17.RandomForest_Classifier_실습.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "matplotlib.rc('font',family = 'NanumGothicCoding')\n",
    "\n",
    "from sklearn.model_selection  import train_test_split\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.tree import export_graphviz\n",
    "import graphviz\n",
    "\n",
    "#ignore warnings\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CHURN</th>\n",
       "      <th>CUSTOMER_ID</th>\n",
       "      <th>GENDER</th>\n",
       "      <th>AGE</th>\n",
       "      <th>CHARGE_TYPE</th>\n",
       "      <th>HANDSET</th>\n",
       "      <th>USAGE_BAND</th>\n",
       "      <th>SERVICE_DURATION</th>\n",
       "      <th>DROPPED_CALLS</th>\n",
       "      <th>PEAK_CALLS_NO</th>\n",
       "      <th>PEAK_CALLS_TIME</th>\n",
       "      <th>WEEKEND_CALLS_NO</th>\n",
       "      <th>WEEKEND_CALLS_TIME</th>\n",
       "      <th>TOTAL_CALLS_NO</th>\n",
       "      <th>TOTAL_CALLS_TIME</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Active</td>\n",
       "      <td>K102990</td>\n",
       "      <td>F</td>\n",
       "      <td>31</td>\n",
       "      <td>CAT 100</td>\n",
       "      <td>ASAD170</td>\n",
       "      <td>Med</td>\n",
       "      <td>33.33</td>\n",
       "      <td>6</td>\n",
       "      <td>218</td>\n",
       "      <td>379.8</td>\n",
       "      <td>9</td>\n",
       "      <td>21.6</td>\n",
       "      <td>366</td>\n",
       "      <td>632.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Active</td>\n",
       "      <td>K103280</td>\n",
       "      <td>M</td>\n",
       "      <td>27</td>\n",
       "      <td>CAT 100</td>\n",
       "      <td>S50</td>\n",
       "      <td>Med</td>\n",
       "      <td>30.87</td>\n",
       "      <td>10</td>\n",
       "      <td>373</td>\n",
       "      <td>656.4</td>\n",
       "      <td>11</td>\n",
       "      <td>28.0</td>\n",
       "      <td>411</td>\n",
       "      <td>810.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Active</td>\n",
       "      <td>K103660</td>\n",
       "      <td>M</td>\n",
       "      <td>27</td>\n",
       "      <td>CAT 100</td>\n",
       "      <td>ASAD170</td>\n",
       "      <td>Med</td>\n",
       "      <td>49.40</td>\n",
       "      <td>11</td>\n",
       "      <td>260</td>\n",
       "      <td>582.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>265</td>\n",
       "      <td>614.1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Active</td>\n",
       "      <td>K103730</td>\n",
       "      <td>M</td>\n",
       "      <td>61</td>\n",
       "      <td>CAT 100</td>\n",
       "      <td>ASAD170</td>\n",
       "      <td>Med</td>\n",
       "      <td>48.60</td>\n",
       "      <td>1</td>\n",
       "      <td>294</td>\n",
       "      <td>661.8</td>\n",
       "      <td>14</td>\n",
       "      <td>101.5</td>\n",
       "      <td>326</td>\n",
       "      <td>844.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Active</td>\n",
       "      <td>K104560</td>\n",
       "      <td>F</td>\n",
       "      <td>52</td>\n",
       "      <td>CAT 100</td>\n",
       "      <td>S50</td>\n",
       "      <td>Med</td>\n",
       "      <td>56.03</td>\n",
       "      <td>0</td>\n",
       "      <td>221</td>\n",
       "      <td>555.6</td>\n",
       "      <td>7</td>\n",
       "      <td>25.0</td>\n",
       "      <td>274</td>\n",
       "      <td>673.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "    CHURN CUSTOMER_ID GENDER  AGE CHARGE_TYPE  HANDSET USAGE_BAND  \\\n",
       "0  Active     K102990      F   31     CAT 100  ASAD170        Med   \n",
       "1  Active     K103280      M   27     CAT 100      S50        Med   \n",
       "2  Active     K103660      M   27     CAT 100  ASAD170        Med   \n",
       "3  Active     K103730      M   61     CAT 100  ASAD170        Med   \n",
       "4  Active     K104560      F   52     CAT 100      S50        Med   \n",
       "\n",
       "   SERVICE_DURATION  DROPPED_CALLS  PEAK_CALLS_NO  PEAK_CALLS_TIME  \\\n",
       "0             33.33              6            218            379.8   \n",
       "1             30.87             10            373            656.4   \n",
       "2             49.40             11            260            582.0   \n",
       "3             48.60              1            294            661.8   \n",
       "4             56.03              0            221            555.6   \n",
       "\n",
       "   WEEKEND_CALLS_NO  WEEKEND_CALLS_TIME  TOTAL_CALLS_NO  TOTAL_CALLS_TIME  \n",
       "0                 9                21.6             366             632.4  \n",
       "1                11                28.0             411             810.4  \n",
       "2                 0                 0.0             265             614.1  \n",
       "3                14               101.5             326             844.0  \n",
       "4                 7                25.0             274             673.0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_raw = pd.read_csv(\"./data/통신고객이탈.csv\", encoding = '949')\n",
    "df_raw.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CHURN                 0\n",
       "CUSTOMER_ID           0\n",
       "GENDER                0\n",
       "AGE                   0\n",
       "CHARGE_TYPE           0\n",
       "HANDSET               0\n",
       "USAGE_BAND            0\n",
       "SERVICE_DURATION      0\n",
       "DROPPED_CALLS         0\n",
       "PEAK_CALLS_NO         0\n",
       "PEAK_CALLS_TIME       0\n",
       "WEEKEND_CALLS_NO      0\n",
       "WEEKEND_CALLS_TIME    0\n",
       "TOTAL_CALLS_NO        0\n",
       "TOTAL_CALLS_TIME      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_raw.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CHURN                  object\n",
       "CUSTOMER_ID            object\n",
       "GENDER                 object\n",
       "AGE                     int64\n",
       "CHARGE_TYPE            object\n",
       "HANDSET                object\n",
       "USAGE_BAND             object\n",
       "SERVICE_DURATION      float64\n",
       "DROPPED_CALLS           int64\n",
       "PEAK_CALLS_NO           int64\n",
       "PEAK_CALLS_TIME       float64\n",
       "WEEKEND_CALLS_NO        int64\n",
       "WEEKEND_CALLS_TIME    float64\n",
       "TOTAL_CALLS_NO          int64\n",
       "TOTAL_CALLS_TIME      float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_raw.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>K102990</td>\n",
       "      <td>F</td>\n",
       "      <td>31</td>\n",
       "      <td>CAT 100</td>\n",
       "      <td>ASAD170</td>\n",
       "      <td>Med</td>\n",
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       "      <td>CAT 100</td>\n",
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       "      <td>CAT 100</td>\n",
       "      <td>ASAD170</td>\n",
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       "      <td>49.40</td>\n",
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       "      <td>14</td>\n",
       "      <td>101.5</td>\n",
       "      <td>326</td>\n",
       "      <td>844.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>K104560</td>\n",
       "      <td>F</td>\n",
       "      <td>52</td>\n",
       "      <td>CAT 100</td>\n",
       "      <td>S50</td>\n",
       "      <td>Med</td>\n",
       "      <td>56.03</td>\n",
       "      <td>0</td>\n",
       "      <td>221</td>\n",
       "      <td>555.6</td>\n",
       "      <td>7</td>\n",
       "      <td>25.0</td>\n",
       "      <td>274</td>\n",
       "      <td>673.0</td>\n",
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      "text/plain": [
       "  CUSTOMER_ID GENDER  AGE CHARGE_TYPE  HANDSET USAGE_BAND  SERVICE_DURATION  \\\n",
       "0     K102990      F   31     CAT 100  ASAD170        Med             33.33   \n",
       "1     K103280      M   27     CAT 100      S50        Med             30.87   \n",
       "2     K103660      M   27     CAT 100  ASAD170        Med             49.40   \n",
       "3     K103730      M   61     CAT 100  ASAD170        Med             48.60   \n",
       "4     K104560      F   52     CAT 100      S50        Med             56.03   \n",
       "\n",
       "   DROPPED_CALLS  PEAK_CALLS_NO  PEAK_CALLS_TIME  WEEKEND_CALLS_NO  \\\n",
       "0              6            218            379.8                 9   \n",
       "1             10            373            656.4                11   \n",
       "2             11            260            582.0                 0   \n",
       "3              1            294            661.8                14   \n",
       "4              0            221            555.6                 7   \n",
       "\n",
       "   WEEKEND_CALLS_TIME  TOTAL_CALLS_NO  TOTAL_CALLS_TIME  \n",
       "0                21.6             366             632.4  \n",
       "1                28.0             411             810.4  \n",
       "2                 0.0             265             614.1  \n",
       "3               101.5             326             844.0  \n",
       "4                25.0             274             673.0  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_raw_y = df_raw['CHURN']\n",
    "df_raw_x = df_raw.drop(\"CHURN\", axis = 1, inplace = False)\n",
    "df_raw_x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
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       "      <td>27</td>\n",
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       "      <td>10</td>\n",
       "      <td>373</td>\n",
       "      <td>656.4</td>\n",
       "      <td>11</td>\n",
       "      <td>28.0</td>\n",
       "      <td>411</td>\n",
       "      <td>810.4</td>\n",
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       "      <td>49.40</td>\n",
       "      <td>11</td>\n",
       "      <td>260</td>\n",
       "      <td>582.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>265</td>\n",
       "      <td>614.1</td>\n",
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       "      <th>3</th>\n",
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       "      <td>48.60</td>\n",
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       "      <td>294</td>\n",
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       "      <th>4</th>\n",
       "      <td>52</td>\n",
       "      <td>56.03</td>\n",
       "      <td>0</td>\n",
       "      <td>221</td>\n",
       "      <td>555.6</td>\n",
       "      <td>7</td>\n",
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       "      <td>673.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 963 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   AGE  SERVICE_DURATION  DROPPED_CALLS  PEAK_CALLS_NO  PEAK_CALLS_TIME  \\\n",
       "0   31             33.33              6            218            379.8   \n",
       "1   27             30.87             10            373            656.4   \n",
       "2   27             49.40             11            260            582.0   \n",
       "3   61             48.60              1            294            661.8   \n",
       "4   52             56.03              0            221            555.6   \n",
       "\n",
       "   WEEKEND_CALLS_NO  WEEKEND_CALLS_TIME  TOTAL_CALLS_NO  TOTAL_CALLS_TIME  \\\n",
       "0                 9                21.6             366             632.4   \n",
       "1                11                28.0             411             810.4   \n",
       "2                 0                 0.0             265             614.1   \n",
       "3                14               101.5             326             844.0   \n",
       "4                 7                25.0             274             673.0   \n",
       "\n",
       "   CUSTOMER_ID_K100420  ...  HANDSET_S80  HANDSET_SOP10  HANDSET_SOP20  \\\n",
       "0                    0  ...            0              0              0   \n",
       "1                    0  ...            0              0              0   \n",
       "2                    0  ...            0              0              0   \n",
       "3                    0  ...            0              0              0   \n",
       "4                    0  ...            0              0              0   \n",
       "\n",
       "   HANDSET_WC95  USAGE_BAND_High  USAGE_BAND_Low  USAGE_BAND_Med  \\\n",
       "0             0                0               0               1   \n",
       "1             0                0               0               1   \n",
       "2             0                0               0               1   \n",
       "3             0                0               0               1   \n",
       "4             0                0               0               1   \n",
       "\n",
       "   USAGE_BAND_MedHigh  USAGE_BAND_MedLow  USAGE_BAND_None  \n",
       "0                   0                  0                0  \n",
       "1                   0                  0                0  \n",
       "2                   0                  0                0  \n",
       "3                   0                  0                0  \n",
       "4                   0                  0                0  \n",
       "\n",
       "[5 rows x 963 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_raw_dummy_x = pd.get_dummies(df_raw_x)\n",
    "df_raw_dummy.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train data X size : (651, 963)\n",
      "train data Y size : (651,)\n",
      "test data X size : (279, 963)\n",
      "test data Y size : (279,)\n"
     ]
    }
   ],
   "source": [
    "df_train_x, df_test_x, df_train_y, df_test_y = train_test_split(df_raw_dummy_x, df_raw_y\n",
    "                                                               , test_size = 0.3, random_state = 1234)\n",
    "print('train data X size : {}'.format(df_train_x.shape))\n",
    "print('train data Y size : {}'.format(df_train_y.shape))\n",
    "print('test data X size : {}'.format(df_test_x.shape))\n",
    "print('test data Y size : {}'.format(df_test_y.shape))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score on trainning set : 0.989\n",
      "Score on test set : 0.867\n"
     ]
    }
   ],
   "source": [
    "rf_uncustomized = RandomForestClassifier(random_state = 1234)\n",
    "rf_uncustomized.fit(df_train_x, df_train_y)\n",
    "\n",
    "print('Score on trainning set : {:.3f}'.format(rf_uncustomized.score(df_train_x, df_train_y)))\n",
    "print('Score on test set : {:.3f}'.format(rf_uncustomized.score(df_test_x, df_test_y)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=None,\n",
       "            oob_score=False, random_state=1234, verbose=0,\n",
       "            warm_start=False)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_uncustomized"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best estimator model: \n",
      "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
      "            max_depth=1, max_features='auto', max_leaf_nodes=None,\n",
      "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "            min_samples_leaf=10, min_samples_split=2,\n",
      "            min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=None,\n",
      "            oob_score=False, random_state=1234, verbose=0,\n",
      "            warm_start=False)\n",
      "\n",
      " best parameter: \n",
      "{'max_depth': 1, 'min_samples_leaf': 10}\n",
      "\n",
      " best score: \n",
      "0.791\n"
     ]
    }
   ],
   "source": [
    "para_depth = [n_leaf for n_leaf in range(1,15)]\n",
    "para_leaf = [n_leaf * 10 for n_leaf in range(1,15)]\n",
    "\n",
    "estimator = RandomForestClassifier(n_estimators=100, random_state=1234)\n",
    "param_grid = {'max_depth':para_depth,\n",
    "             'min_samples_leaf':para_leaf}\n",
    "\n",
    "\n",
    "grid_rf = GridSearchCV(estimator, param_grid, scoring='accuracy', n_jobs=-1)\n",
    "grid_rf.fit(df_train_x, df_train_y)\n",
    "print('best estimator model: \\n{}'.format(grid_rf.best_estimator_))\n",
    "print('\\n best parameter: \\n{}'.format(grid_rf.best_params_))\n",
    "print('\\n best score: \\n{}'.format(grid_rf.best_score_.round(3)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>n_estimators</th>\n",
       "      <th>TrainAccuracy</th>\n",
       "      <th>TestAccuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>0.989</td>\n",
       "      <td>0.867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
       "      <td>0.998</td>\n",
       "      <td>0.882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30</td>\n",
       "      <td>0.998</td>\n",
       "      <td>0.878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>60</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>70</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>80</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>90</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>100</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>110</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>120</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>130</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>140</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>150</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.896</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    n_estimators  TrainAccuracy  TestAccuracy\n",
       "0             10          0.989         0.867\n",
       "1             20          0.998         0.882\n",
       "2             30          0.998         0.878\n",
       "3             40          1.000         0.882\n",
       "4             50          1.000         0.892\n",
       "5             60          1.000         0.892\n",
       "6             70          1.000         0.896\n",
       "7             80          1.000         0.896\n",
       "8             90          1.000         0.892\n",
       "9            100          1.000         0.892\n",
       "10           110          1.000         0.892\n",
       "11           120          1.000         0.896\n",
       "12           130          1.000         0.892\n",
       "13           140          1.000         0.896\n",
       "14           150          1.000         0.896"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_accuracy = [];test_accuracy=[]\n",
    "para_n_tree = [n_tree * 10 for n_tree in range(1,16)]\n",
    "\n",
    "for v_n_estimators in para_n_tree:\n",
    "    rf = RandomForestClassifier(random_state = 1234, \n",
    "                                 n_estimators = v_n_estimators)\n",
    "    \n",
    "    rf.fit(df_train_x, df_train_y)\n",
    "    train_accuracy.append(rf.score(df_train_x, df_train_y))\n",
    "    test_accuracy.append(rf.score(df_test_x, df_test_y))\n",
    "    \n",
    "df_accuracy_n = pd.DataFrame()\n",
    "df_accuracy_n['n_estimators'] = para_n_tree\n",
    "df_accuracy_n['TrainAccuracy'] = train_accuracy\n",
    "df_accuracy_n['TestAccuracy'] = test_accuracy\n",
    "df_accuracy_n.round(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7ff5c717e400>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(para_n_tree, train_accuracy, linestyle = '-', label = 'Train Score')\n",
    "plt.plot(para_n_tree, test_accuracy, linestyle = '--', label = 'Test Score')\n",
    "plt.xlabel('n_estimators'); plt.ylabel('accuracy')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_estimators = 70"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Depth</th>\n",
       "      <th>TrainAccuracy</th>\n",
       "      <th>TestAccuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.791</td>\n",
       "      <td>0.842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.791</td>\n",
       "      <td>0.842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.791</td>\n",
       "      <td>0.842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.791</td>\n",
       "      <td>0.842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.793</td>\n",
       "      <td>0.842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0.800</td>\n",
       "      <td>0.842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0.808</td>\n",
       "      <td>0.846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>0.814</td>\n",
       "      <td>0.846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>0.822</td>\n",
       "      <td>0.849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>0.848</td>\n",
       "      <td>0.853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>0.873</td>\n",
       "      <td>0.857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>0.905</td>\n",
       "      <td>0.860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>0.912</td>\n",
       "      <td>0.889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>0.939</td>\n",
       "      <td>0.878</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Depth  TrainAccuracy  TestAccuracy\n",
       "0       1          0.791         0.842\n",
       "1       2          0.791         0.842\n",
       "2       3          0.791         0.842\n",
       "3       4          0.791         0.842\n",
       "4       5          0.793         0.842\n",
       "5       6          0.800         0.842\n",
       "6       7          0.808         0.846\n",
       "7       8          0.814         0.846\n",
       "8       9          0.822         0.849\n",
       "9      10          0.848         0.853\n",
       "10     11          0.873         0.857\n",
       "11     12          0.905         0.860\n",
       "12     13          0.912         0.889\n",
       "13     14          0.939         0.878"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_accuracy = [];test_accuracy=[]\n",
    "para_depth = [depth for depth in range(1,15)]\n",
    "\n",
    "for v_max_depth in para_depth:\n",
    "    tree = RandomForestClassifier(random_state = 1234, \n",
    "                                 n_estimators = 70,\n",
    "                                 max_depth = v_max_depth)\n",
    "    \n",
    "    tree.fit(df_train_x, df_train_y)\n",
    "    train_accuracy.append(tree.score(df_train_x, df_train_y))\n",
    "    test_accuracy.append(tree.score(df_test_x, df_test_y))\n",
    "    \n",
    "df_accuracy_depth = pd.DataFrame()\n",
    "df_accuracy_depth['Depth'] = para_depth\n",
    "df_accuracy_depth['TrainAccuracy'] = train_accuracy\n",
    "df_accuracy_depth['TestAccuracy'] = test_accuracy\n",
    "df_accuracy_depth.round(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7ff5c7003e80>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(para_depth, train_accuracy, linestyle = '-', label = 'Train Score')\n",
    "plt.plot(para_depth, test_accuracy, linestyle = '--', label = 'Test Score')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>split</th>\n",
       "      <th>TrainAccuracy</th>\n",
       "      <th>TestAccuracy</th>\n",
       "      <th>gap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>0.912</td>\n",
       "      <td>0.889</td>\n",
       "      <td>0.024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>0.912</td>\n",
       "      <td>0.875</td>\n",
       "      <td>0.038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>0.906</td>\n",
       "      <td>0.864</td>\n",
       "      <td>0.042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8</td>\n",
       "      <td>0.888</td>\n",
       "      <td>0.860</td>\n",
       "      <td>0.028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10</td>\n",
       "      <td>0.883</td>\n",
       "      <td>0.849</td>\n",
       "      <td>0.034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>12</td>\n",
       "      <td>0.883</td>\n",
       "      <td>0.849</td>\n",
       "      <td>0.034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>14</td>\n",
       "      <td>0.883</td>\n",
       "      <td>0.860</td>\n",
       "      <td>0.023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>16</td>\n",
       "      <td>0.880</td>\n",
       "      <td>0.864</td>\n",
       "      <td>0.016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>18</td>\n",
       "      <td>0.871</td>\n",
       "      <td>0.860</td>\n",
       "      <td>0.011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>20</td>\n",
       "      <td>0.856</td>\n",
       "      <td>0.860</td>\n",
       "      <td>-0.005</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   split  TrainAccuracy  TestAccuracy    gap\n",
       "0      2          0.912         0.889  0.024\n",
       "1      4          0.912         0.875  0.038\n",
       "2      6          0.906         0.864  0.042\n",
       "3      8          0.888         0.860  0.028\n",
       "4     10          0.883         0.849  0.034\n",
       "5     12          0.883         0.849  0.034\n",
       "6     14          0.883         0.860  0.023\n",
       "7     16          0.880         0.864  0.016\n",
       "8     18          0.871         0.860  0.011\n",
       "9     20          0.856         0.860 -0.005"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_accuracy = [];test_accuracy=[]\n",
    "para_split = [n_split*2 for n_split in range(1,11)]\n",
    "\n",
    "for v_min_samples_split in para_split:\n",
    "    rf = RandomForestClassifier(random_state = 1234, \n",
    "                                 n_estimators = 70,\n",
    "                                 max_depth = 13,\n",
    "                                 min_samples_split = v_min_samples_split)\n",
    "    \n",
    "    rf.fit(df_train_x, df_train_y)\n",
    "    train_accuracy.append(rf.score(df_train_x, df_train_y))\n",
    "    test_accuracy.append(rf.score(df_test_x, df_test_y))\n",
    "    \n",
    "df_accuracy_split = pd.DataFrame()\n",
    "df_accuracy_split['split'] = para_split\n",
    "df_accuracy_split['TrainAccuracy'] = train_accuracy\n",
    "df_accuracy_split['TestAccuracy'] = test_accuracy\n",
    "df_accuracy_split['gap'] = np.array(np.array(train_accuracy)-np.array(test_accuracy))\n",
    "df_accuracy_split.round(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7ff5c78f5cc0>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(para_split, train_accuracy, linestyle = '-', label = 'Train Score')\n",
    "plt.plot(para_split, test_accuracy, linestyle = '--', label = 'Test Score')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>leaf</th>\n",
       "      <th>TrainAccuracy</th>\n",
       "      <th>TestAccuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.880</td>\n",
       "      <td>0.864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.791</td>\n",
       "      <td>0.842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.791</td>\n",
       "      <td>0.842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.791</td>\n",
       "      <td>0.842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.791</td>\n",
       "      <td>0.842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0.791</td>\n",
       "      <td>0.842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0.791</td>\n",
       "      <td>0.842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>0.791</td>\n",
       "      <td>0.842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>0.791</td>\n",
       "      <td>0.842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>0.791</td>\n",
       "      <td>0.842</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   leaf  TrainAccuracy  TestAccuracy\n",
       "0     1          0.880         0.864\n",
       "1     2          0.791         0.842\n",
       "2     3          0.791         0.842\n",
       "3     4          0.791         0.842\n",
       "4     5          0.791         0.842\n",
       "5     6          0.791         0.842\n",
       "6     7          0.791         0.842\n",
       "7     8          0.791         0.842\n",
       "8     9          0.791         0.842\n",
       "9    10          0.791         0.842"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_accuracy = [];test_accuracy=[]\n",
    "para_leaf = [n_leaf for n_leaf in range(1,11)]\n",
    "\n",
    "for v_min_samples_leaf in para_leaf:\n",
    "    rf = RandomForestClassifier(random_state = 1234, \n",
    "                                 n_estimators = 70,\n",
    "                                 max_depth = 13,\n",
    "                                 min_samples_split = 16,\n",
    "                                 min_samples_leaf = v_min_samples_leaf)\n",
    "    \n",
    "    rf.fit(df_train_x, df_train_y)\n",
    "    train_accuracy.append(rf.score(df_train_x, df_train_y))\n",
    "    test_accuracy.append(rf.score(df_test_x, df_test_y))\n",
    "    \n",
    "df_accuracy_leaf = pd.DataFrame()\n",
    "df_accuracy_leaf['leaf'] = para_leaf\n",
    "df_accuracy_leaf['TrainAccuracy'] = train_accuracy\n",
    "df_accuracy_leaf['TestAccuracy'] = test_accuracy\n",
    "df_accuracy_leaf.round(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7ff5c6c7dfd0>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(para_leaf, train_accuracy, linestyle = '-', label = 'Train Score')\n",
    "plt.plot(para_leaf, test_accuracy, linestyle = '--', label = 'Test Score')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_final = RandomForestClassifier(random_state = 1234, \n",
    "                                 n_estimators = 100,\n",
    "                                 max_depth = 4,\n",
    "                                 min_samples_leaf = 20)\n",
    "rf_final.fit(df_train_x, df_train_y)\n",
    "y_pred = rf_final.predict(df_test_x)\n",
    "\n",
    "print('Accuacy on training set : {:.3f}'.format(rf_final.score(df_train_x, df_train_y)))\n",
    "print('Accuacy on ttest set : {:.3f}'.format(rf_final.score(df_test_x, df_test_y)))\n",
    "print('Confusion matrix : \\n{}'.format(confusion_matrix(df_test_y, y_pred)))\n"
   ]
  }
 ],
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