spam_detection.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 269,
   "id": "60e33a2d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.linear_model import LinearRegression\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import string\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.model_selection import train_test_split\n",
    "from nltk.stem import SnowballStemmer\n",
    "from nltk.corpus import stopwords\n",
    "%matplotlib inline\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "id": "38ba28f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>v1</th>\n",
       "      <th>v2</th>\n",
       "      <th>Unnamed: 2</th>\n",
       "      <th>Unnamed: 3</th>\n",
       "      <th>Unnamed: 4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ham</td>\n",
       "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ham</td>\n",
       "      <td>Ok lar... Joking wif u oni...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>spam</td>\n",
       "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ham</td>\n",
       "      <td>U dun say so early hor... U c already then say...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ham</td>\n",
       "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "     v1                                                 v2 Unnamed: 2  \\\n",
       "0   ham  Go until jurong point, crazy.. Available only ...        NaN   \n",
       "1   ham                      Ok lar... Joking wif u oni...        NaN   \n",
       "2  spam  Free entry in 2 a wkly comp to win FA Cup fina...        NaN   \n",
       "3   ham  U dun say so early hor... U c already then say...        NaN   \n",
       "4   ham  Nah I don't think he goes to usf, he lives aro...        NaN   \n",
       "\n",
       "  Unnamed: 3 Unnamed: 4  \n",
       "0        NaN        NaN  \n",
       "1        NaN        NaN  \n",
       "2        NaN        NaN  \n",
       "3        NaN        NaN  \n",
       "4        NaN        NaN  "
      ]
     },
     "execution_count": 270,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sms = pd.read_csv('spam.csv', encoding='latin-1')\n",
    "sms.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "id": "b1bf4e5b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>labels</th>\n",
       "      <th>message</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ham</td>\n",
       "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ham</td>\n",
       "      <td>Ok lar... Joking wif u oni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>spam</td>\n",
       "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ham</td>\n",
       "      <td>U dun say so early hor... U c already then say...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ham</td>\n",
       "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "  labels                                            message\n",
       "0    ham  Go until jurong point, crazy.. Available only ...\n",
       "1    ham                      Ok lar... Joking wif u oni...\n",
       "2   spam  Free entry in 2 a wkly comp to win FA Cup fina...\n",
       "3    ham  U dun say so early hor... U c already then say...\n",
       "4    ham  Nah I don't think he goes to usf, he lives aro..."
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# X = df[['FirstDigitTouch','BothStartLoadPhase']].values\n",
    "# y = df['LiftOff'] == \"a\"\n",
    "# pipe = Pipeline([\n",
    "#     (\"scale\",StandardScaler()),\n",
    "#     (\"model\",LinearRegression())\n",
    "# ])\n",
    "# pipe.get_params\n",
    "sms=sms.drop(['Unnamed: 2', 'Unnamed: 3', 'Unnamed: 4'],axis=1)\n",
    "sms=sms.rename(columns={'v1':'labels','v2': 'message'})\n",
    "sms.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "id": "406d2b3c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# parameters = {'kernel':('linear', 'rbf'), 'C':[1, 10]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "id": "9251623e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# plt.scatter(X[:, 0],X[:,1], c=y);\n",
    "# mod = GridSearchCV(estimator=pipe,cv=3,)\n",
    "#                   param_grid={'': [1,2,3,4,5,6,7,8,9,10]},\n",
    "       #           cv=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "id": "f1f6ef02",
   "metadata": {},
   "outputs": [],
   "source": [
    "# sms['labels']=df.labels.map({'spam':0, 'ham':1})\n",
    "# sms.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "id": "1800a646",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>labels</th>\n",
       "      <th>message</th>\n",
       "      <th>length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ham</td>\n",
       "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ham</td>\n",
       "      <td>Ok lar... Joking wif u oni...</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>spam</td>\n",
       "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
       "      <td>155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ham</td>\n",
       "      <td>U dun say so early hor... U c already then say...</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ham</td>\n",
       "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  labels                                            message  length\n",
       "0    ham  Go until jurong point, crazy.. Available only ...     111\n",
       "1    ham                      Ok lar... Joking wif u oni...      29\n",
       "2   spam  Free entry in 2 a wkly comp to win FA Cup fina...     155\n",
       "3    ham  U dun say so early hor... U c already then say...      49\n",
       "4    ham  Nah I don't think he goes to usf, he lives aro...      61"
      ]
     },
     "execution_count": 275,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sms['length']=sms['message'].apply(len)\n",
    "sms.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "id": "aef779c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# plt.figure(figsize=(16,6))\n",
    "# sns.distplot(a=df['length'])\n",
    "# # plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "id": "d3012e70",
   "metadata": {},
   "outputs": [],
   "source": [
    "# asd=df[df['length']==111]['sms'].iloc[0]\n",
    "# asd\n",
    "text_feat = sms['message'].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "id": "bebbfaae",
   "metadata": {},
   "outputs": [],
   "source": [
    "# text_feat = df['message'].copy()\n",
    "def text_process(text):\n",
    "    \n",
    "    text = text.translate(str.maketrans('', '', string.punctuation))\n",
    "    text = [word for word in text.split() if word.lower() not in stopwords.words('english')]\n",
    "    \n",
    "    return \" \".join(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "id": "19f25c08",
   "metadata": {},
   "outputs": [],
   "source": [
    "text_feat = text_feat.apply(text_process)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "id": "37a0bd3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "vectorizer = TfidfVectorizer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "id": "baa759cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "features = vectorizer.fit_transform(text_feat)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "id": "51e034b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "features_train, features_test, labels_train, labels_test = train_test_split(features, sms['labels'], test_size=0.3, random_state=111)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "id": "1ba8507a",
   "metadata": {},
   "outputs": [],
   "source": [
    "svc = SVC(kernel='sigmoid', gamma=1.0)\n",
    "knc = KNeighborsClassifier(n_neighbors=49)\n",
    "lrc = LogisticRegression(solver='liblinear', penalty='l1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "id": "9aa34758",
   "metadata": {},
   "outputs": [],
   "source": [
    "clfs = {'SVC' : svc,'KN' : knc,'LR': lrc}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "id": "95e5fb56",
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_classifier(clf, feature_train, labels_train):    \n",
    "    clf.fit(feature_train, labels_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "id": "8d3af1f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict_labels(clf, features):\n",
    "    return (clf.predict(features))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "id": "8cbc09bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('SVC', [0.9784688995215312]),\n",
       " ('KN', [0.9246411483253588]),\n",
       " ('LR', [0.9431818181818182])]"
      ]
     },
     "execution_count": 291,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "pred_scores = []\n",
    "for k,v in clfs.items():\n",
    "    train_classifier(v, features_train, labels_train)\n",
    "    pred = predict_labels(v,features_test)\n",
    "    pred_scores.append((k, [accuracy_score(labels_test,pred)]))\n",
    "pred_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "id": "80cf46db",
   "metadata": {},
   "outputs": [
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       "      <th>2</th>\n",
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       "     0                     1\n",
       "0  SVC  [0.9784688995215312]\n",
       "1   KN  [0.9246411483253588]\n",
       "2   LR  [0.9431818181818182]"
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   "source": [
    "df = pd.DataFrame(pred_scores)\n",
    "df "
   ]
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   "execution_count": null,
   "id": "6fec821f",
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