clustering_modeling.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import warnings \n",
    "warnings.filterwarnings('ignore')\n",
    "pd.options.display.float_format = '{:.5f}'.format\n",
    "\n",
    "# 시각화 라이브러리\n",
    "import matplotlib.pyplot as plt\n",
    "from pylab import cm\n",
    "\n",
    "# 한글 폰트 문제\n",
    "from matplotlib import font_manager, rc\n",
    "font_path = \"C:/Windows/Fonts/malgun.ttf\"\n",
    "font = font_manager.FontProperties(fname=font_path).get_name()\n",
    "rc('font', family=font)\n",
    "\n",
    "# 모델 관련\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.metrics import silhouette_score\n",
    "from sklearn.metrics import silhouette_samples\n",
    "\n",
    "# 클러스터링\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.cluster import DBSCAN\n",
    "from sklearn.mixture import GaussianMixture\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 데이터 프레임 전처리"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 1. city(그룹화 X) - 지역, 나이 제외 클러스터링"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>avg_score</th>\n",
       "      <th>num_usecard</th>\n",
       "      <th>monthly_card_spend</th>\n",
       "      <th>monthly_bk_loan</th>\n",
       "      <th>monthly_cd_loan</th>\n",
       "      <th>monthly_installments_loan</th>\n",
       "      <th>monthly_insurance_loan</th>\n",
       "      <th>monthly_sbk_loan</th>\n",
       "      <th>loan_commitment</th>\n",
       "      <th>inst_rep_loanb</th>\n",
       "      <th>ls_rep_loanb</th>\n",
       "      <th>credit_loan</th>\n",
       "      <th>mortgage_loan</th>\n",
       "      <th>credit_card_installments_payment</th>\n",
       "      <th>monthly_spend</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city_ages</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>서울 20대</th>\n",
       "      <td>740</td>\n",
       "      <td>1.99000</td>\n",
       "      <td>810000</td>\n",
       "      <td>18500000</td>\n",
       "      <td>620000</td>\n",
       "      <td>2110000</td>\n",
       "      <td>680000</td>\n",
       "      <td>1320000</td>\n",
       "      <td>3800000</td>\n",
       "      <td>10520000</td>\n",
       "      <td>9750000</td>\n",
       "      <td>14920000</td>\n",
       "      <td>35450000</td>\n",
       "      <td>83000</td>\n",
       "      <td>727000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>서울 20대</th>\n",
       "      <td>782</td>\n",
       "      <td>1.75000</td>\n",
       "      <td>790000</td>\n",
       "      <td>24150000</td>\n",
       "      <td>680000</td>\n",
       "      <td>2300000</td>\n",
       "      <td>790000</td>\n",
       "      <td>1160000</td>\n",
       "      <td>5420000</td>\n",
       "      <td>12220000</td>\n",
       "      <td>12240000</td>\n",
       "      <td>19070000</td>\n",
       "      <td>42420000</td>\n",
       "      <td>83000</td>\n",
       "      <td>707000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>서울 20대</th>\n",
       "      <td>782</td>\n",
       "      <td>1.74000</td>\n",
       "      <td>790000</td>\n",
       "      <td>24240000</td>\n",
       "      <td>680000</td>\n",
       "      <td>2300000</td>\n",
       "      <td>790000</td>\n",
       "      <td>1210000</td>\n",
       "      <td>5350000</td>\n",
       "      <td>12280000</td>\n",
       "      <td>12370000</td>\n",
       "      <td>19190000</td>\n",
       "      <td>42480000</td>\n",
       "      <td>78000</td>\n",
       "      <td>712000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>서울 20대</th>\n",
       "      <td>782</td>\n",
       "      <td>1.75000</td>\n",
       "      <td>860000</td>\n",
       "      <td>24570000</td>\n",
       "      <td>680000</td>\n",
       "      <td>2300000</td>\n",
       "      <td>790000</td>\n",
       "      <td>1230000</td>\n",
       "      <td>5380000</td>\n",
       "      <td>12320000</td>\n",
       "      <td>12700000</td>\n",
       "      <td>19360000</td>\n",
       "      <td>42980000</td>\n",
       "      <td>89000</td>\n",
       "      <td>771000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>서울 20대</th>\n",
       "      <td>781</td>\n",
       "      <td>1.75000</td>\n",
       "      <td>820000</td>\n",
       "      <td>24830000</td>\n",
       "      <td>700000</td>\n",
       "      <td>2320000</td>\n",
       "      <td>770000</td>\n",
       "      <td>1250000</td>\n",
       "      <td>5430000</td>\n",
       "      <td>12420000</td>\n",
       "      <td>12870000</td>\n",
       "      <td>19580000</td>\n",
       "      <td>43370000</td>\n",
       "      <td>82000</td>\n",
       "      <td>738000</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>제주 80대</th>\n",
       "      <td>804</td>\n",
       "      <td>0.27000</td>\n",
       "      <td>170000</td>\n",
       "      <td>60420000</td>\n",
       "      <td>90000</td>\n",
       "      <td>180000</td>\n",
       "      <td>2950000</td>\n",
       "      <td>0</td>\n",
       "      <td>13310000</td>\n",
       "      <td>7630000</td>\n",
       "      <td>50930000</td>\n",
       "      <td>19950000</td>\n",
       "      <td>109270000</td>\n",
       "      <td>29000</td>\n",
       "      <td>141000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>제주 80대</th>\n",
       "      <td>802</td>\n",
       "      <td>0.27000</td>\n",
       "      <td>140000</td>\n",
       "      <td>61450000</td>\n",
       "      <td>90000</td>\n",
       "      <td>190000</td>\n",
       "      <td>2890000</td>\n",
       "      <td>0</td>\n",
       "      <td>13160000</td>\n",
       "      <td>7640000</td>\n",
       "      <td>51740000</td>\n",
       "      <td>20300000</td>\n",
       "      <td>110210000</td>\n",
       "      <td>23000</td>\n",
       "      <td>117000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>제주 80대</th>\n",
       "      <td>802</td>\n",
       "      <td>0.27000</td>\n",
       "      <td>170000</td>\n",
       "      <td>63420000</td>\n",
       "      <td>90000</td>\n",
       "      <td>190000</td>\n",
       "      <td>2840000</td>\n",
       "      <td>30000</td>\n",
       "      <td>14500000</td>\n",
       "      <td>7630000</td>\n",
       "      <td>52190000</td>\n",
       "      <td>20580000</td>\n",
       "      <td>112510000</td>\n",
       "      <td>37000</td>\n",
       "      <td>133000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>제주 80대</th>\n",
       "      <td>802</td>\n",
       "      <td>0.27000</td>\n",
       "      <td>160000</td>\n",
       "      <td>64170000</td>\n",
       "      <td>90000</td>\n",
       "      <td>190000</td>\n",
       "      <td>2820000</td>\n",
       "      <td>110000</td>\n",
       "      <td>14670000</td>\n",
       "      <td>8020000</td>\n",
       "      <td>52530000</td>\n",
       "      <td>20510000</td>\n",
       "      <td>113690000</td>\n",
       "      <td>28000</td>\n",
       "      <td>132000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>제주 80대</th>\n",
       "      <td>802</td>\n",
       "      <td>0.28000</td>\n",
       "      <td>160000</td>\n",
       "      <td>63200000</td>\n",
       "      <td>90000</td>\n",
       "      <td>190000</td>\n",
       "      <td>2790000</td>\n",
       "      <td>110000</td>\n",
       "      <td>13710000</td>\n",
       "      <td>8130000</td>\n",
       "      <td>52900000</td>\n",
       "      <td>20580000</td>\n",
       "      <td>112110000</td>\n",
       "      <td>27000</td>\n",
       "      <td>133000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2688 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           avg_score  num_usecard  monthly_card_spend  monthly_bk_loan  \\\n",
       "city_ages                                                                \n",
       "서울 20대           740      1.99000              810000         18500000   \n",
       "서울 20대           782      1.75000              790000         24150000   \n",
       "서울 20대           782      1.74000              790000         24240000   \n",
       "서울 20대           782      1.75000              860000         24570000   \n",
       "서울 20대           781      1.75000              820000         24830000   \n",
       "...              ...          ...                 ...              ...   \n",
       "제주 80대           804      0.27000              170000         60420000   \n",
       "제주 80대           802      0.27000              140000         61450000   \n",
       "제주 80대           802      0.27000              170000         63420000   \n",
       "제주 80대           802      0.27000              160000         64170000   \n",
       "제주 80대           802      0.28000              160000         63200000   \n",
       "\n",
       "           monthly_cd_loan  monthly_installments_loan  monthly_insurance_loan  \\\n",
       "city_ages                                                                       \n",
       "서울 20대              620000                    2110000                  680000   \n",
       "서울 20대              680000                    2300000                  790000   \n",
       "서울 20대              680000                    2300000                  790000   \n",
       "서울 20대              680000                    2300000                  790000   \n",
       "서울 20대              700000                    2320000                  770000   \n",
       "...                    ...                        ...                     ...   \n",
       "제주 80대               90000                     180000                 2950000   \n",
       "제주 80대               90000                     190000                 2890000   \n",
       "제주 80대               90000                     190000                 2840000   \n",
       "제주 80대               90000                     190000                 2820000   \n",
       "제주 80대               90000                     190000                 2790000   \n",
       "\n",
       "           monthly_sbk_loan  loan_commitment  inst_rep_loanb  ls_rep_loanb  \\\n",
       "city_ages                                                                    \n",
       "서울 20대              1320000          3800000        10520000       9750000   \n",
       "서울 20대              1160000          5420000        12220000      12240000   \n",
       "서울 20대              1210000          5350000        12280000      12370000   \n",
       "서울 20대              1230000          5380000        12320000      12700000   \n",
       "서울 20대              1250000          5430000        12420000      12870000   \n",
       "...                     ...              ...             ...           ...   \n",
       "제주 80대                    0         13310000         7630000      50930000   \n",
       "제주 80대                    0         13160000         7640000      51740000   \n",
       "제주 80대                30000         14500000         7630000      52190000   \n",
       "제주 80대               110000         14670000         8020000      52530000   \n",
       "제주 80대               110000         13710000         8130000      52900000   \n",
       "\n",
       "           credit_loan  mortgage_loan  credit_card_installments_payment  \\\n",
       "city_ages                                                                 \n",
       "서울 20대        14920000       35450000                             83000   \n",
       "서울 20대        19070000       42420000                             83000   \n",
       "서울 20대        19190000       42480000                             78000   \n",
       "서울 20대        19360000       42980000                             89000   \n",
       "서울 20대        19580000       43370000                             82000   \n",
       "...                ...            ...                               ...   \n",
       "제주 80대        19950000      109270000                             29000   \n",
       "제주 80대        20300000      110210000                             23000   \n",
       "제주 80대        20580000      112510000                             37000   \n",
       "제주 80대        20510000      113690000                             28000   \n",
       "제주 80대        20580000      112110000                             27000   \n",
       "\n",
       "           monthly_spend  \n",
       "city_ages                 \n",
       "서울 20대            727000  \n",
       "서울 20대            707000  \n",
       "서울 20대            712000  \n",
       "서울 20대            771000  \n",
       "서울 20대            738000  \n",
       "...                  ...  \n",
       "제주 80대            141000  \n",
       "제주 80대            117000  \n",
       "제주 80대            133000  \n",
       "제주 80대            132000  \n",
       "제주 80대            133000  \n",
       "\n",
       "[2688 rows x 15 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# city - 지역, 나이 그룹화 X\n",
    "city = pd.read_csv('./dataset/credit_card_dataset_city.csv', encoding='utf-8')\n",
    "\n",
    "city['city_ages'] = city['city'] + ' ' +city['ages']\n",
    "city.set_index('city_ages', inplace=True)\n",
    "city.drop(['avg_rat', 'monthly_lc','monthly_loan','new_avg_rat','num_opencard','monthly_spend_rat','credit_card_payment','debit_card_payment', 'year', 'month', 'city', 'ages', 'pop_cd', 'date', 'population'], axis=1, inplace=True)\n",
    "city"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 2. city_total(그룹화 O) - 지역, 나이 제외 클러스터링"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>total_avg_score</th>\n",
       "      <th>total_num_usecard</th>\n",
       "      <th>total_monthly_card_spend</th>\n",
       "      <th>total_monthly_bk_loan</th>\n",
       "      <th>total_monthly_cd_loan</th>\n",
       "      <th>total_monthly_installments_loan</th>\n",
       "      <th>total_monthly_insurance_loan</th>\n",
       "      <th>total_monthly_sbk_loan</th>\n",
       "      <th>total_loan_commitment</th>\n",
       "      <th>total_inst_rep_loanb</th>\n",
       "      <th>total_ls_rep_loanb</th>\n",
       "      <th>total_credit_loan</th>\n",
       "      <th>total_mortgage_loan</th>\n",
       "      <th>total_credit_card_installments_payment</th>\n",
       "      <th>total_monthly_spend</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city_ages</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>강원20대</th>\n",
       "      <td>765.33836</td>\n",
       "      <td>1.75778</td>\n",
       "      <td>800765.83894</td>\n",
       "      <td>13342803.43467</td>\n",
       "      <td>584604.31655</td>\n",
       "      <td>4634859.59619</td>\n",
       "      <td>596586.21490</td>\n",
       "      <td>1633940.58946</td>\n",
       "      <td>3373334.88048</td>\n",
       "      <td>10951297.28475</td>\n",
       "      <td>8272949.64029</td>\n",
       "      <td>15542148.99049</td>\n",
       "      <td>26982527.26851</td>\n",
       "      <td>84106.05709</td>\n",
       "      <td>716659.78185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>강원30대</th>\n",
       "      <td>788.39515</td>\n",
       "      <td>2.17371</td>\n",
       "      <td>1581476.53992</td>\n",
       "      <td>32802203.69135</td>\n",
       "      <td>1121483.21103</td>\n",
       "      <td>5455085.61263</td>\n",
       "      <td>1859737.60285</td>\n",
       "      <td>1256551.03402</td>\n",
       "      <td>9153709.13943</td>\n",
       "      <td>19678325.55037</td>\n",
       "      <td>17963637.98088</td>\n",
       "      <td>25898390.03780</td>\n",
       "      <td>58283809.20614</td>\n",
       "      <td>249136.53547</td>\n",
       "      <td>1332340.00445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>강원40대</th>\n",
       "      <td>782.09554</td>\n",
       "      <td>2.08269</td>\n",
       "      <td>1748968.55563</td>\n",
       "      <td>41674360.74637</td>\n",
       "      <td>1308363.85625</td>\n",
       "      <td>5477081.89357</td>\n",
       "      <td>2389690.73946</td>\n",
       "      <td>990509.67519</td>\n",
       "      <td>14234708.01659</td>\n",
       "      <td>21862116.44782</td>\n",
       "      <td>24259758.12025</td>\n",
       "      <td>28700872.49482</td>\n",
       "      <td>76020979.61299</td>\n",
       "      <td>308625.60470</td>\n",
       "      <td>1440342.95093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>강원50대</th>\n",
       "      <td>787.82106</td>\n",
       "      <td>1.69734</td>\n",
       "      <td>1382013.44430</td>\n",
       "      <td>48399198.14341</td>\n",
       "      <td>1010505.76184</td>\n",
       "      <td>4283950.06402</td>\n",
       "      <td>1674521.44686</td>\n",
       "      <td>604651.08835</td>\n",
       "      <td>20027752.88092</td>\n",
       "      <td>17728277.84891</td>\n",
       "      <td>30016842.18950</td>\n",
       "      <td>27567828.10499</td>\n",
       "      <td>88855243.27785</td>\n",
       "      <td>281201.50448</td>\n",
       "      <td>1100811.93982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>강원60대</th>\n",
       "      <td>814.41700</td>\n",
       "      <td>1.26892</td>\n",
       "      <td>886899.01478</td>\n",
       "      <td>47033884.23645</td>\n",
       "      <td>682283.25123</td>\n",
       "      <td>2741894.08867</td>\n",
       "      <td>951364.53202</td>\n",
       "      <td>244903.94089</td>\n",
       "      <td>21054224.13793</td>\n",
       "      <td>13707046.79803</td>\n",
       "      <td>28765751.23153</td>\n",
       "      <td>20402118.22660</td>\n",
       "      <td>88609460.59113</td>\n",
       "      <td>193071.92118</td>\n",
       "      <td>693827.09360</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>충북40대</th>\n",
       "      <td>790.48589</td>\n",
       "      <td>2.05531</td>\n",
       "      <td>1695413.89153</td>\n",
       "      <td>44121227.40247</td>\n",
       "      <td>1335028.54424</td>\n",
       "      <td>5199863.62195</td>\n",
       "      <td>4290870.59943</td>\n",
       "      <td>1124418.01459</td>\n",
       "      <td>14724552.80685</td>\n",
       "      <td>28009575.00793</td>\n",
       "      <td>26445328.25880</td>\n",
       "      <td>26814839.83508</td>\n",
       "      <td>88078517.28513</td>\n",
       "      <td>280844.43387</td>\n",
       "      <td>1414569.45766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>충북50대</th>\n",
       "      <td>796.93214</td>\n",
       "      <td>1.70257</td>\n",
       "      <td>1319461.94862</td>\n",
       "      <td>48094152.52868</td>\n",
       "      <td>1027232.18614</td>\n",
       "      <td>4106568.10470</td>\n",
       "      <td>2612585.23186</td>\n",
       "      <td>669562.12635</td>\n",
       "      <td>20818392.30894</td>\n",
       "      <td>21280373.24285</td>\n",
       "      <td>32728872.19260</td>\n",
       "      <td>25475794.15091</td>\n",
       "      <td>97553478.75263</td>\n",
       "      <td>254728.38908</td>\n",
       "      <td>1064733.55954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>충북60대</th>\n",
       "      <td>819.99817</td>\n",
       "      <td>1.23465</td>\n",
       "      <td>823453.78371</td>\n",
       "      <td>45963582.08955</td>\n",
       "      <td>693896.30793</td>\n",
       "      <td>2379984.28908</td>\n",
       "      <td>1399743.38832</td>\n",
       "      <td>335326.00157</td>\n",
       "      <td>21972793.92511</td>\n",
       "      <td>15439837.65384</td>\n",
       "      <td>30714003.66588</td>\n",
       "      <td>18995428.12255</td>\n",
       "      <td>92980934.79969</td>\n",
       "      <td>166095.83661</td>\n",
       "      <td>657357.94711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>충북70대</th>\n",
       "      <td>821.57788</td>\n",
       "      <td>0.63896</td>\n",
       "      <td>374555.30474</td>\n",
       "      <td>37807697.51693</td>\n",
       "      <td>233300.22573</td>\n",
       "      <td>933169.30023</td>\n",
       "      <td>667647.85553</td>\n",
       "      <td>183507.90068</td>\n",
       "      <td>17092794.58239</td>\n",
       "      <td>10008555.30474</td>\n",
       "      <td>26452478.55530</td>\n",
       "      <td>12877729.11964</td>\n",
       "      <td>73733133.18284</td>\n",
       "      <td>72956.65914</td>\n",
       "      <td>301598.64560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>충북80대</th>\n",
       "      <td>814.23772</td>\n",
       "      <td>0.27907</td>\n",
       "      <td>138191.96429</td>\n",
       "      <td>37881551.33929</td>\n",
       "      <td>51529.01786</td>\n",
       "      <td>217332.58929</td>\n",
       "      <td>433549.10714</td>\n",
       "      <td>182042.41071</td>\n",
       "      <td>13667220.98214</td>\n",
       "      <td>8023404.01786</td>\n",
       "      <td>28134118.30357</td>\n",
       "      <td>9629241.07143</td>\n",
       "      <td>66113816.96429</td>\n",
       "      <td>22989.95536</td>\n",
       "      <td>115202.00893</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>112 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           total_avg_score  total_num_usecard  total_monthly_card_spend  \\\n",
       "city_ages                                                                 \n",
       "강원20대            765.33836            1.75778              800765.83894   \n",
       "강원30대            788.39515            2.17371             1581476.53992   \n",
       "강원40대            782.09554            2.08269             1748968.55563   \n",
       "강원50대            787.82106            1.69734             1382013.44430   \n",
       "강원60대            814.41700            1.26892              886899.01478   \n",
       "...                    ...                ...                       ...   \n",
       "충북40대            790.48589            2.05531             1695413.89153   \n",
       "충북50대            796.93214            1.70257             1319461.94862   \n",
       "충북60대            819.99817            1.23465              823453.78371   \n",
       "충북70대            821.57788            0.63896              374555.30474   \n",
       "충북80대            814.23772            0.27907              138191.96429   \n",
       "\n",
       "           total_monthly_bk_loan  total_monthly_cd_loan  \\\n",
       "city_ages                                                 \n",
       "강원20대             13342803.43467           584604.31655   \n",
       "강원30대             32802203.69135          1121483.21103   \n",
       "강원40대             41674360.74637          1308363.85625   \n",
       "강원50대             48399198.14341          1010505.76184   \n",
       "강원60대             47033884.23645           682283.25123   \n",
       "...                          ...                    ...   \n",
       "충북40대             44121227.40247          1335028.54424   \n",
       "충북50대             48094152.52868          1027232.18614   \n",
       "충북60대             45963582.08955           693896.30793   \n",
       "충북70대             37807697.51693           233300.22573   \n",
       "충북80대             37881551.33929            51529.01786   \n",
       "\n",
       "           total_monthly_installments_loan  total_monthly_insurance_loan  \\\n",
       "city_ages                                                                  \n",
       "강원20대                        4634859.59619                  596586.21490   \n",
       "강원30대                        5455085.61263                 1859737.60285   \n",
       "강원40대                        5477081.89357                 2389690.73946   \n",
       "강원50대                        4283950.06402                 1674521.44686   \n",
       "강원60대                        2741894.08867                  951364.53202   \n",
       "...                                    ...                           ...   \n",
       "충북40대                        5199863.62195                 4290870.59943   \n",
       "충북50대                        4106568.10470                 2612585.23186   \n",
       "충북60대                        2379984.28908                 1399743.38832   \n",
       "충북70대                         933169.30023                  667647.85553   \n",
       "충북80대                         217332.58929                  433549.10714   \n",
       "\n",
       "           total_monthly_sbk_loan  total_loan_commitment  \\\n",
       "city_ages                                                  \n",
       "강원20대               1633940.58946          3373334.88048   \n",
       "강원30대               1256551.03402          9153709.13943   \n",
       "강원40대                990509.67519         14234708.01659   \n",
       "강원50대                604651.08835         20027752.88092   \n",
       "강원60대                244903.94089         21054224.13793   \n",
       "...                           ...                    ...   \n",
       "충북40대               1124418.01459         14724552.80685   \n",
       "충북50대                669562.12635         20818392.30894   \n",
       "충북60대                335326.00157         21972793.92511   \n",
       "충북70대                183507.90068         17092794.58239   \n",
       "충북80대                182042.41071         13667220.98214   \n",
       "\n",
       "           total_inst_rep_loanb  total_ls_rep_loanb  total_credit_loan  \\\n",
       "city_ages                                                                \n",
       "강원20대            10951297.28475       8272949.64029     15542148.99049   \n",
       "강원30대            19678325.55037      17963637.98088     25898390.03780   \n",
       "강원40대            21862116.44782      24259758.12025     28700872.49482   \n",
       "강원50대            17728277.84891      30016842.18950     27567828.10499   \n",
       "강원60대            13707046.79803      28765751.23153     20402118.22660   \n",
       "...                         ...                 ...                ...   \n",
       "충북40대            28009575.00793      26445328.25880     26814839.83508   \n",
       "충북50대            21280373.24285      32728872.19260     25475794.15091   \n",
       "충북60대            15439837.65384      30714003.66588     18995428.12255   \n",
       "충북70대            10008555.30474      26452478.55530     12877729.11964   \n",
       "충북80대             8023404.01786      28134118.30357      9629241.07143   \n",
       "\n",
       "           total_mortgage_loan  total_credit_card_installments_payment  \\\n",
       "city_ages                                                                \n",
       "강원20대           26982527.26851                             84106.05709   \n",
       "강원30대           58283809.20614                            249136.53547   \n",
       "강원40대           76020979.61299                            308625.60470   \n",
       "강원50대           88855243.27785                            281201.50448   \n",
       "강원60대           88609460.59113                            193071.92118   \n",
       "...                        ...                                     ...   \n",
       "충북40대           88078517.28513                            280844.43387   \n",
       "충북50대           97553478.75263                            254728.38908   \n",
       "충북60대           92980934.79969                            166095.83661   \n",
       "충북70대           73733133.18284                             72956.65914   \n",
       "충북80대           66113816.96429                             22989.95536   \n",
       "\n",
       "           total_monthly_spend  \n",
       "city_ages                       \n",
       "강원20대             716659.78185  \n",
       "강원30대            1332340.00445  \n",
       "강원40대            1440342.95093  \n",
       "강원50대            1100811.93982  \n",
       "강원60대             693827.09360  \n",
       "...                        ...  \n",
       "충북40대            1414569.45766  \n",
       "충북50대            1064733.55954  \n",
       "충북60대             657357.94711  \n",
       "충북70대             301598.64560  \n",
       "충북80대             115202.00893  \n",
       "\n",
       "[112 rows x 15 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 지역, 나이 그룹화\n",
    "city_total = pd.read_csv('./dataset/credit_card_dataset_city_total.csv', encoding='utf-8')\n",
    "\n",
    "city_total['city_ages'] = city_total['city'] + city_total['ages']\n",
    "city_total = city_total.groupby('city_ages').sum().divide(city_total.groupby('city_ages')['population'].sum(), axis='index')\n",
    "city_total.drop(['avg_rat', 'total_avg_rat', 'total_monthly_lc','total_monthly_loan','new_avg_rat','total_num_opencard','monthly_spend_rat','total_credit_card_payment','total_debit_card_payment', 'avg_score', 'year', 'month', 'population'], axis=1, inplace=True)\n",
    "city_total"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 3. city_encoding - 지역, 나이 one-hot-encoding 후 포함하여 클러스터링"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "city_encoding = pd.read_csv('./dataset/credit_card_dataset_city.csv', encoding='utf-8')\n",
    "\n",
    "city_encoding['city_ages'] = city_encoding['city'] + ' ' + city_encoding['ages']\n",
    "city_encoding.set_index('city_ages', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 원핫인코딩\n",
    "city = pd.get_dummies(data=city_encoding, columns=['city'], prefix = 'city').iloc[:,31:]\n",
    "ages = pd.get_dummies(data=city_encoding, columns=['ages'], prefix = 'ages').iloc[:,31:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>avg_score</th>\n",
       "      <th>num_usecard</th>\n",
       "      <th>monthly_card_spend</th>\n",
       "      <th>monthly_bk_loan</th>\n",
       "      <th>monthly_cd_loan</th>\n",
       "      <th>monthly_installments_loan</th>\n",
       "      <th>monthly_insurance_loan</th>\n",
       "      <th>monthly_sbk_loan</th>\n",
       "      <th>loan_commitment</th>\n",
       "      <th>inst_rep_loanb</th>\n",
       "      <th>...</th>\n",
       "      <th>city_전남</th>\n",
       "      <th>city_전북</th>\n",
       "      <th>city_제주</th>\n",
       "      <th>city_충남</th>\n",
       "      <th>city_충북</th>\n",
       "      <th>ages_40대</th>\n",
       "      <th>ages_50대</th>\n",
       "      <th>ages_60대</th>\n",
       "      <th>ages_70대</th>\n",
       "      <th>ages_80대</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city_ages</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>서울 20대</th>\n",
       "      <td>740</td>\n",
       "      <td>1.99000</td>\n",
       "      <td>810000</td>\n",
       "      <td>18500000</td>\n",
       "      <td>620000</td>\n",
       "      <td>2110000</td>\n",
       "      <td>680000</td>\n",
       "      <td>1320000</td>\n",
       "      <td>3800000</td>\n",
       "      <td>10520000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>서울 20대</th>\n",
       "      <td>782</td>\n",
       "      <td>1.75000</td>\n",
       "      <td>790000</td>\n",
       "      <td>24150000</td>\n",
       "      <td>680000</td>\n",
       "      <td>2300000</td>\n",
       "      <td>790000</td>\n",
       "      <td>1160000</td>\n",
       "      <td>5420000</td>\n",
       "      <td>12220000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>서울 20대</th>\n",
       "      <td>782</td>\n",
       "      <td>1.74000</td>\n",
       "      <td>790000</td>\n",
       "      <td>24240000</td>\n",
       "      <td>680000</td>\n",
       "      <td>2300000</td>\n",
       "      <td>790000</td>\n",
       "      <td>1210000</td>\n",
       "      <td>5350000</td>\n",
       "      <td>12280000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>서울 20대</th>\n",
       "      <td>782</td>\n",
       "      <td>1.75000</td>\n",
       "      <td>860000</td>\n",
       "      <td>24570000</td>\n",
       "      <td>680000</td>\n",
       "      <td>2300000</td>\n",
       "      <td>790000</td>\n",
       "      <td>1230000</td>\n",
       "      <td>5380000</td>\n",
       "      <td>12320000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>서울 20대</th>\n",
       "      <td>781</td>\n",
       "      <td>1.75000</td>\n",
       "      <td>820000</td>\n",
       "      <td>24830000</td>\n",
       "      <td>700000</td>\n",
       "      <td>2320000</td>\n",
       "      <td>770000</td>\n",
       "      <td>1250000</td>\n",
       "      <td>5430000</td>\n",
       "      <td>12420000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>제주 80대</th>\n",
       "      <td>804</td>\n",
       "      <td>0.27000</td>\n",
       "      <td>170000</td>\n",
       "      <td>60420000</td>\n",
       "      <td>90000</td>\n",
       "      <td>180000</td>\n",
       "      <td>2950000</td>\n",
       "      <td>0</td>\n",
       "      <td>13310000</td>\n",
       "      <td>7630000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>제주 80대</th>\n",
       "      <td>802</td>\n",
       "      <td>0.27000</td>\n",
       "      <td>140000</td>\n",
       "      <td>61450000</td>\n",
       "      <td>90000</td>\n",
       "      <td>190000</td>\n",
       "      <td>2890000</td>\n",
       "      <td>0</td>\n",
       "      <td>13160000</td>\n",
       "      <td>7640000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>제주 80대</th>\n",
       "      <td>802</td>\n",
       "      <td>0.27000</td>\n",
       "      <td>170000</td>\n",
       "      <td>63420000</td>\n",
       "      <td>90000</td>\n",
       "      <td>190000</td>\n",
       "      <td>2840000</td>\n",
       "      <td>30000</td>\n",
       "      <td>14500000</td>\n",
       "      <td>7630000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>제주 80대</th>\n",
       "      <td>802</td>\n",
       "      <td>0.27000</td>\n",
       "      <td>160000</td>\n",
       "      <td>64170000</td>\n",
       "      <td>90000</td>\n",
       "      <td>190000</td>\n",
       "      <td>2820000</td>\n",
       "      <td>110000</td>\n",
       "      <td>14670000</td>\n",
       "      <td>8020000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>제주 80대</th>\n",
       "      <td>802</td>\n",
       "      <td>0.28000</td>\n",
       "      <td>160000</td>\n",
       "      <td>63200000</td>\n",
       "      <td>90000</td>\n",
       "      <td>190000</td>\n",
       "      <td>2790000</td>\n",
       "      <td>110000</td>\n",
       "      <td>13710000</td>\n",
       "      <td>8130000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2688 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           avg_score  num_usecard  monthly_card_spend  monthly_bk_loan  \\\n",
       "city_ages                                                                \n",
       "서울 20대           740      1.99000              810000         18500000   \n",
       "서울 20대           782      1.75000              790000         24150000   \n",
       "서울 20대           782      1.74000              790000         24240000   \n",
       "서울 20대           782      1.75000              860000         24570000   \n",
       "서울 20대           781      1.75000              820000         24830000   \n",
       "...              ...          ...                 ...              ...   \n",
       "제주 80대           804      0.27000              170000         60420000   \n",
       "제주 80대           802      0.27000              140000         61450000   \n",
       "제주 80대           802      0.27000              170000         63420000   \n",
       "제주 80대           802      0.27000              160000         64170000   \n",
       "제주 80대           802      0.28000              160000         63200000   \n",
       "\n",
       "           monthly_cd_loan  monthly_installments_loan  monthly_insurance_loan  \\\n",
       "city_ages                                                                       \n",
       "서울 20대              620000                    2110000                  680000   \n",
       "서울 20대              680000                    2300000                  790000   \n",
       "서울 20대              680000                    2300000                  790000   \n",
       "서울 20대              680000                    2300000                  790000   \n",
       "서울 20대              700000                    2320000                  770000   \n",
       "...                    ...                        ...                     ...   \n",
       "제주 80대               90000                     180000                 2950000   \n",
       "제주 80대               90000                     190000                 2890000   \n",
       "제주 80대               90000                     190000                 2840000   \n",
       "제주 80대               90000                     190000                 2820000   \n",
       "제주 80대               90000                     190000                 2790000   \n",
       "\n",
       "           monthly_sbk_loan  loan_commitment  inst_rep_loanb  ...  city_전남  \\\n",
       "city_ages                                                     ...            \n",
       "서울 20대              1320000          3800000        10520000  ...        0   \n",
       "서울 20대              1160000          5420000        12220000  ...        0   \n",
       "서울 20대              1210000          5350000        12280000  ...        0   \n",
       "서울 20대              1230000          5380000        12320000  ...        0   \n",
       "서울 20대              1250000          5430000        12420000  ...        0   \n",
       "...                     ...              ...             ...  ...      ...   \n",
       "제주 80대                    0         13310000         7630000  ...        0   \n",
       "제주 80대                    0         13160000         7640000  ...        0   \n",
       "제주 80대                30000         14500000         7630000  ...        0   \n",
       "제주 80대               110000         14670000         8020000  ...        0   \n",
       "제주 80대               110000         13710000         8130000  ...        0   \n",
       "\n",
       "           city_전북  city_제주  city_충남  city_충북  ages_40대  ages_50대  ages_60대  \\\n",
       "city_ages                                                                     \n",
       "서울 20대           0        0        0        0         0         0         0   \n",
       "서울 20대           0        0        0        0         0         0         0   \n",
       "서울 20대           0        0        0        0         0         0         0   \n",
       "서울 20대           0        0        0        0         0         0         0   \n",
       "서울 20대           0        0        0        0         0         0         0   \n",
       "...            ...      ...      ...      ...       ...       ...       ...   \n",
       "제주 80대           0        1        0        0         0         0         0   \n",
       "제주 80대           0        1        0        0         0         0         0   \n",
       "제주 80대           0        1        0        0         0         0         0   \n",
       "제주 80대           0        1        0        0         0         0         0   \n",
       "제주 80대           0        1        0        0         0         0         0   \n",
       "\n",
       "           ages_70대  ages_80대  \n",
       "city_ages                      \n",
       "서울 20대            0         0  \n",
       "서울 20대            0         0  \n",
       "서울 20대            0         0  \n",
       "서울 20대            0         0  \n",
       "서울 20대            0         0  \n",
       "...             ...       ...  \n",
       "제주 80대            0         1  \n",
       "제주 80대            0         1  \n",
       "제주 80대            0         1  \n",
       "제주 80대            0         1  \n",
       "제주 80대            0         1  \n",
       "\n",
       "[2688 rows x 34 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_encoding.drop(['avg_rat', 'monthly_lc','monthly_loan','new_avg_rat','num_opencard','monthly_spend_rat','credit_card_payment','debit_card_payment', 'year', 'month', 'city', 'ages', 'pop_cd', 'date', 'population'], axis=1, inplace=True)\n",
    "city_encoding = pd.concat([city_encoding, city, ages], axis = 1)\n",
    "city_encoding"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 4. df_total - 성별, 지역, 나이 제외"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>avg_score</th>\n",
       "      <th>num_usecard</th>\n",
       "      <th>monthly_card_spend</th>\n",
       "      <th>monthly_bk_loan</th>\n",
       "      <th>monthly_cd_loan</th>\n",
       "      <th>monthly_installments_loan</th>\n",
       "      <th>monthly_insurance_loan</th>\n",
       "      <th>monthly_sbk_loan</th>\n",
       "      <th>loan_commitment</th>\n",
       "      <th>inst_rep_loanb</th>\n",
       "      <th>ls_rep_loanb</th>\n",
       "      <th>credit_loan</th>\n",
       "      <th>mortgage_loan</th>\n",
       "      <th>credit_card_installments_payment</th>\n",
       "      <th>monthly_spend</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>740</td>\n",
       "      <td>1.99000</td>\n",
       "      <td>810000</td>\n",
       "      <td>18500000</td>\n",
       "      <td>620000</td>\n",
       "      <td>2110000</td>\n",
       "      <td>680000</td>\n",
       "      <td>1320000</td>\n",
       "      <td>3800000</td>\n",
       "      <td>10520000</td>\n",
       "      <td>9750000</td>\n",
       "      <td>14920000</td>\n",
       "      <td>35450000</td>\n",
       "      <td>83000</td>\n",
       "      <td>727000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>782</td>\n",
       "      <td>1.75000</td>\n",
       "      <td>790000</td>\n",
       "      <td>24150000</td>\n",
       "      <td>680000</td>\n",
       "      <td>2300000</td>\n",
       "      <td>790000</td>\n",
       "      <td>1160000</td>\n",
       "      <td>5420000</td>\n",
       "      <td>12220000</td>\n",
       "      <td>12240000</td>\n",
       "      <td>19070000</td>\n",
       "      <td>42420000</td>\n",
       "      <td>83000</td>\n",
       "      <td>707000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>782</td>\n",
       "      <td>1.74000</td>\n",
       "      <td>790000</td>\n",
       "      <td>24240000</td>\n",
       "      <td>680000</td>\n",
       "      <td>2300000</td>\n",
       "      <td>790000</td>\n",
       "      <td>1210000</td>\n",
       "      <td>5350000</td>\n",
       "      <td>12280000</td>\n",
       "      <td>12370000</td>\n",
       "      <td>19190000</td>\n",
       "      <td>42480000</td>\n",
       "      <td>78000</td>\n",
       "      <td>712000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>782</td>\n",
       "      <td>1.75000</td>\n",
       "      <td>860000</td>\n",
       "      <td>24570000</td>\n",
       "      <td>680000</td>\n",
       "      <td>2300000</td>\n",
       "      <td>790000</td>\n",
       "      <td>1230000</td>\n",
       "      <td>5380000</td>\n",
       "      <td>12320000</td>\n",
       "      <td>12700000</td>\n",
       "      <td>19360000</td>\n",
       "      <td>42980000</td>\n",
       "      <td>89000</td>\n",
       "      <td>771000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>781</td>\n",
       "      <td>1.75000</td>\n",
       "      <td>820000</td>\n",
       "      <td>24830000</td>\n",
       "      <td>700000</td>\n",
       "      <td>2320000</td>\n",
       "      <td>770000</td>\n",
       "      <td>1250000</td>\n",
       "      <td>5430000</td>\n",
       "      <td>12420000</td>\n",
       "      <td>12870000</td>\n",
       "      <td>19580000</td>\n",
       "      <td>43370000</td>\n",
       "      <td>82000</td>\n",
       "      <td>738000</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3019</th>\n",
       "      <td>818</td>\n",
       "      <td>0.18000</td>\n",
       "      <td>200000</td>\n",
       "      <td>58460000</td>\n",
       "      <td>230000</td>\n",
       "      <td>260000</td>\n",
       "      <td>1610000</td>\n",
       "      <td>290000</td>\n",
       "      <td>8600000</td>\n",
       "      <td>18710000</td>\n",
       "      <td>41980000</td>\n",
       "      <td>14140000</td>\n",
       "      <td>84270000</td>\n",
       "      <td>43000</td>\n",
       "      <td>157000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3020</th>\n",
       "      <td>818</td>\n",
       "      <td>0.18000</td>\n",
       "      <td>190000</td>\n",
       "      <td>58330000</td>\n",
       "      <td>230000</td>\n",
       "      <td>270000</td>\n",
       "      <td>1610000</td>\n",
       "      <td>310000</td>\n",
       "      <td>8550000</td>\n",
       "      <td>18750000</td>\n",
       "      <td>41870000</td>\n",
       "      <td>14230000</td>\n",
       "      <td>84120000</td>\n",
       "      <td>39000</td>\n",
       "      <td>151000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3021</th>\n",
       "      <td>818</td>\n",
       "      <td>0.18000</td>\n",
       "      <td>210000</td>\n",
       "      <td>58190000</td>\n",
       "      <td>240000</td>\n",
       "      <td>290000</td>\n",
       "      <td>1610000</td>\n",
       "      <td>430000</td>\n",
       "      <td>8630000</td>\n",
       "      <td>18760000</td>\n",
       "      <td>41770000</td>\n",
       "      <td>14250000</td>\n",
       "      <td>84200000</td>\n",
       "      <td>49000</td>\n",
       "      <td>161000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3022</th>\n",
       "      <td>819</td>\n",
       "      <td>0.18000</td>\n",
       "      <td>200000</td>\n",
       "      <td>58420000</td>\n",
       "      <td>240000</td>\n",
       "      <td>300000</td>\n",
       "      <td>1610000</td>\n",
       "      <td>470000</td>\n",
       "      <td>8570000</td>\n",
       "      <td>18690000</td>\n",
       "      <td>42130000</td>\n",
       "      <td>14330000</td>\n",
       "      <td>84390000</td>\n",
       "      <td>44000</td>\n",
       "      <td>156000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3023</th>\n",
       "      <td>819</td>\n",
       "      <td>0.18000</td>\n",
       "      <td>200000</td>\n",
       "      <td>58170000</td>\n",
       "      <td>250000</td>\n",
       "      <td>300000</td>\n",
       "      <td>1640000</td>\n",
       "      <td>430000</td>\n",
       "      <td>8590000</td>\n",
       "      <td>18670000</td>\n",
       "      <td>41890000</td>\n",
       "      <td>14240000</td>\n",
       "      <td>84050000</td>\n",
       "      <td>44000</td>\n",
       "      <td>156000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3024 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      avg_score  num_usecard  monthly_card_spend  monthly_bk_loan  \\\n",
       "0           740      1.99000              810000         18500000   \n",
       "1           782      1.75000              790000         24150000   \n",
       "2           782      1.74000              790000         24240000   \n",
       "3           782      1.75000              860000         24570000   \n",
       "4           781      1.75000              820000         24830000   \n",
       "...         ...          ...                 ...              ...   \n",
       "3019        818      0.18000              200000         58460000   \n",
       "3020        818      0.18000              190000         58330000   \n",
       "3021        818      0.18000              210000         58190000   \n",
       "3022        819      0.18000              200000         58420000   \n",
       "3023        819      0.18000              200000         58170000   \n",
       "\n",
       "      monthly_cd_loan  monthly_installments_loan  monthly_insurance_loan  \\\n",
       "0              620000                    2110000                  680000   \n",
       "1              680000                    2300000                  790000   \n",
       "2              680000                    2300000                  790000   \n",
       "3              680000                    2300000                  790000   \n",
       "4              700000                    2320000                  770000   \n",
       "...               ...                        ...                     ...   \n",
       "3019           230000                     260000                 1610000   \n",
       "3020           230000                     270000                 1610000   \n",
       "3021           240000                     290000                 1610000   \n",
       "3022           240000                     300000                 1610000   \n",
       "3023           250000                     300000                 1640000   \n",
       "\n",
       "      monthly_sbk_loan  loan_commitment  inst_rep_loanb  ls_rep_loanb  \\\n",
       "0              1320000          3800000        10520000       9750000   \n",
       "1              1160000          5420000        12220000      12240000   \n",
       "2              1210000          5350000        12280000      12370000   \n",
       "3              1230000          5380000        12320000      12700000   \n",
       "4              1250000          5430000        12420000      12870000   \n",
       "...                ...              ...             ...           ...   \n",
       "3019            290000          8600000        18710000      41980000   \n",
       "3020            310000          8550000        18750000      41870000   \n",
       "3021            430000          8630000        18760000      41770000   \n",
       "3022            470000          8570000        18690000      42130000   \n",
       "3023            430000          8590000        18670000      41890000   \n",
       "\n",
       "      credit_loan  mortgage_loan  credit_card_installments_payment  \\\n",
       "0        14920000       35450000                             83000   \n",
       "1        19070000       42420000                             83000   \n",
       "2        19190000       42480000                             78000   \n",
       "3        19360000       42980000                             89000   \n",
       "4        19580000       43370000                             82000   \n",
       "...           ...            ...                               ...   \n",
       "3019     14140000       84270000                             43000   \n",
       "3020     14230000       84120000                             39000   \n",
       "3021     14250000       84200000                             49000   \n",
       "3022     14330000       84390000                             44000   \n",
       "3023     14240000       84050000                             44000   \n",
       "\n",
       "      monthly_spend  \n",
       "0            727000  \n",
       "1            707000  \n",
       "2            712000  \n",
       "3            771000  \n",
       "4            738000  \n",
       "...             ...  \n",
       "3019         157000  \n",
       "3020         151000  \n",
       "3021         161000  \n",
       "3022         156000  \n",
       "3023         156000  \n",
       "\n",
       "[3024 rows x 15 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_total = pd.read_csv('./dataset/credit_card_dataset.csv', encoding='utf-8')\n",
    "\n",
    "# 클러스터링 후 합쳐줄 dataframe\n",
    "tmp_df = df_total[['city', 'ages', 'sex']]\n",
    "tmp_total_df = df_total[['avg_rat', 'monthly_loan', 'credit_card_payment', 'debit_card_payment']]\n",
    "\n",
    "df_total.drop(['avg_rat', 'monthly_lc','monthly_loan','new_avg_rat','num_opencard','monthly_spend_rat','credit_card_payment','debit_card_payment', 'year', 'month', 'city', 'ages', 'sex', 'pop_cd', 'date', 'population'], axis=1, inplace=True)\n",
    "df_total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['avg_score', 'num_usecard', 'monthly_card_spend', 'monthly_bk_loan',\n",
       "       'monthly_cd_loan', 'monthly_installments_loan',\n",
       "       'monthly_insurance_loan', 'monthly_sbk_loan', 'loan_commitment',\n",
       "       'inst_rep_loanb', 'ls_rep_loanb', 'credit_loan', 'mortgage_loan',\n",
       "       'credit_card_installments_payment', 'monthly_spend'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_total.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 클러스터링 시각화 코드"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cluster_visualization(dataframe):\n",
    "    dataframe_new = dataframe[['city_ages', 'cluster', 'city', 'age']]\n",
    "    dataframe_new.set_index('city_ages', inplace=True)\n",
    "    \n",
    "    cluster_pivot = pd.pivot_table(dataframe_new, index='city', columns='age',values=['cluster'])\n",
    "    cluster_pivot = cluster_pivot.loc[['서울', '경기', '인천', '대구', '부산', '울산', '대전', '광주', '강원', '경남', '충남', '충북', '경북', '전남', '전북', '제주']].copy()\n",
    "    \n",
    "    # 지역별 연령별 클러스터링 결과 시각화\n",
    "    cmap = cm.get_cmap('Accent', cluster_num)\n",
    "    fig, ax = plt.subplots(figsize=(14, 10))\n",
    "    im = ax.imshow(cluster_pivot, cmap = cmap, vmin=-0.5, vmax=cluster_num-0.5)\n",
    "    fig.colorbar(im)\n",
    "    plt.xticks(range(0,7), cluster_pivot.columns.get_level_values(1))\n",
    "    plt.yticks(range(0,16), cluster_pivot.index)\n",
    "    plt.show()\n",
    "\n",
    "    return cluster_pivot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### K_Means Clustering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 최적의 군집수 찾기"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. elbow_methond\n",
    "\n",
    "- Cluster 간의 거리의 합을 나타내는 inertia가 급격히 떨어지는 구간 생기는데, 이 지점의 k값을 군집의 개수로 사용\n",
    "- inertia 값이 작을수록 응집도가 높고, 군집화가 잘 되었다고 평가함\n",
    "- inertia_ 속성으로 확인"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "def elbow_method(dataframe):\n",
    "    inertia = []\n",
    "    for i in range(1, 16):\n",
    "        kmeans_plus = KMeans(n_clusters=i, init = 'k-means++', random_state=121)\n",
    "        kmeans_plus.fit(dataframe)\n",
    "        print('k :', i, '    inertia :', kmeans_plus.inertia_)\n",
    "        inertia.append(kmeans_plus.inertia_)\n",
    "    inertia = np.array(inertia)\n",
    "\n",
    "    plt.plot(range(1, 16), inertia, marker='o')\n",
    "    plt.vlines(4, ymin=inertia.min(), ymax=inertia.max(), linestyles='--', colors='g')\n",
    "    plt.vlines(5, ymin=inertia.min(), ymax=inertia.max(), linestyles='--', colors='r')\n",
    "    plt.xlabel('number of clusters')\n",
    "    plt.ylabel('inertia')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. 실루엣 계수\n",
    "- 1에 가까울 수록 군집화 잘 되었다고 판단"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Silhouette(dataframe):\n",
    "    silhouette = []\n",
    "    best_n = -1\n",
    "    best_silhouette_score = -1\n",
    "\n",
    "    for i in range(2, 15):\n",
    "        kmeans_plus = KMeans(n_clusters=i, init = 'k-means++', random_state=121)\n",
    "        pred = kmeans_plus.fit_predict(dataframe)\n",
    "        \n",
    "        score = silhouette_score(dataframe, pred)\n",
    "        print('k :', i, '      score :', score)\n",
    "        silhouette.append(score)\n",
    "        if score > best_silhouette_score:\n",
    "            best_n = i\n",
    "            best_silhouette_score = score\n",
    "        \n",
    "    print('best n :', best_n, '     best score :', best_silhouette_score)\n",
    "\n",
    "    plt.plot(range(2, 15), silhouette, marker='o')\n",
    "    plt.xlabel('Number of Cluster')\n",
    "    plt.ylabel('Silhouette')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 데이터들의 실루엣 계수를 계산해서 시각화 해주는 함수\n",
    "### 여러개의 클러스터링 갯수를 List로 입력 받아 각각의 실루엣 계수를 면적으로 시각화한 함수 작성\n",
    "\n",
    "def visualize_silhouette(cluster_lists, dataframe):\n",
    "    from sklearn.datasets import make_blobs\n",
    "    from sklearn.cluster import KMeans\n",
    "    from sklearn.metrics import silhouette_samples, silhouette_score\n",
    "\n",
    "    import matplotlib.pyplot as plt\n",
    "    import matplotlib.cm as cm\n",
    "    import math\n",
    "\n",
    "    # 입력값으로 클러스터링 갯수들을 리스트로 받아서, 각 갯수별로 클러스터링을 적용하고 실루엣 개수를 구함\n",
    "    n_cols = len(cluster_lists)\n",
    "\n",
    "    # plt.subplots()으로 리스트에 기재된 클러스터링 수만큼의 sub figures를 가지는 axs 생성\n",
    "    fig, axs = plt.subplots(figsize=(4*n_cols, 4), nrows=1, ncols=n_cols)\n",
    "\n",
    "    # 리스트에 기재된 클러스터링 갯수들을 차례로 iteration 수행하면서 실루엣 개수 시각화\n",
    "    for ind, n_cluster in enumerate(cluster_lists):\n",
    "\n",
    "        # KMeans 클러스터링 수행하고, 실루엣 스코어와 개별 데이터의 실루엣 값 계산\n",
    "        clusterer = KMeans(n_clusters = n_cluster, max_iter=500, random_state=0)\n",
    "        cluster_labels = clusterer.fit_predict(dataframe)\n",
    "\n",
    "        sil_avg = silhouette_score(dataframe, cluster_labels)\n",
    "        sil_values = silhouette_samples(dataframe, cluster_labels)\n",
    "\n",
    "        y_lower = 10\n",
    "        axs[ind].set_title('Number of Cluster : ' + str(n_cluster) + '\\n' + 'Silhouette Score : ' + str(round(sil_avg, 3)))\n",
    "        axs[ind].set_xlabel(\"The Silhouette Coefficient Values\")\n",
    "        axs[ind].set_ylabel(\"Cluster Label\")\n",
    "        axs[ind].set_xlim([-0.1, 1])\n",
    "        axs[ind].set_ylim([0, len(dataframe) + (n_cluster + 1) * 10])\n",
    "        axs[ind].set_yticks([])\n",
    "        axs[ind].set_xticks([0, 0.2, 0.6, 0.8, 1])\n",
    "\n",
    "        # 클러스터링 갯수별로 fill_betweenx() 형태의 막대 그래프 표형\n",
    "        for i in range(n_cluster):\n",
    "            ith_cluster_sil_values = sil_values[cluster_labels == i]\n",
    "            ith_cluster_sil_values.sort()\n",
    "\n",
    "            size_cluster_i = ith_cluster_sil_values.shape[0]\n",
    "            y_upper = y_lower + size_cluster_i\n",
    "\n",
    "            color = cm.nipy_spectral(float(i) / n_cluster)\n",
    "            axs[ind].fill_betweenx(np.arange(y_lower, y_upper), 0, ith_cluster_sil_values, facecolor = color, edgecolor = color, alpha = 0.7)\n",
    "            axs[ind].text(-0.05, y_lower + 0.5 * size_cluster_i, str(i))\n",
    "            y_lower = y_upper + 10\n",
    "        \n",
    "        axs[ind].axvline(x=sil_avg, color='red', linestyle='--')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### K_means 클러스터링 함수"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "def k_means_clustering(dataframe, cluster_num, scaler):\n",
    "    kmeans = KMeans(n_clusters=cluster_num, init='k-means++', max_iter=1000000, random_state=121)\n",
    "\n",
    "    df_scaler = scaler.fit_transform(dataframe)\n",
    "    predict = kmeans.fit_predict(df_scaler)\n",
    "\n",
    "    output_df = pd.DataFrame(dataframe, columns = dataframe.columns, index=dataframe.index)\n",
    "    output_df['cluster'] = predict\n",
    "    if 'city_ages' == dataframe.index.name:\n",
    "        output_df.reset_index(inplace=True)\n",
    "        output_df['city'] = output_df['city_ages'].str.slice(start=0, stop=2)\n",
    "        output_df['age'] = output_df['city_ages'].str.slice(start=2, stop=6)\n",
    "\n",
    "    return output_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "cluster_num = 6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 1. 지역, 나이 포함 (city_encoding)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cluster\n",
       "0    384\n",
       "1    392\n",
       "2    384\n",
       "3    384\n",
       "4    760\n",
       "5    384\n",
       "Name: avg_score, dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_encoding_df = city_encoding.copy()\n",
    "city_encoding_kmeans = k_means_clustering(city_encoding_df, cluster_num, scaler)\n",
    "city_encoding_kmeans.groupby(['cluster'])['avg_score'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "서울 20대 [1]\n",
      "인천 20대 [1]\n",
      "대전 20대 [1]\n",
      "대구 20대 [1]\n",
      "광주 20대 [1]\n",
      "부산 20대 [1]\n",
      "울산 20대 [1]\n",
      "경기 20대 [1]\n",
      "강원 20대 [1]\n",
      "충북 20대 [1]\n",
      "충남 20대 [1]\n",
      "전북 20대 [1]\n",
      "전남 20대 [1]\n",
      "경북 20대 [1]\n",
      "경남 20대 [1]\n",
      "제주 20대 [1]\n",
      "서울 30대 [4]\n",
      "인천 30대 [4]\n",
      "대전 30대 [4]\n",
      "대구 30대 [4]\n",
      "광주 30대 [4]\n",
      "부산 30대 [4]\n",
      "울산 30대 [4]\n",
      "경기 30대 [4]\n",
      "강원 30대 [1 4]\n",
      "충북 30대 [4]\n",
      "충남 30대 [4]\n",
      "전북 30대 [1 4]\n",
      "전남 30대 [1 4]\n",
      "경북 30대 [1 4]\n",
      "경남 30대 [4]\n",
      "제주 30대 [1 4]\n",
      "서울 40대 [4]\n",
      "인천 40대 [4]\n",
      "대전 40대 [4]\n",
      "대구 40대 [4]\n",
      "광주 40대 [4]\n",
      "부산 40대 [4]\n",
      "울산 40대 [4]\n",
      "경기 40대 [4]\n",
      "강원 40대 [4]\n",
      "충북 40대 [4]\n",
      "충남 40대 [4]\n",
      "전북 40대 [4]\n",
      "전남 40대 [4]\n",
      "경북 40대 [4]\n",
      "경남 40대 [4]\n",
      "제주 40대 [4]\n",
      "서울 50대 [3]\n",
      "인천 50대 [3]\n",
      "대전 50대 [3]\n",
      "대구 50대 [3]\n",
      "광주 50대 [3]\n",
      "부산 50대 [3]\n",
      "울산 50대 [3]\n",
      "경기 50대 [3]\n",
      "강원 50대 [3]\n",
      "충북 50대 [3]\n",
      "충남 50대 [3]\n",
      "전북 50대 [3]\n",
      "전남 50대 [3]\n",
      "경북 50대 [3]\n",
      "경남 50대 [3]\n",
      "제주 50대 [3]\n",
      "서울 60대 [2]\n",
      "인천 60대 [2]\n",
      "대전 60대 [2]\n",
      "대구 60대 [2]\n",
      "광주 60대 [2]\n",
      "부산 60대 [2]\n",
      "울산 60대 [2]\n",
      "경기 60대 [2]\n",
      "강원 60대 [2]\n",
      "충북 60대 [2]\n",
      "충남 60대 [2]\n",
      "전북 60대 [2]\n",
      "전남 60대 [2]\n",
      "경북 60대 [2]\n",
      "경남 60대 [2]\n",
      "제주 60대 [2]\n",
      "서울 70대 [5]\n",
      "인천 70대 [5]\n",
      "대전 70대 [5]\n",
      "대구 70대 [5]\n",
      "광주 70대 [5]\n",
      "부산 70대 [5]\n",
      "울산 70대 [5]\n",
      "경기 70대 [5]\n",
      "강원 70대 [5]\n",
      "충북 70대 [5]\n",
      "충남 70대 [5]\n",
      "전북 70대 [5]\n",
      "전남 70대 [5]\n",
      "경북 70대 [5]\n",
      "경남 70대 [5]\n",
      "제주 70대 [5]\n",
      "서울 80대 [0]\n",
      "인천 80대 [0]\n",
      "대전 80대 [0]\n",
      "대구 80대 [0]\n",
      "광주 80대 [0]\n",
      "부산 80대 [0]\n",
      "울산 80대 [0]\n",
      "경기 80대 [0]\n",
      "강원 80대 [0]\n",
      "충북 80대 [0]\n",
      "충남 80대 [0]\n",
      "전북 80대 [0]\n",
      "전남 80대 [0]\n",
      "경북 80대 [0]\n",
      "경남 80대 [0]\n",
      "제주 80대 [0]\n"
     ]
    }
   ],
   "source": [
    "for city_age in city_encoding_kmeans['city_ages'].unique():\n",
    "    print(city_age, city_encoding_kmeans[city_encoding_kmeans['city_ages']==city_age]['cluster'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "city_encoding_kmeans.loc[(city_encoding_kmeans['city_ages']=='강원 30대'), 'cluster'] = 4\n",
    "city_encoding_kmeans.loc[(city_encoding_kmeans['city_ages']=='전북 30대'), 'cluster'] = 4\n",
    "city_encoding_kmeans.loc[(city_encoding_kmeans['city_ages']=='전남 30대'), 'cluster'] = 4\n",
    "city_encoding_kmeans.loc[(city_encoding_kmeans['city_ages']=='경북 30대'), 'cluster'] = 4\n",
    "city_encoding_kmeans.loc[(city_encoding_kmeans['city_ages']=='제주 30대'), 'cluster'] = 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1008x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"7\" halign=\"left\">cluster</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <th>20대</th>\n",
       "      <th>30대</th>\n",
       "      <th>40대</th>\n",
       "      <th>50대</th>\n",
       "      <th>60대</th>\n",
       "      <th>70대</th>\n",
       "      <th>80대</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>서울</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>경기</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>인천</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>대구</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>부산</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>울산</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>대전</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>광주</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>강원</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>경남</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>충남</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>충북</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>경북</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>전남</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>전북</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>제주</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     cluster                              \n",
       "age      20대  30대  40대  50대  60대  70대  80대\n",
       "city                                      \n",
       "서울         1    4    4    3    2    5    0\n",
       "경기         1    4    4    3    2    5    0\n",
       "인천         1    4    4    3    2    5    0\n",
       "대구         1    4    4    3    2    5    0\n",
       "부산         1    4    4    3    2    5    0\n",
       "울산         1    4    4    3    2    5    0\n",
       "대전         1    4    4    3    2    5    0\n",
       "광주         1    4    4    3    2    5    0\n",
       "강원         1    4    4    3    2    5    0\n",
       "경남         1    4    4    3    2    5    0\n",
       "충남         1    4    4    3    2    5    0\n",
       "충북         1    4    4    3    2    5    0\n",
       "경북         1    4    4    3    2    5    0\n",
       "전남         1    4    4    3    2    5    0\n",
       "전북         1    4    4    3    2    5    0\n",
       "제주         1    4    4    3    2    5    0"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster_visualization(city_encoding_kmeans)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "연령과의 상관관계가 너무 큰 것으로 보임\n",
    "\n",
    "다른 변수들에 연령이 영향을 이미 미쳤을텐데, 연령 변수까지 추가해서 더욱 커진 것으로 생각됨"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 2. 지역, 나이 제외 + 그룹화 X (city)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cluster\n",
       "0    642\n",
       "1    419\n",
       "2    496\n",
       "3    384\n",
       "4    310\n",
       "5    437\n",
       "Name: avg_score, dtype: int64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_df = city.copy()\n",
    "city_kmeans = k_means_clustering(city_df, cluster_num, scaler)\n",
    "city_kmeans.groupby(['cluster'])['avg_score'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "서울 20대 [3]\n",
      "인천 20대 [3]\n",
      "대전 20대 [3]\n",
      "대구 20대 [3]\n",
      "광주 20대 [3]\n",
      "부산 20대 [3]\n",
      "울산 20대 [3]\n",
      "경기 20대 [3]\n",
      "강원 20대 [3]\n",
      "충북 20대 [3]\n",
      "충남 20대 [3]\n",
      "전북 20대 [3]\n",
      "전남 20대 [3]\n",
      "경북 20대 [3]\n",
      "경남 20대 [3]\n",
      "제주 20대 [3]\n",
      "서울 30대 [5 1]\n",
      "인천 30대 [5]\n",
      "대전 30대 [5]\n",
      "대구 30대 [5 1]\n",
      "광주 30대 [5]\n",
      "부산 30대 [5 1]\n",
      "울산 30대 [5 1]\n",
      "경기 30대 [5 1]\n",
      "강원 30대 [5]\n",
      "충북 30대 [5]\n",
      "충남 30대 [5]\n",
      "전북 30대 [5]\n",
      "전남 30대 [5]\n",
      "경북 30대 [5]\n",
      "경남 30대 [5]\n",
      "제주 30대 [5]\n",
      "서울 40대 [1]\n",
      "인천 40대 [1]\n",
      "대전 40대 [5 1]\n",
      "대구 40대 [1]\n",
      "광주 40대 [5 1]\n",
      "부산 40대 [1]\n",
      "울산 40대 [1]\n",
      "경기 40대 [1]\n",
      "강원 40대 [5]\n",
      "충북 40대 [5 1]\n",
      "충남 40대 [5 1]\n",
      "전북 40대 [5]\n",
      "전남 40대 [5]\n",
      "경북 40대 [5]\n",
      "경남 40대 [5 1]\n",
      "제주 40대 [4]\n",
      "서울 50대 [1]\n",
      "인천 50대 [1]\n",
      "대전 50대 [4 1]\n",
      "대구 50대 [4 1]\n",
      "광주 50대 [4]\n",
      "부산 50대 [1 4]\n",
      "울산 50대 [4 1]\n",
      "경기 50대 [1]\n",
      "강원 50대 [4]\n",
      "충북 50대 [4]\n",
      "충남 50대 [4]\n",
      "전북 50대 [4]\n",
      "전남 50대 [4]\n",
      "경북 50대 [4]\n",
      "경남 50대 [4]\n",
      "제주 50대 [4]\n",
      "서울 60대 [4 2]\n",
      "인천 60대 [4 2]\n",
      "대전 60대 [4 2]\n",
      "대구 60대 [2]\n",
      "광주 60대 [4 2]\n",
      "부산 60대 [4 2]\n",
      "울산 60대 [4 2]\n",
      "경기 60대 [4 2]\n",
      "강원 60대 [4 2]\n",
      "충북 60대 [4 2]\n",
      "충남 60대 [4 2]\n",
      "전북 60대 [4 0 2]\n",
      "전남 60대 [4 0 2]\n",
      "경북 60대 [2 0]\n",
      "경남 60대 [4 2]\n",
      "제주 60대 [4 2]\n",
      "서울 70대 [2]\n",
      "인천 70대 [2 0]\n",
      "대전 70대 [0]\n",
      "대구 70대 [0]\n",
      "광주 70대 [0]\n",
      "부산 70대 [2 0]\n",
      "울산 70대 [2 0]\n",
      "경기 70대 [2]\n",
      "강원 70대 [0]\n",
      "충북 70대 [0]\n",
      "충남 70대 [0]\n",
      "전북 70대 [0]\n",
      "전남 70대 [0]\n",
      "경북 70대 [0]\n",
      "경남 70대 [0]\n",
      "제주 70대 [0 2]\n",
      "서울 80대 [2]\n",
      "인천 80대 [0]\n",
      "대전 80대 [0]\n",
      "대구 80대 [0]\n",
      "광주 80대 [0]\n",
      "부산 80대 [0]\n",
      "울산 80대 [0]\n",
      "경기 80대 [2]\n",
      "강원 80대 [0]\n",
      "충북 80대 [0]\n",
      "충남 80대 [0]\n",
      "전북 80대 [0]\n",
      "전남 80대 [0]\n",
      "경북 80대 [0]\n",
      "경남 80대 [0]\n",
      "제주 80대 [0]\n"
     ]
    }
   ],
   "source": [
    "for city_age in city_kmeans['city_ages'].unique():\n",
    "    print(city_age, city_kmeans[city_kmeans['city_ages']==city_age]['cluster'].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 3. 지역, 나이 제외 + 그룹화 O (city_total)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cluster\n",
       "0    21\n",
       "1    27\n",
       "2    13\n",
       "3    14\n",
       "4    21\n",
       "5    16\n",
       "Name: total_avg_score, dtype: int64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_total_df = city_total.copy()\n",
    "city_total_kmeans = k_means_clustering(city_total_df, cluster_num, scaler)\n",
    "city_total_kmeans.groupby(['cluster'])['total_avg_score'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "강원20대 [5]\n",
      "강원30대 [0]\n",
      "강원40대 [0]\n",
      "강원50대 [2]\n",
      "강원60대 [4]\n",
      "강원70대 [1]\n",
      "강원80대 [1]\n",
      "경기20대 [5]\n",
      "경기30대 [0]\n",
      "경기40대 [3]\n",
      "경기50대 [3]\n",
      "경기60대 [4]\n",
      "경기70대 [4]\n",
      "경기80대 [4]\n",
      "경남20대 [5]\n",
      "경남30대 [0]\n",
      "경남40대 [3]\n",
      "경남50대 [2]\n",
      "경남60대 [4]\n",
      "경남70대 [1]\n",
      "경남80대 [1]\n",
      "경북20대 [5]\n",
      "경북30대 [0]\n",
      "경북40대 [0]\n",
      "경북50대 [2]\n",
      "경북60대 [4]\n",
      "경북70대 [1]\n",
      "경북80대 [1]\n",
      "광주20대 [5]\n",
      "광주30대 [0]\n",
      "광주40대 [0]\n",
      "광주50대 [2]\n",
      "광주60대 [4]\n",
      "광주70대 [1]\n",
      "광주80대 [1]\n",
      "대구20대 [5]\n",
      "대구30대 [0]\n",
      "대구40대 [3]\n",
      "대구50대 [2]\n",
      "대구60대 [4]\n",
      "대구70대 [1]\n",
      "대구80대 [1]\n",
      "대전20대 [5]\n",
      "대전30대 [0]\n",
      "대전40대 [3]\n",
      "대전50대 [2]\n",
      "대전60대 [4]\n",
      "대전70대 [1]\n",
      "대전80대 [1]\n",
      "부산20대 [5]\n",
      "부산30대 [0]\n",
      "부산40대 [3]\n",
      "부산50대 [3]\n",
      "부산60대 [4]\n",
      "부산70대 [1]\n",
      "부산80대 [1]\n",
      "서울20대 [5]\n",
      "서울30대 [3]\n",
      "서울40대 [3]\n",
      "서울50대 [3]\n",
      "서울60대 [4]\n",
      "서울70대 [4]\n",
      "서울80대 [4]\n",
      "울산20대 [5]\n",
      "울산30대 [3]\n",
      "울산40대 [3]\n",
      "울산50대 [2]\n",
      "울산60대 [4]\n",
      "울산70대 [4]\n",
      "울산80대 [1]\n",
      "인천20대 [5]\n",
      "인천30대 [0]\n",
      "인천40대 [3]\n",
      "인천50대 [3]\n",
      "인천60대 [4]\n",
      "인천70대 [1]\n",
      "인천80대 [1]\n",
      "전남20대 [5]\n",
      "전남30대 [0]\n",
      "전남40대 [0]\n",
      "전남50대 [2]\n",
      "전남60대 [1]\n",
      "전남70대 [1]\n",
      "전남80대 [1]\n",
      "전북20대 [5]\n",
      "전북30대 [0]\n",
      "전북40대 [0]\n",
      "전북50대 [2]\n",
      "전북60대 [4]\n",
      "전북70대 [1]\n",
      "전북80대 [1]\n",
      "제주20대 [5]\n",
      "제주30대 [0]\n",
      "제주40대 [2]\n",
      "제주50대 [2]\n",
      "제주60대 [4]\n",
      "제주70대 [4]\n",
      "제주80대 [1]\n",
      "충남20대 [5]\n",
      "충남30대 [0]\n",
      "충남40대 [0]\n",
      "충남50대 [2]\n",
      "충남60대 [4]\n",
      "충남70대 [1]\n",
      "충남80대 [1]\n",
      "충북20대 [5]\n",
      "충북30대 [0]\n",
      "충북40대 [0]\n",
      "충북50대 [2]\n",
      "충북60대 [4]\n",
      "충북70대 [1]\n",
      "충북80대 [1]\n"
     ]
    }
   ],
   "source": [
    "for city_age in city_total_kmeans['city_ages'].unique():\n",
    "    print(city_age, city_total_kmeans[city_total_kmeans['city_ages']==city_age]['cluster'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1008x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"7\" halign=\"left\">cluster</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <th>20대</th>\n",
       "      <th>30대</th>\n",
       "      <th>40대</th>\n",
       "      <th>50대</th>\n",
       "      <th>60대</th>\n",
       "      <th>70대</th>\n",
       "      <th>80대</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>서울</th>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>경기</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>인천</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>대구</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>부산</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>울산</th>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>대전</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>광주</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>강원</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>경남</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>충남</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>충북</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>경북</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>전남</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>전북</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>제주</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     cluster                        \n",
       "age      20대 30대 40대 50대 60대 70대 80대\n",
       "city                                \n",
       "서울         5   3   3   3   4   4   4\n",
       "경기         5   0   3   3   4   4   4\n",
       "인천         5   0   3   3   4   1   1\n",
       "대구         5   0   3   2   4   1   1\n",
       "부산         5   0   3   3   4   1   1\n",
       "울산         5   3   3   2   4   4   1\n",
       "대전         5   0   3   2   4   1   1\n",
       "광주         5   0   0   2   4   1   1\n",
       "강원         5   0   0   2   4   1   1\n",
       "경남         5   0   3   2   4   1   1\n",
       "충남         5   0   0   2   4   1   1\n",
       "충북         5   0   0   2   4   1   1\n",
       "경북         5   0   0   2   4   1   1\n",
       "전남         5   0   0   2   1   1   1\n",
       "전북         5   0   0   2   4   1   1\n",
       "제주         5   0   2   2   4   4   1"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster_visualization(city_total_kmeans)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 4. 성별, 도시, 나이 제외 (df_total)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cluster\n",
       "0    497\n",
       "1    329\n",
       "2    717\n",
       "3    432\n",
       "4    485\n",
       "5    564\n",
       "Name: avg_score, dtype: int64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_total1 = df_total.copy()\n",
    "\n",
    "df_total_kmeans = k_means_clustering(df_total1, cluster_num, StandardScaler())\n",
    "df_total_kmeans.groupby(['cluster'])['avg_score'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>avg_score</th>\n",
       "      <th>num_usecard</th>\n",
       "      <th>monthly_card_spend</th>\n",
       "      <th>monthly_bk_loan</th>\n",
       "      <th>monthly_cd_loan</th>\n",
       "      <th>monthly_installments_loan</th>\n",
       "      <th>monthly_insurance_loan</th>\n",
       "      <th>monthly_sbk_loan</th>\n",
       "      <th>loan_commitment</th>\n",
       "      <th>inst_rep_loanb</th>\n",
       "      <th>ls_rep_loanb</th>\n",
       "      <th>credit_loan</th>\n",
       "      <th>mortgage_loan</th>\n",
       "      <th>credit_card_installments_payment</th>\n",
       "      <th>monthly_spend</th>\n",
       "      <th>cluster</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>740</td>\n",
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       "      <td>1.75000</td>\n",
       "      <td>790000</td>\n",
       "      <td>24150000</td>\n",
       "      <td>680000</td>\n",
       "      <td>2300000</td>\n",
       "      <td>790000</td>\n",
       "      <td>1160000</td>\n",
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       "      <td>12220000</td>\n",
       "      <td>12240000</td>\n",
       "      <td>19070000</td>\n",
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       "      <td>83000</td>\n",
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       "      <td>782</td>\n",
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       "      <td>2300000</td>\n",
       "      <td>790000</td>\n",
       "      <td>1230000</td>\n",
       "      <td>5380000</td>\n",
       "      <td>12320000</td>\n",
       "      <td>12700000</td>\n",
       "      <td>19360000</td>\n",
       "      <td>42980000</td>\n",
       "      <td>89000</td>\n",
       "      <td>771000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>781</td>\n",
       "      <td>1.75000</td>\n",
       "      <td>820000</td>\n",
       "      <td>24830000</td>\n",
       "      <td>700000</td>\n",
       "      <td>2320000</td>\n",
       "      <td>770000</td>\n",
       "      <td>1250000</td>\n",
       "      <td>5430000</td>\n",
       "      <td>12420000</td>\n",
       "      <td>12870000</td>\n",
       "      <td>19580000</td>\n",
       "      <td>43370000</td>\n",
       "      <td>82000</td>\n",
       "      <td>738000</td>\n",
       "      <td>3</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   avg_score  num_usecard  monthly_card_spend  monthly_bk_loan  \\\n",
       "0        740      1.99000              810000         18500000   \n",
       "1        782      1.75000              790000         24150000   \n",
       "2        782      1.74000              790000         24240000   \n",
       "3        782      1.75000              860000         24570000   \n",
       "4        781      1.75000              820000         24830000   \n",
       "\n",
       "   monthly_cd_loan  monthly_installments_loan  monthly_insurance_loan  \\\n",
       "0           620000                    2110000                  680000   \n",
       "1           680000                    2300000                  790000   \n",
       "2           680000                    2300000                  790000   \n",
       "3           680000                    2300000                  790000   \n",
       "4           700000                    2320000                  770000   \n",
       "\n",
       "   monthly_sbk_loan  loan_commitment  inst_rep_loanb  ls_rep_loanb  \\\n",
       "0           1320000          3800000        10520000       9750000   \n",
       "1           1160000          5420000        12220000      12240000   \n",
       "2           1210000          5350000        12280000      12370000   \n",
       "3           1230000          5380000        12320000      12700000   \n",
       "4           1250000          5430000        12420000      12870000   \n",
       "\n",
       "   credit_loan  mortgage_loan  credit_card_installments_payment  \\\n",
       "0     14920000       35450000                             83000   \n",
       "1     19070000       42420000                             83000   \n",
       "2     19190000       42480000                             78000   \n",
       "3     19360000       42980000                             89000   \n",
       "4     19580000       43370000                             82000   \n",
       "\n",
       "   monthly_spend  cluster  \n",
       "0         727000        3  \n",
       "1         707000        3  \n",
       "2         712000        3  \n",
       "3         771000        3  \n",
       "4         738000        3  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_total_kmeans.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>avg_score</th>\n",
       "      <th>num_usecard</th>\n",
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       "      <th>ls_rep_loanb</th>\n",
       "      <th>credit_loan</th>\n",
       "      <th>mortgage_loan</th>\n",
       "      <th>credit_card_installments_payment</th>\n",
       "      <th>monthly_spend</th>\n",
       "      <th>cluster</th>\n",
       "      <th>city</th>\n",
       "      <th>ages</th>\n",
       "      <th>sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>740</td>\n",
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       "      <td>727000</td>\n",
       "      <td>3</td>\n",
       "      <td>서울</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>782</td>\n",
       "      <td>1.75000</td>\n",
       "      <td>790000</td>\n",
       "      <td>24150000</td>\n",
       "      <td>680000</td>\n",
       "      <td>2300000</td>\n",
       "      <td>790000</td>\n",
       "      <td>1160000</td>\n",
       "      <td>5420000</td>\n",
       "      <td>12220000</td>\n",
       "      <td>12240000</td>\n",
       "      <td>19070000</td>\n",
       "      <td>42420000</td>\n",
       "      <td>83000</td>\n",
       "      <td>707000</td>\n",
       "      <td>3</td>\n",
       "      <td>서울</td>\n",
       "      <td>20대</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>782</td>\n",
       "      <td>1.74000</td>\n",
       "      <td>790000</td>\n",
       "      <td>24240000</td>\n",
       "      <td>680000</td>\n",
       "      <td>2300000</td>\n",
       "      <td>790000</td>\n",
       "      <td>1210000</td>\n",
       "      <td>5350000</td>\n",
       "      <td>12280000</td>\n",
       "      <td>12370000</td>\n",
       "      <td>19190000</td>\n",
       "      <td>42480000</td>\n",
       "      <td>78000</td>\n",
       "      <td>712000</td>\n",
       "      <td>3</td>\n",
       "      <td>서울</td>\n",
       "      <td>20대</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>782</td>\n",
       "      <td>1.75000</td>\n",
       "      <td>860000</td>\n",
       "      <td>24570000</td>\n",
       "      <td>680000</td>\n",
       "      <td>2300000</td>\n",
       "      <td>790000</td>\n",
       "      <td>1230000</td>\n",
       "      <td>5380000</td>\n",
       "      <td>12320000</td>\n",
       "      <td>12700000</td>\n",
       "      <td>19360000</td>\n",
       "      <td>42980000</td>\n",
       "      <td>89000</td>\n",
       "      <td>771000</td>\n",
       "      <td>3</td>\n",
       "      <td>서울</td>\n",
       "      <td>20대</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>781</td>\n",
       "      <td>1.75000</td>\n",
       "      <td>820000</td>\n",
       "      <td>24830000</td>\n",
       "      <td>700000</td>\n",
       "      <td>2320000</td>\n",
       "      <td>770000</td>\n",
       "      <td>1250000</td>\n",
       "      <td>5430000</td>\n",
       "      <td>12420000</td>\n",
       "      <td>12870000</td>\n",
       "      <td>19580000</td>\n",
       "      <td>43370000</td>\n",
       "      <td>82000</td>\n",
       "      <td>738000</td>\n",
       "      <td>3</td>\n",
       "      <td>서울</td>\n",
       "      <td>20대</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",
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       "    <tr>\n",
       "      <th>3019</th>\n",
       "      <td>818</td>\n",
       "      <td>0.18000</td>\n",
       "      <td>200000</td>\n",
       "      <td>58460000</td>\n",
       "      <td>230000</td>\n",
       "      <td>260000</td>\n",
       "      <td>1610000</td>\n",
       "      <td>290000</td>\n",
       "      <td>8600000</td>\n",
       "      <td>18710000</td>\n",
       "      <td>41980000</td>\n",
       "      <td>14140000</td>\n",
       "      <td>84270000</td>\n",
       "      <td>43000</td>\n",
       "      <td>157000</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80대</td>\n",
       "      <td>여자</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3020</th>\n",
       "      <td>818</td>\n",
       "      <td>0.18000</td>\n",
       "      <td>190000</td>\n",
       "      <td>58330000</td>\n",
       "      <td>230000</td>\n",
       "      <td>270000</td>\n",
       "      <td>1610000</td>\n",
       "      <td>310000</td>\n",
       "      <td>8550000</td>\n",
       "      <td>18750000</td>\n",
       "      <td>41870000</td>\n",
       "      <td>14230000</td>\n",
       "      <td>84120000</td>\n",
       "      <td>39000</td>\n",
       "      <td>151000</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80대</td>\n",
       "      <td>여자</td>\n",
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       "    <tr>\n",
       "      <th>3021</th>\n",
       "      <td>818</td>\n",
       "      <td>0.18000</td>\n",
       "      <td>210000</td>\n",
       "      <td>58190000</td>\n",
       "      <td>240000</td>\n",
       "      <td>290000</td>\n",
       "      <td>1610000</td>\n",
       "      <td>430000</td>\n",
       "      <td>8630000</td>\n",
       "      <td>18760000</td>\n",
       "      <td>41770000</td>\n",
       "      <td>14250000</td>\n",
       "      <td>84200000</td>\n",
       "      <td>49000</td>\n",
       "      <td>161000</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80대</td>\n",
       "      <td>여자</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3022</th>\n",
       "      <td>819</td>\n",
       "      <td>0.18000</td>\n",
       "      <td>200000</td>\n",
       "      <td>58420000</td>\n",
       "      <td>240000</td>\n",
       "      <td>300000</td>\n",
       "      <td>1610000</td>\n",
       "      <td>470000</td>\n",
       "      <td>8570000</td>\n",
       "      <td>18690000</td>\n",
       "      <td>42130000</td>\n",
       "      <td>14330000</td>\n",
       "      <td>84390000</td>\n",
       "      <td>44000</td>\n",
       "      <td>156000</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80대</td>\n",
       "      <td>여자</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3023</th>\n",
       "      <td>819</td>\n",
       "      <td>0.18000</td>\n",
       "      <td>200000</td>\n",
       "      <td>58170000</td>\n",
       "      <td>250000</td>\n",
       "      <td>300000</td>\n",
       "      <td>1640000</td>\n",
       "      <td>430000</td>\n",
       "      <td>8590000</td>\n",
       "      <td>18670000</td>\n",
       "      <td>41890000</td>\n",
       "      <td>14240000</td>\n",
       "      <td>84050000</td>\n",
       "      <td>44000</td>\n",
       "      <td>156000</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80대</td>\n",
       "      <td>여자</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3024 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      avg_score  num_usecard  monthly_card_spend  monthly_bk_loan  \\\n",
       "0           740      1.99000              810000         18500000   \n",
       "1           782      1.75000              790000         24150000   \n",
       "2           782      1.74000              790000         24240000   \n",
       "3           782      1.75000              860000         24570000   \n",
       "4           781      1.75000              820000         24830000   \n",
       "...         ...          ...                 ...              ...   \n",
       "3019        818      0.18000              200000         58460000   \n",
       "3020        818      0.18000              190000         58330000   \n",
       "3021        818      0.18000              210000         58190000   \n",
       "3022        819      0.18000              200000         58420000   \n",
       "3023        819      0.18000              200000         58170000   \n",
       "\n",
       "      monthly_cd_loan  monthly_installments_loan  monthly_insurance_loan  \\\n",
       "0              620000                    2110000                  680000   \n",
       "1              680000                    2300000                  790000   \n",
       "2              680000                    2300000                  790000   \n",
       "3              680000                    2300000                  790000   \n",
       "4              700000                    2320000                  770000   \n",
       "...               ...                        ...                     ...   \n",
       "3019           230000                     260000                 1610000   \n",
       "3020           230000                     270000                 1610000   \n",
       "3021           240000                     290000                 1610000   \n",
       "3022           240000                     300000                 1610000   \n",
       "3023           250000                     300000                 1640000   \n",
       "\n",
       "      monthly_sbk_loan  loan_commitment  inst_rep_loanb  ls_rep_loanb  \\\n",
       "0              1320000          3800000        10520000       9750000   \n",
       "1              1160000          5420000        12220000      12240000   \n",
       "2              1210000          5350000        12280000      12370000   \n",
       "3              1230000          5380000        12320000      12700000   \n",
       "4              1250000          5430000        12420000      12870000   \n",
       "...                ...              ...             ...           ...   \n",
       "3019            290000          8600000        18710000      41980000   \n",
       "3020            310000          8550000        18750000      41870000   \n",
       "3021            430000          8630000        18760000      41770000   \n",
       "3022            470000          8570000        18690000      42130000   \n",
       "3023            430000          8590000        18670000      41890000   \n",
       "\n",
       "      credit_loan  mortgage_loan  credit_card_installments_payment  \\\n",
       "0        14920000       35450000                             83000   \n",
       "1        19070000       42420000                             83000   \n",
       "2        19190000       42480000                             78000   \n",
       "3        19360000       42980000                             89000   \n",
       "4        19580000       43370000                             82000   \n",
       "...           ...            ...                               ...   \n",
       "3019     14140000       84270000                             43000   \n",
       "3020     14230000       84120000                             39000   \n",
       "3021     14250000       84200000                             49000   \n",
       "3022     14330000       84390000                             44000   \n",
       "3023     14240000       84050000                             44000   \n",
       "\n",
       "      monthly_spend  cluster city ages  sex  \n",
       "0            727000        3   서울  20대  NaN  \n",
       "1            707000        3   서울  20대  NaN  \n",
       "2            712000        3   서울  20대  NaN  \n",
       "3            771000        3   서울  20대  NaN  \n",
       "4            738000        3   서울  20대  NaN  \n",
       "...             ...      ...  ...  ...  ...  \n",
       "3019         157000        2  NaN  80대   여자  \n",
       "3020         151000        2  NaN  80대   여자  \n",
       "3021         161000        2  NaN  80대   여자  \n",
       "3022         156000        2  NaN  80대   여자  \n",
       "3023         156000        2  NaN  80대   여자  \n",
       "\n",
       "[3024 rows x 19 columns]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_total_kmeans = pd.concat([df_total_kmeans, tmp_df], axis=1)\n",
    "df_total_kmeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\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>avg_score</th>\n",
       "      <th>num_usecard</th>\n",
       "      <th>monthly_card_spend</th>\n",
       "      <th>monthly_bk_loan</th>\n",
       "      <th>monthly_cd_loan</th>\n",
       "      <th>monthly_installments_loan</th>\n",
       "      <th>monthly_insurance_loan</th>\n",
       "      <th>monthly_sbk_loan</th>\n",
       "      <th>loan_commitment</th>\n",
       "      <th>inst_rep_loanb</th>\n",
       "      <th>ls_rep_loanb</th>\n",
       "      <th>credit_loan</th>\n",
       "      <th>mortgage_loan</th>\n",
       "      <th>credit_card_installments_payment</th>\n",
       "      <th>monthly_spend</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>792.32998</td>\n",
       "      <td>2.17781</td>\n",
       "      <td>1602736.41851</td>\n",
       "      <td>44150281.69014</td>\n",
       "      <td>1292374.24547</td>\n",
       "      <td>4725070.42254</td>\n",
       "      <td>2679919.51710</td>\n",
       "      <td>1133581.48893</td>\n",
       "      <td>10543360.16097</td>\n",
       "      <td>25924768.61167</td>\n",
       "      <td>24070523.13883</td>\n",
       "      <td>27319014.08451</td>\n",
       "      <td>78740482.89738</td>\n",
       "      <td>257752.51509</td>\n",
       "      <td>1344983.90342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>795.90881</td>\n",
       "      <td>1.81401</td>\n",
       "      <td>1423069.90881</td>\n",
       "      <td>58841246.20061</td>\n",
       "      <td>1072826.74772</td>\n",
       "      <td>3784133.73860</td>\n",
       "      <td>2241458.96657</td>\n",
       "      <td>621306.99088</td>\n",
       "      <td>18585136.77812</td>\n",
       "      <td>22824650.45593</td>\n",
       "      <td>38323586.62614</td>\n",
       "      <td>28697264.43769</td>\n",
       "      <td>105045653.49544</td>\n",
       "      <td>283121.58055</td>\n",
       "      <td>1139948.32827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>818.15760</td>\n",
       "      <td>0.47888</td>\n",
       "      <td>291729.42817</td>\n",
       "      <td>45579051.60391</td>\n",
       "      <td>207377.96374</td>\n",
       "      <td>550697.35007</td>\n",
       "      <td>915955.36960</td>\n",
       "      <td>145913.52859</td>\n",
       "      <td>13057099.02371</td>\n",
       "      <td>11718744.76987</td>\n",
       "      <td>32270655.50907</td>\n",
       "      <td>14383542.53835</td>\n",
       "      <td>73955732.21757</td>\n",
       "      <td>61518.82845</td>\n",
       "      <td>230210.59972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>770.55787</td>\n",
       "      <td>1.78613</td>\n",
       "      <td>788888.88889</td>\n",
       "      <td>18667615.74074</td>\n",
       "      <td>699907.40741</td>\n",
       "      <td>3828472.22222</td>\n",
       "      <td>889537.03704</td>\n",
       "      <td>1611458.33333</td>\n",
       "      <td>3533055.55556</td>\n",
       "      <td>13209050.92593</td>\n",
       "      <td>11676527.77778</td>\n",
       "      <td>16360833.33333</td>\n",
       "      <td>38640555.55556</td>\n",
       "      <td>84115.74074</td>\n",
       "      <td>704773.14815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>801.31959</td>\n",
       "      <td>2.14812</td>\n",
       "      <td>1700742.26804</td>\n",
       "      <td>63170164.94845</td>\n",
       "      <td>1551216.49485</td>\n",
       "      <td>4119154.63918</td>\n",
       "      <td>4172350.51546</td>\n",
       "      <td>1054886.59794</td>\n",
       "      <td>12483649.48454</td>\n",
       "      <td>37871731.95876</td>\n",
       "      <td>30950515.46392</td>\n",
       "      <td>29340969.07216</td>\n",
       "      <td>108520226.80412</td>\n",
       "      <td>300868.04124</td>\n",
       "      <td>1399874.22680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>826.47518</td>\n",
       "      <td>1.14059</td>\n",
       "      <td>779237.58865</td>\n",
       "      <td>64239734.04255</td>\n",
       "      <td>732695.03546</td>\n",
       "      <td>1853812.05674</td>\n",
       "      <td>1868031.91489</td>\n",
       "      <td>347375.88652</td>\n",
       "      <td>17720372.34043</td>\n",
       "      <td>22261382.97872</td>\n",
       "      <td>40544858.15603</td>\n",
       "      <td>20367251.77305</td>\n",
       "      <td>109477712.76596</td>\n",
       "      <td>171420.21277</td>\n",
       "      <td>607817.37589</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         avg_score  num_usecard  monthly_card_spend  monthly_bk_loan  \\\n",
       "cluster                                                                \n",
       "0        792.32998      2.17781       1602736.41851   44150281.69014   \n",
       "1        795.90881      1.81401       1423069.90881   58841246.20061   \n",
       "2        818.15760      0.47888        291729.42817   45579051.60391   \n",
       "3        770.55787      1.78613        788888.88889   18667615.74074   \n",
       "4        801.31959      2.14812       1700742.26804   63170164.94845   \n",
       "5        826.47518      1.14059        779237.58865   64239734.04255   \n",
       "\n",
       "         monthly_cd_loan  monthly_installments_loan  monthly_insurance_loan  \\\n",
       "cluster                                                                       \n",
       "0          1292374.24547              4725070.42254           2679919.51710   \n",
       "1          1072826.74772              3784133.73860           2241458.96657   \n",
       "2           207377.96374               550697.35007            915955.36960   \n",
       "3           699907.40741              3828472.22222            889537.03704   \n",
       "4          1551216.49485              4119154.63918           4172350.51546   \n",
       "5           732695.03546              1853812.05674           1868031.91489   \n",
       "\n",
       "         monthly_sbk_loan  loan_commitment  inst_rep_loanb   ls_rep_loanb  \\\n",
       "cluster                                                                     \n",
       "0           1133581.48893   10543360.16097  25924768.61167 24070523.13883   \n",
       "1            621306.99088   18585136.77812  22824650.45593 38323586.62614   \n",
       "2            145913.52859   13057099.02371  11718744.76987 32270655.50907   \n",
       "3           1611458.33333    3533055.55556  13209050.92593 11676527.77778   \n",
       "4           1054886.59794   12483649.48454  37871731.95876 30950515.46392   \n",
       "5            347375.88652   17720372.34043  22261382.97872 40544858.15603   \n",
       "\n",
       "           credit_loan   mortgage_loan  credit_card_installments_payment  \\\n",
       "cluster                                                                    \n",
       "0       27319014.08451  78740482.89738                      257752.51509   \n",
       "1       28697264.43769 105045653.49544                      283121.58055   \n",
       "2       14383542.53835  73955732.21757                       61518.82845   \n",
       "3       16360833.33333  38640555.55556                       84115.74074   \n",
       "4       29340969.07216 108520226.80412                      300868.04124   \n",
       "5       20367251.77305 109477712.76596                      171420.21277   \n",
       "\n",
       "         monthly_spend  \n",
       "cluster                 \n",
       "0        1344983.90342  \n",
       "1        1139948.32827  \n",
       "2         230210.59972  \n",
       "3         704773.14815  \n",
       "4        1399874.22680  \n",
       "5         607817.37589  "
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_total_kmeans.groupby('cluster').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### DBSCAN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "필요인자\n",
    "\n",
    "* 최대 탐색 거리 epsilon\n",
    "* 최소 샘플 개수 min_samples "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "밀도가 다양할 때 수행이 잘 안 되는 것이 단점\n",
    "\n",
    "-> 고차원 데이터에서 단점이 부각\n",
    "\n",
    "--> 우리 데이터는 고차원이기 때문에 문제가 될 것 같음"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import DBSCAN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "e = 1\n",
    "m = 2\n",
    "\n",
    "dbscan = DBSCAN(eps = e, min_samples=m, metric = 'euclidean')\n",
    "city_total_dbscan = scaler.fit_transform(city_total)\n",
    "clusters_dbscan = dbscan.fit_predict(city_total_dbscan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>total_avg_score</th>\n",
       "      <th>total_num_usecard</th>\n",
       "      <th>total_monthly_card_spend</th>\n",
       "      <th>total_monthly_bk_loan</th>\n",
       "      <th>total_monthly_cd_loan</th>\n",
       "      <th>total_monthly_installments_loan</th>\n",
       "      <th>total_monthly_insurance_loan</th>\n",
       "      <th>total_monthly_sbk_loan</th>\n",
       "      <th>total_loan_commitment</th>\n",
       "      <th>total_inst_rep_loanb</th>\n",
       "      <th>total_ls_rep_loanb</th>\n",
       "      <th>total_credit_loan</th>\n",
       "      <th>total_mortgage_loan</th>\n",
       "      <th>total_credit_card_installments_payment</th>\n",
       "      <th>total_monthly_spend</th>\n",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city_ages</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>강원20대</th>\n",
       "      <td>-1.91144</td>\n",
       "      <td>0.39381</td>\n",
       "      <td>-0.38005</td>\n",
       "      <td>-1.97021</td>\n",
       "      <td>-0.54200</td>\n",
       "      <td>1.01785</td>\n",
       "      <td>-1.02602</td>\n",
       "      <td>1.64883</td>\n",
       "      <td>-1.72060</td>\n",
       "      <td>-0.98023</td>\n",
       "      <td>-1.85258</td>\n",
       "      <td>-0.93947</td>\n",
       "      <td>-2.05700</td>\n",
       "      <td>-0.93965</td>\n",
       "      <td>-0.25275</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>강원30대</th>\n",
       "      <td>-0.75324</td>\n",
       "      <td>0.99750</td>\n",
       "      <td>1.00607</td>\n",
       "      <td>-0.88116</td>\n",
       "      <td>0.53699</td>\n",
       "      <td>1.49914</td>\n",
       "      <td>-0.10833</td>\n",
       "      <td>0.94593</td>\n",
       "      <td>-0.66113</td>\n",
       "      <td>-0.14097</td>\n",
       "      <td>-1.01963</td>\n",
       "      <td>0.60313</td>\n",
       "      <td>-0.93497</td>\n",
       "      <td>0.68540</td>\n",
       "      <td>1.05898</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>강원40대</th>\n",
       "      <td>-1.06969</td>\n",
       "      <td>0.86539</td>\n",
       "      <td>1.30344</td>\n",
       "      <td>-0.38463</td>\n",
       "      <td>0.91257</td>\n",
       "      <td>1.51204</td>\n",
       "      <td>0.27668</td>\n",
       "      <td>0.45043</td>\n",
       "      <td>0.27015</td>\n",
       "      <td>0.06904</td>\n",
       "      <td>-0.47845</td>\n",
       "      <td>1.02057</td>\n",
       "      <td>-0.29916</td>\n",
       "      <td>1.27118</td>\n",
       "      <td>1.28909</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>강원50대</th>\n",
       "      <td>-0.78208</td>\n",
       "      <td>0.30610</td>\n",
       "      <td>0.65193</td>\n",
       "      <td>-0.00827</td>\n",
       "      <td>0.31395</td>\n",
       "      <td>0.81194</td>\n",
       "      <td>-0.24289</td>\n",
       "      <td>-0.26824</td>\n",
       "      <td>1.33194</td>\n",
       "      <td>-0.32851</td>\n",
       "      <td>0.01640</td>\n",
       "      <td>0.85179</td>\n",
       "      <td>0.16090</td>\n",
       "      <td>1.00114</td>\n",
       "      <td>0.56570</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>강원60대</th>\n",
       "      <td>0.55390</td>\n",
       "      <td>-0.31572</td>\n",
       "      <td>-0.22713</td>\n",
       "      <td>-0.08468</td>\n",
       "      <td>-0.34569</td>\n",
       "      <td>-0.09290</td>\n",
       "      <td>-0.76827</td>\n",
       "      <td>-0.93828</td>\n",
       "      <td>1.52008</td>\n",
       "      <td>-0.71522</td>\n",
       "      <td>-0.09114</td>\n",
       "      <td>-0.21556</td>\n",
       "      <td>0.15209</td>\n",
       "      <td>0.13333</td>\n",
       "      <td>-0.30140</td>\n",
       "      <td>3</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>충북40대</th>\n",
       "      <td>-0.64822</td>\n",
       "      <td>0.82565</td>\n",
       "      <td>1.20836</td>\n",
       "      <td>-0.24769</td>\n",
       "      <td>0.96616</td>\n",
       "      <td>1.34938</td>\n",
       "      <td>1.65790</td>\n",
       "      <td>0.69983</td>\n",
       "      <td>0.35993</td>\n",
       "      <td>0.66023</td>\n",
       "      <td>-0.29059</td>\n",
       "      <td>0.73964</td>\n",
       "      <td>0.13306</td>\n",
       "      <td>0.99762</td>\n",
       "      <td>1.23418</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>충북50대</th>\n",
       "      <td>-0.32441</td>\n",
       "      <td>0.31368</td>\n",
       "      <td>0.54087</td>\n",
       "      <td>-0.02534</td>\n",
       "      <td>0.34757</td>\n",
       "      <td>0.70786</td>\n",
       "      <td>0.43862</td>\n",
       "      <td>-0.14735</td>\n",
       "      <td>1.47685</td>\n",
       "      <td>0.01309</td>\n",
       "      <td>0.24951</td>\n",
       "      <td>0.54018</td>\n",
       "      <td>0.47270</td>\n",
       "      <td>0.74046</td>\n",
       "      <td>0.48883</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>충북60대</th>\n",
       "      <td>0.83425</td>\n",
       "      <td>-0.36546</td>\n",
       "      <td>-0.33977</td>\n",
       "      <td>-0.14458</td>\n",
       "      <td>-0.32235</td>\n",
       "      <td>-0.30526</td>\n",
       "      <td>-0.44252</td>\n",
       "      <td>-0.76987</td>\n",
       "      <td>1.68844</td>\n",
       "      <td>-0.54858</td>\n",
       "      <td>0.07632</td>\n",
       "      <td>-0.42509</td>\n",
       "      <td>0.30879</td>\n",
       "      <td>-0.13230</td>\n",
       "      <td>-0.37910</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>충북70대</th>\n",
       "      <td>0.91361</td>\n",
       "      <td>-1.23005</td>\n",
       "      <td>-1.13677</td>\n",
       "      <td>-0.60103</td>\n",
       "      <td>-1.24804</td>\n",
       "      <td>-1.15422</td>\n",
       "      <td>-0.97440</td>\n",
       "      <td>-1.05263</td>\n",
       "      <td>0.79400</td>\n",
       "      <td>-1.07090</td>\n",
       "      <td>-0.28997</td>\n",
       "      <td>-1.33634</td>\n",
       "      <td>-0.38117</td>\n",
       "      <td>-1.04944</td>\n",
       "      <td>-1.13706</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>충북80대</th>\n",
       "      <td>0.54489</td>\n",
       "      <td>-1.75239</td>\n",
       "      <td>-1.55643</td>\n",
       "      <td>-0.59689</td>\n",
       "      <td>-1.61335</td>\n",
       "      <td>-1.57426</td>\n",
       "      <td>-1.14447</td>\n",
       "      <td>-1.05536</td>\n",
       "      <td>0.16613</td>\n",
       "      <td>-1.26180</td>\n",
       "      <td>-0.14543</td>\n",
       "      <td>-1.82021</td>\n",
       "      <td>-0.65429</td>\n",
       "      <td>-1.54146</td>\n",
       "      <td>-1.53419</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>112 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           total_avg_score  total_num_usecard  total_monthly_card_spend  \\\n",
       "city_ages                                                                 \n",
       "강원20대             -1.91144            0.39381                  -0.38005   \n",
       "강원30대             -0.75324            0.99750                   1.00607   \n",
       "강원40대             -1.06969            0.86539                   1.30344   \n",
       "강원50대             -0.78208            0.30610                   0.65193   \n",
       "강원60대              0.55390           -0.31572                  -0.22713   \n",
       "...                    ...                ...                       ...   \n",
       "충북40대             -0.64822            0.82565                   1.20836   \n",
       "충북50대             -0.32441            0.31368                   0.54087   \n",
       "충북60대              0.83425           -0.36546                  -0.33977   \n",
       "충북70대              0.91361           -1.23005                  -1.13677   \n",
       "충북80대              0.54489           -1.75239                  -1.55643   \n",
       "\n",
       "           total_monthly_bk_loan  total_monthly_cd_loan  \\\n",
       "city_ages                                                 \n",
       "강원20대                   -1.97021               -0.54200   \n",
       "강원30대                   -0.88116                0.53699   \n",
       "강원40대                   -0.38463                0.91257   \n",
       "강원50대                   -0.00827                0.31395   \n",
       "강원60대                   -0.08468               -0.34569   \n",
       "...                          ...                    ...   \n",
       "충북40대                   -0.24769                0.96616   \n",
       "충북50대                   -0.02534                0.34757   \n",
       "충북60대                   -0.14458               -0.32235   \n",
       "충북70대                   -0.60103               -1.24804   \n",
       "충북80대                   -0.59689               -1.61335   \n",
       "\n",
       "           total_monthly_installments_loan  total_monthly_insurance_loan  \\\n",
       "city_ages                                                                  \n",
       "강원20대                              1.01785                      -1.02602   \n",
       "강원30대                              1.49914                      -0.10833   \n",
       "강원40대                              1.51204                       0.27668   \n",
       "강원50대                              0.81194                      -0.24289   \n",
       "강원60대                             -0.09290                      -0.76827   \n",
       "...                                    ...                           ...   \n",
       "충북40대                              1.34938                       1.65790   \n",
       "충북50대                              0.70786                       0.43862   \n",
       "충북60대                             -0.30526                      -0.44252   \n",
       "충북70대                             -1.15422                      -0.97440   \n",
       "충북80대                             -1.57426                      -1.14447   \n",
       "\n",
       "           total_monthly_sbk_loan  total_loan_commitment  \\\n",
       "city_ages                                                  \n",
       "강원20대                     1.64883               -1.72060   \n",
       "강원30대                     0.94593               -0.66113   \n",
       "강원40대                     0.45043                0.27015   \n",
       "강원50대                    -0.26824                1.33194   \n",
       "강원60대                    -0.93828                1.52008   \n",
       "...                           ...                    ...   \n",
       "충북40대                     0.69983                0.35993   \n",
       "충북50대                    -0.14735                1.47685   \n",
       "충북60대                    -0.76987                1.68844   \n",
       "충북70대                    -1.05263                0.79400   \n",
       "충북80대                    -1.05536                0.16613   \n",
       "\n",
       "           total_inst_rep_loanb  total_ls_rep_loanb  total_credit_loan  \\\n",
       "city_ages                                                                \n",
       "강원20대                  -0.98023            -1.85258           -0.93947   \n",
       "강원30대                  -0.14097            -1.01963            0.60313   \n",
       "강원40대                   0.06904            -0.47845            1.02057   \n",
       "강원50대                  -0.32851             0.01640            0.85179   \n",
       "강원60대                  -0.71522            -0.09114           -0.21556   \n",
       "...                         ...                 ...                ...   \n",
       "충북40대                   0.66023            -0.29059            0.73964   \n",
       "충북50대                   0.01309             0.24951            0.54018   \n",
       "충북60대                  -0.54858             0.07632           -0.42509   \n",
       "충북70대                  -1.07090            -0.28997           -1.33634   \n",
       "충북80대                  -1.26180            -0.14543           -1.82021   \n",
       "\n",
       "           total_mortgage_loan  total_credit_card_installments_payment  \\\n",
       "city_ages                                                                \n",
       "강원20대                 -2.05700                                -0.93965   \n",
       "강원30대                 -0.93497                                 0.68540   \n",
       "강원40대                 -0.29916                                 1.27118   \n",
       "강원50대                  0.16090                                 1.00114   \n",
       "강원60대                  0.15209                                 0.13333   \n",
       "...                        ...                                     ...   \n",
       "충북40대                  0.13306                                 0.99762   \n",
       "충북50대                  0.47270                                 0.74046   \n",
       "충북60대                  0.30879                                -0.13230   \n",
       "충북70대                 -0.38117                                -1.04944   \n",
       "충북80대                 -0.65429                                -1.54146   \n",
       "\n",
       "           total_monthly_spend  cluster  \n",
       "city_ages                                \n",
       "강원20대                 -0.25275        0  \n",
       "강원30대                  1.05898        1  \n",
       "강원40대                  1.28909        2  \n",
       "강원50대                  0.56570       -1  \n",
       "강원60대                 -0.30140        3  \n",
       "...                        ...      ...  \n",
       "충북40대                  1.23418       -1  \n",
       "충북50대                  0.48883       -1  \n",
       "충북60대                 -0.37910        3  \n",
       "충북70대                 -1.13706        4  \n",
       "충북80대                 -1.53419        5  \n",
       "\n",
       "[112 rows x 16 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_total_dbscan = pd.DataFrame(city_total_dbscan, columns=city_total.columns, index=city_total.index)\n",
    "city_total_dbscan['cluster'] = clusters_dbscan\n",
    "city_total_dbscan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cluster\n",
       "-1     56\n",
       " 0     15\n",
       " 1      2\n",
       " 2      2\n",
       " 3      3\n",
       " 4      5\n",
       " 5      7\n",
       " 6      4\n",
       " 7      2\n",
       " 8      2\n",
       " 9      2\n",
       " 10     2\n",
       " 11     2\n",
       " 12     2\n",
       " 13     2\n",
       " 14     2\n",
       " 15     2\n",
       "Name: total_avg_score, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_total_dbscan.groupby(['cluster'])['total_avg_score'].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### GMM(Gaussian Mixture Model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 타원형으로 길게 늘어진 데이터 분포에 적용이 용이\n",
    "- 개별 가우시안 분포에 속한다고 가정을 한 상태에서 특정 정규분포에 속할 확률을 추정하는 것\n",
    "- n_components : 사전적으로 정의하는 군집화 개수"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 최적의 클러스터 개수 - GMM이 확률 생성 모델이기 때문에 AIC, BIC 방식 적용 가능\n",
    "- AIC(Akaike Information Criterion)\n",
    "- BIC(Bayesian information criterion)\n",
    "- AIC, BIC가 작을수록 좋음\n",
    "- AIC는 과적합될 수 있는 더 복잡한 모델을 선택하는 경향, BIC는 과소적합될 수 있는 더 단순한 모델을 선택하는 경향"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "def aic_bic(dataframe, n_components):\n",
    "    gmm = [GaussianMixture(n, reg_covar = 1e-1, random_state=121).fit(dataframe)\n",
    "        for n in n_components]\n",
    "\n",
    "    plt.plot(n_components, [m.bic(dataframe) for m in gmm], 'bo-', label='BIC', color='red')\n",
    "    plt.plot(n_components, [m.aic(dataframe) for m in gmm], 'bo-', label='AIC')\n",
    "    plt.legend(loc='best')\n",
    "    plt.xlabel('n_components')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### GMM 클러스터링 함수"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gmm_clustering(dataframe, cluster_num, scaler):\n",
    "    gmm = GaussianMixture(n_components = cluster_num, random_state=0)\n",
    "    dataframe_gmm = scaler.fit_transform(dataframe)\n",
    "    predict = gmm.fit_predict(dataframe_gmm)\n",
    "\n",
    "    output_df = pd.DataFrame(dataframe, columns = dataframe.columns, index=dataframe.index)\n",
    "    output_df['cluster'] = predict\n",
    "\n",
    "    if 'city_ages' == dataframe.index.name:\n",
    "        output_df.reset_index(inplace=True)\n",
    "        output_df['city'] = output_df['city_ages'].str.slice(start=0, stop=2)\n",
    "        output_df['age'] = output_df['city_ages'].str.slice(start=2, stop=6)\n",
    "        \n",
    "    return output_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 1. city_total : 지역, 나이 제외 + 그룹화"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "city_total2 = city_total.copy()\n",
    "\n",
    "aic_bic(city_total2, np.arange(1, 15, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "n = 7\n",
    "scaler = StandardScaler()\n",
    "city_total2_gmm = gmm_clustering(city_total2, n, scaler)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cluster\n",
       "0    15\n",
       "1    24\n",
       "2    16\n",
       "3    22\n",
       "4    17\n",
       "5     6\n",
       "6    12\n",
       "Name: total_avg_score, dtype: int64"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_total2_gmm.groupby('cluster').count()['total_avg_score']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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s5HyS9CB3WJL+sJ3qnkP7d0nOAzYD/whcNNfJRj1jlqRxtKuqJucqqKqLgYuTnAr8CX0T25m8iZEkLbEkB+X+J+duBg6cq94ZsyQtvR8ELklyN/Bt4MVzFRvMkrTEqurT9N4ZPRCXMiSpMQazJDXGYJakxhjMktQYg1mSGmMwS1JjfLlco676yyeudAsAPJ5bV7oFaew4Y5akxhjMktQYg1mSGmMwS1JjDGZJaozBLEmNMZglqTEGsyQ1xmCWpMYYzJLUGINZkhpjMEtSYxYVzElOSHLAqJqRJA14d7kkLwV+um/oR6rqcOBM4AbgW0lOBV7W7d/Qfb2p+3pJVf3VCPqVpFVvoGCuqtcn+a/AAVX1b0m2z1LzIeBDSb4X+DPgPuCXqmrnSDuWpFVumPsxHwm8CngRcG/f+DuTbKU3Oz4BOKirCfAHSe4Erq2qK0fSsSStcvMGc5IjgAlgPXBEkp8C3pjkR7uSF1TVLUkmgTdU1df6Dv+1JOu5f2lDkjSPQWbMRwA/Tm9p4n3Ao4Bvd9sAJDkJOL/7ftaTJLmwqrbNGNsMbAaYmFg3fPeStArNG8xV9ffA3yc5Gngp8H3drpuAtwK3VtUtwNXTxyTZBOxbVVvnOfcUMAUwObmhFvIDSNJqM+irMvYF/jvwK11Qk+QYek/yHUff7FmStDiDvo55v+7rl/rGvgTs7tsnSRqBQV8ud1eSC4D3J9nTDe8DvLqqvjnLITsHPbck6YEGDs/u5W4DveStqq5acEeSNOa8V4YkNcZglqTGGMyS1BiDWZIaYzBLUmMMZklqjMEsSY0xmCWpMQazJDXGYJakxhjMktQYbzQkaVX6wYl7+eQf/8uyXCtvGu35nDFLUmMMZklqjMEsSY0xmCWpMQazJDXGYJakxhjMktQYg1mSGmMwS1JjDGZJaozBLEmNWVQwJzl7gJr1SX5iMdeRpHEyUDAn+S9JtnePG5K8qNv1szPqLkhybZJrkrwjyUOBRwPPGHHfkrRqDXR3uap6xfT3SS4HrppZk+SHgKOr6qe67V8HfgH4v6NpVZLGw1BLGUmeA9xcVf+vb+zj3fhNwMFJTkvyk8DTgU+OtFtJGgMDzZiT7AP8JnAkcF7/vqp6al/d84ATgPXAK4FvA+tG1awkjYN5gznJIcA7gD+rqjfM2P2lruYhwKXAffSC+HuBjwLfBK6f49ybgc0AExPmtyTBAMFcVXcApyV5QpK/Avbrdu0DvK6ruRv4JYAkTwFOAf478FLgycDn9nLuKWAKYHJyQy3i55CkVWOYj5a6FDi9qr4KkORA4MNJ/raq/rUbexPwF8DfAbcAlwA/CvzAKJuWpNVsmGCu7tG/zYyxQ+g9Ofh33fYXuockaUDDBPM5wJuTrO221wCvrqrbZ9S9O8k9M8Y+W1X/eaFNStI4GTiYq+pzwGnz1GxabEOSNO68V4YkNcZglqTGGMyS1BiDWZIaYzBLUmMMZklqjMEsSY0xmCWpMQazJDXGYJakxgxzrwwto5PP3OttrJfVrW9d6Q6k8eOMWZIaYzBLUmMMZklqjMEsSY0xmCWpMQazJDXGYJakxhjMktQYg1mSlliSQ5JcnmRHkr9J8ui56g1mSVp6DwVeWlUbgT8CfnuuYt+SLUlLrKr+uW/zduBbc9UbzJK0eIclua5ve6qqpmYWJVlPb7b84rlOtqhgTjIBfF9V7Zij5tSq+tBiriNJjdtVVZNzFSR5JvAs4Fer6utz1Q60xpzkrG7RekeSzya5tNs1AWzsai5NctQsh79skGtI0mqV5PHAs6rq7PlCGQacMVfVVmBrd4FXALuS7AAeBnxggKYmgZuqaucg15OkVeYU4GldbgLcXFW/uLfioZYykjwWOBZ4dlW9OclTgRPnqN8fCDAJ3AUYzJLGTlVdBFw0aP3AwZzkNOAXgRcCP5DkMh44Y74N+Isk9wDVPd7d66kuneWUkqRZzBvMSdbQW8b4PLCpqu6l93KPjUmOBY4HqKotwJZZjj9yjnNvBjYDTEysG7p5SVqN5g3mqroPODPJQ4CLkjwB2A2sBT4FvGq6Nsnb6D0h2O9g4IK9nHsKmAKYnNxQC+hfkladYdaYzwNurKqXTA8kOY/e6/Eu6YYeWVUPWHNOsm2xTUrSOBnmLdmzzWj3AfaMqBdJEsPNmF8LXJjko9y/lPG3wPl9NWuTbJ9x3OMW16IkjZeBg7mq7gZeMk/NCYvuSJLGnHeXk6TGGMyS1BiDWZIaYzBLUmMMZklqjMEsSY0xmCWpMQazJDXGYJakxhjMktQYPyW7UVf95RNXugUAHs+tK92CNHacMUtSYwxmSWqMwSxJjTGYJakxBrMkNcZglqTGGMyS1BiDWZIaYzBLUmMMZklqjMEsSY0xmCWpMUMHc5KNSZ4xRP2pw15DksbZQMHchfH2JNuBrcCbp7eTnNDVXJrkqFkOf9nIupWkMTDQbT+ragewI8nTgN8H9gC/V1WfGOT4JJPATVW1c6GNStK4GHTG/JIk/wN4OvAs4NnAKUk+kOR35jhufyDAJHD4CPqVpFVv3hlzksOAzwCf7IaO6b5e2T0OSHI4cBvwF0nuAap7vBuoqrp01I1L0mo1yFLGocAP920/CVjD/UENcEtVbQG2zDw4yZF7O3GSzcBmgImJdQO0Ikmr37zBXFU3Ajcm2QD8Nr1gDvAN4OKqumm6NsnbgIkZpzgYuGAv554CpgAmJzfUQn4ASVpthvnMv8uBl3P/qyyOBd4FPKWv5pFVdWL/QUm2LapDSRozwwTzGuD6qroHIMln8Q0qkjRywwTzecAVSaa3A8x8Rcba7rXO/R63wN4kaSwNHMxVdS1w7Tw1Jyy6I0kagW/ccSAffd9xK93GgrgUIUmNMZglqTEGsyQ1xmCWpMYYzJLUGINZkhpjMEtSYwxmSWqMwSxJjTGYJakxBrMkNcZglqTGGMyS1BiDWZIaYzBLUmMMZklqjMEsSY0xmCWpMQazJDXGYJakxhjMktQYg1mSGmMwS1Jjhg7mJBuTPGMpmpEkwb6DFCXZCLyq23xMbyg3dtv/paqu6eq2V9WJM47dVlWnjKZdSVr9BgrmqtoB7EjyNOD3gT3A71XVJ2aUHpVk+4yxDYvuUpLGyKAz5pcATwP+F/Csbvg/J/kd4BNV9Ufd2J3A5TMO3zyKRiVpXMwbzEkOAz4DfLIbOqb7emX3OCDJ4VW1EzgDWDvjFB+b49yb6YJ7YmLdcJ1L0io1yIz5UOCH+7afBKzh/qAGeEySF/RtHwEEuA0gCcCFVbWt/8RVNQVMAUxObqhhm5ek1WjeYK6qG4Ebk2wAfpteMAf4BnBxVd3UlV42fUySTcC+VbV15B1L0io30Bpz53Lg5cDLuu1jgXcBTwFIcjhwRf8BSc4Cjq6q9YvuVJLGxDDBvAa4vqruAUjyWfpeB92tMW+ceVCSbTPHJEl7N0wwnwdc0a0XQ28543dG3pEkjbmBg7mqrgWuXcA1zl3AMZI0tpb8Xhndk4eSpAF5EyNJaozBLEmNMZglqTEGsyQ1xmCWpMYYzJLUmGHeYCJJWoDulhW/Beypqt+dr94ZsyQtvdcBd/Pdt0WelcEsSUusqn4R+JtB613KkKTFOyzJdX3bU9395hfEYJakxdtVVZOjOplLGZLUGINZkhrjUoYkLYOq2gHsGKTWGbMkNcZglqTGGMyS1BiDWZIaYzBLUmMMZklqjMEsSY0xmCWpMYsK5iQTSTaOphVJEgwYzEnOSrKje3w2yaXdrglgY1/d9lmO3TaSTiVpTAz0luyq2gpsBUjyCmBXkh3Aw4AP9JUeNUs4b1h0l5I0Roa6V0aSxwLHAs+uqjcneSpwYl/JncDlMw7bvLgWJWm8DBzMSU4DfhF4IfADSS7ju2fMZ/DdH53ysTnOuZkuuCcm1g3aiiStavMGc5I19JYxPg9sqqp7gduBjUmOBY5PchJwft9hRwABbuvOAXBhVT1gvbm7w/8UwOTkhlrsDyNJq8G8wVxV9wFnJnkIcFGSJwC76c2MPwW8qqp2A1dPH5NkE7BvtzYtSRrCMGvM5wE3VtVLpgeSnAe8GLik+3juK/oPSHIWcHRVrR9Br5I0FoYJ5tmWGvYB9gBU1U76Xjo3zZfLSdJwhnmDyWuBo5N8NMnV3cvl1gF/uiSdSdKYGnjGXFV3Ay+Zt/C7nbuAYyRpbC35vTKq6salvoYkrSbexEiSGmMwS1JjDGZJaozBLEmNMZglqTEGsyQ1xmCWpMYYzJLUGINZkhpjMEtSYwxmSWqMwSxJjTGYJakxBrMkNcZglqTGGMyS1BiDWZIaYzBLUmMMZklqjMEsSY0xmCWpMfuudAOStBS+sfZ2rl1/xUq3sSCLmjEnOSHJxnlqTl3MNSRp3AwUzEk+nGRHkq93Xy/vdh0JPKqruTTJUbMc/rLRtCpJ42GgpYyqegZAkn+oqo3DXiTJJHBTVe0c9lhJGjcDrzEneTLwiCTHAT8GPB2YAF4/xzH7AwEmgbsAg1mS5jFQMHcB+0rgOOCPgV+oqjcmOauv7DbgL5LcA1T3eDdQVXXpXs67GdgMMDGxbqE/gyStKvMGc5JDgbcDr6mqG5L8BrA1yXP766pqC7BlluOP3Nu5q2oKmAKYnNxQQ3UuSavUIDPmO4Czgd1JDqiqG4HTAJI8oDDJ2+gtb/Q7GLhg0Z1K0piYN5irqoBbkmwBtgMf79u3dUb5I6vqxP6BJNsW36YkjQ/f+SdJjRn2nX9vSHLnjLErq2r6lRlrk2yfsf9xC2tNksbTwMG8tyf3ZtScsMh+JGnsuZQhSY0xmCWpMQazJDXGYJakxhjMktQYg1mSGmMwS1JjDGZJaozBLEmNMZglqTEGsyQ1xmCWpMYYzJLUGINZkhpjMEtSYwxmSWqMwSxJjTGYJakxBrMkNcZglqTGGMyS1BiDWZIaYzBLUmMWFcxJTkiycTStSJJgwGBO8uEkO5J8vft6ebfrSOBRfXXbZzl222halaTxsO8gRVX1DIAk/1BVG+coPWqWcN6wwN4kaSwNFMwASZ4MPCLJccCPAU8HJoDX95XdCVw+49DNi21SksbJQMGcZH/glcBxwB8Dv1BVb0xy1ozSM4C1M8Y+Nsd5N9MF98TEugFblqTVbd5gTnIo8HbgNVV1Q5LfALYmeW5fzUnA+X2HHQEEuK3bD3BhVT1gvbmqpoApgMnJDbW4H0WSVodBZsx3AGcDu5McUFU3AqfBvwcuVXU1cPX0AUk2AftW1dYR9ytJq968wVxVBdySZAuwHfh4376t098nORy4ov/Ybqnj6KpaP5p2JenBKckfAD9JL3c3V9X/2VvtwE/+zaeqdgIbZ2nGl8tJGmtJngYcUVU/leQY4GLgp/dWP2wwvyHJnTPGrqyq189aLUkCeAbwToCq+nySOV/tMHAwV9UWYMsCGjp3AcdI0oPJYUmu69ue6l7cMO0RwM6+7d1J9qmqPbOdbGRLGXvTPVkoSavZrqqanGP/ncChfdt79hbK4E2MJGk5fAx4LkCSHwZumat4yWfMkiT+GvjpJB8DvkHvJch7ZTBL0hLrli3OGbTepQxJaozBLEmNMZglqTEGsyQ1xmCWpMYYzJLUGF8u16jH33PlSrfQlHce8DMr3QIAL3jv01e6BQDq9I+sdAtaQs6YJakxBrMkNcZglqTGGMyS1BiDWZIaYzBLUmMMZklqjMEsSY0xmCWpMQazJDXGYJakxhjMktSYRQVzkoOSHD+qZiRJAwZzku1JPpPkxu77H0myDXg48Atdzandvu1d3Y19289cyh9CklaTgW77WVUnJtkIPKWqLgRIMrPmQ8CHknwv8GfAfcAvVdXOUTYsSavdMPdjfjhwSJJjgCcAB/fvTHIicAJwEPAiIMAfJLkTuLaqvMGwJA1gmGB+InAMsAfYDdSM/XcAb6iqr/WN/VqS9cCG2U6YZDOwGWBiYt0QrUjS6jVQMCfZl14wfwX4ZlVdnuSsvv0nAed33+/tHBdW1bb+saqaAqYAJic3zAx6SRpLg86YfxN4O/C/gNcAm/p3VtXVwNXT20k2AftW1dbRtClJ42PeYE6yAZisqhd02x9L8rgl70ySxtS8wVxVNyU5o2/7Mtj7koUkaXEGfbncsOu/Owc9tyTpgRYcnlV1Svfti2bZd9WCO5KkMee9MiSpMQazJDXGYJakxhjMktQYg1mSGmMwS1JjDGZJaozBLEmNMZglqTEGsyQ1JsPfBmNpJNkJ3LTI0xwG7BpBO4tlHw9kHw+0mvrYUFWHj6KZUes+l/SwZbrcrr7bVCxaM8E8Ckmuq6pJ+7AP+3jw9KHv5lKGJDXGYJakxqy2YJ5a6QY69vFA9vFA9qE5rao1ZklaDVbbjHmvkhyQ5AT7uF+SZzfQQxO/D/tos49xteqCOcm2JNu7x7unx4BDgTPHsI9Lk3y8r5e1XR8A5y5jH638PuyjwT70QA+Kz+VLcghwKfC99P4xeSGwH/CnwP7AJ6rqvOn6qjpxifrYD3gvcBAQ4AzgwOXuY0ZPnwHOBz4HvAV4GPAl4Fer6t6ubFNVfaXvmFFe/1bghm5zCvgMK/D7SPIk4LXAGuAD3WO5/z5eDDy3b+gY4Ljl7qPr5WXAzwG7gRcDd61EH1qYB0UwAw8FXlpV/5zkNOC3ge8DfqWqvpLkiiRPrqq/XeI+dgM/X1V3JdlE7x+Ip61AHwAkeS5wSLf5auA1VfWJJBcDzwHetQxtfLGqNvb19CGW+feRZC1wAfDsqrp9pfqoqjcBb+qufzrwaOCS5e4jyRHAs4GnAN8PvIHe/9dX5O9Uw3tQLGVU1T9X1T93m7cD9wD7980C3wscO8cpnkhvNrnYPvZU1V3d5tHA/16JPgCSHETvPzXf0Q39x6r6xIB9rE/yfkbzv//tfT2tZWV+H6cCXwHemeSaJE9eoT4ASLIP8BvAZSvUx/Tf6H7c/+6+Fft9aHgPimCelmQ9vdnya4Gv9+36Or01sb35HHD2iHo4L8mNwCTw2ZXqA3gj8IfAnm67/3/L+fr4Kr3/5N4zR82gJpJcm+QK4JGszO/jaGAd8EzgV4DLV6iPac8Grqa3zLXsfVTVN4C/Af4v8EHgbSvRhxbuwbKUQZJnAs8CfhX4Nvf/Jzz0/sh29tU+ht5a4wHAl4EC7htFH1V1MXBxklPp/SfisvfRLaPcXFWf7pZ2oLfmPWsfwIZuffyhwG3dz7F7FGvNVfWErqfj6f2Decje+ljC/112Ax+uqt3AV5LcwQODZ9n+Pjq/3D2+xcr8fZwGrKW3jHEovRly/z/Cy/370JAeFDPmJI8HnlVVZ1fV17vlhId0M2joradu775/H3AOvfXfk4GDR9jHQbk/zW6m9/tb9j6AFwA/nORyejPflwO3JfnRbv/pfX1cQ28G90vAaYzwpi5J1vRt3k7v/9Ar8fv4JL3ljOn11TuB/VagD5I8nN6ywc6V+jsFNgD/Ur03KfwbvSer163E70ML82CZMZ8CPC3Jjm77ZuClwHuS3A18sKq+AFBVl808eISvQvhB4JLumt+m92z3YcvdR1VNz5JJsgX4FHAj8LYke4BPA1d1tVcAVyxFH/SWMd4B3E1v3f8c4OEs/+/j75LckOR/0ps9v5TeP5rL/fcB8JP0/qGYthJ/p1vp/S1cCzyE3lr351agDy3QgyKYq+oi4KJZds31BMZS9PFpei9/6vdPy91Hv6ra0rf5U8t87X8CfmLG8JdZgd9HVf0u8Lszhleij/fRm4VOb396ufvoZurPn2XXiv2daji+JVuSGvOgWGOWpHFiMEtSYwxmSWqMwSxJjTGYJakxBrMkNcZglqTG/H9Xl7nBNQK7bgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1008x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"7\" halign=\"left\">cluster</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <th>20대</th>\n",
       "      <th>30대</th>\n",
       "      <th>40대</th>\n",
       "      <th>50대</th>\n",
       "      <th>60대</th>\n",
       "      <th>70대</th>\n",
       "      <th>80대</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>서울</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>경기</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>인천</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>대구</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>부산</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>울산</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>대전</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>광주</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>강원</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>경남</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>충남</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>충북</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>경북</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>전남</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>전북</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>제주</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     cluster                        \n",
       "age      20대 30대 40대 50대 60대 70대 80대\n",
       "city                                \n",
       "서울         2   3   0   0   5   5   5\n",
       "경기         2   3   0   0   5   5   5\n",
       "인천         2   3   0   0   4   4   1\n",
       "대구         2   3   0   0   4   1   1\n",
       "부산         2   3   0   0   4   4   1\n",
       "울산         2   0   0   0   4   4   1\n",
       "대전         2   3   0   6   4   1   1\n",
       "광주         2   3   3   6   4   1   1\n",
       "강원         2   3   3   6   4   1   1\n",
       "경남         2   3   0   6   4   1   1\n",
       "충남         2   3   3   6   4   1   1\n",
       "충북         2   3   3   6   4   1   1\n",
       "경북         2   3   3   6   4   1   1\n",
       "전남         2   3   3   6   4   1   1\n",
       "전북         2   3   3   6   4   1   1\n",
       "제주         2   3   6   6   6   4   1"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster_visualization(city_total2_gmm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 2. df_total : 성별, 지역, 나이 제외"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_total2 = df_total.copy()\n",
    "\n",
    "aic_bic(df_total2, np.arange(1, 15, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "n = 7\n",
    "scaler = StandardScaler()\n",
    "df_total2_gmm = gmm_clustering(df_total2, n, scaler)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "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>avg_score</th>\n",
       "      <th>num_usecard</th>\n",
       "      <th>monthly_card_spend</th>\n",
       "      <th>monthly_bk_loan</th>\n",
       "      <th>monthly_cd_loan</th>\n",
       "      <th>monthly_installments_loan</th>\n",
       "      <th>monthly_insurance_loan</th>\n",
       "      <th>monthly_sbk_loan</th>\n",
       "      <th>loan_commitment</th>\n",
       "      <th>inst_rep_loanb</th>\n",
       "      <th>...</th>\n",
       "      <th>credit_card_installments_payment</th>\n",
       "      <th>monthly_spend</th>\n",
       "      <th>cluster</th>\n",
       "      <th>city</th>\n",
       "      <th>ages</th>\n",
       "      <th>sex</th>\n",
       "      <th>avg_rat</th>\n",
       "      <th>monthly_loan</th>\n",
       "      <th>credit_card_payment</th>\n",
       "      <th>debit_card_payment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>740</td>\n",
       "      <td>1.99000</td>\n",
       "      <td>810000</td>\n",
       "      <td>18500000</td>\n",
       "      <td>620000</td>\n",
       "      <td>2110000</td>\n",
       "      <td>680000</td>\n",
       "      <td>1320000</td>\n",
       "      <td>3800000</td>\n",
       "      <td>10520000</td>\n",
       "      <td>...</td>\n",
       "      <td>83000</td>\n",
       "      <td>727000</td>\n",
       "      <td>2</td>\n",
       "      <td>서울</td>\n",
       "      <td>20대</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>24940000</td>\n",
       "      <td>254000</td>\n",
       "      <td>473000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>782</td>\n",
       "      <td>1.75000</td>\n",
       "      <td>790000</td>\n",
       "      <td>24150000</td>\n",
       "      <td>680000</td>\n",
       "      <td>2300000</td>\n",
       "      <td>790000</td>\n",
       "      <td>1160000</td>\n",
       "      <td>5420000</td>\n",
       "      <td>12220000</td>\n",
       "      <td>...</td>\n",
       "      <td>83000</td>\n",
       "      <td>707000</td>\n",
       "      <td>2</td>\n",
       "      <td>서울</td>\n",
       "      <td>20대</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>30880000</td>\n",
       "      <td>278000</td>\n",
       "      <td>429000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>782</td>\n",
       "      <td>1.74000</td>\n",
       "      <td>790000</td>\n",
       "      <td>24240000</td>\n",
       "      <td>680000</td>\n",
       "      <td>2300000</td>\n",
       "      <td>790000</td>\n",
       "      <td>1210000</td>\n",
       "      <td>5350000</td>\n",
       "      <td>12280000</td>\n",
       "      <td>...</td>\n",
       "      <td>78000</td>\n",
       "      <td>712000</td>\n",
       "      <td>2</td>\n",
       "      <td>서울</td>\n",
       "      <td>20대</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>31000000</td>\n",
       "      <td>267000</td>\n",
       "      <td>445000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>782</td>\n",
       "      <td>1.75000</td>\n",
       "      <td>860000</td>\n",
       "      <td>24570000</td>\n",
       "      <td>680000</td>\n",
       "      <td>2300000</td>\n",
       "      <td>790000</td>\n",
       "      <td>1230000</td>\n",
       "      <td>5380000</td>\n",
       "      <td>12320000</td>\n",
       "      <td>...</td>\n",
       "      <td>89000</td>\n",
       "      <td>771000</td>\n",
       "      <td>2</td>\n",
       "      <td>서울</td>\n",
       "      <td>20대</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>31390000</td>\n",
       "      <td>292000</td>\n",
       "      <td>479000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>781</td>\n",
       "      <td>1.75000</td>\n",
       "      <td>820000</td>\n",
       "      <td>24830000</td>\n",
       "      <td>700000</td>\n",
       "      <td>2320000</td>\n",
       "      <td>770000</td>\n",
       "      <td>1250000</td>\n",
       "      <td>5430000</td>\n",
       "      <td>12420000</td>\n",
       "      <td>...</td>\n",
       "      <td>82000</td>\n",
       "      <td>738000</td>\n",
       "      <td>2</td>\n",
       "      <td>서울</td>\n",
       "      <td>20대</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>31730000</td>\n",
       "      <td>285000</td>\n",
       "      <td>453000</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3019</th>\n",
       "      <td>818</td>\n",
       "      <td>0.18000</td>\n",
       "      <td>200000</td>\n",
       "      <td>58460000</td>\n",
       "      <td>230000</td>\n",
       "      <td>260000</td>\n",
       "      <td>1610000</td>\n",
       "      <td>290000</td>\n",
       "      <td>8600000</td>\n",
       "      <td>18710000</td>\n",
       "      <td>...</td>\n",
       "      <td>43000</td>\n",
       "      <td>157000</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80대</td>\n",
       "      <td>여자</td>\n",
       "      <td>3</td>\n",
       "      <td>69610000</td>\n",
       "      <td>82000</td>\n",
       "      <td>75000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3020</th>\n",
       "      <td>818</td>\n",
       "      <td>0.18000</td>\n",
       "      <td>190000</td>\n",
       "      <td>58330000</td>\n",
       "      <td>230000</td>\n",
       "      <td>270000</td>\n",
       "      <td>1610000</td>\n",
       "      <td>310000</td>\n",
       "      <td>8550000</td>\n",
       "      <td>18750000</td>\n",
       "      <td>...</td>\n",
       "      <td>39000</td>\n",
       "      <td>151000</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80대</td>\n",
       "      <td>여자</td>\n",
       "      <td>3</td>\n",
       "      <td>69500000</td>\n",
       "      <td>82000</td>\n",
       "      <td>69000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3021</th>\n",
       "      <td>818</td>\n",
       "      <td>0.18000</td>\n",
       "      <td>210000</td>\n",
       "      <td>58190000</td>\n",
       "      <td>240000</td>\n",
       "      <td>290000</td>\n",
       "      <td>1610000</td>\n",
       "      <td>430000</td>\n",
       "      <td>8630000</td>\n",
       "      <td>18760000</td>\n",
       "      <td>...</td>\n",
       "      <td>49000</td>\n",
       "      <td>161000</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80대</td>\n",
       "      <td>여자</td>\n",
       "      <td>3</td>\n",
       "      <td>69510000</td>\n",
       "      <td>87000</td>\n",
       "      <td>74000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3022</th>\n",
       "      <td>819</td>\n",
       "      <td>0.18000</td>\n",
       "      <td>200000</td>\n",
       "      <td>58420000</td>\n",
       "      <td>240000</td>\n",
       "      <td>300000</td>\n",
       "      <td>1610000</td>\n",
       "      <td>470000</td>\n",
       "      <td>8570000</td>\n",
       "      <td>18690000</td>\n",
       "      <td>...</td>\n",
       "      <td>44000</td>\n",
       "      <td>156000</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80대</td>\n",
       "      <td>여자</td>\n",
       "      <td>3</td>\n",
       "      <td>69750000</td>\n",
       "      <td>80000</td>\n",
       "      <td>76000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3023</th>\n",
       "      <td>819</td>\n",
       "      <td>0.18000</td>\n",
       "      <td>200000</td>\n",
       "      <td>58170000</td>\n",
       "      <td>250000</td>\n",
       "      <td>300000</td>\n",
       "      <td>1640000</td>\n",
       "      <td>430000</td>\n",
       "      <td>8590000</td>\n",
       "      <td>18670000</td>\n",
       "      <td>...</td>\n",
       "      <td>44000</td>\n",
       "      <td>156000</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80대</td>\n",
       "      <td>여자</td>\n",
       "      <td>3</td>\n",
       "      <td>69510000</td>\n",
       "      <td>81000</td>\n",
       "      <td>75000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3024 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      avg_score  num_usecard  monthly_card_spend  monthly_bk_loan  \\\n",
       "0           740      1.99000              810000         18500000   \n",
       "1           782      1.75000              790000         24150000   \n",
       "2           782      1.74000              790000         24240000   \n",
       "3           782      1.75000              860000         24570000   \n",
       "4           781      1.75000              820000         24830000   \n",
       "...         ...          ...                 ...              ...   \n",
       "3019        818      0.18000              200000         58460000   \n",
       "3020        818      0.18000              190000         58330000   \n",
       "3021        818      0.18000              210000         58190000   \n",
       "3022        819      0.18000              200000         58420000   \n",
       "3023        819      0.18000              200000         58170000   \n",
       "\n",
       "      monthly_cd_loan  monthly_installments_loan  monthly_insurance_loan  \\\n",
       "0              620000                    2110000                  680000   \n",
       "1              680000                    2300000                  790000   \n",
       "2              680000                    2300000                  790000   \n",
       "3              680000                    2300000                  790000   \n",
       "4              700000                    2320000                  770000   \n",
       "...               ...                        ...                     ...   \n",
       "3019           230000                     260000                 1610000   \n",
       "3020           230000                     270000                 1610000   \n",
       "3021           240000                     290000                 1610000   \n",
       "3022           240000                     300000                 1610000   \n",
       "3023           250000                     300000                 1640000   \n",
       "\n",
       "      monthly_sbk_loan  loan_commitment  inst_rep_loanb  ...  \\\n",
       "0              1320000          3800000        10520000  ...   \n",
       "1              1160000          5420000        12220000  ...   \n",
       "2              1210000          5350000        12280000  ...   \n",
       "3              1230000          5380000        12320000  ...   \n",
       "4              1250000          5430000        12420000  ...   \n",
       "...                ...              ...             ...  ...   \n",
       "3019            290000          8600000        18710000  ...   \n",
       "3020            310000          8550000        18750000  ...   \n",
       "3021            430000          8630000        18760000  ...   \n",
       "3022            470000          8570000        18690000  ...   \n",
       "3023            430000          8590000        18670000  ...   \n",
       "\n",
       "      credit_card_installments_payment  monthly_spend  cluster  city  ages  \\\n",
       "0                                83000         727000        2    서울   20대   \n",
       "1                                83000         707000        2    서울   20대   \n",
       "2                                78000         712000        2    서울   20대   \n",
       "3                                89000         771000        2    서울   20대   \n",
       "4                                82000         738000        2    서울   20대   \n",
       "...                                ...            ...      ...   ...   ...   \n",
       "3019                             43000         157000        0   NaN   80대   \n",
       "3020                             39000         151000        0   NaN   80대   \n",
       "3021                             49000         161000        0   NaN   80대   \n",
       "3022                             44000         156000        0   NaN   80대   \n",
       "3023                             44000         156000        0   NaN   80대   \n",
       "\n",
       "      sex avg_rat monthly_loan credit_card_payment  debit_card_payment  \n",
       "0     NaN       4     24940000              254000              473000  \n",
       "1     NaN       4     30880000              278000              429000  \n",
       "2     NaN       4     31000000              267000              445000  \n",
       "3     NaN       4     31390000              292000              479000  \n",
       "4     NaN       4     31730000              285000              453000  \n",
       "...   ...     ...          ...                 ...                 ...  \n",
       "3019   여자       3     69610000               82000               75000  \n",
       "3020   여자       3     69500000               82000               69000  \n",
       "3021   여자       3     69510000               87000               74000  \n",
       "3022   여자       3     69750000               80000               76000  \n",
       "3023   여자       3     69510000               81000               75000  \n",
       "\n",
       "[3024 rows x 23 columns]"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_total2_gmm =  pd.concat([df_total2_gmm, tmp_df, tmp_total_df], axis=1)\n",
    "df_total2_gmm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cluster\n",
       "0    454\n",
       "1    553\n",
       "2    432\n",
       "3    337\n",
       "4    375\n",
       "5    407\n",
       "6    466\n",
       "Name: avg_score, dtype: int64"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_total2_gmm.groupby('cluster')['avg_score'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>avg_score</th>\n",
       "      <th>num_usecard</th>\n",
       "      <th>monthly_card_spend</th>\n",
       "      <th>monthly_bk_loan</th>\n",
       "      <th>monthly_cd_loan</th>\n",
       "      <th>monthly_installments_loan</th>\n",
       "      <th>monthly_insurance_loan</th>\n",
       "      <th>monthly_sbk_loan</th>\n",
       "      <th>loan_commitment</th>\n",
       "      <th>inst_rep_loanb</th>\n",
       "      <th>ls_rep_loanb</th>\n",
       "      <th>credit_loan</th>\n",
       "      <th>mortgage_loan</th>\n",
       "      <th>credit_card_installments_payment</th>\n",
       "      <th>monthly_spend</th>\n",
       "      <th>avg_rat</th>\n",
       "      <th>monthly_loan</th>\n",
       "      <th>credit_card_payment</th>\n",
       "      <th>debit_card_payment</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>825.71586</td>\n",
       "      <td>0.52196</td>\n",
       "      <td>371519.82379</td>\n",
       "      <td>60881872.24670</td>\n",
       "      <td>291828.19383</td>\n",
       "      <td>616674.00881</td>\n",
       "      <td>1552863.43612</td>\n",
       "      <td>228656.38767</td>\n",
       "      <td>14235484.58150</td>\n",
       "      <td>17295704.84581</td>\n",
       "      <td>43131960.35242</td>\n",
       "      <td>16409647.57709</td>\n",
       "      <td>95174735.68282</td>\n",
       "      <td>80418.50220</td>\n",
       "      <td>291101.32159</td>\n",
       "      <td>2.99780</td>\n",
       "      <td>75079867.84141</td>\n",
       "      <td>187310.57269</td>\n",
       "      <td>103790.74890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>793.69078</td>\n",
       "      <td>2.18490</td>\n",
       "      <td>1623508.13743</td>\n",
       "      <td>45346708.86076</td>\n",
       "      <td>1323273.05606</td>\n",
       "      <td>4749909.58409</td>\n",
       "      <td>2808010.84991</td>\n",
       "      <td>1167830.01808</td>\n",
       "      <td>10621916.81736</td>\n",
       "      <td>26858933.09222</td>\n",
       "      <td>24526112.11573</td>\n",
       "      <td>27582893.30922</td>\n",
       "      <td>80646618.44485</td>\n",
       "      <td>260956.60036</td>\n",
       "      <td>1362551.53707</td>\n",
       "      <td>3.11573</td>\n",
       "      <td>63906274.86438</td>\n",
       "      <td>907949.36709</td>\n",
       "      <td>454602.16998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>770.55787</td>\n",
       "      <td>1.78613</td>\n",
       "      <td>788888.88889</td>\n",
       "      <td>18667615.74074</td>\n",
       "      <td>699907.40741</td>\n",
       "      <td>3828472.22222</td>\n",
       "      <td>889537.03704</td>\n",
       "      <td>1611458.33333</td>\n",
       "      <td>3533055.55556</td>\n",
       "      <td>13209050.92593</td>\n",
       "      <td>11676527.77778</td>\n",
       "      <td>16360833.33333</td>\n",
       "      <td>38640555.55556</td>\n",
       "      <td>84115.74074</td>\n",
       "      <td>704773.14815</td>\n",
       "      <td>4.00000</td>\n",
       "      <td>29305115.74074</td>\n",
       "      <td>255993.05556</td>\n",
       "      <td>448780.09259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>796.64392</td>\n",
       "      <td>1.84567</td>\n",
       "      <td>1492967.35905</td>\n",
       "      <td>58922611.27596</td>\n",
       "      <td>1121038.57567</td>\n",
       "      <td>3940712.16617</td>\n",
       "      <td>2275816.02374</td>\n",
       "      <td>676468.84273</td>\n",
       "      <td>18503471.81009</td>\n",
       "      <td>23559406.52819</td>\n",
       "      <td>38218516.32047</td>\n",
       "      <td>29527833.82789</td>\n",
       "      <td>104908367.95252</td>\n",
       "      <td>289077.15134</td>\n",
       "      <td>1203890.20772</td>\n",
       "      <td>3.07715</td>\n",
       "      <td>81926320.47478</td>\n",
       "      <td>820216.61721</td>\n",
       "      <td>383673.59050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>814.95467</td>\n",
       "      <td>0.44608</td>\n",
       "      <td>247653.33333</td>\n",
       "      <td>38578240.00000</td>\n",
       "      <td>142933.33333</td>\n",
       "      <td>496160.00000</td>\n",
       "      <td>455173.33333</td>\n",
       "      <td>110053.33333</td>\n",
       "      <td>13005626.66667</td>\n",
       "      <td>8770160.00000</td>\n",
       "      <td>26295520.00000</td>\n",
       "      <td>13231840.00000</td>\n",
       "      <td>64361200.00000</td>\n",
       "      <td>49413.33333</td>\n",
       "      <td>198240.00000</td>\n",
       "      <td>3.00000</td>\n",
       "      <td>48286266.66667</td>\n",
       "      <td>117557.33333</td>\n",
       "      <td>80682.66667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>800.98280</td>\n",
       "      <td>2.12007</td>\n",
       "      <td>1657641.27764</td>\n",
       "      <td>64351769.04177</td>\n",
       "      <td>1539287.46929</td>\n",
       "      <td>3938378.37838</td>\n",
       "      <td>4293488.94349</td>\n",
       "      <td>986707.61671</td>\n",
       "      <td>12403292.38329</td>\n",
       "      <td>38489164.61916</td>\n",
       "      <td>31111621.62162</td>\n",
       "      <td>28839238.32924</td>\n",
       "      <td>110511400.49140</td>\n",
       "      <td>300560.19656</td>\n",
       "      <td>1357081.08108</td>\n",
       "      <td>3.10811</td>\n",
       "      <td>84173046.68305</td>\n",
       "      <td>932842.75184</td>\n",
       "      <td>424238.32924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>822.25322</td>\n",
       "      <td>1.30968</td>\n",
       "      <td>860815.45064</td>\n",
       "      <td>59266845.49356</td>\n",
       "      <td>831330.47210</td>\n",
       "      <td>2162789.69957</td>\n",
       "      <td>1846223.17597</td>\n",
       "      <td>342296.13734</td>\n",
       "      <td>17741652.36052</td>\n",
       "      <td>21718476.39485</td>\n",
       "      <td>36566802.57511</td>\n",
       "      <td>20806309.01288</td>\n",
       "      <td>104771287.55365</td>\n",
       "      <td>190697.42489</td>\n",
       "      <td>670118.02575</td>\n",
       "      <td>2.99785</td>\n",
       "      <td>77261781.11588</td>\n",
       "      <td>463630.90129</td>\n",
       "      <td>206487.12446</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         avg_score  num_usecard  monthly_card_spend  monthly_bk_loan  \\\n",
       "cluster                                                                \n",
       "0        825.71586      0.52196        371519.82379   60881872.24670   \n",
       "1        793.69078      2.18490       1623508.13743   45346708.86076   \n",
       "2        770.55787      1.78613        788888.88889   18667615.74074   \n",
       "3        796.64392      1.84567       1492967.35905   58922611.27596   \n",
       "4        814.95467      0.44608        247653.33333   38578240.00000   \n",
       "5        800.98280      2.12007       1657641.27764   64351769.04177   \n",
       "6        822.25322      1.30968        860815.45064   59266845.49356   \n",
       "\n",
       "         monthly_cd_loan  monthly_installments_loan  monthly_insurance_loan  \\\n",
       "cluster                                                                       \n",
       "0           291828.19383               616674.00881           1552863.43612   \n",
       "1          1323273.05606              4749909.58409           2808010.84991   \n",
       "2           699907.40741              3828472.22222            889537.03704   \n",
       "3          1121038.57567              3940712.16617           2275816.02374   \n",
       "4           142933.33333               496160.00000            455173.33333   \n",
       "5          1539287.46929              3938378.37838           4293488.94349   \n",
       "6           831330.47210              2162789.69957           1846223.17597   \n",
       "\n",
       "         monthly_sbk_loan  loan_commitment  inst_rep_loanb   ls_rep_loanb  \\\n",
       "cluster                                                                     \n",
       "0            228656.38767   14235484.58150  17295704.84581 43131960.35242   \n",
       "1           1167830.01808   10621916.81736  26858933.09222 24526112.11573   \n",
       "2           1611458.33333    3533055.55556  13209050.92593 11676527.77778   \n",
       "3            676468.84273   18503471.81009  23559406.52819 38218516.32047   \n",
       "4            110053.33333   13005626.66667   8770160.00000 26295520.00000   \n",
       "5            986707.61671   12403292.38329  38489164.61916 31111621.62162   \n",
       "6            342296.13734   17741652.36052  21718476.39485 36566802.57511   \n",
       "\n",
       "           credit_loan   mortgage_loan  credit_card_installments_payment  \\\n",
       "cluster                                                                    \n",
       "0       16409647.57709  95174735.68282                       80418.50220   \n",
       "1       27582893.30922  80646618.44485                      260956.60036   \n",
       "2       16360833.33333  38640555.55556                       84115.74074   \n",
       "3       29527833.82789 104908367.95252                      289077.15134   \n",
       "4       13231840.00000  64361200.00000                       49413.33333   \n",
       "5       28839238.32924 110511400.49140                      300560.19656   \n",
       "6       20806309.01288 104771287.55365                      190697.42489   \n",
       "\n",
       "         monthly_spend  avg_rat   monthly_loan  credit_card_payment  \\\n",
       "cluster                                                               \n",
       "0         291101.32159  2.99780 75079867.84141         187310.57269   \n",
       "1        1362551.53707  3.11573 63906274.86438         907949.36709   \n",
       "2         704773.14815  4.00000 29305115.74074         255993.05556   \n",
       "3        1203890.20772  3.07715 81926320.47478         820216.61721   \n",
       "4         198240.00000  3.00000 48286266.66667         117557.33333   \n",
       "5        1357081.08108  3.10811 84173046.68305         932842.75184   \n",
       "6         670118.02575  2.99785 77261781.11588         463630.90129   \n",
       "\n",
       "         debit_card_payment  \n",
       "cluster                      \n",
       "0              103790.74890  \n",
       "1              454602.16998  \n",
       "2              448780.09259  \n",
       "3              383673.59050  \n",
       "4               80682.66667  \n",
       "5              424238.32924  \n",
       "6              206487.12446  "
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_total2_gmm.groupby('cluster').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#clustering = df_total2_gmm.to_csv('dataset/gmm_clustering.csv', encoding='utf-8', index=False)"
   ]
  }
 ],
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