Outcome_regression_HeartDisease.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5563f26c",
   "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": "d4d8a4d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_raw = pd.read_csv('./stroke_data_dummies.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bb60236f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_r = df_raw[df_raw.Gender_Other == 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d940fa27",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df_r.drop(columns = ['Unnamed: 0','id','Obesity','Gender_Other'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0e652b83",
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
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       "      <th>hypertension</th>\n",
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       "      <th>avg_glucose_level</th>\n",
       "      <th>bmi</th>\n",
       "      <th>stroke</th>\n",
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       "      <th>Residence_Urban</th>\n",
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      "text/plain": [
       "    age  hypertension  heart_disease  avg_glucose_level   bmi  stroke  \\\n",
       "0  67.0             0              1             228.69  36.6       1   \n",
       "1  80.0             0              1             105.92  32.5       1   \n",
       "2  49.0             0              0             171.23  34.4       1   \n",
       "3  79.0             1              0             174.12  24.0       1   \n",
       "4  81.0             0              0             186.21  29.0       1   \n",
       "\n",
       "   marriage  Gender_Female  Gender_Male  Work_Govt_job  Work_Never_worked  \\\n",
       "0         1              0            1              0                  0   \n",
       "1         1              0            1              0                  0   \n",
       "2         1              1            0              0                  0   \n",
       "3         1              1            0              0                  0   \n",
       "4         1              0            1              0                  0   \n",
       "\n",
       "   Work_Private  Work_Self-employed  Work_children  smoking_Unknown  \\\n",
       "0             1                   0              0                0   \n",
       "1             1                   0              0                0   \n",
       "2             1                   0              0                0   \n",
       "3             0                   1              0                0   \n",
       "4             1                   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  \n",
       "0                0                1  \n",
       "1                1                0  \n",
       "2                0                1  \n",
       "3                1                0  \n",
       "4                0                1  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "09a10860",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "       age  hypertension  avg_glucose_level   bmi  marriage  Gender_Female  \\\n",
       "0     67.0             0             228.69  36.6         1              0   \n",
       "1     80.0             0             105.92  32.5         1              0   \n",
       "2     49.0             0             171.23  34.4         1              1   \n",
       "3     79.0             1             174.12  24.0         1              1   \n",
       "4     81.0             0             186.21  29.0         1              0   \n",
       "...    ...           ...                ...   ...       ...            ...   \n",
       "4904  13.0             0             103.08  18.6         0              1   \n",
       "4905  81.0             0             125.20  40.0         1              1   \n",
       "4906  35.0             0              82.99  30.6         1              1   \n",
       "4907  51.0             0             166.29  25.6         1              0   \n",
       "4908  44.0             0              85.28  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",
       "3               0              0                  0             0   \n",
       "4               1              0                  0             1   \n",
       "...           ...            ...                ...           ...   \n",
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       "4906            0              0                  0             0   \n",
       "4907            1              0                  0             1   \n",
       "4908            0              1                  0             0   \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",
       "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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df.drop(columns = ['stroke', 'heart_disease'])\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7cdff4d9",
   "metadata": {
    "scrolled": false
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    {
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      ],
      "text/plain": [
       "       age  hypertension  avg_glucose_level   bmi  stroke  marriage  \\\n",
       "2     49.0             0             171.23  34.4       1         1   \n",
       "3     79.0             1             174.12  24.0       1         1   \n",
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       "...    ...           ...                ...   ...     ...       ...   \n",
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       "4907  51.0             0             166.29  25.6       0         1   \n",
       "4908  44.0             0              85.28  26.2       0         1   \n",
       "\n",
       "      Gender_Female  Gender_Male  Work_Govt_job  Work_Never_worked  \\\n",
       "2                 1            0              0                  0   \n",
       "3                 1            0              0                  0   \n",
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       "\n",
       "      Work_Private  Work_Self-employed  Work_children  smoking_Unknown  \\\n",
       "2                1                   0              0                0   \n",
       "3                0                   1              0                0   \n",
       "4                1                   0              0                0   \n",
       "6                1                   0              0                0   \n",
       "7                1                   0              0                1   \n",
       "...            ...                 ...            ...              ...   \n",
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       "4906             0                   1              0                0   \n",
       "4907             1                   0              0                0   \n",
       "4908             0                   0              0                1   \n",
       "\n",
       "      smoking_formerly smoked  smoking_never smoked  smoking_smokes  \\\n",
       "2                           0                     0               1   \n",
       "3                           0                     1               0   \n",
       "4                           1                     0               0   \n",
       "6                           0                     1               0   \n",
       "7                           0                     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",
       "2                   0                1  \n",
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       "6                   0                1  \n",
       "7                   0                1  \n",
       "...               ...              ...  \n",
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       "4907                1                0  \n",
       "4908                0                1  \n",
       "\n",
       "[4665 rows x 19 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_0 = df[df['heart_disease'] == 0]\n",
    "df_0 = df_0.drop(columns = ['heart_disease'])\n",
    "df_0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f4d3b7a0",
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       "    <tr>\n",
       "      <th>4593</th>\n",
       "      <td>81.0</td>\n",
       "      <td>1</td>\n",
       "      <td>126.34</td>\n",
       "      <td>27.4</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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4663</th>\n",
       "      <td>57.0</td>\n",
       "      <td>1</td>\n",
       "      <td>231.72</td>\n",
       "      <td>45.7</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4677</th>\n",
       "      <td>50.0</td>\n",
       "      <td>0</td>\n",
       "      <td>193.80</td>\n",
       "      <td>26.4</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>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4790</th>\n",
       "      <td>58.0</td>\n",
       "      <td>0</td>\n",
       "      <td>225.35</td>\n",
       "      <td>26.5</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4812</th>\n",
       "      <td>79.0</td>\n",
       "      <td>0</td>\n",
       "      <td>88.51</td>\n",
       "      <td>24.5</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",
       "  </tbody>\n",
       "</table>\n",
       "<p>243 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age  hypertension  avg_glucose_level   bmi  stroke  marriage  \\\n",
       "0     67.0             0             228.69  36.6       1         1   \n",
       "1     80.0             0             105.92  32.5       1         1   \n",
       "5     74.0             1              70.09  27.4       1         1   \n",
       "9     61.0             0             120.46  36.8       1         1   \n",
       "11    79.0             0             214.09  28.2       1         1   \n",
       "...    ...           ...                ...   ...     ...       ...   \n",
       "4593  81.0             1             126.34  27.4       0         1   \n",
       "4663  57.0             1             231.72  45.7       0         1   \n",
       "4677  50.0             0             193.80  26.4       0         1   \n",
       "4790  58.0             0             225.35  26.5       0         1   \n",
       "4812  79.0             0              88.51  24.5       0         1   \n",
       "\n",
       "      Gender_Female  Gender_Male  Work_Govt_job  Work_Never_worked  \\\n",
       "0                 0            1              0                  0   \n",
       "1                 0            1              0                  0   \n",
       "5                 0            1              0                  0   \n",
       "9                 1            0              1                  0   \n",
       "11                1            0              0                  0   \n",
       "...             ...          ...            ...                ...   \n",
       "4593              1            0              0                  0   \n",
       "4663              1            0              0                  0   \n",
       "4677              1            0              0                  0   \n",
       "4790              0            1              0                  0   \n",
       "4812              1            0              0                  0   \n",
       "\n",
       "      Work_Private  Work_Self-employed  Work_children  smoking_Unknown  \\\n",
       "0                1                   0              0                0   \n",
       "1                1                   0              0                0   \n",
       "5                1                   0              0                0   \n",
       "9                0                   0              0                0   \n",
       "11               1                   0              0                0   \n",
       "...            ...                 ...            ...              ...   \n",
       "4593             1                   0              0                0   \n",
       "4663             1                   0              0                0   \n",
       "4677             1                   0              0                0   \n",
       "4790             1                   0              0                0   \n",
       "4812             1                   0              0                0   \n",
       "\n",
       "      smoking_formerly smoked  smoking_never smoked  smoking_smokes  \\\n",
       "0                           1                     0               0   \n",
       "1                           0                     1               0   \n",
       "5                           0                     1               0   \n",
       "9                           0                     0               1   \n",
       "11                          0                     1               0   \n",
       "...                       ...                   ...             ...   \n",
       "4593                        0                     0               1   \n",
       "4663                        1                     0               0   \n",
       "4677                        0                     1               0   \n",
       "4790                        0                     0               1   \n",
       "4812                        0                     1               0   \n",
       "\n",
       "      Residence_Rural  Residence_Urban  \n",
       "0                   0                1  \n",
       "1                   1                0  \n",
       "5                   1                0  \n",
       "9                   1                0  \n",
       "11                  0                1  \n",
       "...               ...              ...  \n",
       "4593                1                0  \n",
       "4663                1                0  \n",
       "4677                0                1  \n",
       "4790                1                0  \n",
       "4812                1                0  \n",
       "\n",
       "[243 rows x 19 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_1 = df[df['heart_disease'] == 1]\n",
    "df_1 = df_1.drop(columns = ['heart_disease'])\n",
    "df_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a80fb9c6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "y_0 = df_0['stroke']\n",
    "x_0 = df_0.drop(columns = ['stroke'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c2612ba0",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_1 = df_1['stroke']\n",
    "x_1 = df_1.drop(columns = ['stroke'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8bfa4273",
   "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": "markdown",
   "id": "445966eb",
   "metadata": {},
   "source": [
    "# Model for controlled group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2250af41",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9631296891747052"
      ]
     },
     "execution_count": 12,
     "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": 13,
   "id": "75951716",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9633440514469453 71\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": 14,
   "id": "50caa82a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9633440514469453 69\n"
     ]
    },
    {
     "data": {
      "image/png": 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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(51,101):\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(51,101)][scorel.index(max(scorel))]))\n",
    "plt.figure(figsize=[60,20])\n",
    "plt.plot(range(51,101),scorel)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b5fc99af",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=RandomForestClassifier(n_estimators=69, 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": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_grid = {'max_depth':np.arange(1, 30, 1)}\n",
    "rfc = RandomForestClassifier(n_estimators=69\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": 16,
   "id": "35099b1a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': 1}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GS.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "56a53ca6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9637727759914254"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GS.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d8438727",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=RandomForestClassifier(n_estimators=69, 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": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_grid = {'max_features':np.arange(1,20,1)}\n",
    "rfc = RandomForestClassifier(n_estimators=69\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": 19,
   "id": "a2ce1964",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_features': 5}"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GS.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "61f4ca98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9637727759914254"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GS.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "041a4745",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=RandomForestClassifier(n_estimators=69, 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": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_grid={'min_samples_leaf':np.arange(1, 20, 1)}\n",
    "rfc = RandomForestClassifier(n_estimators=69\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": 22,
   "id": "69b5605a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'min_samples_leaf': 2}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GS.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "7e6e4d3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9637727759914254"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GS.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d577bcce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=RandomForestClassifier(n_estimators=69, 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": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_grid={'min_samples_split':np.arange(2, 2+20, 1)}\n",
    "rfc = RandomForestClassifier(n_estimators=69\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": 25,
   "id": "fff77457",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'min_samples_split': 3}"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GS.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "5315b785",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9637727759914254"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GS.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "35d5a303",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9637727759914254"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc_0 = RandomForestClassifier(n_estimators=69,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": 28,
   "id": "625befab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0006430868167202064"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_0 - score_pre_0"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46d8b047",
   "metadata": {},
   "source": [
    "# Model for treatment group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "118c2d19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8314625850340136"
      ]
     },
     "execution_count": 29,
     "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": 30,
   "id": "518fd43e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8396258503401359 151\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": 31,
   "id": "a20f6b17",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8396258503401359 147\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(130,171):\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(130,171)][scorel.index(max(scorel))]))\n",
    "plt.figure(figsize=[20,5])\n",
    "plt.plot(range(130,171),scorel)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "2e791d5b",
   "metadata": {
    "scrolled": true
   },
   "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": 32,
     "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": 33,
   "id": "8e4fe8c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': 14}"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GS.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "ed762c26",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8396258503401359"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GS.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "e4d62d19",
   "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": 35,
     "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": 36,
   "id": "5b8d2a7f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_features': 4}"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GS.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "044f4cfa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8396258503401359"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GS.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "5b9afecb",
   "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": 38,
     "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": 39,
   "id": "d06c6f38",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'min_samples_leaf': 1}"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GS.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "b877f4a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8396258503401359"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GS.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "5d217c08",
   "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": 41,
     "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": 42,
   "id": "41f2c63b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'min_samples_split': 2}"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GS.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "4d44cd8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8396258503401359"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GS.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "0d8cb3a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8396258503401359"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc_1 = RandomForestClassifier(n_estimators=151,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": 45,
   "id": "e6311425",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.008163265306122325"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_1 - score_pre_1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5dee8914",
   "metadata": {},
   "source": [
    "# Outcome modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "7823ba7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import stats\n",
    "import scipy.stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "727458a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "rfc0 = rfc_0.fit(x_0, y_0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "7a4faaa8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 57,
     "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": 58,
   "id": "a7806102",
   "metadata": {},
   "outputs": [],
   "source": [
    "rfc1 = rfc_1.fit(x_1, y_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "39676bf5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.01365118174409128"
      ]
     },
     "execution_count": 59,
     "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": 60,
   "id": "20398cf5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.01365118174409128"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ATE_outcome = mean_1 - mean_0\n",
    "ATE_outcome"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "0c8d858e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-8.240961572218755 1.0854856320818677e-16\n"
     ]
    }
   ],
   "source": [
    "t,pval = scipy.stats.ttest_ind(predict_0,predict_1,equal_var=False,alternative='less')\n",
    "print(t,pval)"
   ]
  }
 ],
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