experiments/XGBClassifier_0.601.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "017e9265",
   "metadata": {},
   "source": [
    "### XGBClassifier(Version2) for Sales Success Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "a315cc58",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.metrics import (\n",
    "    accuracy_score,\n",
    "    confusion_matrix,\n",
    "    f1_score,\n",
    "    precision_score,\n",
    "    recall_score,\n",
    ")\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "import xgboost as xgb\n",
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "412f98cc",
   "metadata": {},
   "source": [
    "Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7ada941e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = pd.read_csv(\"train.csv\") \n",
    "df_test = pd.read_csv(\"submission.csv\") "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af526c13",
   "metadata": {},
   "source": [
    "Data Processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0b42b1a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def label_encoding(series: pd.Series) -> pd.Series:\n",
    "\n",
    "    my_dict = {}\n",
    "\n",
    "    series = series.astype(str)\n",
    "\n",
    "    for idx, value in enumerate(sorted(series.unique())):\n",
    "        my_dict[value] = idx\n",
    "    series = series.map(my_dict)\n",
    "\n",
    "    return series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "881a4612",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_country</th>\n",
       "      <th>business_subarea</th>\n",
       "      <th>business_area</th>\n",
       "      <th>business_unit</th>\n",
       "      <th>customer_type</th>\n",
       "      <th>enterprise</th>\n",
       "      <th>customer_job</th>\n",
       "      <th>inquiry_type</th>\n",
       "      <th>product_category</th>\n",
       "      <th>product_subcategory</th>\n",
       "      <th>product_modelname</th>\n",
       "      <th>customer_country.1</th>\n",
       "      <th>customer_position</th>\n",
       "      <th>response_corporate</th>\n",
       "      <th>expected_timeline</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>6535</td>\n",
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       "      <td>349</td>\n",
       "      <td>699</td>\n",
       "      <td>3388</td>\n",
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       "      <td>21</td>\n",
       "      <td>246</td>\n",
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       "      <th>4</th>\n",
       "      <td>5799</td>\n",
       "      <td>86</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>84</td>\n",
       "      <td>32</td>\n",
       "      <td>180</td>\n",
       "      <td>349</td>\n",
       "      <td>699</td>\n",
       "      <td>5799</td>\n",
       "      <td>80</td>\n",
       "      <td>21</td>\n",
       "      <td>246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <th>5266</th>\n",
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       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>468</td>\n",
       "      <td>30</td>\n",
       "      <td>183</td>\n",
       "      <td>349</td>\n",
       "      <td>699</td>\n",
       "      <td>10650</td>\n",
       "      <td>9</td>\n",
       "      <td>43</td>\n",
       "      <td>271</td>\n",
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       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>323</td>\n",
       "      <td>30</td>\n",
       "      <td>183</td>\n",
       "      <td>349</td>\n",
       "      <td>699</td>\n",
       "      <td>16640</td>\n",
       "      <td>74</td>\n",
       "      <td>50</td>\n",
       "      <td>271</td>\n",
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       "    <tr>\n",
       "      <th>5268</th>\n",
       "      <td>1079</td>\n",
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       "      <td>29</td>\n",
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       "      <td>58</td>\n",
       "      <td>180</td>\n",
       "      <td>349</td>\n",
       "      <td>699</td>\n",
       "      <td>1079</td>\n",
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       "      <td>246</td>\n",
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       "      <td>1</td>\n",
       "      <td>323</td>\n",
       "      <td>58</td>\n",
       "      <td>52</td>\n",
       "      <td>349</td>\n",
       "      <td>699</td>\n",
       "      <td>24</td>\n",
       "      <td>74</td>\n",
       "      <td>12</td>\n",
       "      <td>271</td>\n",
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       "    <tr>\n",
       "      <th>5270</th>\n",
       "      <td>1082</td>\n",
       "      <td>86</td>\n",
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       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>323</td>\n",
       "      <td>30</td>\n",
       "      <td>180</td>\n",
       "      <td>349</td>\n",
       "      <td>699</td>\n",
       "      <td>1082</td>\n",
       "      <td>74</td>\n",
       "      <td>21</td>\n",
       "      <td>271</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>64570 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      customer_country  business_subarea  business_area  business_unit  \\\n",
       "0                 9070                28              0              0   \n",
       "1                 8406                 0              0              0   \n",
       "2                 6535                17              0              0   \n",
       "3                 3388                44              0              0   \n",
       "4                 5799                86              0              0   \n",
       "...                ...               ...            ...            ...   \n",
       "5266             10650                86              6              0   \n",
       "5267             16640                86              6              3   \n",
       "5268              1079                86              6              0   \n",
       "5269                24                86              6              3   \n",
       "5270              1082                86              6              0   \n",
       "\n",
       "      customer_type  enterprise  customer_job  inquiry_type  product_category  \\\n",
       "0                10           0           420            32               180   \n",
       "1                10           0           303            32               180   \n",
       "2                10           0           160            27               238   \n",
       "3                10           0           166            32               318   \n",
       "4                29           0            84            32               180   \n",
       "...             ...         ...           ...           ...               ...   \n",
       "5266             33           0           468            30               183   \n",
       "5267             33           0           323            30               183   \n",
       "5268             29           0           166            58               180   \n",
       "5269              9           1           323            58                52   \n",
       "5270              9           0           323            30               180   \n",
       "\n",
       "      product_subcategory  product_modelname  customer_country.1  \\\n",
       "0                     349                699                9070   \n",
       "1                     349                699                8406   \n",
       "2                     349                699                6535   \n",
       "3                     349                699                3388   \n",
       "4                     349                699                5799   \n",
       "...                   ...                ...                 ...   \n",
       "5266                  349                699               10650   \n",
       "5267                  349                699               16640   \n",
       "5268                  349                699                1079   \n",
       "5269                  349                699                  24   \n",
       "5270                  349                699                1082   \n",
       "\n",
       "      customer_position  response_corporate  expected_timeline  \n",
       "0                    47                  33                246  \n",
       "1                    21                  33                246  \n",
       "2                    80                  21                246  \n",
       "3                    21                  21                246  \n",
       "4                    80                  21                246  \n",
       "...                 ...                 ...                ...  \n",
       "5266                  9                  43                271  \n",
       "5267                 74                  50                271  \n",
       "5268                 64                  43                246  \n",
       "5269                 74                  12                271  \n",
       "5270                 74                  21                271  \n",
       "\n",
       "[64570 rows x 15 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_columns = [\n",
    "    \"customer_country\",\n",
    "    \"business_subarea\",\n",
    "    \"business_area\",\n",
    "    \"business_unit\",\n",
    "    \"customer_type\",\n",
    "    \"enterprise\",\n",
    "    \"customer_job\",\n",
    "    \"inquiry_type\",\n",
    "    \"product_category\",\n",
    "    \"product_subcategory\",\n",
    "    \"product_modelname\",\n",
    "    \"customer_country.1\",\n",
    "    \"customer_position\",\n",
    "    \"response_corporate\",\n",
    "    \"expected_timeline\",\n",
    "]\n",
    "\n",
    "df_all = pd.concat([df_train[label_columns], df_test[label_columns]])\n",
    "\n",
    "for col in label_columns:\n",
    "    df_all[col] = label_encoding(df_all[col])\n",
    "    \n",
    "df_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "ff10c5fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in label_columns:  \n",
    "    df_train[col] = df_all.iloc[: len(df_train)][col]\n",
    "    df_test[col] = df_all.iloc[len(df_train) :][col]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "6e72b0e5",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 59299 entries, 0 to 59298\n",
      "Data columns (total 29 columns):\n",
      " #   Column                   Non-Null Count  Dtype  \n",
      "---  ------                   --------------  -----  \n",
      " 0   bant_submit              59299 non-null  float64\n",
      " 1   customer_country         59299 non-null  int64  \n",
      " 2   business_unit            59299 non-null  int64  \n",
      " 3   com_reg_ver_win_rate     14568 non-null  float64\n",
      " 4   customer_idx             59299 non-null  int64  \n",
      " 5   customer_type            59299 non-null  int64  \n",
      " 6   enterprise               59299 non-null  int64  \n",
      " 7   historical_existing_cnt  13756 non-null  float64\n",
      " 8   id_strategic_ver         3444 non-null   float64\n",
      " 9   it_strategic_ver         1121 non-null   float64\n",
      " 10  idit_strategic_ver       4565 non-null   float64\n",
      " 11  customer_job             59299 non-null  int64  \n",
      " 12  lead_desc_length         59299 non-null  int64  \n",
      " 13  inquiry_type             59299 non-null  int64  \n",
      " 14  product_category         59299 non-null  int64  \n",
      " 15  product_subcategory      59299 non-null  int64  \n",
      " 16  product_modelname        59299 non-null  int64  \n",
      " 17  customer_country.1       59299 non-null  int64  \n",
      " 18  customer_position        59299 non-null  int64  \n",
      " 19  response_corporate       59299 non-null  int64  \n",
      " 20  expected_timeline        59299 non-null  int64  \n",
      " 21  ver_cus                  59299 non-null  int64  \n",
      " 22  ver_pro                  59299 non-null  int64  \n",
      " 23  ver_win_rate_x           18417 non-null  float64\n",
      " 24  ver_win_ratio_per_bu     15304 non-null  float64\n",
      " 25  business_area            59299 non-null  int64  \n",
      " 26  business_subarea         59299 non-null  int64  \n",
      " 27  lead_owner               59299 non-null  int64  \n",
      " 28  is_converted             59299 non-null  bool   \n",
      "dtypes: bool(1), float64(8), int64(20)\n",
      "memory usage: 12.7 MB\n"
     ]
    }
   ],
   "source": [
    "df_train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "370373d4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 29850 entries, 0 to 27624\n",
      "Data columns (total 19 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   bant_submit           29850 non-null  float64\n",
      " 1   customer_country      29850 non-null  int64  \n",
      " 2   business_unit         29850 non-null  int64  \n",
      " 3   com_reg_ver_win_rate  29850 non-null  float64\n",
      " 4   customer_idx          29850 non-null  int64  \n",
      " 5   enterprise            29850 non-null  int64  \n",
      " 6   customer_job          29850 non-null  int64  \n",
      " 7   inquiry_type          29850 non-null  int64  \n",
      " 8   product_category      29850 non-null  int64  \n",
      " 9   customer_position     29850 non-null  int64  \n",
      " 10  response_corporate    29850 non-null  int64  \n",
      " 11  expected_timeline     29850 non-null  int64  \n",
      " 12  ver_cus               29850 non-null  int64  \n",
      " 13  ver_pro               29850 non-null  int64  \n",
      " 14  ver_win_rate_x        29850 non-null  float64\n",
      " 15  ver_win_ratio_per_bu  29850 non-null  float64\n",
      " 16  business_area         29850 non-null  int64  \n",
      " 17  lead_owner            29850 non-null  int64  \n",
      " 18  is_converted          29850 non-null  bool   \n",
      "dtypes: bool(1), float64(4), int64(14)\n",
      "memory usage: 4.4 MB\n"
     ]
    }
   ],
   "source": [
    "# Proceed with undersampling due to the large difference in T/F distribution of the is_converted column\n",
    "\n",
    "# Separate rows where is_converted is T/F\n",
    "df_train1 = df_train[df_train['is_converted'] == 1]\n",
    "df_train2 = df_train[df_train['is_converted'] == 0]\n",
    "\n",
    "# Select the first 25000 rows where is_converted is False (more rows)\n",
    "df_train2 = df_train2.iloc[:25000]\n",
    "#df_train2.info()\n",
    "\n",
    "# Combine approximately 4800 True rows and 25000 False rows\n",
    "df_train = pd.concat([df_train1, df_train2])\n",
    "\n",
    "# Remove columns\n",
    "df_train = df_train.drop(['customer_country.1', 'id_strategic_ver', 'it_strategic_ver', 'idit_strategic_ver', 'lead_desc_length',\n",
    "                         'customer_type', 'historical_existing_cnt', 'product_subcategory', \n",
    "                          'product_modelname', 'business_subarea'], axis=1)\n",
    "\n",
    "# Handle missing values in the dataset with approximately 30000 rows (using median or 0)\n",
    "df_train.loc[np.isnan(df_train.com_reg_ver_win_rate)==True,'com_reg_ver_win_rate'] = df_train.com_reg_ver_win_rate.median()\n",
    "df_train.loc[np.isnan(df_train.ver_win_rate_x)==True,'ver_win_rate_x'] = df_train.ver_win_rate_x.median()\n",
    "df_train.loc[np.isnan(df_train.ver_win_ratio_per_bu)==True,'ver_win_ratio_per_bu'] = df_train.ver_win_ratio_per_bu.median()\n",
    "\n",
    "df_train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "27c9e205",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_val, y_train, y_val = train_test_split(\n",
    "    df_train.drop(\"is_converted\", axis=1),\n",
    "    df_train[\"is_converted\"],\n",
    "    test_size=0.2,\n",
    "    shuffle=True,\n",
    "    random_state=400,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3caf257b",
   "metadata": {},
   "source": [
    "Model Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "e4509af0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#model = DecisionTreeClassifier()\n",
    "model = xgb.XGBClassifier(n_estimators=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "766d1980",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=100, n_jobs=None,\n",
       "              num_parallel_tree=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=100, n_jobs=None,\n",
       "              num_parallel_tree=None, random_state=None, ...)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=100, n_jobs=None,\n",
       "              num_parallel_tree=None, random_state=None, ...)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#model.fit(x_train.fillna(0), y_train)\n",
    "model.fit(x_train.fillna(0), y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "c8871444",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_clf_eval(y_test, y_pred=None):\n",
    "    confusion = confusion_matrix(y_test, y_pred, labels=[True, False])\n",
    "    accuracy = accuracy_score(y_test, y_pred)\n",
    "    precision = precision_score(y_test, y_pred, labels=[True, False])\n",
    "    recall = recall_score(y_test, y_pred)\n",
    "    F1 = f1_score(y_test, y_pred, labels=[True, False])\n",
    "\n",
    "    print(\"오차행렬:\\n\", confusion)\n",
    "    print(\"\\n정확도: {:.4f}\".format(accuracy))\n",
    "    print(\"정밀도: {:.4f}\".format(precision))\n",
    "    print(\"재현율: {:.4f}\".format(recall))\n",
    "    print(\"F1: {:.4f}\".format(F1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "56a86373",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "오차행렬:\n",
      " [[ 806  164]\n",
      " [  74 4926]]\n",
      "\n",
      "정확도: 0.9601\n",
      "정밀도: 0.9159\n",
      "재현율: 0.8309\n",
      "F1: 0.8714\n"
     ]
    }
   ],
   "source": [
    "pred = model.predict(x_val.fillna(0))\n",
    "get_clf_eval(y_val, pred)"
   ]
  }
 ],
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  "kernelspec": {
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