Diabetics.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "13359fa4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2aeaad5b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.627   50        1  \n",
       "1                     0.351   31        0  \n",
       "2                     0.672   32        1  \n",
       "3                     0.167   21        0  \n",
       "4                     2.288   33        1  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('diabetes.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ffa37cac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(768, 9)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "11015530",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pregnancies                 0\n",
       "Glucose                     0\n",
       "BloodPressure               0\n",
       "SkinThickness               0\n",
       "Insulin                     0\n",
       "BMI                         0\n",
       "DiabetesPedigreeFunction    0\n",
       "Age                         0\n",
       "Outcome                     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d491e3a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pregnancies                   int64\n",
       "Glucose                       int64\n",
       "BloodPressure                 int64\n",
       "SkinThickness                 int64\n",
       "Insulin                       int64\n",
       "BMI                         float64\n",
       "DiabetesPedigreeFunction    float64\n",
       "Age                           int64\n",
       "Outcome                       int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5eb3eec5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(768,)\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "<class 'pandas.core.series.Series'>\n"
     ]
    }
   ],
   "source": [
    "x = df.iloc[:,:-1]   \n",
    "y = df.iloc[:,-1]  \n",
    "print(y.shape)\n",
    "print(type(x))\n",
    "print(type(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d58efdac",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  \n",
       "0                     0.627   50  \n",
       "1                     0.351   31  \n",
       "2                     0.672   32  \n",
       "3                     0.167   21  \n",
       "4                     2.288   33  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2315c194",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2e67e863",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(576, 8)\n",
      "(192, 8)\n",
      "(576,)\n",
      "(192,)\n"
     ]
    }
   ],
   "source": [
    "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.25)\n",
    "print(x_train.shape)\n",
    "print(x_test.shape)\n",
    "print(y_train.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1918a90f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "576.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "768*0.75"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7642670a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix,classification_report,accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "915e789d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def gen_metrics(ytest,ypred):\n",
    "    cm = confusion_matrix(ytest,ypred)\n",
    "    print('confusion matrix\\n',cm)\n",
    "    print('Classification report\\n',classification_report(ytest,ypred))\n",
    "    print('Acc score',accuracy_score(ytest,ypred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "eaf49b73",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8d0377d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(max_iter=1000)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m1 = LogisticRegression(max_iter=1000)\n",
    "m1.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4b20696d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training score 0.7829861111111112\n",
      "Testing score 0.734375\n"
     ]
    }
   ],
   "source": [
    "print('Training score',m1.score(x_train,y_train))\n",
    "print('Testing score',m1.score(x_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ff8eac0b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 0 1 0 1 1 1 1 0 0 0 1 0 0 0 1 0 0 0 0 0 1 0 1 1 1 1 0 0 0 0 0 0 0 0 0 1\n",
      " 0 0 1 0 0 0 0 1 0 1 0 0 0 1 0 0 0 1 0 0 0 1 0 1 0 0 0 0 0 0 1 0 1 1 0 1 0\n",
      " 1 0 0 0 1 1 1 0 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 1 0\n",
      " 0 1 0 0 0 1 0 0 0 0 0 1 1 0 0 1 0 0 0 0 1 0 0 0 1 0 0 0 0 1 1 1 1 0 0 0 0\n",
      " 0 0 0 0 1 0 0 1 0 1 0 0 0 0 0 1 1 0 1 1 0 0 0 0 1 0 0 1 1 1 0 1 0 0 1 0 0\n",
      " 0 0 0 1 0 1 0]\n"
     ]
    }
   ],
   "source": [
    "ypred_m1 = m1.predict(x_test)\n",
    "print(ypred_m1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "631b6215",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "confusion matrix\n",
      " [[108  28]\n",
      " [ 23  33]]\n",
      "Classification report\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      0.79      0.81       136\n",
      "           1       0.54      0.59      0.56        56\n",
      "\n",
      "    accuracy                           0.73       192\n",
      "   macro avg       0.68      0.69      0.69       192\n",
      "weighted avg       0.74      0.73      0.74       192\n",
      "\n",
      "Acc score 0.734375\n"
     ]
    }
   ],
   "source": [
    "gen_metrics(y_test,ypred_m1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9c799e43",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "fb7ab7b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=17)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m2 = KNeighborsClassifier(n_neighbors=17)\n",
    "m2.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "ab14a9f8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training score 0.78125\n",
      "Testing score 0.7291666666666666\n"
     ]
    }
   ],
   "source": [
    "print('Training score',m2.score(x_train,y_train))\n",
    "print('Testing score',m2.score(x_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "dfb1c5e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 0 1 0 1 1 1 1 0 0 0 1 0 0 0 1 0 0 0 0 0 1 0 1 1 1 1 0 1 0 0 0 0 1 0 1 1\n",
      " 0 0 1 0 0 0 0 1 0 1 0 0 0 1 0 0 0 0 0 0 0 1 0 1 1 0 0 0 0 0 1 0 0 1 0 1 0\n",
      " 1 0 0 0 0 1 1 0 1 0 1 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0\n",
      " 0 1 0 0 1 1 0 0 0 0 0 0 1 0 0 1 0 0 0 0 1 0 0 0 1 0 0 1 0 1 1 1 1 0 1 0 0\n",
      " 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 1 0 1 0 1 0 0 0 0 1 0 0 1 1 1 0 1 0 0 1 0 0\n",
      " 0 0 0 1 0 1 0]\n"
     ]
    }
   ],
   "source": [
    "ypred_m2 = m2.predict(x_test)\n",
    "print(ypred_m2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "50df2589",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "confusion matrix\n",
      " [[107  29]\n",
      " [ 23  33]]\n",
      "Classification report\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      0.79      0.80       136\n",
      "           1       0.53      0.59      0.56        56\n",
      "\n",
      "    accuracy                           0.73       192\n",
      "   macro avg       0.68      0.69      0.68       192\n",
      "weighted avg       0.74      0.73      0.73       192\n",
      "\n",
      "Acc score 0.7291666666666666\n"
     ]
    }
   ],
   "source": [
    "gen_metrics(y_test,ypred_m2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "15cbafad",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a77d19a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(max_depth=5, min_samples_split=15)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m3 = DecisionTreeClassifier(criterion='gini',max_depth=5,min_samples_split=15)\n",
    "# \n",
    "m3.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "0a324348",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training score 0.8368055555555556\n",
      "Testing score 0.6927083333333334\n"
     ]
    }
   ],
   "source": [
    "print('Training score',m3.score(x_train,y_train))\n",
    "print('Testing score',m3.score(x_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "5359394c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 0 0 0 1 1 0 1 0 0 0 1 0 0 1 1 0 0 0 0 1 1 0 1 1 1 1 0 0 0 0 0 0 0 0 1 1\n",
      " 0 0 1 0 0 0 0 1 0 1 0 0 0 0 1 1 0 1 0 0 0 0 1 1 0 1 0 0 0 0 1 0 0 1 1 1 0\n",
      " 0 0 1 0 1 1 1 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0\n",
      " 0 1 0 0 1 0 0 1 0 0 0 1 1 0 0 1 0 0 0 0 1 0 0 0 1 1 1 0 0 1 0 0 1 0 1 0 0\n",
      " 0 0 0 0 1 0 0 1 0 1 0 0 1 0 0 1 1 1 0 1 1 0 0 0 1 0 0 1 0 0 0 1 0 0 0 0 0\n",
      " 0 1 0 1 1 1 0]\n"
     ]
    }
   ],
   "source": [
    "ypred_m3 = m3.predict(x_test)\n",
    "print(ypred_m3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "530e4cad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "confusion matrix\n",
      " [[102  34]\n",
      " [ 25  31]]\n",
      "Classification report\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.80      0.75      0.78       136\n",
      "           1       0.48      0.55      0.51        56\n",
      "\n",
      "    accuracy                           0.69       192\n",
      "   macro avg       0.64      0.65      0.64       192\n",
      "weighted avg       0.71      0.69      0.70       192\n",
      "\n",
      "Acc score 0.6927083333333334\n"
     ]
    }
   ],
   "source": [
    "gen_metrics(y_test,ypred_m3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "d1432073",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "15fa1566",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(criterion='entropy', max_depth=6, min_samples_split=12,\n",
       "                       n_estimators=50)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m4 = RandomForestClassifier(n_estimators=50,criterion='entropy',max_depth=6,min_samples_split=12)\n",
    "m4.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "88fce177",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training score 0.8732638888888888\n",
      "Testing score 0.7395833333333334\n"
     ]
    }
   ],
   "source": [
    "print('Training score',m4.score(x_train,y_train))\n",
    "print('Testing score',m4.score(x_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "33c4b8d6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 0 1 0 1 1 1 1 0 0 0 1 0 0 0 1 0 0 0 0 0 1 0 1 1 1 1 0 1 0 0 0 0 0 0 0 1\n",
      " 0 0 1 0 0 0 0 1 0 1 0 0 0 1 0 0 0 0 0 0 0 1 0 1 0 1 0 0 0 0 1 0 0 1 0 1 0\n",
      " 1 0 0 0 1 1 1 0 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0\n",
      " 0 1 0 0 0 0 0 1 0 0 0 1 1 0 0 1 0 0 0 0 1 0 0 0 1 0 0 0 0 1 0 1 1 0 0 0 0\n",
      " 0 0 0 0 1 0 0 1 0 1 0 0 0 0 1 1 1 1 1 1 0 0 0 0 1 0 0 1 1 1 0 1 0 0 0 0 0\n",
      " 0 0 0 1 0 1 0]\n"
     ]
    }
   ],
   "source": [
    "ypred_m4 = m4.predict(x_test)\n",
    "print(ypred_m4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "94aea661",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "confusion matrix\n",
      " [[109  27]\n",
      " [ 23  33]]\n",
      "Classification report\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      0.80      0.81       136\n",
      "           1       0.55      0.59      0.57        56\n",
      "\n",
      "    accuracy                           0.74       192\n",
      "   macro avg       0.69      0.70      0.69       192\n",
      "weighted avg       0.75      0.74      0.74       192\n",
      "\n",
      "Acc score 0.7395833333333334\n"
     ]
    }
   ],
   "source": [
    "gen_metrics(y_test,ypred_m4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "11da1e01",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "1931745c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1, kernel='linear')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m5 = SVC(kernel='linear',C=1)\n",
    "m5.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "606f5c7b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training score 0.7847222222222222\n",
      "Testing score 0.7291666666666666\n"
     ]
    }
   ],
   "source": [
    "print('Training score',m5.score(x_train,y_train))\n",
    "print('Testing score',m5.score(x_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "45e6f231",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 0 1 0 1 1 1 1 0 0 0 1 0 0 0 1 0 0 0 0 0 1 0 1 1 1 1 0 0 0 0 0 0 0 0 0 1\n",
      " 0 0 1 0 0 0 0 1 0 1 0 0 0 1 0 0 0 1 0 0 0 1 0 1 0 0 0 0 0 0 1 0 1 1 0 1 0\n",
      " 1 0 0 0 1 1 1 0 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 1 0\n",
      " 0 1 0 0 0 0 0 0 0 0 0 1 1 0 0 1 0 0 0 0 1 0 0 0 1 0 0 0 0 1 1 1 1 0 0 0 0\n",
      " 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 1 1 0 1 1 0 0 0 0 1 0 0 1 1 1 0 1 0 0 0 0 0\n",
      " 0 0 0 1 0 1 0]\n"
     ]
    }
   ],
   "source": [
    "ypred_m5 = m5.predict(x_test)\n",
    "print(ypred_m5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "aa5d9da8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "confusion matrix\n",
      " [[109  27]\n",
      " [ 25  31]]\n",
      "Classification report\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.81      0.80      0.81       136\n",
      "           1       0.53      0.55      0.54        56\n",
      "\n",
      "    accuracy                           0.73       192\n",
      "   macro avg       0.67      0.68      0.68       192\n",
      "weighted avg       0.73      0.73      0.73       192\n",
      "\n",
      "Acc score 0.7291666666666666\n"
     ]
    }
   ],
   "source": [
    "gen_metrics(y_test,ypred_m5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f19f5149",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "31841dc5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Conclusion\n",
    "#1) Log_Reg is the best performining model in terms of accuracy.\n",
    "#2) Log_Reg and SVC are the best performining model in terms of recall"
   ]
  }
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