Samsung_card_competition.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 데이터 불러오기 및 기본 정보 알아보기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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>YM</th>\n",
       "      <th>Category</th>\n",
       "      <th>성별구분</th>\n",
       "      <th>연령대</th>\n",
       "      <th>기혼스코어</th>\n",
       "      <th>유아자녀스코어</th>\n",
       "      <th>초등학생자녀스코어</th>\n",
       "      <th>중고생자녀스코어</th>\n",
       "      <th>대학생자녀스코어</th>\n",
       "      <th>전업주부스코어</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>202005</td>\n",
       "      <td>할인점</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>high</td>\n",
       "      <td>low</td>\n",
       "      <td>high</td>\n",
       "      <td>mid</td>\n",
       "      <td>low</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>202005</td>\n",
       "      <td>취미</td>\n",
       "      <td>0</td>\n",
       "      <td>B</td>\n",
       "      <td>high</td>\n",
       "      <td>low</td>\n",
       "      <td>mid</td>\n",
       "      <td>mid</td>\n",
       "      <td>low</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>202005</td>\n",
       "      <td>오픈마켓/소셜</td>\n",
       "      <td>1</td>\n",
       "      <td>D</td>\n",
       "      <td>mid</td>\n",
       "      <td>mid</td>\n",
       "      <td>mid</td>\n",
       "      <td>mid</td>\n",
       "      <td>low</td>\n",
       "      <td>mid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>202005</td>\n",
       "      <td>뷰티</td>\n",
       "      <td>0</td>\n",
       "      <td>D</td>\n",
       "      <td>mid</td>\n",
       "      <td>mid</td>\n",
       "      <td>mid</td>\n",
       "      <td>mid</td>\n",
       "      <td>low</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>202005</td>\n",
       "      <td>오픈마켓/소셜</td>\n",
       "      <td>0</td>\n",
       "      <td>G</td>\n",
       "      <td>high</td>\n",
       "      <td>low</td>\n",
       "      <td>mid</td>\n",
       "      <td>mid</td>\n",
       "      <td>mid</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       YM Category  성별구분 연령대 기혼스코어 유아자녀스코어 초등학생자녀스코어 중고생자녀스코어 대학생자녀스코어 전업주부스코어\n",
       "0  202005      할인점     0   F  high     low      high      mid      low     low\n",
       "1  202005       취미     0   B  high     low       mid      mid      low     low\n",
       "2  202005  오픈마켓/소셜     1   D   mid     mid       mid      mid      low     mid\n",
       "3  202005       뷰티     0   D   mid     mid       mid      mid      low     low\n",
       "4  202005  오픈마켓/소셜     0   G  high     low       mid      mid      mid     low"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "df = pd.read_csv('./Track2_1_trend_w_demo.csv')#encoding='UTF-8'\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(452038, 10)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 452038 entries, 0 to 452037\n",
      "Data columns (total 10 columns):\n",
      " #   Column     Non-Null Count   Dtype \n",
      "---  ------     --------------   ----- \n",
      " 0   YM         452038 non-null  int64 \n",
      " 1   Category   452038 non-null  object\n",
      " 2   성별구분       452038 non-null  int64 \n",
      " 3   연령대        452038 non-null  object\n",
      " 4   기혼스코어      452038 non-null  object\n",
      " 5   유아자녀스코어    452038 non-null  object\n",
      " 6   초등학생자녀스코어  452038 non-null  object\n",
      " 7   중고생자녀스코어   452038 non-null  object\n",
      " 8   대학생자녀스코어   452038 non-null  object\n",
      " 9   전업주부스코어    452038 non-null  object\n",
      "dtypes: int64(2), object(8)\n",
      "memory usage: 34.5+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>YM</th>\n",
       "      <th>성별구분</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>452038.000000</td>\n",
       "      <td>452038.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>201951.069815</td>\n",
       "      <td>0.531672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>49.891842</td>\n",
       "      <td>0.498996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>201904.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>201904.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>201905.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>202004.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>202005.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  YM           성별구분\n",
       "count  452038.000000  452038.000000\n",
       "mean   201951.069815       0.531672\n",
       "std        49.891842       0.498996\n",
       "min    201904.000000       0.000000\n",
       "25%    201904.000000       0.000000\n",
       "50%    201905.000000       1.000000\n",
       "75%    202004.000000       1.000000\n",
       "max    202005.000000       1.000000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "YM 요소 : \n",
      "[202005 202004 201905 201904]\n",
      "Category 요소 : \n",
      "['할인점' '취미' '오픈마켓/소셜' '뷰티' '디저트' '전문몰' '항공/여행사' '종합몰' '호텔/숙박' '면세점']\n",
      "성별구분 요소 : \n",
      "[0 1]\n",
      "연령대 요소 : \n",
      "['F' 'B' 'D' 'G' 'E' 'A' 'H' 'C']\n",
      "기혼스코어 요소 : \n",
      "['high' 'mid' 'low']\n",
      "유아자녀스코어 요소 : \n",
      "['low' 'mid' 'high']\n",
      "초등학생자녀스코어 요소 : \n",
      "['high' 'mid' 'low']\n",
      "중고생자녀스코어 요소 : \n",
      "['mid' 'low' 'high']\n",
      "대학생자녀스코어 요소 : \n",
      "['low' 'mid' 'high']\n",
      "전업주부스코어 요소 : \n",
      "['low' 'mid' 'high']\n"
     ]
    }
   ],
   "source": [
    "#데이터는 범주형이다\n",
    "#명목형 변수와 확률로 나눠져 있다\n",
    "\n",
    "#column명 모두 리스트로 뽑기\n",
    "#column_list = []\n",
    "#for i in range()\n",
    "column_list = df.columns.tolist()\n",
    "#column 별로 요소 뽑기\n",
    "for i in column_list:\n",
    "    #print(df[i].head())\n",
    "    print(i + ' 요소 : ')\n",
    "    print(df[i].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import font_manager, rc\n",
    "from matplotlib import style\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "font_name = font_manager.FontProperties(fname=\"C:\\\\Users\\\\esthe\\\\Downloads\\\\NanumBarunGothic.ttf\").get_name()\n",
    "rc('font', family=font_name)\n",
    "style.use('ggplot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "전문몰        126062\n",
       "디저트         89743\n",
       "할인점         84636\n",
       "오픈마켓/소셜     80742\n",
       "뷰티          20453\n",
       "취미          18449\n",
       "종합몰         16119\n",
       "호텔/숙박        8005\n",
       "항공/여행사       5761\n",
       "면세점          2068\n",
       "Name: Category, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Category'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams['axes.unicode_minus'] = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Category', ylabel='count'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ftKcm+/fv5+LFizz33HN8//vf57nnniMcDrN///6bKuDAgQN4vV6ef/55vv3tb7Nw4UKamprIy8ujpqaGvLw8mpqaAOjp6SEQCLBnzx52795NQ0OD+UNu+/btY9u2bdTU1HDmzBk6OjoA8Pv9zJkzh71797JhwwYOHjx4U/WKiMj1m3bYdHR0sHPnTjIzM0lMTCQzM5MdO3bc1JOgL1y4wC9/+UvWrVsHQEJCAnPmzCEYDFJcXAxAcXExwWAQgGAwSFFREYmJiWRkZLBgwQK6uroYHBxkZGSEnJwcbDYbq1evNrc5evQoa9asAaCwsJDjx49jGMYN1ywiItdv2qfRkpKSOH/+POnp6Wbb+fPnSUiY9i6u0tfXx7x586ivr+fXv/41S5YsYdOmTZw7dw6XywWAy+Xi/PnzAIRCIbKzs83t3W43oVAIh8OBx+Mx2z0eD6FQyNzm8jKHw0FKSgpDQ0PMmzdvQi3Nzc00NzcDUFlZSVpamrns3A0f4fW7st/ZTmPxsXgYi3ioQW5d006KdevW8Q//8A9s2LCB9PR0zp49y2uvvcb69etvuPNIJML7779PWVkZ2dnZHDhwwDxlNpmpZiSfNFOZbNlkPwDn8/nw+Xzm+/7+fvN14pR7n3lX9jvbaSw+Fg9jEQ81SHzLzMycctm0w6akpAS3283PfvYzQqEQbrebz3/+8+YpsBvh8XjweDzmbKWwsJCmpiZSU1MZHBzE5XIxODhozkI8Hg8DAwPm9pfr+L/tAwMDuN3uCdt4PB4ikQgXLlwwbzYQEZHomPY1mwMHDpCZmck3v/lNqqur+eY3v8nChQv53ve+d8Od33XXXXg8Hk6fPg3AO++8wz333ENBQQGtra0AtLa2smLFCgAKCgoIBAKMjo7S19dHb28vWVlZuFwukpOT6ezsxDAM2traKCgoAGD58uW0tLQA0N7eTm5urn7aWkQkyqY9s/n5z3/OV7/61QltS5Ys4dvf/jabNm264QLKysqoqalhbGyMjIwMduzYgWEYVFdX4/f7SUtLo6KiAoBFixaxatUqKioqsNvtbN682fxbny1btlBfX084HMbr9ZKfnw9cOv1XW1vLzp07cTqdlJeX33CtIiJyY6YdNjabzbzN+LLx8fGbvrNr8eLFVFZWXtX+1FNPTbp+SUkJJSUlV7Xfe++9VFVVXdWelJRkhpWIiMTGtE+jLV26lB/+8Idm4IyPj/PP//zPLF261LLiRETk9jDtmU1paSmVlZVs27aNtLQ0+vv7cblcPP7441bWJyIit4Fph43H4+Fb3/oWXV1d5t1dWVlZej6aiIhc03X9RabdbicnJ8eqWkRE5DalaYmIiFhOYSMiIpZT2IiIiOUUNiIiYjmFjYiIWE5hIyIillPYiIiI5RQ2IiJiOYWNiIhYTmEjIiKWU9iIiIjlFDYiImI5hY2IiFhOYSMiIpZT2IiIiOUUNiIiYjmFjYiIWE5hIyIillPYiIiI5RQ2IiJiuYRYFwAwPj7OE088gdvt5oknnmB4eJjq6mrOnj1Leno6u3btwul0AtDY2Ijf78dut1NaWorX6wWgu7uburo6wuEw+fn5lJaWYrPZGB0dpba2lu7ububOnUt5eTkZGRkxPFoRkdknLmY2r7/+OgsXLjTfNzU1kZeXR01NDXl5eTQ1NQHQ09NDIBBgz5497N69m4aGBsbHxwHYt28f27Zto6amhjNnztDR0QGA3+9nzpw57N27lw0bNnDw4MFoH56IyKwX87AZGBjgrbfeYv369WZbMBikuLgYgOLiYoLBoNleVFREYmIiGRkZLFiwgK6uLgYHBxkZGSEnJwebzcbq1avNbY4ePcqaNWsAKCws5Pjx4xiGEd2DFBGZ5WJ+Gu173/sejz76KCMjI2bbuXPncLlcALhcLs6fPw9AKBQiOzvbXM/tdhMKhXA4HHg8HrPd4/EQCoXMbS4vczgcpKSkMDQ0xLx58ybU0dzcTHNzMwCVlZWkpaV9XM9MHvA1XNnvbKex+Fg8jEU81CC3rpiGzZtvvklqaipLlizh3Xffveb6U81IPmmmMtkym812VZvP58Pn85nv+/v7zdeJ16xs5lzZ72ynsfhYPIxFPNQg8S0zM3PKZTENm/fee4+jR4/y9ttvEw6HGRkZoaamhtTUVAYHB3G5XAwODpqzEI/Hw8DAgLl9KBTC7XZf1T4wMIDb7Z6wjcfjIRKJcOHCBfNmAxERiY6YXrP5yle+wgsvvEBdXR3l5eXcf//9fO1rX6OgoIDW1lYAWltbWbFiBQAFBQUEAgFGR0fp6+ujt7eXrKwsXC4XycnJdHZ2YhgGbW1tFBQUALB8+XJaWloAaG9vJzc3d9KZjYiIWCfm12wm8/DDD1NdXY3f7yctLY2KigoAFi1axKpVq6ioqMBut7N582bs9kt5uWXLFurr6wmHw3i9XvLz8wFYt24dtbW17Ny5E6fTSXl5eawOS0Rk1rIZujVrUqdPnzZfJ77qj1q/o59fF7W+bkTgJ2VR66voD/dHra8bseUX0avvuysnH/etgSNRq+HForVR60tuTZ90zSbmtz6LiMjtT2EjIiKWU9iIiIjlFDYiImI5hY2IiFhOYSMiIpZT2IiIiOUUNiIiYjmFjYiIWE5hIyIillPYiIiI5RQ2IiJiOYWNiIhYTmEjIiKWU9iIiIjlFDYiImI5hY2IiFhOYSMiIpZT2IiIiOUUNiIiYjmFjYiIWE5hIyIillPYiIiI5RQ2IiJiuYRYdt7f309dXR2//e1vsdls+Hw+HnroIYaHh6murubs2bOkp6eza9cunE4nAI2Njfj9fux2O6WlpXi9XgC6u7upq6sjHA6Tn59PaWkpNpuN0dFRamtr6e7uZu7cuZSXl5ORkRHDoxYRmX1iOrNxOBz8+Z//OdXV1TzzzDP85Cc/oaenh6amJvLy8qipqSEvL4+mpiYAenp6CAQC7Nmzh927d9PQ0MD4+DgA+/btY9u2bdTU1HDmzBk6OjoA8Pv9zJkzh71797JhwwYOHjwYo6MVEZm9YjqzcblcuFwuAJKTk1m4cCGhUIhgMMjTTz8NQHFxMU8//TSPPvoowWCQoqIiEhMTycjIYMGCBXR1dZGens7IyAg5OTkArF69mmAwSH5+PkePHuWRRx4BoLCwkP3792MYBjabLSbHLDfvxZ+VRq2vrZ89ELW+RG5nMQ2bK/X19fH++++TlZXFuXPnzBByuVycP38egFAoRHZ2trmN2+0mFArhcDjweDxmu8fjIRQKmdtcXuZwOEhJSWFoaIh58+ZN6L+5uZnm5mYAKisrSUtLM5eds+B4p3Jlv7NdPIxFPNQA8VFHPNQgt664CJuLFy9SVVXFpk2bSElJmXI9wzCuq32qZZPNanw+Hz6fz3zf399vvk6ccu8z78p+Z7t4GIt4qAHio454qEHiW2Zm5pTLYh42Y2NjVFVV8eCDD7Jy5UoAUlNTGRwcxOVyMTg4aM5CPB4PAwMD5rahUAi3231V+8DAAG63e8I2Ho+HSCTChQsXzJsNbiXnG7dHra95X/hO1PoSkdkhpjcIGIbBCy+8wMKFC/mjP/ojs72goIDW1lYAWltbWbFihdkeCAQYHR2lr6+P3t5esrKycLlcJCcn09nZiWEYtLW1UVBQAMDy5ctpaWkBoL29ndzcXF2vERGJspjObN577z3a2tr4vd/7Pf76r/8agC9/+cs8/PDDVFdX4/f7SUtLo6KiAoBFixaxatUqKioqsNvtbN68Gbv9Ul5u2bKF+vp6wuEwXq+X/Px8ANatW0dtbS07d+7E6XRSXl4ek2MVEZnNYho2S5cu5eWXX5502VNPPTVpe0lJCSUlJVe133vvvVRVVV3VnpSUZIaViIjERsyv2YiIzITGXzii1tcXVkai1tftQo+rERERyylsRETEcgobERGxnMJGREQsp7ARERHLKWxERMRyChsREbGcwkZERCynsBEREcspbERExHIKGxERsZzCRkRELKewERERy+mpzyIiM+jD16Pz3+r8h8ai0s9M0cxGREQsp7ARERHL6TSaiNy0//ef3VHpp27Vkqj0IzNPMxsREbGcwkZERCynsBEREcspbERExHIKGxERsZzuRhMRuc04Xu6NWl+RjXdPa71ZEzYdHR0cOHCA8fFx1q9fz8MPPxzrkkREZo1ZcRptfHychoYGnnzySaqrq/n5z39OT09PrMsSEZk1ZkXYdHV1sWDBAubPn09CQgJFRUUEg8FYlyUiMmvYDMMwYl2E1drb2+no6OAv//IvAWhra+PkyZNs3rzZXKe5uZnm5mYAKisrY1KniMjtalbMbCbLU5vNNuG9z+ejsrJyxoLmiSeemJH93Kx4qCMeaoD4qCMeaoD4qCMeaoD4qCMeagBr65gVYePxeBgYGDDfDwwM4HK5YliRiMjsMivC5t5776W3t5e+vj7GxsYIBAIUFBTEuiwRkVljVtz67HA4KCsr45lnnmF8fJy1a9eyaNEiS/v0+XyW7n+64qGOeKgB4qOOeKgB4qOOeKgB4qOOeKgBrK1jVtwgICIisTUrTqOJiEhsKWxERMRyChuJmRdffJH/+Z//iXUZEkX6zCc3G8ZlVtwgYIWXX36ZkydP4nA4AIhEImRnZ0/aBkzavnHjxpjUM51+Z+L4rnXcJ0+e5E/+5E+oqKggIyPD7HtoaIivf/3rVFZWXtX+zDPPRH0srkc8fC+iVcONjPONfObR+i7czFjEYlymc/yxHosJDLkhL730kjE8PGy+Hx4enrJtqvZY1TPT+7uR4/7Nb35jVFVVGR9++KFx+PDhCX0fOHBgyvaZrn2mxcP3Ilo1XO843+hnHq3vwkx9HtEaFyvrsuK7qdNoEhMdHR14vd5YlyFRpM98crNlXBQ2EhPHjh2bFf/A5GP6zCc3W8ZFYSNR99FHH/G73/0Ot9sd61IkSvSZT242jYvCRqLu3XffJTc3N9ZlSBTpM5/cbBoXhY1E3dtvvz0rThvIx/SZT242jYvCRqKus7OTz3zmM7EuQ6JIn/nkZtO46O9sblBqaiq1tbXm7+IYhoHX6520DZiyPVb1zPT+rqf9W9/6ltlPQkICR48e5d133zXb7Hb7lO2xGIvrEQ/fi2jVcD393MxnHs3vwkx8HtEaFyvrgpn/bupBnCIiYjmdRhMREcspbERExHIKGxERsZzCRkRELKe70URm0M9+9jMOHz7MBx98QHJyMosXL6akpISlS5d+4nYbN26kpqaGBQsWRKlSkehS2IjMkMOHD9PU1MRjjz3GAw88QEJCAh0dHQSDwWuGTaxEIhHzMfIiVtKtzyIz4MKFC2zbto0dO3awatWqq5Z3dXVx4MABPvjgA5KSkli5ciV/8Rd/QUJCAn/7t3/LL3/5S+644w4Atm/fTlFREW+++SY//OEPOXv2LPfccw+PPfYYn/rUpwDo7u7mhRde4MyZM3i9Xmw2G3fffTdf+tKXAGhububVV19leHiYpUuX8thjj5nP39q4cSNlZWW8/vrrRCIR8vPzSUpK4qtf/apZb2VlJXl5eWzYsMHqoZPZ4qZ+oEBEDMMwjLffftv44he/aIyNjU26/NSpU8Z7771njI2NGR9++KFRXl4+4TdKHnnkEaO3t3fC+ps3bzY6OzuNSCRiHDlyxNixY4cRDoeN0dFRY/v27cZrr71mjI6OGu3t7caXvvQl4wc/+IFhGIbxzjvvGGVlZcapU6eMcDhsNDQ0GE899dSEvv7u7/7OGBoaMj766CPj5MmTxtatW41IJGIYhmGcO3fO+LM/+zNjcHDQgpGS2Uo3CIjMgKGhIebOnTvlKaklS5aQk5ODw+EgIyMDn8/HiRMnptzff/zHf+Dz+cjOzsZut7NmzRoSEhI4efIknZ2dRCIRPve5z5GQkMDKlSvJysoyt33jjTdYu3YtS5YsITExka985St0dnbS19dnrvOFL3wBp9NJUlISWVlZpKSkcPz4cQACgQC5ubncddddMzM4IuiajciMmDt3LkNDQ1NeAzl9+jT/+I//yKlTpwiHw0QiEZYsWTLl/vr7+2ltbeXHP/6x2TY2NkYoFMJms+F2u81HiQB4PB7z9eDgIJ/+9KfN93feeSdOp5NQKGT+tPCV6wMUFxfT1tbGsmXLeOONN/jc5z53/YMg8gkUNiIzICcnh8TERILBIIWFhVct/+53v8vixYv5+te/TnJyMq+99hrt7e1T7s/j8VBSUkJJSclVy06cOEEoFMIwDDNwBgYGzDvZXC4X/f395voXL15keHh4wm+mXBlUAA8++CDf+MY3+NWvfkVPTw+///u/f30DIHINOo0mMgNSUlL44he/SENDA//1X//FRx99xNjYGG+//Tbf//73GRkZISUlhTvvvJMPPviAn/70pxO2T01N5cMPPzTfr1+/nn//93/n5MmTGIbBxYsXeeuttxgZGSEnJwe73c6Pf/xjIpEIwWCQrq4uc9vPfvazHDlyhF/96leMjo7ygx/8gKysLHNWMxmPx8O9995LbW0tK1euJCkpaeYHSWY13Y0mMoPeeOMNXnvtNT744APuvPNOlixZQklJCZFIhBdffJGBgQE+/elPk5uby/Hjx/n7v/97AH76059y6NAhwuEwW7dupaioiI6ODl566SV6e3tJSkpi6dKlbN++neTkZE6dOmXejZafn8/4+DiLFy/mT//0T839/eu//ivDw8N85jOf4bHHHjNPnU31Nz1tbW3U1tby1FNPcf/990d34OS2p7ARuQ08+eST/MEf/AFr16694X2cOHGCvXv3UldXd0OPsBf5JPpGidyCTpw4wW9/+1sikQgtLS38+te/vqnfGxkbG+P1119n/fr1ChqxhG4QELkFnT59murqai5evMj8+fP5xje+gcvluqF99fT08Dd/8zd86lOf4qGHHprhSkUu0Wk0ERGxnObLIiJiOYWNiIhYTmEjIiKWU9iIiIjlFDYiImK5/w/EwrrcXGWJzAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(df['Category'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.rename(columns = {'성별구분' : 'gender', '연령대' : 'age', '기혼스코어' : 'marriage', '유아자녀스코어' : 'infant', \n",
    "                     '초등학생자녀스코어' : 'elementary', '중고생자녀스코어' : 'mid_high', '대학생자녀스코어' : 'univ',\n",
    "                    '전업주부스코어' : 'housewife'}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.marriage.replace(['high', 'mid', 'low'], [0.8,0.5,0.2], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.infant.replace(['high', 'mid', 'low'], [0.8,0.5,0.2], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.elementary.replace(['high', 'mid', 'low'],[0.8,0.5,0.2], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.mid_high.replace(['high', 'mid', 'low'], [0.8,0.5,0.2], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.univ.replace(['high', 'mid', 'low'], [0.8,0.5,0.2], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.housewife.replace(['high', 'mid', 'low'], [0.8,0.5,0.2], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "scrolled": true
   },
   "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>YM</th>\n",
       "      <th>Category</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>marriage</th>\n",
       "      <th>infant</th>\n",
       "      <th>elementary</th>\n",
       "      <th>mid_high</th>\n",
       "      <th>univ</th>\n",
       "      <th>housewife</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>202005</td>\n",
       "      <td>할인점</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>202005</td>\n",
       "      <td>취미</td>\n",
       "      <td>0</td>\n",
       "      <td>B</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>202005</td>\n",
       "      <td>오픈마켓/소셜</td>\n",
       "      <td>1</td>\n",
       "      <td>D</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>202005</td>\n",
       "      <td>뷰티</td>\n",
       "      <td>0</td>\n",
       "      <td>D</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>202005</td>\n",
       "      <td>오픈마켓/소셜</td>\n",
       "      <td>0</td>\n",
       "      <td>G</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       YM Category  gender age  marriage  infant  elementary  mid_high  univ  \\\n",
       "0  202005      할인점       0   F       0.8     0.2         0.8       0.5   0.2   \n",
       "1  202005       취미       0   B       0.8     0.2         0.5       0.5   0.2   \n",
       "2  202005  오픈마켓/소셜       1   D       0.5     0.5         0.5       0.5   0.2   \n",
       "3  202005       뷰티       0   D       0.5     0.5         0.5       0.5   0.2   \n",
       "4  202005  오픈마켓/소셜       0   G       0.8     0.2         0.5       0.5   0.5   \n",
       "\n",
       "   housewife  \n",
       "0        0.2  \n",
       "1        0.2  \n",
       "2        0.5  \n",
       "3        0.2  \n",
       "4        0.2  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_data = df[lst]\n",
    "y_data = df['age']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>YM</th>\n",
       "      <th>gender</th>\n",
       "      <th>marriage</th>\n",
       "      <th>infant</th>\n",
       "      <th>elementary</th>\n",
       "      <th>mid_high</th>\n",
       "      <th>univ</th>\n",
       "      <th>housewife</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>202005</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>202005</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>202005</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>202005</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>202005</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.2</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>452033</th>\n",
       "      <td>201904</td>\n",
       "      <td>0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>452034</th>\n",
       "      <td>201904</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>452035</th>\n",
       "      <td>201904</td>\n",
       "      <td>1</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>452036</th>\n",
       "      <td>201904</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>452037</th>\n",
       "      <td>201904</td>\n",
       "      <td>1</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>452038 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            YM  gender  marriage  infant  elementary  mid_high  univ  \\\n",
       "0       202005       0       0.8     0.2         0.8       0.5   0.2   \n",
       "1       202005       0       0.8     0.2         0.5       0.5   0.2   \n",
       "2       202005       1       0.5     0.5         0.5       0.5   0.2   \n",
       "3       202005       0       0.5     0.5         0.5       0.5   0.2   \n",
       "4       202005       0       0.8     0.2         0.5       0.5   0.5   \n",
       "...        ...     ...       ...     ...         ...       ...   ...   \n",
       "452033  201904       0       0.2     0.5         0.2       0.2   0.2   \n",
       "452034  201904       0       0.8     0.2         0.2       0.2   0.2   \n",
       "452035  201904       1       0.8     0.2         0.2       0.2   0.2   \n",
       "452036  201904       0       0.8     0.2         0.2       0.2   0.2   \n",
       "452037  201904       1       0.8     0.2         0.5       0.5   0.2   \n",
       "\n",
       "        housewife  \n",
       "0             0.2  \n",
       "1             0.2  \n",
       "2             0.5  \n",
       "3             0.2  \n",
       "4             0.2  \n",
       "...           ...  \n",
       "452033        0.2  \n",
       "452034        0.2  \n",
       "452035        0.2  \n",
       "452036        0.2  \n",
       "452037        0.5  \n",
       "\n",
       "[452038 rows x 8 columns]"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['gender', 'marriage', 'infant', 'elementary', 'mid_high', 'univ', 'housewife']"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lst = df.columns.tolist()\n",
    "lst.remove('age') # list만 쓸 수 있음\n",
    "lst.remove('Category')\n",
    "lst.remove('YM')\n",
    "lst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(x_data, y_data, test_size=0.2, random_state=777)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(ccp_alpha=0.0001)"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor\n",
    "model = DecisionTreeClassifier(ccp_alpha=0.0001)\n",
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(ccp_alpha=0.0001)"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7103906733917352\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[ 8754,   143,     0,  3608,     0,  1666,    71,     0],\n",
       "       [  593,  2361,     0,     2,     0,  1196,  1478,     0],\n",
       "       [   19,     0, 10218,    15,    58,     0,     0,     0],\n",
       "       [ 1669,     0,     0, 11461,     0,    58,     0,     0],\n",
       "       [    0,     0,  1071,     0, 10740,     0,     0,     0],\n",
       "       [ 2625,  1512,     0,    13,     0,  7000,  2547,     0],\n",
       "       [  567,  3068,     0,     5,     0,  4080,  6287,     0],\n",
       "       [    0,    43,     0,     0,    76,     0,     0,  7404]],\n",
       "      dtype=int64)"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score, confusion_matrix\n",
    "print(accuracy_score(y_test, y_pred))\n",
    "confusion_matrix(y_pred, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "raw_data1 = {'gender': [1],\n",
    "            'marriage' : [0.8],\n",
    "            'infant' : [0.8],\n",
    "            'elementary' : [0.5],\n",
    "            'mid_high': [0.2],\n",
    "            'univ':[0.2],\n",
    "            'housewife':[0.8]}\n",
    "\n",
    "data1 = pd.DataFrame(raw_data1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "raw_data2 = {'gender': [0],\n",
    "            'marriage' : [0.8],\n",
    "            'infant' : [0.2],\n",
    "            'elementary' : [0.2],\n",
    "            'mid_high': [0.5],\n",
    "            'univ':[0.8],\n",
    "            'housewife':[0.2]}\n",
    "\n",
    "data2 = pd.DataFrame(raw_data2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['C'], dtype=object)"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result=model.predict(data1)\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['G'], dtype=object)"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result=model.predict(data2)\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['C'], dtype=object)"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7338620476064065\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[ 9259,    63,     0,  3610,     0,  1990,    32,     0],\n",
       "       [  223,  4339,     0,     2,     0,  1260,  2745,     0],\n",
       "       [   19,     0, 10218,    15,    38,     0,     0,     0],\n",
       "       [ 1669,     0,     0, 11459,     0,    58,     0,     0],\n",
       "       [    0,     0,  1071,     0, 10760,     0,     0,     0],\n",
       "       [ 2848,   746,     0,    18,     0,  7686,  2384,     0],\n",
       "       [  209,  1936,     0,     0,     0,  3006,  5222,     0],\n",
       "       [    0,    43,     0,     0,    76,     0,     0,  7404]],\n",
       "      dtype=int64)"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score, confusion_matrix\n",
    "print(accuracy_score(y_test, y_pred))\n",
    "confusion_matrix(y_pred, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7337551249151992\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[13942,    95,     0,  5396,     0,  2957,    44,     0],\n",
       "       [  337,  6408,     0,     5,     0,  1920,  4168,     0],\n",
       "       [   25,     0, 15198,    27,    67,     0,     0,     0],\n",
       "       [ 2513,     0,     0, 17354,     0,    95,     0,     0],\n",
       "       [    0,     0,  1613,     0, 16157,     0,     0,     0],\n",
       "       [ 4295,  1134,     0,    26,     0, 11685,  3650,     0],\n",
       "       [  300,  2864,     0,     0,     0,  4383,  7689,     0],\n",
       "       [    0,    73,     0,     0,   119,     0,     0, 11073]],\n",
       "      dtype=int64)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score, confusion_matrix\n",
    "print(accuracy_score(y_test, y_pred))\n",
    "confusion_matrix(y_pred, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 카테고리별 데이터 분포를 파이차트로 나타내기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "남성 여성 인구수:\n",
      "1    240336\n",
      "0    211702\n",
      "Name: 성별구분, dtype: int64\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#필요한 칼럼만 df로 추출하기\n",
    "#woman = df2.loc[:, ['Category', '성별구분', '연령대', '기혼스코어', '유아자녀스코어', '전업주부스코어', '']]\n",
    "\n",
    "\n",
    "#남성 여성 인구수 추출\n",
    "gender_df = df['성별구분'].value_counts()\n",
    "print('남성 여성 인구수:')\n",
    "print(gender_df)\n",
    "#print(type(gender_df))\n",
    "print('\\n')\n",
    "\n",
    "\n",
    "#남성 여성 인구수 파이\n",
    "colors = ['blue', 'red']\n",
    "labels = ['남성', '여성']\n",
    "ratio = [int(gender_df[0]), int(gender_df[1])]\n",
    "gender_pie = plt.pie(ratio, labels=labels, colors=colors, shadow=True, startangle=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "연령별 인구수: \n",
      "D    76172\n",
      "A    71640\n",
      "F    70151\n",
      "C    56270\n",
      "E    54574\n",
      "G    51095\n",
      "H    37111\n",
      "B    35025\n",
      "Name: 연령대, dtype: int64\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#연령별 인구수 추출\n",
    "age_df = df['연령대'].value_counts()\n",
    "#age_df = age_df(ascending = False)\n",
    "print('연령별 인구수: ') \n",
    "print(age_df)\n",
    "print('\\n')\n",
    "\n",
    "#print('여기')\n",
    "#print(int(age_df.iloc[0]))\n",
    "\n",
    "#연령별 인구수 파이\n",
    "colors = ['red', 'orange', 'yellow', 'green', 'blue', 'navy', 'purple', 'pink']\n",
    "labels = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H']\n",
    "ratio = [int(age_df.iloc[4]), int(age_df.iloc[3]), int(age_df.iloc[0]), int(age_df.iloc[5]), \n",
    "         int(age_df.iloc[2]), int(age_df.iloc[6]), int(age_df.iloc[7]), int(age_df.iloc[1]),]\n",
    "age_pie = plt.pie(ratio, labels=labels, colors=colors, shadow=True, startangle=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "기혼스코어 인구수: \n",
      "high    314088\n",
      "mid     107368\n",
      "low      30582\n",
      "Name: 기혼스코어, dtype: int64\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "유아자녀스코어 인구수: \n",
      "low     233328\n",
      "mid     188531\n",
      "high     30179\n",
      "Name: 유아자녀스코어, dtype: int64\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "초등학생자녀스코어 인구수:\n",
      "low     173564\n",
      "mid     159939\n",
      "high    118535\n",
      "Name: 초등학생자녀스코어, dtype: int64\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "중고생자녀스코어 인구수:\n",
      "mid     270714\n",
      "low     149922\n",
      "high     31402\n",
      "Name: 중고생자녀스코어, dtype: int64\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "대학생자녀스코어 인구수:\n",
      "low     348745\n",
      "mid      99510\n",
      "high      3783\n",
      "Name: 대학생자녀스코어, dtype: int64\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "전업주부 인구수: \n",
      "low     229843\n",
      "mid     200448\n",
      "high     21747\n",
      "Name: 전업주부스코어, dtype: int64\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#기혼스코어 인구수 추출\n",
    "marriage_df = df['기혼스코어'].value_counts()\n",
    "print('기혼스코어 인구수: ' )\n",
    "print(marriage_df)\n",
    "print('\\n')\n",
    "\n",
    "#기혼스코어 인구수 파이\n",
    "colors = ['black', 'gray', 'lightgray',]\n",
    "labels = ['high', 'mid', 'low']\n",
    "ratio = [int(marriage_df.iloc[0]), int(marriage_df.iloc[1]), int(marriage_df.iloc[2]), ]\n",
    "marriage_pie = plt.pie(ratio, labels=labels, colors=colors, shadow=True, startangle=90)\n",
    "plt.show()\n",
    "\n",
    "\n",
    "#유아자녀스코어 추출\n",
    "child_df= df['유아자녀스코어'].value_counts()\n",
    "print('유아자녀스코어 인구수: ' )\n",
    "print(child_df)\n",
    "print('\\n')\n",
    "\n",
    "#유아스코어 인구수 파이\n",
    "colors = ['black', 'gray', 'lightgray']\n",
    "labels = ['high', 'mid', 'low']\n",
    "ratio = [int(child_df.iloc[0]), int(child_df.iloc[1]), int(child_df.iloc[2]), ]\n",
    "child_pie = plt.pie(ratio, labels=labels, colors=colors, shadow=True, startangle=90)\n",
    "plt.show()\n",
    "\n",
    "\n",
    "#초등학생자녀스코어 추출\n",
    "element_df = df['초등학생자녀스코어'].value_counts()\n",
    "print('초등학생자녀스코어 인구수:')\n",
    "print(element_df)\n",
    "print('\\n')\n",
    "\n",
    "#초등학생자녀스코어 인구수 파이\n",
    "colors = ['black', 'gray', 'lightgray']\n",
    "labels = ['high', 'mid', 'low']\n",
    "ratio = [int(element_df.iloc[0]), int(element_df.iloc[1]), int(element_df.iloc[2]), ]\n",
    "element_pie = plt.pie(ratio, labels=labels, colors=colors, shadow=True, startangle=90)\n",
    "plt.show()\n",
    "\n",
    "\n",
    "#중고생자녀스코어 추출\n",
    "middle_df = df['중고생자녀스코어'].value_counts()\n",
    "print('중고생자녀스코어 인구수:')\n",
    "print(middle_df)\n",
    "print('\\n')\n",
    "\n",
    "#중고생자녀스코어 인구수 파이\n",
    "colors = ['black', 'gray', 'lightgray']\n",
    "labels = ['high', 'mid', 'low']\n",
    "ratio = [int(middle_df.iloc[0]), int(middle_df.iloc[1]), int(middle_df.iloc[2]), ]\n",
    "middle_pie = plt.pie(ratio, labels=labels, colors=colors, shadow=True, startangle=90)\n",
    "plt.show()\n",
    "\n",
    "\n",
    "#대학생자녀스코어 추출\n",
    "uni_df = df['대학생자녀스코어'].value_counts()\n",
    "print('대학생자녀스코어 인구수:')\n",
    "print(uni_df)\n",
    "print('\\n')\n",
    "\n",
    "#대학생자녀스코어 인구수 파이\n",
    "colors = ['black', 'gray', 'lightgray']\n",
    "labels = ['high', 'mid', 'low']\n",
    "ratio = [int(uni_df.iloc[0]), int(uni_df.iloc[1]), int(uni_df.iloc[2]), ]\n",
    "uni_pie = plt.pie(ratio, labels=labels, colors=colors, shadow=True, startangle=90)\n",
    "plt.show()\n",
    "\n",
    "\n",
    "#전업주부 인구수 추출\n",
    "house_df = df['전업주부스코어'].value_counts()\n",
    "print('전업주부 인구수: ' )\n",
    "print(house_df)\n",
    "print('\\n')\n",
    "\n",
    "#전업주부스코어 인구수 파이\n",
    "colors = ['black', 'gray', 'lightgray']\n",
    "labels = ['high', 'mid', 'low']\n",
    "ratio = [int(house_df.iloc[0]), int(house_df.iloc[1]), int(house_df.iloc[2]), ]\n",
    "house_pie = plt.pie(ratio, labels=labels, colors=colors, shadow=True, startangle=90)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 범주형 데이터 시각화를 통한 EDA 보고서"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>YM</th>\n",
       "      <th>Category</th>\n",
       "      <th>Counts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>201904</td>\n",
       "      <td>디저트</td>\n",
       "      <td>22918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>201904</td>\n",
       "      <td>면세점</td>\n",
       "      <td>947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>201904</td>\n",
       "      <td>뷰티</td>\n",
       "      <td>5734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>201904</td>\n",
       "      <td>오픈마켓/소셜</td>\n",
       "      <td>18766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>201904</td>\n",
       "      <td>전문몰</td>\n",
       "      <td>30911</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       YM Category  Counts\n",
       "0  201904      디저트   22918\n",
       "1  201904      면세점     947\n",
       "2  201904       뷰티    5734\n",
       "3  201904  오픈마켓/소셜   18766\n",
       "4  201904      전문몰   30911"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pivot_df = df.groupby(['YM', 'Category']).size().reset_index(name='Counts')\n",
    "pivot_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# category 라벨 추출\n",
    "categories = pivot_df['Category'].unique().tolist()\n",
    "\n",
    "# 연도별 데이터 추출 - 201904\n",
    "df_201904 = pivot_df.loc[pivot_df['YM']== 201904]\n",
    "counts = df_201904['Counts'].tolist()\n",
    "\n",
    "# 연도별 데이터 추출 - 201905\n",
    "df_201905 = pivot_df.loc[pivot_df['YM']== 201905]\n",
    "counts_201905 = df_201905['Counts'].tolist()\n",
    "\n",
    "# 연도별 데이터 추출 - 202004\n",
    "df_202004 = pivot_df.loc[pivot_df['YM']== 202004]\n",
    "counts_202004 = df_202004['Counts'].tolist()\n",
    "\n",
    "# 연도별 데이터 추출 - 202005\n",
    "df_202005 = pivot_df.loc[pivot_df['YM']== 202005]\n",
    "counts_202005 = df_202005['Counts'].tolist()\n",
    "\n",
    "cat_lab = np.arange(len(categories)) # x축 라벨의 위치를 말한다. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['디저트', '면세점', '뷰티', '오픈마켓/소셜', '전문몰', '종합몰', '취미', '할인점', '항공/여행사', '호텔/숙박']\n",
      "[22918, 947, 5734, 18766, 30911, 4189, 5469, 26721, 2132, 2332]\n",
      "[23820, 926, 6130, 19343, 31655, 4175, 5507, 25216, 2165, 2514]\n",
      "[20259, 56, 3978, 20909, 31139, 4073, 3341, 15756, 589, 1274]\n",
      "[22746, 139, 4611, 21724, 32357, 3682, 4132, 16943, 875, 1885]\n"
     ]
    }
   ],
   "source": [
    "print(categories)\n",
    "print(counts)\n",
    "print(counts_201905)\n",
    "print(counts_202004)\n",
    "print(counts_202005)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 상응분석하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.3, 0.3, '한글')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import matplotlib.font_manager as fm\n",
    "\n",
    "'''\n",
    "for font in fm.fontManager.ttflist:\n",
    "    if 'Nanum' in font.name:\n",
    "        print(font.name, font.fname)\n",
    "'''\n",
    "\n",
    "fontpath = 'C:\\\\Users\\\\user\\\\Downloads\\\\NanumBarunGothic.ttf'\n",
    "font = fm.FontProperties(fname=fontpath, size=9)\n",
    "plt.rc('font', family='NanumBarunGothic')\n",
    "plt.rcParams[\"figure.figsize\"] = (20, 10)\n",
    "plt.rcParams[\"lines.linewidth\"] = 2\n",
    "plt.rcParams[\"lines.color\"] = 'r'\n",
    "plt.rcParams['axes.grid']=True\n",
    "mpl.font_manager._rebuild()\n",
    "fm._rebuild()\n",
    "\n",
    "font = {'family': 'NanumBarunGothic', 'weight':'bold', 'size':'22'}\n",
    "plt.rc('font', **font)\n",
    "plt.text(0.3, 0.3, '한글', size=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((452038, 10), (210468, 10))"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#코로나 이후의 상황 보기\n",
    "\n",
    "new_df = df[df['YM'].isin([202004, 202005])]\n",
    "df.shape, new_df.shape\n",
    "#df = pd.read_csv('C:\\\\samsung_card\\\\(Track2_데이터1) trend_w_demo_3.csv', )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "#단순상응분석\n",
    "#좌표계 매핑\n",
    "\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import prince\n",
    "\n",
    "#폰트\n",
    "font = {'family': 'NanumBarunGothic', 'weight':'bold', 'size':14}\n",
    "plt.rc('font', **font)\n",
    "\n",
    "def plot_mca(X = None, var_list=None):\n",
    "    '''\n",
    "    다중 상응분석 그래프\n",
    "    \n",
    "    Keyword arguments:\n",
    "    X--명목형 판다스데이터프레임 (default None)\n",
    "    var_list -- 입력 변수명 리스트로 작성\n",
    "    '''\n",
    "    \n",
    "    input_X = X[var_list]\n",
    "    mca = prince.MCA(n_components=2).fit(input_X)\n",
    "    \n",
    "    #시각화\n",
    "    ax = mca.plot_coordinates(X = input_X, figsize=(10, 10), show_column_labels=True)\n",
    "    ax.set_title(\"다중 상응분석\", fontsize=24)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\user\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 8722 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\user\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 8722 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_mca(new_df, var_list = ['Category','연령대']) #연령대도 상관 없음.."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\user\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 8722 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\user\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 8722 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_mca(new_df, var_list = ['Category','기혼스코어', ]) \n",
    "#호텔 숙박은 기혼여부에 영향을 받지 않음 -> 미혼/기혼 둘 다 즐길 수 있는 프로그램 필요!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_mca(new_df, var_list = ['Category','유아자녀스코어', '초등학생자녀스코어' ]) \n",
    "#유아도, 초등도 낮을 수록 호텔/숙박에 가까움->초등학생 대상 프로그램"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_mca(new_df, var_list = ['Category','중고생자녀스코어', '대학생자녀스코어' ]) \n",
    "#중고생, 대학생 스코어 작을수록 호텔숙박에 가까움"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ==> 다중 상응분석으로부터 얻은 인사이트: 자녀스코어가 낮을 수록 호텔을 많이 이용함\n",
    "## 그렇다면 싱글을 대상으로 전략을 세워야 하는가?.."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 호텔/숙박 집중"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>YM</th>\n",
       "      <th>Category</th>\n",
       "      <th>성별구분</th>\n",
       "      <th>연령대</th>\n",
       "      <th>기혼스코어</th>\n",
       "      <th>유아자녀스코어</th>\n",
       "      <th>초등학생자녀스코어</th>\n",
       "      <th>중고생자녀스코어</th>\n",
       "      <th>대학생자녀스코어</th>\n",
       "      <th>전업주부스코어</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>202005</td>\n",
       "      <td>호텔/숙박</td>\n",
       "      <td>1</td>\n",
       "      <td>E</td>\n",
       "      <td>low</td>\n",
       "      <td>mid</td>\n",
       "      <td>low</td>\n",
       "      <td>low</td>\n",
       "      <td>low</td>\n",
       "      <td>mid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>202005</td>\n",
       "      <td>호텔/숙박</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "      <td>high</td>\n",
       "      <td>low</td>\n",
       "      <td>mid</td>\n",
       "      <td>mid</td>\n",
       "      <td>low</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>202005</td>\n",
       "      <td>호텔/숙박</td>\n",
       "      <td>1</td>\n",
       "      <td>A</td>\n",
       "      <td>high</td>\n",
       "      <td>mid</td>\n",
       "      <td>low</td>\n",
       "      <td>high</td>\n",
       "      <td>low</td>\n",
       "      <td>mid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>202005</td>\n",
       "      <td>호텔/숙박</td>\n",
       "      <td>0</td>\n",
       "      <td>D</td>\n",
       "      <td>mid</td>\n",
       "      <td>mid</td>\n",
       "      <td>mid</td>\n",
       "      <td>mid</td>\n",
       "      <td>low</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>202005</td>\n",
       "      <td>호텔/숙박</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>high</td>\n",
       "      <td>low</td>\n",
       "      <td>low</td>\n",
       "      <td>high</td>\n",
       "      <td>low</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         YM Category  성별구분 연령대 기혼스코어 유아자녀스코어 초등학생자녀스코어 중고생자녀스코어 대학생자녀스코어  \\\n",
       "97   202005    호텔/숙박     1   E   low     mid       low      low      low   \n",
       "119  202005    호텔/숙박     0   A  high     low       mid      mid      low   \n",
       "184  202005    호텔/숙박     1   A  high     mid       low     high      low   \n",
       "299  202005    호텔/숙박     0   D   mid     mid       mid      mid      low   \n",
       "500  202005    호텔/숙박     0   F  high     low       low     high      low   \n",
       "\n",
       "    전업주부스코어  \n",
       "97      mid  \n",
       "119     low  \n",
       "184     mid  \n",
       "299     low  \n",
       "500     low  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Category에서 호텔만 뽑아내기\n",
    "\n",
    "df2 = df[df['Category']=='호텔/숙박']\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((3159, 10), (4846, 10))"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#2019년에 비해 2020년에는 약 35% 건수가 하락\n",
    "\n",
    "df_20 = df2[df2['YM'].isin([202004, 202005])]\n",
    "df_19 = df2[df2['YM'].isin([201904, 201905])]\n",
    "df_20.shape, df_19.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "유아자녀스코어 인구수:\n",
      "low     4205\n",
      "mid     3438\n",
      "high     362\n",
      "Name: 유아자녀스코어, dtype: int64\n",
      "\n",
      "\n",
      "초등학생자녀스코어 인구수:\n",
      "low     3396\n",
      "mid     2531\n",
      "high    2078\n",
      "Name: 초등학생자녀스코어, dtype: int64\n",
      "\n",
      "\n",
      "(87, 6)\n"
     ]
    },
    {
     "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>Category</th>\n",
       "      <th>연령대</th>\n",
       "      <th>기혼스코어</th>\n",
       "      <th>유아자녀스코어</th>\n",
       "      <th>초등학생자녀스코어</th>\n",
       "      <th>전업주부스코어</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>332140</td>\n",
       "      <td>호텔/숙박</td>\n",
       "      <td>D</td>\n",
       "      <td>high</td>\n",
       "      <td>high</td>\n",
       "      <td>high</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>332965</td>\n",
       "      <td>호텔/숙박</td>\n",
       "      <td>D</td>\n",
       "      <td>high</td>\n",
       "      <td>high</td>\n",
       "      <td>high</td>\n",
       "      <td>mid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>333452</td>\n",
       "      <td>호텔/숙박</td>\n",
       "      <td>A</td>\n",
       "      <td>high</td>\n",
       "      <td>high</td>\n",
       "      <td>high</td>\n",
       "      <td>mid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>336894</td>\n",
       "      <td>호텔/숙박</td>\n",
       "      <td>C</td>\n",
       "      <td>high</td>\n",
       "      <td>high</td>\n",
       "      <td>high</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>337504</td>\n",
       "      <td>호텔/숙박</td>\n",
       "      <td>C</td>\n",
       "      <td>high</td>\n",
       "      <td>high</td>\n",
       "      <td>high</td>\n",
       "      <td>mid</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Category 연령대 기혼스코어 유아자녀스코어 초등학생자녀스코어 전업주부스코어\n",
       "332140    호텔/숙박   D  high    high      high     low\n",
       "332965    호텔/숙박   D  high    high      high     mid\n",
       "333452    호텔/숙박   A  high    high      high     mid\n",
       "336894    호텔/숙박   C  high    high      high     low\n",
       "337504    호텔/숙박   C  high    high      high     mid"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "#필요한 칼럼만 df로 추출하기\n",
    "fam = df2.loc[:, ['Category', '연령대', '기혼스코어', '유아자녀스코어', '초등학생자녀스코어', '전업주부스코어']]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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