Keyword Upsert and Disambiguation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Problem 1:  \n",
    "\n",
    "<h1>Learning new skills from the resumes\n",
    "    New skills keep getting added in today’s professional world every week. \n",
    "    There is no way someone can manually keep track of them. \n",
    "    Online Job Boards get thousands of resumes from their users every day. \n",
    "    How would you learn new skills that have never been seen before?\n",
    "    Dataset: data set of resumes from the internet. \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\nAPPROACH:\\n    task1. Extract skills:\\n        - Make use of unsupervised keyword extraction like YAKE, \\n          since a fixed vocabulary based kw extractor will not be able to see new skills\\n          \\n    task2. Maintaining and updating index:\\n        - Keep index of previously found skills\\n        - Update any new ones, \\n            -- Apply deduplication to extracted new skills, \\n            -- Perform cosine similarity or l2 similarity search on index to find skills close\\n               to ones found before\\n            \\n\\n    use word embeddings to cluster tokens and do pca to see if clusters are formed --optional\\n\\ntask1 :\\n    1. tokenise\\n    2. Preprocess:\\n        i. drop the stopwords\\n        ii. clean non alphabetical chars\\n        iii. Clean person names, locations, company names etc. using NER\\n    3. Apply YAKE kw extractor to find new KWs\\n    4. Deduplicate kws using fuzzywuzzy\\n\\ntask2 :\\n    1. Make HNSW Index with cosine sim\\n    2. Load Pretrained glove embeddings \\n    3. Add new skills to index\\n        i. embed new skills \\n        ii. search index with new skill embeddings, eliminate if found close embeddings (e.g. if cos_sim > 0.8)\\n        iii. update index with new reduced set of skills\\n\\n\\nIMPLEMENTED SO FAR:\\n    1. Task 1 is complete, \\n    2. Task 2:\\n        i. unable to install annoy or hnsw in local env, simulating functionality using dictionary and fuzzywuzzy again :(\\n        ii. index is built using sklearn NearestNeighbor, which does not support adding new vectors incrementally, only refresh\\n        \\n        \\nTHINGS I'D DO IF I HAD MORE TIME:\\n    1. Fine tune ner to catch all indian names, gpe entities\\n    2. Fine Tune WORD EMBEDDINGS ON RESUME DATA\\n    2. Improve KW Extraction -- look into bert based kw extractors\\n        i. handle multi word keyphrase extraction\\n    3. Improve nearest neighbor search code\\n    4. Add Human in the loop feedback ?\\n\""
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "APPROACH:\n",
    "    task1. Extract skills:\n",
    "        - Make use of unsupervised keyword extraction like YAKE, \n",
    "          since a fixed vocabulary based kw extractor will not be able to see new skills\n",
    "          \n",
    "    task2. Maintaining and updating index:\n",
    "        - Keep index of previously found skills\n",
    "        - Update any new ones, \n",
    "            -- Apply deduplication to extracted new skills, \n",
    "            -- Perform cosine similarity or l2 similarity search on index to find skills close\n",
    "               to ones found before\n",
    "            \n",
    "\n",
    "    use word embeddings to cluster tokens and do pca to see if clusters are formed --optional\n",
    "\n",
    "task1 :\n",
    "    1. tokenise\n",
    "    2. Preprocess:\n",
    "        i. drop the stopwords\n",
    "        ii. clean non alphabetical chars\n",
    "        iii. Clean person names, locations, company names etc. using NER\n",
    "    3. Apply YAKE kw extractor to find new KWs\n",
    "    4. Deduplicate kws using fuzzywuzzy\n",
    "\n",
    "task2 :\n",
    "    1. Make HNSW Index with cosine sim\n",
    "    2. Load Pretrained glove embeddings \n",
    "    3. Add new skills to index\n",
    "        i. embed new skills \n",
    "        ii. search index with new skill embeddings, eliminate if found close embeddings (e.g. if cos_sim > 0.8)\n",
    "        iii. update index with new reduced set of skills\n",
    "\n",
    "\n",
    "IMPLEMENTED SO FAR:\n",
    "    1. Task 1 is complete, \n",
    "    2. Task 2:\n",
    "        i. unable to install annoy or hnsw in local env, simulating functionality using dictionary and fuzzywuzzy again :(\n",
    "        ii. index is built using sklearn NearestNeighbor, which does not support adding new vectors incrementally, only refresh\n",
    "        \n",
    "        \n",
    "THINGS I'D DO IF I HAD MORE TIME:\n",
    "    1. Fine tune ner to catch all indian names, gpe entities\n",
    "    2. Fine Tune WORD EMBEDDINGS ON RESUME DATA\n",
    "    2. Improve KW Extraction -- look into bert based kw extractors\n",
    "        i. handle multi word keyphrase extraction\n",
    "    3. Improve nearest neighbor search code\n",
    "    4. Add Human in the loop feedback ?\n",
    "\"\"\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Error loading punkt: <urlopen error [Errno 11001]\n",
      "[nltk_data]     getaddrinfo failed>\n",
      "[nltk_data] Error loading stopwords: <urlopen error [Errno 11001]\n",
      "[nltk_data]     getaddrinfo failed>\n",
      "[nltk_data] Error loading wordnet: <urlopen error [Errno 11001]\n",
      "[nltk_data]     getaddrinfo failed>\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import transformers\n",
    "import re\n",
    "import spacy\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import nltk; \n",
    "nltk.download('punkt')\n",
    "nltk.download('stopwords')\n",
    "nltk.download('wordnet')\n",
    "\n",
    "from thefuzz import fuzz\n",
    "from thefuzz import process\n",
    "from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer, TfidfVectorizer\n",
    "from sklearn.cluster import KMeans, DBSCAN, AgglomerativeClustering\n",
    "from sklearn.decomposition import PCA \n",
    "from sklearn.metrics import silhouette_score  # to determine how many clusters should be used in kmeans/hierarchical clustering algorithms \n",
    "from nltk.corpus import stopwords\n",
    "from nltk.tokenize import word_tokenize\n",
    "from nltk.stem import WordNetLemmatizer\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pre Process"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Make Growing Index "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "nlp = spacy.load(\"en_core_web_sm\")\n",
    "\n",
    "# Clean any non alphabetics\n",
    "def clean_resume(text):\n",
    "    # remove non-alphabetic characters and extra spaces\n",
    "    text = re.sub('[^a-zA-Z]', ' ', text)\n",
    "    text = re.sub(' +', ' ', text)\n",
    "    \n",
    "    # do ner to clean person and geo names:\n",
    "    doc = nlp(text)\n",
    "\n",
    "    # TODO: Fine Tune NER model on Indian GPE and PERSONs\n",
    "    \n",
    "    for ent in doc.ents:\n",
    "        if (ent.label_ == 'PERSON') or (ent.label_ == 'GPE'):\n",
    "            text = re.sub(ent.text, ' ', text)\n",
    "        \n",
    "    # make all lower case and tokenize into words\n",
    "    # This does split things like machine learning into seperate tokens\n",
    "    words = word_tokenize(text.lower())\n",
    "    \n",
    "    # remove stop words from job description to increase accuracy of matching skills with resume\n",
    "    stop_words = set(stopwords.words('english'))  \n",
    "    cleaned_resume = [word for word in words if not word in stop_words]\n",
    "    \n",
    "    return \" \".join(cleaned_resume)\n",
    "\n",
    "# clean_resume(\"Education Details \\r\\nMay 2013 to May 2017 B.E   UIT-RGPV\\r\\nData Scientist \\r\\n\\r\\nData Scientist - Matelabs\\r\\nSkill Details \\r\\nPython- Exprience - Less than 1 year months\\r\\nStatsmodels- Exprience - 12 months\\r\\nAWS- Exprience - Less than 1 year months\\r\\nMachine learning- Exprience - Less than 1 year months\\r\\nSklearn- Exprience - Less than 1 year months\\r\\nScipy- Exprience - Less than 1 year months\\r\\nKeras- Exprience - Less than 1 year monthsCompany Details \\r\\ncompany - Matelabs\\r\\ndescription - ML Platform for business professionals, dummies and enthusiasts.\\r\\n60/A Koramangala 5th block,\\r\\nAchievements/Tasks behind sukh sagar, Bengaluru,\\r\\nIndia                               Developed and deployed auto preprocessing steps of machine learning mainly missing value\\r\\ntreatment, outlier detection, encoding, scaling, feature selection and dimensionality reduction.\\r\\nDeployed automated classification and regression model.\\r\\nlinkedin.com/in/aditya-rathore-\\r\\nb4600b146                           Reasearch and deployed the time series forecasting model ARIMA, SARIMAX, Holt-winter and\\r\\nProphet.\\r\\nWorked on meta-feature extracting problem.\\r\\ngithub.com/rathorology\\r\\nImplemented a state of the art research paper on outlier detection for mixed attributes.\\r\\ncompany - Matelabs\\r\\ndescription - \")  #(df.Resume.values[5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Store Embeddings in Index\n",
    "import yake\n",
    "from thefuzz import fuzz\n",
    "from thefuzz import process\n",
    "from sklearn.neighbors import NearestNeighbors\n",
    "import torch\n",
    "import torchtext\n",
    "\n",
    "\n",
    "# Create Key index mapping\n",
    "class KWIndex():\n",
    "    def __init__(self, n_neighbors=15, algorithm='brute', metric='l2'):\n",
    "        self.n_neighbors = n_neighbors\n",
    "        self.algorithm = algorithm\n",
    "        self.metric = metric\n",
    "        \n",
    "        # ANN Search -- Unused here rn, need to implement deduplication using this, for now using fuzzywuzzy\n",
    "        self.index = NearestNeighbors(n_neighbors=self.n_neighbors, \n",
    "                                      algorithm=self.algorithm, \n",
    "                                      metric=self.metric)\n",
    "        \n",
    "        # KW Extraction\n",
    "        self.kw_emb_dict = {}\n",
    "        self.language = \"en\"\n",
    "        self.max_ngram_size = 4\n",
    "        self.deduplication_thresold = 0.9\n",
    "        self.deduplication_algo = 'seqm'\n",
    "        self.windowSize = 5\n",
    "        self.numOfKeywords = 10\n",
    "        \n",
    "        # Fine tuned kws:\n",
    "        \n",
    "        months = [\n",
    "            'January', 'February', 'March', 'April',\n",
    "            'May', 'June', 'July', 'August',\n",
    "            'September', 'October', 'November', 'December' \n",
    "        ]\n",
    "        # use list comprehension to create a new list of abbreviations\n",
    "        abbreviated_months = [month[:3].upper() for month in months]\n",
    "        \n",
    "        # use list comprehension to create a new list\n",
    "        lowercase_months = [month.lower() for month in months]\n",
    "        lowercase_abv_months = [month.lower() for month in abbreviated_months]\n",
    "        \n",
    "        all_months = [*months, *lowercase_abv_months, *lowercase_months]\n",
    "        self.extra_specific_kws = [\"skill\", \"experience\", \"year\", \"years\", \"month\", \"months\", \"achievement\", \n",
    "                                   \"achievements\", \"details\", \"detail\", \"responsibility\", \n",
    "                                   \"responsibilities\", \"role\", \"roles\", \"description\"]\n",
    "        self.extra_specific_kws.extend(all_months)\n",
    "        \n",
    "        # Ngram Extractors\n",
    "        self.custom_kw_ex_list = []\n",
    "        for n in range(1, self.max_ngram_size):\n",
    "            nX_custom_kw_extractor = yake.KeywordExtractor(\n",
    "                                    lan=self.language, \n",
    "                                    n=n, \n",
    "                                    dedupLim=self.deduplication_thresold, \n",
    "                                    dedupFunc=self.deduplication_algo, \n",
    "                                    windowsSize=self.windowSize, \n",
    "                                    top=self.numOfKeywords, \n",
    "                                    features=None\n",
    "                                )\n",
    "\n",
    "            for k in self.extra_specific_kws:\n",
    "                nX_custom_kw_extractor.stopword_set.add(k)\n",
    "                \n",
    "            self.custom_kw_ex_list.append(nX_custom_kw_extractor)\n",
    "        \n",
    "        # Word Embeddings\n",
    "        self.VECTOR_DIM = 50\n",
    "        self.glove = torchtext.vocab.GloVe(name=\"6B\", # trained on Wikipedia 2014 corpus of 6 billion words\n",
    "                                      dim=self.VECTOR_DIM)   # embedding size = 100\n",
    "        \n",
    "        \n",
    "        # NER for cleaning GPE and PERSONs\n",
    "        self.nlp = spacy.load(\"en_core_web_sm\")\n",
    "        \n",
    "        # initialise index:\n",
    "        self.index.fit(np.stack([self.glove[\"first\"] for _ in range(self.n_neighbors)]))\n",
    "\n",
    "    def combine_similar_terms(self, input_list):\n",
    "        \"\"\"\n",
    "        input: list of terms\n",
    "        output: list of terms with similar terms combined\n",
    "        \"\"\"\n",
    "        # first create a list of similar terms\n",
    "        similar_terms = []\n",
    "        for i in range(len(input_list)):\n",
    "            for j in range(i+1, len(input_list)):\n",
    "                if fuzz.partial_token_sort_ratio(input_list[i], input_list[j]) > 70:\n",
    "                    similar_terms.append([input_list[i], input_list[j]])\n",
    "\n",
    "        # then combine them into one term\n",
    "        for pair in similar_terms:\n",
    "            try:\n",
    "                if pair[0] != pair[1]:\n",
    "                    input_list.remove(pair[0])\n",
    "                    continue\n",
    "                input_list.remove(pair[1])\n",
    "            except Exception as e:\n",
    "                continue\n",
    "            new = pair[0] + \" \" + pair[1]\n",
    "            new = \" \".join(set(new.split()))\n",
    "            input_list.append(new)\n",
    "        return input_list\n",
    "\n",
    "    # Clean any non alphabetics\n",
    "    def clean_resume(self, text):\n",
    "        # remove non-alphabetic characters and extra spaces\n",
    "        text = re.sub('[^a-zA-Z]', ' ', text)\n",
    "        text = re.sub(' +', ' ', text)\n",
    "\n",
    "        # do ner to clean person and geo names:\n",
    "        doc = self.nlp(text)\n",
    "\n",
    "        # TODO: Fine Tune NER model on Indian GPE and PERSONs\n",
    "\n",
    "        for ent in doc.ents:\n",
    "            if (ent.label_ == 'PERSON') or (ent.label_ == 'GPE'):\n",
    "                text = re.sub(ent.text, ' ', text)\n",
    "\n",
    "        # make all lower case and tokenize into words\n",
    "        # This does split things like machine learning into seperate tokens\n",
    "        words = word_tokenize(text.lower())\n",
    "\n",
    "        # remove stop words from job description to increase accuracy of matching skills with resume\n",
    "        stop_words = set(stopwords.words('english'))  \n",
    "        cleaned_resume = [word for word in words if not word in stop_words]\n",
    "\n",
    "        return \" \".join(cleaned_resume)\n",
    "        \n",
    "\n",
    "    def fit(self, X_dict):\n",
    "        \"\"\"\n",
    "        Requires X_dict = {\"kw\": [0,1,1,2]...}\n",
    "        Apply calc_avg_emb_dict on df to save and embed kws first\n",
    "        \n",
    "        \"\"\"\n",
    "        self.X = np.stack(list(X_dict.values()))\n",
    "        self.u_id_keys = dict(zip(range(len(X_dict.keys())), X_dict.keys()))\n",
    "        print(f\"Fitting: {self.X.shape}\")\n",
    "        self.index.fit(self.X)\n",
    "        print(self.index)\n",
    "        \n",
    "    def search_and_dedup(self, update_dict):\n",
    "        dedup_dict = {k:v for k, v in update_dict.items()}\n",
    "        # take query, compare with index, if dist < 1, dont add\n",
    "        for kw, kw_emb in update_dict.items():\n",
    "            res_dist, res_ind = self.index.kneighbors([kw_emb], n_neighbors=10)\n",
    "            for ri, rd in zip(res_ind[0], res_dist[0]):\n",
    "                flag = False\n",
    "                if rd < 2:\n",
    "                    # too similar, dont add key, emb to dict\n",
    "                    print(\"too similar \\t\", kw, \"->\", self.u_id_keys[ri], \"|dist: \", rd)\n",
    "                    flag = True\n",
    "                    break\n",
    "                else:\n",
    "                    continue\n",
    "            if flag:\n",
    "                dedup_dict.pop(kw)\n",
    "        return dedup_dict\n",
    "    \n",
    "    def store_kws(self, keywords):\n",
    "        update_dict = {}\n",
    "        for kw in keywords:\n",
    "            if self.kw_emb_dict.get(kw) is None:\n",
    "                embs = []\n",
    "                for tok in kw.split():\n",
    "                    embs.append(np.array(self.glove[tok]))\n",
    "                embs = np.average(np.stack(embs), axis=0)\n",
    "                update_dict[kw] = embs\n",
    "            else:\n",
    "                update_dict[kw] = self.kw_emb_dict[kw]\n",
    "                \n",
    "        # Deduplication TODO: USE WORD VEC BASED DEDUP HERE\n",
    "#         keywords = self.combine_similar_terms(keywords)\n",
    "#         update_dict = {k:update_dict[k] for k in keywords}\n",
    "        update_dict = self.search_and_dedup(update_dict)\n",
    "        \n",
    "        # Update any new into the kw_emb_dict and fit index\n",
    "        self.kw_emb_dict.update(update_dict)\n",
    "        self.fit(self.kw_emb_dict)\n",
    "        return update_dict\n",
    "    \n",
    "    def extract_kws(self, text):\n",
    "        ngram_kws = []\n",
    "        for ckwe in self.custom_kw_ex_list:\n",
    "            ngram_kws.extend(ckwe.extract_keywords(self.clean_resume(text)))\n",
    "        \n",
    "        ngram_kws = [i[0] for i in ngram_kws]\n",
    "        keywords = list(set(ngram_kws))\n",
    "        return keywords\n",
    "    \n",
    "    def clean_terms_by_embedding_dist(self):\n",
    "        pass\n",
    "    \n",
    "    def upsert(self, text):\n",
    "        # Extract KWs        \n",
    "        keywords = self.extract_kws(text)\n",
    "        \n",
    "        # Store and Update KWs\n",
    "        update_dict = self.store_kws(keywords)\n",
    "        \n",
    "        # TODO: Clean related terms based on Cos Sim\n",
    "        \n",
    "        return update_dict\n",
    "      \n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Demo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# sklearn.neighbors.VALID_METRICS['ball_tree']\n",
    "\n",
    "# Read the Data\n",
    "df = pd.read_csv('datasets/kaggle_resume_dataset/UpdatedResumeDataSet.csv')\n",
    "\n",
    "kwi = KWIndex(metric='euclidean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting: (30, 50)\n",
      "NearestNeighbors(algorithm='brute', metric='euclidean', n_neighbors=15)\n",
      "too similar \t data -> data |dist:  0.0\n",
      "too similar \t data scientist data -> data science |dist:  1.7395974\n",
      "Fitting: (58, 50)\n",
      "NearestNeighbors(algorithm='brute', metric='euclidean', n_neighbors=15)\n",
      "too similar \t python -> python |dist:  8.4293696e-08\n",
      "too similar \t data -> data |dist:  0.0\n",
      "too similar \t science data science -> data science |dist:  0.9465557\n",
      "too similar \t learning -> learning |dist:  0.0\n",
      "too similar \t data science -> data science |dist:  0.0\n",
      "too similar \t science data -> data science |dist:  0.0\n",
      "too similar \t data science electrical -> data science |dist:  1.6840291\n",
      "too similar \t higher data science -> data science |dist:  1.628984\n",
      "too similar \t data science data -> data science |dist:  0.9465557\n",
      "too similar \t machine learning -> machine learning |dist:  0.0\n",
      "too similar \t data analysis -> analysis |dist:  1.8113198\n",
      "Fitting: (77, 50)\n",
      "NearestNeighbors(algorithm='brute', metric='euclidean', n_neighbors=15)\n",
      "too similar \t data -> data |dist:  0.0\n",
      "too similar \t marketing months scm -> company matelabs |dist:  1.9662002\n",
      "too similar \t analytics -> analytics |dist:  0.0\n",
      "Fitting: (104, 50)\n",
      "NearestNeighbors(algorithm='brute', metric='euclidean', n_neighbors=15)\n",
      "too similar \t ymcaust -> matelabs |dist:  0.0\n",
      "too similar \t analysis exprience -> data scientist matelabs |dist:  1.723217\n",
      "too similar \t science -> science |dist:  0.0\n",
      "too similar \t data -> data |dist:  0.0\n",
      "too similar \t data science internship -> data science assurance |dist:  1.8958572\n",
      "too similar \t data analysis exprience -> rgpv data |dist:  1.1106238\n",
      "too similar \t data structure exprience -> rgpv data |dist:  1.5294436\n",
      "too similar \t database management exprience -> rgpv data |dist:  1.8560535\n",
      "too similar \t company itechpower -> company matelabs |dist:  0.0\n",
      "too similar \t exprience -> matelabs |dist:  0.0\n",
      "Fitting: (123, 50)\n",
      "NearestNeighbors(algorithm='brute', metric='euclidean', n_neighbors=15)\n",
      "too similar \t matlab -> matlab |dist:  0.0\n",
      "too similar \t science -> science |dist:  0.0\n",
      "too similar \t data -> data |dist:  0.0\n",
      "too similar \t machine -> machine |dist:  8.4293696e-08\n",
      "too similar \t learning -> learning |dist:  0.0\n",
      "too similar \t education -> education |dist:  0.0\n",
      "too similar \t data science -> data science |dist:  0.0\n",
      "too similar \t basics iot python -> python exprience |dist:  1.908187\n",
      "too similar \t machine learning -> machine learning |dist:  0.0\n",
      "too similar \t exprience -> matelabs |dist:  0.0\n",
      "Fitting: (143, 50)\n",
      "NearestNeighbors(algorithm='brute', metric='euclidean', n_neighbors=15)\n",
      "too similar \t python -> python |dist:  8.4293696e-08\n",
      "too similar \t bringing business -> industry cross industry |dist:  1.9801528\n",
      "too similar \t science -> science |dist:  0.0\n",
      "too similar \t science consultant datamites -> science assurance associate |dist:  1.7481291\n",
      "too similar \t data -> data |dist:  0.0\n",
      "too similar \t machine -> machine |dist:  8.4293696e-08\n",
      "too similar \t data science consultant -> data science |dist:  1.6172836\n",
      "too similar \t learning -> learning |dist:  0.0\n",
      "too similar \t consultant data science -> data science |dist:  1.6172837\n",
      "too similar \t data science -> data science |dist:  0.0\n",
      "too similar \t science consultant data -> data science |dist:  1.6172837\n",
      "too similar \t machine learning -> machine learning |dist:  0.0\n",
      "too similar \t data visualization -> data modelling |dist:  1.8619689\n",
      "Fitting: (159, 50)\n",
      "NearestNeighbors(algorithm='brute', metric='euclidean', n_neighbors=15)\n",
      "too similar \t python -> python |dist:  8.4293696e-08\n",
      "too similar \t machine learning deep -> machine learning |dist:  1.6090264\n",
      "too similar \t learning data -> develop data |dist:  1.9294571\n",
      "too similar \t machine -> machine |dist:  8.4293696e-08\n",
      "too similar \t data -> data |dist:  0.0\n",
      "too similar \t learning deep learning -> learning |dist:  1.7613347\n",
      "too similar \t learning -> learning |dist:  0.0\n",
      "too similar \t deep machine learning -> machine learning |dist:  1.6090264\n",
      "too similar \t data science -> data science |dist:  0.0\n",
      "too similar \t learning data science -> data science |dist:  1.2651149\n",
      "too similar \t machine learning -> machine learning |dist:  0.0\n",
      "Fitting: (178, 50)\n",
      "NearestNeighbors(algorithm='brute', metric='euclidean', n_neighbors=15)\n",
      "too similar \t python -> python |dist:  8.4293696e-08\n",
      "too similar \t engineering -> engineering |dist:  0.0\n",
      "too similar \t machine learning deep -> deep machine |dist:  1.4554808\n",
      "too similar \t science -> science |dist:  0.0\n",
      "too similar \t deep -> deep |dist:  8.4293696e-08\n",
      "too similar \t machine -> machine |dist:  8.4293696e-08\n",
      "too similar \t learning -> learning |dist:  0.0\n",
      "too similar \t deep learning -> learning deep |dist:  8.4293696e-08\n",
      "too similar \t learning deep -> learning deep |dist:  8.4293696e-08\n",
      "too similar \t machine learning -> machine learning |dist:  0.0\n",
      "Fitting: (198, 50)\n",
      "NearestNeighbors(algorithm='brute', metric='euclidean', n_neighbors=15)\n",
      "too similar \t company -> company |dist:  0.0\n",
      "too similar \t school data science -> data science |dist:  1.8003596\n",
      "too similar \t called data science -> data science |dist:  1.5367012\n",
      "too similar \t science business -> science consultant |dist:  1.9894137\n",
      "too similar \t science -> science |dist:  0.0\n",
      "too similar \t data -> data |dist:  0.0\n",
      "too similar \t aegis school data -> technology assisted review |dist:  1.9676486\n",
      "too similar \t data science mumbai -> haryana data science |dist:  1.7915723\n",
      "too similar \t analysis -> analysis |dist:  0.0\n",
      "too similar \t data science business -> data science |dist:  1.3739043\n",
      "too similar \t project -> project |dist:  0.0\n",
      "too similar \t data science -> data science |dist:  0.0\n",
      "too similar \t business -> business |dist:  0.0\n",
      "too similar \t data scientist -> data scientist |dist:  0.0\n",
      "too similar \t school data -> academy higher data |dist:  1.9033911\n",
      "Fitting: (212, 50)\n",
      "NearestNeighbors(algorithm='brute', metric='euclidean', n_neighbors=15)\n",
      "too similar \t modelling -> modelling |dist:  8.4293696e-08\n",
      "too similar \t python -> python |dist:  8.4293696e-08\n",
      "too similar \t technology assisted review -> technology assisted review |dist:  0.0\n",
      "too similar \t analytics investigative platform -> analytics investigative platform |dist:  5.9604645e-08\n",
      "too similar \t scikit learn -> scikit learn |dist:  0.0\n",
      "too similar \t information -> information |dist:  0.0\n",
      "too similar \t platform -> platform |dist:  0.0\n",
      "too similar \t assurance associate -> assurance associate |dist:  0.0\n",
      "too similar \t data -> data |dist:  0.0\n",
      "too similar \t fraud -> fraud |dist:  0.0\n",
      "too similar \t ernst young llp -> ernst young llp |dist:  0.0\n",
      "too similar \t topic modelling -> topic modelling |dist:  0.0\n",
      "too similar \t elastic search -> elastic search |dist:  0.0\n",
      "too similar \t analytics -> analytics |dist:  0.0\n",
      "too similar \t science assurance -> science assurance |dist:  0.0\n",
      "too similar \t review -> review |dist:  1.1920929e-07\n",
      "too similar \t data science assurance -> data science assurance |dist:  8.4293696e-08\n",
      "too similar \t tools technologies python -> tools technologies python |dist:  0.0\n",
      "too similar \t topic -> topic |dist:  0.0\n",
      "too similar \t analysis -> analysis |dist:  0.0\n",
      "too similar \t fraud analytics investigative -> fraud analytics investigative |dist:  0.0\n",
      "too similar \t tools technologies -> tools technologies |dist:  0.0\n",
      "too similar \t coding topic modelling -> coding topic modelling |dist:  0.0\n",
      "too similar \t data science -> data science |dist:  0.0\n",
      "too similar \t predictive coding topic -> predictive coding topic |dist:  0.0\n",
      "too similar \t technologies python -> technologies python |dist:  0.0\n",
      "too similar \t natural language processing -> natural language processing |dist:  0.0\n",
      "too similar \t sentiment analysis -> sentiment analysis |dist:  0.0\n",
      "too similar \t science assurance associate -> science assurance associate |dist:  0.0\n",
      "too similar \t technology assisted -> technology assisted |dist:  0.0\n",
      "Fitting: (212, 50)\n",
      "NearestNeighbors(algorithm='brute', metric='euclidean', n_neighbors=15)\n",
      "too similar \t company -> company |dist:  0.0\n",
      "too similar \t business professionals dummies -> business professionals dummies |dist:  0.0\n",
      "too similar \t matelabs -> matelabs |dist:  0.0\n",
      "too similar \t detection -> detection |dist:  0.0\n",
      "too similar \t rgpv data -> rgpv data |dist:  0.0\n",
      "too similar \t uit rgpv data -> uit rgpv data |dist:  0.0\n",
      "too similar \t uit rgpv -> uit rgpv |dist:  0.0\n",
      "too similar \t outlier detection -> outlier detection |dist:  0.0\n",
      "too similar \t company matelabs -> company matelabs |dist:  0.0\n",
      "too similar \t data -> data |dist:  0.0\n",
      "too similar \t machine -> machine |dist:  8.4293696e-08\n",
      "too similar \t scientist matelabs -> scientist matelabs |dist:  4.2146848e-08\n",
      "too similar \t data scientist matelabs -> rgpv data scientist |dist:  0.0\n",
      "too similar \t time series forecasting -> time series forecasting |dist:  0.0\n",
      "too similar \t time series -> time series |dist:  0.0\n",
      "too similar \t learning -> learning |dist:  0.0\n",
      "too similar \t sukh sagar bengaluru -> sukh sagar bengaluru |dist:  0.0\n",
      "too similar \t scientist data -> data scientist |dist:  0.0\n",
      "too similar \t outlier -> outlier |dist:  0.0\n",
      "too similar \t data scientist data -> data scientist |dist:  1.0836436\n",
      "too similar \t data scientist -> data scientist |dist:  0.0\n",
      "too similar \t statsmodels months aws -> statsmodels months aws |dist:  0.0\n",
      "too similar \t model -> model |dist:  0.0\n",
      "too similar \t rgpv data scientist -> rgpv data scientist |dist:  0.0\n",
      "too similar \t deployed -> deployed |dist:  0.0\n",
      "too similar \t scientist data scientist -> scientist data scientist |dist:  0.0\n",
      "too similar \t professionals dummies enthusiasts -> professionals dummies enthusiasts |dist:  0.0\n",
      "too similar \t scientist -> scientist |dist:  8.4293696e-08\n",
      "too similar \t aditya rathore -> aditya rathore |dist:  0.0\n",
      "too similar \t machine learning -> machine learning |dist:  0.0\n",
      "Fitting: (212, 50)\n",
      "NearestNeighbors(algorithm='brute', metric='euclidean', n_neighbors=15)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "too similar \t python -> python |dist:  8.4293696e-08\n",
      "too similar \t higher data -> higher data |dist:  0.0\n",
      "too similar \t webframework intermediate -> webframework intermediate |dist:  0.0\n",
      "too similar \t matlab -> matlab |dist:  0.0\n",
      "too similar \t engineering -> engineering |dist:  0.0\n",
      "too similar \t areas interest -> areas interest |dist:  0.0\n",
      "too similar \t programming -> programming |dist:  0.0\n",
      "too similar \t science -> science |dist:  0.0\n",
      "too similar \t intermediate mipower intermediate -> intermediate mipower intermediate |dist:  0.0\n",
      "too similar \t intermediate xampp mysql -> intermediate xampp mysql |dist:  0.0\n",
      "too similar \t data -> data |dist:  0.0\n",
      "too similar \t science data science -> data science |dist:  0.9465557\n",
      "too similar \t customers -> customers |dist:  0.0\n",
      "too similar \t intermediate xampp -> webframework intermediate |dist:  0.0\n",
      "too similar \t learning -> learning |dist:  0.0\n",
      "too similar \t science electrical -> science electrical |dist:  0.0\n",
      "too similar \t intermediate -> intermediate |dist:  0.0\n",
      "too similar \t data science -> data science |dist:  0.0\n",
      "too similar \t sql year monthscompany -> sql year monthscompany |dist:  0.0\n",
      "too similar \t technology january deeksha -> technology january deeksha |dist:  0.0\n",
      "too similar \t science data -> data science |dist:  0.0\n",
      "too similar \t academy higher data -> academy higher data |dist:  0.0\n",
      "too similar \t data science electrical -> science electrical |dist:  1.5569732\n",
      "too similar \t higher data science -> academy higher data |dist:  1.4324616\n",
      "too similar \t science electrical enthusiast -> science electrical enthusiast |dist:  0.0\n",
      "too similar \t data science data -> data science |dist:  0.9465557\n",
      "too similar \t machine learning -> machine learning |dist:  0.0\n",
      "too similar \t academy higher -> academy higher |dist:  0.0\n",
      "too similar \t data analysis -> analysis |dist:  1.8113198\n",
      "too similar \t electrical -> electrical |dist:  0.0\n",
      "Fitting: (212, 50)\n",
      "NearestNeighbors(algorithm='brute', metric='euclidean', n_neighbors=15)\n",
      "too similar \t industry cross -> industry cross |dist:  8.4293696e-08\n",
      "too similar \t analytics infrastructure top -> analytics infrastructure top |dist:  0.0\n",
      "too similar \t technical environment -> technical environment |dist:  0.0\n",
      "too similar \t sql -> sql |dist:  1.6858739e-07\n",
      "too similar \t design develop data -> design develop data |dist:  0.0\n",
      "too similar \t data -> data |dist:  0.0\n",
      "too similar \t models reporting develop -> models reporting develop |dist:  0.0\n",
      "too similar \t marketing months scm -> management exprience |dist:  1.6975633\n",
      "too similar \t analytics -> analytics |dist:  0.0\n",
      "too similar \t project develop analytics -> project develop analytics |dist:  0.0\n",
      "too similar \t project -> project |dist:  0.0\n",
      "too similar \t accenture solutions pvt -> accenture solutions pvt |dist:  0.0\n",
      "too similar \t develop data -> develop data |dist:  0.0\n",
      "too similar \t cross industry -> industry cross |dist:  8.4293696e-08\n",
      "too similar \t hana -> hana |dist:  0.0\n",
      "too similar \t advance analytics -> advance analytics |dist:  0.0\n",
      "too similar \t develop analytics infrastructure -> develop analytics infrastructure |dist:  8.4293696e-08\n",
      "too similar \t develop -> develop |dist:  0.0\n",
      "too similar \t environment -> environment |dist:  0.0\n",
      "too similar \t reports technical environment -> reports technical environment |dist:  0.0\n",
      "too similar \t technical -> technical |dist:  1.1920929e-07\n",
      "too similar \t develop data models -> develop data models |dist:  0.0\n",
      "too similar \t service area -> service area |dist:  0.0\n",
      "too similar \t data modelling -> data modelling |dist:  0.0\n",
      "too similar \t sql server -> sql server |dist:  0.0\n",
      "too similar \t project involved -> project involved |dist:  0.0\n",
      "too similar \t industry cross industry -> industry cross industry |dist:  0.0\n",
      "too similar \t design develop -> design develop |dist:  8.4293696e-08\n",
      "too similar \t involved -> involved |dist:  8.4293696e-08\n",
      "too similar \t industry -> industry |dist:  0.0\n",
      "Fitting: (212, 50)\n",
      "NearestNeighbors(algorithm='brute', metric='euclidean', n_neighbors=15)\n",
      "too similar \t ymcaust -> matelabs |dist:  0.0\n",
      "too similar \t analysis exprience -> rgpv data scientist |dist:  1.723217\n",
      "too similar \t education details mca -> education details mca |dist:  0.0\n",
      "too similar \t science -> science |dist:  0.0\n",
      "too similar \t mca ymcaust faridabad -> mca ymcaust faridabad |dist:  0.0\n",
      "too similar \t faridabad haryana -> faridabad haryana |dist:  0.0\n",
      "too similar \t mca -> mca |dist:  0.0\n",
      "too similar \t data -> data |dist:  0.0\n",
      "too similar \t structure exprience -> structure exprience |dist:  0.0\n",
      "too similar \t data science internship -> science internship |dist:  1.8893199\n",
      "too similar \t science internship -> science internship |dist:  0.0\n",
      "too similar \t database -> database |dist:  0.0\n",
      "too similar \t management -> management |dist:  0.0\n",
      "too similar \t mca ymcaust -> mca ymcaust |dist:  0.0\n",
      "too similar \t faridabad -> faridabad |dist:  0.0\n",
      "too similar \t database management -> database management |dist:  0.0\n",
      "too similar \t python exprience -> python exprience |dist:  4.2146848e-08\n",
      "too similar \t education -> education |dist:  0.0\n",
      "too similar \t data analysis exprience -> rgpv data |dist:  1.1106238\n",
      "too similar \t data structure exprience -> rgpv data |dist:  1.5294436\n",
      "too similar \t haryana -> haryana |dist:  8.4293696e-08\n",
      "too similar \t management exprience -> management exprience |dist:  0.0\n",
      "too similar \t faridabad haryana data -> faridabad haryana data |dist:  0.0\n",
      "too similar \t database management exprience -> database management |dist:  1.531201\n",
      "too similar \t ymcaust faridabad -> ymcaust faridabad |dist:  0.0\n",
      "too similar \t ymcaust faridabad haryana -> ymcaust faridabad haryana |dist:  0.0\n",
      "too similar \t company itechpower -> company matelabs |dist:  0.0\n",
      "too similar \t exprience -> matelabs |dist:  0.0\n",
      "too similar \t haryana data science -> haryana data science |dist:  0.0\n",
      "Fitting: (212, 50)\n",
      "NearestNeighbors(algorithm='brute', metric='euclidean', n_neighbors=15)\n",
      "too similar \t matlab -> matlab |dist:  0.0\n",
      "too similar \t microsoft word -> microsoft word |dist:  0.0\n",
      "too similar \t powerpoint recognition -> powerpoint recognition |dist:  5.9604645e-08\n",
      "too similar \t science -> science |dist:  0.0\n",
      "too similar \t data -> data |dist:  0.0\n",
      "too similar \t machine -> machine |dist:  8.4293696e-08\n",
      "too similar \t word microsoft -> microsoft word |dist:  0.0\n",
      "too similar \t microsoft excel -> excel microsoft |dist:  0.0\n",
      "too similar \t learning html microsoft -> learning html microsoft |dist:  8.4293696e-08\n",
      "too similar \t microsoft powerpoint -> microsoft powerpoint |dist:  0.0\n",
      "too similar \t html microsoft word -> html microsoft word |dist:  0.0\n",
      "too similar \t microsoft word microsoft -> microsoft word microsoft |dist:  0.0\n",
      "too similar \t learning -> learning |dist:  0.0\n",
      "too similar \t powerpoint recognition academic -> powerpoint recognition academic |dist:  0.0\n",
      "too similar \t microsoft excel microsoft -> microsoft excel microsoft |dist:  8.4293696e-08\n",
      "too similar \t tech -> tech |dist:  8.4293696e-08\n",
      "too similar \t education -> education |dist:  0.0\n",
      "too similar \t data science -> data science |dist:  0.0\n",
      "too similar \t recognition academic -> recognition academic |dist:  8.4293696e-08\n",
      "too similar \t word microsoft excel -> word microsoft excel |dist:  5.9604645e-08\n",
      "too similar \t basics iot python -> python exprience |dist:  1.908187\n",
      "too similar \t html microsoft -> html microsoft |dist:  0.0\n",
      "too similar \t html -> html |dist:  0.0\n",
      "too similar \t microsoft powerpoint recognition -> microsoft powerpoint recognition |dist:  5.9604645e-08\n",
      "too similar \t excel microsoft -> excel microsoft |dist:  0.0\n",
      "too similar \t machine learning -> machine learning |dist:  0.0\n",
      "too similar \t exprience -> matelabs |dist:  0.0\n",
      "too similar \t microsoft -> microsoft |dist:  0.0\n",
      "too similar \t excel microsoft powerpoint -> excel microsoft powerpoint |dist:  0.0\n",
      "too similar \t recognition academic secured -> recognition academic secured |dist:  0.0\n",
      "Fitting: (212, 50)\n",
      "NearestNeighbors(algorithm='brute', metric='euclidean', n_neighbors=15)\n",
      "too similar \t python -> python |dist:  8.4293696e-08\n",
      "too similar \t processing data -> processing data |dist:  0.0\n",
      "too similar \t data visualization company -> data visualization company |dist:  0.0\n",
      "too similar \t consultant -> consultant |dist:  0.0\n",
      "too similar \t bringing business -> industry cross industry |dist:  1.9801528\n",
      "too similar \t visualization company -> visualization company |dist:  0.0\n",
      "too similar \t science -> science |dist:  0.0\n",
      "too similar \t python excel -> python excel |dist:  0.0\n",
      "too similar \t science consultant datamites -> consultant datamites |dist:  1.5498204\n",
      "too similar \t advanced querying visualization -> advanced querying visualization |dist:  0.0\n",
      "too similar \t consultant datamites -> consultant datamites |dist:  0.0\n",
      "too similar \t data -> data |dist:  0.0\n",
      "too similar \t machine -> machine |dist:  8.4293696e-08\n",
      "too similar \t python data visualization -> python data visualization |dist:  0.0\n",
      "too similar \t querying visualization analytics -> querying visualization analytics |dist:  5.9604645e-08\n",
      "too similar \t visualization -> visualization |dist:  0.0\n",
      "too similar \t data science consultant -> data science |dist:  1.6172836\n",
      "too similar \t science consultant -> science consultant |dist:  0.0\n",
      "too similar \t learning -> learning |dist:  0.0\n",
      "too similar \t manipulating cleaning processing -> manipulating cleaning processing |dist:  8.4293696e-08\n",
      "too similar \t solutions -> solutions |dist:  0.0\n",
      "too similar \t consultant data science -> data science |dist:  1.6172837\n",
      "too similar \t data science -> data science |dist:  0.0\n",
      "too similar \t processing -> processing |dist:  0.0\n",
      "too similar \t science consultant data -> data science |dist:  1.6172837\n",
      "too similar \t business -> business |dist:  0.0\n",
      "too similar \t visualization company heretic -> visualization company heretic |dist:  0.0\n",
      "too similar \t machine learning -> machine learning |dist:  0.0\n",
      "too similar \t data visualization -> data modelling |dist:  1.8619689\n",
      "Fitting: (212, 50)\n",
      "NearestNeighbors(algorithm='brute', metric='euclidean', n_neighbors=15)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "too similar \t python -> python |dist:  8.4293696e-08\n",
      "too similar \t communication skills problem -> communication skills problem |dist:  8.4293696e-08\n",
      "too similar \t entities -> entities |dist:  0.0\n",
      "too similar \t machine learning deep -> deep machine |dist:  1.4554808\n",
      "too similar \t learning data -> deep learning data |dist:  1.6702614\n",
      "too similar \t deep -> deep |dist:  8.4293696e-08\n",
      "too similar \t knowledge python -> knowledge python |dist:  5.9604645e-08\n",
      "too similar \t machine -> machine |dist:  8.4293696e-08\n",
      "too similar \t data -> data |dist:  0.0\n",
      "too similar \t learning deep learning -> deep learning |dist:  0.8806673\n",
      "too similar \t neural intelligence -> neural intelligence |dist:  0.0\n",
      "too similar \t neural intelligence platform -> neural intelligence platform |dist:  0.0\n",
      "too similar \t knowledge -> knowledge |dist:  0.0\n",
      "too similar \t wipro neural intelligence -> wipro neural intelligence |dist:  0.0\n",
      "too similar \t laterally think medium -> laterally think medium |dist:  0.0\n",
      "too similar \t learning -> learning |dist:  0.0\n",
      "too similar \t deep machine learning -> deep machine |dist:  1.4554808\n",
      "too similar \t data science -> data science |dist:  0.0\n",
      "too similar \t intelligence platform -> intelligence platform |dist:  0.0\n",
      "too similar \t learning data science -> data science |dist:  1.2651149\n",
      "too similar \t deep learning -> deep learning |dist:  8.4293696e-08\n",
      "too similar \t queries -> queries |dist:  0.0\n",
      "too similar \t wipro -> wipro |dist:  0.0\n",
      "too similar \t user queries -> user queries |dist:  0.0\n",
      "too similar \t machine learning nlp -> machine learning nlp |dist:  0.0\n",
      "too similar \t learning deep -> deep learning |dist:  8.4293696e-08\n",
      "too similar \t machine learning -> machine learning |dist:  0.0\n",
      "too similar \t neural -> neural |dist:  1.6858739e-07\n",
      "too similar \t deep machine -> deep machine |dist:  0.0\n",
      "too similar \t deep learning data -> deep learning data |dist:  0.0\n",
      "Fitting: (212, 50)\n",
      "NearestNeighbors(algorithm='brute', metric='euclidean', n_neighbors=15)\n",
      "too similar \t python -> python |dist:  8.4293696e-08\n",
      "too similar \t madhya pradesh -> madhya pradesh |dist:  1.1920929e-07\n",
      "too similar \t python sql web -> python sql web |dist:  0.0\n",
      "too similar \t information technology -> information technology |dist:  0.0\n",
      "too similar \t bhopal madhya -> bhopal madhya |dist:  0.0\n",
      "too similar \t engineering -> engineering |dist:  0.0\n",
      "too similar \t machine learning deep -> deep machine |dist:  1.4554808\n",
      "too similar \t python sql -> python sql |dist:  0.0\n",
      "too similar \t deep leaning nlp -> deep leaning nlp |dist:  0.0\n",
      "too similar \t science -> science |dist:  0.0\n",
      "too similar \t technology -> technology |dist:  0.0\n",
      "too similar \t deep -> deep |dist:  8.4293696e-08\n",
      "too similar \t nlp python -> nlp python |dist:  0.0\n",
      "too similar \t machine -> machine |dist:  8.4293696e-08\n",
      "too similar \t pradesh truba institute -> pradesh rkdf institute |dist:  0.0\n",
      "too similar \t pradesh rkdf institute -> pradesh rkdf institute |dist:  0.0\n",
      "too similar \t products shared customers -> products shared customers |dist:  8.4293696e-08\n",
      "too similar \t learning -> learning |dist:  0.0\n",
      "too similar \t bhopal madhya pradesh -> bhopal madhya pradesh |dist:  0.0\n",
      "too similar \t madhya -> madhya |dist:  0.0\n",
      "too similar \t deep learning -> deep learning |dist:  8.4293696e-08\n",
      "too similar \t pradesh -> pradesh |dist:  0.0\n",
      "too similar \t sql web -> sql web |dist:  0.0\n",
      "too similar \t quickly grasp technical -> quickly grasp technical |dist:  5.9604645e-08\n",
      "too similar \t nlp python sql -> nlp python sql |dist:  0.0\n",
      "too similar \t life insurance -> life insurance |dist:  8.4293696e-08\n",
      "too similar \t personal ability quickly -> personal ability quickly |dist:  8.4293696e-08\n",
      "too similar \t learning deep -> deep learning |dist:  8.4293696e-08\n",
      "too similar \t web -> web |dist:  0.0\n",
      "too similar \t machine learning -> machine learning |dist:  0.0\n",
      "Fitting: (212, 50)\n",
      "NearestNeighbors(algorithm='brute', metric='euclidean', n_neighbors=15)\n",
      "too similar \t company -> company |dist:  0.0\n",
      "too similar \t school data science -> school data business |dist:  1.6046574\n",
      "too similar \t called data science -> data science |dist:  1.5367012\n",
      "too similar \t school -> school |dist:  0.0\n",
      "too similar \t science business -> science consultant |dist:  1.9894137\n",
      "too similar \t school business mumbai -> school business mumbai |dist:  8.4293696e-08\n",
      "too similar \t science -> science |dist:  0.0\n",
      "too similar \t service provider -> service provider |dist:  0.0\n",
      "too similar \t data -> data |dist:  0.0\n",
      "too similar \t aegis school data -> aegis school business |dist:  1.7298081\n",
      "too similar \t data science mumbai -> haryana data science |dist:  1.7915723\n",
      "too similar \t science mumbai -> science mumbai |dist:  0.0\n",
      "too similar \t company aegis -> company aegis |dist:  0.0\n",
      "too similar \t aegis school -> aegis school |dist:  0.0\n",
      "too similar \t aegis -> aegis |dist:  0.0\n",
      "too similar \t analysis -> analysis |dist:  0.0\n",
      "too similar \t data science business -> data science |dist:  1.3739043\n",
      "too similar \t project -> project |dist:  0.0\n",
      "too similar \t mumbai -> mumbai |dist:  0.0\n",
      "too similar \t data science -> data science |dist:  0.0\n",
      "too similar \t business -> business |dist:  0.0\n",
      "too similar \t data scientist -> data scientist |dist:  0.0\n",
      "too similar \t face -> face |dist:  0.0\n",
      "too similar \t aegis school business -> aegis school business |dist:  5.9604645e-08\n",
      "too similar \t school data -> school data business |dist:  1.3150095\n",
      "too similar \t goal project -> goal project |dist:  0.0\n",
      "too similar \t internet service provider -> internet service provider |dist:  0.0\n",
      "too similar \t school data business -> school data business |dist:  0.0\n",
      "too similar \t company aegis school -> company aegis school |dist:  0.0\n",
      "Fitting: (212, 50)\n",
      "NearestNeighbors(algorithm='brute', metric='euclidean', n_neighbors=15)\n",
      "too similar \t modelling -> modelling |dist:  8.4293696e-08\n",
      "too similar \t python -> python |dist:  8.4293696e-08\n",
      "too similar \t technology assisted review -> technology assisted review |dist:  0.0\n",
      "too similar \t analytics investigative platform -> analytics investigative platform |dist:  5.9604645e-08\n",
      "too similar \t scikit learn -> scikit learn |dist:  0.0\n",
      "too similar \t information -> information |dist:  0.0\n",
      "too similar \t platform -> platform |dist:  0.0\n",
      "too similar \t assurance associate -> assurance associate |dist:  0.0\n",
      "too similar \t data -> data |dist:  0.0\n",
      "too similar \t fraud -> fraud |dist:  0.0\n",
      "too similar \t ernst young llp -> ernst young llp |dist:  0.0\n",
      "too similar \t topic modelling -> topic modelling |dist:  0.0\n",
      "too similar \t elastic search -> elastic search |dist:  0.0\n",
      "too similar \t analytics -> analytics |dist:  0.0\n",
      "too similar \t science assurance -> science assurance |dist:  0.0\n",
      "too similar \t review -> review |dist:  1.1920929e-07\n",
      "too similar \t data science assurance -> data science assurance |dist:  8.4293696e-08\n",
      "too similar \t tools technologies python -> tools technologies python |dist:  0.0\n",
      "too similar \t topic -> topic |dist:  0.0\n",
      "too similar \t analysis -> analysis |dist:  0.0\n",
      "too similar \t fraud analytics investigative -> fraud analytics investigative |dist:  0.0\n",
      "too similar \t tools technologies -> tools technologies |dist:  0.0\n",
      "too similar \t coding topic modelling -> coding topic modelling |dist:  0.0\n",
      "too similar \t data science -> data science |dist:  0.0\n",
      "too similar \t predictive coding topic -> predictive coding topic |dist:  0.0\n",
      "too similar \t technologies python -> technologies python |dist:  0.0\n",
      "too similar \t natural language processing -> natural language processing |dist:  0.0\n",
      "too similar \t sentiment analysis -> sentiment analysis |dist:  0.0\n",
      "too similar \t science assurance associate -> science assurance associate |dist:  0.0\n",
      "too similar \t technology assisted -> technology assisted |dist:  0.0\n",
      "Fitting: (212, 50)\n",
      "NearestNeighbors(algorithm='brute', metric='euclidean', n_neighbors=15)\n",
      "too similar \t company -> company |dist:  0.0\n",
      "too similar \t business professionals dummies -> business professionals dummies |dist:  0.0\n",
      "too similar \t matelabs -> matelabs |dist:  0.0\n",
      "too similar \t detection -> detection |dist:  0.0\n",
      "too similar \t rgpv data -> rgpv data |dist:  0.0\n",
      "too similar \t uit rgpv data -> uit rgpv data |dist:  0.0\n",
      "too similar \t uit rgpv -> uit rgpv |dist:  0.0\n",
      "too similar \t outlier detection -> outlier detection |dist:  0.0\n",
      "too similar \t company matelabs -> company matelabs |dist:  0.0\n",
      "too similar \t data -> data |dist:  0.0\n",
      "too similar \t machine -> machine |dist:  8.4293696e-08\n",
      "too similar \t scientist matelabs -> scientist matelabs |dist:  4.2146848e-08\n",
      "too similar \t data scientist matelabs -> rgpv data scientist |dist:  0.0\n",
      "too similar \t time series forecasting -> time series forecasting |dist:  0.0\n",
      "too similar \t time series -> time series |dist:  0.0\n",
      "too similar \t learning -> learning |dist:  0.0\n",
      "too similar \t sukh sagar bengaluru -> sukh sagar bengaluru |dist:  0.0\n",
      "too similar \t scientist data -> data scientist |dist:  0.0\n",
      "too similar \t outlier -> outlier |dist:  0.0\n",
      "too similar \t data scientist data -> data scientist |dist:  1.0836436\n",
      "too similar \t data scientist -> data scientist |dist:  0.0\n",
      "too similar \t statsmodels months aws -> statsmodels months aws |dist:  0.0\n",
      "too similar \t model -> model |dist:  0.0\n",
      "too similar \t rgpv data scientist -> rgpv data scientist |dist:  0.0\n",
      "too similar \t deployed -> deployed |dist:  0.0\n",
      "too similar \t scientist data scientist -> scientist data scientist |dist:  0.0\n",
      "too similar \t professionals dummies enthusiasts -> professionals dummies enthusiasts |dist:  0.0\n",
      "too similar \t scientist -> scientist |dist:  8.4293696e-08\n",
      "too similar \t aditya rathore -> aditya rathore |dist:  0.0\n",
      "too similar \t machine learning -> machine learning |dist:  0.0\n",
      "Fitting: (212, 50)\n",
      "NearestNeighbors(algorithm='brute', metric='euclidean', n_neighbors=15)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "too similar \t python -> python |dist:  8.4293696e-08\n",
      "too similar \t higher data -> higher data |dist:  0.0\n",
      "too similar \t webframework intermediate -> webframework intermediate |dist:  0.0\n",
      "too similar \t matlab -> matlab |dist:  0.0\n",
      "too similar \t engineering -> engineering |dist:  0.0\n",
      "too similar \t areas interest -> areas interest |dist:  0.0\n",
      "too similar \t programming -> programming |dist:  0.0\n",
      "too similar \t science -> science |dist:  0.0\n",
      "too similar \t intermediate mipower intermediate -> intermediate mipower intermediate |dist:  0.0\n",
      "too similar \t intermediate xampp mysql -> intermediate xampp mysql |dist:  0.0\n",
      "too similar \t data -> data |dist:  0.0\n",
      "too similar \t science data science -> data science |dist:  0.9465557\n",
      "too similar \t customers -> customers |dist:  0.0\n",
      "too similar \t intermediate xampp -> webframework intermediate |dist:  0.0\n",
      "too similar \t learning -> learning |dist:  0.0\n",
      "too similar \t science electrical -> science electrical |dist:  0.0\n",
      "too similar \t intermediate -> intermediate |dist:  0.0\n",
      "too similar \t data science -> data science |dist:  0.0\n",
      "too similar \t sql year monthscompany -> sql year monthscompany |dist:  0.0\n",
      "too similar \t technology january deeksha -> technology january deeksha |dist:  0.0\n",
      "too similar \t science data -> data science |dist:  0.0\n",
      "too similar \t academy higher data -> academy higher data |dist:  0.0\n",
      "too similar \t data science electrical -> science electrical |dist:  1.5569732\n",
      "too similar \t higher data science -> academy higher data |dist:  1.4324616\n",
      "too similar \t science electrical enthusiast -> science electrical enthusiast |dist:  0.0\n",
      "too similar \t data science data -> data science |dist:  0.9465557\n",
      "too similar \t machine learning -> machine learning |dist:  0.0\n",
      "too similar \t academy higher -> academy higher |dist:  0.0\n",
      "too similar \t data analysis -> analysis |dist:  1.8113198\n",
      "too similar \t electrical -> electrical |dist:  0.0\n",
      "Fitting: (212, 50)\n",
      "NearestNeighbors(algorithm='brute', metric='euclidean', n_neighbors=15)\n",
      "too similar \t industry cross -> industry cross |dist:  8.4293696e-08\n",
      "too similar \t analytics infrastructure top -> analytics infrastructure top |dist:  0.0\n",
      "too similar \t technical environment -> technical environment |dist:  0.0\n",
      "too similar \t sql -> sql |dist:  1.6858739e-07\n",
      "too similar \t design develop data -> design develop data |dist:  0.0\n",
      "too similar \t data -> data |dist:  0.0\n",
      "too similar \t models reporting develop -> models reporting develop |dist:  0.0\n",
      "too similar \t marketing months scm -> management exprience |dist:  1.6975633\n",
      "too similar \t analytics -> analytics |dist:  0.0\n",
      "too similar \t project develop analytics -> project develop analytics |dist:  0.0\n",
      "too similar \t project -> project |dist:  0.0\n",
      "too similar \t accenture solutions pvt -> accenture solutions pvt |dist:  0.0\n",
      "too similar \t develop data -> develop data |dist:  0.0\n",
      "too similar \t cross industry -> industry cross |dist:  8.4293696e-08\n",
      "too similar \t hana -> hana |dist:  0.0\n",
      "too similar \t advance analytics -> advance analytics |dist:  0.0\n",
      "too similar \t develop analytics infrastructure -> develop analytics infrastructure |dist:  8.4293696e-08\n",
      "too similar \t develop -> develop |dist:  0.0\n",
      "too similar \t environment -> environment |dist:  0.0\n",
      "too similar \t reports technical environment -> reports technical environment |dist:  0.0\n",
      "too similar \t technical -> technical |dist:  1.1920929e-07\n",
      "too similar \t develop data models -> develop data models |dist:  0.0\n",
      "too similar \t service area -> service area |dist:  0.0\n",
      "too similar \t data modelling -> data modelling |dist:  0.0\n",
      "too similar \t sql server -> sql server |dist:  0.0\n",
      "too similar \t project involved -> project involved |dist:  0.0\n",
      "too similar \t industry cross industry -> industry cross industry |dist:  0.0\n",
      "too similar \t design develop -> design develop |dist:  8.4293696e-08\n",
      "too similar \t involved -> involved |dist:  8.4293696e-08\n",
      "too similar \t industry -> industry |dist:  0.0\n",
      "Fitting: (212, 50)\n",
      "NearestNeighbors(algorithm='brute', metric='euclidean', n_neighbors=15)\n",
      "too similar \t ymcaust -> matelabs |dist:  0.0\n",
      "too similar \t analysis exprience -> rgpv data scientist |dist:  1.723217\n",
      "too similar \t education details mca -> education details mca |dist:  0.0\n",
      "too similar \t science -> science |dist:  0.0\n",
      "too similar \t mca ymcaust faridabad -> mca ymcaust faridabad |dist:  0.0\n",
      "too similar \t faridabad haryana -> faridabad haryana |dist:  0.0\n",
      "too similar \t mca -> mca |dist:  0.0\n",
      "too similar \t data -> data |dist:  0.0\n",
      "too similar \t structure exprience -> structure exprience |dist:  0.0\n",
      "too similar \t data science internship -> science internship |dist:  1.8893199\n",
      "too similar \t science internship -> science internship |dist:  0.0\n",
      "too similar \t database -> database |dist:  0.0\n",
      "too similar \t management -> management |dist:  0.0\n",
      "too similar \t mca ymcaust -> mca ymcaust |dist:  0.0\n",
      "too similar \t faridabad -> faridabad |dist:  0.0\n",
      "too similar \t database management -> database management |dist:  0.0\n",
      "too similar \t python exprience -> python exprience |dist:  4.2146848e-08\n",
      "too similar \t education -> education |dist:  0.0\n",
      "too similar \t data analysis exprience -> rgpv data |dist:  1.1106238\n",
      "too similar \t data structure exprience -> rgpv data |dist:  1.5294436\n",
      "too similar \t haryana -> haryana |dist:  8.4293696e-08\n",
      "too similar \t management exprience -> management exprience |dist:  0.0\n",
      "too similar \t faridabad haryana data -> faridabad haryana data |dist:  0.0\n",
      "too similar \t database management exprience -> database management |dist:  1.531201\n",
      "too similar \t ymcaust faridabad -> ymcaust faridabad |dist:  0.0\n",
      "too similar \t ymcaust faridabad haryana -> ymcaust faridabad haryana |dist:  0.0\n",
      "too similar \t company itechpower -> company matelabs |dist:  0.0\n",
      "too similar \t exprience -> matelabs |dist:  0.0\n",
      "too similar \t haryana data science -> haryana data science |dist:  0.0\n",
      "Fitting: (212, 50)\n",
      "NearestNeighbors(algorithm='brute', metric='euclidean', n_neighbors=15)\n",
      "too similar \t matlab -> matlab |dist:  0.0\n",
      "too similar \t microsoft word -> microsoft word |dist:  0.0\n",
      "too similar \t powerpoint recognition -> powerpoint recognition |dist:  5.9604645e-08\n",
      "too similar \t science -> science |dist:  0.0\n",
      "too similar \t data -> data |dist:  0.0\n",
      "too similar \t machine -> machine |dist:  8.4293696e-08\n",
      "too similar \t word microsoft -> microsoft word |dist:  0.0\n",
      "too similar \t microsoft excel -> excel microsoft |dist:  0.0\n",
      "too similar \t learning html microsoft -> learning html microsoft |dist:  8.4293696e-08\n",
      "too similar \t microsoft powerpoint -> microsoft powerpoint |dist:  0.0\n",
      "too similar \t html microsoft word -> html microsoft word |dist:  0.0\n",
      "too similar \t microsoft word microsoft -> microsoft word microsoft |dist:  0.0\n",
      "too similar \t learning -> learning |dist:  0.0\n",
      "too similar \t powerpoint recognition academic -> powerpoint recognition academic |dist:  0.0\n",
      "too similar \t microsoft excel microsoft -> microsoft excel microsoft |dist:  8.4293696e-08\n",
      "too similar \t tech -> tech |dist:  8.4293696e-08\n",
      "too similar \t education -> education |dist:  0.0\n",
      "too similar \t data science -> data science |dist:  0.0\n",
      "too similar \t recognition academic -> recognition academic |dist:  8.4293696e-08\n",
      "too similar \t word microsoft excel -> word microsoft excel |dist:  5.9604645e-08\n",
      "too similar \t basics iot python -> python exprience |dist:  1.908187\n",
      "too similar \t html microsoft -> html microsoft |dist:  0.0\n",
      "too similar \t html -> html |dist:  0.0\n",
      "too similar \t microsoft powerpoint recognition -> microsoft powerpoint recognition |dist:  5.9604645e-08\n",
      "too similar \t excel microsoft -> excel microsoft |dist:  0.0\n",
      "too similar \t machine learning -> machine learning |dist:  0.0\n",
      "too similar \t exprience -> matelabs |dist:  0.0\n",
      "too similar \t microsoft -> microsoft |dist:  0.0\n",
      "too similar \t excel microsoft powerpoint -> excel microsoft powerpoint |dist:  0.0\n",
      "too similar \t recognition academic secured -> recognition academic secured |dist:  0.0\n",
      "Fitting: (212, 50)\n",
      "NearestNeighbors(algorithm='brute', metric='euclidean', n_neighbors=15)\n",
      "too similar \t python -> python |dist:  8.4293696e-08\n",
      "too similar \t processing data -> processing data |dist:  0.0\n",
      "too similar \t data visualization company -> data visualization company |dist:  0.0\n",
      "too similar \t consultant -> consultant |dist:  0.0\n",
      "too similar \t bringing business -> industry cross industry |dist:  1.9801528\n",
      "too similar \t visualization company -> visualization company |dist:  0.0\n",
      "too similar \t science -> science |dist:  0.0\n",
      "too similar \t python excel -> python excel |dist:  0.0\n",
      "too similar \t science consultant datamites -> consultant datamites |dist:  1.5498204\n",
      "too similar \t advanced querying visualization -> advanced querying visualization |dist:  0.0\n",
      "too similar \t consultant datamites -> consultant datamites |dist:  0.0\n",
      "too similar \t data -> data |dist:  0.0\n",
      "too similar \t machine -> machine |dist:  8.4293696e-08\n",
      "too similar \t python data visualization -> python data visualization |dist:  0.0\n",
      "too similar \t querying visualization analytics -> querying visualization analytics |dist:  5.9604645e-08\n",
      "too similar \t visualization -> visualization |dist:  0.0\n",
      "too similar \t data science consultant -> data science |dist:  1.6172836\n",
      "too similar \t science consultant -> science consultant |dist:  0.0\n",
      "too similar \t learning -> learning |dist:  0.0\n",
      "too similar \t manipulating cleaning processing -> manipulating cleaning processing |dist:  8.4293696e-08\n",
      "too similar \t solutions -> solutions |dist:  0.0\n",
      "too similar \t consultant data science -> data science |dist:  1.6172837\n",
      "too similar \t data science -> data science |dist:  0.0\n",
      "too similar \t processing -> processing |dist:  0.0\n",
      "too similar \t science consultant data -> data science |dist:  1.6172837\n",
      "too similar \t business -> business |dist:  0.0\n",
      "too similar \t visualization company heretic -> visualization company heretic |dist:  0.0\n",
      "too similar \t machine learning -> machine learning |dist:  0.0\n",
      "too similar \t data visualization -> data modelling |dist:  1.8619689\n",
      "Fitting: (212, 50)\n",
      "NearestNeighbors(algorithm='brute', metric='euclidean', n_neighbors=15)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "too similar \t python -> python |dist:  8.4293696e-08\n",
      "too similar \t communication skills problem -> communication skills problem |dist:  8.4293696e-08\n",
      "too similar \t entities -> entities |dist:  0.0\n",
      "too similar \t machine learning deep -> deep machine |dist:  1.4554808\n",
      "too similar \t learning data -> deep learning data |dist:  1.6702614\n",
      "too similar \t deep -> deep |dist:  8.4293696e-08\n",
      "too similar \t knowledge python -> knowledge python |dist:  5.9604645e-08\n",
      "too similar \t machine -> machine |dist:  8.4293696e-08\n",
      "too similar \t data -> data |dist:  0.0\n",
      "too similar \t learning deep learning -> deep learning |dist:  0.8806673\n",
      "too similar \t neural intelligence -> neural intelligence |dist:  0.0\n",
      "too similar \t neural intelligence platform -> neural intelligence platform |dist:  0.0\n",
      "too similar \t knowledge -> knowledge |dist:  0.0\n",
      "too similar \t wipro neural intelligence -> wipro neural intelligence |dist:  0.0\n",
      "too similar \t laterally think medium -> laterally think medium |dist:  0.0\n",
      "too similar \t learning -> learning |dist:  0.0\n",
      "too similar \t deep machine learning -> deep machine |dist:  1.4554808\n",
      "too similar \t data science -> data science |dist:  0.0\n",
      "too similar \t intelligence platform -> intelligence platform |dist:  0.0\n",
      "too similar \t learning data science -> data science |dist:  1.2651149\n",
      "too similar \t deep learning -> deep learning |dist:  8.4293696e-08\n",
      "too similar \t queries -> queries |dist:  0.0\n",
      "too similar \t wipro -> wipro |dist:  0.0\n",
      "too similar \t user queries -> user queries |dist:  0.0\n",
      "too similar \t machine learning nlp -> machine learning nlp |dist:  0.0\n",
      "too similar \t learning deep -> deep learning |dist:  8.4293696e-08\n",
      "too similar \t machine learning -> machine learning |dist:  0.0\n",
      "too similar \t neural -> neural |dist:  1.6858739e-07\n",
      "too similar \t deep machine -> deep machine |dist:  0.0\n",
      "too similar \t deep learning data -> deep learning data |dist:  0.0\n",
      "Fitting: (212, 50)\n",
      "NearestNeighbors(algorithm='brute', metric='euclidean', n_neighbors=15)\n",
      "too similar \t python -> python |dist:  8.4293696e-08\n",
      "too similar \t madhya pradesh -> madhya pradesh |dist:  1.1920929e-07\n",
      "too similar \t python sql web -> python sql web |dist:  0.0\n",
      "too similar \t information technology -> information technology |dist:  0.0\n",
      "too similar \t bhopal madhya -> bhopal madhya |dist:  0.0\n",
      "too similar \t engineering -> engineering |dist:  0.0\n",
      "too similar \t machine learning deep -> deep machine |dist:  1.4554808\n",
      "too similar \t python sql -> python sql |dist:  0.0\n",
      "too similar \t deep leaning nlp -> deep leaning nlp |dist:  0.0\n",
      "too similar \t science -> science |dist:  0.0\n",
      "too similar \t technology -> technology |dist:  0.0\n",
      "too similar \t deep -> deep |dist:  8.4293696e-08\n",
      "too similar \t nlp python -> nlp python |dist:  0.0\n",
      "too similar \t machine -> machine |dist:  8.4293696e-08\n",
      "too similar \t pradesh truba institute -> pradesh rkdf institute |dist:  0.0\n",
      "too similar \t pradesh rkdf institute -> pradesh rkdf institute |dist:  0.0\n",
      "too similar \t products shared customers -> products shared customers |dist:  8.4293696e-08\n",
      "too similar \t learning -> learning |dist:  0.0\n",
      "too similar \t bhopal madhya pradesh -> bhopal madhya pradesh |dist:  0.0\n",
      "too similar \t madhya -> madhya |dist:  0.0\n",
      "too similar \t deep learning -> deep learning |dist:  8.4293696e-08\n",
      "too similar \t pradesh -> pradesh |dist:  0.0\n",
      "too similar \t sql web -> sql web |dist:  0.0\n",
      "too similar \t quickly grasp technical -> quickly grasp technical |dist:  5.9604645e-08\n",
      "too similar \t nlp python sql -> nlp python sql |dist:  0.0\n",
      "too similar \t life insurance -> life insurance |dist:  8.4293696e-08\n",
      "too similar \t personal ability quickly -> personal ability quickly |dist:  8.4293696e-08\n",
      "too similar \t learning deep -> deep learning |dist:  8.4293696e-08\n",
      "too similar \t web -> web |dist:  0.0\n",
      "too similar \t machine learning -> machine learning |dist:  0.0\n",
      "Fitting: (212, 50)\n",
      "NearestNeighbors(algorithm='brute', metric='euclidean', n_neighbors=15)\n",
      "too similar \t company -> company |dist:  0.0\n",
      "too similar \t school data science -> school data business |dist:  1.6046574\n",
      "too similar \t called data science -> data science |dist:  1.5367012\n",
      "too similar \t school -> school |dist:  0.0\n",
      "too similar \t science business -> science consultant |dist:  1.9894137\n",
      "too similar \t school business mumbai -> school business mumbai |dist:  8.4293696e-08\n",
      "too similar \t science -> science |dist:  0.0\n",
      "too similar \t service provider -> service provider |dist:  0.0\n",
      "too similar \t data -> data |dist:  0.0\n",
      "too similar \t aegis school data -> aegis school business |dist:  1.7298081\n",
      "too similar \t data science mumbai -> haryana data science |dist:  1.7915723\n",
      "too similar \t science mumbai -> science mumbai |dist:  0.0\n",
      "too similar \t company aegis -> company aegis |dist:  0.0\n",
      "too similar \t aegis school -> aegis school |dist:  0.0\n",
      "too similar \t aegis -> aegis |dist:  0.0\n",
      "too similar \t analysis -> analysis |dist:  0.0\n",
      "too similar \t data science business -> data science |dist:  1.3739043\n",
      "too similar \t project -> project |dist:  0.0\n",
      "too similar \t mumbai -> mumbai |dist:  0.0\n",
      "too similar \t data science -> data science |dist:  0.0\n",
      "too similar \t business -> business |dist:  0.0\n",
      "too similar \t data scientist -> data scientist |dist:  0.0\n",
      "too similar \t face -> face |dist:  0.0\n",
      "too similar \t aegis school business -> aegis school business |dist:  5.9604645e-08\n",
      "too similar \t school data -> school data business |dist:  1.3150095\n",
      "too similar \t goal project -> goal project |dist:  0.0\n",
      "too similar \t internet service provider -> internet service provider |dist:  0.0\n",
      "too similar \t school data business -> school data business |dist:  0.0\n",
      "too similar \t company aegis school -> company aegis school |dist:  0.0\n",
      "Fitting: (212, 50)\n",
      "NearestNeighbors(algorithm='brute', metric='euclidean', n_neighbors=15)\n",
      "too similar \t modelling -> modelling |dist:  8.4293696e-08\n",
      "too similar \t python -> python |dist:  8.4293696e-08\n",
      "too similar \t technology assisted review -> technology assisted review |dist:  0.0\n",
      "too similar \t analytics investigative platform -> analytics investigative platform |dist:  5.9604645e-08\n",
      "too similar \t scikit learn -> scikit learn |dist:  0.0\n",
      "too similar \t information -> information |dist:  0.0\n",
      "too similar \t platform -> platform |dist:  0.0\n",
      "too similar \t assurance associate -> assurance associate |dist:  0.0\n",
      "too similar \t data -> data |dist:  0.0\n",
      "too similar \t fraud -> fraud |dist:  0.0\n",
      "too similar \t ernst young llp -> ernst young llp |dist:  0.0\n",
      "too similar \t topic modelling -> topic modelling |dist:  0.0\n",
      "too similar \t elastic search -> elastic search |dist:  0.0\n",
      "too similar \t analytics -> analytics |dist:  0.0\n",
      "too similar \t science assurance -> science assurance |dist:  0.0\n",
      "too similar \t review -> review |dist:  1.1920929e-07\n",
      "too similar \t data science assurance -> data science assurance |dist:  8.4293696e-08\n",
      "too similar \t tools technologies python -> tools technologies python |dist:  0.0\n",
      "too similar \t topic -> topic |dist:  0.0\n",
      "too similar \t analysis -> analysis |dist:  0.0\n",
      "too similar \t fraud analytics investigative -> fraud analytics investigative |dist:  0.0\n",
      "too similar \t tools technologies -> tools technologies |dist:  0.0\n",
      "too similar \t coding topic modelling -> coding topic modelling |dist:  0.0\n",
      "too similar \t data science -> data science |dist:  0.0\n",
      "too similar \t predictive coding topic -> predictive coding topic |dist:  0.0\n",
      "too similar \t technologies python -> technologies python |dist:  0.0\n",
      "too similar \t natural language processing -> natural language processing |dist:  0.0\n",
      "too similar \t sentiment analysis -> sentiment analysis |dist:  0.0\n",
      "too similar \t science assurance associate -> science assurance associate |dist:  0.0\n",
      "too similar \t technology assisted -> technology assisted |dist:  0.0\n",
      "Fitting: (212, 50)\n",
      "NearestNeighbors(algorithm='brute', metric='euclidean', n_neighbors=15)\n",
      "too similar \t company -> company |dist:  0.0\n",
      "too similar \t business professionals dummies -> business professionals dummies |dist:  0.0\n",
      "too similar \t matelabs -> matelabs |dist:  0.0\n",
      "too similar \t detection -> detection |dist:  0.0\n",
      "too similar \t rgpv data -> rgpv data |dist:  0.0\n",
      "too similar \t uit rgpv data -> uit rgpv data |dist:  0.0\n",
      "too similar \t uit rgpv -> uit rgpv |dist:  0.0\n",
      "too similar \t outlier detection -> outlier detection |dist:  0.0\n",
      "too similar \t company matelabs -> company matelabs |dist:  0.0\n",
      "too similar \t data -> data |dist:  0.0\n",
      "too similar \t machine -> machine |dist:  8.4293696e-08\n",
      "too similar \t scientist matelabs -> scientist matelabs |dist:  4.2146848e-08\n",
      "too similar \t data scientist matelabs -> rgpv data scientist |dist:  0.0\n",
      "too similar \t time series forecasting -> time series forecasting |dist:  0.0\n",
      "too similar \t time series -> time series |dist:  0.0\n",
      "too similar \t learning -> learning |dist:  0.0\n",
      "too similar \t sukh sagar bengaluru -> sukh sagar bengaluru |dist:  0.0\n",
      "too similar \t scientist data -> data scientist |dist:  0.0\n",
      "too similar \t outlier -> outlier |dist:  0.0\n",
      "too similar \t data scientist data -> data scientist |dist:  1.0836436\n",
      "too similar \t data scientist -> data scientist |dist:  0.0\n",
      "too similar \t statsmodels months aws -> statsmodels months aws |dist:  0.0\n",
      "too similar \t model -> model |dist:  0.0\n",
      "too similar \t rgpv data scientist -> rgpv data scientist |dist:  0.0\n",
      "too similar \t deployed -> deployed |dist:  0.0\n",
      "too similar \t scientist data scientist -> scientist data scientist |dist:  0.0\n",
      "too similar \t professionals dummies enthusiasts -> professionals dummies enthusiasts |dist:  0.0\n",
      "too similar \t scientist -> scientist |dist:  8.4293696e-08\n",
      "too similar \t aditya rathore -> aditya rathore |dist:  0.0\n",
      "too similar \t machine learning -> machine learning |dist:  0.0\n",
      "Fitting: (212, 50)\n",
      "NearestNeighbors(algorithm='brute', metric='euclidean', n_neighbors=15)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "too similar \t python -> python |dist:  8.4293696e-08\n",
      "too similar \t higher data -> higher data |dist:  0.0\n",
      "too similar \t webframework intermediate -> webframework intermediate |dist:  0.0\n",
      "too similar \t matlab -> matlab |dist:  0.0\n",
      "too similar \t engineering -> engineering |dist:  0.0\n",
      "too similar \t areas interest -> areas interest |dist:  0.0\n",
      "too similar \t programming -> programming |dist:  0.0\n",
      "too similar \t science -> science |dist:  0.0\n",
      "too similar \t intermediate mipower intermediate -> intermediate mipower intermediate |dist:  0.0\n",
      "too similar \t intermediate xampp mysql -> intermediate xampp mysql |dist:  0.0\n",
      "too similar \t data -> data |dist:  0.0\n",
      "too similar \t science data science -> data science |dist:  0.9465557\n",
      "too similar \t customers -> customers |dist:  0.0\n",
      "too similar \t intermediate xampp -> webframework intermediate |dist:  0.0\n",
      "too similar \t learning -> learning |dist:  0.0\n",
      "too similar \t science electrical -> science electrical |dist:  0.0\n",
      "too similar \t intermediate -> intermediate |dist:  0.0\n",
      "too similar \t data science -> data science |dist:  0.0\n",
      "too similar \t sql year monthscompany -> sql year monthscompany |dist:  0.0\n",
      "too similar \t technology january deeksha -> technology january deeksha |dist:  0.0\n",
      "too similar \t science data -> data science |dist:  0.0\n",
      "too similar \t academy higher data -> academy higher data |dist:  0.0\n",
      "too similar \t data science electrical -> science electrical |dist:  1.5569732\n",
      "too similar \t higher data science -> academy higher data |dist:  1.4324616\n",
      "too similar \t science electrical enthusiast -> science electrical enthusiast |dist:  0.0\n",
      "too similar \t data science data -> data science |dist:  0.9465557\n",
      "too similar \t machine learning -> machine learning |dist:  0.0\n",
      "too similar \t academy higher -> academy higher |dist:  0.0\n",
      "too similar \t data analysis -> analysis |dist:  1.8113198\n",
      "too similar \t electrical -> electrical |dist:  0.0\n",
      "Fitting: (212, 50)\n",
      "NearestNeighbors(algorithm='brute', metric='euclidean', n_neighbors=15)\n",
      "too similar \t industry cross -> industry cross |dist:  8.4293696e-08\n",
      "too similar \t analytics infrastructure top -> analytics infrastructure top |dist:  0.0\n",
      "too similar \t technical environment -> technical environment |dist:  0.0\n",
      "too similar \t sql -> sql |dist:  1.6858739e-07\n",
      "too similar \t design develop data -> design develop data |dist:  0.0\n",
      "too similar \t data -> data |dist:  0.0\n",
      "too similar \t models reporting develop -> models reporting develop |dist:  0.0\n",
      "too similar \t marketing months scm -> management exprience |dist:  1.6975633\n",
      "too similar \t analytics -> analytics |dist:  0.0\n",
      "too similar \t project develop analytics -> project develop analytics |dist:  0.0\n",
      "too similar \t project -> project |dist:  0.0\n",
      "too similar \t accenture solutions pvt -> accenture solutions pvt |dist:  0.0\n",
      "too similar \t develop data -> develop data |dist:  0.0\n",
      "too similar \t cross industry -> industry cross |dist:  8.4293696e-08\n",
      "too similar \t hana -> hana |dist:  0.0\n",
      "too similar \t advance analytics -> advance analytics |dist:  0.0\n",
      "too similar \t develop analytics infrastructure -> develop analytics infrastructure |dist:  8.4293696e-08\n",
      "too similar \t develop -> develop |dist:  0.0\n",
      "too similar \t environment -> environment |dist:  0.0\n",
      "too similar \t reports technical environment -> reports technical environment |dist:  0.0\n",
      "too similar \t technical -> technical |dist:  1.1920929e-07\n",
      "too similar \t develop data models -> develop data models |dist:  0.0\n",
      "too similar \t service area -> service area |dist:  0.0\n",
      "too similar \t data modelling -> data modelling |dist:  0.0\n",
      "too similar \t sql server -> sql server |dist:  0.0\n",
      "too similar \t project involved -> project involved |dist:  0.0\n",
      "too similar \t industry cross industry -> industry cross industry |dist:  0.0\n",
      "too similar \t design develop -> design develop |dist:  8.4293696e-08\n",
      "too similar \t involved -> involved |dist:  8.4293696e-08\n",
      "too similar \t industry -> industry |dist:  0.0\n",
      "Fitting: (212, 50)\n",
      "NearestNeighbors(algorithm='brute', metric='euclidean', n_neighbors=15)\n",
      "too similar \t ymcaust -> matelabs |dist:  0.0\n",
      "too similar \t analysis exprience -> rgpv data scientist |dist:  1.723217\n",
      "too similar \t education details mca -> education details mca |dist:  0.0\n",
      "too similar \t science -> science |dist:  0.0\n",
      "too similar \t mca ymcaust faridabad -> mca ymcaust faridabad |dist:  0.0\n",
      "too similar \t faridabad haryana -> faridabad haryana |dist:  0.0\n",
      "too similar \t mca -> mca |dist:  0.0\n",
      "too similar \t data -> data |dist:  0.0\n",
      "too similar \t structure exprience -> structure exprience |dist:  0.0\n",
      "too similar \t data science internship -> science internship |dist:  1.8893199\n",
      "too similar \t science internship -> science internship |dist:  0.0\n",
      "too similar \t database -> database |dist:  0.0\n",
      "too similar \t management -> management |dist:  0.0\n",
      "too similar \t mca ymcaust -> mca ymcaust |dist:  0.0\n",
      "too similar \t faridabad -> faridabad |dist:  0.0\n",
      "too similar \t database management -> database management |dist:  0.0\n",
      "too similar \t python exprience -> python exprience |dist:  4.2146848e-08\n",
      "too similar \t education -> education |dist:  0.0\n",
      "too similar \t data analysis exprience -> rgpv data |dist:  1.1106238\n",
      "too similar \t data structure exprience -> rgpv data |dist:  1.5294436\n",
      "too similar \t haryana -> haryana |dist:  8.4293696e-08\n",
      "too similar \t management exprience -> management exprience |dist:  0.0\n",
      "too similar \t faridabad haryana data -> faridabad haryana data |dist:  0.0\n",
      "too similar \t database management exprience -> database management |dist:  1.531201\n",
      "too similar \t ymcaust faridabad -> ymcaust faridabad |dist:  0.0\n",
      "too similar \t ymcaust faridabad haryana -> ymcaust faridabad haryana |dist:  0.0\n",
      "too similar \t company itechpower -> company matelabs |dist:  0.0\n",
      "too similar \t exprience -> matelabs |dist:  0.0\n",
      "too similar \t haryana data science -> haryana data science |dist:  0.0\n",
      "Fitting: (212, 50)\n",
      "NearestNeighbors(algorithm='brute', metric='euclidean', n_neighbors=15)\n",
      "too similar \t matlab -> matlab |dist:  0.0\n",
      "too similar \t microsoft word -> microsoft word |dist:  0.0\n",
      "too similar \t powerpoint recognition -> powerpoint recognition |dist:  5.9604645e-08\n",
      "too similar \t science -> science |dist:  0.0\n",
      "too similar \t data -> data |dist:  0.0\n",
      "too similar \t machine -> machine |dist:  8.4293696e-08\n",
      "too similar \t word microsoft -> microsoft word |dist:  0.0\n",
      "too similar \t microsoft excel -> excel microsoft |dist:  0.0\n",
      "too similar \t learning html microsoft -> learning html microsoft |dist:  8.4293696e-08\n",
      "too similar \t microsoft powerpoint -> microsoft powerpoint |dist:  0.0\n",
      "too similar \t html microsoft word -> html microsoft word |dist:  0.0\n",
      "too similar \t microsoft word microsoft -> microsoft word microsoft |dist:  0.0\n",
      "too similar \t learning -> learning |dist:  0.0\n",
      "too similar \t powerpoint recognition academic -> powerpoint recognition academic |dist:  0.0\n",
      "too similar \t microsoft excel microsoft -> microsoft excel microsoft |dist:  8.4293696e-08\n",
      "too similar \t tech -> tech |dist:  8.4293696e-08\n",
      "too similar \t education -> education |dist:  0.0\n",
      "too similar \t data science -> data science |dist:  0.0\n",
      "too similar \t recognition academic -> recognition academic |dist:  8.4293696e-08\n",
      "too similar \t word microsoft excel -> word microsoft excel |dist:  5.9604645e-08\n",
      "too similar \t basics iot python -> python exprience |dist:  1.908187\n",
      "too similar \t html microsoft -> html microsoft |dist:  0.0\n",
      "too similar \t html -> html |dist:  0.0\n",
      "too similar \t microsoft powerpoint recognition -> microsoft powerpoint recognition |dist:  5.9604645e-08\n",
      "too similar \t excel microsoft -> excel microsoft |dist:  0.0\n",
      "too similar \t machine learning -> machine learning |dist:  0.0\n",
      "too similar \t exprience -> matelabs |dist:  0.0\n",
      "too similar \t microsoft -> microsoft |dist:  0.0\n",
      "too similar \t excel microsoft powerpoint -> excel microsoft powerpoint |dist:  0.0\n",
      "too similar \t recognition academic secured -> recognition academic secured |dist:  0.0\n",
      "Fitting: (212, 50)\n",
      "NearestNeighbors(algorithm='brute', metric='euclidean', n_neighbors=15)\n",
      "too similar \t python -> python |dist:  8.4293696e-08\n",
      "too similar \t processing data -> processing data |dist:  0.0\n",
      "too similar \t data visualization company -> data visualization company |dist:  0.0\n",
      "too similar \t consultant -> consultant |dist:  0.0\n",
      "too similar \t bringing business -> industry cross industry |dist:  1.9801528\n",
      "too similar \t visualization company -> visualization company |dist:  0.0\n",
      "too similar \t science -> science |dist:  0.0\n",
      "too similar \t python excel -> python excel |dist:  0.0\n",
      "too similar \t science consultant datamites -> consultant datamites |dist:  1.5498204\n",
      "too similar \t advanced querying visualization -> advanced querying visualization |dist:  0.0\n",
      "too similar \t consultant datamites -> consultant datamites |dist:  0.0\n",
      "too similar \t data -> data |dist:  0.0\n",
      "too similar \t machine -> machine |dist:  8.4293696e-08\n",
      "too similar \t python data visualization -> python data visualization |dist:  0.0\n",
      "too similar \t querying visualization analytics -> querying visualization analytics |dist:  5.9604645e-08\n",
      "too similar \t visualization -> visualization |dist:  0.0\n",
      "too similar \t data science consultant -> data science |dist:  1.6172836\n",
      "too similar \t science consultant -> science consultant |dist:  0.0\n",
      "too similar \t learning -> learning |dist:  0.0\n",
      "too similar \t manipulating cleaning processing -> manipulating cleaning processing |dist:  8.4293696e-08\n",
      "too similar \t solutions -> solutions |dist:  0.0\n",
      "too similar \t consultant data science -> data science |dist:  1.6172837\n",
      "too similar \t data science -> data science |dist:  0.0\n",
      "too similar \t processing -> processing |dist:  0.0\n",
      "too similar \t science consultant data -> data science |dist:  1.6172837\n",
      "too similar \t business -> business |dist:  0.0\n",
      "too similar \t visualization company heretic -> visualization company heretic |dist:  0.0\n",
      "too similar \t machine learning -> machine learning |dist:  0.0\n",
      "too similar \t data visualization -> data modelling |dist:  1.8619689\n",
      "Fitting: (212, 50)\n",
      "NearestNeighbors(algorithm='brute', metric='euclidean', n_neighbors=15)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "too similar \t python -> python |dist:  8.4293696e-08\n",
      "too similar \t communication skills problem -> communication skills problem |dist:  8.4293696e-08\n",
      "too similar \t entities -> entities |dist:  0.0\n",
      "too similar \t machine learning deep -> deep machine |dist:  1.4554808\n",
      "too similar \t learning data -> deep learning data |dist:  1.6702614\n",
      "too similar \t deep -> deep |dist:  8.4293696e-08\n",
      "too similar \t knowledge python -> knowledge python |dist:  5.9604645e-08\n",
      "too similar \t machine -> machine |dist:  8.4293696e-08\n",
      "too similar \t data -> data |dist:  0.0\n",
      "too similar \t learning deep learning -> deep learning |dist:  0.8806673\n",
      "too similar \t neural intelligence -> neural intelligence |dist:  0.0\n",
      "too similar \t neural intelligence platform -> neural intelligence platform |dist:  0.0\n",
      "too similar \t knowledge -> knowledge |dist:  0.0\n",
      "too similar \t wipro neural intelligence -> wipro neural intelligence |dist:  0.0\n",
      "too similar \t laterally think medium -> laterally think medium |dist:  0.0\n",
      "too similar \t learning -> learning |dist:  0.0\n",
      "too similar \t deep machine learning -> deep machine |dist:  1.4554808\n",
      "too similar \t data science -> data science |dist:  0.0\n",
      "too similar \t intelligence platform -> intelligence platform |dist:  0.0\n",
      "too similar \t learning data science -> data science |dist:  1.2651149\n",
      "too similar \t deep learning -> deep learning |dist:  8.4293696e-08\n",
      "too similar \t queries -> queries |dist:  0.0\n",
      "too similar \t wipro -> wipro |dist:  0.0\n",
      "too similar \t user queries -> user queries |dist:  0.0\n",
      "too similar \t machine learning nlp -> machine learning nlp |dist:  0.0\n",
      "too similar \t learning deep -> deep learning |dist:  8.4293696e-08\n",
      "too similar \t machine learning -> machine learning |dist:  0.0\n",
      "too similar \t neural -> neural |dist:  1.6858739e-07\n",
      "too similar \t deep machine -> deep machine |dist:  0.0\n",
      "too similar \t deep learning data -> deep learning data |dist:  0.0\n",
      "Fitting: (212, 50)\n",
      "NearestNeighbors(algorithm='brute', metric='euclidean', n_neighbors=15)\n",
      "too similar \t python -> python |dist:  8.4293696e-08\n",
      "too similar \t madhya pradesh -> madhya pradesh |dist:  1.1920929e-07\n",
      "too similar \t python sql web -> python sql web |dist:  0.0\n",
      "too similar \t information technology -> information technology |dist:  0.0\n",
      "too similar \t bhopal madhya -> bhopal madhya |dist:  0.0\n",
      "too similar \t engineering -> engineering |dist:  0.0\n",
      "too similar \t machine learning deep -> deep machine |dist:  1.4554808\n",
      "too similar \t python sql -> python sql |dist:  0.0\n",
      "too similar \t deep leaning nlp -> deep leaning nlp |dist:  0.0\n",
      "too similar \t science -> science |dist:  0.0\n",
      "too similar \t technology -> technology |dist:  0.0\n",
      "too similar \t deep -> deep |dist:  8.4293696e-08\n",
      "too similar \t nlp python -> nlp python |dist:  0.0\n",
      "too similar \t machine -> machine |dist:  8.4293696e-08\n",
      "too similar \t pradesh truba institute -> pradesh rkdf institute |dist:  0.0\n",
      "too similar \t pradesh rkdf institute -> pradesh rkdf institute |dist:  0.0\n",
      "too similar \t products shared customers -> products shared customers |dist:  8.4293696e-08\n",
      "too similar \t learning -> learning |dist:  0.0\n",
      "too similar \t bhopal madhya pradesh -> bhopal madhya pradesh |dist:  0.0\n",
      "too similar \t madhya -> madhya |dist:  0.0\n",
      "too similar \t deep learning -> deep learning |dist:  8.4293696e-08\n",
      "too similar \t pradesh -> pradesh |dist:  0.0\n",
      "too similar \t sql web -> sql web |dist:  0.0\n",
      "too similar \t quickly grasp technical -> quickly grasp technical |dist:  5.9604645e-08\n",
      "too similar \t nlp python sql -> nlp python sql |dist:  0.0\n",
      "too similar \t life insurance -> life insurance |dist:  8.4293696e-08\n",
      "too similar \t personal ability quickly -> personal ability quickly |dist:  8.4293696e-08\n",
      "too similar \t learning deep -> deep learning |dist:  8.4293696e-08\n",
      "too similar \t web -> web |dist:  0.0\n",
      "too similar \t machine learning -> machine learning |dist:  0.0\n",
      "Fitting: (212, 50)\n",
      "NearestNeighbors(algorithm='brute', metric='euclidean', n_neighbors=15)\n",
      "too similar \t company -> company |dist:  0.0\n",
      "too similar \t school data science -> school data business |dist:  1.6046574\n",
      "too similar \t called data science -> data science |dist:  1.5367012\n",
      "too similar \t school -> school |dist:  0.0\n",
      "too similar \t science business -> science consultant |dist:  1.9894137\n",
      "too similar \t school business mumbai -> school business mumbai |dist:  8.4293696e-08\n",
      "too similar \t science -> science |dist:  0.0\n",
      "too similar \t service provider -> service provider |dist:  0.0\n",
      "too similar \t data -> data |dist:  0.0\n",
      "too similar \t aegis school data -> aegis school business |dist:  1.7298081\n",
      "too similar \t data science mumbai -> haryana data science |dist:  1.7915723\n",
      "too similar \t science mumbai -> science mumbai |dist:  0.0\n",
      "too similar \t company aegis -> company aegis |dist:  0.0\n",
      "too similar \t aegis school -> aegis school |dist:  0.0\n",
      "too similar \t aegis -> aegis |dist:  0.0\n",
      "too similar \t analysis -> analysis |dist:  0.0\n",
      "too similar \t data science business -> data science |dist:  1.3739043\n",
      "too similar \t project -> project |dist:  0.0\n",
      "too similar \t mumbai -> mumbai |dist:  0.0\n",
      "too similar \t data science -> data science |dist:  0.0\n",
      "too similar \t business -> business |dist:  0.0\n",
      "too similar \t data scientist -> data scientist |dist:  0.0\n",
      "too similar \t face -> face |dist:  0.0\n",
      "too similar \t aegis school business -> aegis school business |dist:  5.9604645e-08\n",
      "too similar \t school data -> school data business |dist:  1.3150095\n",
      "too similar \t goal project -> goal project |dist:  0.0\n",
      "too similar \t internet service provider -> internet service provider |dist:  0.0\n",
      "too similar \t school data business -> school data business |dist:  0.0\n",
      "too similar \t company aegis school -> company aegis school |dist:  0.0\n",
      "Fitting: (212, 50)\n",
      "NearestNeighbors(algorithm='brute', metric='euclidean', n_neighbors=15)\n",
      "too similar \t school -> school |dist:  0.0\n",
      "too similar \t engineering -> engineering |dist:  0.0\n",
      "too similar \t technical -> technical |dist:  1.1920929e-07\n",
      "too similar \t spsseducation -> matelabs |dist:  0.0\n",
      "Fitting: (238, 50)\n",
      "NearestNeighbors(algorithm='brute', metric='euclidean', n_neighbors=15)\n",
      "too similar \t management -> management |dist:  0.0\n",
      "Fitting: (267, 50)\n",
      "NearestNeighbors(algorithm='brute', metric='euclidean', n_neighbors=15)\n",
      "too similar \t company -> company |dist:  0.0\n",
      "too similar \t mumbai -> mumbai |dist:  0.0\n",
      "too similar \t monthscompany details company -> company matelabs |dist:  1.5237632\n",
      "too similar \t education -> education |dist:  0.0\n",
      "too similar \t exprience -> matelabs |dist:  0.0\n",
      "too similar \t monthscompany -> matelabs |dist:  0.0\n",
      "Fitting: (272, 50)\n",
      "NearestNeighbors(algorithm='brute', metric='euclidean', n_neighbors=15)\n",
      "too similar \t company -> company |dist:  0.0\n",
      "too similar \t tamil nadu sdnbvc -> tamil nadu sns |dist:  1.4634869\n",
      "too similar \t education -> education |dist:  0.0\n",
      "too similar \t tamil nadu -> tamil nadu |dist:  0.0\n",
      "too similar \t company details company -> company |dist:  1.9206945\n",
      "too similar \t chennai tamil nadu -> tamil nadu |dist:  1.8037288\n",
      "too similar \t sdnbvc -> matelabs |dist:  0.0\n",
      "Fitting: (282, 50)\n",
      "NearestNeighbors(algorithm='brute', metric='euclidean', n_neighbors=15)\n",
      "too similar \t company -> company |dist:  0.0\n",
      "too similar \t economics chennai -> economics chennai |dist:  0.0\n",
      "too similar \t solution -> solution |dist:  8.4293696e-08\n",
      "too similar \t tamil nadu sdnbvc -> nadu sdnbvc |dist:  1.4546527\n",
      "too similar \t economics -> economics |dist:  0.0\n",
      "too similar \t nadu -> nadu |dist:  0.0\n",
      "too similar \t nadu sdnbvc -> nadu sdnbvc |dist:  0.0\n",
      "too similar \t economics chennai tamil -> economics chennai tamil |dist:  0.0\n",
      "too similar \t education -> education |dist:  0.0\n",
      "too similar \t tamil nadu -> tamil nadu |dist:  0.0\n",
      "too similar \t company details company -> company |dist:  1.9206945\n",
      "too similar \t tamil -> tamil |dist:  0.0\n",
      "too similar \t company anything solution -> company anything solution |dist:  0.0\n",
      "too similar \t chennai tamil nadu -> chennai tamil |dist:  1.0504411\n",
      "too similar \t chennai -> chennai |dist:  0.0\n",
      "too similar \t sdnbvc -> matelabs |dist:  0.0\n",
      "too similar \t chennai tamil -> chennai tamil |dist:  1.1920929e-07\n",
      "Fitting: (282, 50)\n",
      "NearestNeighbors(algorithm='brute', metric='euclidean', n_neighbors=15)\n",
      "too similar \t company -> company |dist:  0.0\n",
      "too similar \t university -> university |dist:  0.0\n",
      "too similar \t monthscompany details company -> company matelabs |dist:  1.5237632\n",
      "too similar \t exprience monthscompany -> matelabs |dist:  0.0\n",
      "too similar \t communication exprience monthscompany -> matelabs |dist:  1.7112768\n",
      "too similar \t education -> education |dist:  0.0\n",
      "too similar \t exprience -> matelabs |dist:  0.0\n",
      "too similar \t monthscompany -> matelabs |dist:  0.0\n",
      "Fitting: (293, 50)\n",
      "NearestNeighbors(algorithm='brute', metric='euclidean', n_neighbors=15)\n",
      "too similar \t company -> company |dist:  0.0\n",
      "too similar \t mba -> mba |dist:  0.0\n",
      "too similar \t college engineering mgt -> sns college engineering |dist:  1.5184338\n",
      "too similar \t education details mba -> education details mca |dist:  1.9480041\n",
      "too similar \t education -> education |dist:  0.0\n",
      "too similar \t company details company -> company |dist:  1.9206945\n",
      "too similar \t engineering -> engineering |dist:  0.0\n",
      "too similar \t college engineering -> college engineering |dist:  0.0\n",
      "too similar \t college -> college |dist:  0.0\n",
      "too similar \t acn college engineering -> sns college engineering |dist:  1.7475334\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting: (301, 50)\n",
      "NearestNeighbors(algorithm='brute', metric='euclidean', n_neighbors=15)\n",
      "too similar \t good communication -> communication skills problem |dist:  1.646467\n",
      "too similar \t good communication leadership -> communication skills problem |dist:  1.9608529\n",
      "too similar \t hyderabad university -> mumbai university |dist:  1.828275\n",
      "too similar \t management -> management |dist:  0.0\n",
      "too similar \t rlt -> matelabs |dist:  0.0\n",
      "Fitting: (326, 50)\n",
      "NearestNeighbors(algorithm='brute', metric='euclidean', n_neighbors=15)\n",
      "too similar \t company -> company |dist:  0.0\n",
      "too similar \t education -> education |dist:  0.0\n",
      "too similar \t college -> college |dist:  0.0\n",
      "Fitting: (353, 50)\n",
      "NearestNeighbors(algorithm='brute', metric='euclidean', n_neighbors=15)\n",
      "too similar \t school -> school |dist:  0.0\n",
      "too similar \t exprience monthscompany -> matelabs |dist:  0.0\n",
      "too similar \t office exprience monthscompany -> matelabs |dist:  1.711691\n",
      "too similar \t exprience months microsoft -> sql year monthscompany |dist:  1.7809306\n",
      "too similar \t gaarment -> matelabs |dist:  0.0\n",
      "too similar \t exprience -> matelabs |dist:  0.0\n",
      "too similar \t microsoft -> microsoft |dist:  0.0\n",
      "Fitting: (376, 50)\n",
      "NearestNeighbors(algorithm='brute', metric='euclidean', n_neighbors=15)\n",
      "too similar \t science engineering -> engineering |dist:  1.9151702\n",
      "too similar \t matlab -> matlab |dist:  0.0\n",
      "too similar \t engineering -> engineering |dist:  0.0\n",
      "too similar \t computer -> computer |dist:  0.0\n",
      "too similar \t science -> science |dist:  0.0\n",
      "too similar \t engineering adichunchanagiri -> engineering mgt |dist:  1.4551269\n",
      "too similar \t adichunchanagiri institute technology -> technology assisted |dist:  1.947909\n",
      "too similar \t adichunchanagiri -> matelabs |dist:  0.0\n",
      "too similar \t science engineering adichunchanagiri -> science assurance associate |dist:  1.8367294\n",
      "too similar \t adichunchanagiri institute -> pradesh rkdf institute |dist:  1.9779966\n",
      "too similar \t monthscompany -> matelabs |dist:  0.0\n",
      "Fitting: (395, 50)\n",
      "NearestNeighbors(algorithm='brute', metric='euclidean', n_neighbors=15)\n",
      "too similar \t tora -> tora |dist:  0.0\n",
      "too similar \t higher secondary school -> higher secondary school |dist:  0.0\n",
      "too similar \t sslc finance kamaraj -> sslc finance kamaraj |dist:  0.0\n",
      "too similar \t school -> school |dist:  0.0\n",
      "too similar \t tora spsseducation -> tora spsseducation |dist:  0.0\n",
      "too similar \t hsc sav -> hsc sav |dist:  0.0\n",
      "too similar \t engineering -> engineering |dist:  0.0\n",
      "too similar \t typewriting tora -> typewriting tora |dist:  0.0\n",
      "too similar \t sav higher secondary -> sav higher secondary |dist:  0.0\n",
      "too similar \t higher secondary -> higher secondary |dist:  0.0\n",
      "too similar \t sns college -> sns college |dist:  5.9604645e-08\n",
      "too similar \t chidambaram tamil nadu -> chidambaram tamil nadu |dist:  0.0\n",
      "too similar \t sns -> sns |dist:  0.0\n",
      "too similar \t kamaraj matriculation -> kamaraj matriculation |dist:  0.0\n",
      "too similar \t chidambaram tamil -> chidambaram tamil |dist:  0.0\n",
      "too similar \t college engineering -> college engineering |dist:  0.0\n",
      "too similar \t tamil nadu sns -> tamil nadu sns |dist:  5.9604645e-08\n",
      "too similar \t mba chidambaram tamil -> mba chidambaram tamil |dist:  0.0\n",
      "too similar \t mba -> mba |dist:  0.0\n",
      "too similar \t sav higher -> sav higher |dist:  0.0\n",
      "too similar \t technical -> technical |dist:  1.1920929e-07\n",
      "too similar \t finance -> finance |dist:  0.0\n",
      "too similar \t hsc sav higher -> hsc sav higher |dist:  0.0\n",
      "too similar \t tamil nadu -> tamil nadu |dist:  0.0\n",
      "too similar \t spsseducation -> matelabs |dist:  0.0\n",
      "too similar \t sns college engineering -> sns college engineering |dist:  5.9604645e-08\n",
      "too similar \t typewriting tora spsseducation -> typewriting tora spsseducation |dist:  0.0\n",
      "too similar \t typewriting -> typewriting |dist:  0.0\n",
      "too similar \t finance kamaraj matriculation -> finance kamaraj matriculation |dist:  0.0\n",
      "too similar \t college -> college |dist:  0.0\n",
      "Fitting: (395, 50)\n",
      "NearestNeighbors(algorithm='brute', metric='euclidean', n_neighbors=15)\n",
      "too similar \t salary -> salary |dist:  0.0\n",
      "too similar \t saral payment package -> saral payment package |dist:  0.0\n",
      "too similar \t payroll -> payroll |dist:  0.0\n",
      "too similar \t compliance related -> compliance related |dist:  0.0\n",
      "too similar \t statutory compliance -> statutory compliance |dist:  0.0\n",
      "too similar \t queen university edinburg -> queen university edinburg |dist:  0.0\n",
      "too similar \t end end facilitation -> end end facilitation |dist:  0.0\n",
      "too similar \t management end end -> management end end |dist:  0.0\n",
      "too similar \t package payroll -> package payroll |dist:  0.0\n",
      "too similar \t employees -> employees |dist:  0.0\n",
      "too similar \t statutory -> statutory |dist:  8.4293696e-08\n",
      "too similar \t act recruitment selection -> act recruitment selection |dist:  0.0\n",
      "too similar \t related -> related |dist:  0.0\n",
      "too similar \t management -> management |dist:  0.0\n",
      "too similar \t end facilitation pms -> end facilitation pms |dist:  5.9604645e-08\n",
      "too similar \t payroll software -> payroll software |dist:  8.4293696e-08\n",
      "too similar \t welfare creation deployment -> welfare creation deployment |dist:  0.0\n",
      "too similar \t payment package -> payment package |dist:  0.0\n",
      "too similar \t recruitment selection recruitment -> recruitment selection recruitment |dist:  0.0\n",
      "too similar \t package payroll software -> package payroll software |dist:  0.0\n",
      "too similar \t payment package payroll -> payment package payroll |dist:  0.0\n",
      "too similar \t payment -> payment |dist:  8.4293696e-08\n",
      "too similar \t compliance -> compliance |dist:  8.4293696e-08\n",
      "too similar \t policies procedure -> policies procedure |dist:  0.0\n",
      "too similar \t annual -> annual |dist:  1.1920929e-07\n",
      "too similar \t hospitality management -> hospitality management |dist:  0.0\n",
      "too similar \t saral payment -> saral payment |dist:  0.0\n",
      "too similar \t employee -> employee |dist:  0.0\n",
      "too similar \t hotel management -> hotel management |dist:  0.0\n",
      "too similar \t management international -> management international |dist:  0.0\n",
      "Fitting: (395, 50)\n",
      "NearestNeighbors(algorithm='brute', metric='euclidean', n_neighbors=15)\n",
      "too similar \t company -> company |dist:  0.0\n",
      "too similar \t operations exprience -> operations exprience |dist:  0.0\n",
      "too similar \t university -> university |dist:  0.0\n",
      "too similar \t mumbai -> mumbai |dist:  0.0\n",
      "too similar \t mumbai university -> mumbai university |dist:  0.0\n",
      "too similar \t monthscompany details company -> company matelabs |dist:  1.5237632\n",
      "too similar \t education -> education |dist:  0.0\n",
      "too similar \t exprience -> matelabs |dist:  0.0\n",
      "too similar \t monthscompany -> matelabs |dist:  0.0\n",
      "too similar \t company mumbai -> company mumbai |dist:  0.0\n",
      "too similar \t operations -> operations |dist:  0.0\n",
      "Fitting: (395, 50)\n",
      "NearestNeighbors(algorithm='brute', metric='euclidean', n_neighbors=15)\n",
      "too similar \t company -> company |dist:  0.0\n",
      "too similar \t economics chennai -> economics chennai |dist:  0.0\n",
      "too similar \t solution -> solution |dist:  8.4293696e-08\n",
      "too similar \t tamil nadu sdnbvc -> nadu sdnbvc |dist:  1.4546527\n",
      "too similar \t economics -> economics |dist:  0.0\n",
      "too similar \t nadu -> nadu |dist:  0.0\n",
      "too similar \t nadu sdnbvc -> nadu sdnbvc |dist:  0.0\n",
      "too similar \t economics chennai tamil -> economics chennai tamil |dist:  0.0\n",
      "too similar \t education -> education |dist:  0.0\n",
      "too similar \t tamil nadu -> tamil nadu |dist:  0.0\n",
      "too similar \t company details company -> company |dist:  1.9206945\n",
      "too similar \t tamil -> tamil |dist:  0.0\n",
      "too similar \t company anything solution -> company anything solution |dist:  0.0\n",
      "too similar \t chennai tamil nadu -> chennai tamil |dist:  1.0504411\n",
      "too similar \t chennai -> chennai |dist:  8.4293696e-08\n",
      "too similar \t sdnbvc -> matelabs |dist:  0.0\n",
      "too similar \t chennai tamil -> chennai tamil |dist:  1.1920929e-07\n",
      "Fitting: (395, 50)\n",
      "NearestNeighbors(algorithm='brute', metric='euclidean', n_neighbors=15)\n",
      "too similar \t company -> company |dist:  0.0\n",
      "too similar \t economics chennai -> economics chennai |dist:  0.0\n",
      "too similar \t solution -> solution |dist:  8.4293696e-08\n",
      "too similar \t tamil nadu sdnbvc -> nadu sdnbvc |dist:  1.4546527\n",
      "too similar \t economics -> economics |dist:  0.0\n",
      "too similar \t nadu -> nadu |dist:  0.0\n",
      "too similar \t nadu sdnbvc -> nadu sdnbvc |dist:  0.0\n",
      "too similar \t economics chennai tamil -> economics chennai tamil |dist:  0.0\n",
      "too similar \t education -> education |dist:  0.0\n",
      "too similar \t tamil nadu -> tamil nadu |dist:  0.0\n",
      "too similar \t company details company -> company |dist:  1.9206945\n",
      "too similar \t tamil -> tamil |dist:  0.0\n",
      "too similar \t company anything solution -> company anything solution |dist:  0.0\n",
      "too similar \t chennai tamil nadu -> chennai tamil |dist:  1.0504411\n",
      "too similar \t chennai -> chennai |dist:  8.4293696e-08\n",
      "too similar \t sdnbvc -> matelabs |dist:  0.0\n",
      "too similar \t chennai tamil -> chennai tamil |dist:  1.1920929e-07\n",
      "Fitting: (395, 50)\n",
      "NearestNeighbors(algorithm='brute', metric='euclidean', n_neighbors=15)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "too similar \t company -> company |dist:  0.0\n",
      "too similar \t university -> university |dist:  0.0\n",
      "too similar \t monthscompany details company -> company matelabs |dist:  1.5237632\n",
      "too similar \t exprience monthscompany -> matelabs |dist:  0.0\n",
      "too similar \t bba lovely -> bba lovely |dist:  5.9604645e-08\n",
      "too similar \t education details bba -> education details bba |dist:  8.4293696e-08\n",
      "too similar \t communication exprience -> communication exprience |dist:  0.0\n",
      "too similar \t lovely -> lovely |dist:  0.0\n",
      "too similar \t bba -> bba |dist:  0.0\n",
      "too similar \t bba lovely professional -> bba lovely professional |dist:  0.0\n",
      "too similar \t communication -> communication |dist:  0.0\n",
      "too similar \t lovely professional -> lovely professional |dist:  0.0\n",
      "too similar \t communication exprience monthscompany -> communication exprience |dist:  0.85563844\n",
      "too similar \t education -> education |dist:  0.0\n",
      "too similar \t lovely professional university -> lovely professional university |dist:  0.0\n",
      "too similar \t professional -> professional |dist:  0.0\n",
      "too similar \t professional university -> professional university |dist:  0.0\n",
      "too similar \t exprience -> matelabs |dist:  0.0\n",
      "too similar \t monthscompany -> matelabs |dist:  0.0\n",
      "Fitting: (395, 50)\n",
      "NearestNeighbors(algorithm='brute', metric='euclidean', n_neighbors=15)\n",
      "too similar \t company -> company |dist:  0.0\n",
      "too similar \t assistant -> assistant |dist:  0.0\n",
      "too similar \t mba -> mba |dist:  0.0\n",
      "too similar \t college engineering mgt -> sns college engineering |dist:  1.5184338\n",
      "too similar \t company hr assistant -> company hr assistant |dist:  0.0\n",
      "too similar \t education details mba -> education details mca |dist:  1.9480041\n",
      "too similar \t mgt -> mgt |dist:  0.0\n",
      "too similar \t mba acn college -> mba acn college |dist:  0.0\n",
      "too similar \t education -> education |dist:  0.0\n",
      "too similar \t company details company -> company |dist:  1.9206945\n",
      "too similar \t engineering -> engineering |dist:  0.0\n",
      "too similar \t acn college -> acn college |dist:  0.0\n",
      "too similar \t engineering mgt -> engineering mgt |dist:  0.0\n",
      "too similar \t acn -> acn |dist:  0.0\n",
      "too similar \t college engineering -> college engineering |dist:  0.0\n",
      "too similar \t mba acn -> mba acn |dist:  0.0\n",
      "too similar \t college -> college |dist:  0.0\n",
      "too similar \t acn college engineering -> acn college |dist:  1.565482\n",
      "Fitting: (395, 50)\n",
      "NearestNeighbors(algorithm='brute', metric='euclidean', n_neighbors=15)\n",
      "too similar \t management accounting -> management accounting |dist:  0.0\n",
      "too similar \t income tax good -> income tax good |dist:  0.0\n",
      "too similar \t rlt scrum master -> rlt scrum master |dist:  0.0\n",
      "too similar \t tax good -> tax good |dist:  0.0\n",
      "too similar \t computer -> computer |dist:  0.0\n",
      "too similar \t tax good communication -> tax good communication |dist:  0.0\n",
      "too similar \t good communication -> communication skills problem |dist:  1.646467\n",
      "too similar \t sincere hard -> sincere hard |dist:  0.0\n",
      "too similar \t good communication leadership -> communication skills problem |dist:  1.9608529\n",
      "too similar \t rlt scrum -> rlt scrum |dist:  0.0\n",
      "too similar \t hyderabad university -> mumbai university |dist:  1.828275\n",
      "too similar \t ecommerce -> ecommerce |dist:  0.0\n",
      "too similar \t driving license internet -> driving license internet |dist:  0.0\n",
      "too similar \t mba finance -> mba finance |dist:  0.0\n",
      "too similar \t system months dbms -> system months dbms |dist:  0.0\n",
      "too similar \t management -> management |dist:  0.0\n",
      "too similar \t scrum -> scrum |dist:  0.0\n",
      "too similar \t form -> form |dist:  0.0\n",
      "too similar \t accounting -> accounting |dist:  0.0\n",
      "too similar \t income tax -> income tax |dist:  0.0\n",
      "too similar \t rlt -> matelabs |dist:  0.0\n",
      "too similar \t sincere hard working -> sincere hard working |dist:  5.9604645e-08\n",
      "too similar \t hard working -> hard working |dist:  0.0\n",
      "too similar \t dbms -> dbms |dist:  0.0\n",
      "too similar \t scrum master -> scrum master |dist:  0.0\n",
      "too similar \t tally -> tally |dist:  0.0\n",
      "too similar \t language web programing -> language web programing |dist:  0.0\n",
      "too similar \t wheeler driving license -> wheeler driving license |dist:  0.0\n",
      "too similar \t inter mec srimedhav -> inter mec srimedhav |dist:  0.0\n",
      "too similar \t master -> master |dist:  0.0\n",
      "Fitting: (395, 50)\n",
      "NearestNeighbors(algorithm='brute', metric='euclidean', n_neighbors=15)\n",
      "too similar \t company -> company |dist:  0.0\n",
      "too similar \t training special -> training special |dist:  0.0\n",
      "too similar \t prajyoti niketan college -> prajyoti niketan college |dist:  0.0\n",
      "too similar \t behaviour malappuram -> behaviour malappuram |dist:  5.9604645e-08\n",
      "too similar \t behaviour malappuram kerala -> behaviour malappuram kerala |dist:  0.0\n",
      "too similar \t specialization organizational behaviour -> specialization organizational behaviour |dist:  0.0\n",
      "too similar \t niketan college -> niketan college |dist:  0.0\n",
      "too similar \t thrissur prajyoti niketan -> thrissur prajyoti niketan |dist:  4.2146848e-08\n",
      "too similar \t malappuram kerala -> malappuram kerala |dist:  0.0\n",
      "too similar \t days -> days |dist:  8.4293696e-08\n",
      "too similar \t special -> special |dist:  0.0\n",
      "too similar \t specialization organizational -> specialization organizational |dist:  0.0\n",
      "too similar \t special education -> special education |dist:  0.0\n",
      "too similar \t counselling counsellor -> counselling counsellor |dist:  0.0\n",
      "too similar \t special education certificate -> special education certificate |dist:  0.0\n",
      "too similar \t institute counselling counsellor -> institute counselling counsellor |dist:  0.0\n",
      "too similar \t training -> training |dist:  0.0\n",
      "too similar \t malappuram kerala calicut -> malappuram kerala calicut |dist:  0.0\n",
      "too similar \t education -> education |dist:  0.0\n",
      "too similar \t kerala calicut university -> kerala calicut university |dist:  0.0\n",
      "too similar \t counsellor -> counsellor |dist:  0.0\n",
      "too similar \t certificate -> certificate |dist:  0.0\n",
      "too similar \t internship -> internship |dist:  8.4293696e-08\n",
      "too similar \t organizational behaviour -> organizational behaviour |dist:  0.0\n",
      "too similar \t training special education -> training special education |dist:  0.0\n",
      "too similar \t psychology -> psychology |dist:  0.0\n",
      "too similar \t days internship -> days internship |dist:  0.0\n",
      "too similar \t organizational behaviour malappuram -> organizational behaviour malappuram |dist:  0.0\n",
      "too similar \t kerala calicut -> kerala calicut |dist:  0.0\n",
      "too similar \t college -> college |dist:  0.0\n",
      "Fitting: (395, 50)\n",
      "NearestNeighbors(algorithm='brute', metric='euclidean', n_neighbors=15)\n",
      "too similar \t computer knowledge proficient -> computer knowledge proficient |dist:  0.0\n",
      "too similar \t labors individual -> labors individual |dist:  0.0\n",
      "too similar \t dynamics exprience -> dynamics exprience |dist:  0.0\n",
      "too similar \t school -> school |dist:  0.0\n",
      "too similar \t ramesh -> ramesh |dist:  8.4293696e-08\n",
      "too similar \t ramesh gaarment -> ramesh gaarment |dist:  4.2146848e-08\n",
      "too similar \t exprience monthscompany -> matelabs |dist:  0.0\n",
      "too similar \t office exprience monthscompany -> matelabs |dist:  1.711691\n",
      "too similar \t avoidance blackbox car -> avoidance blackbox car |dist:  0.0\n",
      "too similar \t exprience months microsoft -> sql year monthscompany |dist:  1.7809306\n",
      "too similar \t microsoft dynamics exprience -> microsoft dynamics exprience |dist:  0.0\n",
      "too similar \t activities attendance maintenance -> activities attendance maintenance |dist:  0.0\n",
      "too similar \t sri -> sri |dist:  0.0\n",
      "too similar \t gaarment -> matelabs |dist:  0.0\n",
      "too similar \t admin bharat -> admin bharat |dist:  0.0\n",
      "too similar \t microsoft dynamics -> microsoft dynamics |dist:  0.0\n",
      "too similar \t sec -> sec |dist:  0.0\n",
      "too similar \t sri ramesh -> sri ramesh |dist:  0.0\n",
      "too similar \t school hr admin -> school hr admin |dist:  0.0\n",
      "too similar \t sec school -> sec school |dist:  0.0\n",
      "too similar \t labors individual records -> labors individual records |dist:  0.0\n",
      "too similar \t activities attendance -> activities attendance |dist:  0.0\n",
      "too similar \t project advanced automobile -> project advanced automobile |dist:  0.0\n",
      "too similar \t tirupur -> tirupur |dist:  1.1920929e-07\n",
      "too similar \t exprience -> matelabs |dist:  0.0\n",
      "too similar \t microsoft -> microsoft |dist:  0.0\n",
      "too similar \t dynamics -> dynamics |dist:  0.0\n",
      "too similar \t maintenance -> maintenance |dist:  0.0\n",
      "too similar \t blackbox car -> blackbox car |dist:  0.0\n",
      "too similar \t sri ramesh gaarment -> sri ramesh gaarment |dist:  0.0\n",
      "Fitting: (395, 50)\n",
      "NearestNeighbors(algorithm='brute', metric='euclidean', n_neighbors=15)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "too similar \t accept challenges -> accept challenges |dist:  0.0\n",
      "too similar \t science engineering -> computer science engineering |dist:  1.602063\n",
      "too similar \t proficiency program -> proficiency program |dist:  0.0\n",
      "too similar \t matlab -> matlab |dist:  0.0\n",
      "too similar \t computer science engineering -> computer science engineering |dist:  8.4293696e-08\n",
      "too similar \t engineering -> engineering |dist:  0.0\n",
      "too similar \t computer -> computer |dist:  0.0\n",
      "too similar \t computer science -> computer science |dist:  0.0\n",
      "too similar \t software -> software |dist:  0.0\n",
      "too similar \t science -> science |dist:  0.0\n",
      "too similar \t engineering adichunchanagiri -> engineering adichunchanagiri institute |dist:  1.3744159\n",
      "too similar \t accept challenges education -> accept challenges education |dist:  0.0\n",
      "too similar \t ready accept challenges -> ready accept challenges |dist:  0.0\n",
      "too similar \t java -> java |dist:  0.0\n",
      "too similar \t program langages -> program langages |dist:  0.0\n",
      "too similar \t web programming -> web programming |dist:  0.0\n",
      "too similar \t programming tools -> programming tools |dist:  0.0\n",
      "too similar \t system -> system |dist:  0.0\n",
      "too similar \t motivated ready accept -> motivated ready accept |dist:  0.0\n",
      "too similar \t adichunchanagiri institute technology -> engineering adichunchanagiri institute |dist:  1.3091557\n",
      "too similar \t adichunchanagiri -> matelabs |dist:  0.0\n",
      "too similar \t web programming tools -> web programming tools |dist:  0.0\n",
      "too similar \t science engineering adichunchanagiri -> engineering adichunchanagiri institute |dist:  1.1061809\n",
      "too similar \t institute technology chikmagalur -> institute technology chikmagalur |dist:  0.0\n",
      "too similar \t adichunchanagiri institute -> engineering adichunchanagiri institute |dist:  1.3166802\n",
      "too similar \t enthusiastic learn -> enthusiastic learn |dist:  0.0\n",
      "too similar \t proficiency program langages -> proficiency program langages |dist:  0.0\n",
      "too similar \t proficiency -> proficiency |dist:  0.0\n",
      "too similar \t monthscompany -> matelabs |dist:  0.0\n",
      "too similar \t engineering adichunchanagiri institute -> engineering adichunchanagiri institute |dist:  0.0\n",
      "Fitting: (395, 50)\n",
      "NearestNeighbors(algorithm='brute', metric='euclidean', n_neighbors=15)\n",
      "too similar \t tora -> tora |dist:  0.0\n",
      "too similar \t higher secondary school -> higher secondary school |dist:  0.0\n",
      "too similar \t sslc finance kamaraj -> sslc finance kamaraj |dist:  0.0\n",
      "too similar \t school -> school |dist:  0.0\n",
      "too similar \t tora spsseducation -> tora spsseducation |dist:  0.0\n",
      "too similar \t hsc sav -> hsc sav |dist:  0.0\n",
      "too similar \t engineering -> engineering |dist:  0.0\n",
      "too similar \t typewriting tora -> typewriting tora |dist:  0.0\n",
      "too similar \t sav higher secondary -> sav higher secondary |dist:  0.0\n",
      "too similar \t higher secondary -> higher secondary |dist:  0.0\n",
      "too similar \t sns college -> sns college |dist:  5.9604645e-08\n",
      "too similar \t chidambaram tamil nadu -> chidambaram tamil nadu |dist:  0.0\n",
      "too similar \t sns -> sns |dist:  0.0\n",
      "too similar \t kamaraj matriculation -> kamaraj matriculation |dist:  0.0\n",
      "too similar \t chidambaram tamil -> chidambaram tamil |dist:  0.0\n",
      "too similar \t college engineering -> college engineering |dist:  0.0\n",
      "too similar \t tamil nadu sns -> tamil nadu sns |dist:  5.9604645e-08\n",
      "too similar \t mba chidambaram tamil -> mba chidambaram tamil |dist:  0.0\n",
      "too similar \t mba -> mba |dist:  0.0\n",
      "too similar \t sav higher -> sav higher |dist:  0.0\n",
      "too similar \t technical -> technical |dist:  1.1920929e-07\n",
      "too similar \t finance -> finance |dist:  0.0\n",
      "too similar \t hsc sav higher -> hsc sav higher |dist:  0.0\n",
      "too similar \t tamil nadu -> tamil nadu |dist:  0.0\n",
      "too similar \t spsseducation -> matelabs |dist:  0.0\n",
      "too similar \t sns college engineering -> sns college engineering |dist:  5.9604645e-08\n",
      "too similar \t typewriting tora spsseducation -> typewriting tora spsseducation |dist:  0.0\n",
      "too similar \t typewriting -> typewriting |dist:  0.0\n",
      "too similar \t finance kamaraj matriculation -> finance kamaraj matriculation |dist:  0.0\n",
      "too similar \t college -> college |dist:  0.0\n",
      "Fitting: (395, 50)\n",
      "NearestNeighbors(algorithm='brute', metric='euclidean', n_neighbors=15)\n",
      "too similar \t salary -> salary |dist:  0.0\n",
      "too similar \t saral payment package -> saral payment package |dist:  0.0\n",
      "too similar \t payroll -> payroll |dist:  0.0\n",
      "too similar \t compliance related -> compliance related |dist:  0.0\n",
      "too similar \t statutory compliance -> statutory compliance |dist:  0.0\n",
      "too similar \t queen university edinburg -> queen university edinburg |dist:  0.0\n",
      "too similar \t end end facilitation -> end end facilitation |dist:  0.0\n",
      "too similar \t management end end -> management end end |dist:  0.0\n",
      "too similar \t package payroll -> package payroll |dist:  0.0\n",
      "too similar \t employees -> employees |dist:  0.0\n",
      "too similar \t statutory -> statutory |dist:  8.4293696e-08\n",
      "too similar \t act recruitment selection -> act recruitment selection |dist:  0.0\n",
      "too similar \t related -> related |dist:  0.0\n",
      "too similar \t management -> management |dist:  0.0\n",
      "too similar \t end facilitation pms -> end facilitation pms |dist:  5.9604645e-08\n",
      "too similar \t payroll software -> payroll software |dist:  8.4293696e-08\n",
      "too similar \t welfare creation deployment -> welfare creation deployment |dist:  0.0\n",
      "too similar \t payment package -> payment package |dist:  0.0\n",
      "too similar \t recruitment selection recruitment -> recruitment selection recruitment |dist:  0.0\n",
      "too similar \t package payroll software -> package payroll software |dist:  0.0\n",
      "too similar \t payment package payroll -> payment package payroll |dist:  0.0\n",
      "too similar \t payment -> payment |dist:  8.4293696e-08\n",
      "too similar \t compliance -> compliance |dist:  8.4293696e-08\n",
      "too similar \t policies procedure -> policies procedure |dist:  0.0\n",
      "too similar \t annual -> annual |dist:  1.1920929e-07\n",
      "too similar \t hospitality management -> hospitality management |dist:  0.0\n",
      "too similar \t saral payment -> saral payment |dist:  0.0\n",
      "too similar \t employee -> employee |dist:  0.0\n",
      "too similar \t hotel management -> hotel management |dist:  0.0\n",
      "too similar \t management international -> management international |dist:  0.0\n",
      "Fitting: (395, 50)\n",
      "NearestNeighbors(algorithm='brute', metric='euclidean', n_neighbors=15)\n",
      "too similar \t company -> company |dist:  0.0\n",
      "too similar \t operations exprience -> operations exprience |dist:  0.0\n",
      "too similar \t university -> university |dist:  0.0\n",
      "too similar \t mumbai -> mumbai |dist:  0.0\n",
      "too similar \t mumbai university -> mumbai university |dist:  0.0\n",
      "too similar \t monthscompany details company -> company matelabs |dist:  1.5237632\n",
      "too similar \t education -> education |dist:  0.0\n",
      "too similar \t exprience -> matelabs |dist:  0.0\n",
      "too similar \t monthscompany -> matelabs |dist:  0.0\n",
      "too similar \t company mumbai -> company mumbai |dist:  0.0\n",
      "too similar \t operations -> operations |dist:  0.0\n",
      "Fitting: (395, 50)\n",
      "NearestNeighbors(algorithm='brute', metric='euclidean', n_neighbors=15)\n",
      "too similar \t company -> company |dist:  0.0\n",
      "too similar \t economics chennai -> economics chennai |dist:  0.0\n",
      "too similar \t solution -> solution |dist:  8.4293696e-08\n",
      "too similar \t tamil nadu sdnbvc -> nadu sdnbvc |dist:  1.4546527\n",
      "too similar \t economics -> economics |dist:  0.0\n",
      "too similar \t nadu -> nadu |dist:  0.0\n",
      "too similar \t nadu sdnbvc -> nadu sdnbvc |dist:  0.0\n",
      "too similar \t economics chennai tamil -> economics chennai tamil |dist:  0.0\n",
      "too similar \t education -> education |dist:  0.0\n",
      "too similar \t tamil nadu -> tamil nadu |dist:  0.0\n",
      "too similar \t company details company -> company |dist:  1.9206945\n",
      "too similar \t tamil -> tamil |dist:  0.0\n",
      "too similar \t company anything solution -> company anything solution |dist:  0.0\n",
      "too similar \t chennai tamil nadu -> chennai tamil |dist:  1.0504411\n",
      "too similar \t chennai -> chennai |dist:  8.4293696e-08\n",
      "too similar \t sdnbvc -> matelabs |dist:  0.0\n",
      "too similar \t chennai tamil -> chennai tamil |dist:  1.1920929e-07\n",
      "Fitting: (395, 50)\n",
      "NearestNeighbors(algorithm='brute', metric='euclidean', n_neighbors=15)\n",
      "too similar \t company -> company |dist:  0.0\n",
      "too similar \t economics chennai -> economics chennai |dist:  0.0\n",
      "too similar \t solution -> solution |dist:  8.4293696e-08\n",
      "too similar \t tamil nadu sdnbvc -> nadu sdnbvc |dist:  1.4546527\n",
      "too similar \t economics -> economics |dist:  0.0\n",
      "too similar \t nadu -> nadu |dist:  0.0\n",
      "too similar \t nadu sdnbvc -> nadu sdnbvc |dist:  0.0\n",
      "too similar \t economics chennai tamil -> economics chennai tamil |dist:  0.0\n",
      "too similar \t education -> education |dist:  0.0\n",
      "too similar \t tamil nadu -> tamil nadu |dist:  0.0\n",
      "too similar \t company details company -> company |dist:  1.9206945\n",
      "too similar \t tamil -> tamil |dist:  0.0\n",
      "too similar \t company anything solution -> company anything solution |dist:  0.0\n",
      "too similar \t chennai tamil nadu -> chennai tamil |dist:  1.0504411\n",
      "too similar \t chennai -> chennai |dist:  8.4293696e-08\n",
      "too similar \t sdnbvc -> matelabs |dist:  0.0\n",
      "too similar \t chennai tamil -> chennai tamil |dist:  1.1920929e-07\n",
      "Fitting: (395, 50)\n",
      "NearestNeighbors(algorithm='brute', metric='euclidean', n_neighbors=15)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "too similar \t company -> company |dist:  0.0\n",
      "too similar \t university -> university |dist:  0.0\n",
      "too similar \t monthscompany details company -> company matelabs |dist:  1.5237632\n",
      "too similar \t exprience monthscompany -> matelabs |dist:  0.0\n",
      "too similar \t bba lovely -> bba lovely |dist:  5.9604645e-08\n",
      "too similar \t education details bba -> education details bba |dist:  8.4293696e-08\n",
      "too similar \t communication exprience -> communication exprience |dist:  0.0\n",
      "too similar \t lovely -> lovely |dist:  0.0\n",
      "too similar \t bba -> bba |dist:  0.0\n",
      "too similar \t bba lovely professional -> bba lovely professional |dist:  0.0\n",
      "too similar \t communication -> communication |dist:  0.0\n",
      "too similar \t lovely professional -> lovely professional |dist:  0.0\n",
      "too similar \t communication exprience monthscompany -> communication exprience |dist:  0.85563844\n",
      "too similar \t education -> education |dist:  0.0\n",
      "too similar \t lovely professional university -> lovely professional university |dist:  0.0\n",
      "too similar \t professional -> professional |dist:  0.0\n",
      "too similar \t professional university -> professional university |dist:  0.0\n",
      "too similar \t exprience -> matelabs |dist:  0.0\n",
      "too similar \t monthscompany -> matelabs |dist:  0.0\n",
      "Fitting: (395, 50)\n",
      "NearestNeighbors(algorithm='brute', metric='euclidean', n_neighbors=15)\n",
      "too similar \t company -> company |dist:  0.0\n",
      "too similar \t assistant -> assistant |dist:  0.0\n",
      "too similar \t mba -> mba |dist:  0.0\n",
      "too similar \t college engineering mgt -> sns college engineering |dist:  1.5184338\n",
      "too similar \t company hr assistant -> company hr assistant |dist:  0.0\n",
      "too similar \t education details mba -> education details mca |dist:  1.9480041\n",
      "too similar \t mgt -> mgt |dist:  0.0\n",
      "too similar \t mba acn college -> mba acn college |dist:  0.0\n",
      "too similar \t education -> education |dist:  0.0\n",
      "too similar \t company details company -> company |dist:  1.9206945\n",
      "too similar \t engineering -> engineering |dist:  0.0\n",
      "too similar \t acn college -> acn college |dist:  0.0\n",
      "too similar \t engineering mgt -> engineering mgt |dist:  0.0\n",
      "too similar \t acn -> acn |dist:  0.0\n",
      "too similar \t college engineering -> college engineering |dist:  0.0\n",
      "too similar \t mba acn -> mba acn |dist:  0.0\n",
      "too similar \t college -> college |dist:  0.0\n",
      "too similar \t acn college engineering -> acn college |dist:  1.565482\n",
      "Fitting: (395, 50)\n",
      "NearestNeighbors(algorithm='brute', metric='euclidean', n_neighbors=15)\n",
      "too similar \t management accounting -> management accounting |dist:  0.0\n",
      "too similar \t income tax good -> income tax good |dist:  0.0\n",
      "too similar \t rlt scrum master -> rlt scrum master |dist:  0.0\n",
      "too similar \t tax good -> tax good |dist:  0.0\n",
      "too similar \t computer -> computer |dist:  0.0\n",
      "too similar \t tax good communication -> tax good communication |dist:  0.0\n",
      "too similar \t good communication -> communication skills problem |dist:  1.646467\n",
      "too similar \t sincere hard -> sincere hard |dist:  0.0\n",
      "too similar \t good communication leadership -> communication skills problem |dist:  1.9608529\n",
      "too similar \t rlt scrum -> rlt scrum |dist:  0.0\n",
      "too similar \t hyderabad university -> mumbai university |dist:  1.828275\n",
      "too similar \t ecommerce -> ecommerce |dist:  0.0\n",
      "too similar \t driving license internet -> driving license internet |dist:  0.0\n",
      "too similar \t mba finance -> mba finance |dist:  0.0\n",
      "too similar \t system months dbms -> system months dbms |dist:  0.0\n",
      "too similar \t management -> management |dist:  0.0\n",
      "too similar \t scrum -> scrum |dist:  0.0\n",
      "too similar \t form -> form |dist:  0.0\n",
      "too similar \t accounting -> accounting |dist:  0.0\n",
      "too similar \t income tax -> income tax |dist:  0.0\n",
      "too similar \t rlt -> matelabs |dist:  0.0\n",
      "too similar \t sincere hard working -> sincere hard working |dist:  5.9604645e-08\n",
      "too similar \t hard working -> hard working |dist:  0.0\n",
      "too similar \t dbms -> dbms |dist:  0.0\n",
      "too similar \t scrum master -> scrum master |dist:  0.0\n",
      "too similar \t tally -> tally |dist:  0.0\n",
      "too similar \t language web programing -> language web programing |dist:  0.0\n",
      "too similar \t wheeler driving license -> wheeler driving license |dist:  0.0\n",
      "too similar \t inter mec srimedhav -> inter mec srimedhav |dist:  0.0\n",
      "too similar \t master -> master |dist:  0.0\n",
      "Fitting: (395, 50)\n",
      "NearestNeighbors(algorithm='brute', metric='euclidean', n_neighbors=15)\n",
      "too similar \t company -> company |dist:  0.0\n",
      "too similar \t training special -> training special |dist:  0.0\n",
      "too similar \t prajyoti niketan college -> prajyoti niketan college |dist:  0.0\n",
      "too similar \t behaviour malappuram -> behaviour malappuram |dist:  5.9604645e-08\n",
      "too similar \t behaviour malappuram kerala -> behaviour malappuram kerala |dist:  0.0\n",
      "too similar \t specialization organizational behaviour -> specialization organizational behaviour |dist:  0.0\n",
      "too similar \t niketan college -> niketan college |dist:  0.0\n",
      "too similar \t thrissur prajyoti niketan -> thrissur prajyoti niketan |dist:  4.2146848e-08\n",
      "too similar \t malappuram kerala -> malappuram kerala |dist:  0.0\n",
      "too similar \t days -> days |dist:  8.4293696e-08\n",
      "too similar \t special -> special |dist:  0.0\n",
      "too similar \t specialization organizational -> specialization organizational |dist:  0.0\n",
      "too similar \t special education -> special education |dist:  0.0\n",
      "too similar \t counselling counsellor -> counselling counsellor |dist:  0.0\n",
      "too similar \t special education certificate -> special education certificate |dist:  0.0\n",
      "too similar \t institute counselling counsellor -> institute counselling counsellor |dist:  0.0\n",
      "too similar \t training -> training |dist:  0.0\n",
      "too similar \t malappuram kerala calicut -> malappuram kerala calicut |dist:  0.0\n",
      "too similar \t education -> education |dist:  0.0\n",
      "too similar \t kerala calicut university -> kerala calicut university |dist:  0.0\n",
      "too similar \t counsellor -> counsellor |dist:  0.0\n",
      "too similar \t certificate -> certificate |dist:  0.0\n",
      "too similar \t internship -> internship |dist:  8.4293696e-08\n",
      "too similar \t organizational behaviour -> organizational behaviour |dist:  0.0\n",
      "too similar \t training special education -> training special education |dist:  0.0\n",
      "too similar \t psychology -> psychology |dist:  0.0\n",
      "too similar \t days internship -> days internship |dist:  0.0\n",
      "too similar \t organizational behaviour malappuram -> organizational behaviour malappuram |dist:  0.0\n",
      "too similar \t kerala calicut -> kerala calicut |dist:  0.0\n",
      "too similar \t college -> college |dist:  0.0\n",
      "Fitting: (395, 50)\n",
      "NearestNeighbors(algorithm='brute', metric='euclidean', n_neighbors=15)\n",
      "too similar \t computer knowledge proficient -> computer knowledge proficient |dist:  0.0\n",
      "too similar \t labors individual -> labors individual |dist:  0.0\n",
      "too similar \t dynamics exprience -> dynamics exprience |dist:  0.0\n",
      "too similar \t school -> school |dist:  0.0\n",
      "too similar \t ramesh -> ramesh |dist:  8.4293696e-08\n",
      "too similar \t ramesh gaarment -> ramesh gaarment |dist:  4.2146848e-08\n",
      "too similar \t exprience monthscompany -> matelabs |dist:  0.0\n",
      "too similar \t office exprience monthscompany -> matelabs |dist:  1.711691\n",
      "too similar \t avoidance blackbox car -> avoidance blackbox car |dist:  0.0\n",
      "too similar \t exprience months microsoft -> sql year monthscompany |dist:  1.7809306\n",
      "too similar \t microsoft dynamics exprience -> microsoft dynamics exprience |dist:  0.0\n",
      "too similar \t activities attendance maintenance -> activities attendance maintenance |dist:  0.0\n",
      "too similar \t sri -> sri |dist:  0.0\n",
      "too similar \t gaarment -> matelabs |dist:  0.0\n",
      "too similar \t admin bharat -> admin bharat |dist:  0.0\n",
      "too similar \t microsoft dynamics -> microsoft dynamics |dist:  0.0\n",
      "too similar \t sec -> sec |dist:  0.0\n",
      "too similar \t sri ramesh -> sri ramesh |dist:  0.0\n",
      "too similar \t school hr admin -> school hr admin |dist:  0.0\n",
      "too similar \t sec school -> sec school |dist:  0.0\n",
      "too similar \t labors individual records -> labors individual records |dist:  0.0\n",
      "too similar \t activities attendance -> activities attendance |dist:  0.0\n",
      "too similar \t project advanced automobile -> project advanced automobile |dist:  0.0\n",
      "too similar \t tirupur -> tirupur |dist:  1.1920929e-07\n",
      "too similar \t exprience -> matelabs |dist:  0.0\n",
      "too similar \t microsoft -> microsoft |dist:  0.0\n",
      "too similar \t dynamics -> dynamics |dist:  0.0\n",
      "too similar \t maintenance -> maintenance |dist:  0.0\n",
      "too similar \t blackbox car -> blackbox car |dist:  0.0\n",
      "too similar \t sri ramesh gaarment -> sri ramesh gaarment |dist:  0.0\n",
      "Fitting: (395, 50)\n",
      "NearestNeighbors(algorithm='brute', metric='euclidean', n_neighbors=15)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "too similar \t accept challenges -> accept challenges |dist:  0.0\n",
      "too similar \t science engineering -> computer science engineering |dist:  1.602063\n",
      "too similar \t proficiency program -> proficiency program |dist:  0.0\n",
      "too similar \t matlab -> matlab |dist:  0.0\n",
      "too similar \t computer science engineering -> computer science engineering |dist:  8.4293696e-08\n",
      "too similar \t engineering -> engineering |dist:  0.0\n",
      "too similar \t computer -> computer |dist:  0.0\n",
      "too similar \t computer science -> computer science |dist:  0.0\n",
      "too similar \t software -> software |dist:  0.0\n",
      "too similar \t science -> science |dist:  0.0\n",
      "too similar \t engineering adichunchanagiri -> engineering adichunchanagiri institute |dist:  1.3744159\n",
      "too similar \t accept challenges education -> accept challenges education |dist:  0.0\n",
      "too similar \t ready accept challenges -> ready accept challenges |dist:  0.0\n",
      "too similar \t java -> java |dist:  0.0\n",
      "too similar \t program langages -> program langages |dist:  0.0\n",
      "too similar \t web programming -> web programming |dist:  0.0\n",
      "too similar \t programming tools -> programming tools |dist:  0.0\n",
      "too similar \t system -> system |dist:  0.0\n",
      "too similar \t motivated ready accept -> motivated ready accept |dist:  0.0\n",
      "too similar \t adichunchanagiri institute technology -> engineering adichunchanagiri institute |dist:  1.3091557\n",
      "too similar \t adichunchanagiri -> matelabs |dist:  0.0\n",
      "too similar \t web programming tools -> web programming tools |dist:  0.0\n",
      "too similar \t science engineering adichunchanagiri -> engineering adichunchanagiri institute |dist:  1.1061809\n",
      "too similar \t institute technology chikmagalur -> institute technology chikmagalur |dist:  0.0\n",
      "too similar \t adichunchanagiri institute -> engineering adichunchanagiri institute |dist:  1.3166802\n",
      "too similar \t enthusiastic learn -> enthusiastic learn |dist:  0.0\n",
      "too similar \t proficiency program langages -> proficiency program langages |dist:  0.0\n",
      "too similar \t proficiency -> proficiency |dist:  0.0\n",
      "too similar \t monthscompany -> matelabs |dist:  0.0\n",
      "too similar \t engineering adichunchanagiri institute -> engineering adichunchanagiri institute |dist:  0.0\n",
      "Fitting: (395, 50)\n",
      "NearestNeighbors(algorithm='brute', metric='euclidean', n_neighbors=15)\n",
      "too similar \t tora -> tora |dist:  0.0\n",
      "too similar \t higher secondary school -> higher secondary school |dist:  0.0\n",
      "too similar \t sslc finance kamaraj -> sslc finance kamaraj |dist:  0.0\n",
      "too similar \t school -> school |dist:  0.0\n",
      "too similar \t tora spsseducation -> tora spsseducation |dist:  0.0\n",
      "too similar \t hsc sav -> hsc sav |dist:  0.0\n",
      "too similar \t engineering -> engineering |dist:  0.0\n",
      "too similar \t typewriting tora -> typewriting tora |dist:  0.0\n",
      "too similar \t sav higher secondary -> sav higher secondary |dist:  0.0\n",
      "too similar \t higher secondary -> higher secondary |dist:  0.0\n",
      "too similar \t sns college -> sns college |dist:  5.9604645e-08\n",
      "too similar \t chidambaram tamil nadu -> chidambaram tamil nadu |dist:  0.0\n",
      "too similar \t sns -> sns |dist:  0.0\n",
      "too similar \t kamaraj matriculation -> kamaraj matriculation |dist:  0.0\n",
      "too similar \t chidambaram tamil -> chidambaram tamil |dist:  0.0\n",
      "too similar \t college engineering -> college engineering |dist:  0.0\n",
      "too similar \t tamil nadu sns -> tamil nadu sns |dist:  5.9604645e-08\n",
      "too similar \t mba chidambaram tamil -> mba chidambaram tamil |dist:  0.0\n",
      "too similar \t mba -> mba |dist:  0.0\n",
      "too similar \t sav higher -> sav higher |dist:  0.0\n",
      "too similar \t technical -> technical |dist:  1.1920929e-07\n",
      "too similar \t finance -> finance |dist:  0.0\n",
      "too similar \t hsc sav higher -> hsc sav higher |dist:  0.0\n",
      "too similar \t tamil nadu -> tamil nadu |dist:  0.0\n",
      "too similar \t spsseducation -> matelabs |dist:  0.0\n",
      "too similar \t sns college engineering -> sns college engineering |dist:  5.9604645e-08\n",
      "too similar \t typewriting tora spsseducation -> typewriting tora spsseducation |dist:  0.0\n",
      "too similar \t typewriting -> typewriting |dist:  0.0\n",
      "too similar \t finance kamaraj matriculation -> finance kamaraj matriculation |dist:  0.0\n",
      "too similar \t college -> college |dist:  0.0\n",
      "Fitting: (395, 50)\n",
      "NearestNeighbors(algorithm='brute', metric='euclidean', n_neighbors=15)\n",
      "too similar \t salary -> salary |dist:  0.0\n",
      "too similar \t saral payment package -> saral payment package |dist:  0.0\n",
      "too similar \t payroll -> payroll |dist:  0.0\n",
      "too similar \t compliance related -> compliance related |dist:  0.0\n",
      "too similar \t statutory compliance -> statutory compliance |dist:  0.0\n",
      "too similar \t queen university edinburg -> queen university edinburg |dist:  0.0\n",
      "too similar \t end end facilitation -> end end facilitation |dist:  0.0\n",
      "too similar \t management end end -> management end end |dist:  0.0\n",
      "too similar \t package payroll -> package payroll |dist:  0.0\n",
      "too similar \t employees -> employees |dist:  0.0\n",
      "too similar \t statutory -> statutory |dist:  8.4293696e-08\n",
      "too similar \t act recruitment selection -> act recruitment selection |dist:  0.0\n",
      "too similar \t related -> related |dist:  0.0\n",
      "too similar \t management -> management |dist:  0.0\n",
      "too similar \t end facilitation pms -> end facilitation pms |dist:  5.9604645e-08\n",
      "too similar \t payroll software -> payroll software |dist:  8.4293696e-08\n",
      "too similar \t welfare creation deployment -> welfare creation deployment |dist:  0.0\n",
      "too similar \t payment package -> payment package |dist:  0.0\n",
      "too similar \t recruitment selection recruitment -> recruitment selection recruitment |dist:  0.0\n",
      "too similar \t package payroll software -> package payroll software |dist:  0.0\n",
      "too similar \t payment package payroll -> payment package payroll |dist:  0.0\n",
      "too similar \t payment -> payment |dist:  8.4293696e-08\n",
      "too similar \t compliance -> compliance |dist:  8.4293696e-08\n",
      "too similar \t policies procedure -> policies procedure |dist:  0.0\n",
      "too similar \t annual -> annual |dist:  1.1920929e-07\n",
      "too similar \t hospitality management -> hospitality management |dist:  0.0\n",
      "too similar \t saral payment -> saral payment |dist:  0.0\n",
      "too similar \t employee -> employee |dist:  0.0\n",
      "too similar \t hotel management -> hotel management |dist:  0.0\n",
      "too similar \t management international -> management international |dist:  0.0\n",
      "Fitting: (395, 50)\n",
      "NearestNeighbors(algorithm='brute', metric='euclidean', n_neighbors=15)\n",
      "too similar \t company -> company |dist:  0.0\n",
      "too similar \t operations exprience -> operations exprience |dist:  0.0\n",
      "too similar \t university -> university |dist:  0.0\n",
      "too similar \t mumbai -> mumbai |dist:  0.0\n",
      "too similar \t mumbai university -> mumbai university |dist:  0.0\n",
      "too similar \t monthscompany details company -> company matelabs |dist:  1.5237632\n",
      "too similar \t education -> education |dist:  0.0\n",
      "too similar \t exprience -> matelabs |dist:  0.0\n",
      "too similar \t monthscompany -> matelabs |dist:  0.0\n",
      "too similar \t company mumbai -> company mumbai |dist:  0.0\n",
      "too similar \t operations -> operations |dist:  0.0\n",
      "Fitting: (395, 50)\n",
      "NearestNeighbors(algorithm='brute', metric='euclidean', n_neighbors=15)\n",
      "too similar \t company -> company |dist:  0.0\n",
      "too similar \t economics chennai -> economics chennai |dist:  0.0\n",
      "too similar \t solution -> solution |dist:  8.4293696e-08\n",
      "too similar \t tamil nadu sdnbvc -> nadu sdnbvc |dist:  1.4546527\n",
      "too similar \t economics -> economics |dist:  0.0\n",
      "too similar \t nadu -> nadu |dist:  0.0\n",
      "too similar \t nadu sdnbvc -> nadu sdnbvc |dist:  0.0\n",
      "too similar \t economics chennai tamil -> economics chennai tamil |dist:  0.0\n",
      "too similar \t education -> education |dist:  0.0\n",
      "too similar \t tamil nadu -> tamil nadu |dist:  0.0\n",
      "too similar \t company details company -> company |dist:  1.9206945\n",
      "too similar \t tamil -> tamil |dist:  0.0\n",
      "too similar \t company anything solution -> company anything solution |dist:  0.0\n",
      "too similar \t chennai tamil nadu -> chennai tamil |dist:  1.0504411\n",
      "too similar \t chennai -> chennai |dist:  8.4293696e-08\n",
      "too similar \t sdnbvc -> matelabs |dist:  0.0\n",
      "too similar \t chennai tamil -> chennai tamil |dist:  1.1920929e-07\n",
      "Fitting: (395, 50)\n",
      "NearestNeighbors(algorithm='brute', metric='euclidean', n_neighbors=15)\n",
      "too similar \t company -> company |dist:  0.0\n",
      "too similar \t economics chennai -> economics chennai |dist:  0.0\n",
      "too similar \t solution -> solution |dist:  8.4293696e-08\n",
      "too similar \t tamil nadu sdnbvc -> nadu sdnbvc |dist:  1.4546527\n",
      "too similar \t economics -> economics |dist:  0.0\n",
      "too similar \t nadu -> nadu |dist:  0.0\n",
      "too similar \t nadu sdnbvc -> nadu sdnbvc |dist:  0.0\n",
      "too similar \t economics chennai tamil -> economics chennai tamil |dist:  0.0\n",
      "too similar \t education -> education |dist:  0.0\n",
      "too similar \t tamil nadu -> tamil nadu |dist:  0.0\n",
      "too similar \t company details company -> company |dist:  1.9206945\n",
      "too similar \t tamil -> tamil |dist:  0.0\n",
      "too similar \t company anything solution -> company anything solution |dist:  0.0\n",
      "too similar \t chennai tamil nadu -> chennai tamil |dist:  1.0504411\n",
      "too similar \t chennai -> chennai |dist:  8.4293696e-08\n",
      "too similar \t sdnbvc -> matelabs |dist:  0.0\n",
      "too similar \t chennai tamil -> chennai tamil |dist:  1.1920929e-07\n",
      "Fitting: (395, 50)\n",
      "NearestNeighbors(algorithm='brute', metric='euclidean', n_neighbors=15)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "too similar \t company -> company |dist:  0.0\n",
      "too similar \t university -> university |dist:  0.0\n",
      "too similar \t monthscompany details company -> company matelabs |dist:  1.5237632\n",
      "too similar \t exprience monthscompany -> matelabs |dist:  0.0\n",
      "too similar \t bba lovely -> bba lovely |dist:  5.9604645e-08\n",
      "too similar \t education details bba -> education details bba |dist:  8.4293696e-08\n",
      "too similar \t communication exprience -> communication exprience |dist:  0.0\n",
      "too similar \t lovely -> lovely |dist:  0.0\n",
      "too similar \t bba -> bba |dist:  0.0\n",
      "too similar \t bba lovely professional -> bba lovely professional |dist:  0.0\n",
      "too similar \t communication -> communication |dist:  0.0\n",
      "too similar \t lovely professional -> lovely professional |dist:  0.0\n",
      "too similar \t communication exprience monthscompany -> communication exprience |dist:  0.85563844\n",
      "too similar \t education -> education |dist:  0.0\n",
      "too similar \t lovely professional university -> lovely professional university |dist:  0.0\n",
      "too similar \t professional -> professional |dist:  0.0\n",
      "too similar \t professional university -> professional university |dist:  0.0\n",
      "too similar \t exprience -> matelabs |dist:  0.0\n",
      "too similar \t monthscompany -> matelabs |dist:  0.0\n",
      "Fitting: (395, 50)\n",
      "NearestNeighbors(algorithm='brute', metric='euclidean', n_neighbors=15)\n",
      "too similar \t company -> company |dist:  0.0\n",
      "too similar \t assistant -> assistant |dist:  0.0\n",
      "too similar \t mba -> mba |dist:  0.0\n",
      "too similar \t college engineering mgt -> sns college engineering |dist:  1.5184338\n",
      "too similar \t company hr assistant -> company hr assistant |dist:  0.0\n",
      "too similar \t education details mba -> education details mca |dist:  1.9480041\n",
      "too similar \t mgt -> mgt |dist:  0.0\n",
      "too similar \t mba acn college -> mba acn college |dist:  0.0\n",
      "too similar \t education -> education |dist:  0.0\n",
      "too similar \t company details company -> company |dist:  1.9206945\n",
      "too similar \t engineering -> engineering |dist:  0.0\n",
      "too similar \t acn college -> acn college |dist:  0.0\n",
      "too similar \t engineering mgt -> engineering mgt |dist:  0.0\n",
      "too similar \t acn -> acn |dist:  0.0\n",
      "too similar \t college engineering -> college engineering |dist:  0.0\n",
      "too similar \t mba acn -> mba acn |dist:  0.0\n",
      "too similar \t college -> college |dist:  0.0\n",
      "too similar \t acn college engineering -> acn college |dist:  1.565482\n",
      "Fitting: (395, 50)\n",
      "NearestNeighbors(algorithm='brute', metric='euclidean', n_neighbors=15)\n",
      "too similar \t management accounting -> management accounting |dist:  0.0\n",
      "too similar \t income tax good -> income tax good |dist:  0.0\n",
      "too similar \t rlt scrum master -> rlt scrum master |dist:  0.0\n",
      "too similar \t tax good -> tax good |dist:  0.0\n",
      "too similar \t computer -> computer |dist:  0.0\n",
      "too similar \t tax good communication -> tax good communication |dist:  0.0\n",
      "too similar \t good communication -> communication skills problem |dist:  1.646467\n",
      "too similar \t sincere hard -> sincere hard |dist:  0.0\n",
      "too similar \t good communication leadership -> communication skills problem |dist:  1.9608529\n",
      "too similar \t rlt scrum -> rlt scrum |dist:  0.0\n",
      "too similar \t hyderabad university -> mumbai university |dist:  1.828275\n",
      "too similar \t ecommerce -> ecommerce |dist:  0.0\n",
      "too similar \t driving license internet -> driving license internet |dist:  0.0\n",
      "too similar \t mba finance -> mba finance |dist:  0.0\n",
      "too similar \t system months dbms -> system months dbms |dist:  0.0\n",
      "too similar \t management -> management |dist:  0.0\n",
      "too similar \t scrum -> scrum |dist:  0.0\n",
      "too similar \t form -> form |dist:  0.0\n",
      "too similar \t accounting -> accounting |dist:  0.0\n",
      "too similar \t income tax -> income tax |dist:  0.0\n",
      "too similar \t rlt -> matelabs |dist:  0.0\n",
      "too similar \t sincere hard working -> sincere hard working |dist:  5.9604645e-08\n",
      "too similar \t hard working -> hard working |dist:  0.0\n",
      "too similar \t dbms -> dbms |dist:  0.0\n",
      "too similar \t scrum master -> scrum master |dist:  0.0\n",
      "too similar \t tally -> tally |dist:  0.0\n",
      "too similar \t language web programing -> language web programing |dist:  0.0\n",
      "too similar \t wheeler driving license -> wheeler driving license |dist:  0.0\n",
      "too similar \t inter mec srimedhav -> inter mec srimedhav |dist:  0.0\n",
      "too similar \t master -> master |dist:  0.0\n",
      "Fitting: (395, 50)\n",
      "NearestNeighbors(algorithm='brute', metric='euclidean', n_neighbors=15)\n",
      "too similar \t company -> company |dist:  0.0\n",
      "too similar \t training special -> training special |dist:  0.0\n",
      "too similar \t prajyoti niketan college -> prajyoti niketan college |dist:  0.0\n",
      "too similar \t behaviour malappuram -> behaviour malappuram |dist:  5.9604645e-08\n",
      "too similar \t behaviour malappuram kerala -> behaviour malappuram kerala |dist:  0.0\n",
      "too similar \t specialization organizational behaviour -> specialization organizational behaviour |dist:  0.0\n",
      "too similar \t niketan college -> niketan college |dist:  0.0\n",
      "too similar \t thrissur prajyoti niketan -> thrissur prajyoti niketan |dist:  4.2146848e-08\n",
      "too similar \t malappuram kerala -> malappuram kerala |dist:  0.0\n",
      "too similar \t days -> days |dist:  8.4293696e-08\n",
      "too similar \t special -> special |dist:  0.0\n",
      "too similar \t specialization organizational -> specialization organizational |dist:  0.0\n",
      "too similar \t special education -> special education |dist:  0.0\n",
      "too similar \t counselling counsellor -> counselling counsellor |dist:  0.0\n",
      "too similar \t special education certificate -> special education certificate |dist:  0.0\n",
      "too similar \t institute counselling counsellor -> institute counselling counsellor |dist:  0.0\n",
      "too similar \t training -> training |dist:  0.0\n",
      "too similar \t malappuram kerala calicut -> malappuram kerala calicut |dist:  0.0\n",
      "too similar \t education -> education |dist:  0.0\n",
      "too similar \t kerala calicut university -> kerala calicut university |dist:  0.0\n",
      "too similar \t counsellor -> counsellor |dist:  0.0\n",
      "too similar \t certificate -> certificate |dist:  0.0\n",
      "too similar \t internship -> internship |dist:  8.4293696e-08\n",
      "too similar \t organizational behaviour -> organizational behaviour |dist:  0.0\n",
      "too similar \t training special education -> training special education |dist:  0.0\n",
      "too similar \t psychology -> psychology |dist:  0.0\n",
      "too similar \t days internship -> days internship |dist:  0.0\n",
      "too similar \t organizational behaviour malappuram -> organizational behaviour malappuram |dist:  0.0\n",
      "too similar \t kerala calicut -> kerala calicut |dist:  0.0\n",
      "too similar \t college -> college |dist:  0.0\n",
      "Fitting: (395, 50)\n",
      "NearestNeighbors(algorithm='brute', metric='euclidean', n_neighbors=15)\n",
      "too similar \t computer knowledge proficient -> computer knowledge proficient |dist:  0.0\n",
      "too similar \t labors individual -> labors individual |dist:  0.0\n",
      "too similar \t dynamics exprience -> dynamics exprience |dist:  0.0\n",
      "too similar \t school -> school |dist:  0.0\n",
      "too similar \t ramesh -> ramesh |dist:  8.4293696e-08\n",
      "too similar \t ramesh gaarment -> ramesh gaarment |dist:  4.2146848e-08\n",
      "too similar \t exprience monthscompany -> matelabs |dist:  0.0\n",
      "too similar \t office exprience monthscompany -> matelabs |dist:  1.711691\n",
      "too similar \t avoidance blackbox car -> avoidance blackbox car |dist:  0.0\n",
      "too similar \t exprience months microsoft -> sql year monthscompany |dist:  1.7809306\n",
      "too similar \t microsoft dynamics exprience -> microsoft dynamics exprience |dist:  0.0\n",
      "too similar \t activities attendance maintenance -> activities attendance maintenance |dist:  0.0\n",
      "too similar \t sri -> sri |dist:  0.0\n",
      "too similar \t gaarment -> matelabs |dist:  0.0\n",
      "too similar \t admin bharat -> admin bharat |dist:  0.0\n",
      "too similar \t microsoft dynamics -> microsoft dynamics |dist:  0.0\n",
      "too similar \t sec -> sec |dist:  0.0\n",
      "too similar \t sri ramesh -> sri ramesh |dist:  0.0\n",
      "too similar \t school hr admin -> school hr admin |dist:  0.0\n",
      "too similar \t sec school -> sec school |dist:  0.0\n",
      "too similar \t labors individual records -> labors individual records |dist:  0.0\n",
      "too similar \t activities attendance -> activities attendance |dist:  0.0\n",
      "too similar \t project advanced automobile -> project advanced automobile |dist:  0.0\n",
      "too similar \t tirupur -> tirupur |dist:  1.1920929e-07\n",
      "too similar \t exprience -> matelabs |dist:  0.0\n",
      "too similar \t microsoft -> microsoft |dist:  0.0\n",
      "too similar \t dynamics -> dynamics |dist:  0.0\n",
      "too similar \t maintenance -> maintenance |dist:  0.0\n",
      "too similar \t blackbox car -> blackbox car |dist:  0.0\n",
      "too similar \t sri ramesh gaarment -> sri ramesh gaarment |dist:  0.0\n",
      "Fitting: (395, 50)\n",
      "NearestNeighbors(algorithm='brute', metric='euclidean', n_neighbors=15)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "too similar \t accept challenges -> accept challenges |dist:  0.0\n",
      "too similar \t science engineering -> computer science engineering |dist:  1.602063\n",
      "too similar \t proficiency program -> proficiency program |dist:  0.0\n",
      "too similar \t matlab -> matlab |dist:  0.0\n",
      "too similar \t computer science engineering -> computer science engineering |dist:  8.4293696e-08\n",
      "too similar \t engineering -> engineering |dist:  0.0\n",
      "too similar \t computer -> computer |dist:  0.0\n",
      "too similar \t computer science -> computer science |dist:  0.0\n",
      "too similar \t software -> software |dist:  0.0\n",
      "too similar \t science -> science |dist:  0.0\n",
      "too similar \t engineering adichunchanagiri -> engineering adichunchanagiri institute |dist:  1.3744159\n",
      "too similar \t accept challenges education -> accept challenges education |dist:  0.0\n",
      "too similar \t ready accept challenges -> ready accept challenges |dist:  0.0\n",
      "too similar \t java -> java |dist:  0.0\n",
      "too similar \t program langages -> program langages |dist:  0.0\n",
      "too similar \t web programming -> web programming |dist:  0.0\n",
      "too similar \t programming tools -> programming tools |dist:  0.0\n",
      "too similar \t system -> system |dist:  0.0\n",
      "too similar \t motivated ready accept -> motivated ready accept |dist:  0.0\n",
      "too similar \t adichunchanagiri institute technology -> engineering adichunchanagiri institute |dist:  1.3091557\n",
      "too similar \t adichunchanagiri -> matelabs |dist:  0.0\n",
      "too similar \t web programming tools -> web programming tools |dist:  0.0\n",
      "too similar \t science engineering adichunchanagiri -> engineering adichunchanagiri institute |dist:  1.1061809\n",
      "too similar \t institute technology chikmagalur -> institute technology chikmagalur |dist:  0.0\n",
      "too similar \t adichunchanagiri institute -> engineering adichunchanagiri institute |dist:  1.3166802\n",
      "too similar \t enthusiastic learn -> enthusiastic learn |dist:  0.0\n",
      "too similar \t proficiency program langages -> proficiency program langages |dist:  0.0\n",
      "too similar \t proficiency -> proficiency |dist:  0.0\n",
      "too similar \t monthscompany -> matelabs |dist:  0.0\n",
      "too similar \t engineering adichunchanagiri institute -> engineering adichunchanagiri institute |dist:  0.0\n",
      "Fitting: (395, 50)\n",
      "NearestNeighbors(algorithm='brute', metric='euclidean', n_neighbors=15)\n",
      "too similar \t company -> company |dist:  0.0\n",
      "too similar \t jagiroad assam jagiroad -> nadu sdnbvc |dist:  1.7552419\n",
      "too similar \t jagiroad -> matelabs |dist:  0.0\n",
      "too similar \t college -> college |dist:  0.0\n",
      "Fitting: (421, 50)\n",
      "NearestNeighbors(algorithm='brute', metric='euclidean', n_neighbors=15)\n",
      "too similar \t income -> income tax |dist:  1.8503561\n",
      "too similar \t university -> university |dist:  0.0\n",
      "too similar \t cases -> cases cases |dist:  8.4293696e-08\n",
      "too similar \t university clacutta -> queen university edinburg |dist:  1.8729931\n",
      "too similar \t tax -> income tax |dist:  1.8503561\n",
      "too similar \t income tax -> income tax |dist:  0.0\n",
      "too similar \t education -> education |dist:  0.0\n",
      "too similar \t clacutta university -> queen university edinburg |dist:  1.8729931\n",
      "too similar \t clacutta -> matelabs |dist:  0.0\n",
      "too similar \t university burdwan advocate -> morigaon college advocate |dist:  1.9386145\n",
      "too similar \t exprience -> matelabs |dist:  0.0\n",
      "too similar \t clacutta university burdwan -> jagiroad college morigaon |dist:  1.9386145\n",
      "Fitting: (439, 50)\n",
      "NearestNeighbors(algorithm='brute', metric='euclidean', n_neighbors=15)\n",
      "too similar \t company -> company |dist:  0.0\n",
      "too similar \t university -> university |dist:  0.0\n",
      "too similar \t monthscompany details company -> company matelabs |dist:  1.5237632\n",
      "too similar \t advocate -> advocate advocate |dist:  0.0\n",
      "too similar \t education -> education |dist:  0.0\n",
      "too similar \t exprience -> matelabs |dist:  0.0\n",
      "too similar \t dibrugarh university advocate -> morigaon college advocate |dist:  1.820397\n",
      "too similar \t monthscompany -> matelabs |dist:  0.0\n",
      "too similar \t university advocate -> college advocate |dist:  1.6144001\n",
      "Fitting: (448, 50)\n",
      "NearestNeighbors(algorithm='brute', metric='euclidean', n_neighbors=15)\n",
      "too similar \t microsoft word -> word microsoft |dist:  0.0\n",
      "too similar \t company lrc office -> company matelabs |dist:  1.7683706\n",
      "too similar \t osmania university -> university burdwan |dist:  1.8794016\n",
      "too similar \t drafting legal correspondence -> legal exprience |dist:  1.9650785\n",
      "too similar \t advocate -> advocate advocate |dist:  0.0\n",
      "too similar \t education -> education |dist:  0.0\n",
      "too similar \t legal -> legal |dist:  0.0\n",
      "too similar \t company lrc -> company matelabs |dist:  1.9309108\n",
      "too similar \t college -> college |dist:  0.0\n",
      "Fitting: (469, 50)\n",
      "NearestNeighbors(algorithm='brute', metric='euclidean', n_neighbors=15)\n",
      "too similar \t advocate -> advocate advocate |dist:  0.0\n",
      "too similar \t legal -> legal |dist:  0.0\n",
      "too similar \t microsoft -> microsoft |dist:  0.0\n",
      "Fitting: (496, 50)\n",
      "NearestNeighbors(algorithm='brute', metric='euclidean', n_neighbors=15)\n",
      "too similar \t company -> company |dist:  0.0\n",
      "too similar \t university -> university |dist:  0.0\n",
      "too similar \t university advocate -> college advocate |dist:  1.6144001\n",
      "too similar \t nadu -> nadu |dist:  0.0\n",
      "too similar \t law -> law |dist:  0.0\n",
      "too similar \t advocate advocate -> advocate advocate |dist:  0.0\n",
      "too similar \t advocate -> advocate advocate |dist:  0.0\n",
      "too similar \t legal -> legal |dist:  0.0\n",
      "too similar \t law university -> college law |dist:  1.6144\n",
      "too similar \t university advocate advocate -> college advocate advocate |dist:  1.0762668\n",
      "too similar \t company practiced -> company practice |dist:  1.4033375\n",
      "too similar \t tamil nadu -> tamil nadu |dist:  0.0\n",
      "too similar \t tamil -> tamil |dist:  0.0\n",
      "too similar \t sundaranar university advocate -> morigaon college advocate |dist:  1.6225007\n",
      "Fitting: (512, 50)\n",
      "NearestNeighbors(algorithm='brute', metric='euclidean', n_neighbors=15)\n",
      "too similar \t company -> company |dist:  0.0\n",
      "too similar \t university -> university |dist:  0.0\n",
      "too similar \t law -> law |dist:  0.0\n",
      "too similar \t company criminal -> company legal |dist:  1.7845832\n",
      "too similar \t mumbai university -> mumbai university |dist:  0.0\n",
      "too similar \t mumbai -> mumbai |dist:  0.0\n",
      "too similar \t llb -> llb |dist:  0.0\n",
      "too similar \t operation education -> training special education |dist:  1.8552386\n",
      "too similar \t college -> college |dist:  0.0\n",
      "Fitting: (533, 50)\n",
      "NearestNeighbors(algorithm='brute', metric='euclidean', n_neighbors=15)\n",
      "too similar \t university -> university |dist:  0.0\n",
      "too similar \t pune advocate advocate -> advocate advocate gauhati |dist:  1.7048606\n",
      "too similar \t pune maharashtra university -> kerala calicut university |dist:  1.9678682\n",
      "too similar \t maharashtra -> maharashtra |dist:  0.0\n",
      "too similar \t law pune -> law hyderabad |dist:  1.5194944\n",
      "too similar \t law -> law |dist:  0.0\n",
      "too similar \t maharashtra university pune -> kerala calicut university |dist:  1.9678683\n",
      "too similar \t advocate advocate -> advocate advocate |dist:  0.0\n",
      "too similar \t advocate -> advocate advocate |dist:  0.0\n",
      "too similar \t university pune advocate -> university clacutta university |dist:  1.7479534\n",
      "too similar \t university pune -> mumbai university |dist:  1.7552285\n",
      "too similar \t legal -> legal |dist:  0.0\n",
      "too similar \t legal writing -> legal correspondence |dist:  1.9219711\n",
      "too similar \t writing -> listening reading writing |dist:  1.9828526\n",
      "too similar \t pune maharashtra -> mumbai maharashtra |dist:  1.7552285\n",
      "Fitting: (548, 50)\n",
      "NearestNeighbors(algorithm='brute', metric='euclidean', n_neighbors=15)\n",
      "too similar \t company -> company |dist:  0.0\n",
      "too similar \t university advocate -> college advocate |dist:  1.6144001\n",
      "too similar \t district -> district |dist:  0.0\n",
      "too similar \t legal -> legal |dist:  0.0\n",
      "too similar \t law solapur maharashtra -> law pune maharashtra |dist:  1.2282581\n",
      "Fitting: (573, 50)\n",
      "NearestNeighbors(algorithm='brute', metric='euclidean', n_neighbors=15)\n",
      "too similar \t company -> company |dist:  0.0\n",
      "too similar \t hard working quick -> hard working |dist:  1.1729223\n",
      "too similar \t university -> university |dist:  0.0\n",
      "too similar \t hsc maharashtra board -> maharashtra board |dist:  1.975917\n",
      "too similar \t maharashtra board advocate -> maharashtra board |dist:  1.7500145\n",
      "too similar \t maharashtra board -> maharashtra board |dist:  0.0\n",
      "too similar \t maharashtra -> maharashtra |dist:  0.0\n",
      "too similar \t maharashtra mumbai -> mumbai maharashtra |dist:  0.0\n",
      "too similar \t vidishtra -> matelabs |dist:  0.0\n",
      "too similar \t mumbai university -> mumbai university |dist:  0.0\n",
      "too similar \t law mumbai maharashtra -> law pune maharashtra |dist:  1.1701523\n",
      "too similar \t mumbai -> mumbai |dist:  0.0\n",
      "too similar \t advocate -> advocate advocate |dist:  0.0\n",
      "too similar \t mumbai maharashtra mumbai -> mumbai maharashtra |dist:  0.79017854\n",
      "too similar \t hard working -> hard working |dist:  0.0\n",
      "too similar \t maharashtra mumbai university -> maharashtra university |dist:  1.5522934\n",
      "too similar \t board -> board |dist:  0.0\n",
      "too similar \t mumbai maharashtra -> mumbai maharashtra |dist:  0.0\n",
      "too similar \t commerce mumbai maharashtra -> mumbai maharashtra |dist:  1.9121542\n",
      "too similar \t working quick learnereducation -> scikit learn |dist:  1.8497323\n",
      "too similar \t law mumbai -> law hyderabad |dist:  1.828275\n",
      "too similar \t llb -> llb |dist:  0.0\n",
      "too similar \t board advocate -> working senior advocate |dist:  1.9528638\n",
      "too similar \t hard -> hard working |dist:  1.8177145\n",
      "too similar \t company vidishtra -> company matelabs |dist:  0.0\n",
      "Fitting: (578, 50)\n",
      "NearestNeighbors(algorithm='brute', metric='euclidean', n_neighbors=15)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "too similar \t company -> company |dist:  0.0\n",
      "too similar \t advocate gauhati -> advocate gauhati |dist:  0.0\n",
      "too similar \t subordinate tribunals -> subordinate tribunals |dist:  0.0\n",
      "too similar \t criminal cases cases -> criminal cases cases |dist:  0.0\n",
      "too similar \t cases -> cases |dist:  8.4293696e-08\n",
      "too similar \t college advocate advocate -> college advocate advocate |dist:  0.0\n",
      "too similar \t gauhati high -> gauhati high |dist:  0.0\n",
      "too similar \t judge judge -> judge |dist:  0.0\n",
      "too similar \t gauhati -> gauhati |dist:  8.4293696e-08\n",
      "too similar \t assam jagiroad college -> assam jagiroad college |dist:  0.0\n",
      "too similar \t corporation -> corporation |dist:  0.0\n",
      "too similar \t subordinate tribunals statutory -> subordinate tribunals statutory |dist:  5.9604645e-08\n",
      "too similar \t morigaon college advocate -> morigaon college advocate |dist:  0.0\n",
      "too similar \t cases cases -> cases |dist:  8.4293696e-08\n",
      "too similar \t technical qualifications -> technical qualifications |dist:  0.0\n",
      "too similar \t advocate advocate -> advocate advocate |dist:  0.0\n",
      "too similar \t advocate advocate gauhati -> advocate advocate gauhati |dist:  0.0\n",
      "too similar \t jagiroad assam jagiroad -> nadu sdnbvc |dist:  1.7552419\n",
      "too similar \t college advocate -> college advocate |dist:  0.0\n",
      "too similar \t judge -> judge |dist:  0.0\n",
      "too similar \t consumer forum -> consumer forum |dist:  0.0\n",
      "too similar \t advocate -> advocate advocate |dist:  0.0\n",
      "too similar \t jagiroad college morigaon -> jagiroad college morigaon |dist:  0.0\n",
      "too similar \t technical qualifications windows -> technical qualifications windows |dist:  0.0\n",
      "too similar \t company gauhati -> company gauhati |dist:  5.9604645e-08\n",
      "too similar \t matters -> matters |dist:  0.0\n",
      "too similar \t cases cases consumer -> cases cases consumer |dist:  8.4293696e-08\n",
      "too similar \t high -> high |dist:  0.0\n",
      "too similar \t jagiroad -> matelabs |dist:  0.0\n",
      "too similar \t college -> college |dist:  0.0\n",
      "Fitting: (578, 50)\n",
      "NearestNeighbors(algorithm='brute', metric='euclidean', n_neighbors=15)\n",
      "too similar \t income -> income tax |dist:  1.8503561\n",
      "too similar \t move relevant authority -> move relevant authority |dist:  8.4293696e-08\n",
      "too similar \t taxation matters income -> taxation matters income |dist:  0.0\n",
      "too similar \t university -> university |dist:  0.0\n",
      "too similar \t returns gst returns -> returns gst returns |dist:  0.0\n",
      "too similar \t cases -> cases |dist:  8.4293696e-08\n",
      "too similar \t university clacutta -> university clacutta university |dist:  1.0991917\n",
      "too similar \t burdwan advocate -> burdwan advocate |dist:  0.0\n",
      "too similar \t university clacutta university -> university clacutta university |dist:  8.4293696e-08\n",
      "too similar \t returns -> returns |dist:  0.0\n",
      "too similar \t gst -> gst |dist:  0.0\n",
      "too similar \t accounts -> accounts |dist:  0.0\n",
      "too similar \t relevant authority hear -> relevant authority hear |dist:  0.0\n",
      "too similar \t university burdwan -> university burdwan |dist:  0.0\n",
      "too similar \t tax -> income tax |dist:  1.8503561\n",
      "too similar \t drafting preparation plaint -> drafting preparation plaint |dist:  0.0\n",
      "too similar \t hear cases -> hear cases |dist:  0.0\n",
      "too similar \t income tax -> income tax |dist:  0.0\n",
      "too similar \t tds air exprience -> tds air exprience |dist:  0.0\n",
      "too similar \t drafting preparation -> drafting preparation |dist:  0.0\n",
      "too similar \t education -> education |dist:  0.0\n",
      "too similar \t clacutta university -> university clacutta university |dist:  1.0991917\n",
      "too similar \t clacutta -> matelabs |dist:  0.0\n",
      "too similar \t university burdwan advocate -> burdwan advocate |dist:  1.8188974\n",
      "too similar \t authority hear cases -> authority hear cases |dist:  0.0\n",
      "too similar \t taxation matters -> taxation matters |dist:  0.0\n",
      "too similar \t tds air -> tds air |dist:  5.9604645e-08\n",
      "too similar \t exprience -> matelabs |dist:  0.0\n",
      "too similar \t clacutta university burdwan -> university burdwan |dist:  1.6136016\n",
      "too similar \t company practice -> company practice |dist:  0.0\n",
      "Fitting: (578, 50)\n",
      "NearestNeighbors(algorithm='brute', metric='euclidean', n_neighbors=15)\n",
      "too similar \t company -> company |dist:  0.0\n",
      "too similar \t llb dibrugarh university -> llb dibrugarh university |dist:  0.0\n",
      "too similar \t llb dibrugarh -> llb dibrugarh |dist:  0.0\n",
      "too similar \t legal exprience -> legal exprience |dist:  0.0\n",
      "too similar \t university -> university |dist:  0.0\n",
      "too similar \t monthscompany details company -> company matelabs |dist:  1.5237632\n",
      "too similar \t dibrugarh -> dibrugarh |dist:  8.4293696e-08\n",
      "too similar \t advocate -> advocate advocate |dist:  0.0\n",
      "too similar \t education -> education |dist:  0.0\n",
      "too similar \t llb -> llb |dist:  0.0\n",
      "too similar \t dibrugarh university -> dibrugarh university |dist:  0.0\n",
      "too similar \t exprience -> matelabs |dist:  0.0\n",
      "too similar \t dibrugarh university advocate -> dibrugarh university |dist:  1.7988851\n",
      "too similar \t monthscompany -> matelabs |dist:  0.0\n",
      "too similar \t university advocate -> college advocate |dist:  1.6144001\n",
      "too similar \t company legal -> company legal |dist:  0.0\n",
      "too similar \t legal -> legal |dist:  0.0\n",
      "too similar \t education details llb -> education details llb |dist:  0.0\n",
      "Fitting: (578, 50)\n",
      "NearestNeighbors(algorithm='brute', metric='euclidean', n_neighbors=15)\n",
      "too similar \t masters -> masters |dist:  0.0\n",
      "too similar \t llb bachelors -> llb bachelors |dist:  0.0\n",
      "too similar \t law hyderabad -> law hyderabad |dist:  0.0\n",
      "too similar \t microsoft word -> microsoft word |dist:  0.0\n",
      "too similar \t recently acquired masters -> recently acquired masters |dist:  0.0\n",
      "too similar \t litigation recently acquired -> litigation recently acquired |dist:  0.0\n",
      "too similar \t working senior advocate -> working senior advocate |dist:  0.0\n",
      "too similar \t company lrc office -> company matelabs |dist:  1.7683706\n",
      "too similar \t hyderabad telangana -> hyderabad telangana |dist:  0.0\n",
      "too similar \t law -> law |dist:  0.0\n",
      "too similar \t experienced litigation recently -> experienced litigation recently |dist:  0.0\n",
      "too similar \t drafting -> drafting |dist:  0.0\n",
      "too similar \t osmania university -> university burdwan |dist:  1.8794016\n",
      "too similar \t drafting legal correspondence -> legal correspondence |dist:  1.4169672\n",
      "too similar \t advocate -> advocate advocate |dist:  0.0\n",
      "too similar \t education -> education |dist:  0.0\n",
      "too similar \t college law -> college law |dist:  0.0\n",
      "too similar \t recently acquired -> recently acquired |dist:  0.0\n",
      "too similar \t acquired masters degree -> acquired masters degree |dist:  0.0\n",
      "too similar \t telangana osmania university -> telangana osmania university |dist:  0.0\n",
      "too similar \t ravichander high court -> ravichander high court |dist:  0.0\n",
      "too similar \t legal -> legal |dist:  0.0\n",
      "too similar \t hyderabad -> hyderabad |dist:  0.0\n",
      "too similar \t company lrc -> company matelabs |dist:  1.9309108\n",
      "too similar \t law hyderabad telangana -> law hyderabad telangana |dist:  0.0\n",
      "too similar \t legal correspondence -> legal correspondence |dist:  0.0\n",
      "too similar \t litigation -> litigation |dist:  0.0\n",
      "too similar \t internet savvy -> internet savvy |dist:  8.4293696e-08\n",
      "too similar \t telangana -> telangana |dist:  0.0\n",
      "too similar \t college -> college |dist:  0.0\n",
      "Fitting: (578, 50)\n",
      "NearestNeighbors(algorithm='brute', metric='euclidean', n_neighbors=15)\n",
      "too similar \t writing speaking hindi -> writing speaking hindi |dist:  8.4293696e-08\n",
      "too similar \t trials -> trials |dist:  0.0\n",
      "too similar \t chandigarh -> chandigarh |dist:  0.0\n",
      "too similar \t bio informatics -> bio informatics |dist:  0.0\n",
      "too similar \t science bio -> science bio |dist:  8.4293696e-08\n",
      "too similar \t district -> district |dist:  0.0\n",
      "too similar \t legal services -> legal services |dist:  8.4293696e-08\n",
      "too similar \t reading writing speaking -> reading writing speaking |dist:  0.0\n",
      "too similar \t hindi bilingual proficiency -> hindi bilingual proficiency |dist:  8.4293696e-08\n",
      "too similar \t bar -> bar |dist:  8.4293696e-08\n",
      "too similar \t practice -> practice |dist:  0.0\n",
      "too similar \t speaking hindi bilingual -> speaking hindi bilingual |dist:  0.0\n",
      "too similar \t office months drafting -> office months drafting |dist:  0.0\n",
      "too similar \t listening reading writing -> listening reading writing |dist:  0.0\n",
      "too similar \t native speaker -> native speaker |dist:  0.0\n",
      "too similar \t bar council -> bar council |dist:  8.4293696e-08\n",
      "too similar \t certified systems engineer -> certified systems engineer |dist:  0.0\n",
      "too similar \t concisely people diverse -> concisely people diverse |dist:  0.0\n",
      "too similar \t microsoft certified -> microsoft certified |dist:  0.0\n",
      "too similar \t systems engineer -> systems engineer |dist:  0.0\n",
      "too similar \t advocate -> advocate advocate |dist:  0.0\n",
      "too similar \t certified systems -> certified systems |dist:  0.0\n",
      "too similar \t services authority -> services authority |dist:  0.0\n",
      "too similar \t authority -> authority |dist:  8.4293696e-08\n",
      "too similar \t legal -> legal |dist:  0.0\n",
      "too similar \t mansa -> mansa |dist:  0.0\n",
      "too similar \t microsoft certified systems -> microsoft certified systems |dist:  0.0\n",
      "too similar \t science bio informatics -> science bio informatics |dist:  0.0\n",
      "too similar \t injury death -> injury death |dist:  0.0\n",
      "too similar \t microsoft -> microsoft |dist:  0.0\n",
      "Fitting: (578, 50)\n",
      "NearestNeighbors(algorithm='brute', metric='euclidean', n_neighbors=15)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "too similar \t company -> company |dist:  0.0\n",
      "too similar \t ambedkar -> ambedkar |dist:  8.4293696e-08\n",
      "too similar \t ambedkar law university -> ambedkar law university |dist:  0.0\n",
      "too similar \t bachelor -> bachelor |dist:  0.0\n",
      "too similar \t ambedkar law -> ambedkar law |dist:  0.0\n",
      "too similar \t university -> university |dist:  0.0\n",
      "too similar \t qualification introduction -> qualification introduction |dist:  0.0\n",
      "too similar \t master law tamil -> master law tamil |dist:  0.0\n",
      "too similar \t english literature tirunelveli -> english literature tirunelveli |dist:  8.4293696e-08\n",
      "too similar \t nessa trust till -> nessa trust till |dist:  4.2146848e-08\n",
      "too similar \t university advocate -> college advocate |dist:  1.6144001\n",
      "too similar \t courts -> courts |dist:  0.0\n",
      "too similar \t nadu -> nadu |dist:  0.0\n",
      "too similar \t bachelor law tamil -> bachelor law tamil |dist:  0.0\n",
      "too similar \t literature tirunelveli sundaranar -> literature tirunelveli sundaranar |dist:  4.2146848e-08\n",
      "too similar \t law -> law |dist:  0.0\n",
      "too similar \t advocate advocate -> advocate advocate |dist:  0.0\n",
      "too similar \t law tamil nadu -> law tamil nadu |dist:  0.0\n",
      "too similar \t legal advisor -> legal advisor |dist:  0.0\n",
      "too similar \t advocate -> advocate advocate |dist:  0.0\n",
      "too similar \t bachelor law -> bachelor law |dist:  0.0\n",
      "too similar \t legal -> legal |dist:  0.0\n",
      "too similar \t law university -> college law |dist:  1.6144\n",
      "too similar \t university advocate advocate -> college advocate advocate |dist:  1.0762668\n",
      "too similar \t company practiced -> company practice |dist:  1.4033375\n",
      "too similar \t tamil nadu -> tamil nadu |dist:  0.0\n",
      "too similar \t tamil -> tamil |dist:  0.0\n",
      "too similar \t law tamil -> law tamil |dist:  1.1920929e-07\n",
      "too similar \t sundaranar university advocate -> morigaon college advocate |dist:  1.6225007\n",
      "too similar \t qualification introduction computer -> qualification introduction computer |dist:  0.0\n",
      "Fitting: (578, 50)\n",
      "NearestNeighbors(algorithm='brute', metric='euclidean', n_neighbors=15)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0     {'modelling': [0.10673, -0.40866, -0.63364, 0....\n",
       "1     {'company': [0.62583, -0.57703, 0.41163, 0.868...\n",
       "2     {'higher data': [-0.05461502, -0.16895, 1.2802...\n",
       "3     {'industry cross': [-0.09434001, 0.15380499, -...\n",
       "4     {'education details mca': [-0.02339667, 0.3066...\n",
       "                            ...                        \n",
       "95                                                   {}\n",
       "96                                                   {}\n",
       "97                                                   {}\n",
       "98                                                   {}\n",
       "99                                                   {}\n",
       "Name: Resume, Length: 100, dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Resume[:100].apply(kwi.upsert)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'modelling': array([ 0.10673 , -0.40866 , -0.63364 ,  0.2717  , -0.96436 ,  0.35033 ,\n",
       "        -0.064713, -0.77608 ,  0.17238 ,  0.12635 ,  1.0346  ,  0.22073 ,\n",
       "        -0.53149 ,  0.43147 , -0.11151 , -0.34564 , -0.10939 ,  0.82782 ,\n",
       "        -0.081633, -0.61482 ,  0.73704 ,  0.35889 , -0.12471 ,  0.30057 ,\n",
       "         0.28201 ,  0.46634 , -0.64082 , -0.20684 ,  0.17151 ,  0.47707 ,\n",
       "         1.1729  , -0.54662 , -0.25898 , -1.5143  ,  0.15249 ,  1.0992  ,\n",
       "        -0.73158 ,  1.2593  ,  0.068409, -0.46882 ,  0.43516 , -0.42893 ,\n",
       "         0.25435 ,  0.22008 , -0.56119 , -1.3936  ,  1.9326  ,  0.41271 ,\n",
       "         0.74327 ,  0.16076 ], dtype=float32),\n",
       " 'python': array([ 0.5897  , -0.55043 , -1.0106  ,  0.41226 ,  0.57348 ,  0.23464 ,\n",
       "        -0.35773 , -1.78    ,  0.10745 ,  0.74913 ,  0.45013 ,  1.0351  ,\n",
       "         0.48348 ,  0.47954 ,  0.51908 , -0.15053 ,  0.32474 ,  1.0789  ,\n",
       "        -0.90894 ,  0.42943 , -0.56388 ,  0.69961 ,  0.13501 ,  0.16557 ,\n",
       "        -0.063592,  0.35435 ,  0.42819 ,  0.1536  , -0.47018 , -1.0935  ,\n",
       "         1.361   , -0.80821 , -0.674   ,  1.2606  ,  0.29554 ,  1.0835  ,\n",
       "         0.2444  , -1.1877  , -0.60203 , -0.068315,  0.66256 ,  0.45336 ,\n",
       "        -1.0178  ,  0.68267 , -0.20788 , -0.73393 ,  1.2597  ,  0.15425 ,\n",
       "        -0.93256 , -0.15025 ], dtype=float32),\n",
       " 'technology assisted review': array([ 0.04143233, -0.17509164,  0.10542067,  0.02396667, -0.29761198,\n",
       "        -0.04494001, -0.4809133 , -0.6465706 ,  0.7238467 ,  0.16772334,\n",
       "         0.24317332,  0.39609662, -0.22636335,  0.29532334, -0.21682002,\n",
       "        -0.01392   , -0.05954667, -0.01492   , -0.02198567, -0.01345199,\n",
       "         0.804534  ,  0.20222376, -0.23751998, -0.3435367 ,  0.11798465,\n",
       "        -1.3267332 ,  0.04624001, -0.82324666, -0.4611257 , -0.04351734,\n",
       "         2.5319    , -0.39181232, -0.42419663, -1.11079   ,  0.02457867,\n",
       "        -0.01194333,  0.13322668,  0.39867997,  0.05818667, -0.1256    ,\n",
       "         0.063093  , -0.14825068,  0.28476667,  0.23502266, -0.07644   ,\n",
       "         0.14659001,  0.2645    ,  0.5873967 , -0.108953  ,  0.53006   ],\n",
       "       dtype=float32),\n",
       " 'analytics investigative platform': array([ 0.09090867,  0.02333228,  0.45901668,  0.6219433 ,  0.06314334,\n",
       "        -0.17098331, -0.09332667, -0.6017967 ,  0.6870467 , -0.109146  ,\n",
       "        -0.30552   ,  0.00253666, -0.33832002,  0.40071666, -1.0916033 ,\n",
       "        -0.4460003 , -0.03353767,  0.08583667,  0.22832465, -0.31488433,\n",
       "         0.81633663,  0.36418998, -0.16782665,  0.19814666, -0.00753319,\n",
       "        -0.46389   , -0.08410668, -0.16886665, -0.55724335,  0.08723467,\n",
       "         1.75961   , -0.6862133 , -0.11284999, -1.0370766 , -0.3329133 ,\n",
       "        -0.05178667, -0.30085668,  0.14379667, -0.21942668, -0.42721668,\n",
       "         0.85974336, -0.32404664, -0.34499002,  0.219142  , -0.23906468,\n",
       "        -0.66848665,  0.48569   ,  0.4671866 ,  0.70771736,  0.5725634 ],\n",
       "       dtype=float32),\n",
       " 'scikit learn': array([ 0.13017  , -0.038327 , -0.121445 , -0.224715 ,  0.25574  ,\n",
       "        -0.2555   , -0.20632  ,  0.192    , -0.268445 ,  0.05099  ,\n",
       "         0.154805 ,  0.38445  ,  0.116895 , -0.052835 ,  0.272205 ,\n",
       "         0.153325 ,  0.08737  ,  0.202155 ,  0.179445 , -0.31879  ,\n",
       "        -0.0399435,  0.446385 ,  0.0973   ,  0.33508  ,  0.83695  ,\n",
       "        -0.69075  , -0.4235   , -0.5375   ,  0.18473  , -0.5889   ,\n",
       "         1.3994   ,  0.4592   , -0.329975 , -0.448505 ,  0.12647  ,\n",
       "         0.118735 , -0.221585 ,  0.16766  ,  0.17771  , -0.0415535,\n",
       "        -0.16381  , -0.240735 ,  0.275025 ,  0.32294  ,  0.459865 ,\n",
       "         0.057745 ,  0.39476  ,  0.097605 , -0.14384  ,  0.25295  ],\n",
       "       dtype=float32),\n",
       " 'information': array([ 0.63591 ,  0.28142 ,  1.103   ,  0.90695 ,  0.58408 , -0.66616 ,\n",
       "        -0.58817 , -0.55119 ,  1.0063  , -0.22333 , -0.021339,  0.59643 ,\n",
       "         0.020229, -0.33389 ,  0.27095 ,  0.099159, -0.62187 , -0.62834 ,\n",
       "         0.87429 , -0.15716 ,  0.97701 ,  0.36715 ,  0.65559 ,  0.15535 ,\n",
       "         0.22763 , -1.4113  , -0.65703 , -0.72715 ,  0.25938 , -0.23776 ,\n",
       "         3.3925  , -0.58473 , -0.34668 , -1.7489  , -0.015439,  0.50899 ,\n",
       "        -0.25659 ,  0.069998,  0.086402,  0.395   ,  1.0702  ,  0.088681,\n",
       "         0.54121 ,  0.53468 ,  0.09773 , -0.25598 , -0.15555 ,  1.5154  ,\n",
       "         0.81081 ,  0.11142 ], dtype=float32),\n",
       " 'platform': array([ 0.098851,  0.59935 ,  1.0168  ,  0.72483 , -0.24534 ,  0.20306 ,\n",
       "        -0.46785 , -1.2689  ,  0.29747 , -0.062728, -0.21183 , -0.30493 ,\n",
       "        -0.20452 ,  0.85655 , -1.3103  ,  0.030989, -0.093103,  0.70541 ,\n",
       "         0.16624 , -0.84804 ,  0.31641 , -0.16909 , -0.62288 ,  0.035483,\n",
       "         0.49894 , -0.63076 ,  0.68848 ,  0.32431 , -0.18392 , -0.085155,\n",
       "         2.8517  , -0.18436 , -0.90013 , -0.16993 , -0.57414 , -0.38403 ,\n",
       "         0.31566 ,  0.12175 , -1.2881  , -0.51956 ,  0.44713 , -1.2054  ,\n",
       "        -0.5704  ,  0.30348 , -0.1844  , -0.2149  , -0.10204 , -0.71244 ,\n",
       "         0.72361 , -0.41411 ], dtype=float32),\n",
       " 'assurance associate': array([-0.3289035 ,  0.53210497, -0.528685  ,  0.188289  ,  0.522     ,\n",
       "        -0.37485498, -0.066765  , -0.016285  ,  0.28832752, -0.069295  ,\n",
       "         0.605895  ,  0.913255  , -0.1424795 , -0.191605  , -0.368035  ,\n",
       "         0.23457   , -0.0876765 ,  0.42440003,  0.58197   ,  0.14056   ,\n",
       "         0.445545  ,  0.941295  ,  0.04159501, -0.3804    , -0.4510885 ,\n",
       "        -1.018885  , -0.16672999, -0.57855   , -0.7313105 ,  0.30639   ,\n",
       "         1.5911    , -0.17609   , -0.25637   , -0.82562596,  0.20912701,\n",
       "        -0.24173999, -0.03456999,  0.17234498,  0.655225  , -0.34242955,\n",
       "         0.31565452, -0.27529   ,  0.01397995,  0.31004   , -0.25718987,\n",
       "        -0.2152925 , -0.44409   ,  0.76816297, -0.11712499,  0.8131    ],\n",
       "       dtype=float32),\n",
       " 'data': array([ 5.3101e-01, -5.5869e-01,  1.7674e+00,  4.4824e-01,  2.2341e-01,\n",
       "        -3.4559e-01, -7.7679e-01, -9.6117e-01,  1.1669e+00,  7.4279e-02,\n",
       "         8.1470e-01, -5.9428e-02,  6.4599e-02,  1.5176e-03,  9.9179e-02,\n",
       "         3.6602e-01, -9.8724e-01, -8.3913e-01,  1.5917e-01, -7.7603e-01,\n",
       "         7.3474e-01, -6.4861e-01,  4.6174e-01,  8.8162e-03,  5.1738e-01,\n",
       "        -6.5976e-01, -7.4010e-01, -1.3928e-01,  8.1094e-02,  2.0657e-01,\n",
       "         3.5652e+00, -8.2264e-01,  5.7360e-01, -1.7268e+00,  6.2356e-03,\n",
       "         6.7672e-02, -2.3411e-01,  3.5163e-02,  2.6507e-01, -2.9966e-01,\n",
       "         7.4323e-01, -4.5027e-01,  1.9406e-01,  4.8611e-01, -4.3075e-01,\n",
       "        -2.5210e-01,  1.2774e+00,  1.5815e+00,  6.5838e-01, -2.0978e-01],\n",
       "       dtype=float32),\n",
       " 'fraud': array([ 0.53081 , -0.56427 ,  0.63769 ,  0.93618 , -0.31586 ,  1.0001  ,\n",
       "         0.6784  , -0.10596 ,  0.43425 ,  0.2019  , -0.9783  , -0.19657 ,\n",
       "        -0.98257 , -0.70396 ,  1.312   , -0.78779 , -0.16478 , -0.43054 ,\n",
       "         0.18849 , -0.19604 ,  1.1539  , -0.37791 ,  0.08559 , -0.23615 ,\n",
       "        -1.2796  , -2.2339  , -0.1372  , -0.31878 ,  0.036506,  0.18124 ,\n",
       "         1.6509  , -0.44528 , -0.49224 , -1.3011  , -0.62379 ,  0.24241 ,\n",
       "        -0.47553 , -0.53558 ,  0.69295 , -0.72759 , -0.22271 ,  0.38729 ,\n",
       "         0.48601 ,  1.1348  , -0.16384 , -0.81687 , -0.73938 ,  0.73955 ,\n",
       "         1.6292  , -0.72649 ], dtype=float32),\n",
       " 'ernst young llp': array([ 0.02467333, -0.13187   , -0.266517  , -0.37018332,  0.22337066,\n",
       "         0.7857134 , -0.65422267, -0.109718  , -0.54992336, -0.5355667 ,\n",
       "         0.08098668,  0.56892   , -0.771692  ,  0.02114999,  0.31733665,\n",
       "        -0.41315666,  0.78877336, -0.11087   , -0.40153337, -0.16613033,\n",
       "         0.7517933 ,  0.50275666, -0.16780333, -0.02290333, -0.41244003,\n",
       "        -0.7193033 , -0.53217   , -1.1211333 , -0.8870034 ,  0.08628   ,\n",
       "         0.8384003 , -0.126849  ,  0.11609668, -0.334053  , -0.46397662,\n",
       "        -0.05160333, -0.4653027 ,  0.21381335,  1.3212174 , -0.22064365,\n",
       "         0.24329002,  0.01436333,  0.19281869,  0.34354803,  0.40457   ,\n",
       "        -0.58859   , -0.11347333, -0.589676  , -0.20510666,  0.4268    ],\n",
       "       dtype=float32),\n",
       " 'topic modelling': array([ 3.1975001e-02,  1.9202501e-01, -6.6725498e-01,  1.2410650e-01,\n",
       "        -3.5927498e-01,  2.9723001e-01, -8.5921496e-02, -6.4750999e-01,\n",
       "        -7.4499846e-04, -1.5929997e-02,  6.5340012e-02, -5.8399990e-02,\n",
       "        -3.3205503e-01,  5.5098999e-01,  4.1153002e-01, -1.4962600e-01,\n",
       "        -1.8653500e-01,  2.9563498e-01,  3.6360347e-01, -2.5501001e-01,\n",
       "         5.8844501e-01,  1.4499050e-01,  3.5650998e-01,  6.6943502e-01,\n",
       "         6.5990496e-01, -2.9727998e-01, -3.2810602e-01,  9.2390008e-02,\n",
       "         2.7004998e-02,  4.4175500e-01,  1.5782499e+00, -4.8093501e-01,\n",
       "        -1.5492851e-01, -1.3731999e+00, -2.6914999e-01,  3.7159503e-01,\n",
       "        -7.4353504e-01,  1.1029900e+00, -3.8692048e-01, -1.8684301e-01,\n",
       "         1.7149150e-01, -2.8363001e-01, -2.6060000e-02,  5.5896497e-01,\n",
       "        -2.4480350e-01, -5.4138499e-01,  1.4092250e+00,  7.7620506e-01,\n",
       "         2.7594501e-01,  2.1733999e-01], dtype=float32),\n",
       " 'elastic search': array([ 3.9456999e-01, -3.9718500e-01,  3.0908048e-01,  3.2666999e-01,\n",
       "         2.2264999e-01,  1.4034501e-01, -4.1700006e-02, -2.3718649e-01,\n",
       "         8.9767498e-01, -1.4950037e-03,  6.1471003e-01,  1.7628402e-01,\n",
       "        -2.7488500e-01,  4.5341998e-01, -3.6178598e-01,  2.6165500e-01,\n",
       "        -7.7260002e-02, -2.2472200e-01,  1.4665100e-01, -8.1081498e-01,\n",
       "        -1.6447499e-01,  2.4687999e-01,  1.7082000e-01, -6.7994982e-02,\n",
       "         2.7290002e-02, -9.0965003e-02, -3.6431500e-01,  1.9392499e-01,\n",
       "         1.0655000e+00,  1.7904997e-02,  2.0674000e+00, -6.6692001e-01,\n",
       "        -2.6845500e-01, -3.9301699e-01, -1.2870498e-01,  5.9783006e-01,\n",
       "        -3.9629999e-01,  3.6440000e-02, -5.2453998e-02,  5.4370001e-02,\n",
       "         4.2385501e-01, -4.2799994e-02, -6.8199992e-02,  4.7925997e-01,\n",
       "        -5.5500001e-02, -1.0327051e+00,  9.6285999e-01, -1.8731499e-01,\n",
       "        -4.4793001e-01, -2.1064000e-01], dtype=float32),\n",
       " 'analytics': array([ 0.12649  , -0.0028231,  0.61663  ,  0.89156  ,  0.29741  ,\n",
       "        -0.61362  , -0.19391  , -0.99463  ,  0.50097  ,  0.23965  ,\n",
       "        -0.11647  ,  0.45654  , -0.11381  , -1.1113   , -1.7181   ,\n",
       "         0.37371  ,  0.18481  , -0.17753  ,  0.090004 ,  0.020507 ,\n",
       "         1.1097   ,  0.43015  , -0.20343  ,  0.59334  ,  0.0024204,\n",
       "         0.58669  ,  0.1364   , -0.41736  , -0.66947  ,  0.26332  ,\n",
       "         0.84783  , -0.79838  ,  0.82132  , -1.2732   ,  0.35831  ,\n",
       "        -0.66776  , -0.86767  , -0.224    ,  0.33858  , -0.58192  ,\n",
       "         1.4692   , -0.22759  , -0.85502  ,  0.42018  , -0.017144 ,\n",
       "        -0.98724  ,  1.3405   ,  1.0925   ,  1.3088   ,  1.3946   ],\n",
       "       dtype=float32),\n",
       " 'science assurance': array([-0.27656353,  0.673025  , -0.42375   ,  0.19973901,  0.10876635,\n",
       "        -0.315885  ,  0.00524001, -0.63330996,  0.45706502,  0.2245685 ,\n",
       "         0.40390998,  0.6497    , -0.0388795 , -0.02588502, -0.591105  ,\n",
       "         0.257425  ,  0.11935   ,  0.72661   ,  0.14447501,  0.28446   ,\n",
       "         0.804955  ,  0.97188497,  0.00707   , -0.54983   ,  0.4106815 ,\n",
       "        -1.040635  , -0.814445  , -0.525515  , -0.53471047,  0.14089   ,\n",
       "         2.1738    , -0.10574999, -0.42706   , -1.054576  , -0.01397499,\n",
       "        -0.030065  , -0.09796499,  0.38598502,  0.57651997, -0.244495  ,\n",
       "         0.3202595 , -0.69371504,  0.1451025 ,  0.351085  , -0.31832   ,\n",
       "         0.3249225 ,  0.16005501,  1.2065051 ,  0.19089419,  0.450405  ],\n",
       "       dtype=float32),\n",
       " 'review': array([-0.094903,  0.11984 , -0.73262 , -0.12816 , -0.45065 , -0.42703 ,\n",
       "        -0.31404 , -0.2888  ,  1.1209  , -0.14501 , -0.6902  ,  0.5469  ,\n",
       "        -0.27214 ,  0.3649  ,  0.54982 , -0.35273 , -0.12575 , -0.72552 ,\n",
       "         0.2356  , -0.094686,  0.9763  ,  0.27642 ,  0.39827 , -0.39296 ,\n",
       "        -0.21534 , -1.4118  , -0.53259 , -0.10365 , -1.0972  ,  0.069262,\n",
       "         2.8649  , -0.98303 , -0.56226 , -1.0736  , -0.12693 , -0.34976 ,\n",
       "         0.70635 , -0.38184 , -0.13337 , -0.75927 , -0.055391, -0.20889 ,\n",
       "         0.27749 ,  0.035698, -0.71724 ,  0.34967 ,  0.31645 ,  1.4766  ,\n",
       "        -0.083603,  0.89653 ], dtype=float32),\n",
       " 'data science assurance': array([-7.3723518e-03,  2.6245335e-01,  3.0663332e-01,  2.8257266e-01,\n",
       "         1.4698090e-01, -3.2578668e-01, -2.5543666e-01, -7.4259669e-01,\n",
       "         6.9367671e-01,  1.7447202e-01,  5.4083997e-01,  4.1332400e-01,\n",
       "        -4.3866686e-03, -1.6750813e-02, -3.6101031e-01,  2.9362333e-01,\n",
       "        -2.4951334e-01,  2.0469670e-01,  1.4937334e-01, -6.9036670e-02,\n",
       "         7.8154999e-01,  4.3172002e-01,  1.5862668e-01, -3.6361459e-01,\n",
       "         4.4624767e-01, -9.1367668e-01, -7.8966331e-01, -3.9677000e-01,\n",
       "        -3.2944229e-01,  1.6278334e-01,  2.6375999e+00, -3.4471333e-01,\n",
       "        -9.3506664e-02, -1.2786506e+00, -7.2381296e-03,  2.5140047e-03,\n",
       "        -1.4334665e-01,  2.6904437e-01,  4.7270334e-01, -2.6288334e-01,\n",
       "         4.6124968e-01, -6.1256671e-01,  1.6142167e-01,  3.9609337e-01,\n",
       "        -3.5579666e-01,  1.3258167e-01,  5.3250337e-01,  1.3315034e+00,\n",
       "         3.4672275e-01,  2.3034334e-01], dtype=float32),\n",
       " 'tools technologies python': array([ 0.4835167 , -0.5549    ,  0.30816004,  0.70309335, -0.00877364,\n",
       "         0.03563624, -0.38083336, -1.6459999 ,  0.36016   ,  0.6795306 ,\n",
       "         0.42036232,  0.63658667,  0.074339  ,  0.04548033, -0.52610666,\n",
       "         0.2839867 ,  0.13420665,  0.70638674,  0.03651335, -0.43620667,\n",
       "         0.4262867 ,  0.24017334, -0.22578335, -0.35474333,  0.22851266,\n",
       "        -0.10742334, -0.14743668, -0.71793336,  0.15992333, -0.576815  ,\n",
       "         2.2449    , -0.6112267 , -0.54399997, -0.19444   ,  0.21370333,\n",
       "         0.71576333, -0.22313066,  0.20689332, -0.18128133,  0.00516067,\n",
       "         0.63067   , -0.33320335, -0.09924664,  0.73349   ,  0.55602664,\n",
       "        -0.010569  ,  0.86951333,  0.37129   , -0.25861236, -0.00833333],\n",
       "       dtype=float32),\n",
       " 'topic': array([-0.04278 ,  0.79271 , -0.70087 , -0.023487,  0.24581 ,  0.24413 ,\n",
       "        -0.10713 , -0.51894 , -0.17387 , -0.15821 , -0.90392 , -0.33753 ,\n",
       "        -0.13262 ,  0.67051 ,  0.93457 ,  0.046388, -0.26368 , -0.23655 ,\n",
       "         0.80884 ,  0.1048  ,  0.43985 , -0.068909,  0.83773 ,  1.0383  ,\n",
       "         1.0378  , -1.0609  , -0.015392,  0.39162 , -0.1175  ,  0.40644 ,\n",
       "         1.9836  , -0.41525 , -0.050877, -1.2321  , -0.69079 , -0.35601 ,\n",
       "        -0.75549 ,  0.94668 , -0.84225 ,  0.095134, -0.092177, -0.13833 ,\n",
       "        -0.30647 ,  0.89785 ,  0.071583,  0.31083 ,  0.88585 ,  1.1397  ,\n",
       "        -0.19138 ,  0.27392 ], dtype=float32),\n",
       " 'analysis': array([ 0.47168 ,  0.25095 ,  0.078363,  0.33009 ,  0.32782 ,  0.47325 ,\n",
       "         0.16805 , -0.99997 ,  0.8607  ,  0.018286, -0.022716, -0.16986 ,\n",
       "        -0.33896 ,  0.10096 , -0.29278 , -0.18954 ,  0.063336, -0.64536 ,\n",
       "        -0.098547, -0.46883 ,  0.26338 , -0.063779,  0.51437 , -0.34739 ,\n",
       "         0.26537 , -0.58654 , -1.2527  , -0.20557 , -0.31604 ,  0.59308 ,\n",
       "         2.8329  , -1.2396  , -0.23409 , -1.66    ,  0.047692, -0.23141 ,\n",
       "        -0.14241 ,  0.74129 ,  0.69887 ,  0.14877 ,  0.6547  , -0.29812 ,\n",
       "         0.10476 ,  0.56112 , -0.015528,  0.099004,  1.3858  ,  1.8129  ,\n",
       "         0.3804  ,  0.83921 ], dtype=float32),\n",
       " 'fraud analytics investigative': array([ 0.23489499, -0.36454105,  0.3326467 ,  0.6923933 ,  0.03963667,\n",
       "         0.09469668,  0.28875664, -0.21415   ,  0.73263997, -0.02093667,\n",
       "        -0.56100994,  0.03865667, -0.59767   , -0.11945331, -0.21750331,\n",
       "        -0.71892667, -0.05743   , -0.29281333,  0.23574133, -0.097551  ,\n",
       "         1.0955    ,  0.29458335,  0.06833   ,  0.10760233, -0.6003799 ,\n",
       "        -0.99827003, -0.35933337, -0.38323   , -0.483768  ,  0.176033  ,\n",
       "         1.3593434 , -0.7731867 ,  0.02311333, -1.4141334 , -0.34946334,\n",
       "         0.15702666, -0.56458664, -0.07531333,  0.44092333, -0.49656   ,\n",
       "         0.63646334,  0.20685   ,  0.00714667,  0.49624863, -0.2322113 ,\n",
       "        -0.8691433 ,  0.27324334,  0.9511833 ,  1.0095807 ,  0.4684367 ],\n",
       "       dtype=float32),\n",
       " 'tools technologies': array([ 0.43042502, -0.557135  ,  0.96754   ,  0.84851   , -0.29990047,\n",
       "        -0.06386565, -0.392385  , -1.579     ,  0.486515  ,  0.644731  ,\n",
       "         0.40547848,  0.43733   , -0.1302315 , -0.1715495 , -1.0487    ,\n",
       "         0.501245  ,  0.03893999,  0.52013004,  0.50924003, -0.869025  ,\n",
       "         0.92137   ,  0.010455  , -0.40618002, -0.6149    ,  0.374565  ,\n",
       "        -0.33831   , -0.43525   , -1.1537    ,  0.474975  , -0.3184725 ,\n",
       "         2.68685   , -0.512735  , -0.479     , -0.92196   ,  0.172785  ,\n",
       "         0.531895  , -0.456896  ,  0.90419   ,  0.029093  ,  0.0418985 ,\n",
       "         0.614725  , -0.726485  ,  0.36003003,  0.75890005,  0.93798   ,\n",
       "         0.3511115 ,  0.67442   ,  0.47981   ,  0.0783615 ,  0.06262501],\n",
       "       dtype=float32),\n",
       " 'coding topic modelling': array([-0.03407   , -0.03459666, -0.22824998,  0.46113768, -0.44752333,\n",
       "         0.49243665,  0.256989  , -0.78343993,  0.19893666,  0.03700333,\n",
       "         0.24254668, -0.07297999, -0.16814668,  0.49575332,  0.17865668,\n",
       "        -0.01026734, -0.31765667,  0.03611   ,  0.58056897, -0.154207  ,\n",
       "         0.22136664, -0.15993966,  0.39739668,  0.79719   ,  0.56396997,\n",
       "        -0.190018  , -0.5671707 , -0.18744667,  0.12325666,  0.15133333,\n",
       "         1.5527998 , -0.54645   , -0.377089  , -1.12769   ,  0.13465334,\n",
       "         0.5676667 , -0.43257   ,  0.78902334, -0.20419365,  0.13855466,\n",
       "         0.37885436,  0.07105333, -0.01949404,  0.4224733 , -0.38451234,\n",
       "        -0.29598665,  1.7016166 ,  0.92960334,  0.4203833 ,  0.26153666],\n",
       "       dtype=float32),\n",
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       "        -0.252165  , -0.58511   , -1.321385  ,  0.71220505,  0.08537801,\n",
       "         0.51863   ,  0.170396  , -0.0342955 ,  0.2512338 , -0.10867549,\n",
       "         0.115825  , -0.312595  ,  0.15058503, -0.150705  ,  0.00424999,\n",
       "         0.91992   ,  0.01282999,  0.39761   , -0.0955169 ,  0.69889   ,\n",
       "        -1.08753   , -0.91385   , -0.558655  , -0.47190297,  0.06261   ,\n",
       "         3.10695   , -0.646945  ,  0.060425  , -1.87775   , -0.2014322 ,\n",
       "         0.167021  , -0.451075  ,  0.57913154,  0.57778   , -0.04834   ,\n",
       "         0.3591345 , -0.45946002,  0.225145  ,  0.43062502, -0.277545  ,\n",
       "         0.23833999,  1.09168   ,  1.208505  ,  0.32930416,  0.118915  ],\n",
       "       dtype=float32),\n",
       " 'predictive coding topic': array([ 0.06138334, -0.20482002,  0.17405666,  0.44899437, -0.17591333,\n",
       "         0.52293   ,  0.5401733 , -1.0276133 ,  0.46031   ,  0.32284668,\n",
       "         0.14421   , -0.03605333, -0.00253534,  0.03906667, -0.21607329,\n",
       "         0.263196  , -0.50057   , -0.17755334,  0.7133867 , -0.10649367,\n",
       "        -0.20927338, -0.22655632,  0.51785   ,  0.7655367 ,  0.72396326,\n",
       "        -0.27590132, -0.735764  , -0.30217   ,  0.09456266,  0.10150333,\n",
       "         1.3919767 , -0.3813223 , -0.38512233, -1.0155233 ,  0.45452332,\n",
       "         0.13522334, -0.35102668,  0.6149034 , -0.40559664,  0.28103065,\n",
       "         0.49230766,  0.20375033, -0.2497307 ,  0.82761335, -0.5141457 ,\n",
       "        -0.02118666,  1.6443167 ,  1.2858    ,  0.51996   ,  0.30629998],\n",
       "       dtype=float32),\n",
       " 'technologies python': array([ 6.28515005e-01, -4.50169981e-01,  1.35150015e-01,  8.92879963e-01,\n",
       "        -4.78049815e-02,  1.13499351e-01, -3.60729992e-01, -1.76189995e+00,\n",
       "         4.00195003e-01,  1.00371504e+00,  6.59529984e-01,  7.66974986e-01,\n",
       "         7.40900040e-02,  7.50650018e-02, -5.10460019e-01,  3.37320000e-01,\n",
       "         5.07049933e-02,  8.97585034e-01, -3.42315018e-01, -1.08759999e-01,\n",
       "         2.65260011e-01,  4.15369987e-01, -1.00600004e-01, -2.88949996e-01,\n",
       "         9.32390019e-02, -5.32200038e-02,  1.31314993e-01, -4.80400026e-01,\n",
       "        -2.65199989e-02, -5.91032505e-01,  1.89560008e+00, -5.21905005e-01,\n",
       "        -5.48174977e-01,  5.12499809e-02,  2.34865010e-01,  2.75995016e-01,\n",
       "        -3.00679982e-01, -1.03595018e-01, -3.21672976e-01, -7.65299983e-03,\n",
       "         5.91009974e-01, -2.19039991e-01, -2.07669973e-01,  5.03185034e-01,\n",
       "         1.49739981e-01,  1.20013952e-04,  7.73819983e-01,  2.20890000e-01,\n",
       "        -4.43773508e-01,  1.13605000e-01], dtype=float32),\n",
       " 'natural language processing': array([ 0.00788999, -0.05488666, -0.48584333,  0.6181798 , -0.02278067,\n",
       "         0.27614868, -0.16364999, -0.75579333,  0.4615933 ,  0.54792   ,\n",
       "         0.6729533 ,  0.21443796,  1.3001    , -0.27673   , -0.130689  ,\n",
       "         0.03766201, -0.11503667,  0.43500003,  0.28361332, -0.24442334,\n",
       "         0.25210333, -0.24265333,  0.06223834,  0.02521   ,  0.61187667,\n",
       "        -0.83064   , -0.33539665, -0.17351   ,  0.45849332, -0.01847334,\n",
       "         3.4111335 , -0.87712   , -0.11297234, -0.59233   ,  0.05870233,\n",
       "         0.382293  , -0.55372   ,  0.18594332,  0.15831667,  0.46702334,\n",
       "         0.33991   ,  0.15368667,  0.3216733 ,  0.27775002, -0.15773   ,\n",
       "         0.24627334,  0.4064467 ,  0.7177603 , -0.049111  , -0.1219773 ],\n",
       "       dtype=float32),\n",
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       "         0.012835  , -0.006055  , -0.60006   ,  0.108035  ,  0.42385298,\n",
       "         0.15620199, -0.48512   , -0.16640629, -0.043135  , -0.52158   ,\n",
       "         0.09747   , -0.22115698, -0.85913   , -0.1259985 , -0.361925  ,\n",
       "         0.366875  , -0.0816095 ,  0.23727201, -0.44515   ,  0.386495  ,\n",
       "        -0.86342   , -0.923645  ,  0.223055  ,  0.090775  ,  0.96744   ,\n",
       "         2.50695   , -0.42071497,  0.502405  , -1.23024   , -0.472154  ,\n",
       "        -0.818005  , -0.615005  , -0.05824003,  0.05911499, -0.23145498,\n",
       "        -0.07892501, -0.37436   , -0.16447501,  0.06110498,  0.521886  ,\n",
       "         0.098432  ,  0.61101   ,  1.36611   ,  0.56592   ,  0.33473998],\n",
       "       dtype=float32),\n",
       " 'science assurance associate': array([-0.42038235,  0.70730335, -0.45396337,  0.14443533,  0.34820756,\n",
       "        -0.30281666, -0.17565334, -0.57139   ,  0.278055  , -0.01403767,\n",
       "         0.47811666,  0.7422433 , -0.139383  ,  0.03924666, -0.35086665,\n",
       "         0.11159001,  0.06223233,  0.6630333 ,  0.23445334,  0.35521665,\n",
       "         0.6653967 ,  0.85228664,  0.13889001, -0.3202167 , -0.00725901,\n",
       "        -1.1843567 , -0.4736867 , -0.71171   , -0.8291736 ,  0.17714334,\n",
       "         1.9436334 , -0.27447668, -0.32183   , -1.2266507 ,  0.00305134,\n",
       "        -0.07237   , -0.24572666,  0.48926333,  0.73364663, -0.16062637,\n",
       "         0.20211601, -0.33974338,  0.09472997,  0.33174   , -0.21290658,\n",
       "         0.09939834,  0.00592668,  0.79061204, -0.0780072 ,  0.69127005],\n",
       "       dtype=float32),\n",
       " 'technology assisted': array([ 0.10959999, -0.32255748,  0.524441  ,  0.10003   , -0.221093  ,\n",
       "         0.14610499, -0.56435   , -0.8254559 ,  0.52532   ,  0.32409   ,\n",
       "         0.70985997,  0.32069498, -0.203475  ,  0.260535  , -0.60014004,\n",
       "         0.155485  , -0.026445  ,  0.34038   , -0.1507785 ,  0.02716501,\n",
       "         0.71865106,  0.16512564, -0.555415  , -0.318825  ,  0.284647  ,\n",
       "        -1.2842    ,  0.335655  , -1.1830449 , -0.1430885 , -0.099907  ,\n",
       "         2.3653998 , -0.09620349, -0.355165  , -1.129385  ,  0.10033301,\n",
       "         0.156965  , -0.153335  ,  0.78893995,  0.153965  ,  0.191235  ,\n",
       "         0.122335  , -0.11793101,  0.288405  ,  0.334685  ,  0.24396   ,\n",
       "         0.04505001,  0.238525  ,  0.14279501, -0.121628  ,  0.346825  ],\n",
       "       dtype=float32),\n",
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       "        -1.48    , -0.38792 ,  0.42727 ,  0.1168  ,  0.7839  ,  0.81352 ,\n",
       "        -0.78763 , -0.6544  , -0.099214,  0.20658 , -0.73315 ,  0.86418 ,\n",
       "        -0.26917 , -0.93792 ,  1.919   , -0.17473 , -0.61905 , -0.5035  ,\n",
       "        -0.9341  , -1.6755  , -0.79399 , -0.32003 , -0.067678, -0.12344 ,\n",
       "         3.2909  , -0.43151 ,  0.3879  ,  0.38733 , -0.018419, -0.98191 ,\n",
       "        -0.53189 ,  0.39866 ,  0.89452 , -0.40963 ,  0.58922 , -0.52377 ,\n",
       "         0.27893 , -0.25766 , -0.30457 , -0.066305, -1.1221  ,  0.21582 ,\n",
       "        -0.15586 ,  0.64018 ], dtype=float32),\n",
       " 'business professionals dummies': array([-0.041999  , -0.26173267,  0.01835367, -0.19325668, -0.06603567,\n",
       "        -0.25411335, -0.67892665, -1.0587834 ,  0.43954334,  0.117666  ,\n",
       "         0.02593634,  0.29999664, -0.24423666,  0.37280932,  0.3073967 ,\n",
       "        -0.25868666, -0.004932  ,  0.70413667,  0.24509998, -0.76956004,\n",
       "         0.66404665,  0.63324666, -0.26549333,  0.5110467 , -0.07231667,\n",
       "        -0.56304   , -0.6838033 , -0.81779337, -0.23327434,  0.10702667,\n",
       "         1.95546   ,  0.52374667, -0.01930666, -0.34873   ,  0.01506667,\n",
       "         0.18984334, -0.41147336,  0.32252666,  0.31636867, -0.15024   ,\n",
       "        -0.04262833, -0.33550668,  0.61073667,  1.0585066 ,  0.34356868,\n",
       "         0.05833365, -0.07182334,  0.40942666,  0.13266   ,  0.43981335],\n",
       "       dtype=float32),\n",
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       "        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "       dtype=float32),\n",
       " 'detection': array([ 1.0515  , -0.71071 ,  1.1432  ,  0.13598 , -0.79249 ,  0.26449 ,\n",
       "         0.82631 , -0.46758 ,  1.5747  ,  0.64758 ,  0.90419 , -0.25229 ,\n",
       "         0.44672 ,  0.38699 , -0.67376 ,  0.15019 , -0.38463 , -0.26589 ,\n",
       "        -1.115   , -0.26627 , -0.2606  , -0.40227 , -0.24216 , -0.43843 ,\n",
       "         0.33342 , -1.1965  , -0.29477 , -0.41592 ,  0.94101 , -0.14759 ,\n",
       "         1.7477  , -0.48682 , -0.49668 , -1.8894  ,  0.35402 ,  0.82188 ,\n",
       "         0.67614 ,  0.15441 ,  0.050774,  0.058885,  0.42524 ,  0.28718 ,\n",
       "         0.83229 ,  0.75788 ,  0.77235 , -0.76995 ,  1.539   ,  0.64898 ,\n",
       "         1.0435  ,  0.23798 ], dtype=float32),\n",
       " 'rgpv data': array([ 2.65505e-01, -2.79345e-01,  8.83700e-01,  2.24120e-01,\n",
       "         1.11705e-01, -1.72795e-01, -3.88395e-01, -4.80585e-01,\n",
       "         5.83450e-01,  3.71395e-02,  4.07350e-01, -2.97140e-02,\n",
       "         3.22995e-02,  7.58800e-04,  4.95895e-02,  1.83010e-01,\n",
       "        -4.93620e-01, -4.19565e-01,  7.95850e-02, -3.88015e-01,\n",
       "         3.67370e-01, -3.24305e-01,  2.30870e-01,  4.40810e-03,\n",
       "         2.58690e-01, -3.29880e-01, -3.70050e-01, -6.96400e-02,\n",
       "         4.05470e-02,  1.03285e-01,  1.78260e+00, -4.11320e-01,\n",
       "         2.86800e-01, -8.63400e-01,  3.11780e-03,  3.38360e-02,\n",
       "        -1.17055e-01,  1.75815e-02,  1.32535e-01, -1.49830e-01,\n",
       "         3.71615e-01, -2.25135e-01,  9.70300e-02,  2.43055e-01,\n",
       "        -2.15375e-01, -1.26050e-01,  6.38700e-01,  7.90750e-01,\n",
       "         3.29190e-01, -1.04890e-01], dtype=float32),\n",
       " 'uit rgpv data': array([ 0.09585666, -0.09295333,  0.66062003, -0.01387333, -0.28239667,\n",
       "        -0.48879668, -0.13270667, -0.26824668,  0.44306   ,  0.149973  ,\n",
       "         0.44039667,  0.009519  , -0.04433033,  0.01309953,  0.10791299,\n",
       "         0.02986999, -0.15946667, -0.20131333,  0.19067667, -0.17436333,\n",
       "         0.23448034, -0.37404665,  0.20055665,  0.2079254 ,  0.22198333,\n",
       "         0.14428   , -0.5167067 , -0.13652332, -0.10959534,  0.10546   ,\n",
       "         0.95173335, -0.40007332, -0.09310333, -0.42984998, -0.12750147,\n",
       "        -0.01830267,  0.05696667,  0.06287766,  0.38022003, -0.01797333,\n",
       "         0.3655633 , -0.43656334,  0.14911667,  0.06433   , -0.09240667,\n",
       "        -0.3693367 ,  0.6428933 ,  0.5365277 ,  0.5839267 ,  0.0248    ],\n",
       "       dtype=float32),\n",
       " 'uit rgpv': array([-0.12172  ,  0.139915 ,  0.10723  , -0.24493  , -0.5353   ,\n",
       "        -0.5604   ,  0.189335 ,  0.078215 ,  0.08114  ,  0.18782  ,\n",
       "         0.253245 ,  0.0439925, -0.098795 ,  0.0188905,  0.11228  ,\n",
       "        -0.138205 ,  0.25442  ,  0.117595 ,  0.20643  ,  0.12647  ,\n",
       "        -0.0156495, -0.236765 ,  0.069965 ,  0.30748  ,  0.074285 ,\n",
       "         0.5463   , -0.40501  , -0.135145 , -0.20494  ,  0.054905 ,\n",
       "        -0.355    , -0.18879  , -0.426455 ,  0.218625 , -0.19437  ,\n",
       "        -0.06129  ,  0.202505 ,  0.076735 ,  0.437795 ,  0.12287  ,\n",
       "         0.17673  , -0.42971  ,  0.126645 , -0.14656  ,  0.076765 ,\n",
       "        -0.427955 ,  0.32564  ,  0.0140415,  0.5467   ,  0.14209  ],\n",
       "       dtype=float32),\n",
       " 'outlier detection': array([ 1.0688    , -0.61617   ,  0.38011003, -0.016465  , -0.47132   ,\n",
       "         0.653895  ,  0.530005  , -0.1509    ,  0.905495  ,  0.46229002,\n",
       "         0.556945  , -0.36983   ,  1.07241   ,  0.31274   , -0.6797    ,\n",
       "         0.15945   ,  0.04547   , -0.069     , -0.85387504,  0.27433997,\n",
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       "        -0.5905195 , -0.47347   , -0.08302999,  0.4718579 , -0.043109  ,\n",
       "         0.8401355 , -0.581905  , -0.06472999, -0.834945  ,  0.306315  ,\n",
       "         0.234305  ,  0.07596999, -0.40027   ,  0.073026  , -0.12395249,\n",
       "         0.20887406,  0.27925   ,  0.4660245 ,  0.819645  ,  0.261725  ,\n",
       "        -0.79429996,  1.3004    ,  1.10129   ,  0.72398496,  0.1359025 ],\n",
       "       dtype=float32),\n",
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       "         0.132775 , -0.74     , -0.19396  ,  0.213635 ,  0.0584   ,\n",
       "         0.39195  ,  0.40676  , -0.393815 , -0.3272   , -0.049607 ,\n",
       "         0.10329  , -0.366575 ,  0.43209  , -0.134585 , -0.46896  ,\n",
       "         0.9595   , -0.087365 , -0.309525 , -0.25175  , -0.46705  ,\n",
       "        -0.83775  , -0.396995 , -0.160015 , -0.033839 , -0.06172  ,\n",
       "         1.64545  , -0.215755 ,  0.19395  ,  0.193665 , -0.0092095,\n",
       "        -0.490955 , -0.265945 ,  0.19933  ,  0.44726  , -0.204815 ,\n",
       "         0.29461  , -0.261885 ,  0.139465 , -0.12883  , -0.152285 ,\n",
       "        -0.0331525, -0.56105  ,  0.10791  , -0.07793  ,  0.32009  ],\n",
       "       dtype=float32),\n",
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       "        -0.040956 , -0.5637   , -0.32191  ,  0.15968  , -0.59756  ,\n",
       "        -0.14571  , -0.77074  ,  1.2955   , -0.72002  , -0.90818  ,\n",
       "         0.76644  ,  0.05346  , -0.0031632, -0.15341  ,  0.22065  ,\n",
       "        -1.191    , -1.0775   , -0.29768  ,  1.327    , -0.51359  ,\n",
       "         2.6229   , -0.67411  , -0.82558  ,  0.14283  , -0.014214 ,\n",
       "         0.90775  ,  0.66828  ,  0.48431  ,  0.1543   ,  0.26044  ,\n",
       "         1.0191   ,  0.015872 , -0.75325  ,  0.58992  ,  0.4546   ,\n",
       "        -0.19678  ,  0.42138  , -0.43168  ,  0.11985  ,  0.14094  ],\n",
       "       dtype=float32),\n",
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       "         0.023912  , -0.369395  , -0.267995  ,  0.16209   ,  0.063805  ,\n",
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       "        -0.0354365 , -0.33084   ,  0.46634   ,  0.59615   ,  0.053825  ,\n",
       "         0.197915  ,  0.300835  ,  0.10898   ,  0.0106945 ,  0.64065   ,\n",
       "        -0.00279415,  0.12923   ,  0.226165  , -0.05757   ,  0.270215  ],\n",
       "       dtype=float32),\n",
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       "         2.36343324e-01, -9.92553309e-02, -5.05193293e-01, -4.99053329e-01,\n",
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       "        -3.08535337e-01,  2.88650006e-01,  1.49731338e+00, -8.80746663e-01,\n",
       "        -4.60733362e-02, -1.07019997e+00, -7.87548050e-02, -1.06700009e-03,\n",
       "        -2.98596650e-01,  3.22614342e-01,  4.85789984e-01, -6.40033334e-02,\n",
       "         3.79686683e-01,  5.04666753e-02,  1.37339994e-01,  1.69166327e-01,\n",
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       "         1.81079984e-01,  1.10216677e-01], dtype=float32),\n",
       " 'time series forecasting': array([ 0.12509133,  0.05651333,  0.2755823 ,  0.29174668, -0.40240002,\n",
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       "        -0.19368666, -0.04117123, -0.00292473, -1.0710801 , -0.24423301,\n",
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       "        -0.6214834 , -0.74437666, -0.41101333,  0.02606333,  0.35828665,\n",
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       "         0.26720166,  0.66389   ,  0.498376  ,  0.01865   ,  0.53216   ],\n",
       "       dtype=float32),\n",
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       "        -0.390183  ,  0.27431852, -1.29194   ,  0.4191075 ,  1.08905   ,\n",
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       "         0.41656   ,  0.414585  , -0.125686  , -0.22463   ,  0.16204   ],\n",
       "       dtype=float32),\n",
       " 'learning': array([ 0.20461 ,  0.48659 , -0.55308 , -0.27019 ,  0.26336 ,  0.15751 ,\n",
       "        -0.28994 , -0.51824 ,  0.051829,  0.36225 ,  0.37077 ,  0.1322  ,\n",
       "        -0.061377, -0.53606 , -0.34733 , -0.043981, -0.086744,  0.78305 ,\n",
       "         0.41422 ,  0.027996,  0.23433 ,  0.98844 , -0.41049 ,  0.6206  ,\n",
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       "         3.227   ,  0.39509 , -0.33042 , -1.229   ,  0.29048 ,  0.33654 ,\n",
       "        -0.24817 ,  0.47105 ,  0.32964 ,  0.23997 ,  0.088302, -0.91779 ,\n",
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       "        -0.14093 ,  0.70785 ], dtype=float32),\n",
       " 'sukh sagar bengaluru': array([ 0.21458668, -0.2766167 ,  0.48939666,  0.42570004, -0.86347   ,\n",
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       "         0.47346666,  0.48962   ,  0.77645665,  0.22491   ,  0.7675533 ,\n",
       "         0.61252   , -0.3189533 , -0.41195667,  0.16614336,  0.42089164,\n",
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       "        -0.6069933 , -0.06623999,  0.08628267, -0.02941333,  0.33247668,\n",
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       "         0.02540668,  0.012261  , -0.06902301,  0.89371675, -0.6206934 ],\n",
       "       dtype=float32),\n",
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       "        -4.62803006e-01,  4.32974994e-01,  2.24597001e+00, -1.32112002e+00,\n",
       "        -6.91100061e-02, -1.60529995e+00, -1.18132204e-01, -1.60050020e-03,\n",
       "        -4.47894990e-01,  4.83921498e-01,  7.28684962e-01, -9.60050002e-02,\n",
       "         5.69530010e-01,  7.57000148e-02,  2.06009999e-01,  2.53749490e-01,\n",
       "         4.25274968e-01, -1.28844142e-01,  7.67930031e-01,  1.01691508e+00,\n",
       "         2.71619976e-01,  1.65325016e-01], dtype=float32),\n",
       " 'outlier': array([ 1.0861   , -0.52163  , -0.38298  , -0.16891  , -0.15015  ,\n",
       "         1.0433   ,  0.2337   ,  0.16578  ,  0.23629  ,  0.277    ,\n",
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       "        -0.5642   , -0.22385  , -0.64103  ,  0.87541  ,  0.32556  ,\n",
       "         0.015461 , -0.65217  ,  0.24986  ,  0.0027058,  0.061372 ,\n",
       "        -0.067429 , -0.67699  ,  0.36722  ,  0.21951  ,  0.25861  ,\n",
       "        -0.35327  , -0.5242   , -0.95495  ,  0.095278 , -0.30679  ,\n",
       "        -0.0074919,  0.27132  ,  0.099759 ,  0.88141  , -0.2489   ,\n",
       "        -0.81865  ,  1.0618   ,  1.5536   ,  0.40447  ,  0.033825 ],\n",
       "       dtype=float32),\n",
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       "         7.45540023e-01,  1.00944504e-01,  7.05635011e-01,  1.70380995e-01,\n",
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       "        -2.37819999e-01,  1.95250213e-02, -2.69924998e-01,  5.93149960e-02,\n",
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       "        -6.91100061e-02, -1.60529995e+00, -1.18132204e-01, -1.60050020e-03,\n",
       "        -4.47894990e-01,  4.83921498e-01,  7.28684962e-01, -9.60050002e-02,\n",
       "         5.69530010e-01,  7.57000148e-02,  2.06009999e-01,  2.53749490e-01,\n",
       "         4.25274968e-01, -1.28844142e-01,  7.67930031e-01,  1.01691508e+00,\n",
       "         2.71619976e-01,  1.65325016e-01], dtype=float32),\n",
       " 'statsmodels months aws': array([ 0.12077668, -0.12683333,  0.28814   ,  0.09965666, -0.278351  ,\n",
       "        -0.05003   , -0.16516332,  0.061956  ,  0.19711   ,  0.07968   ,\n",
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       "         0.12811333, -0.42967   , -0.04294668, -0.161876  ,  0.23356669,\n",
       "        -0.05031999,  0.15982   ,  0.54117334, -0.12566002, -0.26953334,\n",
       "        -0.24457   ,  0.175077  , -0.19288534,  0.14893667,  0.6326967 ,\n",
       "         1.0777134 ,  0.13716334, -0.39233002,  0.10752667, -0.05276333,\n",
       "        -0.03089667,  0.08404166, -0.099     , -0.01067333,  0.03392667,\n",
       "         0.01393334, -0.06920467, -0.43306533,  0.33863333, -0.12176999,\n",
       "        -0.19059165, -0.29516998,  0.08346666, -0.11115   , -0.14221233],\n",
       "       dtype=float32),\n",
       " 'model': array([ 0.35093  ,  0.67075  ,  0.3862   , -0.12275  , -0.011067 ,\n",
       "         0.96002  , -0.43233  , -1.9641   , -0.16677  , -0.027022 ,\n",
       "         0.88861  , -0.0033811, -0.66293  ,  0.032598 ,  0.23932  ,\n",
       "         0.14299  , -0.5518   ,  1.2313   ,  0.079988 , -0.81698  ,\n",
       "        -0.098454 , -0.10228  , -0.8761   ,  0.46941  ,  0.19033  ,\n",
       "        -0.77669  , -0.92219  , -0.099159 ,  0.030179 ,  0.51725  ,\n",
       "         2.5706   , -0.61656  , -0.72079  , -0.49696  ,  0.7871   ,\n",
       "        -0.23723  ,  0.22709  ,  0.60641  , -0.57691  , -0.31989  ,\n",
       "         0.2975   , -0.38839  ,  0.31212  , -0.34082  , -0.35483  ,\n",
       "        -0.28197  ,  0.71581  , -0.29069  ,  0.22914  ,  0.62689  ],\n",
       "       dtype=float32),\n",
       " 'rgpv data scientist': array([ 1.24536656e-01,  1.69866681e-02,  6.99156702e-01,  1.82641014e-01,\n",
       "         2.36343324e-01, -9.92553309e-02, -5.05193293e-01, -4.99053329e-01,\n",
       "         4.97026682e-01,  6.72963336e-02,  4.70423341e-01,  1.13587327e-01,\n",
       "         3.56296636e-02,  2.26889193e-01,  8.98429975e-02,  1.18919969e-01,\n",
       "        -1.58546671e-01,  1.30166812e-02, -1.79949999e-01,  3.95433307e-02,\n",
       "         2.03209996e-01,  6.02400117e-02,  1.94133326e-01, -8.77012610e-02,\n",
       "         5.26093304e-01, -9.63686705e-01, -4.21360016e-01, -3.40146661e-01,\n",
       "        -3.08535337e-01,  2.88650006e-01,  1.49731338e+00, -8.80746663e-01,\n",
       "        -4.60733362e-02, -1.07019997e+00, -7.87548050e-02, -1.06700009e-03,\n",
       "        -2.98596650e-01,  3.22614342e-01,  4.85789984e-01, -6.40033334e-02,\n",
       "         3.79686683e-01,  5.04666753e-02,  1.37339994e-01,  1.69166327e-01,\n",
       "         2.83516645e-01, -8.58960971e-02,  5.11953354e-01,  6.77943408e-01,\n",
       "         1.81079984e-01,  1.10216677e-01], dtype=float32),\n",
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       "         0.086085,  0.17993 ,  0.4024  , -1.7883  ,  0.74287 , -0.77056 ,\n",
       "         0.22475 , -0.18219 , -0.61257 ,  0.33988 ,  0.1599  ,  0.66913 ,\n",
       "        -1.129   , -0.35126 , -0.18731 , -0.10743 ,  0.1591  , -0.27441 ,\n",
       "        -0.76649 , -0.85794 ,  0.45987 , -0.32447 , -0.088069,  0.32122 ,\n",
       "         2.3506  ,  0.53953 , -0.74575 , -0.67977 ,  0.23359 ,  0.9029  ,\n",
       "         0.25574 , -0.41205 , -1.3189  ,  0.1743  ,  0.10634 ,  0.22666 ,\n",
       "         0.87236 , -0.97435 , -0.048312, -0.189   , -0.036793,  0.075626,\n",
       "        -1.2913  , -0.33155 ], dtype=float32),\n",
       " 'scientist data scientist': array([ 0.07206999,  0.22020334,  0.80918   ,  0.21586867,  0.39821663,\n",
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       "         0.66928005,  0.246984  ,  0.04972633,  0.4532725 ,  0.14662634,\n",
       "         0.11583328,  0.01198667,  0.30574337, -0.41295668,  0.3377633 ,\n",
       "         0.16150667,  0.33668336,  0.23435332, -0.17834127,  0.87972665,\n",
       "        -1.7074534 , -0.59602004, -0.63386667, -0.64410204,  0.50844336,\n",
       "         1.8062267 , -1.48728   , -0.28334668, -1.5647999 , -0.15958814,\n",
       "        -0.02469133, -0.51915663,  0.63350767,  0.8832233 , -0.02812   ,\n",
       "         0.51163   ,  0.25102335,  0.20999332,  0.176296  ,  0.7106166 ,\n",
       "        -0.08775886,  0.5981067 ,  0.8287201 ,  0.14269999,  0.29036   ],\n",
       "       dtype=float32),\n",
       " 'professionals dummies enthusiasts': array([-0.067678  , -0.23723269,  0.00800897, -0.4103333 , -0.40351033,\n",
       "        -0.27141666, -0.78496   , -1.0087433 ,  0.44015667,  0.29391232,\n",
       "         0.10926668,  0.15236901,  0.10219667,  0.46242   ,  0.41173434,\n",
       "        -0.41033998,  0.341988  ,  0.91224   , -0.25228333, -1.0880533 ,\n",
       "         0.18271665,  0.612414  , -0.30918667,  0.7277767 ,  0.34215   ,\n",
       "        -0.23081668, -0.8514233 , -0.85964996, -0.247391  ,  0.03254   ,\n",
       "         1.0897266 ,  0.5603833 , -0.32213667, -0.32666   ,  0.15734066,\n",
       "         0.30624664, -0.11805335,  0.00837334, -0.08907801, -0.12533468,\n",
       "         0.10407833, -0.37741998,  0.52069867,  1.3520001 ,  0.70454997,\n",
       "         0.02011666,  0.15668665,  0.28168765,  0.00397334, -0.02259334],\n",
       "       dtype=float32),\n",
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       "         0.047824 , -0.73879  , -0.53599  ,  0.32418  ,  0.12761  ,\n",
       "         0.59657  ,  0.40019  ,  0.04229  ,  0.67915  ,  0.17035  ,\n",
       "        -0.0092601,  0.5116   ,  0.87818  , -0.69902  ,  0.89466  ,\n",
       "        -0.12511  ,  0.82933  ,  0.12066  , -0.27192  ,  1.0609   ,\n",
       "        -2.2313   , -0.52398  , -0.88116  , -1.0067   ,  0.65938  ,\n",
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       "        -0.070873 , -0.66168  ,  0.93268  ,  1.1923   ,  0.10765  ,\n",
       "         0.39583  ,  0.60167  ,  0.21796  ,  0.021389 ,  1.2813   ,\n",
       "        -0.0055883,  0.25846  ,  0.45233  , -0.11514  ,  0.54043  ],\n",
       "       dtype=float32),\n",
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       "         0.06631   ,  1.39384   ,  0.10649099, -0.2970035 , -0.02390999,\n",
       "         0.491265  ,  0.931445  , -0.755685  , -0.005684  , -0.21993649,\n",
       "         0.64114   , -0.087115  ,  0.771945  , -0.59895504,  0.19962549,\n",
       "         0.851305  ,  0.56069   , -0.21768501, -0.151725  ,  0.78551996,\n",
       "        -0.072903  , -0.168105  , -0.88831997, -1.337565  , -0.2025422 ,\n",
       "        -0.691325  ,  0.096936  ,  0.91411996,  0.636295  ,  0.36034498,\n",
       "        -0.01944499,  0.46101   , -0.66442   ,  0.268485  ,  0.25760502,\n",
       "        -0.049255  ,  0.36398   , -0.585525  ,  0.08703001,  0.10697502,\n",
       "         0.29775   ,  0.311285  , -0.42486   , -0.06349501, -0.2987005 ],\n",
       "       dtype=float32),\n",
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       "         0.27659   , -0.09465   , -0.53252   , -0.0685205 ,  0.237945  ,\n",
       "         0.16490701, -0.21575001, -0.19164349, -0.18818998, -0.472445  ,\n",
       "        -0.0948455 , -0.428742  ,  1.039275  , -0.1529    , -0.440092  ,\n",
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       "        -0.92263496, -0.629855  , -0.66349   ,  0.65612954, -0.38371497,\n",
       "         2.9249501 , -0.13950999, -0.578     , -0.543085  ,  0.13813299,\n",
       "         0.622145  ,  0.210055  ,  0.47768   ,  0.24197   ,  0.25020498,\n",
       "         0.553701  , -0.450959  , -0.55998003,  0.79126   ,  0.33655   ,\n",
       "        -0.25639   ,  0.81219   , -0.08089   , -0.01054   ,  0.424395  ],\n",
       "       dtype=float32),\n",
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       "        -0.278705  , -0.263875  , -0.79601   ,  0.599972  ,  0.2909195 ,\n",
       "         0.734355  , -0.278189  ,  0.3554495 , -0.3641012 , -0.0072855 ,\n",
       "         0.58605   , -0.661215  , -0.61896   , -0.22581501, -0.88351   ,\n",
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       "         3.75335   , -0.03949001,  0.81515   , -0.971915  ,  0.3787778 ,\n",
       "        -0.549464  , -0.340945  ,  0.0638285 ,  0.4264    , -0.37302   ,\n",
       "        -0.05135   , -0.2618    ,  0.44187498,  0.48509002, -0.224947  ,\n",
       "        -0.37120998,  0.93165505,  0.98659503,  0.571645  , -0.083717  ],\n",
       "       dtype=float32),\n",
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       "         0.17043   ,  0.467995  ,  0.116525  , -0.00128295,  0.054535  ,\n",
       "         0.254745  , -0.264695  ,  0.18214   , -0.24059   , -0.38302   ,\n",
       "         0.224205  ,  0.00814   ,  0.443765  , -0.367865  , -0.15089   ,\n",
       "         0.30192   , -0.62745   ,  0.253625  ,  0.29008   , -0.158365  ,\n",
       "        -0.322935  ,  0.383275  , -0.041506  , -0.116995  , -0.1384    ,\n",
       "         1.04685   , -0.18519   ,  0.180565  , -0.231985  ,  0.8609    ,\n",
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       "        -0.205625  , -0.41877   , -0.0284315 ,  0.25389   ,  0.098305  ,\n",
       "        -0.14227   ,  0.53825   ,  0.088195  ,  0.285315  , -0.070555  ],\n",
       "       dtype=float32),\n",
       " 'matlab': array([-2.0907e-02, -1.6838e+00, -1.0598e+00, -1.4593e-01, -1.6336e-01,\n",
       "        -6.1256e-01,  1.5718e-01, -1.8879e+00,  6.8809e-01,  4.4239e-01,\n",
       "         1.2380e-01,  6.9130e-01,  9.6312e-01, -3.9939e-01, -7.7259e-01,\n",
       "        -2.9018e-01, -3.4824e-01,  3.3638e-01,  1.0554e+00, -8.6035e-02,\n",
       "        -3.1845e-01, -4.2310e-01,  2.3041e-01,  5.5542e-01, -1.6449e-04,\n",
       "         1.3446e+00,  6.6181e-01,  1.6650e-02, -8.5203e-03, -1.4934e-01,\n",
       "         2.3993e-01, -6.5127e-01, -6.2326e-01,  4.5709e-01,  7.9208e-01,\n",
       "         5.6091e-01, -9.4128e-02, -2.8578e-01, -1.1280e-01,  1.0109e+00,\n",
       "         1.7860e-01,  1.9625e-01, -1.0380e+00,  9.6714e-01, -6.4822e-01,\n",
       "         1.3865e-01,  1.4350e+00,  6.5626e-01,  1.9888e-01,  1.9672e-01],\n",
       "       dtype=float32),\n",
       " 'engineering': array([-0.30847 ,  0.23461 ,  0.11819 ,  0.50325 , -0.4622  ,  0.33465 ,\n",
       "        -0.24591 , -0.90292 ,  0.39837 , -0.3622  ,  1.0792  ,  0.22552 ,\n",
       "        -0.60442 , -0.30231 , -1.41    , -0.22967 , -0.075232,  1.8487  ,\n",
       "        -0.23882 , -0.21088 ,  1.3641  ,  0.4565  , -0.75138 , -1.007   ,\n",
       "        -0.029306, -0.94489 , -0.86208 , -1.4729  , -0.98162 ,  0.25306 ,\n",
       "         2.6589  , -0.48034 ,  0.020104, -1.063   ,  0.71672 ,  0.5565  ,\n",
       "        -0.42515 ,  1.3095  ,  1.4427  ,  0.51156 , -0.022319, -1.0668  ,\n",
       "         0.39002 ,  0.16765 ,  0.045271,  0.2151  ,  0.49155 ,  0.78136 ,\n",
       "        -0.31409 ,  0.05833 ], dtype=float32),\n",
       " 'areas interest': array([ 0.350765  ,  0.2504325 , -0.042202  , -0.129565  , -0.09423   ,\n",
       "         0.01662001, -0.67551506, -0.28607   ,  0.20291999, -0.09103   ,\n",
       "         0.063078  , -0.24531502,  0.4936795 , -0.668195  , -0.13705999,\n",
       "         0.77024996,  0.58503   , -0.345443  ,  0.10122101, -0.12010399,\n",
       "         0.47860003, -0.210167  , -0.10192001,  0.02009   , -0.16835   ,\n",
       "        -1.013765  ,  0.0217405 ,  0.03591999,  0.44098002,  0.455105  ,\n",
       "         3.80465   ,  0.34354448,  0.58435   , -1.0344551 ,  0.077215  ,\n",
       "        -0.02509001, -0.8895    , -0.08638451, -0.171339  , -0.07798499,\n",
       "        -0.71701   ,  0.05564   ,  0.409915  ,  0.2741    , -0.229685  ,\n",
       "        -0.27228698, -0.159985  ,  0.4578075 ,  0.1023498 , -0.20595099],\n",
       "       dtype=float32),\n",
       " 'programming': array([ 0.14957 , -0.16036 , -0.1369  ,  1.0076  , -0.37291 ,  0.63466 ,\n",
       "        -0.93203 , -1.5188  , -0.24924 ,  0.67075 ,  0.51193 ,  0.28389 ,\n",
       "         0.18781 ,  0.8798  , -0.023971,  0.39789 , -0.72769 ,  1.0153  ,\n",
       "         0.11723 , -0.14114 ,  0.974   ,  0.48037 ,  0.25464 ,  1.3262  ,\n",
       "         0.17958 ,  0.19717 ,  0.074712, -0.24844 ,  0.26248 , -0.084427,\n",
       "         3.156   ,  0.38308 , -0.095827, -0.99383 , -0.20497 ,  0.38918 ,\n",
       "        -0.13281 , -0.21775 , -0.93632 ,  0.37943 ,  0.78475 ,  0.36969 ,\n",
       "        -1.3904  ,  0.54891 , -1.3798  ,  0.18898 ,  0.60928 ,  0.98271 ,\n",
       "        -0.53934 ,  1.4439  ], dtype=float32),\n",
       " 'science': array([-6.0334e-01,  1.0577e+00, -3.0452e-01,  5.6728e-02,  6.2269e-04,\n",
       "        -1.5874e-01, -3.9343e-01, -1.6816e+00,  2.5751e-01,  9.6477e-02,\n",
       "         2.2256e-01,  4.0022e-01, -1.3319e-01,  5.0095e-01, -3.1653e-01,\n",
       "        -1.3437e-01,  3.6205e-01,  1.1403e+00, -4.6058e-01,  7.8453e-01,\n",
       "         1.1051e+00,  6.7427e-01,  3.3348e-01, -1.9985e-01,  8.8040e-01,\n",
       "        -1.5153e+00, -1.0876e+00, -9.7803e-01, -1.0249e+00, -8.1350e-02,\n",
       "         2.6487e+00, -4.7125e-01, -4.5275e-01, -2.0287e+00, -4.0910e-01,\n",
       "         2.6637e-01, -6.6804e-01,  1.1231e+00,  8.9049e-01,  2.0298e-01,\n",
       "        -2.4961e-02, -4.6865e-01,  2.5623e-01,  3.7514e-01, -1.2434e-01,\n",
       "         7.2878e-01,  9.0596e-01,  8.3551e-01,  2.2837e-04,  4.4761e-01],\n",
       "       dtype=float32),\n",
       " 'intermediate mipower intermediate': array([-0.46948   ,  0.35786   , -0.18092   ,  0.46010002, -0.06984   ,\n",
       "         0.22724001,  0.62399334,  0.15536667, -0.0017106 ,  0.07271334,\n",
       "         0.33966002, -0.35292664,  0.24285333, -0.32078668, -0.5106934 ,\n",
       "         0.29894   ,  0.01085333,  0.59168667, -0.49048665, -0.20118666,\n",
       "         0.40256   , -0.8366    ,  0.33816668,  0.38677335, -0.21115333,\n",
       "        -0.43058   ,  0.51103336, -0.05534133, -0.15599333, -0.18453334,\n",
       "         1.3958    , -0.24692   ,  0.24075334, -0.30931333,  1.1478666 ,\n",
       "        -0.048476  ,  0.25220665,  0.4349133 , -0.10916   ,  0.19464667,\n",
       "        -0.27416667, -0.55836   , -0.03790867,  0.33852002,  0.13107334,\n",
       "        -0.18969333,  0.7176667 ,  0.11759334,  0.38042   , -0.09407333],\n",
       "       dtype=float32),\n",
       " 'intermediate xampp mysql': array([-0.15096334,  0.10618   ,  0.15273333,  0.9113167 , -0.1874    ,\n",
       "         0.08139367,  0.15751332, -0.14880668,  0.22254135,  0.26226333,\n",
       "         0.5248633 , -0.04336999,  0.3601133 , -0.36713   , -0.5689667 ,\n",
       "         0.137491  , -0.37184   ,  0.65997666,  0.10419001,  0.01130001,\n",
       "        -0.23935334, -0.7202267 ,  0.12791   ,  0.216612  , -0.10179033,\n",
       "        -0.18116999,  0.3683    ,  0.07304601, -0.40611336, -0.63696665,\n",
       "         0.88256997, -0.48032668,  0.21410668,  0.04405667,  0.7963833 ,\n",
       "         0.02656867, -0.00730667,  0.16881333,  0.19313668,  0.03328334,\n",
       "         0.22498333, -0.7020133 , -0.08456767,  0.36664334,  0.19687666,\n",
       "         0.01001   ,  0.6589567 ,  0.54529667,  0.11150334, -0.23587   ],\n",
       "       dtype=float32),\n",
       " 'customers': array([ 1.081    , -0.42035  ,  1.2383   ,  0.24359  ,  0.17261  ,\n",
       "        -0.18405  , -1.2897   ,  0.19998  ,  0.28152  ,  0.38429  ,\n",
       "         0.13456  ,  0.25351  ,  0.63025  , -0.5403   ,  0.47859  ,\n",
       "         0.38151  , -0.81858  ,  0.19375  ,  0.6638   , -1.853    ,\n",
       "         1.2349   , -0.31138  , -0.48747  ,  1.1099   , -0.19867  ,\n",
       "        -1.1284   , -0.011382 , -0.36449  ,  0.69066  , -0.67945  ,\n",
       "         2.9075   ,  1.3669   ,  0.76154  , -0.03224  ,  0.019912 ,\n",
       "        -0.60335  , -0.73934  , -0.095316 ,  0.024081 , -0.59674  ,\n",
       "         0.64656  ,  0.11109  ,  0.36185  ,  0.96213  ,  0.0072497,\n",
       "        -0.20237  , -0.54532  ,  0.37054  , -0.31478  ,  0.65331  ],\n",
       "       dtype=float32),\n",
       " 'intermediate xampp': array([-0.35211   ,  0.268395  , -0.13569   ,  0.345075  , -0.05238   ,\n",
       "         0.17043   ,  0.467995  ,  0.116525  , -0.00128295,  0.054535  ,\n",
       "         0.254745  , -0.264695  ,  0.18214   , -0.24059   , -0.38302   ,\n",
       "         0.224205  ,  0.00814   ,  0.443765  , -0.367865  , -0.15089   ,\n",
       "         0.30192   , -0.62745   ,  0.253625  ,  0.29008   , -0.158365  ,\n",
       "        -0.322935  ,  0.383275  , -0.041506  , -0.116995  , -0.1384    ,\n",
       "         1.04685   , -0.18519   ,  0.180565  , -0.231985  ,  0.8609    ,\n",
       "        -0.036357  ,  0.189155  ,  0.326185  , -0.08187   ,  0.145985  ,\n",
       "        -0.205625  , -0.41877   , -0.0284315 ,  0.25389   ,  0.098305  ,\n",
       "        -0.14227   ,  0.53825   ,  0.088195  ,  0.285315  , -0.070555  ],\n",
       "       dtype=float32),\n",
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       "       dtype=float32),\n",
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       "       dtype=float32),\n",
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       "       dtype=float32),\n",
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       "       dtype=float32),\n",
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       "        -0.83513   , -0.26515   , -0.457095  ,  0.255082  , -0.04394001,\n",
       "         3.4726    , -0.05865499, -0.039415  , -1.3656    ,  0.0393068 ,\n",
       "         0.14247599, -0.475085  ,  0.4790965 , -0.02134   , -0.33899   ,\n",
       "         0.24199499, -0.63583   ,  0.37131   ,  0.19593251,  0.20631999,\n",
       "         0.04465   ,  0.6613295 ,  1.031255  ,  0.06330499,  0.09151001],\n",
       "       dtype=float32),\n",
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       "         0.31137502,  0.182436  , -0.35846052,  0.2307375 , -0.029375  ,\n",
       "        -0.04255   ,  0.5698    , -0.07819501,  0.01521999, -0.673225  ,\n",
       "         0.69093   , -0.1988055 , -0.571435  , -0.23037949, -0.0357115 ,\n",
       "        -1.4222    ,  0.03801501, -0.074995  , -0.18476951,  0.02441999,\n",
       "         3.1968    ,  0.10293499,  0.09605999, -0.0350615 , -0.52662504,\n",
       "         0.00911   , -0.502619  ,  0.186095  ,  0.04116002,  0.15792951,\n",
       "        -0.17622998,  0.15975499,  0.371775  , -0.23085399,  0.46109998,\n",
       "        -0.28556502, -0.19166002,  0.07997999,  0.019015  , -0.3965    ],\n",
       "       dtype=float32),\n",
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       "        -2.3277e-02,  4.2345e-02,  6.6937e-01, -4.5155e-01, -4.4203e-02,\n",
       "         1.6033e-01, -1.4077e-02, -1.1212e-02,  4.2519e-02, -7.7097e-02,\n",
       "         6.5044e-01, -1.5160e-01,  4.9895e-01,  9.6790e-01,  1.2796e+00,\n",
       "         4.2358e-01,  3.8855e-01, -2.0658e-01,  5.1661e-01,  3.5197e-01,\n",
       "        -8.1113e-02, -4.2093e-01,  3.9534e-01, -2.9839e-01, -1.1995e+00,\n",
       "        -1.6586e-01, -1.4927e-01,  4.5746e-01,  1.7718e+00,  1.4369e-01,\n",
       "        -7.9121e-01, -4.4490e-01, -7.0845e-01,  6.9269e-01, -4.5482e-01,\n",
       "        -4.0255e-02, -6.4075e-01,  6.0694e-01, -7.4370e-01, -3.7708e-01,\n",
       "        -7.2169e-01, -7.3330e-04,  1.5087e-01,  7.7879e-01, -4.1905e-01],\n",
       "       dtype=float32),\n",
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       "        -0.67887497, -0.30692   , -0.18822   ,  0.2849625 , -0.05735501,\n",
       "        -0.11722501, -0.16229501, -0.25377   , -0.74701   , -0.77266496,\n",
       "         0.1759735 , -0.10022499, -0.69656503, -0.11504801, -0.1243815 ,\n",
       "         0.75909996, -0.05766501, -0.09789   ,  0.2975921 , -0.0704148 ,\n",
       "        -0.18485498, -0.007175  , -0.593825  , -0.08428502,  0.042385  ,\n",
       "         1.840865  , -0.09772   ,  0.05500999, -0.76326   ,  0.11148001,\n",
       "        -0.501415  , -0.60784   ,  0.15407999, -0.16277999, -0.651495  ,\n",
       "         0.98484004, -0.44246   , -0.14466998, -0.03998999,  0.043613  ,\n",
       "        -0.42662   ,  1.05101   ,  0.84536   ,  0.496755  ,  0.738334  ],\n",
       "       dtype=float32),\n",
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       "        -0.05803999, -0.19304335, -0.87768334,  0.5069467 ,  0.24123335,\n",
       "         0.24486966,  0.3041068 , -0.08668667, -0.50051004, -1.16149   ,\n",
       "         0.64863664,  0.4019867 ,  0.52469665,  0.65416133,  0.096839  ,\n",
       "         0.6302933 ,  0.17260332, -0.47882998, -0.14504334,  0.19748347,\n",
       "        -0.20135331,  0.29844335, -0.5498367 ,  0.21162666,  0.26610333,\n",
       "         2.5865767 ,  0.15145333,  0.22566001, -0.9993267 ,  0.02941268,\n",
       "         0.29444   , -0.58163667,  0.3616067 , -0.01384633, -0.18937333,\n",
       "         0.24295665, -0.4643967 ,  0.07775   , -0.022675  ,  0.16483532,\n",
       "        -0.11744668,  0.12001967,  0.78386664,  0.4835    ,  0.65505666],\n",
       "       dtype=float32),\n",
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       "        -0.19879 , -0.49762 ,  0.66364 ,  0.5909  ,  0.86272 ,  0.44877 ,\n",
       "         0.22857 ,  0.10304 , -0.87668 ,  0.95663 ,  0.36236 ,  0.812   ,\n",
       "         0.64478 , -0.1477  ,  0.11169 ,  0.20213 ,  0.17404 , -0.24893 ,\n",
       "         0.41506 , -1.0105  ,  0.2098  , -0.77491 ,  0.42907 , -0.29445 ,\n",
       "         3.38    ,  0.70533 , -0.65243 , -1.0044  ,  0.072378,  0.21728 ,\n",
       "        -0.71606 ,  0.92303 , -0.30775 , -0.37832 , -0.25924 , -0.82139 ,\n",
       "         0.54856 , -0.094245,  0.84339 ,  0.3414  ,  0.045259,  0.48101 ,\n",
       "        -0.53177 ,  0.3928  ], dtype=float32),\n",
       " 'environment': array([ 0.24989 ,  0.21958 , -0.44505 ,  0.58602 , -0.010814, -0.39337 ,\n",
       "        -0.12406 , -1.1307  ,  0.43009 , -0.050078,  0.72717 ,  0.37415 ,\n",
       "         0.51648 ,  0.55305 , -0.014879,  0.8714  ,  1.1501  ,  0.30417 ,\n",
       "         0.62127 , -0.42837 , -0.021624,  0.58691 , -0.36424 , -0.20318 ,\n",
       "         0.87405 , -0.33359 ,  0.3524  ,  0.086728,  0.18502 ,  1.364   ,\n",
       "         3.3352  , -0.064015, -0.10601 , -1.8814  , -0.9336  ,  0.31382 ,\n",
       "        -0.39991 ,  0.45189 ,  0.31445 , -0.075881, -0.55053 , -0.090056,\n",
       "         0.8603  , -0.026639,  0.2439  ,  0.30208 , -0.44115 , -0.036279,\n",
       "         1.1414  ,  0.7708  ], dtype=float32),\n",
       " 'reports technical environment': array([ 2.1268000e-01, -7.8266710e-03, -1.4663666e-01,  5.4270333e-01,\n",
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       "         2.4229668e-01, -5.8842588e-02, -3.0296663e-02,  1.2319404e+00,\n",
       "         5.5281430e-01,  1.7343332e-01], dtype=float32),\n",
       " 'technical': array([-4.0852e-01,  1.7814e-01, -4.7991e-01,  6.3322e-01, -4.2886e-01,\n",
       "        -3.6955e-01, -6.1283e-02, -8.9297e-02,  1.9720e-01, -4.1649e-01,\n",
       "         3.9508e-01,  1.8402e-01, -7.9311e-01, -3.4480e-01, -2.8445e-01,\n",
       "        -2.6479e-01, -1.8368e-02,  5.0540e-01, -6.6843e-02, -7.5194e-01,\n",
       "         8.6298e-01,  4.3668e-01, -6.8933e-01, -4.4791e-01,  3.2017e-01,\n",
       "        -8.5253e-01, -2.3586e-01, -5.9545e-01, -6.8002e-01,  4.3973e-02,\n",
       "         3.1604e+00,  1.7226e-02,  2.5016e-01, -1.3353e+00,  7.4771e-01,\n",
       "         5.7270e-01,  1.0707e-01,  9.0485e-01,  2.2390e-01,  2.0535e-01,\n",
       "         1.0388e-01, -3.5281e-01,  3.5609e-01,  2.8824e-01, -3.3449e-01,\n",
       "         8.5223e-04,  8.9128e-01,  1.9979e+00,  5.9333e-02,  1.9473e-01],\n",
       "       dtype=float32),\n",
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       "         0.8672033 ,  0.1362    , -0.14752701,  0.07592253, -0.12975033,\n",
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       "       dtype=float32),\n",
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       "         7.41714984e-02,  2.53655016e-01], dtype=float32),\n",
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       "         0.92465   ,  0.080651  , -0.23344551,  0.2164938 , -0.0061655 ,\n",
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       "         2.36905   , -0.68463004,  0.15731   , -1.6205499 ,  0.0793628 ,\n",
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       "        -0.82285   ,  1.605     ,  0.997105  ,  0.700825  , -0.02451   ],\n",
       "       dtype=float32),\n",
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       "        -0.13113749,  1.3201001 ,  0.18323499,  0.31044   ,  0.148425  ],\n",
       "       dtype=float32),\n",
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       "        -4.5844987e-02, -4.3911999e-01, -3.1333849e-01,  3.8635001e-01,\n",
       "        -2.5776500e-01,  1.3935050e-01], dtype=float32),\n",
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       "       dtype=float32),\n",
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       "         3.8859749e-01,  3.1195000e-01,  2.7798951e-01,  2.1792349e-01,\n",
       "        -2.0535000e-01,  4.8319000e-01], dtype=float32),\n",
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       "        -0.27451 , -0.13589 , -0.28074 ,  0.57252 , -0.49239 , -0.12945 ,\n",
       "         0.32308 ,  0.35871 ,  0.49173 , -0.73118 , -0.55319 ,  0.47959 ,\n",
       "        -0.2153  ,  0.015891], dtype=float32),\n",
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       "         0.8376667 ,  0.09819   ,  0.9141    ,  0.21334332,  0.54247   ,\n",
       "         0.45818338,  0.0822    ,  0.01871333,  0.6156233 ,  0.05661667,\n",
       "         0.0193    ,  0.24180667,  0.05770333, -0.32359168,  0.03034733,\n",
       "         0.00395668,  0.173535  ,  0.23291333, -0.003588  ,  0.70773333,\n",
       "         0.31261334,  0.07846334,  0.08402   ,  0.13411666,  0.23081666,\n",
       "        -0.58484   ,  0.04965267, -0.2895533 , -0.17457335, -0.21474333,\n",
       "         0.8204667 , -0.02492667,  0.029995  ,  0.44438997, -0.311632  ],\n",
       "       dtype=float32),\n",
       " 'haryana data science': array([-0.2738067 , -0.28433   ,  0.24910001,  0.5268227 , -0.1406491 ,\n",
       "         0.09297001, -0.06855335, -0.8401633 ,  0.65047336, -0.38821468,\n",
       "         0.28207666, -0.21344931,  0.23350966, -0.31911078, -0.262817  ,\n",
       "         0.40868333, -0.08810667,  0.64479   ,  0.05949334,  0.32709333,\n",
       "         0.86867   , -0.10676668,  0.4151633 ,  0.21237874,  0.60279995,\n",
       "        -0.91113335, -0.49811336, -0.45400667, -0.612122  ,  0.012924  ,\n",
       "         2.2168367 , -0.426685  ,  0.12816332, -1.221358  ,  0.22297853,\n",
       "         0.33869067, -0.39192   ,  0.5225944 ,  0.44491997,  0.18997334,\n",
       "        -0.17157702, -0.23415   , -0.00393   ,  0.19721334, -0.32703   ,\n",
       "         0.51456   ,  0.61655   ,  0.8410433 ,  0.4896728 , -0.21559334],\n",
       "       dtype=float32),\n",
       " 'microsoft word': array([ 0.21008001, -0.64994   ,  0.32199502,  0.69045   ,  0.18494   ,\n",
       "        -0.2804    , -0.469055  , -0.75707   , -0.36318   ,  0.59523   ,\n",
       "        -0.0847155 ,  0.60377   , -0.14603502, -0.64081   ,  0.586675  ,\n",
       "         0.09560375, -0.96499   , -0.0030768 ,  0.489645  , -0.59657   ,\n",
       "        -0.33349594, -0.06185   , -0.165375  ,  0.29085   ,  0.45759004,\n",
       "        -1.405     , -0.44329002, -0.02878   ,  0.089135  , -0.65350497,\n",
       "         2.7488499 , -0.88899004, -0.497795  , -0.10079499, -0.197357  ,\n",
       "        -0.57392   , -0.11446501, -0.67923   , -0.38366002, -0.80826503,\n",
       "         0.786505  , -0.1590635 , -0.41386   ,  0.84484506, -0.07047901,\n",
       "         0.36588502,  0.49142498,  0.484755  , -0.020875  ,  0.22779998],\n",
       "       dtype=float32),\n",
       " 'powerpoint recognition': array([-0.162195  ,  0.02164501, -0.06208   ,  0.3176335 , -0.028855  ,\n",
       "        -0.56273   ,  0.26218998, -0.522035  , -0.02561   ,  0.900475  ,\n",
       "         0.162655  , -0.1253005 ,  0.36745   , -0.51538503,  0.227665  ,\n",
       "        -0.259675  , -0.91858   , -0.09432   ,  0.88319504, -0.58245003,\n",
       "         0.17711249,  0.458315  , -0.3120015 , -0.59761   ,  0.378425  ,\n",
       "        -0.272735  , -0.701705  , -0.100825  , -0.61309004, -0.64126   ,\n",
       "         1.70929   ,  0.038945  , -0.73504996, -0.91392   , -0.20626001,\n",
       "         0.312354  ,  0.140587  , -0.1224685 , -0.4787905 , -0.88741505,\n",
       "         0.50190496, -0.177645  , -0.5935    ,  0.12391499, -0.41877   ,\n",
       "        -0.36856002,  0.8424955 ,  0.67522   , -0.15495   , -0.38153   ],\n",
       "       dtype=float32),\n",
       " 'word microsoft': array([ 0.21008001, -0.64994   ,  0.32199502,  0.69045   ,  0.18494   ,\n",
       "        -0.2804    , -0.469055  , -0.75707   , -0.36318   ,  0.59523   ,\n",
       "        -0.0847155 ,  0.60377   , -0.14603502, -0.64081   ,  0.586675  ,\n",
       "         0.09560375, -0.96499   , -0.0030768 ,  0.489645  , -0.59657   ,\n",
       "        -0.33349594, -0.06185   , -0.165375  ,  0.29085   ,  0.45759004,\n",
       "        -1.405     , -0.44329002, -0.02878   ,  0.089135  , -0.65350497,\n",
       "         2.7488499 , -0.88899004, -0.497795  , -0.10079499, -0.197357  ,\n",
       "        -0.57392   , -0.11446501, -0.67923   , -0.38366002, -0.80826503,\n",
       "         0.786505  , -0.1590635 , -0.41386   ,  0.84484506, -0.07047901,\n",
       "         0.36588502,  0.49142498,  0.484755  , -0.020875  ,  0.22779998],\n",
       "       dtype=float32),\n",
       " 'microsoft excel': array([ 0.18388501, -0.95051   ,  0.852965  ,  1.19532   , -0.26950002,\n",
       "        -0.74265003, -0.506165  , -1.00173   , -0.102245  ,  0.961375  ,\n",
       "         0.16398501,  0.36861998, -0.523132  , -0.831655  ,  0.2681825 ,\n",
       "         0.29686   , -1.01573   ,  0.6459461 ,  0.74794996, -0.78664   ,\n",
       "         0.09640405,  0.32018998, -0.375127  ,  0.19363001,  0.03456   ,\n",
       "        -0.43939   ,  0.13216   , -0.55606   , -0.264565  , -0.89525   ,\n",
       "         1.88645   , -0.26572502, -0.29842   , -0.549575  ,  0.004355  ,\n",
       "        -0.5113125 , -0.28940502, -0.2157755 , -0.193275  , -1.259115  ,\n",
       "         0.593715  , -0.818025  , -0.113186  ,  0.95884   , -0.07702351,\n",
       "         0.382935  ,  0.599045  ,  0.29928   , -0.1544615 ,  0.29544067],\n",
       "       dtype=float32),\n",
       " 'learning html microsoft': array([ 1.18736684e-01, -4.67070013e-01,  5.17943323e-01,  6.02420032e-01,\n",
       "         6.79133236e-02, -2.32056662e-01, -2.33266667e-01, -1.32484663e+00,\n",
       "        -2.57057011e-01,  5.59729993e-01,  5.25606722e-02,  3.13473344e-01,\n",
       "        -1.78905666e-01, -6.43769979e-01,  2.70746678e-01,  1.18663358e-02,\n",
       "        -6.07501328e-01,  2.03114077e-01,  7.97863305e-01, -5.21017969e-01,\n",
       "        -4.00772989e-02,  2.16333330e-01, -4.04686689e-01,  5.04283369e-01,\n",
       "         5.08549988e-01, -3.77193332e-01,  1.30690664e-01, -4.88927603e-01,\n",
       "         3.74596678e-02, -5.50643325e-01,  2.33150005e+00, -5.93096673e-01,\n",
       "        -5.15033305e-01, -7.54633367e-01, -9.86466706e-02,  1.05179988e-01,\n",
       "        -2.90623337e-01, -2.49196693e-01, -1.03332603e-03, -5.25373340e-01,\n",
       "         1.07486725e+00, -4.52129990e-01, -8.51533413e-02,  8.98846626e-01,\n",
       "        -2.27136657e-01,  2.55966663e-01,  1.06096995e+00,  5.43439984e-01,\n",
       "         1.39773324e-01,  2.53523320e-01], dtype=float32),\n",
       " 'microsoft powerpoint': array([ 0.36283   , -1.1235    ,  0.703965  ,  1.138875  , -0.42858   ,\n",
       "        -1.0968    , -0.17770001, -1.238     , -0.20051   ,  1.045585  ,\n",
       "        -0.52471   ,  0.29267448, -0.34667003, -0.674755  ,  0.59598   ,\n",
       "        -0.21801999, -1.48735   ,  0.0883811 ,  0.990835  , -1.05995   ,\n",
       "         0.20438404,  0.448655  , -0.3502665 , -0.43812498,  0.275975  ,\n",
       "        -0.14313501,  0.03802   , -0.002     , -0.57817   , -0.8341    ,\n",
       "         1.69269   , -0.632075  , -0.523165  , -0.49677998, -0.307985  ,\n",
       "        -0.158535  , -0.1222    , -0.30962002, -0.2722305 , -1.4014001 ,\n",
       "         0.794945  , -0.211205  , -0.490725  ,  0.807595  , -0.01487   ,\n",
       "         0.156295  ,  1.051405  ,  0.41889998,  0.0476    ,  0.158055  ],\n",
       "       dtype=float32),\n",
       " 'html microsoft word': array([-0.00423332, -0.57686   ,  0.5189    ,  0.58238333,  0.20166999,\n",
       "        -0.32296   , -0.21198666, -1.2310133 , -0.56139666,  0.5200433 ,\n",
       "        -0.04619633,  0.47300997,  0.08716333, -0.58249664,  0.59101003,\n",
       "         0.02822583, -0.7870467 , -0.05958453,  0.7210967 , -0.57106334,\n",
       "        -0.3380973 , -0.21539001, -0.15106332,  0.5501    ,  0.38245   ,\n",
       "        -0.7399034 , -0.28447267, -0.01905425,  0.16808333, -0.71583337,\n",
       "         2.2358665 , -0.99469334, -0.6278966 , -0.26788998, -0.21987133,\n",
       "        -0.05241334, -0.12648667, -0.7438467 , -0.19397669, -0.62834   ,\n",
       "         1.2795367 , -0.15359233, -0.17853   ,  0.76664335, -0.29093933,\n",
       "         0.39864334,  0.8640167 ,  0.70127004,  0.2653233 , -0.02806001],\n",
       "       dtype=float32),\n",
       " 'microsoft word microsoft': array([ 3.3487335e-01, -9.1899329e-01,  6.1273003e-01,  1.0307001e+00,\n",
       "         2.5043329e-02, -3.3546665e-01, -5.5004001e-01, -9.3051338e-01,\n",
       "        -1.9717668e-01,  7.1257663e-01, -1.3778700e-01,  6.0142332e-01,\n",
       "        -4.4032335e-01, -7.3699999e-01,  5.7774663e-01,  1.2577249e-01,\n",
       "        -1.0781933e+00, -2.4204666e-03,  5.9155333e-01, -7.5471336e-01,\n",
       "        -2.2475125e-01,  1.9776663e-02, -3.3729330e-01,  1.3511668e-01,\n",
       "         2.7068672e-01, -1.2925333e+00, -1.1515335e-01, -1.6514666e-01,\n",
       "        -6.8633356e-03, -6.2153667e-01,  2.6850998e+00, -9.1542006e-01,\n",
       "        -4.4072333e-01, -2.1146999e-01, -2.3874466e-01, -7.1981335e-01,\n",
       "        -2.3403335e-01, -5.6800669e-01, -4.2848334e-01, -1.0547100e+00,\n",
       "         8.1456995e-01, -2.0469232e-01, -3.3620000e-01,  9.2779666e-01,\n",
       "        -1.0300267e-01,  4.5050335e-01,  4.5118666e-01,  3.9854336e-01,\n",
       "        -1.0640667e-01,  3.4936666e-01], dtype=float32),\n",
       " 'powerpoint recognition academic': array([-0.57146335,  0.47009668, -0.35336003, -0.09404099, -0.010373  ,\n",
       "        -0.45885667,  0.09347999, -0.63038   , -0.00824667,  0.5999742 ,\n",
       "         0.09711833, -0.20250034,  0.15322666, -0.39422336,  0.18205233,\n",
       "        -0.36252666, -0.57895666,  0.08053333,  0.80999666, -0.24054003,\n",
       "         0.30284166,  0.4909567 , -0.17627001, -0.39567208,  0.16983001,\n",
       "        -0.48415336, -0.6253233 , -0.41128334, -0.92912674, -0.16263336,\n",
       "         2.05496   ,  0.09330001, -0.43634665, -1.20688   , -0.06727   ,\n",
       "         0.46929264, -0.09648534,  0.36652097, -0.3358333 , -0.68684006,\n",
       "         0.25216332, -0.32858   , -0.41635737,  0.39731666, -0.39777997,\n",
       "        -0.42406335,  0.73226696,  0.8642133 , -0.25404334, -0.22465366],\n",
       "       dtype=float32),\n",
       " 'microsoft excel microsoft': array([ 0.31741002, -1.1193733 ,  0.96671003,  1.36728   , -0.27791667,\n",
       "        -0.64363337, -0.57478005, -1.09362   , -0.02322   ,  0.9566733 ,\n",
       "         0.02801334,  0.44465664, -0.6917214 , -0.86423   ,  0.36541831,\n",
       "         0.25994334, -1.11202   ,  0.43026146,  0.7637566 , -0.88142663,\n",
       "         0.06184873,  0.27447   , -0.477128  ,  0.07030334, -0.01133333,\n",
       "        -0.64879334,  0.26847997, -0.5166667 , -0.24266332, -0.78270006,\n",
       "         2.1101668 , -0.49991003, -0.30780667, -0.51065665, -0.10427   ,\n",
       "        -0.678075  , -0.35066   , -0.25903702, -0.30156   , -1.3552767 ,\n",
       "         0.6860433 , -0.644     , -0.13575067,  1.0037934 , -0.10736567,\n",
       "         0.46186998,  0.5229333 ,  0.2748933 , -0.19546433,  0.39446044],\n",
       "       dtype=float32),\n",
       " 'tech': array([-1.4284  , -0.24064 ,  1.0716  ,  0.95506 , -0.4638  , -0.9633  ,\n",
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       "        -0.59718 ,  0.029111,  0.24891 , -0.14459 , -0.71886 , -0.57624 ,\n",
       "        -0.20726 , -1.3759  , -0.32223 , -0.80346 ,  0.41116 , -0.80033 ,\n",
       "         2.263   ,  0.41214 ,  0.3832  , -0.63751 ,  0.55706 ,  0.33757 ,\n",
       "        -0.7698  ,  0.36486 ,  0.47082 , -0.61175 , -0.56498 , -0.10927 ,\n",
       "         0.24216 ,  0.54632 ,  1.1373  ,  0.66224 ,  0.76762 ,  0.34601 ,\n",
       "         0.10207 ,  0.46033 ], dtype=float32),\n",
       " 'recognition academic': array([-0.927795  ,  1.100095  , -0.636905  , -0.4243365 ,  0.2656455 ,\n",
       "         0.185715  , -0.038085  , -0.34627   ,  0.255555  ,  0.32801124,\n",
       "         0.5484225 , -0.29806   ,  0.06206   , -0.38127   , -0.0429565 ,\n",
       "        -0.23271498, -0.033385  ,  0.031865  ,  0.621845  ,  0.16364   ,\n",
       "         0.2462475 ,  0.37929502, -0.25470352, -0.2435581 , -0.07279   ,\n",
       "        -1.116895  , -0.705445  , -0.833865  , -0.91495   ,  0.31135   ,\n",
       "         2.66855   ,  0.287885  , -0.294645  , -1.5299499 ,  0.04632   ,\n",
       "         0.356674  , -0.259113  ,  0.6866215 , -0.4905845 , -0.40266   ,\n",
       "         0.01865   , -0.42964   , -0.224251  ,  0.33523   , -0.665825  ,\n",
       "        -0.48251998,  0.2323505 ,  0.99048   , -0.5674    , -0.1987855 ],\n",
       "       dtype=float32),\n",
       " 'word microsoft excel': array([ 0.06782334, -0.5812667 ,  0.38524   ,  0.68678   ,  0.04187666,\n",
       "        -0.5335    , -0.41281   , -0.74673337, -0.35522667,  0.72198004,\n",
       "         0.13415633,  0.44935   , -0.10314468, -0.67185   ,  0.383275  ,\n",
       "         0.19960582, -0.8856134 ,  0.4289488 ,  0.55994004, -0.56514   ,\n",
       "        -0.15564063,  0.11121666, -0.13329132,  0.38177   ,  0.36247337,\n",
       "        -0.87372667, -0.38779333, -0.24393332, -0.05066666, -0.8466366 ,\n",
       "         2.2376666 , -0.44705   , -0.42194998, -0.28930667, -0.02149467,\n",
       "        -0.38628832, -0.11152334, -0.48148367, -0.21191335, -0.8623867 ,\n",
       "         0.62991333, -0.55274236, -0.29107067,  0.83788997, -0.04231834,\n",
       "         0.29263332,  0.60341   ,  0.44731668, -0.024401  ,  0.15132712],\n",
       "       dtype=float32),\n",
       " 'html microsoft': array([ 0.07580002, -0.9439    ,  1.053455  ,  1.038725  , -0.02981   ,\n",
       "        -0.42684   , -0.20493001, -1.72815   , -0.4115    ,  0.65847   ,\n",
       "        -0.106544  ,  0.40411   , -0.23767   , -0.697625  ,  0.579785  ,\n",
       "         0.03979   , -0.86788   , -0.0868539 ,  0.989685  , -0.79552495,\n",
       "        -0.17728095, -0.16972   , -0.401785  ,  0.44612503,  0.064525  ,\n",
       "        -0.238655  ,  0.287141  , -0.21874139,  0.06356   , -0.699045  ,\n",
       "         1.88375   , -1.08719   , -0.60734   , -0.51745   , -0.29321   ,\n",
       "        -0.01050001, -0.31185   , -0.60932004, -0.16637   , -0.908045  ,\n",
       "         1.56815   , -0.2193    ,  0.055625  ,  0.85197   , -0.449955  ,\n",
       "         0.54195   ,  0.989955  ,  0.68021   ,  0.280125  ,  0.02635998],\n",
       "       dtype=float32),\n",
       " 'html': array([-4.3286e-01, -4.3070e-01,  9.1271e-01,  3.6625e-01,  2.3513e-01,\n",
       "        -4.0808e-01,  3.0215e-01, -2.1789e+00, -9.5783e-01,  3.6967e-01,\n",
       "         3.0842e-02,  2.1149e-01,  5.5356e-01, -4.6587e-01,  5.9968e-01,\n",
       "        -1.0653e-01, -4.3116e-01, -1.7260e-01,  1.1840e+00, -5.2005e-01,\n",
       "        -3.4730e-01, -5.2247e-01, -1.2244e-01,  1.0686e+00,  2.3217e-01,\n",
       "         5.9029e-01,  3.3162e-02,  3.9725e-04,  3.2598e-01, -8.4049e-01,\n",
       "         1.2099e+00, -1.2061e+00, -8.8810e-01, -6.0208e-01, -2.6490e-01,\n",
       "         9.9060e-01, -1.5053e-01, -8.7308e-01,  1.8539e-01, -2.6849e-01,\n",
       "         2.2656e+00, -1.4265e-01,  2.9213e-01,  6.1024e-01, -7.3186e-01,\n",
       "         4.6416e-01,  1.6092e+00,  1.1343e+00,  8.3772e-01, -5.3978e-01],\n",
       "       dtype=float32),\n",
       " 'microsoft powerpoint recognition': array([ 0.08669001, -0.47127   ,  0.35668   ,  0.7821557 , -0.11748666,\n",
       "        -0.52368665, -0.06254334, -0.7738233 ,  0.02787   ,  0.9160733 ,\n",
       "         0.02712667,  0.11537632, -0.09800002, -0.6533833 ,  0.33840665,\n",
       "        -0.11107999, -1.0472533 , -0.06324927,  0.85392   , -0.7453    ,\n",
       "         0.11565436,  0.36655334, -0.43504432, -0.45719   ,  0.21790999,\n",
       "        -0.53769   , -0.28743002, -0.21317667, -0.47501335, -0.61337334,\n",
       "         1.9920601 , -0.29679668, -0.59889334, -0.75355333, -0.24468   ,\n",
       "        -0.128964  , -0.06399867, -0.19683234, -0.49190366, -1.1074767 ,\n",
       "         0.6248367 , -0.21708   , -0.45596   ,  0.4471767 , -0.33519664,\n",
       "        -0.03912666,  0.68523365,  0.52552   , -0.19579   , -0.05685334],\n",
       "       dtype=float32),\n",
       " 'excel microsoft': array([ 0.18388501, -0.95051   ,  0.852965  ,  1.19532   , -0.26950002,\n",
       "        -0.74265003, -0.506165  , -1.00173   , -0.102245  ,  0.961375  ,\n",
       "         0.16398501,  0.36861998, -0.523132  , -0.831655  ,  0.2681825 ,\n",
       "         0.29686   , -1.01573   ,  0.6459461 ,  0.74794996, -0.78664   ,\n",
       "         0.09640405,  0.32018998, -0.375127  ,  0.19363001,  0.03456   ,\n",
       "        -0.43939   ,  0.13216   , -0.55606   , -0.264565  , -0.89525   ,\n",
       "         1.88645   , -0.26572502, -0.29842   , -0.549575  ,  0.004355  ,\n",
       "        -0.5113125 , -0.28940502, -0.2157755 , -0.193275  , -1.259115  ,\n",
       "         0.593715  , -0.818025  , -0.113186  ,  0.95884   , -0.07702351,\n",
       "         0.382935  ,  0.599045  ,  0.29928   , -0.1544615 ,  0.29544067],\n",
       "       dtype=float32),\n",
       " 'microsoft': array([ 5.8446e-01, -1.4571e+00,  1.1942e+00,  1.7112e+00, -2.9475e-01,\n",
       "        -4.4560e-01, -7.1201e-01, -1.2774e+00,  1.3483e-01,  9.4727e-01,\n",
       "        -2.4393e-01,  5.9673e-01, -1.0289e+00, -9.2938e-01,  5.5989e-01,\n",
       "         1.8611e-01, -1.3046e+00, -1.1078e-03,  7.9537e-01, -1.0710e+00,\n",
       "        -7.2619e-03,  1.8303e-01, -6.8113e-01, -1.7635e-01, -1.0312e-01,\n",
       "        -1.0676e+00,  5.4112e-01, -4.3788e-01, -1.9886e-01, -5.5760e-01,\n",
       "         2.5576e+00, -9.6828e-01, -3.2658e-01, -4.3282e-01, -3.2152e-01,\n",
       "        -1.0116e+00, -4.7317e-01, -3.4556e-01, -5.1813e-01, -1.5476e+00,\n",
       "         8.7070e-01, -2.9595e-01, -1.8088e-01,  1.0937e+00, -1.6805e-01,\n",
       "         6.1974e-01,  3.7071e-01,  2.2612e-01, -2.7747e-01,  5.9250e-01],\n",
       "       dtype=float32),\n",
       " 'excel microsoft powerpoint': array([ 0.16965668, -0.8969734 ,  0.6398867 ,  0.98573   , -0.3671367 ,\n",
       "        -1.0777668 , -0.21857335, -1.0673534 , -0.24678   ,  1.0222167 ,\n",
       "        -0.15917332,  0.24195297, -0.23690136, -0.69448   ,  0.38947833,\n",
       "        -0.00947666, -1.2338533 ,  0.48992074,  0.89406663, -0.87406   ,\n",
       "         0.20294602,  0.45155334, -0.2565523 , -0.10421332,  0.24139667,\n",
       "        -0.03248334, -0.06692   , -0.22608002, -0.49553666, -0.9670334 ,\n",
       "         1.5335602 , -0.27577335, -0.43886337, -0.5532966 , -0.09524667,\n",
       "        -0.10936499, -0.11668   , -0.23507701, -0.137627  , -1.25781   ,\n",
       "         0.63554   , -0.5875033 , -0.342314  ,  0.81305665, -0.00524567,\n",
       "         0.15290666,  0.97673005,  0.4034133 ,  0.021249  ,  0.10483045],\n",
       "       dtype=float32),\n",
       " 'recognition academic secured': array([-0.60169834,  0.76154107, -0.19234334, -0.20296101,  0.27815035,\n",
       "         0.18373334, -0.16148667,  0.05318   ,  0.18390535,  0.15590084,\n",
       "         0.3631976 , -0.27432   , -0.11677334, -0.38480997, -0.11388767,\n",
       "        -0.18316399,  0.11861334, -0.16147333,  0.41800764,  0.04125334,\n",
       "         0.26677167,  0.14548668, -0.2923557 , -0.35436538, -0.28169334,\n",
       "        -1.0027333 , -0.26364332, -0.65860003, -0.7407834 ,  0.14199333,\n",
       "         2.4317667 ,  0.30983666, -0.27986333, -0.9761233 ,  0.18138666,\n",
       "         0.30700266, -0.04994534,  0.73181766, -0.5275597 , -0.66234   ,\n",
       "         0.14272334, -0.4558533 , -0.14717574,  0.18974666, -0.83141667,\n",
       "        -0.4213833 , -0.101993  ,  0.70296   , -0.29149997, -0.37878034],\n",
       "       dtype=float32),\n",
       " 'processing data': array([ 0.34596497, -0.73045003,  0.962685  ,  0.81292003,  0.111394  ,\n",
       "        -0.18229699, -0.4638    , -0.7749    ,  1.33985   ,  0.2514795 ,\n",
       "         0.74194   , -0.02872205,  0.67504954, -0.1751762 ,  0.044616  ,\n",
       "         0.17675799, -0.81732   , -0.07033998,  0.40759498, -0.749085  ,\n",
       "         1.0509701 , -0.81307   ,  0.181822  , -0.1488569 ,  0.358105  ,\n",
       "        -0.50574   , -0.16086501, -0.24862   ,  0.512092  , -0.11076   ,\n",
       "         3.3873    , -0.878775  ,  0.2683315 , -1.069945  ,  0.1057378 ,\n",
       "         0.48848101, -0.307345  ,  0.4220565 ,  0.448875  ,  0.11248   ,\n",
       "         0.57528496, -0.359645  ,  0.21232   ,  0.32242   , -0.06826501,\n",
       "        -0.19652998,  1.09279   ,  1.1377051 ,  0.26454997, -0.10075594],\n",
       "       dtype=float32),\n",
       " 'data visualization company': array([ 6.29036665e-01, -5.15753329e-01,  7.15295017e-01,  7.41286695e-01,\n",
       "        -4.30823378e-02, -2.44623348e-01, -5.16016662e-01, -8.65796626e-01,\n",
       "         8.34973335e-01,  3.44409674e-01,  6.70303345e-01,  3.75740677e-01,\n",
       "        -1.58436999e-01, -2.00396135e-01, -3.06938350e-01,  1.01699986e-01,\n",
       "        -6.04510009e-01,  5.34933396e-02,  4.91400063e-02, -6.57329977e-01,\n",
       "         1.01111662e+00, -1.99083328e-01, -1.30840018e-01, -2.71497935e-01,\n",
       "         1.25176653e-01, -4.23686653e-01, -7.11746693e-01, -4.68870014e-01,\n",
       "         4.69053276e-02, -1.81133356e-02,  2.71170020e+00, -6.94383323e-01,\n",
       "         2.09416673e-01, -9.43989933e-01, -2.17327818e-01,  6.24001026e-04,\n",
       "        -3.39156657e-01,  4.69784349e-01,  4.06647325e-01, -2.69322008e-01,\n",
       "         8.38349998e-01, -4.85886663e-01,  4.29466665e-02,  3.96943331e-01,\n",
       "        -2.65297025e-01, -1.87328324e-01,  6.79133415e-01,  6.60470009e-01,\n",
       "         2.20313326e-01,  2.80339986e-01], dtype=float32),\n",
       " 'consultant': array([-0.46413  ,  0.32619  , -0.31003  ,  0.57073  , -0.005063 ,\n",
       "         0.10132  , -1.605    , -0.54195  ,  0.41325  , -0.56555  ,\n",
       "        -0.35644  ,  0.35198  , -0.51459  ,  0.24217  , -0.054245 ,\n",
       "        -0.48365  ,  0.35191  ,  0.64869  ,  0.34505  , -0.025982 ,\n",
       "         0.83562  ,  0.88454  , -0.84204  ,  0.0093599, -0.41031  ,\n",
       "        -1.2115   , -0.28877  , -0.82745  , -0.50806  ,  0.61159  ,\n",
       "         1.4628   , -0.4494   ,  0.024408 , -0.99515  , -0.16685  ,\n",
       "         0.064839 , -0.98224  ,  0.80211  ,  1.3755   , -0.024594 ,\n",
       "         0.21947  ,  0.6581   , -0.40235  ,  0.32384  ,  0.28802  ,\n",
       "        -0.563    , -0.41027  ,  0.45402  ,  0.06173  ,  1.9012   ],\n",
       "       dtype=float32),\n",
       " 'visualization company': array([ 6.7805004e-01, -4.9428499e-01,  1.8924250e-01,  8.8780999e-01,\n",
       "        -1.7632851e-01, -1.9414000e-01, -3.8563001e-01, -8.1810999e-01,\n",
       "         6.6900998e-01,  4.7947499e-01,  5.9810501e-01,  5.9332502e-01,\n",
       "        -2.6995501e-01, -3.0135301e-01, -5.0999701e-01, -3.0460007e-02,\n",
       "        -4.1314501e-01,  4.9980500e-01, -5.8749914e-03, -5.9798002e-01,\n",
       "         1.1493050e+00,  2.5679998e-02, -4.2713001e-01, -4.1165501e-01,\n",
       "        -7.0924997e-02, -3.0565000e-01, -6.9756997e-01, -6.3366503e-01,\n",
       "         2.9810999e-02, -1.3045500e-01,  2.2849500e+00, -6.3025498e-01,\n",
       "         2.7325004e-02, -5.5258501e-01, -3.2910952e-01, -3.2900006e-02,\n",
       "        -3.9168000e-01,  6.8709505e-01,  4.7743601e-01, -2.5415301e-01,\n",
       "         8.8591003e-01, -5.0369501e-01, -3.2609999e-02,  3.5236001e-01,\n",
       "        -1.8257049e-01, -1.5494250e-01,  3.8000000e-01,  1.9995500e-01,\n",
       "         1.2799948e-03,  5.2539998e-01], dtype=float32),\n",
       " 'python excel': array([ 0.18650499, -0.49717498, -0.24943498,  0.54585004,  0.164615  ,\n",
       "        -0.40253   , -0.329025  , -1.25303   , -0.115935  ,  0.86230505,\n",
       "         0.511015  ,  0.587805  ,  0.233058  , -0.127195  ,  0.24777749,\n",
       "         0.12854001, -0.20106   ,  1.18595   , -0.10420501, -0.03642499,\n",
       "        -0.18190502,  0.57848   ,  0.032943  ,  0.36459002,  0.054324  ,\n",
       "         0.271585  ,  0.07569499, -0.26032   , -0.40022498, -1.1632    ,\n",
       "         1.28815   , -0.18569   , -0.47213   ,  0.297135  ,  0.312885  ,\n",
       "         0.53623754,  0.06938   , -0.6368455 , -0.23522499, -0.5194725 ,\n",
       "         0.489645  , -0.44337004, -0.531646  ,  0.753325  , -0.09693851,\n",
       "        -0.2939    ,  1.04354   ,  0.263345  , -0.48200652, -0.0759343 ],\n",
       "       dtype=float32),\n",
       " 'advanced querying visualization': array([ 0.23729669, -0.50714   ,  0.321935  ,  0.532887  , -0.121053  ,\n",
       "        -0.279604  ,  0.26792398, -0.65825   ,  0.30353665,  0.47562   ,\n",
       "         0.57544667,  0.02264001,  0.07676733,  0.04939133, -0.61074996,\n",
       "        -0.16478467, -0.40125   ,  0.10803777,  0.21854001, -0.23884733,\n",
       "         0.173337  ,  0.032471  , -0.16295233, -0.02198333,  0.4565257 ,\n",
       "         0.28706   ,  0.00204335, -0.8306167 , -0.04282967, -0.10272733,\n",
       "         1.32597   , -0.37899664, -0.07443067, -0.81701994,  0.04613532,\n",
       "         0.4103233 , -0.34956333,  0.87413   , -0.08959467, -0.19735533,\n",
       "         0.8582167 , -0.55689335,  0.3069153 ,  0.57877576,  0.14823966,\n",
       "        -0.12948333,  1.22158   ,  0.3962933 ,  0.01471334,  0.07256333],\n",
       "       dtype=float32),\n",
       " 'consultant datamites': array([-0.232065  ,  0.163095  , -0.155015  ,  0.285365  , -0.0025315 ,\n",
       "         0.05066   , -0.8025    , -0.270975  ,  0.206625  , -0.282775  ,\n",
       "        -0.17822   ,  0.17599   , -0.257295  ,  0.121085  , -0.0271225 ,\n",
       "        -0.241825  ,  0.175955  ,  0.324345  ,  0.172525  , -0.012991  ,\n",
       "         0.41781   ,  0.44227   , -0.42102   ,  0.00467995, -0.205155  ,\n",
       "        -0.60575   , -0.144385  , -0.413725  , -0.25403   ,  0.305795  ,\n",
       "         0.7314    , -0.2247    ,  0.012204  , -0.497575  , -0.083425  ,\n",
       "         0.0324195 , -0.49112   ,  0.401055  ,  0.68775   , -0.012297  ,\n",
       "         0.109735  ,  0.32905   , -0.201175  ,  0.16192   ,  0.14401   ,\n",
       "        -0.2815    , -0.205135  ,  0.22701   ,  0.030865  ,  0.9506    ],\n",
       "       dtype=float32),\n",
       " 'python data visualization': array([ 0.6169933 , -0.50688666,  0.24121834,  0.58933336,  0.17577668,\n",
       "        -0.25492665, -0.14192669, -1.3298234 ,  0.7283667 ,  0.55518633,\n",
       "         0.5590467 ,  0.44960067,  0.26526633,  0.17758386, -0.10084035,\n",
       "        -0.01733667, -0.25188002,  0.12506665, -0.16411668, -0.20154667,\n",
       "         0.18349   ,  0.09236334,  0.12051334, -0.0484746 ,  0.415346  ,\n",
       "         0.25293002, -0.30435333, -0.31099334, -0.087262  , -0.34146667,\n",
       "         2.0684001 , -0.81995004, -0.14455001, -0.6529    , -0.1126748 ,\n",
       "         0.689094  , -0.08039334, -0.05900232, -0.09220266, -0.15555033,\n",
       "         0.8627966 , -0.16017666, -0.38929668,  0.7103867 , -0.23306702,\n",
       "        -0.40987   ,  1.4730667 ,  0.6399467 , -0.03858669,  0.01686334],\n",
       "       dtype=float32),\n",
       " 'querying visualization analytics': array([ 0.37978336, -0.46998438,  0.30463502,  0.6156704 ,  0.02699033,\n",
       "        -0.47803998,  0.20222867, -0.86963004,  0.5421533 ,  0.45087   ,\n",
       "         0.17599   ,  0.39017   ,  0.075354  , -0.28613535, -0.84564996,\n",
       "        -0.03212   , -0.13927335,  0.04989999,  0.50854135, -0.20446767,\n",
       "         0.55189663,  0.23379433, -0.12598233,  0.29675665,  0.45896015,\n",
       "         0.73766   , -0.10078333, -0.5008367 , -0.19225633, -0.02503333,\n",
       "         0.53434664, -0.60523665,  0.152886  , -1.1004933 , -0.08631801,\n",
       "         0.24151333, -0.5434367 ,  0.26293004,  0.03568734, -0.17085868,\n",
       "         1.0922567 , -0.35322   , -0.41119134,  0.71716   , -0.09243166,\n",
       "        -0.54138   ,  1.3181133 ,  0.7127733 ,  0.56667334,  0.46062335],\n",
       "       dtype=float32),\n",
       " 'visualization': array([ 0.73027 , -0.41154 , -0.033145,  0.9075  , -0.26956 , -0.65383 ,\n",
       "         0.70874 , -1.2483  ,  0.91075 ,  0.84215 ,  0.41231 ,  0.37313 ,\n",
       "         0.24772 ,  0.051694, -0.92078 , -0.2675  , -0.09314 ,  0.13543 ,\n",
       "         0.25742 , -0.25804 ,  0.37961 ,  0.22609 , -0.23521 , -0.31981 ,\n",
       "         0.79225 ,  1.0642  , -0.60115 , -0.9473  ,  0.1273  , -0.13747 ,\n",
       "         1.279   , -0.829   , -0.33325 , -1.4925  , -0.6398  ,  0.91611 ,\n",
       "        -0.25147 ,  0.97553 ,  0.060352, -0.098676,  1.1826  , -0.48362 ,\n",
       "        -0.34415 ,  0.96238 , -0.060571, -0.24358 ,  1.8821  ,  0.18409 ,\n",
       "         0.15842 ,  0.41062 ], dtype=float32),\n",
       " 'science consultant': array([-0.53373504,  0.691945  , -0.307275  ,  0.313729  , -0.00222015,\n",
       "        -0.02871   , -0.999215  , -1.1117749 ,  0.33538002, -0.23453651,\n",
       "        -0.06694   ,  0.3761    , -0.32389003,  0.37155998, -0.18538749,\n",
       "        -0.30901   ,  0.35698   ,  0.894495  , -0.05776499,  0.37927398,\n",
       "         0.97036004,  0.779405  , -0.25428   , -0.09524505,  0.235045  ,\n",
       "        -1.3634    , -0.688185  , -0.90274   , -0.76647997,  0.26512003,\n",
       "         2.05575   , -0.460325  , -0.21417099, -1.5119251 , -0.287975  ,\n",
       "         0.1656045 , -0.82514   ,  0.962605  ,  1.132995  ,  0.089193  ,\n",
       "         0.0972545 ,  0.094725  , -0.07306001,  0.34949   ,  0.08184001,\n",
       "         0.08288997,  0.24784501,  0.644765  ,  0.03097919,  1.174405  ],\n",
       "       dtype=float32),\n",
       " 'manipulating cleaning processing': array([ 0.16631667, -0.89411336,  0.18173666,  0.35926667, -0.01375068,\n",
       "         0.10823967,  0.13637333, -0.37614003,  0.8475833 ,  0.33854   ,\n",
       "         0.15554999,  0.17640798,  0.45972002,  0.05161667,  0.02734167,\n",
       "         0.05224367, -0.27528402,  0.4973457 ,  0.6324933 , -0.78974   ,\n",
       "         1.2284333 , -0.15755667,  0.27116802, -0.40174332,  0.04368333,\n",
       "        -0.52559334,  0.051446  ,  0.07732334,  0.9033733 , -0.25858566,\n",
       "         1.9842    , -0.21699667, -0.142899  , -0.6005667 , -0.06301   ,\n",
       "         0.92735   , -0.44738665,  0.72851664,  0.5788567 ,  0.12415   ,\n",
       "         0.06547133, -0.18926966,  0.33640334,  0.44120336,  0.31388518,\n",
       "        -0.27881667,  0.292696  ,  0.3292    ,  0.49867   , -0.27599397],\n",
       "       dtype=float32),\n",
       " 'solutions': array([ 1.3012  , -0.013244,  0.1448  ,  0.66044 , -0.33617 ,  0.45017 ,\n",
       "         0.11766 , -0.89443 ,  0.25785 ,  0.5859  ,  0.019647,  0.37024 ,\n",
       "        -0.51061 , -0.047094, -0.82948 ,  0.47848 ,  0.68967 ,  0.32658 ,\n",
       "         1.2933  , -0.92081 ,  0.53691 ,  0.34984 , -0.29374 ,  0.32138 ,\n",
       "        -0.13005 ,  0.13223 , -0.10042 , -0.45223 ,  0.62178 ,  0.69297 ,\n",
       "         2.8184  , -0.14738 , -0.93059 , -0.7813  ,  0.14007 , -0.1808  ,\n",
       "        -0.72063 ,  0.4107  ,  0.37131 , -0.25575 ,  0.69743 , -0.69195 ,\n",
       "         0.071957,  0.51546 ,  0.14277 ,  0.60711 ,  0.35964 ,  1.2288  ,\n",
       "         0.33879 ,  0.76845 ], dtype=float32),\n",
       " 'processing': array([ 1.6092e-01, -9.0221e-01,  1.5797e-01,  1.1776e+00, -6.2201e-04,\n",
       "        -1.9004e-02, -1.5081e-01, -5.8863e-01,  1.5128e+00,  4.2868e-01,\n",
       "         6.6918e-01,  1.9839e-03,  1.2855e+00, -3.5187e-01, -9.9470e-03,\n",
       "        -1.2504e-02, -6.4740e-01,  6.9845e-01,  6.5602e-01, -7.2214e-01,\n",
       "         1.3672e+00, -9.7753e-01, -9.8096e-02, -3.0653e-01,  1.9883e-01,\n",
       "        -3.5172e-01,  4.1837e-01, -3.5796e-01,  9.4309e-01, -4.2809e-01,\n",
       "         3.2094e+00, -9.3491e-01, -3.6937e-02, -4.1309e-01,  2.0524e-01,\n",
       "         9.0929e-01, -3.8058e-01,  8.0895e-01,  6.3268e-01,  5.2462e-01,\n",
       "         4.0734e-01, -2.6902e-01,  2.3058e-01,  1.5873e-01,  2.9422e-01,\n",
       "        -1.4096e-01,  9.0818e-01,  6.9391e-01, -1.2928e-01,  8.2681e-03],\n",
       "       dtype=float32),\n",
       " 'business': array([ 0.023693,  0.13316 ,  0.023131,  0.49833 ,  0.026874, -0.43252 ,\n",
       "        -1.1364  , -0.82001 ,  0.22388 , -0.032119, -0.069651,  0.39857 ,\n",
       "        -0.58275 ,  0.095008, -0.023643,  0.23237 , -0.42441 ,  0.65709 ,\n",
       "         0.57802 , -0.51602 ,  1.8253  ,  0.12951 , -0.61773 ,  0.39281 ,\n",
       "        -0.35754 , -1.6778  , -0.45201 , -0.47075 ,  0.19487 ,  0.35828 ,\n",
       "         3.6034  ,  0.32865 ,  0.47288 , -0.33787 , -0.46234 , -0.51628 ,\n",
       "        -1.3755  ,  0.70789 ,  0.4648  , -0.16186 , -0.0961  , -0.28523 ,\n",
       "         0.30047 ,  0.50902 ,  0.081356, -0.015639, -0.51021 ,  0.34585 ,\n",
       "         0.24201 ,  0.82237 ], dtype=float32),\n",
       " 'visualization company heretic': array([ 5.7576668e-01, -5.9533000e-01, -7.9955012e-02,  2.3157334e-01,\n",
       "         1.4278765e-01,  7.0999959e-03, -5.8293343e-02, -6.5669996e-01,\n",
       "         1.9276333e-01,  6.6224998e-01,  4.6849334e-01,  6.3029003e-01,\n",
       "        -2.3706001e-01, -1.1638200e-01, -2.8837800e-01, -5.7004392e-05,\n",
       "        -3.5418999e-01,  4.0637669e-01, -8.3493330e-02, -1.6153668e-01,\n",
       "         6.3919336e-01, -7.2523333e-02, -2.3803668e-01, -2.0619333e-01,\n",
       "        -1.0713333e-01, -3.8626000e-01, -6.5646333e-01, -4.7466001e-01,\n",
       "         8.3880663e-02, -1.2746334e-01,  1.2537533e+00, -6.3655335e-01,\n",
       "        -1.8909000e-01, -2.7921334e-01, -3.5571635e-01, -8.2940005e-02,\n",
       "         6.7866645e-03,  3.9157334e-01,  1.7543733e-01, -1.4571534e-01,\n",
       "         7.4214005e-01, -5.2053666e-01, -2.1329999e-01,  5.1087666e-01,\n",
       "         4.4329673e-02,  1.2514833e-01,  5.6059998e-02, -9.2766685e-03,\n",
       "         1.9622330e-02,  2.3111331e-01], dtype=float32),\n",
       " 'communication skills problem': array([ 0.04066001,  0.131258  , -0.075441  ,  0.23309767,  0.02757333,\n",
       "         0.26737735,  0.08518067, -0.38107333,  0.38169   , -0.06217334,\n",
       "         0.5691367 ,  0.40355334, -0.46636   , -0.21514992, -0.44533002,\n",
       "         0.01066667, -0.05051001,  0.48305002,  0.4673833 , -0.46885633,\n",
       "         0.03663665,  0.61854   , -0.35597   ,  0.04467332,  0.87837   ,\n",
       "        -1.0946733 , -0.04151333, -0.6504634 ,  0.50351   ,  0.263255  ,\n",
       "         3.2016    ,  0.47274664, -0.09472833, -1.18277   , -0.00887666,\n",
       "         0.72284335, -0.51851004,  0.54079336,  0.12195334,  0.33977666,\n",
       "         0.18696666, -0.06164333, -0.03580667,  0.75057334,  0.17504667,\n",
       "        -0.26524335,  0.7229266 ,  0.82813334,  0.00710467,  0.32739002],\n",
       "       dtype=float32),\n",
       " 'entities': array([ 0.58019  , -0.28998  ,  0.15752  ,  0.55535  ,  0.53318  ,\n",
       "         0.64791  ,  0.58663  , -0.52759  , -0.28532  , -0.04167  ,\n",
       "         0.56985  ,  0.46603  ,  0.0046983,  0.59877  , -0.51163  ,\n",
       "         0.18911  ,  0.55224  ,  0.18515  ,  1.1625   ,  0.45308  ,\n",
       "         0.71981  , -0.29315  ,  0.64717  ,  0.6917   , -1.5954   ,\n",
       "        -0.27557  , -0.83618  , -0.91532  ,  0.086256 , -0.2464   ,\n",
       "         2.4725   , -0.21346  ,  0.33416  , -0.86008  , -0.19316  ,\n",
       "         0.18192  , -0.83322  , -0.55001  , -0.43995  ,  0.4636   ,\n",
       "        -0.42725  , -0.81229  ,  1.1704   ,  1.1342   , -1.3862   ,\n",
       "        -0.46385  , -1.1726   ,  0.88067  , -0.30808  , -0.2986   ],\n",
       "       dtype=float32),\n",
       " 'deep': array([ 0.31445 ,  1.2024  ,  0.066651, -0.20096 , -0.049636,  0.66882 ,\n",
       "        -0.049386,  0.44174 ,  0.1799  , -0.10196 , -0.43674 ,  0.12076 ,\n",
       "        -0.12495 ,  0.43378 , -0.87784 ,  0.010281,  0.54592 , -0.28928 ,\n",
       "        -0.46115 , -0.32058 , -0.69094 ,  0.49733 ,  0.40657 , -0.90062 ,\n",
       "         0.69699 , -1.1536  , -0.12229 ,  1.0657  ,  0.93207 ,  0.20439 ,\n",
       "         3.3004  ,  0.14223 ,  0.46493 ,  0.075359, -0.56755 ,  0.30769 ,\n",
       "        -1.1251  , -0.37871 ,  0.57479 , -0.12629 ,  0.13589 ,  0.10633 ,\n",
       "         0.058432,  0.40321 ,  0.10243 ,  0.12004 ,  0.41383 ,  0.051987,\n",
       "        -0.5835  , -1.1159  ], dtype=float32),\n",
       " 'knowledge python': array([ 0.506565  , -0.006145  , -0.73079   ,  0.226853  ,  0.777895  ,\n",
       "         0.112383  , -0.13902   , -0.997     ,  0.31087   ,  0.68027   ,\n",
       "         0.512645  ,  0.95189   ,  0.17906001, -0.159455  ,  0.11202499,\n",
       "        -0.1065895 ,  0.249325  ,  0.71538496, -0.22614501,  0.22649701,\n",
       "        -0.14342502,  0.704895  ,  0.18101001, -0.03946999,  0.375929  ,\n",
       "        -0.48342502, -0.22266999, -0.33958   , -0.07341   , -0.5031445 ,\n",
       "         2.1326    , -0.47563002, -0.51104003, -0.19095004,  0.18339151,\n",
       "         0.74469   , -0.32775497, -0.34757   , -0.3139945 ,  0.0438525 ,\n",
       "         0.64989   , -0.13437001, -0.5076534 ,  0.868235  , -0.24656501,\n",
       "        -0.513975  ,  0.81517494,  0.675075  , -0.373005  , -0.262995  ],\n",
       "       dtype=float32),\n",
       " 'neural intelligence': array([ 0.90311503, -0.08037499,  0.82287   ,  0.5423735 ,  0.01842001,\n",
       "         0.78062505,  0.53855497, -0.607075  ,  0.6315    , -0.3435    ,\n",
       "         0.44600952,  0.34676   , -0.3134235 ,  0.58564997, -1.171025  ,\n",
       "         0.33316   , -0.541305  ,  0.09390049,  0.397005  ,  0.34716502,\n",
       "         0.086624  , -0.03942001,  0.745345  , -0.46554798,  0.218435  ,\n",
       "        -1.1417999 , -0.47990152, -0.253717  , -0.1760745 , -0.27401498,\n",
       "         2.04675   , -0.543525  ,  0.20674   , -1.9326    , -0.0391735 ,\n",
       "         0.59274   , -0.329394  ,  0.097863  , -0.14284   ,  0.25679305,\n",
       "         0.02503002, -0.24197   , -0.285075  ,  0.74748296,  0.333245  ,\n",
       "        -0.68285   ,  0.986293  ,  1.04929   , -0.175055  ,  0.060745  ],\n",
       "       dtype=float32),\n",
       " 'neural intelligence platform': array([ 6.35026991e-01,  1.46200001e-01,  8.87513340e-01,  6.03192329e-01,\n",
       "        -6.94999918e-02,  5.88103354e-01,  2.03086659e-01, -8.27683270e-01,\n",
       "         5.20156682e-01, -2.49909326e-01,  2.26729676e-01,  1.29529998e-01,\n",
       "        -2.77122349e-01,  6.75949991e-01, -1.21745002e+00,  2.32436344e-01,\n",
       "        -3.91904354e-01,  2.97737002e-01,  3.20083350e-01, -5.12366481e-02,\n",
       "         1.63219333e-01, -8.26433375e-02,  2.89270014e-01, -2.98537642e-01,\n",
       "         3.11936647e-01, -9.71453249e-01, -9.04410109e-02, -6.10413365e-02,\n",
       "        -1.78689674e-01, -2.11061656e-01,  2.31506658e+00, -4.23803329e-01,\n",
       "        -1.62216648e-01, -1.34504330e+00, -2.17495680e-01,  2.67150015e-01,\n",
       "        -1.14376009e-01,  1.05825335e-01, -5.24593353e-01, -1.99129176e-03,\n",
       "         1.65730014e-01, -5.63113332e-01, -3.80183339e-01,  5.99482000e-01,\n",
       "         1.60696670e-01, -5.26866674e-01,  6.23515308e-01,  4.62046623e-01,\n",
       "         1.24499999e-01, -9.75399986e-02], dtype=float32),\n",
       " 'knowledge': array([ 4.2343e-01,  5.3814e-01, -4.5098e-01,  4.1446e-02,  9.8231e-01,\n",
       "        -9.8740e-03,  7.9690e-02, -2.1400e-01,  5.1429e-01,  6.1141e-01,\n",
       "         5.7516e-01,  8.6868e-01, -1.2536e-01, -7.9845e-01, -2.9503e-01,\n",
       "        -6.2649e-02,  1.7391e-01,  3.5187e-01,  4.5665e-01,  2.3564e-02,\n",
       "         2.7703e-01,  7.1018e-01,  2.2701e-01, -2.4451e-01,  8.1545e-01,\n",
       "        -1.3212e+00, -8.7353e-01, -8.3276e-01,  3.2336e-01,  8.7211e-02,\n",
       "         2.9042e+00, -1.4305e-01, -3.4808e-01, -1.6425e+00,  7.1243e-02,\n",
       "         4.0588e-01, -8.9991e-01,  4.9256e-01, -2.5959e-02,  1.5602e-01,\n",
       "         6.3722e-01, -7.2210e-01,  2.4931e-03,  1.0538e+00, -2.8525e-01,\n",
       "        -2.9402e-01,  3.7065e-01,  1.1959e+00,  1.8655e-01, -3.7574e-01],\n",
       "       dtype=float32),\n",
       " 'wipro neural intelligence': array([ 0.33348668, -0.7297166 ,  0.69457   ,  0.9563157 ,  0.18209334,\n",
       "         0.42255005,  0.21976666, -0.71180004,  1.0009333 , -0.17596667,\n",
       "         0.45108965,  0.35505334, -0.46218237,  0.10746   , -1.0122033 ,\n",
       "         0.3567067 , -0.47242332,  0.27170366,  0.39602003,  0.211215  ,\n",
       "         0.4707493 ,  0.00787665,  0.45382667, -0.358402  ,  0.35224998,\n",
       "        -0.53125995, -0.33844635, -0.561678  , -0.36945298, -0.10196998,\n",
       "         1.6494766 , -0.25433335,  0.61756   , -0.6942    ,  0.040521  ,\n",
       "         0.30589   , -0.346856  ,  0.2851087 ,  0.11812333,  0.31245205,\n",
       "         0.04018434, -0.26823   , -0.5836167 ,  0.7774053 ,  0.43764004,\n",
       "        -0.05003333,  0.612732  ,  0.98021334,  0.08245333, -0.07428   ],\n",
       "       dtype=float32),\n",
       " 'laterally think medium': array([ 1.0482999e-01, -5.5640233e-01,  2.1096902e-01, -7.6106668e-01,\n",
       "         5.5143666e-01,  9.2129670e-02,  2.7292666e-01, -4.6583000e-01,\n",
       "        -9.5070004e-02, -1.2045667e-02,  7.2146662e-02,  3.1646666e-01,\n",
       "         1.0784999e-01,  5.1532668e-01, -5.3786331e-01,  4.0303335e-01,\n",
       "         5.4091066e-01,  3.0203366e-01, -1.7986034e-01, -6.3590997e-01,\n",
       "        -4.2034665e-01, -1.5553667e-01,  7.9851669e-01,  2.2834434e-01,\n",
       "         4.5398334e-01, -7.0644337e-01,  2.7041003e-01,  7.6618671e-01,\n",
       "         4.7405669e-01, -2.2177334e-01,  2.4851334e+00,  1.5797001e-01,\n",
       "         3.1966665e-01, -1.7900331e-01, -9.3450010e-02, -1.1600057e-03,\n",
       "        -3.0296400e-01,  3.6829498e-01, -4.5848001e-02, -5.2915335e-01,\n",
       "         7.3076665e-02, -9.7956680e-02, -3.7736665e-02,  9.2323989e-02,\n",
       "        -6.7900002e-02,  4.0925398e-01,  5.9167331e-01,  5.5016667e-02,\n",
       "        -1.7156839e-01,  5.0514668e-01], dtype=float32),\n",
       " 'intelligence platform': array([ 0.4885255 ,  0.07381999,  0.992085  ,  0.38258848,  0.25850502,\n",
       "        -0.217595  , -0.13920501, -0.767615  ,  0.44310498, -0.49940398,\n",
       "        -0.1385155 ,  0.10368501, -0.447815  ,  0.69231   , -1.011125  ,\n",
       "        -0.1262055 , -0.2195065 ,  0.3598805 ,  0.141935  , -0.12698498,\n",
       "         0.26172   ,  0.26089   ,  0.23131001, -0.3980485 ,  0.06963499,\n",
       "        -1.5123299 , -0.097635  , -0.046345  , -0.275545  ,  0.2519075 ,\n",
       "         2.63245   , -0.46358502, -0.631015  , -1.081665  , -0.2405035 ,\n",
       "         0.240945  , -0.218095  ,  0.15624   , -0.91547   , -0.26402193,\n",
       "         0.511335  , -0.37895   , -0.042625  ,  0.179173  ,  0.08701   ,\n",
       "        -0.36594   , -0.064027  ,  0.63913   ,  0.30491498, -0.05642   ],\n",
       "       dtype=float32),\n",
       " 'deep learning': array([ 2.59530008e-01,  8.44494998e-01, -2.43214518e-01, -2.35574991e-01,\n",
       "         1.06861994e-01,  4.13165003e-01, -1.69662997e-01, -3.82499844e-02,\n",
       "         1.15864500e-01,  1.30144998e-01, -3.29850018e-02,  1.26479998e-01,\n",
       "        -9.31634977e-02, -5.11399806e-02, -6.12585008e-01, -1.68500002e-02,\n",
       "         2.29588002e-01,  2.46885002e-01, -2.34649926e-02, -1.46292001e-01,\n",
       "        -2.28305012e-01,  7.42884994e-01, -1.96000934e-03, -1.40009999e-01,\n",
       "         1.04679501e+00, -9.03934956e-01, -1.52249992e-01,  1.82000399e-02,\n",
       "         4.58664507e-01, -2.47249976e-02,  3.26370001e+00,  2.68660009e-01,\n",
       "         6.72550052e-02, -5.76820493e-01, -1.38535008e-01,  3.22115004e-01,\n",
       "        -6.86635017e-01,  4.61699963e-02,  4.52215016e-01,  5.68400025e-02,\n",
       "         1.12096004e-01, -4.05730009e-01, -1.54138997e-01,  6.97905004e-01,\n",
       "         1.60465002e-01, -9.79800075e-02,  8.08414996e-01,  1.60943493e-01,\n",
       "        -3.62215012e-01, -2.04025030e-01], dtype=float32),\n",
       " 'queries': array([ 0.70772 , -0.40859 ,  0.63112 ,  0.61999 , -0.0186  , -0.64993 ,\n",
       "         0.40827 ,  0.067658,  0.20809 , -0.85033 , -0.66244 ,  0.43871 ,\n",
       "         0.29196 , -0.5788  ,  0.19258 , -0.61278 , -1.2175  , -0.41868 ,\n",
       "         0.72047 , -1.0026  ,  0.46085 ,  0.11439 ,  0.90493 ,  0.78868 ,\n",
       "         1.3595  , -0.29392 ,  0.14462 , -0.22866 , -0.52495 , -0.39659 ,\n",
       "         1.1764  , -0.15961 ,  0.47672 , -0.45595 , -0.30057 , -0.18149 ,\n",
       "        -0.45133 , -0.52018 , -0.28699 ,  0.292   ,  1.4165  , -0.046636,\n",
       "         0.29065 ,  1.0866  ,  0.22367 , -0.10031 ,  1.0986  ,  1.4299  ,\n",
       "         0.28468 ,  0.12875 ], dtype=float32),\n",
       " 'wipro': array([-0.80577 , -2.0284  ,  0.43797 ,  1.7842  ,  0.50944 , -0.2936  ,\n",
       "        -0.41781 , -0.92125 ,  1.7398  ,  0.1591  ,  0.46125 ,  0.37164 ,\n",
       "        -0.7597  , -0.84892 , -0.69456 ,  0.4038  , -0.33466 ,  0.62731 ,\n",
       "         0.39405 , -0.060685,  1.239   ,  0.10247 , -0.12921 , -0.14411 ,\n",
       "         0.61988 ,  0.68982 , -0.055536, -1.1776  , -0.75621 ,  0.24212 ,\n",
       "         0.85493 ,  0.32405 ,  1.4392  ,  1.7826  ,  0.19991 , -0.26781 ,\n",
       "        -0.38178 ,  0.6596  ,  0.64005 ,  0.42377 ,  0.070493, -0.32075 ,\n",
       "        -1.1807  ,  0.83725 ,  0.64643 ,  1.2156  , -0.13439 ,  0.84206 ,\n",
       "         0.59747 , -0.34433 ], dtype=float32),\n",
       " 'user queries': array([ 0.679     , -0.288845  ,  0.87741005,  0.64845   ,  0.267155  ,\n",
       "        -0.447765  ,  0.02189   , -0.436591  ,  0.559655  , -0.11379001,\n",
       "        -0.07907501,  0.56775   ,  0.43578   , -0.37317502,  0.28762   ,\n",
       "        -0.40714997, -1.1105499 , -0.189471  ,  0.862985  , -0.96499   ,\n",
       "         0.22729875, -0.18715501,  0.29081   ,  0.99984   ,  1.109215  ,\n",
       "        -0.46388   ,  0.30149502, -0.390365  , -0.12904002, -0.38145   ,\n",
       "         2.0493    , -0.265535  ,  0.08618501, -0.727335  , -0.142921  ,\n",
       "         0.17879501, -0.123855  , -0.221131  , -0.46743   , -0.18101999,\n",
       "         1.4354    , -0.089873  , -0.13501999,  1.12605   , -0.139155  ,\n",
       "        -0.299905  ,  1.12765   ,  0.818115  ,  0.59067   ,  0.214625  ],\n",
       "       dtype=float32),\n",
       " 'machine learning nlp': array([-0.26971334, -0.16822   ,  0.36670002,  0.14158668, -0.04349367,\n",
       "         0.5904933 , -0.10748   , -0.7475467 , -0.13571034,  0.33275   ,\n",
       "         0.10706107, -0.16257034,  0.321671  , -0.46316   , -0.68113   ,\n",
       "        -0.143517  , -0.2880549 ,  0.64344335, -0.116824  , -0.11182133,\n",
       "         0.02370332,  0.11708331,  0.16769893,  0.17396033,  0.46971667,\n",
       "        -0.2417233 , -0.11299998, -0.85216   ,  0.47308636, -0.03632331,\n",
       "         1.6673833 , -0.14159666, -0.61847   , -0.08367666,  0.39540532,\n",
       "         0.17927332, -0.12167335,  0.5938234 ,  0.52081335,  0.26615664,\n",
       "         0.7126673 , -0.23534267, -0.55872005,  0.67312   ,  0.42366663,\n",
       "         0.08716333,  0.6755867 ,  0.13630332,  0.09071001,  0.6403633 ],\n",
       "       dtype=float32),\n",
       " 'learning deep': array([ 2.59530008e-01,  8.44494998e-01, -2.43214518e-01, -2.35574991e-01,\n",
       "         1.06861994e-01,  4.13165003e-01, -1.69662997e-01, -3.82499844e-02,\n",
       "         1.15864500e-01,  1.30144998e-01, -3.29850018e-02,  1.26479998e-01,\n",
       "        -9.31634977e-02, -5.11399806e-02, -6.12585008e-01, -1.68500002e-02,\n",
       "         2.29588002e-01,  2.46885002e-01, -2.34649926e-02, -1.46292001e-01,\n",
       "        -2.28305012e-01,  7.42884994e-01, -1.96000934e-03, -1.40009999e-01,\n",
       "         1.04679501e+00, -9.03934956e-01, -1.52249992e-01,  1.82000399e-02,\n",
       "         4.58664507e-01, -2.47249976e-02,  3.26370001e+00,  2.68660009e-01,\n",
       "         6.72550052e-02, -5.76820493e-01, -1.38535008e-01,  3.22115004e-01,\n",
       "        -6.86635017e-01,  4.61699963e-02,  4.52215016e-01,  5.68400025e-02,\n",
       "         1.12096004e-01, -4.05730009e-01, -1.54138997e-01,  6.97905004e-01,\n",
       "         1.60465002e-01, -9.79800075e-02,  8.08414996e-01,  1.60943493e-01,\n",
       "        -3.62215012e-01, -2.04025030e-01], dtype=float32),\n",
       " 'neural': array([ 0.92803 ,  0.29096 ,  0.67837 ,  1.0444  , -0.72551 ,  2.1995  ,\n",
       "         0.88767 , -0.94782 ,  0.67426 ,  0.24908 ,  0.95722 ,  0.18122 ,\n",
       "         0.064263,  0.64323 , -1.6301  ,  0.94972 , -0.7367  ,  0.17345 ,\n",
       "         0.67638 ,  0.10026 , -0.033782, -0.76971 ,  0.40519 , -0.099516,\n",
       "         0.79654 ,  0.1103  , -0.076053, -0.090434,  0.015021, -1.137   ,\n",
       "         1.6803  , -0.34424 ,  0.77538 , -1.8718  , -0.17148 ,  0.31956 ,\n",
       "         0.093062,  0.004996,  0.25716 ,  0.52207 , -0.52548 , -0.93144 ,\n",
       "        -1.0553  ,  1.4401  ,  0.30807 , -0.84872 ,  1.9986  ,  0.10788 ,\n",
       "        -0.23633 , -0.17978 ], dtype=float32),\n",
       " 'deep machine': array([-0.01360001,  0.19486499,  0.7589755 , -0.07091   , -0.0652185 ,\n",
       "         0.53224504,  0.025627  , -0.05253001, -0.004485  ,  0.00584   ,\n",
       "        -0.238848  , -0.22147001, -0.22343   ,  0.29673   , -0.7377    ,\n",
       "        -0.0677145 , -0.11240998,  0.50311005, -0.590585  , -0.61438   ,\n",
       "         0.03774998,  0.275395  ,  0.2017034 , -0.527015  ,  0.45882002,\n",
       "        -1.1723    , -0.599895  ,  0.38401002,  1.129535  , -0.1546    ,\n",
       "         2.96165   , -0.26594   , -0.180325  ,  0.1090945 , -0.290882  ,\n",
       "         0.60772   , -0.22841   ,  0.0528    ,  0.364545  ,  0.067075  ,\n",
       "         0.577495  ,  0.061101  , -0.347409  ,  0.49656498,  0.278515  ,\n",
       "        -0.03837   ,  0.417605  , -0.1898465 , -0.23182501, -0.48748   ],\n",
       "       dtype=float32),\n",
       " 'deep learning data': array([ 0.35002336,  0.37676668,  0.42699036, -0.00763666,  0.14571132,\n",
       "         0.16024667, -0.3720387 , -0.34589002,  0.46620968,  0.111523  ,\n",
       "         0.24957667,  0.06451067, -0.040576  , -0.03358745, -0.37533033,\n",
       "         0.11077333, -0.17602134, -0.11511999,  0.03741334, -0.35620466,\n",
       "         0.09271   ,  0.27905333,  0.15260665, -0.09040126,  0.87032336,\n",
       "        -0.8225433 , -0.3482    , -0.03429331,  0.33280766,  0.05237333,\n",
       "         3.3641999 , -0.09510666,  0.23603666, -0.96014696, -0.09027814,\n",
       "         0.23730068, -0.53579336,  0.042501  ,  0.38983333, -0.06199333,\n",
       "         0.322474  , -0.42057666, -0.03807266,  0.62730664, -0.03660667,\n",
       "        -0.14935334,  0.9647433 ,  0.63446236, -0.02201668, -0.20594335],\n",
       "       dtype=float32),\n",
       " 'madhya pradesh': array([-0.65063   , -0.3236715 , -0.035531  ,  0.63128996, -0.992275  ,\n",
       "         0.55922496,  1.2601    ,  0.352425  ,  0.50398004, -0.93966   ,\n",
       "        -0.69235003, -0.8823    ,  1.127255  , -0.49012   , -1.0948601 ,\n",
       "         0.912665  ,  0.316935  ,  1.4247    ,  0.27656   ,  1.4907    ,\n",
       "         1.130755  , -0.53184   ,  1.1491001 ,  0.64841497,  0.49482   ,\n",
       "        -1.2335    , -1.1347901 , -0.13207884, -1.2867    ,  0.048831  ,\n",
       "         0.98798496,  0.14404202,  0.210305  ,  0.45428002,  1.01153   ,\n",
       "         0.381495  , -0.062213  , -0.204952  ,  0.49252498,  0.71178997,\n",
       "        -1.7393999 ,  0.595965  , -0.71284497, -0.19240999, -1.2564    ,\n",
       "         1.01796   , -0.65183496, -0.016113  ,  1.0158    , -1.21855   ],\n",
       "       dtype=float32),\n",
       " 'python sql web': array([ 0.75730664, -0.58078   ,  0.14779   ,  1.1572367 , -0.10977333,\n",
       "        -0.21900666, -0.22680998, -1.3557833 ,  0.43128332,  0.60951334,\n",
       "         0.02674667,  0.41944703,  0.12128401, -0.11882   ,  0.33377   ,\n",
       "        -0.44439664, -0.7241233 ,  0.58083004,  0.16611667, -0.17632668,\n",
       "        -0.22235   ,  0.22942333,  0.040162  ,  0.76382667,  0.095446  ,\n",
       "        -0.14925332,  0.25255   , -0.04010667, -0.54113567, -0.96929   ,\n",
       "         1.8131332 , -1.05408   , -0.17548335, -0.04831668, -0.09517336,\n",
       "         0.41149333, -0.03253   , -0.83764   , -0.56562334, -0.504555  ,\n",
       "         1.1745534 , -0.17825933, -0.33059666,  0.88467   , -0.13124001,\n",
       "        -0.38607332,  1.0533667 ,  0.37658334, -0.03400001,  0.04004667],\n",
       "       dtype=float32),\n",
       " 'information technology': array([ 1.89344987e-01,  9.40174907e-02,  1.12004995e+00,  9.29210007e-01,\n",
       "         6.62599951e-02, -6.09589994e-01, -5.37270010e-01, -1.09939504e+00,\n",
       "         8.00259948e-01,  2.64389992e-01,  3.60195488e-01,  4.30079997e-01,\n",
       "        -1.35790497e-01, -6.07499927e-02, -2.96680033e-01,  4.85009491e-01,\n",
       "        -4.81899977e-01,  1.81089997e-01,  4.50301498e-01, -2.04750001e-01,\n",
       "         1.19735503e+00,  1.84840649e-01, -1.41999125e-03, -1.02184996e-01,\n",
       "         3.98725003e-01, -1.50959992e+00, -4.26990002e-01, -8.62169981e-01,\n",
       "         1.30806506e-01, -9.56720039e-02,  3.32499981e+00, -4.26590025e-01,\n",
       "        -2.20634997e-01, -1.54030001e+00, -2.76864991e-02,  1.75194994e-01,\n",
       "        -5.55745006e-01,  5.76848984e-01,  1.44245997e-01,  1.03120007e-01,\n",
       "         7.08179951e-01, -1.16029508e-01,  4.03365016e-01,  5.11829972e-01,\n",
       "         3.77054989e-01,  1.24170005e-01,  3.23009998e-01,  1.04860997e+00,\n",
       "         3.70927006e-01,  2.52395004e-01], dtype=float32),\n",
       " 'bhopal madhya': array([-0.46418   ,  0.10209849,  0.259689  ,  0.69091   , -1.1527    ,\n",
       "         0.617175  ,  1.40345   ,  1.0059199 ,  0.57908   , -0.49842352,\n",
       "        -0.37988898, -0.75857997,  0.55157   , -0.33011   , -0.77126503,\n",
       "         0.45694003,  0.1991925 ,  1.5762999 ,  0.08061001,  1.161425  ,\n",
       "         1.46875   , -0.6348    ,  0.6301125 ,  0.40016   ,  0.339535  ,\n",
       "        -1.2678499 , -1.2232001 , -0.10761385, -0.718595  , -0.0620545 ,\n",
       "         0.08798499,  0.01411001,  0.08652   ,  0.46061498,  0.612465  ,\n",
       "         0.184777  ,  0.106585  , -0.341622  ,  1.07671   ,  1.1538999 ,\n",
       "        -1.055645  , -0.08156499, -0.3251685 , -0.29645002, -0.779135  ,\n",
       "         0.92964   , -0.59624   ,  0.31012198,  0.69047   , -1.3162501 ],\n",
       "       dtype=float32),\n",
       " 'python sql': array([ 0.79463   , -0.98536503, -0.35396498,  1.29033   , -0.04939499,\n",
       "        -0.02178501,  0.452385  , -1.257225  ,  0.238545  ,  0.71895504,\n",
       "         0.29084   ,  0.655075  ,  0.217148  , -0.34608   ,  0.24842998,\n",
       "        -0.550785  , -0.78622997,  0.939915  , -0.05673   , -0.088     ,\n",
       "        -0.588     ,  0.2733    ,  0.10513701,  0.48669   ,  0.027279  ,\n",
       "         0.271525  ,  0.689825  ,  0.14098   , -0.80014   , -1.1561    ,\n",
       "         1.2186    , -1.359355  , -0.11443502,  0.37731498,  0.40769   ,\n",
       "         0.426055  ,  0.18503   , -1.1427    , -0.54468   , -0.4695775 ,\n",
       "         1.03863   , -0.227105  , -0.33585998,  0.857185  , -0.39590502,\n",
       "        -0.49300498,  1.4209    ,  0.365435  , -0.26682502, -0.02219   ],\n",
       "       dtype=float32),\n",
       " 'deep leaning nlp': array([-0.34406665,  0.27895668,  0.21245366, -0.15305333, -0.15098132,\n",
       "         0.61191565, -0.24212535, -0.25883636, -0.34741667,  0.08666667,\n",
       "        -0.33887693, -0.356617  ,  0.380746  ,  0.17979331, -1.0689467 ,\n",
       "        -0.21620633,  0.1676384 , -0.33359   ,  0.13100934, -0.04480334,\n",
       "        -0.76708   ,  0.01487668,  0.47004333, -0.11765299,  0.18958499,\n",
       "        -0.48023334,  0.21298002,  0.3612667 ,  0.13505   ,  0.19202667,\n",
       "         1.3591166 , -0.019539  , -0.18047667,  0.24160634, -0.34493336,\n",
       "        -0.42081666, -0.8213167 ,  0.31649664,  0.63266665,  0.00347334,\n",
       "         0.41397765,  0.14556666, -0.274376  ,  0.39535332,  0.13467999,\n",
       "         0.12509334,  0.05969666,  0.11005899,  0.15783998, -0.11599001],\n",
       "       dtype=float32),\n",
       " 'technology': array([-2.5722e-01, -9.3385e-02,  1.1371e+00,  9.5147e-01, -4.5156e-01,\n",
       "        -5.5302e-01, -4.8637e-01, -1.6476e+00,  5.9422e-01,  7.5211e-01,\n",
       "         7.4173e-01,  2.6373e-01, -2.9181e-01,  2.1239e-01, -8.6431e-01,\n",
       "         8.7086e-01, -3.4193e-01,  9.9052e-01,  2.6313e-02, -2.5234e-01,\n",
       "         1.4177e+00,  2.5313e-03, -6.5843e-01, -3.5972e-01,  5.6982e-01,\n",
       "        -1.6079e+00, -1.9695e-01, -9.9719e-01,  2.2330e-03,  4.6416e-02,\n",
       "         3.2575e+00, -2.6845e-01, -9.4590e-02, -1.3317e+00, -3.9934e-02,\n",
       "        -1.5860e-01, -8.5490e-01,  1.0837e+00,  2.0209e-01, -1.8876e-01,\n",
       "         3.4616e-01, -3.2074e-01,  2.6552e-01,  4.8898e-01,  6.5638e-01,\n",
       "         5.0432e-01,  8.0157e-01,  5.8182e-01, -6.8956e-02,  3.9337e-01],\n",
       "       dtype=float32),\n",
       " 'nlp python': array([-0.04120001, -0.364505  , -0.40436   ,  0.524035  ,  0.13022001,\n",
       "         0.72647   , -0.245435  , -1.4788    , -0.08132   ,  0.63574505,\n",
       "         0.22074959,  0.4894445 ,  0.91589   , -0.26678002, -0.28971002,\n",
       "        -0.195695  ,  0.1590296 ,  0.46534   , -0.476806  ,  0.487075  ,\n",
       "        -0.74677   ,  0.00448   ,  0.52588   ,  0.1101305 , -0.135846  ,\n",
       "         0.737225  ,  0.67445004, -0.53795004, -0.18159   , -0.21752   ,\n",
       "         0.25662497, -0.47699   , -0.686705  ,  1.0478699 ,  0.602745  ,\n",
       "         0.18851501, -0.270365  , -0.18079501,  0.23823503,  0.1148725 ,\n",
       "         0.84657997,  0.324625  , -0.787     ,  0.55975497,  0.19500998,\n",
       "         0.02017   ,  0.83103997,  0.36246997, -0.31967503,  0.46102497],\n",
       "       dtype=float32),\n",
       " 'pradesh truba institute': array([-0.28329   , -0.01318665, -0.19167668,  0.18615   , -0.31472   ,\n",
       "         0.08098334,  0.04333333, -0.38846335,  0.38080335, -0.45239   ,\n",
       "         0.10724666, -0.50369996,  0.48346666, -0.32739666, -0.50794   ,\n",
       "         0.37826666,  0.09549233,  0.7388733 , -0.14530666,  0.71149665,\n",
       "         0.6436834 ,  0.10593334,  0.18720333, -0.03506001,  0.3193533 ,\n",
       "        -0.9404667 , -0.59639335, -0.39054668, -1.2574    , -0.01393867,\n",
       "         1.2872334 , -0.15315533, -0.04985667, -0.54091   ,  0.0692    ,\n",
       "        -0.04297332, -0.07126533,  0.17667   ,  0.8095767 ,  0.36060667,\n",
       "        -0.6041639 ,  0.16136   , -0.14383332, -0.19625334, -0.24061   ,\n",
       "         0.5558767 , -0.12661667,  0.23599666,  0.29207   , -0.32066667],\n",
       "       dtype=float32),\n",
       " 'pradesh rkdf institute': array([-0.28329   , -0.01318665, -0.19167668,  0.18615   , -0.31472   ,\n",
       "         0.08098334,  0.04333333, -0.38846335,  0.38080335, -0.45239   ,\n",
       "         0.10724666, -0.50369996,  0.48346666, -0.32739666, -0.50794   ,\n",
       "         0.37826666,  0.09549233,  0.7388733 , -0.14530666,  0.71149665,\n",
       "         0.6436834 ,  0.10593334,  0.18720333, -0.03506001,  0.3193533 ,\n",
       "        -0.9404667 , -0.59639335, -0.39054668, -1.2574    , -0.01393867,\n",
       "         1.2872334 , -0.15315533, -0.04985667, -0.54091   ,  0.0692    ,\n",
       "        -0.04297332, -0.07126533,  0.17667   ,  0.8095767 ,  0.36060667,\n",
       "        -0.6041639 ,  0.16136   , -0.14383332, -0.19625334, -0.24061   ,\n",
       "         0.5558767 , -0.12661667,  0.23599666,  0.29207   , -0.32066667],\n",
       "       dtype=float32),\n",
       " 'products shared customers': array([ 0.45807996,  0.03729001,  0.39250132,  0.45812333,  0.31314   ,\n",
       "         0.4204067 , -0.86476   , -0.5915033 ,  0.506789  ,  0.41978237,\n",
       "         0.36235666,  0.29627332,  0.18067332, -0.39562902,  0.24906333,\n",
       "         0.37244332, -0.40872666,  0.15641999,  0.5501    , -1.1784867 ,\n",
       "         0.8524637 , -0.03831334, -0.05232701,  0.28430066, -0.20442002,\n",
       "        -0.8352067 , -0.266934  , -0.2732527 ,  0.4395567 , -0.27562198,\n",
       "         2.9401    ,  0.6115733 ,  0.43658665, -0.14304332,  0.166324  ,\n",
       "        -0.20712632, -0.4968067 ,  0.5285347 , -0.07475967, -0.21690534,\n",
       "         0.44658336, -0.14084   ,  0.43443   ,  0.47735068,  0.34753326,\n",
       "         0.37853667, -0.58877   ,  0.26892135, -0.07931667,  0.09126467],\n",
       "       dtype=float32),\n",
       " 'bhopal madhya pradesh': array([-0.47904333, -0.16119766,  0.13695599,  0.66702   , -1.0317832 ,\n",
       "         0.5969933 ,  1.2893667 ,  0.7427499 ,  0.51857334, -0.64995235,\n",
       "        -0.48411265, -0.87538666,  0.79584664, -0.4610667 , -0.8227833 ,\n",
       "         0.57417005,  0.20273499,  1.5471665 ,  0.09252667,  1.2157832 ,\n",
       "         1.3118366 , -0.49813   ,  0.79527503,  0.49729332,  0.50992996,\n",
       "        -1.2638999 , -1.1142601 , -0.15873922, -0.98009664, -0.032176  ,\n",
       "         0.5935233 ,  0.02074467,  0.10357666,  0.39343333,  0.7344634 ,\n",
       "         0.263608  ,  0.091338  , -0.34185132,  0.86135   ,  0.9775266 ,\n",
       "        -1.3112301 ,  0.21094   , -0.49475232, -0.27024   , -0.87712336,\n",
       "         1.0045266 , -0.5936267 ,  0.21022798,  0.80068   , -1.3011334 ],\n",
       "       dtype=float32),\n",
       " 'madhya': array([-0.79249  ,  0.040447 ,  0.037448 ,  0.64334  , -1.1946   ,\n",
       "         0.56182  ,  1.459    ,  0.48844  ,  0.6104   , -0.92631  ,\n",
       "        -0.69214  , -0.6556   ,  0.97011  , -0.25726  , -1.2639   ,\n",
       "         1.0167   ,  0.42405  ,  1.3605   ,  0.43676  ,  1.6569   ,\n",
       "         1.2635   , -0.83889  ,  1.1726   ,  0.60527  ,  0.13892  ,\n",
       "        -1.211    , -1.3732   , -0.0031677, -1.0703   ,  0.070081 ,\n",
       "         0.37137  ,  0.25407  ,  0.28292  ,  0.64949  ,  1.0446   ,\n",
       "         0.34172  , -0.18527  , -0.067594 ,  0.55442  ,  0.7988   ,\n",
       "        -1.6564   ,  0.39598  , -0.59177  , -0.167    , -1.4397   ,\n",
       "         0.88162  , -0.71527  , -0.042666 ,  1.0105   , -1.1662   ],\n",
       "       dtype=float32),\n",
       " 'pradesh': array([-0.50877 , -0.68779 , -0.10851 ,  0.61924 , -0.78995 ,  0.55663 ,\n",
       "         1.0612  ,  0.21641 ,  0.39756 , -0.95301 , -0.69256 , -1.109   ,\n",
       "         1.2844  , -0.72298 , -0.92582 ,  0.80863 ,  0.20982 ,  1.4889  ,\n",
       "         0.11636 ,  1.3245  ,  0.99801 , -0.22479 ,  1.1256  ,  0.69156 ,\n",
       "         0.85072 , -1.256   , -0.89638 , -0.26099 , -1.5031  ,  0.027581,\n",
       "         1.6046  ,  0.034014,  0.13769 ,  0.25907 ,  0.97846 ,  0.42127 ,\n",
       "         0.060844, -0.34231 ,  0.43063 ,  0.62478 , -1.8224  ,  0.79595 ,\n",
       "        -0.83392 , -0.21782 , -1.0731  ,  1.1543  , -0.5884  ,  0.01044 ,\n",
       "         1.0211  , -1.2709  ], dtype=float32),\n",
       " 'sql web': array([ 0.84111   , -0.595955  ,  0.726985  ,  1.5297251 , -0.45139998,\n",
       "        -0.44583   , -0.16134995, -1.143675  ,  0.59319997,  0.53970504,\n",
       "        -0.18494499,  0.11162051, -0.059814  , -0.41799998,  0.241115  ,\n",
       "        -0.59133   , -1.248555  ,  0.331795  ,  0.703645  , -0.479205  ,\n",
       "        -0.05158499, -0.00567   , -0.007262  ,  1.062955  ,  0.174965  ,\n",
       "        -0.40105498,  0.16473001, -0.13696   , -0.5766135 , -0.907185  ,\n",
       "         2.0391998 , -1.1770151 ,  0.07377499, -0.702775  , -0.29053003,\n",
       "         0.07549   , -0.170995  , -0.66261   , -0.54741997, -0.72267497,\n",
       "         1.4305501 , -0.494069  ,  0.013005  ,  0.98567   , -0.09292001,\n",
       "        -0.212145  ,  0.9502    ,  0.48775   ,  0.41527998,  0.135195  ],\n",
       "       dtype=float32),\n",
       " 'quickly grasp technical': array([ 0.03416901, -0.47492   ,  0.07288999, -0.12996334,  0.19923769,\n",
       "        -0.32296   , -0.12239099,  0.34102765, -0.08040667, -0.13755666,\n",
       "         0.31828666,  0.3494767 , -0.62272996, -0.04882963, -0.28127667,\n",
       "         0.31155333,  0.07117733, -0.11547333,  0.05064233, -0.61478   ,\n",
       "         0.18257666,  0.32292297, -0.05553065, -0.38689336,  0.78827   ,\n",
       "        -1.12277   , -0.24313666, -0.112775  ,  0.21178336, -0.11162233,\n",
       "         2.5404    ,  0.16922867,  0.02327867, -0.90878993,  0.12684233,\n",
       "         0.23222214, -0.35882   ,  0.6763367 , -0.00564813, -0.41748667,\n",
       "        -0.06535599, -0.40649998,  0.15185   ,  0.26036665, -0.24681266,\n",
       "         0.14267407,  0.6824246 ,  0.66802   ,  0.042421  , -0.17556667],\n",
       "       dtype=float32),\n",
       " 'nlp python sql': array([ 0.30572   , -0.7164367 , -0.16868334,  1.0721567 , -0.13727666,\n",
       "         0.39157665,  0.25721002, -1.2306833 ,  0.069     ,  0.65342337,\n",
       "         0.19101639,  0.4179797 ,  0.59419864, -0.56842   , -0.20054668,\n",
       "        -0.44747666, -0.52638024,  0.57720333, -0.05271067,  0.12290666,\n",
       "        -0.70188665, -0.04801666,  0.37567464,  0.34269035, -0.05118067,\n",
       "         0.55438334,  0.76678675, -0.31584668, -0.49776003, -0.5512467 ,\n",
       "         0.5298166 , -0.95482665, -0.30942667,  0.52992326,  0.57511   ,\n",
       "         0.04854667, -0.13835667, -0.48643002, -0.00361998, -0.21369834,\n",
       "         1.0359534 , -0.08610668, -0.40930668,  0.7170699 , -0.06463668,\n",
       "        -0.07058   ,  1.0813934 ,  0.4338533 , -0.08014669,  0.34263995],\n",
       "       dtype=float32),\n",
       " 'life insurance': array([ 0.336745  ,  0.4349325 , -0.156185  , -0.2500525 ,  0.23838998,\n",
       "         0.81247   , -0.63232   , -0.181505  ,  0.44562   , -0.03827497,\n",
       "        -0.032458  ,  0.5653    ,  0.01158001, -0.117746  ,  0.52863   ,\n",
       "        -0.01994555, -0.1362635 , -0.072     ,  0.3110095 , -0.079645  ,\n",
       "         0.5161375 ,  0.29281   , -0.73555505, -0.24158749, -0.09326503,\n",
       "        -1.5991    , -0.73758996, -0.65079   ,  0.379989  ,  0.1409345 ,\n",
       "         2.9488    ,  0.27259448,  0.3056765 , -0.188765  , -0.200385  ,\n",
       "        -0.14935   , -0.250835  , -0.194702  ,  0.912725  , -0.62555003,\n",
       "        -0.53023   , -0.292523  ,  0.68698   ,  0.77753496, -0.26025498,\n",
       "        -0.3186265 , -0.484045  , -0.08243999,  0.309726  ,  0.62069005],\n",
       "       dtype=float32),\n",
       " 'personal ability quickly': array([ 0.26142067, -0.10891332,  0.46938   , -0.19110668,  0.443411  ,\n",
       "         0.12987666, -0.20248334,  0.24064668,  0.19797568,  0.31495333,\n",
       "         0.26482   ,  0.5966333 , -0.42420664, -0.23706631,  0.04337334,\n",
       "         0.24558   , -0.23474334, -0.2923467 ,  0.3129633 , -0.86499   ,\n",
       "         0.12429333,  0.25917634,  0.04613734, -0.47237667,  0.5884333 ,\n",
       "        -1.5466    , -0.33629334, -0.4093733 ,  0.65055674,  0.04984667,\n",
       "         3.1268666 ,  0.6133533 , -0.049948  , -0.9802566 , -0.16014333,\n",
       "         0.31462333, -0.4577267 ,  0.34651002, -0.34437147, -0.53527004,\n",
       "         0.11494666, -0.141136  , -0.19164968,  0.47616002, -0.04239834,\n",
       "        -0.16653998,  0.06612133,  0.04511333, -0.18977134, -0.030407  ],\n",
       "       dtype=float32),\n",
       " 'web': array([ 0.68266 ,  0.22839 ,  1.1513  ,  0.89105 , -0.23053 , -0.61345 ,\n",
       "        -1.5852  , -1.5529  ,  0.81676 ,  0.39063 , -0.50144 , -0.051809,\n",
       "        -0.070444,  0.3357  ,  0.50445 , -0.23162 , -0.59991 , -0.13734 ,\n",
       "         0.61181 , -0.35298 ,  0.50895 ,  0.14167 , -0.089788,  1.3181  ,\n",
       "         0.23178 , -0.99081 , -0.622   , -0.40228 , -0.023127, -0.59567 ,\n",
       "         3.0022  , -0.44353 , -0.29758 , -0.89958 , -1.1009  ,  0.38237 ,\n",
       "        -0.46765 , -0.22752 , -0.60751 , -0.57451 ,  1.4464  , -0.080568,\n",
       "        -0.32007 ,  0.93964 ,  0.39809 , -0.17221 ,  0.3183  ,  0.39888 ,\n",
       "         0.43165 ,  0.16452 ], dtype=float32),\n",
       " 'school': array([-0.90629  ,  1.2485   , -0.79692  , -1.4027   , -0.038458 ,\n",
       "        -0.25177  , -1.2838   , -0.58413  , -0.11179  , -0.56908  ,\n",
       "        -0.34842  , -0.39626  , -0.0090178, -1.0691   , -0.35368  ,\n",
       "        -0.052826 , -0.37056  ,  1.0931   , -0.19205  ,  0.44648  ,\n",
       "         0.45169  ,  0.72104  , -0.61103  ,  0.6315   , -0.49044  ,\n",
       "        -1.7517   ,  0.055979 , -0.52281  , -1.0248   , -0.89142  ,\n",
       "         3.0695   ,  0.14483  , -0.13938  , -1.3907   ,  1.2123   ,\n",
       "         0.40173  ,  0.4171   ,  0.27364  ,  0.98673  ,  0.027599 ,\n",
       "        -0.8724   , -0.51648  , -0.30662  ,  0.37784  ,  0.016734 ,\n",
       "         0.23813  ,  0.49411  , -0.56643  , -0.18744  ,  0.62809  ],\n",
       "       dtype=float32),\n",
       " 'school business mumbai': array([-0.10622233,  0.23693334, -0.312493  , -0.14056665,  0.04194866,\n",
       "        -0.40312663, -0.66519   , -0.5073033 ,  0.07534999, -0.08990299,\n",
       "        -0.25420368, -0.16971667, -0.49058262, -0.381254  , -0.22500099,\n",
       "         0.24314801, -0.33055   ,  0.84242004, -0.27421   ,  0.53695333,\n",
       "         1.38823   ,  0.62378335, -0.4576467 ,  0.76646996, -0.27749935,\n",
       "        -1.5135332 , -0.187187  , -0.46321   , -0.49372664, -0.33474   ,\n",
       "         2.6425667 ,  0.32239333,  0.4228767 , -0.66781664,  0.59058666,\n",
       "        -0.03080033, -0.2750633 , -0.00518002,  0.38638   ,  0.17824966,\n",
       "        -0.6817667 , -0.29620433,  0.06594666,  0.25620332,  0.1335    ,\n",
       "         0.22126698,  0.02544534, -0.02525999,  0.52678996,  0.14628665],\n",
       "       dtype=float32),\n",
       " 'service provider': array([ 0.310195  ,  0.336783  ,  0.82398003,  0.568552  , -0.14105399,\n",
       "        -0.109185  , -1.0869    , -0.040607  ,  1.34165   , -0.5532    ,\n",
       "         0.58829   ,  0.26529   ,  0.234015  , -0.01109   , -0.54309   ,\n",
       "        -0.093355  , -1.24951   ,  0.301355  ,  0.357511  ,  0.13193999,\n",
       "         0.822255  ,  0.0071935 , -0.817795  ,  0.83029   , -0.26202   ,\n",
       "        -1.3085849 ,  0.1903225 , -0.826805  ,  0.13557349,  0.374145  ,\n",
       "         2.99145   ,  0.351365  ,  0.2073175 , -0.06452499, -0.294395  ,\n",
       "        -0.15908499,  0.08869499, -0.11969   ,  0.220125  , -0.12564999,\n",
       "         1.208045  , -0.317975  ,  0.050015  ,  0.54318   , -0.576495  ,\n",
       "        -0.043801  , -0.31474   ,  0.509465  ,  0.1719265 ,  0.85886   ],\n",
       "       dtype=float32),\n",
       " 'science mumbai': array([-0.01970503,  0.19342002, -0.234105  ,  0.269699  ,  0.06902634,\n",
       "        -0.34191498,  0.0156    , -0.89968497,  0.185735  ,  0.2139835 ,\n",
       "        -0.06099   , -0.05562   , -0.506585  ,  0.16564   , -0.307105  ,\n",
       "         0.207765  ,  0.082685  ,  0.95868504, -0.83459   ,  1.232465  ,\n",
       "         1.4964    ,  0.847535  ,  0.09465   ,  0.537625  ,  0.447941  ,\n",
       "        -1.3132    , -0.626565  , -0.68705   , -0.838075  , -0.27621502,\n",
       "         1.95175   ,  0.011225  ,  0.24119   , -1.15179   ,  0.30635002,\n",
       "         0.1442595 , -0.267415  ,  0.06301501,  0.29905   ,  0.43599498,\n",
       "        -0.5508805 , -0.2777765 ,  0.23010999,  0.128445  ,  0.089035  ,\n",
       "         0.585045  ,  0.49919802,  0.490155  ,  0.7630142 , -0.281995  ],\n",
       "       dtype=float32),\n",
       " 'company aegis': array([ 0.712885  , -0.65342   ,  0.40107   ,  0.40626502, -0.14836851,\n",
       "        -0.14363001, -0.62717503, -0.684465  ,  0.370385  , -0.18737   ,\n",
       "         0.68752   ,  0.870185  , -0.3886015 , -0.00391498, -0.843907  ,\n",
       "         0.06579   , -0.57119   ,  0.91892505, -0.5006    , -0.29648998,\n",
       "         1.065825  , -0.03486   , -0.63946   , -0.747925  , -0.721295  ,\n",
       "        -1.24053   , -0.28381002, -0.397975  , -0.319719  , -0.29219002,\n",
       "         1.805205  , -0.101175  , -0.06255502,  0.149172  ,  0.2154505 ,\n",
       "        -0.4978915 , -0.21467498,  0.1535265 , -0.24548998, -0.51550496,\n",
       "         0.62239003, -0.792385  ,  0.94651496, -0.29018998, -0.05937999,\n",
       "         0.0539525 , -0.537647  ,  0.598625  , -0.159225  ,  0.45639   ],\n",
       "       dtype=float32),\n",
       " 'aegis school': array([-5.31750023e-02,  2.59344995e-01, -2.03205004e-01, -7.29144990e-01,\n",
       "        -1.26048997e-01, -4.02289987e-01, -5.29075027e-01, -7.82570004e-01,\n",
       "         1.00854993e-01, -5.30310035e-01,  1.21359989e-01,  2.65295029e-01,\n",
       "         7.04600010e-04, -2.11264998e-01, -9.71140027e-01, -6.39130026e-02,\n",
       "        -3.89894992e-01,  1.03338504e+00, -4.62039977e-01,  3.95709991e-01,\n",
       "         3.32170010e-01,  4.13025022e-01, -6.35450006e-01, -1.80424988e-01,\n",
       "        -4.99465019e-01, -1.27863002e+00,  1.41174495e-01, -4.99364972e-01,\n",
       "        -7.98280001e-01, -6.76180005e-01,  1.69450498e+00,  1.86995000e-01,\n",
       "        -3.26195002e-01, -7.39843011e-01,  8.30809951e-01,  1.93928495e-01,\n",
       "         2.59820014e-01,  9.10165012e-02, -1.99384987e-01, -2.96890497e-01,\n",
       "        -1.08419985e-01, -7.88740039e-01,  6.53739989e-01,  2.75600106e-02,\n",
       "         1.01272002e-01,  2.06169993e-01,  2.70457983e-01,  2.07500011e-01,\n",
       "        -1.75015002e-01,  4.50345010e-01], dtype=float32),\n",
       " 'aegis': array([ 0.79994 , -0.72981 ,  0.39051 , -0.05559 , -0.21364 , -0.55281 ,\n",
       "         0.22565 , -0.98101 ,  0.3135  , -0.49154 ,  0.59114 ,  0.92685 ,\n",
       "         0.010427,  0.64657 , -1.5886  , -0.075   , -0.40923 ,  0.97367 ,\n",
       "        -0.73203 ,  0.34494 ,  0.21265 ,  0.10501 , -0.65987 , -0.99235 ,\n",
       "        -0.50849 , -0.80556 ,  0.22637 , -0.47592 , -0.57176 , -0.46094 ,\n",
       "         0.31951 ,  0.22916 , -0.51301 , -0.088986,  0.44932 , -0.013873,\n",
       "         0.10254 , -0.091607, -1.3855  , -0.62138 ,  0.65556 , -1.061   ,\n",
       "         1.6141  , -0.32272 ,  0.18581 ,  0.17421 ,  0.046806,  0.98143 ,\n",
       "        -0.16259 ,  0.2726  ], dtype=float32),\n",
       " 'mumbai': array([ 0.56393 , -0.67086 , -0.16369 ,  0.48267 ,  0.13743 , -0.52509 ,\n",
       "         0.42463 , -0.11777 ,  0.11396 ,  0.33149 , -0.34454 , -0.51146 ,\n",
       "        -0.87998 , -0.16967 , -0.29768 ,  0.5499  , -0.19668 ,  0.77707 ,\n",
       "        -1.2086  ,  1.6804  ,  1.8877  ,  1.0208  , -0.14418 ,  1.2751  ,\n",
       "         0.015482, -1.1111  , -0.16553 , -0.39607 , -0.65125 , -0.47108 ,\n",
       "         1.2548  ,  0.4937  ,  0.93513 , -0.27488 ,  1.0218  ,  0.022149,\n",
       "         0.13321 , -0.99707 , -0.29239 ,  0.66901 , -1.0768  , -0.086903,\n",
       "         0.20399 , -0.11825 ,  0.30241 ,  0.44131 ,  0.092436,  0.1448  ,\n",
       "         1.5258  , -1.0116  ], dtype=float32),\n",
       " 'face': array([-0.051524 ,  0.25137  , -0.14722  , -0.061506 ,  0.32593  ,\n",
       "         0.38156  ,  0.55556  ,  0.62217  , -0.16847  , -0.21296  ,\n",
       "        -0.28909  , -0.15927  , -0.65887  ,  0.083074 ,  0.58494  ,\n",
       "         0.0089347,  0.11029  , -0.81686  , -0.15163  , -0.78485  ,\n",
       "        -0.78144  ,  0.21021  , -0.034455 , -0.2988   , -0.46719  ,\n",
       "        -2.1546   ,  0.15288  ,  0.50366  ,  0.3593   , -0.12171  ,\n",
       "         2.9504   ,  0.76009  ,  0.025124 , -0.36938  , -0.40831  ,\n",
       "         0.40751  , -0.16736  , -0.15224  , -0.09004  , -1.3537   ,\n",
       "        -0.24557  ,  0.53153  ,  0.56965  ,  0.18923  ,  0.61818  ,\n",
       "         0.066045 , -0.029378 , -0.40707  ,  0.0065966, -0.61169  ],\n",
       "       dtype=float32),\n",
       " 'aegis school business': array([-0.02755233,  0.21728332, -0.12775967, -0.31998667, -0.07507467,\n",
       "        -0.41236666, -0.73151666, -0.79504997,  0.14186333, -0.36424637,\n",
       "         0.05768966,  0.30972   , -0.19378029, -0.10917399, -0.6553077 ,\n",
       "         0.034848  , -0.4014    ,  0.9079533 , -0.11535332,  0.0918    ,\n",
       "         0.82988   ,  0.31852   , -0.62954336,  0.01065334, -0.4521567 ,\n",
       "        -1.4116868 , -0.05655367, -0.48982665, -0.46723   , -0.33136   ,\n",
       "         2.3308032 ,  0.23421334, -0.05983667, -0.605852  ,  0.39975998,\n",
       "        -0.04280767, -0.28528664,  0.296641  ,  0.02201001, -0.25188032,\n",
       "        -0.10431332, -0.6209034 ,  0.5359833 ,  0.18804665,  0.09463333,\n",
       "         0.13223366,  0.01023533,  0.25361666, -0.03600667,  0.57435334],\n",
       "       dtype=float32),\n",
       " 'goal project': array([ 0.461715  ,  0.33268404,  0.2574    , -0.11140001,  0.15451503,\n",
       "        -0.0522945 , -0.921785  , -0.4053975 ,  0.53724504,  0.714345  ,\n",
       "         0.02336   ,  0.062395  , -0.837345  ,  0.12466   , -0.1422252 ,\n",
       "        -0.4674265 ,  1.3519499 , -0.2338    , -0.655055  ,  0.41071498,\n",
       "         0.09349501, -0.203225  , -0.70893   , -0.600325  ,  0.36140501,\n",
       "        -1.0919499 ,  0.48471498, -0.30817   , -0.16040999, -0.585595  ,\n",
       "         3.0662    ,  0.40131   , -0.65004003, -0.52547   , -0.43774998,\n",
       "         0.266375  ,  0.276675  ,  0.6869314 ,  0.30089498, -0.24696   ,\n",
       "         0.05403002, -0.45805   , -0.38481   , -0.66308   , -0.82831   ,\n",
       "         0.19798   ,  0.2765715 ,  0.44758502,  0.12710501, -0.023755  ],\n",
       "       dtype=float32),\n",
       " 'internet service provider': array([ 3.9720002e-01,  1.4644535e-01,  1.0515534e+00,  6.5415806e-01,\n",
       "        -2.4658601e-01, -2.7811667e-01, -1.1396667e+00, -4.7930464e-01,\n",
       "         1.2030734e+00,  1.6666453e-04,  3.2668000e-01,  1.9971000e-01,\n",
       "         9.7623326e-02,  8.7103337e-02, -2.1631669e-01, -7.4409999e-02,\n",
       "        -1.1470600e+00,  1.5303667e-01,  4.0650734e-01,  3.8343329e-02,\n",
       "         8.6313993e-01,  4.1875672e-02, -6.3241667e-01,  1.0201267e+00,\n",
       "        -5.0970003e-02, -1.3983568e+00,  5.9358332e-02, -8.2049328e-01,\n",
       "         2.1460564e-01,  1.9145000e-01,  3.0640001e+00,  2.5806001e-01,\n",
       "         1.9124167e-01, -3.3281335e-01, -5.9386331e-01, -2.1411999e-01,\n",
       "        -2.1339001e-01, -1.8386668e-01, -6.2726654e-02, -1.6901666e-01,\n",
       "         1.1505966e+00, -1.9850767e-01,  1.0371000e-02,  8.5798663e-01,\n",
       "        -3.5226798e-01, -9.0470664e-02, -2.0045167e-01,  3.1571767e-01,\n",
       "         2.5805101e-01,  6.3570666e-01], dtype=float32),\n",
       " 'school data business': array([-0.11719567,  0.2743233 ,  0.33120367, -0.1520433 ,  0.07060867,\n",
       "        -0.34329334, -1.0656633 , -0.78843665,  0.42633   , -0.17563999,\n",
       "         0.13220967, -0.01903933, -0.17572294, -0.32419145, -0.09271467,\n",
       "         0.18185467, -0.59407   ,  0.30368665,  0.18171333, -0.28185666,\n",
       "         1.00391   ,  0.06731334, -0.25567332,  0.34437537, -0.11020001,\n",
       "        -1.3630867 , -0.37871036, -0.37761334, -0.24961197, -0.10885666,\n",
       "         3.4127    , -0.11638667,  0.30236667, -1.15179   ,  0.25206518,\n",
       "        -0.01562599, -0.3975033 ,  0.33889768,  0.5722    , -0.14464033,\n",
       "        -0.07509   , -0.4173267 ,  0.06263667,  0.45765665, -0.11088667,\n",
       "        -0.00986966,  0.42043337,  0.45364   ,  0.23764999,  0.41356   ],\n",
       "       dtype=float32),\n",
       " 'company aegis school': array([ 1.73160017e-01, -1.94466505e-02,  1.73999870e-03, -1.96723297e-01,\n",
       "        -1.11731671e-01, -1.79676667e-01, -8.46050024e-01, -6.51019990e-01,\n",
       "         2.09659994e-01, -3.14606667e-01,  3.42206687e-01,  4.48036671e-01,\n",
       "        -2.62073606e-01, -3.58976692e-01, -6.80497944e-01,  2.62513310e-02,\n",
       "        -5.04313290e-01,  9.76983368e-01, -3.97749990e-01, -4.88333218e-02,\n",
       "         8.61113310e-01,  2.17106655e-01, -6.29983366e-01, -2.88116664e-01,\n",
       "        -6.44343317e-01, -1.41092002e+00, -1.70547009e-01, -4.39586639e-01,\n",
       "        -5.54745972e-01, -4.91933346e-01,  2.22663665e+00, -1.91733334e-02,\n",
       "        -8.81633386e-02, -3.64118665e-01,  5.47733605e-01, -1.98017657e-01,\n",
       "        -4.08331538e-03,  1.93564340e-01,  1.65250003e-01, -3.34470302e-01,\n",
       "         1.24126695e-01, -7.00416625e-01,  5.28803289e-01, -6.75133169e-02,\n",
       "        -3.40086631e-02,  1.15345001e-01, -1.93728015e-01,  2.10273340e-01,\n",
       "        -1.68630004e-01,  5.13623297e-01], dtype=float32),\n",
       " 'tora': array([ 1.7389  , -0.12718 ,  1.1178  ,  0.87671 , -0.21076 , -1.4335  ,\n",
       "         0.066069,  0.058776, -0.56002 , -0.51815 , -0.58791 , -0.70967 ,\n",
       "        -1.0419  , -0.50504 ,  0.33254 ,  0.50478 ,  0.93596 ,  0.48462 ,\n",
       "        -0.58291 ,  1.7281  , -0.80712 ,  0.099472,  1.2643  ,  0.13972 ,\n",
       "        -0.096143, -0.19274 , -0.59223 ,  0.62822 ,  0.74673 , -0.7363  ,\n",
       "        -0.31218 , -1.1536  , -0.70219 , -0.13492 ,  0.48761 ,  0.49233 ,\n",
       "        -1.4303  , -0.14609 , -0.63162 ,  0.5056  , -0.35306 ,  0.24331 ,\n",
       "         0.522   , -1.4423  ,  0.51727 , -1.2151  ,  1.266   , -0.34287 ,\n",
       "        -0.66354 , -0.4009  ], dtype=float32),\n",
       " 'higher secondary school': array([-0.5988667 ,  0.88562995, -0.020781  , -0.89672333,  0.09251732,\n",
       "         0.00962   , -0.39218   , -0.5211933 , -0.04048233, -0.10329332,\n",
       "         0.20594335, -0.24413998,  0.37107074, -0.73379666, -0.5197433 ,\n",
       "         0.39346465, -0.28412333,  0.40555   , -0.36707667, -0.03437667,\n",
       "         0.45555997, -0.15012665, -0.13800865,  0.2530667 , -0.36193332,\n",
       "        -1.1071967 ,  0.24476634, -0.36502   , -0.5267933 , -0.02202368,\n",
       "         3.2697    ,  0.37524998,  0.54767996, -1.0003101 ,  1.1401399 ,\n",
       "        -0.01714333,  0.20192333,  0.10224801,  0.63168   , -0.06015033,\n",
       "        -0.75651336, -0.38211668,  0.02166999,  0.4819467 , -0.02861   ,\n",
       "        -0.02393   ,  0.54432666, -0.00950666,  0.31513998,  0.29263535],\n",
       "       dtype=float32),\n",
       " 'sslc finance kamaraj': array([-0.4584    , -0.6919467 ,  0.64946336, -0.52967   , -0.21744466,\n",
       "         0.22021002,  0.38354668, -0.26261   , -0.27656   , -0.52881336,\n",
       "        -0.44382635,  0.31552002,  0.01836333,  0.13989   , -0.40965667,\n",
       "         0.40959665, -0.06257001,  0.20664366,  0.486075  ,  0.55384797,\n",
       "         1.0265633 , -0.24961467, -0.21705668, -0.5947933 ,  0.32062998,\n",
       "        -0.39705   ,  0.21819668, -0.65333337, -1.2627968 ,  0.30044666,\n",
       "         0.39866665, -0.18313335,  0.21738768, -0.21257667,  0.30709   ,\n",
       "         0.44239667,  0.47571662,  0.15992066,  0.55896664, -0.04956333,\n",
       "        -0.6231713 ,  0.02175669, -0.5656667 ,  0.25156334, -0.38298666,\n",
       "         0.35906732,  0.22533333,  0.82439995,  0.41769662,  0.06431334],\n",
       "       dtype=float32),\n",
       " 'tora spsseducation': array([ 0.86945  , -0.06359  ,  0.5589   ,  0.438355 , -0.10538  ,\n",
       "        -0.71675  ,  0.0330345,  0.029388 , -0.28001  , -0.259075 ,\n",
       "        -0.293955 , -0.354835 , -0.52095  , -0.25252  ,  0.16627  ,\n",
       "         0.25239  ,  0.46798  ,  0.24231  , -0.291455 ,  0.86405  ,\n",
       "        -0.40356  ,  0.049736 ,  0.63215  ,  0.06986  , -0.0480715,\n",
       "        -0.09637  , -0.296115 ,  0.31411  ,  0.373365 , -0.36815  ,\n",
       "        -0.15609  , -0.5768   , -0.351095 , -0.06746  ,  0.243805 ,\n",
       "         0.246165 , -0.71515  , -0.073045 , -0.31581  ,  0.2528   ,\n",
       "        -0.17653  ,  0.121655 ,  0.261    , -0.72115  ,  0.258635 ,\n",
       "        -0.60755  ,  0.633    , -0.171435 , -0.33177  , -0.20045  ],\n",
       "       dtype=float32),\n",
       " 'hsc sav': array([ 1.3495000e-01, -5.1351500e-01, -4.7911000e-01, -3.6353999e-01,\n",
       "        -8.2955503e-01, -5.2804500e-02,  5.7764500e-01, -6.8302500e-01,\n",
       "         7.5821000e-01,  1.1883650e-01,  2.5306499e-01,  6.7251995e-02,\n",
       "         3.2716650e-01, -4.6308398e-01, -3.9212000e-01, -3.1240052e-01,\n",
       "        -9.4167995e-01,  1.2995550e-01,  4.7931802e-01,  1.3316500e-01,\n",
       "        -3.6751500e-01, -1.3870500e-01,  2.8104600e-01,  4.5768499e-01,\n",
       "        -1.4954500e-01,  6.5561497e-01,  2.9329002e-01, -2.0499527e-04,\n",
       "        -8.8343495e-01, -4.6730500e-01, -4.2598748e-01,  5.8854502e-01,\n",
       "        -2.7437651e-01,  6.8413401e-01,  5.0680494e-01,  1.3718849e-01,\n",
       "         3.5993648e-01, -7.7325016e-02,  3.2002500e-01, -3.8628399e-01,\n",
       "         4.0352502e-01, -5.4204744e-01,  1.4568500e-01,  1.7091949e-01,\n",
       "         2.5074500e-01, -3.3483499e-01,  9.7608501e-01, -2.2404599e-01,\n",
       "         1.1296500e+00, -1.9992000e-01], dtype=float32),\n",
       " 'typewriting tora': array([ 0.59773   , -0.43671   ,  0.586213  ,  0.61329   , -0.37788   ,\n",
       "        -0.51118004,  0.4473545 ,  0.115263  , -0.38133502,  0.08476001,\n",
       "        -0.227435  , -0.384761  , -0.20026001, -0.15230998, -0.18521999,\n",
       "         0.12512   ,  0.04249501,  0.85326   , -0.17201501,  0.95238495,\n",
       "        -0.03720501,  0.148131  ,  0.5211    ,  0.436325  ,  0.29814848,\n",
       "        -0.06917749, -0.36669   ,  0.10742001,  0.4168645 , -0.46947   ,\n",
       "        -0.72364   , -0.746365  , -0.31476298, -0.173385  ,  0.470725  ,\n",
       "         0.584275  , -0.775505  ,  0.22877999, -0.37713498,  0.49663997,\n",
       "         0.26506498,  0.044205  ,  0.20520501, -0.18325001,  0.536255  ,\n",
       "        -0.69183505,  1.03407   ,  0.378865  ,  0.01866001, -0.251395  ],\n",
       "       dtype=float32),\n",
       " 'sav higher secondary': array([-0.26226667,  0.18518667,  0.158029  , -0.33221668, -0.14605668,\n",
       "         0.04131667,  0.16662   , -0.66961664,  0.259011  ,  0.08984768,\n",
       "         0.27302334, -0.130142  ,  0.34652102, -0.40024266, -0.36544   ,\n",
       "         0.37817967, -0.35699   ,  0.02647033,  0.03700667, -0.06843   ,\n",
       "         0.12491667, -0.4883667 ,  0.04516867,  0.30681667, -0.31128332,\n",
       "        -0.29805332,  0.30513   , -0.09589332, -0.21128333,  0.07985633,\n",
       "         1.9343166 ,  0.4256333 ,  0.6195857 , -0.07451002,  0.64201   ,\n",
       "        -0.17762099,  0.03896767, -0.18895201,  0.36695334, -0.04907933,\n",
       "        -0.40947   , -0.2067883 ,  0.17308998,  0.45083666,  0.17388201,\n",
       "        -0.35593665,  0.54991335,  0.00437333,  0.65038663,  0.15147533],\n",
       "       dtype=float32),\n",
       " 'higher secondary': array([-0.44515502,  0.704195  ,  0.3672885 , -0.64373505,  0.15800498,\n",
       "         0.140315  ,  0.05362999, -0.489725  , -0.0048285 ,  0.12960002,\n",
       "         0.483125  , -0.16808   ,  0.561115  , -0.566145  , -0.602775  ,\n",
       "         0.61661   , -0.240905  ,  0.061775  , -0.45459002, -0.274805  ,\n",
       "         0.45749497, -0.58571   ,  0.098502  ,  0.06385002, -0.29768   ,\n",
       "        -0.784945  ,  0.33916   , -0.286125  , -0.27779   ,  0.4126745 ,\n",
       "         3.3698    ,  0.49045998,  0.89120996, -0.80511504,  1.1040599 ,\n",
       "        -0.22658   ,  0.09433499,  0.016552  ,  0.454155  , -0.10402499,\n",
       "        -0.69857   , -0.314935  ,  0.18581499,  0.53400004, -0.051282  ,\n",
       "        -0.15496   ,  0.569435  ,  0.268955  ,  0.56643   ,  0.124908  ],\n",
       "       dtype=float32),\n",
       " 'sns college': array([-1.09325   ,  0.61358   ,  0.172225  , -0.3011    , -0.07931636,\n",
       "        -0.19667   , -0.543945  , -0.28188   , -0.654945  , -0.36232   ,\n",
       "         0.35253498,  0.0694135 ,  0.237195  , -0.41316497, -0.675025  ,\n",
       "         0.43440002, -0.01690499,  0.5403005 , -0.116355  ,  0.69819   ,\n",
       "         0.136235  , -0.31642002, -0.17663151,  0.58237004, -0.32774   ,\n",
       "        -0.72755   ,  0.102051  , -0.72915   , -1.4398501 , -0.51538   ,\n",
       "         1.247815  ,  0.39305   ,  0.41891798, -0.72303003, -0.01735002,\n",
       "        -0.055365  , -0.05919   , -0.19069001,  1.03094   , -0.059235  ,\n",
       "        -0.13187501, -0.33776   , -0.894185  ,  0.80493   , -0.21522   ,\n",
       "        -0.357305  ,  0.12603   ,  0.26372   ,  0.197255  ,  0.075045  ],\n",
       "       dtype=float32),\n",
       " 'chidambaram tamil nadu': array([-0.12472134, -1.2064    ,  0.12107766, -0.06471334,  0.20433998,\n",
       "         0.01690332,  0.14483103, -0.45575765,  0.10416899, -0.34846666,\n",
       "        -0.77638334, -0.46648335,  0.6036367 , -0.59868336, -0.46324566,\n",
       "         0.5437534 ,  1.1303333 ,  0.9139034 , -0.13223733,  1.0384766 ,\n",
       "         0.93922997,  0.5007283 ,  0.39861533,  0.39894667,  0.91265994,\n",
       "        -0.42562246, -0.08713669, -0.59675336, -1.3601999 ,  0.48165667,\n",
       "         1.1914467 , -0.63335997, -0.21630667,  0.4196297 ,  0.56606334,\n",
       "         0.42601535,  0.07164667, -0.65241164, -0.32733765,  0.37004933,\n",
       "        -0.83123666,  0.7253167 , -1.1293167 , -0.31003332, -0.62585664,\n",
       "         0.7376067 , -0.58644336,  0.49017334,  0.58734   , -0.96857935],\n",
       "       dtype=float32),\n",
       " 'sns': array([-0.9582  , -0.19044 ,  1.0307  ,  0.5593  , -0.15667 ,  0.13243 ,\n",
       "         0.50981 , -0.31069 , -1.0929  , -0.15892 ,  0.84755 , -0.018823,\n",
       "         0.28676 ,  0.49097 , -0.81567 ,  0.66307 ,  0.44604 ,  0.033001,\n",
       "         0.27479 ,  0.62312 , -0.12527 , -1.3363  , -0.030943,  0.56303 ,\n",
       "        -0.52196 ,  0.3869  ,  0.2967  , -0.3295  , -1.3938  , -0.16841 ,\n",
       "        -0.10197 ,  0.12464 ,  0.89193 ,  0.12944 , -1.1532  , -0.24984 ,\n",
       "         0.15077 , -0.95297 ,  0.72908 ,  0.13014 ,  0.42179 , -0.23596 ,\n",
       "        -0.92093 ,  0.65214 ,  0.24305 , -1.0751  , -0.36746 ,  0.791   ,\n",
       "         0.76601 , -0.17084 ], dtype=float32),\n",
       " 'kamaraj matriculation': array([-0.78259003, -0.199145  ,  0.18686497, -1.02384   , -0.204637  ,\n",
       "         0.3109385 ,  0.585815  , -0.351304  ,  0.31464   , -0.45514   ,\n",
       "        -0.25673002,  0.37254   ,  0.20077501, -0.00884001, -0.90504   ,\n",
       "         0.97396   , -0.291615  ,  0.08438499,  0.0880175 ,  1.00211   ,\n",
       "         1.2277501 , -0.04215501, -0.085875  , -0.401165  ,  0.73175   ,\n",
       "        -0.33179548,  0.183625  , -0.69726   , -1.5777    , -0.30165   ,\n",
       "        -0.16969502,  0.09049501,  0.3191255 , -0.3050625 ,  0.914305  ,\n",
       "         0.39252   ,  1.0091751 ,  0.04158   ,  0.18394499,  0.382503  ,\n",
       "        -0.98345   , -0.15591499, -1.13239   ,  0.727355  , -0.60135996,\n",
       "         0.06084999,  1.14723   ,  0.33965498,  0.21827498, -0.42630002],\n",
       "       dtype=float32),\n",
       " 'chidambaram tamil': array([ 1.0380800e-01, -1.2021000e+00,  2.1368149e-01, -2.1755500e-01,\n",
       "         3.8477999e-01, -1.7061502e-01, -1.7344998e-04, -6.5085500e-01,\n",
       "        -3.5669652e-01, -1.9058001e-01, -9.5752001e-01, -2.5200999e-01,\n",
       "         3.4505501e-01, -5.0862002e-01, -3.4810349e-01,  4.9893504e-01,\n",
       "         1.1255500e+00,  7.6545501e-01, -2.3395249e-01,  1.1587850e+00,\n",
       "         7.3439503e-01,  5.7668751e-01,  3.9340299e-01,  1.8415001e-01,\n",
       "         9.1773498e-01, -3.5857871e-01,  2.4407497e-01, -4.0410501e-01,\n",
       "        -1.2255000e+00,  4.9771500e-01,  1.2288700e+00, -6.5718001e-01,\n",
       "        -1.9913000e-01,  6.0675502e-01,  4.4318500e-01,  3.9715800e-01,\n",
       "         1.5820001e-01, -8.3422750e-01, -4.7530997e-01,  2.9705900e-01,\n",
       "        -8.1217003e-01,  5.7282495e-01, -1.1646750e+00, -2.8320998e-01,\n",
       "        -1.0668501e-01,  6.8775499e-01, -5.7156998e-01,  8.7604499e-01,\n",
       "         6.1467499e-01, -7.8171903e-01], dtype=float32),\n",
       " 'college engineering': array([-0.768385  ,  0.826105  , -0.28403   , -0.329125  , -0.23208134,\n",
       "        -0.09556   , -0.921805  , -0.577995  ,  0.09069   , -0.46396   ,\n",
       "         0.46836   ,  0.191585  , -0.208395  , -0.809805  , -0.97219   ,\n",
       "        -0.01197   , -0.27754098,  1.44815   , -0.37316   ,  0.28119   ,\n",
       "         0.88092   ,  0.57998   , -0.53685004, -0.20264497, -0.081413  ,\n",
       "        -1.393445  , -0.477339  , -1.30085   , -1.23376   , -0.304645  ,\n",
       "         2.6282501 ,  0.09055999, -0.016995  , -1.31925   ,  0.91761   ,\n",
       "         0.34780502, -0.34715   ,  0.94054496,  1.38775   ,  0.131475  ,\n",
       "        -0.35392952, -0.75318   , -0.23870999,  0.562685  , -0.3141095 ,\n",
       "         0.287795  ,  0.555535  ,  0.2589    , -0.342795  ,  0.18963   ],\n",
       "       dtype=float32),\n",
       " 'tamil nadu sns': array([-0.49140468, -1.02428   ,  0.33605433,  0.17522334, -0.13784   ,\n",
       "         0.33024666,  0.31813335, -0.213221  ,  0.001809  , -0.31394333,\n",
       "         0.03043332, -0.6120077 ,  0.66723675, -0.20953333, -0.74043673,\n",
       "         0.3745767 ,  0.94458   ,  0.75570035, -0.01106233,  0.9681601 ,\n",
       "         0.52944   ,  0.08040333,  0.36081564,  0.73387337,  0.3814733 ,\n",
       "        -0.29910666, -0.3727367 , -0.79401666, -1.3889666 ,  0.20135666,\n",
       "         1.1096767 , -0.50596   , -0.07663667,  0.33543965,  0.12000664,\n",
       "         0.3533033 , -0.13206667, -1.0020834 , -0.13897099,  0.24789268,\n",
       "        -0.37406996,  0.45903668, -0.91316   , -0.20722997, -0.6799066 ,\n",
       "        -0.05152665, -0.42047334,  0.43633333,  0.37457666, -1.0562134 ],\n",
       "       dtype=float32),\n",
       " 'mba chidambaram tamil': array([-0.362928  , -0.52466005,  0.30967098, -0.37829664,  0.00380334,\n",
       "        -0.51941   , -0.11062563, -0.6233633 , -0.404521  ,  0.06649999,\n",
       "        -0.51400334, -0.23377334,  0.17650999, -0.31253567, -0.39224565,\n",
       "         0.18921001,  0.5958733 ,  0.92507   ,  0.12515502,  0.94811004,\n",
       "         0.6929    ,  0.694535  ,  0.26461557,  0.13334401,  0.42083   ,\n",
       "        -0.3289658 ,  0.10926998, -0.6424033 , -1.38      ,  0.52525   ,\n",
       "         1.1671133 , -0.27103335, -0.34952   ,  0.29002333,  0.5687    ,\n",
       "         0.43467867, -0.02201999, -0.04735168,  0.09626003, -0.031474  ,\n",
       "        -0.40484   ,  0.31782666, -0.7396633 , -0.12270667, -0.38620004,\n",
       "         0.23241664, -0.08684999,  0.83897334,  0.4045093 , -0.27622268],\n",
       "       dtype=float32),\n",
       " 'mba': array([-1.2964   ,  0.83022  ,  0.50165  , -0.69978  , -0.75815  ,\n",
       "        -1.217    , -0.33153  , -0.56838  , -0.50017  ,  0.58066  ,\n",
       "         0.37303  , -0.1973   , -0.16058  ,  0.079633 , -0.48053  ,\n",
       "        -0.43024  , -0.46348  ,  1.2443   ,  0.84337  ,  0.52676  ,\n",
       "         0.60991  ,  0.93023  ,  0.0070407,  0.031732 , -0.57298  ,\n",
       "        -0.26974  , -0.16034  , -1.119    , -1.689    ,  0.58032  ,\n",
       "         1.0436   ,  0.50126  , -0.6503   , -0.34344  ,  0.81973  ,\n",
       "         0.50972  , -0.38246  ,  1.5264   ,  1.2394   , -0.68854  ,\n",
       "         0.40982  , -0.19217  ,  0.11036  ,  0.1983   , -0.94523  ,\n",
       "        -0.67826  ,  0.88259  ,  0.76483  , -0.015822 ,  0.73477  ],\n",
       "       dtype=float32),\n",
       " 'sav higher': array([-0.268365  , -0.31602   ,  0.266335  , -0.211885  , -0.07739502,\n",
       "        -0.18425   ,  0.32082   , -0.830125  ,  0.409867  ,  0.25895151,\n",
       "         0.253415  , -0.275608  ,  0.2818165 , -0.399079  , -0.00226   ,\n",
       "         0.3536995 , -0.462175  , -0.2214645 ,  0.2047    , -0.323335  ,\n",
       "         0.123725  , -0.478895  ,  0.020911  ,  0.159875  , -0.28553   ,\n",
       "        -0.251135  ,  0.22587   ,  0.05799001, -0.110095  ,  0.15902999,\n",
       "         1.502425  ,  0.51982   ,  0.5665185 ,  0.584835  ,  0.234615  ,\n",
       "        -0.6231515 , -0.2597735 , -0.253733  ,  0.39014   , -0.192784  ,\n",
       "        -0.33859998, -0.03191245,  0.418665  ,  0.38428998,  0.302533  ,\n",
       "        -0.624105  ,  0.54839003, -0.06655   ,  0.651605  ,  0.123478  ],\n",
       "       dtype=float32),\n",
       " 'finance': array([ 0.26065 , -0.2486  ,  0.33918 ,  0.17447 ,  0.16059 , -0.17241 ,\n",
       "        -0.49592 , -1.1057  , -1.1515  , -0.67266 , -0.52137 ,  0.98864 ,\n",
       "        -0.39519 ,  0.38565 ,  0.46111 ,  0.17909 ,  0.53703 , -0.071299,\n",
       "         1.0576  ,  0.32059 ,  1.5695  , -0.58055 , -0.54938 , -0.69697 ,\n",
       "        -0.4368  , -1.3162  ,  0.73868 , -0.56189 , -1.5138  ,  0.9422  ,\n",
       "         2.8278  ,  0.18323 , -0.023847,  0.12808 , -0.23547 ,  0.20016 ,\n",
       "        -0.6252  ,  0.67822 ,  1.1125  , -0.16363 , -0.66115 ,  0.64392 ,\n",
       "         0.31846 , -0.40373 ,  0.19716 ,  0.045172, -0.92688 ,  1.5485  ,\n",
       "         1.5444  ,  0.51316 ], dtype=float32),\n",
       " 'hsc sav higher': array([-0.12344667, -0.26874664, -0.05501999, -0.48055664, -0.35324   ,\n",
       "        -0.10580967,  0.46811   , -0.6656334 ,  0.516488  ,  0.248411  ,\n",
       "         0.38671336, -0.12081534,  0.4335443 , -0.5519627 , -0.29933   ,\n",
       "         0.06042631, -0.7395167 , -0.046293  ,  0.11594534, -0.24155332,\n",
       "         0.01755333, -0.31384   ,  0.22180401,  0.14745666, -0.17722   ,\n",
       "         0.04440999,  0.26708335, -0.05633333, -0.6362633 , -0.01025667,\n",
       "         1.0298417 ,  0.64025   ,  0.16931565,  0.383746  ,  0.58830994,\n",
       "        -0.29740766,  0.09069765, -0.02071868,  0.40926   , -0.406316  ,\n",
       "        -0.01295998, -0.38580832,  0.32702002,  0.275303  ,  0.160782  ,\n",
       "        -0.38666332,  0.84602666, -0.01880067,  0.9147367 , -0.11916467],\n",
       "       dtype=float32),\n",
       " 'tamil nadu': array([-0.25800702, -1.4412    , -0.0112685 , -0.016815  , -0.128425  ,\n",
       "         0.429155  ,  0.22229505, -0.1644865 ,  0.5491635 , -0.391455  ,\n",
       "        -0.378125  , -0.9086    ,  0.85747504, -0.559785  , -0.70282   ,\n",
       "         0.23033   ,  1.19385   ,  1.11705   , -0.1539885 ,  1.1406801 ,\n",
       "         0.85679495,  0.788755  ,  0.556695  ,  0.81929505,  0.83318996,\n",
       "        -0.64211   , -0.70745504, -1.026275  , -1.38655   ,  0.38624   ,\n",
       "         1.7155    , -0.82126   , -0.56092   ,  0.4384395 ,  0.75661   ,\n",
       "         0.654875  , -0.273485  , -1.02664   , -0.5729965 ,  0.306769  ,\n",
       "        -0.77199996,  0.806535  , -0.90927494, -0.63691497, -1.141385  ,\n",
       "         0.46026   , -0.44698   ,  0.259     ,  0.17886001, -1.4989    ],\n",
       "       dtype=float32),\n",
       " 'sns college engineering': array([-0.8316567 ,  0.48725665,  0.15421332, -0.03298332, -0.20694423,\n",
       "        -0.01956333, -0.44460002, -0.48889336, -0.30384   , -0.36228   ,\n",
       "         0.59475666,  0.12144899, -0.04334334, -0.3762133 , -0.9200166 ,\n",
       "         0.21304335, -0.03634733,  0.9764337 , -0.15717667,  0.39516664,\n",
       "         0.54552335, -0.05878001, -0.36821437,  0.05258004, -0.228262  ,\n",
       "        -0.7999967 , -0.21932597, -0.9770667 , -1.2871068 , -0.25923336,\n",
       "         1.7181767 ,  0.10191999,  0.28598   , -0.8363533 ,  0.22733998,\n",
       "         0.14859001, -0.18117666,  0.30937332,  1.1681933 ,  0.13103001,\n",
       "        -0.09535634, -0.58077335, -0.46611667,  0.5925033 , -0.12838967,\n",
       "        -0.16650333,  0.24787001,  0.43626666,  0.02680666,  0.06947333],\n",
       "       dtype=float32),\n",
       " 'typewriting tora spsseducation': array([ 0.39848664, -0.29114   ,  0.39080867,  0.40886   , -0.25192   ,\n",
       "        -0.3407867 ,  0.29823634,  0.076842  , -0.25422335,  0.05650667,\n",
       "        -0.15162332, -0.25650734, -0.13350667, -0.10153999, -0.12347999,\n",
       "         0.08341333,  0.02833001,  0.56883997, -0.11467668,  0.6349233 ,\n",
       "        -0.02480334,  0.098754  ,  0.34739998,  0.29088333,  0.19876565,\n",
       "        -0.04611833, -0.24446   ,  0.07161334,  0.27790967, -0.31298   ,\n",
       "        -0.48242667, -0.49757668, -0.20984198, -0.11559   ,  0.31381667,\n",
       "         0.38951668, -0.51700336,  0.15251999, -0.25142333,  0.3310933 ,\n",
       "         0.17671   ,  0.02947   ,  0.13680334, -0.12216667,  0.35750332,\n",
       "        -0.46122336,  0.68938   ,  0.25257668,  0.01244001, -0.16759665],\n",
       "       dtype=float32),\n",
       " 'typewriting': array([-0.54344 , -0.74624 ,  0.054626,  0.34987 , -0.545   ,  0.41114 ,\n",
       "         0.82864 ,  0.17175 , -0.20265 ,  0.68767 ,  0.13304 , -0.059852,\n",
       "         0.64138 ,  0.20042 , -0.70298 , -0.25454 , -0.85097 ,  1.2219  ,\n",
       "         0.23888 ,  0.17667 ,  0.73271 ,  0.19679 , -0.2221  ,  0.73293 ,\n",
       "         0.69244 ,  0.054385, -0.14115 , -0.41338 ,  0.086999, -0.20264 ,\n",
       "        -1.1351  , -0.33913 ,  0.072664, -0.21185 ,  0.45384 ,  0.67622 ,\n",
       "        -0.12071 ,  0.60365 , -0.12265 ,  0.48768 ,  0.88319 , -0.1549  ,\n",
       "        -0.11159 ,  1.0758  ,  0.55524 , -0.16857 ,  0.80214 ,  1.1006  ,\n",
       "         0.70086 , -0.10189 ], dtype=float32),\n",
       " 'finance kamaraj matriculation': array([-0.43484333, -0.21563001,  0.23763667, -0.6244033 , -0.08289468,\n",
       "         0.14982234,  0.22523667, -0.6027693 , -0.17407334, -0.52764666,\n",
       "        -0.3449433 ,  0.57790667,  0.00212   ,  0.12265667, -0.44965664,\n",
       "         0.70900327, -0.01540001,  0.03249033,  0.4112117 ,  0.7749367 ,\n",
       "         1.3416667 , -0.22162001, -0.24037667, -0.49976668,  0.34223333,\n",
       "        -0.65993035,  0.3686433 , -0.6521366 , -1.5564    ,  0.11296668,\n",
       "         0.82947   ,  0.12140667,  0.20480134, -0.16068166,  0.5310467 ,\n",
       "         0.3284    ,  0.46438336,  0.25379333,  0.49346328,  0.20045866,\n",
       "        -0.8760166 ,  0.11069667, -0.6487734 ,  0.3503267 , -0.33518663,\n",
       "         0.05562399,  0.45586   ,  0.7426033 ,  0.66031665, -0.11314669],\n",
       "       dtype=float32),\n",
       " 'college': array([-1.2283e+00,  1.4176e+00, -6.8625e-01, -1.1615e+00, -1.9627e-03,\n",
       "        -5.2577e-01, -1.5977e+00, -2.5307e-01, -2.1699e-01, -5.6572e-01,\n",
       "        -1.4248e-01,  1.5765e-01,  1.8763e-01, -1.3173e+00, -5.3438e-01,\n",
       "         2.0573e-01, -4.7985e-01,  1.0476e+00, -5.0750e-01,  7.7326e-01,\n",
       "         3.9774e-01,  7.0346e-01, -3.2232e-01,  6.0171e-01, -1.3352e-01,\n",
       "        -1.8420e+00, -9.2598e-02, -1.1288e+00, -1.4859e+00, -8.6235e-01,\n",
       "         2.5976e+00,  6.6146e-01, -5.4094e-02, -1.5755e+00,  1.1185e+00,\n",
       "         1.3911e-01, -2.6915e-01,  5.7159e-01,  1.3328e+00, -2.4861e-01,\n",
       "        -6.8554e-01, -4.3956e-01, -8.6744e-01,  9.5772e-01, -6.7349e-01,\n",
       "         3.6049e-01,  6.1952e-01, -2.6356e-01, -3.7150e-01,  3.2093e-01],\n",
       "       dtype=float32),\n",
       " 'salary': array([-7.3751e-01,  8.2157e-01,  4.0644e-01, -7.5612e-01,  7.4616e-01,\n",
       "         5.5063e-02, -3.1218e-01,  8.3916e-02, -4.8283e-01, -8.0958e-02,\n",
       "        -1.0130e-02, -8.9587e-02,  3.1889e-01, -5.2252e-01,  1.8701e+00,\n",
       "        -1.7341e-01,  2.3058e-01,  1.6724e-01,  2.1844e-01, -8.0360e-01,\n",
       "        -4.0263e-02, -9.5571e-01, -5.4382e-01, -7.6451e-01, -1.0864e+00,\n",
       "        -6.5438e-01,  5.7078e-01,  5.3928e-02,  1.1188e-01,  4.3511e-01,\n",
       "         2.5398e+00,  8.1287e-01, -1.0570e-03,  5.3784e-01,  2.7165e-01,\n",
       "         1.5605e-01,  3.4646e-01, -4.5518e-02,  9.1915e-01, -7.1363e-01,\n",
       "        -6.1983e-01,  3.8306e-01, -2.9985e-01,  9.8619e-01, -1.5391e+00,\n",
       "        -1.5450e+00, -1.5935e-02,  1.6215e+00, -7.5851e-01,  6.3004e-01],\n",
       "       dtype=float32),\n",
       " 'saral payment package': array([ 0.39604   , -0.14506331,  0.53119   , -0.01162333, -0.12482673,\n",
       "         0.23583667, -0.00283666,  0.00896335,  0.62414336,  0.22165366,\n",
       "        -0.45242134, -0.23686999,  0.9279066 , -0.15062   ,  0.40996996,\n",
       "        -0.07848299, -0.14717667,  0.20408033,  1.1427066 ,  0.03783667,\n",
       "         0.65195996, -0.5091333 , -0.33134332,  0.14593999, -0.26685   ,\n",
       "        -0.3197033 ,  0.36892334, -0.07170733,  0.17482167,  0.180088  ,\n",
       "         1.7774767 , -0.30656597, -0.44997334,  0.7527434 ,  0.37279332,\n",
       "         0.24260734,  0.05619   ,  0.12323195,  0.18647666, -0.47403002,\n",
       "         0.10141033,  0.22030567,  0.11166   , -0.25363335, -0.79803663,\n",
       "        -0.29928336,  0.08360667,  0.93904   ,  0.12337047,  0.58391005],\n",
       "       dtype=float32),\n",
       " 'payroll': array([-0.2749  , -0.14625 ,  1.4295  , -0.8054  ,  0.34623 , -0.30173 ,\n",
       "        -0.85363 , -0.22404 ,  0.10629 , -0.38504 , -0.058729, -0.37136 ,\n",
       "        -0.014787, -0.40648 ,  1.0285  , -0.5514  , -0.28905 ,  0.016809,\n",
       "         0.76534 , -0.48286 ,  0.055111, -1.586   , -0.3472  , -0.6286  ,\n",
       "        -1.4581  , -0.42869 , -0.029951, -0.054452,  0.77075 ,  0.15996 ,\n",
       "         1.835   ,  1.0015  ,  0.43572 , -0.094213, -0.32152 ,  0.352   ,\n",
       "        -0.2424  ,  0.19088 ,  0.92095 , -0.59649 , -0.74519 , -0.56393 ,\n",
       "         0.034764,  0.71585 , -1.0435  , -1.5196  , -0.13692 ,  1.2072  ,\n",
       "        -0.024712,  0.7852  ], dtype=float32),\n",
       " 'compliance related': array([ 0.13214502, -0.08815999, -0.06712499,  0.15770999, -0.29321998,\n",
       "         0.247695  ,  0.473055  , -0.5981    ,  0.939435  , -0.1876975 ,\n",
       "         0.1803615 ,  0.28811002, -0.497926  , -0.368635  ,  0.51598   ,\n",
       "         0.14562   , -0.33758348, -0.2599225 ,  0.83914   , -0.41565502,\n",
       "         0.5068    , -0.155895  ,  0.21697901, -0.433085  , -0.65072   ,\n",
       "        -1.01747   , -0.2191043 , -0.431465  ,  0.19872999,  0.30581   ,\n",
       "         2.74645   , -0.31984   , -0.29961997, -0.963295  ,  0.19574499,\n",
       "        -0.04069   ,  0.3979    , -0.38938   ,  0.009627  ,  0.428835  ,\n",
       "        -0.459505  , -0.53979003,  0.944255  ,  0.542015  , -0.01159   ,\n",
       "        -0.031613  , -0.076814  ,  1.38221   , -0.150007  ,  0.3109451 ],\n",
       "       dtype=float32),\n",
       " 'statutory compliance': array([-0.605415  , -0.274505  , -0.551615  , -0.4849    , -0.069175  ,\n",
       "         0.24684   ,  0.96269   , -0.27613   ,  1.0132    , -0.49787   ,\n",
       "         0.29635   ,  0.50722003, -0.222875  , -0.24515   ,  0.18858   ,\n",
       "         0.39703   , -0.1045185 , -0.45372498,  1.14688   , -0.4644    ,\n",
       "         0.59504503,  0.0716765 , -0.329901  , -0.54147   , -0.904605  ,\n",
       "        -0.73526   , -0.0630043 , -0.20041999,  0.0984205 ,  0.264285  ,\n",
       "         2.0756998 , -0.191165  , -0.71791   , -0.69808996,  0.553995  ,\n",
       "         0.0887    ,  1.1818    , -0.838705  ,  0.070132  ,  0.108368  ,\n",
       "        -0.473345  , -0.68284   ,  0.86977005,  0.696035  , -1.4341199 ,\n",
       "        -0.2116715 , -0.0385135 ,  1.4807999 ,  0.22228   ,  0.22560501],\n",
       "       dtype=float32),\n",
       " 'queen university edinburg': array([-0.32231665,  1.0155541 , -0.8416967 , -0.163896  , -0.10656668,\n",
       "        -0.33876   , -0.46796334,  0.14259101,  0.09242066, -0.28532   ,\n",
       "         0.39604333, -0.20641667,  0.45084634, -0.32616335, -0.49216   ,\n",
       "         0.11340999, -0.37893334,  0.64438367, -0.64758   ,  0.92597336,\n",
       "         0.13651665,  0.22444999, -0.08752101,  0.28046104, -0.24213134,\n",
       "        -1.3699266 , -0.29411432, -0.25148   , -0.68055004, -0.3437873 ,\n",
       "         1.0126334 , -0.06091799, -0.252025  , -0.7174967 ,  0.32694831,\n",
       "        -0.2628106 , -0.08488999,  0.7526093 ,  0.4423233 ,  0.2327733 ,\n",
       "        -0.01791334, -0.04175534,  0.03316001, -0.22318333, -0.4407213 ,\n",
       "         0.52309996,  0.10651001, -0.62536997, -0.61032486,  0.10686133],\n",
       "       dtype=float32),\n",
       " 'end end facilitation': array([ 0.06206   ,  0.10923667, -0.4182533 , -0.03868999, -0.11916867,\n",
       "         0.16664799, -0.17458   , -0.45057   ,  0.01002002, -0.17548132,\n",
       "        -0.05925667,  0.16107   , -0.9842866 , -0.14337   ,  0.41848668,\n",
       "         0.17935665,  0.3886793 , -0.42208266,  0.02133334,  0.131942  ,\n",
       "         0.46819666, -0.19837   , -0.40896335, -0.01723333, -0.06744967,\n",
       "        -0.8451633 ,  0.8349867 , -0.20889   ,  0.54035395,  0.20813332,\n",
       "         2.8765666 ,  0.43305334, -0.6728733 ,  0.220101  ,  0.05248333,\n",
       "        -0.11192334, -0.0232    , -0.24169   , -0.43295667,  0.05632665,\n",
       "        -0.6839466 , -0.24010666, -0.07635   , -0.23795335, -0.28389636,\n",
       "        -0.33335337,  0.56938   ,  0.2791    ,  0.1401533 , -0.091736  ],\n",
       "       dtype=float32),\n",
       " 'management end end': array([ 1.35926679e-01,  1.27980664e-01, -1.70176670e-01, -5.89099936e-02,\n",
       "        -7.80786648e-02, -2.16438651e-01, -5.96350014e-01, -3.94696683e-01,\n",
       "        -2.21189976e-01, -3.48199993e-01, -2.13649988e-01,  2.77279973e-01,\n",
       "        -9.90546644e-01, -4.62689996e-01,  3.76489997e-01,  4.57486659e-01,\n",
       "         1.54179335e-01, -1.08526669e-01, -3.21749985e-01, -4.28713374e-02,\n",
       "         3.90210003e-01, -8.16533342e-02, -4.15553331e-01, -3.65219980e-01,\n",
       "        -3.11614662e-01, -1.30196667e+00,  3.32690686e-01, -6.06033243e-02,\n",
       "         3.04293305e-01,  3.20199996e-01,  3.66846657e+00,  1.24092333e-01,\n",
       "        -2.90426642e-01, -7.64733478e-02, -3.66667897e-04, -1.14829659e-01,\n",
       "        -4.31200005e-02, -2.32456684e-01,  1.92733362e-01, -3.72179985e-01,\n",
       "        -5.32406628e-01, -3.04603338e-01, -2.23707005e-01,  1.11800015e-01,\n",
       "        -3.85686666e-01,  3.41499932e-02,  3.45358342e-01,  2.55500048e-01,\n",
       "        -1.53410003e-01,  2.68073320e-01], dtype=float32),\n",
       " 'package payroll': array([ 0.354775  ,  0.05704501,  0.95695496, -0.47890502,  0.09128   ,\n",
       "         0.092015  , -0.697605  , -0.601875  ,  0.122845  , -0.062915  ,\n",
       "        -0.5041695 , -0.069585  ,  0.2230065 ,  0.160985  ,  0.88329   ,\n",
       "        -0.270185  , -0.25139   ,  0.049235  ,  0.72465   , -0.551315  ,\n",
       "         0.4804755 , -0.99492997, -0.43997002, -0.521905  , -0.94791496,\n",
       "        -0.591035  ,  0.1710545 ,  0.23583901,  0.4081125 ,  0.105757  ,\n",
       "         2.4597    ,  0.69753504, -0.19230501,  0.45879352, -0.01146001,\n",
       "         0.34971   ,  0.029975  ,  0.09265295,  0.40026   , -0.969245  ,\n",
       "        -0.33848453, -0.24946648,  0.168242  ,  0.119645  , -0.737095  ,\n",
       "        -0.40377003, -0.005365  ,  1.3978    ,  0.177529  ,  0.62596   ],\n",
       "       dtype=float32),\n",
       " 'employees': array([ 0.72931 , -0.45016 ,  0.9179  , -0.15627 ,  0.23472 , -0.23346 ,\n",
       "        -0.83127 ,  0.32546 ,  0.11581 , -0.89044 , -0.040481, -0.30806 ,\n",
       "         0.010728, -0.60179 ,  0.83919 , -0.4009  , -0.68278 ,  1.0203  ,\n",
       "         0.49221 , -1.5164  ,  0.79965 ,  0.44942 , -0.4315  ,  0.20588 ,\n",
       "        -1.0949  , -1.1657  , -0.014455, -0.67832 , -0.28029 , -0.2141  ,\n",
       "         2.8741  ,  0.57669 ,  0.18353 , -0.39262 , -0.20661 , -0.34731 ,\n",
       "        -0.059411, -0.64094 ,  0.84288 ,  0.062736, -0.16679 , -0.30599 ,\n",
       "         0.84191 ,  1.1817  , -0.088266, -0.83507 , -1.2226  ,  0.82789 ,\n",
       "        -1.0209  ,  0.80056 ], dtype=float32),\n",
       " 'statutory': array([-0.60458 , -0.10392 , -1.2171  , -0.50789 ,  0.34146 ,  0.98683 ,\n",
       "         1.1945  , -0.30256 ,  0.599   , -0.54389 ,  0.14206 ,  0.7789  ,\n",
       "         0.52881 , -0.15683 ,  0.19685 ,  0.27681 , -0.225   , -0.43877 ,\n",
       "         0.79926 , -0.252   ,  0.71855 ,  0.010503, -0.64565 , -0.48278 ,\n",
       "        -0.89552 , -0.54228 , -0.12478 , -0.22377 , -0.056889,  0.16052 ,\n",
       "         2.0017  , -0.16657 , -0.40882 , -0.55789 ,  0.90804 ,  0.46803 ,\n",
       "         1.3063  , -1.3297  ,  0.084161,  0.020456, -0.76041 , -0.42215 ,\n",
       "         0.73274 ,  1.1501  , -2.1666  , -0.37078 , -0.047999,  1.145   ,\n",
       "         0.83101 , -0.16673 ], dtype=float32),\n",
       " 'act recruitment selection': array([-0.35696697, -0.29852334, -0.7296147 , -0.28950667,  0.08374333,\n",
       "         0.31817666, -0.18248332, -0.36899337,  0.6880967 , -0.22623001,\n",
       "        -0.06995767,  0.11291665,  0.01472666, -0.01071399,  0.0926    ,\n",
       "        -0.266222  ,  0.23898466, -0.18005335,  0.00521401, -0.17539233,\n",
       "         0.49541867, -0.30247334,  0.09096333,  0.04723667, -0.56654334,\n",
       "        -1.04294   , -0.07256   , -0.5877597 , -0.00326967,  0.20005132,\n",
       "         2.3014    ,  0.10552601, -0.7743266 , -1.1317734 ,  0.21623234,\n",
       "         0.45913196,  0.45723   , -0.00825334, -0.6430066 , -0.35429668,\n",
       "        -0.38568333, -0.30752665, -0.40698335,  0.250681  , -0.00568334,\n",
       "        -0.3896533 , -0.10550668,  0.48425332,  0.4050367 ,  0.59579664],\n",
       "       dtype=float32),\n",
       " 'related': array([ 0.87054  ,  0.26877  , -0.24812  ,  0.77733  , -0.10663  ,\n",
       "         0.98854  ,  0.21523  , -0.9465   ,  0.45147  ,  0.076455 ,\n",
       "        -0.089917 ,  0.34068  , -0.021292 , -0.4038   ,  0.85165  ,\n",
       "        -0.22601  , -0.69113  , -0.051165 ,  0.18378  , -0.15451  ,\n",
       "         0.54206  , -0.44464  ,  0.44811  , -0.26601  , -0.38775  ,\n",
       "        -1.1067   , -0.43698  , -0.68586  ,  0.14373  ,  0.24357  ,\n",
       "         3.3432   , -0.42392  ,  0.42776  , -1.0883   ,  0.19154  ,\n",
       "         0.20925  , -0.2615   , -0.43105  , -0.036849 ,  0.66139  ,\n",
       "        -0.73273  , -0.13605  ,  0.88171  ,  0.84206  ,  0.67846  ,\n",
       "        -0.010663 , -0.1246   ,  0.94782  ,  0.086436 ,  0.0039502],\n",
       "       dtype=float32),\n",
       " 'end facilitation pms': array([ 0.01137333,  0.36734   , -0.5168433 ,  0.39468002, -0.43289602,\n",
       "         0.06458234,  0.21098334, -0.13323331, -0.1888233 , -0.56162465,\n",
       "         0.10649303,  0.36547998, -0.32995665,  0.24426334,  0.32031998,\n",
       "         0.19750333, -0.5310104 , -0.416996  ,  0.37366   ,  0.13737267,\n",
       "         0.70955664,  0.06904999, -0.04476335,  0.30313   , -0.06703433,\n",
       "        -0.21294665,  0.8198533 , -0.4595767 ,  0.221754  ,  0.39244   ,\n",
       "         1.7479067 ,  0.81531   , -0.58401334,  0.21216433, -0.02721   ,\n",
       "        -0.035503  , -0.043911  , -0.47995672, -0.29863566,  0.18363033,\n",
       "        -0.26485667, -0.30484667,  0.31669334,  0.03734666,  0.24459033,\n",
       "        -0.12723666,  0.52450997,  0.69283336,  0.30467665,  0.00827733],\n",
       "       dtype=float32),\n",
       " 'payroll software': array([ 0.063015  , -0.657275  ,  1.3440499 ,  0.25945002, -0.12995501,\n",
       "        -0.335435  , -1.060215  , -1.00562   ,  0.392165  ,  0.39703003,\n",
       "         0.3338005 ,  0.18592   , -0.1691235 , -0.35912502,  0.501632  ,\n",
       "        -0.239509  , -0.56831   ,  0.3614145 ,  0.82679   , -0.579715  ,\n",
       "         0.29756048, -0.851675  , -0.623865  , -0.10295001, -0.620375  ,\n",
       "        -0.50269496, -0.21545051, -0.594476  ,  0.4175235 , -0.143665  ,\n",
       "         2.5694    , -0.03504997,  0.029475  , -0.2887115 , -0.21592   ,\n",
       "         0.31567   , -0.377855  ,  0.207625  ,  0.634525  , -0.63644505,\n",
       "         0.26240498, -0.47166   , -0.070318  ,  0.828255  , -0.581795  ,\n",
       "        -0.559315  ,  0.400405  ,  0.89169   ,  0.14073901,  0.543535  ],\n",
       "       dtype=float32),\n",
       " 'welfare creation deployment': array([ 0.5789266 , -0.31490365,  0.00282   , -0.7530634 ,  0.04078833,\n",
       "         0.39761332, -0.20934667, -0.68505   ,  0.52577996, -0.5443167 ,\n",
       "         0.01092001, -0.19977666,  0.30551967, -0.08605033,  0.03528066,\n",
       "        -0.04908033,  0.23563667, -0.05817733,  0.5810133 ,  0.02828333,\n",
       "         0.19290316, -0.34506598, -0.8083167 , -0.43415666,  0.04035533,\n",
       "        -1.1179634 ,  0.04781334, -0.5406433 , -0.165224  ,  0.6558433 ,\n",
       "         2.5302334 ,  0.22343767, -1.0166433 , -0.43240067, -0.31941333,\n",
       "         0.20186667,  0.099913  , -0.23471765, -0.5131133 , -0.28432   ,\n",
       "        -0.57109   , -0.22859667,  0.03795667, -0.2991433 , -0.40085003,\n",
       "        -0.09562667, -0.11617532,  0.7015133 , -0.14654402,  0.23520334],\n",
       "       dtype=float32),\n",
       " 'payment package': array([ 0.91557   ,  0.20032   ,  0.506615  , -0.21031   , -0.0834801 ,\n",
       "         0.558435  , -0.29394   , -0.24935   ,  0.27586502,  0.376265  ,\n",
       "        -0.500132  ,  0.433095  ,  0.54996   ,  0.00308001,  0.98239   ,\n",
       "        -0.13572   , -0.37335503, -0.1175245 ,  0.83866   , -0.525845  ,\n",
       "         1.03562   , -0.673045  , -0.62851   , -0.27084   , -0.519405  ,\n",
       "        -0.87352   ,  0.39604002, -0.079355  ,  0.1664025 ,  0.122797  ,\n",
       "         2.968     ,  0.204251  , -0.561045  ,  1.0178001 ,  0.313675  ,\n",
       "         0.134331  ,  0.416485  , -0.20015205,  0.15596001, -1.065555  ,\n",
       "         0.2338355 , -0.0544615 ,  0.284025  ,  0.15509999, -0.75249505,\n",
       "        -0.25817   ,  0.07093   ,  1.49845   ,  0.1821215 ,  0.30211502],\n",
       "       dtype=float32),\n",
       " 'recruitment selection recruitment': array([-0.14074732, -0.29939333, -0.3514627 , -0.33267668,  0.10477   ,\n",
       "         0.01711   , -0.45945665, -0.42922664,  0.96586996, -0.3163    ,\n",
       "        -0.04120266, -0.42054   , -0.08858333, -0.12709999, -0.13968335,\n",
       "        -0.4558867 ,  0.117966  ,  0.01891998,  0.15636334, -0.01944334,\n",
       "         0.31840864, -0.35663   , -0.02795667,  0.25080332, -0.8272433 ,\n",
       "        -0.70927   ,  0.08633999, -0.64779633, -0.31801265,  0.37065664,\n",
       "         1.9490666 ,  0.42363605, -0.51015   , -1.0314467 ,  0.33146134,\n",
       "         0.88861006, -0.01065   ,  0.2638267 , -0.56474   , -0.30858   ,\n",
       "        -0.17728667, -0.19246668, -0.79201   ,  0.37583002,  0.6299033 ,\n",
       "        -1.0106866 ,  0.36600998,  1.00806   ,  0.33419   ,  0.55898   ],\n",
       "       dtype=float32),\n",
       " 'package payroll software': array([ 0.37016   , -0.35140336,  1.0575033 ,  0.12216333, -0.14119335,\n",
       "        -0.06170334, -0.88733673, -0.99698335,  0.30791   ,  0.35109004,\n",
       "        -0.09400302,  0.20134334,  0.040851  ,  0.0034    ,  0.580448  ,\n",
       "        -0.155996  , -0.45011666,  0.26816332,  0.77918005, -0.5930667 ,\n",
       "         0.5003203 , -0.7024033 , -0.59349   , -0.20703667, -0.5594933 ,\n",
       "        -0.5862567 , -0.01961367, -0.22094066,  0.29350734, -0.078592  ,\n",
       "         2.7410667 ,  0.10782337, -0.25379333,  0.14479235, -0.04441334,\n",
       "         0.32625332, -0.15112   ,  0.13655864,  0.38287333, -0.87163   ,\n",
       "         0.19767697, -0.29277432,  0.05369466,  0.39331666, -0.53142667,\n",
       "        -0.13552335,  0.30900002,  1.1239266 ,  0.22041601,  0.51793   ],\n",
       "       dtype=float32),\n",
       " 'payment package payroll': array([ 5.18746674e-01,  8.47966671e-02,  8.14243317e-01, -4.08673316e-01,\n",
       "         5.97565956e-02,  2.71713346e-01, -4.80503321e-01, -2.40913332e-01,\n",
       "         2.19340011e-01,  1.22496665e-01, -3.52997690e-01,  1.64943337e-01,\n",
       "         3.61711025e-01, -1.33440003e-01,  9.97759998e-01, -2.74279982e-01,\n",
       "        -3.45253348e-01, -7.27466643e-02,  8.14219952e-01, -5.11516631e-01,\n",
       "         7.08783627e-01, -9.77363288e-01, -5.34740031e-01, -3.90093327e-01,\n",
       "        -8.32303345e-01, -7.25243330e-01,  2.54043013e-01, -7.10540041e-02,\n",
       "         3.67851645e-01,  1.35184661e-01,  2.59033322e+00,  4.70000654e-01,\n",
       "        -2.28790000e-01,  6.47129059e-01,  1.01943322e-01,  2.06887320e-01,\n",
       "         1.96856678e-01, -6.98080361e-02,  4.10956651e-01, -9.09200013e-01,\n",
       "        -9.25063416e-02, -2.24284336e-01,  2.00938001e-01,  3.42016667e-01,\n",
       "        -8.49496663e-01, -6.78646743e-01,  1.64666772e-03,  1.40136671e+00,\n",
       "         1.13177001e-01,  4.63143349e-01], dtype=float32),\n",
       " 'payment': array([ 0.84669  ,  0.1403   ,  0.52882  , -0.26821  , -0.0032902,\n",
       "         0.63111  , -0.0463   ,  0.48101  ,  0.41233  ,  0.49332  ,\n",
       "        -0.050654 ,  0.634    ,  0.63912  , -0.72229  ,  1.2267   ,\n",
       "        -0.28247  , -0.53298  , -0.31671  ,  0.99336  , -0.43192  ,\n",
       "         1.1654   , -0.94223  , -0.72428  , -0.12647  , -0.60108  ,\n",
       "        -0.99366  ,  0.42002  , -0.68484  ,  0.28733  ,  0.19404  ,\n",
       "         2.8516   ,  0.014932 , -0.30176  ,  1.0238   ,  0.32875  ,\n",
       "        -0.078758 ,  0.53062  , -0.39473  ,  0.43235  , -0.78911  ,\n",
       "         0.39945  , -0.17392  ,  0.26633  ,  0.78676  , -1.0743   ,\n",
       "        -1.2284   ,  0.01567  ,  1.4085   , -0.015527 ,  0.13751  ],\n",
       "       dtype=float32),\n",
       " 'compliance': array([-6.0625e-01, -4.4509e-01,  1.1387e-01, -4.6191e-01, -4.7981e-01,\n",
       "        -4.9315e-01,  7.3088e-01, -2.4970e-01,  1.4274e+00, -4.5185e-01,\n",
       "         4.5064e-01,  2.3554e-01, -9.7456e-01, -3.3347e-01,  1.8031e-01,\n",
       "         5.1725e-01,  1.5963e-02, -4.6868e-01,  1.4945e+00, -6.7680e-01,\n",
       "         4.7154e-01,  1.3285e-01, -1.4152e-02, -6.0016e-01, -9.1369e-01,\n",
       "        -9.2824e-01, -1.2286e-03, -1.7707e-01,  2.5373e-01,  3.6805e-01,\n",
       "         2.1497e+00, -2.1576e-01, -1.0270e+00, -8.3829e-01,  1.9995e-01,\n",
       "        -2.9063e-01,  1.0573e+00, -3.4771e-01,  5.6103e-02,  1.9628e-01,\n",
       "        -1.8628e-01, -9.4353e-01,  1.0068e+00,  2.4197e-01, -7.0164e-01,\n",
       "        -5.2563e-02, -2.9028e-02,  1.8166e+00, -3.8645e-01,  6.1794e-01],\n",
       "       dtype=float32),\n",
       " 'policies procedure': array([ 0.32547   , -0.621365  , -0.59849   , -0.63542   , -0.74602497,\n",
       "         0.87336   ,  0.4776305 , -0.38926002,  0.04980502,  0.1187255 ,\n",
       "        -0.103895  ,  0.020385  ,  0.174751  ,  0.354995  , -0.01859501,\n",
       "         0.18263501, -0.306769  , -0.74336505,  0.95176494, -0.202074  ,\n",
       "         0.114105  , -0.194725  , -0.44431   , -0.459435  ,  0.015397  ,\n",
       "        -1.44      ,  0.09105   , -0.29010502,  0.124575  ,  0.43033698,\n",
       "         2.7915    , -0.11758998, -0.97371495, -0.72277504, -0.3919995 ,\n",
       "        -0.1128775 ,  0.748815  ,  0.639445  , -0.31361002, -0.05847215,\n",
       "        -0.62441504, -0.24992499,  0.45257998,  0.97087497, -0.09854   ,\n",
       "        -0.37208   , -0.03643999,  0.79781   ,  0.481165  ,  0.65583   ],\n",
       "       dtype=float32),\n",
       " 'annual': array([-0.37791  ,  1.0773   , -0.66116  , -0.11279  ,  0.45041  ,\n",
       "        -0.56139  , -0.58576  , -0.93223  ,  1.4882   , -0.06564  ,\n",
       "        -0.44609  , -0.98404  ,  0.88088  , -0.18864  ,  1.1978   ,\n",
       "        -0.67449  , -0.02678  , -0.012949 , -1.1207   , -0.34115  ,\n",
       "         1.4087   , -1.3492   , -0.24899  , -0.54433  , -0.68191  ,\n",
       "        -0.55549  , -0.66159  , -0.35694  , -1.1895   ,  0.62435  ,\n",
       "         3.2609   ,  1.2453   , -0.22373  ,  0.40704  , -0.43906  ,\n",
       "        -0.44327  ,  0.13702  ,  0.0054712,  0.12888  , -0.61378  ,\n",
       "        -1.0401   , -0.12735  , -0.79365  , -0.45138  , -0.33485  ,\n",
       "        -0.19037  ,  0.39579  ,  0.53896  , -0.35373  ,  0.50487  ],\n",
       "       dtype=float32),\n",
       " 'hospitality management': array([ 0.327505  ,  0.437061  , -0.727135  ,  0.39431   ,  0.00974999,\n",
       "        -0.825555  , -0.68716   , -0.544455  ,  0.36171502, -0.157825  ,\n",
       "         0.17296176,  0.677275  , -0.00722   , -0.53086495, -0.515605  ,\n",
       "         0.2503705 ,  0.036525  ,  1.0105901 ,  0.32353   , -0.32913   ,\n",
       "         0.74022996,  0.77699   , -0.94228   , -0.597785  , -0.61775   ,\n",
       "        -0.39504498, -0.285039  , -0.3485    , -0.33305952,  0.46332002,\n",
       "         2.52915   ,  0.8111585 ,  0.19088   , -0.310575  , -0.24718499,\n",
       "         0.2639505 , -0.445665  , -0.060929  ,  0.787105  , -0.10745901,\n",
       "         0.15336   , -0.444035  , -0.1421155 ,  0.859345  , -0.23254001,\n",
       "        -0.15073851, -0.6282825 ,  0.82142   , -0.05912499,  0.83258   ],\n",
       "       dtype=float32),\n",
       " 'saral payment': array([ 0.10183501, -0.34776497,  0.55458   ,  0.05877   , -0.1054051 ,\n",
       "         0.11087501,  0.266535  ,  0.5033    ,  0.86651504,  0.2028755 ,\n",
       "        -0.203827  , -0.4714    ,  1.1614599 , -0.590155  ,  0.24591497,\n",
       "        -0.12323949, -0.11390001,  0.26529   ,  1.37208   ,  0.36664   ,\n",
       "         0.52502   , -0.56176996, -0.230645  ,  0.42651498, -0.18141   ,\n",
       "        -0.10286498,  0.367355  , -0.370626  ,  0.23949501,  0.244355  ,\n",
       "         1.124015  , -0.656634  , -0.264795  ,  0.623215  ,  0.40989   ,\n",
       "         0.190201  , -0.06689   ,  0.18763499,  0.33993   , -0.04004499,\n",
       "         0.118005  ,  0.29795998,  0.01663   , -0.14217001, -0.98171   ,\n",
       "        -0.804955  ,  0.062315  ,  0.61436   , -0.0048293 ,  0.64250505],\n",
       "       dtype=float32),\n",
       " 'employee': array([ 0.23666  ,  0.18914  ,  0.4051   ,  0.084121 ,  0.79485  ,\n",
       "         0.24362  , -0.75882  ,  0.39081  ,  0.67731  , -0.60095  ,\n",
       "         0.36774  ,  0.030937 , -0.25487  , -0.37063  ,  0.57514  ,\n",
       "        -0.39748  , -0.92277  ,  0.77213  ,  0.69639  , -0.44781  ,\n",
       "         0.51802  ,  0.50076  , -0.95403  ,  0.18261  , -0.91081  ,\n",
       "        -1.5448   ,  0.059238 , -0.25646  ,  0.091397 ,  0.13902  ,\n",
       "         2.0347   , -0.0022858,  0.13005  , -0.21161  ,  0.19615  ,\n",
       "        -0.0024776,  0.077975 , -0.40218  ,  1.0372   , -0.15382  ,\n",
       "         0.17604  , -0.23392  , -0.021668 ,  0.79639  , -0.11123  ,\n",
       "        -1.4084   , -0.68147  ,  0.84508  ,  0.22588  ,  1.1423   ],\n",
       "       dtype=float32),\n",
       " 'hotel management': array([ 1.04995   ,  0.484241  , -0.39340502,  0.44814998,  0.03807999,\n",
       "        -1.0467551 , -0.96108496, -0.334585  ,  0.027775  , -0.37606502,\n",
       "        -0.31160998,  0.40982997, -0.48510003, -0.19079998, -0.02955998,\n",
       "         0.427405  , -0.02176499,  0.789135  , -0.21969   , -0.321095  ,\n",
       "         1.038045  ,  0.61959   , -1.07748   , -0.08307499, -0.930235  ,\n",
       "        -0.916605  , -0.201269  ,  0.204985  , -0.37667403,  0.0473    ,\n",
       "         2.7614002 ,  0.062291  ,  0.14493   , -0.283635  ,  0.318475  ,\n",
       "         0.0515976 ,  0.028935  , -0.20672   ,  0.84733003, -0.13754925,\n",
       "        -0.154755  , -0.26866502,  0.2851945 ,  0.53207004, -0.23249   ,\n",
       "        -0.19608316, -0.59868246, -0.08124995,  0.49334502,  0.65852   ],\n",
       "       dtype=float32),\n",
       " 'management international': array([ 0.16174999,  0.314511  , -0.521375  ,  0.5272    , -0.2975    ,\n",
       "        -0.761605  , -0.48722002, -0.62609   ,  0.678775  , -0.1228    ,\n",
       "         0.608325  ,  0.66486   , -0.94907   , -0.11439998, -0.17986   ,\n",
       "         0.15328   ,  0.44120502,  0.473341  , -0.27633   , -0.27759   ,\n",
       "         0.969895  ,  0.436845  , -0.73555   , -0.313615  , -0.81153   ,\n",
       "        -1.3262501 ,  0.202161  , -0.54025495, -0.97136   ,  0.392685  ,\n",
       "         3.4543    ,  0.44865352, -0.162595  , -0.886725  , -0.37650502,\n",
       "        -0.1810245 , -0.30225998, -0.10362   ,  0.52833503, -0.121401  ,\n",
       "        -0.010095  , -0.337615  ,  0.5228495 ,  0.171295  , -0.17594406,\n",
       "         0.04945   , -0.2149025 ,  1.023035  ,  0.10212401,  0.35086498],\n",
       "       dtype=float32),\n",
       " 'operations exprience': array([ 0.57955  , -0.59995  ,  0.0363265,  0.294095 , -0.148395 ,\n",
       "        -0.255005 , -0.246855 ,  0.008846 ,  0.5308   , -0.5047   ,\n",
       "         0.263545 ,  0.13635  , -0.42052  ,  0.0468845, -0.36735  ,\n",
       "        -0.076675 , -0.049118 ,  0.24435  , -0.32156  ,  0.114975 ,\n",
       "         0.5388   , -0.13043  , -0.121705 , -0.311415 , -0.6449   ,\n",
       "        -0.5807   , -0.030222 , -0.2927   ,  0.415385 ,  0.191415 ,\n",
       "         1.72005  , -0.0228395, -0.140915 , -0.174565 ,  0.051035 ,\n",
       "         0.213    , -0.204665 , -0.09762  , -0.0274455,  0.332215 ,\n",
       "        -0.245045 , -0.116525 ,  0.22478  , -0.35477  , -0.0465515,\n",
       "        -0.28513  , -0.08712  ,  0.29535  , -0.271055 ,  0.497825 ],\n",
       "       dtype=float32),\n",
       " 'university': array([-1.1082e+00,  1.2916e+00, -7.8751e-01, -4.5955e-01, -4.0788e-01,\n",
       "        -5.3387e-01, -9.1468e-01, -4.8577e-01, -3.1858e-01, -3.3494e-01,\n",
       "         5.0093e-01, -5.0392e-01,  2.9449e-02, -1.1748e+00, -4.7245e-01,\n",
       "         2.7201e-01, -1.1093e-01,  1.0152e+00, -1.0427e+00,  1.4929e+00,\n",
       "         7.0221e-01,  7.2141e-01, -1.0898e-01, -1.9169e-03, -7.6224e-02,\n",
       "        -2.5398e+00,  1.2008e-01, -8.6093e-01, -2.1220e+00, -9.0913e-01,\n",
       "         2.6149e+00, -4.9744e-02, -9.6555e-02, -2.2488e+00, -6.0325e-02,\n",
       "         8.3282e-03, -1.0552e-01,  1.2435e+00,  1.7389e+00,  6.6512e-01,\n",
       "        -1.1836e-01, -6.1496e-02, -2.9160e-01,  3.7698e-01, -9.7014e-02,\n",
       "         4.8428e-01,  3.6570e-01,  2.6268e-01, -5.4315e-01,  3.5944e-02],\n",
       "       dtype=float32),\n",
       " 'mumbai university': array([-2.7213499e-01,  3.1037000e-01, -4.7560000e-01,  1.1560008e-02,\n",
       "        -1.3522500e-01, -5.2947998e-01, -2.4502501e-01, -3.0177000e-01,\n",
       "        -1.0231000e-01, -1.7249882e-03,  7.8195006e-02, -5.0769001e-01,\n",
       "        -4.2526552e-01, -6.7223501e-01, -3.8506499e-01,  4.1095501e-01,\n",
       "        -1.5380500e-01,  8.9613497e-01, -1.1256500e+00,  1.5866500e+00,\n",
       "         1.2949550e+00,  8.7110496e-01, -1.2658000e-01,  6.3659155e-01,\n",
       "        -3.0370999e-02, -1.8254499e+00, -2.2724997e-02, -6.2849998e-01,\n",
       "        -1.3866251e+00, -6.9010496e-01,  1.9348500e+00,  2.2197799e-01,\n",
       "         4.1928750e-01, -1.2618400e+00,  4.8073751e-01,  1.5238600e-02,\n",
       "         1.3845000e-02,  1.2321499e-01,  7.2325498e-01,  6.6706502e-01,\n",
       "        -5.9758002e-01, -7.4199498e-02, -4.3804996e-02,  1.2936500e-01,\n",
       "         1.0269800e-01,  4.6279499e-01,  2.2906801e-01,  2.0374000e-01,\n",
       "         4.9132499e-01, -4.8782802e-01], dtype=float32),\n",
       " 'company mumbai': array([ 5.9487998e-01, -6.2394500e-01,  1.2397000e-01,  6.7539501e-01,\n",
       "         2.7166497e-02, -1.2977000e-01, -5.2768505e-01, -2.5284499e-01,\n",
       "         2.7061498e-01,  2.2414501e-01,  2.1968001e-01,  1.5103000e-01,\n",
       "        -8.3380502e-01, -4.1203499e-01, -1.9844699e-01,  3.7823999e-01,\n",
       "        -4.6491501e-01,  8.2062501e-01, -7.3888505e-01,  3.7124002e-01,\n",
       "         1.9033500e+00,  4.2303500e-01, -3.8161501e-01,  3.8580000e-01,\n",
       "        -4.5930898e-01, -1.3933001e+00, -4.7975999e-01, -3.5804999e-01,\n",
       "        -3.5946399e-01, -2.9725999e-01,  2.2728500e+00,  3.1094998e-02,\n",
       "         6.6151500e-01,  5.6225002e-02,  5.0169051e-01, -4.7988048e-01,\n",
       "        -1.9933999e-01, -2.9920501e-01,  3.0106500e-01,  1.2968999e-01,\n",
       "        -2.4379000e-01, -3.0533648e-01,  2.4146000e-01, -1.8795499e-01,\n",
       "        -1.0799915e-03,  1.8750250e-01, -5.1483202e-01,  1.8031001e-01,\n",
       "         6.8497002e-01, -1.8571001e-01], dtype=float32),\n",
       " 'operations': array([ 1.1591  , -1.1999  ,  0.072653,  0.58819 , -0.29679 , -0.51001 ,\n",
       "        -0.49371 ,  0.017692,  1.0616  , -1.0094  ,  0.52709 ,  0.2727  ,\n",
       "        -0.84104 ,  0.093769, -0.7347  , -0.15335 , -0.098236,  0.4887  ,\n",
       "        -0.64312 ,  0.22995 ,  1.0776  , -0.26086 , -0.24341 , -0.62283 ,\n",
       "        -1.2898  , -1.1614  , -0.060444, -0.5854  ,  0.83077 ,  0.38283 ,\n",
       "         3.4401  , -0.045679, -0.28183 , -0.34913 ,  0.10207 ,  0.426   ,\n",
       "        -0.40933 , -0.19524 , -0.054891,  0.66443 , -0.49009 , -0.23305 ,\n",
       "         0.44956 , -0.70954 , -0.093103, -0.57026 , -0.17424 ,  0.5907  ,\n",
       "        -0.54211 ,  0.99565 ], dtype=float32),\n",
       " 'economics chennai': array([-0.67394996,  0.530402  , -0.28506452,  0.41601998, -0.828245  ,\n",
       "        -0.702125  ,  0.208442  , -0.510795  , -0.49194998,  0.0612925 ,\n",
       "         0.090838  , -0.092375  , -0.40194   , -0.2528605 , -0.1693995 ,\n",
       "         0.71220505,  0.123924  ,  0.796175  , -0.32138002,  0.959255  ,\n",
       "         1.1056    ,  0.30718   , -0.20062   ,  0.731735  ,  0.60734   ,\n",
       "        -0.82616   , -0.2392    , -0.77644503, -1.14166   ,  0.21239498,\n",
       "         1.4853001 ,  0.03397   ,  0.2051895 , -0.44973   ,  0.32281   ,\n",
       "        -0.252145  , -0.25857002,  0.95175505,  0.90894   ,  0.43464   ,\n",
       "        -0.24303   ,  0.101824  ,  0.2158165 ,  0.2877315 , -0.566523  ,\n",
       "         0.79745   ,  0.54025   ,  0.16729999,  0.79841   , -0.09301999],\n",
       "       dtype=float32),\n",
       " 'solution': array([ 1.1908   ,  0.23095  , -0.169    ,  0.043158 ,  0.16602  ,\n",
       "         0.61424  ,  0.74083  ,  0.050958 , -0.033029 ,  0.57433  ,\n",
       "         0.27801  ,  0.032734 , -0.16744  ,  0.039031 , -0.0091897,\n",
       "         1.0416   ,  1.2401   ,  0.066394 ,  1.1815   , -0.5456   ,\n",
       "         0.032248 ,  0.336    , -0.015792 ,  0.30239  ,  0.39266  ,\n",
       "        -0.66283  ,  0.29985  ,  0.40486  ,  0.92657  ,  0.95652  ,\n",
       "         3.1577   , -0.56382  , -1.8908   , -0.20699  ,  0.074541 ,\n",
       "        -0.22807  , -0.25308  ,  0.38774  ,  0.20464  , -0.19395  ,\n",
       "         0.55547  , -0.20615  ,  0.050134 , -0.13841  , -0.020059 ,\n",
       "         0.42713  ,  0.42016  ,  1.2225   ,  0.32241  ,  0.16752  ],\n",
       "       dtype=float32),\n",
       " 'economics': array([-1.4991  ,  1.1124  , -0.50814 , -0.52046 , -0.29619 , -0.72921 ,\n",
       "        -0.082186, -1.3696  , -1.2656  , -0.091535,  0.17002 ,  0.38438 ,\n",
       "        -0.44561 ,  0.037239,  0.049661,  0.40331 ,  0.31469 ,  0.54425 ,\n",
       "         0.15109 ,  0.51171 ,  1.0199  ,  0.48717 , -0.25363 , -0.18163 ,\n",
       "         0.84534 , -1.1826  , -0.32939 , -0.73919 , -1.2962  ,  1.2578  ,\n",
       "         2.0116  , -0.41193 , -0.080511, -1.1402  , -0.47288 , -0.70195 ,\n",
       "        -0.90143 ,  1.1943  ,  1.6968  ,  0.53972 , -0.28337 ,  0.064448,\n",
       "         0.5208  ,  0.48846 , -0.088546,  0.2897  ,  0.75557 ,  1.1493  ,\n",
       "         0.70663 ,  0.50772 ], dtype=float32),\n",
       " 'nadu': array([-0.58178 , -1.215   , -0.06413 ,  0.24097 , -0.15654 ,  0.39194 ,\n",
       "         0.43484 , -0.065563,  1.0259  , -0.66424 , -0.41411 , -0.89543 ,\n",
       "         1.1208  , -0.77881 , -0.69353 ,  0.63339 ,  1.1399  ,  1.2108  ,\n",
       "         0.071193,  0.79786 ,  1.3489  ,  0.34881 ,  0.40904 ,  0.82854 ,\n",
       "         0.90251 , -0.55971 , -0.74956 , -0.98205 , -1.6296  ,  0.44954 ,\n",
       "         1.1166  , -0.58572 , -0.25066 ,  0.045379,  0.81182 ,  0.48373 ,\n",
       "        -0.10146 , -0.28878 , -0.031393,  0.51603 , -0.86937 ,  1.0303  ,\n",
       "        -1.0586  , -0.36368 , -1.6642  ,  0.83731 , -0.61619 , -0.28157 ,\n",
       "         0.53267 , -1.3423  ], dtype=float32),\n",
       " 'nadu sdnbvc': array([-0.29089  , -0.6075   , -0.032065 ,  0.120485 , -0.07827  ,\n",
       "         0.19597  ,  0.21742  , -0.0327815,  0.51295  , -0.33212  ,\n",
       "        -0.207055 , -0.447715 ,  0.5604   , -0.389405 , -0.346765 ,\n",
       "         0.316695 ,  0.56995  ,  0.6054   ,  0.0355965,  0.39893  ,\n",
       "         0.67445  ,  0.174405 ,  0.20452  ,  0.41427  ,  0.451255 ,\n",
       "        -0.279855 , -0.37478  , -0.491025 , -0.8148   ,  0.22477  ,\n",
       "         0.5583   , -0.29286  , -0.12533  ,  0.0226895,  0.40591  ,\n",
       "         0.241865 , -0.05073  , -0.14439  , -0.0156965,  0.258015 ,\n",
       "        -0.434685 ,  0.51515  , -0.5293   , -0.18184  , -0.8321   ,\n",
       "         0.418655 , -0.308095 , -0.140785 ,  0.266335 , -0.67115  ],\n",
       "       dtype=float32),\n",
       " 'economics chennai tamil': array([-4.27377969e-01, -2.02198669e-01, -1.76178679e-01,  1.85813308e-01,\n",
       "        -5.85599959e-01, -3.12626690e-01,  1.42211363e-01, -4.28333312e-01,\n",
       "        -3.03824335e-01,  1.30499899e-03, -5.34879975e-02, -3.68839979e-01,\n",
       "        -6.99099898e-02, -2.82160312e-01, -3.50303024e-01,  4.17226702e-01,\n",
       "         4.98549342e-01,  8.71883392e-01, -3.40643317e-01,  1.13400328e+00,\n",
       "         8.58630002e-01,  6.14353359e-01,  1.01036668e-01,  7.57839978e-01,\n",
       "         6.59516633e-01, -7.92276680e-01, -3.81249994e-01, -8.74463379e-01,\n",
       "        -1.14227331e+00,  2.49243319e-01,  1.76166666e+00, -3.29620004e-01,\n",
       "        -1.53600335e-01, -2.26533413e-02,  4.49006647e-01,  1.07243337e-01,\n",
       "        -3.20883363e-01,  4.63366918e-02,  2.34426692e-01,  3.22262675e-01,\n",
       "        -3.86896640e-01,  2.62139320e-01, -1.09438993e-01, -1.11562334e-01,\n",
       "        -5.83872020e-01,  5.59369981e-01,  2.67576665e-01,  3.78056675e-01,\n",
       "         4.73956674e-01, -6.13846719e-01], dtype=float32),\n",
       " 'tamil': array([ 0.065766 , -1.6674   ,  0.041593 , -0.2746   , -0.10031  ,\n",
       "         0.46637  ,  0.0097501, -0.26341  ,  0.072427 , -0.11867  ,\n",
       "        -0.34214  , -0.92177  ,  0.59415  , -0.34076  , -0.71211  ,\n",
       "        -0.17273  ,  1.2478   ,  1.0233   , -0.37917  ,  1.4835   ,\n",
       "         0.36469  ,  1.2287   ,  0.70435  ,  0.81005  ,  0.76387  ,\n",
       "        -0.72451  , -0.66535  , -1.0705   , -1.1435   ,  0.32294  ,\n",
       "         2.3144   , -1.0568   , -0.87118  ,  0.8315   ,  0.7014   ,\n",
       "         0.82602  , -0.44551  , -1.7645   , -1.1146   ,  0.097508 ,\n",
       "        -0.67463  ,  0.58277  , -0.75995  , -0.91015  , -0.61857  ,\n",
       "         0.08321  , -0.27777  ,  0.79957  , -0.17495  , -1.6555   ],\n",
       "       dtype=float32),\n",
       " 'company anything solution': array([ 0.64564997, -0.16471334,  0.08284337,  0.253326  ,  0.27256432,\n",
       "         0.28651834, -0.28915665, -0.01818733,  0.007257  ,  0.41011   ,\n",
       "         0.24566334,  0.4919647 , -0.47509336, -0.292173  ,  0.35849878,\n",
       "         0.68619996,  0.35711336,  0.27863565,  0.3517233 , -0.81583   ,\n",
       "         0.58412933,  0.20182   , -0.075244  , -0.08049465,  0.09988666,\n",
       "        -1.4453768 , -0.47650668,  0.15525666,  0.788564  , -0.04996334,\n",
       "         3.0656002 , -0.21797667, -0.6854833 , -0.11375   , -0.06917933,\n",
       "        -0.46453333, -0.20424666,  0.31148997,  0.34618533, -0.30069   ,\n",
       "         0.3245333 , -0.23502564,  0.17590801,  0.07167999, -0.05662966,\n",
       "         0.07912501, -0.30597666,  0.6198933 ,  0.06971233,  0.51160336],\n",
       "       dtype=float32),\n",
       " 'chennai': array([ 0.1512  , -0.051596, -0.061989,  1.3525  , -1.3603  , -0.67504 ,\n",
       "         0.49907 ,  0.34801 ,  0.2817  ,  0.21412 ,  0.011656, -0.56913 ,\n",
       "        -0.35827 , -0.54296 , -0.38846 ,  1.0211  , -0.066842,  1.0481  ,\n",
       "        -0.79385 ,  1.4068  ,  1.1913  ,  0.12719 , -0.14761 ,  1.6451  ,\n",
       "         0.36934 , -0.46972 , -0.14901 , -0.8137  , -0.98712 , -0.83301 ,\n",
       "         0.959   ,  0.47987 ,  0.49089 ,  0.24074 ,  1.1185  ,  0.19766 ,\n",
       "         0.38429 ,  0.70921 ,  0.12108 ,  0.32956 , -0.20269 ,  0.1392  ,\n",
       "        -0.089167,  0.087003, -1.0445  ,  1.3052  ,  0.32493 , -0.8147  ,\n",
       "         0.89019 , -0.69376 ], dtype=float32),\n",
       " 'chennai tamil': array([ 0.108483  , -0.859498  , -0.010198  ,  0.53894997, -0.73030496,\n",
       "        -0.10433501,  0.25441006,  0.0423    ,  0.1770635 ,  0.047725  ,\n",
       "        -0.165242  , -0.74545   ,  0.11794001, -0.44186   , -0.550285  ,\n",
       "         0.42418504,  0.590479  ,  1.0357001 , -0.58651   ,  1.44515   ,\n",
       "         0.777995  ,  0.677945  ,  0.27837   ,  1.2275751 ,  0.566605  ,\n",
       "        -0.59711504, -0.40718   , -0.94210005, -1.06531   , -0.255035  ,\n",
       "         1.6366999 , -0.28846502, -0.190145  ,  0.53612   ,  0.90995   ,\n",
       "         0.51184   , -0.03061   , -0.527645  , -0.49675998,  0.213534  ,\n",
       "        -0.43866   ,  0.36098498, -0.4245585 , -0.4115735 , -0.831535  ,\n",
       "         0.694205  ,  0.02358   , -0.00756499,  0.35762   , -1.17463   ],\n",
       "       dtype=float32),\n",
       " 'bba lovely': array([ 0.1825515 ,  1.4672999 , -0.46164498,  0.16133499,  0.12325999,\n",
       "        -0.3840065 , -0.412795  , -0.3146915 , -0.558795  ,  0.42422003,\n",
       "        -0.31560498,  0.4705235 ,  0.3816205 ,  0.58605003, -0.005935  ,\n",
       "         0.127776  , -0.61815   , -0.03871001,  0.1883385 ,  0.367135  ,\n",
       "        -0.711795  ,  0.30664504,  0.00755   , -0.1694389 , -0.32725   ,\n",
       "        -0.29778   , -0.5401525 ,  0.643956  ,  0.17561999, -0.806575  ,\n",
       "         0.85144   ,  0.20941551,  0.07434499,  0.85412997, -0.24616998,\n",
       "        -0.33444   , -0.34266248, -0.191375  ,  0.5896405 , -0.47096398,\n",
       "         0.70196295, -0.22883499,  0.35619748,  0.44561502,  0.072245  ,\n",
       "         0.5998    ,  0.431975  ,  0.49645   , -0.79559   ,  0.35754   ],\n",
       "       dtype=float32),\n",
       " 'education details bba': array([ 1.28380999e-01,  1.02249670e+00, -1.04986645e-01,  7.02799782e-02,\n",
       "        -1.45470008e-01, -1.25743344e-01, -4.37166691e-01, -6.25600040e-01,\n",
       "        -2.66680002e-01, -2.10233331e-01, -2.05510005e-01,  3.29643339e-01,\n",
       "        -4.58729863e-02,  5.97396903e-02,  1.12830013e-01,  1.82066709e-02,\n",
       "        -7.51098633e-01, -1.61383331e-01,  7.26009071e-01,  5.40976644e-01,\n",
       "         9.24366713e-02,  1.83916688e-01, -9.30433273e-02, -2.10102007e-01,\n",
       "        -4.83963341e-01, -1.04033327e+00, -2.12021649e-01, -3.60199332e-01,\n",
       "        -4.12726641e-01, -1.76896989e-01,  2.23452663e+00,  1.99733332e-01,\n",
       "        -3.94730002e-01, -2.76450008e-01, -9.04993191e-02,  1.85833368e-02,\n",
       "        -1.89126670e-01,  1.80509891e-02,  4.60106641e-01, -1.00266561e-03,\n",
       "         3.63676637e-01, -2.96006620e-01,  3.99337679e-01,  5.68991661e-01,\n",
       "        -1.42726675e-01,  3.24376673e-01,  2.14286670e-01,  1.49928999e+00,\n",
       "        -3.32456678e-01,  3.52053314e-01], dtype=float32),\n",
       " 'communication exprience': array([ 1.36610e-01,  1.77280e-01,  1.59640e-01,  4.50940e-01,\n",
       "        -1.19235e-01, -3.51230e-02,  5.59550e-02, -3.19125e-01,\n",
       "         2.54325e-01, -1.05085e-01,  4.41865e-01,  2.27030e-01,\n",
       "        -8.09050e-02,  7.75100e-04, -6.96000e-01,  1.81000e-02,\n",
       "        -4.22445e-01,  1.56540e-01,  2.57805e-01,  5.63050e-03,\n",
       "         3.63100e-01,  4.54780e-01, -1.65815e-01,  6.93450e-02,\n",
       "         4.71330e-01, -3.18200e-01,  2.31915e-01, -4.61070e-01,\n",
       "         2.26385e-01,  1.44005e-01,  1.59255e+00,  9.28000e-02,\n",
       "        -5.30550e-02, -7.13700e-01, -2.57670e-01,  1.89985e-01,\n",
       "        -2.08280e-01,  1.17985e-01, -6.54150e-02,  5.22750e-01,\n",
       "         5.05650e-01, -1.18935e-01,  2.27370e-02,  2.84520e-01,\n",
       "        -5.63650e-02, -8.54850e-02,  2.33545e-01,  3.51995e-01,\n",
       "         4.47270e-02,  1.92735e-01], dtype=float32),\n",
       " 'lovely': array([ 0.44626  ,  1.1243   , -1.5112   , -0.33919  ,  0.96984  ,\n",
       "        -0.023973 , -0.24074  ,  0.012837 , -0.19629  ,  0.50242  ,\n",
       "        -0.84935  ,  0.029707 ,  0.81295  ,  0.0195   ,  0.29288  ,\n",
       "         0.050392 ,  0.2858   ,  0.19708  ,  0.28841  , -0.69433  ,\n",
       "        -0.36479  ,  1.0792   , -0.13448  ,  0.0026922,  1.1306   ,\n",
       "         0.04454  , -1.0981   ,  1.3454   ,  0.69688  , -0.88612  ,\n",
       "         1.488    , -0.060989 ,  0.45463  ,  0.54266  ,  0.36983  ,\n",
       "         0.19683  , -0.018415 ,  0.37568  , -0.076219 , -0.9148   ,\n",
       "         0.0026259,  0.11982  ,  0.048395 , -0.51267  ,  0.28838  ,\n",
       "         0.1114   ,  0.38044  , -1.1832   ,  0.23562  ,  0.41061  ],\n",
       "       dtype=float32),\n",
       " 'bba': array([-0.081157,  1.8103  ,  0.58791 ,  0.66186 , -0.72332 , -0.74404 ,\n",
       "        -0.58485 , -0.64222 , -0.9213  ,  0.34602 ,  0.21814 ,  0.91134 ,\n",
       "        -0.049709,  1.1526  , -0.30475 ,  0.20516 , -1.5221  , -0.2745  ,\n",
       "         0.088267,  1.4286  , -1.0588  , -0.46591 ,  0.14958 , -0.34157 ,\n",
       "        -1.7851  , -0.6401  ,  0.017795, -0.057488, -0.34564 , -0.72703 ,\n",
       "         0.21488 ,  0.47982 , -0.30594 ,  1.1656  , -0.86217 , -0.86571 ,\n",
       "        -0.66691 , -0.75843 ,  1.2555  , -0.027128,  1.4013  , -0.57749 ,\n",
       "         0.664   ,  1.4039  , -0.14389 ,  1.0882  ,  0.48351 ,  2.1761  ,\n",
       "        -1.8268  ,  0.30447 ], dtype=float32),\n",
       " 'bba lovely professional': array([-0.38023233,  1.1763066 , -0.62367666,  0.19104667, -0.03695334,\n",
       "        -0.26100767, -0.62523   , -0.032561  , -0.49466667,  0.32787335,\n",
       "         0.17799668,  0.42389235,  0.136747  ,  0.60762334,  0.12665333,\n",
       "        -0.25594932, -0.33541667,  0.37522665, -0.082861  ,  0.08994333,\n",
       "        -0.49757132,  0.42491004, -0.08375334,  0.17432408, -0.42373   ,\n",
       "        -0.58681995, -0.490235  , -0.01182934, -0.06202   , -0.43602   ,\n",
       "         1.5346266 ,  0.288417  , -0.01029667,  0.24267997, -0.01612332,\n",
       "         0.0425    , -0.28894165,  0.06494666,  0.40815067, -0.6199027 ,\n",
       "         0.525952  , -0.36277333,  0.320305  ,  0.5358367 ,  0.00475667,\n",
       "         0.19848333,  0.20758335,  0.4670367 , -0.57008666,  0.36067334],\n",
       "       dtype=float32),\n",
       " 'communication': array([ 2.7322e-01,  3.5456e-01,  3.1928e-01,  9.0188e-01, -2.3847e-01,\n",
       "        -7.0246e-02,  1.1191e-01, -6.3825e-01,  5.0865e-01, -2.1017e-01,\n",
       "         8.8373e-01,  4.5406e-01, -1.6181e-01,  1.5502e-03, -1.3920e+00,\n",
       "         3.6200e-02, -8.4489e-01,  3.1308e-01,  5.1561e-01,  1.1261e-02,\n",
       "         7.2620e-01,  9.0956e-01, -3.3163e-01,  1.3869e-01,  9.4266e-01,\n",
       "        -6.3640e-01,  4.6383e-01, -9.2214e-01,  4.5277e-01,  2.8801e-01,\n",
       "         3.1851e+00,  1.8560e-01, -1.0611e-01, -1.4274e+00, -5.1534e-01,\n",
       "         3.7997e-01, -4.1656e-01,  2.3597e-01, -1.3083e-01,  1.0455e+00,\n",
       "         1.0113e+00, -2.3787e-01,  4.5474e-02,  5.6904e-01, -1.1273e-01,\n",
       "        -1.7097e-01,  4.6709e-01,  7.0399e-01,  8.9454e-02,  3.8547e-01],\n",
       "       dtype=float32),\n",
       " 'lovely professional': array([-0.52977   ,  0.85931003, -1.22947   , -0.04436   ,  0.30623   ,\n",
       "        -0.0194915 , -0.64541996,  0.2722685 , -0.28135   ,  0.3188    ,\n",
       "         0.15792501,  0.18016851,  0.22997501,  0.335135  ,  0.342355  ,\n",
       "        -0.486504  ,  0.257925  ,  0.70009   , -0.168425  , -0.579385  ,\n",
       "        -0.216957  ,  0.87032   , -0.20042   ,  0.43227112,  0.256955  ,\n",
       "        -0.56017995, -0.74424994,  0.01099998,  0.07979   , -0.290515  ,\n",
       "         2.1945    ,  0.19271551,  0.13752499, -0.21878001,  0.4069    ,\n",
       "         0.49660498, -0.0999575 ,  0.47663498, -0.015524  , -0.91629   ,\n",
       "         0.08827795, -0.255415  ,  0.1484575 ,  0.101805  ,  0.07908   ,\n",
       "        -0.246375  ,  0.06962   , -0.38749498,  0.05827   ,  0.388775  ],\n",
       "       dtype=float32),\n",
       " 'lovely professional university': array([-7.2257996e-01,  1.0034066e+00, -1.0821500e+00, -1.8275666e-01,\n",
       "         6.8193339e-02, -1.9095099e-01, -7.3517329e-01,  1.9589007e-02,\n",
       "        -2.9375997e-01,  1.0088667e-01,  2.7226001e-01, -4.7860999e-02,\n",
       "         1.6313301e-01, -1.6817667e-01,  7.0753343e-02, -2.3366599e-01,\n",
       "         1.3497333e-01,  8.0512667e-01, -4.5984998e-01,  1.1137668e-01,\n",
       "         8.9432001e-02,  8.2068330e-01, -1.6993999e-01,  2.8754178e-01,\n",
       "         1.4589533e-01, -1.2200533e+00, -4.5613995e-01, -2.7964336e-01,\n",
       "        -6.5414000e-01, -4.9671999e-01,  2.3346334e+00,  1.1189567e-01,\n",
       "         5.9498329e-02, -8.9545339e-01,  2.5115833e-01,  3.3384606e-01,\n",
       "        -1.0181167e-01,  7.3225665e-01,  5.6928396e-01, -3.8915333e-01,\n",
       "         1.9398635e-02, -1.9077533e-01,  1.7716686e-03,  1.9353001e-01,\n",
       "         2.0382000e-02, -2.8233330e-03,  1.6831334e-01, -1.7076999e-01,\n",
       "        -1.4220333e-01,  2.7116466e-01], dtype=float32),\n",
       " 'professional': array([-1.5058  ,  0.59432 , -0.94774 ,  0.25047 , -0.35738 , -0.01501 ,\n",
       "        -1.0501  ,  0.5317  , -0.36641 ,  0.13518 ,  1.1652  ,  0.33063 ,\n",
       "        -0.353   ,  0.65077 ,  0.39183 , -1.0234  ,  0.23005 ,  1.2031  ,\n",
       "        -0.62526 , -0.46444 , -0.069124,  0.66144 , -0.26636 ,  0.86185 ,\n",
       "        -0.61669 , -1.1649  , -0.3904  , -1.3234  , -0.5373  ,  0.30509 ,\n",
       "         2.901   ,  0.44642 , -0.17958 , -0.98022 ,  0.44397 ,  0.79638 ,\n",
       "        -0.1815  ,  0.57759 ,  0.045171, -0.91778 ,  0.17393 , -0.63065 ,\n",
       "         0.24852 ,  0.71628 , -0.13022 , -0.60415 , -0.2412  ,  0.40821 ,\n",
       "        -0.11908 ,  0.36694 ], dtype=float32),\n",
       " 'professional university': array([-1.3069999 ,  0.94296   , -0.867625  , -0.10453999, -0.38263   ,\n",
       "        -0.27444   , -0.98239   ,  0.02296501, -0.342495  , -0.09987999,\n",
       "         0.83306503, -0.08664501, -0.1617755 , -0.262015  , -0.04031   ,\n",
       "        -0.375695  ,  0.05956   ,  1.1091499 , -0.83398   ,  0.51423   ,\n",
       "         0.316543  ,  0.69142497, -0.18767   ,  0.42996657, -0.346457  ,\n",
       "        -1.85235   , -0.13516   , -1.092165  , -1.3296499 , -0.30201998,\n",
       "         2.75795   ,  0.198338  , -0.1380675 , -1.61451   ,  0.1918225 ,\n",
       "         0.4023541 , -0.14351   ,  0.910545  ,  0.8920355 , -0.12632999,\n",
       "         0.027785  , -0.346073  , -0.02153999,  0.54663   , -0.113617  ,\n",
       "        -0.059935  ,  0.06225   ,  0.335445  , -0.331115  ,  0.201442  ],\n",
       "       dtype=float32),\n",
       " 'assistant': array([-1.1453  ,  0.35973 , -0.77082 ,  0.023133,  0.59523 , -0.80477 ,\n",
       "        -1.2182  ,  0.070484, -0.2934  , -1.767   ,  0.46365 ,  1.2107  ,\n",
       "        -0.78048 , -0.080696,  0.28928 , -0.093388, -0.30316 ,  0.74663 ,\n",
       "        -0.075706,  0.34656 ,  0.027788,  0.56972 , -0.082096, -0.15734 ,\n",
       "        -0.6096  , -1.81    ,  0.73415 , -0.41556 , -1.0881  ,  0.06216 ,\n",
       "         1.8189  , -0.36865 , -0.072859, -1.3569  ,  0.37075 ,  0.7292  ,\n",
       "         0.080541,  0.80372 ,  1.172   ,  0.086435, -0.37322 ,  0.91535 ,\n",
       "        -0.36493 , -0.37147 ,  0.12733 , -0.16291 , -0.18144 ,  0.22021 ,\n",
       "        -0.351   ,  1.4706  ], dtype=float32),\n",
       " 'company hr assistant': array([-5.63390017e-01,  5.86233325e-02,  3.43569994e-01,  5.56264341e-01,\n",
       "         3.11887652e-01, -2.98633337e-01, -6.60253346e-01, -4.42578673e-01,\n",
       "         2.68583328e-01, -6.38133347e-01,  2.35546663e-01,  8.62689972e-01,\n",
       "        -6.87200069e-01, -1.20501995e-01, -1.64598003e-01, -3.97135973e-01,\n",
       "        -3.35698694e-01,  7.02830017e-01, -1.29314661e-01,  9.91400108e-02,\n",
       "         7.20512629e-01, -7.91333541e-02,  9.24946591e-02, -1.14466595e-02,\n",
       "        -3.98826689e-01, -8.83419991e-01,  1.93149984e-01, -6.99429989e-01,\n",
       "        -2.65726000e-01, -1.06006660e-01,  2.15573335e+00, -7.76966587e-02,\n",
       "         2.98620343e-01, -1.59479991e-01,  1.45065010e-01, -1.00862347e-01,\n",
       "         7.99703375e-02,  4.36913341e-01,  8.36513340e-01,  2.65234977e-01,\n",
       "         7.70002604e-04, -1.04706667e-01, -8.91133323e-02,  5.84500134e-02,\n",
       "        -3.56023312e-01, -1.77954987e-01, -2.69246668e-01,  2.59283334e-01,\n",
       "         1.97780013e-01,  1.31932676e+00], dtype=float32),\n",
       " 'mgt': array([ 0.051105, -0.33011 ,  0.092912,  0.14733 , -0.48198 ,  0.17336 ,\n",
       "         0.97791 , -0.35338 , -0.1404  , -0.31944 , -0.045383,  0.22029 ,\n",
       "        -0.21636 , -0.10049 , -0.29901 ,  0.44867 ,  0.19843 ,  0.77298 ,\n",
       "         0.5135  ,  0.33245 ,  0.039152,  0.052839, -0.24771 ,  0.20256 ,\n",
       "         0.083131,  1.3183  ,  0.119   , -0.41864 , -0.098971,  0.25209 ,\n",
       "        -0.93474 ,  0.038683,  0.33097 ,  0.47647 , -0.24101 ,  0.42515 ,\n",
       "        -0.25346 , -0.62982 , -0.040897, -0.25082 ,  0.066174, -0.86449 ,\n",
       "         0.092951,  0.29505 , -0.13285 ,  0.046236,  0.49307 , -0.41415 ,\n",
       "        -0.19395 , -0.20777 ], dtype=float32),\n",
       " 'mba acn college': array([-1.0645833 ,  0.5225833 ,  0.10271666, -0.26362666, -0.30913422,\n",
       "        -0.67560667, -0.22097667, -0.33088335,  0.16738002,  0.20188   ,\n",
       "         0.2524633 ,  0.08169667,  0.14125   , -0.17691232, -0.50327665,\n",
       "        -0.08149533, -0.24715333,  0.9408667 , -0.05867332,  0.3739033 ,\n",
       "         0.41590667,  0.36676335,  0.14984022,  0.173214  , -0.2821    ,\n",
       "        -0.5596833 ,  0.06331401, -0.9290433 , -1.3348132 ,  0.03717667,\n",
       "         1.1292766 ,  0.41531166, -0.407138  , -0.47356334,  0.5678    ,\n",
       "         0.04226334, -0.25800002,  0.45420995,  0.66877   , -0.44113335,\n",
       "         0.12688667, -0.38851666, -0.22019565,  0.40789333, -0.9274066 ,\n",
       "         0.2876433 ,  0.43971333,  0.41267666, -0.19271399,  0.16175334],\n",
       "       dtype=float32),\n",
       " 'acn college': array([-0.948675  ,  0.36876503, -0.09674999, -0.04554999, -0.08462635,\n",
       "        -0.40491   , -0.16570002, -0.212135  ,  0.501155  ,  0.01248997,\n",
       "         0.19217998,  0.221195  ,  0.29216498, -0.305185  , -0.51465   ,\n",
       "         0.092877  , -0.13899   ,  0.78915   , -0.509695  ,  0.29747498,\n",
       "         0.318905  ,  0.08502999,  0.22124   ,  0.24395502, -0.13666001,\n",
       "        -0.704655  ,  0.175141  , -0.834065  , -1.1577201 , -0.234395  ,\n",
       "         1.172115  ,  0.3723375 , -0.285557  , -0.538625  ,  0.441835  ,\n",
       "        -0.191465  , -0.19577   , -0.08188501,  0.383455  , -0.31743   ,\n",
       "        -0.01458001, -0.48668998, -0.3854735 ,  0.51269   , -0.91849494,\n",
       "         0.77059495,  0.21827501,  0.23660001, -0.28116   , -0.124755  ],\n",
       "       dtype=float32),\n",
       " 'engineering mgt': array([-0.12868251, -0.04775   ,  0.105551  ,  0.32529   , -0.47209   ,\n",
       "         0.254005  ,  0.366     , -0.62815   ,  0.12898499, -0.34082   ,\n",
       "         0.5169085 ,  0.22290501, -0.41039002, -0.2014    , -0.854505  ,\n",
       "         0.1095    ,  0.061599  ,  1.31084   ,  0.13733998,  0.060785  ,\n",
       "         0.701626  ,  0.2546695 , -0.499545  , -0.40221998,  0.0269125 ,\n",
       "         0.186705  , -0.37153998, -0.94577   , -0.5402955 ,  0.252575  ,\n",
       "         0.86208   , -0.2208285 ,  0.17553699, -0.29326499,  0.23785499,\n",
       "         0.490825  , -0.33930498,  0.33984   ,  0.7009015 ,  0.13037   ,\n",
       "         0.0219275 , -0.96564496,  0.2414855 ,  0.23135   , -0.0437895 ,\n",
       "         0.130668  ,  0.49231   ,  0.18360499, -0.25402   , -0.07472   ],\n",
       "       dtype=float32),\n",
       " 'acn': array([-0.66905 , -0.68007 ,  0.49275 ,  1.0704  , -0.16729 , -0.28405 ,\n",
       "         1.2663  , -0.1712  ,  1.2193  ,  0.5907  ,  0.52684 ,  0.28474 ,\n",
       "         0.3967  ,  0.70693 , -0.49492 , -0.019976,  0.20187 ,  0.5307  ,\n",
       "        -0.51189 , -0.17831 ,  0.24007 , -0.5334  ,  0.7648  , -0.1138  ,\n",
       "        -0.1398  ,  0.43269 ,  0.44288 , -0.53933 , -0.82954 ,  0.39356 ,\n",
       "        -0.25337 ,  0.083215, -0.51702 ,  0.49825 , -0.23483 , -0.52204 ,\n",
       "        -0.12239 , -0.73536 , -0.56589 , -0.38625 ,  0.65638 , -0.53382 ,\n",
       "         0.096493,  0.06766 , -1.1635  ,  1.1807  , -0.18297 ,  0.73676 ,\n",
       "        -0.19082 , -0.57044 ], dtype=float32),\n",
       " 'mba acn': array([-0.98272496,  0.075075  ,  0.49719998,  0.18531   , -0.46271998,\n",
       "        -0.750525  ,  0.467385  , -0.36979002,  0.35956502,  0.58568   ,\n",
       "         0.449935  ,  0.04372   ,  0.11806   ,  0.3932815 , -0.487725  ,\n",
       "        -0.225108  , -0.130805  ,  0.88750005,  0.16574001,  0.17422499,\n",
       "         0.42499   ,  0.19841501,  0.38592035, -0.041034  , -0.35639   ,\n",
       "         0.081475  ,  0.14127001, -0.829165  , -1.25927   ,  0.48694   ,\n",
       "         0.395115  ,  0.2922375 , -0.58366   ,  0.07740501,  0.29244998,\n",
       "        -0.00615999, -0.25242501,  0.39551997,  0.336755  , -0.537395  ,\n",
       "         0.5331    , -0.36299497,  0.1034265 ,  0.13298   , -1.0543649 ,\n",
       "         0.25121996,  0.34981   ,  0.750795  , -0.103321  ,  0.082165  ],\n",
       "       dtype=float32),\n",
       " 'management accounting': array([ 2.14671493e-01, -6.17008984e-01, -4.64550033e-02,  4.36964989e-01,\n",
       "        -2.44610012e-01,  1.71799958e-02, -2.53225029e-01, -9.50784981e-01,\n",
       "         3.67915004e-01, -3.48120004e-01,  6.56800047e-02,  6.51649952e-01,\n",
       "        -8.06815028e-01, -4.35319990e-01,  1.27560019e-01, -1.70300007e-02,\n",
       "        -2.79992819e-04,  5.40359974e-01,  2.70339996e-01, -3.01310003e-01,\n",
       "         9.59645033e-01,  1.97084993e-01, -1.53384984e-01, -7.93154955e-01,\n",
       "        -6.70590043e-01, -9.21839952e-01, -3.73324007e-01, -5.11439979e-01,\n",
       "        -5.41750014e-01,  4.85000014e-01,  2.96374989e+00, -1.80656508e-01,\n",
       "         2.37264991e-01, -6.45754993e-01, -1.06910504e-01,  5.81780002e-02,\n",
       "        -6.56679988e-01,  1.52904987e-01,  1.24005008e+00, -3.25834990e-01,\n",
       "        -4.17555004e-01, -4.72945005e-01,  6.00049496e-01,  9.97630000e-01,\n",
       "        -6.73920035e-01, -5.36965013e-01,  7.21650198e-03,  1.35249996e+00,\n",
       "         5.26120007e-01,  6.27604485e-01], dtype=float32),\n",
       " 'income tax good': array([-0.22320999,  0.25345   ,  0.32207668, -0.7029433 ,  0.50082   ,\n",
       "         0.49233666, -0.38065663, -0.6704633 ,  0.09393734,  0.24460799,\n",
       "        -0.43968365,  0.16751666,  0.30860665, -0.769625  ,  0.65142   ,\n",
       "        -0.09162167,  0.43650067, -0.10215333,  0.5478112 , -0.5610166 ,\n",
       "         0.4487033 , -0.6389367 , -0.20583999, -0.24470933, -0.39659667,\n",
       "        -1.4205467 , -0.4100813 , -0.26016667,  0.61198   ,  0.28069332,\n",
       "         3.3201666 ,  0.8253533 ,  0.11393333,  0.13793   ,  0.117787  ,\n",
       "        -0.41495502, -0.41737998,  0.35036302,  0.48360336, -0.73114663,\n",
       "        -0.49388668,  0.11059672,  0.39287665,  0.60220665, -0.8916133 ,\n",
       "        -0.6254501 , -0.52418065,  0.5408099 ,  0.6464134 ,  0.7694733 ],\n",
       "       dtype=float32),\n",
       " 'rlt scrum master': array([-0.37497333, -0.04694001, -0.30249667, -0.4767867 , -0.01384   ,\n",
       "        -0.10052333, -0.20391732, -0.09523001, -0.28936335,  0.02384333,\n",
       "         0.23859668,  0.54922   , -0.31092533,  0.20237064, -0.18201333,\n",
       "        -0.08750067,  0.60267466,  0.11430999, -0.5448433 , -0.21820001,\n",
       "        -0.06824001,  0.25884   , -0.46767664,  0.10403133,  0.09098333,\n",
       "        -0.49765268,  0.21691664, -0.4337333 , -0.46695998,  0.17029233,\n",
       "         1.1357666 , -0.17266   , -0.21864666,  0.13144   ,  0.26910767,\n",
       "         0.6842534 ,  0.16552667,  0.25323668,  0.34108666, -0.35596335,\n",
       "         0.5696933 ,  0.23004   , -0.15842666,  0.30505332,  0.09890199,\n",
       "         0.02699   ,  0.48511663, -0.23043734,  0.15806466, -0.30803233],\n",
       "       dtype=float32),\n",
       " 'tax good': array([-0.24236   ,  0.20897001, -0.01212502, -0.61603   ,  0.32664502,\n",
       "         0.332145  , -0.38816   , -0.431695  , -0.005119  ,  0.35311997,\n",
       "        -0.652855  ,  0.457335  , -0.01704499, -0.2661375 ,  0.64170504,\n",
       "        -0.1378355 ,  0.614565  , -0.09268999,  0.4664768 , -0.74824   ,\n",
       "         0.352635  , -0.44610497, -0.19692999, -0.251584  , -0.03419501,\n",
       "        -1.78545   , -0.426702  , -0.05306499,  0.69543004,  0.060675  ,\n",
       "         3.3602    ,  0.64883   , -0.34595   ,  0.1675425 ,  0.07654551,\n",
       "        -0.2874975 , -0.204855  ,  0.49501002,  0.38501   , -0.710625  ,\n",
       "        -0.370215  ,  0.20362909,  0.25334   ,  0.51826   , -0.58987   ,\n",
       "        -0.288975  , -0.433306  ,  0.56242   ,  0.66597503,  0.752985  ],\n",
       "       dtype=float32),\n",
       " 'computer': array([ 0.079084, -0.81504 ,  1.7901  ,  0.91653 ,  0.10797 , -0.55628 ,\n",
       "        -0.84427 , -1.4951  ,  0.13418 ,  0.63627 ,  0.35146 ,  0.25813 ,\n",
       "        -0.55029 ,  0.51056 ,  0.37409 ,  0.12092 , -1.6166  ,  0.83653 ,\n",
       "         0.14202 , -0.52348 ,  0.73453 ,  0.12207 , -0.49079 ,  0.32533 ,\n",
       "         0.45306 , -1.585   , -0.63848 , -1.0053  ,  0.10454 , -0.42984 ,\n",
       "         3.181   , -0.62187 ,  0.16819 , -1.0139  ,  0.064058,  0.57844 ,\n",
       "        -0.4556  ,  0.73783 ,  0.37203 , -0.57722 ,  0.66441 ,  0.055129,\n",
       "         0.037891,  1.3275  ,  0.30991 ,  0.50697 ,  1.2357  ,  0.1274  ,\n",
       "        -0.11434 ,  0.20709 ], dtype=float32),\n",
       " 'tax good communication': array([-0.07049999,  0.25750002,  0.09834332, -0.11005998,  0.13827334,\n",
       "         0.19801468, -0.22147   , -0.5005467 ,  0.16613734,  0.16535665,\n",
       "        -0.14065999,  0.4562433 , -0.0653    , -0.17690827, -0.03619663,\n",
       "        -0.07982367,  0.12808001,  0.04256668,  0.48285452, -0.495073  ,\n",
       "         0.47715664,  0.00578336, -0.24182999, -0.12149266,  0.29142332,\n",
       "        -1.4024334 , -0.129858  , -0.34275666,  0.6145434 ,  0.13645333,\n",
       "         3.3018334 ,  0.49442002, -0.26600334, -0.364105  , -0.12074965,\n",
       "        -0.06500832, -0.27542332,  0.40866336,  0.21306334, -0.12524998,\n",
       "         0.09028999,  0.05646273,  0.18405133,  0.5351867 , -0.43082333,\n",
       "        -0.24963999, -0.133174  ,  0.60961   ,  0.47380137,  0.63048   ],\n",
       "       dtype=float32),\n",
       " 'sincere hard': array([ 0.18223003,  0.25321   , -0.20299003, -0.2546    ,  0.70681953,\n",
       "        -0.012004  ,  0.322585  ,  0.72307503, -0.49755502,  0.96046   ,\n",
       "        -0.35877502,  0.34307998, -0.13657   , -0.07041499, -0.3265245 ,\n",
       "        -0.15054499,  0.33461902,  0.034515  ,  0.80586004, -0.479598  ,\n",
       "        -0.5004    ,  1.01128   ,  0.11028799, -0.245565  ,  1.23555   ,\n",
       "        -1.285605  , -0.23600149,  0.153025  ,  0.74239004, -0.317775  ,\n",
       "         2.6352    ,  0.60214996, -0.238435  ,  0.1183095 , -0.284415  ,\n",
       "        -0.321405  , -0.261495  , -0.06305499, -0.55869   , -0.45375   ,\n",
       "         0.130705  , -0.34521502, -0.18697199,  0.13100001,  0.44603503,\n",
       "         0.239165  , -0.5281955 ,  0.538345  , -0.06197   ,  0.101759  ],\n",
       "       dtype=float32),\n",
       " 'rlt scrum': array([-0.28152   , -0.461025  , -0.20979   , -0.46347   , -0.31866   ,\n",
       "         0.06308   , -0.26209   , -0.059805  , -0.099355  , -0.158995  ,\n",
       "         0.1155    ,  0.23478   , -0.4878    , -0.00462405, -0.100255  ,\n",
       "         0.041314  ,  0.9489    , -0.371835  , -0.57705   , -0.379915  ,\n",
       "        -0.5388    ,  0.26703   , -0.389655  ,  0.202475  ,  0.05306   ,\n",
       "         0.025521  ,  0.71175   ,  0.0601    , -0.311745  ,  0.28809   ,\n",
       "         0.57075   ,  0.23746   ,  0.114025  ,  0.343     ,  0.0303815 ,\n",
       "         0.66025   ,  0.17194   ,  0.114925  ,  0.148065  , -0.390895  ,\n",
       "         0.634     ,  0.429795  , -0.08027   ,  0.240345  ,  0.111695  ,\n",
       "         0.20932   ,  0.29387   ,  0.029439  ,  0.262195  , -0.44603   ],\n",
       "       dtype=float32),\n",
       " 'ecommerce': array([ 0.11415  ,  0.15047  ,  0.67296  ,  1.231    , -0.65175  ,\n",
       "        -0.44908  , -0.7593   , -0.9653   ,  1.0161   ,  0.32806  ,\n",
       "        -0.16527  , -0.36912  , -0.35843  ,  0.060718 , -0.41706  ,\n",
       "        -0.26613  , -0.14739  ,  0.33398  ,  1.1997   ,  0.12102  ,\n",
       "         1.1511   , -0.4962   , -0.80816  ,  1.1746   , -0.32588  ,\n",
       "         0.95211  , -0.36892  , -0.59857  ,  0.201    ,  0.60576  ,\n",
       "         0.16535  , -0.20607  , -0.27595  ,  0.071692 , -0.49211  ,\n",
       "         0.3222   , -0.59462  , -0.0052707, -0.48777  ,  0.033348 ,\n",
       "         1.245    , -0.97362  , -0.61898  ,  0.21425  ,  0.29488  ,\n",
       "        -0.39053  ,  0.16289  ,  0.62758  ,  0.90824  , -0.060733 ],\n",
       "       dtype=float32),\n",
       " 'driving license internet': array([-0.25954664, -0.17428666,  0.76640004,  0.37388667, -0.41344333,\n",
       "         0.05111333, -1.0075401 , -0.5554843 ,  0.61163664,  0.49059   ,\n",
       "         0.32606664,  0.00667334, -0.2935433 , -0.21453667,  0.5191867 ,\n",
       "         0.07771667, -0.7209633 ,  0.27864334, -0.03974634, -0.8364933 ,\n",
       "         0.65779996,  0.1365    , -0.49975666,  0.70136   ,  0.08298334,\n",
       "        -1.6040667 , -0.04827334, -0.35279334,  0.8566366 , -0.14265333,\n",
       "         2.5579002 ,  0.0376    , -0.46029666, -0.39374003,  0.22620666,\n",
       "        -0.19095866, -0.01845368, -0.18557066, -0.35654667,  0.02163666,\n",
       "         0.41164002, -0.02583633,  0.126794  ,  0.90222996, -0.26486468,\n",
       "        -0.71614337,  0.09618833, -0.04955567,  0.48216334,  0.03444307],\n",
       "       dtype=float32),\n",
       " 'mba finance': array([-0.51787496,  0.29081   ,  0.42041498, -0.262655  , -0.29878   ,\n",
       "        -0.694705  , -0.41372502, -0.83704   , -0.825835  , -0.046     ,\n",
       "        -0.07416999,  0.39567   , -0.277885  ,  0.2326415 , -0.00971   ,\n",
       "        -0.125575  ,  0.03677499,  0.5865005 ,  0.950485  ,  0.423675  ,\n",
       "         1.089705  ,  0.17484   , -0.27116966, -0.33261898, -0.50488997,\n",
       "        -0.79297   ,  0.28917   , -0.840445  , -1.6014    ,  0.76126003,\n",
       "         1.9356999 ,  0.34224498, -0.3370735 , -0.10768   ,  0.29213   ,\n",
       "         0.35494   , -0.50382996,  1.10231   ,  1.17595   , -0.426085  ,\n",
       "        -0.125665  ,  0.225875  ,  0.21440999, -0.102715  , -0.374035  ,\n",
       "        -0.31654403, -0.022145  ,  1.156665  ,  0.76428896,  0.623965  ],\n",
       "       dtype=float32),\n",
       " 'system months dbms': array([ 0.58894   , -0.5046666 ,  0.43411663,  0.12346667, -0.54353434,\n",
       "         0.27177   , -0.09960967, -0.38431668,  0.11005667, -0.19520001,\n",
       "         0.46254238,  0.07755666, -0.17244251,  0.05494767,  0.08510666,\n",
       "         0.06748668, -0.6044933 ,  0.29880667,  0.19055332, -0.20381999,\n",
       "        -0.03549333, -0.5202233 , -0.35025668,  0.14684667, -0.15808   ,\n",
       "        -0.83043337,  0.17974667, -0.16861866,  0.05311667,  0.25539997,\n",
       "         2.554193  , -0.25326   , -0.14675666, -0.45591664, -0.06017999,\n",
       "         0.25782332, -0.039245  ,  0.04486001,  0.22892666, -0.314029  ,\n",
       "        -0.05184666, -0.351958  ,  0.20903899,  0.67803335, -0.52123   ,\n",
       "        -0.20054667,  0.49332666,  0.41210666,  0.18026668,  0.00526467],\n",
       "       dtype=float32),\n",
       " 'scrum': array([-0.56304  , -0.92205  , -0.41958  , -0.92694  , -0.63732  ,\n",
       "         0.12616  , -0.52418  , -0.11961  , -0.19871  , -0.31799  ,\n",
       "         0.231    ,  0.46956  , -0.9756   , -0.0092481, -0.20051  ,\n",
       "         0.082628 ,  1.8978   , -0.74367  , -1.1541   , -0.75983  ,\n",
       "        -1.0776   ,  0.53406  , -0.77931  ,  0.40495  ,  0.10612  ,\n",
       "         0.051042 ,  1.4235   ,  0.1202   , -0.62349  ,  0.57618  ,\n",
       "         1.1415   ,  0.47492  ,  0.22805  ,  0.686    ,  0.060763 ,\n",
       "         1.3205   ,  0.34388  ,  0.22985  ,  0.29613  , -0.78179  ,\n",
       "         1.268    ,  0.85959  , -0.16054  ,  0.48069  ,  0.22339  ,\n",
       "         0.41864  ,  0.58774  ,  0.058878 ,  0.52439  , -0.89206  ],\n",
       "       dtype=float32),\n",
       " 'form': array([ 0.56753 ,  0.27592 , -0.63927 , -0.056709,  0.18394 ,  1.1685  ,\n",
       "         0.30643 , -0.27317 , -0.30291 ,  0.43855 ,  0.54986 ,  0.35057 ,\n",
       "         0.065629,  0.2697  , -0.47189 ,  0.29592 ,  0.32129 , -0.31124 ,\n",
       "        -0.1542  , -0.31495 , -0.29969 , -0.69391 ,  0.48761 ,  0.49253 ,\n",
       "        -0.33012 , -0.93747 , -0.44062 ,  0.051767, -0.092574,  0.21899 ,\n",
       "         3.7494  , -0.47923 , -0.26518 , -0.36895 ,  0.041559,  0.19942 ,\n",
       "         0.20267 ,  0.2354  , -0.26481 , -0.49624 ,  0.3448  , -0.44459 ,\n",
       "        -0.45436 ,  0.54088 , -0.17108 ,  0.43885 ,  0.16974 , -0.21389 ,\n",
       "         0.096059, -0.66869 ], dtype=float32),\n",
       " 'accounting': array([-0.060757, -1.1731  ,  0.18846 ,  0.3781  , -0.17724 ,  0.55007 ,\n",
       "         0.11642 , -1.1169  ,  0.37766 , -0.23238 , -0.21123 ,  0.2008  ,\n",
       "        -0.78939 , -0.31377 ,  0.76239 , -0.44848 , -0.30536 ,  0.19994 ,\n",
       "         0.35953 , -0.34278 ,  1.1711  ,  0.22089 ,  0.39379 , -0.91729 ,\n",
       "        -0.56081 , -0.87678 , -0.81646 , -0.59667 , -0.39168 ,  0.52532 ,\n",
       "         2.4125  , -0.44773 ,  0.31469 , -0.36885 ,  0.042919,  0.022925,\n",
       "        -0.8817  ,  0.40938 ,  1.143   , -0.37375 , -0.71801 , -0.24206 ,\n",
       "         1.2361  ,  1.182   , -1.0029  , -0.69144 ,  0.094998,  1.5783  ,\n",
       "         0.76583 ,  0.090209], dtype=float32),\n",
       " 'income tax': array([-0.156885  ,  0.119525  ,  0.788465  , -0.90376   ,  0.27692002,\n",
       "         0.8962    , -0.27183   , -1.066635  ,  0.1568775 ,  0.088437  ,\n",
       "        -0.60642046, -0.06572001,  0.69961   , -1.11649   ,  0.785895  ,\n",
       "        -0.17821701,  0.243681  , -0.26433   ,  0.82590497, -0.458425  ,\n",
       "         0.95431995, -1.2672    , -0.41022   , -0.342765  , -1.03397   ,\n",
       "        -1.30337   , -0.228032  , -0.467425  ,  0.443855  ,  0.61864   ,\n",
       "         3.11515   ,  0.823755  ,  0.24142   ,  0.1986975 ,  0.0711055 ,\n",
       "        -0.60439   , -0.548135  ,  0.0926295 ,  0.59386003, -0.741645  ,\n",
       "        -0.722445  ,  0.164981  ,  0.677835  ,  0.76815   , -1.39255   ,\n",
       "        -1.0088401 , -0.75760996,  0.67517996,  0.81309503,  0.690355  ],\n",
       "       dtype=float32),\n",
       " 'sincere hard working': array([ 0.20746   ,  0.12076334, -0.14720468, -0.37157   ,  0.507893  ,\n",
       "        -0.02760233, -0.19191003,  0.47151503, -0.33960304,  0.51557666,\n",
       "        -0.14277   ,  0.2698233 , -0.19739   ,  0.16978002, -0.12314299,\n",
       "        -0.17021666,  0.32448936,  0.27491334,  0.69712   , -0.45965198,\n",
       "        -0.10385666,  0.9809399 ,  0.09712467, -0.03210999,  0.9061034 ,\n",
       "        -1.3250033 , -0.20469765, -0.12951666,  0.48324266, -0.2104765 ,\n",
       "         2.8909333 ,  0.41352364, -0.3539    , -0.16148366, -0.177278  ,\n",
       "        -0.10405333, -0.26543665,  0.13095334, -0.25582668, -0.28210035,\n",
       "        -0.03345666, -0.31859002,  0.01771867,  0.09003937,  0.36350334,\n",
       "         0.03089666, -0.47058034,  0.369874  , -0.20816332,  0.24903266],\n",
       "       dtype=float32),\n",
       " 'hard working': array([-0.13643499, -0.18189001,  0.24852298, -0.418475  ,  0.09218951,\n",
       "        -0.062152  , -0.66255504,  0.1378225 , -0.2600695 ,  0.13976501,\n",
       "        -0.0478    , -0.019635  , -0.482115  ,  0.50894   ,  0.18658051,\n",
       "         0.060875  ,  0.514875  ,  0.35221002,  0.42413002, -0.65936   ,\n",
       "         0.07847002,  0.62781   ,  0.132439  ,  0.289365  ,  0.387455  ,\n",
       "        -1.61795   , -0.33888   , -0.287485  ,  0.419019  , -0.20250475,\n",
       "         3.4886    ,  0.10213549, -0.427325  , -0.26578498, -0.059142  ,\n",
       "         0.2806    , -0.290395  ,  0.48149502,  0.24661   , -0.2522755 ,\n",
       "        -0.24597499,  0.01447999,  0.187268  ,  0.20570405,  0.32818002,\n",
       "        -0.01702   , -0.18752049, -0.083839  , -0.3739    ,  0.256514  ],\n",
       "       dtype=float32),\n",
       " 'dbms': array([ 0.76037 , -1.059   ,  0.15464 ,  0.63645 , -0.64624 ,  0.2936  ,\n",
       "         0.48793 , -0.64371 ,  0.08927 , -0.3007  ,  0.66202 ,  0.29919 ,\n",
       "         0.24136 , -0.018467, -0.43143 , -0.41169 , -0.39008 ,  0.71306 ,\n",
       "         0.84755 , -0.548   , -0.61252 , -1.0813  , -0.96449 ,  1.088   ,\n",
       "        -0.14839 ,  0.804   ,  0.22642 , -0.21225 , -0.41786 , -0.36142 ,\n",
       "         0.099979, -0.64769 ,  0.16598 , -0.14458 , -0.2462  ,  0.51601 ,\n",
       "        -0.49551 , -0.11255 ,  0.31178 , -0.3421  ,  1.0703  , -0.77257 ,\n",
       "         0.53338 ,  1.1225  , -0.43294 , -0.3828  ,  1.2749  ,  1.012   ,\n",
       "         0.60904 , -0.016419], dtype=float32),\n",
       " 'scrum master': array([-0.56246   , -0.07041001, -0.453745  , -0.71518004, -0.02076   ,\n",
       "        -0.150785  , -0.305876  , -0.142845  , -0.43404502,  0.03576499,\n",
       "         0.35789502,  0.82383   , -0.466388  ,  0.30355597, -0.27302   ,\n",
       "        -0.131251  ,  0.90401196,  0.17146498, -0.817265  , -0.3273    ,\n",
       "        -0.10236001,  0.38826   , -0.70151496,  0.156047  ,  0.136475  ,\n",
       "        -0.74647903,  0.32537496, -0.65059996, -0.70044   ,  0.2554385 ,\n",
       "         1.70365   , -0.25899   , -0.32797   ,  0.19715999,  0.4036615 ,\n",
       "         1.0263801 ,  0.24829   ,  0.379855  ,  0.51163   , -0.533945  ,\n",
       "         0.85454   ,  0.34506   , -0.23764   ,  0.45758   ,  0.148353  ,\n",
       "         0.04048499,  0.72767496, -0.345656  ,  0.237097  , -0.4620485 ],\n",
       "       dtype=float32),\n",
       " 'tally': array([-0.34056 , -0.21187 ,  1.1197  ,  0.29947 ,  0.65839 , -0.1686  ,\n",
       "        -0.24351 ,  0.065828,  1.2194  , -0.35218 , -0.59722 , -1.2438  ,\n",
       "        -0.37476 , -0.13782 ,  1.0304  , -0.84043 , -0.14222 , -1.3613  ,\n",
       "        -0.73229 , -0.26599 , -0.30857 , -0.46951 ,  0.81266 , -0.4451  ,\n",
       "         0.17481 , -0.29003 , -0.64529 , -0.36514 , -0.76197 ,  0.36409 ,\n",
       "         1.4893  ,  0.67893 ,  0.11238 , -0.15833 ,  0.13717 ,  0.10724 ,\n",
       "         0.79623 ,  0.77881 , -0.39478 , -0.41165 , -0.18273 ,  0.090378,\n",
       "        -0.13646 ,  0.012953, -0.76588 ,  0.17533 , -0.18129 ,  1.017   ,\n",
       "         0.76405 , -0.60967 ], dtype=float32),\n",
       " 'language web programing': array([ 0.15345998,  0.13939333,  0.06197   ,  0.8327198 , -0.38342667,\n",
       "        -0.18280666, -1.0711033 , -1.1662799 , -0.11567666,  0.17114   ,\n",
       "         0.01160967, -0.03423966,  0.46711668,  0.18661   , -0.19162999,\n",
       "         0.16771   , -0.82444   ,  0.24207   ,  0.54837334,  0.20695667,\n",
       "         0.36084998,  0.7978366 ,  0.01779401,  1.2606    ,  0.31414667,\n",
       "        -0.44353664, -0.6698033 , -0.11456334, -0.36353233, -0.317805  ,\n",
       "         2.5396168 , -0.63310665,  0.10054668, -0.61146665, -0.32687333,\n",
       "         0.15906933, -0.4133433 , -0.66045   , -0.7883933 , -0.05145166,\n",
       "         1.16142   ,  0.19353966, -0.37409332,  0.6509333 , -0.39966998,\n",
       "        -0.14049666,  0.26133668,  0.76634336, -0.053564  ,  0.28834334],\n",
       "       dtype=float32),\n",
       " 'wheeler driving license': array([-0.58675003, -0.04969   ,  0.31387666,  0.17436667, -0.2618226 ,\n",
       "         0.34999335, -0.86551   , -0.05796434,  0.23256999, -0.14212   ,\n",
       "         0.29697332,  0.22580333, -0.43265   , -0.5291233 ,  0.37021104,\n",
       "         0.03212335, -0.28337002,  0.42318335, -0.35407636, -0.88706666,\n",
       "         0.45911002,  0.12456267, -0.52639   ,  0.22346467,  0.18571334,\n",
       "        -1.2041534 ,  0.12288666, -0.28408334,  0.74805564, -0.21781333,\n",
       "         1.6289167 , -0.34358335, -0.35968003, -0.14753   ,  0.8109366 ,\n",
       "        -0.19541867,  0.24508466, -0.08015843, -0.02108333,  0.23178999,\n",
       "         0.01876001,  0.049198  ,  0.07309967,  0.23289001, -0.15543668,\n",
       "        -0.8075034 ,  0.21936   , -0.36113003,  0.20849   ,  0.32404307],\n",
       "       dtype=float32),\n",
       " 'inter mec srimedhav': array([ 0.28340533, -0.01256   , -0.46481666,  0.32134667, -0.20199668,\n",
       "        -0.24894696,  0.09963999, -0.35681832, -0.08605999, -0.03678333,\n",
       "         0.36628   ,  0.21020333, -0.49182   , -0.05242001, -0.23782001,\n",
       "        -0.08699001, -0.05442666,  0.24721332,  0.17909999,  0.5308933 ,\n",
       "        -0.34194002, -0.16194533, -0.11561   ,  0.19930333, -0.32322   ,\n",
       "         0.06810667,  0.6904003 ,  0.1426    , -0.39110002,  0.00931397,\n",
       "         0.7648433 ,  0.53686666, -0.11207666,  0.32469335, -0.25962666,\n",
       "         0.145282  , -0.07338332,  0.01308667,  0.08884332,  0.21902435,\n",
       "         0.08818334, -0.3381    , -0.14130999,  0.03234   , -0.24621   ,\n",
       "         0.24952666, -0.05214334,  0.5228267 ,  0.4270133 , -0.36540067],\n",
       "       dtype=float32),\n",
       " 'master': array([-0.56188 ,  0.78123 , -0.48791 , -0.50342 ,  0.5958  , -0.42773 ,\n",
       "        -0.087572, -0.16608 , -0.66938 ,  0.38952 ,  0.48479 ,  1.1781  ,\n",
       "         0.042824,  0.61636 , -0.34553 , -0.34513 , -0.089776,  1.0866  ,\n",
       "        -0.48043 ,  0.10523 ,  0.87288 ,  0.24246 , -0.62372 , -0.092856,\n",
       "         0.16683 , -1.544   , -0.77275 , -1.4214  , -0.77739 , -0.065303,\n",
       "         2.2658  , -0.9929  , -0.88399 , -0.29168 ,  0.74656 ,  0.73226 ,\n",
       "         0.1527  ,  0.52986 ,  0.72713 , -0.2861  ,  0.44108 , -0.16947 ,\n",
       "        -0.31474 ,  0.43447 ,  0.073316, -0.33767 ,  0.86761 , -0.75019 ,\n",
       "        -0.050196, -0.032037], dtype=float32),\n",
       " 'training special': array([ 0.219855  ,  0.4707    , -0.63138497, -0.10813999,  0.154085  ,\n",
       "        -0.546635  , -0.4149115 , -0.26494148,  0.826815  , -0.861655  ,\n",
       "         0.32014   ,  0.106915  , -0.21105   ,  0.252465  ,  0.01194498,\n",
       "        -0.360875  , -0.211745  ,  0.639825  , -0.422593  , -0.195865  ,\n",
       "         0.25563002,  0.491695  , -0.03561   , -0.1534885 , -0.604805  ,\n",
       "        -1.3155999 , -0.07323   , -0.72676   , -0.15496999,  0.067715  ,\n",
       "         3.2863998 ,  0.58447003, -0.93106997, -0.735075  ,  0.713525  ,\n",
       "         1.1144    ,  0.37985998,  0.35123   , -0.45148498,  0.119803  ,\n",
       "        -0.079677  ,  0.05089498,  0.051565  , -0.02998999,  0.09443501,\n",
       "        -0.49673   ,  0.350102  ,  0.53354   , -0.64672   ,  0.83772504],\n",
       "       dtype=float32),\n",
       " 'prajyoti niketan college': array([-0.48825   ,  0.68782663, -0.19190331, -0.8972333 , -0.03599757,\n",
       "        -0.02323333, -0.6484633 , -0.5488233 , -0.13471334, -0.1873753 ,\n",
       "        -0.13056   ,  0.02032567,  0.18812001, -0.7431433 , -0.3711767 ,\n",
       "         0.12162   , -0.02772333,  0.67327666, -0.04919333,  0.3722267 ,\n",
       "         0.28344333,  0.45448002, -0.19668333,  0.52989   ,  0.28972664,\n",
       "        -0.31160334,  0.220574  , -0.39198837, -0.9738667 , -0.4350233 ,\n",
       "         0.5071    ,  0.44697332, -0.07647467, -0.502646  ,  0.59488666,\n",
       "        -0.06903666, -0.11735433, -0.03059667,  0.6531233 , -0.10098767,\n",
       "        -0.35396   , -0.42331   , -0.4605333 ,  0.40975332, -0.04489666,\n",
       "        -0.22520335,  0.68964   , -0.10231534,  0.3498    , -0.04306333],\n",
       "       dtype=float32),\n",
       " 'behaviour malappuram': array([-0.0282506 , -0.88383996, -1.31945   ,  0.01875   ,  0.13314065,\n",
       "         0.00686499,  0.87567997, -0.521545  ,  0.145055  ,  0.07566001,\n",
       "         0.22318   , -0.400125  ,  0.4078905 , -0.76266503, -0.10929   ,\n",
       "         0.172386  ,  0.2363245 ,  0.7375013 ,  0.06591499,  0.11842501,\n",
       "         0.015285  , -0.23175001, -0.033385  ,  0.857165  ,  0.263286  ,\n",
       "        -0.296155  ,  0.211555  ,  0.2885885 , -0.18876001,  0.14331001,\n",
       "         0.597075  ,  0.11834   ,  0.0337221 , -0.9659345 ,  0.013035  ,\n",
       "         0.11438099, -0.05657001,  0.31056   , -0.46229497,  0.448794  ,\n",
       "        -0.44273   ,  0.03828001, -0.38206849,  0.543805  , -0.04794997,\n",
       "        -0.4414    ,  0.28858   ,  0.00249499,  1.2514501 ,  0.22912501],\n",
       "       dtype=float32),\n",
       " 'behaviour malappuram kerala': array([-0.14415707, -0.89639664, -1.3678001 , -0.04724334,  0.00906377,\n",
       "        -0.09824333,  0.70636326, -0.6112933 ,  0.30464333,  0.027975  ,\n",
       "         0.175125  , -0.42827   ,  0.7476937 , -0.87471   , -0.32378334,\n",
       "         0.04152733,  0.3394263 ,  0.98460084, -0.09094334,  0.40346003,\n",
       "         0.24098332, -0.34289002,  0.01230333,  0.8838    ,  0.35868397,\n",
       "        -0.41327667,  0.03355333,  0.08566233, -0.7306733 , -0.06729666,\n",
       "         0.7988167 ,  0.12966667,  0.14312474, -0.659342  ,  0.06129   ,\n",
       "         0.166474  , -0.07511667,  0.34954667, -0.38975665,  0.63802934,\n",
       "        -0.59339666,  0.28173667, -0.5771423 ,  0.34571135, -0.50693333,\n",
       "        -0.07575333, -0.00435001, -0.15118334,  1.0414934 , -0.20845   ],\n",
       "       dtype=float32),\n",
       " 'specialization organizational behaviour': array([-0.23882973,  0.08899333, -0.9368    , -0.15100335,  0.00463467,\n",
       "         0.40911666,  0.59471333, -0.46433666,  0.052501  ,  0.008358  ,\n",
       "         0.57923   ,  0.22535335, -0.18420298,  0.087255  , -0.5992933 ,\n",
       "        -0.12038267, -0.021097  ,  0.1911452 ,  0.33291665, -0.22957332,\n",
       "        -0.05014667,  0.03367   , -0.16641332, -0.01155999,  0.1149413 ,\n",
       "        -0.12579702, -0.29413334, -0.50839764,  0.23837757,  0.76454   ,\n",
       "         1.7472334 ,  0.29018667, -0.11558593, -1.5105433 , -0.12492333,\n",
       "         0.51589   , -0.35656667,  0.32982996, -0.15072997,  0.35540268,\n",
       "        -0.29738   , -0.7208233 , -0.262622  ,  1.0409366 ,  0.03514667,\n",
       "        -0.75960666,  0.8078467 ,  0.90296674,  0.481487  ,  0.42392   ],\n",
       "       dtype=float32),\n",
       " 'niketan college': array([-0.73237497,  1.03174   , -0.28785497, -1.34585   , -0.05399635,\n",
       "        -0.03485   , -0.972695  , -0.823235  , -0.20207   , -0.28106296,\n",
       "        -0.19584   ,  0.0304885 ,  0.28218   , -1.114715  , -0.556765  ,\n",
       "         0.18243   , -0.041585  ,  1.009915  , -0.07379   ,  0.55834   ,\n",
       "         0.425165  ,  0.68172   , -0.295025  ,  0.794835  ,  0.43458998,\n",
       "        -0.467405  ,  0.330861  , -0.58798254, -1.4608    , -0.65253496,\n",
       "         0.76065   ,  0.67046   , -0.114712  , -0.753969  ,  0.89233   ,\n",
       "        -0.10355499, -0.1760315 , -0.04589501,  0.979685  , -0.15148151,\n",
       "        -0.53094   , -0.634965  , -0.69079995,  0.61463   , -0.06734499,\n",
       "        -0.33780503,  1.03446   , -0.153473  ,  0.5247    , -0.064595  ],\n",
       "       dtype=float32),\n",
       " 'thrissur prajyoti niketan': array([-0.097187  , -0.02030667, -0.28541002, -0.32943   , -0.03487113,\n",
       "         0.18039767,  0.02601667, -0.56114334, -0.08800866,  0.0314427 ,\n",
       "        -0.07106134, -0.41045764,  0.4569433 , -0.6648433 , -0.4406167 ,\n",
       "         0.13595334,  0.20809667,  0.8067767 ,  0.08825767,  0.4321867 ,\n",
       "         0.54839665, -0.09707334, -0.24121666,  0.66246   ,  0.42615333,\n",
       "         0.26087   ,  0.23111768,  0.11984833, -0.8684333 , -0.5535067 ,\n",
       "        -0.42605665,  0.26584998,  0.19634335, -0.093146  ,  0.22282194,\n",
       "        -0.22713666,  0.08191233,  0.05672999,  0.4564767 ,  0.47248232,\n",
       "        -0.34519336, -0.23456667, -0.49701667,  0.08547599, -0.2397    ,\n",
       "        -0.11452335,  0.23148666, -0.04562767,  0.8850333 , -0.33686998],\n",
       "       dtype=float32),\n",
       " 'malappuram kerala': array([-0.217856  , -1.082505  , -1.23295   ,  0.08066   , -0.11519935,\n",
       "        -0.43661   ,  0.626415  , -0.74230003,  0.65945   , -0.2160475 ,\n",
       "        -0.1343675 , -1.03038   ,  1.095715  , -1.25775   , -0.556855  ,\n",
       "         0.04136   ,  0.47162   ,  1.4766    ,  0.14517   ,  0.813865  ,\n",
       "         0.54190004, -0.74199   , -0.039855  ,  1.1606851 ,  0.259316  ,\n",
       "        -0.23797   , -0.106875  ,  0.16691   , -1.43535   , -0.54639   ,\n",
       "         0.303075  ,  0.13848999,  0.21611601, -0.042163  ,  0.339605  ,\n",
       "         0.12444101, -0.21981001,  0.85021   , -0.057835  ,  0.93353   ,\n",
       "        -0.780555  ,  0.67249   , -0.859385  , -0.172533  , -1.3266001 ,\n",
       "         0.45736998, -0.07238001, -0.48004502,  0.77699   , -0.59474504],\n",
       "       dtype=float32),\n",
       " 'days': array([ 6.2236e-01,  1.9730e-01,  2.3326e-03, -4.4924e-01,  8.4905e-02,\n",
       "        -3.3628e-01, -9.0642e-01,  6.2154e-01, -2.0657e-01, -2.0043e-01,\n",
       "        -6.2690e-01, -6.4429e-01, -3.1616e-01,  2.3314e-01,  1.2348e+00,\n",
       "        -8.0113e-02, -7.3820e-01, -3.4970e-01, -9.7589e-01, -4.6964e-02,\n",
       "         2.9777e-01,  6.8288e-01,  8.7876e-01, -2.8921e-01,  1.4560e-01,\n",
       "        -1.4110e+00, -1.1301e-01,  2.1320e-01,  4.9548e-01,  3.8685e-01,\n",
       "         3.3970e+00,  4.0639e-01, -5.4343e-01, -3.7414e-04,  4.4268e-01,\n",
       "        -1.0150e-01,  3.7693e-01,  4.7623e-02,  4.4414e-02,  1.0038e-01,\n",
       "        -9.9794e-01,  2.0820e-01,  6.3226e-02,  1.2436e-01, -5.9387e-02,\n",
       "         4.6682e-02, -2.2335e-01, -2.2082e-01, -4.0414e-01, -1.8502e-01],\n",
       "       dtype=float32),\n",
       " 'special': array([ 0.16163 ,  0.78638 , -0.50019 ,  0.17855 ,  0.64519 , -0.28504 ,\n",
       "        -0.078533, -0.57302 ,  0.75151 , -0.82732 , -0.19134 ,  0.35527 ,\n",
       "         0.20475 ,  0.39756 ,  0.53953 , -0.25249 , -0.12972 ,  0.18465 ,\n",
       "         0.034124, -0.65288 ,  0.73289 ,  0.1267  ,  0.24617 , -0.22166 ,\n",
       "        -0.73607 , -1.1947  , -0.48604 , -0.26072 , -0.27699 , -0.11712 ,\n",
       "         3.162   ,  0.40051 , -0.96605 , -0.63214 ,  0.47338 ,  1.0037  ,\n",
       "         0.53081 , -0.1093  , -0.69595 , -0.030794,  0.032276,  0.71254 ,\n",
       "        -0.12191 , -0.54396 , -0.33492 , -0.23341 , -0.088966,  0.45571 ,\n",
       "        -0.44395 ,  0.68005 ], dtype=float32),\n",
       " 'specialization organizational': array([-0.359865  ,  0.39558   , -0.58645   , -0.07498001, -0.121843  ,\n",
       "         0.32443   ,  0.45894   , -0.521865  ,  0.2812365 , -0.245473  ,\n",
       "         0.47179002, -0.049945  , -0.30212998,  0.1851975 , -0.97011995,\n",
       "        -0.201505  , -0.06916499,  0.2864165 ,  0.78095996, -0.135685  ,\n",
       "         0.105205  , -0.17715   , -0.30793   , -0.18235499, -0.10629805,\n",
       "         0.1932495 , -0.598405  , -0.72418   ,  0.01822635,  0.701365  ,\n",
       "         1.7257    ,  0.37927002, -0.17195   , -1.318965  ,  0.060285  ,\n",
       "         0.648565  , -0.641985  ,  0.82063496,  0.30070502,  0.50959   ,\n",
       "        -0.33653   , -0.83134997, -0.3876045 ,  0.870305  , -0.51348   ,\n",
       "        -0.56841004,  1.145915  ,  1.1011801 , -0.0630195 ,  0.35381   ],\n",
       "       dtype=float32),\n",
       " 'special education': array([-0.38248497,  0.740525  , -0.54549   , -0.43782502,  0.239905  ,\n",
       "        -0.011325  , -0.2282065 , -1.00916   ,  0.58018005, -0.56509995,\n",
       "        -0.07976   ,  0.087555  ,  0.316705  , -0.250055  , -0.01181501,\n",
       "        -0.26138002, -0.042013  ,  0.4052    ,  0.649262  ,  0.05467498,\n",
       "         0.64278   ,  0.309855  , -0.17919001, -0.088633  , -0.262925  ,\n",
       "        -1.3148999 , -0.08551499, -0.84696   , -0.706645  ,  0.07274   ,\n",
       "         3.402     ,  0.55781996, -0.74143505, -0.97647   ,  0.242591  ,\n",
       "         0.679415  ,  0.182325  ,  0.390685  ,  0.01986501,  0.104763  ,\n",
       "        -0.30337203,  0.20223999, -0.0150285 , -0.10728499, -0.42576   ,\n",
       "        -0.057165  , -0.12557301,  0.53144   ,  0.03644499,  0.755835  ],\n",
       "       dtype=float32),\n",
       " 'counselling counsellor': array([ 0.10300499,  0.37080002, -1.27595   , -0.550835  ,  0.19215001,\n",
       "        -0.06774499,  0.43383998,  0.304525  ,  0.57427   , -0.23537   ,\n",
       "         0.39318502,  0.57427645,  0.04651675,  0.11670999, -0.183455  ,\n",
       "        -0.2510215 , -0.846105  ,  0.15987998,  0.86355495,  0.88108003,\n",
       "         0.928325  ,  1.31132   , -0.46975   ,  0.295339  , -0.24125001,\n",
       "         0.06542   ,  0.707055  , -1.00641   , -0.47851002,  0.87665   ,\n",
       "         0.65681   ,  0.48848003,  0.0029495 , -0.55069   ,  0.222135  ,\n",
       "         0.817105  ,  0.329659  , -0.00235   ,  0.59201   , -0.100033  ,\n",
       "         0.430985  , -0.468395  ,  0.09108999,  0.738623  ,  0.07596   ,\n",
       "        -0.415895  ,  0.178545  ,  0.660335  ,  0.60290504,  0.45016003],\n",
       "       dtype=float32),\n",
       " 'special education certificate': array([-0.56631   ,  0.88825005, -0.40253666, -0.42450333,  0.30305   ,\n",
       "         0.011018  , -0.27548766, -0.8003767 ,  0.81842   , -0.35639527,\n",
       "         0.30796   ,  0.32968667,  0.5573366 , -0.36862335,  0.0742    ,\n",
       "        -0.13665001, -0.51477534,  0.35203668,  0.538428  ,  0.01863432,\n",
       "         0.6724367 ,  0.31367   , -0.24154   , -0.24288534, -0.19171166,\n",
       "        -1.2790666 , -0.19866331, -0.82733005, -0.71090335,  0.00638   ,\n",
       "         2.7571332 ,  0.32090664, -0.9201567 , -0.7386833 ,  0.6458607 ,\n",
       "         0.51198334,  0.56738335,  0.35176334,  0.06434   , -0.17551135,\n",
       "         0.00937198, -0.20207334,  0.06348767,  0.20760667, -0.66460663,\n",
       "        -0.36928335,  0.05811466,  0.67026   ,  0.33484998,  0.38762   ],\n",
       "       dtype=float32),\n",
       " 'institute counselling counsellor': array([-0.04503001,  0.4632767 , -1.00614   , -0.38748667,  0.07669667,\n",
       "        -0.14972332, -0.02117336, -0.25758335,  0.63113004, -0.29163334,\n",
       "         0.60022336,  0.24881764,  0.0863445 , -0.00859668, -0.32163665,\n",
       "        -0.05862433, -0.53851765,  0.34916   ,  0.39161   ,  0.8573833 ,\n",
       "         0.92989665,  1.0550767 , -0.50116336, -0.06868734, -0.12505333,\n",
       "        -0.4781867 ,  0.17377001, -0.97449   , -1.0753733 ,  0.561301  ,\n",
       "         1.19024   ,  0.16116004, -0.09378701, -0.99439335, -0.10886333,\n",
       "         0.36134002,  0.128226  ,  0.28920665,  1.0607067 ,  0.08565801,\n",
       "         0.2906261 , -0.41622004,  0.19486666,  0.3687687 ,  0.16772999,\n",
       "        -0.10615334,  0.18854666,  0.67274   ,  0.35364   ,  0.40307334],\n",
       "       dtype=float32),\n",
       " 'training': array([ 0.27808 ,  0.15502 , -0.76258 , -0.39483 , -0.33702 , -0.80823 ,\n",
       "        -0.75129 ,  0.043137,  0.90212 , -0.89599 ,  0.83162 , -0.14144 ,\n",
       "        -0.62685 ,  0.10737 , -0.51564 , -0.46926 , -0.29377 ,  1.095   ,\n",
       "        -0.87931 ,  0.26115 , -0.22163 ,  0.85669 , -0.31739 , -0.085317,\n",
       "        -0.47354 , -1.4365  ,  0.33958 , -1.1928  , -0.03295 ,  0.25255 ,\n",
       "         3.4108  ,  0.76843 , -0.89609 , -0.83801 ,  0.95367 ,  1.2251  ,\n",
       "         0.22891 ,  0.81176 , -0.20702 ,  0.2704  , -0.19163 , -0.61075 ,\n",
       "         0.22504 ,  0.48398 ,  0.52379 , -0.76005 ,  0.78917 ,  0.61137 ,\n",
       "        -0.84949 ,  0.9954  ], dtype=float32),\n",
       " 'malappuram kerala calicut': array([ 1.0932266e-01, -1.1586033e+00, -1.0249200e+00,  6.0156669e-02,\n",
       "        -2.5125626e-01, -7.7697331e-01,  6.3113999e-01, -5.4362005e-01,\n",
       "         4.1911468e-01, -2.5506166e-01,  2.2378500e-01, -6.7629868e-01,\n",
       "         9.4266337e-01, -1.3092333e+00, -6.9262999e-01,  5.7858333e-02,\n",
       "         1.4179999e-01,  1.4723333e+00, -3.9355335e-01,  8.4629327e-01,\n",
       "         4.5126000e-01, -6.9358999e-01, -8.6283334e-02,  8.8742667e-01,\n",
       "         2.3788066e-01, -4.3569669e-01, -7.0026934e-02, -7.7760004e-02,\n",
       "        -1.2814833e+00, -5.6669003e-01, -3.6676724e-05,  4.2669335e-01,\n",
       "        -1.0465990e-02, -1.2427200e-01,  2.8219000e-01,  1.5856734e-01,\n",
       "        -3.9058670e-01,  7.6260000e-01, -7.2509997e-02,  8.8296670e-01,\n",
       "        -3.2753000e-01,  4.4380799e-01, -5.6154168e-01,  2.9864660e-02,\n",
       "        -1.2048668e+00,  4.0092000e-01,  1.7962332e-01, -1.9068335e-01,\n",
       "         5.8247000e-01, -7.3563004e-01], dtype=float32),\n",
       " 'kerala calicut university': array([-0.24016333, -0.31356993, -0.9536233 , -0.20654333, -0.39011335,\n",
       "        -0.76667666,  0.03121332, -0.47427332,  0.08122801, -0.24514167,\n",
       "         0.50667834, -0.318872  ,  0.69776964, -1.2286    , -0.7298    ,\n",
       "         0.04755833, -0.02771335,  1.3192667 , -0.97278666,  1.1258601 ,\n",
       "         0.5548567 , -0.14685   , -0.06148   ,  0.42535436,  0.22275536,\n",
       "        -1.3394899 , -0.06623361, -0.58274   , -1.6367499 , -0.66831   ,\n",
       "         1.0703133 ,  0.36855868, -0.06608499, -0.861149  ,  0.08827833,\n",
       "         0.16860275, -0.31662333,  0.7528    ,  0.46412   ,  0.82115334,\n",
       "        -0.14485665,  0.23119932, -0.40824834,  0.25372133, -0.82777137,\n",
       "         0.47594666,  0.15303999,  0.06406   ,  0.09062   , -0.68835205],\n",
       "       dtype=float32),\n",
       " 'counsellor': array([-0.58673 ,  0.64104 , -1.4561  , -0.36628 ,  0.74282 , -0.62349 ,\n",
       "         0.62095 ,  0.29399 , -0.20586 , -0.87807 ,  0.39502 ,  1.1376  ,\n",
       "         0.09687 ,  0.66839 , -0.12068 , -0.069683, -0.87797 ,  0.576   ,\n",
       "         0.63381 ,  1.2381  ,  1.1169  ,  0.51314 , -0.23572 , -0.017712,\n",
       "        -0.30226 , -0.44409 ,  0.5416  , -0.42122 , -0.40293 ,  1.1518  ,\n",
       "        -0.10238 , -0.23024 , -0.02726 , -0.27053 ,  0.24636 ,  0.62781 ,\n",
       "         0.061228,  0.52834 ,  0.25186 , -0.076126,  0.10749 , -0.24161 ,\n",
       "         0.31565 , -0.019254, -0.23799 , -0.20736 , -0.30995 ,  0.21167 ,\n",
       "         1.0231  ,  0.24629 ], dtype=float32),\n",
       " 'certificate': array([-0.93396 ,  1.1837  , -0.11663 , -0.39786 ,  0.42934 ,  0.055704,\n",
       "        -0.37005 , -0.38281 ,  1.2949  ,  0.061014,  1.0834  ,  0.81395 ,\n",
       "         1.0386  , -0.60576 ,  0.24623 ,  0.11281 , -1.4603  ,  0.24571 ,\n",
       "         0.31676 , -0.053447,  0.73175 ,  0.3213  , -0.36624 , -0.55139 ,\n",
       "        -0.049285, -1.2074  , -0.42496 , -0.78807 , -0.71942 , -0.12634 ,\n",
       "         1.4674  , -0.15292 , -1.2776  , -0.26311 ,  1.4524  ,  0.17712 ,\n",
       "         1.3375  ,  0.27392 ,  0.15329 , -0.73606 ,  0.63486 , -1.0107  ,\n",
       "         0.22052 ,  0.83739 , -1.1423  , -0.99352 ,  0.42549 ,  0.9479  ,\n",
       "         0.93166 , -0.34881 ], dtype=float32),\n",
       " 'internship': array([ 0.19251 ,  0.67908 , -0.94133 , -0.93238 , -0.59629 , -0.18152 ,\n",
       "        -0.97681 ,  0.44614 ,  0.53997 , -0.40487 , -0.035171, -0.40235 ,\n",
       "         0.42968 ,  0.35493 ,  0.28085 , -0.34657 , -0.60675 ,  0.27727 ,\n",
       "         0.71261 ,  0.78443 ,  1.1917  ,  0.67468 , -0.60345 ,  0.087905,\n",
       "        -0.4318  ,  0.0973  ,  0.41627 , -0.89658 , -0.39814 , -0.038744,\n",
       "         1.089   ,  0.80367 , -0.7224  , -0.25152 ,  0.23112 ,  0.95972 ,\n",
       "        -0.22075 ,  1.3311  ,  1.0307  , -0.41713 , -0.28042 , -0.71416 ,\n",
       "        -0.27605 ,  0.76489 , -0.91167 , -1.4874  ,  1.417   ,  0.73904 ,\n",
       "        -0.70597 ,  1.1635  ], dtype=float32),\n",
       " 'organizational behaviour': array([-0.0889346 , -0.10062   , -0.948755  , -0.03699501,  0.177092  ,\n",
       "         0.44528502,  0.681615  , -0.355655  , -0.22115849,  0.03681   ,\n",
       "         0.49586   ,  0.46775   , -0.5294745 ,  0.160795  , -0.47332   ,\n",
       "        -0.287004  , -0.11973549,  0.0364078 ,  0.187385  , -0.41723502,\n",
       "        -0.23314   ,  0.21763   , -0.15258999, -0.11209999,  0.27924696,\n",
       "        -0.3575155 , -0.33630502, -0.5063565 ,  0.34106034,  0.8849    ,\n",
       "         1.99505   ,  0.252605  , -0.1205639 , -1.7890999 , -0.385635  ,\n",
       "         0.45410997, -0.15618   , -0.01965502, -0.41549498,  0.234069  ,\n",
       "        -0.24217   , -0.785035  , -0.3450485 ,  1.119625  ,  0.29991   ,\n",
       "        -0.72991   ,  0.45822   ,  0.88552   ,  0.8107955 ,  0.53137004],\n",
       "       dtype=float32),\n",
       " 'training special education': array([-1.6229667e-01,  5.4535669e-01, -6.1785334e-01, -4.2349336e-01,\n",
       "         4.7596663e-02, -2.7695999e-01, -4.0256765e-01, -6.5839434e-01,\n",
       "         6.8749332e-01, -6.7539668e-01,  2.2403334e-01,  1.1223331e-02,\n",
       "         2.1866660e-03, -1.3091332e-01, -1.7975669e-01, -3.3067334e-01,\n",
       "        -1.2593199e-01,  6.3513333e-01,  1.3973801e-01,  1.2349999e-01,\n",
       "         3.5464334e-01,  4.9213335e-01, -2.2525667e-01, -8.7527670e-02,\n",
       "        -3.3313000e-01, -1.3554333e+00,  5.6183338e-02, -9.6224004e-01,\n",
       "        -4.8207998e-01,  1.3267668e-01,  3.4049332e+00,  6.2802333e-01,\n",
       "        -7.9298663e-01, -9.3031663e-01,  4.7961733e-01,  8.6131001e-01,\n",
       "         1.9785333e-01,  5.3104335e-01, -5.5763323e-02,  1.5997534e-01,\n",
       "        -2.6612470e-01, -6.8756677e-02,  6.4994335e-02,  8.9803338e-02,\n",
       "        -1.0924333e-01, -2.9146001e-01,  1.7934133e-01,  5.5808336e-01,\n",
       "        -2.5886667e-01,  8.3569002e-01], dtype=float32),\n",
       " 'psychology': array([-0.96949 ,  0.93756 , -0.96815 , -0.63307 , -0.081726,  0.56352 ,\n",
       "         0.12968 , -1.0084  , -0.13071 ,  0.34126 ,  0.5136  ,  0.087984,\n",
       "        -0.43784 ,  0.081012, -0.58064 , -0.28724 , -0.32626 ,  0.35577 ,\n",
       "        -0.016916,  0.82933 ,  0.28275 ,  0.95336 ,  0.27084 ,  0.27341 ,\n",
       "         0.49444 , -1.0657  , -0.99669 , -0.95407 , -0.57929 ,  0.54103 ,\n",
       "         1.7225  , -0.54281 , -0.025387, -2.1854  , -0.39124 , -0.22007 ,\n",
       "        -1.0152  ,  0.6483  ,  1.6239  ,  0.60581 , -0.064025, -0.6806  ,\n",
       "        -0.02143 ,  1.1568  ,  0.17143 , -0.032123,  1.6882  ,  0.89031 ,\n",
       "        -0.097621,  0.86147 ], dtype=float32),\n",
       " 'days internship': array([ 0.407435  ,  0.43819   , -0.4694987 , -0.69081   , -0.25569248,\n",
       "        -0.2589    , -0.941615  ,  0.53384   ,  0.16669999, -0.30265   ,\n",
       "        -0.3310355 , -0.52331996,  0.05676   ,  0.29403502,  0.757825  ,\n",
       "        -0.21334149, -0.672475  , -0.03621501, -0.13163999,  0.36873302,\n",
       "         0.744735  ,  0.67877996,  0.13765499, -0.1006525 , -0.1431    ,\n",
       "        -0.65685   ,  0.15163   , -0.34169   ,  0.04866999,  0.174053  ,\n",
       "         2.243     ,  0.60503   , -0.632915  , -0.12594707,  0.3369    ,\n",
       "         0.42911002,  0.07809   ,  0.6893615 ,  0.537557  , -0.158375  ,\n",
       "        -0.63918   , -0.25298002, -0.106412  ,  0.44462502, -0.48552853,\n",
       "        -0.720359  ,  0.596825  ,  0.25911   , -0.555055  ,  0.48924   ],\n",
       "       dtype=float32),\n",
       " 'organizational behaviour malappuram': array([-0.07920373, -0.48158002, -0.96630335,  0.08885333,  0.12095844,\n",
       "         0.10860334,  0.74944335, -0.46837333,  0.08425433, -0.09702667,\n",
       "         0.21465667, -0.21356666, -0.098273  , -0.36503664, -0.43586   ,\n",
       "        -0.090366  ,  0.05271301,  0.51573855,  0.35659   , -0.06009001,\n",
       "        -0.02495334, -0.16118334, -0.16285665,  0.3867    ,  0.17588197,\n",
       "        -0.18115033, -0.18797003, -0.11956766, -0.12469312,  0.38851   ,\n",
       "         1.1313167 ,  0.20995666, -0.05694194, -1.2054563 , -0.08328667,\n",
       "         0.29548064, -0.21325667,  0.41119662, -0.2339933 ,  0.43956602,\n",
       "        -0.38357332, -0.33124667, -0.48052564,  0.64822   , -0.20949332,\n",
       "        -0.40020669,  0.45396337,  0.42316332,  0.8513303 ,  0.31895003],\n",
       "       dtype=float32),\n",
       " 'kerala calicut': array([ 0.19385499, -1.1161549 , -1.03668   , -0.08004   , -0.38123   ,\n",
       "        -0.88308   ,  0.50416   , -0.468525  ,  0.281132  , -0.2002425 ,\n",
       "         0.5095525 , -0.22634801,  1.03193   , -1.2555    , -0.85847497,\n",
       "        -0.0646675 ,  0.01389498,  1.4713    , -0.93783   ,  0.94234   ,\n",
       "         0.48118   , -0.58098   , -0.03773   ,  0.63899   ,  0.372245  ,\n",
       "        -0.739335  , -0.1593904 , -0.443645  , -1.394125  , -0.5479    ,\n",
       "         0.29801998,  0.57771003, -0.05084999, -0.1673235 ,  0.16258   ,\n",
       "         0.24874002, -0.422175  ,  0.50745   , -0.17327   ,  0.89917004,\n",
       "        -0.15810499,  0.377547  , -0.4665725 ,  0.192092  , -1.19315   ,\n",
       "         0.47178   ,  0.04670998, -0.03524999,  0.407505  , -1.0505    ],\n",
       "       dtype=float32),\n",
       " 'computer knowledge proficient': array([-0.36942866, -0.43793336,  0.6140266 , -0.17260802,  0.65543336,\n",
       "        -0.35315466, -0.17222667, -0.6775734 , -0.10176667,  0.7934933 ,\n",
       "         0.6714733 ,  0.2095267 ,  0.11015002,  0.02594999, -0.19807334,\n",
       "        -0.214283  , -0.38013664,  0.67761   ,  0.10541   , -0.16087633,\n",
       "         0.04236998,  0.34531665,  0.12426001,  0.25484002,  0.69622   ,\n",
       "        -0.96244496, -0.55862   , -0.8075733 ,  0.02905333, -0.17279302,\n",
       "         2.4793003 , -0.42011333,  0.22872   , -1.0707301 ,  0.52143365,\n",
       "         0.36110833, -0.29298   ,  0.86573   , -0.05073966, -0.24511002,\n",
       "         0.464012  , -0.46764365,  0.29707137,  1.0454966 , -0.10591334,\n",
       "        -0.17358   ,  0.81306   ,  0.68998337,  0.08642   ,  0.17882667],\n",
       "       dtype=float32),\n",
       " 'labors individual': array([ 2.0479999e-02,  1.7766999e-01, -1.2718500e-01, -2.7765501e-01,\n",
       "         6.0654998e-01,  4.2496949e-01,  3.0185199e-01,  1.4061901e-01,\n",
       "         3.6287999e-01,  1.4004998e-02, -8.2979994e-03,  3.9489999e-02,\n",
       "         3.4170002e-01, -6.9855995e-02,  2.9671001e-01, -6.6547000e-01,\n",
       "         1.3257000e-01,  1.1247500e-01,  5.9084952e-01, -6.0094303e-01,\n",
       "         1.7070399e-01, -1.1070000e-01,  2.1004000e-01,  6.9499016e-04,\n",
       "         3.8024500e-01, -6.4344972e-02, -4.2659000e-01, -9.5693505e-01,\n",
       "         1.6658001e-01,  3.2240003e-02,  1.7029351e+00,  7.8504503e-01,\n",
       "         2.0275500e-02, -1.3839498e-01, -1.3375901e-01,  3.8907799e-01,\n",
       "        -3.3173499e-01,  4.9008501e-01, -3.1346551e-01, -1.5296701e-01,\n",
       "        -2.0805499e-01, -8.0570000e-01,  4.6581450e-01,  7.8318000e-01,\n",
       "        -6.4069003e-02, -4.7387001e-01, -1.1667350e-01,  1.7315000e-02,\n",
       "        -1.5513800e-01, -2.1184550e-01], dtype=float32),\n",
       " 'dynamics exprience': array([ 0.33372  , -0.138225 , -0.043841 ,  0.43357  , -0.12496  ,\n",
       "         0.55335  ,  0.152715 , -0.413395 , -0.177465 , -0.10667  ,\n",
       "         0.38474  ,  0.214995 , -0.357205 ,  0.115475 , -0.6298   ,\n",
       "         0.22672  , -0.171915 ,  0.440015 , -0.24103  , -0.51015  ,\n",
       "         0.07898  ,  0.17986  , -0.107685 , -0.27861  ,  0.157835 ,\n",
       "        -0.0460795, -0.45355  , -0.09758  ,  0.240635 ,  0.419805 ,\n",
       "         0.8922   , -0.198205 , -0.152695 , -0.486875 ,  0.0145005,\n",
       "        -0.45953  , -0.5489   , -0.134365 ,  0.392965 ,  0.0471785,\n",
       "        -0.0497685, -0.398675 ,  0.07282  ,  0.33043  , -0.267115 ,\n",
       "         0.035802 ,  0.6387   ,  0.308245 , -0.141475 ,  0.32111  ],\n",
       "       dtype=float32),\n",
       " 'ramesh': array([ 0.077293 , -1.4059   , -0.30836  ,  1.2156   , -0.029151 ,\n",
       "        -0.21391  ,  0.36436  , -0.52465  ,  0.042738 ,  0.26748  ,\n",
       "         0.26824  ,  1.0705   ,  0.15404  ,  0.54118  ,  0.2079   ,\n",
       "        -0.014777 ,  0.54454  ,  0.65287  ,  0.32551  ,  0.085549 ,\n",
       "         0.030066 ,  1.3295   ,  0.65804  , -0.31414  ,  2.1719   ,\n",
       "         0.37574  ,  1.0532   , -0.38309  , -1.4743   ,  0.47828  ,\n",
       "         0.19131  , -0.57701  , -0.14553  ,  0.34223  ,  0.47609  ,\n",
       "         0.18458  ,  0.14093  ,  0.28985  ,  0.2081   ,  0.71447  ,\n",
       "        -1.3281   ,  1.0089   ,  0.0096065, -0.33909  ,  0.18393  ,\n",
       "         0.090431 , -0.058779 , -0.43937  ,  1.7312   , -0.36575  ],\n",
       "       dtype=float32),\n",
       " 'ramesh gaarment': array([ 0.0386465 , -0.70295   , -0.15418   ,  0.6078    , -0.0145755 ,\n",
       "        -0.106955  ,  0.18218   , -0.262325  ,  0.021369  ,  0.13374   ,\n",
       "         0.13412   ,  0.53525   ,  0.07702   ,  0.27059   ,  0.10395   ,\n",
       "        -0.0073885 ,  0.27227   ,  0.326435  ,  0.162755  ,  0.0427745 ,\n",
       "         0.015033  ,  0.66475   ,  0.32902   , -0.15707   ,  1.08595   ,\n",
       "         0.18787   ,  0.5266    , -0.191545  , -0.73715   ,  0.23914   ,\n",
       "         0.095655  , -0.288505  , -0.072765  ,  0.171115  ,  0.238045  ,\n",
       "         0.09229   ,  0.070465  ,  0.144925  ,  0.10405   ,  0.357235  ,\n",
       "        -0.66405   ,  0.50445   ,  0.00480325, -0.169545  ,  0.091965  ,\n",
       "         0.0452155 , -0.0293895 , -0.219685  ,  0.8656    , -0.182875  ],\n",
       "       dtype=float32),\n",
       " 'avoidance blackbox car': array([ 0.73948336, -0.03805733,  0.287086  ,  0.060698  , -0.41046333,\n",
       "         0.25428   ,  0.19333999, -0.25517663,  0.14021634,  0.34834668,\n",
       "         0.22602434,  0.15333368, -0.5811753 ,  0.57608336, -0.21020667,\n",
       "        -0.07302433, -0.38593534,  0.61634165, -0.22304332, -0.56944335,\n",
       "         0.23492335, -0.05905666, -1.0518433 ,  0.01082335,  0.05552499,\n",
       "        -0.44783005, -0.19398333,  0.32476267,  0.832623  ,  0.30551332,\n",
       "         0.97939664,  0.02970333, -0.12668999, -0.00974668,  0.45418334,\n",
       "         0.18204333,  0.3981967 , -0.38341334, -0.02299666,  0.39081666,\n",
       "         0.00508   , -0.24426667,  0.21277533,  0.23304869,  0.16527666,\n",
       "        -0.38545367,  0.36168   ,  0.26418334,  0.7383376 ,  0.15981667],\n",
       "       dtype=float32),\n",
       " 'microsoft dynamics exprience': array([ 0.4173    , -0.57785004,  0.36883935,  0.8594467 , -0.18155666,\n",
       "         0.22036664, -0.13552667, -0.70139664, -0.07336667,  0.24464333,\n",
       "         0.17518334,  0.34224   , -0.5811033 , -0.23281   , -0.23323669,\n",
       "         0.21318334, -0.5494767 ,  0.29297405,  0.10443667, -0.6971    ,\n",
       "         0.0502327 ,  0.18091667, -0.29883334, -0.24452333,  0.07085001,\n",
       "        -0.38658634, -0.12199334, -0.21101333,  0.09413666,  0.09400332,\n",
       "         1.4473333 , -0.4548967 , -0.21065666, -0.46885666, -0.09750634,\n",
       "        -0.6435533 , -0.52365667, -0.20476334,  0.08926666, -0.48441434,\n",
       "         0.25705433, -0.36443332, -0.01174666,  0.58485335, -0.23409332,\n",
       "         0.23044801,  0.54937   ,  0.28087   , -0.18680668,  0.41157332],\n",
       "       dtype=float32),\n",
       " 'activities attendance maintenance': array([ 0.15908334,  0.487077  , -0.19645667, -0.21383335, -0.15890332,\n",
       "        -0.45571932, -0.42574668, -0.15119542,  0.77200395, -0.29317787,\n",
       "         0.091687  , -0.30401334, -0.04264   , -0.3472543 ,  0.297476  ,\n",
       "        -0.17966567, -0.21040666,  0.36048666,  0.07082333, -0.44005933,\n",
       "         0.6418433 , -0.32084432, -0.35904   , -0.22012   , -0.5255067 ,\n",
       "        -0.5786567 ,  0.02826167, -0.28395298,  0.3009567 ,  0.46369663,\n",
       "         2.7770665 ,  0.6359507 ,  0.19822668, -0.77893335, -0.02689666,\n",
       "         0.4278833 ,  0.36788666,  0.08031   , -0.23981665,  0.42006   ,\n",
       "        -0.65665   , -0.61704   , -0.10657332,  0.60437334, -0.10462001,\n",
       "        -0.71213   , -0.24738933,  0.50404334, -0.21864934,  0.41578865],\n",
       "       dtype=float32),\n",
       " 'sri': array([ 0.31326 , -0.998   , -0.25783 ,  0.58968 ,  0.58295 , -0.78743 ,\n",
       "         0.21652 , -0.085245,  0.79313 , -0.31748 , -0.095959,  0.053049,\n",
       "        -0.20802 , -1.0512  ,  0.061849, -0.027036,  0.74234 ,  0.39778 ,\n",
       "        -0.45061 ,  1.1143  , -0.31441 ,  1.3555  ,  0.088508, -0.051022,\n",
       "         1.0908  , -0.38769 ,  0.96642 , -1.1537  , -1.3374  ,  0.75115 ,\n",
       "         2.6922  ,  0.057631, -0.38686 ,  1.0149  ,  0.86939 ,  1.0885  ,\n",
       "         0.32114 , -0.81618 , -0.90925 , -0.34178 , -0.70956 ,  0.23238 ,\n",
       "        -0.34988 , -0.68892 ,  0.23334 ,  0.92899 , -0.58765 ,  0.27188 ,\n",
       "         0.83884 , -1.6536  ], dtype=float32),\n",
       " 'admin bharat': array([-6.78020000e-01, -5.12089968e-01,  5.30445039e-01,  6.16077960e-01,\n",
       "        -5.81851959e-01, -3.95214975e-01,  6.25059962e-01, -5.95998764e-03,\n",
       "         8.30525011e-02, -3.81260008e-01, -5.52500188e-02,  6.11590028e-01,\n",
       "         1.86235011e-01,  2.20973000e-01, -1.13863504e+00, -7.97400028e-02,\n",
       "         1.66579992e-01,  9.59715009e-01,  8.41645002e-01,  1.72339994e-02,\n",
       "         6.10339999e-01, -8.21449980e-02, -2.76250482e-01, -2.36545011e-01,\n",
       "         2.57714987e-01,  1.44030005e-01, -5.52240014e-01, -2.68289983e-01,\n",
       "        -8.29365015e-01, -2.64609993e-01,  8.00400078e-02, -3.71385008e-01,\n",
       "         8.25555027e-02,  7.61986494e-01,  5.30004501e-04,  4.15455014e-01,\n",
       "         3.17291498e-01, -6.43310010e-01,  7.88760006e-01,  1.78229988e-01,\n",
       "         2.79695004e-01,  7.88650066e-02, -5.70405006e-01, -1.22335002e-01,\n",
       "        -4.56510007e-01, -4.36168998e-01, -1.17864996e-01,  2.41386995e-01,\n",
       "         7.96109974e-01,  8.86997581e-03], dtype=float32),\n",
       " 'microsoft dynamics': array([ 0.62595   , -0.86677504,  0.553259  ,  1.28917   , -0.272335  ,\n",
       "         0.33054996, -0.20329002, -1.0520949 , -0.11005001,  0.366965  ,\n",
       "         0.262775  ,  0.51336   , -0.871655  , -0.349215  , -0.34985504,\n",
       "         0.31977502, -0.824215  ,  0.43946108,  0.156655  , -1.04565   ,\n",
       "         0.07534905,  0.271375  , -0.44825   , -0.366785  ,  0.10627501,\n",
       "        -0.5798795 , -0.18299001, -0.31652   ,  0.14120498,  0.14100498,\n",
       "         2.171     , -0.68234503, -0.315985  , -0.703285  , -0.1462595 ,\n",
       "        -0.96533   , -0.785485  , -0.307145  ,  0.13389999, -0.7266215 ,\n",
       "         0.3855815 , -0.54665   , -0.01762   ,  0.87728   , -0.35114   ,\n",
       "         0.345672  ,  0.824055  ,  0.421305  , -0.28021002,  0.61736   ],\n",
       "       dtype=float32),\n",
       " 'sec': array([-1.3228   , -0.62585  ,  0.12525  ,  1.8365   ,  0.085129 ,\n",
       "         0.045201 ,  0.10156  , -0.21027  ,  0.0069678, -1.085    ,\n",
       "        -0.46635  , -0.13163  , -1.2277   , -0.83934  ,  1.0401   ,\n",
       "        -0.28436  , -0.16776  , -0.60704  , -0.25893  , -0.86368  ,\n",
       "         0.4918   , -0.17181  ,  0.29862  , -0.44673  , -0.27424  ,\n",
       "        -0.98205  ,  0.7875   , -0.59607  , -0.88628  , -0.972    ,\n",
       "         1.1253   , -0.35283  , -0.53458  , -0.38625  , -0.02002  ,\n",
       "        -0.83835  , -0.41351  ,  0.53456  ,  0.62073  , -1.1065   ,\n",
       "        -0.064498 , -0.74036  ,  0.72271  ,  1.1065   , -0.76332  ,\n",
       "        -0.57285  ,  0.97699  ,  0.94177  ,  0.87187  ,  0.038798 ],\n",
       "       dtype=float32),\n",
       " 'sri ramesh': array([ 0.1952765 , -1.2019501 , -0.283095  ,  0.90264   ,  0.2768995 ,\n",
       "        -0.50067   ,  0.29044   , -0.3049475 ,  0.417934  , -0.02500001,\n",
       "         0.0861405 ,  0.5617745 , -0.02699   , -0.25501   ,  0.13487451,\n",
       "        -0.0209065 ,  0.64344   ,  0.525325  , -0.06255001,  0.5999245 ,\n",
       "        -0.142172  ,  1.3425    ,  0.373274  , -0.182581  ,  1.63135   ,\n",
       "        -0.00597501,  1.00981   , -0.768395  , -1.4058499 ,  0.614715  ,\n",
       "         1.4417549 , -0.25968948, -0.266195  ,  0.67856497,  0.67274   ,\n",
       "         0.63654   ,  0.231035  , -0.263165  , -0.350575  ,  0.18634501,\n",
       "        -1.01883   ,  0.62064004, -0.17013675, -0.514005  ,  0.208635  ,\n",
       "         0.5097105 , -0.3232145 , -0.083745  ,  1.28502   , -1.009675  ],\n",
       "       dtype=float32),\n",
       " 'school hr admin': array([-0.7383433 ,  0.48446   ,  0.47838998, -0.20320131,  0.09595601,\n",
       "        -0.41810665,  0.05036001, -0.67195994,  0.20000167, -0.61206   ,\n",
       "        -0.64304334,  0.10465667, -0.10409927, -0.21179134, -0.62327   ,\n",
       "        -0.601022  , -0.2781987 ,  0.68977   ,  0.36376068,  0.4730487 ,\n",
       "         0.16627334, -0.10365667, -0.08013332,  0.47740665, -0.12506667,\n",
       "        -0.14756668,  0.32902965, -0.5479633 , -0.32697663, -0.47875667,\n",
       "         1.3925133 ,  0.14661999,  0.13215701, -0.311999  ,  0.23741466,\n",
       "         0.421841  ,  0.56065005, -0.09838334,  0.6085567 ,  0.20052963,\n",
       "        -0.05661998, -0.5021567 , -0.33448997,  0.4924133 , -0.36652532,\n",
       "         0.00914067,  0.45016003,  0.06407667,  0.4945267 ,  1.0709933 ],\n",
       "       dtype=float32),\n",
       " 'sec school': array([-1.11454499e+00,  3.11324984e-01, -3.35835010e-01,  2.16900051e-01,\n",
       "         2.33354997e-02, -1.03284493e-01, -5.91120005e-01, -3.97199988e-01,\n",
       "        -5.24111018e-02, -8.27040017e-01, -4.07384992e-01, -2.63944983e-01,\n",
       "        -6.18358910e-01, -9.54219997e-01,  3.43209982e-01, -1.68592989e-01,\n",
       "        -2.69160002e-01,  2.43029982e-01, -2.25490004e-01, -2.08600000e-01,\n",
       "         4.71745014e-01,  2.74614990e-01, -1.56204998e-01,  9.23850089e-02,\n",
       "        -3.82340014e-01, -1.36687505e+00,  4.21739519e-01, -5.59440017e-01,\n",
       "        -9.55539942e-01, -9.31710005e-01,  2.09739995e+00, -1.03999995e-01,\n",
       "        -3.36979985e-01, -8.88475001e-01,  5.96139967e-01, -2.18309999e-01,\n",
       "         1.79500878e-03,  4.04100001e-01,  8.03730011e-01, -5.39450526e-01,\n",
       "        -4.68448997e-01, -6.28419995e-01,  2.08045006e-01,  7.42170036e-01,\n",
       "        -3.73293012e-01, -1.67359993e-01,  7.35549986e-01,  1.87670022e-01,\n",
       "         3.42215002e-01,  3.33443999e-01], dtype=float32),\n",
       " 'labors individual records': array([-0.23404665,  0.22826   ,  0.00748666, -0.04447334,  0.21251665,\n",
       "         0.25946167, -0.19006534,  0.16957267,  0.60411996,  0.16404332,\n",
       "         0.121018  ,  0.22727668,  0.02793334, -0.03463   ,  0.48475337,\n",
       "        -0.5857    , -0.19883333, -0.09540999,  0.32395634, -0.453882  ,\n",
       "         0.23452933, -0.06940066,  0.39171997, -0.08684667,  0.07685333,\n",
       "        -0.32763332, -0.85672665, -0.7738634 ,  0.08055667, -0.21177334,\n",
       "         2.0364902 ,  0.16869669,  0.07507367, -0.22808665, -0.12775934,\n",
       "         0.32843533,  0.05671334,  0.07561334, -0.09657034, -0.36524466,\n",
       "        -0.07835332, -0.87350005,  0.256613  ,  0.60457665, -0.25190935,\n",
       "        -0.38931668, -0.11993233, -0.10059333, -0.23790534, -0.386307  ],\n",
       "       dtype=float32),\n",
       " 'activities attendance': array([-0.20527   ,  0.71051997, -0.35498   , -0.17525002,  0.13185   ,\n",
       "        -0.637365  , -0.410955  , -0.27153614,  0.442156  , -0.01158185,\n",
       "        -0.0320395 , -0.60134   ,  0.045745  , -0.43383148,  0.648574  ,\n",
       "        -0.2057535 ,  0.05890499,  0.02508001, -0.117235  , -0.24201399,\n",
       "         0.66091996, -0.31444648,  0.02924   ,  0.025265  , -0.59603   ,\n",
       "        -0.583675  ,  0.09429251, -0.3215645 , -0.106815  ,  0.38794   ,\n",
       "         2.73335   ,  0.94463   ,  0.21415001, -1.069185  , -0.36431   ,\n",
       "         0.23051   ,  0.11372501, -0.09166   , -0.73335   ,  0.23862   ,\n",
       "        -0.79303   , -0.47382998, -0.37652498,  0.55171   ,  0.07452999,\n",
       "        -0.811965  , -0.349365  ,  0.4612    , -0.292196  ,  0.13634801],\n",
       "       dtype=float32),\n",
       " 'project advanced automobile': array([ 0.1491267 ,  0.20429933,  0.42691335,  0.46254334, -0.5875667 ,\n",
       "         0.237873  , -0.62686133, -0.59540665,  0.2872367 ,  0.29841766,\n",
       "         0.7003133 , -0.18046999, -0.29539332, -0.0786    , -0.23623677,\n",
       "        -0.013619  , -0.16347666,  0.9030945 , -0.18275332, -0.407944  ,\n",
       "         0.73301363, -0.43543664, -0.97499996, -0.35130295,  0.13321173,\n",
       "        -0.93334   , -0.22954667, -0.545632  , -0.15802999,  0.05065266,\n",
       "         2.673867  , -0.22001667, -0.07709333, -0.23950331,  0.26481667,\n",
       "        -0.18804668,  0.00797633,  0.67837423,  0.19050467, -0.04704666,\n",
       "        -0.08408999, -0.79405665,  0.6333933 , -0.45327425,  0.23983668,\n",
       "        -0.02248334,  0.12688766,  0.20053333,  0.08114   ,  0.14408232],\n",
       "       dtype=float32),\n",
       " 'tirupur': array([-1.2895e+00, -1.1974e+00,  4.6540e-01,  6.0055e-02, -9.4296e-01,\n",
       "         5.4250e-02,  8.8387e-01, -2.7617e-01,  1.0250e+00,  6.7873e-01,\n",
       "        -1.0417e-01, -1.5153e+00,  3.8395e-01, -1.1423e+00, -9.9094e-01,\n",
       "         9.9006e-01,  8.6054e-01,  5.6940e-01,  7.1515e-01,  5.4085e-01,\n",
       "         9.6696e-01, -1.6097e-01,  2.9660e-01,  1.0566e+00, -4.5866e-01,\n",
       "         7.7478e-02, -5.9824e-01,  2.8699e-01, -3.3568e-04, -5.5505e-01,\n",
       "        -9.9396e-01, -4.6294e-01,  1.1702e-01,  7.0654e-02,  6.1626e-01,\n",
       "        -5.0506e-01, -4.3523e-01,  5.6459e-02, -3.6380e-02,  5.9313e-01,\n",
       "        -1.8379e-01,  6.1909e-01, -2.7897e-01, -5.7188e-01, -7.5110e-01,\n",
       "         4.9686e-02,  2.7723e-02, -6.5077e-01,  4.8647e-01, -5.1151e-01],\n",
       "       dtype=float32),\n",
       " 'dynamics': array([ 0.66744 , -0.27645 , -0.087682,  0.86714 , -0.24992 ,  1.1067  ,\n",
       "         0.30543 , -0.82679 , -0.35493 , -0.21334 ,  0.76948 ,  0.42999 ,\n",
       "        -0.71441 ,  0.23095 , -1.2596  ,  0.45344 , -0.34383 ,  0.88003 ,\n",
       "        -0.48206 , -1.0203  ,  0.15796 ,  0.35972 , -0.21537 , -0.55722 ,\n",
       "         0.31567 , -0.092159, -0.9071  , -0.19516 ,  0.48127 ,  0.83961 ,\n",
       "         1.7844  , -0.39641 , -0.30539 , -0.97375 ,  0.029001, -0.91906 ,\n",
       "        -1.0978  , -0.26873 ,  0.78593 ,  0.094357, -0.099537, -0.79735 ,\n",
       "         0.14564 ,  0.66086 , -0.53423 ,  0.071604,  1.2774  ,  0.61649 ,\n",
       "        -0.28295 ,  0.64222 ], dtype=float32),\n",
       " 'maintenance': array([ 0.88779 ,  0.040191,  0.12059 , -0.291   , -0.74041 , -0.092428,\n",
       "        -0.45533 ,  0.089486,  1.4317  , -0.85637 ,  0.33914 ,  0.29064 ,\n",
       "        -0.21941 , -0.1741  , -0.40472 , -0.12749 , -0.74903 ,  1.0313  ,\n",
       "         0.44694 , -0.83615 ,  0.60369 , -0.33364 , -1.1356  , -0.71089 ,\n",
       "        -0.38446 , -0.56862 , -0.1038  , -0.20873 ,  1.1165  ,  0.61521 ,\n",
       "         2.8645  ,  0.018592,  0.16638 , -0.19843 ,  0.64793 ,  0.82263 ,\n",
       "         0.87621 ,  0.42425 ,  0.74725 ,  0.78294 , -0.38389 , -0.90346 ,\n",
       "         0.43333 ,  0.7097  , -0.46292 , -0.51246 , -0.043438,  0.58973 ,\n",
       "        -0.071556,  0.97467 ], dtype=float32),\n",
       " 'blackbox car': array([ 7.27699995e-01, -1.19136006e-01,  6.96004033e-01,  6.93745017e-02,\n",
       "        -4.85449955e-02, -3.60749960e-02, -1.54305011e-01, -4.46000025e-02,\n",
       "         1.02359496e-01,  4.52920020e-01,  3.59500021e-01, -3.07499498e-01,\n",
       "        -8.53849530e-01,  6.94379985e-01, -4.14350033e-02,  1.08073503e-01,\n",
       "        -3.51593018e-01,  9.55179989e-01, -2.88904995e-01, -7.79874980e-01,\n",
       "         4.82120007e-01,  1.84495002e-01, -1.16875005e+00,  3.51665020e-01,\n",
       "        -1.55067503e-01, -3.66105020e-01, -4.85830009e-01,  4.85029995e-01,\n",
       "         5.46484470e-01,  2.15499997e-01,  9.65245008e-01, -1.67369992e-01,\n",
       "         8.10250044e-02,  1.12424992e-01,  7.41559982e-01,  1.73600003e-01,\n",
       "         6.49875045e-01, -3.62625003e-01,  9.96900052e-02,  2.50490010e-01,\n",
       "         1.11170001e-01, -2.31135011e-01, -2.62669995e-02, -2.76677012e-01,\n",
       "         1.53774992e-01, -1.62845507e-01,  8.91849920e-02, -2.01775014e-01,\n",
       "         5.58156371e-01,  5.60000539e-04], dtype=float32),\n",
       " 'sri ramesh gaarment': array([ 0.13018434, -0.80130005, -0.18873   ,  0.60175997,  0.18459965,\n",
       "        -0.33378002,  0.19362666, -0.20329833,  0.27862266, -0.01666667,\n",
       "         0.057427  ,  0.37451634, -0.01799334, -0.17000668,  0.08991634,\n",
       "        -0.01393767,  0.42896   ,  0.35021666, -0.04170001,  0.39994967,\n",
       "        -0.09478133,  0.895     ,  0.24884933, -0.12172066,  1.0875667 ,\n",
       "        -0.00398334,  0.6732066 , -0.51226336, -0.93723327,  0.40980998,\n",
       "         0.96116996, -0.17312633, -0.17746334,  0.45237663,  0.44849333,\n",
       "         0.42436   ,  0.15402333, -0.17544334, -0.23371667,  0.12423   ,\n",
       "        -0.67921996,  0.41376004, -0.1134245 , -0.34267   ,  0.13909   ,\n",
       "         0.339807  , -0.21547633, -0.05583   ,  0.85668   , -0.6731167 ],\n",
       "       dtype=float32),\n",
       " 'accept challenges': array([ 0.3838    ,  0.12205601, -0.0404165 , -0.07846999,  0.15823801,\n",
       "         0.217755  ,  0.383335  ,  0.29402   , -0.01114997,  0.066065  ,\n",
       "        -0.349095  ,  0.338075  , -0.285255  , -0.219987  ,  0.478775  ,\n",
       "         0.43449   ,  0.52324   , -0.41413   ,  0.93415   , -0.768385  ,\n",
       "         0.030495  ,  0.11893851, -0.2984525 , -0.0972325 ,  0.37998748,\n",
       "        -1.4784999 , -0.1702815 , -0.590255  , -0.0924155 ,  0.308     ,\n",
       "         2.52405   ,  0.56462   , -0.7843095 , -0.7499665 , -0.03102   ,\n",
       "        -0.307065  , -0.14096001, -0.1317005 , -0.72705996, -0.49585998,\n",
       "        -0.440652  , -0.0798885 ,  0.25838   ,  0.443945  , -0.16988501,\n",
       "         0.29793   , -0.20956501,  0.922915  , -0.31824   , -0.003118  ],\n",
       "       dtype=float32),\n",
       " 'proficiency program': array([-0.72825   ,  0.2712265 ,  0.028205  , -0.78980005, -0.3640135 ,\n",
       "        -0.0662685 , -0.223715  , -0.69748   ,  0.992815  ,  0.34403   ,\n",
       "         0.807185  , -0.15208851,  0.53457   , -0.038105  , -0.337555  ,\n",
       "        -0.26550502, -0.36369252,  0.713835  ,  0.12047099,  0.09301999,\n",
       "         0.23797499,  0.382301  , -0.27447   , -0.21665   ,  0.429635  ,\n",
       "        -1.12028   , -0.19577001, -0.72419   , -0.553325  , -0.17746   ,\n",
       "         2.51695   ,  0.50827503, -0.73154503, -0.89033496,  0.804815  ,\n",
       "         0.324135  ,  0.486355  ,  0.30469   , -0.38199452, -0.301889  ,\n",
       "        -0.09582199, -0.65257   ,  0.27664298,  0.0366    , -0.355285  ,\n",
       "        -0.604735  ,  1.096215  ,  1.56635   , -0.22719   ,  0.610145  ],\n",
       "       dtype=float32),\n",
       " 'computer science engineering': array([-0.27757534,  0.15909003,  0.53459   ,  0.49216935, -0.1178691 ,\n",
       "        -0.12679   , -0.49453667, -1.3598734 ,  0.26335335,  0.12351566,\n",
       "         0.5510734 ,  0.29462335, -0.42929998,  0.23639996, -0.45081332,\n",
       "        -0.08104   , -0.44326067,  1.2751766 , -0.18579334,  0.01672333,\n",
       "         1.0679101 ,  0.41761336, -0.30289668, -0.29384   ,  0.43471798,\n",
       "        -1.3483967 , -0.86272   , -1.1520767 , -0.6339933 , -0.08604333,\n",
       "         2.8295333 , -0.52448666, -0.088152  , -1.3685335 ,  0.12389266,\n",
       "         0.46710333, -0.5162633 ,  1.05681   ,  0.90174   ,  0.04577333,\n",
       "         0.20571   , -0.49344036,  0.22804701,  0.62343   ,  0.076947  ,\n",
       "         0.48361668,  0.8777366 ,  0.58142334, -0.14273389,  0.23767667],\n",
       "       dtype=float32),\n",
       " 'computer science': array([-0.26212803,  0.12133002,  0.74279   ,  0.486629  ,  0.05429634,\n",
       "        -0.35751   , -0.61885   , -1.58835   ,  0.19584501,  0.36637348,\n",
       "         0.28701   ,  0.329175  , -0.34174   ,  0.50575495,  0.02878   ,\n",
       "        -0.006725  , -0.627275  ,  0.988415  , -0.15928   ,  0.130525  ,\n",
       "         0.919815  ,  0.39817   , -0.078655  ,  0.06274   ,  0.66673   ,\n",
       "        -1.55015   , -0.86304   , -0.991665  , -0.46017998, -0.255595  ,\n",
       "         2.91485   , -0.54656   , -0.14228   , -1.5213001 , -0.172521  ,\n",
       "         0.422405  , -0.56182   ,  0.930465  ,  0.63126   , -0.18712002,\n",
       "         0.3197245 , -0.20676051,  0.1470605 ,  0.85132   ,  0.092785  ,\n",
       "         0.617875  ,  1.07083   ,  0.481455  , -0.05705582,  0.32735   ],\n",
       "       dtype=float32),\n",
       " 'software': array([ 0.40093 , -1.1683  ,  1.2586  ,  1.3243  , -0.60614 , -0.36914 ,\n",
       "        -1.2668  , -1.7872  ,  0.67804 ,  1.1791  ,  0.72633 ,  0.7432  ,\n",
       "        -0.32346 , -0.31177 , -0.025236,  0.072382, -0.84757 ,  0.70602 ,\n",
       "         0.88824 , -0.67657 ,  0.54001 , -0.11735 , -0.90053 ,  0.4227  ,\n",
       "         0.21735 , -0.5767  , -0.40095 , -1.1345  ,  0.064297, -0.44729 ,\n",
       "         3.3038  , -1.0716  , -0.37677 , -0.48321 , -0.11032 ,  0.27934 ,\n",
       "        -0.51331 ,  0.22437 ,  0.3481  , -0.6764  ,  1.27    , -0.37939 ,\n",
       "        -0.1754  ,  0.94066 , -0.12009 ,  0.40097 ,  0.93773 ,  0.57618 ,\n",
       "         0.30619 ,  0.30187 ], dtype=float32),\n",
       " 'accept challenges education': array([-0.05299999,  0.31292734, -0.22387433, -0.40371335,  0.05036534,\n",
       "         0.23263334,  0.12959667, -0.28575334,  0.12885003, -0.05691667,\n",
       "        -0.22212332,  0.16533001, -0.04728334, -0.44588134,  0.13146333,\n",
       "         0.19957   ,  0.364058  , -0.06750333,  1.0442333 , -0.25818   ,\n",
       "         0.20455332,  0.24362902, -0.400485  , -0.05002366,  0.32339832,\n",
       "        -1.4640332 , -0.00851766, -0.8712366 , -0.440377  ,  0.29286668,\n",
       "         2.8967    ,  0.61479   , -0.6951463 , -0.9402444 , -0.016746  ,\n",
       "        -0.08633334, -0.14936   ,  0.20908968, -0.23947997, -0.25046667,\n",
       "        -0.50677466, -0.15594566,  0.20287101,  0.40575996, -0.2854567 ,\n",
       "         0.23831333, -0.19377   ,  0.8176667 , -0.03988   ,  0.27512798],\n",
       "       dtype=float32),\n",
       " 'ready accept challenges': array([ 0.37861666,  0.01299734,  0.09524234, -0.13133   ,  0.31245866,\n",
       "        -0.17182332,  0.12129333,  0.38929334, -0.08796998, -0.03937   ,\n",
       "        -0.16826   ,  0.29117   , -0.29130667, -0.04832133,  0.50904995,\n",
       "         0.63255996,  0.51039   , -0.22869332,  0.649149  , -0.75768334,\n",
       "         0.06733333,  0.16017233, -0.10419167, -0.09732467,  0.32672167,\n",
       "        -1.4880333 , -0.03677766, -0.35582665,  0.17693633,  0.03594333,\n",
       "         2.6875    ,  0.67298   , -1.0211064 , -0.37432766, -0.04863967,\n",
       "        -0.16911668,  0.03717999,  0.02196301, -0.58124   , -0.60885334,\n",
       "        -0.28882632, -0.141069  ,  0.17970999,  0.10809666,  0.08071667,\n",
       "         0.33742   , -0.14671135,  0.74971336, -0.37002   ,  0.17847133],\n",
       "       dtype=float32),\n",
       " 'java': array([ 1.028    , -1.5226   ,  0.18132  ,  1.2824   , -0.43872  ,\n",
       "        -1.594    , -0.13842  , -1.2056   , -0.19988  ,  1.1825   ,\n",
       "         0.39901  , -0.23756  ,  1.085    , -0.4708   , -0.31306  ,\n",
       "         0.31879  , -0.82113  ,  0.89139  ,  0.0065573,  0.64755  ,\n",
       "        -0.31353  ,  0.27502  , -0.4065   ,  0.95904  ,  0.76842  ,\n",
       "        -0.15136  ,  0.4732   , -0.18431  , -0.39377  , -0.63589  ,\n",
       "         2.3685   , -1.5863   , -0.88763  , -0.22783  ,  0.90984  ,\n",
       "         0.42915  , -0.14046  , -1.0618   , -0.21038  ,  0.40454  ,\n",
       "        -0.098995 ,  0.46445  ,  0.0024477, -0.24164  , -0.42419  ,\n",
       "         1.0077   ,  1.0097   , -0.18129  ,  0.53966  , -0.82229  ],\n",
       "       dtype=float32),\n",
       " 'program langages': array([ 0.0606   ,  0.281975 ,  0.090555 , -0.19875  , -0.37553  ,\n",
       "         0.0365515, -0.5137   , -0.49456  ,  0.6389   ,  0.086925 ,\n",
       "         0.078285 ,  0.0269765,  0.066255 ,  0.2285   ,  0.192595 ,\n",
       "        -0.120805 , -0.0357575,  0.54335  ,  0.1581   ,  0.325745 ,\n",
       "         0.305095 ,  0.043801 , -0.220775 , -0.059655 , -0.071765 ,\n",
       "        -0.85735  ,  0.167165 , -0.25107  , -0.197845 , -0.072575 ,\n",
       "         1.76235  ,  0.087975 , -0.5407   , -0.48782  , -0.171385 ,\n",
       "         0.269125 ,  0.1784   ,  0.098175 ,  0.0133955, -0.34178  ,\n",
       "        -0.14375  , -0.286575 , -0.046032 , -0.056175 , -0.14187  ,\n",
       "        -0.172115 ,  0.262515 ,  0.60535  , -0.30649  ,  0.68705  ],\n",
       "       dtype=float32),\n",
       " 'web programming': array([ 0.416115  ,  0.034015  ,  0.5072    ,  0.94932497, -0.30172   ,\n",
       "         0.01060501, -1.258615  , -1.53585   ,  0.28376   ,  0.53069   ,\n",
       "         0.005245  ,  0.11604051,  0.058683  ,  0.60775   ,  0.24023952,\n",
       "         0.083135  , -0.6638    ,  0.43898   ,  0.36452   , -0.24706   ,\n",
       "         0.741475  ,  0.31102   ,  0.08242601,  1.32215   ,  0.20568   ,\n",
       "        -0.39681998, -0.273644  , -0.32536   ,  0.11967649, -0.3400485 ,\n",
       "         3.0791    , -0.03022499, -0.1967035 , -0.946705  , -0.652935  ,\n",
       "         0.385775  , -0.30023   , -0.222635  , -0.77191496, -0.09753999,\n",
       "         1.1155751 ,  0.144561  , -0.85523504,  0.744275  , -0.49085498,\n",
       "         0.008385  ,  0.46379   ,  0.690795  , -0.053845  ,  0.80421   ],\n",
       "       dtype=float32),\n",
       " 'programming tools': array([ 0.171545  , -0.46236   ,  0.25864   ,  0.66556   , -0.1518105 ,\n",
       "         0.257285  , -0.676535  , -1.4665    ,  0.015425  ,  0.35095602,\n",
       "         0.2269785 ,  0.32985002,  0.1313235 ,  0.43305552, -0.2906855 ,\n",
       "         0.287605  , -0.21324   ,  0.669645  ,  0.4557    , -0.61612   ,\n",
       "         0.86117   ,  0.18507499, -0.110755  ,  0.419935  ,  0.33932   ,\n",
       "        -0.00933   , -0.31511402, -0.72072   ,  0.397645  , -0.3164035 ,\n",
       "         3.0497499 , -0.20339501, -0.3157385 , -0.83982503, -0.016795  ,\n",
       "         0.99223995, -0.100421  ,  0.30506   , -0.418409  ,  0.205109  ,\n",
       "         0.74737   , -0.09592   , -0.63640004,  0.871505  , -0.00559998,\n",
       "         0.0785165 ,  0.83509   ,  0.82739997, -0.213815  ,  0.595845  ],\n",
       "       dtype=float32),\n",
       " 'system': array([ 0.33811  , -0.23768  ,  0.77132  ,  0.20897  , -0.96102  ,\n",
       "         0.21753  ,  0.069671 , -0.79317  ,  0.35003  , -0.18187  ,\n",
       "         0.80426  ,  0.6604   ,  0.0062125,  0.4198   , -0.83985  ,\n",
       "         0.39408  , -0.57603  ,  0.69031  ,  0.26979  , -0.26878  ,\n",
       "        -0.11203  , -0.85813  , -0.82293  , -0.01665  ,  0.10325  ,\n",
       "        -1.8031   , -0.1156   , -0.26856  ,  0.31724  ,  0.17981  ,\n",
       "         4.0673   , -0.36699  , -0.42908  , -1.0838   , -0.20061  ,\n",
       "         0.49008  ,  0.46461  , -0.12059  ,  0.23733  , -0.097047 ,\n",
       "         0.01566  , -0.37673  ,  0.065233 ,  1.0646   , -1.0214   ,\n",
       "         0.30089  ,  0.33471  ,  0.23508  ,  0.40676  ,  0.11479  ],\n",
       "       dtype=float32),\n",
       " 'motivated ready accept': array([ 0.29012   , -0.444306  ,  0.058459  , -0.44077334,  0.46905002,\n",
       "        -0.04039334,  0.16982998,  0.56765336, -0.53115   ,  0.31677333,\n",
       "        -0.02905334,  0.04649999, -0.3126833 , -0.30711666,  0.6330633 ,\n",
       "         0.47565666,  0.54082   ,  0.02084333,  0.41564897, -0.5419    ,\n",
       "         0.10233033,  0.27056566,  0.20294501,  0.13310534,  0.06965566,\n",
       "        -1.6275667 ,  0.01254567, -0.20171   ,  0.41737637, -0.210573  ,\n",
       "         2.3350334 ,  0.46453667, -1.030489  , -0.47099432, -0.10438633,\n",
       "        -0.3333    ,  0.17035334, -0.04993   , -0.55427   , -0.5524833 ,\n",
       "        -0.12700967, -0.26911333,  0.12661   ,  0.164     ,  0.23617335,\n",
       "        -0.11614001, -0.49835134,  0.7839834 , -0.14739667,  0.316692  ],\n",
       "       dtype=float32),\n",
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       "        -1.78050324e-01, -3.29599939e-02, -9.79423344e-01, -1.49529994e+00,\n",
       "         2.82536656e-01,  3.64180684e-01, -1.58276670e-02,  2.02630341e-01,\n",
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       "         3.03473324e-01, -3.36490005e-01, -4.17409331e-01, -6.14573300e-01,\n",
       "         2.57387668e-01, -4.09492344e-01,  3.03390002e+00, -2.83439994e-01,\n",
       "        -3.09685677e-01, -8.59743297e-01, -3.78163338e-01,  7.88949966e-01,\n",
       "        -2.22830653e-01,  1.27533332e-01, -4.81442660e-01, -5.47639914e-02,\n",
       "         9.80380058e-01, -9.08026695e-02, -5.30956686e-01,  8.94216716e-01,\n",
       "         1.28963351e-01, -5.05899871e-03,  6.62826657e-01,  6.84560001e-01,\n",
       "         1.33999682e-03,  4.52069998e-01], dtype=float32),\n",
       " 'institute technology chikmagalur': array([-0.18226235, -0.05962833,  0.00357332,  0.22517002, -0.4124333 ,\n",
       "        -0.5015733 , -0.05652332, -0.9741967 ,  0.49739   ,  0.21809001,\n",
       "         0.44821334, -0.42425668,  0.18407667, -0.40057334, -0.63164   ,\n",
       "         0.48937997, -0.196521  ,  0.7032233 ,  0.13424432,  0.32168332,\n",
       "         0.9462233 ,  0.1372171 , -0.27121666,  0.01327999,  0.07192665,\n",
       "        -0.79844   , -0.32001665, -0.47097668, -1.0967222 , -0.019432  ,\n",
       "         1.5758799 , -0.282567  , -0.01058   , -0.98255664,  0.11853532,\n",
       "        -0.18710665, -0.32323667,  0.9434333 ,  0.65670335,  0.20746   ,\n",
       "         0.12244377,  0.03044999,  0.08713001, -0.07887   ,  0.10503334,\n",
       "         0.31288534,  0.42140666,  0.14508669,  0.18494134,  0.12330668],\n",
       "       dtype=float32),\n",
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       "         0.07393   ,  0.02009499,  0.616695  , -0.093227  ,  0.20982999,\n",
       "        -0.23547499,  0.28725   ,  0.547     ,  0.0416    , -0.88443995,\n",
       "        -0.1016585 ,  0.68494   , -0.09688   ,  0.3536145 ,  1.310495  ,\n",
       "        -1.11906   , -0.443336  , -0.41862503, -0.11018002, -0.40136003,\n",
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       "        -0.23920499, -0.28499   ,  0.39201   , -0.32524002, -0.4946835 ,\n",
       "         0.145665  , -0.22544399, -0.17901999,  0.510925  ,  0.80438   ,\n",
       "        -0.21598999, -0.07201999,  0.37089   , -0.39748   ,  0.63909   ],\n",
       "       dtype=float32),\n",
       " 'proficiency program langages': array([-0.4855    ,  0.18081766,  0.01880333, -0.52653337, -0.24267566,\n",
       "        -0.044179  , -0.14914334, -0.46498668,  0.6618767 ,  0.22935332,\n",
       "         0.5381233 , -0.10139234,  0.35638   , -0.02540333, -0.22503667,\n",
       "        -0.17700334, -0.24246168,  0.47589   ,  0.080314  ,  0.06201333,\n",
       "         0.15865   ,  0.25486735, -0.18298   , -0.14443333,  0.28642333,\n",
       "        -0.74685335, -0.13051334, -0.48279333, -0.36888334, -0.11830667,\n",
       "         1.6779666 ,  0.33885002, -0.48769668, -0.59355664,  0.5365433 ,\n",
       "         0.21609001,  0.32423666,  0.20312667, -0.25466302, -0.20125933,\n",
       "        -0.06388133, -0.43504667,  0.18442865,  0.0244    , -0.23685665,\n",
       "        -0.40315667,  0.73081   ,  1.0442333 , -0.15146   ,  0.40676332],\n",
       "       dtype=float32),\n",
       " 'proficiency': array([-1.5777  , -0.021497, -0.1247  , -1.1821  ,  0.023033, -0.20564 ,\n",
       "         0.57997 , -0.40584 ,  0.70783 ,  0.51421 ,  1.4578  , -0.35813 ,\n",
       "         0.93663 , -0.53321 , -1.0603  , -0.2894  , -0.65587 ,  0.34097 ,\n",
       "        -0.075258, -0.46545 , -0.13424 ,  0.677   , -0.10739 , -0.31399 ,\n",
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       "         0.61591 ,  0.41303 , -0.79078 ,  0.079782,  0.095856, -0.73199 ,\n",
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       "         0.1586  , -0.15381 ], dtype=float32),\n",
       " 'engineering adichunchanagiri institute': array([-2.16523334e-01,  2.94280022e-01, -1.16110004e-01,  1.47486672e-01,\n",
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       "        -1.52993336e-01,  1.22409999e-01], dtype=float32),\n",
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       "         0.327865  , -0.218315  ,  0.54331005, -0.12039851, -0.11140001],\n",
       "       dtype=float32),\n",
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       "        -0.29592997, -0.38324   , -0.1155645 , -0.37945998,  0.36253   ],\n",
       "       dtype=float32),\n",
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       "        -0.69076663, -0.5792617 , -0.37789002, -0.005898  , -0.3405497 ,\n",
       "         0.01209666,  0.00469   ,  0.5429355 ,  0.27674666, -0.8548866 ,\n",
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       "         2.7771666 , -0.59580666,  0.12195667, -1.3598667 ,  0.26833665,\n",
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       "        -0.7868633 ,  0.30990663,  1.3823668 ,  1.26271   ,  0.17667735,\n",
       "        -0.12632333, -0.4360533 ,  0.34048   ,  0.19694398, -0.16143768],\n",
       "       dtype=float32),\n",
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       "         0.87191 , -0.12948 ,  0.98434 , -0.060635, -0.22155 , -0.48973 ,\n",
       "         0.17967 , -0.72795 ,  1.5319  , -0.518   , -0.89722 , -0.47947 ,\n",
       "        -0.099917, -0.53544 , -0.25357 , -0.14473 ,  0.8177  ,  0.21028 ,\n",
       "        -0.53528 , -1.7229  , -0.23533 , -0.42676 , -0.22517 , -0.033998,\n",
       "         3.0681  , -0.38766 ,  0.43658 , -1.0692  ,  0.32944 , -0.16995 ,\n",
       "         0.54683 , -0.18123 ,  0.18144 ,  0.15913 , -0.87828 ,  0.28833 ,\n",
       "         1.4865  ,  1.4837  ,  0.3142  ,  0.20116 , -0.45547 ,  0.32681 ,\n",
       "         0.031446, -0.2658  ], dtype=float32),\n",
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       "        -0.39665   , -0.17427666,  0.24762134,  0.32208002, -0.18033667,\n",
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       "         0.01073   , -0.42707333, -0.27217332, -0.11429799,  0.8184433 ],\n",
       "       dtype=float32),\n",
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       "         0.39049998,  0.5026435 , -0.10012   ,  0.27058   , -0.089725  ,\n",
       "        -1.2371199 ,  0.217855  ,  0.202258  , -0.6365485 ,  0.02768501,\n",
       "         1.6738349 , -0.1559615 ,  0.71429   , -0.25447   ,  0.954075  ,\n",
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       "         0.4329675 ,  0.61706   ,  0.45337003, -0.22299501, -0.59532446],\n",
       "       dtype=float32),\n",
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       "        -2.5510e+00,  1.9407e-01, -1.6332e-01, -1.1196e+00, -5.8010e-01,\n",
       "         1.4963e+00, -1.6072e+00, -5.7570e-01, -1.1390e+00,  4.6445e-01,\n",
       "        -9.2836e-01,  1.0848e+00, -1.6223e-01,  4.8931e-01, -1.3666e-01,\n",
       "        -1.9366e-01,  1.6199e+00,  3.4303e-01,  1.6869e-02, -6.5361e-01,\n",
       "        -1.6779e-03, -5.3340e-01, -6.0813e-01,  2.5397e-01,  9.6259e-01],\n",
       "       dtype=float32),\n",
       " 'gauhati': array([-0.62295 , -0.11314 ,  0.64923 ,  0.050387, -0.41227 , -1.1653  ,\n",
       "         1.057   ,  0.86594 ,  1.0937  , -0.097243, -0.50276 , -1.3636  ,\n",
       "         0.070188, -1.0533  , -0.75631 ,  0.086468,  0.39239 ,  0.80695 ,\n",
       "        -0.57928 ,  0.83491 ,  0.66589 ,  0.93914 ,  0.18773 ,  0.72355 ,\n",
       "         0.39645 , -0.59664 , -0.17474 , -0.029824, -1.1826  , -0.12181 ,\n",
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       "        -0.47968 , -1.012   ,  1.1701  ,  1.8861  ,  0.113   ,  1.317   ,\n",
       "         0.15573 , -0.0508  , -0.93762 ,  0.81988 ,  0.51374 ,  1.3631  ,\n",
       "        -0.2551  , -1.29    ], dtype=float32),\n",
       " 'assam jagiroad college': array([-0.6311967 , -0.04683332, -0.31981334, -0.40197733, -0.11879089,\n",
       "        -0.23333667, -0.51984066, -0.04550333,  0.26723668, -0.5541733 ,\n",
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       "         0.06910547, -0.08853   ,  0.69156665, -0.06005999,  0.56616664,\n",
       "         0.10255167,  0.36326334,  0.18844669,  0.49097332,  0.06188667,\n",
       "        -0.7469933 , -0.23170935, -0.39840832, -0.77731997, -0.11679665,\n",
       "         1.2252666 ,  0.08869333,  0.004773  , -0.30373   ,  0.5015467 ,\n",
       "         0.29137334, -0.39255333, -0.13991   ,  0.6052367 ,  0.28713   ,\n",
       "        -0.47877002,  0.20921332, -0.58149   ,  0.08517333, -0.44608665,\n",
       "         0.44471002,  0.10337334,  0.11101667, -0.13975833, -0.45415667],\n",
       "       dtype=float32),\n",
       " 'corporation': array([-0.27177  , -0.22943  ,  0.26692  ,  1.5533   , -0.48634  ,\n",
       "         0.11197  , -0.81666  , -0.59874  ,  0.5598   , -0.19032  ,\n",
       "         0.98432  ,  1.1684   , -0.22649  , -0.34209  , -0.92088  ,\n",
       "         0.78469  , -0.34462  ,  1.8457   , -0.20655  ,  0.070788 ,\n",
       "         1.8536   , -0.014443 , -0.66193  , -0.48167  , -0.6879   ,\n",
       "        -1.1242   , -0.35965  , -0.48269  , -0.67432  , -0.29731  ,\n",
       "         2.2105   , -0.62108  ,  0.37132  , -0.1432   , -0.11969  ,\n",
       "        -0.59883  ,  0.046388 , -0.35669  ,  0.6355   , -0.018717 ,\n",
       "         0.25765  , -0.90633  ,  0.0053882, -0.25177  , -1.021    ,\n",
       "         0.43729  , -1.4498   ,  0.21358  ,  0.096074 ,  0.15452  ],\n",
       "       dtype=float32),\n",
       " 'subordinate tribunals statutory': array([ 0.21405667, -0.5898833 , -0.70680666, -0.21966833,  0.57808936,\n",
       "        -0.06012332,  1.3730999 ,  0.10960466,  0.00307167, -0.77573663,\n",
       "         0.02797666,  0.63046664,  0.05974001,  0.24299   , -0.13984334,\n",
       "        -0.04064333, -0.276872  , -0.23014998,  0.71091336, -0.07345734,\n",
       "         0.22428   , -0.24197567, -0.13776101, -0.1263808 , -1.1536733 ,\n",
       "        -0.8036267 , -0.10882767, -0.5211933 , -0.30046967, -0.04818567,\n",
       "         1.30053   , -0.33008865, -0.43006667, -0.9728833 ,  0.417124  ,\n",
       "         0.28609666,  0.6903267 , -0.60684663, -0.64554626, -0.102148  ,\n",
       "        -0.48512003, -0.32279667,  0.8969833 ,  0.91578335, -1.1461233 ,\n",
       "        -0.32087997, -0.27149302,  0.30462366,  0.02403001,  0.18611   ],\n",
       "       dtype=float32),\n",
       " 'morigaon college advocate': array([-0.79062337,  0.20905036, -0.5027533 , -0.6166733 , -0.05664756,\n",
       "        -0.24318336, -0.158988  ,  0.23746336,  0.22758333, -0.08896702,\n",
       "        -0.14001334, -0.47677836,  0.35049334, -0.6586573 , -0.54192334,\n",
       "        -0.19187666, -0.00862   ,  0.34494868,  0.29659   ,  0.5178967 ,\n",
       "         0.17824668,  0.22115   , -0.37008438,  0.4280591 , -0.12342999,\n",
       "        -0.92894   ,  0.12048066, -0.62309   , -0.61048096, -0.05935999,\n",
       "         1.1772267 ,  0.05855998,  0.05190198, -1.0062966 ,  0.3037733 ,\n",
       "        -0.11573666, -0.27898   ,  0.43698564,  0.31826666,  0.09840999,\n",
       "        -0.43116036, -0.03941667, -0.15300333,  0.26281333, -0.26767668,\n",
       "        -0.02265667,  0.02557335, -0.21808334, -0.105115  ,  0.49455896],\n",
       "       dtype=float32),\n",
       " 'cases cases': array([ 1.0667  , -0.58909 , -0.1097  ,  0.24335 , -0.12807 ,  1.383   ,\n",
       "         0.87191 , -0.12948 ,  0.98434 , -0.060635, -0.22155 , -0.48973 ,\n",
       "         0.17967 , -0.72795 ,  1.5319  , -0.518   , -0.89722 , -0.47947 ,\n",
       "        -0.099917, -0.53544 , -0.25357 , -0.14473 ,  0.8177  ,  0.21028 ,\n",
       "        -0.53528 , -1.7229  , -0.23533 , -0.42676 , -0.22517 , -0.033998,\n",
       "         3.0681  , -0.38766 ,  0.43658 , -1.0692  ,  0.32944 , -0.16995 ,\n",
       "         0.54683 , -0.18123 ,  0.18144 ,  0.15913 , -0.87828 ,  0.28833 ,\n",
       "         1.4865  ,  1.4837  ,  0.3142  ,  0.20116 , -0.45547 ,  0.32681 ,\n",
       "         0.031446, -0.2658  ], dtype=float32),\n",
       " 'technical qualifications': array([-0.79151   ,  0.469315  , -0.38114   ,  0.20034   , -0.09708501,\n",
       "        -0.12266999,  0.1676285 ,  0.0426065 ,  0.22299   , -0.578265  ,\n",
       "         0.46890497,  0.17715   , -0.258915  , -0.18376   , -0.336995  ,\n",
       "        -0.036265  , -0.117724  ,  0.28133202,  0.2640385 , -0.845315  ,\n",
       "         0.5289    ,  0.653575  , -0.34403783, -0.25042   ,  0.272905  ,\n",
       "        -0.662975  , -0.23038   , -0.67959   , -0.80649996,  0.2443665 ,\n",
       "         2.25225   ,  0.394763  , -0.25954998, -1.145645  ,  0.988155  ,\n",
       "         0.34266   ,  0.248705  ,  0.943215  , -0.077425  , -0.20084   ,\n",
       "         0.125895  , -0.582745  ,  0.470905  ,  0.85696995, -0.799945  ,\n",
       "        -0.4154889 ,  0.66775   ,  1.7358    ,  0.32838652,  0.188375  ],\n",
       "       dtype=float32),\n",
       " 'advocate advocate': array([-9.0568e-01,  4.5471e-02, -6.6214e-01, -9.2491e-01,  1.2680e-01,\n",
       "         5.0345e-01,  1.6536e-02, -2.0854e-01,  6.9282e-01,  3.6681e-01,\n",
       "        -1.0928e-01, -3.9085e-02,  2.7495e-01, -9.1972e-02, -7.8872e-01,\n",
       "        -5.6768e-01,  5.8896e-01, -8.2603e-02,  7.5603e-01,  1.9777e-01,\n",
       "        -1.4918e-01,  3.3272e-01, -8.7888e-01, -2.8276e-04,  1.6454e-01,\n",
       "        -1.8378e+00,  2.5705e-01, -9.0744e-01, -3.3503e-01,  1.0719e+00,\n",
       "         1.8214e+00, -3.0226e-01, -6.8954e-01, -9.2692e-01, -8.5948e-01,\n",
       "        -6.6453e-01, -1.2684e-01,  8.5637e-02, -1.8328e-01, -1.4620e-01,\n",
       "         6.0849e-02,  2.7326e-01, -2.8726e-01,  3.2397e-01,  4.9825e-01,\n",
       "        -1.6415e-01, -9.5037e-01, -2.7648e-01,  1.4303e-02,  1.0672e+00],\n",
       "       dtype=float32),\n",
       " 'advocate advocate gauhati': array([-0.81143665, -0.00739933, -0.22501667, -0.599811  , -0.05289   ,\n",
       "        -0.05280002,  0.36335734,  0.14962   ,  0.8264467 ,  0.21212566,\n",
       "        -0.24044   , -0.48059002,  0.20669599, -0.41241467, -0.77791667,\n",
       "        -0.34963068,  0.52343667,  0.21391465,  0.31092668,  0.41014996,\n",
       "         0.12250999,  0.53486   , -0.5233434 ,  0.24099483,  0.24184334,\n",
       "        -1.42408   ,  0.11312   , -0.61490136, -0.6175533 ,  0.6739967 ,\n",
       "         1.16989   , -0.31438336, -0.18512003, -0.52476   , -0.35293666,\n",
       "        -0.5205067 , -0.24445333, -0.280242  ,  0.26784667,  0.5312334 ,\n",
       "         0.07823267,  0.6211733 , -0.13959666,  0.19904666,  0.01962668,\n",
       "         0.16386001, -0.46233335,  0.27004668, -0.07549801,  0.28146663],\n",
       "       dtype=float32),\n",
       " 'college advocate': array([-1.06699   ,  0.7315355 , -0.674195  , -1.043205  ,  0.06241865,\n",
       "        -0.01116002, -0.790582  , -0.230805  ,  0.23791501, -0.09945501,\n",
       "        -0.12588   ,  0.0592825 ,  0.23129   , -0.704636  , -0.66155005,\n",
       "        -0.18097499,  0.054555  ,  0.48249853,  0.12426502,  0.485515  ,\n",
       "         0.12428001,  0.51809   , -0.6006    ,  0.30071363,  0.01550999,\n",
       "        -1.8399    ,  0.08222601, -1.01812   , -0.910465  ,  0.10477501,\n",
       "         2.2095    ,  0.17959999, -0.37181702, -1.25121   ,  0.12950999,\n",
       "        -0.26270998, -0.19799499,  0.32861352,  0.57476   , -0.19740501,\n",
       "        -0.3123455 , -0.08315   , -0.57735   ,  0.640845  , -0.08761999,\n",
       "         0.09817   , -0.165425  , -0.27002   , -0.1785985 ,  0.694065  ],\n",
       "       dtype=float32),\n",
       " 'judge': array([-4.1753e-01, -7.7215e-01, -7.7358e-01,  1.0329e-01,  1.0391e+00,\n",
       "         1.4551e-01,  5.2221e-01,  1.0225e+00,  5.4505e-02, -2.2843e-01,\n",
       "        -4.5173e-01,  4.7498e-01, -4.9818e-01, -2.4674e-01,  1.2705e+00,\n",
       "        -1.2240e-01, -1.3576e-01, -1.3487e+00,  1.9960e-01, -8.5554e-01,\n",
       "         5.5979e-01,  6.6522e-01, -3.5259e-02,  2.0750e-01, -4.9616e-01,\n",
       "        -2.5510e+00,  1.9407e-01, -1.6332e-01, -1.1196e+00, -5.8010e-01,\n",
       "         1.4963e+00, -1.6072e+00, -5.7570e-01, -1.1390e+00,  4.6445e-01,\n",
       "        -9.2836e-01,  1.0848e+00, -1.6223e-01,  4.8931e-01, -1.3666e-01,\n",
       "        -1.9366e-01,  1.6199e+00,  3.4303e-01,  1.6869e-02, -6.5361e-01,\n",
       "        -1.6779e-03, -5.3340e-01, -6.0813e-01,  2.5397e-01,  9.6259e-01],\n",
       "       dtype=float32),\n",
       " 'consumer forum': array([-0.32336497, -0.015625  , -0.07971501,  0.55800605,  0.188055  ,\n",
       "        -0.55805653, -0.62031496, -1.196615  ,  0.424867  ,  0.11371999,\n",
       "         0.079585  , -0.320245  ,  0.0347545 ,  0.4241485 ,  0.10062   ,\n",
       "         0.24531752, -0.25594002, -0.3040765 ,  0.358083  , -0.41654497,\n",
       "         1.15305   , -0.15429899, -0.411825  ,  0.209111  , -0.05003   ,\n",
       "        -0.809395  , -0.04886502, -0.340015  , -0.654325  ,  0.515638  ,\n",
       "         2.7865    ,  0.677745  ,  0.05766499, -0.83213496, -1.04584   ,\n",
       "        -0.93464005, -0.60158   , -0.16126199,  0.03034499, -0.30884498,\n",
       "        -0.326115  , -0.3249935 ,  0.00932   ,  0.0345045 ,  0.574135  ,\n",
       "         0.575835  , -0.19609   ,  0.620785  ,  0.431195  ,  0.53601503],\n",
       "       dtype=float32),\n",
       " 'advocate': array([-9.0568e-01,  4.5471e-02, -6.6214e-01, -9.2491e-01,  1.2680e-01,\n",
       "         5.0345e-01,  1.6536e-02, -2.0854e-01,  6.9282e-01,  3.6681e-01,\n",
       "        -1.0928e-01, -3.9085e-02,  2.7495e-01, -9.1972e-02, -7.8872e-01,\n",
       "        -5.6768e-01,  5.8896e-01, -8.2603e-02,  7.5603e-01,  1.9777e-01,\n",
       "        -1.4918e-01,  3.3272e-01, -8.7888e-01, -2.8276e-04,  1.6454e-01,\n",
       "        -1.8378e+00,  2.5705e-01, -9.0744e-01, -3.3503e-01,  1.0719e+00,\n",
       "         1.8214e+00, -3.0226e-01, -6.8954e-01, -9.2692e-01, -8.5948e-01,\n",
       "        -6.6453e-01, -1.2684e-01,  8.5637e-02, -1.8328e-01, -1.4620e-01,\n",
       "         6.0849e-02,  2.7326e-01, -2.8726e-01,  3.2397e-01,  4.9825e-01,\n",
       "        -1.6415e-01, -9.5037e-01, -2.7648e-01,  1.4303e-02,  1.0672e+00],\n",
       "       dtype=float32),\n",
       " 'jagiroad college morigaon': array([-0.48872998,  0.19389336, -0.28204   , -0.30837   , -0.09891423,\n",
       "        -0.411     , -0.1645    ,  0.30697668, -0.00335667, -0.21123701,\n",
       "        -0.10358667, -0.46375   ,  0.25884333, -0.62799996, -0.27901667,\n",
       "        -0.00265   , -0.20494   ,  0.37248302,  0.04458   ,  0.45197335,\n",
       "         0.22797334,  0.11024333, -0.07712433,  0.42815337, -0.17827666,\n",
       "        -0.31634   ,  0.03479733, -0.32061002, -0.49880433, -0.41665998,\n",
       "         0.57009333,  0.15931332,  0.28174865, -0.6973233 ,  0.59026664,\n",
       "         0.10577333, -0.2367    ,  0.40844   ,  0.37936   ,  0.14714332,\n",
       "        -0.45144334, -0.13050334, -0.05725   ,  0.15482332, -0.43376002,\n",
       "         0.03206   ,  0.34236336, -0.12592334, -0.10988266,  0.13882567],\n",
       "       dtype=float32),\n",
       " 'technical qualifications windows': array([-0.25939998,  0.18098001,  0.23727334,  0.26543334, -0.05638867,\n",
       "        -0.32721666, -0.13371767, -0.48126236, -0.06761333, -0.26867   ,\n",
       "         0.285386  ,  0.05980666, -0.28338334, -0.04778667, -0.16551667,\n",
       "         0.14452   , -0.58374935,  0.17481233,  0.41280898, -1.10441   ,\n",
       "         0.29792336,  0.50019336, -0.6906252 , -0.10300335,  0.21040465,\n",
       "        -0.4085767 , -0.04489666, -0.18596   , -0.35399997, -0.12700899,\n",
       "         2.5938332 ,  0.10404533, -0.15515165, -0.84188   ,  0.59268665,\n",
       "         0.6017733 ,  0.56847   ,  0.38122335, -0.09347   , -0.35279   ,\n",
       "         0.38632998, -0.46959338,  0.22383   ,  0.8365533 , -0.49333334,\n",
       "         0.05947407,  0.57304335,  0.8028334 ,  0.42438102, -0.24991667],\n",
       "       dtype=float32),\n",
       " 'company gauhati': array([ 1.4399886e-03, -3.4508500e-01,  5.3043002e-01,  4.5925352e-01,\n",
       "        -2.4768351e-01, -4.4987500e-01, -2.1149999e-01,  2.3900999e-01,\n",
       "         7.6048505e-01,  9.7784996e-03,  1.4057001e-01, -2.7504000e-01,\n",
       "        -3.5872102e-01, -8.5385001e-01, -4.2776200e-01,  1.4652400e-01,\n",
       "        -1.7038000e-01,  8.3556497e-01, -4.2422500e-01, -5.1504999e-02,\n",
       "         1.2924449e+00,  3.8220501e-01, -2.1566001e-01,  1.1002502e-01,\n",
       "        -2.6882499e-01, -1.1360700e+00, -4.8436502e-01, -1.7492700e-01,\n",
       "        -6.2513900e-01, -1.2262499e-01,  1.5788850e+00, -3.8507000e-01,\n",
       "         6.0580999e-01,  3.3344501e-01,  3.2086548e-01, -6.0718501e-01,\n",
       "        -5.0578499e-01, -3.0666998e-01,  1.0323100e+00,  7.3823500e-01,\n",
       "         3.5110998e-01,  3.9661503e-01,  2.1733001e-01, -1.5423000e-01,\n",
       "        -6.2109500e-01,  3.7678751e-01, -3.0418000e-01,  7.8946000e-01,\n",
       "        -2.0548001e-01, -3.2490999e-01], dtype=float32),\n",
       " 'matters': array([ 1.9401e-02,  1.9517e-01, -1.3228e+00,  2.5030e-01,  7.4223e-01,\n",
       "         1.9011e-01,  5.6188e-01,  2.0873e-01, -7.0394e-02, -8.6462e-01,\n",
       "        -6.6332e-01,  8.9657e-01, -4.4610e-01,  7.6695e-02,  4.8169e-01,\n",
       "         1.6642e-01, -1.8647e-03, -4.4935e-01,  1.2064e+00, -3.5145e-01,\n",
       "         6.5061e-01,  4.6204e-01,  1.6162e-01,  5.2952e-02,  3.5994e-01,\n",
       "        -1.3597e+00,  1.0989e-01, -2.2694e-01,  3.4432e-01,  1.3781e-01,\n",
       "         2.7007e+00,  1.8912e-01, -1.1095e-01, -1.2695e+00, -2.5030e-01,\n",
       "         1.3010e-01, -6.2452e-01,  1.9491e-01, -2.6513e-01,  2.9525e-01,\n",
       "        -5.6429e-01, -1.1289e-01,  4.1936e-01,  1.1322e+00, -6.6061e-01,\n",
       "         3.5446e-01, -1.1714e-01,  1.0093e+00,  1.2138e-02,  4.1032e-01],\n",
       "       dtype=float32),\n",
       " 'cases cases consumer': array([ 0.34243333, -0.6889667 ,  0.19872   ,  0.15793733, -0.05146   ,\n",
       "         0.9453623 ,  0.26447666, -0.56372   ,  0.92183334,  0.10994333,\n",
       "         0.05709   , -0.40410665,  0.136222  , -0.50484765,  1.1233433 ,\n",
       "        -0.15102333, -0.88703996, -0.50068337,  0.14493866, -0.7445933 ,\n",
       "         0.28115335, -0.22285335,  0.35155   ,  0.11921734, -0.43107   ,\n",
       "        -1.4995333 , -0.38298666, -0.34075665, -0.02189667,  0.29616132,\n",
       "         3.0368335 , -0.03311   ,  0.62672   , -0.98238665, -0.02220001,\n",
       "        -0.38316   ,  0.09274   , -0.20425999,  0.30205998, -0.19068332,\n",
       "        -0.65697664,  0.21529098,  1.06295   ,  0.9743364 ,  0.43696666,\n",
       "         0.21802334, -0.34054336,  0.46366668,  0.29461733,  0.08339334],\n",
       "       dtype=float32),\n",
       " 'high': array([-0.65758 ,  1.2502  ,  0.19082 , -0.6742  , -0.10125 , -0.046932,\n",
       "        -0.27648 , -0.48253 ,  0.39254 ,  0.3704  , -0.24111 , -0.6429  ,\n",
       "         0.27676 , -0.62422 , -0.52203 ,  0.29799 , -0.1996  ,  0.36884 ,\n",
       "        -0.81686 , -0.48865 ,  0.11511 ,  0.066147, -0.38797 , -0.18239 ,\n",
       "        -0.5759  , -1.8776  ,  0.61045 ,  0.43434 , -0.090497,  0.17718 ,\n",
       "         3.4808  ,  0.026707,  0.60486 , -0.7885  ,  1.248   , -0.32864 ,\n",
       "        -0.15526 ,  0.12868 ,  0.65885 , -0.27297 , -0.69856 ,  0.46953 ,\n",
       "         0.19621 ,  0.40444 ,  0.061996,  0.046055,  0.72038 , -0.45636 ,\n",
       "        -0.19089 ,  0.099351], dtype=float32),\n",
       " 'move relevant authority': array([ 0.28812   , -0.23895667, -0.14187667,  0.01982   ,  0.23466866,\n",
       "        -0.4071133 ,  0.11113   , -0.01133633,  0.19532664, -0.256673  ,\n",
       "         0.06016   ,  0.47885534, -0.19019   ,  0.02131433, -0.07694667,\n",
       "         0.64923   ,  0.31852266, -0.4707267 ,  0.7061267 , -0.32222667,\n",
       "         0.56774   , -0.04681282, -0.08537666, -0.11809701, -0.04591733,\n",
       "        -1.4698033 ,  0.08186001, -0.18108666,  0.02966333, -0.10155334,\n",
       "         3.0377    ,  0.19534999, -0.53459334, -1.0508133 , -0.15765001,\n",
       "        -0.10779084,  0.29676467, -0.14341001, -0.51716334,  0.14128666,\n",
       "        -0.25536   , -0.21492006,  0.36480305,  0.31052333, -0.66602266,\n",
       "         0.11445833, -0.47707167,  0.7738533 ,  0.18375234,  0.07338334],\n",
       "       dtype=float32),\n",
       " 'taxation matters income': array([-0.25008634,  0.15079166, -0.3974967 , -0.32784   ,  0.32344332,\n",
       "         0.50446004,  0.34484664, -0.6179467 ,  0.15622868, -0.45868537,\n",
       "        -0.27538034,  0.36298335,  0.29627332, -0.77834827,  0.3758023 ,\n",
       "         0.04188466,  0.12856911, -0.38859665,  1.0483266 , -0.30344   ,\n",
       "         0.8041167 , -0.6048867 , -0.20714332, -0.255886  , -0.42239666,\n",
       "        -0.9640166 ,  0.02892667, -0.40973663,  0.32637334,  0.6145666 ,\n",
       "         2.6156666 ,  0.60485   ,  0.2707633 , -0.4245447 , -0.14705001,\n",
       "        -0.22165334, -0.59969   ,  0.19816302,  0.1921    ,  0.02930667,\n",
       "        -0.5844733 , -0.041535  ,  0.65398663,  0.89653665, -1.0675367 ,\n",
       "        -0.60862   , -0.38689336,  0.8054133 ,  0.6470093 ,  0.44150665],\n",
       "       dtype=float32),\n",
       " 'returns gst returns': array([ 0.15155554,  0.12783666,  0.71418333, -0.19580267,  0.14540668,\n",
       "         0.4138887 , -0.15447001,  0.06540664,  0.16256998,  0.00676999,\n",
       "         0.06936534,  0.38594   , -0.21964334, -0.40565333,  0.6742101 ,\n",
       "         0.42830333, -0.073578  , -0.55436   ,  0.17805667,  0.00850543,\n",
       "         0.60494   , -0.6523    , -0.35041198,  0.16404001,  0.19900334,\n",
       "        -0.58858997,  0.19114132, -0.28551337,  0.20358032,  0.18666066,\n",
       "         1.37399   ,  0.64649665,  0.17337   ,  0.330064  ,  0.10619   ,\n",
       "        -0.12474466,  0.11290333, -0.06925467,  0.26410332, -0.6111233 ,\n",
       "         0.09444666,  0.18445332, -0.19252999, -0.269809  , -0.6839533 ,\n",
       "        -0.22312666,  0.39619336,  0.06588333,  0.7991266 ,  0.165466  ],\n",
       "       dtype=float32),\n",
       " 'burdwan advocate': array([-0.87107503, -0.54456455, -0.21733502, -0.74643505, -0.24667001,\n",
       "         0.22993198,  0.646018  ,  0.04215001,  0.26268   , -0.18820001,\n",
       "         0.03071   , -0.38407248,  0.117185  , -0.693536  , -1.17956   ,\n",
       "         0.30626   ,  0.402925  ,  0.5071485 ,  0.548645  ,  0.923135  ,\n",
       "         0.29158   , -0.10298   , -0.60122   ,  0.3563536 ,  0.15437   ,\n",
       "        -1.173345  , -0.29259998, -0.85925996, -0.671915  , -0.01880002,\n",
       "         0.78909504, -0.280245  ,  0.10651499, -0.496524  , -0.07230002,\n",
       "        -0.21590498,  0.142165  ,  0.26573348,  0.17252001,  0.260035  ,\n",
       "        -0.0419705 , -0.13564   , -0.52706504,  0.44037497, -0.00426   ,\n",
       "        -0.20154   , -0.028815  , -0.301935  , -0.1093485 ,  0.584355  ],\n",
       "       dtype=float32),\n",
       " 'university clacutta university': array([-7.3879999e-01,  8.6106664e-01, -5.2500665e-01, -3.0636665e-01,\n",
       "        -2.7192000e-01, -3.5591331e-01, -6.0978669e-01, -3.2384667e-01,\n",
       "        -2.1238667e-01, -2.2329332e-01,  3.3395335e-01, -3.3594668e-01,\n",
       "         1.9632665e-02, -7.8320003e-01, -3.1496665e-01,  1.8133999e-01,\n",
       "        -7.3953338e-02,  6.7680001e-01, -6.9513339e-01,  9.9526668e-01,\n",
       "         4.6814001e-01,  4.8093998e-01, -7.2653331e-02, -1.2779334e-03,\n",
       "        -5.0816000e-02, -1.6932000e+00,  8.0053337e-02, -5.7395333e-01,\n",
       "        -1.4146667e+00, -6.0608667e-01,  1.7432667e+00, -3.3162665e-02,\n",
       "        -6.4369999e-02, -1.4992000e+00, -4.0216666e-02,  5.5521335e-03,\n",
       "        -7.0346668e-02,  8.2900000e-01,  1.1592666e+00,  4.4341335e-01,\n",
       "        -7.8906663e-02, -4.0997334e-02, -1.9440000e-01,  2.5132000e-01,\n",
       "        -6.4676002e-02,  3.2285333e-01,  2.4380000e-01,  1.7512000e-01,\n",
       "        -3.6210001e-01,  2.3962667e-02], dtype=float32),\n",
       " 'returns': array([ 0.22497 ,  0.31136 ,  0.62103 , -0.086114,  0.76716 , -0.035767,\n",
       "        -0.48738 ,  0.74011 ,  0.37802 ,  0.36501 , -0.081457,  0.44969 ,\n",
       "        -0.14553 , -0.15688 ,  0.93006 ,  0.22268 , -0.084491, -0.73172 ,\n",
       "        -0.15269 ,  0.013496,  0.37681 , -0.1524  , -0.013518, -0.21034 ,\n",
       "         0.23704 , -1.0653  ,  0.024777, -0.21746 ,  0.29252 ,  0.241   ,\n",
       "         2.1733  ,  0.55485 ,  0.20309 , -0.063654,  0.4346  ,  0.013688,\n",
       "        -0.2201  , -0.018012,  0.16148 , -1.2382  , -0.35863 ,  0.35953 ,\n",
       "        -0.4412  , -0.36283 , -0.79422 , -0.40696 ,  0.47206 , -0.13873 ,\n",
       "         0.53209 ,  0.2686  ], dtype=float32),\n",
       " 'gst': array([ 4.7266e-03, -2.3921e-01,  9.0049e-01, -4.1518e-01, -1.0981e+00,\n",
       "         1.3132e+00,  5.1135e-01, -1.2840e+00, -2.6833e-01, -7.0971e-01,\n",
       "         3.7101e-01,  2.5844e-01, -3.6787e-01, -9.0320e-01,  1.6251e-01,\n",
       "         8.3955e-01, -5.1752e-02, -1.9964e-01,  8.3955e-01, -1.4757e-03,\n",
       "         1.0612e+00, -1.6521e+00, -1.0242e+00,  9.1280e-01,  1.2293e-01,\n",
       "         3.6483e-01,  5.2387e-01, -4.2162e-01,  2.5701e-02,  7.7982e-02,\n",
       "        -2.2463e-01,  8.2979e-01,  1.1393e-01,  1.1175e+00, -5.5063e-01,\n",
       "        -4.0161e-01,  7.7891e-01, -1.7174e-01,  4.6935e-01,  6.4303e-01,\n",
       "         1.0006e+00, -1.6570e-01,  3.0481e-01, -8.3767e-02, -4.6342e-01,\n",
       "         1.4454e-01,  2.4446e-01,  4.7511e-01,  1.3332e+00, -4.0802e-02],\n",
       "       dtype=float32),\n",
       " 'accounts': array([ 1.0939   , -0.0099262,  0.7331   , -0.14339  ,  0.55397  ,\n",
       "         0.35902  , -0.58619  , -0.18143  ,  0.24906  ,  0.097163 ,\n",
       "        -0.335    ,  0.27104  ,  0.32756  , -0.67002  ,  0.97168  ,\n",
       "        -0.43364  , -0.56155  , -0.7524   ,  0.73677  ,  0.12162  ,\n",
       "         0.92902  , -0.38939  ,  0.53444  , -0.18601  , -0.44739  ,\n",
       "        -0.99913  , -0.88915  , -0.40437  , -0.0030246,  0.2587   ,\n",
       "         2.7463   , -0.42818  ,  0.70243  , -0.29018  , -0.16727  ,\n",
       "        -0.036128 , -0.85417  , -0.047352 ,  0.058258 , -0.26152  ,\n",
       "         0.1612   , -0.087957 ,  0.8174   ,  0.6846   , -0.84013  ,\n",
       "        -1.0561   , -0.50342  ,  1.0835   ,  0.29758  , -0.6222   ],\n",
       "       dtype=float32),\n",
       " 'relevant authority hear': array([ 1.76686659e-01, -1.74093321e-01, -3.21226686e-01, -1.29833324e-02,\n",
       "         3.56139988e-01, -4.34923321e-01,  2.97644019e-01,  2.45983645e-01,\n",
       "         1.11799978e-01, -9.00733471e-02, -1.63803324e-01,  5.15730023e-01,\n",
       "         1.62313301e-02,  4.65199985e-02,  1.90916672e-01,  5.16346693e-01,\n",
       "         7.10693300e-02, -3.25240016e-01,  8.51566732e-01, -6.00283325e-01,\n",
       "         4.88852978e-01,  4.35540527e-01,  2.12720022e-01,  1.23843355e-02,\n",
       "         3.12296659e-01, -1.46083677e+00, -3.88509989e-01, -1.30613312e-01,\n",
       "        -1.77380010e-01, -4.35476661e-01,  2.69563317e+00,  1.39799595e-04,\n",
       "        -5.01793325e-01, -1.11434329e+00, -6.99599981e-02, -2.06070840e-01,\n",
       "         5.61410010e-01, -4.45713371e-01, -3.34253341e-01,  3.38378549e-01,\n",
       "        -2.46743321e-01, -2.25366745e-02,  2.00704351e-01,  5.82683325e-01,\n",
       "        -5.01973391e-01,  2.64808327e-01, -5.33197641e-01,  7.61919975e-01,\n",
       "        -3.18033285e-02,  1.48850009e-01], dtype=float32),\n",
       " 'university burdwan': array([-0.972335  ,  0.07849997, -0.28002   , -0.513755  , -0.51401   ,\n",
       "        -0.288728  ,  0.18041003, -0.09646499, -0.24302   , -0.539075  ,\n",
       "         0.335815  , -0.61649   , -0.0055655 , -1.2349501 , -1.021425  ,\n",
       "         0.726105  ,  0.05298   ,  1.0560501 , -0.35072005,  1.5706999 ,\n",
       "         0.717275  ,  0.09136498, -0.21627   ,  0.35553655,  0.033988  ,\n",
       "        -1.5243449 , -0.361085  , -0.836005  , -1.5654    , -1.009315  ,\n",
       "         1.185845  , -0.153987  ,  0.4030075 , -1.157464  ,  0.32727748,\n",
       "         0.1205241 ,  0.152825  ,  0.844665  ,  1.13361   ,  0.665695  ,\n",
       "        -0.13157499, -0.303018  , -0.529235  ,  0.46688   , -0.301892  ,\n",
       "         0.12267499,  0.62922   , -0.032355  , -0.388075  ,  0.068727  ],\n",
       "       dtype=float32),\n",
       " 'drafting preparation plaint': array([ 0.03098333, -0.29517066, -0.7813733 , -0.09038667, -0.15806367,\n",
       "         0.06573667,  0.27893665,  0.16478668,  0.17503893, -0.03141667,\n",
       "        -0.55853003, -0.09939667, -0.18264966,  0.34472   ,  0.09303301,\n",
       "        -0.3987433 , -0.21884966, -0.18049936,  0.5634567 , -0.29840967,\n",
       "         0.34426668, -0.09338483, -0.23617667, -0.27317667,  0.20859833,\n",
       "        -0.27103332, -0.15208633, -0.08175999,  0.23038001,  0.06181667,\n",
       "         1.1779667 , -0.16046   , -0.78600997, -0.02543   ,  0.28536993,\n",
       "         0.25963667,  0.36589813,  0.39109334, -0.37255666,  0.04827334,\n",
       "        -0.3604063 , -0.6166666 , -0.170653  ,  0.38660336, -0.0681    ,\n",
       "         0.04188666,  0.5980533 ,  0.92476004, -0.07142567,  0.19501966],\n",
       "       dtype=float32),\n",
       " 'hear cases': array([ 6.1610001e-01, -3.6541000e-01, -1.4162500e-01,  7.5499713e-04,\n",
       "         1.3720000e-01,  3.9950001e-01,  4.6773100e-01,  5.2775997e-01,\n",
       "         1.2758499e-01,  1.9900750e-01, -5.2099001e-01, -1.6134501e-01,\n",
       "         9.7517006e-02, -3.0115497e-01,  1.2281400e+00, -8.4820002e-02,\n",
       "        -5.5549502e-01, -4.0125501e-01,  5.0851498e-02, -1.0014200e+00,\n",
       "        -1.2353060e-01,  4.5918500e-01,  6.6228002e-01,  3.0657500e-01,\n",
       "         3.1305999e-01, -1.7376500e+00, -5.6127501e-01, -3.5059988e-02,\n",
       "        -1.8277000e-01, -6.6149902e-01,  2.7150998e+00, -1.9842030e-01,\n",
       "        -1.7519996e-02, -8.2126498e-01,  1.0078500e-01, -4.7655001e-01,\n",
       "         6.2918001e-01, -6.1251497e-01,  1.7300001e-01,  7.5802803e-02,\n",
       "        -5.3842998e-01,  4.2927498e-01,  4.8088500e-01,  1.0744500e+00,\n",
       "         4.1786999e-01,  4.4124001e-01, -2.7121651e-01,  2.6673001e-01,\n",
       "        -3.5642701e-01,  7.2105005e-02], dtype=float32),\n",
       " 'tds air exprience': array([-0.40606332, -0.13859333,  0.5362567 , -0.16105   , -0.14371467,\n",
       "         0.02756667, -0.1517    ,  0.15548666,  0.17709334, -0.44098   ,\n",
       "        -0.03001668,  0.087359  , -0.06961333,  0.41824332, -0.46070334,\n",
       "        -0.4144167 , -0.09943   ,  0.16543666, -0.91416335, -0.20836   ,\n",
       "        -0.01940667, -0.05632666,  0.69863003, -0.11860001, -0.29834333,\n",
       "        -0.52565   ,  0.40716   , -0.01231001,  0.48925337,  0.20421667,\n",
       "         1.3479266 ,  0.33473   , -0.29648   ,  0.12005001,  0.58918   ,\n",
       "         0.38571033,  0.27378   , -0.06367434,  0.04443   ,  0.4587733 ,\n",
       "         0.09312668,  0.29774   , -0.05368334, -0.01251666, -0.5253467 ,\n",
       "         0.27839336, -0.14003666,  0.16393332,  0.00952333,  0.09395334],\n",
       "       dtype=float32),\n",
       " 'drafting preparation': array([-0.04391   , -0.346816  , -0.93386495, -0.194175  , -0.1242955 ,\n",
       "        -0.08748999, -0.073275  , -0.02162999,  0.3140834 , -0.424605  ,\n",
       "        -0.39682502, -0.043345  , -0.29705998,  0.36276   ,  0.147485  ,\n",
       "        -0.284195  ,  0.0283805 , -0.28146502,  0.48011503, -0.41439003,\n",
       "         0.69938004, -0.14047   , -0.06301999, -0.441015  ,  0.0331625 ,\n",
       "        -0.54712   ,  0.2378905 , -0.42556   ,  0.11140501,  0.00812501,\n",
       "         2.15405   ,  0.13304001, -1.4047999 , -0.599295  ,  0.431655  ,\n",
       "         0.67926   ,  0.54751   ,  0.914745  , -0.34722498,  0.12701501,\n",
       "        -0.5264085 , -0.684845  , -0.12641451,  0.13774501,  0.05490001,\n",
       "         0.23454499,  0.63312995,  1.143625  , -0.06986099,  0.27048   ],\n",
       "       dtype=float32),\n",
       " 'authority hear cases': array([ 0.46053663, -0.26020667, -0.14270332, -0.03763   ,  0.14983332,\n",
       "         0.11924668,  0.41655397,  0.34072363,  0.32225332, -0.048815  ,\n",
       "        -0.28803998,  0.15146667, -0.032322  , -0.2707    ,  0.6439033 ,\n",
       "         0.26233998, -0.20108669, -0.34633002,  0.22599433, -0.58052   ,\n",
       "         0.12171295,  0.30593383,  0.21989335,  0.05556333, -0.04713001,\n",
       "        -1.8534333 , -0.33828   , -0.10815999, -0.19364   , -0.2807527 ,\n",
       "         2.7483332 , -0.08982686, -0.24709333, -0.88107663, -0.00607   ,\n",
       "        -0.31639084,  0.61547333, -0.67868334,  0.07622667,  0.2711552 ,\n",
       "        -0.47407666,  0.36971998,  0.32543766,  0.9301767 , -0.24102001,\n",
       "         0.28685835, -0.5957443 ,  0.36305666, -0.14035466, -0.0233    ],\n",
       "       dtype=float32),\n",
       " 'taxation matters': array([-0.2826745 ,  0.0549825 , -1.091485  , -0.05337501,  0.06057999,\n",
       "         0.35033   ,  0.700095  , -0.35292   ,  0.08831801, -0.70182   ,\n",
       "        -0.4064    ,  0.750535  , -0.03554499, -0.2792225 ,  0.22827849,\n",
       "         0.062424  ,  0.15266766, -0.52235496,  1.21725   , -0.361875  ,\n",
       "         0.88575506, -0.39503   , -0.198885  , -0.268349  , -0.07289499,\n",
       "        -1.100655  ,  0.23181   , -0.27741998,  0.26702   ,  0.561485  ,\n",
       "         2.30345   ,  0.318075  , -0.110705  , -0.6761695 , -0.32071   ,\n",
       "         0.002455  , -0.47832   ,  0.26671   , -0.05224501,  0.430055  ,\n",
       "        -0.506095  , -0.0245685 ,  0.645005  ,  0.959755  , -0.853755  ,\n",
       "        -0.26373   , -0.227375  ,  0.95932496,  0.666869  ,  0.261035  ],\n",
       "       dtype=float32),\n",
       " 'tds air': array([-0.609095  , -0.20789   ,  0.804385  , -0.241575  , -0.215572  ,\n",
       "         0.04135001, -0.22755   ,  0.23323   ,  0.26564002, -0.66147   ,\n",
       "        -0.04502502,  0.1310385 , -0.10442   ,  0.627365  , -0.691055  ,\n",
       "        -0.62162507, -0.149145  ,  0.24815498, -1.371245  , -0.31254   ,\n",
       "        -0.02911   , -0.08448999,  1.047945  , -0.17790002, -0.447515  ,\n",
       "        -0.78847504,  0.61074   , -0.01846501,  0.73388004,  0.30632502,\n",
       "         2.02189   ,  0.502095  , -0.44472   ,  0.180075  ,  0.88377   ,\n",
       "         0.5785655 ,  0.41066998, -0.0955115 ,  0.066645  ,  0.68815994,\n",
       "         0.13969001,  0.44661   , -0.08052501, -0.01877499, -0.78802   ,\n",
       "         0.41759002, -0.210055  ,  0.24589999,  0.014285  ,  0.14093001],\n",
       "       dtype=float32),\n",
       " 'company practice': array([-0.09005001, -0.41545   , -0.50488496,  0.294005  , -0.17086351,\n",
       "         0.187265  , -1.165495  , -0.2171965 ,  0.237351  , -0.03875   ,\n",
       "         0.51099   ,  0.4417085 , -0.75413   , -0.451435  ,  0.059863  ,\n",
       "         0.1407605 , -0.523015  ,  0.31986502, -0.40106   , -0.659175  ,\n",
       "         0.9815    ,  0.141915  , -0.43202502, -0.184955  , -0.4242    ,\n",
       "        -1.5792    , -0.353022  , -0.44888002,  0.224761  , -0.263935  ,\n",
       "         3.16605   , -0.14094001, -0.06235501, -0.14054501,  0.0497905 ,\n",
       "        -0.4500205 , -0.01222998,  0.58543   ,  0.4636535 ,  0.15004   ,\n",
       "         0.00959501, -0.64821994,  0.101228  ,  0.76157   , -0.02293999,\n",
       "        -0.10769249, -0.35726   ,  0.152906  , -0.147735  ,  0.44333   ],\n",
       "       dtype=float32),\n",
       " 'llb dibrugarh university': array([-1.2309166 ,  0.27797666, -0.70588   , -0.56109667, -0.71288997,\n",
       "         0.06147568,  0.37289333, -0.39278364,  0.01956   , -0.17269666,\n",
       "         0.20066333, -0.48304668,  0.4141867 , -1.0442033 , -0.64019334,\n",
       "         0.07475   , -0.59856665,  1.0908533 , -0.55828   ,  0.911678  ,\n",
       "         0.8646967 ,  0.35801664,  0.01477333,  0.44331774,  0.06973034,\n",
       "        -1.5792233 ,  0.436115  , -0.27981332, -1.1230966 , -0.43601665,\n",
       "         0.59110004,  0.19472198, -0.12739834, -0.6951657 ,  0.247945  ,\n",
       "        -0.12077627, -0.10339334,  0.6427341 ,  0.8052933 ,  0.60723   ,\n",
       "         0.35577998,  0.21641631, -0.17420666,  0.602104  , -0.60800797,\n",
       "        -0.09384003,  0.68952674,  0.34912333,  0.19611   , -0.171012  ],\n",
       "       dtype=float32),\n",
       " 'llb dibrugarh': array([-1.292275  , -0.228835  , -0.665065  , -0.61187   , -0.86539495,\n",
       "         0.3591485 ,  1.01668   , -0.3462905 ,  0.18863   , -0.091575  ,\n",
       "         0.05053   , -0.47261003,  0.6065555 , -0.97890496, -0.724065  ,\n",
       "        -0.02388   , -0.842385  ,  1.12868   , -0.31607   ,  0.621067  ,\n",
       "         0.94594   ,  0.17632002,  0.07664999,  0.66593504,  0.14270751,\n",
       "        -1.098935  ,  0.5941325 ,  0.01074502, -0.623645  , -0.19945998,\n",
       "        -0.4208    ,  0.31695497, -0.14282002,  0.08165149,  0.40208   ,\n",
       "        -0.1853285 , -0.10233   ,  0.34235117,  0.33849   ,  0.578285  ,\n",
       "         0.59284997,  0.3553725 , -0.11551   ,  0.714666  , -0.863505  ,\n",
       "        -0.38290003,  0.85144   ,  0.392345  ,  0.56574   , -0.27449   ],\n",
       "       dtype=float32),\n",
       " 'legal exprience': array([ 2.75260e-02, -7.96250e-02, -3.93660e-01,  2.61020e-01,\n",
       "         1.40370e-03,  3.64670e-01,  1.58525e-01,  4.90270e-02,\n",
       "         2.24645e-01, -1.11240e-01, -2.56840e-01,  1.73120e-01,\n",
       "        -1.67260e-01,  1.57470e-02,  3.31110e-01, -1.11585e-01,\n",
       "         7.79850e-02, -3.89150e-01,  4.28850e-01, -2.39060e-01,\n",
       "         3.03080e-01,  1.96790e-01, -2.69070e-01, -1.25085e-01,\n",
       "        -1.86900e-01, -1.05245e+00,  4.96565e-03, -4.06535e-01,\n",
       "        -9.22000e-02,  3.12200e-01,  1.54220e+00, -3.93845e-01,\n",
       "        -3.56825e-01, -4.89675e-01, -1.45535e-01, -5.87900e-02,\n",
       "        -1.90745e-01, -5.54250e-02, -8.98050e-02, -1.37065e-01,\n",
       "        -6.08250e-02,  6.28950e-02,  3.76865e-01,  8.38900e-01,\n",
       "        -3.70705e-01, -2.17110e-01, -1.28590e-01,  3.87725e-01,\n",
       "        -8.96450e-02,  2.60115e-02], dtype=float32),\n",
       " 'dibrugarh': array([-0.29755 , -0.95185 , -0.33826 , -0.40981 , -0.68439 , -0.070093,\n",
       "         0.64386 , -0.038421,  0.53884 , -0.62604 , -0.35672 , -1.4937  ,\n",
       "         1.1789  , -1.3569  , -0.74088 ,  0.11783 , -0.66267 ,  1.4507  ,\n",
       "         0.13027 ,  0.031234,  0.71298 , -0.72436 ,  0.22467 ,  1.7595  ,\n",
       "         0.31038 , -0.70307 ,  0.018265,  0.67308 , -0.95823 , -0.5085  ,\n",
       "        -0.73149 ,  0.50213 ,  0.36817 ,  0.0764  ,  0.416   , -0.30456 ,\n",
       "        -0.69175 ,  0.67926 ,  0.33853 ,  0.60641 ,  0.38491 ,  0.67191 ,\n",
       "        -0.4801  ,  0.029232, -0.54391 ,  0.5858  ,  0.63648 ,  0.37294 ,\n",
       "         0.56322 , -0.3862  ], dtype=float32),\n",
       " 'llb': array([-2.287    ,  0.49418  , -0.99187  , -0.81393  , -1.0464   ,\n",
       "         0.78839  ,  1.3895   , -0.65416  , -0.16158  ,  0.44289  ,\n",
       "         0.45778  ,  0.54848  ,  0.034211 , -0.60091  , -0.70725  ,\n",
       "        -0.16559  , -1.0221   ,  0.80666  , -0.76241  ,  1.2109   ,\n",
       "         1.1789   ,  1.077    , -0.07137  , -0.42763  , -0.024965 ,\n",
       "        -1.4948   ,  1.17     , -0.65159  , -0.28906  ,  0.10958  ,\n",
       "        -0.11011  ,  0.13178  , -0.65381  ,  0.086903 ,  0.38816  ,\n",
       "        -0.066097 ,  0.48709  ,  0.0054423,  0.33845  ,  0.55016  ,\n",
       "         0.80079  ,  0.038835 ,  0.24908  ,  1.4001   , -1.1831   ,\n",
       "        -1.3516   ,  1.0664   ,  0.41175  ,  0.56826  , -0.16278  ],\n",
       "       dtype=float32),\n",
       " 'dibrugarh university': array([-7.02874959e-01,  1.69874996e-01, -5.62884986e-01, -4.34679985e-01,\n",
       "        -5.46135008e-01, -3.01981479e-01, -1.35410011e-01, -2.62095481e-01,\n",
       "         1.10129997e-01, -4.80489969e-01,  7.21050054e-02, -9.98810053e-01,\n",
       "         6.04174495e-01, -1.26585007e+00, -6.06665015e-01,  1.94920003e-01,\n",
       "        -3.86800021e-01,  1.23294997e+00, -4.56215024e-01,  7.62067020e-01,\n",
       "         7.07594991e-01, -1.47500634e-03,  5.78449965e-02,  8.78791571e-01,\n",
       "         1.17078006e-01, -1.62143493e+00,  6.91725016e-02, -9.39249992e-02,\n",
       "        -1.54011500e+00, -7.08814979e-01,  9.41705048e-01,  2.26192981e-01,\n",
       "         1.35807499e-01, -1.08620000e+00,  1.77837506e-01, -1.48115903e-01,\n",
       "        -3.98635000e-01,  9.61380005e-01,  1.03871500e+00,  6.35765016e-01,\n",
       "         1.33275002e-01,  3.05206984e-01, -3.85850012e-01,  2.03106001e-01,\n",
       "        -3.20462018e-01,  5.35040021e-01,  5.01089990e-01,  3.17809999e-01,\n",
       "         1.00350082e-02, -1.75128013e-01], dtype=float32),\n",
       " 'company legal': array([ 0.340441  , -0.36814   , -0.187845  ,  0.69508004, -0.0401448 ,\n",
       "         0.497445  , -0.581475  , -0.144933  ,  0.43828   , -0.05284   ,\n",
       "         0.13511002,  0.57988   , -0.56107503, -0.31145298,  0.281503  ,\n",
       "        -0.008295  , -0.28859   ,  0.04294002,  0.294265  , -0.70802   ,\n",
       "         1.26258   ,  0.10942499, -0.57859504, -0.376835  , -0.65395   ,\n",
       "        -1.8901999 , -0.39202935, -0.56655   , -0.126039  ,  0.25048   ,\n",
       "         3.18765   , -0.6096    , -0.162875  , -0.29601   , -0.1547445 ,\n",
       "        -0.54974496, -0.45668998,  0.143905  ,  0.357455  , -0.34188   ,\n",
       "         0.23378499, -0.19898999,  0.51633   ,  0.71007   , -0.52299   ,\n",
       "        -0.2502625 , -0.68964   ,  0.495635  , -0.167575  ,  0.3461015 ],\n",
       "       dtype=float32),\n",
       " 'legal': array([ 5.5052e-02, -1.5925e-01, -7.8732e-01,  5.2204e-01,  2.8074e-03,\n",
       "         7.2934e-01,  3.1705e-01,  9.8054e-02,  4.4929e-01, -2.2248e-01,\n",
       "        -5.1368e-01,  3.4624e-01, -3.3452e-01,  3.1494e-02,  6.6222e-01,\n",
       "        -2.2317e-01,  1.5597e-01, -7.7830e-01,  8.5770e-01, -4.7812e-01,\n",
       "         6.0616e-01,  3.9358e-01, -5.3814e-01, -2.5017e-01, -3.7380e-01,\n",
       "        -2.1049e+00,  9.9313e-03, -8.1307e-01, -1.8440e-01,  6.2440e-01,\n",
       "         3.0844e+00, -7.8769e-01, -7.1365e-01, -9.7935e-01, -2.9107e-01,\n",
       "        -1.1758e-01, -3.8149e-01, -1.1085e-01, -1.7961e-01, -2.7413e-01,\n",
       "        -1.2165e-01,  1.2579e-01,  7.5373e-01,  1.6778e+00, -7.4141e-01,\n",
       "        -4.3422e-01, -2.5718e-01,  7.7545e-01, -1.7929e-01,  5.2023e-02],\n",
       "       dtype=float32),\n",
       " 'education details llb': array([-0.6069    ,  0.58379   , -0.63157994, -0.42165002, -0.2531633 ,\n",
       "         0.38506666,  0.22095   , -0.62958   , -0.01343999, -0.17794333,\n",
       "        -0.12563   ,  0.20868999, -0.01789966, -0.52476364, -0.02133665,\n",
       "        -0.10537666, -0.58443195,  0.19900334,  0.44245005,  0.46841   ,\n",
       "         0.83833665,  0.69822   , -0.16669333, -0.23878866,  0.10274833,\n",
       "        -1.3252333 ,  0.17204666, -0.5582333 , -0.39386666,  0.101973  ,\n",
       "         2.1261966 ,  0.08372   , -0.5106867 , -0.63601565,  0.32627735,\n",
       "         0.285121  ,  0.19554   ,  0.2726751 ,  0.15442334,  0.19142668,\n",
       "         0.16350667, -0.09056497,  0.261031  ,  0.567725  , -0.48913002,\n",
       "        -0.48889002,  0.40858337,  0.91117334,  0.4658967 ,  0.19630332],\n",
       "       dtype=float32),\n",
       " 'masters': array([-0.77853 ,  1.0441  , -0.73998 ,  0.72821 , -0.51173 , -0.82441 ,\n",
       "        -0.19271 ,  0.24802 , -0.66313 ,  0.45599 , -0.03629 , -0.19384 ,\n",
       "        -1.3522  ,  0.35353 ,  0.98083 , -0.097999,  0.3914  , -0.095067,\n",
       "        -1.1018  ,  0.31636 ,  0.21458 ,  0.80457 , -0.057907, -0.13034 ,\n",
       "         0.099164, -0.72229 , -1.0711  , -1.3602  , -1.3212  , -0.20993 ,\n",
       "         1.8171  , -0.021796, -0.45882 ,  0.72062 ,  0.42016 ,  0.030756,\n",
       "        -0.031811,  1.572   ,  0.31928 , -1.2134  ,  0.37149 , -0.96957 ,\n",
       "         0.79405 ,  0.75293 ,  0.19878 ,  0.15453 ,  0.93681 , -0.5151  ,\n",
       "        -0.42714 , -0.57828 ], dtype=float32),\n",
       " 'llb bachelors': array([-1.878     ,  0.88044   , -0.5110525 , -0.77804   , -0.5511995 ,\n",
       "         0.454575  ,  0.634425  , -0.275045  , -0.16686   ,  0.62912   ,\n",
       "         0.30941   ,  0.22081998,  0.5255555 , -0.10094501, -0.43198   ,\n",
       "        -0.314315  , -0.57515997,  0.88806   , -0.324065  ,  0.97712   ,\n",
       "         1.079525  ,  1.03277   ,  0.27945998, -0.20081751, -0.14035249,\n",
       "        -0.994465  ,  0.21950498, -0.858345  , -0.31917   , -0.00696   ,\n",
       "         0.198315  ,  0.2291    , -0.42572   , -0.1471985 ,  0.70612997,\n",
       "         0.2125065 , -0.09036002,  0.5568711 ,  0.612665  ,  0.11652499,\n",
       "         0.24921   , -0.2381675 ,  0.386685  ,  1.3659    , -1.27565   ,\n",
       "        -1.079615  ,  0.719205  ,  0.2521955 ,  0.14823   , -0.269325  ],\n",
       "       dtype=float32),\n",
       " 'law hyderabad': array([-0.652202  , -0.2702    , -0.42082202, -0.038175  , -0.523705  ,\n",
       "        -0.23766497,  0.54996   , -0.19478501,  0.0708258 , -0.10219   ,\n",
       "        -0.08291075, -0.01627001, -0.15126   , -0.41806   , -0.222365  ,\n",
       "         0.5090672 , -0.2619315 ,  0.387325  ,  0.06395   ,  0.91253847,\n",
       "         1.0741    ,  0.32326   , -0.15675   ,  0.377145  , -0.320005  ,\n",
       "        -1.98915   , -0.42098   , -0.854365  , -0.57717   , -0.26128998,\n",
       "         1.859215  , -0.18088001, -0.41141498, -0.60717   ,  0.5078065 ,\n",
       "         0.049365  ,  0.228035  , -0.04794   ,  0.494545  ,  0.72714996,\n",
       "        -0.65477   ,  0.37877   ,  0.362095  ,  0.611535  , -0.88806   ,\n",
       "         0.593933  , -0.242772  , -0.09890001,  0.3456    , -0.39546996],\n",
       "       dtype=float32),\n",
       " 'recently acquired masters': array([ 0.03530335,  0.32044336, -0.14918   ,  0.47100297, -0.16503   ,\n",
       "        -0.05863734, -1.1525034 , -0.20310335, -0.315437  ,  0.20015998,\n",
       "         0.52629334,  0.23410003, -0.73411006, -0.17128332,  0.46623   ,\n",
       "         0.19043034, -0.25374338,  0.031088  , -0.6547267 ,  0.28687233,\n",
       "         0.30017334,  0.24685001, -0.071359  , -0.26964667, -0.45648536,\n",
       "        -1.05503   , -0.46020135, -0.6041167 , -0.51904   , -0.08436334,\n",
       "         2.2551334 , -0.29300532,  0.09597001, -0.07280668,  0.19515665,\n",
       "        -0.17468466, -0.195897  ,  0.7411067 ,  0.15969333, -0.51331   ,\n",
       "         0.21106666, -0.44845334,  0.26893935,  0.29671666, -0.10132667,\n",
       "         0.05010333, -0.29827666, -0.22813065, -0.44850668, -0.16480866],\n",
       "       dtype=float32),\n",
       " 'litigation recently acquired': array([ 0.39124668, -0.30788332, -0.28258666,  0.51517636, -0.28562   ,\n",
       "         0.60373265, -0.8167    , -0.07103667,  0.15257634,  0.02029034,\n",
       "         0.21653664,  0.36943004, -0.5092267 , -0.45615998,  0.35454   ,\n",
       "         0.16269334, -0.27571666, -0.004813  , -0.13458   , -0.07296767,\n",
       "         0.57254666, -0.10791668, -0.29123   , -0.36352333, -0.73845005,\n",
       "        -1.2257334 , -0.04081467, -0.36568   , -0.01097666,  0.13382667,\n",
       "         2.2219    , -0.62634003,  0.273283  , -0.61155003, -0.21777332,\n",
       "        -0.36666667, -0.36503664,  0.12695001,  0.31340334, -0.38248667,\n",
       "        -0.14469002, -0.33244   ,  0.19837266,  0.67894   , -0.22359334,\n",
       "        -0.05768334, -0.53217   ,  0.068556  , -0.5419433 ,  0.31367132],\n",
       "       dtype=float32),\n",
       " 'working senior advocate': array([-0.50155663,  0.211257  , -0.17985468, -0.37866998,  0.38744998,\n",
       "        -0.03493967, -0.637408  , -0.01882833,  0.07020366, -0.51296   ,\n",
       "         0.00668667,  0.07015166, -0.3401133 ,  0.20189233, -0.20078667,\n",
       "        -0.22771901,  0.40984002,  0.38757566,  0.36853334,  0.02064667,\n",
       "         0.27840334,  0.6120867 , -0.184804  ,  0.18617575, -0.14609002,\n",
       "        -1.7086334 ,  0.1488    , -0.8187433 , -0.6975607 ,  0.49425018,\n",
       "         2.586     , -0.12529968, -0.4235562 , -0.7822533 , -0.01596133,\n",
       "        -0.116243  , -0.26637998,  0.13446234,  0.16947334, -0.08567033,\n",
       "        -0.25990364,  0.11522333, -0.02185667,  0.05864936,  0.47991666,\n",
       "        -0.3637167 , -0.62715   ,  0.2910507 , -0.20694898,  0.75546664],\n",
       "       dtype=float32),\n",
       " 'hyderabad telangana': array([-0.197187  , -0.657995  , -0.16396199,  0.16066   , -1.16238   ,\n",
       "        -0.31711996,  0.86926   ,  0.41261   , -0.23949002,  0.130925  ,\n",
       "         0.07877425, -0.85548997,  0.49739498, -0.2217085 , -0.908045  ,\n",
       "         0.4839985 , -0.35308   ,  1.062665  ,  0.51985   ,  1.3271999 ,\n",
       "         0.592375  , -0.10506499,  0.61753   ,  0.594705  , -0.21045001,\n",
       "        -0.39054   , -0.831355  , -0.309452  , -0.7484    , -0.315105  ,\n",
       "         0.569815  ,  0.271844  , -0.614515  , -0.02266   ,  0.26902497,\n",
       "         0.25103   , -0.06080501, -0.47713   ,  0.396485  ,  0.959365  ,\n",
       "        -1.0448151 ,  0.16318999, -0.75770503, -0.3438    , -1.02622   ,\n",
       "         0.784925  , -0.134142  ,  0.13183   ,  0.02451499, -1.06672   ],\n",
       "       dtype=float32),\n",
       " 'law': array([-1.2328e+00, -1.1042e-01, -7.7951e-01, -5.0614e-01, -6.8150e-02,\n",
       "         6.9457e-01,  1.8967e-01, -7.6766e-01,  5.4416e-03, -3.6045e-01,\n",
       "        -1.6309e-01,  5.9114e-01, -1.4221e-01, -3.3803e-01,  2.2356e-01,\n",
       "         1.5343e-03, -1.2043e-02, -5.9315e-01,  6.2070e-01,  8.7477e-02,\n",
       "         7.2180e-01,  3.1269e-01, -4.9196e-01, -1.4968e-01, -5.2249e-01,\n",
       "        -2.9321e+00,  1.3136e-01, -1.0542e+00, -2.5628e-01, -2.7729e-01,\n",
       "         2.8322e+00, -8.0952e-01, -1.2425e+00, -1.3874e+00, -5.5187e-02,\n",
       "        -4.8155e-01,  1.7897e-01, -2.2712e-01,  5.2925e-01,  4.7739e-01,\n",
       "        -5.5841e-01,  2.5676e-01,  1.0353e+00,  1.0491e+00, -9.5648e-01,\n",
       "        -3.3834e-02, -4.3219e-01,  1.0342e-01,  2.0543e-01,  3.6536e-01],\n",
       "       dtype=float32),\n",
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       "         0.43497267, -0.37061667,  0.44968334,  0.18095   , -0.02088633,\n",
       "        -0.00386001,  0.01784933, -0.48087668, -0.217746  ,  0.54212004,\n",
       "        -0.09834667, -0.07990333, -0.047069  , -0.16342999, -0.41353765,\n",
       "         0.19695334,  0.14241333, -0.26715335, -0.14980666, -0.16328001,\n",
       "        -1.3178467 , -0.081678  , -0.41505668, -0.11256   ,  0.66950005,\n",
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       "        -0.58381003,  0.10922333,  0.24307334,  0.7324733 ,  0.24974333,\n",
       "        -0.17959666, -0.19123434,  0.21656601, -0.4693133 ,  0.3626093 ],\n",
       "       dtype=float32),\n",
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       "         0.33365  , -0.3258   , -0.59006  ,  0.0020568, -0.6246   ,\n",
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       "         0.61887  , -0.17566  , -0.29566  , -0.50788  ,  0.024346 ,\n",
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       "         1.6432   , -0.16306  , -1.6144   , -0.67785  ,  0.48611  ,\n",
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       "        -0.97324  , -0.62347  , -0.1805   ,  0.29126  , -0.32099  ,\n",
       "         0.24321  ,  0.5146   ,  1.433    , -0.11831  ,  0.10079  ],\n",
       "       dtype=float32),\n",
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       "         0.0844    , -0.704015  , -0.510365  , -0.10577419, -0.463085  ,\n",
       "        -0.152785  ,  0.37439498,  0.02271   , -0.827665  , -0.15541   ,\n",
       "         0.10363215, -0.2459465 ,  0.227225  ,  0.0566    ,  0.43036848,\n",
       "         0.55977   ,  0.508075  , -0.40714002,  0.226015  , -0.32800502,\n",
       "        -2.3870502 ,  0.019381  , -1.0915    , -0.87109005, -0.56982   ,\n",
       "         2.7149    , -0.07403001, -0.64829695, -1.4814501 ,  0.5316565 ,\n",
       "        -0.17122   , -0.04509   ,  0.17223501,  0.931025  ,  0.11438999,\n",
       "        -0.621975  , -0.0914    ,  0.08393002,  1.00341   , -0.81498504,\n",
       "         0.16332799,  0.093665  , -0.08007   , -0.08303499,  0.343145  ],\n",
       "       dtype=float32),\n",
       " 'recently acquired': array([ 4.4222003e-01, -4.1384999e-02,  1.4622000e-01,  3.4239951e-01,\n",
       "         8.3200037e-03,  3.2424900e-01, -1.6324000e+00, -4.2866501e-01,\n",
       "        -1.4159051e-01,  7.2245002e-02,  8.0758500e-01,  4.4807002e-01,\n",
       "        -4.2506498e-01, -4.3368998e-01,  2.0893002e-01,  3.3464500e-01,\n",
       "        -5.7631505e-01,  9.4165504e-02, -4.3118998e-01,  2.7212849e-01,\n",
       "         3.4297001e-01, -3.2010004e-02, -7.8085005e-02, -3.3930001e-01,\n",
       "        -7.3431003e-01, -1.2214000e+00, -1.5475200e-01, -2.2607501e-01,\n",
       "        -1.1795999e-01, -2.1580003e-02,  2.4741502e+00, -4.2861000e-01,\n",
       "         3.7336501e-01, -4.6952000e-01,  8.2654998e-02, -2.7740499e-01,\n",
       "        -2.7794001e-01,  3.2565999e-01,  7.9899997e-02, -1.6326499e-01,\n",
       "         1.3085499e-01, -1.8789500e-01,  6.3840002e-03,  6.8609998e-02,\n",
       "        -2.5138000e-01, -2.1100044e-03, -9.1582000e-01, -8.4646001e-02,\n",
       "        -4.5919001e-01,  4.1926999e-02], dtype=float32),\n",
       " 'acquired masters degree': array([-0.5487833 ,  0.88756007, -0.44067335,  0.19535   ,  0.02289   ,\n",
       "         0.07194332, -0.2946367 , -0.09883735, -0.29700032,  0.40236333,\n",
       "         0.67078996,  0.22364335, -0.5492497 , -0.44908333,  0.11654667,\n",
       "        -0.01157967, -0.18412668,  0.29253766, -0.93264335,  0.43616334,\n",
       "         0.47412667,  0.40220332, -0.09116901, -0.32898667, -0.373662  ,\n",
       "        -1.0862633 , -0.5504133 , -0.96186   , -0.31967336, -0.01042   ,\n",
       "         2.1462333 , -0.14344566, -0.10418332, -0.54631   ,  0.5221366 ,\n",
       "        -0.153458  , -0.21017367,  0.8490767 ,  0.67379   , -0.37576997,\n",
       "         0.27914998, -0.6201867 ,  0.34636602,  0.5902333 , -0.6861367 ,\n",
       "        -0.06782333,  0.40813002, -0.10656333, -0.28329697, -0.046868  ],\n",
       "       dtype=float32),\n",
       " 'telangana osmania university': array([-0.6723733 ,  0.03816001, -0.37465897, -0.34773   , -0.74205   ,\n",
       "        -0.2875533 ,  0.22911   ,  0.12265   , -0.53619   , -0.07112467,\n",
       "         0.43318334, -0.7313333 ,  0.55975634, -0.617409  , -0.8719933 ,\n",
       "         0.37391233, -0.50815666,  1.0614433 ,  0.13539167,  1.3819333 ,\n",
       "         0.50085336,  0.10219   ,  0.302522  ,  0.20098436, -0.22037466,\n",
       "        -1.0946933 , -0.32943332, -0.3335347 , -1.2962466 , -0.68497664,\n",
       "         0.80619   ,  0.12828799, -0.3535317 , -1.10434   , -0.24712835,\n",
       "         0.16777273, -0.15616134,  0.20342667,  1.1303433 ,  0.8495334 ,\n",
       "        -0.35873333, -0.09653333, -0.8093234 ,  0.18287002, -0.58561796,\n",
       "         0.37277332,  0.16967332,  0.51334333, -0.46788335, -0.56132865],\n",
       "       dtype=float32),\n",
       " 'ravichander high court': array([-0.33734334,  0.23279667, -0.27772668, -0.02678666,  0.15928668,\n",
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       "        -0.23314333, -0.32347667, -0.07653666, -0.34241667,  0.14822666,\n",
       "         0.1763    , -0.26694   , -0.19722001, -0.26933527, -0.30952334,\n",
       "         0.3277    ,  0.20042567, -0.25449   , -0.02783167, -0.5115067 ,\n",
       "        -1.6450332 ,  0.24844335,  0.0522    , -0.323439  , -0.10030667,\n",
       "         2.0229332 , -0.33816433, -0.05327   , -0.54174   ,  0.53949666,\n",
       "        -0.30339333,  0.26343   ,  0.04012943,  0.23675866, -0.21474667,\n",
       "        -0.28431666,  0.38059   ,  0.25563332,  0.40327668, -0.215568  ,\n",
       "         0.11133166,  0.03050667, -0.30917335, -0.02017666, -0.07862633],\n",
       "       dtype=float32),\n",
       " 'hyderabad': array([-0.071604 , -0.42998  , -0.062134 ,  0.42979  , -0.97926  ,\n",
       "        -1.1699   ,  0.91025  ,  0.37809  ,  0.13621  ,  0.15607  ,\n",
       "        -0.0027315, -0.62368  , -0.16031  , -0.49809  , -0.66829  ,\n",
       "         1.0166   , -0.51182  ,  1.3678   , -0.4928   ,  1.7376   ,\n",
       "         1.4264   ,  0.33383  ,  0.17846  ,  0.90397  , -0.11752  ,\n",
       "        -1.0462   , -0.97332  , -0.65453  , -0.89806  , -0.24529  ,\n",
       "         0.88623  ,  0.44776  ,  0.41967  ,  0.17306  ,  1.0708   ,\n",
       "         0.58028  ,  0.2771   ,  0.13124  ,  0.45984  ,  0.97691  ,\n",
       "        -0.75113  ,  0.50078  , -0.31111  ,  0.17397  , -0.81964  ,\n",
       "         1.2217   , -0.053354 , -0.30122  ,  0.48577  , -1.1563   ],\n",
       "       dtype=float32),\n",
       " 'law hyderabad telangana': array([-0.54239136, -0.47546998, -0.36914468, -0.06160666, -0.7976367 ,\n",
       "         0.02011003,  0.64273   ,  0.01918666, -0.15784614, -0.03286667,\n",
       "        -0.00184717, -0.37327996,  0.2841933 , -0.26048234, -0.5308433 ,\n",
       "         0.32317713, -0.239401  ,  0.51072663,  0.5534667 ,  0.913959  ,\n",
       "         0.6355167 ,  0.03418668,  0.24769999,  0.34657666, -0.31446335,\n",
       "        -1.2377267 , -0.51045   , -0.5577013 , -0.58436   , -0.30249998,\n",
       "         1.3239434 , -0.08861067, -0.8238433 , -0.4775733 ,  0.16095431,\n",
       "         0.00683667,  0.01912   , -0.39379334,  0.44074002,  0.7987067 ,\n",
       "        -0.88268   ,  0.19438   , -0.16003668,  0.12050001, -1.0029733 ,\n",
       "         0.5120053 , -0.23349135,  0.12236   ,  0.08482   , -0.58936   ],\n",
       "       dtype=float32),\n",
       " 'legal correspondence': array([ 1.03895999e-01,  5.08024991e-01, -6.35195017e-01,  1.22250006e-01,\n",
       "         4.53107022e-02,  4.47700024e-01,  1.44501030e-03,  1.98302001e-01,\n",
       "         1.10840000e-01, -4.05129999e-01, -4.33484972e-01,  6.46449983e-01,\n",
       "        -1.98153004e-01, -2.45408013e-01,  5.31774998e-01, -5.89605033e-01,\n",
       "        -5.12115002e-01, -6.82289958e-01,  8.01095009e-01, -2.81732500e-01,\n",
       "         7.61675000e-01,  2.20918000e-01,  6.96800053e-02, -1.31149992e-01,\n",
       "         8.54499638e-03, -1.35297489e+00, -1.32784352e-01, -6.58910036e-01,\n",
       "        -2.92600006e-01,  3.52220505e-01,  2.25005007e+00, -8.56135011e-01,\n",
       "        -4.03813988e-01, -9.78729963e-01, -1.99500009e-01,  2.15615004e-01,\n",
       "        -3.38029981e-01,  2.83830017e-01, -2.81794995e-01,  7.53600001e-02,\n",
       "         6.86174989e-01, -8.37199986e-02,  2.32525006e-01,  1.35109997e+00,\n",
       "        -5.56385040e-01, -5.59579968e-01,  1.95904985e-01,  7.34259963e-01,\n",
       "        -4.28499997e-01, -3.01943511e-01], dtype=float32),\n",
       " 'litigation': array([ 0.2893  , -0.84088 , -1.1402  ,  0.86073 , -0.8735  ,  1.1627  ,\n",
       "         0.8147  ,  0.64422 ,  0.74091 , -0.083619, -0.96556 ,  0.21215 ,\n",
       "        -0.67755 , -0.5011  ,  0.64576 , -0.18121 ,  0.32548 , -0.20277 ,\n",
       "         0.45864 , -0.76316 ,  1.0317  , -0.25973 , -0.71752 , -0.41197 ,\n",
       "        -0.74673 , -1.2344  ,  0.18706 , -0.64489 ,  0.20299 ,  0.44464 ,\n",
       "         1.7174  , -1.0218  ,  0.073119, -0.89561 , -0.81863 , -0.54519 ,\n",
       "        -0.53923 , -0.27047 ,  0.78041 , -0.82093 , -0.69578 , -0.62153 ,\n",
       "         0.58235 ,  1.8996  , -0.16802 , -0.16883 ,  0.23513 ,  0.37496 ,\n",
       "        -0.70745 ,  0.85716 ], dtype=float32),\n",
       " 'internet savvy': array([ 0.21281001, -0.32098   ,  1.16487   ,  0.3621    ,  0.00567   ,\n",
       "        -0.662795  , -0.94162   , -0.78311497,  0.34533   ,  1.1489    ,\n",
       "        -0.26407498,  0.043913  , -0.01929   ,  0.546255  , -0.028475  ,\n",
       "        -0.09377   , -0.375875  ,  0.29717502,  0.6058    , -0.300875  ,\n",
       "         0.423377  ,  0.369595  , -0.487135  ,  1.18678   ,  0.37362498,\n",
       "        -1.39795   , -0.48835   , -0.702545  ,  0.399115  ,  0.021945  ,\n",
       "         2.164     ,  0.33234   ,  0.31907   , -0.754965  , -0.542675  ,\n",
       "        -0.1276065 , -1.1578801 ,  0.273785  , -0.56238   , -0.666175  ,\n",
       "         0.786935  ,  0.2256785 , -0.03492505,  1.3092    ,  0.43159798,\n",
       "        -0.286995  ,  0.026193  ,  0.2516215 ,  0.3388    ,  0.62689996],\n",
       "       dtype=float32),\n",
       " 'telangana': array([-0.32277 , -0.88601 , -0.26579 , -0.10847 , -1.3455  ,  0.53566 ,\n",
       "         0.82827 ,  0.44713 , -0.61519 ,  0.10578 ,  0.16028 , -1.0873  ,\n",
       "         1.1551  ,  0.054673, -1.1478  , -0.048603, -0.19434 ,  0.75753 ,\n",
       "         1.5325  ,  0.9168  , -0.24165 , -0.54396 ,  1.0566  ,  0.28544 ,\n",
       "        -0.30338 ,  0.26512 , -0.68939 ,  0.035626, -0.59874 , -0.38492 ,\n",
       "         0.2534  ,  0.095928, -1.6487  , -0.21838 , -0.53275 , -0.07822 ,\n",
       "        -0.39871 , -1.0855  ,  0.33313 ,  0.94182 , -1.3385  , -0.1744  ,\n",
       "        -1.2043  , -0.86157 , -1.2328  ,  0.34815 , -0.21493 ,  0.56488 ,\n",
       "        -0.43674 , -0.97714 ], dtype=float32),\n",
       " 'writing speaking hindi': array([-3.8279334e-01, -1.1853200e-01, -7.3276997e-01, -5.9864998e-01,\n",
       "         1.5751266e-01, -7.9050064e-03, -3.2080999e-01, -5.5195671e-01,\n",
       "        -6.8112665e-01,  4.1320333e-01, -1.0060068e-01,  2.0649667e-01,\n",
       "         3.3792999e-01,  3.2315633e-01,  2.8068331e-01, -4.8340502e-01,\n",
       "        -1.3204333e-01,  4.6374130e-01,  2.6997232e-01,  6.0579067e-01,\n",
       "         5.3622669e-01,  8.1472999e-01,  8.0861998e-01,  9.2399001e-01,\n",
       "         9.6104336e-01, -1.1999766e+00, -7.7550334e-01, -1.5217666e-01,\n",
       "        -8.7800997e-01, -1.8919766e-01,  2.4912333e+00, -3.9337265e-01,\n",
       "         1.3744332e-01, -1.5034999e-01, -7.1000058e-04,  7.7200003e-02,\n",
       "        -4.0919665e-02,  1.3170666e-01, -7.9684669e-01,  4.1449800e-01,\n",
       "         2.1605301e-01,  7.8337002e-01, -3.2781002e-01, -1.7592335e-01,\n",
       "        -4.1350499e-01, -2.0295031e-01, -8.2417332e-02,  5.6171662e-01,\n",
       "        -1.7087667e-01,  2.2622333e-01], dtype=float32),\n",
       " 'trials': array([ 0.84351 , -0.35263 , -0.64145 ,  0.32791 , -0.81336 ,  0.44497 ,\n",
       "         0.75537 ,  0.45157 ,  1.001   ,  0.36485 , -0.1166  , -0.6396  ,\n",
       "        -0.94911 , -0.25553 ,  1.0027  , -0.4486  , -0.068898, -0.15422 ,\n",
       "        -1.0979  , -0.42068 ,  0.61317 ,  0.30092 ,  0.41475 ,  0.025165,\n",
       "        -0.67237 , -1.5223  , -0.36164 , -1.0104  , -0.51191 ,  0.19631 ,\n",
       "         1.6244  , -0.28319 , -0.96883 , -1.4109  ,  0.67185 , -0.30938 ,\n",
       "         0.73317 ,  0.91876 , -0.59228 , -0.32123 , -0.51435 , -0.64251 ,\n",
       "         0.74606 ,  1.0758  ,  0.48091 , -0.22086 ,  0.40681 , -0.16248 ,\n",
       "        -0.54139 , -0.19423 ], dtype=float32),\n",
       " 'chandigarh': array([-0.21322  , -0.47615  , -0.86932  ,  0.71365  , -0.70212  ,\n",
       "        -0.17129  ,  0.62196  , -0.072773 , -0.3422   , -0.43526  ,\n",
       "         0.092696 , -0.83653  ,  0.43017  , -1.0424   , -1.0134   ,\n",
       "         0.73792  , -0.22343  ,  1.3136   , -0.69894  ,  1.5827   ,\n",
       "         0.84529  ,  0.31426  , -0.07283  ,  0.42473  ,  0.41534  ,\n",
       "        -0.35223  ,  0.13236  ,  0.14911  , -0.60835  , -0.87223  ,\n",
       "         0.0085122,  0.78174  ,  0.32971  , -0.034443 ,  0.56136  ,\n",
       "         0.19694  , -0.2602   ,  0.28311  ,  0.39523  ,  1.0888   ,\n",
       "        -0.34727  ,  0.012145 , -0.012251 , -0.16453  , -1.0667   ,\n",
       "         0.6771   ,  0.24801  ,  0.71525  ,  0.62612  , -0.65976  ],\n",
       "       dtype=float32),\n",
       " 'bio informatics': array([-0.525993  ,  0.065108  ,  0.198645  ,  0.89284   , -0.57724   ,\n",
       "        -0.49717999,  0.0606285 , -1.4921    ,  1.016015  ,  0.231425  ,\n",
       "         0.752865  ,  0.085475  ,  0.15292001, -0.20274   , -0.55199504,\n",
       "         0.0894255 , -0.096401  ,  0.969105  ,  0.33249003,  0.57440996,\n",
       "         0.20929149, -0.0392255 , -0.00404499, -0.36698997, -0.115646  ,\n",
       "        -0.019665  , -0.61790997, -0.863785  , -0.28933   ,  0.240865  ,\n",
       "         0.63944   , -0.007065  , -0.723485  , -1.04461   , -0.150345  ,\n",
       "         0.7595    , -0.434915  ,  0.386285  ,  0.99460495,  0.615375  ,\n",
       "         0.23353899, -0.388315  , -0.012248  ,  0.04752499,  0.626145  ,\n",
       "         0.784045  ,  0.458815  ,  1.01933   ,  0.428355  ,  0.014825  ],\n",
       "       dtype=float32),\n",
       " 'science bio': array([-0.330148  ,  0.49795303,  0.143915  ,  0.468964  , -0.33729365,\n",
       "        -0.3692    , -0.2203615 , -1.54855   ,  0.659055  ,  0.5102735 ,\n",
       "         0.24564502,  0.396925  , -0.00447   ,  0.17856999, -0.34956   ,\n",
       "        -0.0750195 ,  0.200174  ,  1.013555  , -0.03600499,  0.49223   ,\n",
       "         0.5797215 ,  0.27392   ,  0.37195   , -0.59305   ,  0.36439002,\n",
       "        -0.92981505, -0.91699   , -0.66155005, -0.339845  ,  0.05955   ,\n",
       "         1.61892   , -0.35132998, -0.774225  , -1.2884101 , -0.069565  ,\n",
       "         0.4866    , -0.659335  ,  0.63060004,  0.77475   ,  0.44663998,\n",
       "         0.0313035 , -0.12697001,  0.0894005 , -0.21309   ,  0.38910002,\n",
       "         0.714965  ,  0.5868    ,  0.898085  ,  0.08324919,  0.312435  ],\n",
       "       dtype=float32),\n",
       " 'district': array([-0.27896 , -0.63728 , -0.50534 ,  0.31013 ,  0.36839 , -0.54706 ,\n",
       "         0.40083 ,  0.44362 ,  0.62641 , -1.3909  , -1.0806  , -1.9063  ,\n",
       "         0.28615 , -1.3979  , -0.42639 ,  0.16845 ,  0.78329 ,  0.49424 ,\n",
       "         0.27511 ,  0.70024 ,  0.49592 , -0.7463  , -0.27301 ,  1.1971  ,\n",
       "        -1.076   , -1.7345  , -0.30244 ,  0.23436 , -1.3131  , -1.0174  ,\n",
       "         2.9439  , -1.1441  ,  0.40474 , -0.81523 ,  1.2169  ,  0.053004,\n",
       "         0.75009 ,  0.36677 ,  0.53634 ,  0.61355 , -1.2986  ,  0.933   ,\n",
       "        -0.18591 , -0.86878 , -1.3558  ,  0.11293 , -0.65068 , -0.96977 ,\n",
       "         0.85731 ,  0.79817 ], dtype=float32),\n",
       " 'legal services': array([ 0.34754598,  0.05551999, -0.26262   ,  0.338885  , -0.1567763 ,\n",
       "         0.321853  , -0.41887504, -0.20809302,  0.82644504, -0.5288    ,\n",
       "        -0.01681998,  0.376715  , -0.139172  , -0.193513  , -0.02974001,\n",
       "        -0.039625  , -0.47246504, -0.25687498,  0.729     , -0.2458785 ,\n",
       "         1.13783   ,  0.25746998, -0.65514004,  0.12027501, -0.49106503,\n",
       "        -1.55905   ,  0.25459567, -0.91403496, -0.165955  ,  0.52026   ,\n",
       "         3.42675   , -0.01365998, -0.14263499, -0.80711997, -0.172784  ,\n",
       "         0.0133    , -0.143274  , -0.11058   ,  0.07977001,  0.13456002,\n",
       "         0.259225  , -0.095035  ,  0.4018445 ,  1.054815  , -0.46625   ,\n",
       "        -0.313655  , -0.431355  ,  0.617875  ,  0.096655  ,  0.4647065 ],\n",
       "       dtype=float32),\n",
       " 'reading writing speaking': array([-0.3891833 ,  0.247688  , -0.60759336, -0.92458993,  0.48776603,\n",
       "        -0.06903335, -0.71811336, -0.33938003, -0.49833664,  0.12638666,\n",
       "        -0.394784  ,  0.01452001,  0.16852002, -0.11015367,  0.31279   ,\n",
       "        -0.47354832, -0.3383533 , -0.15775199,  0.16337334, -0.06718601,\n",
       "         0.56724334,  0.8227    ,  0.47074667,  0.49786666,  1.01251   ,\n",
       "        -1.2993666 , -0.5513    ,  0.04125667, -0.4945433 , -0.35298768,\n",
       "         2.7489002 , -0.133326  ,  0.07386667, -0.66969997, -0.11500666,\n",
       "        -0.118656  ,  0.18614702,  0.39999333, -0.36778235,  0.06483799,\n",
       "         0.49811402,  0.22379   , -0.28723666,  0.23705333, -0.19656833,\n",
       "         0.02825966,  0.17006667,  0.50595003, -0.07713667,  0.27291816],\n",
       "       dtype=float32),\n",
       " 'hindi bilingual proficiency': array([-1.1178067 , -0.337419  , -0.40013   , -1.0151267 , -0.270839  ,\n",
       "        -0.07447233,  0.05440001, -0.87882996, -0.10796001,  0.60242003,\n",
       "         0.66891336, -0.30576333,  1.0471901 , -0.04463333, -0.21221334,\n",
       "        -0.4798467 , -0.36046335,  0.73326665,  0.49419633,  0.5460766 ,\n",
       "         0.16896832,  0.38114667,  0.25858667,  0.91823673,  0.83909005,\n",
       "        -0.5275033 , -0.7669266 , -0.5615234 , -0.96453   , -0.3040333 ,\n",
       "         1.6724    ,  0.10487334, -0.15662001, -0.1675    ,  0.770592  ,\n",
       "         0.40399003,  0.29816332,  0.06315667, -0.80765   ,  0.22348766,\n",
       "         0.17815101,  0.11597669,  0.04359199, -0.2117743 , -0.58764   ,\n",
       "        -0.43927667,  0.6635327 ,  1.2168268 ,  0.22448333, -0.03422667],\n",
       "       dtype=float32),\n",
       " 'bar': array([-9.4531e-01,  3.9686e-01, -8.0605e-01, -3.0215e-01,  2.7736e-01,\n",
       "        -1.0019e-01, -4.0500e-01, -1.0095e-01, -6.5934e-02, -4.7258e-02,\n",
       "        -2.0828e-01, -2.5721e-01,  6.8750e-02,  9.3751e-01, -8.1483e-02,\n",
       "         1.3460e-01,  2.7302e-02, -1.8096e-01, -3.5638e-01, -8.8104e-01,\n",
       "         1.1951e+00,  5.5556e-02, -3.1741e-01,  1.0244e+00, -8.4768e-01,\n",
       "        -1.5959e+00,  2.1657e-02,  4.3628e-01,  8.8388e-04, -4.1820e-01,\n",
       "         2.1247e+00, -4.3332e-01, -1.0816e+00,  3.3616e-01,  3.3399e-01,\n",
       "        -2.0064e-01,  5.8633e-01,  9.0186e-02,  7.5054e-01,  4.8500e-01,\n",
       "         1.7370e-01,  6.8129e-01, -1.6810e-01,  6.1265e-01,  7.6875e-02,\n",
       "        -1.9797e-01, -9.9555e-02, -1.0231e+00,  9.5394e-01, -6.3500e-02],\n",
       "       dtype=float32),\n",
       " 'practice': array([-0.80593 , -0.25387 , -1.4214  , -0.28011 , -0.25863 ,  0.10898 ,\n",
       "        -0.85099 , -0.046473,  0.047432, -0.1943  ,  0.23808 ,  0.069897,\n",
       "        -0.72063 , -0.24847 ,  0.21894 ,  0.074941, -0.31288 , -0.22445 ,\n",
       "        -0.53295 , -0.38043 ,  0.044   ,  0.45856 , -0.245   ,  0.13359 ,\n",
       "         0.0857  , -1.4829  ,  0.087946, -0.57773 ,  0.5172  , -0.40443 ,\n",
       "         3.0412  ,  0.14963 , -0.51261 , -0.66842 ,  0.118   ,  0.081869,\n",
       "         0.50743 ,  0.7722  ,  0.032787,  0.70971 , -0.57003 , -0.77267 ,\n",
       "        -0.076474,  1.7808  ,  0.25869 , -0.14908 ,  0.40758 ,  0.089992,\n",
       "        -0.13961 ,  0.24648 ], dtype=float32),\n",
       " 'speaking hindi bilingual': array([-0.5205333 , -0.33621868, -0.65368336, -0.7007833 , -0.01368   ,\n",
       "        -0.13893901, -0.38678002, -0.64811337, -0.66457665,  0.26935336,\n",
       "         0.20960933, -0.37619665,  0.91873664,  0.24179667,  0.18115668,\n",
       "        -0.364135  , -0.18788333,  0.65074795,  0.64227563,  0.67190295,\n",
       "         0.20569165,  0.49361333,  0.43238005,  1.33473   ,  0.7755334 ,\n",
       "        -0.8891167 , -0.5621566 , -0.08881667, -1.0548066 , -0.21209435,\n",
       "         2.0532334 , -0.15813933, -0.07661001, -0.03712667,  0.06381866,\n",
       "         0.15639001,  0.03644   ,  0.00396667, -0.82681996,  0.40872034,\n",
       "         0.16805632,  0.6753033 , -0.22040467, -0.32796097, -0.42517498,\n",
       "        -0.21847665, -0.14082733,  0.86956006,  0.09814   ,  0.14198667],\n",
       "       dtype=float32),\n",
       " 'office months drafting': array([ 0.08626336, -0.313167  , -0.01612999, -0.18033667,  0.13477866,\n",
       "        -0.03880333, -0.6543667 , -0.20017333, -0.02753207, -0.67264336,\n",
       "        -0.46333098, -0.2835333 , -0.61007   ,  0.21840008,  0.63813   ,\n",
       "         0.04011667, -0.52975005, -0.37443602,  0.43237004, -0.158747  ,\n",
       "         0.76784664,  0.14823668, -0.03167   , -0.48461333, -0.381928  ,\n",
       "        -1.3814901 ,  0.31374633, -0.05792865, -0.2556    ,  0.23257999,\n",
       "         2.6722667 , -0.05477333, -0.78611   , -0.6446733 ,  0.15112333,\n",
       "         0.15594333,  0.28172168,  0.38269338,  0.09066334, -0.10011667,\n",
       "        -0.8342366 ,  0.02280865, -0.14256199,  0.04444684, -0.3886467 ,\n",
       "        -0.1433133 , -0.20217   ,  0.458755  ,  0.04404001,  0.145031  ],\n",
       "       dtype=float32),\n",
       " 'listening reading writing': array([-0.56645995,  0.27990633, -0.24005   , -1.0346533 ,  0.311616  ,\n",
       "        -0.12535001, -0.70939666, -0.29036665, -0.26460335,  0.5295966 ,\n",
       "        -0.5171167 ,  0.224876  ,  0.02953334,  0.04669635,  0.32975665,\n",
       "        -0.45290336, -0.56118   , -0.07606667,  0.22782   , -0.36342564,\n",
       "         0.73882335,  0.98803335,  0.5863634 ,  0.39876   ,  1.1734667 ,\n",
       "        -1.0305899 , -0.68363667, -0.02041333, -0.04439333, -0.5747167 ,\n",
       "         2.6064332 , -0.16509567,  0.10432067, -0.8175133 , -0.10648999,\n",
       "         0.08835933,  0.35920036,  0.33331332, -0.18499903, -0.10209867,\n",
       "         0.56824   , -0.08286133, -0.47192332,  0.54833335, -0.08551333,\n",
       "         0.11112333,  0.53462   ,  0.12352333, -0.29533336,  0.28450814],\n",
       "       dtype=float32),\n",
       " 'native speaker': array([-5.31309962e-01,  2.64524996e-01, -3.57195020e-01,  4.07819986e-01,\n",
       "         6.77219987e-01,  3.84554982e-01, -5.26130021e-01,  1.03415012e-01,\n",
       "        -8.84760022e-01, -1.41329989e-01, -1.36164993e-01, -5.02335012e-01,\n",
       "         1.33852506e+00,  3.24949920e-02, -2.03834996e-01, -1.19692996e-01,\n",
       "        -1.45599991e-01,  1.52920008e-01,  1.86180025e-01, -1.80395007e-01,\n",
       "        -3.49494994e-01,  3.83350030e-02,  6.75050020e-02,  4.92080003e-01,\n",
       "         5.18980026e-01, -1.38010001e+00,  2.22009987e-01, -4.26265001e-01,\n",
       "        -6.79486036e-01,  3.07844996e-01,  2.27670002e+00, -7.19449997e-01,\n",
       "        -6.09910011e-01, -3.31070006e-01,  2.44660005e-01, -5.23635030e-01,\n",
       "        -2.45229989e-01,  3.18670005e-01, -1.32400077e-03,  2.53679991e-01,\n",
       "         1.42745003e-01,  9.28829968e-01, -4.81274992e-01, -1.21200010e-02,\n",
       "         4.81449962e-02,  8.20245028e-01, -6.35390043e-01, -3.64128500e-01,\n",
       "        -2.55270004e-01,  2.60654986e-01], dtype=float32),\n",
       " 'bar council': array([-6.6557503e-01,  3.1740001e-01, -9.5617497e-01, -2.5283501e-01,\n",
       "         3.7413502e-01, -2.9572499e-01, -5.5454999e-02, -1.0591000e-01,\n",
       "         1.3812301e-01, -8.3152902e-01, -1.9912000e-01, -1.5436350e-01,\n",
       "         1.0926001e-02,  3.7373501e-01, -2.7611148e-01,  4.4381499e-01,\n",
       "         3.2121101e-01, -1.9090500e-01,  9.5495015e-02, -4.2515999e-01,\n",
       "         9.5693499e-01, -4.1599944e-04, -2.5014997e-02,  4.6867651e-01,\n",
       "        -9.7384000e-01, -1.6584500e+00,  2.7488849e-01,  1.4993000e-01,\n",
       "        -8.0060804e-01,  2.3130000e-02,  2.4959002e+00, -2.0340349e-01,\n",
       "        -1.2758501e+00, -3.8361999e-01,  1.6647650e-01, -3.8237500e-01,\n",
       "         6.9549000e-01, -9.1817006e-02,  3.0062002e-01,  3.2607001e-01,\n",
       "        -2.3213001e-01,  2.1492499e-01, -1.6666999e-01,  6.3814983e-02,\n",
       "        -2.7948248e-01,  4.7946501e-01, -7.5582749e-01, -1.6446000e-01,\n",
       "         4.8533148e-01,  5.3580001e-02], dtype=float32),\n",
       " 'certified systems engineer': array([-0.2656933 , -0.32730332,  0.88631994,  0.23776467, -0.3537967 ,\n",
       "        -0.16649868, -0.8649066 , -0.28571332,  0.82304335, -0.15375833,\n",
       "         1.1269666 ,  0.38749996,  0.13763   ,  0.49178663, -0.58032334,\n",
       "        -0.02339834, -0.50185335,  0.8166134 ,  0.08049332, -0.50503   ,\n",
       "         0.26282734,  0.08178534, -0.30989668, -0.21075533, -0.05976667,\n",
       "        -0.94582003, -0.56826   , -0.97803336, -0.07594999,  0.5363267 ,\n",
       "         2.2782333 , -0.9796467 , -0.44849   , -0.62593   ,  0.6091133 ,\n",
       "         0.2415096 ,  0.41910267,  0.79016   ,  0.69866997, -0.11340669,\n",
       "         0.30882317, -0.47822866,  0.41207066,  0.29450667, -0.11766333,\n",
       "         0.03956999, -0.17149667,  0.19992334,  0.01191533,  0.57248336],\n",
       "       dtype=float32),\n",
       " 'concisely people diverse': array([ 0.52919   , -0.21632336, -0.0631    , -0.19426668,  0.6992133 ,\n",
       "         0.22769232,  0.02574002, -0.454342  , -0.23835002,  0.18332332,\n",
       "        -0.18100528, -0.44814667,  0.61511666, -0.14658333,  0.04590867,\n",
       "        -0.16512933,  0.2684213 ,  0.12696667,  0.68518335, -0.62858003,\n",
       "        -0.30479   ,  0.42505002,  0.5227297 ,  0.53341   ,  0.84193665,\n",
       "        -0.26729   , -0.5563333 , -0.16735333, -0.08194533, -0.29946667,\n",
       "         2.0973833 ,  0.17370401,  0.35950664, -1.0730133 , -0.22218998,\n",
       "        -0.18684101, -0.44032988,  0.13545667, -0.69834   ,  0.3948917 ,\n",
       "        -0.25094667, -0.12160334,  0.44736442,  0.47669664, -0.07977999,\n",
       "         0.39058003, -0.45092332,  0.26165998, -0.40743002,  0.02319333],\n",
       "       dtype=float32),\n",
       " 'microsoft certified': array([-0.35061997, -0.87437004,  0.900855  ,  0.847327  , -0.2565    ,\n",
       "        -0.46361   , -0.836485  , -0.56787   ,  1.003615  ,  0.52380496,\n",
       "         0.48313498,  0.24623999,  0.04820001, -0.19999999,  0.58756   ,\n",
       "         0.0475625 , -0.807335  ,  0.2707361 ,  0.639615  , -0.896815  ,\n",
       "         0.04061505,  0.106468  ,  0.038095  , -0.07683299, -0.32893503,\n",
       "        -0.777295  , -0.23194   , -0.72549003, -0.400645  , -0.09384501,\n",
       "         2.05055   , -0.976385  , -0.60831   , -0.29096   ,  0.25348997,\n",
       "        -0.5011656 ,  0.324965  ,  0.28566998,  0.14105001, -1.3673    ,\n",
       "         0.43341476, -0.663375  ,  0.28943   ,  0.80258   , -0.152215  ,\n",
       "         0.135075  , -0.10107501,  0.29008502, -0.11274199,  0.610535  ],\n",
       "       dtype=float32),\n",
       " 'systems engineer': array([ 0.24431   , -0.345135  ,  1.025725  ,  0.36492002, -0.42157   ,\n",
       "        -0.008938  , -0.81688   , -0.49939996,  0.298365  , -0.2808075 ,\n",
       "         1.08535   ,  0.633375  , -0.35620502,  0.47298998, -1.1781    ,\n",
       "         0.01039499, -0.597745  ,  0.95363003, -0.12119   , -0.39622998,\n",
       "         0.34999502,  0.10772501, -0.843505  , -0.327475  ,  0.18772501,\n",
       "        -1.175235  , -0.34989   , -0.9605    ,  0.18729001,  0.619535  ,\n",
       "         2.6456    , -0.977225  , -0.22771499, -0.86434495,  0.49942   ,\n",
       "         0.35763   ,  0.067104  ,  0.72679   ,  0.64789   ,  0.42339   ,\n",
       "         0.46517   , -0.201943  ,  0.238236  ,  0.18603003, -0.10830501,\n",
       "         0.23414999,  0.029185  ,  0.12286001, -0.00812   ,  0.54444   ],\n",
       "       dtype=float32),\n",
       " 'certified systems': array([-2.3081997e-01, -3.1587499e-01,  9.2475498e-01,  4.7328201e-01,\n",
       "        -6.6402501e-01, -2.1570501e-01, -6.3716000e-01, -6.1686999e-01,\n",
       "         1.0855650e+00,  1.9325018e-03,  1.1101000e+00,  3.3530998e-01,\n",
       "         5.1033002e-01,  3.6863500e-01, -3.9613500e-01,  2.5482750e-01,\n",
       "        -6.1566001e-01,  6.7772001e-01,  2.8527498e-01, -6.8820500e-01,\n",
       "         1.5434600e-01, -1.3595700e-01,  1.3579994e-02, -1.6769299e-01,\n",
       "        -3.1364501e-01, -6.7948002e-01, -6.3973498e-01, -8.8290000e-01,\n",
       "        -4.7049820e-03,  3.6220002e-01,  2.6001501e+00, -7.3126996e-01,\n",
       "        -5.4183501e-01, -6.6610003e-01,  4.3166998e-01,  1.8997939e-01,\n",
       "         5.7652402e-01,  6.1733997e-01,  6.0350502e-01, -3.7445003e-01,\n",
       "         2.8700975e-01, -7.6498997e-01,  5.8484501e-01,  8.7832999e-01,\n",
       "        -2.8671500e-01,  1.2591499e-01, -9.3620002e-02,  4.4830000e-01,\n",
       "        -1.8475998e-02,  5.2055502e-01], dtype=float32),\n",
       " 'services authority': array([ 0.394725  ,  0.11024499,  0.05861001,  0.020665  , -0.07063   ,\n",
       "        -0.26344702, -0.4203    , -0.2737945 ,  0.957595  , -0.68979   ,\n",
       "         0.32895002,  0.59214   , -0.11791199, -0.314155  , -0.623135  ,\n",
       "         0.55029   , -0.29658502,  0.014035  ,  0.58829   ,  0.1238215 ,\n",
       "         1.1408501 ,  0.06039576, -0.71851003,  0.02213   , -0.68792   ,\n",
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       "       dtype=float32),\n",
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       "        -4.4126e-01,  3.1420e-01, -3.3349e-02,  7.1159e-01, -5.4446e-01,\n",
       "         1.7786e-01,  7.7709e-01, -2.9200e-01, -2.0979e-01, -5.2457e-01,\n",
       "         9.5666e-01,  5.0773e-01, -2.3648e-01,  5.7628e-01,  2.6128e-01,\n",
       "         6.1220e-01, -5.6847e-04, -6.6488e-01, -4.4646e-01, -7.6751e-01,\n",
       "        -2.0850e+00,  1.0771e-01, -2.5436e-01, -2.1538e-01,  4.8074e-01,\n",
       "         2.8148e+00,  1.2736e-01, -7.0624e-01, -1.0007e+00, -2.1978e-01,\n",
       "         3.9275e-03,  5.8806e-01, -8.1102e-01, -1.1732e-01,  6.6186e-01,\n",
       "        -3.4537e-01,  2.5061e-01,  1.4543e-02,  6.4163e-01, -1.5588e+00,\n",
       "        -2.1905e-02, -1.2448e+00,  5.5571e-01,  2.9179e-01, -2.1411e-01],\n",
       "       dtype=float32),\n",
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       "         1.3683  ,  0.3146  ,  0.012001,  0.061853,  0.069705,  0.11661 ,\n",
       "         0.3954  , -1.7699  , -0.79544 ,  0.39389 ,  0.050912,  0.56292 ,\n",
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       "         0.28069 ,  0.95327 , -0.040267,  0.11293 , -0.8534  ,  0.047612,\n",
       "        -0.31393 , -0.14994 , -0.21364 ,  0.95505 ,  0.91899 ,  0.31467 ,\n",
       "         0.37181 , -0.078722, -0.20784 ,  0.66908 , -0.62909 ,  0.4406  ,\n",
       "        -1.6163  , -0.27332 , -0.89967 , -1.0748  , -0.011106, -1.5042  ,\n",
       "         0.38821 , -0.29575 ], dtype=float32),\n",
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       "        -0.29233667, -0.66211003, -0.8370466 ,  0.76865333,  0.31704497,\n",
       "         0.6587567 ,  0.42244998, -0.00274666, -0.06403666, -0.07746001,\n",
       "         0.23192167, -0.8453067 ,  0.45144406,  0.45530665, -0.81580335,\n",
       "         0.1004767 , -0.029628  , -0.21799   , -0.17057866, -0.24347001,\n",
       "        -0.8088533 , -0.24611668, -0.73456   , -0.06942332,  0.0556    ,\n",
       "         2.5859668 , -0.81027335, -0.47008333, -0.58834   ,  0.18060665,\n",
       "        -0.21054709,  0.226626  ,  0.29637334,  0.22962667, -0.7655    ,\n",
       "         0.4815732 , -0.60864335,  0.3296033 ,  0.95012   , -0.24716   ,\n",
       "         0.29052332,  0.06115666,  0.37424   , -0.10480732,  0.54453665],\n",
       "       dtype=float32),\n",
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       "         0.5760967 ,  0.19038999,  0.05755   ,  0.03182333, -0.4735067 ,\n",
       "         0.014827  ,  0.056416  ,  1.02617   ,  0.06813335,  0.64445   ,\n",
       "         0.50789434,  0.19860633,  0.10846334, -0.31127667,  0.21636935,\n",
       "        -0.51821   , -0.77447337, -0.90186673, -0.53452   ,  0.13346   ,\n",
       "         1.3091933 , -0.16179332, -0.63324   , -1.37264   , -0.23659666,\n",
       "         0.59512335, -0.51262337,  0.63189   ,  0.95989996,  0.47790995,\n",
       "         0.14737232, -0.41509333,  0.07724467,  0.15673   ,  0.37598336,\n",
       "         0.76562333,  0.60786337,  0.9580566 ,  0.2856461 ,  0.15908666],\n",
       "       dtype=float32),\n",
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       "         1.0977    ,  0.069475  ,  1.07874   ,  0.43707   ,  0.0605225 ,\n",
       "         0.472771  ,  0.058655  , -1.272245  , -0.5653    ,  1.4782    ,\n",
       "        -0.08266   , -0.540925  , -0.99017   , -0.93649995,  0.407075  ,\n",
       "        -1.0045635 ,  0.37116   , -0.08949001, -0.384055  ,  0.21757   ,\n",
       "        -1.9749    ,  0.07964   , -0.273065  ,  0.387295  ,  0.37164   ,\n",
       "         2.5255    ,  0.040685  ,  0.22106501, -0.50258   ,  0.2038    ,\n",
       "         0.778941  ,  1.1894951 , -0.09551999,  0.75023   , -0.23191498,\n",
       "        -0.53275996,  0.494595  , -0.309745  ,  0.17469999,  0.442895  ,\n",
       "        -0.036556  ,  0.0499    , -0.38590503,  0.206295  , -0.42796   ],\n",
       "       dtype=float32),\n",
       " 'ambedkar': array([ 0.1236   , -0.75712  , -0.3818   ,  0.13695  , -0.42251  ,\n",
       "         0.67435  ,  0.87674  , -1.13     , -0.015831 ,  0.27297  ,\n",
       "        -0.39698  ,  0.25899  , -0.086739 , -0.73304  , -0.065131 ,\n",
       "         0.91047  ,  0.72632  ,  0.51041  ,  0.26444  ,  0.57567  ,\n",
       "         0.22388  , -0.13752  , -0.68648  ,  0.076355 ,  0.32862  ,\n",
       "        -0.01495  ,  0.91746  , -0.87659  , -1.852    , -0.44172  ,\n",
       "        -0.88846  , -1.003    , -0.17803  , -0.6998   ,  0.0061994,\n",
       "         0.35878  ,  1.1737   , -0.016347 ,  0.75249  ,  0.5334   ,\n",
       "        -0.54762  ,  0.65512  , -1.4217   ,  0.28005  ,  0.73775  ,\n",
       "         1.6109   ,  0.38021  , -1.3723   , -0.83126  , -0.15281  ],\n",
       "       dtype=float32),\n",
       " 'ambedkar law university': array([-0.73913336,  0.14135332, -0.64960665, -0.27624667, -0.29951334,\n",
       "         0.27835003,  0.05057665, -0.7944767 , -0.10965646, -0.14080666,\n",
       "        -0.01971332,  0.11540332, -0.0665    , -0.7486234 , -0.10467366,\n",
       "         0.39467144,  0.20111567,  0.31082   , -0.05252002,  0.7186823 ,\n",
       "         0.5492967 ,  0.29885998, -0.42914   , -0.02508063, -0.09003135,\n",
       "        -1.8289499 ,  0.38963333, -0.9305734 , -1.4100933 , -0.54271334,\n",
       "         1.5195469 , -0.62075466, -0.505695  , -1.4453335 , -0.03643753,\n",
       "        -0.03814727,  0.41571665,  0.33334434,  1.0068799 ,  0.55863667,\n",
       "        -0.40813002,  0.28346133, -0.226     ,  0.56871   , -0.10524801,\n",
       "         0.6871154 ,  0.10457334, -0.33540002, -0.38966003,  0.08283133],\n",
       "       dtype=float32),\n",
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       "         5.4995e-01, -5.5369e-01, -3.5791e-01, -2.7327e-01,  5.6298e-01,\n",
       "         4.8538e-01,  1.9560e-01,  7.6609e-01,  1.4633e-03, -4.9319e-01,\n",
       "        -2.5365e-01, -6.0558e-01,  1.3433e+00, -4.7967e-01,  1.0888e+00,\n",
       "         9.3805e-01,  1.1932e+00, -3.1010e-02, -2.9201e-01, -4.4510e-01,\n",
       "        -1.2520e+00, -9.7210e-02, -1.1680e+00, -3.7394e-01,  2.4645e-01,\n",
       "         1.5268e+00,  1.2353e-01, -9.8737e-01, -8.2833e-01,  8.4111e-01,\n",
       "         4.8287e-01, -4.5142e-01,  9.8250e-01,  1.3721e+00, -3.4363e-01,\n",
       "        -1.5758e-01, -3.4484e-01,  4.8065e-03,  8.4408e-01, -1.3145e+00,\n",
       "        -6.1091e-01,  7.1880e-01, -1.9112e-01, -9.0826e-02,  2.6399e-01],\n",
       "       dtype=float32),\n",
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       "         0.68446004,  0.533205  , -0.94883   , -0.0051947 , -0.04374   ,\n",
       "        -0.280035  ,  0.42506498, -0.11447451, -0.535535  ,  0.0792145 ,\n",
       "         0.45600215,  0.3571385 , -0.04137   ,  0.44257   ,  0.3315735 ,\n",
       "         0.47284   ,  0.08758499, -0.58922   , -0.0366625 , -0.09693502,\n",
       "        -1.473525  ,  0.52441   , -0.96539503, -1.05414   , -0.359505  ,\n",
       "         0.97187006, -0.90626   , -0.710265  , -1.0436001 , -0.0244938 ,\n",
       "        -0.06138501,  0.676335  , -0.1217335 ,  0.64087   ,  0.505395  ,\n",
       "        -0.553015  ,  0.45594   , -0.19319999,  0.66457504, -0.10936502,\n",
       "         0.78853303, -0.02598999, -0.63444   , -0.31291503,  0.10627499],\n",
       "       dtype=float32),\n",
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       "        -4.5366499e-01,  4.8250005e-02,  5.3654999e-02, -2.9507503e-01,\n",
       "         3.8585499e-01,  2.7880013e-02,  6.6657501e-01, -4.1884989e-02,\n",
       "        -2.0060501e-01, -7.6619998e-02,  3.4988499e-01, -5.5273995e-02,\n",
       "        -8.2265005e-02, -1.4226000e-01, -5.4984498e-01, -1.4751650e-01,\n",
       "         1.4265999e-01, -4.9257001e-01, -8.8265002e-02,  4.1309997e-02,\n",
       "         1.6679502e-01, -5.2670503e-01,  1.9599944e-03, -5.6671500e-01,\n",
       "        -2.6427501e-01,  3.5901999e-01,  2.2759500e+00,  2.6578999e-01,\n",
       "        -7.0577496e-01, -3.0552000e-01,  4.2046002e-01,  3.2992202e-01,\n",
       "         6.8211001e-01,  9.5305002e-01, -8.3984506e-01, -7.2692001e-01,\n",
       "         2.1720000e-01, -8.2608497e-01,  1.4282499e-01,  2.3233500e-01,\n",
       "        -4.7617900e-01, -4.6049953e-03,  8.1259000e-01,  6.6548502e-01,\n",
       "         3.0338103e-01, -1.7159000e-01], dtype=float32),\n",
       " 'master law tamil': array([-0.5763047 , -0.33219668, -0.40860903, -0.42805335,  0.14244667,\n",
       "         0.24440332,  0.0372827 , -0.39905   , -0.19717048, -0.02986667,\n",
       "        -0.00681334,  0.28249   ,  0.16492133, -0.02081   , -0.27802667,\n",
       "        -0.17210858,  0.38199365,  0.50558335, -0.07963333,  0.55873567,\n",
       "         0.6531234 ,  0.59461665, -0.13711   ,  0.18917133,  0.13607   ,\n",
       "        -1.7335367 , -0.43558002, -1.1820334 , -0.72572327, -0.006551  ,\n",
       "         2.4708002 , -0.9530733 , -0.9992233 , -0.28252667,  0.46425763,\n",
       "         0.35891   , -0.03794667, -0.48725334,  0.04726005,  0.09626599,\n",
       "        -0.26398668,  0.22335333, -0.01312999,  0.19114004, -0.500578  ,\n",
       "        -0.09609801,  0.05254999,  0.05093334, -0.006572  , -0.44072568],\n",
       "       dtype=float32),\n",
       " 'english literature tirunelveli': array([-0.50537664,  0.25186   , -0.92760664, -0.68555135, -0.153567  ,\n",
       "         0.47985664, -0.38854328, -0.32083368, -0.48988998,  0.07896   ,\n",
       "         0.05795665, -0.07371   ,  0.76555663, -0.55376333, -0.15062101,\n",
       "        -0.23598336,  0.18139632,  0.4648633 , -0.31619504,  0.84989333,\n",
       "         0.61402667,  0.11503667,  0.13274665,  1.2618433 ,  0.56436664,\n",
       "        -0.9695868 , -0.8383567 , -0.66068333, -1.0561867 , -0.13148   ,\n",
       "         1.7025132 , -0.6644    ,  0.20346732, -0.42152748,  0.49049667,\n",
       "         0.25034967, -0.33079   ,  0.17637731, -0.06267333,  0.11866666,\n",
       "         0.5826333 ,  0.25876567, -0.17997666,  0.21295567, -0.691149  ,\n",
       "         0.43567   ,  0.48583332,  0.42498   ,  0.11260333, -0.69629335],\n",
       "       dtype=float32),\n",
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       "        -0.07142   ,  0.56942004, -0.15665233, -0.64739937,  0.37243667,\n",
       "         0.78129673,  0.06010668,  0.381994  ,  0.43157998,  0.49650002,\n",
       "         0.6610767 ,  0.277234  ,  0.0805467 , -0.04581   ,  0.50407   ,\n",
       "        -0.34516335, -0.17645735, -0.49601933, -0.10777333,  0.07291966,\n",
       "         1.1682667 , -0.08491   , -0.24616434,  0.29744   , -0.01359666,\n",
       "        -0.10580667,  0.28827667, -0.40326667,  0.46444607, -0.23345333,\n",
       "        -0.4648    , -0.8299133 , -0.5701373 ,  0.38640666, -0.36518666,\n",
       "        -0.12693201, -0.17884332, -0.21508068,  0.21875234,  0.06213133],\n",
       "       dtype=float32),\n",
       " 'courts': array([-0.63199 , -0.7651  , -1.0161  ,  0.66418 , -0.45588 , -0.64957 ,\n",
       "         1.1307  ,  0.9599  ,  0.038835, -0.31315 , -0.40853 , -0.45901 ,\n",
       "        -0.1096  , -0.21919 ,  0.44653 ,  0.50354 ,  0.42558 , -0.45744 ,\n",
       "         0.48728 , -1.1543  ,  0.26975 ,  0.25054 , -0.23078 ,  0.59083 ,\n",
       "        -1.0941  , -1.7787  , -0.094447, -0.45007 , -0.6186  , -0.18298 ,\n",
       "         2.614   , -0.64498 , -0.76375 , -1.0087  ,  0.32886 ,  0.10261 ,\n",
       "         0.66781 ,  0.10538 , -0.3828  , -0.25343 , -0.51281 ,  0.082526,\n",
       "         1.1184  ,  1.8658  , -0.55232 ,  0.43038 , -0.80147 , -0.71519 ,\n",
       "        -0.23106 , -0.60306 ], dtype=float32),\n",
       " 'bachelor law tamil': array([-0.88207793, -0.01907333, -0.49415234, -0.48062   , -0.13680999,\n",
       "         0.57029665, -0.11808997, -0.46299335, -0.0651338 ,  0.02795333,\n",
       "        -0.00661666, -0.04501001,  0.40601   , -0.22577555, -0.32724667,\n",
       "        -0.14161523,  0.210059  ,  0.59115   , -0.07938   ,  0.8865923 ,\n",
       "         0.67484665,  0.91153   ,  0.06046001,  0.12278666, -0.06790668,\n",
       "        -1.6362034 , -0.2104    , -1.0975666 , -0.59124   ,  0.09736667,\n",
       "         2.2244666 , -0.58093   , -1.0336833 , -0.46140996,  0.49577436,\n",
       "         0.27578   , -0.23932   , -0.33637333,  0.26225004,  0.07708933,\n",
       "        -0.46354   ,  0.16489667,  0.09338552,  0.32767668, -0.96318334,\n",
       "        -0.187178  ,  0.00294666,  0.23729001, -0.02011533, -0.34205   ],\n",
       "       dtype=float32),\n",
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       "         0.43416333, -0.02441   , -0.31104   , -0.04482334,  0.13677001,\n",
       "        -0.11242   , -0.31118   ,  0.56508   , -0.37983668, -0.16563   ,\n",
       "        -0.05850001,  0.1955    ,  0.40562332, -0.01128167,  0.7388833 ,\n",
       "         0.57830334, -0.03382667,  0.08039667,  0.8216767 ,  0.36368665,\n",
       "        -0.53628665, -0.67189336, -0.42838   , -0.8064334 , -0.27968666,\n",
       "         0.5638467 , -0.37847337,  0.08733734, -0.41886735,  0.23148334,\n",
       "         0.11226967, -0.37842667,  0.14401333,  0.09011   ,  0.32684   ,\n",
       "         0.1635    ,  0.27525   , -0.21764334,  0.16905232, -0.66173667,\n",
       "         0.28775665,  0.42493668,  0.37586334,  0.00631   , -0.53692   ],\n",
       "       dtype=float32),\n",
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       "         0.51762664,  0.21142001, -0.36554432,  0.36792287, -0.38112   ,\n",
       "        -0.30644667, -0.40868667,  0.5242467 , -0.48586667, -0.39402667,\n",
       "         0.15406477,  0.7918857 ,  0.54698336,  0.10424101,  0.7896123 ,\n",
       "         0.81179667,  0.6300667 ,  0.20714335,  0.49630332,  0.38129663,\n",
       "        -1.40544   , -0.42785   , -1.0355834 , -1.0097934 ,  0.16506334,\n",
       "         2.0877333 , -0.81734663, -0.7881133 , -0.17017369,  0.486011  ,\n",
       "         0.27606666, -0.12266666, -0.7601333 , -0.20558096,  0.3636427 ,\n",
       "        -0.7008033 ,  0.62327665, -0.2610833 , -0.07490999, -1.0797501 ,\n",
       "         0.295562  , -0.44204998,  0.20714001,  0.18771668, -0.87748003],\n",
       "       dtype=float32),\n",
       " 'legal advisor': array([-0.14705399,  0.674025  , -0.495525  ,  0.44160002,  0.3620987 ,\n",
       "        -0.08796999,  0.165919  , -0.099378  ,  0.158225  , -0.58931   ,\n",
       "        -0.39314   ,  0.69647   , -0.49071002,  0.172777  ,  0.18866   ,\n",
       "        -0.154814  ,  0.19165   , -0.15812999,  0.92101   ,  0.32024002,\n",
       "         0.66445   ,  0.51695   , -0.12119   , -0.47948998, -0.39346   ,\n",
       "        -1.6264    ,  0.25063565, -0.7568    , -0.76484996,  1.0699    ,\n",
       "         2.2501    , -0.65551496, -0.58799   , -0.857165  , -0.22448   ,\n",
       "         0.174435  , -0.59081995,  0.293915  , -0.063585  , -0.252415  ,\n",
       "         0.094695  ,  0.31493   ,  0.134935  ,  0.5573751 , -0.343674  ,\n",
       "        -0.62281   , -0.35472   ,  0.709925  , -0.0546855 ,  0.4637265 ],\n",
       "       dtype=float32),\n",
       " 'bachelor law': array([-1.3559999e+00,  8.0509001e-01, -7.6202500e-01, -5.8362997e-01,\n",
       "        -1.5505999e-01,  6.2225997e-01, -1.8201001e-01, -5.6278503e-01,\n",
       "        -1.3391420e-01,  1.0126500e-01,  1.6114500e-01,  3.9336997e-01,\n",
       "         3.1193998e-01, -1.6828336e-01, -1.3481499e-01, -1.2605785e-01,\n",
       "        -3.0881149e-01,  3.7507498e-01,  7.0515007e-02,  5.8813846e-01,\n",
       "         8.2992500e-01,  7.5294501e-01, -2.6148498e-01, -2.2084501e-01,\n",
       "        -4.8379502e-01, -2.0920501e+00,  1.7074998e-02, -1.1111000e+00,\n",
       "        -3.1511000e-01, -1.5419990e-02,  2.1795001e+00, -3.4299499e-01,\n",
       "        -1.1149349e+00, -1.1078650e+00,  3.9296150e-01,  6.6000223e-04,\n",
       "        -1.3622501e-01,  3.7769002e-01,  9.5067501e-01,  6.6880003e-02,\n",
       "        -3.5799500e-01, -4.4039994e-02,  5.2005327e-01,  9.4659001e-01,\n",
       "        -1.1354899e+00, -3.2237199e-01,  1.4330500e-01, -4.3850001e-02,\n",
       "         5.7302002e-02,  3.1467500e-01], dtype=float32),\n",
       " 'law tamil': array([-0.583517  , -0.88891   , -0.3689585 , -0.39037   , -0.08423   ,\n",
       "         0.58046997,  0.09971005, -0.515535  ,  0.0389343 , -0.23956001,\n",
       "        -0.252615  , -0.165315  ,  0.22597   , -0.339395  , -0.24427499,\n",
       "        -0.08559785,  0.6178785 ,  0.21507502,  0.120765  ,  0.7854885 ,\n",
       "         0.543245  ,  0.77069503,  0.106195  ,  0.330185  ,  0.12068999,\n",
       "        -1.828305  , -0.266995  , -1.06235   , -0.69989   ,  0.022825  ,\n",
       "         2.5733    , -0.93316   , -1.05684   , -0.27795002,  0.3231065 ,\n",
       "         0.172235  , -0.13327   , -0.99581003, -0.29267496,  0.287449  ,\n",
       "        -0.61652   ,  0.419765  ,  0.13767502,  0.06947502, -0.78752506,\n",
       "         0.024688  , -0.35498   ,  0.45149502,  0.01524   , -0.64507   ],\n",
       "       dtype=float32),\n",
       " 'qualification introduction computer': array([-0.36162198, -0.03117665,  0.17701335,  0.38638666, -0.26645333,\n",
       "        -0.15326001, -0.24565333, -0.6950834 ,  0.30196333,  0.23067667,\n",
       "         0.56153667,  0.05812001, -0.31716666,  0.11910666,  0.3579533 ,\n",
       "         0.00345734, -0.59371   ,  0.18400335, -0.3192233 , -0.27283767,\n",
       "         0.33995   , -0.28769   , -0.22244   ,  0.13598333,  0.2622167 ,\n",
       "        -0.87947005, -0.21152   , -0.71291   , -0.14133668,  0.09606666,\n",
       "         2.5776331 , -0.03009667, -0.4144533 , -0.54164666,  0.30165935,\n",
       "         0.41276136,  0.30287334,  0.88131   , -0.4358867 , -0.67702   ,\n",
       "         0.36626998, -0.53234696,  0.107847  ,  0.59739   , -0.21414934,\n",
       "         0.16592   ,  0.95362663,  0.48612335,  0.16414069, -0.04536333],\n",
       "       dtype=float32),\n",
       " 'lawyer law firm': array([-0.49106666, -0.17220502, -0.37803003,  0.31709   ,  0.46551666,\n",
       "         0.41819334, -0.4587233 , -0.04967001, -0.05195614, -0.19838701,\n",
       "        -0.19981033,  0.42629   , -0.59781003, -0.33895   ,  0.07203003,\n",
       "        -0.15227456,  0.035082  , -0.29735002,  0.36546066, -0.19765532,\n",
       "         0.86151004,  0.77595663, -0.5252667 , -0.23448332, -0.65684   ,\n",
       "        -2.2847002 ,  0.105784  , -0.70376664, -0.50335   ,  0.03760567,\n",
       "         2.2806    , -0.99477   , -0.4805833 , -0.67548496, -0.08282233,\n",
       "        -0.77216667, -0.259287  ,  0.06945366,  0.89648336, -0.04161068,\n",
       "         0.27147332,  0.27703664,  0.49297   ,  0.61359   , -0.18339002,\n",
       "        -0.33208466, -0.79972667,  0.21909298,  0.07672334,  0.68844   ],\n",
       "       dtype=float32),\n",
       " 'natural languages proficient': array([-0.35034335,  0.0282733 , -0.23028332, -0.24423736,  0.346177  ,\n",
       "         0.25839004, -0.08799666, -0.7714567 , -0.49129334,  0.6489096 ,\n",
       "         0.99699336,  0.10523999,  1.2615    , -0.0178    , -0.3452933 ,\n",
       "        -0.20423333,  0.24076666,  0.53811   , -0.07390333,  0.01025233,\n",
       "        -0.58419335,  0.06752334,  0.5928137 ,  0.45538667,  0.82888335,\n",
       "        -0.286935  , -0.28397334, -0.42668667, -0.18161333, -0.02883666,\n",
       "         2.6773665 , -0.65220666,  0.38766333, -0.62264997,  0.6929824 ,\n",
       "         0.11415734, -0.37831667,  0.49302998, -0.18935998,  0.4431367 ,\n",
       "         0.16255534, -0.12403   ,  0.7445267 ,  0.49326333, -0.45750332,\n",
       "         0.09499332,  0.44851336,  0.662907  , -0.17238033, -0.13353002],\n",
       "       dtype=float32),\n",
       " 'criminal lawyer': array([-0.124235  , -0.3709075 , -0.539145  ,  0.428385  ,  0.813565  ,\n",
       "         0.44403   ,  0.38787997,  0.485205  , -0.11543751, -0.074155  ,\n",
       "        -0.45426002,  0.20373   , -0.867195  , -0.163815  ,  0.77289   ,\n",
       "        -0.77312493,  0.034152  , -0.29969   ,  0.29716998,  0.002769  ,\n",
       "         0.39687997,  0.889615  , -0.2791918 ,  0.29009   , -0.965465  ,\n",
       "        -2.5068002 ,  0.06071001, -0.71842   , -0.329515  , -0.1021115 ,\n",
       "         1.7813501 , -1.25375   , -0.502985  , -1.2979801 ,  0.1321    ,\n",
       "        -0.13633499, -0.0463655 , -0.4097195 ,  0.44393   ,  0.0147945 ,\n",
       "        -0.03435501,  0.78418   ,  0.67269003,  0.71908   ,  0.059506  ,\n",
       "        -0.785495  , -0.79376   ,  0.13697949,  0.32656997,  0.5188235 ],\n",
       "       dtype=float32),\n",
       " 'maharashtra board': array([-0.0111475 , -1.02987   , -0.43989   ,  0.370125  , -0.230315  ,\n",
       "         0.04005999,  0.00564501,  0.03303   ,  0.2588225 , -0.80322504,\n",
       "        -0.03605501,  0.11801499,  0.2927    , -0.06243001, -0.378984  ,\n",
       "         0.227385  ,  0.10256   ,  1.0500851 , -0.361105  , -0.06964499,\n",
       "         1.1142    , -0.348681  ,  0.0929715 ,  0.35047996, -0.07215999,\n",
       "        -1.297405  , -0.05404   , -0.199618  , -1.5431    , -0.01553   ,\n",
       "         1.8765451 ,  0.069833  , -0.03016502, -0.12218   ,  0.251795  ,\n",
       "        -0.147025  ,  0.2404355 , -0.11113949,  0.680595  ,  0.311885  ,\n",
       "        -0.83478   ,  0.168765  , -0.403666  ,  0.208343  , -0.95773494,\n",
       "         0.959545  , -0.866705  ,  0.510975  ,  0.90163   , -0.36193   ],\n",
       "       dtype=float32),\n",
       " 'languages proficient marathi': array([-0.68804   , -0.50088   , -0.37732998, -0.7241373 ,  0.19105034,\n",
       "         0.17604667,  0.12451333, -0.84304   , -1.0683299 ,  0.6869696 ,\n",
       "         0.7621567 ,  0.05946666,  1.2850333 ,  0.29063335, -0.43692002,\n",
       "        -0.57339   , -0.10055367,  0.7845066 ,  0.04066667,  0.74435234,\n",
       "        -0.22979002,  0.17796868,  0.98088664,  1.3989334 ,  1.08615   ,\n",
       "        -0.16307166, -0.6757867 , -0.67181   , -1.13208   , -0.35199332,\n",
       "         1.8896867 , -0.37423   ,  0.73316336, -0.18366666,  0.7610934 ,\n",
       "         0.12484733, -0.03926334,  0.33771133, -0.69297665,  0.43759668,\n",
       "         0.269192  ,  0.14733332,  0.30629334,  0.31701335, -0.68897   ,\n",
       "         0.08796999,  0.52641   ,  0.84256667,  0.08592334, -0.14528336],\n",
       "       dtype=float32),\n",
       " 'maharashtra': array([-0.098312, -1.4559  , -0.69223 ,  0.62598 , -0.73315 ,  0.20222 ,\n",
       "         0.7498  ,  0.17962 ,  0.075065, -0.29235 , -0.22863 , -0.38155 ,\n",
       "         0.86142 , -0.51389 , -0.66455 ,  0.29121 ,  0.61349 ,  1.5851  ,\n",
       "        -0.3047  ,  0.95521 ,  1.2413  , -0.78292 ,  0.25514 ,  1.0902  ,\n",
       "         0.48186 , -0.83731 , -0.24629 , -0.37071 , -1.5723  ,  0.10153 ,\n",
       "         0.95389 ,  0.1822  ,  0.64935 ,  0.07683 ,  0.37403 ,  0.28475 ,\n",
       "        -0.021229, -0.1554  ,  0.44956 ,  0.87028 , -1.2398  ,  0.4741  ,\n",
       "        -0.78616 , -0.077684, -1.0458  ,  1.4808  , -1.0777  ,  0.42499 ,\n",
       "         1.5076  , -1.0453  ], dtype=float32),\n",
       " 'internet operation education': array([ 0.47390333, -0.08173665,  0.2547367 , -0.02559668, -0.30825332,\n",
       "        -0.12744467, -0.55894434, -0.79437333,  0.81279   ,  0.05684332,\n",
       "         0.00457667, -0.05913234, -0.15338333, -0.17603166, -0.15149334,\n",
       "        -0.15751   , -0.26414868,  0.32337335,  0.32920334,  0.44212332,\n",
       "         0.71616   ,  0.10243667, -0.313881  ,  0.297788  , -0.03298334,\n",
       "        -1.4699668 , -0.08860999, -0.765498  ,  0.05892336,  0.11057333,\n",
       "         3.3049667 ,  0.19963999, -0.45964333, -0.6623833 , -0.40117905,\n",
       "         0.28195333, -0.21464334,  0.15879999, -0.03764667,  0.07687333,\n",
       "        -0.03557668, -0.17601435, -0.09831467,  0.32060665, -0.055088  ,\n",
       "        -0.34769332,  0.02499833,  0.25807765,  0.22394998,  0.4923233 ],\n",
       "       dtype=float32),\n",
       " 'asmita girls junior': array([-0.8610127 ,  0.53763336, -0.53684336, -0.20847766, -0.03636   ,\n",
       "        -0.13965999, -0.18413667,  0.06446666, -0.17723668, -0.05052   ,\n",
       "         0.33723333,  0.03269333, -0.08725667,  0.10301001, -0.03240767,\n",
       "        -0.11860933,  0.07996667,  0.75634336,  0.01946233,  0.22410367,\n",
       "        -0.09335334,  0.81166905,  0.14439332,  0.89355665, -0.41127667,\n",
       "        -0.28016666,  0.06575666, -0.472     , -0.7304    , -0.48860666,\n",
       "         1.0379333 ,  0.54813635,  0.08752766, -0.17279665,  0.6024733 ,\n",
       "         0.35261336,  0.04943967, -0.17708667,  0.06150733, -0.580528  ,\n",
       "        -0.39679667, -0.49661636,  0.23363666,  0.04619901,  0.355788  ,\n",
       "        -0.38447937,  0.1844    , -0.82289666, -0.004354  ,  0.033237  ],\n",
       "       dtype=float32),\n",
       " 'natural languages': array([ 2.7988499e-01,  5.6085998e-01, -5.9690499e-01,  3.7154397e-01,\n",
       "         8.1255503e-02,  6.3424003e-01, -2.5594500e-01, -9.9537504e-01,\n",
       "        -2.6005501e-01,  4.0696442e-01,  9.5159000e-01,  4.0697500e-01,\n",
       "         1.3892000e+00, -2.0957001e-01, -1.8130000e-01,  4.4210017e-02,\n",
       "         2.1000999e-01,  3.8495001e-01,  3.0365005e-02,  6.7349970e-03,\n",
       "        -4.3406501e-01, -5.6499243e-04,  5.7094049e-01,  3.4123001e-01,\n",
       "         8.3324999e-01, -4.3983498e-01, -3.4403500e-01, -3.4770000e-01,\n",
       "        -1.0205001e-01,  4.4620007e-02,  3.3397000e+00, -7.3060000e-01,\n",
       "         1.4847000e-01, -6.5608001e-01,  3.2497349e-01,  1.2173350e-01,\n",
       "        -8.0576003e-01,  5.6144997e-02, -3.4894973e-02,  8.2177001e-01,\n",
       "         1.9863001e-01,  1.8193500e-01,  6.9137502e-01,  3.6229998e-01,\n",
       "        -5.1505500e-01,  5.0933498e-01,  2.5635502e-01,  6.2103552e-01,\n",
       "        -3.5209548e-01, -5.5286002e-01], dtype=float32),\n",
       " 'proficient': array([-1.6108  , -1.0369  ,  0.50296 , -1.4758  ,  0.87602 , -0.49331 ,\n",
       "         0.2479  , -0.32362 , -0.95377 ,  1.1328  ,  1.0878  , -0.49823 ,\n",
       "         1.0061  ,  0.36574 , -0.67328 , -0.70112 ,  0.30228 ,  0.84443 ,\n",
       "        -0.28244 ,  0.017287, -0.88445 ,  0.2037  ,  0.63656 ,  0.6837  ,\n",
       "         0.82015 ,  0.018865, -0.16385 , -0.58466 , -0.34074 , -0.17575 ,\n",
       "         1.3527  , -0.49542 ,  0.86605 , -0.55579 ,  1.429   ,  0.099005,\n",
       "         0.47657 ,  1.3668  , -0.49829 , -0.31413 ,  0.090406, -0.73596 ,\n",
       "         0.85083 ,  0.75519 , -0.3424  , -0.73369 ,  0.83283 ,  0.74665 ,\n",
       "         0.18705 ,  0.70513 ], dtype=float32),\n",
       " 'company criminal lawyer': array([ 0.12578665, -0.43961498, -0.22221999,  0.57496333,  0.5146777 ,\n",
       "         0.38453665, -0.2347467 ,  0.19416332,  0.065465  , -0.01050333,\n",
       "        -0.04154001,  0.40699336, -0.8406734 , -0.32734334,  0.48218867,\n",
       "        -0.44655666, -0.22161533,  0.08826667,  0.10839   , -0.310794  ,\n",
       "         0.9042533 ,  0.5348334 , -0.39247787,  0.02556001, -0.95501   ,\n",
       "        -2.2297    , -0.22419   , -0.5856233 , -0.24223602, -0.109221  ,\n",
       "         2.2845335 , -0.97967   , -0.20602334, -0.73621005,  0.081927  ,\n",
       "        -0.41819334, -0.208207  , -0.14025967,  0.59412664, -0.12668033,\n",
       "         0.17350332,  0.34819666,  0.54143673,  0.39349997, -0.06185266,\n",
       "        -0.545765  , -0.90320665,  0.16325967,  0.16576   ,  0.5592757 ],\n",
       "       dtype=float32),\n",
       " 'office internet operation': array([ 0.8677066 , -0.29499033,  0.55958337,  0.39723334, -0.09267   ,\n",
       "        -0.46632132, -0.6932743 , -0.41073665,  0.6846657 , -0.2722967 ,\n",
       "        -0.23761334, -0.11238901, -0.41917   ,  0.12281173,  0.20664664,\n",
       "         0.02413   , -0.56736666,  0.13833399,  0.11603334,  0.17367633,\n",
       "         0.88746995,  0.01863667, -0.291031  ,  0.17793997, -0.35006666,\n",
       "        -1.6929334 , -0.18547034, -0.14221466,  0.21066   ,  0.08651666,\n",
       "         3.0504    , -0.12412333, -0.47629   , -0.5943833 , -0.50478303,\n",
       "         0.2625967 , -0.02535001, -0.06493334, -0.06782667, -0.06473   ,\n",
       "         0.08143999,  0.1261623 , -0.22082901,  0.20917015, -0.12808801,\n",
       "        -0.43852666, -0.251435  ,  0.04036267,  0.29348   ,  0.35407665],\n",
       "       dtype=float32),\n",
       " 'board': array([ 0.076017, -0.60384 , -0.18755 ,  0.11427 ,  0.27252 , -0.1221  ,\n",
       "        -0.73851 , -0.11356 ,  0.44258 , -1.3141  ,  0.15652 ,  0.61758 ,\n",
       "        -0.27602 ,  0.38903 , -0.093418,  0.16356 , -0.40837 ,  0.51507 ,\n",
       "        -0.41751 , -1.0945  ,  0.9871  ,  0.085558, -0.069197, -0.38924 ,\n",
       "        -0.62618 , -1.7575  ,  0.13821 , -0.028526, -1.5139  , -0.13259 ,\n",
       "         2.7992  , -0.042534, -0.70968 , -0.32119 ,  0.12956 , -0.5788  ,\n",
       "         0.5021  , -0.066879,  0.91163 , -0.24651 , -0.42976 , -0.13657 ,\n",
       "        -0.021172,  0.49437 , -0.86967 ,  0.43829 , -0.65571 ,  0.59696 ,\n",
       "         0.29566 ,  0.32144 ], dtype=float32),\n",
       " 'office internet': array([ 0.41301   , -0.0896605 ,  0.915225  ,  0.51983   ,  0.01186   ,\n",
       "        -0.68511   , -1.013035  , -0.82554495,  0.4751985 , -0.09170002,\n",
       "        -0.445645  , -0.13569   , -0.27193   ,  0.1411751 ,  0.47424498,\n",
       "         0.119065  , -0.90306   , -0.036484  ,  0.564695  , -0.0959805 ,\n",
       "         1.005755  ,  0.17642501, -0.39883   ,  0.54232496, -0.18495   ,\n",
       "        -1.84095   , -0.0890705 , -0.18560998, -0.15421   ,  0.008245  ,\n",
       "         3.0437    , -0.092355  , -0.20383501, -0.993095  , -0.74590504,\n",
       "        -0.01356499, -0.20792001, -0.04637501,  0.00835499, -0.22012   ,\n",
       "         0.37386498,  0.31944847, -0.1723035 ,  0.7413402 , -0.319707  ,\n",
       "        -0.168615  , -0.4816775 , -0.058876  ,  0.577865  ,  0.30314   ],\n",
       "       dtype=float32),\n",
       " 'criminal lawyer law': array([-0.49375668, -0.28407833, -0.6192667 ,  0.11687666,  0.51966   ,\n",
       "         0.5275433 ,  0.32180998,  0.06758332, -0.07514447, -0.16958666,\n",
       "        -0.35720336,  0.33286667, -0.62553334, -0.22188668,  0.58978   ,\n",
       "        -0.51490515,  0.01875367, -0.39751002,  0.40501332,  0.031005  ,\n",
       "         0.5051866 ,  0.69730663, -0.3501145 ,  0.14349999, -0.8178067 ,\n",
       "        -2.648567  ,  0.08426001, -0.83034676, -0.30510333, -0.16050433,\n",
       "         2.1316335 , -1.1056733 , -0.74948996, -1.3277868 ,  0.069671  ,\n",
       "        -0.25140667,  0.02874633, -0.348853  ,  0.47237   ,  0.168993  ,\n",
       "        -0.20904   ,  0.60837334,  0.79356   ,  0.8290866 , -0.279156  ,\n",
       "        -0.5349413 , -0.67323667,  0.125793  ,  0.28619   ,  0.467669  ],\n",
       "       dtype=float32),\n",
       " 'mumbai maharashtra': array([ 0.23280899, -1.06338   , -0.42795998,  0.554325  , -0.29786   ,\n",
       "        -0.161435  ,  0.587215  ,  0.030925  ,  0.0945125 ,  0.01957001,\n",
       "        -0.286585  , -0.446505  , -0.00928003, -0.34178   , -0.48111498,\n",
       "         0.420555  ,  0.20840499,  1.181085  , -0.75665003,  1.317805  ,\n",
       "         1.5645    ,  0.11894   ,  0.05548   ,  1.18265   ,  0.24867101,\n",
       "        -0.974205  , -0.20591   , -0.38339   , -1.1117749 , -0.184775  ,\n",
       "         1.104345  ,  0.33795   ,  0.79224   , -0.099025  ,  0.697915  ,\n",
       "         0.1534495 ,  0.0559905 , -0.576235  ,  0.078585  ,  0.769645  ,\n",
       "        -1.1582999 ,  0.1935985 , -0.291085  , -0.097967  , -0.37169498,\n",
       "         0.96105504, -0.492632  ,  0.284895  ,  1.5167    , -1.02845   ],\n",
       "       dtype=float32),\n",
       " 'proficient marathi computer': array([-0.7007187 , -0.8639167 ,  0.46404   , -0.43967998,  0.21900666,\n",
       "        -0.3006867 , -0.04408999, -0.89134   , -0.56210333,  0.89983004,\n",
       "         0.49737668,  0.01133666,  0.53997004,  0.50030667, -0.21964335,\n",
       "        -0.38994336, -0.41709366,  0.95691997, -0.09755334,  0.36320233,\n",
       "         0.24859999,  0.12384534,  0.42209002,  0.99271005,  0.79486996,\n",
       "        -0.6489817 , -0.7699433 , -0.6928833 , -0.69516665, -0.28527665,\n",
       "         1.82612   , -0.31030333,  0.74527997, -0.38975   ,  0.5540093 ,\n",
       "         0.30915168,  0.05305335,  0.6722279 , -0.32325998, -0.16247667,\n",
       "         0.286952  ,  0.09747633,  0.03215702,  0.58660334, -0.29801002,\n",
       "         0.11092999,  0.6446467 ,  0.48286664,  0.28724   ,  0.20959665],\n",
       "       dtype=float32),\n",
       " 'student journalist': array([-1.00292   ,  0.62444   ,  0.026182  , -0.64785   ,  0.49962997,\n",
       "         0.1632235 , -0.838055  ,  0.044295  , -0.08892   ,  0.0800965 ,\n",
       "         0.18496001, -0.44481498, -0.178597  ,  0.305582  ,  0.60564   ,\n",
       "        -1.07617   , -0.261215  ,  0.44475   ,  0.04796001,  0.71842   ,\n",
       "         0.471305  ,  1.405755  , -0.06635   ,  0.89517   ,  0.23950998,\n",
       "        -1.8393    , -0.06103501, -0.95493007, -0.86882997,  0.41107702,\n",
       "         2.0249    , -0.64699   , -0.18775001, -0.99512494, -0.33078498,\n",
       "         0.328235  , -0.177295  , -0.03459   ,  0.4139455 ,  0.0312    ,\n",
       "         0.41000998,  0.58646303,  0.02234501, -0.18646498,  0.31078652,\n",
       "        -0.914345  , -0.266685  ,  0.0558955 ,  0.16071701,  0.25073   ],\n",
       "       dtype=float32),\n",
       " 'llb student journalist': array([-1.4309467 ,  0.58102   , -0.31316867, -0.70320994, -0.01571331,\n",
       "         0.3716123 , -0.09553667, -0.18852334, -0.11313999,  0.20102768,\n",
       "         0.2759    , -0.11371667, -0.107661  ,  0.00341799,  0.16801   ,\n",
       "        -0.7726433 , -0.5148433 ,  0.5653867 , -0.22216332,  0.88258   ,\n",
       "         0.70717   ,  1.2961701 , -0.06802333,  0.45423666,  0.15135165,\n",
       "        -1.7244667 ,  0.34931   , -0.8538167 , -0.67557335,  0.31057802,\n",
       "         1.31323   , -0.3874    , -0.34310332, -0.634449  , -0.09113667,\n",
       "         0.19679101,  0.04416665, -0.0212459 ,  0.38878033,  0.20418666,\n",
       "         0.54027   ,  0.40392032,  0.09792334,  0.34239003, -0.18717565,\n",
       "        -1.0600966 ,  0.1776767 ,  0.17451365,  0.29656467,  0.11289334],\n",
       "       dtype=float32),\n",
       " 'journalist': array([-0.93294 ,  0.30785 , -0.03254 , -0.2191  ,  0.5706  ,  0.22657 ,\n",
       "        -1.1653  ,  0.3382  ,  0.13099 , -0.035337,  0.17342 , -0.15811 ,\n",
       "        -0.45411 ,  0.67385 ,  1.0905  , -1.4285  , -0.14043 ,  0.1961  ,\n",
       "        -0.23364 ,  1.0344  ,  0.40776 ,  1.8937  ,  0.31283 ,  1.0723  ,\n",
       "         0.61537 , -1.988   , -0.28025 , -0.67316 , -0.50986 ,  0.88072 ,\n",
       "         1.2954  , -1.4807  , -0.1125  , -0.71105 , -0.83149 ,  0.24899 ,\n",
       "        -0.47707 , -0.18129 ,  0.044711,  0.026008,  1.2281  ,  1.2314  ,\n",
       "         0.32401 , -0.70328 ,  0.097733, -0.77999 , -0.80902 ,  0.075491,\n",
       "         0.27283 ,  0.21907 ], dtype=float32),\n",
       " 'internet operation': array([ 1.17415500e+00, -4.69939977e-01,  6.77500010e-01,  4.88705009e-01,\n",
       "        -3.79689991e-01, -3.22362006e-01, -6.49476528e-01, -4.68909979e-01,\n",
       "         1.01476002e+00,  2.36704975e-01, -9.04499739e-03,  1.38149783e-03,\n",
       "        -4.44404989e-01,  1.84787497e-01,  5.43399900e-02, -1.01130001e-01,\n",
       "        -4.19070005e-01,  1.72185004e-01, -1.38395011e-01,  2.82069981e-01,\n",
       "         7.97904968e-01, -9.28499997e-02, -1.68546498e-01,  4.24484968e-01,\n",
       "        -1.54585004e-01, -1.48740005e+00, -2.90419996e-01, -4.31646973e-01,\n",
       "         6.56535029e-01,  3.45599949e-02,  3.13645005e+00, -5.81049956e-02,\n",
       "        -4.31054980e-01, -3.33175004e-01, -6.07669532e-01,  2.45365009e-01,\n",
       "        -2.38885015e-01, -2.07135007e-01, -4.24309999e-01, -4.85000014e-03,\n",
       "         2.66144991e-01, -1.09991506e-01, -1.93398505e-01,  3.16214979e-01,\n",
       "         1.75668001e-01, -5.81079960e-01,  1.18587501e-01,  8.35314989e-02,\n",
       "         7.75049925e-02,  3.22674990e-01], dtype=float32),\n",
       " 'bank chinchwad': array([ 0.44583   ,  0.21459   ,  0.33790958,  0.183865  ,  0.2473    ,\n",
       "        -0.883485  ,  0.11735   ,  0.32389998, -0.74016   ,  0.29088998,\n",
       "         0.319995  ,  0.13751   ,  0.286485  , -0.32551   , -0.6368835 ,\n",
       "         1.0378749 ,  0.33189002,  0.282015  ,  0.322632  ,  0.81288505,\n",
       "         1.1748999 , -0.54214   , -0.392955  ,  0.17759   , -0.85604   ,\n",
       "        -0.2439    ,  0.59561   ,  0.327515  , -0.32120502, -0.247435  ,\n",
       "         1.08095   ,  0.068755  ,  0.65276504,  0.208365  ,  0.15903002,\n",
       "        -0.4119675 , -0.206395  , -0.260045  ,  0.680145  ,  0.02261999,\n",
       "         0.04516001, -0.740265  ,  0.157935  ,  0.162395  , -0.1724425 ,\n",
       "        -0.51409   , -0.633355  ,  0.4110785 ,  0.7401    , -0.25309998],\n",
       "       dtype=float32),\n",
       " 'family law': array([-0.03460002,  0.39672   , -0.76114   , -0.00670999,  0.905925  ,\n",
       "         0.811535  , -0.426615  , -0.377115  , -0.25679418, -0.524035  ,\n",
       "        -0.0773752 ,  0.54934   ,  0.187855  , -0.54690003,  0.62382996,\n",
       "         0.08881715, -0.4654665 , -0.1893    ,  0.40651   ,  0.1415485 ,\n",
       "         0.36311412,  0.35985   , -0.43658   ,  0.18158999, -0.18985501,\n",
       "        -2.19865   , -0.21029001, -0.9452    ,  0.08124499,  0.06532   ,\n",
       "         2.68285   , -0.508695  , -0.5768695 , -0.748565  ,  0.2327765 ,\n",
       "        -0.2276455 , -0.23809001, -0.18481   ,  0.48498002,  0.21479699,\n",
       "        -0.395585  ,  0.21444   ,  0.43343002,  0.82516   , -0.24246001,\n",
       "         0.039763  , -0.69624996, -0.61889   ,  0.0919815 ,  0.2072885 ],\n",
       "       dtype=float32),\n",
       " 'banks clients federal': array([ 0.506079  , -0.32811335,  0.58803004, -0.05981334, -0.09902034,\n",
       "        -0.09587333, -0.61756665,  0.07669633, -0.09757667, -0.30798832,\n",
       "        -0.39430535,  0.6329688 ,  0.16865534, -0.14994833,  0.29796997,\n",
       "         0.17666666, -0.04677   , -0.59392   ,  0.55597335, -0.57859665,\n",
       "         1.1578399 , -0.36895666, -0.13449301, -0.02030665, -1.00344   ,\n",
       "        -1.5240198 ,  0.21690635, -0.38070166, -0.33323666, -0.23281467,\n",
       "         2.9694998 ,  0.03057335,  0.52772   , -0.5442167 , -0.05715503,\n",
       "        -0.33809665, -0.514999  , -0.49191666,  0.63551337, -0.66447335,\n",
       "        -0.28764665,  0.09939233,  1.08137   ,  0.70411664, -0.33654332,\n",
       "        -0.45242333, -0.63849664,  0.46215668,  0.26593   ,  0.41778   ],\n",
       "       dtype=float32),\n",
       " 'bank': array([ 0.66488 , -0.11391 ,  0.67844 ,  0.17951 ,  0.6828  , -0.47787 ,\n",
       "        -0.30761 ,  0.17489 , -0.70512 , -0.55022 ,  0.1514  ,  0.10214 ,\n",
       "        -0.45063 , -0.33069 ,  0.056133,  1.2271  ,  0.55607 , -0.68297 ,\n",
       "         0.037364,  0.70266 ,  1.9093  , -0.61483 , -0.83329 , -0.3023  ,\n",
       "        -1.1118  , -1.55    ,  0.2604  ,  0.22957 , -1.0375  , -0.31789 ,\n",
       "         3.5091  , -0.25871 ,  1.0151  ,  0.65927 , -0.18231 , -0.75859 ,\n",
       "        -0.30927 , -0.91678 ,  1.0633  , -0.66761 , -0.37464 , -0.29143 ,\n",
       "         0.65606 , -0.44642 , -0.075495, -1.0552  , -0.60501 ,  0.73582 ,\n",
       "         1.0139  , -0.27749 ], dtype=float32),\n",
       " 'chinchwad pune': array([ 0.25154   ,  0.00980499, -0.19736539,  0.19793001, -0.49476   ,\n",
       "        -0.887035  ,  0.47556502,  0.23441324, -0.4139175 ,  0.65573   ,\n",
       "         0.260471  , -0.17467502,  0.50766766, -0.64722   , -1.1805    ,\n",
       "         0.56878495,  0.093582  ,  1.67555   , -0.31419998,  1.286905  ,\n",
       "         0.83405   , -0.183655  ,  0.07823   ,  0.99319   , -0.18415499,\n",
       "         0.18630996,  0.4274425 ,  0.09781001, -0.130475  , -0.535845  ,\n",
       "        -0.56757504,  0.53118   ,  0.342825  , -0.34174   ,  0.680115  ,\n",
       "         0.1520575 ,  0.00288   ,  0.269475  ,  0.358145  ,  0.772775  ,\n",
       "        -0.03311001, -0.92117   , -0.36207   ,  0.44721502, -0.38483998,\n",
       "         0.450075  , -0.2996035 ,  0.1436135 ,  0.87605   , -0.52816   ],\n",
       "       dtype=float32),\n",
       " 'banks': array([ 8.2710e-01, -4.8219e-01,  6.7966e-01, -2.6328e-01, -4.5816e-01,\n",
       "        -1.3364e-01, -7.3247e-01,  3.1852e-01, -4.6337e-01, -3.4561e-02,\n",
       "        -9.9436e-02,  1.0159e+00,  8.2146e-02, -1.4959e-01, -1.7242e-01,\n",
       "         7.8729e-01,  7.5757e-01, -8.5362e-01,  3.9677e-01, -3.7476e-01,\n",
       "         1.4576e+00, -7.3945e-01, -9.7499e-02, -1.3451e-01, -8.4306e-01,\n",
       "        -1.0507e+00,  2.5437e-01, -1.3626e-02, -4.8004e-01, -4.0503e-01,\n",
       "         3.5636e+00,  1.8436e-01,  1.5831e+00,  3.2820e-01,  7.0492e-04,\n",
       "        -5.0349e-01, -7.0440e-01, -7.7461e-01,  8.5403e-01, -8.4153e-01,\n",
       "        -6.8792e-01, -2.8112e-01,  1.2088e+00,  2.2196e-01, -1.0488e-01,\n",
       "        -5.1722e-01, -6.7723e-01,  3.1619e-01,  6.4571e-01, -2.9856e-01],\n",
       "       dtype=float32),\n",
       " 'banks scheduled banks': array([ 0.86951333, -0.09142667,  0.37546334,  0.13185002, -0.42501667,\n",
       "        -0.32232666, -0.7113967 ,  0.45196334, -0.36106333, -0.29529732,\n",
       "        -0.20366733,  0.48393002, -0.02741934,  0.14998667,  0.32891998,\n",
       "         0.65457666,  0.23665668, -0.71225   ,  0.03518333, -0.32053998,\n",
       "         1.3828667 , -0.56180334,  0.08345733, -0.08393034, -0.66438335,\n",
       "        -1.0375    ,  0.4787433 , -0.08211733, -0.55147004, -0.23164333,\n",
       "         3.3085668 ,  0.27599   ,  0.5699333 ,  0.19770102,  0.17814995,\n",
       "        -0.51282   , -0.06986666, -0.39893332,  0.36403334, -0.50897336,\n",
       "        -0.7636233 , -0.17178734,  0.7504633 ,  0.03145666, -0.13062   ,\n",
       "        -0.22082001, -0.45154238,  0.2626767 ,  0.27640668, -0.01286999],\n",
       "       dtype=float32),\n",
       " 'private banks clients': array([ 0.82703996,  0.09084333,  0.47253665, -0.04201   , -0.00659334,\n",
       "        -0.18572001, -0.92465335,  0.07169967, -0.17853665, -0.11794166,\n",
       "        -0.26298535,  0.75219995,  0.17651533, -0.10028333,  0.12729   ,\n",
       "         0.187254  ,  0.02464668, -0.21403666,  0.5617567 , -0.4724833 ,\n",
       "         1.3365332 ,  0.06292667, -0.20799632,  0.26658335, -0.8157833 ,\n",
       "        -1.0715866 ,  0.11513301, -0.546082  , -0.36706004, -0.14876667,\n",
       "         3.069833  ,  0.32017002,  0.82336664, -0.2693833 , -0.0779537 ,\n",
       "        -0.18019032, -0.62309   , -0.36069265,  0.40920666, -0.601     ,\n",
       "         0.09527668, -0.20503433,  0.8941633 ,  0.7700367 , -0.04890333,\n",
       "        -0.59282   , -0.8474333 ,  0.28623   ,  0.16332667,  0.37395   ],\n",
       "       dtype=float32),\n",
       " 'maharashtra university': array([-0.603256  , -0.08214998, -0.73986995,  0.08321501, -0.57051504,\n",
       "        -0.165825  , -0.08243999, -0.153075  , -0.1217575 , -0.313645  ,\n",
       "         0.13615   , -0.44273502,  0.44543448, -0.84434503, -0.5685    ,\n",
       "         0.28161   ,  0.25127998,  1.30015   , -0.67370003,  1.224055  ,\n",
       "         0.971755  , -0.03075501,  0.07308   ,  0.54414153,  0.202818  ,\n",
       "        -1.688555  , -0.063105  , -0.61582   , -1.84715   , -0.40379998,\n",
       "         1.7843951 ,  0.066228  ,  0.2763975 , -1.0859851 ,  0.1568525 ,\n",
       "         0.1465391 , -0.0633745 ,  0.54405   ,  1.0942299 ,  0.7677    ,\n",
       "        -0.67908   ,  0.206302  , -0.53888   ,  0.14964801, -0.57140696,\n",
       "         0.98254   , -0.356     ,  0.343835  ,  0.48222497, -0.504678  ],\n",
       "       dtype=float32),\n",
       " 'family': array([ 1.1636   ,  0.90386  , -0.74277  ,  0.49272  ,  1.88     ,\n",
       "         0.9285   , -1.0429   ,  0.01343  , -0.51903  , -0.68762  ,\n",
       "         0.0083396,  0.50754  ,  0.51792  , -0.75577  ,  1.0241   ,\n",
       "         0.1761   , -0.91889  ,  0.21455  ,  0.19232  ,  0.19562  ,\n",
       "         0.0044282,  0.40701  , -0.3812   ,  0.51286  ,  0.14278  ,\n",
       "        -1.4652   , -0.55194  , -0.8362   ,  0.41877  ,  0.40793  ,\n",
       "         2.5335   , -0.20787  ,  0.088761 , -0.10973  ,  0.52074  ,\n",
       "         0.026259 , -0.65515  , -0.1425   ,  0.44071  , -0.047796 ,\n",
       "        -0.23276  ,  0.17212  , -0.16844  ,  0.60122  ,  0.47156  ,\n",
       "         0.11336  , -0.96031  , -1.3412   , -0.021467 ,  0.049217 ],\n",
       "       dtype=float32),\n",
       " 'bank chinchwad pune': array([ 0.38932   , -0.03143333,  0.09456972,  0.19179   , -0.10224   ,\n",
       "        -0.75064665,  0.21450667,  0.21457215, -0.51098496,  0.25374666,\n",
       "         0.22411399, -0.08240334,  0.18823509, -0.54170996, -0.768289  ,\n",
       "         0.78822327,  0.24774468,  0.88937664, -0.19701199,  1.0921568 ,\n",
       "         1.1924666 , -0.32738   , -0.22561   ,  0.56136   , -0.49337003,\n",
       "        -0.39246002,  0.37176168,  0.14173001, -0.43281665, -0.46319333,\n",
       "         0.7913167 ,  0.26788333,  0.5669167 , -0.00807   ,  0.39264002,\n",
       "        -0.15149166, -0.10117   , -0.12594332,  0.5931967 ,  0.29264668,\n",
       "        -0.14695333, -0.7112567 , -0.02269334,  0.14933667, -0.281725  ,\n",
       "        -0.05168333, -0.4014057 ,  0.34101567,  0.922     , -0.44460332],\n",
       "       dtype=float32),\n",
       " 'law pune maharashtra': array([-0.35160398, -0.69659996, -0.62128335,  0.10916001, -0.5342067 ,\n",
       "         0.13727333,  0.44943   , -0.19737451,  0.00929053, -0.15777999,\n",
       "        -0.11978934, -0.10421336,  0.23698175, -0.6086767 , -0.49069667,\n",
       "         0.19388811,  0.22696699,  1.0320166 , -0.30676666,  0.8977957 ,\n",
       "         1.0635667 , -0.12269667, -0.04257999,  0.7564733 ,  0.06378   ,\n",
       "        -1.48633   , -0.06362167, -0.55158335, -0.8282067 , -0.35682333,\n",
       "         1.3327134 ,  0.01294   , -0.06597664, -0.5838367 ,  0.39290103,\n",
       "         0.05755333,  0.089007  , -0.08008666,  0.46603665,  0.72679   ,\n",
       "        -0.7764633 ,  0.02587332, -0.04493666,  0.36487865, -0.83418995,\n",
       "         0.7733653 , -0.48246565,  0.2431    ,  0.99960995, -0.5025167 ],\n",
       "       dtype=float32),\n",
       " 'pune advocate': array([-3.14689994e-01, -2.39004493e-01, -5.27125001e-01, -3.58635008e-01,\n",
       "        -3.37260008e-01,  9.23998654e-03,  2.12678000e-01, -1.06311746e-01,\n",
       "         3.20092499e-01,  2.73135006e-01, -3.84639986e-02, -2.80657530e-01,\n",
       "         1.33342654e-01, -5.33041000e-01, -9.09910023e-01, -1.39380008e-01,\n",
       "         3.34206998e-01,  1.01074851e+00, -2.40134984e-01,  9.24234986e-01,\n",
       "         5.39209962e-01,  2.17430010e-01, -3.84900004e-01,  6.64308608e-01,\n",
       "         1.98255002e-01, -1.26368999e+00,  9.05575007e-02, -5.68639994e-01,\n",
       "        -4.95535016e-01,  8.85950029e-02,  1.01672506e+00,  1.81940004e-01,\n",
       "        -1.47160009e-01, -6.83929980e-01,  1.89989805e-04, -1.47534996e-01,\n",
       "        -8.77999887e-03,  1.13948502e-01,  1.18009992e-01,  3.43250006e-01,\n",
       "        -2.35165507e-01, -1.89990014e-01, -3.35604995e-01,  2.23594993e-01,\n",
       "        -1.01998448e-03,  3.54490012e-01, -4.43938494e-01, -3.77949923e-02,\n",
       "         6.50051475e-01,  1.19794965e-01], dtype=float32),\n",
       " 'banks private banks': array([ 0.85824996, -0.16057667,  0.50166005, -0.25783333, -0.23525001,\n",
       "        -0.14521666, -0.90318006,  0.15816666, -0.24909668, -0.098674  ,\n",
       "        -0.13750066,  0.7931967 ,  0.16195066, -0.06632   , -0.17941666,\n",
       "         0.5038673 ,  0.43491337, -0.36856666,  0.43111667, -0.30491665,\n",
       "         1.4404334 , -0.29334334, -0.28004268, -0.00669333, -0.81988335,\n",
       "        -1.1295666 ,  0.22309335, -0.19065732, -0.49016333, -0.16865332,\n",
       "         3.4699001 ,  0.20251667,  1.1377    ,  0.03383334, -0.02778872,\n",
       "        -0.30585697, -0.55417997, -0.543176  ,  0.64697   , -0.61323   ,\n",
       "        -0.41502333, -0.31305668,  0.92259663,  0.41469   , -0.15621333,\n",
       "        -0.58794004, -0.8830867 ,  0.24914335,  0.47153333, -0.05583666],\n",
       "       dtype=float32),\n",
       " 'pune': array([ 0.2763   , -0.52348  , -0.39211  ,  0.20764  , -0.80132  ,\n",
       "        -0.48497  ,  0.40882  , -0.0040835, -0.052635 ,  0.17946  ,\n",
       "         0.032352 , -0.52223  , -0.0082647, -0.97411  , -1.0311   ,\n",
       "         0.28892  ,  0.079454 ,  2.1041   , -1.2363   ,  1.6507   ,\n",
       "         1.2276   ,  0.10214  ,  0.10908  ,  1.3289   ,  0.23197  ,\n",
       "        -0.68958  , -0.075935 , -0.22984  , -0.65604  , -0.89471  ,\n",
       "         0.21205  ,  0.66614  ,  0.39522  , -0.44094  ,  0.85986  ,\n",
       "         0.36946  ,  0.10928  ,  0.14226  ,  0.4193   ,  0.8327   ,\n",
       "        -0.53118  , -0.65324  , -0.38395  ,  0.12322  , -0.50029  ,\n",
       "         0.87313  ,  0.062493 ,  0.20089  ,  1.2858   , -0.82761  ],\n",
       "       dtype=float32),\n",
       " 'forward thinking': array([-0.086157  , -0.149389  ,  0.03177501, -0.710855  ,  0.83675504,\n",
       "        -0.08446684, -0.61095   , -0.0543495 , -0.61467004,  0.2751765 ,\n",
       "         0.19387498,  0.544435  , -1.27661   ,  0.42908502, -0.15686001,\n",
       "         0.07888   ,  0.581705  , -0.291425  , -0.146815  , -0.477675  ,\n",
       "        -0.498535  ,  0.53178   , -0.098665  , -0.11743499,  0.76222   ,\n",
       "        -1.3898001 ,  0.284175  ,  0.13467   ,  0.4873748 , -0.752375  ,\n",
       "         2.72255   ,  0.64968   , -0.472335  , -0.76596   , -0.12404999,\n",
       "        -0.203099  , -0.256663  ,  0.602     ,  0.127515  , -0.190055  ,\n",
       "        -0.2346205 , -0.17546001, -0.13040501,  0.02572   ,  0.00540501,\n",
       "         0.1085595 ,  0.62599   ,  0.368455  ,  0.208565  ,  0.3245505 ],\n",
       "       dtype=float32),\n",
       " 'knowledge typing': array([ 2.5077149e-01, -1.3791999e-01, -9.2160001e-02, -1.8500200e-01,\n",
       "         5.9910500e-01,  2.7749967e-04,  2.8428501e-01, -2.7771500e-01,\n",
       "         3.9111000e-01,  3.6198002e-01,  2.1103501e-01,  6.8159497e-01,\n",
       "        -9.7199976e-03, -4.0065613e-01, -3.2398000e-01, -3.4741950e-01,\n",
       "        -6.5504497e-01,  2.0624700e-01,  6.5169001e-01, -3.0799299e-01,\n",
       "         5.5229999e-02,  3.2169849e-01,  3.5602999e-01,  2.4233001e-01,\n",
       "         1.2368250e+00, -8.2653499e-01, -5.8369499e-01, -5.9697497e-01,\n",
       "         1.7692100e-01, -4.8659450e-01,  2.1979501e+00, -1.2601000e-01,\n",
       "        -1.3958400e-01, -8.4576952e-01,  1.0252650e-01,  5.3217500e-01,\n",
       "        -2.8671998e-01,  5.8363497e-01,  2.0200551e-01,  1.6069500e-01,\n",
       "         7.6832002e-01, -5.0031000e-01, -3.4839347e-01,  1.3320999e+00,\n",
       "         2.7000010e-03, -3.3235002e-01,  1.2796750e+00,  6.4500248e-01,\n",
       "         3.2797500e-01, -1.3128000e-01], dtype=float32),\n",
       " 'grasping quality skillful': array([-0.13796668, -0.50486666, -0.44651   , -0.35194233,  0.41068998,\n",
       "        -0.01490333,  0.28071666, -0.013876  ,  0.24813998,  0.592469  ,\n",
       "         0.15173568,  0.33121002,  0.08420666,  0.28672335, -0.533209  ,\n",
       "        -0.38044134,  0.40273234,  0.45216003,  0.41727003, -0.9687433 ,\n",
       "        -0.17206667,  0.43759   , -0.06651334, -0.29843333,  0.6774667 ,\n",
       "        -0.48544335, -0.331413  ,  0.16114333,  0.34224668, -0.05570202,\n",
       "         1.5326934 ,  0.31758   ,  0.19777334, -0.37111866,  0.14596999,\n",
       "         0.6177767 , -0.46775088,  0.8731534 , -0.24329133, -0.39147   ,\n",
       "         0.153228  , -0.09882501,  0.050559  ,  0.67234993,  0.16636   ,\n",
       "        -0.25059667,  0.30821523,  0.6829233 ,  0.16018699, -0.06835001],\n",
       "       dtype=float32),\n",
       " 'thinking individual refined': array([-1.3885133e-01, -6.5010011e-02, -2.0396334e-01, -3.3252335e-01,\n",
       "         6.5117997e-01,  2.8437331e-01,  1.0315899e-01, -4.1100398e-01,\n",
       "         1.6336650e-02,  5.7999998e-01,  2.5716567e-01,  1.4939667e-01,\n",
       "        -7.2409987e-02,  1.3326705e-03,  2.1990533e-01,  1.1641001e-01,\n",
       "         1.9648667e-01,  3.1560001e-01,  1.5218966e-01, -9.0466332e-01,\n",
       "         2.1712331e-01,  5.0616670e-02,  2.8998667e-01, -3.9240003e-02,\n",
       "         5.4997998e-01, -8.3063656e-01, -6.5984666e-01, -1.5518667e-01,\n",
       "         6.0190332e-01, -2.4900038e-03,  2.4322667e+00,  1.2150333e-01,\n",
       "         2.2157660e-02, -5.2998263e-01,  1.1689999e-01, -1.2891068e-01,\n",
       "        -3.0196333e-01,  9.0992337e-01, -3.5287666e-01,  1.2929996e-02,\n",
       "        -4.0133297e-03, -5.2562517e-01,  4.2905000e-01,  6.5231329e-01,\n",
       "         1.4446667e-01,  7.0203327e-02,  1.1130667e-01,  2.3538665e-01,\n",
       "        -1.5420668e-01,  1.6613966e-01], dtype=float32),\n",
       " 'court solapur': array([-0.29126   , -0.724125  , -0.84019005,  0.58008504, -0.22099501,\n",
       "        -0.138204  ,  1.042055  ,  0.66366553,  0.12549001, -0.351805  ,\n",
       "        -0.5378    , -0.515175  , -0.08044501, -0.904065  , -0.02359504,\n",
       "        -0.05904999, -0.62857497,  0.3183    ,  0.1430421 ,  0.17096499,\n",
       "         0.71076   , -0.19287997,  0.053295  ,  0.79899746, -0.62699   ,\n",
       "        -1.9116349 ,  0.347045  ,  0.19413501, -0.5633    , -0.79770005,\n",
       "         1.09074   , -0.28489003, -0.01129001, -0.58001   ,  0.333     ,\n",
       "        -0.21467   ,  0.38265502,  0.10490914, -0.10434701,  0.21356499,\n",
       "        -0.07178   ,  0.276445  ,  0.22064999,  0.531075  , -0.650195  ,\n",
       "         0.26028   , -0.3568525 , -0.325755  ,  0.70508003, -0.27803499],\n",
       "       dtype=float32),\n",
       " 'work': array([ 5.1359e-01,  1.9695e-01, -5.1944e-01, -8.6218e-01,  1.5494e-02,\n",
       "         1.0973e-01, -8.0293e-01, -3.3361e-01, -1.6119e-04,  1.0189e-02,\n",
       "         4.6734e-02,  4.6751e-01, -4.7475e-01,  1.1038e-01,  3.9327e-01,\n",
       "        -4.3652e-01,  3.9984e-01,  2.7109e-01,  4.2650e-01, -6.0640e-01,\n",
       "         8.1145e-01,  4.5630e-01, -1.2726e-01, -2.2474e-01,  6.4071e-01,\n",
       "        -1.2767e+00, -7.2231e-01, -6.9590e-01,  2.8045e-02, -2.3072e-01,\n",
       "         3.7996e+00, -1.2625e-01, -4.7967e-01, -9.9972e-01, -2.1976e-01,\n",
       "         5.0565e-01,  2.5953e-02,  8.0514e-01,  1.9929e-01,  2.8796e-01,\n",
       "        -1.5915e-01, -3.0438e-01,  1.6025e-01, -1.8290e-01, -3.8563e-02,\n",
       "        -1.7619e-01,  2.7041e-02,  4.6842e-02, -6.2897e-01,  3.5726e-01],\n",
       "       dtype=float32),\n",
       " 'individual refined interpersonal': array([ 3.0330012e-02,  2.3579665e-01, -3.9504004e-01,  1.6842668e-01,\n",
       "         4.2315832e-01,  7.2730333e-01,  7.4197561e-01, -4.1579399e-01,\n",
       "         1.3709998e-01,  5.8820337e-01,  4.6830568e-01,  1.6816668e-01,\n",
       "        -5.2900114e-03, -1.5813734e-01, -1.4845799e-01, -1.9863248e-03,\n",
       "        -2.5956670e-02,  4.2092332e-01,  3.9429966e-01, -7.1479672e-01,\n",
       "         3.1130365e-01,  2.5237671e-01,  1.5590332e-01, -5.1123340e-02,\n",
       "         3.6197332e-01, -1.1908666e-01, -2.6840335e-01, -4.9535000e-01,\n",
       "         6.4524335e-01,  4.3366668e-01,  2.0548334e+00,  2.2085001e-01,\n",
       "         3.1267765e-01, -7.2020602e-01,  2.1361667e-01,  1.3726600e-01,\n",
       "        -6.6962332e-01,  5.9231001e-01, -4.1272101e-01,  2.4504666e-01,\n",
       "         1.6003333e-01, -5.0818515e-01,  4.9394503e-01,  7.1926004e-01,\n",
       "         5.5005997e-02, -2.5450668e-01,  4.3998000e-01,  4.3181333e-01,\n",
       "        -3.3710337e-01,  2.2292633e-01], dtype=float32),\n",
       " 'session court solapur': array([-4.1398335e-01, -5.9801334e-01, -8.6024666e-01,  4.4322333e-01,\n",
       "         6.2243301e-02, -3.7389600e-01,  5.6964666e-01,  6.2254703e-01,\n",
       "         8.6166663e-03, -2.9584333e-01, -5.5603331e-01, -6.6644335e-01,\n",
       "        -2.6821670e-01, -2.2324336e-01,  1.2818663e-01, -1.6330667e-01,\n",
       "        -7.3532337e-01, -2.0166667e-01, -1.5605261e-02, -1.5720999e-01,\n",
       "         9.2397332e-01, -8.8332099e-04,  1.7482333e-01,  4.2199168e-01,\n",
       "        -3.7233666e-01, -1.4090233e+00,  4.6388331e-01,  1.2434801e-01,\n",
       "        -6.9287664e-01, -2.4372667e-01,  1.8343935e+00, -1.6845769e-01,\n",
       "        -2.3114665e-01, -2.4988668e-01,  3.5022330e-01, -4.2916667e-01,\n",
       "         4.6862665e-01,  2.0372944e-01, -1.4644466e-01,  9.8376654e-02,\n",
       "        -3.9855328e-01,  1.5628566e-01, -2.7640000e-01,  3.1653002e-01,\n",
       "        -3.8361669e-01,  2.9370666e-01, -1.7878331e-02, -1.5862000e-01,\n",
       "         4.6862817e-01, -1.2837000e-01], dtype=float32),\n",
       " 'grasping quality': array([-0.12097001, -0.18609999, -0.29415   , -0.29157352,  0.5337    ,\n",
       "         0.08303   ,  0.19103   , -0.304759  ,  0.557835  ,  0.40124348,\n",
       "         0.21360001,  0.34031   ,  0.005945  ,  0.08436   , -0.4002085 ,\n",
       "        -0.037862  ,  0.13621351,  0.39042503,  0.528285  , -0.97465   ,\n",
       "        -0.09313   ,  0.37808   ,  0.228335  , -0.353165  ,  0.542895  ,\n",
       "        -0.51242   , -0.4775345 ,  0.460785  ,  0.43155003, -0.31648803,\n",
       "         1.853835  ,  0.56821   ,  0.36401   , -0.45165798,  0.09542999,\n",
       "         0.501595  , -0.38333133,  0.70873   , -0.030657  , -0.379835  ,\n",
       "         0.027107  , -0.32699752,  0.11570501,  0.63690996,  0.139455  ,\n",
       "        -0.02702501,  0.2911128 ,  0.603475  ,  0.20217499, -0.21630502],\n",
       "       dtype=float32),\n",
       " 'quality skillful': array([-0.425215  , -0.43716502, -0.581975  , -0.1898185 ,  0.15028   ,\n",
       "        -0.227025  ,  0.12095501, -0.05127501,  0.43277496,  0.89857495,\n",
       "         0.32989353,  0.34539002,  0.31293   ,  0.185875  , -0.4390135 ,\n",
       "        -0.508632  ,  0.4773835 ,  0.483505  ,  0.431735  , -1.146515  ,\n",
       "        -0.10556   ,  0.38079   , -0.26654   , -0.330825  ,  0.53613997,\n",
       "        -0.569325  , -0.505325  , -0.07858001,  0.34752002,  0.278157  ,\n",
       "         2.2216551 ,  0.10389   ,  0.235995  , -0.565925  ,  0.44692498,\n",
       "         0.72534   , -0.31572634,  1.05572   , -0.37393352, -0.48885   ,\n",
       "         0.208554  ,  0.13606751,  0.17973351,  0.473425  ,  0.052625  ,\n",
       "        -0.26226002,  0.1724928 ,  0.831855  ,  0.1659755 ,  0.31903   ],\n",
       "       dtype=float32),\n",
       " 'refined interpersonal multitasking': array([ 0.34655666, -0.47439   , -0.38990334,  0.19034667, -0.00739166,\n",
       "         0.36605   ,  0.68620235, -0.8546634 ,  0.01062   ,  0.91520995,\n",
       "         0.37516332,  0.54041666, -0.00489   , -0.17129926, -0.45124802,\n",
       "         0.17406501, -0.31879666,  0.8228    ,  0.46095666, -0.28963515,\n",
       "        -0.04206634,  0.49813   , -0.14296666, -0.18917334,  0.6070233 ,\n",
       "         0.23921669,  0.21440999, -0.33394334,  1.0689334 ,  0.5780466 ,\n",
       "         1.1350534 ,  0.03941667,  0.05491332, -0.464543  ,  0.28745964,\n",
       "         0.266704  , -0.6914146 ,  0.2474    , -0.510601  ,  0.24138002,\n",
       "         0.01917667, -0.30641517, -0.03291833,  0.75862664,  0.24881601,\n",
       "        -0.17316668,  0.81637335,  0.3817651 , -0.23123665,  0.5804333 ],\n",
       "       dtype=float32),\n",
       " 'solapur': array([-0.22807 , -0.89644 , -0.65638 ,  0.56633 , -1.0211  , -0.21462 ,\n",
       "         1.3551  ,  0.061431,  0.38471 , -0.36172 , -0.61728 , -0.70282 ,\n",
       "         0.34548 , -1.4051  , -1.0139  , -0.34901 , -0.65593 ,  1.5971  ,\n",
       "         0.27723 ,  0.78185 ,  0.55353 , -0.92089 ,  0.48209 ,  1.4991  ,\n",
       "        -0.29536 , -0.76577 ,  0.55921 ,  0.66601 , -0.24678 , -1.1173  ,\n",
       "        -0.40652 ,  0.47142 ,  0.74209 , -0.3233  ,  0.29551 ,  0.1522  ,\n",
       "        -0.18024 ,  0.21811 , -0.26012 ,  0.7984  ,  0.01083 , -0.11935 ,\n",
       "        -0.12939 ,  0.25676 , -0.59169 ,  0.23262 , -0.084845, -0.18035 ,\n",
       "         1.2798  , -0.22084 ], dtype=float32),\n",
       " 'company district session': array([-1.0418668e-01, -5.2003330e-01, -3.3135664e-01,  4.4925001e-01,\n",
       "         3.0467099e-01, -3.7559667e-01, -4.8477998e-01,  1.9867001e-01,\n",
       "         2.7618331e-01, -4.8600671e-01, -2.9639998e-01, -6.8725330e-01,\n",
       "        -3.8174668e-01, -3.0463335e-01, -3.1284671e-02,  1.0699928e-03,\n",
       "        -2.9955998e-01,  3.8939994e-02, -1.0898665e-01, -3.5041332e-01,\n",
       "         1.2551067e+00, -1.7930667e-01, -1.5806001e-01,  1.2052668e-01,\n",
       "        -6.2437660e-01, -1.2712667e+00, -1.3295668e-01, -3.3632003e-02,\n",
       "        -7.7760267e-01, -9.2206679e-02,  3.1855001e+00, -5.0373429e-01,\n",
       "         4.0593326e-02, -5.8466694e-03,  5.2771699e-01, -5.9568864e-01,\n",
       "         2.8625667e-01,  3.8893333e-01,  4.0007338e-01,  2.3973336e-02,\n",
       "        -5.8715993e-01,  1.0839901e-01, -3.9249334e-01, -4.1300002e-01,\n",
       "        -5.0360996e-01,  1.3572834e-01, -3.7090334e-01, -1.9276667e-01,\n",
       "         2.3239146e-01,  5.3643662e-01], dtype=float32),\n",
       " 'district session': array([-4.69195008e-01, -4.91535008e-01, -7.02849984e-01,  2.39814997e-01,\n",
       "         4.98555005e-01, -6.96169972e-01,  1.28300041e-02,  4.91964996e-01,\n",
       "         2.00639993e-01, -7.87410021e-01, -8.36549997e-01, -1.43763995e+00,\n",
       "        -1.78805009e-01, -1.29750013e-01,  2.68000364e-03, -1.01685002e-01,\n",
       "        -8.27649832e-02, -3.73680025e-01, -2.88949907e-02, -5.66599965e-02,\n",
       "         9.23159957e-01, -1.81594998e-01,  7.24350065e-02,  4.32539999e-01,\n",
       "        -4.69514996e-01, -1.06914997e+00,  1.97560012e-01,  1.09566994e-01,\n",
       "        -1.13256502e+00, -7.65900016e-02,  3.13280010e+00, -5.39846480e-01,\n",
       "        -1.33059993e-01, -2.02435002e-01,  8.00785005e-01, -4.02577996e-01,\n",
       "         6.95330024e-01,  3.84069979e-01,  1.52850002e-01,  2.40775004e-01,\n",
       "        -1.17534995e+00,  4.24483508e-01, -7.28204966e-01, -4.90669996e-01,\n",
       "        -6.03129983e-01,  2.36745000e-01,  4.69499826e-03, -3.97060007e-01,\n",
       "         4.26517189e-01,  4.84564990e-01], dtype=float32),\n",
       " 'court': array([-0.35445  , -0.55181  , -1.024    ,  0.59384  ,  0.57911  ,\n",
       "        -0.061788 ,  0.72901  ,  1.2659   , -0.13373  , -0.34189  ,\n",
       "        -0.45832  , -0.32753  , -0.50637  , -0.40303  ,  0.96671  ,\n",
       "         0.23091  , -0.60122  , -0.9605   ,  0.0088542, -0.43992  ,\n",
       "         0.86799  ,  0.53513  , -0.3755   ,  0.098895 , -0.95862  ,\n",
       "        -3.0575   ,  0.13488  , -0.27774  , -0.87982  , -0.4781   ,\n",
       "         2.588    , -1.0412   , -0.76467  , -0.83672  ,  0.37049  ,\n",
       "        -0.58154  ,  0.94555  , -0.0082917,  0.051426 , -0.37127  ,\n",
       "        -0.15439  ,  0.67224  ,  0.57069  ,  0.80539  , -0.7087   ,\n",
       "         0.28794  , -0.62886  , -0.47116  ,  0.13036  , -0.33523  ],\n",
       "       dtype=float32),\n",
       " 'assistance': array([ 1.0603   , -0.00358  ,  0.19913  , -0.47867  ,  0.22712  ,\n",
       "        -0.07895  , -0.073569 ,  0.20873  ,  1.2049   , -0.48919  ,\n",
       "         0.20163  ,  0.48798  ,  0.72553  , -0.60995  ,  0.034981 ,\n",
       "        -0.45106  ,  0.34116  ,  0.14434  ,  1.102    ,  0.32146  ,\n",
       "         0.41441  ,  0.47278  , -0.54738  , -0.70964  ,  0.13742  ,\n",
       "        -1.0253   ,  0.80988  , -1.1551   , -0.1258   ,  0.15007  ,\n",
       "         3.2658   ,  0.78545  , -0.54558  , -0.50242  , -0.0082065,\n",
       "         0.90215  ,  0.082199 , -0.42365  ,  0.087276 , -0.29254  ,\n",
       "        -0.088295 ,  0.11749  ,  0.88427  , -0.85118  , -0.36069  ,\n",
       "        -0.28476  , -0.54633  ,  1.2059   , -0.2927   ,  0.50238  ],\n",
       "       dtype=float32),\n",
       " 'good': array([-3.5586e-01,  5.2130e-01, -6.1070e-01, -3.0131e-01,  9.4862e-01,\n",
       "        -3.1539e-01, -5.9831e-01,  1.2188e-01, -3.1943e-02,  5.5695e-01,\n",
       "        -1.0621e-01,  6.3399e-01, -4.7340e-01, -7.5895e-02,  3.8247e-01,\n",
       "         8.1569e-02,  8.2214e-01,  2.2220e-01, -8.3764e-03, -7.6620e-01,\n",
       "        -5.6253e-01,  6.1759e-01,  2.0292e-01, -4.8598e-02,  8.7815e-01,\n",
       "        -1.6549e+00, -7.7418e-01,  1.5435e-01,  9.4823e-01, -3.9520e-01,\n",
       "         3.7302e+00,  8.2855e-01, -1.4104e-01,  1.6395e-02,  2.1115e-01,\n",
       "        -3.6085e-02, -1.5587e-01,  8.6583e-01,  2.6309e-01, -7.1015e-01,\n",
       "        -3.6770e-02,  1.8282e-03, -1.7704e-01,  2.7032e-01,  1.1026e-01,\n",
       "         1.4133e-01, -5.7322e-02,  2.7207e-01,  3.1305e-01,  9.2771e-01],\n",
       "       dtype=float32),\n",
       " 'forward thinking individual': array([-0.09561467,  0.14422733,  0.06046334, -0.35216   ,  0.88707334,\n",
       "         0.21217543, -0.20233667, -0.05336367, -0.20751004,  0.10114434,\n",
       "         0.13761233,  0.27188003, -0.76672333,  0.30034602,  0.00811666,\n",
       "        -0.13024333,  0.51746   , -0.23949333, -0.07474367, -0.74254996,\n",
       "        -0.2397    ,  0.20879   ,  0.00938667,  0.01551667,  0.53077   ,\n",
       "        -1.1546401 , -0.05547333, -0.32978666,  0.2470932 , -0.5867533 ,\n",
       "         2.8899667 ,  0.67149   , -0.30553567, -0.7791133 , -0.16064332,\n",
       "        -0.123844  , -0.12354533,  0.5584767 , -0.14885333, -0.21832667,\n",
       "        -0.12307033, -0.4531733 ,  0.19037999,  0.5076467 , -0.03090999,\n",
       "        -0.094807  ,  0.33161664,  0.29855332,  0.05866667,  0.22576   ],\n",
       "       dtype=float32),\n",
       " 'session': array([-0.65943  , -0.34579  , -0.90036  ,  0.1695   ,  0.62872  ,\n",
       "        -0.84528  , -0.37517  ,  0.54031  , -0.22513  , -0.18392  ,\n",
       "        -0.5925   , -0.96898  , -0.64376  ,  1.1384   ,  0.43175  ,\n",
       "        -0.37182  , -0.94882  , -1.2416   , -0.3329   , -0.81356  ,\n",
       "         1.3504   ,  0.38311  ,  0.41788  , -0.33202  ,  0.13697  ,\n",
       "        -0.4038   ,  0.69756  , -0.015226 , -0.95203  ,  0.86422  ,\n",
       "         3.3217   ,  0.064407 , -0.67086  ,  0.41036  ,  0.38467  ,\n",
       "        -0.85816  ,  0.64057  ,  0.40137  , -0.23064  , -0.132    ,\n",
       "        -1.0521   , -0.084033 , -1.2705   , -0.11256  ,  0.14954  ,\n",
       "         0.36056  ,  0.66007  ,  0.17565  , -0.0042756,  0.17096  ],\n",
       "       dtype=float32),\n",
       " 'law solapur': array([-7.30435014e-01, -5.03430009e-01, -7.17944980e-01,  3.00950110e-02,\n",
       "        -5.44625044e-01,  2.39975005e-01,  7.72385001e-01, -3.53114516e-01,\n",
       "         1.95075810e-01, -3.61084998e-01, -3.90184999e-01, -5.58400154e-02,\n",
       "         1.01634994e-01, -8.71564984e-01, -3.95170033e-01, -1.73737839e-01,\n",
       "        -3.33986491e-01,  5.01975000e-01,  4.48965013e-01,  4.34663475e-01,\n",
       "         6.37665033e-01, -3.04099977e-01, -4.93499637e-03,  6.74709976e-01,\n",
       "        -4.08924997e-01, -1.84893501e+00,  3.45284998e-01, -1.94095016e-01,\n",
       "        -2.51529992e-01, -6.97295010e-01,  1.21284008e+00, -1.69050008e-01,\n",
       "        -2.50204980e-01, -8.55350018e-01,  1.20161496e-01, -1.64674997e-01,\n",
       "        -6.35005534e-04, -4.50500101e-03,  1.34565011e-01,  6.37894988e-01,\n",
       "        -2.73790002e-01,  6.87050000e-02,  4.52955008e-01,  6.52930021e-01,\n",
       "        -7.74085045e-01,  9.93930027e-02, -2.58517504e-01, -3.84650044e-02,\n",
       "         7.42615044e-01,  7.22599924e-02], dtype=float32),\n",
       " 'company district': array([ 1.73435003e-01, -6.07154965e-01, -4.68549877e-02,  5.89125037e-01,\n",
       "         1.42646492e-01, -1.40755013e-01, -5.39584994e-01,  2.78500021e-02,\n",
       "         5.26839972e-01, -6.37050033e-01, -1.48350000e-01, -5.46389937e-01,\n",
       "        -2.50739992e-01, -1.02614999e+00, -2.62802005e-01,  1.87514991e-01,\n",
       "         2.50700116e-02,  6.79210007e-01,  2.97001004e-03, -1.18839979e-01,\n",
       "         1.20746005e+00, -4.60514992e-01, -4.46030021e-01,  3.46800029e-01,\n",
       "        -1.00504994e+00, -1.70500004e+00, -5.48215032e-01, -4.28350046e-02,\n",
       "        -6.90388978e-01, -5.70420027e-01,  3.11740017e+00, -7.87804961e-01,\n",
       "         3.96319985e-01, -2.13950008e-01,  5.99240482e-01, -4.64452982e-01,\n",
       "         1.09100014e-01,  3.82714987e-01,  7.15430021e-01,  1.01960003e-01,\n",
       "        -3.54689986e-01,  2.04615027e-01,  4.65100035e-02, -5.63220024e-01,\n",
       "        -8.30184996e-01,  2.33125016e-02, -8.86389971e-01, -3.76975000e-01,\n",
       "         3.50724995e-01,  7.19174981e-01], dtype=float32),\n",
       " 'join progressive organization': array([-0.15607937,  0.16819601, -0.18806   , -0.04694453, -0.01484966,\n",
       "         0.24214335, -0.576317  , -0.08318668, -0.10482669, -0.09942999,\n",
       "         0.52180666, -0.13097735, -0.19338   ,  0.6254167 , -0.6533833 ,\n",
       "        -0.19398166,  0.45808336, -0.03783999,  0.383953  ,  0.14387   ,\n",
       "        -0.260779  , -0.09543133, -0.048164  ,  0.23157091, -0.37203994,\n",
       "        -1.1708432 , -0.01598732, -0.83389   , -0.83511335,  0.17726535,\n",
       "         2.8490667 ,  0.45719668, -0.88259   , -0.61045   , -0.8826534 ,\n",
       "        -0.54501003, -0.01859666, -0.193147  , -0.12087067, -0.13078   ,\n",
       "        -0.38399532, -0.5681767 , -0.5213764 , -0.09193634,  0.19770001,\n",
       "         0.0471    , -0.88160676,  0.06342334, -0.22867668,  0.24652   ],\n",
       "       dtype=float32),\n",
       " 'district session court': array([-0.43094668, -0.51162666, -0.8099    ,  0.35782334,  0.52540666,\n",
       "        -0.4847093 ,  0.25155666,  0.7499433 ,  0.08918333, -0.6389033 ,\n",
       "        -0.7104733 , -1.0676032 , -0.28799334, -0.22084336,  0.32402334,\n",
       "         0.00918   , -0.25558332, -0.56928664, -0.01631193, -0.18441333,\n",
       "         0.90476996,  0.05731334, -0.07687666,  0.321325  , -0.63255   ,\n",
       "        -1.7319332 ,  0.17666668, -0.01953534, -1.0483166 , -0.21042667,\n",
       "         2.9512002 , -0.7069643 , -0.34359667, -0.41386333,  0.65735334,\n",
       "        -0.462232  ,  0.77873665,  0.25328276,  0.119042  ,  0.03676   ,\n",
       "        -0.83503   ,  0.50706905, -0.29523998, -0.05865   , -0.63831997,\n",
       "         0.25381002, -0.20649   , -0.42176   ,  0.32779813,  0.2113    ],\n",
       "       dtype=float32),\n",
       " 'session court': array([-0.50694   , -0.44880003, -0.96218   ,  0.38167   ,  0.603915  ,\n",
       "        -0.453534  ,  0.17692   ,  0.903105  , -0.17943001, -0.262905  ,\n",
       "        -0.52541   , -0.648255  , -0.575065  ,  0.36768496,  0.69922996,\n",
       "        -0.070455  , -0.77502   , -1.10105   , -0.16202289, -0.62674   ,\n",
       "         1.109195  ,  0.45912   ,  0.02119   , -0.11656251, -0.410825  ,\n",
       "        -1.73065   ,  0.41622   , -0.146483  , -0.915925  ,  0.19306001,\n",
       "         2.9548502 , -0.48839653, -0.717765  , -0.21317999,  0.37758   ,\n",
       "        -0.71985   ,  0.79306   ,  0.19653915, -0.089607  , -0.25163502,\n",
       "        -0.60324496,  0.2941035 , -0.34990498,  0.346415  , -0.27958   ,\n",
       "         0.32424998,  0.015605  , -0.147755  ,  0.0630422 , -0.082135  ],\n",
       "       dtype=float32),\n",
       " 'provide': array([ 1.0435  ,  0.36095 ,  0.48787 , -0.068629,  0.072353, -0.17164 ,\n",
       "        -0.43456 , -0.18883 ,  0.94188 , -0.072146,  0.031514,  0.25461 ,\n",
       "         0.78294 , -0.27697 , -0.37145 ,  0.27563 ,  0.20983 ,  0.049158,\n",
       "         0.81629 , -0.8902  ,  0.5463  ,  0.17822 , -0.39007 , -0.25892 ,\n",
       "        -0.094446, -0.76073 ,  0.13599 , -0.66691 ,  0.10599 , -0.088948,\n",
       "         3.9529  ,  0.47956 , -0.53525 , -0.85311 ,  0.47729 ,  0.5493  ,\n",
       "         0.27984 ,  0.16038 , -0.19831 , -0.39836 ,  0.45721 , -0.26262 ,\n",
       "         0.59203 ,  0.34297 , -0.27254 , -0.073066, -0.16261 ,  1.0334  ,\n",
       "        -0.094584,  0.50767 ], dtype=float32),\n",
       " 'llb law mumbai': array([-0.98529   , -0.0957    , -0.6450233 , -0.27913335, -0.32570666,\n",
       "         0.31929   ,  0.6679333 , -0.51319665, -0.01405946,  0.13797666,\n",
       "        -0.01661666,  0.20938665, -0.32932636, -0.36953667, -0.26045665,\n",
       "         0.12861477, -0.4102743 ,  0.3301933 , -0.45010337,  0.99292564,\n",
       "         1.2628001 ,  0.80349666, -0.23583667,  0.23259665, -0.17732434,\n",
       "        -1.8460001 ,  0.37861   , -0.70062   , -0.39886332, -0.21293001,\n",
       "         1.3256301 , -0.06134666, -0.32039332, -0.5251257 ,  0.451591  ,\n",
       "        -0.175166  ,  0.2664233 , -0.40624925,  0.19177002,  0.56552   ,\n",
       "        -0.27813998,  0.06956399,  0.4961233 ,  0.77698344, -0.61239   ,\n",
       "        -0.31470802,  0.24221535,  0.21999   ,  0.76649666, -0.26967335],\n",
       "       dtype=float32),\n",
       " 'hsc': array([ 0.16639 , -0.1742  , -0.69773 , -1.0179  , -0.90493 ,  0.051071,\n",
       "         0.76269 , -0.33665 ,  0.72973 ,  0.22733 ,  0.65331 ,  0.18877 ,\n",
       "         0.737   , -0.85773 , -0.89347 , -0.52612 , -1.2942  ,  0.30405 ,\n",
       "        -0.061564, -0.07799 , -0.19479 ,  0.01627 ,  0.62359 ,  0.12262 ,\n",
       "         0.0394  ,  0.6355  ,  0.34951 , -0.28498 , -1.6886  , -0.34883 ,\n",
       "         0.084675,  0.88111 , -0.62509 , -0.018432,  1.2957  ,  0.35408 ,\n",
       "         0.79164 ,  0.44531 ,  0.4475  , -0.83338 ,  0.63832 , -1.0936  ,\n",
       "         0.14373 ,  0.057329, -0.12272 ,  0.08822 ,  1.4413  ,  0.076698,\n",
       "         1.441   , -0.60445 ], dtype=float32),\n",
       " 'llb law': array([-1.7599    ,  0.19187999, -0.88569   , -0.660035  , -0.557275  ,\n",
       "         0.74148   ,  0.789585  , -0.71091   , -0.0780692 ,  0.04121999,\n",
       "         0.147345  ,  0.56981   , -0.05399951, -0.46947002, -0.241845  ,\n",
       "        -0.08202785, -0.5170715 ,  0.10675499, -0.07085499,  0.64918846,\n",
       "         0.95035005,  0.694845  , -0.281665  , -0.288655  , -0.2737275 ,\n",
       "        -2.21345   ,  0.65067995, -0.852895  , -0.27267   , -0.08385499,\n",
       "         1.361045  , -0.33887   , -0.948155  , -0.6502485 ,  0.1664865 ,\n",
       "        -0.2738235 ,  0.33303   , -0.11083885,  0.43385002,  0.513775  ,\n",
       "         0.12119001,  0.1477975 ,  0.64219   ,  1.2246001 , -1.06979   ,\n",
       "        -0.692717  ,  0.31710503,  0.257585  ,  0.386845  ,  0.10129   ],\n",
       "       dtype=float32),\n",
       " 'commerce mumbai': array([-0.19710001, -0.43328   , -0.0952535 ,  0.73774505,  0.21329   ,\n",
       "        -0.73644495, -0.13170001, -0.68808496,  0.263115  , -0.210045  ,\n",
       "        -0.205635  , -0.1808    , -0.35627002, -0.1859    , -0.183223  ,\n",
       "         0.376475  , -0.186225  ,  0.25756   , -0.24415502,  0.619985  ,\n",
       "         1.7662499 ,  0.2807    , -0.20264   ,  0.41355   ,  0.09469099,\n",
       "        -1.07765   ,  0.14282   , -0.442265  , -0.61034   ,  0.026205  ,\n",
       "         1.8783    ,  0.052435  ,  0.40089   , -0.40484   ,  0.15589002,\n",
       "        -0.3967205 , -0.399565  , -0.32542002,  0.00605001,  0.790145  ,\n",
       "        -0.862445  ,  0.1542285 ,  0.20217   , -0.0929975 ,  0.0798    ,\n",
       "         0.18062149, -0.213752  ,  0.23870501,  1.1371    , -0.263305  ],\n",
       "       dtype=float32),\n",
       " 'university january hsc': array([-0.34786662,  0.33045   , -0.50832266, -0.6025367 , -0.52349   ,\n",
       "        -0.12184633, -0.45712996, -0.2798533 , -0.05760666, -0.23658669,\n",
       "         0.5639567 , -0.26170334,  0.11269633, -0.87164664, -0.01797332,\n",
       "        -0.03818   , -0.9187767 ,  0.32652   , -0.8801214 ,  0.77196664,\n",
       "         0.60244   ,  0.10849667,  0.42573   , -0.1196323 , -0.25922135,\n",
       "        -1.0127    ,  0.36037335, -0.5172567 , -1.33037   , -0.02795332,\n",
       "         1.8515583 ,  0.12468201, -0.49735832, -0.876184  ,  0.47543165,\n",
       "        -0.07912394,  0.43815336,  0.596042  ,  0.77329665, -0.20015   ,\n",
       "        -0.01414998, -0.51084536, -0.15415   , -0.31803033, -0.36366466,\n",
       "         0.20923233,  0.5101967 , -0.07668734,  0.32388797, -0.16437466],\n",
       "       dtype=float32)}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kwi.kw_emb_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Clustering for vizualisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\GFApps\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3338: FutureWarning: arrays to stack must be passed as a \"sequence\" type such as list or tuple. Support for non-sequence iterables such as generators is deprecated as of NumPy 1.16 and will raise an error in the future.\n",
      "  if (await self.run_code(code, result,  async_=asy)):\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([16, 15, 22, 16, 14, 23, 19,  7, 23,  6, 27, 13, 19, 16, 33, 21, 23,\n",
       "        2, 14, 23, 16, 23, 16, 23, 16, 15, 23, 23, 33, 23, 31, 13, 10, 34,\n",
       "       19, 16, 10, 34, 31,  2, 12, 12, 19,  1, 13, 11, 12, 16, 12, 19, 19,\n",
       "       12, 22, 12, 13, 12, 17,  2, 23,  3, 15, 33, 22,  2, 33,  3,  3, 18,\n",
       "        3, 33,  3, 19, 19,  7, 33,  7, 23, 22, 19, 23, 28, 23, 23, 23, 22,\n",
       "       16, 23, 22, 10, 16, 23, 23, 23, 22,  7, 23, 22, 23, 28, 22, 22, 23,\n",
       "       22, 31,  7, 10, 11, 10, 19, 33, 28, 31, 10, 11, 23, 15,  7, 24, 19,\n",
       "       11, 11, 11, 33,  9,  9,  9,  9,  9,  9,  9,  9, 26,  9,  7, 26,  9,\n",
       "        9, 28,  9,  9,  9,  9, 26, 23, 23, 31, 23, 15, 16, 19,  2, 16, 16,\n",
       "       33, 13, 23, 23, 31, 19, 13,  8,  1, 15, 34, 23, 13, 23,  1, 22,  1,\n",
       "       28, 17, 28, 13,  1, 34,  1, 23, 24, 28, 23, 24, 15, 19, 23, 15, 25,\n",
       "       25, 31, 24, 24, 24, 28,  1, 15, 31,  1,  2, 30,  7, 22, 25, 31,  7,\n",
       "       10, 25,  1,  7, 22, 22,  7, 31, 16,  7,  4, 16, 10, 16,  3,  3,  4,\n",
       "        5, 10,  4,  5, 30,  5,  5,  4,  3, 18,  3,  5,  4, 16, 16,  4, 30,\n",
       "       32, 32, 32,  8,  8, 30, 19, 22, 32, 18,  8, 21,  6, 19, 32, 22, 32,\n",
       "       21, 32, 32, 32,  8, 21, 32, 31, 19, 31, 31, 31, 19, 30, 30, 31, 22,\n",
       "       25,  1,  0,  5,  5, 25,  5,  1, 11,  5, 10,  7, 19, 14, 10, 14, 23,\n",
       "       14,  7,  7, 30, 30, 31, 10,  4,  4,  4, 10,  4, 31, 32, 27, 32,  2,\n",
       "        1,  1, 27, 16, 19,  4, 19, 27,  1,  8, 32,  1,  1, 16, 27, 32,  2,\n",
       "       19, 10, 27,  7,  4, 11, 11, 13,  4, 11, 11,  1, 22, 13,  7,  4, 26,\n",
       "        4,  7, 11, 30,  4, 26,  4, 13,  7,  0,  7, 13, 11, 13, 13, 19, 17,\n",
       "       17, 19, 19, 22, 17, 10,  9, 21, 17,  4,  7, 13, 22, 31, 11, 23, 22,\n",
       "       19, 17, 21, 26, 33, 33,  2, 21,  1, 15, 19,  2,  2, 23,  1,  2, 33,\n",
       "       14, 26, 26, 33, 25,  8,  6,  6,  7, 25, 29, 24, 25, 31,  8,  4,  6,\n",
       "       26, 21, 25, 30, 29, 22, 21,  4, 26, 25, 21,  6,  7, 21,  8, 19, 25,\n",
       "       30, 32, 10, 18, 21, 30, 21,  6, 19, 21,  6,  8, 19, 31,  4, 11, 21,\n",
       "       11,  4, 30, 31, 21, 26, 27,  4, 25, 31, 31,  7, 24, 29, 21, 21, 30,\n",
       "       31,  7, 30, 20, 24, 25, 21, 21,  2,  5, 14,  6, 11, 33, 33, 20, 22,\n",
       "       14, 26, 20,  7, 14, 21, 14, 14, 20, 23, 14,  9, 23, 23, 22,  8, 11,\n",
       "       23, 33,  6, 17, 30,  4, 25, 26, 25, 25, 10, 29, 25, 25,  5, 21,  7,\n",
       "       25, 19, 29, 14, 29, 25, 14, 24, 22,  4, 14, 13, 29, 22, 20, 22, 29,\n",
       "       24, 14, 12,  4, 12, 22, 10, 29, 18, 18, 11, 18, 18, 18, 30, 14, 10,\n",
       "       25, 25, 18, 11,  1, 13, 13, 13, 25,  1, 13, 20, 13, 13, 13, 11, 20,\n",
       "       20, 29, 22,  1,  1, 20, 25, 20, 22, 20, 20, 22, 25,  4, 25, 25, 30])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "from sklearn.decomposition import PCA\n",
    "from scipy.spatial.distance import cdist\n",
    "\n",
    "\n",
    "def plot_elbow(X):\n",
    "    distortions = []\n",
    "    K = range(1,40)\n",
    "    for k in K:\n",
    "        kmeanModel = KMeans(n_clusters=k).fit(X)\n",
    "        kmeanModel.fit(X)\n",
    "        distortions.append(sum(np.min(cdist(X, kmeanModel.cluster_centers_, 'euclidean'), axis=1)) / X.shape[0])\n",
    "\n",
    "    # Plot the elbow\n",
    "    plt.plot(K, distortions, 'bx-')\n",
    "    plt.xlabel('k')\n",
    "    plt.ylabel('Distortion')\n",
    "    plt.title('The Elbow Method showing the optimal k')\n",
    "    plt.show()\n",
    "    \n",
    "    \n",
    "def cluster_high_dim_space(data, n_clusters=35, n_components=3):\n",
    "    # clustering\n",
    "    kmeans = KMeans(n_clusters=n_clusters)\n",
    "    kmeans.fit(data)\n",
    "    y = kmeans.predict(data)\n",
    "\n",
    "    # visualisation of clusters in 2D embedding space\n",
    "    pca = PCA(n_components=n_components)\n",
    "    X = pca.fit_transform(data)\n",
    "\n",
    "    plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='viridis')\n",
    "    plt.show()\n",
    "    plt.scatter(X[:, 1], X[:, 2], c=y, s=50, cmap='viridis')\n",
    "    plt.show()\n",
    "    plt.scatter(X[:, 2], X[:, 0], c=y, s=50, cmap='viridis')\n",
    "    centers = kmeans.cluster_centers_\n",
    "    plt.scatter(centers[:, 0], centers[:, 1], c='black', s=200, alpha=0.5);\n",
    "    plt.show()\n",
    "    return y\n",
    "\n",
    "\n",
    "\n",
    "vis_data = np.stack(kwi.kw_emb_dict.values())\n",
    "# plot_elbow(vis_data)\n",
    "cluster_high_dim_space(vis_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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