Outcome_regression_Hypertension.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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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>hypertension</th>\n",
       "      <th>heart_disease</th>\n",
       "      <th>avg_glucose_level</th>\n",
       "      <th>bmi</th>\n",
       "      <th>stroke</th>\n",
       "      <th>marriage</th>\n",
       "      <th>Gender_Female</th>\n",
       "      <th>Gender_Male</th>\n",
       "      <th>Work_Govt_job</th>\n",
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       "      <th>Residence_Rural</th>\n",
       "      <th>Residence_Urban</th>\n",
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       "      <td>174.12</td>\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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       "<p>4908 rows × 18 columns</p>\n",
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      "text/plain": [
       "       age  heart_disease  avg_glucose_level   bmi  marriage  Gender_Female  \\\n",
       "0     67.0              1             228.69  36.6         1              0   \n",
       "1     80.0              1             105.92  32.5         1              0   \n",
       "2     49.0              0             171.23  34.4         1              1   \n",
       "3     79.0              0             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",
       "4904            0              0                  0             0   \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', 'hypertension'])\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7cdff4d9",
   "metadata": {
    "scrolled": false
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      "text/plain": [
       "       age  heart_disease  avg_glucose_level   bmi  stroke  marriage  \\\n",
       "0     67.0              1             228.69  36.6       1         1   \n",
       "1     80.0              1             105.92  32.5       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",
       "0                 0            1              0                  0   \n",
       "1                 0            1              0                  0   \n",
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       "4908              1            0              1                  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",
       "4                1                   0              0                0   \n",
       "6                1                   0              0                0   \n",
       "...            ...                 ...            ...              ...   \n",
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       "4908             0                   0              0                1   \n",
       "\n",
       "      smoking_formerly smoked  smoking_never smoked  smoking_smokes  \\\n",
       "0                           1                     0               0   \n",
       "1                           0                     1               0   \n",
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       "4                           1                     0               0   \n",
       "6                           0                     1               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",
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       "6                   0                1  \n",
       "...               ...              ...  \n",
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       "4907                1                0  \n",
       "4908                0                1  \n",
       "\n",
       "[4457 rows x 19 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_0 = df[df['hypertension'] == 0]\n",
    "df_0 = df_0.drop(columns = ['hypertension'])\n",
    "df_0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f4d3b7a0",
   "metadata": {},
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4788</th>\n",
       "      <td>76.0</td>\n",
       "      <td>0</td>\n",
       "      <td>267.60</td>\n",
       "      <td>30.5</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4791</th>\n",
       "      <td>64.0</td>\n",
       "      <td>0</td>\n",
       "      <td>196.26</td>\n",
       "      <td>34.5</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>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>4890</th>\n",
       "      <td>64.0</td>\n",
       "      <td>0</td>\n",
       "      <td>76.89</td>\n",
       "      <td>30.2</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>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>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4893</th>\n",
       "      <td>59.0</td>\n",
       "      <td>0</td>\n",
       "      <td>95.05</td>\n",
       "      <td>30.9</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4900</th>\n",
       "      <td>82.0</td>\n",
       "      <td>0</td>\n",
       "      <td>71.97</td>\n",
       "      <td>28.3</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>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>451 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age  heart_disease  avg_glucose_level   bmi  stroke  marriage  \\\n",
       "3     79.0              0             174.12  24.0       1         1   \n",
       "5     74.0              1              70.09  27.4       1         1   \n",
       "8     81.0              0              80.43  29.7       1         1   \n",
       "12    50.0              0             167.41  30.9       1         1   \n",
       "14    75.0              0             221.29  25.8       1         1   \n",
       "...    ...            ...                ...   ...     ...       ...   \n",
       "4788  76.0              0             267.60  30.5       0         1   \n",
       "4791  64.0              0             196.26  34.5       0         1   \n",
       "4890  64.0              0              76.89  30.2       0         1   \n",
       "4893  59.0              0              95.05  30.9       0         1   \n",
       "4900  82.0              0              71.97  28.3       0         1   \n",
       "\n",
       "      Gender_Female  Gender_Male  Work_Govt_job  Work_Never_worked  \\\n",
       "3                 1            0              0                  0   \n",
       "5                 0            1              0                  0   \n",
       "8                 1            0              0                  0   \n",
       "12                1            0              0                  0   \n",
       "14                0            1              0                  0   \n",
       "...             ...          ...            ...                ...   \n",
       "4788              0            1              0                  0   \n",
       "4791              0            1              0                  0   \n",
       "4890              1            0              0                  0   \n",
       "4893              0            1              0                  0   \n",
       "4900              0            1              0                  0   \n",
       "\n",
       "      Work_Private  Work_Self-employed  Work_children  smoking_Unknown  \\\n",
       "3                0                   1              0                0   \n",
       "5                1                   0              0                0   \n",
       "8                1                   0              0                0   \n",
       "12               0                   1              0                0   \n",
       "14               1                   0              0                0   \n",
       "...            ...                 ...            ...              ...   \n",
       "4788             1                   0              0                0   \n",
       "4791             1                   0              0                1   \n",
       "4890             0                   1              0                1   \n",
       "4893             1                   0              0                0   \n",
       "4900             0                   1              0                0   \n",
       "\n",
       "      smoking_formerly smoked  smoking_never smoked  smoking_smokes  \\\n",
       "3                           0                     1               0   \n",
       "5                           0                     1               0   \n",
       "8                           0                     1               0   \n",
       "12                          0                     1               0   \n",
       "14                          0                     0               1   \n",
       "...                       ...                   ...             ...   \n",
       "4788                        0                     1               0   \n",
       "4791                        0                     0               0   \n",
       "4890                        0                     0               0   \n",
       "4893                        0                     1               0   \n",
       "4900                        0                     1               0   \n",
       "\n",
       "      Residence_Rural  Residence_Urban  \n",
       "3                   1                0  \n",
       "5                   1                0  \n",
       "8                   1                0  \n",
       "12                  1                0  \n",
       "14                  0                1  \n",
       "...               ...              ...  \n",
       "4788                1                0  \n",
       "4791                1                0  \n",
       "4890                0                1  \n",
       "4893                1                0  \n",
       "4900                1                0  \n",
       "\n",
       "[451 rows x 19 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_1 = df[df['hypertension'] == 1]\n",
    "df_1 = df_1.drop(columns = ['hypertension'])\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": [
       "0.9654479523687296"
      ]
     },
     "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": [
      "0.9663450649997735 151\n"
     ]
    },
    {
     "data": {
      "image/png": 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U/+nlY/zZ/3eQF46c4u0zE5fWD6zv5e7b+/gX926b7X+0uY+NfT0NrFSSJEmSJC0GQyUtij/6/j9y+MR5fu49Gy81z75r82pW2/9IkiRJkqRlyVBJN+zMhSmePzLG7/7sO/lXv/juRpcjSZIkSZJugbZGF6Dmt+9gmemZZHD7+kaXIkmSJEmSbhFDJd2w4VKZjrbgA3eubXQpkiRJkiTpFjFU0g0bKpW5Z+saerucTSlJkiRJUqswVNINOT8xzXOHTzr1TZIkSZKkFmOopBvyzOsnmJxO9u5Y1+hSJEmSJEnSLWSopBvy41KZtoA9d/Y3uhRJkiRJknQLGSrphgyXRnnvljWs7ulsdCmSJEmSJOkWMlTSdbswNc0zr59k73anvkmSJEmS1GoMlXTdnjt8igtTMwwaKkmSJEmS1HIMlXTdhkZGAQyVJEmSJElqQYZKum5DpTLvecdq1vZ2NboUSZIkSZJ0ixkq6bpMTs/w9Gsn7KckSZIkSVKLMlTSdXn+jVOcm5hm7471jS5FkiRJkiQ1QF2hUkTcFxEvR8SrEfFQje39EfF4RDwXEcMRcXfFtrUR8VcR8VJEvBgR/1mxfl1EfD8iXik+9y/ey9LNNlQqA3DvgCOVJEmSJElqRfOGShHRDnwN+BiwC/hUROyq2u1LwP7MvAf4NPBIxbZHgL/JzPcA7wNeLNY/BPwgM3cCPyiW1SSGS2XeuWElG1Z3N7oUSZIkSZLUAPWMVBoEXs3MkcycAL4N3F+1zy5mgyEy8yVgICI2RUQf8BHgT4ttE5l5sjjmfuCbxeNvAr92A69Dt9D0TPJUqezUN0mSJEmSWlg9odLtwKGK5cPFukrPAp8AiIhB4E5gK7ADOA78WUQ8ExF/EhEri2M2ZeZRgOLzxut+FbqlXjw6xukLUzbpliRJkiSphdUTKkWNdVm1/DDQHxH7gS8CzwBTQAfwAeDrmfl+4CwLnOYWEQ9ExL6I2Hf8+PGFHKqb5GI/pUFDJUmSJEmSWlY9odJhYFvF8lbgSOUOmTmWmZ/NzN3M9lTaAJSKYw9n5lCx618xGzIBvBURmwGKz8dqffHM/EZm7snMPRs2bKjvVemmGhoZ5Y51vWxes6LRpUiSJEmSpAapJ1R6CtgZEdsjogv4JPBE5Q7FHd66isXPAz8sgqY3gUMR8e5i20eBF4rHTwCfKR5/BvjrG3gdukVmZpKnDpad+iZJkiRJUovrmG+HzJyKiAeB7wHtwGOZeSAivlBsfxS4C/hWREwzGxp9ruIpvgj8eRE6jQCfLdY/DHwnIj4HvA78xiK9Jt1Erxw7w4lzkzbpliRJkiSpxc0bKgFk5pPAk1XrHq14/CNg5xzH7gf21Fg/yuzIJTWR4dIogCOVJEmSJElqcfVMf5Mu+XGpzJY1PWztt5+SJEmSJEmtzFBJdctMhkbKDG5fR0StmwJKkiRJkqRWYaikupXePsvbZy7YT0mSJEmSJBkqqX5DpTJgPyVJkiRJkmSopAUYGhnltlXdbL9tZaNLkSRJkiRJDWaopLpkJkOlMnt32E9JkiRJkiQZKqlOh0+c5+ipcae+SZIkSZIkwFBJdbrcT8km3ZIkSZIkyVBJdRoaGaW/t5OdG1c1uhRJkiRJkrQEGCqpLkOlMvcOrKOtzX5KkiRJkiTJUEl1OHrqPK+Xz7F3h1PfJEmSJEnSLEMlzWv4Uj8lm3RLkiRJkqRZhkqa11CpzOqeDu7a3NfoUiRJkiRJ0hJhqKR5DY2Mcu/AOtrtpyRJkiRJkgqGSrqm46cv8NPjZxl06pskSZIkSapgqKRreuqg/ZQkSZIkSdLVDJV0TUMjo/R2tXP37WsaXYokSZIkSVpCDJV0TUOlMh+8s5/Odk8VSZIkSZJ0mUmB5nTy3AQvvXnaqW+SJEmSJOkqhkqa03Bptp/S4Pb1Da5EkiRJkiQtNYZKmtNwqUx3Rxvv22Y/JUmSJEmSdCVDJc1pqFTm/XespbujvdGlSJIkSZKkJcZQSTWNjU9y4Mgpp75JkiRJkqSaDJVU09OvnWAm4UM26ZYkSZIkSTUYKqmmoZEyne3B++/ob3QpkiRJkiRpCTJUUk1DpVHu2bqWFV32U5IkSZIkSVczVNJVzk1M8ZPDp9jr1DdJkiRJkjQHQyVd5R9eO8nUTDJoqCRJkiRJkuZgqKSrDJdGaW8L9gwYKkmSJEmSpNrqCpUi4r6IeDkiXo2Ih2ps74+IxyPiuYgYjoi7K7YdjIifRMT+iNhXsf7LEfFGsX5/RPzS4rwk3agfl8rcvaWPVd0djS5FkiRJkiQtUfOGShHRDnwN+BiwC/hUROyq2u1LwP7MvAf4NPBI1fZ/npm7M3NP1fo/LNbvzswnr+8laDGNT06z/9BJp75JkiRJkqRrqmek0iDwamaOZOYE8G3g/qp9dgE/AMjMl4CBiNi0qJXqlnj20EkmpmbYu319o0uRJEmSJElLWD2h0u3AoYrlw8W6Ss8CnwCIiEHgTmBrsS2Bv42IpyPigarjHiymzD0WEf0Lrl6LbqhUJgLutZ+SJEmSJEm6hnpCpaixLquWHwb6I2I/8EXgGWCq2PbhzPwAs9PnfjciPlKs/zrwTmA3cBT4as0vHvFAROyLiH3Hjx+vo1zdiOFSmfe8o481vZ2NLkWSJEmSJC1h9YRKh4FtFctbgSOVO2TmWGZ+NjN3M9tTaQNQKrYdKT4fAx5ndjodmflWZk5n5gzwxxfXV8vMb2Tmnszcs2HDhoW8Ni3Q5PQMT792gr32U5IkSZIkSfOoJ1R6CtgZEdsjogv4JPBE5Q4RsbbYBvB54IeZORYRKyNidbHPSuAXgeeL5c0VT/Hxi+vVOM8dPsX5yWlDJUmSJEmSNK957xmfmVMR8SDwPaAdeCwzD0TEF4rtjwJ3Ad+KiGngBeBzxeGbgMcj4uLX+ovM/Jti21ciYjezU+kOAr+zWC9K12e4VAbwzm+SJEmSJGle84ZKAJn5JPBk1bpHKx7/CNhZ47gR4H1zPOdvLahS3XRDpVF2blzF+lXdjS5FkiRJkiQtcfVMf1MLmJqeYd/BE45SkiRJkiRJdTFUEgAvHj3NmQtT7N2xvtGlSJIkSZKkJmCoJGB26htgk25JkiRJklQXQyUBMFQqM7C+l019PY0uRZIkSZIkNQFDJTEzkzx1sMze7U59kyRJkiRJ9TFUEi+/dZqT5yZt0i1JkiRJkupmqCSGS2UA9u4wVJIkSZIkSfUxVBJDpVFuX7uCrf29jS5FkiRJkiQ1CUOlFpeZDJfK3vVNkiRJkiQtiKFSi/vp8bO8fWbCqW+SJEmSJGlBDJVa3FBpFIBB7/wmSZIkSZIWwFCpxQ2Xymxc3c3AevspSZIkSZKk+hkqtbDMZGikzOD2dUREo8uRJEmSJElNxFCphb1ePsebY+Ps3eHUN0mSJEmStDCGSi1sqFQG4EPe+U2SJEmSJC2QoVILGxops25lF/9k46pGlyJJkiRJkpqMoVILGz44yuCA/ZQkSZIkSdLCGSq1qCMnz3OofJ69O5z6JkmSJEmSFs5QqUUNlUYBGLSfkiRJkiRJug6GSi1quFSmr6eD97yjr9GlSJIkSZKkJmSo1KKGRsrcO7CO9jb7KUmSJEmSpIUzVGpBx06PM/L2WfspSZIkSZKk62ao1IKGS2UA9m5f3+BKJEmSJElSszJUakFDI2VWdrXz3i32U5IkSZIkSdfHUKkFDZfKfHBgHR3tfvslSZIkSdL1MVVoMeWzE7z81mn2brefkiRJkiRJun6GSi3mcj8lQyVJkiRJknT9DJVazHCpTHdHG/dsXdvoUiRJkiRJUhMzVGoxQ6VRPnBHP10dfuslSZIkSdL1qytZiIj7IuLliHg1Ih6qsb0/Ih6PiOciYjgi7q7YdjAifhIR+yNiX8X6dRHx/Yh4pfjcvzgvSXMZG5/khaNj7N3h1DdJkiRJknRj5g2VIqId+BrwMWAX8KmI2FW125eA/Zl5D/Bp4JGq7f88M3dn5p6KdQ8BP8jMncAPimXdRPsOlsmEvdvXN7oUSZIkSZLU5OoZqTQIvJqZI5k5AXwbuL9qn13MBkNk5kvAQERsmud57we+WTz+JvBr9Rat6zM0UqarvY3337G20aVIkiRJkqQmV0+odDtwqGL5cLGu0rPAJwAiYhC4E9habEvgbyPi6Yh4oOKYTZl5FKD4vHHh5Wshhkpl3rdtDT2d7Y0uRZIkSZIkNbl6QqWosS6rlh8G+iNiP/BF4Blgqtj24cz8ALPT5343Ij6ykAIj4oGI2BcR+44fP76QQ1Xh7IUpfvLGKQa3209JkiRJkiTduHpCpcPAtorlrcCRyh0ycywzP5uZu5ntqbQBKBXbjhSfjwGPMzudDuCtiNgMUHw+VuuLZ+Y3MnNPZu7ZsGFDva9LVZ5+7QTTM2k/JUmSJEmStCjqCZWeAnZGxPaI6AI+CTxRuUNErC22AXwe+GFmjkXEyohYXeyzEvhF4PlivyeAzxSPPwP89Y29FF3LcKlMe1vwwTu9yZ4kSZIkSbpxHfPtkJlTEfEg8D2gHXgsMw9ExBeK7Y8CdwHfiohp4AXgc8Xhm4DHI+Li1/qLzPybYtvDwHci4nPA68BvLN7LUrWh0ih3376Gld3zfsslSZIkSZLmVVfCkJlPAk9WrXu04vGPgJ01jhsB3jfHc44CH11Isbo+45PTPHvoFJ/98ECjS5EkSZIkSctEPdPf1OSeef0kE9MzNumWJEmSJEmLxlCpBQyVRomAPQOGSpIkSZIkaXEYKrWA4VKZXZv7WLOis9GlSJIkSZKkZcJQaZmbmJrhH14/4dQ3SZIkSZK0qAyVlrmfvHGS8ckZ9m5f3+hSJEmSJEnSMmKotMz9eKQM4EglSZIkSZK0qAyVlrmhUpl3bVrFupVdjS5FkiRJkiQtI4ZKy9jU9AxPHyw79U2SJEmSJC06Q6Vl7MCRMc5OTDv1TZIkSZIkLTpDpWVsqDQKwN4dhkqSJEmSJGlxGSotY8OlMjtuW8nG1T2NLkWSJEmSJC0zhkrL1PRMMlwqO/VNkiRJkiTdFIZKy9TLb55mbHzKqW+SJEmSJOmmMFRapi72Uxr0zm+SJEmSJOkmMFRapoZGymztX8Hta1c0uhRJkiRJkrQMGSotQ5nJ8MEyex2lJEmSJEmSbhJDpWXo1WNnKJ+dYK9NuiVJkiRJ0k1iqLQMDZXKADbpliRJkiRJN42h0jI0VCqzqa+bO9b1NroUSZIkSZK0TBkqLTOZydDIKHu3ryciGl2OJEmSJElapgyVlpnXRs9x7PQFp75JkiRJkqSbylBpmRkqjQLYpFuSJEmSJN1UhkrLzNBImdtWdfHODasaXYokSZIkSVrGDJWWmaFSmcHt6+ynJEmSJEmSbipDpWXk8IlzvHHyPIMDTn2TJEmSJEk3l6HSMjJcKgOwd8f6BlciSZIkSZKWO0OlZWRopMyaFZ28e9PqRpciSZIkSZKWOUOlZWSoNMq9A+toa7OfkiRJkiRJurkMlZaJt8bGOTh6jg/tsJ+SJEmSJEm6+eoKlSLivoh4OSJejYiHamzvj4jHI+K5iBiOiLurtrdHxDMR8d2KdV+OiDciYn/x8Us3/nJa11DRT2lwu6GSJEmSJEm6+eYNlSKiHfga8DFgF/CpiNhVtduXgP2ZeQ/waeCRqu2/B7xY4+n/MDN3Fx9PLrh6XTJcGmVVdwe7Nvc1uhRJkiRJktQC6hmpNAi8mpkjmTkBfBu4v2qfXcAPADLzJWAgIjYBRMRW4JeBP1m0qnWVoZEyH7yzn452ZzRKkiRJkqSbr54E4nbgUMXy4WJdpWeBTwBExCBwJ7C12PZHwL8GZmo894PFlLnHIqK/1hePiAciYl9E7Dt+/Hgd5bae0TMXeOXYGfbaT0mSJEmSJN0i9YRKtW4lllXLDwP9EbEf+CLwDDAVEb8CHMvMp2s8x9eBdwK7gaPAV2t98cz8Rmbuycw9GzZsqKPc1vPUwdl+Snu3r29wJZIkSZIkqVV01LHPYWBbxfJW4EjlDpk5BnwWICICKBUfnwR+tWjC3QP0RcR/yMzfzMy3Lh4fEX8MfBddlx+PlOnpbONnbl/T6FIkSZIkSVKLqGek0lPAzojYHhFdzAZFT1TuEBFri20Anwd+mJljmfkHmbk1MweK4/4uM3+zOGZzxVN8HHj+Bl9LyxoqzfZT6uqwn5IkSZIkSbo15h2plJlTEfEg8D2gHXgsMw9ExBeK7Y8CdwHfiohp4AXgc3V87a9ExG5mp9IdBH7nul5Bizt1bpKX3hzjv/35dzW6FEmSJEmS1ELqmf5GZj4JPFm17tGKxz8Cds7zHH8P/H3F8m8toE7N4amDZTJhcLtNuiVJkiRJ0q3jfKkmN3ywTFd7G7u3rW10KZIkSZIkqYUYKjW5oZFRdm9bS09ne6NLkSRJkiRJLcRQqYmduTDF80fG2LvDqW+SJEmSJOnWMlRqYk+/doLpmWTv9vWNLkWSJEmSJLUYQ6UmNjQySkdb8IE71za6FEmSJEmS1GIMlZrYcKnMz2xdQ29XXTfxkyRJkiRJWjSGSk3q/MQ0zx4+yeB2+ylJkiRJkqRbz1CpST3z+gkmp5MP2U9JkiRJkiQ1gKFSkxoqlWkL+OBAf6NLkSRJkiRJLchQqUkNlUbZtaWPvp7ORpciSZIkSZJakKFSE7owNc0zr59kr1PfJEmSJElSgxgqNaHnDp/iwtQMe23SLUmSJEmSGsRQqQkNjYwCcO+AoZIkSZIkSWoMQ6UmNFQq8553rKZ/ZVejS5EkSZIkSS3KUKnJTE7P8PRrJxh06pskSZIkSWogQ6Um8/wbpzg3MW2TbkmSJEmS1FCGSk1muFQGcKSSJEmSJElqKEOlJjNUKrNjw0o2rO5udCmSJEmSJKmFGSo1kemZ5KlS2alvkiRJkiSp4QyVmsiLR8c4fWGKvU59kyRJkiRJDWao1ESGin5Ke3cYKkmSJEmSpMYyVGoiw6VR7ljXy+Y1KxpdiiRJkiRJanGGSk1iZiYZLpW965skSZIkSVoSDJWaxCvHznDi3KT9lCRJkiRJ0pJgqNQkhkujAHxoh3d+kyRJkiRJjWeo1CR+XCqzeU0PW/vtpyRJkiRJkhrPUKkJZM72U9q7fR0R0ehyJEmSJEmSDJWaQentsxw/fYHB7U59kyRJkiRJS0NdoVJE3BcRL0fEqxHxUI3t/RHxeEQ8FxHDEXF31fb2iHgmIr5bsW5dRHw/Il4pPvff+MtZnoZKZQD27rBJtyRJkiRJWhrmDZUioh34GvAxYBfwqYjYVbXbl4D9mXkP8Gngkartvwe8WLXuIeAHmbkT+EGxrBqGS2VuW9XNjttWNroUSZIkSZIkoL6RSoPAq5k5kpkTwLeB+6v22cVsMERmvgQMRMQmgIjYCvwy8CdVx9wPfLN4/E3g167nBSx3mcnQyKj9lCRJkiRJ0pJST6h0O3CoYvlwsa7Ss8AnACJiELgT2Fps+yPgXwMzVcdsysyjAMXnjQspvFUcPnGeI6fGnfomSZIkSZKWlHpCpVrDY7Jq+WGgPyL2A18EngGmIuJXgGOZ+fT1FhgRD0TEvojYd/z48et9mqZ1sZ/S4HZDJUmSJEmStHTUEyodBrZVLG8FjlTukJljmfnZzNzNbE+lDUAJ+DDwqxFxkNlpcz8XEf+hOOytiNgMUHw+VuuLZ+Y3MnNPZu7ZsGFD3S9suRgaGWVtbyfv2ri60aVIkiRJkiRdUk+o9BSwMyK2R0QX8EngicodImJtsQ3g88APi6DpDzJza2YOFMf9XWb+ZrHfE8BnisefAf76Bl/LsjR8sMzgwDra2uynJEmSJEmSlo55Q6XMnAIeBL7H7B3cvpOZByLiCxHxhWK3u4ADEfESs3eJ+706vvbDwC9ExCvALxTLqvDmqXFeGz3n1DdJkiRJkrTkdNSzU2Y+CTxZte7Risc/AnbO8xx/D/x9xfIo8NH6S209Q6VRAD60Y32DK5EkSZIkSbpSPdPf1CBDpTKruzu4a3Nfo0uRJEmSJEm6gqHSEjY0MsqegX7a7ackSZIkSZKWGEOlJertMxf46fGz7HXqmyRJkiRJWoIMlZao4VIZwCbdkiRJkiRpSTJUWqKGRkbp7WrnZ25f0+hSJEmSJEmSrmKotEQNlcp88M5+Otv9FkmSJEmSpKXHxGIJOnlugpffOs3ggFPfJEmSJEnS0mSotAQNl8pkYpNuSZIkSZK0ZBkqLUHDpTJdHW3cs9V+SpIkSZIkaWkyVFqChkpl3r9tLT2d7Y0uRZIkSZIkqSZDpSXm9PgkB46ccuqbJEmSJEla0gyVlph9r51gJmHvdpt0S5IkSZKkpctQaYkZGinT0RZ84I7+RpciSZIkSZI0J0OlJWa4NMo9W9ewost+SpIkSZIkaekyVFpCzk1M8dxh+ylJkiRJkqSlz1BpCXnm9ZNMzaT9lCRJkiRJ0pJnqLSEDI2M0hbwwTvtpyRJkiRJkpY2Q6Ul5MelMnffvobVPZ2NLkWSJEmSJOmaDJWWiPHJafYfOunUN0mSJEmS1BQMlZaIZw+dZGJqhsHtNumWJEmSJElLn6HSEjFUKhMBgwOOVJIkSZIkSUufodISMVwq8+5Nq1nTaz8lSZIkSZK09BkqLQGT0zM8/doJPrTDqW+SJEmSJKk5GCotAT954xTnJ6dt0i1JkiRJkpqGodISMDRSBuBeQyVJkiRJktQkDJWWgKHSKP9k4ypuW9Xd6FIkSZIkSZLqYqjUYNMzyb6DJxh0lJIkSZIkSWoihkoN9sKRMc5cmLKfkiRJkiRJaiqGSg02VBoFYO927/wmSZIkSZKaR12hUkTcFxEvR8SrEfFQje39EfF4RDwXEcMRcXexvqdYfjYiDkTE/1RxzJcj4o2I2F98/NLivazmMVQqc+f6Xt6xpqfRpUiSJEmSJNVt3lApItqBrwEfA3YBn4qIXVW7fQnYn5n3AJ8GHinWXwB+LjPfB+wG7ouID1Uc94eZubv4ePLGXkrzmZlJnjpYduqbJEmSJElqOvWMVBoEXs3MkcycAL4N3F+1zy7gBwCZ+RIwEBGbctaZYp/O4iMXp/Tm94/HTnPy3KRT3yRJkiRJUtOpJ1S6HThUsXy4WFfpWeATABExCNwJbC2W2yNiP3AM+H5mDlUc92AxZe6xiOi/vpfQvIZGygDe+U2SJEmSJDWdekKlqLGuerTRw0B/ER59EXgGmALIzOnM3M1syDR4sd8S8HXgncxOizsKfLXmF494ICL2RcS+48eP11Fu8xgqjXL72hVsW9fb6FIkSZIkSZIWpJ5Q6TCwrWJ5K3CkcofMHMvMzxbh0aeBDUCpap+TwN8D9xXLbxWB0wzwx8xOs7tKZn4jM/dk5p4NGzbU85qaQmYyXCo7SkmSJEmSJDWlekKlp4CdEbE9IrqATwJPVO4QEWuLbQCfB36YmWMRsSEi1hb7rAB+HnipWN5c8RQfB56/oVfSZH56/Cxvn5mwSbckSZIkSWpKHfPtkJlTEfEg8D2gHXgsMw9ExBeK7Y8CdwHfiohp4AXgc8Xhm4FvFneQawO+k5nfLbZ9JSJ2MzuV7iDwO4v2qprAcGm2n9LeHTbpliRJkiRJzWfeUAkgM58Enqxa92jF4x8BO2sc9xzw/jme87cWVOkyM1QaZcPqbgbW209JkiRJkiQ1n3qmv2mRZSZDI2X2bl9HRK0+6JIkSZIkSUuboVIDHCqf582xcfspSZIkSZKkpmWo1AA/Lo0C9lOSJEmSJEnNy1CpAYZGyqxb2cXOjasaXYokSZIkSdJ1MVRqgOGDo9w70G8/JUmSJEmS1LQMlW6xIyfPc6h8nr3bnfomSZIkSZKaV0ejC2g17+jr4Xu//xHWrexqdCmSJEmSJEnXzVDpFmtrC979jtWNLkOSJEmSJOmGOP1NkiRJkiRJC2aoJEmSJEmSpAUzVJIkSZIkSdKCGSpJkiRJkiRpwQyVJEmSJEmStGCGSpIkSZIkSVowQyVJkiRJkiQtmKGSJEmSJEmSFsxQSZIkSZIkSQtmqCRJkiRJkqQFi8xsdA11i4jjwGuNrmOBbgPebnQRWnI8L1TNc0K1eF6omueEavG8UDXPCdXieaFqlefEnZm5YaFP0FShUjOKiH2ZuafRdWhp8bxQNc8J1eJ5oWqeE6rF80LVPCdUi+eFqi3GOeH0N0mSJEmSJC2YoZIkSZIkSZIWzFDp5vtGowvQkuR5oWqeE6rF80LVPCdUi+eFqnlOqBbPC1W74XPCnkqSJEmSJElaMEcqSZIkSZIkacEMlW6iiLgvIl6OiFcj4qFG16NbLyK2RcR/iogXI+JARPxesf7LEfFGROwvPn6p0bXq1oqIgxHxk+L7v69Yty4ivh8RrxSf+xtdp26NiHh3xfVgf0SMRcTve61oPRHxWEQci4jnK9bNeW2IiD8ofs94OSL+i8ZUrZtpjnPif42IlyLiuYh4PCLWFusHIuJ8xTXj0YYVrptqjvNizp8ZXiuWvznOif9YcT4cjIj9xXqvFS3gGu9FF/X3Cqe/3SQR0Q78I/ALwGHgKeBTmflCQwvTLRURm4HNmfkPEbEaeBr4NeC/BM5k5r9vZH1qnIg4COzJzLcr1n0FKGfmw0UQ3Z+Z/6ZRNaoxip8fbwB7gc/itaKlRMRHgDPAtzLz7mJdzWtDROwC/hIYBLYA/zfwrsycblD5ugnmOCd+Efi7zJyKiP8FoDgnBoDvXtxPy9cc58WXqfEzw2tFa6h1TlRt/ypwKjP/rdeK1nCN96L/kkX8vcKRSjfPIPBqZo5k5gTwbeD+BtekWywzj2bmPxSPTwMvArc3tiotYfcD3ywef5PZi75az0eBn2bma40uRLdeZv4QKFetnuvacD/w7cy8kJkl4FVmf//QMlLrnMjMv83MqWLxx8DWW16YGmqOa8VcvFa0gGudExERzP5R+y9vaVFqqGu8F13U3ysMlW6e24FDFcuHMUxoacVfBN4PDBWrHiyGrT/mNKeWlMDfRsTTEfFAsW5TZh6F2R8CwMaGVadG+iRX/tLntUJzXRv8XUMAvw38XxXL2yPimYj4fyLinzWqKDVMrZ8ZXiv0z4C3MvOVinVeK1pI1XvRRf29wlDp5oka65xr2KIiYhXwfwC/n5ljwNeBdwK7gaPAVxtXnRrkw5n5AeBjwO8WQ5bV4iKiC/hV4H8vVnmt0LX4u0aLi4j/HpgC/rxYdRS4IzPfD/wr4C8ioq9R9emWm+tnhtcKfYor/2DltaKF1HgvOueuNdbNe60wVLp5DgPbKpa3AkcaVIsaKCI6mf1P/OeZ+X8CZOZbmTmdmTPAH+MQ5JaTmUeKz8eAx5k9B94q5j5fnAN9rHEVqkE+BvxDZr4FXit0yVzXBn/XaGER8RngV4D/OosmqcWUhdHi8dPAT4F3Na5K3UrX+JnhtaKFRUQH8AngP15c57WiddR6L8oi/15hqHTzPAXsjIjtxV+ePwk80eCadIsV85f/FHgxM/+3ivWbK3b7OPB89bFaviJiZdEsj4hYCfwis+fAE8Bnit0+A/x1YypUA13xl0SvFSrMdW14AvhkRHRHxHZgJzDcgPp0i0XEfcC/AX41M89VrN9QNPsnInYwe06MNKZK3WrX+JnhtaK1/TzwUmYevrjCa0VrmOu9KIv8e0XH4pWsSsXdOB4Evge0A49l5oEGl6Vb78PAbwE/uXgLT+BLwKciYjezwwkPAr/TiOLUMJuAx2ev83QAf5GZfxMRTwHfiYjPAa8Dv9HAGnWLRUQvs3cMrbwefMVrRWuJiL8Efha4LSIOA/8j8DA1rg2ZeSAivgO8wOwUqN/1bk7LzxznxB8A3cD3i58lP87MLwAfAf5tREwB08AXMrPeZs5qInOcFz9b62eG14rWUOucyMw/5epejeC1olXM9V50UX+viGK0rCRJkiRJklQ3p79JkiRJkiRpwQyVJEmSJEmStGCGSpIkSZIkSVowQyVJkiRJkiQtmKGSJEmSJEmSFsxQSZIkSZIkSQtmqCRJkiRJkqQFM1SSJEmSJEnSgv3/L8vsNzx9vsIAAAAASUVORK5CYII=\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": [
      "0.9663450649997735 150\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(130,170):\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(130,170)][scorel.index(max(scorel))]))\n",
    "plt.figure(figsize=[60,20])\n",
    "plt.plot(range(130,170),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=150,\n",
       "                                              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=150\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": [
       "0.9665695318909071"
      ]
     },
     "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=150,\n",
       "                                              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=150\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': 4}"
      ]
     },
     "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": [
       "0.9661208497531367"
      ]
     },
     "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=150,\n",
       "                                              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=150\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": [
       "0.9665695318909071"
      ]
     },
     "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=150,\n",
       "                                              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=150\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': 6}"
      ]
     },
     "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": [
       "0.9665695318909071"
      ]
     },
     "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": [
       "0.9665695318909071"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc_0 = RandomForestClassifier(n_estimators=150,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": [
       "0.0011215795221775071"
      ]
     },
     "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": [
       "0.8581196581196581"
      ]
     },
     "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": [
      "0.8581196581196581 101\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": [
      "0.8581196581196581 84\n"
     ]
    },
    {
     "data": {
      "image/png": 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RctqWDoxWKuZVe3Sog+bsVF/ERcX1dH0tp3Ty8bcapeHEncUK5UaNjI+b8gz+biq10x9Xf+tdKOsYfB96HAFHIfQsvUcCcDqCIwB4gpf3Gur27KmfIJc38qrueur1ZqMfyLA5L1vVxuJPaZqlk+i4eFKvl/ICnlAcdrra2W881sA5bm4WHBkzWxVH1Sf0bSsX87pdC6dnU6XmyZj+YwgsuqPXef79BOYBwREAPMFR0HJ6xVGr09Pdh80wl3VhVbeubCqhp5aXol7KTCjxxvdCrLW6XTv9ZH5zOJFwcR7X7b2GejbeE9UkaSmd1NWVpZk55pvtrnYeNE8N5MLs2VR167q6sqSldHLq3wuIu3UnreWl1EJ9QADMM4IjAHiCs3pWRDHueRIV11Op6CiRMGdfGUdTmWbk9xsX+15bB83Oqc+blaW0ivnMQp1QDCeqxbziSOqvcVaO+Tu7g+23p1RRhvkaXXHrM/H7BcJgjFGp6CzUBwTAPCM4AoAnqLqenExSly9lR37dPzHempE3RpUaJzbn4WRSurKcVaU2GyfRcVHdPbuXVmnBJqsdTZmLd8WR1A9hZuU17fbguXnqVrWN8KoGq67HNmDgmFIxv1AfEADzjOAIAJ7An6hmzOgKnaurOaWTZiY+Uev1rKq7Ho1bz6lczC9UwBGEo+qa00+iSwv2uFZdT5eyKRXymaiXcqbNQl5uvaWHM9AU/iiQG/26drOQkzFHAdO0PGy25dZbDB4AjikXHW3vNdTu9qJeCoAJERwBwBNUntB0VZKSCaObhdmYrHb3YVOtTo9R0edU3nBmZttOXIzTJLhUdLTzoKFmuxviyqJTGTQLPy2EjpPyDFVSVty61py0Vp30yK9nU0ldW81N/TW6WvO3NRMcAb5SMa9Oz2pnvxH1UgBMiOAIAE7R7Vnd2W2odMbWg/6459k4wZKevH0IjysV87p3cCiv1Yl6KTNjnCbB5WJe1krbe/F/7gSh6noz89wrzVDvtqrrnRnW9MPf6R5nTKwEHsdkNWB+EBwBwCleedBQq9s782Sv36slnHHPkzir0TdG443v+Y3TJHjYeLw2/49rp9vTnV1vZp57/jpn4ZjvV4U++XENo8/KcHtmYTbCQSAM5eFrSfxDaABPRnAEAKfYGjNoKRfz8lpd1R61wljWhVVqdWVSCV1bzUW9lJlyFHDwxndcW7tnNwkeBnK78Q8nJvXKg6Y6PTszFUf5bEqXl7OxP9k77HS1s98483G9Vcxrz2vrgTe9nk23a56eXllSLnN6lR2waC4vZ5VLJ2eiKhvAkxEcAcApKmc0XfWVZuQTtcqg0XciEf8eK3HiT2Xije94Dppt1R61zqw4WnPSWllKxf55E4TKGM3C46Y8A2O07+w21LNnbw8bhr9TPNaqgx5WAI4YY4ZV2QBmG8ERAJyi6vYrdJ5eWXri9Y76gcT7JKvfY4UTm/O6lE1p41L8qy/iYrgl8gmNsSX/hGI2+oNNquLOXuPkzUL8x2gfTe87q8fR9Hs2VWaohxUQptIMhNAAzkZwBACnGLdC5/paTsmEifVJlrV2rL4zGK1ffRHf32+cVM8RkizKJ9HVWl1L6YSuLGejXsrYykVHrz6Md1P4catCNwvT7dn06LCj2qPDMwcpAIuoXMxra9dTrxfvPpAAnozgCABOMW6FTiaV0PW1XKw/UXv14aGa7d7wk3ecT6mYX4gmzkE4z7ascjGv7b2G2t3etJcVqYrrqVTIz9Q20dLgtWIrxj2oKrW6VpZSWnfST7zeUjqpa6tLU+tT5t/vLYJ54DGlYl6tTk93HzajXgqACRAcAcAI1tqxxjz74l45MRwVzVa1CykXHd192FSj1Y16KbFXdeu6vJxVPps687qloqNuz+rlvUYIK4vOLPa/Kc/A1LuKW1d5Iy9jzg7k+tsip/MafZ4qO2DRlEPoMQZg+giOAGCE+weHarS7Ywct5WI+1qOrq8PgiBObiyjPQPVFXJynl5b/uM7zZLVezw6mzM3Wc88fK7+1G9+TvfOE++UNZ2qv0bPY/BwIy2ZxultFAYSD4AgARjhvM9tS0dGDRlv7Xmuay7qwiuspnTS6uvrkRt8YrVycfnPdeXHeSr3+beb3cX31oKnDTm/YZ2dWrDpprTnp2G7BbXV62t4bP6QsFfNy6y09bLYDX8t5quyARXN1NadMMsG/n8CMIzgCgBHO+wly3CerVd26bq47SiV52b+IzQUIOILQaHV192HzzIlqvsuXsnIyyVhvh5qU/7PNYrVfqRjfyWov7zfUs+M/rv71tqbwGl1hYiVwqmTC6GYhp+ocv84Di4AzCAAYoerWlUoYXV/LjXX9csyDhdu12dsqEyerubQK+Yxu88b3ifytfKUxjzVjjDYL8e4PNqnqDG9jKhed2IZ6fkPq8piTzPzr3Z5Cg+xKrT6TwSAQlvIUe4wBCAfBEQCMUHE93VjPjV2hc7PgyJh4NpLtN/qevea8cVOOeQP0OLhIE/Z5P6Hwt4leGzOEjpNSMa+dBw0dduLXFP6oKnS8wMbfKhj0c9hrdXTv4JBgHniCUjGvrV1P1tqolwLgggiOAGCEftAy/onAUjqpqytLsQwW7j86lNfq8on4hOLeAD0OhtU1hfGPtdKGozu7DXV783lCUXXrullwlEycPfkrbspFR9ZKd3bjN/Wu6nq6lE2pmM+MdX0nk9JTK9nAtxMfTVQjmAdOUyo68lpd3X90GPVSAFwQwREAnNCv0Dl/z4pSMR/L6VCc2ATDr75otuNXfREXVdfTupPWqpMe+zblYl6tbk93HzanuLLo9F9LZjO09cPzOE5WqwyqKI0ZP5CbRs8mJlYCZysxWQ2YeQRHAHDCntfWQbOjzXOeCJRiupXJ7+lxi60UEylv+NUXvPE9TdX1zv+88bcQTaH3TNT8baKzNlHN55/sxXELbqVWP/f2sFvFfOB9yvz7I5gHTjecTDqHr/PAoiA4AoATLtKnRep/ml171NLBFMY9T+K8jb4xWtwn58VBxa2f/3mzMb+Pa+1RS/VWd2YnbhXzGV3KpmIXiLe7PW3vNS5wrDmqPTrUo8NOYGupunVtXMpoeWn8Kjtg0VxfzymZMFQcATOM4AgATqies+mqrxzTUuzzNvrGaLcGx0PcTqLj4rDT1c5+49zPm6srS8qkEnP5uA5fS2a02s8Yo1LRiV2ot7PfUKdnL/AaHfxzuHLOfnjAIkonE7qxnpvrQQjAvOMsAgBOqNQ8GSPdLJyvQqc0PCmJ10nWeRt9Y7RVJ601J80b31Ns7zXUs+ev1EskjDYLzlw+rn7gMsv9b8pT6As0qYs+rtPos1J1PbapAWPwJ6sBmE0ERwBwQtWt69pqTtlU8ly3G/YDidFJlrVWldr5G31jtFIxH8t+L3Fw0Uo9qR82xS1wDULVrSs549tES0VH23sNdbq9qJcy5PdJKW+c73Vt2GcloNfoZrurVx40ZzoYBMJSKji6XavL2vmcoAnMO4IjADihuuud+4REkvLZlC4vZ7UVoxNgt97So8POuZvIYrRycT4rY4JQHVaBnP+505925c3dCUXV9XR9LadManbfbpWLeXV6Vjv78Zl6V3HrcjJJXb6UPdft/NfooBr0Do95Xl+BM5WKjg6aHe178eoDCWA8s/tOBgCmpOp62ixc7ESgFLMtN4yKDlapmNfOfkOHnW7US4mdquvpUjalQj5z7tuWio4a7a7uHxxOYWXRqQ5Gxs+yzRhWUva3h+VljDn3bcsB9my66CAFYBEFXfEHIFwERwBwzINGW7v11oVPBPzKibioMCo6ULc2HPVsv58PXqsyCEkucjI/rxPrKnPQ/2YaDaUndZHpfb5SgD2bhtszL/hBA7BI/EruOL1HAjA+giMAOMbfZnbRZtLloqO7D5tqtOJRkVJ160oY6cb6bJ+8xkUphifRcVF1vQtXtpVjWNUyqX2vpQeN9sxX+11ZzmopnYhNqNftWd3Z9SZ6jX714aG8VmfitVRcT+tOWqtOeuL7AubdjXVHxhAcAbOK4AgAjhluPbhAjyPpaOx2XCaH3HY93Vh3ZrrHSpz4IcBtGmS/RqfbG5zMX+x5c30tp1TCzFUgV5kwhI6LRMKoVIjPZLWd/YbaXatbF3yN9vsRBXHyWqnV6W8EjGkpndS11VxsXksAnM9YZxLGmPcZY75sjHnRGPMDI76+aoz5eWPMF4wxXzLGfOjY1yrGmBeMMc8bY547dvk/H1z2/OA6zwfyEwHABPw3NJuFC56UxKxyYh56rMTJupPW8lKKN74n7Ow31enZC1fXpJIJ3VjPxaaqJQjVOep/UwqwL9CkKhNM75OC3Xo3SZUdsIg2Y9YHEsD4zgyOjDFJST8m6f2S3iLpO40xbzlxtY9K+j1r7dslvVvSjxpjjnfHfI+19hlr7bP+BdbaPz+47BlJ/0rSJyf6SQAgAFXX01MrWTmZ1IVu7/e6iMNkNWutbtfqnNgEyBijWxv52JxEx0V11z+Zv3hIUirmY/G8CUrV9WSMdPOCIXSclDfy2tr11OtFP/WuMpzed7HXtaNm35Mda812VzsPGgTzwDmUNxy2qgEzapyKo3dIetFa+5K1tiXpZyV94MR1rKRl0++IeUnSrqSxNo8PbvNfSfqZsVcNAFNSdb2JGp2uOmmtOelYfKK277V10OxwYhOwIJvrzosgtmX1q1rqsjb6cCIIFbeup1eWtJRORr2UiW0WHLU6Pd192Ix6KarW6lpKJ3RlOXuh268spVXMZyZ+Dm/vebKWiZXAeZSKebn1lh4221EvBcA5jRMcXZd059jftweXHfcxSW+WtCPpBUnfa63tDb5mJX3OGPN5Y8yHR9z/N0t61Vr71XOtHACmoBLA1q64TFa7PTgxukUPjkCVi4629xpqd3tnX3lBTHoyL/WfNwfNjva8+TihqM7BRDVfnMZo9yeq5ZVInH96n6+8kdft2mQ/i9/njB5HwPj8rbvzVF0KLIpxgqNR/zKf/DjwvZKel3RN0jOSPmaMWRl87Z3W2q9Xf6vbR40x7zpx2+/UE6qNjDEfNsY8Z4x57v79+2MsFwAuxmt1dO/gcOITgXIxHnv4qxP2AsFopWJe3Z7V9l4j6qXERmVQqTfRyXzM+oNNqurOzzZRPwCLQyBeCSCQKxUn3y4zTz2sgLAcTSaN/rUEwPmMExxtS7p57O831K8sOu5Dkj5p+16UdFvSmyTJWrsz+P89SZ9Sf+ubJMkYk5L0ZyX989O+ubX2x621z1prn718+fIYywWAi6kOt9tMXnG0s99QqxNtRUql5vdYyUW6jnkzbwFHEIJowl4KsGlx1B4ddlR71Jqb0PbaWk7ppIn8mO/2rLYCaEhdLub1yoOmmu3uhe+j4ta1mktrzcmcfWUAko7eX0X9WgLg/MYJjn5L0huMMbcGDa8/KOnTJ66zJelbJMkY85SkN0p6yRiTN8YsDy7PS/pWSV88drs/IekPrLXbk/0YADC56oRNV33loqOe7ffAiFLVrevaak7Z1Oz3WImT4TjvCbe6zItez2pr15u4Uu9mISdj5uOT6HmrRkkmjG4WnMi3l9x92FSr25s4kPNPXrd2L/7z9CeqzcfvFwiLk0np8nJ2Lj4gABbNmcGRtbYj6Xsk/ZKk35f0c9baLxljPmKM+cjgaj8s6Y8aY16Q9CuSvt9aW5P0lKRfN8Z8QdJvSvp/rbWfPXb3HxRNsQHEhP9GZjOwyoloT7IqrqfyBic2QSvmM7qUTTFZbeDVg6YOO72JK46yqaSureYif94EoRpAs/C4KRejnyboh7WTBjbDnk0ThL/9fnjz8/sFwtLfzj/7r/PAohlr3rS19jOSPnPiso8f+/OO+tVEJ2/3kqS3P+F+v3vchQLAtFVcT4V8RitL6YnuJy6l2BW3rv/i665GuoZ5ZIwZTgBDf0ukpImmEfrm5XGtBBRCx8lmwdFvvOTKWqv+QNzw+Q3/J+9DN1mz71anp5f3GvqO/+zGROsAFlGpmNevfZW+tcCsGWerGgAshCD6tEhHFSlRVk7sey3te+25ac4bN+WYTM6Lg6Mm7JM/d+IykXBS1ZqnjUtZXcqO9fncTCgXHXmtru4/OoxsDVXXUyaV0NMrSxPdz6qT1rqTvnDVw509Tz07P1sRgTCVi45efXioRuviPcYAhI/gCAAGqgE0XZXiUZESVKNvjFbecHRn11OnG20D9DiouJ7SSaNra5M3YS8XHe3WW3rQaAewsuj0R8bP13OvtBH9FtxKra5SwZloep+vH1Je7DWaiZXAxfnPm0l6jAEIH8ERAEg67HS186ARWNASdUVKJaAtHRitVMyr07Pa2W9GvZTIVd26bhYcJQM6mZcUeRPmSVVdb+5ChSD6Ak0qyMe1XHSG2yzPy7/dvIWDQBjisp0fwPkQHAGApDu7DVk7+UQ1X6noaHsvuoqUSs2TMf2+JAief5zc5o1vYJV6kobN3Ku7s/u4Nttd3X3YnLtQ4fpaTsmEiaxKoNezqrh13Qqo4X95I6+dBw012+ffLlNx61peSqmQzwSyFmCR+P3wmKwGzBaCIwBQsH1apH6w0O5avfIgmoqUqlvX1ZUlLaWTkXz/eeeHAov+xtdaG1hvMOko6JzlPkd+sFKas2q/TCqh62u5yKYhHU3vC6riKC9rpe298/88lUFYGlWTcGCWTdpjDEA0CI4AQBq+gQnqpGQz4lJsRkVP1+XlrJxM8sJbXeZF7VFL9VZXpYAq25xMSleWs5Fuh5qUv/agHpM4KRWdyMLSo+1hwVWFHr/f8wgyLAUW0SQ9xgBEg+AIANQ/EVheSmndSQdyf0fjnqMJFqquN9z2g+D1G6DzxndYqRdgdU3U/cEm5a99HicaloqObtfqstaG/r2nURUqnT/cb3d72t5rzOXvFwjLJD3GAESD4AgAFPzWgyvLWS2lE6pGUDnxsNmWW29RcTRl5aKz8D2OKlMISaKeSDipilvXmpPWakAhdJyUi3kdNDva98KfenfbrSuTTAQyvU+S1vMZrebS5w4pX95rqNuzVBwBEygV83rlQUOHnfP3GAMQDYIjAFDwWw8SCaNSIR9JxVE14C0dGK1UzOvOrqduL/zqi7iounUlE0bXAzqZl/pNi+8dHMprdQK7zzDN40Q1X+mCVTpBqNY83SzkApne5ytfIKT0w+Jbc9bDCghTqeioZ6XtvUbUSwEwJoIjAAuv3e3p5SlsPSgVHW1FMB3KPxFiq9p0lYuO2l2rnf3FfeNbdT1dX8spkwru7YQf4EY1vWtS1d363E1U85Uj/N1U3PoUXqPz5w6O/CrSeQ0HgTD4z59F3+4NzBKCIwALb2e/oU7PDhtaB6XfSNZTL+SKFP+N2OYcNueNk/KG/8Z3NgOOIEyjSbA/qnkW+1+0Ov0Qeh4bY0vSzcLFG0pPoj+9L/hKrnLR0ct7DbU6vbFvU3E95TNJbVzKBLoWYJGUJ2hODyAaBEcAFt40+rRI/U/UDjs9vXrQDPR+z1JxPT21kpWTSYX6fRfNRZvrzpOK6wUeHPkB7ix+Er2956ln57caZSmd1NXVpdB/N/cPDtVodwOvoiwV84PtMuOfvFYHEyuD6ocHLKJCPqPlbGomX+eBRUVwBGDh+W9cgt5eMgwWQv5ErVILfksHHuc3QJ/l0fGT2PdaetBoB36srebSKuQzkU0knMRwotocbxONonn57Zr/Gh1wxdHG+cPfiuvR3wiYkDFGpQ1nJl/ngUVFcARg4VVqnnLppC4vZwO931JElRP+hDhMV5QN0OPA/7mnUV3T3+Y5e4FcxZ3//jflYj707ZnVKVWFnne7TKfb053d4KvsgEVUKuZntpcdsIgIjgAsPL9PS9BbD66t5ZROmlCDhUeHHdUeHao0xxUPcTKrAUcQplWp17/P8MOJIFRdT5eyKRXz89v/plTMy623dNBsh/Y9K25dqYTRtbWlQO/3vNtldvab6vQswTwQgFLB0Z1dT53u+D3GAESH4AjAwqvuTqdCJ5kwulkId7La0ck8JzZhuLWRV3U3/AbocVB1PRlz1DA5SKWio50HDR12uoHf9zRNK4SOk/KwkjK8YK/qerpZcJRKBvu29bzbZY4qygjmgUmVi3l1elY7++H2gQRwMQRHABZat2e1NYUGv75SwQm1x5H/vQiOwlEq5tXq9PTKw8V741tx63p6ZUlL6WTg910qOrJWurPbCPy+p6k6xdeSuNiMIDi6XatPpbJN8qvbxgv3/evR4wiYnP9aucgDJoBZQnAEYKHdfdhUq9ubWk+S0uCkxNpwKlL4RDxcw+qLBWyQPc2QxH8+ztI2wE63pzt7wY+Mj5tSyNMErbXDSWbTUC7mdWevofYY22VuT6kfHrCI/Ob0s/Q6DywygiMAC61am16fFv9+662uao9aU7n/k6puXZeXs8pnU6F8v0VXGk5lmr1+PJOqutOb3jecSDhDj+srD5pqd+3UXkvi4lI2pY1L2dBO9mqPWqq3ulN7XEtFR92e1ct7Z1e3LcJWRCAsw8mkM/Q6DywygiMAC204GWpKWw9KIX+i1p+oNt8nrnFydWVJmVRi4T4x7Tdhb02tCmTdSWt5afymxXGwCBPVfOVieGO0/WNgWq/R5Y3xK6gqUwxLgUVjjJnZQQjAIiI4ArDQqrt1ZVIJXV0JdlqPrzzcchPOG6NKbXpbOvC4RMKoVHB0e8G2qk1zopo0mycU0xoZH0elYl5bIf1ubtem2/C/NGbPpm7P6s5ug4mVQIA2C4s7mRSYNQRHABZateZps+AokZjO1oPrazklEyaUN0Zeq6N7B4c0bg1ZacYCjiD4P+80Q8pScbZOKKpuXUvphK4sQP+bctHR3YdNNVrTn3pXdT0lE0Y31nNTuf/Ll7LKZ5Jnhr87+w21uj3dWoBgEAhLeYEnkwKzhuAIwEKruHWVpjBO3JdJJXRtbSmUbR1HJ/N8Ih6mctFRdbe+UG98/W09m1M81kpFR9tjNi2Og4o73RA6Tvzf+9bu9F/XKm5dN9ZzSien85bVGDMcYvAkYYSlwKIpFR21Oj3dXcDJpMCsITgCsLD603qmPwXpPOOeJ3G0fYgTmzCVNvJqtnu6d3AY9VJCU6152riU1aUpNmEvFfPq9Kx29s9uWhwH05z8FTflECerhfIaveGcWTXo/6xltqoBgQnztQTAZAiOACys+weHarS7Uz8RKIXUSLZCxVEkbi3gG99+k+DpHmezNFmt1+uH0IvSmP6od9t0j3lrbSjHWqmY1509T50nVLdV3bqyqYSeWp5OPzxgEfnvV8LqmQbg4giOACysSkhbD8rFvB402tr3WlP9PpVaXRuXMlpeSk/1++C1/De+lQVqkB1OpZ7ftDj+j+urB00ddnoLU3G06qS15qSnHurt1ls6aHamXkV5q5hXu2u1s3/6dpnbNU/lYn4htiICYbm6mlMmmZiJDwiARUdwBGBhTXsylK8U0mS1ygJtlYmTa2s5pZNmYd74Nttd3X3YnPrz5vJyVrl0ciYajy/SRDVfGJPV/OdUGFWh/e93ekjZ34q4GBVlQFiSCaMbhdxMfEAALDqCIwALq+p6SiWMrq9NZ1qPrzzGSUkQ+lUgnNiELZkwurlAI4X9hsilKU/v6zctno3H1V/jIj3/ykUnhNc0/3Gddo+jJ2+96/Wsqrve8HoAglMu5hfmgxdglhEcAVhYFbeu6+s5paY0rcd3s+BvuZneG6Nmu6tXHjQXquIhThbpja+/JW+a0wh9YfUHm1TF9ZROGl2bcggdJ6WCo539hg473al9j4rrKWGkm+vTPdauDKrbTjvW7j5sqtXpLVQwCITF/4DA2sWZTArMIoIjAAsrjD4tkrSUTurq6tJUP52v0hg7Uv7kvEV44xvmtqzyYDtUtxfvx7Xq1nVz3VFygfrflIp59ay0vTe9qXeVWj/cz6Sm+3b1rOq2ChMrgakpF/PyWl3df7Q4k0mBWURwBGAhhTWtx9c/KZle5YR/YnOLrRSRKG84/Te+B/P/xrfi1rXmpLXqTL8Je6mYV6vb092HpzctjoNKbfG2ifp9h6a5lbDq1kMLa8rFvG6f0uC+UvN7LfH6CgSNyWrAbCA4ArCQ9ry2Dpqd0JpJ+xUp0zLsBVLgxCYKpRkaHT+psCr1pGOT1WI8sc5aO2icvFjPveExX5tmIB5eIFfacHRntzGyuq3q1pVJJXR1ZSmUtQCLZJH+/QRmGcERgIUU1kQ1X6mYV+1RS48OO1O5/4rraT2kKhA8LqwG6HFQ3Q2xUs9vWrwb3xMKt95SvdUN7TGJi2I+o0vZ1LBZetD2vZYeNNqhVhy1uj298uDxrXcVt67NgqPEAm1FBMJyfS2nZMLMxCAEYJERHAFYSEc9gUKunJjSG6NFrHiIk+trOaUW4I1vq9PTy3uN0I61qytLyqQSsQ7khtV+C7aNye8LNK3fTSXk1+hS8fQhBlXXW7hgEAhLJpXQ9bUcFUdAzBEcAVhIFbcuY6SbhXCmIG0+4aQkCJWaR3+jCKWSCd0sOFPdthMH23ueejaciWqSlEgY3VzPqRrjx9X/nYf1mMTJNHu3+dP7bm2E87j6r58n+xz1en4/PF5fgWl5UnN6APFAcARgIVVdT9dWc8qmkqF8v6M9/MG/MWq2u9p50Fi45rxxM83qi7gYTlQL6WRe6m8hivPjWnXrShjpxpRHxsdRqZjXnV1PnW4v8Pv2w/2wHtenlpeUTSUeO3m9d3CoZru3cBVlQJjKxfwwLAYQTwRHABZSxa2HGrRcyqa0cSk7lcqJ7T1P1jIqOmr9BuierI336PhJ+AFOmNsiSzF/XCuuF8rI+DgqFx11elY7+8FPvfPD/aV0OOF+IuFvvXvta3Ql5H54wCIqFR09bHa077WiXgqAUyzeuxwAULiToXzlKVWkDLfKcGITqVLR0aPDjtz6/L7xrbqeLmVTKuYzoX3P8oajRrur+weHoX3P8whzZHzcTLOSMuxwX/JDytf+LEeDFBbzdwyEgclqQPwRHAFYOA+bbe3WW6F/glwq5qcygcg/aaPHUbTKG/548vktt68OTuaNCW+6lH9CEdfJatXd8EbGx015ir+bSq0+fE6F5dZGv7qt1zuqbqu4ntJJo2tr4fTDAxbRtAeIAJgcwRGAhbPlRlOhUy46euVBU812N9D7rbh1rebSWnPCqwLB48oL8Ilpv1Iv/OeNFM9Abt9rad9rq1RYzND2ynK23xco4N/NA6+tPa8dQbjv6LDT092HR1vvKrW6bhYcJRPhhaXAorlZcGSM5n7ABDDLCI4ALJwo+rRIR5PVgq46YlR0PFxfyymZMHP7iWmn29OdvfC3eF4bPq7xO6GoRhRCx8VpfYEmVd2N5jW6PGLrXcX12KYGTNlSOqmrK0tz++8nMA8IjgAsnKhO9oYnJQF/Ot/vBcKJTdQyqYSur+XmtuLolQdNtbs29JAynUzoxnoulpPVho2TF3ib6Ki+QJPyn0NhBzal4XaZ/ve31g63ZwKYrlLMJ2gCi47gCMDCqdTqurKclZNJhfp9h/1AAgwWWp2eXt5rUHEUE+WN+R0pHFWlnv8941xxtFlY3OdfueiouvvavkCT8p9DYT+uV1dzyiQTw2P9/qNDea0uFUdACMobzlT6QAIIBsERgIVTjWjrwaqT1pqTDvQTtTt7nnp2sSse4sSfnBfX0fGTiKoKpP894/m4Vty6rq4uhTYyPo5KxbxaJ/oCTcp/XHOZcB/XZMJos+gMgyu/3wqvr8D0bRbyqj1q6aDZjnopAEYgOAKwcKq70W09CHqyWjXCKhA8rlTM66DZ0Z43f298t9y6ltIJXVnOhv69/cd1P2aP61YEzcLjZhqVlFE0YfeVi87wZxluRVzw3zEQhvKJraIA4oXgCMBC8VodvfrwMLrgqOAEWnE0/EScE5tYGE4Am8M+DRXX02bBUSKC6VKlQjwf14rrLexENV9pCmO0q249su1hfp8Vv79RKmF0fS0XyVqARVKaQggNIDgERwAWil/tE1WFTrno6OW9hlqdXiD3V3XrWs6mVMhnArk/TObojW+8Ao4gVCNswl7eiN8n0Y8OO6o9OlRpY7FD26urS0onTWBN4Q+abdUetSJ9jW62e7p3cKiK6+nGek6pJG+XgWkrzfEHL8A8GOtfQmPM+4wxXzbGvGiM+YERX181xvy8MeYLxpgvGWM+dOxrFWPMC8aY540xz5243V8b3O+XjDE/MvmPAwBPdlShE92n2T0rbe8Fc5J12/VU3sjLmPCrQPC4m4WcEka6XYtPwBGEXs8OeoNFE5LcWHdkTLxOKKrDbUyLXXGUSiZ0c90JLCz1w8FbEQVyfj+jSq3er3yivxEQinw2pcvL2bn84AWYB2eOFDLGJCX9mKQ/KWlb0m8ZYz5trf29Y1f7qKTfs9Z+mzHmsqQvG2P+mbW2Nfj6e6y1tRP3+x5JH5D0NmvtoTHmShA/EAA8if+GZDOq/hnHKided/nSxPdXdev6uuurE98PgpFNJXVtLTd3b3xfPWjqsNOLrApkKZ3UtdVcrCqO/LUseo8jqf8YBFVxFOX0PukoCKy4dVVqnp4tFSJZB7CIjvcYAxAv41QcvUPSi9balwZB0M+qH/gcZyUtm/5H3pck7UrqnHG/f1XS37PWHkqStfbeuVYOABdQ3fVUyGe0mktH8v2D3MrU7va0vddY+IqHuCkX84GdRMdFNcKJar5SMbiqliAcBUc8/0rFvLYCmnoXdSDnb737fHVPjw47BINAiDYLeYIjIKbGCY6uS7pz7O/bg8uO+5ikN0vakfSCpO+11voNPKykzxljPm+M+fCx23ytpG82xvxHY8y/N8b84Qv9BABwDv0+LdGdCBTzGV3KpgIJFl7ea6jbs5zYxEzcAo4gHE3vi+5YKxXjdUJRdevauJTVpeyZxdtzr1x0VG91VXvUOvvKZ6jU6rqynJWTieZx9bfe/fuv3JfEVkQgTOWio7sPm2q0ulEvBcAJ4wRHoxpnnPxI6b2Snpd0TdIzkj5mjFkZfO2d1tqvl/R+SR81xrxrcHlK0rqkPyLpf5D0c2ZEkw5jzIeNMc8ZY567f//+GMsFgNNVat5wQlMUjDHaLAQTLNz2e6zQgyNWbm3kte+1te9NfhIdFxXXUzppdC3C6VKloiO33tLDZjuyNRxXiTiEjpMgKykrEU5U85WKjl59eDj8M4BwlAbvZ/xBJgDiY5zgaFvSzWN/v6F+ZdFxH5L0Sdv3oqTbkt4kSdbancH/70n6lPpb3/z79W/zm5J6kjZOfnNr7Y9ba5+11j57+fLl8X8yADjhsNPVzoNG5FtLyhvB7OGv1mjOG0elYY+U+XnjW3XrurnuKJmIrgm735h7KyaPa9X1CBUGjqYhTf67qbjesBdcVPwwPpkwurHO7xgIS5nJakBsjRMc/ZakNxhjbhljMpI+KOnTJ66zJelbJMkY85SkN0p6yRiTN8YsDy7PS/pWSV8c3OZfS/rjg699raSMpJoAYEru7DZkrSI/KSkV87qz56nT7Z195SeouJ7ymaQ2LmUCWhmC4L/xnaftapVa9CFJ6VjT4qg121298qBJaDtwY91Rwkx+zNcPO7p/cBh9uD/4/tfXcsqkxhpADCAApcKg4igmHxAAOHLmBnJrbccY8z2SfklSUtInrLVfMsZ8ZPD1j0v6YUk/aYx5Qf2tbd9vra0ZY14n6VODHWgpST9trf3s4K4/IekTxpgvSmpJ+i4bRFdFADhFNeJpPb5y0VG7a/XKg6ZuTrBtrt+vKa8Ru3wRoZuFwej42ny88bXWqurW9Y5b0U6XKhWPJhJGzd9GEXWYFheZVELX13MTVxzFoQm7dPR75fcLhGvVSWvNScfiAwIArzVW50Fr7WckfebEZR8/9ucd9auJTt7uJUlvP+U+W5L+4nkWC5zGWquf+g8V3Ts4jHopiLEv7TyUFIeTkv73/99++Su6urp04ft54eUHkZ/M43FL6aSurizpl750V4ed2W/w2e72VG91h5VUUXEyKV1ZzuoXv/iK6odnDW6drjt7DUnRv5bESbmY1+cru/qRz/7Bhe8j6olqPv/3yu8XCF/cBiEA6GMUyIyqH3aUTiYiL6Hu9qzqrY5WlqIZbe6rup7+zs//npIJowhbcGAGvPX6itadaI/XNz+9oqdWsvqF3z3ZLu58jIze+TWPtYZDDPyxN17Wv/z8tr567yDqpQRiOZvSN5SiDynf9bWX9W+ef1lfvhv943p9LaevuXIp6mXExjd9zYZ+4yVX/+TXXprofm6s5/T6y9E+rjfWc3rbjVV90xt4fQXCVi46+s3bu/qDuw+jXgowFiOjNz69HPUyps7M0u6wZ5991j733HNRLyMW3v+Pfk3f+Lqifujb3hLpOn7qP1T0D//tV/Qf/8dvUTaVjGwd/+7L9/TdP/Fb+hcf+Ub94XL0JzcAAAAAzucf/8pX9aO//JWolwGMLZNM6Ct/9/1RLyMQxpjPW2ufHfU1Ko5m0GGnqz+4+1DL2eh/fS+8/ED7Xlvbe41IPyGMS3k7AAAAgIv5S990S1/79LJ6vdkpbsBiW5Reo9EnDzg3fzJUHBrH+c2Gq2490uCo4tblZJK6fCkb2RoAAAAAXFw+m9J7/9DTUS8DwAnMGJ1Bflhz7+BQXivaBqH+BJWopwdtuR7TpQAAAAAACBjB0Qw6Pu42yqkD9cOO7g+mmEVd/VRx6ypNMNYcAAAAAAA8juBoBlWPhTTVCAOb46FVJcIAq9uzurPbUGmD4AgAAAAAgCARHM2gqusNm0BHGdj4odXrNvKRBlivPGio1e2pXMxHtgYAAAAAAOYRwdEMqrp1fd31VRXymUi3qlV3+9/7XV97Wdt7DbW7vWjWwUQ1AAAAAACmguBoxrS7PW3vNVQqOtosOBFvVatr41JGb72+qm7PanuvEck6/P5KVBwBAAAAABAsgqMZs7PfUKdnVSrmVS46kVYcVWqeNguOysNtc9GEWFuup0wqoadXliL5/gAAAAAAzCuCoxnj9zQqF/MqFfPaedBQs92NZC1Vtz5chyRVa9EERxW3rs2Co0TCRPL9AQAAAACYVwRHM6Y63JblqLzhyFppey/8qqNmu6tXHjZVKua1cSmjfCYZWaPuqusNq54AAAAAAEBwCI5mTNX1lEsndXk5e1TpE0Fgs73nyVqpvOHIGKNSMZrJatZaVdz68LEAAAAAAADBITiaMVW3rlKxH9b4zaCjqPSp1PxJZv013NrIR7KOeweHarZ7VBwBAAAAADAFBEczpuJ6w7Hz605ay9lUJJU+fiPsUqG/llLR0Z1dT51uL9x1DPoqUXEEAAAAAEDwCI5mSLdnteV6w0ojY4xKG04klT5V19PKUkprTlpSv1l3p2e1s98Mdx27R83CAQAAAABAsAiOZsjdh021ur3XVNdE1Vuo4tZV3sjLGDNYhzO8PExVt65Uwuja2lKo3xcAAAAAgEVAcDRD/HH3x/v5lIuOtvcaaoe8Razqeq8JsG5t+P2Wwg2OKq6nG+s5pZIcygAAAAAABI2z7Rnib8sqbby24qjbs9rZb4S2jna3p5f3G68JsC4vZ5VLJ4dNs8NSZaIaAAAAAABTQ3A0QypuXZlkQk+vHG3LimKy2st7DXV79jWBjTFGpaIT6rY5a62qNY+JagAAAAAATAnB0Qyp1jzdLOSUTJjhZX5voTADm+FEtROBTbmYD3Wr2m69pYPDDhVHAAAAAABMCcHRDKm49cemh11ZzmopnQh1i1h1UN10MjgqbTi6s9uvRgplHf5EtQ0qjgAAAAAAmAaCoxlhrdXWrvdYdY0xRuWQJ6tV3LqcTFKXL2Vfc/mtYl6tbi+0fkv+z7xZoOIIAAAAAIBpIDiaEfcfHcprdUdW15SKTqhbxPyJasaY11zuh1rVkPotVWqejJFuFnKhfD8AAAAAABYNwdGMONoe9nh1TbmYD3eLmFsf2ZDaD7XCCrGqbl3XVnPKppKhfD8AAAAAABYNwdGMqNQGDakLjwc2m0VHrW5Pdx82p76Obs/qzm5jZID11PKSsqlEaNvmKq5HfyMAAAAAAKaI4GhGVF1PyYTR9fXHt2X5DbOrtekHNq88aKjV7T3WGFuSEol+v6XbITXqHtXzCQAAAAAABIfgaEZU3LpurOeUTj7+K/NDnEoIvYVOm6h2fC1hVBw9aLS1W2+N3DIHAAAAAACCQXA0I/yG1KNcXc0pkwxni5jfv6h8ylrKG3lVdz31ptxvaWsQYDFRDQAAAACA6SE4mgHWWlVOaUgtScmE0c1CLpSm1FXXUyaV0NMrSyO/Xio6anWm329pGGDR4wgAAAAAgKkhOJoB+15bB82ONkc0xvaVi/nhNrJpqrp1lQqOEglz6jqk6U9W86urnvSYAAAAAACAyRAczYCztodJ/clqVdeTtdPdIvakLXNSf6uaJFWm3CC74np6aiUrJ5Oa6vcBAAAAAGCRERzNAL+S6EnbssrFvBrtru4fHE5tHf6WudMaY0vS1ZUlZVLT77e0dUaABQAAAAAAJkdwNAMqbl3GSDfWTw9swpisdu/gUM1274mTzBIJo82CM/WtapXBljkAAAAAADA9BEczYMv1dG01p6V08tTrhNFbqFLr3/dZlT7lwba5afFaHd07OBxuiwMAAAAAANNBcDQDztoeJknX13NKJsxUt4gNt8ydGRzlVXHr6vWm02/JX8dZjwkAAAAAAJgMwdEM6DekfnJIkk4mdGM9N9VKn+puXamE0bW1pSder7SRV7Pd070p9VuqjtEsHAAAAAAATI7gKOYeNtty662xGkFvFqa7RazierqxnlMq+eTDpjzstzSd6ie/j9MmFUcAAAAAAEwVwVHMbQ23h50dkvhbxKyd1hax+lgBll8JNK1tc1XXUzGf0cpSeir3DwAAAAAA+giOYs6v2hknsCkVHR00O9rz2oGvw1qras0bK8C6tpZTOml0uzad6qeqW6faCAAAAACAEBAcxdx5GkFPc7Labr2lg8POWAFWMmF0s+BMteKI/kYAAAAAAEwfwVHMVd26rixn5WRSZ163vOEMbxM0v6+Q/z3OXEsxP7xNkJrtrnYeNJioBgAAAABACAiOYq4yxkQ13411R8ZoKg2yt3bH3zLXv16/4ijofkvbe56sZaIaAAAAAABhIDiKuXEbUkvSUjqpqytLUwmOKjVPxkg31nNjXb9czMtrdXX/0WHg65DG27oHAAAAAAAmQ3AUY16ro1cfHo7VkNpXGkxWC1rVrevaak7ZVHKs65c3Bv2WAm6QXd31p8xRcQQAAAAAwLSNFRwZY95njPmyMeZFY8wPjPj6qjHm540xXzDGfMkY86FjX6sYY14wxjxvjHnu2OV/xxjz8uDy540xfyqYH2l+bO361TXjhyTlDWc6FUeuN3Z/I0nDsCvoEKvq1rW8lNKakw70fgEAAAAAwOPODI6MMUlJPybp/ZLeIuk7jTFvOXG1j0r6PWvt2yW9W9KPGmMyx77+HmvtM9baZ0/c7h8OLn/GWvuZC/8Uc8oPgM5TXVMq5rVbb+lhsx3wWsbfMidJ19dySiVM4I26K4OJasaYQO8XAAAAAAA8bpyKo3dIetFa+5K1tiXpZyV94MR1rKRl0z+bvyRpV1In0JUuID902TzHVjW/0mcrwKqjB4229rz2ubbMpZIJ3VjPBT5ZrR9g0d8IAAAAAIAwjBMcXZd059jftweXHfcxSW+WtCPpBUnfa63tDb5mJX3OGPN5Y8yHT9zue4wxv2uM+YQxZv38y59vFdfTupPWam78bVl+VVCQW8T8EOo8FUdSv89RpRbcOtrdnrb3GvQ3AgAAAAAgJOMER6P2BJ2csf5eSc9LuibpGUkfM8asDL72Tmvt16u/1e2jxph3DS7/PyS9fnD9VyT96MhvbsyHjTHPGWOeu3///hjLnR/n3R4mSZsFZ3Db4Cp9/BDqvJU+5WJeVdeTtScPl4vZ2W+o27NUHAEAAAAAEJJxgqNtSTeP/f2G+pVFx31I0idt34uSbkt6kyRZa3cG/78n6VPqb32TtfZVa213UJn0T/zLT7LW/ri19llr7bOXL18e/yebA5Wad67tYZKUz6Z0eTkbaKXPcMtc4XxrKRUdPTrsyK23AlmHv+3Nn9gGAAAAAACma5zg6LckvcEYc2vQ8PqDkj594jpbkr5FkowxT0l6o6SXjDF5Y8zy4PK8pG+V9MXB368eu/13+Jej77DT1SsPGueuOJL6fY6CrTjy9NRKVk4mdc519NceVINs/35K5wywAAAAAADAxZyZBFhrO8aY75H0S5KSkj5hrf2SMeYjg69/XNIPS/pJY8wL6m9t+35rbc0Y8zpJnxpMwEpJ+mlr7WcHd/0jxphn1N/2VpH03wb6k8247b2GelYqb5w/JCkV8/rVrwS3re8iW+b66+ivvVLz9A2lwsTrqNQ85dJJXV7OTnxfAAAAAADgbGOVkFhrPyPpMycu+/ixP++oX0108nYvSXr7Kff5X59rpQvmaHvYxSqO/uXBobxW59xVQqPX4undbzz/NsEb646SCRNYo25/otogiAQAAAAAAFM2zlY1RKBSG/TzuUAj6M1BddDW7uTb1bxWR/cODi9UcZRJJXR9LTfsTTSpiltnohoAAAAAACEiOIqpqlvXcjalQj5z7tuWj20Rm3wd/fu46CSzUtEJpMdRt2d1Z7eh0gW27gEAAAAAgIshOIqpiuuptHGxbVmlQnBNqf37uGilT7mY1+1aXdbaidZx92FTrW6PiiMAAAAAAEJEcBRTW7vehbaHSdKqk9a6kw5ki5h/H5sXrDgqb+R10Oxoz2tPtI5qjYlqAAAAAACEjeAohjrdnu7sehfqb+QrFfOBVRwV8xmtLKUvdPvhtrkJ1+IHWKUNKo4AAAAAAAgLwVEM7ew31enZ4ZaziygXnWF/oklUXe/C/Y0kDaumJg2xqm5dmVRCV1eWJrofAAAAAAAwPoKjGPKrcyYJbDaLee08aOiw051oLVXXm6iv0M1CTsZM3qi74ta1WXCUSJy/5xMAAAAAALgYgqMYGjaknmBbVrnoyFrpzm7jwvfRbHe186Bx4f5GkpRNJXVtNRdAxdFkW/cAAAAAAMD5ERzFUNX1tJRO6Mpy9sL3EcQWse09T9ZefKKa79ZGXrcn2DZnrR1smaO/EQAAAAAAYSI4iqHKYHuYMRfflnXUlPrigY2/vWySLXP+7ScJsO4fHKrR7k68DgAAAAAAcD4ERzFUHfTzmUQhn9FyNjVRYOP3Wpq04qhczGvfa2vfa11wHX6ARcURAAAAAABhIjiKmV7PqrrrTdTfSJKMMSptTDZZbWvX08pSSmtOeqK1+JVCF13LUYBFxREAAAAAAGEiOIqZuw+banV6gWzLKhXyE1Yc9fsKTbJlTur3OOrf38XWUnXrSiWMrq/lJloHAAAAAAA4H4KjmAlqe5jUr/TZ3muo3e1d6PZVtx5IgHWz4MiYo55J51+HpxvrOaWSHK4AAAAAAISJM/GY2XKDaUgt9cOnTs9qZ79x7tu2uz1t7zUCCbCW0kldXVm6cPVT1fW0SX8jAAAAAABCR3AUMxXXUyaZ0NXVybdllSaYrPbyXkPdng1sklmpmL/QVjVrrSpunf5GAAAAAABEgOAoZqpuXTcKOSUTk/UVkjRssH2RSp/hlrkJm3QfX8tFmmPveW0dNDtMVAMAAAAAIAIERzFTcb1AtodJ0pXlrJbSiQsFNlu7wW2Zk/oT0dx6Sw+b7XPdjolqAAAAAABEh+AoRqy1gTWkliRjzIUnq1VqnpxMUpcvZQNZi18xVD1ng2x/7VQcAQAAAAAQPoKjGLn/6FBeqxtYxZHUrxi6SI+jqlvXZsGRMZNvmZOk8obfb+l8IVbV9WSMdLMwec8nAAAAAABwPgRHMRLkRDVfeSOvLddTt2fPdbt+Q+oAA6zCxfotVV1P11ZzyqaSga0FAAAAAACMh+AoRvzKoKArjlrdnu4+bI59m27P6s5uQ6WN4AKsXCapp1eWdPucW9UqAW7dAwAAAAAA50NwFCN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      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "scorel = []\n",
    "for i in range(70,130):\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(70,130)][scorel.index(max(scorel))]))\n",
    "plt.figure(figsize=[20,5])\n",
    "plt.plot(range(70,130),scorel)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "2e791d5b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, estimator=RandomForestClassifier(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=100\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': 1}"
      ]
     },
     "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": [
       "0.866959706959707"
      ]
     },
     "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, estimator=RandomForestClassifier(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=100\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': 6}"
      ]
     },
     "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": [
       "0.8603174603174603"
      ]
     },
     "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, estimator=RandomForestClassifier(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=100\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': 3}"
      ]
     },
     "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": [
       "0.866959706959707"
      ]
     },
     "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, estimator=RandomForestClassifier(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=100\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': 6}"
      ]
     },
     "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": [
       "0.8691819291819293"
      ]
     },
     "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": [
       "0.866959706959707"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc_1 = RandomForestClassifier(n_estimators=100,max_depth = 1, 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": [
       "0.008840048840048964"
      ]
     },
     "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": 46,
   "id": "7823ba7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import stats\n",
    "import scipy.stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "727458a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "rfc0 = rfc_0.fit(x_0, y_0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "7a4faaa8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 48,
     "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": 49,
   "id": "a7806102",
   "metadata": {},
   "outputs": [],
   "source": [
    "rfc1 = rfc_1.fit(x_1, y_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "39676bf5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 50,
     "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": 51,
   "id": "20398cf5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ATE_outcome = mean_1 - mean_0\n",
    "ATE_outcome"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "21fca7c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nan nan\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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