avg_glucose_level_dr.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "af31ff62",
   "metadata": {},
   "source": [
    "# Outcome regression for avg-glucose level"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "51126262",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4377566b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_raw = pd.read_csv('./stroke_data_dummies.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cac33d8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_r = df_raw[df_raw.Gender_Other == 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3bae73be",
   "metadata": {},
   "outputs": [],
   "source": [
    "def glucose_check(x):\n",
    "    if x >= 140:\n",
    "        return 1\n",
    "    else:\n",
    "        return 0\n",
    "    \n",
    "df_r['diabetes'] = df_r.avg_glucose_level.apply(glucose_check)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "106eb5d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df_r.drop(columns = ['Unnamed: 0','id','Obesity','Gender_Other','avg_glucose_level'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6d8d4040",
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "    age  hypertension  heart_disease   bmi  stroke  marriage  Gender_Female  \\\n",
       "0  67.0             0              1  36.6       1         1              0   \n",
       "1  80.0             0              1  32.5       1         1              0   \n",
       "2  49.0             0              0  34.4       1         1              1   \n",
       "3  79.0             1              0  24.0       1         1              1   \n",
       "4  81.0             0              0  29.0       1         1              0   \n",
       "\n",
       "   Gender_Male  Work_Govt_job  Work_Never_worked  Work_Private  \\\n",
       "0            1              0                  0             1   \n",
       "1            1              0                  0             1   \n",
       "2            0              0                  0             1   \n",
       "3            0              0                  0             0   \n",
       "4            1              0                  0             1   \n",
       "\n",
       "   Work_Self-employed  Work_children  smoking_Unknown  \\\n",
       "0                   0              0                0   \n",
       "1                   0              0                0   \n",
       "2                   0              0                0   \n",
       "3                   1              0                0   \n",
       "4                   0              0                0   \n",
       "\n",
       "   smoking_formerly smoked  smoking_never smoked  smoking_smokes  \\\n",
       "0                        1                     0               0   \n",
       "1                        0                     1               0   \n",
       "2                        0                     0               1   \n",
       "3                        0                     1               0   \n",
       "4                        1                     0               0   \n",
       "\n",
       "   Residence_Rural  Residence_Urban  diabetes  \n",
       "0                0                1         1  \n",
       "1                1                0         0  \n",
       "2                0                1         1  \n",
       "3                1                0         1  \n",
       "4                0                1         1  "
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     "execution_count": 6,
     "metadata": {},
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   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "725d15b5",
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "       age  hypertension  heart_disease   bmi  marriage  Gender_Female  \\\n",
       "0     67.0             0              1  36.6         1              0   \n",
       "1     80.0             0              1  32.5         1              0   \n",
       "2     49.0             0              0  34.4         1              1   \n",
       "3     79.0             1              0  24.0         1              1   \n",
       "4     81.0             0              0  29.0         1              0   \n",
       "...    ...           ...            ...   ...       ...            ...   \n",
       "4904  13.0             0              0  18.6         0              1   \n",
       "4905  81.0             0              0  40.0         1              1   \n",
       "4906  35.0             0              0  30.6         1              1   \n",
       "4907  51.0             0              0  25.6         1              0   \n",
       "4908  44.0             0              0  26.2         1              1   \n",
       "\n",
       "      Gender_Male  Work_Govt_job  Work_Never_worked  Work_Private  \\\n",
       "0               1              0                  0             1   \n",
       "1               1              0                  0             1   \n",
       "2               0              0                  0             1   \n",
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       "...           ...            ...                ...           ...   \n",
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       "\n",
       "      Work_Self-employed  Work_children  smoking_Unknown  \\\n",
       "0                      0              0                0   \n",
       "1                      0              0                0   \n",
       "2                      0              0                0   \n",
       "3                      1              0                0   \n",
       "4                      0              0                0   \n",
       "...                  ...            ...              ...   \n",
       "4904                   0              1                1   \n",
       "4905                   1              0                0   \n",
       "4906                   1              0                0   \n",
       "4907                   0              0                0   \n",
       "4908                   0              0                1   \n",
       "\n",
       "      smoking_formerly smoked  smoking_never smoked  smoking_smokes  \\\n",
       "0                           1                     0               0   \n",
       "1                           0                     1               0   \n",
       "2                           0                     0               1   \n",
       "3                           0                     1               0   \n",
       "4                           1                     0               0   \n",
       "...                       ...                   ...             ...   \n",
       "4904                        0                     0               0   \n",
       "4905                        0                     1               0   \n",
       "4906                        0                     1               0   \n",
       "4907                        1                     0               0   \n",
       "4908                        0                     0               0   \n",
       "\n",
       "      Residence_Rural  Residence_Urban  \n",
       "0                   0                1  \n",
       "1                   1                0  \n",
       "2                   0                1  \n",
       "3                   1                0  \n",
       "4                   0                1  \n",
       "...               ...              ...  \n",
       "4904                1                0  \n",
       "4905                0                1  \n",
       "4906                1                0  \n",
       "4907                1                0  \n",
       "4908                0                1  \n",
       "\n",
       "[4908 rows x 18 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df.drop(columns = ['stroke', 'diabetes'])\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fb3c54b3",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "       age  hypertension  heart_disease   bmi  stroke  marriage  \\\n",
       "1     80.0             0              1  32.5       1         1   \n",
       "5     74.0             1              1  27.4       1         1   \n",
       "6     69.0             0              0  22.8       1         0   \n",
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       "4908  44.0             0              0  26.2       0         1   \n",
       "\n",
       "      Gender_Female  Gender_Male  Work_Govt_job  Work_Never_worked  \\\n",
       "1                 0            1              0                  0   \n",
       "5                 0            1              0                  0   \n",
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       "8                 1            0              0                  0   \n",
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       "\n",
       "      Work_Private  Work_Self-employed  Work_children  smoking_Unknown  \\\n",
       "1                1                   0              0                0   \n",
       "5                1                   0              0                0   \n",
       "6                1                   0              0                0   \n",
       "7                1                   0              0                1   \n",
       "8                1                   0              0                0   \n",
       "...            ...                 ...            ...              ...   \n",
       "4903             1                   0              0                1   \n",
       "4904             0                   0              1                1   \n",
       "4905             0                   1              0                0   \n",
       "4906             0                   1              0                0   \n",
       "4908             0                   0              0                1   \n",
       "\n",
       "      smoking_formerly smoked  smoking_never smoked  smoking_smokes  \\\n",
       "1                           0                     1               0   \n",
       "5                           0                     1               0   \n",
       "6                           0                     1               0   \n",
       "7                           0                     0               0   \n",
       "8                           0                     1               0   \n",
       "...                       ...                   ...             ...   \n",
       "4903                        0                     0               0   \n",
       "4904                        0                     0               0   \n",
       "4905                        0                     1               0   \n",
       "4906                        0                     1               0   \n",
       "4908                        0                     0               0   \n",
       "\n",
       "      Residence_Rural  Residence_Urban  \n",
       "1                   1                0  \n",
       "5                   1                0  \n",
       "6                   0                1  \n",
       "7                   0                1  \n",
       "8                   1                0  \n",
       "...               ...              ...  \n",
       "4903                0                1  \n",
       "4904                1                0  \n",
       "4905                0                1  \n",
       "4906                1                0  \n",
       "4908                0                1  \n",
       "\n",
       "[4154 rows x 19 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_0 = df[df['diabetes'] == 0]\n",
    "df_0 = df_0.drop(columns = ['diabetes'])\n",
    "df_0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "16613bbc",
   "metadata": {},
   "outputs": [
    {
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4865</th>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>27.7</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>0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4866</th>\n",
       "      <td>70.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24.3</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>0</td>\n",
       "      <td>1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4878</th>\n",
       "      <td>34.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4888</th>\n",
       "      <td>51.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>21.8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4907</th>\n",
       "      <td>51.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25.6</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>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",
       "  </tbody>\n",
       "</table>\n",
       "<p>754 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age  hypertension  heart_disease   bmi  stroke  marriage  \\\n",
       "0     67.0             0              1  36.6       1         1   \n",
       "2     49.0             0              0  34.4       1         1   \n",
       "3     79.0             1              0  24.0       1         1   \n",
       "4     81.0             0              0  29.0       1         1   \n",
       "11    79.0             0              1  28.2       1         1   \n",
       "...    ...           ...            ...   ...     ...       ...   \n",
       "4865  39.0             0              0  27.7       0         1   \n",
       "4866  70.0             0              0  24.3       0         1   \n",
       "4878  34.0             0              0  23.0       0         1   \n",
       "4888  51.0             0              0  21.8       0         1   \n",
       "4907  51.0             0              0  25.6       0         1   \n",
       "\n",
       "      Gender_Female  Gender_Male  Work_Govt_job  Work_Never_worked  \\\n",
       "0                 0            1              0                  0   \n",
       "2                 1            0              0                  0   \n",
       "3                 1            0              0                  0   \n",
       "4                 0            1              0                  0   \n",
       "11                1            0              0                  0   \n",
       "...             ...          ...            ...                ...   \n",
       "4865              0            1              0                  0   \n",
       "4866              0            1              0                  0   \n",
       "4878              1            0              0                  0   \n",
       "4888              1            0              0                  0   \n",
       "4907              0            1              0                  0   \n",
       "\n",
       "      Work_Private  Work_Self-employed  Work_children  smoking_Unknown  \\\n",
       "0                1                   0              0                0   \n",
       "2                1                   0              0                0   \n",
       "3                0                   1              0                0   \n",
       "4                1                   0              0                0   \n",
       "11               1                   0              0                0   \n",
       "...            ...                 ...            ...              ...   \n",
       "4865             1                   0              0                1   \n",
       "4866             0                   1              0                1   \n",
       "4878             1                   0              0                0   \n",
       "4888             1                   0              0                1   \n",
       "4907             1                   0              0                0   \n",
       "\n",
       "      smoking_formerly smoked  smoking_never smoked  smoking_smokes  \\\n",
       "0                           1                     0               0   \n",
       "2                           0                     0               1   \n",
       "3                           0                     1               0   \n",
       "4                           1                     0               0   \n",
       "11                          0                     1               0   \n",
       "...                       ...                   ...             ...   \n",
       "4865                        0                     0               0   \n",
       "4866                        0                     0               0   \n",
       "4878                        0                     1               0   \n",
       "4888                        0                     0               0   \n",
       "4907                        1                     0               0   \n",
       "\n",
       "      Residence_Rural  Residence_Urban  \n",
       "0                   0                1  \n",
       "2                   0                1  \n",
       "3                   1                0  \n",
       "4                   0                1  \n",
       "11                  0                1  \n",
       "...               ...              ...  \n",
       "4865                0                1  \n",
       "4866                1                0  \n",
       "4878                1                0  \n",
       "4888                0                1  \n",
       "4907                1                0  \n",
       "\n",
       "[754 rows x 19 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_1 = df[df['diabetes'] == 1]\n",
    "df_1 = df_1.drop(columns = ['diabetes'])\n",
    "df_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "82611dd5",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_0 = df_0['stroke']\n",
    "x_0 = df_0.drop(columns = ['stroke'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b78daf01",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_1 = df_1['stroke']\n",
    "x_1 = df_1.drop(columns = ['stroke'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7cc03b88",
   "metadata": {},
   "source": [
    "# Model for controlled group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "820ec431",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import cross_val_score\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f5ac53ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9665382106041495"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc = RandomForestClassifier(random_state=60)\n",
    "score_pre_0 = cross_val_score(rfc,x_0,y_0,cv=5).mean()\n",
    "score_pre_0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4b3ac478",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nan 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "scorel = []\n",
    "for i in range(0,200,10):\n",
    "    rfc = RandomForestClassifier(n_estimators=i+1,\n",
    "                                 n_jobs=-1,\n",
    "                                random_state=60)\n",
    "    score = cross_val_score(rfc,x_0,y_0,cv=5).mean()\n",
    "    scorel.append(score)\n",
    "\n",
    "print(max(scorel),(scorel.index(max(scorel))*10)+1)\n",
    "plt.figure(figsize=[20,5])\n",
    "plt.plot(range(1,201,10),scorel)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9b5520fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nan 1\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 4320x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "scorel = []\n",
    "for i in range(1,71):\n",
    "    rfc = RandomForestClassifier(n_estimators=i+1,\n",
    "                                 n_jobs=-1,\n",
    "                                random_state=60)\n",
    "    score = cross_val_score(rfc,x_0,y_0,cv=5).mean()\n",
    "    scorel.append(score)\n",
    "\n",
    "print(max(scorel),([*range(1,71)][scorel.index(max(scorel))]))\n",
    "plt.figure(figsize=[60,20])\n",
    "plt.plot(range(1,71),scorel)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "eaac93d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=RandomForestClassifier(n_estimators=40, random_state=60),\n",
       "             param_grid={'max_depth': array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29])})"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_grid = {'max_depth':np.arange(1, 30, 1)}\n",
    "rfc = RandomForestClassifier(n_estimators=40\n",
    "                             ,random_state=60\n",
    "                             )\n",
    "GS = GridSearchCV(rfc,param_grid,cv=5)\n",
    "GS.fit(x_0,y_0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1e5173ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': 1}"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GS.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ed7de8f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9689458193785974"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GS.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "526280e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=RandomForestClassifier(n_estimators=40, random_state=60),\n",
       "             param_grid={'max_features': array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19])})"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_grid = {'max_features':np.arange(1,20,1)}\n",
    "rfc = RandomForestClassifier(n_estimators=40\n",
    "                             ,random_state=60\n",
    "                             )\n",
    "GS = GridSearchCV(rfc,param_grid,cv=5)\n",
    "GS.fit(x_0,y_0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "536884f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_features': 4}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GS.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f5c5e743",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9665382106041495"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GS.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "b5294fd8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=RandomForestClassifier(n_estimators=40, random_state=60),\n",
       "             param_grid={'min_samples_leaf': array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19])})"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_grid={'min_samples_leaf':np.arange(1, 20, 1)}\n",
    "rfc = RandomForestClassifier(n_estimators=40\n",
    "                             ,random_state=60\n",
    "                             )\n",
    "GS = GridSearchCV(rfc,param_grid,cv=5)\n",
    "GS.fit(x_0,y_0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "de0fe4fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'min_samples_leaf': 4}"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GS.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "50f7a1ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9689458193785974"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GS.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b297cbda",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=RandomForestClassifier(n_estimators=40, random_state=60),\n",
       "             param_grid={'min_samples_split': array([ 2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18,\n",
       "       19, 20, 21])})"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_grid={'min_samples_split':np.arange(2, 2+20, 1)}\n",
    "rfc = RandomForestClassifier(n_estimators=40\n",
    "                             ,random_state=60\n",
    "                             )\n",
    "GS = GridSearchCV(rfc,param_grid,cv=5)\n",
    "GS.fit(x_0,y_0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "f9faaa85",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'min_samples_split': 11}"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GS.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e6e132dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9689458193785974"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GS.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "a7745961",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9689458193785974"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc_0 = RandomForestClassifier(n_estimators=40,max_depth = 1,random_state=60)\n",
    "score_0 = cross_val_score(rfc_0,x_0,y_0,cv=5).mean()\n",
    "score_0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "b5f59476",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0024076087744479002"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_0 - score_pre_0"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "892fe777",
   "metadata": {},
   "source": [
    "# Model for treatment group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "434eba58",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8833024282560705"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc = RandomForestClassifier(n_estimators=100,random_state=90)\n",
    "score_pre_1 = cross_val_score(rfc,x_1,y_1,cv=5).mean()\n",
    "score_pre_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "baa9a9d7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nan 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "scorel = []\n",
    "for i in range(0,200,10):\n",
    "    rfc = RandomForestClassifier(n_estimators=i+1,\n",
    "                                 n_jobs=-1,\n",
    "                                random_state=80)\n",
    "    score = cross_val_score(rfc,x_1,y_1,cv=5).mean()\n",
    "    scorel.append(score)\n",
    "\n",
    "print(max(scorel),(scorel.index(max(scorel))*10)+1)\n",
    "plt.figure(figsize=[20,5])\n",
    "plt.plot(range(0,200,10),scorel)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "162b4a9d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nan 141\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "scorel = []\n",
    "for i in range(141,201):\n",
    "    rfc = RandomForestClassifier(n_estimators=i+1,\n",
    "                                 n_jobs=-1,\n",
    "                                random_state=80)\n",
    "    score = cross_val_score(rfc,x_1,y_1,cv=5).mean()\n",
    "    scorel.append(score)\n",
    "\n",
    "print(max(scorel),([*range(141,201)][scorel.index(max(scorel))]))\n",
    "plt.figure(figsize=[20,5])\n",
    "plt.plot(range(141,201),scorel)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "1a2ed6b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=RandomForestClassifier(n_estimators=151,\n",
       "                                              random_state=80),\n",
       "             param_grid={'max_depth': array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29])})"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_grid = {'max_depth':np.arange(1, 30, 1)}\n",
    "rfc = RandomForestClassifier(n_estimators=151\n",
    "                             ,random_state=80\n",
    "                             )\n",
    "GS = GridSearchCV(rfc,param_grid,cv=5)\n",
    "GS.fit(x_1,y_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "61491a59",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': 7}"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GS.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "3dd25e34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8952317880794702"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GS.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "7a1e2b62",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=RandomForestClassifier(n_estimators=151,\n",
       "                                              random_state=80),\n",
       "             param_grid={'max_features': array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19])})"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_grid = {'max_features':np.arange(1,20,1)}\n",
    "rfc = RandomForestClassifier(n_estimators=151\n",
    "                             ,random_state=80\n",
    "                             )\n",
    "GS = GridSearchCV(rfc,param_grid,cv=5)\n",
    "GS.fit(x_1,y_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "6c1913d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_features': 8}"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GS.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "423a4018",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8833112582781457"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GS.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "02a307e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=RandomForestClassifier(n_estimators=151,\n",
       "                                              random_state=80),\n",
       "             param_grid={'min_samples_leaf': array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19])})"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_grid={'min_samples_leaf':np.arange(1, 20, 1)}\n",
    "rfc = RandomForestClassifier(n_estimators=151\n",
    "                             ,random_state=80\n",
    "                             )\n",
    "GS = GridSearchCV(rfc,param_grid,cv=5)\n",
    "GS.fit(x_1,y_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "8a180cdc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'min_samples_leaf': 4}"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GS.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "60c46acf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8938984547461368"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GS.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "9e5d93fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=RandomForestClassifier(n_estimators=151,\n",
       "                                              random_state=80),\n",
       "             param_grid={'min_samples_split': array([ 2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18,\n",
       "       19, 20, 21])})"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_grid={'min_samples_split':np.arange(2, 2+20, 1)}\n",
    "rfc = RandomForestClassifier(n_estimators=151\n",
    "                             ,random_state=80\n",
    "                             )\n",
    "GS = GridSearchCV(rfc,param_grid,cv=5)\n",
    "GS.fit(x_1,y_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "4654eca6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'min_samples_split': 12}"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GS.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "1345eca6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8938984547461368"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GS.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "25769073",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8952317880794702"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc_1 = RandomForestClassifier(n_estimators=151,max_depth = 7, random_state=80)\n",
    "score_1 = cross_val_score(rfc_1,x_1,y_1,cv=5).mean()\n",
    "score_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "82679f2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.01192935982339971"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_1 - score_pre_1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d8f7eb4",
   "metadata": {},
   "source": [
    "# Outcome modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "b9da1e79",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import stats\n",
    "import scipy.stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "0ae5e18f",
   "metadata": {},
   "outputs": [],
   "source": [
    "rfc0 = rfc_0.fit(x_0, y_0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "7b4f68bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_0 = rfc0.predict(X)\n",
    "mean_0 = predict_0.mean()\n",
    "mean_0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "c6781c34",
   "metadata": {},
   "outputs": [],
   "source": [
    "rfc1 = rfc_1.fit(x_1, y_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "20ebd8a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.002037489812550937"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_1 = rfc1.predict(X)\n",
    "mean_1 = predict_1.mean()\n",
    "mean_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "ef151b4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.002037489812550937"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ATE_outcome = mean_1 - mean_0\n",
    "ATE_outcome"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "1f256d6f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-3.165181645162925 0.0007796715253973722\n"
     ]
    }
   ],
   "source": [
    "t,pval = scipy.stats.ttest_ind(predict_0,predict_1,equal_var=False,alternative='less')\n",
    "print(t,pval)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35fedcb7",
   "metadata": {},
   "source": [
    "# IPW for avg_glucose_level"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "8ef566c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression as lr\n",
    "\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.neighbors import NearestNeighbors\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "efaab543",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Enabled to remove warnings for demo purposes.\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "6125256f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "# import scipy.stats as stats\n",
    "import statsmodels.api as sm\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('classic')\n",
    "%matplotlib inline\n",
    "\n",
    "import seaborn as sns\n",
    "sns.set(rc={'figure.figsize':(16,10)}, font_scale=1.3)\n",
    "\n",
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "379bbb44",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('./healthcare-dataset-stroke-data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "cabb2922",
   "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>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",
       "    </tr>\n",
       "    <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",
       "      <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",
       "    </tr>\n",
       "    <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>5110 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",
       "[5110 rows x 12 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "b7dcc31d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# delete row with NA value\n",
    "df_na = df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "26a87a38",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_na.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "c8b2a3e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "index                False\n",
       "id                   False\n",
       "gender               False\n",
       "age                  False\n",
       "hypertension         False\n",
       "heart_disease        False\n",
       "ever_married         False\n",
       "work_type            False\n",
       "Residence_type       False\n",
       "avg_glucose_level    False\n",
       "bmi                  False\n",
       "smoking_status       False\n",
       "stroke               False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.isnull(df_na).any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "ba491684",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>stroke</th>\n",
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       "  </thead>\n",
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       "      <th>0</th>\n",
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       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>105.92</td>\n",
       "      <td>32.5</td>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\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>3</th>\n",
       "      <td>4</td>\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",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>56669</td>\n",
       "      <td>Male</td>\n",
       "      <td>81.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>186.21</td>\n",
       "      <td>29.0</td>\n",
       "      <td>formerly smoked</td>\n",
       "      <td>1</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",
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       "    <tr>\n",
       "      <th>4904</th>\n",
       "      <td>5104</td>\n",
       "      <td>14180</td>\n",
       "      <td>Female</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>children</td>\n",
       "      <td>Rural</td>\n",
       "      <td>103.08</td>\n",
       "      <td>18.6</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4905</th>\n",
       "      <td>5106</td>\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>4906</th>\n",
       "      <td>5107</td>\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>4907</th>\n",
       "      <td>5108</td>\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>4908</th>\n",
       "      <td>5109</td>\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>4909 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      index     id  gender   age  hypertension  heart_disease ever_married  \\\n",
       "0         0   9046    Male  67.0             0              1          Yes   \n",
       "1         2  31112    Male  80.0             0              1          Yes   \n",
       "2         3  60182  Female  49.0             0              0          Yes   \n",
       "3         4   1665  Female  79.0             1              0          Yes   \n",
       "4         5  56669    Male  81.0             0              0          Yes   \n",
       "...     ...    ...     ...   ...           ...            ...          ...   \n",
       "4904   5104  14180  Female  13.0             0              0           No   \n",
       "4905   5106  44873  Female  81.0             0              0          Yes   \n",
       "4906   5107  19723  Female  35.0             0              0          Yes   \n",
       "4907   5108  37544    Male  51.0             0              0          Yes   \n",
       "4908   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           Private          Rural             105.92  32.5     never smoked   \n",
       "2           Private          Urban             171.23  34.4           smokes   \n",
       "3     Self-employed          Rural             174.12  24.0     never smoked   \n",
       "4           Private          Urban             186.21  29.0  formerly smoked   \n",
       "...             ...            ...                ...   ...              ...   \n",
       "4904       children          Rural             103.08  18.6          Unknown   \n",
       "4905  Self-employed          Urban             125.20  40.0     never smoked   \n",
       "4906  Self-employed          Rural              82.99  30.6     never smoked   \n",
       "4907        Private          Rural             166.29  25.6  formerly smoked   \n",
       "4908       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",
       "4904       0  \n",
       "4905       0  \n",
       "4906       0  \n",
       "4907       0  \n",
       "4908       0  \n",
       "\n",
       "[4909 rows x 13 columns]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_na"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "f83939ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_na['ever_married'] = df_na.ever_married.map({'Yes':1, 'No':0})\n",
    "# For gender, 1 means Male, 0 means Female\n",
    "df_na['gender'] = df_na.gender.map({'Male':1, 'Female':0})\n",
    "# For Residence_type, 1 means Urban, 0 means Rural\n",
    "df_na['Residence_type'] = df_na.Residence_type.map({'Urban':1, 'Rural':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "83d53d3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "index                False\n",
       "id                   False\n",
       "gender                True\n",
       "age                  False\n",
       "hypertension         False\n",
       "heart_disease        False\n",
       "ever_married         False\n",
       "work_type            False\n",
       "Residence_type       False\n",
       "avg_glucose_level    False\n",
       "bmi                  False\n",
       "smoking_status       False\n",
       "stroke               False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.isnull(df_na).any()\n",
    "# there is one sample with 'Other' gender'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "7e81377f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "index                False\n",
       "id                   False\n",
       "gender               False\n",
       "age                  False\n",
       "hypertension         False\n",
       "heart_disease        False\n",
       "ever_married         False\n",
       "work_type            False\n",
       "Residence_type       False\n",
       "avg_glucose_level    False\n",
       "bmi                  False\n",
       "smoking_status       False\n",
       "stroke               False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# delete the row with 'gender' value of 'Other'\n",
    "df_na = df_na.dropna()\n",
    "df_na.reset_index(drop=True,inplace=True)\n",
    "pd.isnull(df_na).any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "11433b60",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>id</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
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       "      <th>0</th>\n",
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       "      <td>9046</td>\n",
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       "      <td>0</td>\n",
       "      <td>105.92</td>\n",
       "      <td>32.5</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>60182</td>\n",
       "      <td>0.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Private</td>\n",
       "      <td>1</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>3</th>\n",
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       "      <td>79.0</td>\n",
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       "      <td>1</td>\n",
       "      <td>Self-employed</td>\n",
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       "      <td>174.12</td>\n",
       "      <td>24.0</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>Private</td>\n",
       "      <td>1</td>\n",
       "      <td>186.21</td>\n",
       "      <td>29.0</td>\n",
       "      <td>formerly smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>5104</td>\n",
       "      <td>14180</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>children</td>\n",
       "      <td>0</td>\n",
       "      <td>103.08</td>\n",
       "      <td>18.6</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4904</th>\n",
       "      <td>5106</td>\n",
       "      <td>44873</td>\n",
       "      <td>0.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>1</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>4905</th>\n",
       "      <td>5107</td>\n",
       "      <td>19723</td>\n",
       "      <td>0.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>0</td>\n",
       "      <td>82.99</td>\n",
       "      <td>30.6</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4906</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Private</td>\n",
       "      <td>0</td>\n",
       "      <td>166.29</td>\n",
       "      <td>25.6</td>\n",
       "      <td>formerly smoked</td>\n",
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       "      <td>44.0</td>\n",
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       "      <td>Govt_job</td>\n",
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       "      <td>Unknown</td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>4908 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      index     id  gender   age  hypertension  heart_disease  ever_married  \\\n",
       "0         0   9046     1.0  67.0             0              1             1   \n",
       "1         2  31112     1.0  80.0             0              1             1   \n",
       "2         3  60182     0.0  49.0             0              0             1   \n",
       "3         4   1665     0.0  79.0             1              0             1   \n",
       "4         5  56669     1.0  81.0             0              0             1   \n",
       "...     ...    ...     ...   ...           ...            ...           ...   \n",
       "4903   5104  14180     0.0  13.0             0              0             0   \n",
       "4904   5106  44873     0.0  81.0             0              0             1   \n",
       "4905   5107  19723     0.0  35.0             0              0             1   \n",
       "4906   5108  37544     1.0  51.0             0              0             1   \n",
       "4907   5109  44679     0.0  44.0             0              0             1   \n",
       "\n",
       "          work_type  Residence_type  avg_glucose_level   bmi   smoking_status  \\\n",
       "0           Private               1             228.69  36.6  formerly smoked   \n",
       "1           Private               0             105.92  32.5     never smoked   \n",
       "2           Private               1             171.23  34.4           smokes   \n",
       "3     Self-employed               0             174.12  24.0     never smoked   \n",
       "4           Private               1             186.21  29.0  formerly smoked   \n",
       "...             ...             ...                ...   ...              ...   \n",
       "4903       children               0             103.08  18.6          Unknown   \n",
       "4904  Self-employed               1             125.20  40.0     never smoked   \n",
       "4905  Self-employed               0              82.99  30.6     never smoked   \n",
       "4906        Private               0             166.29  25.6  formerly smoked   \n",
       "4907       Govt_job               1              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",
       "4903       0  \n",
       "4904       0  \n",
       "4905       0  \n",
       "4906       0  \n",
       "4907       0  \n",
       "\n",
       "[4908 rows x 13 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_na"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "baf82981",
   "metadata": {},
   "outputs": [],
   "source": [
    "def Glucose_check(x):\n",
    "    if x >= 140:\n",
    "        return 1\n",
    "    else:\n",
    "        return 0\n",
    "# 1 means it is higher than the bound. 0 means lower.\n",
    "df_na['treatment_glucose_level'] = df_na.avg_glucose_level.apply(Glucose_check)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "de6f72e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>children</td>\n",
       "      <td>0</td>\n",
       "      <td>18.6</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4904</th>\n",
       "      <td>0.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>1</td>\n",
       "      <td>40.0</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4905</th>\n",
       "      <td>0.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>0</td>\n",
       "      <td>30.6</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4906</th>\n",
       "      <td>1.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Private</td>\n",
       "      <td>0</td>\n",
       "      <td>25.6</td>\n",
       "      <td>formerly smoked</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4907</th>\n",
       "      <td>0.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Govt_job</td>\n",
       "      <td>1</td>\n",
       "      <td>26.2</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4908 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      gender   age  hypertension  heart_disease  ever_married      work_type  \\\n",
       "0        1.0  67.0             0              1             1        Private   \n",
       "1        1.0  80.0             0              1             1        Private   \n",
       "2        0.0  49.0             0              0             1        Private   \n",
       "3        0.0  79.0             1              0             1  Self-employed   \n",
       "4        1.0  81.0             0              0             1        Private   \n",
       "...      ...   ...           ...            ...           ...            ...   \n",
       "4903     0.0  13.0             0              0             0       children   \n",
       "4904     0.0  81.0             0              0             1  Self-employed   \n",
       "4905     0.0  35.0             0              0             1  Self-employed   \n",
       "4906     1.0  51.0             0              0             1        Private   \n",
       "4907     0.0  44.0             0              0             1       Govt_job   \n",
       "\n",
       "      Residence_type   bmi   smoking_status  stroke  treatment_glucose_level  \n",
       "0                  1  36.6  formerly smoked       1                        1  \n",
       "1                  0  32.5     never smoked       1                        0  \n",
       "2                  1  34.4           smokes       1                        1  \n",
       "3                  0  24.0     never smoked       1                        1  \n",
       "4                  1  29.0  formerly smoked       1                        1  \n",
       "...              ...   ...              ...     ...                      ...  \n",
       "4903               0  18.6          Unknown       0                        0  \n",
       "4904               1  40.0     never smoked       0                        0  \n",
       "4905               0  30.6     never smoked       0                        0  \n",
       "4906               0  25.6  formerly smoked       0                        1  \n",
       "4907               1  26.2          Unknown       0                        0  \n",
       "\n",
       "[4908 rows x 11 columns]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_data = df_na.drop(columns=['index','id','avg_glucose_level'])\n",
    "df_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "a37a1a06",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4906</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>25.6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "<p>4908 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      gender   age  hypertension  heart_disease  ever_married  Residence_type  \\\n",
       "0        1.0  67.0             0              1             1               1   \n",
       "1        1.0  80.0             0              1             1               0   \n",
       "2        0.0  49.0             0              0             1               1   \n",
       "3        0.0  79.0             1              0             1               0   \n",
       "4        1.0  81.0             0              0             1               1   \n",
       "...      ...   ...           ...            ...           ...             ...   \n",
       "4903     0.0  13.0             0              0             0               0   \n",
       "4904     0.0  81.0             0              0             1               1   \n",
       "4905     0.0  35.0             0              0             1               0   \n",
       "4906     1.0  51.0             0              0             1               0   \n",
       "4907     0.0  44.0             0              0             1               1   \n",
       "\n",
       "       bmi  stroke  treatment_glucose_level  work_type_Govt_job  \\\n",
       "0     36.6       1                        1                   0   \n",
       "1     32.5       1                        0                   0   \n",
       "2     34.4       1                        1                   0   \n",
       "3     24.0       1                        1                   0   \n",
       "4     29.0       1                        1                   0   \n",
       "...    ...     ...                      ...                 ...   \n",
       "4903  18.6       0                        0                   0   \n",
       "4904  40.0       0                        0                   0   \n",
       "4905  30.6       0                        0                   0   \n",
       "4906  25.6       0                        1                   0   \n",
       "4907  26.2       0                        0                   1   \n",
       "\n",
       "      work_type_Never_worked  work_type_Private  work_type_Self-employed  \\\n",
       "0                          0                  1                        0   \n",
       "1                          0                  1                        0   \n",
       "2                          0                  1                        0   \n",
       "3                          0                  0                        1   \n",
       "4                          0                  1                        0   \n",
       "...                      ...                ...                      ...   \n",
       "4903                       0                  0                        0   \n",
       "4904                       0                  0                        1   \n",
       "4905                       0                  0                        1   \n",
       "4906                       0                  1                        0   \n",
       "4907                       0                  0                        0   \n",
       "\n",
       "      work_type_children  smoking_status_Unknown  \\\n",
       "0                      0                       0   \n",
       "1                      0                       0   \n",
       "2                      0                       0   \n",
       "3                      0                       0   \n",
       "4                      0                       0   \n",
       "...                  ...                     ...   \n",
       "4903                   1                       1   \n",
       "4904                   0                       0   \n",
       "4905                   0                       0   \n",
       "4906                   0                       0   \n",
       "4907                   0                       1   \n",
       "\n",
       "      smoking_status_formerly smoked  smoking_status_never smoked  \\\n",
       "0                                  1                            0   \n",
       "1                                  0                            1   \n",
       "2                                  0                            0   \n",
       "3                                  0                            1   \n",
       "4                                  1                            0   \n",
       "...                              ...                          ...   \n",
       "4903                               0                            0   \n",
       "4904                               0                            1   \n",
       "4905                               0                            1   \n",
       "4906                               1                            0   \n",
       "4907                               0                            0   \n",
       "\n",
       "      smoking_status_smokes  \n",
       "0                         0  \n",
       "1                         0  \n",
       "2                         1  \n",
       "3                         0  \n",
       "4                         0  \n",
       "...                     ...  \n",
       "4903                      0  \n",
       "4904                      0  \n",
       "4905                      0  \n",
       "4906                      0  \n",
       "4907                      0  \n",
       "\n",
       "[4908 rows x 18 columns]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_encoded = pd.get_dummies(df_data, columns = ['work_type','smoking_status'], \\\n",
    "                           prefix = {'work_type':'work_type', 'smoking_status' : 'smoking_status'}, drop_first=False)\n",
    "df_encoded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "be00e8d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_treatment = df_encoded[df_encoded['treatment_glucose_level']==1]\n",
    "df_control = df_encoded[df_encoded['treatment_glucose_level']==0]\n",
    "treat_mean = df_treatment.mean()\n",
    "treat_std = df_treatment.std()\n",
    "control_mean = df_control.mean()\n",
    "control_std = df_control.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "cc422123",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_list = ['gender','age','hypertension','heart_disease','ever_married','Residence_type','bmi','work_type_Govt_job',\n",
    "          'work_type_Never_worked','work_type_Private','work_type_Self-employed','work_type_children','smoking_status_Unknown',\n",
    "          'smoking_status_formerly smoked','smoking_status_never smoked','smoking_status_smokes']\n",
    "X_descrip = ['0 to Female and 1 to Male','age of the sample','observation of hypertension','observation of heart_disease',\n",
    "             'Marriage states','0 to Rural and 1 to Urban','bmi','Whether is government job',\n",
    "             'Whether is never worked','Whether job is private','Whether job is self-employed','Whether job is children',\n",
    "             'Whether smoking status is unknown','Whether is formerly smoked','Whether is never smoked','Whether is smokes']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "997b99c1",
   "metadata": {},
   "outputs": [
    {
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       "      <th>1</th>\n",
       "      <td>age</td>\n",
       "      <td>age of the sample</td>\n",
       "      <td>40.536081</td>\n",
       "      <td>22.300920</td>\n",
       "      <td>55.720477</td>\n",
       "      <td>19.421345</td>\n",
       "      <td>-19.284613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>hypertension</td>\n",
       "      <td>observation of hypertension</td>\n",
       "      <td>0.069090</td>\n",
       "      <td>0.253638</td>\n",
       "      <td>0.217507</td>\n",
       "      <td>0.412824</td>\n",
       "      <td>-9.550201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>heart_disease</td>\n",
       "      <td>observation of heart_disease</td>\n",
       "      <td>0.035388</td>\n",
       "      <td>0.184780</td>\n",
       "      <td>0.127321</td>\n",
       "      <td>0.333553</td>\n",
       "      <td>-7.365844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ever_married</td>\n",
       "      <td>Marriage states</td>\n",
       "      <td>0.620366</td>\n",
       "      <td>0.485354</td>\n",
       "      <td>0.831565</td>\n",
       "      <td>0.374501</td>\n",
       "      <td>-13.556302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Residence_type</td>\n",
       "      <td>0 to Rural and 1 to Urban</td>\n",
       "      <td>0.506259</td>\n",
       "      <td>0.500021</td>\n",
       "      <td>0.513263</td>\n",
       "      <td>0.500156</td>\n",
       "      <td>-0.353752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>bmi</td>\n",
       "      <td>bmi</td>\n",
       "      <td>28.298387</td>\n",
       "      <td>7.679817</td>\n",
       "      <td>32.179045</td>\n",
       "      <td>7.997494</td>\n",
       "      <td>-12.331939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>work_type_Govt_job</td>\n",
       "      <td>Whether is government job</td>\n",
       "      <td>0.125662</td>\n",
       "      <td>0.331508</td>\n",
       "      <td>0.143236</td>\n",
       "      <td>0.350546</td>\n",
       "      <td>-1.276873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>work_type_Never_worked</td>\n",
       "      <td>Whether is never worked</td>\n",
       "      <td>0.004815</td>\n",
       "      <td>0.069229</td>\n",
       "      <td>0.002653</td>\n",
       "      <td>0.051468</td>\n",
       "      <td>1.000831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>work_type_Private</td>\n",
       "      <td>Whether job is private</td>\n",
       "      <td>0.570053</td>\n",
       "      <td>0.495128</td>\n",
       "      <td>0.586207</td>\n",
       "      <td>0.492839</td>\n",
       "      <td>-0.827426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>work_type_Self-employed</td>\n",
       "      <td>Whether job is self-employed</td>\n",
       "      <td>0.146124</td>\n",
       "      <td>0.353273</td>\n",
       "      <td>0.222812</td>\n",
       "      <td>0.416409</td>\n",
       "      <td>-4.755837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>work_type_children</td>\n",
       "      <td>Whether job is children</td>\n",
       "      <td>0.153346</td>\n",
       "      <td>0.360364</td>\n",
       "      <td>0.045093</td>\n",
       "      <td>0.207646</td>\n",
       "      <td>11.510718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>smoking_status_Unknown</td>\n",
       "      <td>Whether smoking status is unknown</td>\n",
       "      <td>0.323062</td>\n",
       "      <td>0.467703</td>\n",
       "      <td>0.187003</td>\n",
       "      <td>0.390172</td>\n",
       "      <td>8.527707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>smoking_status_formerly smoked</td>\n",
       "      <td>Whether is formerly smoked</td>\n",
       "      <td>0.157920</td>\n",
       "      <td>0.364710</td>\n",
       "      <td>0.238727</td>\n",
       "      <td>0.426588</td>\n",
       "      <td>-4.887332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>smoking_status_never smoked</td>\n",
       "      <td>Whether is never smoked</td>\n",
       "      <td>0.370246</td>\n",
       "      <td>0.482928</td>\n",
       "      <td>0.416446</td>\n",
       "      <td>0.493296</td>\n",
       "      <td>-2.373522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>smoking_status_smokes</td>\n",
       "      <td>Whether is smokes</td>\n",
       "      <td>0.148772</td>\n",
       "      <td>0.355907</td>\n",
       "      <td>0.157825</td>\n",
       "      <td>0.364819</td>\n",
       "      <td>-0.629190</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Label               Variable Description  \\\n",
       "0                           gender          0 to Female and 1 to Male   \n",
       "1                              age                  age of the sample   \n",
       "2                     hypertension        observation of hypertension   \n",
       "3                    heart_disease       observation of heart_disease   \n",
       "4                     ever_married                    Marriage states   \n",
       "5                   Residence_type          0 to Rural and 1 to Urban   \n",
       "6                              bmi                                bmi   \n",
       "7               work_type_Govt_job          Whether is government job   \n",
       "8           work_type_Never_worked            Whether is never worked   \n",
       "9                work_type_Private             Whether job is private   \n",
       "10         work_type_Self-employed       Whether job is self-employed   \n",
       "11              work_type_children            Whether job is children   \n",
       "12          smoking_status_Unknown  Whether smoking status is unknown   \n",
       "13  smoking_status_formerly smoked         Whether is formerly smoked   \n",
       "14     smoking_status_never smoked            Whether is never smoked   \n",
       "15           smoking_status_smokes                  Whether is smokes   \n",
       "\n",
       "    Controls Mean  Controls STD  Treated Mean  Treated STD  t-Statistics  \n",
       "0        0.398411      0.489630      0.472149     0.499555     -3.740135  \n",
       "1       40.536081     22.300920     55.720477    19.421345    -19.284613  \n",
       "2        0.069090      0.253638      0.217507     0.412824     -9.550201  \n",
       "3        0.035388      0.184780      0.127321     0.333553     -7.365844  \n",
       "4        0.620366      0.485354      0.831565     0.374501    -13.556302  \n",
       "5        0.506259      0.500021      0.513263     0.500156     -0.353752  \n",
       "6       28.298387      7.679817     32.179045     7.997494    -12.331939  \n",
       "7        0.125662      0.331508      0.143236     0.350546     -1.276873  \n",
       "8        0.004815      0.069229      0.002653     0.051468      1.000831  \n",
       "9        0.570053      0.495128      0.586207     0.492839     -0.827426  \n",
       "10       0.146124      0.353273      0.222812     0.416409     -4.755837  \n",
       "11       0.153346      0.360364      0.045093     0.207646     11.510718  \n",
       "12       0.323062      0.467703      0.187003     0.390172      8.527707  \n",
       "13       0.157920      0.364710      0.238727     0.426588     -4.887332  \n",
       "14       0.370246      0.482928      0.416446     0.493296     -2.373522  \n",
       "15       0.148772      0.355907      0.157825     0.364819     -0.629190  "
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary_list = []\n",
    "\n",
    "for i in range(0, len(X_list)):\n",
    "    summary_list.append([X_list[i],X_descrip[i],control_mean[X_list[i]],control_std[X_list[i]],treat_mean[X_list[i]],treat_std[X_list[i]],\n",
    "                    stats.ttest_ind(df_control[X_list[i]], df_treatment[X_list[i]], equal_var=False)[0]])\n",
    "\n",
    "summary_df = pd.DataFrame(summary_list,columns=\n",
    "                          ['Label', 'Variable Description','Controls Mean','Controls STD','Treated Mean','Treated STD','t-Statistics'])\n",
    "summary_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "e6a9c639",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df_encoded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "52ec1049",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       1\n",
       "1       0\n",
       "2       1\n",
       "3       1\n",
       "4       1\n",
       "       ..\n",
       "4903    0\n",
       "4904    0\n",
       "4905    0\n",
       "4906    1\n",
       "4907    0\n",
       "Name: treatment_glucose_level, Length: 4908, dtype: int64"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y = df.stroke\n",
    "\n",
    "df_data = df.loc[:,df.columns !='stroke']\n",
    "T = df_data.treatment_glucose_level\n",
    "X = df_data.loc[:,df_data.columns !='treatment_glucose_level']\n",
    "T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "2cb30403",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_g = ['age','hypertension','heart_disease','bmi']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "062d9b55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.15362673186634065"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "9a104bad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.7618\n",
      "\n",
      "Confusion matrix:\n",
      "[[3325  829]\n",
      " [ 340  414]]\n",
      "\n",
      "F1 score is: 0.4146\n"
     ]
    }
   ],
   "source": [
    "model_initial = Pipeline([('scaler', StandardScaler()),('logistic_classifier', lr(class_weight={0:1,1:4}))])\n",
    "model_initial.fit(X.loc[:,X_g], T)\n",
    "\n",
    "predictions = model_initial.predict_proba(X.loc[:,X_g])\n",
    "predictions_binary = model_initial.predict(X.loc[:,X_g])\n",
    "print('Accuracy: {:.4f}\\n'.format(metrics.accuracy_score(T, predictions_binary)))\n",
    "print('Confusion matrix:\\n{}\\n'.format(metrics.confusion_matrix(T, predictions_binary)))\n",
    "print('F1 score is: {:.4f}'.format(metrics.f1_score(T, predictions_binary)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "2450eb0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "likelihood_ratio_table = []\n",
    "X_l = X_g.copy()\n",
    "\n",
    "while not likelihood_ratio_table or max([x for x in likelihood_ratio_table[-1].values() if x != '-']) >= 1:\n",
    "    if likelihood_ratio_table:\n",
    "        max_likelihood_ratio = max([x for x in likelihood_ratio_table[-1].values() if x != '-'])\n",
    "        next_label = [key for key in likelihood_ratio_table[-1] if likelihood_ratio_table[-1][key] == max_likelihood_ratio][0]\n",
    "        X_l.append(next_label)\n",
    "    model = Pipeline([('scaler', StandardScaler()),('logistic_classifier', lr(class_weight={0:1,1:4}))])\n",
    "    model.fit(X.loc[:,X_l], T)\n",
    "    step = {}\n",
    "    for x in X.columns:\n",
    "        if x in X_l:\n",
    "            step[x] = '-'\n",
    "        else:\n",
    "            X_new = X_l.copy()\n",
    "            X_new.append(x)\n",
    "            model_new = Pipeline([('scaler', StandardScaler()),('logistic_classifier', lr(class_weight={0:1,1:4}))])\n",
    "            model_new.fit(X.loc[:,X_new], T)\n",
    "            \n",
    "            step[x] = -2*((-metrics.log_loss(T, model.predict_proba(X.loc[:,X_l]))*len(T))\n",
    "                      -(-metrics.log_loss(T, model_new.predict_proba(X.loc[:,X_new]))*len(T)))\n",
    "    likelihood_ratio_table.append(step)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "2e2b2eae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['age',\n",
       " 'hypertension',\n",
       " 'heart_disease',\n",
       " 'bmi',\n",
       " 'gender',\n",
       " 'work_type_children',\n",
       " 'smoking_status_Unknown',\n",
       " 'ever_married',\n",
       " 'work_type_Never_worked']"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "40467ba3",
   "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>Label</th>\n",
       "      <th>Step 1</th>\n",
       "      <th>Step 2</th>\n",
       "      <th>Step 3</th>\n",
       "      <th>Step 4</th>\n",
       "      <th>Step 5</th>\n",
       "      <th>Step 6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>gender</td>\n",
       "      <td>19.073622</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>age</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>2</th>\n",
       "      <td>hypertension</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>3</th>\n",
       "      <td>heart_disease</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>4</th>\n",
       "      <td>ever_married</td>\n",
       "      <td>0.634818</td>\n",
       "      <td>0.540819</td>\n",
       "      <td>2.501759</td>\n",
       "      <td>2.296151</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Residence_type</td>\n",
       "      <td>0.070596</td>\n",
       "      <td>0.074609</td>\n",
       "      <td>0.069361</td>\n",
       "      <td>0.084035</td>\n",
       "      <td>0.079983</td>\n",
       "      <td>0.059932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>bmi</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>7</th>\n",
       "      <td>work_type_Govt_job</td>\n",
       "      <td>0.115045</td>\n",
       "      <td>0.090918</td>\n",
       "      <td>-0.016364</td>\n",
       "      <td>-0.007365</td>\n",
       "      <td>-0.000397</td>\n",
       "      <td>0.000324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>work_type_Never_worked</td>\n",
       "      <td>0.657222</td>\n",
       "      <td>0.589452</td>\n",
       "      <td>0.987675</td>\n",
       "      <td>1.015231</td>\n",
       "      <td>1.323178</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>work_type_Private</td>\n",
       "      <td>-0.047031</td>\n",
       "      <td>0.077384</td>\n",
       "      <td>0.238391</td>\n",
       "      <td>0.305258</td>\n",
       "      <td>0.271985</td>\n",
       "      <td>0.408997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>work_type_Self-employed</td>\n",
       "      <td>0.316727</td>\n",
       "      <td>0.212119</td>\n",
       "      <td>0.364408</td>\n",
       "      <td>0.375967</td>\n",
       "      <td>0.317737</td>\n",
       "      <td>0.318677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>work_type_children</td>\n",
       "      <td>13.771959</td>\n",
       "      <td>12.552948</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>smoking_status_Unknown</td>\n",
       "      <td>2.358109</td>\n",
       "      <td>2.678801</td>\n",
       "      <td>7.150382</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>smoking_status_formerly smoked</td>\n",
       "      <td>1.260345</td>\n",
       "      <td>0.517434</td>\n",
       "      <td>0.521816</td>\n",
       "      <td>-0.187335</td>\n",
       "      <td>-0.179125</td>\n",
       "      <td>-0.176085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>smoking_status_never smoked</td>\n",
       "      <td>0.530763</td>\n",
       "      <td>1.356023</td>\n",
       "      <td>2.52985</td>\n",
       "      <td>0.274352</td>\n",
       "      <td>0.331632</td>\n",
       "      <td>0.297426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>smoking_status_smokes</td>\n",
       "      <td>0.271251</td>\n",
       "      <td>0.396282</td>\n",
       "      <td>-0.075639</td>\n",
       "      <td>0.611313</td>\n",
       "      <td>0.687603</td>\n",
       "      <td>0.614897</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Label     Step 1     Step 2    Step 3    Step 4  \\\n",
       "0                           gender  19.073622          -         -         -   \n",
       "1                              age          -          -         -         -   \n",
       "2                     hypertension          -          -         -         -   \n",
       "3                    heart_disease          -          -         -         -   \n",
       "4                     ever_married   0.634818   0.540819  2.501759  2.296151   \n",
       "5                   Residence_type   0.070596   0.074609  0.069361  0.084035   \n",
       "6                              bmi          -          -         -         -   \n",
       "7               work_type_Govt_job   0.115045   0.090918 -0.016364 -0.007365   \n",
       "8           work_type_Never_worked   0.657222   0.589452  0.987675  1.015231   \n",
       "9                work_type_Private  -0.047031   0.077384  0.238391  0.305258   \n",
       "10         work_type_Self-employed   0.316727   0.212119  0.364408  0.375967   \n",
       "11              work_type_children  13.771959  12.552948         -         -   \n",
       "12          smoking_status_Unknown   2.358109   2.678801  7.150382         -   \n",
       "13  smoking_status_formerly smoked   1.260345   0.517434  0.521816 -0.187335   \n",
       "14     smoking_status_never smoked   0.530763   1.356023   2.52985  0.274352   \n",
       "15           smoking_status_smokes   0.271251   0.396282 -0.075639  0.611313   \n",
       "\n",
       "      Step 5    Step 6  \n",
       "0          -         -  \n",
       "1          -         -  \n",
       "2          -         -  \n",
       "3          -         -  \n",
       "4          -         -  \n",
       "5   0.079983  0.059932  \n",
       "6          -         -  \n",
       "7  -0.000397  0.000324  \n",
       "8   1.323178         -  \n",
       "9   0.271985  0.408997  \n",
       "10  0.317737  0.318677  \n",
       "11         -         -  \n",
       "12         -         -  \n",
       "13 -0.179125 -0.176085  \n",
       "14  0.331632  0.297426  \n",
       "15  0.687603  0.614897  "
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linear_terms_list = []\n",
    "for x in X.columns:\n",
    "    row = [step[x] for step in likelihood_ratio_table]\n",
    "    row.insert(0, x)\n",
    "    linear_terms_list.append(row)\n",
    "    \n",
    "columns = ['Label']\n",
    "for i in range(0,len(likelihood_ratio_table)):\n",
    "    step = 'Step ' + str(i+1)\n",
    "    columns.append(step)\n",
    "    \n",
    "linear_terms_df = pd.DataFrame(linear_terms_list,columns = columns)\n",
    "linear_terms_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "3a071556",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.7612\n",
      "\n",
      "Confusion matrix:\n",
      "[[3291  863]\n",
      " [ 309  445]]\n",
      "\n",
      "F1 score is: 0.4316\n"
     ]
    }
   ],
   "source": [
    "model_linear = Pipeline([('scaler', StandardScaler()),('logistic_classifier', lr(class_weight={0:1,1:4}))])\n",
    "model_linear.fit(X.loc[:,X_l], T)\n",
    "\n",
    "predictions_binary = model_linear.predict(X.loc[:,X_l])\n",
    "print('Accuracy: {:.4f}\\n'.format(metrics.accuracy_score(T, predictions_binary)))\n",
    "print('Confusion matrix:\\n{}\\n'.format(metrics.confusion_matrix(T, predictions_binary)))\n",
    "print('F1 score is: {:.4f}'.format(metrics.f1_score(T, predictions_binary)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "ad451cdb",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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>bmi</th>\n",
       "      <th>gender</th>\n",
       "      <th>work_type_children</th>\n",
       "      <th>smoking_status_Unknown</th>\n",
       "      <th>ever_married</th>\n",
       "      <th>work_type_Never_worked</th>\n",
       "      <th>age*age</th>\n",
       "      <th>...</th>\n",
       "      <th>work_type_children*work_type_children</th>\n",
       "      <th>work_type_children*smoking_status_Unknown</th>\n",
       "      <th>work_type_children*ever_married</th>\n",
       "      <th>work_type_children*work_type_Never_worked</th>\n",
       "      <th>smoking_status_Unknown*smoking_status_Unknown</th>\n",
       "      <th>smoking_status_Unknown*ever_married</th>\n",
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       "      <th>ever_married*ever_married</th>\n",
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       "      <th>work_type_Never_worked*work_type_Never_worked</th>\n",
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       "      <th>3</th>\n",
       "      <td>79.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>81.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0.0</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",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <th>4905</th>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2601.0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
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       "      <td>44.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26.2</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1936.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>4908 rows × 54 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age  hypertension  heart_disease   bmi  gender  work_type_children  \\\n",
       "0     67.0             0              1  36.6     1.0                   0   \n",
       "1     80.0             0              1  32.5     1.0                   0   \n",
       "2     49.0             0              0  34.4     0.0                   0   \n",
       "3     79.0             1              0  24.0     0.0                   0   \n",
       "4     81.0             0              0  29.0     1.0                   0   \n",
       "...    ...           ...            ...   ...     ...                 ...   \n",
       "4903  13.0             0              0  18.6     0.0                   1   \n",
       "4904  81.0             0              0  40.0     0.0                   0   \n",
       "4905  35.0             0              0  30.6     0.0                   0   \n",
       "4906  51.0             0              0  25.6     1.0                   0   \n",
       "4907  44.0             0              0  26.2     0.0                   0   \n",
       "\n",
       "      smoking_status_Unknown  ever_married  work_type_Never_worked  age*age  \\\n",
       "0                          0             1                       0   4489.0   \n",
       "1                          0             1                       0   6400.0   \n",
       "2                          0             1                       0   2401.0   \n",
       "3                          0             1                       0   6241.0   \n",
       "4                          0             1                       0   6561.0   \n",
       "...                      ...           ...                     ...      ...   \n",
       "4903                       1             0                       0    169.0   \n",
       "4904                       0             1                       0   6561.0   \n",
       "4905                       0             1                       0   1225.0   \n",
       "4906                       0             1                       0   2601.0   \n",
       "4907                       1             1                       0   1936.0   \n",
       "\n",
       "      ...  work_type_children*work_type_children  \\\n",
       "0     ...                                      0   \n",
       "1     ...                                      0   \n",
       "2     ...                                      0   \n",
       "3     ...                                      0   \n",
       "4     ...                                      0   \n",
       "...   ...                                    ...   \n",
       "4903  ...                                      1   \n",
       "4904  ...                                      0   \n",
       "4905  ...                                      0   \n",
       "4906  ...                                      0   \n",
       "4907  ...                                      0   \n",
       "\n",
       "      work_type_children*smoking_status_Unknown  \\\n",
       "0                                             0   \n",
       "1                                             0   \n",
       "2                                             0   \n",
       "3                                             0   \n",
       "4                                             0   \n",
       "...                                         ...   \n",
       "4903                                          1   \n",
       "4904                                          0   \n",
       "4905                                          0   \n",
       "4906                                          0   \n",
       "4907                                          0   \n",
       "\n",
       "      work_type_children*ever_married  \\\n",
       "0                                   0   \n",
       "1                                   0   \n",
       "2                                   0   \n",
       "3                                   0   \n",
       "4                                   0   \n",
       "...                               ...   \n",
       "4903                                0   \n",
       "4904                                0   \n",
       "4905                                0   \n",
       "4906                                0   \n",
       "4907                                0   \n",
       "\n",
       "      work_type_children*work_type_Never_worked  \\\n",
       "0                                             0   \n",
       "1                                             0   \n",
       "2                                             0   \n",
       "3                                             0   \n",
       "4                                             0   \n",
       "...                                         ...   \n",
       "4903                                          0   \n",
       "4904                                          0   \n",
       "4905                                          0   \n",
       "4906                                          0   \n",
       "4907                                          0   \n",
       "\n",
       "      smoking_status_Unknown*smoking_status_Unknown  \\\n",
       "0                                                 0   \n",
       "1                                                 0   \n",
       "2                                                 0   \n",
       "3                                                 0   \n",
       "4                                                 0   \n",
       "...                                             ...   \n",
       "4903                                              1   \n",
       "4904                                              0   \n",
       "4905                                              0   \n",
       "4906                                              0   \n",
       "4907                                              1   \n",
       "\n",
       "      smoking_status_Unknown*ever_married  \\\n",
       "0                                       0   \n",
       "1                                       0   \n",
       "2                                       0   \n",
       "3                                       0   \n",
       "4                                       0   \n",
       "...                                   ...   \n",
       "4903                                    0   \n",
       "4904                                    0   \n",
       "4905                                    0   \n",
       "4906                                    0   \n",
       "4907                                    1   \n",
       "\n",
       "      smoking_status_Unknown*work_type_Never_worked  \\\n",
       "0                                                 0   \n",
       "1                                                 0   \n",
       "2                                                 0   \n",
       "3                                                 0   \n",
       "4                                                 0   \n",
       "...                                             ...   \n",
       "4903                                              0   \n",
       "4904                                              0   \n",
       "4905                                              0   \n",
       "4906                                              0   \n",
       "4907                                              0   \n",
       "\n",
       "      ever_married*ever_married  ever_married*work_type_Never_worked  \\\n",
       "0                             1                                    0   \n",
       "1                             1                                    0   \n",
       "2                             1                                    0   \n",
       "3                             1                                    0   \n",
       "4                             1                                    0   \n",
       "...                         ...                                  ...   \n",
       "4903                          0                                    0   \n",
       "4904                          1                                    0   \n",
       "4905                          1                                    0   \n",
       "4906                          1                                    0   \n",
       "4907                          1                                    0   \n",
       "\n",
       "      work_type_Never_worked*work_type_Never_worked  \n",
       "0                                                 0  \n",
       "1                                                 0  \n",
       "2                                                 0  \n",
       "3                                                 0  \n",
       "4                                                 0  \n",
       "...                                             ...  \n",
       "4903                                              0  \n",
       "4904                                              0  \n",
       "4905                                              0  \n",
       "4906                                              0  \n",
       "4907                                              0  \n",
       "\n",
       "[4908 rows x 54 columns]"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_copy = X.loc[:,X_l]\n",
    "\n",
    "for i in range(0, len(X_l)):\n",
    "    j = i\n",
    "    while j < len(X_l):\n",
    "        X_copy[X_l[i]+'*'+X_l[j]] = X_copy[X_l[i]] * X_copy[X_l[j]]\n",
    "        j += 1\n",
    "\n",
    "X_copy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "fc61948d",
   "metadata": {},
   "outputs": [],
   "source": [
    "likelihood_ratio_table = []\n",
    "X_2 = X_l.copy()\n",
    "\n",
    "while not likelihood_ratio_table or max([x for x in likelihood_ratio_table[-1].values() if x != '-']) >= 2.71:\n",
    "    if likelihood_ratio_table:\n",
    "        max_likelihood_ratio = max([x for x in likelihood_ratio_table[-1].values() if x != '-'])\n",
    "        next_label = [key for key in likelihood_ratio_table[-1] if likelihood_ratio_table[-1][key] == max_likelihood_ratio][0]\n",
    "        X_2.append(next_label)\n",
    "    model = Pipeline([('scaler', StandardScaler()),('logistic_classifier', lr(class_weight={0:1,1:4}))])\n",
    "    model.fit(X_copy.loc[:,X_2], T)\n",
    "    step = {}\n",
    "    for x in X_copy.columns:\n",
    "        if x in X_2:\n",
    "            step[x] = '-'\n",
    "        else:\n",
    "            X_new = X_2.copy()\n",
    "            X_new.append(x)\n",
    "            model_new = Pipeline([('scaler', StandardScaler()),('logistic_classifier', lr(class_weight={0:1,1:4}))])\n",
    "            model_new.fit(X_copy.loc[:,X_new], T)\n",
    "            \n",
    "            step[x] = -2*((-metrics.log_loss(T, model.predict_proba(X_copy.loc[:,X_2]))*len(T))\n",
    "                      -(-metrics.log_loss(T, model_new.predict_proba(X_copy.loc[:,X_new]))*len(T)))\n",
    "    likelihood_ratio_table.append(step)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "b2ac36f3",
   "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>Label</th>\n",
       "      <th>Step 1</th>\n",
       "      <th>Step 2</th>\n",
       "      <th>Step 3</th>\n",
       "      <th>Step 4</th>\n",
       "      <th>Step 5</th>\n",
       "      <th>Step 6</th>\n",
       "      <th>Step 7</th>\n",
       "      <th>Step 8</th>\n",
       "      <th>Step 9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>age</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>1</th>\n",
       "      <td>hypertension</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>2</th>\n",
       "      <td>heart_disease</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>3</th>\n",
       "      <td>bmi</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>4</th>\n",
       "      <td>gender</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>5</th>\n",
       "      <td>work_type_children</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>6</th>\n",
       "      <td>smoking_status_Unknown</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>7</th>\n",
       "      <td>ever_married</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>8</th>\n",
       "      <td>work_type_Never_worked</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>9</th>\n",
       "      <td>age*age</td>\n",
       "      <td>-0.456462</td>\n",
       "      <td>0.243005</td>\n",
       "      <td>0.253178</td>\n",
       "      <td>0.677132</td>\n",
       "      <td>0.770905</td>\n",
       "      <td>0.304231</td>\n",
       "      <td>0.285913</td>\n",
       "      <td>0.208414</td>\n",
       "      <td>-0.075912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>age*hypertension</td>\n",
       "      <td>1.163047</td>\n",
       "      <td>0.132468</td>\n",
       "      <td>0.237823</td>\n",
       "      <td>0.08189</td>\n",
       "      <td>0.077368</td>\n",
       "      <td>0.236806</td>\n",
       "      <td>0.351834</td>\n",
       "      <td>0.254705</td>\n",
       "      <td>0.562107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>age*heart_disease</td>\n",
       "      <td>2.127541</td>\n",
       "      <td>1.210647</td>\n",
       "      <td>1.403954</td>\n",
       "      <td>1.428035</td>\n",
       "      <td>1.426393</td>\n",
       "      <td>1.456227</td>\n",
       "      <td>1.50279</td>\n",
       "      <td>1.439743</td>\n",
       "      <td>1.844461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>age*bmi</td>\n",
       "      <td>54.138688</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>13</th>\n",
       "      <td>age*gender</td>\n",
       "      <td>0.05787</td>\n",
       "      <td>-0.095055</td>\n",
       "      <td>0.25709</td>\n",
       "      <td>0.239258</td>\n",
       "      <td>0.285911</td>\n",
       "      <td>0.382127</td>\n",
       "      <td>0.449561</td>\n",
       "      <td>0.491176</td>\n",
       "      <td>0.541178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>age*work_type_children</td>\n",
       "      <td>1.767124</td>\n",
       "      <td>0.05581</td>\n",
       "      <td>0.075106</td>\n",
       "      <td>1.178103</td>\n",
       "      <td>1.148216</td>\n",
       "      <td>0.692934</td>\n",
       "      <td>0.600285</td>\n",
       "      <td>0.638912</td>\n",
       "      <td>0.525518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>age*smoking_status_Unknown</td>\n",
       "      <td>2.3128</td>\n",
       "      <td>0.725027</td>\n",
       "      <td>0.729133</td>\n",
       "      <td>7.215401</td>\n",
       "      <td>7.035552</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>age*ever_married</td>\n",
       "      <td>4.310151</td>\n",
       "      <td>3.211995</td>\n",
       "      <td>3.485944</td>\n",
       "      <td>3.765841</td>\n",
       "      <td>3.970435</td>\n",
       "      <td>3.060495</td>\n",
       "      <td>2.888479</td>\n",
       "      <td>2.858226</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>age*work_type_Never_worked</td>\n",
       "      <td>4.913865</td>\n",
       "      <td>3.832793</td>\n",
       "      <td>3.793363</td>\n",
       "      <td>3.805741</td>\n",
       "      <td>3.714955</td>\n",
       "      <td>3.048683</td>\n",
       "      <td>3.091455</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>hypertension*hypertension</td>\n",
       "      <td>-0.002597</td>\n",
       "      <td>-0.014679</td>\n",
       "      <td>0.012135</td>\n",
       "      <td>-0.031522</td>\n",
       "      <td>0.017215</td>\n",
       "      <td>-0.039688</td>\n",
       "      <td>-0.021209</td>\n",
       "      <td>-0.038317</td>\n",
       "      <td>0.013825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>hypertension*heart_disease</td>\n",
       "      <td>1.608648</td>\n",
       "      <td>1.778749</td>\n",
       "      <td>1.70514</td>\n",
       "      <td>1.75385</td>\n",
       "      <td>2.598907</td>\n",
       "      <td>2.712054</td>\n",
       "      <td>2.564763</td>\n",
       "      <td>2.515632</td>\n",
       "      <td>2.450449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>hypertension*bmi</td>\n",
       "      <td>3.803128</td>\n",
       "      <td>7.41797</td>\n",
       "      <td>7.36449</td>\n",
       "      <td>7.540721</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>hypertension*gender</td>\n",
       "      <td>1.732238</td>\n",
       "      <td>1.570839</td>\n",
       "      <td>1.178718</td>\n",
       "      <td>1.227277</td>\n",
       "      <td>1.257828</td>\n",
       "      <td>1.295593</td>\n",
       "      <td>1.373346</td>\n",
       "      <td>1.320046</td>\n",
       "      <td>1.214316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>hypertension*work_type_children</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>hypertension*smoking_status_Unknown</td>\n",
       "      <td>3.510132</td>\n",
       "      <td>5.089607</td>\n",
       "      <td>4.921737</td>\n",
       "      <td>4.064291</td>\n",
       "      <td>4.094102</td>\n",
       "      <td>6.758882</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>hypertension*ever_married</td>\n",
       "      <td>2.243175</td>\n",
       "      <td>0.813899</td>\n",
       "      <td>0.756981</td>\n",
       "      <td>0.794076</td>\n",
       "      <td>0.839652</td>\n",
       "      <td>0.753853</td>\n",
       "      <td>0.789453</td>\n",
       "      <td>0.767771</td>\n",
       "      <td>0.136283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>hypertension*work_type_Never_worked</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>heart_disease*heart_disease</td>\n",
       "      <td>0.000615</td>\n",
       "      <td>-0.028844</td>\n",
       "      <td>-0.00477</td>\n",
       "      <td>-0.017541</td>\n",
       "      <td>0.036112</td>\n",
       "      <td>-0.021672</td>\n",
       "      <td>0.038764</td>\n",
       "      <td>-0.003342</td>\n",
       "      <td>0.063541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>heart_disease*bmi</td>\n",
       "      <td>0.614434</td>\n",
       "      <td>0.573071</td>\n",
       "      <td>0.599491</td>\n",
       "      <td>0.780449</td>\n",
       "      <td>0.643298</td>\n",
       "      <td>0.532715</td>\n",
       "      <td>0.564667</td>\n",
       "      <td>0.511311</td>\n",
       "      <td>0.526227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>heart_disease*gender</td>\n",
       "      <td>7.992542</td>\n",
       "      <td>8.189158</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>29</th>\n",
       "      <td>heart_disease*work_type_children</td>\n",
       "      <td>0.383467</td>\n",
       "      <td>0.197377</td>\n",
       "      <td>0.070641</td>\n",
       "      <td>0.136387</td>\n",
       "      <td>0.106911</td>\n",
       "      <td>0.113345</td>\n",
       "      <td>0.091934</td>\n",
       "      <td>0.098569</td>\n",
       "      <td>0.167001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>heart_disease*smoking_status_Unknown</td>\n",
       "      <td>0.025782</td>\n",
       "      <td>0.009524</td>\n",
       "      <td>0.063134</td>\n",
       "      <td>0.023675</td>\n",
       "      <td>0.029683</td>\n",
       "      <td>0.706105</td>\n",
       "      <td>0.944529</td>\n",
       "      <td>0.893165</td>\n",
       "      <td>0.809558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>heart_disease*ever_married</td>\n",
       "      <td>0.521164</td>\n",
       "      <td>0.63312</td>\n",
       "      <td>0.177541</td>\n",
       "      <td>0.218774</td>\n",
       "      <td>0.071201</td>\n",
       "      <td>0.124809</td>\n",
       "      <td>0.315296</td>\n",
       "      <td>0.24549</td>\n",
       "      <td>1.803644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>heart_disease*work_type_Never_worked</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>bmi*bmi</td>\n",
       "      <td>6.261838</td>\n",
       "      <td>1.641247</td>\n",
       "      <td>1.655858</td>\n",
       "      <td>1.253187</td>\n",
       "      <td>0.193784</td>\n",
       "      <td>0.269143</td>\n",
       "      <td>0.288889</td>\n",
       "      <td>0.31736</td>\n",
       "      <td>0.49503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>bmi*gender</td>\n",
       "      <td>1.651982</td>\n",
       "      <td>0.459839</td>\n",
       "      <td>0.656254</td>\n",
       "      <td>0.618357</td>\n",
       "      <td>0.29797</td>\n",
       "      <td>0.32161</td>\n",
       "      <td>0.298124</td>\n",
       "      <td>0.221504</td>\n",
       "      <td>0.240442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>bmi*work_type_children</td>\n",
       "      <td>3.384275</td>\n",
       "      <td>1.067993</td>\n",
       "      <td>1.277111</td>\n",
       "      <td>0.190833</td>\n",
       "      <td>0.155467</td>\n",
       "      <td>0.169528</td>\n",
       "      <td>0.228926</td>\n",
       "      <td>0.143591</td>\n",
       "      <td>0.108283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>bmi*smoking_status_Unknown</td>\n",
       "      <td>1.941419</td>\n",
       "      <td>-0.003727</td>\n",
       "      <td>0.00221</td>\n",
       "      <td>1.112765</td>\n",
       "      <td>1.783073</td>\n",
       "      <td>1.393147</td>\n",
       "      <td>1.586389</td>\n",
       "      <td>1.357448</td>\n",
       "      <td>1.323011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>bmi*ever_married</td>\n",
       "      <td>19.364377</td>\n",
       "      <td>1.048992</td>\n",
       "      <td>0.771451</td>\n",
       "      <td>0.868681</td>\n",
       "      <td>0.88364</td>\n",
       "      <td>0.720495</td>\n",
       "      <td>0.813099</td>\n",
       "      <td>0.701763</td>\n",
       "      <td>0.418226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>bmi*work_type_Never_worked</td>\n",
       "      <td>3.647114</td>\n",
       "      <td>1.308878</td>\n",
       "      <td>1.27251</td>\n",
       "      <td>1.164766</td>\n",
       "      <td>1.225267</td>\n",
       "      <td>0.833939</td>\n",
       "      <td>0.95704</td>\n",
       "      <td>0.070896</td>\n",
       "      <td>0.09756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>gender*gender</td>\n",
       "      <td>-0.006144</td>\n",
       "      <td>-0.000112</td>\n",
       "      <td>0.013067</td>\n",
       "      <td>-0.024381</td>\n",
       "      <td>-0.000588</td>\n",
       "      <td>-0.027428</td>\n",
       "      <td>0.020984</td>\n",
       "      <td>-0.050332</td>\n",
       "      <td>-0.036592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>gender*work_type_children</td>\n",
       "      <td>0.839269</td>\n",
       "      <td>0.649541</td>\n",
       "      <td>0.359345</td>\n",
       "      <td>0.367197</td>\n",
       "      <td>0.358325</td>\n",
       "      <td>0.389439</td>\n",
       "      <td>0.392965</td>\n",
       "      <td>0.332317</td>\n",
       "      <td>0.42315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>gender*smoking_status_Unknown</td>\n",
       "      <td>0.567502</td>\n",
       "      <td>0.516528</td>\n",
       "      <td>0.37387</td>\n",
       "      <td>0.657756</td>\n",
       "      <td>0.613219</td>\n",
       "      <td>0.52732</td>\n",
       "      <td>0.537798</td>\n",
       "      <td>0.413609</td>\n",
       "      <td>0.547091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>gender*ever_married</td>\n",
       "      <td>0.107195</td>\n",
       "      <td>0.094598</td>\n",
       "      <td>0.276238</td>\n",
       "      <td>0.267854</td>\n",
       "      <td>0.287818</td>\n",
       "      <td>0.347555</td>\n",
       "      <td>0.335588</td>\n",
       "      <td>0.383227</td>\n",
       "      <td>0.542877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>gender*work_type_Never_worked</td>\n",
       "      <td>0.028615</td>\n",
       "      <td>-0.002301</td>\n",
       "      <td>0.056816</td>\n",
       "      <td>0.046685</td>\n",
       "      <td>0.039157</td>\n",
       "      <td>0.1358</td>\n",
       "      <td>0.185711</td>\n",
       "      <td>1.062367</td>\n",
       "      <td>1.093538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>work_type_children*work_type_children</td>\n",
       "      <td>0.012223</td>\n",
       "      <td>-0.018875</td>\n",
       "      <td>0.016895</td>\n",
       "      <td>-0.000603</td>\n",
       "      <td>0.031092</td>\n",
       "      <td>-0.003442</td>\n",
       "      <td>0.008597</td>\n",
       "      <td>0.025028</td>\n",
       "      <td>0.116663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>work_type_children*smoking_status_Unknown</td>\n",
       "      <td>3.832006</td>\n",
       "      <td>7.827519</td>\n",
       "      <td>7.858593</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>46</th>\n",
       "      <td>work_type_children*ever_married</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>work_type_children*work_type_Never_worked</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>smoking_status_Unknown*smoking_status_Unknown</td>\n",
       "      <td>-0.015014</td>\n",
       "      <td>-0.019833</td>\n",
       "      <td>-0.045925</td>\n",
       "      <td>0.008916</td>\n",
       "      <td>0.013361</td>\n",
       "      <td>0.072614</td>\n",
       "      <td>-0.00764</td>\n",
       "      <td>-0.005844</td>\n",
       "      <td>0.027963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>smoking_status_Unknown*ever_married</td>\n",
       "      <td>-0.039058</td>\n",
       "      <td>0.41806</td>\n",
       "      <td>0.457145</td>\n",
       "      <td>0.423279</td>\n",
       "      <td>0.573822</td>\n",
       "      <td>0.259643</td>\n",
       "      <td>0.22575</td>\n",
       "      <td>0.252026</td>\n",
       "      <td>0.56304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>smoking_status_Unknown*work_type_Never_worked</td>\n",
       "      <td>0.851486</td>\n",
       "      <td>0.259727</td>\n",
       "      <td>0.276857</td>\n",
       "      <td>0.278218</td>\n",
       "      <td>0.240895</td>\n",
       "      <td>-0.04344</td>\n",
       "      <td>0.017212</td>\n",
       "      <td>0.322401</td>\n",
       "      <td>0.333099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>ever_married*ever_married</td>\n",
       "      <td>-0.005185</td>\n",
       "      <td>-0.018046</td>\n",
       "      <td>-0.048927</td>\n",
       "      <td>0.019668</td>\n",
       "      <td>0.02534</td>\n",
       "      <td>-0.038591</td>\n",
       "      <td>0.01953</td>\n",
       "      <td>-0.028051</td>\n",
       "      <td>0.033046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>ever_married*work_type_Never_worked</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>work_type_Never_worked*work_type_Never_worked</td>\n",
       "      <td>-0.009526</td>\n",
       "      <td>-0.023721</td>\n",
       "      <td>0.005399</td>\n",
       "      <td>-0.003395</td>\n",
       "      <td>-0.017815</td>\n",
       "      <td>0.012228</td>\n",
       "      <td>0.02795</td>\n",
       "      <td>-0.030036</td>\n",
       "      <td>0.028805</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            Label     Step 1    Step 2  \\\n",
       "0                                             age          -         -   \n",
       "1                                    hypertension          -         -   \n",
       "2                                   heart_disease          -         -   \n",
       "3                                             bmi          -         -   \n",
       "4                                          gender          -         -   \n",
       "5                              work_type_children          -         -   \n",
       "6                          smoking_status_Unknown          -         -   \n",
       "7                                    ever_married          -         -   \n",
       "8                          work_type_Never_worked          -         -   \n",
       "9                                         age*age  -0.456462  0.243005   \n",
       "10                               age*hypertension   1.163047  0.132468   \n",
       "11                              age*heart_disease   2.127541  1.210647   \n",
       "12                                        age*bmi  54.138688         -   \n",
       "13                                     age*gender    0.05787 -0.095055   \n",
       "14                         age*work_type_children   1.767124   0.05581   \n",
       "15                     age*smoking_status_Unknown     2.3128  0.725027   \n",
       "16                               age*ever_married   4.310151  3.211995   \n",
       "17                     age*work_type_Never_worked   4.913865  3.832793   \n",
       "18                      hypertension*hypertension  -0.002597 -0.014679   \n",
       "19                     hypertension*heart_disease   1.608648  1.778749   \n",
       "20                               hypertension*bmi   3.803128   7.41797   \n",
       "21                            hypertension*gender   1.732238  1.570839   \n",
       "22                hypertension*work_type_children       -0.0      -0.0   \n",
       "23            hypertension*smoking_status_Unknown   3.510132  5.089607   \n",
       "24                      hypertension*ever_married   2.243175  0.813899   \n",
       "25            hypertension*work_type_Never_worked       -0.0      -0.0   \n",
       "26                    heart_disease*heart_disease   0.000615 -0.028844   \n",
       "27                              heart_disease*bmi   0.614434  0.573071   \n",
       "28                           heart_disease*gender   7.992542  8.189158   \n",
       "29               heart_disease*work_type_children   0.383467  0.197377   \n",
       "30           heart_disease*smoking_status_Unknown   0.025782  0.009524   \n",
       "31                     heart_disease*ever_married   0.521164   0.63312   \n",
       "32           heart_disease*work_type_Never_worked       -0.0      -0.0   \n",
       "33                                        bmi*bmi   6.261838  1.641247   \n",
       "34                                     bmi*gender   1.651982  0.459839   \n",
       "35                         bmi*work_type_children   3.384275  1.067993   \n",
       "36                     bmi*smoking_status_Unknown   1.941419 -0.003727   \n",
       "37                               bmi*ever_married  19.364377  1.048992   \n",
       "38                     bmi*work_type_Never_worked   3.647114  1.308878   \n",
       "39                                  gender*gender  -0.006144 -0.000112   \n",
       "40                      gender*work_type_children   0.839269  0.649541   \n",
       "41                  gender*smoking_status_Unknown   0.567502  0.516528   \n",
       "42                            gender*ever_married   0.107195  0.094598   \n",
       "43                  gender*work_type_Never_worked   0.028615 -0.002301   \n",
       "44          work_type_children*work_type_children   0.012223 -0.018875   \n",
       "45      work_type_children*smoking_status_Unknown   3.832006  7.827519   \n",
       "46                work_type_children*ever_married       -0.0      -0.0   \n",
       "47      work_type_children*work_type_Never_worked       -0.0      -0.0   \n",
       "48  smoking_status_Unknown*smoking_status_Unknown  -0.015014 -0.019833   \n",
       "49            smoking_status_Unknown*ever_married  -0.039058   0.41806   \n",
       "50  smoking_status_Unknown*work_type_Never_worked   0.851486  0.259727   \n",
       "51                      ever_married*ever_married  -0.005185 -0.018046   \n",
       "52            ever_married*work_type_Never_worked       -0.0      -0.0   \n",
       "53  work_type_Never_worked*work_type_Never_worked  -0.009526 -0.023721   \n",
       "\n",
       "      Step 3    Step 4    Step 5    Step 6    Step 7    Step 8    Step 9  \n",
       "0          -         -         -         -         -         -         -  \n",
       "1          -         -         -         -         -         -         -  \n",
       "2          -         -         -         -         -         -         -  \n",
       "3          -         -         -         -         -         -         -  \n",
       "4          -         -         -         -         -         -         -  \n",
       "5          -         -         -         -         -         -         -  \n",
       "6          -         -         -         -         -         -         -  \n",
       "7          -         -         -         -         -         -         -  \n",
       "8          -         -         -         -         -         -         -  \n",
       "9   0.253178  0.677132  0.770905  0.304231  0.285913  0.208414 -0.075912  \n",
       "10  0.237823   0.08189  0.077368  0.236806  0.351834  0.254705  0.562107  \n",
       "11  1.403954  1.428035  1.426393  1.456227   1.50279  1.439743  1.844461  \n",
       "12         -         -         -         -         -         -         -  \n",
       "13   0.25709  0.239258  0.285911  0.382127  0.449561  0.491176  0.541178  \n",
       "14  0.075106  1.178103  1.148216  0.692934  0.600285  0.638912  0.525518  \n",
       "15  0.729133  7.215401  7.035552         -         -         -         -  \n",
       "16  3.485944  3.765841  3.970435  3.060495  2.888479  2.858226         -  \n",
       "17  3.793363  3.805741  3.714955  3.048683  3.091455         -         -  \n",
       "18  0.012135 -0.031522  0.017215 -0.039688 -0.021209 -0.038317  0.013825  \n",
       "19   1.70514   1.75385  2.598907  2.712054  2.564763  2.515632  2.450449  \n",
       "20   7.36449  7.540721         -         -         -         -         -  \n",
       "21  1.178718  1.227277  1.257828  1.295593  1.373346  1.320046  1.214316  \n",
       "22      -0.0       0.0       0.0       0.0      -0.0      -0.0      -0.0  \n",
       "23  4.921737  4.064291  4.094102  6.758882         -         -         -  \n",
       "24  0.756981  0.794076  0.839652  0.753853  0.789453  0.767771  0.136283  \n",
       "25      -0.0       0.0       0.0       0.0      -0.0      -0.0      -0.0  \n",
       "26  -0.00477 -0.017541  0.036112 -0.021672  0.038764 -0.003342  0.063541  \n",
       "27  0.599491  0.780449  0.643298  0.532715  0.564667  0.511311  0.526227  \n",
       "28         -         -         -         -         -         -         -  \n",
       "29  0.070641  0.136387  0.106911  0.113345  0.091934  0.098569  0.167001  \n",
       "30  0.063134  0.023675  0.029683  0.706105  0.944529  0.893165  0.809558  \n",
       "31  0.177541  0.218774  0.071201  0.124809  0.315296   0.24549  1.803644  \n",
       "32      -0.0       0.0       0.0       0.0      -0.0      -0.0      -0.0  \n",
       "33  1.655858  1.253187  0.193784  0.269143  0.288889   0.31736   0.49503  \n",
       "34  0.656254  0.618357   0.29797   0.32161  0.298124  0.221504  0.240442  \n",
       "35  1.277111  0.190833  0.155467  0.169528  0.228926  0.143591  0.108283  \n",
       "36   0.00221  1.112765  1.783073  1.393147  1.586389  1.357448  1.323011  \n",
       "37  0.771451  0.868681   0.88364  0.720495  0.813099  0.701763  0.418226  \n",
       "38   1.27251  1.164766  1.225267  0.833939   0.95704  0.070896   0.09756  \n",
       "39  0.013067 -0.024381 -0.000588 -0.027428  0.020984 -0.050332 -0.036592  \n",
       "40  0.359345  0.367197  0.358325  0.389439  0.392965  0.332317   0.42315  \n",
       "41   0.37387  0.657756  0.613219   0.52732  0.537798  0.413609  0.547091  \n",
       "42  0.276238  0.267854  0.287818  0.347555  0.335588  0.383227  0.542877  \n",
       "43  0.056816  0.046685  0.039157    0.1358  0.185711  1.062367  1.093538  \n",
       "44  0.016895 -0.000603  0.031092 -0.003442  0.008597  0.025028  0.116663  \n",
       "45  7.858593         -         -         -         -         -         -  \n",
       "46      -0.0       0.0       0.0       0.0      -0.0      -0.0      -0.0  \n",
       "47      -0.0       0.0       0.0       0.0      -0.0      -0.0      -0.0  \n",
       "48 -0.045925  0.008916  0.013361  0.072614  -0.00764 -0.005844  0.027963  \n",
       "49  0.457145  0.423279  0.573822  0.259643   0.22575  0.252026   0.56304  \n",
       "50  0.276857  0.278218  0.240895  -0.04344  0.017212  0.322401  0.333099  \n",
       "51 -0.048927  0.019668   0.02534 -0.038591   0.01953 -0.028051  0.033046  \n",
       "52      -0.0       0.0       0.0       0.0      -0.0      -0.0      -0.0  \n",
       "53  0.005399 -0.003395 -0.017815  0.012228   0.02795 -0.030036  0.028805  "
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_terms_list = []\n",
    "for x in X_copy.columns:\n",
    "    row = [step[x] for step in likelihood_ratio_table]\n",
    "    row.insert(0, x)\n",
    "    final_terms_list.append(row)\n",
    "    \n",
    "final_columns = ['Label']\n",
    "for i in range(0,len(likelihood_ratio_table)):\n",
    "    step = 'Step ' + str(i+1)\n",
    "    final_columns.append(step)\n",
    "    \n",
    "final_terms_df = pd.DataFrame(final_terms_list,columns = final_columns)\n",
    "final_terms_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "522fcac6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['age',\n",
       " 'hypertension',\n",
       " 'heart_disease',\n",
       " 'bmi',\n",
       " 'gender',\n",
       " 'work_type_children',\n",
       " 'smoking_status_Unknown',\n",
       " 'ever_married',\n",
       " 'work_type_Never_worked',\n",
       " 'age*bmi',\n",
       " 'heart_disease*gender',\n",
       " 'work_type_children*smoking_status_Unknown',\n",
       " 'hypertension*bmi',\n",
       " 'age*smoking_status_Unknown',\n",
       " 'hypertension*smoking_status_Unknown',\n",
       " 'age*work_type_Never_worked',\n",
       " 'age*ever_married']"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "4780f9e7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.7710\n",
      "\n",
      "Confusion matrix:\n",
      "[[3347  807]\n",
      " [ 317  437]]\n",
      "\n",
      "F1 score is: 0.4374\n"
     ]
    }
   ],
   "source": [
    "model_final = Pipeline([('scaler', StandardScaler()),('logistic_classifier', lr(class_weight={0:1,1:4}))])\n",
    "model_final.fit(X_copy.loc[:,X_2], T)\n",
    "predictions = model_initial.predict_proba(X.loc[:,X_g])\n",
    "predictions_binary = model_final.predict(X_copy.loc[:,X_2])\n",
    "print('Accuracy: {:.4f}\\n'.format(metrics.accuracy_score(T, predictions_binary)))\n",
    "print('Confusion matrix:\\n{}\\n'.format(metrics.confusion_matrix(T, predictions_binary)))\n",
    "print('F1 score is: {:.4f}'.format(metrics.f1_score(T, predictions_binary)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "f189a4c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "treat_index = []\n",
    "control_index = []\n",
    "for i in range(0,len(T)):\n",
    "    if T[i] == 1:\n",
    "        treat_index.append(i)\n",
    "    elif T[i] == 0:\n",
    "        control_index.append(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "7bc18b85",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.07073972750569343"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions = model_final.predict_proba(X_copy.loc[:,X_2])\n",
    "\n",
    "A = sum([T[i]*Y[i]/predictions[i][1] for i in treat_index]) / sum([T[i]/predictions[i][1] for i in treat_index])\n",
    "A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "ca7e60b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.04456253470832627"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "B = sum([(1-T[i])*Y[i]/(1-predictions[i][1]) for i in control_index]) / sum([(1-T[i])/(1-predictions[i][1]) for i in control_index])\n",
    "B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "740d0e02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.026177192797367167"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ate_ipw = A - B\n",
    "ate_ipw"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "578a7218",
   "metadata": {},
   "source": [
    "# Doubly-robust estimator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "957edee3",
   "metadata": {},
   "outputs": [],
   "source": [
    "ate_dr = ATE_outcome + ate_ipw - sum([T[i]*mean_1/predictions[i][1] for i in treat_index])/ sum([T[i]/predictions[i][1] for i in treat_index]) + sum([(1-T[i])*mean_0/(1-predictions[i][1]) for i in control_index]) / sum([(1-T[i])/(1-predictions[i][1]) for i in control_index])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "b44b5022",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.026177192797367164"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ate_dr"
   ]
  }
 ],
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