SVM.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from IPython.display import display, clear_output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\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>id</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>hypertension</th>\n",
       "      <th>heart_disease</th>\n",
       "      <th>ever_married</th>\n",
       "      <th>work_type</th>\n",
       "      <th>Residence_type</th>\n",
       "      <th>avg_glucose_level</th>\n",
       "      <th>bmi</th>\n",
       "      <th>smoking_status</th>\n",
       "      <th>stroke</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30669</td>\n",
       "      <td>Male</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>children</td>\n",
       "      <td>Rural</td>\n",
       "      <td>95.12</td>\n",
       "      <td>18.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30468</td>\n",
       "      <td>Male</td>\n",
       "      <td>58.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>87.96</td>\n",
       "      <td>39.2</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16523</td>\n",
       "      <td>Female</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>110.89</td>\n",
       "      <td>17.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56543</td>\n",
       "      <td>Female</td>\n",
       "      <td>70.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>69.04</td>\n",
       "      <td>35.9</td>\n",
       "      <td>formerly smoked</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>46136</td>\n",
       "      <td>Male</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>Never_worked</td>\n",
       "      <td>Rural</td>\n",
       "      <td>161.28</td>\n",
       "      <td>19.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id  gender   age  hypertension  heart_disease ever_married  \\\n",
       "0  30669    Male   3.0             0              0           No   \n",
       "1  30468    Male  58.0             1              0          Yes   \n",
       "2  16523  Female   8.0             0              0           No   \n",
       "3  56543  Female  70.0             0              0          Yes   \n",
       "4  46136    Male  14.0             0              0           No   \n",
       "\n",
       "      work_type Residence_type  avg_glucose_level   bmi   smoking_status  \\\n",
       "0      children          Rural              95.12  18.0              NaN   \n",
       "1       Private          Urban              87.96  39.2     never smoked   \n",
       "2       Private          Urban             110.89  17.6              NaN   \n",
       "3       Private          Rural              69.04  35.9  formerly smoked   \n",
       "4  Never_worked          Rural             161.28  19.1              NaN   \n",
       "\n",
       "   stroke  \n",
       "0       0  \n",
       "1       0  \n",
       "2       0  \n",
       "3       0  \n",
       "4       0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset= pd.read_csv(\"stroke prediction.csv\")\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset.drop(columns=[\"id\",\"Residence_type\"],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset[\"gender\"]=dataset[\"gender\"].astype('category')\n",
    "dataset[\"ever_married\"]=dataset[\"ever_married\"].astype('category')\n",
    "# dataset[\"Residence_type\"]=dataset[\"Residence_type\"].astype('category')\n",
    "dataset[\"work_type\"]=dataset[\"work_type\"].astype('category')\n",
    "dataset[\"smoking_status\"]=dataset.smoking_status.astype(\"category\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_imputed=dataset.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender               category\n",
       "age                   float64\n",
       "hypertension            int64\n",
       "heart_disease           int64\n",
       "ever_married         category\n",
       "work_type            category\n",
       "avg_glucose_level     float64\n",
       "bmi                   float64\n",
       "smoking_status       category\n",
       "stroke                  int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_imputed.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "encoder=LabelEncoder()\n",
    "\n",
    "dataset_imputed.gender=encoder.fit_transform(dataset_imputed.gender)\n",
    "\n",
    "dataset_imputed.ever_married=encoder.fit_transform(dataset_imputed.ever_married)\n",
    "\n",
    "dataset_imputed.work_type=encoder.fit_transform(dataset_imputed.work_type)\n",
    "\n",
    "# dataset_imputed.Residence_type=encoder.fit_transform(dataset_imputed.Residence_type)\n",
    "\n",
    "dataset_imputed.smoking_status=dataset_imputed.smoking_status.map({'never smoked':0,'formerly smoked':1,'smokes':2})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=dataset_imputed.drop(columns=\"stroke\",axis=1)\n",
    "y=dataset_imputed[\"stroke\"]\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train,X_test,y_train,y_test= train_test_split(X,y,test_size=0.2,random_state=88,stratify=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.impute import KNNImputer\n",
    "\n",
    "imputer=KNNImputer()\n",
    "\n",
    "X_trimputed=pd.DataFrame(imputer.fit_transform(X_train),columns=X_train.columns)\n",
    "\n",
    "X_teimputed=pd.DataFrame(imputer.transform(X_test),columns=X_test.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_trimputed[\"smoking_status\"]=X_trimputed[\"smoking_status\"].apply(round)\n",
    "X_trimputed[\"smoking_status\"].value_counts()\n",
    "\n",
    "X_trimputed[\"smoking_status\"]=X_trimputed[\"smoking_status\"].astype('category')\n",
    "X_trimputed[\"work_type\"]=X_trimputed[\"work_type\"].apply(int)\n",
    "X_trimputed[\"work_type\"]=X_trimputed[\"work_type\"].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_teimputed[\"smoking_status\"]=X_teimputed[\"smoking_status\"].apply(round)\n",
    "X_teimputed[\"smoking_status\"].value_counts()\n",
    "\n",
    "X_teimputed[\"smoking_status\"]=X_teimputed[\"smoking_status\"].astype('category')\n",
    "X_teimputed[\"work_type\"]=X_teimputed[\"work_type\"].apply(int)\n",
    "X_teimputed[\"work_type\"]=X_teimputed[\"work_type\"].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_trimputed[\"gender\"]=X_trimputed[\"gender\"].astype(\"category\")\n",
    "X_trimputed[[\"hypertension\",\"heart_disease\",\"ever_married\"]]=X_trimputed[[\"hypertension\",\"heart_disease\",\"ever_married\"]].astype(\"int\")\n",
    "\n",
    "X_teimputed[\"gender\"]=X_teimputed[\"gender\"].astype(\"category\")\n",
    "X_teimputed[[\"hypertension\",\"heart_disease\",\"ever_married\"]]=X_teimputed[[\"hypertension\",\"heart_disease\",\"ever_married\"]].astype(\"int\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_trimputed=pd.get_dummies(X_trimputed,drop_first=True)\n",
    "X_teimputed=pd.get_dummies(X_teimputed,drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "        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>age</th>\n",
       "      <th>hypertension</th>\n",
       "      <th>heart_disease</th>\n",
       "      <th>ever_married</th>\n",
       "      <th>avg_glucose_level</th>\n",
       "      <th>bmi</th>\n",
       "      <th>gender_1.0</th>\n",
       "      <th>gender_2.0</th>\n",
       "      <th>work_type_1</th>\n",
       "      <th>work_type_2</th>\n",
       "      <th>work_type_3</th>\n",
       "      <th>work_type_4</th>\n",
       "      <th>smoking_status_1</th>\n",
       "      <th>smoking_status_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>136.51</td>\n",
       "      <td>17.1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>90.55</td>\n",
       "      <td>27.3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>29.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>108.69</td>\n",
       "      <td>29.6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>63.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>84.57</td>\n",
       "      <td>31.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>67.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>70.43</td>\n",
       "      <td>26.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34715</th>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>58.90</td>\n",
       "      <td>29.1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34716</th>\n",
       "      <td>43.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>94.82</td>\n",
       "      <td>29.7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34717</th>\n",
       "      <td>23.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>98.66</td>\n",
       "      <td>28.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34718</th>\n",
       "      <td>9.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>150.00</td>\n",
       "      <td>17.4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34719</th>\n",
       "      <td>31.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>77.38</td>\n",
       "      <td>39.6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>34720 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        age  hypertension  heart_disease  ever_married  avg_glucose_level  \\\n",
       "0       4.0             0              0             0             136.51   \n",
       "1       9.0             0              0             0              90.55   \n",
       "2      29.0             0              0             1             108.69   \n",
       "3      63.0             0              0             1              84.57   \n",
       "4      67.0             0              0             1              70.43   \n",
       "...     ...           ...            ...           ...                ...   \n",
       "34715  62.0             0              0             1              58.90   \n",
       "34716  43.0             0              0             0              94.82   \n",
       "34717  23.0             0              0             0              98.66   \n",
       "34718   9.0             0              0             0             150.00   \n",
       "34719  31.0             0              0             1              77.38   \n",
       "\n",
       "        bmi  gender_1.0  gender_2.0  work_type_1  work_type_2  work_type_3  \\\n",
       "0      17.1           1           0            0            0            0   \n",
       "1      27.3           1           0            0            0            0   \n",
       "2      29.6           1           0            0            1            0   \n",
       "3      31.2           1           0            0            1            0   \n",
       "4      26.3           0           0            0            0            1   \n",
       "...     ...         ...         ...          ...          ...          ...   \n",
       "34715  29.1           0           0            0            1            0   \n",
       "34716  29.7           0           0            0            0            0   \n",
       "34717  28.3           0           0            0            1            0   \n",
       "34718  17.4           1           0            0            0            0   \n",
       "34719  39.6           0           0            0            0            1   \n",
       "\n",
       "       work_type_4  smoking_status_1  smoking_status_2  \n",
       "0                1                 0                 0  \n",
       "1                1                 0                 0  \n",
       "2                0                 0                 0  \n",
       "3                0                 1                 0  \n",
       "4                0                 0                 0  \n",
       "...            ...               ...               ...  \n",
       "34715            0                 1                 0  \n",
       "34716            0                 0                 0  \n",
       "34717            0                 0                 0  \n",
       "34718            1                 0                 0  \n",
       "34719            0                 0                 0  \n",
       "\n",
       "[34720 rows x 14 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_trimputed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "scaler=MinMaxScaler()\n",
    "\n",
    "scaler.fit(X_trimputed)\n",
    "\n",
    "X_trimputed=pd.DataFrame(scaler.transform(X_trimputed),columns=X_trimputed.columns)\n",
    "\n",
    "X_teimputed=pd.DataFrame(scaler.transform(X_teimputed),columns=X_teimputed.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\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>age</th>\n",
       "      <th>hypertension</th>\n",
       "      <th>heart_disease</th>\n",
       "      <th>ever_married</th>\n",
       "      <th>avg_glucose_level</th>\n",
       "      <th>bmi</th>\n",
       "      <th>gender_1.0</th>\n",
       "      <th>gender_2.0</th>\n",
       "      <th>work_type_1</th>\n",
       "      <th>work_type_2</th>\n",
       "      <th>work_type_3</th>\n",
       "      <th>work_type_4</th>\n",
       "      <th>smoking_status_1</th>\n",
       "      <th>smoking_status_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.047852</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.345280</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.108887</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.150568</td>\n",
       "      <td>0.196571</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        age  hypertension  heart_disease  ever_married  avg_glucose_level  \\\n",
       "0  0.047852           0.0            0.0           0.0           0.345280   \n",
       "1  0.108887           0.0            0.0           0.0           0.150568   \n",
       "\n",
       "        bmi  gender_1.0  gender_2.0  work_type_1  work_type_2  work_type_3  \\\n",
       "0  0.080000         1.0         0.0          0.0          0.0          0.0   \n",
       "1  0.196571         1.0         0.0          0.0          0.0          0.0   \n",
       "\n",
       "   work_type_4  smoking_status_1  smoking_status_2  \n",
       "0          1.0               0.0               0.0  \n",
       "1          1.0               0.0               0.0  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_trimputed.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SVM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Kernel='rbf'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import classification_report as rep\n",
    "from sklearn.metrics import f1_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "support=SVC(class_weight='balanced',random_state=40,probability=True)\n",
    "\n",
    "support.fit(X_trimputed,y_train)\n",
    "y_train_pred=support.predict(X_trimputed)\n",
    "\n",
    "y_test_pred=support.predict(X_teimputed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "print(\"Train Report\")\n",
    "\n",
    "print(rep(y_train, y_train_pred))\n",
    "\n",
    "print(\"Test Report\")\n",
    "\n",
    "print(rep(y_test, y_test_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "f1_score(y_train,y_train_pred),f1_score(y_test,y_test_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_prob=support.predict_proba(X_trimputed)[:,1]\n",
    "y_train_prob\n",
    "\n",
    "y_test_prob=support.predict_proba(X_teimputed)[:,1]\n",
    "y_test_prob\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_auc_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "roc_auc_score(y_train,y_train_prob),roc_auc_score(y_test,y_test_prob)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Kernel='linear'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import classification_report as rep\n",
    "from sklearn.metrics import f1_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "support=SVC(class_weight='balanced',random_state=40,kernel='linear',probability=True)\n",
    "\n",
    "support.fit(X_trimputed,y_train)\n",
    "y_train_pred=support.predict(X_trimputed)\n",
    "\n",
    "y_test_pred=support.predict(X_teimputed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"Train Report\")\n",
    "\n",
    "print(rep(y_train, y_train_pred))\n",
    "\n",
    "print(\"Test Report\")\n",
    "\n",
    "print(rep(y_test, y_test_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "f1_score(y_train,y_train_pred),f1_score(y_test,y_test_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_prob=support.predict_proba(X_trimputed)[:,1]\n",
    "y_train_prob\n",
    "\n",
    "y_test_prob=support.predict_proba(X_teimputed)[:,1]\n",
    "y_test_prob\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_auc_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "roc_auc_score(y_train,y_train_prob),roc_auc_score(y_test,y_test_prob)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Kernel='polynomial'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import classification_report as rep\n",
    "from sklearn.metrics import f1_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "support=SVC(class_weight='balanced',random_state=40,kernel='poly',probability=True)\n",
    "\n",
    "support.fit(X_trimputed,y_train)\n",
    "y_train_pred=support.predict(X_trimputed)\n",
    "\n",
    "y_test_pred=support.predict(X_teimputed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"Train Report\")\n",
    "\n",
    "print(rep(y_train, y_train_pred))\n",
    "\n",
    "print(\"Test Report\")\n",
    "\n",
    "print(rep(y_test, y_test_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "f1_score(y_train,y_train_pred),f1_score(y_test,y_test_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "y_train_prob=support.predict_proba(X_trimputed)[:,1]\n",
    "y_train_prob\n",
    "\n",
    "y_test_prob=support.predict_proba(X_teimputed)[:,1]\n",
    "y_test_prob\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_auc_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "roc_auc_score(y_train,y_train_prob),roc_auc_score(y_test,y_test_prob)"
   ]
  },
  {
   "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": []
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "__init__() got an unexpected keyword argument 'n_jobs'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-19-bc4987a9cc37>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m     \u001b[0msupport\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mSVC\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mclass_weight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'balanced'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mrandom_state\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m40\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mkernel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'poly'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mprobability\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mdegree\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mn_jobs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m     \u001b[0msupport\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_trimputed\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0my_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[0my_train_pred\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msupport\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_trimputed\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: __init__() got an unexpected keyword argument 'n_jobs'"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "for i in range(2,10):\n",
    "    support=SVC(class_weight='balanced',random_state=40,kernel='poly',probability=True,degree=i)\n",
    "\n",
    "    support.fit(X_trimputed,y_train)\n",
    "    y_train_pred=support.predict(X_trimputed)\n",
    "\n",
    "    y_test_pred=support.predict(X_teimputed)\n",
    "\n",
    "    roc_auc_score(y_train,y_train_prob),roc_auc_score(y_test,y_test_prob)\n",
    "\n",
    "    y_train_prob=support.predict_proba(X_trimputed)[:,1]\n",
    "    y_train_prob\n",
    "\n",
    "    y_test_prob=support.predict_proba(X_teimputed)[:,1]\n",
    "    y_test_prob\n",
    "\n",
    "    print(i,f1_score(y_train,y_train_pred),f1_score(y_test,y_test_pred),roc_auc_score(y_train,y_train_prob),roc_auc_score(y_test,y_test_prob))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ensembling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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