leafpredift.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "fc3a655f",
   "metadata": {},
   "source": [
    "## Install Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8b6c39ca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: seaborn in /opt/anaconda3/lib/python3.9/site-packages (0.11.2)\n",
      "Requirement already satisfied: matplotlib>=2.2 in /opt/anaconda3/lib/python3.9/site-packages (from seaborn) (3.4.3)\n",
      "Requirement already satisfied: pandas>=0.23 in /opt/anaconda3/lib/python3.9/site-packages (from seaborn) (1.4.2)\n",
      "Requirement already satisfied: numpy>=1.15 in /opt/anaconda3/lib/python3.9/site-packages (from seaborn) (1.22.3)\n",
      "Requirement already satisfied: scipy>=1.0 in /opt/anaconda3/lib/python3.9/site-packages (from seaborn) (1.7.3)\n",
      "Requirement already satisfied: pyparsing>=2.2.1 in /opt/anaconda3/lib/python3.9/site-packages (from matplotlib>=2.2->seaborn) (3.0.4)\n",
      "Requirement already satisfied: pillow>=6.2.0 in /opt/anaconda3/lib/python3.9/site-packages (from matplotlib>=2.2->seaborn) (9.0.1)\n",
      "Requirement already satisfied: cycler>=0.10 in /opt/anaconda3/lib/python3.9/site-packages (from matplotlib>=2.2->seaborn) (0.11.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /opt/anaconda3/lib/python3.9/site-packages (from matplotlib>=2.2->seaborn) (1.3.2)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in /opt/anaconda3/lib/python3.9/site-packages (from matplotlib>=2.2->seaborn) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2020.1 in /opt/anaconda3/lib/python3.9/site-packages (from pandas>=0.23->seaborn) (2021.3)\n",
      "Requirement already satisfied: six>=1.5 in /opt/anaconda3/lib/python3.9/site-packages (from python-dateutil>=2.7->matplotlib>=2.2->seaborn) (1.16.0)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install seaborn\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d9d522b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: plotly in /opt/anaconda3/lib/python3.9/site-packages (5.7.0)\n",
      "Requirement already satisfied: six in /opt/anaconda3/lib/python3.9/site-packages (from plotly) (1.16.0)\n",
      "Requirement already satisfied: tenacity>=6.2.0 in /opt/anaconda3/lib/python3.9/site-packages (from plotly) (8.0.1)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install plotly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0e2bcc3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "from time import time\n",
    "from sklearn.model_selection import ShuffleSplit, GridSearchCV\n",
    "from scipy.stats import randint\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1efde46f",
   "metadata": {},
   "source": [
    "## Import CSV FILES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2694ba9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = pd.read_csv(\"/Users/isa/Desktop/LeafClassification/train.csv\")\n",
    "df_test = pd.read_csv(\"/Users/isa/Desktop/LeafClassification/test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a9c6b285",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train.head()\n",
    "df = df_train.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2c843b30",
   "metadata": {},
   "outputs": [
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     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3b90cb5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import StratifiedShuffleSplit\n",
    "\n",
    "\n",
    "encoder=LabelEncoder()\n",
    "le=encoder.fit(df_train.species)\n",
    "labels=le.transform(df_train.species)\n",
    "classes=list(le.classes_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "129f1f8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train=df_train.drop(['id','species'],axis=1)\n",
    "test_id=df_test.id\n",
    "df_test=df_test.drop(['id'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ab2924cd",
   "metadata": {},
   "outputs": [
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>589</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003906</td>\n",
       "      <td>0.015625</td>\n",
       "      <td>0.041016</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.017578</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.005859</td>\n",
       "      <td>0.013672</td>\n",
       "      <td>...</td>\n",
       "      <td>0.098633</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.004883</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003906</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.018555</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>590</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003906</td>\n",
       "      <td>0.003906</td>\n",
       "      <td>0.005859</td>\n",
       "      <td>0.017578</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.017578</td>\n",
       "      <td>0.005859</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.005859</td>\n",
       "      <td>...</td>\n",
       "      <td>0.012695</td>\n",
       "      <td>0.004883</td>\n",
       "      <td>0.004883</td>\n",
       "      <td>0.002930</td>\n",
       "      <td>0.009766</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.090820</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.016602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>591</th>\n",
       "      <td>0.017578</td>\n",
       "      <td>0.029297</td>\n",
       "      <td>0.015625</td>\n",
       "      <td>0.013672</td>\n",
       "      <td>0.003906</td>\n",
       "      <td>0.015625</td>\n",
       "      <td>0.025391</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.009766</td>\n",
       "      <td>...</td>\n",
       "      <td>0.073242</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.028320</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001953</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.042969</td>\n",
       "      <td>0.006836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>592</th>\n",
       "      <td>0.013672</td>\n",
       "      <td>0.009766</td>\n",
       "      <td>0.060547</td>\n",
       "      <td>0.025391</td>\n",
       "      <td>0.035156</td>\n",
       "      <td>0.025391</td>\n",
       "      <td>0.039062</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003906</td>\n",
       "      <td>0.023438</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003906</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000977</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.011719</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.011719</td>\n",
       "      <td>0.018555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>593</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.117190</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.019531</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.136720</td>\n",
       "      <td>0.001953</td>\n",
       "      <td>0.005859</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.007812</td>\n",
       "      <td>...</td>\n",
       "      <td>0.107420</td>\n",
       "      <td>0.012695</td>\n",
       "      <td>0.016602</td>\n",
       "      <td>0.000977</td>\n",
       "      <td>0.004883</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.015625</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.017578</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>594 rows × 192 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      margin1   margin2   margin3   margin4   margin5   margin6   margin7  \\\n",
       "0    0.019531  0.009766  0.078125  0.011719  0.003906  0.015625  0.005859   \n",
       "1    0.007812  0.005859  0.064453  0.009766  0.003906  0.013672  0.007812   \n",
       "2    0.000000  0.000000  0.001953  0.021484  0.041016  0.000000  0.023438   \n",
       "3    0.000000  0.000000  0.009766  0.011719  0.017578  0.000000  0.003906   \n",
       "4    0.001953  0.000000  0.015625  0.009766  0.039062  0.000000  0.009766   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "589  0.000000  0.000000  0.003906  0.015625  0.041016  0.000000  0.017578   \n",
       "590  0.000000  0.003906  0.003906  0.005859  0.017578  0.000000  0.017578   \n",
       "591  0.017578  0.029297  0.015625  0.013672  0.003906  0.015625  0.025391   \n",
       "592  0.013672  0.009766  0.060547  0.025391  0.035156  0.025391  0.039062   \n",
       "593  0.000000  0.117190  0.000000  0.019531  0.000000  0.136720  0.001953   \n",
       "\n",
       "      margin8   margin9  margin10  ...  texture55  texture56  texture57  \\\n",
       "0    0.000000  0.005859  0.023438  ...   0.006836   0.000000   0.015625   \n",
       "1    0.000000  0.033203  0.023438  ...   0.000000   0.000000   0.006836   \n",
       "2    0.000000  0.011719  0.005859  ...   0.128910   0.000000   0.000977   \n",
       "3    0.000000  0.003906  0.001953  ...   0.012695   0.015625   0.002930   \n",
       "4    0.000000  0.005859  0.000000  ...   0.000000   0.042969   0.016602   \n",
       "..        ...       ...       ...  ...        ...        ...        ...   \n",
       "589  0.000000  0.005859  0.013672  ...   0.098633   0.000000   0.004883   \n",
       "590  0.005859  0.000000  0.005859  ...   0.012695   0.004883   0.004883   \n",
       "591  0.000000  0.000000  0.009766  ...   0.073242   0.000000   0.028320   \n",
       "592  0.000000  0.003906  0.023438  ...   0.003906   0.000000   0.000977   \n",
       "593  0.005859  0.000000  0.007812  ...   0.107420   0.012695   0.016602   \n",
       "\n",
       "     texture58  texture59  texture60  texture61  texture62  texture63  \\\n",
       "0     0.000977   0.015625        0.0        0.0   0.000000   0.003906   \n",
       "1     0.001953   0.013672        0.0        0.0   0.000977   0.037109   \n",
       "2     0.000000   0.000000        0.0        0.0   0.015625   0.000000   \n",
       "3     0.036133   0.013672        0.0        0.0   0.089844   0.000000   \n",
       "4     0.010742   0.041016        0.0        0.0   0.007812   0.009766   \n",
       "..         ...        ...        ...        ...        ...        ...   \n",
       "589   0.000000   0.003906        0.0        0.0   0.018555   0.000000   \n",
       "590   0.002930   0.009766        0.0        0.0   0.090820   0.000000   \n",
       "591   0.000000   0.001953        0.0        0.0   0.000000   0.042969   \n",
       "592   0.000000   0.011719        0.0        0.0   0.000000   0.011719   \n",
       "593   0.000977   0.004883        0.0        0.0   0.015625   0.000000   \n",
       "\n",
       "     texture64  \n",
       "0     0.053711  \n",
       "1     0.044922  \n",
       "2     0.000000  \n",
       "3     0.008789  \n",
       "4     0.007812  \n",
       "..         ...  \n",
       "589   0.000977  \n",
       "590   0.016602  \n",
       "591   0.006836  \n",
       "592   0.018555  \n",
       "593   0.017578  \n",
       "\n",
       "[594 rows x 192 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b4427e4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "uniquelables=np.unique(labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3965eb2b",
   "metadata": {},
   "source": [
    "## Train and Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c8cdd6c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test=train_test_split(df_train,labels,test_size=0.3,shuffle=True,stratify=labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4152b039",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9494949494949495\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    }
   ],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "pipe_lr=Pipeline([('scl', StandardScaler()), \n",
    "                  ('pca', PCA(n_components=10)), \n",
    "                  ('clf', LogisticRegression(random_state=1))])\n",
    "pipe_lr.fit(X_train, y_train)\n",
    "print(pipe_lr.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "548a9604",
   "metadata": {},
   "source": [
    "## K Fold Cross Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "71d83901",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross Validation Scores are [0.21212121 0.22222222 0.13131313 0.13131313 0.18181818 0.17171717\n",
      " 0.25252525 0.21212121 0.12121212 0.18181818]\n",
      "Average Cross Validation score :0.18181818181818182\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score,KFold\n",
    "X=df_train\n",
    "Y=df['species']\n",
    "logreg=LogisticRegression()\n",
    "kf=KFold(n_splits=10)\n",
    "score=cross_val_score(logreg,X,Y,cv=kf)\n",
    "print(\"Cross Validation Scores are {}\".format(score))\n",
    "print(\"Average Cross Validation score :{}\".format(score.mean()))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4eb46aa",
   "metadata": {},
   "source": [
    "### Most suitable parameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "294471a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "cv_sets = ShuffleSplit(n_splits=10,test_size=0.30,random_state=42)\n",
    "classifiers = [SVC(), KNeighborsClassifier()]\n",
    "params = [{'kernel':('linear','poly','sigmoid','rbf'),'C':[0.01,0.05,0.025,0.07,0.09,1.0], 'gamma':['scale'], 'probability':[True]},\n",
    "          {'n_neighbors': [3,5,7,9]}]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "19d07c44",
   "metadata": {},
   "outputs": [],
   "source": [
    "best_estimators = []\n",
    "for classifier, param in zip(classifiers, params):\n",
    "    grid = GridSearchCV(classifier,param,cv=cv_sets)\n",
    "    grid = grid.fit(X_train,y_train)\n",
    "    best_estimators.append(grid.best_estimator_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "fd048003",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[SVC(probability=True), KNeighborsClassifier(n_neighbors=3)]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_estimators"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c9dc127e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, estimator=DecisionTreeClassifier(),\n",
       "                   param_distributions={'criterion': ['gini', 'entropy'],\n",
       "                                        'max_depth': [3, None],\n",
       "                                        'max_features': <scipy.stats._distn_infrastructure.rv_frozen object at 0x7fed694f9550>,\n",
       "                                        'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x7fed69532310>})"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_dist = {\"max_depth\": [3, None],\n",
    "              \"max_features\": randint(1, 9),\n",
    "              \"min_samples_leaf\": randint(1, 9),\n",
    "              \"criterion\": [\"gini\", \"entropy\"]}\n",
    "tree = DecisionTreeClassifier()\n",
    "\n",
    "tree_cv = RandomizedSearchCV(tree, param_dist, cv=5)\n",
    "\n",
    "tree_cv.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b37a16d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(max_features=7, min_samples_leaf=5)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_cv.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db076fa3",
   "metadata": {},
   "source": [
    "## MLOPS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "17e662c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline(steps=[('standardscaler', StandardScaler()),\n",
      "                ('logisticregression', LogisticRegression())])\n",
      "0.9764309764309764\n",
      "[[3 0 0 ... 0 0 0]\n",
      " [0 3 0 ... 0 0 0]\n",
      " [0 0 3 ... 0 0 0]\n",
      " ...\n",
      " [0 0 0 ... 3 0 0]\n",
      " [0 0 0 ... 0 3 0]\n",
      " [0 0 0 ... 0 0 3]]\n",
      "Recall:  0.9764309764309764\n",
      "Precision: 0.9764309764309764\n",
      "F1 Puanı: 0.9764309764309764\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score\n",
    "model_1 = make_pipeline(StandardScaler(), LogisticRegression())\n",
    "\n",
    "print(model_1.fit(X_train,y_train))\n",
    "\n",
    "print(model_1.score(X_test,y_test))\n",
    "\n",
    "\n",
    "y_pred = model_1.predict(X_test)\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "print(cm)\n",
    "print(\"Recall: \",recall_score(y_test, y_pred, average='micro'))\n",
    "print(\"Precision: \"+str(precision_score(y_test,y_pred, average='micro')))\n",
    "print(\"F1 Puanı: \"+str(precision_score(y_test,y_pred, average='micro')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "6c0f4d6c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline(steps=[('standardscaler', StandardScaler()), ('svc', SVC())])\n",
      "score Model: 0.9764309764309764\n",
      "[[3 0 0 ... 0 0 0]\n",
      " [0 3 0 ... 0 0 0]\n",
      " [0 0 3 ... 0 0 0]\n",
      " ...\n",
      " [0 0 0 ... 3 0 0]\n",
      " [0 0 0 ... 0 2 0]\n",
      " [0 0 0 ... 0 0 3]]\n",
      "Recall:  0.9764309764309764\n",
      "Precision: 0.9764309764309764\n",
      "F1 Puanı: 0.9764309764309764\n"
     ]
    }
   ],
   "source": [
    "model_2 = make_pipeline(StandardScaler(), SVC())\n",
    "\n",
    "print(model_2.fit(X_train,y_train))\n",
    "\n",
    "print(f'score Model:',model_2.score(X_test,y_test))\n",
    "\n",
    "y_pred = model_2.predict(X_test)\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "print(cm)\n",
    "print(\"Recall: \",recall_score(y_test, y_pred, average='micro'))\n",
    "print(\"Precision: \"+str(precision_score(y_test,y_pred, average='micro')))\n",
    "print(\"F1 Puanı: \"+str(precision_score(y_test,y_pred, average='micro')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "f0206a63",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline(steps=[('standardscaler', StandardScaler()),\n",
      "                ('kneighborsclassifier', KNeighborsClassifier())])\n",
      "score Model: 0.9595959595959596\n",
      "[[3 0 0 ... 0 0 0]\n",
      " [0 3 0 ... 0 0 0]\n",
      " [0 0 3 ... 0 0 0]\n",
      " ...\n",
      " [0 0 0 ... 3 0 0]\n",
      " [0 0 0 ... 0 2 0]\n",
      " [0 0 0 ... 0 0 2]]\n",
      "Recall:  0.9595959595959596\n",
      "Precision: 0.9595959595959596\n",
      "F1 Puanı: 0.9595959595959596\n"
     ]
    }
   ],
   "source": [
    "model_3 = make_pipeline(StandardScaler(), KNeighborsClassifier())\n",
    "\n",
    "print(model_3.fit(X_train,y_train))\n",
    "\n",
    "print(f'score Model:',model_3.score(X_test,y_test))\n",
    "\n",
    "y_pred = model_3.predict(X_test)\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "print(cm)\n",
    "print(\"Recall: \",recall_score(y_test, y_pred, average='micro'))\n",
    "print(\"Precision: \"+str(precision_score(y_test,y_pred, average='micro')))\n",
    "print(\"F1 Puanı: \"+str(precision_score(y_test,y_pred, average='micro')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "aa1022cd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline(steps=[('standardscaler', StandardScaler()),\n",
      "                ('decisiontreeclassifier', DecisionTreeClassifier())])\n",
      "score Model: 0.6599326599326599\n",
      "[[3 0 0 ... 0 0 0]\n",
      " [0 3 0 ... 0 0 0]\n",
      " [0 0 1 ... 0 0 0]\n",
      " ...\n",
      " [0 0 0 ... 1 0 0]\n",
      " [0 0 0 ... 0 0 0]\n",
      " [0 0 0 ... 0 0 2]]\n",
      "Recall:  0.6599326599326599\n",
      "Precision: 0.6599326599326599\n",
      "F1 Puanı: 0.6599326599326599\n"
     ]
    }
   ],
   "source": [
    "model_4 = make_pipeline(StandardScaler(),DecisionTreeClassifier())\n",
    "\n",
    "print(model_4.fit(X_train,y_train))\n",
    "\n",
    "print(f'score Model:',model_4.score(X_test,y_test))\n",
    "\n",
    "y_pred = model_4.predict(X_test)\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "print(cm)\n",
    "print(\"Recall: \",recall_score(y_test, y_pred, average='micro'))\n",
    "print(\"Precision: \"+str(precision_score(y_test,y_pred, average='micro')))\n",
    "print(\"F1 Puanı: \"+str(precision_score(y_test,y_pred, average='micro')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "df95572f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline(steps=[('standardscaler', StandardScaler()),\n",
      "                ('gaussiannb', GaussianNB())])\n",
      "score Model: 0.48484848484848486\n",
      "[[2 0 0 ... 0 0 0]\n",
      " [0 2 0 ... 0 0 0]\n",
      " [0 0 3 ... 0 0 0]\n",
      " ...\n",
      " [0 0 0 ... 1 0 0]\n",
      " [0 0 0 ... 0 0 0]\n",
      " [0 0 0 ... 0 0 1]]\n",
      "Recall:  0.48484848484848486\n",
      "Precision: 0.48484848484848486\n",
      "F1 Puanı: 0.48484848484848486\n"
     ]
    }
   ],
   "source": [
    "model_5 = make_pipeline(StandardScaler(),GaussianNB())\n",
    "\n",
    "print(model_5.fit(X_train,y_train))\n",
    "\n",
    "print(f'score Model:',model_5.score(X_test,y_test))\n",
    "\n",
    "y_pred = model_5.predict(X_test)\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "print(cm)\n",
    "accuracy= accuracy_score(y_test, y_pred)\n",
    "print(\"Recall: \",recall_score(y_test, y_pred, average='micro'))\n",
    "print(\"Precision: \"+str(precision_score(y_test,y_pred, average='micro')))\n",
    "print(\"F1 Puanı: \"+str(precision_score(y_test,y_pred, average='micro')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "244dede8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogisticRegression\n",
      "Accuracy: 59.26\n",
      "Recall:  0.5925925925925926\n",
      "Precision: 0.5925925925925926\n",
      "F1 Puanı: 0.5925925925925926\n",
      "SVC\n",
      "Accuracy: 91.58\n",
      "Recall:  0.9158249158249159\n",
      "Precision: 0.9158249158249159\n",
      "F1 Puanı: 0.9158249158249159\n",
      "KNeighborsClassifier\n",
      "Accuracy: 87.21\n",
      "Recall:  0.8720538720538721\n",
      "Precision: 0.8720538720538721\n",
      "F1 Puanı: 0.8720538720538721\n",
      "DecisionTreeClassifier\n",
      "Accuracy: 65.99\n",
      "Recall:  0.6599326599326599\n",
      "Precision: 0.6599326599326599\n",
      "F1 Puanı: 0.6599326599326599\n",
      "GaussianNB\n",
      "Accuracy: 48.82\n",
      "Recall:  0.4882154882154882\n",
      "Precision: 0.4882154882154882\n",
      "F1 Puanı: 0.4882154882154882\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score,recall_score,precision_score,f1_score,roc_auc_score\n",
    "values= [LogisticRegression(), SVC(), KNeighborsClassifier(), DecisionTreeClassifier(), GaussianNB()]\n",
    "\n",
    "keys= ['LogisticRegression',\n",
    "      'SVC', \n",
    "       'KNeighborsClassifier', \n",
    "       'DecisionTreeClassifier', \n",
    "       'GaussianNB']\n",
    "\n",
    "models= dict(zip(keys,values))\n",
    "accuracy_scores=[]\n",
    "train_times=[]\n",
    "\n",
    "for key,value in models.items():\n",
    "  t = time()\n",
    "  value.fit(X_train,y_train)\n",
    "  duration = (time() - t) / 60\n",
    "  y_pred= value.predict(X_test)\n",
    "  accuracy= accuracy_score(y_test, y_pred)\n",
    "  accuracy_scores.append(accuracy)\n",
    "  train_times.append(duration)   \n",
    "  print(key)\n",
    "  print(\"Accuracy:\",round(accuracy * 100, 2))\n",
    "  #cm = confusion_matrix(y_test, y_pred)\n",
    "  #print(cm)\n",
    "  #print(classification_report(y_test, y_pred))\n",
    "  print(\"Recall: \",recall_score(y_test, y_pred, average='micro'))\n",
    "  print(\"Precision: \"+str(precision_score(y_test,y_pred, average='micro')))\n",
    "  print(\"F1 Puanı: \"+str(precision_score(y_test,y_pred, average='micro')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "0426d574",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([50, 50,  1, 19, 14, 50, 50, 28, 16, 19, 43, 16, 75, 10, 52, 41, 69,\n",
       "       19, 19, 71, 61, 69, 44, 77,  1, 87, 69, 15, 35, 70, 53, 16, 47, 50,\n",
       "        4, 73, 93, 93, 69, 93, 19, 19, 19,  9, 71, 70, 38, 70, 19, 69, 86,\n",
       "       19, 69, 94, 93, 86, 62, 69, 87,  0, 88, 26, 87, 59, 12, 11, 48, 78,\n",
       "       13,  4, 85, 57, 79, 19, 31, 12, 19, 41, 47, 10, 61, 73, 85, 24, 19,\n",
       "        2, 23, 56, 87, 73, 41, 69, 70, 83, 70, 56, 26, 17, 19, 48, 54, 93,\n",
       "       50, 12, 69, 69, 12, 96, 64, 78, 93, 93, 36, 30, 16, 73, 60, 87,  6,\n",
       "       44, 34, 69, 91, 14, 75, 27, 50, 92, 39, 23, 46,  2, 41, 68, 24, 93,\n",
       "       28, 28, 69, 81, 42, 50, 50, 83, 60, 69, 14, 64, 42, 96, 14, 20, 59,\n",
       "       35, 86,  1, 26, 38, 96, 83, 11, 60, 93, 79, 73, 79, 93, 47, 65, 77,\n",
       "       26, 21, 79, 87, 11, 10, 73, 94, 27, 73, 19, 45, 73, 85, 47, 20, 87,\n",
       "       94, 40, 49, 93, 70, 36, 93, 69, 95, 69, 91, 29, 64, 80,  6, 78, 45,\n",
       "       12,  9, 78, 85, 77, 89, 92, 78,  2, 59, 41, 93, 70, 32, 79, 78, 91,\n",
       "       69, 44, 34,  5, 79, 49, 58, 94, 67, 92, 88, 90, 11, 20, 39, 49, 12,\n",
       "       76, 27, 70, 71, 11, 70, 33, 47, 98, 82, 34, 69, 36, 96, 48, 20, 62,\n",
       "       64, 56, 50, 93, 73,  9, 19, 26, 19, 30, 71, 11, 36, 79, 47, 12, 78,\n",
       "       73, 11, 27, 65, 36, 38,  0, 65, 50, 73, 47, 10, 80, 44, 73, 69, 54,\n",
       "       54, 11, 51, 33, 93, 41, 11, 77,  1, 73,  2, 43, 83, 42, 86,  2, 73,\n",
       "       96, 83, 73,  9, 63, 50, 16, 38, 87, 95, 50, 35, 41, 60, 59, 58, 87,\n",
       "       93, 77, 38, 93, 50, 93, 93, 19, 46, 19, 93,  0, 33, 69, 70, 73, 48,\n",
       "       42, 19, 69, 91, 73, 81, 12, 85, 47,  6, 79,  2, 73, 35, 43,  6, 80,\n",
       "       78, 82, 56, 61, 70, 43, 33, 50, 47, 71, 58, 59, 69, 66, 86, 98, 50,\n",
       "       90, 64, 78, 11, 11, 56, 85, 49, 48, 85, 16, 15, 54, 49, 89, 44, 17,\n",
       "       35, 93, 54, 54, 96, 44, 19, 69, 26,  9, 36, 69, 61, 50, 82, 69, 79,\n",
       "       22, 86, 72, 11, 19, 78, 87, 26, 12,  2, 74, 69, 66, 43, 26, 73, 80,\n",
       "        4, 26,  3, 73, 87, 36, 93, 39, 69, 73, 76, 78,  7, 36, 70,  8, 95,\n",
       "       85, 62, 10, 87, 48,  2, 52, 86, 48, 52,  3, 49, 47, 87, 52, 41, 26,\n",
       "       88, 71, 45, 69, 27, 87, 75, 74, 11, 64, 65, 78, 63, 83, 21, 54, 88,\n",
       "       83, 11, 26, 12, 16, 36, 85, 12, 77, 57, 50, 82, 70, 19, 69, 19, 36,\n",
       "       77, 73, 66, 83, 35, 48, 48, 54, 91, 20, 19, 15, 10, 24, 71, 73, 12,\n",
       "       54, 85, 16, 19, 45, 74,  6, 47, 42, 34, 80, 12, 85, 69, 85, 47, 47,\n",
       "       27, 58, 68, 41, 97, 63, 91, 76, 96, 87, 36, 14,  0, 19, 41, 14, 12,\n",
       "        0, 86, 46, 19,  4, 47, 69, 19, 58, 19, 93, 12, 78, 14, 72, 93, 87,\n",
       "       78, 69, 50, 26, 92, 16, 81, 37, 14, 93, 19, 56, 46, 25, 50, 17, 37,\n",
       "       93,  0, 20, 41, 10, 91, 69, 81, 53, 18, 27, 11, 12, 65, 19, 36])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_pred = model_5.predict(df_test)\n",
    "test_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "0daf63a8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       id  Acer_Capillipes  Acer_Circinatum  Acer_Mono  Acer_Opalus  \\\n",
      "0       4         0.010101         0.010101   0.010101     0.010101   \n",
      "1       7         0.010101         0.010101   0.010101     0.010101   \n",
      "2       9         0.010101         0.010101   0.010101     0.010101   \n",
      "3      12         0.010101         0.010101   0.010101     0.010101   \n",
      "4      13         0.010101         0.010101   0.010101     0.010101   \n",
      "..    ...              ...              ...        ...          ...   \n",
      "589  1576         0.010101         0.010101   0.010101     0.010101   \n",
      "590  1577         0.010101         0.010101   0.010101     0.010101   \n",
      "591  1579         0.010101         0.010101   0.010101     0.010101   \n",
      "592  1580         0.010101         0.010101   0.010101     0.010101   \n",
      "593  1583         0.010101         0.010101   0.010101     0.010101   \n",
      "\n",
      "     Acer_Palmatum  Acer_Pictum  Acer_Platanoids  Acer_Rubrum  Acer_Rufinerve  \\\n",
      "0         0.010101     0.010101         0.010101     0.010101        0.010101   \n",
      "1         0.010101     0.010101         0.010101     0.010101        0.010101   \n",
      "2         0.010101     0.010101         0.010101     0.010101        0.010101   \n",
      "3         0.010101     0.010101         0.010101     0.010101        0.010101   \n",
      "4         0.010101     0.010101         0.010101     0.010101        0.010101   \n",
      "..             ...          ...              ...          ...             ...   \n",
      "589       0.010101     0.010101         0.010101     0.010101        0.010101   \n",
      "590       0.010101     0.010101         0.010101     0.010101        0.010101   \n",
      "591       0.010101     0.010101         0.010101     0.010101        0.010101   \n",
      "592       0.010101     0.010101         0.010101     0.010101        0.010101   \n",
      "593       0.010101     0.010101         0.010101     0.010101        0.010101   \n",
      "\n",
      "     ...  Salix_Fragilis  Salix_Intergra  Sorbus_Aria  Tilia_Oliveri  \\\n",
      "0    ...        0.010101        0.010101     0.010101       0.010101   \n",
      "1    ...        0.010101        0.010101     0.010101       0.010101   \n",
      "2    ...        0.010101        0.010101     0.010101       0.010101   \n",
      "3    ...        0.010101        0.010101     0.010101       0.010101   \n",
      "4    ...        0.010101        0.010101     0.010101       0.010101   \n",
      "..   ...             ...             ...          ...            ...   \n",
      "589  ...        0.010101        0.010101     0.010101       0.010101   \n",
      "590  ...        0.010101        0.010101     0.010101       0.010101   \n",
      "591  ...        0.010101        0.010101     0.010101       0.010101   \n",
      "592  ...        0.010101        0.010101     0.010101       0.010101   \n",
      "593  ...        0.010101        0.010101     0.010101       0.010101   \n",
      "\n",
      "     Tilia_Platyphyllos  Tilia_Tomentosa  Ulmus_Bergmanniana  Viburnum_Tinus  \\\n",
      "0              0.010101         0.010101            0.010101        0.010101   \n",
      "1              0.010101         0.010101            0.010101        0.010101   \n",
      "2              0.010101         0.010101            0.010101        0.010101   \n",
      "3              0.010101         0.010101            0.010101        0.010101   \n",
      "4              0.010101         0.010101            0.010101        0.010101   \n",
      "..                  ...              ...                 ...             ...   \n",
      "589            0.010101         0.010101            0.010101        0.010101   \n",
      "590            0.010101         0.010101            0.010101        0.010101   \n",
      "591            0.010101         0.010101            0.010101        0.010101   \n",
      "592            0.010101         0.010101            0.010101        0.010101   \n",
      "593            0.010101         0.010101            0.010101        0.010101   \n",
      "\n",
      "     Viburnum_x_Rhytidophylloides  Zelkova_Serrata  \n",
      "0                        0.010101         0.010101  \n",
      "1                        0.010101         0.010101  \n",
      "2                        0.010101         0.010101  \n",
      "3                        0.010101         0.010101  \n",
      "4                        0.010101         0.010101  \n",
      "..                            ...              ...  \n",
      "589                      0.010101         0.010101  \n",
      "590                      0.010101         0.010101  \n",
      "591                      0.010101         0.010101  \n",
      "592                      0.010101         0.010101  \n",
      "593                      0.010101         0.010101  \n",
      "\n",
      "[594 rows x 100 columns]\n"
     ]
    }
   ],
   "source": [
    "sample_df=pd.read_csv('/Users/isa/Desktop/LeafClassification/sample_submission.csv',index_col=False)\n",
    "print(sample_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c4381afb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>Labels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>50</td>\n",
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       "      <th>3</th>\n",
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       "      <td>19</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  Labels\n",
       "0   4      50\n",
       "1   7      50\n",
       "2   9       1\n",
       "3  12      19\n",
       "4  13      14"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output = pd.DataFrame({'Id': test_id,\n",
    "                       \n",
    "                       'Labels': test_pred})\n",
    "output.to_csv('submission.csv', index=False)\n",
    "output.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "90b57f2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>Labels</th>\n",
       "      <th>id</th>\n",
       "      <th>Acer_Capillipes</th>\n",
       "      <th>Acer_Circinatum</th>\n",
       "      <th>Acer_Mono</th>\n",
       "      <th>Acer_Opalus</th>\n",
       "      <th>Acer_Palmatum</th>\n",
       "      <th>Acer_Pictum</th>\n",
       "      <th>Acer_Platanoids</th>\n",
       "      <th>...</th>\n",
       "      <th>Salix_Fragilis</th>\n",
       "      <th>Salix_Intergra</th>\n",
       "      <th>Sorbus_Aria</th>\n",
       "      <th>Tilia_Oliveri</th>\n",
       "      <th>Tilia_Platyphyllos</th>\n",
       "      <th>Tilia_Tomentosa</th>\n",
       "      <th>Ulmus_Bergmanniana</th>\n",
       "      <th>Viburnum_Tinus</th>\n",
       "      <th>Viburnum_x_Rhytidophylloides</th>\n",
       "      <th>Zelkova_Serrata</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>0.010101</td>\n",
       "      <td>0.010101</td>\n",
       "      <td>0.010101</td>\n",
       "      <td>0.010101</td>\n",
       "      <td>0.010101</td>\n",
       "      <td>0.010101</td>\n",
       "      <td>0.010101</td>\n",
       "      <td>0.010101</td>\n",
       "      <td>0.010101</td>\n",
       "      <td>0.010101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>0.010101</td>\n",
       "      <td>0.010101</td>\n",
       "      <td>0.010101</td>\n",
       "      <td>0.010101</td>\n",
       "      <td>0.010101</td>\n",
       "      <td>0.010101</td>\n",
       "      <td>0.010101</td>\n",
       "      <td>...</td>\n",
       "      <td>0.010101</td>\n",
       "      <td>0.010101</td>\n",
       "      <td>0.010101</td>\n",
       "      <td>0.010101</td>\n",
       "      <td>0.010101</td>\n",
       "      <td>0.010101</td>\n",
       "      <td>0.010101</td>\n",
       "      <td>0.010101</td>\n",
       "      <td>0.010101</td>\n",
       "      <td>0.010101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12</td>\n",
       "      <td>19</td>\n",
       "      <td>12</td>\n",
       "      <td>0.010101</td>\n",
       "      <td>0.010101</td>\n",
       "      <td>0.010101</td>\n",
       "      <td>0.010101</td>\n",
       "      <td>0.010101</td>\n",
       "      <td>0.010101</td>\n",
       "      <td>0.010101</td>\n",
       "      <td>...</td>\n",
       "      <td>0.010101</td>\n",
       "      <td>0.010101</td>\n",
       "      <td>0.010101</td>\n",
       "      <td>0.010101</td>\n",
       "      <td>0.010101</td>\n",
       "      <td>0.010101</td>\n",
       "      <td>0.010101</td>\n",
       "      <td>0.010101</td>\n",
       "      <td>0.010101</td>\n",
       "      <td>0.010101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>13</td>\n",
       "      <td>0.010101</td>\n",
       "      <td>0.010101</td>\n",
       "      <td>0.010101</td>\n",
       "      <td>0.010101</td>\n",
       "      <td>0.010101</td>\n",
       "      <td>0.010101</td>\n",
       "      <td>0.010101</td>\n",
       "      <td>...</td>\n",
       "      <td>0.010101</td>\n",
       "      <td>0.010101</td>\n",
       "      <td>0.010101</td>\n",
       "      <td>0.010101</td>\n",
       "      <td>0.010101</td>\n",
       "      <td>0.010101</td>\n",
       "      <td>0.010101</td>\n",
       "      <td>0.010101</td>\n",
       "      <td>0.010101</td>\n",
       "      <td>0.010101</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 102 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  Labels  id  Acer_Capillipes  Acer_Circinatum  Acer_Mono  Acer_Opalus  \\\n",
       "0   4      50   4         0.010101         0.010101   0.010101     0.010101   \n",
       "1   7      50   7         0.010101         0.010101   0.010101     0.010101   \n",
       "2   9       1   9         0.010101         0.010101   0.010101     0.010101   \n",
       "3  12      19  12         0.010101         0.010101   0.010101     0.010101   \n",
       "4  13      14  13         0.010101         0.010101   0.010101     0.010101   \n",
       "\n",
       "   Acer_Palmatum  Acer_Pictum  Acer_Platanoids  ...  Salix_Fragilis  \\\n",
       "0       0.010101     0.010101         0.010101  ...        0.010101   \n",
       "1       0.010101     0.010101         0.010101  ...        0.010101   \n",
       "2       0.010101     0.010101         0.010101  ...        0.010101   \n",
       "3       0.010101     0.010101         0.010101  ...        0.010101   \n",
       "4       0.010101     0.010101         0.010101  ...        0.010101   \n",
       "\n",
       "   Salix_Intergra  Sorbus_Aria  Tilia_Oliveri  Tilia_Platyphyllos  \\\n",
       "0        0.010101     0.010101       0.010101            0.010101   \n",
       "1        0.010101     0.010101       0.010101            0.010101   \n",
       "2        0.010101     0.010101       0.010101            0.010101   \n",
       "3        0.010101     0.010101       0.010101            0.010101   \n",
       "4        0.010101     0.010101       0.010101            0.010101   \n",
       "\n",
       "   Tilia_Tomentosa  Ulmus_Bergmanniana  Viburnum_Tinus  \\\n",
       "0         0.010101            0.010101        0.010101   \n",
       "1         0.010101            0.010101        0.010101   \n",
       "2         0.010101            0.010101        0.010101   \n",
       "3         0.010101            0.010101        0.010101   \n",
       "4         0.010101            0.010101        0.010101   \n",
       "\n",
       "   Viburnum_x_Rhytidophylloides  Zelkova_Serrata  \n",
       "0                      0.010101         0.010101  \n",
       "1                      0.010101         0.010101  \n",
       "2                      0.010101         0.010101  \n",
       "3                      0.010101         0.010101  \n",
       "4                      0.010101         0.010101  \n",
       "\n",
       "[5 rows x 102 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final=pd.concat([output,sample_df],axis=1)\n",
    "final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5d595c48",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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