preparation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "republican-requirement",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "continent-contemporary",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        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>9046</td>\n",
       "      <td>Male</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>228.69</td>\n",
       "      <td>36.6</td>\n",
       "      <td>formerly smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>51676</td>\n",
       "      <td>Female</td>\n",
       "      <td>61.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>Rural</td>\n",
       "      <td>202.21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>31112</td>\n",
       "      <td>Male</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>105.92</td>\n",
       "      <td>32.5</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>60182</td>\n",
       "      <td>Female</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>171.23</td>\n",
       "      <td>34.4</td>\n",
       "      <td>smokes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1665</td>\n",
       "      <td>Female</td>\n",
       "      <td>79.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>Rural</td>\n",
       "      <td>174.12</td>\n",
       "      <td>24.0</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>5105</th>\n",
       "      <td>18234</td>\n",
       "      <td>Female</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>83.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5106</th>\n",
       "      <td>44873</td>\n",
       "      <td>Female</td>\n",
       "      <td>81.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>Urban</td>\n",
       "      <td>125.20</td>\n",
       "      <td>40.0</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5107</th>\n",
       "      <td>19723</td>\n",
       "      <td>Female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>Rural</td>\n",
       "      <td>82.99</td>\n",
       "      <td>30.6</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5108</th>\n",
       "      <td>37544</td>\n",
       "      <td>Male</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>166.29</td>\n",
       "      <td>25.6</td>\n",
       "      <td>formerly smoked</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5109</th>\n",
       "      <td>44679</td>\n",
       "      <td>Female</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Govt_job</td>\n",
       "      <td>Urban</td>\n",
       "      <td>85.28</td>\n",
       "      <td>26.2</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4194 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         id  gender   age  hypertension  heart_disease ever_married  \\\n",
       "0      9046    Male  67.0             0              1          Yes   \n",
       "1     51676  Female  61.0             0              0          Yes   \n",
       "2     31112    Male  80.0             0              1          Yes   \n",
       "3     60182  Female  49.0             0              0          Yes   \n",
       "4      1665  Female  79.0             1              0          Yes   \n",
       "...     ...     ...   ...           ...            ...          ...   \n",
       "5105  18234  Female  80.0             1              0          Yes   \n",
       "5106  44873  Female  81.0             0              0          Yes   \n",
       "5107  19723  Female  35.0             0              0          Yes   \n",
       "5108  37544    Male  51.0             0              0          Yes   \n",
       "5109  44679  Female  44.0             0              0          Yes   \n",
       "\n",
       "          work_type Residence_type  avg_glucose_level   bmi   smoking_status  \\\n",
       "0           Private          Urban             228.69  36.6  formerly smoked   \n",
       "1     Self-employed          Rural             202.21   NaN     never smoked   \n",
       "2           Private          Rural             105.92  32.5     never smoked   \n",
       "3           Private          Urban             171.23  34.4           smokes   \n",
       "4     Self-employed          Rural             174.12  24.0     never smoked   \n",
       "...             ...            ...                ...   ...              ...   \n",
       "5105        Private          Urban              83.75   NaN     never smoked   \n",
       "5106  Self-employed          Urban             125.20  40.0     never smoked   \n",
       "5107  Self-employed          Rural              82.99  30.6     never smoked   \n",
       "5108        Private          Rural             166.29  25.6  formerly smoked   \n",
       "5109       Govt_job          Urban              85.28  26.2          Unknown   \n",
       "\n",
       "      stroke  \n",
       "0          1  \n",
       "1          1  \n",
       "2          1  \n",
       "3          1  \n",
       "4          1  \n",
       "...      ...  \n",
       "5105       0  \n",
       "5106       0  \n",
       "5107       0  \n",
       "5108       0  \n",
       "5109       0  \n",
       "\n",
       "[4194 rows x 12 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('E:\\\\term8\\\\datamining\\\\HW\\\\project\\\\healthcare-dataset-stroke-data.csv')\n",
    "df = df[df.age > 18]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "loose-filename",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                     0\n",
       "gender                 0\n",
       "age                    0\n",
       "hypertension           0\n",
       "heart_disease          0\n",
       "ever_married           0\n",
       "work_type              0\n",
       "Residence_type         0\n",
       "avg_glucose_level      0\n",
       "bmi                  180\n",
       "smoking_status         0\n",
       "stroke                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "patient-multimedia",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5109</th>\n",
       "      <td>-0.380039</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.486479</td>\n",
       "      <td>26.2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4014 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           age  hypertension  heart_disease  avg_glucose_level   bmi  stroke  \\\n",
       "0     0.931846             0              1           2.500974  36.6       1   \n",
       "2     1.673346             0              1          -0.056516  32.5       1   \n",
       "3    -0.094846             0              0           1.303993  34.4       1   \n",
       "4     1.616307             1              0           1.364196  24.0       1   \n",
       "5     1.730384             0              0           1.616050  29.0       1   \n",
       "...        ...           ...            ...                ...   ...     ...   \n",
       "5102  0.361461             0              0          -0.639591  21.7       0   \n",
       "5106  1.730384             0              0           0.345117  40.0       0   \n",
       "5107 -0.893385             0              0          -0.534184  30.6       0   \n",
       "5108  0.019230             0              0           1.201085  25.6       0   \n",
       "5109 -0.380039             0              0          -0.486479  26.2       0   \n",
       "\n",
       "      work_type_Govt_job  work_type_Never_worked  work_type_Private  \\\n",
       "0                      0                       0                  1   \n",
       "2                      0                       0                  1   \n",
       "3                      0                       0                  1   \n",
       "4                      0                       0                  0   \n",
       "5                      0                       0                  1   \n",
       "...                  ...                     ...                ...   \n",
       "5102                   0                       0                  1   \n",
       "5106                   0                       0                  0   \n",
       "5107                   0                       0                  0   \n",
       "5108                   0                       0                  1   \n",
       "5109                   1                       0                  0   \n",
       "\n",
       "      work_type_Self-employed  ...  smoking_status_formerly smoked  \\\n",
       "0                           0  ...                               1   \n",
       "2                           0  ...                               0   \n",
       "3                           0  ...                               0   \n",
       "4                           1  ...                               0   \n",
       "5                           0  ...                               1   \n",
       "...                       ...  ...                             ...   \n",
       "5102                        0  ...                               0   \n",
       "5106                        1  ...                               0   \n",
       "5107                        1  ...                               0   \n",
       "5108                        0  ...                               1   \n",
       "5109                        0  ...                               0   \n",
       "\n",
       "      smoking_status_never smoked  smoking_status_smokes  gender_Female  \\\n",
       "0                               0                      0              0   \n",
       "2                               1                      0              0   \n",
       "3                               0                      1              1   \n",
       "4                               1                      0              1   \n",
       "5                               0                      0              0   \n",
       "...                           ...                    ...            ...   \n",
       "5102                            1                      0              1   \n",
       "5106                            1                      0              1   \n",
       "5107                            1                      0              1   \n",
       "5108                            0                      0              0   \n",
       "5109                            0                      0              1   \n",
       "\n",
       "      gender_Male  gender_Other  ever_married_No  ever_married_Yes  \\\n",
       "0               1             0                0                 1   \n",
       "2               1             0                0                 1   \n",
       "3               0             0                0                 1   \n",
       "4               0             0                0                 1   \n",
       "5               1             0                0                 1   \n",
       "...           ...           ...              ...               ...   \n",
       "5102            0             0                0                 1   \n",
       "5106            0             0                0                 1   \n",
       "5107            0             0                0                 1   \n",
       "5108            1             0                0                 1   \n",
       "5109            0             0                0                 1   \n",
       "\n",
       "      Residence_type_Rural  Residence_type_Urban  \n",
       "0                        0                     1  \n",
       "2                        1                     0  \n",
       "3                        0                     1  \n",
       "4                        1                     0  \n",
       "5                        0                     1  \n",
       "...                    ...                   ...  \n",
       "5102                     1                     0  \n",
       "5106                     0                     1  \n",
       "5107                     1                     0  \n",
       "5108                     1                     0  \n",
       "5109                     0                     1  \n",
       "\n",
       "[4014 rows x 21 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = df\n",
    "df1 = pd.get_dummies(df1, columns = ['work_type','smoking_status','gender','ever_married','Residence_type'])\n",
    "df1 = df1.drop(columns = ['id'])\n",
    "df1.age = (df1.age - df1.age.mean())/df1.age.std()\n",
    "df1.avg_glucose_level = (df1.avg_glucose_level - df1.avg_glucose_level.mean())/df1.avg_glucose_level.std()\n",
    "df_bmi_na = df1[df1.bmi.isnull()]\n",
    "df1 = df1.dropna()\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "vital-discretion",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "import random\n",
    "df_train, df_test = train_test_split(df1, test_size = 0.2, random_state = 45)\n",
    "ds_train = df_train.values\n",
    "ds_test = df_test.values\n",
    "X = np.concatenate((ds_train[:,0:4],ds_train[:,5:20]),axis = 1)\n",
    "y = ds_train[:,4]\n",
    "X_test = np.concatenate((ds_test[:,0:4],ds_test[:,5:20]),axis = 1)\n",
    "y_test = ds_test[:,4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "alike-netherlands",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "reg = LinearRegression().fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "driven-destiny",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.329332883675776"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = reg.predict(X)\n",
    "temp = np.abs(y_pred - y)\n",
    "temp.sum()/len(temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "amateur-leave",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.365419799809714"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = reg.predict(X_test)\n",
    "temp = np.abs(y_pred - y_test)\n",
    "temp.sum()/len(temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "voluntary-richardson",
   "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>avg_glucose_level</th>\n",
       "      <th>bmi</th>\n",
       "      <th>stroke</th>\n",
       "      <th>work_type_Govt_job</th>\n",
       "      <th>work_type_Never_worked</th>\n",
       "      <th>work_type_Private</th>\n",
       "      <th>work_type_Self-employed</th>\n",
       "      <th>...</th>\n",
       "      <th>smoking_status_formerly smoked</th>\n",
       "      <th>smoking_status_never smoked</th>\n",
       "      <th>smoking_status_smokes</th>\n",
       "      <th>gender_Female</th>\n",
       "      <th>gender_Male</th>\n",
       "      <th>gender_Other</th>\n",
       "      <th>ever_married_No</th>\n",
       "      <th>ever_married_Yes</th>\n",
       "      <th>Residence_type_Rural</th>\n",
       "      <th>Residence_type_Urban</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>0.931846</td>\n",
       "      <td>0</td>\n",
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       "      <td>2.500974</td>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.673346</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.056516</td>\n",
       "      <td>32.500000</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.094846</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.303993</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.616307</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.364196</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.730384</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.616050</td>\n",
       "      <td>29.000000</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>5039</th>\n",
       "      <td>-0.551154</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.801661</td>\n",
       "      <td>28.908675</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
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       "      <td>-0.608193</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.718958</td>\n",
       "      <td>32.961869</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5093</th>\n",
       "      <td>-0.323000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.283580</td>\n",
       "      <td>33.366133</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
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       "      <td>-0.608193</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.514394</td>\n",
       "      <td>30.698684</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5105</th>\n",
       "      <td>1.673346</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.518352</td>\n",
       "      <td>31.786700</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4194 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           age  hypertension  heart_disease  avg_glucose_level        bmi  \\\n",
       "0     0.931846             0              1           2.500974  36.600000   \n",
       "2     1.673346             0              1          -0.056516  32.500000   \n",
       "3    -0.094846             0              0           1.303993  34.400000   \n",
       "4     1.616307             1              0           1.364196  24.000000   \n",
       "5     1.730384             0              0           1.616050  29.000000   \n",
       "...        ...           ...            ...                ...        ...   \n",
       "5039 -0.551154             0              0          -0.801661  28.908675   \n",
       "5048 -0.608193             0              0           1.718958  32.961869   \n",
       "5093 -0.323000             1              0          -0.283580  33.366133   \n",
       "5099 -0.608193             0              0          -0.514394  30.698684   \n",
       "5105  1.673346             1              0          -0.518352  31.786700   \n",
       "\n",
       "      stroke  work_type_Govt_job  work_type_Never_worked  work_type_Private  \\\n",
       "0          1                   0                       0                  1   \n",
       "2          1                   0                       0                  1   \n",
       "3          1                   0                       0                  1   \n",
       "4          1                   0                       0                  0   \n",
       "5          1                   0                       0                  1   \n",
       "...      ...                 ...                     ...                ...   \n",
       "5039       0                   0                       0                  1   \n",
       "5048       0                   0                       0                  1   \n",
       "5093       0                   1                       0                  0   \n",
       "5099       0                   0                       0                  1   \n",
       "5105       0                   0                       0                  1   \n",
       "\n",
       "      work_type_Self-employed  ...  smoking_status_formerly smoked  \\\n",
       "0                           0  ...                               1   \n",
       "2                           0  ...                               0   \n",
       "3                           0  ...                               0   \n",
       "4                           1  ...                               0   \n",
       "5                           0  ...                               1   \n",
       "...                       ...  ...                             ...   \n",
       "5039                        0  ...                               1   \n",
       "5048                        0  ...                               0   \n",
       "5093                        0  ...                               0   \n",
       "5099                        0  ...                               0   \n",
       "5105                        0  ...                               0   \n",
       "\n",
       "      smoking_status_never smoked  smoking_status_smokes  gender_Female  \\\n",
       "0                               0                      0              0   \n",
       "2                               1                      0              0   \n",
       "3                               0                      1              1   \n",
       "4                               1                      0              1   \n",
       "5                               0                      0              0   \n",
       "...                           ...                    ...            ...   \n",
       "5039                            0                      0              0   \n",
       "5048                            0                      1              0   \n",
       "5093                            0                      1              1   \n",
       "5099                            0                      1              0   \n",
       "5105                            1                      0              1   \n",
       "\n",
       "      gender_Male  gender_Other  ever_married_No  ever_married_Yes  \\\n",
       "0               1             0                0                 1   \n",
       "2               1             0                0                 1   \n",
       "3               0             0                0                 1   \n",
       "4               0             0                0                 1   \n",
       "5               1             0                0                 1   \n",
       "...           ...           ...              ...               ...   \n",
       "5039            1             0                1                 0   \n",
       "5048            1             0                0                 1   \n",
       "5093            0             0                0                 1   \n",
       "5099            1             0                0                 1   \n",
       "5105            0             0                0                 1   \n",
       "\n",
       "      Residence_type_Rural  Residence_type_Urban  \n",
       "0                        0                     1  \n",
       "2                        1                     0  \n",
       "3                        0                     1  \n",
       "4                        1                     0  \n",
       "5                        0                     1  \n",
       "...                    ...                   ...  \n",
       "5039                     1                     0  \n",
       "5048                     0                     1  \n",
       "5093                     1                     0  \n",
       "5099                     1                     0  \n",
       "5105                     0                     1  \n",
       "\n",
       "[4194 rows x 21 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bmi_na.bmi = reg.predict(np.concatenate((df_bmi_na.values[:,0:4],df_bmi_na.values[:,5:20]),axis = 1))\n",
    "df2 = pd.concat([df1, df_bmi_na])\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "exterior-array",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                               0\n",
       "hypertension                      0\n",
       "heart_disease                     0\n",
       "avg_glucose_level                 0\n",
       "bmi                               0\n",
       "stroke                            0\n",
       "work_type_Govt_job                0\n",
       "work_type_Never_worked            0\n",
       "work_type_Private                 0\n",
       "work_type_Self-employed           0\n",
       "smoking_status_Unknown            0\n",
       "smoking_status_formerly smoked    0\n",
       "smoking_status_never smoked       0\n",
       "smoking_status_smokes             0\n",
       "gender_Female                     0\n",
       "gender_Male                       0\n",
       "gender_Other                      0\n",
       "ever_married_No                   0\n",
       "ever_married_Yes                  0\n",
       "Residence_type_Rural              0\n",
       "Residence_type_Urban              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "exposed-alloy",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0015664246986517583 38.715880343904615 5.365419799809714\n",
      "0.006078724464366303 41.40607872446437 5.530866810497012\n"
     ]
    }
   ],
   "source": [
    "y_pred = reg.predict(X_test)\n",
    "print(np.min(np.abs(y_pred - y_test)), np.max(np.abs(y_pred - y_test)), np.mean(np.abs(y_pred - y_test)))\n",
    "print(np.min(np.abs(df_test.bmi - df.bmi.dropna().mean())), np.max(np.abs(df_test.bmi - df.bmi.dropna().mean())),\n",
    "     np.mean(np.abs(df_test.bmi - df.bmi.dropna().mean())))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "central-alabama",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    .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>avg_glucose_level</th>\n",
       "      <th>bmi</th>\n",
       "      <th>work_type_Govt_job</th>\n",
       "      <th>work_type_Never_worked</th>\n",
       "      <th>work_type_Private</th>\n",
       "      <th>work_type_Self-employed</th>\n",
       "      <th>smoking_status_Unknown</th>\n",
       "      <th>...</th>\n",
       "      <th>smoking_status_never smoked</th>\n",
       "      <th>smoking_status_smokes</th>\n",
       "      <th>gender_Female</th>\n",
       "      <th>gender_Male</th>\n",
       "      <th>gender_Other</th>\n",
       "      <th>ever_married_No</th>\n",
       "      <th>ever_married_Yes</th>\n",
       "      <th>Residence_type_Rural</th>\n",
       "      <th>Residence_type_Urban</th>\n",
       "      <th>stroke</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>4</th>\n",
       "      <td>1.616307</td>\n",
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       "      <td>1.364196</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>5105</th>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.518352</td>\n",
       "      <td>0.179082</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4194 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           age  hypertension  heart_disease  avg_glucose_level       bmi  \\\n",
       "0     0.931846             0              1           2.500974  0.859059   \n",
       "2     1.673346             0              1          -0.056516  0.279850   \n",
       "3    -0.094846             0              0           1.303993  0.548264   \n",
       "4     1.616307             1              0           1.364196 -0.920950   \n",
       "5     1.730384             0              0           1.616050 -0.214597   \n",
       "...        ...           ...            ...                ...       ...   \n",
       "5039 -0.551154             0              0          -0.801661 -0.227499   \n",
       "5048 -0.608193             0              0           1.718958  0.345098   \n",
       "5093 -0.323000             1              0          -0.283580  0.402209   \n",
       "5099 -0.608193             0              0          -0.514394  0.025377   \n",
       "5105  1.673346             1              0          -0.518352  0.179082   \n",
       "\n",
       "      work_type_Govt_job  work_type_Never_worked  work_type_Private  \\\n",
       "0                      0                       0                  1   \n",
       "2                      0                       0                  1   \n",
       "3                      0                       0                  1   \n",
       "4                      0                       0                  0   \n",
       "5                      0                       0                  1   \n",
       "...                  ...                     ...                ...   \n",
       "5039                   0                       0                  1   \n",
       "5048                   0                       0                  1   \n",
       "5093                   1                       0                  0   \n",
       "5099                   0                       0                  1   \n",
       "5105                   0                       0                  1   \n",
       "\n",
       "      work_type_Self-employed  smoking_status_Unknown  ...  \\\n",
       "0                           0                       0  ...   \n",
       "2                           0                       0  ...   \n",
       "3                           0                       0  ...   \n",
       "4                           1                       0  ...   \n",
       "5                           0                       0  ...   \n",
       "...                       ...                     ...  ...   \n",
       "5039                        0                       0  ...   \n",
       "5048                        0                       0  ...   \n",
       "5093                        0                       0  ...   \n",
       "5099                        0                       0  ...   \n",
       "5105                        0                       0  ...   \n",
       "\n",
       "      smoking_status_never smoked  smoking_status_smokes  gender_Female  \\\n",
       "0                               0                      0              0   \n",
       "2                               1                      0              0   \n",
       "3                               0                      1              1   \n",
       "4                               1                      0              1   \n",
       "5                               0                      0              0   \n",
       "...                           ...                    ...            ...   \n",
       "5039                            0                      0              0   \n",
       "5048                            0                      1              0   \n",
       "5093                            0                      1              1   \n",
       "5099                            0                      1              0   \n",
       "5105                            1                      0              1   \n",
       "\n",
       "      gender_Male  gender_Other  ever_married_No  ever_married_Yes  \\\n",
       "0               1             0                0                 1   \n",
       "2               1             0                0                 1   \n",
       "3               0             0                0                 1   \n",
       "4               0             0                0                 1   \n",
       "5               1             0                0                 1   \n",
       "...           ...           ...              ...               ...   \n",
       "5039            1             0                1                 0   \n",
       "5048            1             0                0                 1   \n",
       "5093            0             0                0                 1   \n",
       "5099            1             0                0                 1   \n",
       "5105            0             0                0                 1   \n",
       "\n",
       "      Residence_type_Rural  Residence_type_Urban  stroke  \n",
       "0                        0                     1       1  \n",
       "2                        1                     0       1  \n",
       "3                        0                     1       1  \n",
       "4                        1                     0       1  \n",
       "5                        0                     1       1  \n",
       "...                    ...                   ...     ...  \n",
       "5039                     1                     0       0  \n",
       "5048                     0                     1       0  \n",
       "5093                     1                     0       0  \n",
       "5099                     1                     0       0  \n",
       "5105                     0                     1       0  \n",
       "\n",
       "[4194 rows x 21 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp = df2.stroke\n",
    "df2 = df2.drop(columns = ['stroke'])\n",
    "df2['stroke'] = temp\n",
    "df2['bmi'] = (df2['bmi'] - df2['bmi'].mean())/df2['bmi'].std()\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "coupled-improvement",
   "metadata": {},
   "outputs": [],
   "source": [
    "df2.to_csv('E:\\\\term8\\\\datamining\\\\HW\\\\project\\\\prepared_data.csv', index = False)"
   ]
  }
 ],
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