5.10. Log Feature Data Cleansing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "47a5ca0c",
   "metadata": {},
   "source": [
    "# <font color='CC3D3D'> Log Feature Data Clansing\n",
    "    \n",
    "### 1. Data Merge\n",
    "- <span style=\"color:green\">**Master_df.csv** </span>와 <span style=\"color:green\">**loan_with_new_feats.csv, loan_path_segment.csv** </span>를 Merge\n",
    "\n",
    "### 2. MissingValue Preprocessing\n",
    "- ['application_id','loanapply_insert_time','loan_rate','credit_score','loan_limit', 'existing_loan_amt','desired_amount','yearly_income','existing_loan_cnt']를 가지고 Knn Imputer를 가지고 결측치 대체\n",
    "- Segment 결측치에 대해서만 새로운 Segment 부여\n",
    "\n",
    "### 3. Deployment CSV\n",
    "- Clansing한 <span style=\"color:blue\">**log_feature_clansing.csv** </span>를 생성"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "edb9e228",
   "metadata": {},
   "source": [
    "# Import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4ab0a025",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from tqdm import tqdm\n",
    "\n",
    "from sklearn.impute import KNNImputer\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from pycaret.classification import *\n",
    "\n",
    "# Visualization\n",
    "import matplotlib.pylab as plt\n",
    "from matplotlib import font_manager, rc\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "# EDA\n",
    "import klib\n",
    "\n",
    "plt.rc(\"font\", family = 'Malgun Gothic')\n",
    "plt.rcParams[\"figure.figsize\"] = (12,8)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6379343",
   "metadata": {},
   "source": [
    "# Data Load"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "359da9fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "log_feature = pd.read_csv('../Data/loan_with_new_feats.csv')\n",
    "log_segment = pd.read_csv('../Data/loan_path_segment.csv')\n",
    "master_df = pd.read_csv('../Data/Master_df.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b6c649b9",
   "metadata": {},
   "outputs": [
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       "          application_id loanapply_insert_time  is_applied  user_id  \\\n",
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       "\n",
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     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "display(log_feature)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4fc7bb3c",
   "metadata": {},
   "outputs": [
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       "          application_id loanapply_insert_time  is_applied  user_id  labels\n",
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   "source": [
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   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "633e2532",
   "metadata": {},
   "outputs": [
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       "      <td>25000000.0</td>\n",
       "      <td>대환대출</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>15000000.0</td>\n",
       "      <td>2022.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>2022-06</td>\n",
       "      <td>1996.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2022-06-07</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1.0</td>\n",
       "      <td>39544504.0</td>\n",
       "      <td>0.0408</td>\n",
       "      <td>2347000.0</td>\n",
       "      <td>0.0224</td>\n",
       "      <td>0.000081</td>\n",
       "      <td>0.0174</td>\n",
       "      <td>2670.65</td>\n",
       "      <td>2679.57</td>\n",
       "      <td>2681.51</td>\n",
       "      <td>2663.00</td>\n",
       "      <td>562470000</td>\n",
       "      <td>0.0044</td>\n",
       "      <td>156</td>\n",
       "      <td>7.8</td>\n",
       "      <td>40세미만</td>\n",
       "      <td>200-300만원</td>\n",
       "      <td>89</td>\n",
       "      <td>94</td>\n",
       "      <td>85</td>\n",
       "      <td>103.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>92.17</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1748340</td>\n",
       "      <td>2022-06-07 13:05:41</td>\n",
       "      <td>25</td>\n",
       "      <td>169</td>\n",
       "      <td>24000000.0</td>\n",
       "      <td>17.9</td>\n",
       "      <td>430982.0</td>\n",
       "      <td>2022-06-07 13:05:39</td>\n",
       "      <td>620.0</td>\n",
       "      <td>24000000.0</td>\n",
       "      <td>EARNEDINCOME</td>\n",
       "      <td>정규직</td>\n",
       "      <td>자가</td>\n",
       "      <td>25000000.0</td>\n",
       "      <td>대환대출</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>15000000.0</td>\n",
       "      <td>2022.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>2022-06</td>\n",
       "      <td>1996.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2022-06-07</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1.0</td>\n",
       "      <td>39544504.0</td>\n",
       "      <td>0.0408</td>\n",
       "      <td>2347000.0</td>\n",
       "      <td>0.0224</td>\n",
       "      <td>0.000081</td>\n",
       "      <td>0.0174</td>\n",
       "      <td>2670.65</td>\n",
       "      <td>2679.57</td>\n",
       "      <td>2681.51</td>\n",
       "      <td>2663.00</td>\n",
       "      <td>562470000</td>\n",
       "      <td>0.0044</td>\n",
       "      <td>156</td>\n",
       "      <td>7.8</td>\n",
       "      <td>40세미만</td>\n",
       "      <td>200-300만원</td>\n",
       "      <td>89</td>\n",
       "      <td>94</td>\n",
       "      <td>85</td>\n",
       "      <td>103.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>92.17</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1748340</td>\n",
       "      <td>2022-06-07 13:05:41</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>24000000.0</td>\n",
       "      <td>18.5</td>\n",
       "      <td>430982.0</td>\n",
       "      <td>2022-06-07 13:05:39</td>\n",
       "      <td>620.0</td>\n",
       "      <td>24000000.0</td>\n",
       "      <td>EARNEDINCOME</td>\n",
       "      <td>정규직</td>\n",
       "      <td>자가</td>\n",
       "      <td>25000000.0</td>\n",
       "      <td>대환대출</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>15000000.0</td>\n",
       "      <td>2022.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>2022-06</td>\n",
       "      <td>1996.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2022-06-07</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1.0</td>\n",
       "      <td>39544504.0</td>\n",
       "      <td>0.0408</td>\n",
       "      <td>2347000.0</td>\n",
       "      <td>0.0224</td>\n",
       "      <td>0.000081</td>\n",
       "      <td>0.0174</td>\n",
       "      <td>2670.65</td>\n",
       "      <td>2679.57</td>\n",
       "      <td>2681.51</td>\n",
       "      <td>2663.00</td>\n",
       "      <td>562470000</td>\n",
       "      <td>0.0044</td>\n",
       "      <td>156</td>\n",
       "      <td>7.8</td>\n",
       "      <td>40세미만</td>\n",
       "      <td>200-300만원</td>\n",
       "      <td>89</td>\n",
       "      <td>94</td>\n",
       "      <td>85</td>\n",
       "      <td>103.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>92.17</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1748340</td>\n",
       "      <td>2022-06-07 13:05:41</td>\n",
       "      <td>4</td>\n",
       "      <td>268</td>\n",
       "      <td>29000000.0</td>\n",
       "      <td>10.8</td>\n",
       "      <td>430982.0</td>\n",
       "      <td>2022-06-07 13:05:39</td>\n",
       "      <td>620.0</td>\n",
       "      <td>24000000.0</td>\n",
       "      <td>EARNEDINCOME</td>\n",
       "      <td>정규직</td>\n",
       "      <td>자가</td>\n",
       "      <td>25000000.0</td>\n",
       "      <td>대환대출</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>15000000.0</td>\n",
       "      <td>2022.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>2022-06</td>\n",
       "      <td>1996.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2022-06-07</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1.0</td>\n",
       "      <td>39544504.0</td>\n",
       "      <td>0.0408</td>\n",
       "      <td>2347000.0</td>\n",
       "      <td>0.0224</td>\n",
       "      <td>0.000081</td>\n",
       "      <td>0.0174</td>\n",
       "      <td>2670.65</td>\n",
       "      <td>2679.57</td>\n",
       "      <td>2681.51</td>\n",
       "      <td>2663.00</td>\n",
       "      <td>562470000</td>\n",
       "      <td>0.0044</td>\n",
       "      <td>156</td>\n",
       "      <td>7.8</td>\n",
       "      <td>40세미만</td>\n",
       "      <td>200-300만원</td>\n",
       "      <td>89</td>\n",
       "      <td>94</td>\n",
       "      <td>85</td>\n",
       "      <td>103.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>92.17</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1748340</td>\n",
       "      <td>2022-06-07 13:05:41</td>\n",
       "      <td>11</td>\n",
       "      <td>118</td>\n",
       "      <td>5000000.0</td>\n",
       "      <td>16.4</td>\n",
       "      <td>430982.0</td>\n",
       "      <td>2022-06-07 13:05:39</td>\n",
       "      <td>620.0</td>\n",
       "      <td>24000000.0</td>\n",
       "      <td>EARNEDINCOME</td>\n",
       "      <td>정규직</td>\n",
       "      <td>자가</td>\n",
       "      <td>25000000.0</td>\n",
       "      <td>대환대출</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>15000000.0</td>\n",
       "      <td>2022.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>2022-06</td>\n",
       "      <td>1996.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2022-06-07</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1.0</td>\n",
       "      <td>39544504.0</td>\n",
       "      <td>0.0408</td>\n",
       "      <td>2347000.0</td>\n",
       "      <td>0.0224</td>\n",
       "      <td>0.000081</td>\n",
       "      <td>0.0174</td>\n",
       "      <td>2670.65</td>\n",
       "      <td>2679.57</td>\n",
       "      <td>2681.51</td>\n",
       "      <td>2663.00</td>\n",
       "      <td>562470000</td>\n",
       "      <td>0.0044</td>\n",
       "      <td>156</td>\n",
       "      <td>7.8</td>\n",
       "      <td>40세미만</td>\n",
       "      <td>200-300만원</td>\n",
       "      <td>89</td>\n",
       "      <td>94</td>\n",
       "      <td>85</td>\n",
       "      <td>103.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>92.17</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13527245</th>\n",
       "      <td>1428218</td>\n",
       "      <td>2022-06-03 12:01:58</td>\n",
       "      <td>62</td>\n",
       "      <td>200</td>\n",
       "      <td>3000000.0</td>\n",
       "      <td>14.8</td>\n",
       "      <td>478802.0</td>\n",
       "      <td>2022-06-03 12:01:47</td>\n",
       "      <td>610.0</td>\n",
       "      <td>45000000.0</td>\n",
       "      <td>EARNEDINCOME</td>\n",
       "      <td>정규직</td>\n",
       "      <td>기타가족소유</td>\n",
       "      <td>50000000.0</td>\n",
       "      <td>대환대출</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>56000000.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>2022-06</td>\n",
       "      <td>1965.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2022-06-03</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1.0</td>\n",
       "      <td>38376912.0</td>\n",
       "      <td>0.0134</td>\n",
       "      <td>2310990.0</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>0.000101</td>\n",
       "      <td>0.0202</td>\n",
       "      <td>2658.99</td>\n",
       "      <td>2670.74</td>\n",
       "      <td>2674.00</td>\n",
       "      <td>2653.59</td>\n",
       "      <td>525260000</td>\n",
       "      <td>-0.0100</td>\n",
       "      <td>68</td>\n",
       "      <td>1.8</td>\n",
       "      <td>50-60세</td>\n",
       "      <td>300-400만원</td>\n",
       "      <td>89</td>\n",
       "      <td>89</td>\n",
       "      <td>87</td>\n",
       "      <td>103.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>91.50</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13527246</th>\n",
       "      <td>1428218</td>\n",
       "      <td>2022-06-03 12:01:49</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>40000000.0</td>\n",
       "      <td>11.8</td>\n",
       "      <td>478802.0</td>\n",
       "      <td>2022-06-03 12:01:47</td>\n",
       "      <td>610.0</td>\n",
       "      <td>45000000.0</td>\n",
       "      <td>EARNEDINCOME</td>\n",
       "      <td>정규직</td>\n",
       "      <td>기타가족소유</td>\n",
       "      <td>50000000.0</td>\n",
       "      <td>대환대출</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>56000000.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>2022-06</td>\n",
       "      <td>1965.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2022-06-03</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1.0</td>\n",
       "      <td>38376912.0</td>\n",
       "      <td>0.0134</td>\n",
       "      <td>2310990.0</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>0.000101</td>\n",
       "      <td>0.0202</td>\n",
       "      <td>2658.99</td>\n",
       "      <td>2670.74</td>\n",
       "      <td>2674.00</td>\n",
       "      <td>2653.59</td>\n",
       "      <td>525260000</td>\n",
       "      <td>-0.0100</td>\n",
       "      <td>68</td>\n",
       "      <td>1.8</td>\n",
       "      <td>50-60세</td>\n",
       "      <td>300-400만원</td>\n",
       "      <td>89</td>\n",
       "      <td>89</td>\n",
       "      <td>87</td>\n",
       "      <td>103.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>91.50</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13527247</th>\n",
       "      <td>1428218</td>\n",
       "      <td>2022-06-03 12:01:48</td>\n",
       "      <td>32</td>\n",
       "      <td>257</td>\n",
       "      <td>15000000.0</td>\n",
       "      <td>7.2</td>\n",
       "      <td>478802.0</td>\n",
       "      <td>2022-06-03 12:01:47</td>\n",
       "      <td>610.0</td>\n",
       "      <td>45000000.0</td>\n",
       "      <td>EARNEDINCOME</td>\n",
       "      <td>정규직</td>\n",
       "      <td>기타가족소유</td>\n",
       "      <td>50000000.0</td>\n",
       "      <td>대환대출</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>56000000.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>2022-06</td>\n",
       "      <td>1965.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2022-06-03</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1.0</td>\n",
       "      <td>38376912.0</td>\n",
       "      <td>0.0134</td>\n",
       "      <td>2310990.0</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>0.000101</td>\n",
       "      <td>0.0202</td>\n",
       "      <td>2658.99</td>\n",
       "      <td>2670.74</td>\n",
       "      <td>2674.00</td>\n",
       "      <td>2653.59</td>\n",
       "      <td>525260000</td>\n",
       "      <td>-0.0100</td>\n",
       "      <td>68</td>\n",
       "      <td>1.8</td>\n",
       "      <td>50-60세</td>\n",
       "      <td>300-400만원</td>\n",
       "      <td>89</td>\n",
       "      <td>89</td>\n",
       "      <td>87</td>\n",
       "      <td>103.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>91.50</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13527248</th>\n",
       "      <td>1428218</td>\n",
       "      <td>2022-06-03 12:01:48</td>\n",
       "      <td>33</td>\n",
       "      <td>110</td>\n",
       "      <td>44000000.0</td>\n",
       "      <td>13.5</td>\n",
       "      <td>478802.0</td>\n",
       "      <td>2022-06-03 12:01:47</td>\n",
       "      <td>610.0</td>\n",
       "      <td>45000000.0</td>\n",
       "      <td>EARNEDINCOME</td>\n",
       "      <td>정규직</td>\n",
       "      <td>기타가족소유</td>\n",
       "      <td>50000000.0</td>\n",
       "      <td>대환대출</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>56000000.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>2022-06</td>\n",
       "      <td>1965.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2022-06-03</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1.0</td>\n",
       "      <td>38376912.0</td>\n",
       "      <td>0.0134</td>\n",
       "      <td>2310990.0</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>0.000101</td>\n",
       "      <td>0.0202</td>\n",
       "      <td>2658.99</td>\n",
       "      <td>2670.74</td>\n",
       "      <td>2674.00</td>\n",
       "      <td>2653.59</td>\n",
       "      <td>525260000</td>\n",
       "      <td>-0.0100</td>\n",
       "      <td>68</td>\n",
       "      <td>1.8</td>\n",
       "      <td>50-60세</td>\n",
       "      <td>300-400만원</td>\n",
       "      <td>89</td>\n",
       "      <td>89</td>\n",
       "      <td>87</td>\n",
       "      <td>103.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>91.50</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13527249</th>\n",
       "      <td>1428218</td>\n",
       "      <td>2022-06-03 12:01:48</td>\n",
       "      <td>5</td>\n",
       "      <td>194</td>\n",
       "      <td>44000000.0</td>\n",
       "      <td>9.7</td>\n",
       "      <td>478802.0</td>\n",
       "      <td>2022-06-03 12:01:47</td>\n",
       "      <td>610.0</td>\n",
       "      <td>45000000.0</td>\n",
       "      <td>EARNEDINCOME</td>\n",
       "      <td>정규직</td>\n",
       "      <td>기타가족소유</td>\n",
       "      <td>50000000.0</td>\n",
       "      <td>대환대출</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>56000000.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>2022-06</td>\n",
       "      <td>1965.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2022-06-03</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1.0</td>\n",
       "      <td>38376912.0</td>\n",
       "      <td>0.0134</td>\n",
       "      <td>2310990.0</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>0.000101</td>\n",
       "      <td>0.0202</td>\n",
       "      <td>2658.99</td>\n",
       "      <td>2670.74</td>\n",
       "      <td>2674.00</td>\n",
       "      <td>2653.59</td>\n",
       "      <td>525260000</td>\n",
       "      <td>-0.0100</td>\n",
       "      <td>68</td>\n",
       "      <td>1.8</td>\n",
       "      <td>50-60세</td>\n",
       "      <td>300-400만원</td>\n",
       "      <td>89</td>\n",
       "      <td>89</td>\n",
       "      <td>87</td>\n",
       "      <td>103.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>91.50</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13527250 rows × 51 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          application_id loanapply_insert_time  bank_id  product_id  \\\n",
       "0                1748340   2022-06-07 13:05:41        7         191   \n",
       "1                1748340   2022-06-07 13:05:41       25         169   \n",
       "2                1748340   2022-06-07 13:05:41        2           7   \n",
       "3                1748340   2022-06-07 13:05:41        4         268   \n",
       "4                1748340   2022-06-07 13:05:41       11         118   \n",
       "...                  ...                   ...      ...         ...   \n",
       "13527245         1428218   2022-06-03 12:01:58       62         200   \n",
       "13527246         1428218   2022-06-03 12:01:49        2           7   \n",
       "13527247         1428218   2022-06-03 12:01:48       32         257   \n",
       "13527248         1428218   2022-06-03 12:01:48       33         110   \n",
       "13527249         1428218   2022-06-03 12:01:48        5         194   \n",
       "\n",
       "          loan_limit  loan_rate   user_id          insert_time  credit_score  \\\n",
       "0         42000000.0       13.6  430982.0  2022-06-07 13:05:39         620.0   \n",
       "1         24000000.0       17.9  430982.0  2022-06-07 13:05:39         620.0   \n",
       "2         24000000.0       18.5  430982.0  2022-06-07 13:05:39         620.0   \n",
       "3         29000000.0       10.8  430982.0  2022-06-07 13:05:39         620.0   \n",
       "4          5000000.0       16.4  430982.0  2022-06-07 13:05:39         620.0   \n",
       "...              ...        ...       ...                  ...           ...   \n",
       "13527245   3000000.0       14.8  478802.0  2022-06-03 12:01:47         610.0   \n",
       "13527246  40000000.0       11.8  478802.0  2022-06-03 12:01:47         610.0   \n",
       "13527247  15000000.0        7.2  478802.0  2022-06-03 12:01:47         610.0   \n",
       "13527248  44000000.0       13.5  478802.0  2022-06-03 12:01:47         610.0   \n",
       "13527249  44000000.0        9.7  478802.0  2022-06-03 12:01:47         610.0   \n",
       "\n",
       "          yearly_income   income_type employment_type houseown_type  \\\n",
       "0            24000000.0  EARNEDINCOME             정규직            자가   \n",
       "1            24000000.0  EARNEDINCOME             정규직            자가   \n",
       "2            24000000.0  EARNEDINCOME             정규직            자가   \n",
       "3            24000000.0  EARNEDINCOME             정규직            자가   \n",
       "4            24000000.0  EARNEDINCOME             정규직            자가   \n",
       "...                 ...           ...             ...           ...   \n",
       "13527245     45000000.0  EARNEDINCOME             정규직        기타가족소유   \n",
       "13527246     45000000.0  EARNEDINCOME             정규직        기타가족소유   \n",
       "13527247     45000000.0  EARNEDINCOME             정규직        기타가족소유   \n",
       "13527248     45000000.0  EARNEDINCOME             정규직        기타가족소유   \n",
       "13527249     45000000.0  EARNEDINCOME             정규직        기타가족소유   \n",
       "\n",
       "          desired_amount purpose  personal_rehabilitation_yn  \\\n",
       "0             25000000.0    대환대출                         0.0   \n",
       "1             25000000.0    대환대출                         0.0   \n",
       "2             25000000.0    대환대출                         0.0   \n",
       "3             25000000.0    대환대출                         0.0   \n",
       "4             25000000.0    대환대출                         0.0   \n",
       "...                  ...     ...                         ...   \n",
       "13527245      50000000.0    대환대출                         0.0   \n",
       "13527246      50000000.0    대환대출                         0.0   \n",
       "13527247      50000000.0    대환대출                         0.0   \n",
       "13527248      50000000.0    대환대출                         0.0   \n",
       "13527249      50000000.0    대환대출                         0.0   \n",
       "\n",
       "          personal_rehabilitation_complete_yn  existing_loan_cnt  \\\n",
       "0                                         0.0                2.0   \n",
       "1                                         0.0                2.0   \n",
       "2                                         0.0                2.0   \n",
       "3                                         0.0                2.0   \n",
       "4                                         0.0                2.0   \n",
       "...                                       ...                ...   \n",
       "13527245                                  0.0                2.0   \n",
       "13527246                                  0.0                2.0   \n",
       "13527247                                  0.0                2.0   \n",
       "13527248                                  0.0                2.0   \n",
       "13527249                                  0.0                2.0   \n",
       "\n",
       "          existing_loan_amt  enter_year  enter_month  enter_birth    month  \\\n",
       "0                15000000.0      2022.0          2.0         26.0  2022-06   \n",
       "1                15000000.0      2022.0          2.0         26.0  2022-06   \n",
       "2                15000000.0      2022.0          2.0         26.0  2022-06   \n",
       "3                15000000.0      2022.0          2.0         26.0  2022-06   \n",
       "4                15000000.0      2022.0          2.0         26.0  2022-06   \n",
       "...                     ...         ...          ...          ...      ...   \n",
       "13527245         56000000.0      2019.0          3.0         54.0  2022-06   \n",
       "13527246         56000000.0      2019.0          3.0         54.0  2022-06   \n",
       "13527247         56000000.0      2019.0          3.0         54.0  2022-06   \n",
       "13527248         56000000.0      2019.0          3.0         54.0  2022-06   \n",
       "13527249         56000000.0      2019.0          3.0         54.0  2022-06   \n",
       "\n",
       "          birth_year  gender   month_day  k_lending_rate  us_lending_rate  \\\n",
       "0             1996.0     1.0  2022-06-07            1.75              1.0   \n",
       "1             1996.0     1.0  2022-06-07            1.75              1.0   \n",
       "2             1996.0     1.0  2022-06-07            1.75              1.0   \n",
       "3             1996.0     1.0  2022-06-07            1.75              1.0   \n",
       "4             1996.0     1.0  2022-06-07            1.75              1.0   \n",
       "...              ...     ...         ...             ...              ...   \n",
       "13527245      1965.0     1.0  2022-06-03            1.75              1.0   \n",
       "13527246      1965.0     1.0  2022-06-03            1.75              1.0   \n",
       "13527247      1965.0     1.0  2022-06-03            1.75              1.0   \n",
       "13527248      1965.0     1.0  2022-06-03            1.75              1.0   \n",
       "13527249      1965.0     1.0  2022-06-03            1.75              1.0   \n",
       "\n",
       "           btc_price  btc_variance  eth_price  eth_variance  lunc_price  \\\n",
       "0         39544504.0        0.0408  2347000.0        0.0224    0.000081   \n",
       "1         39544504.0        0.0408  2347000.0        0.0224    0.000081   \n",
       "2         39544504.0        0.0408  2347000.0        0.0224    0.000081   \n",
       "3         39544504.0        0.0408  2347000.0        0.0224    0.000081   \n",
       "4         39544504.0        0.0408  2347000.0        0.0224    0.000081   \n",
       "...              ...           ...        ...           ...         ...   \n",
       "13527245  38376912.0        0.0134  2310990.0        0.0005    0.000101   \n",
       "13527246  38376912.0        0.0134  2310990.0        0.0005    0.000101   \n",
       "13527247  38376912.0        0.0134  2310990.0        0.0005    0.000101   \n",
       "13527248  38376912.0        0.0134  2310990.0        0.0005    0.000101   \n",
       "13527249  38376912.0        0.0134  2310990.0        0.0005    0.000101   \n",
       "\n",
       "          lunc_variance    Close     Open     High      Low     Volume  \\\n",
       "0                0.0174  2670.65  2679.57  2681.51  2663.00  562470000   \n",
       "1                0.0174  2670.65  2679.57  2681.51  2663.00  562470000   \n",
       "2                0.0174  2670.65  2679.57  2681.51  2663.00  562470000   \n",
       "3                0.0174  2670.65  2679.57  2681.51  2663.00  562470000   \n",
       "4                0.0174  2670.65  2679.57  2681.51  2663.00  562470000   \n",
       "...                 ...      ...      ...      ...      ...        ...   \n",
       "13527245         0.0202  2658.99  2670.74  2674.00  2653.59  525260000   \n",
       "13527246         0.0202  2658.99  2670.74  2674.00  2653.59  525260000   \n",
       "13527247         0.0202  2658.99  2670.74  2674.00  2653.59  525260000   \n",
       "13527248         0.0202  2658.99  2670.74  2674.00  2653.59  525260000   \n",
       "13527249         0.0202  2658.99  2670.74  2674.00  2653.59  525260000   \n",
       "\n",
       "          Change  실업자  실업률     연령대       소득수준  현재생활형편CSI_성별  현재생활형편CSI_나이  \\\n",
       "0         0.0044  156  7.8   40세미만  200-300만원            89            94   \n",
       "1         0.0044  156  7.8   40세미만  200-300만원            89            94   \n",
       "2         0.0044  156  7.8   40세미만  200-300만원            89            94   \n",
       "3         0.0044  156  7.8   40세미만  200-300만원            89            94   \n",
       "4         0.0044  156  7.8   40세미만  200-300만원            89            94   \n",
       "...          ...  ...  ...     ...        ...           ...           ...   \n",
       "13527245 -0.0100   68  1.8  50-60세  300-400만원            89            89   \n",
       "13527246 -0.0100   68  1.8  50-60세  300-400만원            89            89   \n",
       "13527247 -0.0100   68  1.8  50-60세  300-400만원            89            89   \n",
       "13527248 -0.0100   68  1.8  50-60세  300-400만원            89            89   \n",
       "13527249 -0.0100   68  1.8  50-60세  300-400만원            89            89   \n",
       "\n",
       "          현재생활형편CSI_소득  현재가계부채CSI  가계부채전망CSI  소비심리지수  is_applied  \n",
       "0                   85      103.0      100.0   92.17         NaN  \n",
       "1                   85      103.0      100.0   92.17         NaN  \n",
       "2                   85      103.0      100.0   92.17         NaN  \n",
       "3                   85      103.0      100.0   92.17         NaN  \n",
       "4                   85      103.0      100.0   92.17         NaN  \n",
       "...                ...        ...        ...     ...         ...  \n",
       "13527245            87      103.0      100.0   91.50         NaN  \n",
       "13527246            87      103.0      100.0   91.50         NaN  \n",
       "13527247            87      103.0      100.0   91.50         NaN  \n",
       "13527248            87      103.0      100.0   91.50         NaN  \n",
       "13527249            87      103.0      100.0   91.50         NaN  \n",
       "\n",
       "[13527250 rows x 51 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(master_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "309e4933",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "application_id                 0\n",
       "loanapply_insert_time          0\n",
       "is_applied               3257239\n",
       "user_id                        0\n",
       "date_cd                        0\n",
       "event                     703483\n",
       "loan_cnt                  703483\n",
       "duration                  703483\n",
       "visit                     703483\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_feature.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c37b1d35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "application_id                0\n",
       "loanapply_insert_time         0\n",
       "is_applied               111972\n",
       "user_id                       0\n",
       "labels                   596630\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_segment.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a96b915b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "application_id                               0\n",
       "loanapply_insert_time                        0\n",
       "bank_id                                      0\n",
       "product_id                                   0\n",
       "loan_limit                                   0\n",
       "loan_rate                                    0\n",
       "user_id                                      0\n",
       "insert_time                                  0\n",
       "credit_score                                 0\n",
       "yearly_income                                0\n",
       "income_type                                  0\n",
       "employment_type                              0\n",
       "houseown_type                                0\n",
       "desired_amount                               0\n",
       "purpose                                      0\n",
       "personal_rehabilitation_yn                   0\n",
       "personal_rehabilitation_complete_yn          0\n",
       "existing_loan_cnt                            0\n",
       "existing_loan_amt                            0\n",
       "enter_year                                   0\n",
       "enter_month                                  0\n",
       "enter_birth                                  0\n",
       "month                                        0\n",
       "birth_year                                   0\n",
       "gender                                       0\n",
       "month_day                                    0\n",
       "k_lending_rate                               0\n",
       "us_lending_rate                              0\n",
       "btc_price                                    0\n",
       "btc_variance                                 0\n",
       "eth_price                                    0\n",
       "eth_variance                                 0\n",
       "lunc_price                                   0\n",
       "lunc_variance                                0\n",
       "Close                                        0\n",
       "Open                                         0\n",
       "High                                         0\n",
       "Low                                          0\n",
       "Volume                                       0\n",
       "Change                                       0\n",
       "실업자                                          0\n",
       "실업률                                          0\n",
       "연령대                                          0\n",
       "소득수준                                         0\n",
       "현재생활형편CSI_성별                                 0\n",
       "현재생활형편CSI_나이                                 0\n",
       "현재생활형편CSI_소득                                 0\n",
       "현재가계부채CSI                                    0\n",
       "가계부채전망CSI                                    0\n",
       "소비심리지수                                       0\n",
       "is_applied                             3257239\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master_df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c6ce122c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 13527250 entries, 0 to 13527249\n",
      "Data columns (total 9 columns):\n",
      " #   Column                 Dtype  \n",
      "---  ------                 -----  \n",
      " 0   application_id         int64  \n",
      " 1   loanapply_insert_time  object \n",
      " 2   is_applied             float64\n",
      " 3   user_id                int64  \n",
      " 4   date_cd                object \n",
      " 5   event                  float64\n",
      " 6   loan_cnt               float64\n",
      " 7   duration               float64\n",
      " 8   visit                  float64\n",
      "dtypes: float64(5), int64(2), object(2)\n",
      "memory usage: 928.8+ MB\n"
     ]
    }
   ],
   "source": [
    "log_feature.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8ee098b0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 13545171 entries, 0 to 13545170\n",
      "Data columns (total 5 columns):\n",
      " #   Column                 Dtype  \n",
      "---  ------                 -----  \n",
      " 0   application_id         int64  \n",
      " 1   loanapply_insert_time  object \n",
      " 2   is_applied             float64\n",
      " 3   user_id                int64  \n",
      " 4   labels                 float64\n",
      "dtypes: float64(2), int64(2), object(1)\n",
      "memory usage: 516.7+ MB\n"
     ]
    }
   ],
   "source": [
    "log_segment.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "db45871e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 13527250 entries, 0 to 13527249\n",
      "Data columns (total 51 columns):\n",
      " #   Column                               Dtype  \n",
      "---  ------                               -----  \n",
      " 0   application_id                       int64  \n",
      " 1   loanapply_insert_time                object \n",
      " 2   bank_id                              int64  \n",
      " 3   product_id                           int64  \n",
      " 4   loan_limit                           float64\n",
      " 5   loan_rate                            float64\n",
      " 6   user_id                              float64\n",
      " 7   insert_time                          object \n",
      " 8   credit_score                         float64\n",
      " 9   yearly_income                        float64\n",
      " 10  income_type                          object \n",
      " 11  employment_type                      object \n",
      " 12  houseown_type                        object \n",
      " 13  desired_amount                       float64\n",
      " 14  purpose                              object \n",
      " 15  personal_rehabilitation_yn           float64\n",
      " 16  personal_rehabilitation_complete_yn  float64\n",
      " 17  existing_loan_cnt                    float64\n",
      " 18  existing_loan_amt                    float64\n",
      " 19  enter_year                           float64\n",
      " 20  enter_month                          float64\n",
      " 21  enter_birth                          float64\n",
      " 22  month                                object \n",
      " 23  birth_year                           float64\n",
      " 24  gender                               float64\n",
      " 25  month_day                            object \n",
      " 26  k_lending_rate                       float64\n",
      " 27  us_lending_rate                      float64\n",
      " 28  btc_price                            float64\n",
      " 29  btc_variance                         float64\n",
      " 30  eth_price                            float64\n",
      " 31  eth_variance                         float64\n",
      " 32  lunc_price                           float64\n",
      " 33  lunc_variance                        float64\n",
      " 34  Close                                float64\n",
      " 35  Open                                 float64\n",
      " 36  High                                 float64\n",
      " 37  Low                                  float64\n",
      " 38  Volume                               int64  \n",
      " 39  Change                               float64\n",
      " 40  실업자                                  int64  \n",
      " 41  실업률                                  float64\n",
      " 42  연령대                                  object \n",
      " 43  소득수준                                 object \n",
      " 44  현재생활형편CSI_성별                         int64  \n",
      " 45  현재생활형편CSI_나이                         int64  \n",
      " 46  현재생활형편CSI_소득                         int64  \n",
      " 47  현재가계부채CSI                            float64\n",
      " 48  가계부채전망CSI                            float64\n",
      " 49  소비심리지수                               float64\n",
      " 50  is_applied                           float64\n",
      "dtypes: float64(33), int64(8), object(10)\n",
      "memory usage: 5.1+ GB\n"
     ]
    }
   ],
   "source": [
    "master_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20ed4381",
   "metadata": {},
   "source": [
    "# Data Merge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f47ce37c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>application_id</th>\n",
       "      <th>loanapply_insert_time</th>\n",
       "      <th>is_applied</th>\n",
       "      <th>user_id</th>\n",
       "      <th>date_cd</th>\n",
       "      <th>event</th>\n",
       "      <th>loan_cnt</th>\n",
       "      <th>duration</th>\n",
       "      <th>visit</th>\n",
       "      <th>labels</th>\n",
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       "      <td>3.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>2022-05-21 23:41:14</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>2022-05-21 23:41:15</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>3.0</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",
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       "    <tr>\n",
       "      <th>4565098</th>\n",
       "      <td>1543548</td>\n",
       "      <td>2022-05-10 20:40:54</td>\n",
       "      <td>0.0</td>\n",
       "      <td>879697</td>\n",
       "      <td>2022-05-10 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4565099</th>\n",
       "      <td>1543548</td>\n",
       "      <td>2022-05-10 20:40:55</td>\n",
       "      <td>0.0</td>\n",
       "      <td>879697</td>\n",
       "      <td>2022-05-10 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4565100</th>\n",
       "      <td>1543548</td>\n",
       "      <td>2022-05-10 20:41:07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>879697</td>\n",
       "      <td>2022-05-10 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4565101</th>\n",
       "      <td>1543548</td>\n",
       "      <td>2022-05-10 20:41:10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>879697</td>\n",
       "      <td>2022-05-10 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>879697</td>\n",
       "      <td>2022-05-10 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4565103 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         application_id loanapply_insert_time  is_applied  user_id  \\\n",
       "0                248576   2022-05-21 23:41:09         0.0        9   \n",
       "1                248576   2022-05-21 23:41:10         0.0        9   \n",
       "2                248576   2022-05-21 23:41:11         0.0        9   \n",
       "3                248576   2022-05-21 23:41:14         0.0        9   \n",
       "4                248576   2022-05-21 23:41:15         0.0        9   \n",
       "...                 ...                   ...         ...      ...   \n",
       "4565098         1543548   2022-05-10 20:40:54         0.0   879697   \n",
       "4565099         1543548   2022-05-10 20:40:55         0.0   879697   \n",
       "4565100         1543548   2022-05-10 20:41:07         0.0   879697   \n",
       "4565101         1543548   2022-05-10 20:41:10         0.0   879697   \n",
       "4565102         1543548   2022-05-10 20:41:11         0.0   879697   \n",
       "\n",
       "                     date_cd  event  loan_cnt  duration  visit  labels  \n",
       "0        2022-05-21 00:00:00    3.0       0.0     354.0    1.0     3.0  \n",
       "1        2022-05-21 00:00:00    3.0       0.0     354.0    1.0     3.0  \n",
       "2        2022-05-21 00:00:00    3.0       0.0     354.0    1.0     3.0  \n",
       "3        2022-05-21 00:00:00    3.0       0.0     354.0    1.0     3.0  \n",
       "4        2022-05-21 00:00:00    3.0       0.0     354.0    1.0     3.0  \n",
       "...                      ...    ...       ...       ...    ...     ...  \n",
       "4565098  2022-05-10 00:00:00    NaN       NaN       NaN    NaN     NaN  \n",
       "4565099  2022-05-10 00:00:00    NaN       NaN       NaN    NaN     NaN  \n",
       "4565100  2022-05-10 00:00:00    NaN       NaN       NaN    NaN     NaN  \n",
       "4565101  2022-05-10 00:00:00    NaN       NaN       NaN    NaN     NaN  \n",
       "4565102  2022-05-10 00:00:00    NaN       NaN       NaN    NaN     NaN  \n",
       "\n",
       "[4565103 rows x 10 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_df = pd.merge(log_feature,log_segment[['application_id','loanapply_insert_time','labels']],\n",
    "         on=['application_id','loanapply_insert_time'],how='left').drop_duplicates().reset_index(drop=True);log_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "bab286a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "log_df.drop(['is_applied','user_id','date_cd'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d6e7e575",
   "metadata": {},
   "outputs": [],
   "source": [
    "log_df.columns = ['application_id','loanapply_insert_time','event','loan_cnt','duration','visit','segment']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "cfd52ab1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>application_id</th>\n",
       "      <th>loanapply_insert_time</th>\n",
       "      <th>event</th>\n",
       "      <th>loan_cnt</th>\n",
       "      <th>duration</th>\n",
       "      <th>visit</th>\n",
       "      <th>segment</th>\n",
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       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>248576</td>\n",
       "      <td>2022-05-21 23:41:15</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>354.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <th>4565098</th>\n",
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       "      <td>2022-05-10 20:40:54</td>\n",
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       "      <th>4565099</th>\n",
       "      <td>1543548</td>\n",
       "      <td>2022-05-10 20:40:55</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4565100</th>\n",
       "      <td>1543548</td>\n",
       "      <td>2022-05-10 20:41:07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4565101</th>\n",
       "      <td>1543548</td>\n",
       "      <td>2022-05-10 20:41:10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>4565102</th>\n",
       "      <td>1543548</td>\n",
       "      <td>2022-05-10 20:41:11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4565103 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         application_id loanapply_insert_time  event  loan_cnt  duration  \\\n",
       "0                248576   2022-05-21 23:41:09    3.0       0.0     354.0   \n",
       "1                248576   2022-05-21 23:41:10    3.0       0.0     354.0   \n",
       "2                248576   2022-05-21 23:41:11    3.0       0.0     354.0   \n",
       "3                248576   2022-05-21 23:41:14    3.0       0.0     354.0   \n",
       "4                248576   2022-05-21 23:41:15    3.0       0.0     354.0   \n",
       "...                 ...                   ...    ...       ...       ...   \n",
       "4565098         1543548   2022-05-10 20:40:54    NaN       NaN       NaN   \n",
       "4565099         1543548   2022-05-10 20:40:55    NaN       NaN       NaN   \n",
       "4565100         1543548   2022-05-10 20:41:07    NaN       NaN       NaN   \n",
       "4565101         1543548   2022-05-10 20:41:10    NaN       NaN       NaN   \n",
       "4565102         1543548   2022-05-10 20:41:11    NaN       NaN       NaN   \n",
       "\n",
       "         visit  segment  \n",
       "0          1.0      3.0  \n",
       "1          1.0      3.0  \n",
       "2          1.0      3.0  \n",
       "3          1.0      3.0  \n",
       "4          1.0      3.0  \n",
       "...        ...      ...  \n",
       "4565098    NaN      NaN  \n",
       "4565099    NaN      NaN  \n",
       "4565100    NaN      NaN  \n",
       "4565101    NaN      NaN  \n",
       "4565102    NaN      NaN  \n",
       "\n",
       "[4565103 rows x 7 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7def462a",
   "metadata": {},
   "source": [
    "# Missing Value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "49c249dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "application_id                0\n",
       "loanapply_insert_time         0\n",
       "event                    216644\n",
       "loan_cnt                 216644\n",
       "duration                 216644\n",
       "visit                    216644\n",
       "segment                  179732\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "529ef5d5",
   "metadata": {},
   "source": [
    "## Knn imputer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a09312b4",
   "metadata": {},
   "source": [
    "### Data Generation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "68fe6775",
   "metadata": {},
   "outputs": [],
   "source": [
    "md = master_df[['application_id','loanapply_insert_time','loan_rate','credit_score','loan_limit',\n",
    "               'existing_loan_amt','desired_amount','yearly_income','existing_loan_cnt']].drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9a856347",
   "metadata": {},
   "outputs": [
    {
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       "         application_id loanapply_insert_time  loan_rate  credit_score  \\\n",
       "0                     1   2022-05-06 11:05:42       17.7         580.0   \n",
       "1                     1   2022-05-06 11:05:43       18.5         580.0   \n",
       "2                     1   2022-05-06 11:05:44       18.9         580.0   \n",
       "3                     1   2022-05-06 11:05:56       17.1         580.0   \n",
       "4                     1   2022-05-06 11:05:57       14.8         580.0   \n",
       "...                 ...                   ...        ...           ...   \n",
       "4293458         2167822   2022-06-29 14:08:35       15.6         570.0   \n",
       "4293459         2167822   2022-06-29 14:08:36       18.3         570.0   \n",
       "4293460         2167826   2022-03-25 11:57:49       18.5         560.0   \n",
       "4293461         2167826   2022-03-25 11:57:51       17.4         560.0   \n",
       "4293462         2167826   2022-03-25 11:58:01       16.9         560.0   \n",
       "\n",
       "           loan_limit  existing_loan_amt  desired_amount  yearly_income  \\\n",
       "0        1.900000e+07         32800000.0      30000000.0     40000000.0   \n",
       "1        1.850000e+07         32800000.0      30000000.0     40000000.0   \n",
       "2        3.200000e+07         32800000.0      30000000.0     40000000.0   \n",
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       "4        8.000000e+06         32800000.0      30000000.0     40000000.0   \n",
       "...               ...                ...             ...            ...   \n",
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       "4293459  1.100000e+07          6000000.0       7000000.0     13000000.0   \n",
       "4293460  1.566667e+07         79000000.0       5000000.0     32000000.0   \n",
       "4293461  1.300000e+07         79000000.0       5000000.0     32000000.0   \n",
       "4293462  2.200000e+07         79000000.0       5000000.0     32000000.0   \n",
       "\n",
       "         existing_loan_cnt  \n",
       "0                      4.2  \n",
       "1                      4.2  \n",
       "2                      4.2  \n",
       "3                      4.2  \n",
       "4                      4.2  \n",
       "...                    ...  \n",
       "4293458                2.0  \n",
       "4293459                2.0  \n",
       "4293460               10.0  \n",
       "4293461               10.0  \n",
       "4293462               10.0  \n",
       "\n",
       "[4293463 rows x 9 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "md_df = md.groupby(['application_id','loanapply_insert_time']).mean().reset_index()\n",
    "md_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "54a8b2cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "knn_log = log_df.iloc[:,:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ff7bf688",
   "metadata": {},
   "outputs": [
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      ],
      "text/plain": [
       "         application_id loanapply_insert_time  event  loan_cnt  duration  \\\n",
       "0                248576   2022-05-21 23:41:09    3.0       0.0     354.0   \n",
       "1                248576   2022-05-21 23:41:10    3.0       0.0     354.0   \n",
       "2                248576   2022-05-21 23:41:11    3.0       0.0     354.0   \n",
       "3                248576   2022-05-21 23:41:14    3.0       0.0     354.0   \n",
       "4                248576   2022-05-21 23:41:15    3.0       0.0     354.0   \n",
       "...                 ...                   ...    ...       ...       ...   \n",
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       "4565102         1543548   2022-05-10 20:41:11    NaN       NaN       NaN   \n",
       "\n",
       "         visit  loan_rate  credit_score    loan_limit  existing_loan_amt  \\\n",
       "0          1.0   9.000000         810.0  1.700000e+07         17800000.0   \n",
       "1          1.0  12.927273         810.0  2.118182e+07         17800000.0   \n",
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       "...        ...        ...           ...           ...                ...   \n",
       "4565098    NaN  11.177778         894.0  3.211111e+07        171000000.0   \n",
       "4565099    NaN   9.285714         894.0  4.442857e+07        171000000.0   \n",
       "4565100    NaN   5.400000         894.0  3.000000e+06        171000000.0   \n",
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       "4565102    NaN  11.900000         894.0  3.400000e+07        171000000.0   \n",
       "\n",
       "         desired_amount  yearly_income  existing_loan_cnt  \n",
       "0            15000000.0     29000000.0                1.0  \n",
       "1            15000000.0     29000000.0                1.0  \n",
       "2            15000000.0     29000000.0                1.0  \n",
       "3            15000000.0     29000000.0                1.0  \n",
       "4            15000000.0     29000000.0                1.0  \n",
       "...                 ...            ...                ...  \n",
       "4565098     200000000.0     70000000.0                1.8  \n",
       "4565099     200000000.0     70000000.0                1.8  \n",
       "4565100     200000000.0     70000000.0                1.8  \n",
       "4565101     200000000.0     70000000.0                1.8  \n",
       "4565102     200000000.0     70000000.0                1.8  \n",
       "\n",
       "[4565103 rows x 13 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_df = pd.merge(knn_log,md_df,on=['application_id','loanapply_insert_time'],how='left')\n",
    "knn_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "94c0e576",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "application_id                0\n",
       "loanapply_insert_time         0\n",
       "event                    216644\n",
       "loan_cnt                 216644\n",
       "duration                 216644\n",
       "visit                    216644\n",
       "loan_rate                     0\n",
       "credit_score                  0\n",
       "loan_limit                    0\n",
       "existing_loan_amt             0\n",
       "desired_amount                0\n",
       "yearly_income                 0\n",
       "existing_loan_cnt             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "021bdd6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "knn_df.drop(['application_id','loanapply_insert_time'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b7cfdcd2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>10000000.0</td>\n",
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       "<p>4348459 rows × 11 columns</p>\n",
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      ],
      "text/plain": [
       "         event  loan_cnt  duration  visit  loan_rate  credit_score  \\\n",
       "0          3.0       0.0     354.0    1.0   9.000000         810.0   \n",
       "1          3.0       0.0     354.0    1.0  12.927273         810.0   \n",
       "2          3.0       0.0     354.0    1.0  13.625000         810.0   \n",
       "3          3.0       0.0     354.0    1.0   7.800000         810.0   \n",
       "4          3.0       0.0     354.0    1.0  13.100000         810.0   \n",
       "...        ...       ...       ...    ...        ...           ...   \n",
       "4348454   11.0       2.0     400.0    1.0   9.400000         610.0   \n",
       "4348455   11.0       2.0     400.0    1.0  16.500000         610.0   \n",
       "4348456   11.0       2.0     400.0    1.0   8.400000         610.0   \n",
       "4348457   11.0       2.0     400.0    1.0  11.300000         610.0   \n",
       "4348458   11.0       2.0     400.0    1.0  11.300000         610.0   \n",
       "\n",
       "           loan_limit  existing_loan_amt  desired_amount  yearly_income  \\\n",
       "0        1.700000e+07         17800000.0      15000000.0     29000000.0   \n",
       "1        2.118182e+07         17800000.0      15000000.0     29000000.0   \n",
       "2        2.200000e+07         17800000.0      15000000.0     29000000.0   \n",
       "3        5.000000e+06         17800000.0      15000000.0     29000000.0   \n",
       "4        3.700000e+07         17800000.0      15000000.0     29000000.0   \n",
       "...               ...                ...             ...            ...   \n",
       "4348454  9.000000e+06         20000000.0      10000000.0     37000000.0   \n",
       "4348455  1.000000e+07         20000000.0      10000000.0     37000000.0   \n",
       "4348456  9.000000e+06         20000000.0      10000000.0     37000000.0   \n",
       "4348457  1.000000e+07         20000000.0      10000000.0     37000000.0   \n",
       "4348458  1.000000e+07         20000000.0      10000000.0     37000000.0   \n",
       "\n",
       "         existing_loan_cnt  \n",
       "0                      1.0  \n",
       "1                      1.0  \n",
       "2                      1.0  \n",
       "3                      1.0  \n",
       "4                      1.0  \n",
       "...                    ...  \n",
       "4348454                3.0  \n",
       "4348455                3.0  \n",
       "4348456                3.0  \n",
       "4348457                3.0  \n",
       "4348458                3.0  \n",
       "\n",
       "[4348459 rows x 11 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_df_train = knn_df[knn_df.event.notna()].reset_index(drop=True);knn_df_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "3e2adc35",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler().fit(knn_df_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "25a4c9bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_knn = scaler.transform(knn_df_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "09a59eaf",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_knn = scaler.transform(knn_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "6a76efb3",
   "metadata": {},
   "outputs": [],
   "source": [
    "imputer = KNNImputer(n_neighbors = 5).fit(train_knn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "7429e72a",
   "metadata": {},
   "outputs": [],
   "source": [
    "knn_test = imputer.transform(test_knn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "bc53ad6f",
   "metadata": {},
   "outputs": [],
   "source": [
    "knn_test_scaler = scaler.inverse_transform(knn_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "8e794da4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</table>\n",
       "<p>4565103 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         event  loan_cnt  duration  visit  loan_rate  credit_score  \\\n",
       "0          3.0       0.0     354.0    1.0   9.000000         810.0   \n",
       "1          3.0       0.0     354.0    1.0  12.927273         810.0   \n",
       "2          3.0       0.0     354.0    1.0  13.625000         810.0   \n",
       "3          3.0       0.0     354.0    1.0   7.800000         810.0   \n",
       "4          3.0       0.0     354.0    1.0  13.100000         810.0   \n",
       "...        ...       ...       ...    ...        ...           ...   \n",
       "4565098    5.0       2.4     181.8    1.0  11.177778         894.0   \n",
       "4565099    6.0       4.0     167.6    1.2   9.285714         894.0   \n",
       "4565100    6.0       4.6     243.6    1.0   5.400000         894.0   \n",
       "4565101    5.0       3.0     115.4    1.2  10.600000         894.0   \n",
       "4565102    4.2       2.8     111.4    1.0  11.900000         894.0   \n",
       "\n",
       "           loan_limit  existing_loan_amt  desired_amount  yearly_income  \\\n",
       "0        1.700000e+07         17800000.0      15000000.0     29000000.0   \n",
       "1        2.118182e+07         17800000.0      15000000.0     29000000.0   \n",
       "2        2.200000e+07         17800000.0      15000000.0     29000000.0   \n",
       "3        5.000000e+06         17800000.0      15000000.0     29000000.0   \n",
       "4        3.700000e+07         17800000.0      15000000.0     29000000.0   \n",
       "...               ...                ...             ...            ...   \n",
       "4565098  3.211111e+07        171000000.0     200000000.0     70000000.0   \n",
       "4565099  4.442857e+07        171000000.0     200000000.0     70000000.0   \n",
       "4565100  3.000000e+06        171000000.0     200000000.0     70000000.0   \n",
       "4565101  5.000000e+07        171000000.0     200000000.0     70000000.0   \n",
       "4565102  3.400000e+07        171000000.0     200000000.0     70000000.0   \n",
       "\n",
       "         existing_loan_cnt  \n",
       "0                      1.0  \n",
       "1                      1.0  \n",
       "2                      1.0  \n",
       "3                      1.0  \n",
       "4                      1.0  \n",
       "...                    ...  \n",
       "4565098                1.8  \n",
       "4565099                1.8  \n",
       "4565100                1.8  \n",
       "4565101                1.8  \n",
       "4565102                1.8  \n",
       "\n",
       "[4565103 rows x 11 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_df = pd.DataFrame(knn_test_scaler,columns = knn_df.columns);knn_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "ceda3eb7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>application_id</th>\n",
       "      <th>loanapply_insert_time</th>\n",
       "      <th>event</th>\n",
       "      <th>loan_cnt</th>\n",
       "      <th>duration</th>\n",
       "      <th>visit</th>\n",
       "      <th>segment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>248576</td>\n",
       "      <td>2022-05-21 23:41:09</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>354.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>248576</td>\n",
       "      <td>2022-05-21 23:41:10</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>354.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>248576</td>\n",
       "      <td>2022-05-21 23:41:11</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>354.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>248576</td>\n",
       "      <td>2022-05-21 23:41:14</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>354.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>248576</td>\n",
       "      <td>2022-05-21 23:41:15</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>354.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</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",
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       "    <tr>\n",
       "      <th>4565098</th>\n",
       "      <td>1543548</td>\n",
       "      <td>2022-05-10 20:40:54</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4565099</th>\n",
       "      <td>1543548</td>\n",
       "      <td>2022-05-10 20:40:55</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4565100</th>\n",
       "      <td>1543548</td>\n",
       "      <td>2022-05-10 20:41:07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4565101</th>\n",
       "      <td>1543548</td>\n",
       "      <td>2022-05-10 20:41:10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4565102</th>\n",
       "      <td>1543548</td>\n",
       "      <td>2022-05-10 20:41:11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4565103 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         application_id loanapply_insert_time  event  loan_cnt  duration  \\\n",
       "0                248576   2022-05-21 23:41:09    3.0       0.0     354.0   \n",
       "1                248576   2022-05-21 23:41:10    3.0       0.0     354.0   \n",
       "2                248576   2022-05-21 23:41:11    3.0       0.0     354.0   \n",
       "3                248576   2022-05-21 23:41:14    3.0       0.0     354.0   \n",
       "4                248576   2022-05-21 23:41:15    3.0       0.0     354.0   \n",
       "...                 ...                   ...    ...       ...       ...   \n",
       "4565098         1543548   2022-05-10 20:40:54    NaN       NaN       NaN   \n",
       "4565099         1543548   2022-05-10 20:40:55    NaN       NaN       NaN   \n",
       "4565100         1543548   2022-05-10 20:41:07    NaN       NaN       NaN   \n",
       "4565101         1543548   2022-05-10 20:41:10    NaN       NaN       NaN   \n",
       "4565102         1543548   2022-05-10 20:41:11    NaN       NaN       NaN   \n",
       "\n",
       "         visit  segment  \n",
       "0          1.0      3.0  \n",
       "1          1.0      3.0  \n",
       "2          1.0      3.0  \n",
       "3          1.0      3.0  \n",
       "4          1.0      3.0  \n",
       "...        ...      ...  \n",
       "4565098    NaN      NaN  \n",
       "4565099    NaN      NaN  \n",
       "4565100    NaN      NaN  \n",
       "4565101    NaN      NaN  \n",
       "4565102    NaN      NaN  \n",
       "\n",
       "[4565103 rows x 7 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "839e268e",
   "metadata": {},
   "outputs": [],
   "source": [
    "log_df.event = knn_df.event\n",
    "log_df.loan_cnt = knn_df.loan_cnt\n",
    "log_df.duration = knn_df.duration\n",
    "log_df.visit = knn_df.visit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "9063d529",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "application_id                0\n",
       "loanapply_insert_time         0\n",
       "event                         0\n",
       "loan_cnt                      0\n",
       "duration                      0\n",
       "visit                         0\n",
       "segment                  179732\n",
       "dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5aad3cd2",
   "metadata": {},
   "source": [
    "## segment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "8e4db226",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3.,  4.,  0., nan,  1.,  5.,  2.])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_df.segment.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "fc84fdfc",
   "metadata": {},
   "outputs": [],
   "source": [
    "log_df.segment.fillna(6,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "4d45353b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "application_id           0\n",
       "loanapply_insert_time    0\n",
       "event                    0\n",
       "loan_cnt                 0\n",
       "duration                 0\n",
       "visit                    0\n",
       "segment                  0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5aa79aff",
   "metadata": {},
   "source": [
    "# Data Generation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "76d62afb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>application_id</th>\n",
       "      <th>loanapply_insert_time</th>\n",
       "      <th>bank_id</th>\n",
       "      <th>product_id</th>\n",
       "      <th>loan_limit</th>\n",
       "      <th>loan_rate</th>\n",
       "      <th>user_id</th>\n",
       "      <th>insert_time</th>\n",
       "      <th>credit_score</th>\n",
       "      <th>yearly_income</th>\n",
       "      <th>income_type</th>\n",
       "      <th>employment_type</th>\n",
       "      <th>houseown_type</th>\n",
       "      <th>desired_amount</th>\n",
       "      <th>purpose</th>\n",
       "      <th>personal_rehabilitation_yn</th>\n",
       "      <th>personal_rehabilitation_complete_yn</th>\n",
       "      <th>existing_loan_cnt</th>\n",
       "      <th>existing_loan_amt</th>\n",
       "      <th>enter_year</th>\n",
       "      <th>enter_month</th>\n",
       "      <th>enter_birth</th>\n",
       "      <th>month</th>\n",
       "      <th>birth_year</th>\n",
       "      <th>gender</th>\n",
       "      <th>month_day</th>\n",
       "      <th>k_lending_rate</th>\n",
       "      <th>us_lending_rate</th>\n",
       "      <th>btc_price</th>\n",
       "      <th>btc_variance</th>\n",
       "      <th>eth_price</th>\n",
       "      <th>eth_variance</th>\n",
       "      <th>lunc_price</th>\n",
       "      <th>lunc_variance</th>\n",
       "      <th>Close</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Change</th>\n",
       "      <th>실업자</th>\n",
       "      <th>실업률</th>\n",
       "      <th>연령대</th>\n",
       "      <th>소득수준</th>\n",
       "      <th>현재생활형편CSI_성별</th>\n",
       "      <th>현재생활형편CSI_나이</th>\n",
       "      <th>현재생활형편CSI_소득</th>\n",
       "      <th>현재가계부채CSI</th>\n",
       "      <th>가계부채전망CSI</th>\n",
       "      <th>소비심리지수</th>\n",
       "      <th>is_applied</th>\n",
       "      <th>event</th>\n",
       "      <th>loan_cnt</th>\n",
       "      <th>duration</th>\n",
       "      <th>visit</th>\n",
       "      <th>segment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1748340</td>\n",
       "      <td>2022-06-07 13:05:41</td>\n",
       "      <td>7</td>\n",
       "      <td>191</td>\n",
       "      <td>42000000.0</td>\n",
       "      <td>13.6</td>\n",
       "      <td>430982.0</td>\n",
       "      <td>2022-06-07 13:05:39</td>\n",
       "      <td>620.0</td>\n",
       "      <td>24000000.0</td>\n",
       "      <td>EARNEDINCOME</td>\n",
       "      <td>정규직</td>\n",
       "      <td>자가</td>\n",
       "      <td>25000000.0</td>\n",
       "      <td>대환대출</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>15000000.0</td>\n",
       "      <td>2022.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>2022-06</td>\n",
       "      <td>1996.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2022-06-07</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1.0</td>\n",
       "      <td>39544504.0</td>\n",
       "      <td>0.0408</td>\n",
       "      <td>2347000.0</td>\n",
       "      <td>0.0224</td>\n",
       "      <td>0.000081</td>\n",
       "      <td>0.0174</td>\n",
       "      <td>2670.65</td>\n",
       "      <td>2679.57</td>\n",
       "      <td>2681.51</td>\n",
       "      <td>2663.00</td>\n",
       "      <td>562470000</td>\n",
       "      <td>0.0044</td>\n",
       "      <td>156</td>\n",
       "      <td>7.8</td>\n",
       "      <td>40세미만</td>\n",
       "      <td>200-300만원</td>\n",
       "      <td>89</td>\n",
       "      <td>94</td>\n",
       "      <td>85</td>\n",
       "      <td>103.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>92.17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>861.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1748340</td>\n",
       "      <td>2022-06-07 13:05:41</td>\n",
       "      <td>25</td>\n",
       "      <td>169</td>\n",
       "      <td>24000000.0</td>\n",
       "      <td>17.9</td>\n",
       "      <td>430982.0</td>\n",
       "      <td>2022-06-07 13:05:39</td>\n",
       "      <td>620.0</td>\n",
       "      <td>24000000.0</td>\n",
       "      <td>EARNEDINCOME</td>\n",
       "      <td>정규직</td>\n",
       "      <td>자가</td>\n",
       "      <td>25000000.0</td>\n",
       "      <td>대환대출</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>15000000.0</td>\n",
       "      <td>2022.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>2022-06</td>\n",
       "      <td>1996.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2022-06-07</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1.0</td>\n",
       "      <td>39544504.0</td>\n",
       "      <td>0.0408</td>\n",
       "      <td>2347000.0</td>\n",
       "      <td>0.0224</td>\n",
       "      <td>0.000081</td>\n",
       "      <td>0.0174</td>\n",
       "      <td>2670.65</td>\n",
       "      <td>2679.57</td>\n",
       "      <td>2681.51</td>\n",
       "      <td>2663.00</td>\n",
       "      <td>562470000</td>\n",
       "      <td>0.0044</td>\n",
       "      <td>156</td>\n",
       "      <td>7.8</td>\n",
       "      <td>40세미만</td>\n",
       "      <td>200-300만원</td>\n",
       "      <td>89</td>\n",
       "      <td>94</td>\n",
       "      <td>85</td>\n",
       "      <td>103.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>92.17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>861.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1748340</td>\n",
       "      <td>2022-06-07 13:05:41</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>24000000.0</td>\n",
       "      <td>18.5</td>\n",
       "      <td>430982.0</td>\n",
       "      <td>2022-06-07 13:05:39</td>\n",
       "      <td>620.0</td>\n",
       "      <td>24000000.0</td>\n",
       "      <td>EARNEDINCOME</td>\n",
       "      <td>정규직</td>\n",
       "      <td>자가</td>\n",
       "      <td>25000000.0</td>\n",
       "      <td>대환대출</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>15000000.0</td>\n",
       "      <td>2022.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>2022-06</td>\n",
       "      <td>1996.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2022-06-07</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1.0</td>\n",
       "      <td>39544504.0</td>\n",
       "      <td>0.0408</td>\n",
       "      <td>2347000.0</td>\n",
       "      <td>0.0224</td>\n",
       "      <td>0.000081</td>\n",
       "      <td>0.0174</td>\n",
       "      <td>2670.65</td>\n",
       "      <td>2679.57</td>\n",
       "      <td>2681.51</td>\n",
       "      <td>2663.00</td>\n",
       "      <td>562470000</td>\n",
       "      <td>0.0044</td>\n",
       "      <td>156</td>\n",
       "      <td>7.8</td>\n",
       "      <td>40세미만</td>\n",
       "      <td>200-300만원</td>\n",
       "      <td>89</td>\n",
       "      <td>94</td>\n",
       "      <td>85</td>\n",
       "      <td>103.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>92.17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>861.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1748340</td>\n",
       "      <td>2022-06-07 13:05:41</td>\n",
       "      <td>4</td>\n",
       "      <td>268</td>\n",
       "      <td>29000000.0</td>\n",
       "      <td>10.8</td>\n",
       "      <td>430982.0</td>\n",
       "      <td>2022-06-07 13:05:39</td>\n",
       "      <td>620.0</td>\n",
       "      <td>24000000.0</td>\n",
       "      <td>EARNEDINCOME</td>\n",
       "      <td>정규직</td>\n",
       "      <td>자가</td>\n",
       "      <td>25000000.0</td>\n",
       "      <td>대환대출</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>15000000.0</td>\n",
       "      <td>2022.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>2022-06</td>\n",
       "      <td>1996.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2022-06-07</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1.0</td>\n",
       "      <td>39544504.0</td>\n",
       "      <td>0.0408</td>\n",
       "      <td>2347000.0</td>\n",
       "      <td>0.0224</td>\n",
       "      <td>0.000081</td>\n",
       "      <td>0.0174</td>\n",
       "      <td>2670.65</td>\n",
       "      <td>2679.57</td>\n",
       "      <td>2681.51</td>\n",
       "      <td>2663.00</td>\n",
       "      <td>562470000</td>\n",
       "      <td>0.0044</td>\n",
       "      <td>156</td>\n",
       "      <td>7.8</td>\n",
       "      <td>40세미만</td>\n",
       "      <td>200-300만원</td>\n",
       "      <td>89</td>\n",
       "      <td>94</td>\n",
       "      <td>85</td>\n",
       "      <td>103.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>92.17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>861.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1748340</td>\n",
       "      <td>2022-06-07 13:05:41</td>\n",
       "      <td>11</td>\n",
       "      <td>118</td>\n",
       "      <td>5000000.0</td>\n",
       "      <td>16.4</td>\n",
       "      <td>430982.0</td>\n",
       "      <td>2022-06-07 13:05:39</td>\n",
       "      <td>620.0</td>\n",
       "      <td>24000000.0</td>\n",
       "      <td>EARNEDINCOME</td>\n",
       "      <td>정규직</td>\n",
       "      <td>자가</td>\n",
       "      <td>25000000.0</td>\n",
       "      <td>대환대출</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>15000000.0</td>\n",
       "      <td>2022.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>2022-06</td>\n",
       "      <td>1996.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2022-06-07</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1.0</td>\n",
       "      <td>39544504.0</td>\n",
       "      <td>0.0408</td>\n",
       "      <td>2347000.0</td>\n",
       "      <td>0.0224</td>\n",
       "      <td>0.000081</td>\n",
       "      <td>0.0174</td>\n",
       "      <td>2670.65</td>\n",
       "      <td>2679.57</td>\n",
       "      <td>2681.51</td>\n",
       "      <td>2663.00</td>\n",
       "      <td>562470000</td>\n",
       "      <td>0.0044</td>\n",
       "      <td>156</td>\n",
       "      <td>7.8</td>\n",
       "      <td>40세미만</td>\n",
       "      <td>200-300만원</td>\n",
       "      <td>89</td>\n",
       "      <td>94</td>\n",
       "      <td>85</td>\n",
       "      <td>103.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>92.17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>861.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15090062</th>\n",
       "      <td>1428218</td>\n",
       "      <td>2022-06-03 12:01:58</td>\n",
       "      <td>62</td>\n",
       "      <td>200</td>\n",
       "      <td>3000000.0</td>\n",
       "      <td>14.8</td>\n",
       "      <td>478802.0</td>\n",
       "      <td>2022-06-03 12:01:47</td>\n",
       "      <td>610.0</td>\n",
       "      <td>45000000.0</td>\n",
       "      <td>EARNEDINCOME</td>\n",
       "      <td>정규직</td>\n",
       "      <td>기타가족소유</td>\n",
       "      <td>50000000.0</td>\n",
       "      <td>대환대출</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>56000000.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>2022-06</td>\n",
       "      <td>1965.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2022-06-03</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1.0</td>\n",
       "      <td>38376912.0</td>\n",
       "      <td>0.0134</td>\n",
       "      <td>2310990.0</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>0.000101</td>\n",
       "      <td>0.0202</td>\n",
       "      <td>2658.99</td>\n",
       "      <td>2670.74</td>\n",
       "      <td>2674.00</td>\n",
       "      <td>2653.59</td>\n",
       "      <td>525260000</td>\n",
       "      <td>-0.0100</td>\n",
       "      <td>68</td>\n",
       "      <td>1.8</td>\n",
       "      <td>50-60세</td>\n",
       "      <td>300-400만원</td>\n",
       "      <td>89</td>\n",
       "      <td>89</td>\n",
       "      <td>87</td>\n",
       "      <td>103.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>91.50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15090063</th>\n",
       "      <td>1428218</td>\n",
       "      <td>2022-06-03 12:01:49</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>40000000.0</td>\n",
       "      <td>11.8</td>\n",
       "      <td>478802.0</td>\n",
       "      <td>2022-06-03 12:01:47</td>\n",
       "      <td>610.0</td>\n",
       "      <td>45000000.0</td>\n",
       "      <td>EARNEDINCOME</td>\n",
       "      <td>정규직</td>\n",
       "      <td>기타가족소유</td>\n",
       "      <td>50000000.0</td>\n",
       "      <td>대환대출</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>56000000.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>2022-06</td>\n",
       "      <td>1965.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2022-06-03</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1.0</td>\n",
       "      <td>38376912.0</td>\n",
       "      <td>0.0134</td>\n",
       "      <td>2310990.0</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>0.000101</td>\n",
       "      <td>0.0202</td>\n",
       "      <td>2658.99</td>\n",
       "      <td>2670.74</td>\n",
       "      <td>2674.00</td>\n",
       "      <td>2653.59</td>\n",
       "      <td>525260000</td>\n",
       "      <td>-0.0100</td>\n",
       "      <td>68</td>\n",
       "      <td>1.8</td>\n",
       "      <td>50-60세</td>\n",
       "      <td>300-400만원</td>\n",
       "      <td>89</td>\n",
       "      <td>89</td>\n",
       "      <td>87</td>\n",
       "      <td>103.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>91.50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15090064</th>\n",
       "      <td>1428218</td>\n",
       "      <td>2022-06-03 12:01:48</td>\n",
       "      <td>32</td>\n",
       "      <td>257</td>\n",
       "      <td>15000000.0</td>\n",
       "      <td>7.2</td>\n",
       "      <td>478802.0</td>\n",
       "      <td>2022-06-03 12:01:47</td>\n",
       "      <td>610.0</td>\n",
       "      <td>45000000.0</td>\n",
       "      <td>EARNEDINCOME</td>\n",
       "      <td>정규직</td>\n",
       "      <td>기타가족소유</td>\n",
       "      <td>50000000.0</td>\n",
       "      <td>대환대출</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>56000000.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>2022-06</td>\n",
       "      <td>1965.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2022-06-03</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1.0</td>\n",
       "      <td>38376912.0</td>\n",
       "      <td>0.0134</td>\n",
       "      <td>2310990.0</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>0.000101</td>\n",
       "      <td>0.0202</td>\n",
       "      <td>2658.99</td>\n",
       "      <td>2670.74</td>\n",
       "      <td>2674.00</td>\n",
       "      <td>2653.59</td>\n",
       "      <td>525260000</td>\n",
       "      <td>-0.0100</td>\n",
       "      <td>68</td>\n",
       "      <td>1.8</td>\n",
       "      <td>50-60세</td>\n",
       "      <td>300-400만원</td>\n",
       "      <td>89</td>\n",
       "      <td>89</td>\n",
       "      <td>87</td>\n",
       "      <td>103.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>91.50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15090065</th>\n",
       "      <td>1428218</td>\n",
       "      <td>2022-06-03 12:01:48</td>\n",
       "      <td>33</td>\n",
       "      <td>110</td>\n",
       "      <td>44000000.0</td>\n",
       "      <td>13.5</td>\n",
       "      <td>478802.0</td>\n",
       "      <td>2022-06-03 12:01:47</td>\n",
       "      <td>610.0</td>\n",
       "      <td>45000000.0</td>\n",
       "      <td>EARNEDINCOME</td>\n",
       "      <td>정규직</td>\n",
       "      <td>기타가족소유</td>\n",
       "      <td>50000000.0</td>\n",
       "      <td>대환대출</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>56000000.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>2022-06</td>\n",
       "      <td>1965.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2022-06-03</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1.0</td>\n",
       "      <td>38376912.0</td>\n",
       "      <td>0.0134</td>\n",
       "      <td>2310990.0</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>0.000101</td>\n",
       "      <td>0.0202</td>\n",
       "      <td>2658.99</td>\n",
       "      <td>2670.74</td>\n",
       "      <td>2674.00</td>\n",
       "      <td>2653.59</td>\n",
       "      <td>525260000</td>\n",
       "      <td>-0.0100</td>\n",
       "      <td>68</td>\n",
       "      <td>1.8</td>\n",
       "      <td>50-60세</td>\n",
       "      <td>300-400만원</td>\n",
       "      <td>89</td>\n",
       "      <td>89</td>\n",
       "      <td>87</td>\n",
       "      <td>103.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>91.50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15090066</th>\n",
       "      <td>1428218</td>\n",
       "      <td>2022-06-03 12:01:48</td>\n",
       "      <td>5</td>\n",
       "      <td>194</td>\n",
       "      <td>44000000.0</td>\n",
       "      <td>9.7</td>\n",
       "      <td>478802.0</td>\n",
       "      <td>2022-06-03 12:01:47</td>\n",
       "      <td>610.0</td>\n",
       "      <td>45000000.0</td>\n",
       "      <td>EARNEDINCOME</td>\n",
       "      <td>정규직</td>\n",
       "      <td>기타가족소유</td>\n",
       "      <td>50000000.0</td>\n",
       "      <td>대환대출</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>56000000.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>2022-06</td>\n",
       "      <td>1965.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2022-06-03</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1.0</td>\n",
       "      <td>38376912.0</td>\n",
       "      <td>0.0134</td>\n",
       "      <td>2310990.0</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>0.000101</td>\n",
       "      <td>0.0202</td>\n",
       "      <td>2658.99</td>\n",
       "      <td>2670.74</td>\n",
       "      <td>2674.00</td>\n",
       "      <td>2653.59</td>\n",
       "      <td>525260000</td>\n",
       "      <td>-0.0100</td>\n",
       "      <td>68</td>\n",
       "      <td>1.8</td>\n",
       "      <td>50-60세</td>\n",
       "      <td>300-400만원</td>\n",
       "      <td>89</td>\n",
       "      <td>89</td>\n",
       "      <td>87</td>\n",
       "      <td>103.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>91.50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13527090 rows × 56 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          application_id loanapply_insert_time  bank_id  product_id  \\\n",
       "0                1748340   2022-06-07 13:05:41        7         191   \n",
       "1                1748340   2022-06-07 13:05:41       25         169   \n",
       "2                1748340   2022-06-07 13:05:41        2           7   \n",
       "3                1748340   2022-06-07 13:05:41        4         268   \n",
       "4                1748340   2022-06-07 13:05:41       11         118   \n",
       "...                  ...                   ...      ...         ...   \n",
       "15090062         1428218   2022-06-03 12:01:58       62         200   \n",
       "15090063         1428218   2022-06-03 12:01:49        2           7   \n",
       "15090064         1428218   2022-06-03 12:01:48       32         257   \n",
       "15090065         1428218   2022-06-03 12:01:48       33         110   \n",
       "15090066         1428218   2022-06-03 12:01:48        5         194   \n",
       "\n",
       "          loan_limit  loan_rate   user_id          insert_time  credit_score  \\\n",
       "0         42000000.0       13.6  430982.0  2022-06-07 13:05:39         620.0   \n",
       "1         24000000.0       17.9  430982.0  2022-06-07 13:05:39         620.0   \n",
       "2         24000000.0       18.5  430982.0  2022-06-07 13:05:39         620.0   \n",
       "3         29000000.0       10.8  430982.0  2022-06-07 13:05:39         620.0   \n",
       "4          5000000.0       16.4  430982.0  2022-06-07 13:05:39         620.0   \n",
       "...              ...        ...       ...                  ...           ...   \n",
       "15090062   3000000.0       14.8  478802.0  2022-06-03 12:01:47         610.0   \n",
       "15090063  40000000.0       11.8  478802.0  2022-06-03 12:01:47         610.0   \n",
       "15090064  15000000.0        7.2  478802.0  2022-06-03 12:01:47         610.0   \n",
       "15090065  44000000.0       13.5  478802.0  2022-06-03 12:01:47         610.0   \n",
       "15090066  44000000.0        9.7  478802.0  2022-06-03 12:01:47         610.0   \n",
       "\n",
       "          yearly_income   income_type employment_type houseown_type  \\\n",
       "0            24000000.0  EARNEDINCOME             정규직            자가   \n",
       "1            24000000.0  EARNEDINCOME             정규직            자가   \n",
       "2            24000000.0  EARNEDINCOME             정규직            자가   \n",
       "3            24000000.0  EARNEDINCOME             정규직            자가   \n",
       "4            24000000.0  EARNEDINCOME             정규직            자가   \n",
       "...                 ...           ...             ...           ...   \n",
       "15090062     45000000.0  EARNEDINCOME             정규직        기타가족소유   \n",
       "15090063     45000000.0  EARNEDINCOME             정규직        기타가족소유   \n",
       "15090064     45000000.0  EARNEDINCOME             정규직        기타가족소유   \n",
       "15090065     45000000.0  EARNEDINCOME             정규직        기타가족소유   \n",
       "15090066     45000000.0  EARNEDINCOME             정규직        기타가족소유   \n",
       "\n",
       "          desired_amount purpose  personal_rehabilitation_yn  \\\n",
       "0             25000000.0    대환대출                         0.0   \n",
       "1             25000000.0    대환대출                         0.0   \n",
       "2             25000000.0    대환대출                         0.0   \n",
       "3             25000000.0    대환대출                         0.0   \n",
       "4             25000000.0    대환대출                         0.0   \n",
       "...                  ...     ...                         ...   \n",
       "15090062      50000000.0    대환대출                         0.0   \n",
       "15090063      50000000.0    대환대출                         0.0   \n",
       "15090064      50000000.0    대환대출                         0.0   \n",
       "15090065      50000000.0    대환대출                         0.0   \n",
       "15090066      50000000.0    대환대출                         0.0   \n",
       "\n",
       "          personal_rehabilitation_complete_yn  existing_loan_cnt  \\\n",
       "0                                         0.0                2.0   \n",
       "1                                         0.0                2.0   \n",
       "2                                         0.0                2.0   \n",
       "3                                         0.0                2.0   \n",
       "4                                         0.0                2.0   \n",
       "...                                       ...                ...   \n",
       "15090062                                  0.0                2.0   \n",
       "15090063                                  0.0                2.0   \n",
       "15090064                                  0.0                2.0   \n",
       "15090065                                  0.0                2.0   \n",
       "15090066                                  0.0                2.0   \n",
       "\n",
       "          existing_loan_amt  enter_year  enter_month  enter_birth    month  \\\n",
       "0                15000000.0      2022.0          2.0         26.0  2022-06   \n",
       "1                15000000.0      2022.0          2.0         26.0  2022-06   \n",
       "2                15000000.0      2022.0          2.0         26.0  2022-06   \n",
       "3                15000000.0      2022.0          2.0         26.0  2022-06   \n",
       "4                15000000.0      2022.0          2.0         26.0  2022-06   \n",
       "...                     ...         ...          ...          ...      ...   \n",
       "15090062         56000000.0      2019.0          3.0         54.0  2022-06   \n",
       "15090063         56000000.0      2019.0          3.0         54.0  2022-06   \n",
       "15090064         56000000.0      2019.0          3.0         54.0  2022-06   \n",
       "15090065         56000000.0      2019.0          3.0         54.0  2022-06   \n",
       "15090066         56000000.0      2019.0          3.0         54.0  2022-06   \n",
       "\n",
       "          birth_year  gender   month_day  k_lending_rate  us_lending_rate  \\\n",
       "0             1996.0     1.0  2022-06-07            1.75              1.0   \n",
       "1             1996.0     1.0  2022-06-07            1.75              1.0   \n",
       "2             1996.0     1.0  2022-06-07            1.75              1.0   \n",
       "3             1996.0     1.0  2022-06-07            1.75              1.0   \n",
       "4             1996.0     1.0  2022-06-07            1.75              1.0   \n",
       "...              ...     ...         ...             ...              ...   \n",
       "15090062      1965.0     1.0  2022-06-03            1.75              1.0   \n",
       "15090063      1965.0     1.0  2022-06-03            1.75              1.0   \n",
       "15090064      1965.0     1.0  2022-06-03            1.75              1.0   \n",
       "15090065      1965.0     1.0  2022-06-03            1.75              1.0   \n",
       "15090066      1965.0     1.0  2022-06-03            1.75              1.0   \n",
       "\n",
       "           btc_price  btc_variance  eth_price  eth_variance  lunc_price  \\\n",
       "0         39544504.0        0.0408  2347000.0        0.0224    0.000081   \n",
       "1         39544504.0        0.0408  2347000.0        0.0224    0.000081   \n",
       "2         39544504.0        0.0408  2347000.0        0.0224    0.000081   \n",
       "3         39544504.0        0.0408  2347000.0        0.0224    0.000081   \n",
       "4         39544504.0        0.0408  2347000.0        0.0224    0.000081   \n",
       "...              ...           ...        ...           ...         ...   \n",
       "15090062  38376912.0        0.0134  2310990.0        0.0005    0.000101   \n",
       "15090063  38376912.0        0.0134  2310990.0        0.0005    0.000101   \n",
       "15090064  38376912.0        0.0134  2310990.0        0.0005    0.000101   \n",
       "15090065  38376912.0        0.0134  2310990.0        0.0005    0.000101   \n",
       "15090066  38376912.0        0.0134  2310990.0        0.0005    0.000101   \n",
       "\n",
       "          lunc_variance    Close     Open     High      Low     Volume  \\\n",
       "0                0.0174  2670.65  2679.57  2681.51  2663.00  562470000   \n",
       "1                0.0174  2670.65  2679.57  2681.51  2663.00  562470000   \n",
       "2                0.0174  2670.65  2679.57  2681.51  2663.00  562470000   \n",
       "3                0.0174  2670.65  2679.57  2681.51  2663.00  562470000   \n",
       "4                0.0174  2670.65  2679.57  2681.51  2663.00  562470000   \n",
       "...                 ...      ...      ...      ...      ...        ...   \n",
       "15090062         0.0202  2658.99  2670.74  2674.00  2653.59  525260000   \n",
       "15090063         0.0202  2658.99  2670.74  2674.00  2653.59  525260000   \n",
       "15090064         0.0202  2658.99  2670.74  2674.00  2653.59  525260000   \n",
       "15090065         0.0202  2658.99  2670.74  2674.00  2653.59  525260000   \n",
       "15090066         0.0202  2658.99  2670.74  2674.00  2653.59  525260000   \n",
       "\n",
       "          Change  실업자  실업률     연령대       소득수준  현재생활형편CSI_성별  현재생활형편CSI_나이  \\\n",
       "0         0.0044  156  7.8   40세미만  200-300만원            89            94   \n",
       "1         0.0044  156  7.8   40세미만  200-300만원            89            94   \n",
       "2         0.0044  156  7.8   40세미만  200-300만원            89            94   \n",
       "3         0.0044  156  7.8   40세미만  200-300만원            89            94   \n",
       "4         0.0044  156  7.8   40세미만  200-300만원            89            94   \n",
       "...          ...  ...  ...     ...        ...           ...           ...   \n",
       "15090062 -0.0100   68  1.8  50-60세  300-400만원            89            89   \n",
       "15090063 -0.0100   68  1.8  50-60세  300-400만원            89            89   \n",
       "15090064 -0.0100   68  1.8  50-60세  300-400만원            89            89   \n",
       "15090065 -0.0100   68  1.8  50-60세  300-400만원            89            89   \n",
       "15090066 -0.0100   68  1.8  50-60세  300-400만원            89            89   \n",
       "\n",
       "          현재생활형편CSI_소득  현재가계부채CSI  가계부채전망CSI  소비심리지수  is_applied  event  \\\n",
       "0                   85      103.0      100.0   92.17         NaN   18.0   \n",
       "1                   85      103.0      100.0   92.17         NaN   18.0   \n",
       "2                   85      103.0      100.0   92.17         NaN   18.0   \n",
       "3                   85      103.0      100.0   92.17         NaN   18.0   \n",
       "4                   85      103.0      100.0   92.17         NaN   18.0   \n",
       "...                ...        ...        ...     ...         ...    ...   \n",
       "15090062            87      103.0      100.0   91.50         NaN    6.0   \n",
       "15090063            87      103.0      100.0   91.50         NaN    6.0   \n",
       "15090064            87      103.0      100.0   91.50         NaN    6.0   \n",
       "15090065            87      103.0      100.0   91.50         NaN    6.0   \n",
       "15090066            87      103.0      100.0   91.50         NaN    6.0   \n",
       "\n",
       "          loan_cnt  duration  visit  segment  \n",
       "0             12.0     861.0    3.0      0.0  \n",
       "1             12.0     861.0    3.0      0.0  \n",
       "2             12.0     861.0    3.0      0.0  \n",
       "3             12.0     861.0    3.0      0.0  \n",
       "4             12.0     861.0    3.0      0.0  \n",
       "...            ...       ...    ...      ...  \n",
       "15090062       3.0     166.0    1.0      1.0  \n",
       "15090063       3.0     166.0    1.0      1.0  \n",
       "15090064       3.0     166.0    1.0      1.0  \n",
       "15090065       3.0     166.0    1.0      1.0  \n",
       "15090066       3.0     166.0    1.0      1.0  \n",
       "\n",
       "[13527090 rows x 56 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log = pd.merge(master_df,log_df,on=['application_id','loanapply_insert_time'],how='left').drop_duplicates(['application_id','loanapply_insert_time','bank_id','product_id'])\n",
    "log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "ef05506e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "application_id                               0\n",
       "loanapply_insert_time                        0\n",
       "bank_id                                      0\n",
       "product_id                                   0\n",
       "loan_limit                                   0\n",
       "loan_rate                                    0\n",
       "user_id                                      0\n",
       "insert_time                                  0\n",
       "credit_score                                 0\n",
       "yearly_income                                0\n",
       "income_type                                  0\n",
       "employment_type                              0\n",
       "houseown_type                                0\n",
       "desired_amount                               0\n",
       "purpose                                      0\n",
       "personal_rehabilitation_yn                   0\n",
       "personal_rehabilitation_complete_yn          0\n",
       "existing_loan_cnt                            0\n",
       "existing_loan_amt                            0\n",
       "enter_year                                   0\n",
       "enter_month                                  0\n",
       "enter_birth                                  0\n",
       "month                                        0\n",
       "birth_year                                   0\n",
       "gender                                       0\n",
       "month_day                                    0\n",
       "k_lending_rate                               0\n",
       "us_lending_rate                              0\n",
       "btc_price                                    0\n",
       "btc_variance                                 0\n",
       "eth_price                                    0\n",
       "eth_variance                                 0\n",
       "lunc_price                                   0\n",
       "lunc_variance                                0\n",
       "Close                                        0\n",
       "Open                                         0\n",
       "High                                         0\n",
       "Low                                          0\n",
       "Volume                                       0\n",
       "Change                                       0\n",
       "실업자                                          0\n",
       "실업률                                          0\n",
       "연령대                                          0\n",
       "소득수준                                         0\n",
       "현재생활형편CSI_성별                                 0\n",
       "현재생활형편CSI_나이                                 0\n",
       "현재생활형편CSI_소득                                 0\n",
       "현재가계부채CSI                                    0\n",
       "가계부채전망CSI                                    0\n",
       "소비심리지수                                       0\n",
       "is_applied                             3257209\n",
       "event                                        0\n",
       "loan_cnt                                     0\n",
       "duration                                     0\n",
       "visit                                        0\n",
       "segment                                      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6d11713",
   "metadata": {},
   "source": [
    "# Data Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "b38fb64d",
   "metadata": {},
   "outputs": [],
   "source": [
    "log_feature = log[['event','loan_cnt','duration','visit','segment']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "5127d569",
   "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>event</th>\n",
       "      <th>loan_cnt</th>\n",
       "      <th>duration</th>\n",
       "      <th>visit</th>\n",
       "      <th>segment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>861.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>861.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>861.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>18.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>861.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>861.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15090062</th>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15090063</th>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15090064</th>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15090065</th>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15090066</th>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13527090 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          event  loan_cnt  duration  visit  segment\n",
       "0          18.0      12.0     861.0    3.0      0.0\n",
       "1          18.0      12.0     861.0    3.0      0.0\n",
       "2          18.0      12.0     861.0    3.0      0.0\n",
       "3          18.0      12.0     861.0    3.0      0.0\n",
       "4          18.0      12.0     861.0    3.0      0.0\n",
       "...         ...       ...       ...    ...      ...\n",
       "15090062    6.0       3.0     166.0    1.0      1.0\n",
       "15090063    6.0       3.0     166.0    1.0      1.0\n",
       "15090064    6.0       3.0     166.0    1.0      1.0\n",
       "15090065    6.0       3.0     166.0    1.0      1.0\n",
       "15090066    6.0       3.0     166.0    1.0      1.0\n",
       "\n",
       "[13527090 rows x 5 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_feature"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f43471c",
   "metadata": {},
   "source": [
    "# Deployment CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "5b56b1e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "log_feature.to_csv('../Data/log_feature_clansing.csv',index=False)"
   ]
  }
 ],
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