05-Pandas/03-입찰공고-데이터분석.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 성공적인 입찰을 위한 데이터 분석 (토건, 토목, 시공 편)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 주요 용어 정리\n",
    "\n",
    "- **기초금액**: 공사 입찰을 위한 기준 금액 (부가가치세가 합산된 금액)\n",
    "- **예가**: 기초금액±3% 으로 형성되며, 이 안에 들어오는 금액으로 입찰 해야함.(결국, 예가에 근접한 가격을 입찰해야함)\n",
    "- **예정가격**: 예가(%)를 기초금액에 곱한 가격\n",
    "- **사정율**: **예정가격**을 기준으로 예정가격을 예측하는 비율. (1등 선정된 업체의 예가)\n",
    "\n",
    "그 외 다소 생소한 용어들이 존재하나, 분석에 있어 주요한 용어만 설명합니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 입찰 선정 방식\n",
    "\n",
    "**예가에 가장 근접**한 기업이 입찰 받음. 단, 예가보다는 **높은 금액**을 입찰해야 합니다.\n",
    "\n",
    "**예시**\n",
    "\n",
    "기초금액: 1억원\n",
    "\n",
    "*입찰급액*\n",
    "- A기업: 9900만원\n",
    "- B기업: 1억 1000만원\n",
    "- C기업: 1억 2000만원\n",
    "\n",
    "*사정율*\n",
    "- A기업: 99.0%, (1억원 대비 99.0% 이므로)\n",
    "- B기업: 101.0%, (1억원 대비 101.0% 이므로)\n",
    "- C기업: 102.0%, (1억원 대비 102.0% 이므로)\n",
    "\n",
    "\n",
    "시나리오 1) 예가: 1억 1500만원\n",
    "\n",
    "* C기업 입찰. B기업이 예가에는 더 근접한 금액을 제시했지만, 예가보다 낮으므로, C기업이 입찰 받습니다.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 데이터 분석 방식"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. [나라장터](http://www.g2b.go.kr/index.jsp)에서 필요한 데이터 크롤링\n",
    "2. `pandas`, `numpy`, `matplotlib`등을 활용하여 분석 및 시각화 진행\n",
    "3. `sklearn`, `xgboost`, `lgbm`을 활용하여 데이터 예측 수행"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "import matplotlib as mpl\n",
    "\n",
    "# 유니코드 깨짐현상 해결\n",
    "mpl.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "# 나눔고딕 폰트 적용\n",
    "plt.rcParams[\"font.family\"] = 'NanumGothic'\n",
    "\n",
    "# 경고 무시\n",
    "warnings.filterwarnings('ignore')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 데이터 로드"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train =  pd.read_csv('data/train.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 컬럼 출력"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['번호', '공고업종', '지역', '공고번호', '발주기관', '입찰일시', '기초금액', '추정가격', '낙찰하한율',\n",
       "       '예정가격', '예가', '낙찰하한가', '투찰', '업체명', '대표자', '투찰금액', '투찰율', '기초대비',\n",
       "       '업체별사정율', 'num_1_num', 'num_1_money', 'num_1_per', 'num_2_num',\n",
       "       'num_2_money', 'num_2_per', 'num_3_num', 'num_3_money', 'num_3_per',\n",
       "       'num_4_num', 'num_4_money', 'num_4_per', 'num_5_num', 'num_5_money',\n",
       "       'num_5_per', 'num_6_num', 'num_6_money', 'num_6_per', 'num_7_num',\n",
       "       'num_7_money', 'num_7_per', 'num_8_num', 'num_8_money', 'num_8_per',\n",
       "       'num_9_num', 'num_9_money', 'num_9_per', 'num_10_num', 'num_10_money',\n",
       "       'num_10_per', 'num_11_num', 'num_11_money', 'num_11_per', 'num_12_num',\n",
       "       'num_12_money', 'num_12_per', 'num_13_num', 'num_13_money',\n",
       "       'num_13_per', 'num_14_num', 'num_14_money', 'num_14_per', 'num_15_num',\n",
       "       'num_15_money', 'num_15_per', 'total', 'Unnamed: 65', 'Unnamed: 66'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. 예가"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    99.4075 % (-0.5924 % )\n",
       "1    99.2730 % (-0.7269 % )\n",
       "2    99.1207 % (-0.8792 % )\n",
       "3    99.8778 % (-0.1221 % )\n",
       "4    99.6818 % (-0.3181 % )\n",
       "Name: 예가, dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['예가'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **예가**에 대한 전처리 작업 수행. 필요한 숫자 소수점 4째자리 까지 추출"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['예가'] = train['예가'].str.extract(r'(\\d+[.]\\d+)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **예가**를 object -> float로 변환"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# float로 변경\n",
    "train['예가'] = train['예가'].astype('float32')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **예가**는 97 ~ 103의 범위를 가져야 함.\n",
    "\n",
    "이에 어긋나는 데이터는 `drop`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7377      0.000000\n",
       "8646      0.000000\n",
       "9522      0.000000\n",
       "10945    90.908997\n",
       "15187    59.200001\n",
       "15268     0.000000\n",
       "16133     0.000000\n",
       "16382     0.000000\n",
       "16543     0.000000\n",
       "17183     0.000000\n",
       "25247    96.362503\n",
       "29316     0.000000\n",
       "Name: 예가, dtype: float32"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.loc[(train['예가'] < 97) | (train['예가'] > 103), '예가']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "drop_idx = train.loc[(train['예가'] < 97) | (train['예가'] > 103), '예가'].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = train.drop(drop_idx)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **예가**가 NaN인 값을 확인하고 `drop`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>번호</th>\n",
       "      <th>공고업종</th>\n",
       "      <th>지역</th>\n",
       "      <th>공고번호</th>\n",
       "      <th>발주기관</th>\n",
       "      <th>입찰일시</th>\n",
       "      <th>기초금액</th>\n",
       "      <th>추정가격</th>\n",
       "      <th>낙찰하한율</th>\n",
       "      <th>예정가격</th>\n",
       "      <th>...</th>\n",
       "      <th>num_13_per</th>\n",
       "      <th>num_14_num</th>\n",
       "      <th>num_14_money</th>\n",
       "      <th>num_14_per</th>\n",
       "      <th>num_15_num</th>\n",
       "      <th>num_15_money</th>\n",
       "      <th>num_15_per</th>\n",
       "      <th>total</th>\n",
       "      <th>Unnamed: 65</th>\n",
       "      <th>Unnamed: 66</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>31788</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>99.890635</td>\n",
       "      <td>7214</td>\n",
       "      <td>NaN</td>\n",
       "      <td>99.905027</td>\n",
       "      <td>7188</td>\n",
       "      <td>NaN</td>\n",
       "      <td>99.899742</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>99.891641</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 67 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       번호 공고업종   지역 공고번호 발주기관 입찰일시 기초금액 추정가격 낙찰하한율 예정가격  ...  num_13_per  \\\n",
       "31788 NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN   NaN  NaN  ...   99.890635   \n",
       "\n",
       "      num_14_num num_14_money num_14_per num_15_num num_15_money  num_15_per  \\\n",
       "31788       7214          NaN  99.905027       7188          NaN   99.899742   \n",
       "\n",
       "       total Unnamed: 65  Unnamed: 66  \n",
       "31788    NaN         NaN    99.891641  \n",
       "\n",
       "[1 rows x 67 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.loc[train['예가'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "drop_idx = train.loc[train['예가'].isnull()].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = train.drop(drop_idx)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **예가**에 대한 데이터 분포 확인"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f5f01cfc3c8>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(train['예가'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    31776.000000\n",
       "mean        99.905418\n",
       "std          0.732136\n",
       "min         97.069199\n",
       "25%         99.406502\n",
       "50%         99.912399\n",
       "75%        100.410723\n",
       "max        102.201698\n",
       "Name: 예가, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['예가'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. 가격"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>기초금액</th>\n",
       "      <th>추정가격</th>\n",
       "      <th>예정가격</th>\n",
       "      <th>투찰금액</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>166,200,000 원</td>\n",
       "      <td>151,090,909 원</td>\n",
       "      <td>165,215,275 원</td>\n",
       "      <td>145,039,600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>49,000,000 원</td>\n",
       "      <td>44,545,455 원</td>\n",
       "      <td>48,643,800 원</td>\n",
       "      <td>42,786,020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35,000,000 원</td>\n",
       "      <td>31,818,182 원</td>\n",
       "      <td>34,692,275 원</td>\n",
       "      <td>30,530,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>27,000,000 원</td>\n",
       "      <td>24,545,455 원</td>\n",
       "      <td>26,967,025 원</td>\n",
       "      <td>23,688,450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>64,460,000 원</td>\n",
       "      <td>58,600,000 원</td>\n",
       "      <td>64,254,925 원</td>\n",
       "      <td>56,382,000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            기초금액           추정가격           예정가격         투찰금액\n",
       "0  166,200,000 원  151,090,909 원  165,215,275 원  145,039,600\n",
       "1   49,000,000 원   44,545,455 원   48,643,800 원   42,786,020\n",
       "2   35,000,000 원   31,818,182 원   34,692,275 원   30,530,000\n",
       "3   27,000,000 원   24,545,455 원   26,967,025 원   23,688,450\n",
       "4   64,460,000 원   58,600,000 원   64,254,925 원   56,382,000"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.filter(regex='금액|가격').head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "가격에 대한 **원** 단위를 제거 및 **,**를 제거합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['기초금액', '추정가격', '예정가격', '투찰금액'], dtype='object')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "price_cols = train.filter(regex='금액|가격').columns\n",
    "price_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>기초금액</th>\n",
       "      <th>추정가격</th>\n",
       "      <th>예정가격</th>\n",
       "      <th>투찰금액</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>166,200,000 원</td>\n",
       "      <td>151,090,909 원</td>\n",
       "      <td>165,215,275 원</td>\n",
       "      <td>145,039,600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>49,000,000 원</td>\n",
       "      <td>44,545,455 원</td>\n",
       "      <td>48,643,800 원</td>\n",
       "      <td>42,786,020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35,000,000 원</td>\n",
       "      <td>31,818,182 원</td>\n",
       "      <td>34,692,275 원</td>\n",
       "      <td>30,530,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>27,000,000 원</td>\n",
       "      <td>24,545,455 원</td>\n",
       "      <td>26,967,025 원</td>\n",
       "      <td>23,688,450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>64,460,000 원</td>\n",
       "      <td>58,600,000 원</td>\n",
       "      <td>64,254,925 원</td>\n",
       "      <td>56,382,000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            기초금액           추정가격           예정가격         투찰금액\n",
       "0  166,200,000 원  151,090,909 원  165,215,275 원  145,039,600\n",
       "1   49,000,000 원   44,545,455 원   48,643,800 원   42,786,020\n",
       "2   35,000,000 원   31,818,182 원   34,692,275 원   30,530,000\n",
       "3   27,000,000 원   24,545,455 원   26,967,025 원   23,688,450\n",
       "4   64,460,000 원   58,600,000 원   64,254,925 원   56,382,000"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[price_cols].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ,를 제거합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in price_cols:\n",
    "    train[col] = train[col].str.replace(',', '')\n",
    "    train[col] = train[col].str.replace('원', '')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>기초금액</th>\n",
       "      <th>추정가격</th>\n",
       "      <th>예정가격</th>\n",
       "      <th>투찰금액</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>166200000</td>\n",
       "      <td>151090909</td>\n",
       "      <td>165215275</td>\n",
       "      <td>145039600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>49000000</td>\n",
       "      <td>44545455</td>\n",
       "      <td>48643800</td>\n",
       "      <td>42786020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35000000</td>\n",
       "      <td>31818182</td>\n",
       "      <td>34692275</td>\n",
       "      <td>30530000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>27000000</td>\n",
       "      <td>24545455</td>\n",
       "      <td>26967025</td>\n",
       "      <td>23688450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>64460000</td>\n",
       "      <td>58600000</td>\n",
       "      <td>64254925</td>\n",
       "      <td>56382000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         기초금액        추정가격        예정가격       투찰금액\n",
       "0  166200000   151090909   165215275   145039600\n",
       "1   49000000    44545455    48643800    42786020\n",
       "2   35000000    31818182    34692275    30530000\n",
       "3   27000000    24545455    26967025    23688450\n",
       "4   64460000    58600000    64254925    56382000"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[price_cols].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### NaN값을 채워줍니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "type을 int로 변경하기 전 NaN 값을 채워줍니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "기초금액     0\n",
       "추정가격    66\n",
       "예정가격     0\n",
       "투찰금액     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[price_cols].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "train[price_cols] = train[price_cols].fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "기초금액    0\n",
       "추정가격    0\n",
       "예정가격    0\n",
       "투찰금액    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[price_cols].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>기초금액</th>\n",
       "      <th>추정가격</th>\n",
       "      <th>예정가격</th>\n",
       "      <th>투찰금액</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>166200000</td>\n",
       "      <td>151090909</td>\n",
       "      <td>165215275</td>\n",
       "      <td>145039600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>49000000</td>\n",
       "      <td>44545455</td>\n",
       "      <td>48643800</td>\n",
       "      <td>42786020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35000000</td>\n",
       "      <td>31818182</td>\n",
       "      <td>34692275</td>\n",
       "      <td>30530000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>27000000</td>\n",
       "      <td>24545455</td>\n",
       "      <td>26967025</td>\n",
       "      <td>23688450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>64460000</td>\n",
       "      <td>58600000</td>\n",
       "      <td>64254925</td>\n",
       "      <td>56382000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         기초금액        추정가격        예정가격       투찰금액\n",
       "0  166200000   151090909   165215275   145039600\n",
       "1   49000000    44545455    48643800    42786020\n",
       "2   35000000    31818182    34692275    30530000\n",
       "3   27000000    24545455    26967025    23688450\n",
       "4   64460000    58600000    64254925    56382000"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[price_cols].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 31776 entries, 0 to 31787\n",
      "Data columns (total 4 columns):\n",
      "기초금액    31776 non-null object\n",
      "추정가격    31776 non-null object\n",
      "예정가격    31776 non-null object\n",
      "투찰금액    31776 non-null object\n",
      "dtypes: object(4)\n",
      "memory usage: 1.2+ MB\n"
     ]
    }
   ],
   "source": [
    "train[price_cols].info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### float 타입으로 가격 컬럼을 변환"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "train[price_cols] = train[price_cols].astype('float32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>기초금액</th>\n",
       "      <th>추정가격</th>\n",
       "      <th>예정가격</th>\n",
       "      <th>투찰금액</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>166200000.0</td>\n",
       "      <td>151090912.0</td>\n",
       "      <td>165215280.0</td>\n",
       "      <td>145039600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>49000000.0</td>\n",
       "      <td>44545456.0</td>\n",
       "      <td>48643800.0</td>\n",
       "      <td>42786020.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35000000.0</td>\n",
       "      <td>31818182.0</td>\n",
       "      <td>34692276.0</td>\n",
       "      <td>30530000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>27000000.0</td>\n",
       "      <td>24545456.0</td>\n",
       "      <td>26967024.0</td>\n",
       "      <td>23688450.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>64460000.0</td>\n",
       "      <td>58600000.0</td>\n",
       "      <td>64254924.0</td>\n",
       "      <td>56382000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          기초금액         추정가격         예정가격         투찰금액\n",
       "0  166200000.0  151090912.0  165215280.0  145039600.0\n",
       "1   49000000.0   44545456.0   48643800.0   42786020.0\n",
       "2   35000000.0   31818182.0   34692276.0   30530000.0\n",
       "3   27000000.0   24545456.0   26967024.0   23688450.0\n",
       "4   64460000.0   58600000.0   64254924.0   56382000.0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[price_cols].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 31776 entries, 0 to 31787\n",
      "Data columns (total 4 columns):\n",
      "기초금액    31776 non-null float32\n",
      "추정가격    31776 non-null float32\n",
      "예정가격    31776 non-null float32\n",
      "투찰금액    31776 non-null float32\n",
      "dtypes: float32(4)\n",
      "memory usage: 744.8 KB\n"
     ]
    }
   ],
   "source": [
    "train[price_cols].info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### NaN 값을 drop 하였으므로, index 를 초기화한다"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = train.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 명시된 예정 가격과 예가 비율 * 기초금액과 가격이 과연 같을까?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    166200000.0\n",
       "1     49000000.0\n",
       "2     35000000.0\n",
       "3     27000000.0\n",
       "4     64460000.0\n",
       "Name: 기초금액, dtype: float32"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['기초금액'][:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    165215280.0\n",
       "1     48643800.0\n",
       "2     34692276.0\n",
       "3     26967024.0\n",
       "4     64254924.0\n",
       "Name: 예정가격, dtype: float32"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['예정가격'][:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    99.407501\n",
       "1    99.273003\n",
       "2    99.120697\n",
       "3    99.877800\n",
       "4    99.681801\n",
       "Name: 예가, dtype: float32"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['예가'][:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**기초금액** x **예가** = **예정가격** 정말 일치할까? (왜냐하면, 소수 4째짜리까지만 반영되기 때문에 다를 수 있다)\n",
    "\n",
    "10개만 테스트해 보겠다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "명시된 가격과 계산된 금액의 차이: 17.28 원\n",
      "명시된 가격과 계산된 금액의 차이: 26.56 원\n",
      "명시된 가격과 계산된 금액의 차이: 31.52 원\n",
      "명시된 가격과 계산된 금액의 차이: 17.28 원\n",
      "명시된 가격과 계산된 금액의 차이: 35.04 원\n",
      "명시된 가격과 계산된 금액의 차이: 46.72 원\n",
      "명시된 가격과 계산된 금액의 차이: 157.44 원\n",
      "명시된 가격과 계산된 금액의 차이: 123.52 원\n",
      "명시된 가격과 계산된 금액의 차이: 18.72 원\n",
      "명시된 가격과 계산된 금액의 차이: 143.36 원\n"
     ]
    }
   ],
   "source": [
    "for i in range(10):\n",
    "    calculated_price = train['기초금액'][i] * train['예가'][i] / 100\n",
    "    printed_price = train['예정가격'][i]\n",
    "    diff = abs(calculated_price - printed_price)\n",
    "    print('명시된 가격과 계산된 금액의 차이: {:.2f} 원'.format(diff))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 전체 평균 차이 계산"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "평균 오차 가격: 358.07 원\n"
     ]
    }
   ],
   "source": [
    "total_size = len(train)\n",
    "diff = 0\n",
    "for i in range(total_size):\n",
    "    calculated_price = train['기초금액'][i] * train['예가'][i] / 100\n",
    "    printed_price = train['예정가격'][i]\n",
    "    diff += abs(calculated_price - printed_price)\n",
    "diff /= total_size\n",
    "print('평균 오차 가격: {:.2f} 원'.format(diff))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 금액&가격 컬럼간 corr() 확인"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "column들이 매우 높은 상관관계를 가지고 있다. \n",
    "\n",
    "따라서, 이중 **하나의 컬럼만 지정해서 활용**하면 될 것으로 보입니다.\n",
    "\n",
    "가장 base가 되는 **기초금액** 컬럼을 활용하겠습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>기초금액</th>\n",
       "      <th>추정가격</th>\n",
       "      <th>예정가격</th>\n",
       "      <th>투찰금액</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>기초금액</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.998769</td>\n",
       "      <td>0.999979</td>\n",
       "      <td>0.998075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>추정가격</th>\n",
       "      <td>0.998769</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.998765</td>\n",
       "      <td>0.996852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>예정가격</th>\n",
       "      <td>0.999979</td>\n",
       "      <td>0.998765</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.998081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>투찰금액</th>\n",
       "      <td>0.998075</td>\n",
       "      <td>0.996852</td>\n",
       "      <td>0.998081</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          기초금액      추정가격      예정가격      투찰금액\n",
       "기초금액  1.000000  0.998769  0.999979  0.998075\n",
       "추정가격  0.998769  1.000000  0.998765  0.996852\n",
       "예정가격  0.999979  0.998765  1.000000  0.998081\n",
       "투찰금액  0.998075  0.996852  0.998081  1.000000"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[price_cols].corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f5f017f0358>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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mYxQVjs9PZVZ7neLQu4F3AR8GPle2NdY7eS3wYN0Xi4hotxGr1q1MtrdXbvPHXGoOcIO9oi9/NTC3cvw54NWVx7OB7SiS99plzxxg3bId4B8peuQA2wPfKe8/D+wr6SuSFkp6RbOfs85OOk8BxwBfBl5F8SfA1ZLOBF5PsTVaRERXqDsM0PYiYNEkp6xHUd5oWAq8qfL8WyVtJek8ivLIo8As2/dJuhw4X9JTwD3ArPJplwCHSVofeAVwR3mtFXlU0n4Uw7E/NVn8dZK3ylrNZygS9cG2z5D0DuBx27+qcY2IiFViuHVfWD4OVCcjzi7bVrB9duO+pEOBh8v2K4Ery/Y9gDUlrQ58CdinLMMsAc7i5au1XktRZ59UnbLJGWUwjwC/krSRpNm2FydxR0S3aeEknSXAzpXyx17Aj8Y7UdKGwH7A9WPa1wKOoiiPrAmsz+is9ReBTce53A7AT5oFV2eSzjWVh//L9sOSbgR2avbciIhVrVWjTWw/KelC4ApJQ8Dttu9qHC+T+lnACLABcLjtZ8tjC4F1yvaFth8o288pr/cYRSI/tmyfD7wNMMXIlaObxSc3+UnL8si/235C0vm2PyTpZts71nkDXnz0V32wBMzKe+Ivs5ZXw7qXf7PTIXSNC7f+ZKdD6CofeuDila553L7J3rVyzrYPXDWt15J0FUXpo6Ob0TSbYTmbYhz3kKRjKJaFjYjoWu1et8T23m19gZqalU2uLP89AbgO2KN8PEPSLMC2l7UruIiIqRrpg0Wn6miWvEWRoJdI+glwf9m+BXAORa3nwPaFFxExNcMDkrybjTb5f5X7X2V0+Mq/2Z5n+8C2RBURMU2DsiTspMnb9t8wOqzlJuDtjUPtDCoiYrpGat56XZ1JOgdBUTuR9GTZ1vu/tiKiL3lA0lOdcd73Vu4fWd79x7ZFFBGxEkYGpC5Qp+f9MrY/0+pAIiJaYbjWxPHeN63kHRHRrfqhnl1HkndE9JXUvCMielB63hERPSjJOyKiB6VsEhHRg4aU5B0R0XMGZJh3kndE9JfUvCMietBIyiYREb0nZZOIiB6UsklERA/KaJOIiB6UsklERA8aGYyOd/+vnTg8PMyZX7+Ig4/6ZKdD6bwZM3jlBz/EOqee1ulIOm54eJgzF13AwUee0OlQOk4zxFuPfi+7XnxMp0NpiUHZSafvk/ctt/2Y7bfbluHh4U6H0nFrbDeH5UsWo5kzOx1Kx91y2xK2n/P2fC6A1+2yDfffeCea2R/pwDVvva7vyyY7bb9dp0PoGi/cdmunQ+gaO20/p9MhdI3fXP/TTofQUkMDUjaZNHlLejMwtpv2a2AtYP3y8UO2H2t9aBERU9cPJZE6mvW830ORvD8AXFD++x7gO8AlFBsR3wL8sH0hRkTU5xb2vCXtD+wLDAGLbZ9WOSbgFGBjYBlwb+O4pCOAbYFngCeA422PSHon8BFgKbAJcITtuyW9DjgLeI4iL3/YdmPD93FNmrxtf7oMZAfbJ0uaa/vnkh6yvWCSH3g+MB/ga6cv4KB5+032MhERLdOqnrektYEDgN1sW9JFkja3fXd5yi7AMtvzyvPnS3oL8Dywle39y/ZdgT2Ba4Djgd1tL5f0J8BHgY8BC4Gjy0S+M3B0ee6Emn5DIekgYPWp/NC2F9ne1va23ZK4V1+978v7tXloqNMhdI3VV8vnomHkxf74XLRwtMkc4Abbje83rwbmVo4/B7y68ng2sB1F8l677JkDrFu2A/wjRY8cYHuKKgbARpVfCjcCb2sWXJ1P7uHA5ZIuo+juQw9+WXv26Sd3OoSu8dQnju10CF3j7C8s7HQIXeMH8z7f6RBaom5yqlYISotsL6o8Xo+ivNGwFHjTitexb5W0laTzKMojjwKzbN8n6XLgfElPAfcAs8qnXQIcJml94BXAHY1wKtd1JfFPqE7yXmr7FElbA58F9q6+UEREN6k72qRM1IsmOeVxYMvK49llW/UaZzfuSzoUeLhsvxK4smzfA1hT0urAl4B9ygS9hKLOfRCV3zll4m76x0HtgZ227wSuKH9b/X3d50VErEotLJssAXau9IL3An403omSNgT2A64f074WcBRFeWRNilF6jeu9CGxa3n9U0ubl/Z2AnzULrk7P+7rGHduXSPqC7aNqPC8iYpVrVU3X9pOSLqTotA4Bt9u+q3G8TOpnUfwu2AA43Paz5bGFwDpl+0LbD5Tt55TXe4wikTdqmB8HvihpGcUIv8Oaxdc0eds+fczjJO6I6FqtXNvE9mXAZdU2SVdRlD6GmSDJ2j5xgvZLgUvHab8feN9UYpvSfFhJ35vK+RERq1q71zaxvXeZuDuq2QzLwxhN8AK2kPS3lVOGbX+1XcFFRExVzw2Fm6ZmZZM7een0+IPGHO/4b5+IiKqhAUnfzWZYZiWjiOgpg5G6a3xhKelSiiEti4ELbD/X9qgiIqZpUBamqvOF5Wso5t/fRzHTcp/2hhQRMX0jqnfrdXWS94jtZ21/3/a7gG3L9U4iIrrOCK5163V1kvdLfkfZ/jiwjaQtJzg/IqJjhmveel2d5D3eyj3HAVu1OJaIiJU2KD3vOjMsbxqn7WnGzDqKiOgGvZ+W65nKJJ2GxbZ/XO4w8aDtW9oVXETEVA3KaJO6k3TOAI4o2x6RdBLFguO7Sppp+8Y2xhgRUVs/lETqqDVJR9JTtn8oaZ/y3x1tz5X0WoqaeJJ3RHSFwUjd9SbpnAR8StJuwBZlc+PL2oeADdsUW0TElA0PSPqus573u4G1gc0YXbKwsd7Ja4EH2xBXRMS0DErNu85QwaeAYyiS9KvKtqslnQl8GTi/TbFFRExZhgqOku0RSZ+h2MPyYNtnSHoH8LjtX7U3xIiI+no/LddTJ3mfAWD7EUm/krSR7YdtL25zbBERU9YPveo6mo3z/mtghqR5ZdNvgV0qu9IP276kjfFFRExJvrAsPEJRFxfj/zXSdImAC7c5eRph9Z+DH72n0yF0jXO3/mSnQ+ga8+5c0OkQ+s6gfGHZLHnPpRhZYkYXqKreHyJjvCOiizg9bwDOoUjelwH7USTtjwOnlsf7YXGuiOgj6XkDth+QtBmwMbAIWA6sASy1/cwqiC8iYkpGnJ53wwXAfNvXA0janaInvmc7A4uImI7BSN31JunMaCRuANvXAbPbF1JExPQNM1Lr1uvq9LxvlvRV4Nry/H2Al63xHRHRDXo/LddTZzOGEyRtDWxbNn3J9s/bG1ZExPRkkk6F7Tsp1vaOiOhqrRwqWG46sy/FsOjFtk+rHBNwCsWAjmXAvY3jko6g6PA+AzwBHE9Rpv4yowv7vR64xfbnJJ1PMRjk2fLY6bbvnSy2Wsk7IqJXtKpsImlt4ABgN9uWdJGkzW3fXZ6yC7DM9rzy/PmS3kKxUc1Wtvcv23cF9rR9DXBo5fpfZ3Q7yZnAx20/UDe+Ol9YRkT0DNu1bmWyvb1ymz/mUnOAG+wVYw+vppi42PAc8OrK49nAdhTJe22NriOybtm+gqRNgNVs31c2PQscKulcSf+z8twJpecdEX1lqGbZxPYiivkrE1kPWFp5vBR4U+X5t0raStJ5FOWRR4FZtu+TdDlwvqSngHuAWWOufQRwZuVa1R75ccAHgG9NFn+Sd0T0lRbWvB8Htqw8nl22jb6WfXbjvqRDgYfL9iuBK8v2PYA1K+etC7zR9h0TvO61wEHNgkvZJCL6Sgs3Y1gC7FwpYewF/Gi8EyVtSLGEyPVj2tcCjgK+U2n+KHDuJK+7A/CTZsGl5x0RfcUtmh5v+0lJFwJXSBoCbrd9V+N4mdTPoviOdAPgcNvPlscWAuuU7QsbX0RKWpOibn5K9bUkfQLYlOKLy/ttf61ZfEneEdFXWjlJx/ZljI4IAUDSVcA+toeBwyZ43okTtC+nGKUytv2UcU6fVJJ3RPSVdk99t713W1+gpiTviOgrrSqbdLsk74joK5keHxHRg7KTTkRED8pmDBERPWgwUneSd0T0maEBWdG772dYaoZ469HvZdeLj+l0KB03Y8YMFpx8DN+/9uJOh9Jx+VyMGh4e5sxFF3DwkSd0OpSWqLswVa/r++T9ul224f4b70Qz+/5HbWrPPXfhuutuZLXV8gdXPhejbrltCdvPeTvDw8OdDqUlWjg9vqv1/X/Fv7n+p50OoWtcc831zU8aEPlcjNpp+zmdDqGlMtokIqIH9UNJpI5Jk7ekE3h5aeW7wFspFlEBuNn2P7U+tIiIqeuHkkgdzXre36ZY5eoCYF7Z9iBwPjAfEPDQ2CeVO1LMBzjg1W9nh1e+aewpERFtMezBGG3SLHkfSJG8NyrvG/g0xb5tv5joSdUdKs7f5K+74tfgyItDnQ6ha7zwwgudDqFr5HMxavU++SJ7UGremqw+VO6z1tjp2ADlFj832X5nnRfoluTdaQc/enOnQ+ga5/7+jp0OoWvMu3NBp0PoKquvv1nTvRub2XLDd9TKOf/nPxev9Gt10qTjpMoFxNcHvggsoLVL5UZEtJxr/q/X1fk76Qxg7/LcBcAhFLXuiIiuk7VNKmwvBZDUKKH8oG0RRUSshHxhOepmSReU968HsH1q+0KKiJi+fiiJ1NE0eds+SdJGwPO2n1gFMUVETNuglE1qLexg+2HbT5Q7KUdEdK18YQlI+jCwLvA4cB+wyZj239r+ZruDjIioywNS827W8z4YuAG4l2KiTsMhwA+ZYNv7iIhOyaqChWds39F4IOnA8u7TtpdIerptkUVETENGmxQm+vXU+7+2IqIvZVXBiIgeNCijTZol74lmUq4laTNgVovjiYhYKa0cSSJpf2BfYAhYbPu0yjEBpwAbA8uAexvHJR0BbAs8AzwBHE/xHeOXGV0v6vXALbY/J+mPymv9DngOmG/7xcliq7Mk7HiuBD4CXNvk+RERq1SryiaS1gYOAHazbUkXSdrc9t3lKbtQrLA6rzx/vqS3AM8DW9nev2zfFdjT9jXAoZXrfx24rHx4CnCA7aWSDqJYxfXrk8XXbGGqc8c0LS3bT7N9tO1PN/n5IyJWqRaONpkD3ODR3wZXA3Mrx58DXl15PBvYjiJ5r132zKEYVr1d9cLliq2rlau0vgIYaixDAlwFNF16s1bNW9Jhtr9i+711zo+I6JThkXqjTaqbxpQWlXsRNKxH2WEtLQVW7Cxj+1ZJW0k6j6I88igwq0zIlwPnS3oKuIeXl5iPAM4s788GnhzzOrObxd9sks5aFPWZ9zdmV9p+WtIrgRdsvyhpa9t3NnuhiIhVoW7ZpLppzAQeB7asPJ5dtlWvcXbjvqRDgYfL9ispystI2gNYs3LeusAbK8OwH6fonVdfp/pLY1zNJul8qbzdS1Fo/5KkfSn+fGgk7C82e5GIiFWlhWWTJcDOlfLHXsCPxjtR0obAfpSL91Xa1wKOAr5Taf4osKIkbXs5sIakRm97b4pJkJOatOdt+6AxgbwZ+K/A6cCxjeZmLxIRsaq06gtL20+WFYcrJA0Bt9u+q3G8TOpnUWxSswFwuO1ny2MLgXXK9oXlxjZIWpOibn7KmJc7hqLM8gywnBqz15vWvCW9D/g5Rbf/AGBxs+dERHRKK8d5276M0REhAEi6CtjH9jATJFnbJ07QvpxilMrY9l8A75lKbM1q3qcCvwe8C3gN8BfjvXBERLdo9/R423u39QVqalbzfgdF4fyDFAPNX1E9WBbeB2M6U0T0BNu1br2uWfL+BUD558GxwEnAr4GHgAsovsTMGt8R0TWynjdg++/KYS7Y/g9JI8C/Aa+w/UuKBB4R0TX6oVddR9OddGx/v3L/cNvPAVe0NaqIiGkalLKJJvshJH3F9mGSTrD9aUkX2T5A0s22m07f7BaS5o+ZOTWw8l6MynsxKu9F72nW8/5v5b/vLP/duI2xtNP85qcMjLwXo/JejMp70WNqbUA83vMkzZL0ey2NJiIiapnuZgxbAOdQzCw6sGXRRERELdNN3v/WWMO2R6SWNyrvxai8F6PyXvSYZmWT9SX9GbCepD+nR9cxyRcxo/JejMp7MSrvRe9pNtrkPcDvV5p+afufe220SUREv2k2Sed/T3DoxjbEMmWSzgeOsP10+fj9FMstApxn++Ix5x9CsWxj1auAc8fZNajxnPoNm6gAAAXASURBVNcCh1BMSNrX9sIW/ggREdMyrdEmq3r7s3LvuFvK279KavT6Z1L5GWx/x/bc8nbx2OvYPqdyfK7tucCHqSyELmmBpH+SdKukt5Wv0XidmWOv2QmSvlG+FzdLWr/G+f9D0uEre0553omStppKvKuCpNdK+oykXSQdMMl5ffdeSPoLSb+s/DfSuH2tcs6ZZdvnKm3nS3pV5fEh41zjZ5IOnuS1G+/75pLGXUkv2mO6X1iuUrZX/Mco6WPAC2PPKdfdfV2laX2K9XcPbHL5V1HujiHpNcAbbP8PSetRbAB6JPBXFGPdr1uJH2OlSfovwHnVJuDbkj5ZlrP2otheCYqf61rbJzH6CwhJOwEnVp7/sO39queU5x0D7F4+nA2cZfvrY8/rFEmfodjnz8A8YJjR2GYO0ntBUdo82vY/jHdQ0p4UC8zdAsyWtK/ty3l55+ccilFk1ee+lcpKopIWULzvonh/f0uXdW4GRU8k7zG2Y5xvxis7OK8B7F+e9/Ea13sd8Jvy/vOV9pnA78r7lwLf4uUll1XK9j1UNkCV9A7gZODfy+NXA1dLmgmcBtwi6RaKTVK/VZ5zI2XZq9zlY8U2TmNe67TyGkg6HjhQ0v7AphQbpHaMpD8GZtueU36RfjPFYmm3NM4ZlPei4jRJx41pu8n2AtvfA74naTXgmxTr8zd8T9J3bJ/J+HqmczNoeip5S9oG+HW5vkq1fQNge4rdnmcCa5SH3l9+uXqXpO9SbCg60bXn2f5rSf+gYkPREeAEih5G1yyEIGkGxX8ohwJvB3a3/Xjl+EbA8cAmFJuhzpU0F9h6nMvNpcnmGpK2Bt5i+0/Lx59a+Z9ipW0LXAtg+x8k3UUxQ/CQ6kkD8l40HGd7wuQp6Y3A5yj+clizcmhP20+O/yyghzo3g6ZnkrekPwA+BXxgnMObUvxZeAKwOcXWQzcCf0SxicRdtvep8zq2LwEuGfPaCyn+LPzu9KJvDUlnUNTnfwwcBLwWeE9ZSvoMxU4cmwPHAU8Dx0qaRfGn7XjGfoF7mKQ/s727JFH8BfN+4Liy1wrd09usfnafH3tQ0tEMznvxS+AzZXmn6kHb+0v6CkWyPQxYBpwgabh6Yr90bgbJpEMFu4Wk/w4cTbFH3H2V9m8BH6v2HMo/ozct63djr3Ms8F7g2UrzOsA51dEmkj4B/NmYp88Cvmb7Wyv9A7WJpI1tPzhO++uBjWwvrrR9Fvip7SvKx3OBrW1/qXz8ReBuYJE9ujVJ2fv8dZPeWluVMXzM9oGSdgf+jqJ32Kjr/gFwwyC8F3WUJaE/BF4/5tDqLrb5Wtnrz6Lo3LyhXCo6VoGu73mXNcsdKYbpvayHBbxJ0umVx7OA1SRVe1GNYYMbAH9Z1o4b138HlToygO1TGLNBaPkfase2P5J0ArBzpekPKRJKowdl4H3luUdS7HQ9TPH/8c0UtXEkrQN8FrijkazGY/vI8pfdTdJL5mZtTPH/R8cSlu07JT0o6V8odnh6P8VfJIdUznkQ+v+9KEd47FRpmuhzsZzRMkfD0ZK+Z/uZ8lor3bmh+CsgVoGuT94udvGZaGz1EPDv5ZC/Op4CLpVUrZmvA3xl+hGuGuXwzBVDNCVdDBxi+yX/QUraDtjM9g6VtuMo/uy/CNgDONvFhqfNXvNzFHXS6vU/RTGS54Fp/zAtYPv46mMVW/Ixpq3v34ty3sGK/z4m+VzsycvXIfoDXjpyq2c7N4Oo65P3ZGwfNMXzX/JBn6KHKHpt3WKE8WuNjwOvl/QG4H5gI+DNFHVybF86wfWGGe2t9aLhMTcYzPdios/FThQ7nj8+zrGGnu3cDKKeqHnH1Ej6U+BvKEZZ/Cdwke0bWnTtHYF/tf1oK67XbnkvCpI+SDEefuz6REfa/lkLrv/7wB/a/uHKXivqSfKOiOhB092MISIiOijJOyKiByV5R0T0oCTviIgelOQdEdGD/j8zDu7ovfr9yAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(train[price_cols].corr(), annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. 날짜"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2020-03-13 16:00\n",
       "1    2020-03-13 16:00\n",
       "2    2020-03-13 16:00\n",
       "3    2020-03-13 16:00\n",
       "4    2020-03-13 16:00\n",
       "Name: 입찰일시, dtype: object"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['입찰일시'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NaN 값 확인"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['입찰일시'].isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 날짜를 변환 (datetime)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['입찰일시'] = pd.to_datetime(train['입찰일시'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['year'] = train['입찰일시'].dt.year\n",
    "train['month'] = train['입찰일시'].dt.month\n",
    "train['day'] = train['입찰일시'].dt.day\n",
    "train['hour'] = train['입찰일시'].dt.hour\n",
    "train['minute'] = train['입찰일시'].dt.minute\n",
    "train['dayofweek'] = train['입찰일시'].dt.dayofweek\n",
    "train['weekofyear'] = train['입찰일시'].dt.weekofyear\n",
    "train['dayofyear'] = train['입찰일시'].dt.dayofyear\n",
    "train['quarter'] = train['입찰일시'].dt.quarter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 연도 확인"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "sns.countplot(train['year'])\n",
    "plt.title('연도별 공고 현황', fontsize=18)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 분기별 공고 현황"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "sns.countplot(train['quarter'])\n",
    "plt.title('분기별 공고 현황', fontsize=18)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 월별 공고 현황"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "sns.countplot(train['month'])\n",
    "plt.title('월별 공고 현황', fontsize=18)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 일별 공고 현황"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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et+UAK3nqcBenJElSYVxBk9SX3Rcd27jvN/c+aYCVSFP3ua/f3bjvW/fdZICVSKsb2gpaRKwZESdFxKUtbTtHxCURcW5EfKJpuyRJ0kw2zF2cewCXUK/SRUQARwP7Zub+wPKI2KXf9iHWK0mSVIShBbTMXJSZP2xp2hK4ITNX1I8XATs1aJckSZrRRnmSwEbAspbHy+q2fttXExELImJxRCxesmTJwIuWJEkatVEGtKXA3JbHc+u2fttXk5kLM3N+Zs4fGxsbeNGSJEmjNsqAdjOwVUSsUz/eG7iqQbskSdKMNorLbKwEyMxHI+IE4JyIeBC4E7gsM7Of9hHUK0mSNK2GHtAyc/eW768EruywTV/tkiRJM5l3EpAkSSqMdxKQJElPCb/++8WN+2565PwBVjJ8rqBJkiQVxoAmSZJUGAOaJElSYQxokiRJhfEkAUnT4nVf/3jjvpfs+74BViJJ5TGgSRLw+vPPbNz34v3ePMBKJMldnJIkScUxoEmSJBXGXZySJGlW+fWnm99FctN37TTASrpzBU2SJKkwBjRJkqTCGNAkSZIKY0CTJEkqjCcJjMAdp/5F476bH/GxAVYiSZKeCgxoE7j7tE817rvJ4e8eYCWSJGk2cRenJElSYQxokiRJhTGgSZIkFcaAJkmSVBgDmiRJUmEMaJIkSYUxoEmSJBXG66BJbT5z1m6N+77jTd8eYCWSpNnKFTRJkqTCGNAkSZIKY0CTJEkqjAFNkiSpMAY0SZKkwhjQJEmSCuNlNtTYFWfs3rjvzm/75gArkSRpZnEFTZIkqTCuoGnaXfCF1zbuu88hlw6wEkmSyuAKmiRJUmEMaJIkSYUxoEmSJBXGY9AkSU85x11wR+O+J+6z+QArkYZj5AEtIt4DbAM8AqwFLAB2AI4CHgRuz8z31tvu3KldkiRpJhvpLs6IeCawa2a+KTMPAX4C7AYcDeybmfsDyyNil4iITu2jrFeSJGk6jHoF7T7gjojYFLgXeA7wL8ANmbmi3mYRsC9wW5f2y0dasaSive780xr3vWS/wwdYiSQNzkgDWmZmRJwJHAosBa4B1gSWtWy2DNio/urUvpqIWEC1m5QttthiOIVLkiSN0Kh3cW4N7J6ZJ2XmZ6mOLXsJMLdls7lU4W1pl/bVZObCzJyfmfPHxsaGV7wkSdKIjPoyG5tTrZiNWwnMA7aKiHXqtr2Bq4Cbu7RLkiTNaKM+Bu0y4NUR8RVgObAe8G5ga+CciHgQuBO4rN4dekJ7+4jrlSRJGrlRH4P2GNWZme2urL/at+/YLkmSNJN5JwFJkqTCGNAkSZIKY0CTJEkqjAFNkiSpMAY0SZKkwhjQJEmSCtNTQKvvAND6eI/hlCNJkqQJA1pEbBIRmwPviYjN66/nAoeMpjxJkqTZZ7IL1X6k3mZb4CQggFXABUOuS5IkadaaMKBl5qEAEfGWzPz8aEqSJEma3Xq61VNmfj4ing48s256NDN/PbyyJEmSZq+eAlpE/D+q3Zx38cRuzkOHWJckSdKs1evN0jfPzN2HWokkSZKA3q+D9thQq5AkSdLjel1B2zAivgD8on78aGb+zZBqkiRJmtV6DWifbXv86KALkSRJUqXXszivGnYhkiRJqvR6Fuc3623nAC8CFmemt3uSJEkagl5X0B4/gzMi1qe6q4AkSZKGoNezOB+XmQ8AOYRaJEmSRO+7OA8A1qwfbg7MG1ZBkiRJs12vK2hrtXzdDPzZ0CqSJEma5XoKaJl5FvAD4H7gp5n50FCrkiRJmsV6CmgRcQhwDLAxcGxEHDzMoiRJkmazXndx7piZB2fmwsw8GNhpiDVJkiTNar0GtOVtjx8YdCGSJEmq9BrQ1oyInSNiTkTszBNndEqSJGnAeg1oFwGvAS6g2r158dAqkiRJmuV6vVn6qzPzL8YfRMTfYUiTJEkail5X0J7W9vhZgy5EkiRJlV5X0G6IiBOA7wK7Aj8fXkmSJEmzW683Sz81Il4NzAcuzcx/Hm5ZkiRJs1evK2hk5lXAVUOsRZIkSfQR0CRJkvSEu0+9qHHfTY7YY8Lnez1JQJIkSSNiQJMkSSqMAU2SJKkwBjRJkqTCjPwkgYj4PeA4IIBHgWOpbh91ALAKuCYzT6m3PbBTuyRJ0kw20oAWEQF8FHh7Zi6t2zYADgL+NDMzIr4cEVsCd3Zqz8ybRlmzJEnSqI16BW0b4JfAhyNifeAHwO3A5ZmZ9TYXAjsCt3ZpN6BJkqQZbdQBbR6wFbBnZq6IiFOB5wC3tWyzDPh94IH6+/b21UTEAmABwBZbbDGcqiVJkkZo1CcJLAeuyMwV9eOLgYeBuS3bzAWW1l+d2leTmQszc35mzh8bGxtO1ZIkSSM06oD2I2C7lsfbATcDO9fHpwHsRXVT9mu7tEuSJM1oI93FmZl3RsSlEXEO1S7MWzLz/IhYG/haRKwCFmfmjQAR8aVO7ZIkSTPZyC+zkZmnA6e3tZ0NnN1h247tkiRJM5kXqpUkSSqMAU2SJKkwBjRJkqTCjPwYtFFY8tmzGvcde/ubBliJJElS/1xBkyRJKowBTZIkqTAGNEmSpMIY0CRJkgpjQJMkSSqMAU2SJKkwBjRJkqTCGNAkSZIKY0CTJEkqjAFNkiSpMAY0SZKkwhjQJEmSCmNAkyRJKowBTZIkqTAGNEmSpMIY0CRJkgpjQJMkSSqMAU2SJKkwBjRJkqTCGNAkSZIKY0CTJEkqjAFNkiSpMAY0SZKkwhjQJEmSCmNAkyRJKowBTZIkqTAGNEmSpMIY0CRJkgpjQJMkSSqMAU2SJKkwBjRJkqTCGNAkSZIKY0CTJEkqjAFNkiSpMAY0SZKkwsyZjh8aEXOALwH3Z+ZhEbEzcBTwIHB7Zr633q5juyRJ0kw2XStoxwFfBNaMiACOBvbNzP2B5RGxS7f2aapXkiRpZEYe0CLiQOA64Ka6aUvghsxcUT9eBOw0QXv7eAsiYnFELF6yZMlwi5ckSRqBkQa0iHgFsFlmXtzSvBGwrOXxsrqtW/tqMnNhZs7PzPljY2NDqFqSJGm0Rn0M2gHAsyLiNGAD4BXAT4C5LdvMBZbWX53aJUmSZrSRBrTM/MD49xExDzgW+AfgiohYp96duTdwFXAzsFWHdkmSpBltWs7irK0CVmXmoxFxAnBORDwI3AlclpnZqX0a65UkSRqJaQtomXk7cHj9/ZXAlR226dguSZI0k3mhWkmSpMIY0CRJkgpjQJMkSSqMAU2SJKkwBjRJkqTCGNAkSZIKY0CTJEkqjAFNkiSpMAY0SZKkwhjQJEmSCmNAkyRJKowBTZIkqTAGNEmSpMIY0CRJkgpjQJMkSSqMAU2SJKkwBjRJkqTCGNAkSZIKY0CTJEkqjAFNkiSpMAY0SZKkwhjQJEmSCmNAkyRJKowBTZIkqTAGNEmSpMIY0CRJkgpjQJMkSSqMAU2SJKkwBjRJkqTCGNAkSZIKY0CTJEkqjAFNkiSpMAY0SZKkwhjQJEmSCmNAkyRJKowBTZIkqTAGNEmSpMIY0CRJkgozZ9Q/MCJOBx4D5gIXZuZZEbEzcBTwIHB7Zr633rZjuyRJ0kw28hW0zDw0Mw8DDgAOj4gAjgb2zcz9geURsUu39lHXK0mSNGrTuYtzbWApsCVwQ2auqNsXATtN0C5JkjSjTWdAOwE4BdgIWNbSvqxu69a+mohYEBGLI2LxkiVLhliuJEnSaExLQIuIo4AfZ+b3qVbR5rY8Pbdu69a+msxcmJnzM3P+2NjYEKuWJEkajZEHtIh4O3BfZp5dN90MbBUR69SP9waumqBdkiRpRhvpWZwRsQPVgf+XRcT2dfOHqHZ3nhMRDwJ3ApdlZkbEk9pHWa8kSdJ0GGlAy8wfAFt0eOpu4MoO21/ZqV2SJGkm80K1kiRJhTGgSZIkFcaAJkmSVBgDmiRJUmEMaJIkSYUxoEmSJBXGgCZJklQYA5okSVJhDGiSJEmFMaBJkiQVxoAmSZJUGAOaJElSYQxokiRJhTGgSZIkFcaAJkmSVBgDmiRJUmEMaJIkSYUxoEmSJBXGgCZJklQYA5okSVJhDGiSJEmFMaBJkiQVxoAmSZJUGAOaJElSYQxokiRJhTGgSZIkFcaAJkmSVBgDmiRJUmEMaJIkSYUxoEmSJBXGgCZJklQYA5okSVJhDGiSJEmFMaBJkiQVxoAmSZJUGAOaJElSYQxokiRJhTGgSZIkFWbOdBcwmYg4EDgAWAVck5mnTHNJkiRJQ1X0ClpEbAAcBOyVmfsCL4mILae5LEmSpKEqOqABOwCXZ2bWjy8Edpy+ciRJkoYvnsg+5YmIPwPWycwv1I9fA2ybmR9t2WYBsKB++ALgPycZdmPgNwMoz3Ecx3Ecx3Ecx3EcZyrjPDczxzo9UfoxaEuBrVoez63bHpeZC4GFvQ4YEYszc/5UC3Mcx3Ecx3Ecx3EcxxnWOKXv4rwW2Dkion68F/DdaaxHkiRp6IpeQcvMeyLiS8DXImIVsDgzb5zuuiRJkoap6IAGkJlnA2cPcMied4c6juM4juM4juM4juNMxzhFnyQgSZI0G5V+DJokSdKsU/wuzkGKiDWB44H5mfnaKYxzOvAY1VmlF2bmWQ3HOZXq72AD4KbM/Ksp1DQH+BJwf2Ye1qD/j6lOygB4BHh3NlxejYjfA44DAngUODYz7+hzjBcCR7Y0bQ8syMxru3SZaKz3ANtQva616nGW9zlGACcDvwM8BPxXP3e16DT3ImJn4CjgQeD2zHxvk3Hq9iOBN2fmy6dQz0epTgtfD/hxZn6s4TgnAJsB6wL3AO/NzFVNXlf93CnASzNzt4b1XAHc3LLZBzPzngbjjAEnUb0/K4FPZub1/YxTj3FiyyZbAZ/OzK82qOeNwN7AA1R/bwsy8+4G4xwFzAfuB34LHJOZj00yzpM+AxvO546fpQ3mc6d6msznTuM0mc9df0f0OZ871dNkPncap8l8Xm0c4Ns0m8+d6mkynzuN02Q+P+l3ccP53PF3er/z+XGZOWu+qP7ytweuGNB4awBXD2isM4EXTKH/8cCuwBkN+w/qPQngXGCjAf69rQlcQr1Lvs++zwQuaXn8AWDvBuPsCny45fECYOs++q829+r36TtU1/mD6oNyl37Hqdt2APbs5+9wsn8LwGXA0wcwzvHAbk3HAY4A/rjX19bl/el7bncZ54vAFlMdp+35rzd9n4Grx/9NUN0O78gG83BL4PMtz+8G7NnH61tjvI4m87l9nKbzudM4TeZzD+P0NJ+7jdPvfO7y/jT+rG4bp+/53MP709N87lJP3/O5wzyc0nyu+5xJdU3VxvO5bZzG83lW7eLMzEWZ+cMBDrk2bddlayIinkn1P4ZfN+x/IHAdcNMUylgjIo6PiM9HxB5TGGcb4JfAhyPicxHx1imMNW4/YFHWs75P9wF3RMSmEbEu8Bzgew3GWQ48q+XxXKpfdD3pMPe2BG7IzBX140XATg3GITN/kJnf6LWWbuO0WUX1mhuPU7/fLwT+u8k4EbET8EhmXj1Z/0nquT8iPhwRZ0TEIU3GiYhNqULIOyPi9Ij44BTqGR/zD4GfZ+aDDce5Fvj9iFgDeBnVf2L6HedhYIOWSxltSB/zmic+AxvN5w7jNJrPncZp09N8nmicfuZzp3GazOcu9fQ9n9vHaTqfu9QD9Defu4zT93zuMM6U5nPL7+JnMYX53Po7fSrzeVbt4hyCE4DGN2+PiOdT/Y/sD4F35STL1F3GeAWwWWZ+JSLmNa0lM19TjzcHODcibszMXzQYah7VMveembkiIk6NiJsys0koGncwsG+TjpmZEXEmcCjVP+BrMrPvUJ2ZV0fESyLiDKql87updg00tRGwrOXxsrpt2tW7hL/YMBATERsCfw9sB/x1k3kUEVsAu2bm0U1qaJWZ+9RjBnBqRPxPZv5Ln8M8F3g58MrMvDci3h8Rb8qGhzfUjgQm3W0ygS9Q/dv4GfAr+gsOAGTmbRHxVeBzEXEv1a6zfub1+GfgVOfzlD5LJxqn4Xx+fJwpzucTgFMGMJ8fr2eK83l8nKnO505/X03mc+s4U5nPJwCnNJ3P7b+Lqfba9D2fB/E7vdWsWkEbpHo/948z8/tNx8jMmzPzQOBFwFsjYrMGwxwAbNFwW7sAAASXSURBVBkRpwEfAf4oIt4xhZpWUS3tvrjhEMuplnLH/+dxMfAHTeupjwP4YWY+3LD/1sDumXlSZn4WeDAiDm0yVmZ+NjPflplHUa3M3dpknNpSqlW4cU+6S8Z0iIj9gbUy89ymY2TmbzPzzVSrDdtExMsaDLMfsGlEnFbP7RdGxHFNa6rrSqr/lb+0QfflwPcy89768UVUx7k0EhFbAg9k5l0N+28KHJWZH8rMr1Dt3jmhyViZeV5mvqWe17fQ47xu+wxsPJ8H8VnabZwm87l9nKbzuW2cxvO52/vT73xuG6fxfO7yPvc9n1vHmcp87vD31fd8bv9dTHWsct/zeUC/0x/nCloDEfF24L6srtE2ZZm5qj54d+0GfT/QUtc8qgPyPzPFkrYHjm3Y90dA67L7dkzt7g/vpPoH09TmVP8bGreSapWvsfrD5I1A4xNNqP5nt1VErFOH2b2Bq6ZS11RFxF7ACzOz0S/6dvXq5Upg/QZ9/66ttisy88Ru2/fhVUCT3Q2/oNr9Mqf+T8x2wE+mUMf7qFZlmtqQ1d/XQczr9anq+vMetm3/DGw0nwf1WdppnCbzeaJ6+pnP7eM0nc89vD89zecO4zSazxPU09d87jBOo/k80fvTz3we1/K7+Bam8Pk8ld/prWZrQFvZtGNE7AAcDVwWEeP7tj+Uk5xt0mGcV1AtBz8APB04PzNva1pXbVX91bd6N+BDVP9IFmXmLU3Gycw7I+LSiDiH6rXdkpnfaVjTy4DbmuySbHEZ8OqI+ArV/xrXA97doJYAPk11xtAY1fJ1P8dajFsJkJmPRnV22DkR8SBwZ11rX+P00NbTOBHxXKqLKl5U78YF+Hhm/rzPcZ4D/C1wL7AOcH2fx9x0ew0rurRPOk5EfILq39i6wLV9rtSM/32tiIhPAV+NiN9QzaW/aFjPpsDczLyhj/7t9dwYEdfUu3Puofof/gcm7Nm9nhOpTqYZA07MzNsn6tjtM5BqxaPn+dzDZ2lP87nLOCfS53zuMs4JwN/Qx3zu8XfEpPN5gvf5g/QxnycYp6/5PME4QR/zeYJx+prPE4zzLvqbz51+F9/a7+dzD7/T+/589kK1kiRJhfEYNEmSpMIY0CRJkgpjQJMkSSqMAU2SJKkwBjRJkqTCGNAkqYOIODoiXjnddUianQxoktTZmqx+kWNJGhmvgyZJtYj4JNVFJh8A/hfwCWBH4BlUt3+5hOqK6ydl5iF1nwvG748oSYPiCpokARGxLbCqvt/qkTxxm5Zbqa7a/hDw9sz8FbBhRDwtIrYDrpueiiXNZLP1Vk+S1O55wE9bHv+I6h6BrwCOoLpN2IX1cxcAr6O6F+LHRlijpFnCFTRJqtwI/O+WxzsAzwe+ldWxILsA48eEnA/sC2w8gHvoStKTuIImSUBm/ntE7BMR/0h1M+tfAtcA74qI11LdMPuuetsHIuIx+rvBvST1zJMEJKmBiDgVeH9mPjjdtUiaedzFKUl9iIj1I+LTwM8NZ5KGxRU0SZKkwriCJkmSVBgDmiRJUmEMaJIkSYUxoEmSJBXGgCZJklQYA5okSVJh/j8JkNUbUnB2RAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "sns.countplot(train['day'])\n",
    "plt.title('일자별 공고 현황', fontsize=18)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 시간대별 공고 현황"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "sns.countplot(train['hour'])\n",
    "plt.title('시간대별 공고 현황', fontsize=18)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 분(minute)별 공고 현황"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "sns.countplot(train['minute'])\n",
    "plt.title('분별 공고 현황', fontsize=18)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 요일별 공고 현황"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 0: 월요일 ~ 6: 일요일\n",
    "- 일요일은 역시 공고가 없다.\n",
    "- 토요일은 공고가 2건 있었다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "sns.countplot(train['dayofweek'])\n",
    "plt.title('요일별 공고 현황', fontsize=18)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. 지역"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "전남                4141\n",
       "경북                3884\n",
       "경기                3756\n",
       "경남                3480\n",
       "강원                3259\n",
       "전북                2544\n",
       "충남                2204\n",
       "충북                1631\n",
       "서울                1608\n",
       "부산                 821\n",
       "제주                 813\n",
       "대구                 729\n",
       "인천                 722\n",
       "대전                 425\n",
       "광주                 344\n",
       "울산                 338\n",
       "전국                 240\n",
       "세종                 138\n",
       "전국,경기              128\n",
       "전국,경북               58\n",
       "전국,서울               57\n",
       "전국,전남               44\n",
       "전국,전북               38\n",
       "전국,경남               37\n",
       "서울,경기               36\n",
       "전국,충남               31\n",
       "전국,충북               28\n",
       "전국,강원               27\n",
       "전국,부산               23\n",
       "전국,인천               23\n",
       "                  ... \n",
       "전국,세종               12\n",
       "충남,세종                9\n",
       "전국,대전                8\n",
       "전국,제주                8\n",
       "부산,경남                7\n",
       "인천,경기                5\n",
       "서울,인천,경기             5\n",
       "전북,전남                3\n",
       "충남,전북                3\n",
       "대전,충북,충남,세종          3\n",
       "대전,충북,세종             2\n",
       "대전,충남                2\n",
       "대전,충북,충남             2\n",
       "인천,경기,전남             1\n",
       "부산,울산                1\n",
       "울산,경남                1\n",
       "충북,충남,전북             1\n",
       "서울,경기,강원             1\n",
       "경남,전남                1\n",
       "전국,서울,경기             1\n",
       "경기,강원                1\n",
       "경기,강원,충북             1\n",
       "대구,경북,경남             1\n",
       "강원,경북,전남             1\n",
       "부산,울산,경북             1\n",
       "전국,울산,경남             1\n",
       "강원,충북                1\n",
       "광주,전북,전남             1\n",
       "서울,인천                1\n",
       "부산,대구,울산,경북,경남       1\n",
       "Name: 지역, Length: 65, dtype: int64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['지역'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 지역을 , 기준으로 분리합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "area = train['지역'].str.split(',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    [경기]\n",
       "1    [충남]\n",
       "2    [충남]\n",
       "3    [강원]\n",
       "4    [전북]\n",
       "Name: 지역, dtype: object"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "area.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 최소 값과, 최대 값을 살펴봅니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 5)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "area_count = area.apply(lambda x: len(x))\n",
    "area_count.min(), area_count.max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 각각의 데이터를 분리하여 column을 만들도록 합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "area_split = train[\"지역\"].str.split(\",\", n=5, expand=True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "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",
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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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>345</th>\n",
       "      <td>전국</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12390</th>\n",
       "      <td>대전</td>\n",
       "      <td>충남</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1646</th>\n",
       "      <td>전국</td>\n",
       "      <td>울산</td>\n",
       "      <td>경남</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16326</th>\n",
       "      <td>부산</td>\n",
       "      <td>대구</td>\n",
       "      <td>울산</td>\n",
       "      <td>경북</td>\n",
       "      <td>경남</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        0     1     2     3     4\n",
       "345    전국  None  None  None  None\n",
       "12390  대전    충남  None  None  None\n",
       "1646   전국    울산    경남  None  None\n",
       "16326  부산    대구    울산    경북    경남"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "area_split.iloc[[345, 12390, 1646, 16326]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['전남',\n",
       " '경북',\n",
       " '경기',\n",
       " '경남',\n",
       " '강원',\n",
       " '전북',\n",
       " '충남',\n",
       " '충북',\n",
       " '서울',\n",
       " '부산',\n",
       " '제주',\n",
       " '대구',\n",
       " '인천',\n",
       " '대전',\n",
       " '광주',\n",
       " '울산',\n",
       " '전국',\n",
       " '세종']"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "area_key = list(train['지역'].value_counts()[:18].keys())\n",
    "area_key"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(31776, 18)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = np.zeros(shape=(len(area), 18))\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "area_df = pd.DataFrame(columns=area_key, data=data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 지역과 지역 값 컬럼을 합칩니다"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "area_merged = pd.concat([area, area_df], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th>지역</th>\n",
       "      <th>전남</th>\n",
       "      <th>경북</th>\n",
       "      <th>경기</th>\n",
       "      <th>경남</th>\n",
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       "      <th>제주</th>\n",
       "      <th>대구</th>\n",
       "      <th>인천</th>\n",
       "      <th>대전</th>\n",
       "      <th>광주</th>\n",
       "      <th>울산</th>\n",
       "      <th>전국</th>\n",
       "      <th>세종</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>31771</th>\n",
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       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31774</th>\n",
       "      <td>[충북]</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>31775</th>\n",
       "      <td>[경남]</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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      ],
      "text/plain": [
       "         지역   전남   경북   경기   경남   강원   전북   충남   충북   서울   부산   제주   대구   인천  \\\n",
       "31771  [경남]  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "31772  [전국]  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "31773  [전국]  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "31774  [충북]  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "31775  [경남]  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "\n",
       "        대전   광주   울산   전국   세종  \n",
       "31771  0.0  0.0  0.0  0.0  0.0  \n",
       "31772  0.0  0.0  0.0  0.0  0.0  \n",
       "31773  0.0  0.0  0.0  0.0  0.0  \n",
       "31774  0.0  0.0  0.0  0.0  0.0  \n",
       "31775  0.0  0.0  0.0  0.0  0.0  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "area_merged.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### list에 지역이 포함된 경우 1로 체크합니다"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "def check_area(data):\n",
    "    for d in data['지역']:\n",
    "        data[d] = 1\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "area = area_merged.apply(check_area, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[충남]</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[충남]</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[강원]</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[전북]</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     지역   전남   경북   경기   경남   강원   전북   충남   충북   서울   부산   제주   대구   인천   대전  \\\n",
       "0  [경기]  0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "1  [충남]  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "2  [충남]  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "3  [강원]  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "4  [전북]  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "\n",
       "    광주   울산   전국   세종  \n",
       "0  0.0  0.0  0.0  0.0  \n",
       "1  0.0  0.0  0.0  0.0  \n",
       "2  0.0  0.0  0.0  0.0  \n",
       "3  0.0  0.0  0.0  0.0  \n",
       "4  0.0  0.0  0.0  0.0  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "area.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "지역    [전국, 울산, 경남]\n",
       "전남               0\n",
       "경북               0\n",
       "경기               0\n",
       "경남               1\n",
       "강원               0\n",
       "전북               0\n",
       "충남               0\n",
       "충북               0\n",
       "서울               0\n",
       "부산               0\n",
       "제주               0\n",
       "대구               0\n",
       "인천               0\n",
       "대전               0\n",
       "광주               0\n",
       "울산               1\n",
       "전국               1\n",
       "세종               0\n",
       "Name: 1646, dtype: object"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "area.iloc[1646]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 지역은 drop합니다"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "area = area.drop('지역', 1)"
   ]
  },
  {
   "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>전남</th>\n",
       "      <th>경북</th>\n",
       "      <th>경기</th>\n",
       "      <th>경남</th>\n",
       "      <th>강원</th>\n",
       "      <th>전북</th>\n",
       "      <th>충남</th>\n",
       "      <th>충북</th>\n",
       "      <th>서울</th>\n",
       "      <th>부산</th>\n",
       "      <th>제주</th>\n",
       "      <th>대구</th>\n",
       "      <th>인천</th>\n",
       "      <th>대전</th>\n",
       "      <th>광주</th>\n",
       "      <th>울산</th>\n",
       "      <th>전국</th>\n",
       "      <th>세종</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    전남   경북   경기   경남   강원   전북   충남   충북   서울   부산   제주   대구   인천   대전   광주  \\\n",
       "0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "1  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "2  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "3  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "4  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "\n",
       "    울산   전국   세종  \n",
       "0  0.0  0.0  0.0  \n",
       "1  0.0  0.0  0.0  \n",
       "2  0.0  0.0  0.0  \n",
       "3  0.0  0.0  0.0  \n",
       "4  0.0  0.0  0.0  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "area.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(31776, 18)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "area.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(31776, 76)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. 업체별 사정율"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "사정율이란 개찰후 결정된 예정가격과 예비가격기초금액같의 차이를 백분율로 나타낸것이라고 정의 할 수 있습니다.\n",
    "\n",
    "법률적인 개념은 아니지만 입찰담당자들 사이에 널리 쓰이는 개념입니다.\n",
    "\n",
    "예를 들어\n",
    "\n",
    "기초금액 : 100억\n",
    "\n",
    "예정가격 : 101억이라면\n",
    "\n",
    "사정율을 1%라고 합니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "즉, 표면적으로 보이는 투찰율은 다르지만 사정율을 계산해서 보면 패턴을 볼 수 있다는 것이지요.\n",
    "\n",
    "특정발주처나 특정업체의 입찰 패턴을 분석할 때 투찰율을 분석하면 아무런 결과를 얻을 수가 없습니다.\n",
    "\n",
    "그러나 **사정율로 분석을 하게 되면 패턴을 발견**할 수도 있습니다.\n",
    "\n",
    "복수예비가격을 사전에 발표하는 발주처의 성향을 분석할때도 금액만으로는 원하는 결과를 얻을수 없습니다.\n",
    "\n",
    "각각의 **예비가격에 대한 사정율을 계산해서 일정기간 분석해 보면 패턴이 있음**을 알 수 있습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    99.4565\n",
       "1    99.5138\n",
       "2    99.4114\n",
       "3    99.9886\n",
       "4    99.6845\n",
       "Name: 업체별사정율, dtype: object"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['업체별사정율'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "288      ( - )\n",
       "345      ( - )\n",
       "854      ( - )\n",
       "1046     ( - )\n",
       "2227     ( - )\n",
       "2292     ( - )\n",
       "2764     ( - )\n",
       "2765     ( - )\n",
       "2798     ( - )\n",
       "3056     ( - )\n",
       "Name: 업체별사정율, dtype: object"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.loc[train['업체별사정율'].str.contains('-'), '업체별사정율'].head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ( - ) 이루어진 데이터는 0으로 치환합니다"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.loc[train['업체별사정율'].str.contains('-'), '업체별사정율'] = np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['업체별사정율'] = train['업체별사정율'].fillna(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 업체별 사정율의 데이터를 float로 변경합니다"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['업체별사정율'] = train['업체별사정율'].astype('float32')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. 불필요한 컬럼 정리"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['번호', '공고업종', '지역', '공고번호', '발주기관', '입찰일시', '기초금액', '추정가격', '낙찰하한율',\n",
       "       '예정가격', '예가', '낙찰하한가', '투찰', '업체명', '대표자', '투찰금액', '투찰율', '기초대비',\n",
       "       '업체별사정율', 'num_1_num', 'num_1_money', 'num_1_per', 'num_2_num',\n",
       "       'num_2_money', 'num_2_per', 'num_3_num', 'num_3_money', 'num_3_per',\n",
       "       'num_4_num', 'num_4_money', 'num_4_per', 'num_5_num', 'num_5_money',\n",
       "       'num_5_per', 'num_6_num', 'num_6_money', 'num_6_per', 'num_7_num',\n",
       "       'num_7_money', 'num_7_per', 'num_8_num', 'num_8_money', 'num_8_per',\n",
       "       'num_9_num', 'num_9_money', 'num_9_per', 'num_10_num', 'num_10_money',\n",
       "       'num_10_per', 'num_11_num', 'num_11_money', 'num_11_per', 'num_12_num',\n",
       "       'num_12_money', 'num_12_per', 'num_13_num', 'num_13_money',\n",
       "       'num_13_per', 'num_14_num', 'num_14_money', 'num_14_per', 'num_15_num',\n",
       "       'num_15_money', 'num_15_per', 'total', 'Unnamed: 65', 'Unnamed: 66',\n",
       "       'year', 'month', 'day', 'hour', 'minute', 'dayofweek', 'weekofyear',\n",
       "       'dayofyear', 'quarter'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = ['업체명', \n",
    "        '기초금액', \n",
    "        '예가', \n",
    "        '업체별사정율',\n",
    "        'year', \n",
    "        'month', \n",
    "        'day', \n",
    "        'hour', \n",
    "        'minute', \n",
    "        'dayofweek', \n",
    "        'weekofyear', \n",
    "        'dayofyear', \n",
    "        'quarter', \n",
    "       ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>업체명</th>\n",
       "      <th>기초금액</th>\n",
       "      <th>예가</th>\n",
       "      <th>업체별사정율</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "      <th>minute</th>\n",
       "      <th>dayofweek</th>\n",
       "      <th>weekofyear</th>\n",
       "      <th>dayofyear</th>\n",
       "      <th>quarter</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>제이티건설(주)</td>\n",
       "      <td>166200000.0</td>\n",
       "      <td>99.407501</td>\n",
       "      <td>99.456497</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>73</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>통일종합건설 주식회사</td>\n",
       "      <td>49000000.0</td>\n",
       "      <td>99.273003</td>\n",
       "      <td>99.513802</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>73</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>성문건설 주식회사</td>\n",
       "      <td>35000000.0</td>\n",
       "      <td>99.120697</td>\n",
       "      <td>99.411400</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>73</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(주)덕산종합건설</td>\n",
       "      <td>27000000.0</td>\n",
       "      <td>99.877800</td>\n",
       "      <td>99.988602</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>73</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(유)아라야</td>\n",
       "      <td>64460000.0</td>\n",
       "      <td>99.681801</td>\n",
       "      <td>99.684502</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>73</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           업체명         기초금액         예가     업체별사정율  year  month  day  hour  \\\n",
       "0     제이티건설(주)  166200000.0  99.407501  99.456497  2020      3   13    16   \n",
       "1  통일종합건설 주식회사   49000000.0  99.273003  99.513802  2020      3   13    16   \n",
       "2    성문건설 주식회사   35000000.0  99.120697  99.411400  2020      3   13    16   \n",
       "3    (주)덕산종합건설   27000000.0  99.877800  99.988602  2020      3   13    16   \n",
       "4       (유)아라야   64460000.0  99.681801  99.684502  2020      3   13    16   \n",
       "\n",
       "   minute  dayofweek  weekofyear  dayofyear  quarter  \n",
       "0       0          4          11         73        1  \n",
       "1       0          4          11         73        1  \n",
       "2       0          4          11         73        1  \n",
       "3       0          4          11         73        1  \n",
       "4       0          4          11         73        1  "
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[cols].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 31776 entries, 0 to 31775\n",
      "Data columns (total 13 columns):\n",
      "업체명           31776 non-null object\n",
      "기초금액          31776 non-null float32\n",
      "예가            31776 non-null float32\n",
      "업체별사정율        31776 non-null float32\n",
      "year          31776 non-null int64\n",
      "month         31776 non-null int64\n",
      "day           31776 non-null int64\n",
      "hour          31776 non-null int64\n",
      "minute        31776 non-null int64\n",
      "dayofweek     31776 non-null int64\n",
      "weekofyear    31776 non-null int64\n",
      "dayofyear     31776 non-null int64\n",
      "quarter       31776 non-null int64\n",
      "dtypes: float32(3), int64(9), object(1)\n",
      "memory usage: 2.8+ MB\n"
     ]
    }
   ],
   "source": [
    "train[cols].info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### df에 정리된 train 데이터와 area 데이터를 합칩니다"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.concat([train[cols], area], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
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       "      <td>35000000.0</td>\n",
       "      <td>99.120697</td>\n",
       "      <td>99.411400</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(주)덕산종합건설</td>\n",
       "      <td>27000000.0</td>\n",
       "      <td>99.877800</td>\n",
       "      <td>99.988602</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(유)아라야</td>\n",
       "      <td>64460000.0</td>\n",
       "      <td>99.681801</td>\n",
       "      <td>99.684502</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
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       "<p>5 rows × 31 columns</p>\n",
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      "text/plain": [
       "           업체명         기초금액         예가     업체별사정율  year  month  day  hour  \\\n",
       "0     제이티건설(주)  166200000.0  99.407501  99.456497  2020      3   13    16   \n",
       "1  통일종합건설 주식회사   49000000.0  99.273003  99.513802  2020      3   13    16   \n",
       "2    성문건설 주식회사   35000000.0  99.120697  99.411400  2020      3   13    16   \n",
       "3    (주)덕산종합건설   27000000.0  99.877800  99.988602  2020      3   13    16   \n",
       "4       (유)아라야   64460000.0  99.681801  99.684502  2020      3   13    16   \n",
       "\n",
       "   minute  dayofweek  ...   서울   부산   제주   대구   인천   대전   광주   울산   전국   세종  \n",
       "0       0          4  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "1       0          4  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "2       0          4  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "3       0          4  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "4       0          4  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 지역도 잘 합쳐져 있는지 확인합니다"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31773</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31774</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31775</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        전남   경북   경기   경남   강원   전북   충남   충북   서울   부산   제주   대구   인천   대전  \\\n",
       "31771  0.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "31772  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "31773  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "31774  0.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "31775  0.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "\n",
       "        광주   울산   전국   세종  \n",
       "31771  0.0  0.0  0.0  0.0  \n",
       "31772  0.0  0.0  1.0  0.0  \n",
       "31773  0.0  0.0  1.0  0.0  \n",
       "31774  0.0  0.0  0.0  0.0  \n",
       "31775  0.0  0.0  0.0  0.0  "
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[:, 13:].tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "31771    경남\n",
       "31772    전국\n",
       "31773    전국\n",
       "31774    충북\n",
       "31775    경남\n",
       "Name: 지역, dtype: object"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['지역'].tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 업체별사정율 -> 사정율 (컬럼명 변환)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.rename(columns={'업체별사정율': '사정율'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "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>업체명</th>\n",
       "      <th>기초금액</th>\n",
       "      <th>예가</th>\n",
       "      <th>사정율</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "      <th>minute</th>\n",
       "      <th>dayofweek</th>\n",
       "      <th>...</th>\n",
       "      <th>서울</th>\n",
       "      <th>부산</th>\n",
       "      <th>제주</th>\n",
       "      <th>대구</th>\n",
       "      <th>인천</th>\n",
       "      <th>대전</th>\n",
       "      <th>광주</th>\n",
       "      <th>울산</th>\n",
       "      <th>전국</th>\n",
       "      <th>세종</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>제이티건설(주)</td>\n",
       "      <td>166200000.0</td>\n",
       "      <td>99.407501</td>\n",
       "      <td>99.456497</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>통일종합건설 주식회사</td>\n",
       "      <td>49000000.0</td>\n",
       "      <td>99.273003</td>\n",
       "      <td>99.513802</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>성문건설 주식회사</td>\n",
       "      <td>35000000.0</td>\n",
       "      <td>99.120697</td>\n",
       "      <td>99.411400</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(주)덕산종합건설</td>\n",
       "      <td>27000000.0</td>\n",
       "      <td>99.877800</td>\n",
       "      <td>99.988602</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(유)아라야</td>\n",
       "      <td>64460000.0</td>\n",
       "      <td>99.681801</td>\n",
       "      <td>99.684502</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           업체명         기초금액         예가        사정율  year  month  day  hour  \\\n",
       "0     제이티건설(주)  166200000.0  99.407501  99.456497  2020      3   13    16   \n",
       "1  통일종합건설 주식회사   49000000.0  99.273003  99.513802  2020      3   13    16   \n",
       "2    성문건설 주식회사   35000000.0  99.120697  99.411400  2020      3   13    16   \n",
       "3    (주)덕산종합건설   27000000.0  99.877800  99.988602  2020      3   13    16   \n",
       "4       (유)아라야   64460000.0  99.681801  99.684502  2020      3   13    16   \n",
       "\n",
       "   minute  dayofweek  ...   서울   부산   제주   대구   인천   대전   광주   울산   전국   세종  \n",
       "0       0          4  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "1       0          4  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "2       0          4  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "3       0          4  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "4       0          4  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8. 사정율 - 예가 컬럼 생성 (1등 업체의 근접도 확인)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['근접율'] = abs(df['예가'] - df['사정율'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "sns.distplot(df['근접율'])\n",
    "plt.title('근접율 분포도', fontsize=18)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "288     100.150597\n",
       "345      99.907997\n",
       "854      99.307800\n",
       "1046    100.894501\n",
       "2227     99.351601\n",
       "2292    100.069702\n",
       "2764     98.986198\n",
       "2765    100.400200\n",
       "2798    100.603203\n",
       "3056     99.776497\n",
       "3298    100.022202\n",
       "3972    100.263702\n",
       "4044    100.124496\n",
       "4242     99.884697\n",
       "4591    100.806999\n",
       "5824    100.657700\n",
       "5826    100.247002\n",
       "6035     99.893700\n",
       "6185    100.836197\n",
       "6189    100.503700\n",
       "Name: 근접율, dtype: float32"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['근접율'] > 30, '근접율'].head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "사정율이 나와 있지 않은 column은 우리가 0으로 채워 주었기 때문에 100 이상의 수치가 나온다. \n",
    "\n",
    "해당 컬럼은 제외하고 다시 시각화"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "sns.distplot(df.loc[df['근접율'] < 1, '근접율'])\n",
    "plt.title('근접율 분포도', fontsize=18)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "선정된 대다수의 업체가 **근접율을 0.0 ~ 0.2 이하**로 가져가고 있다는 점을 볼 수 있다"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8. 상위 업체 사정율 분석"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "주로 활용할 유의미한 feature 정의\n",
    "- 예가 (예측 label)\n",
    "- 일자 (datetime) 관련 column\n",
    "\n",
    "안타깝께도... 기타 활용할만한 지표는 크게 보이지 않는다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 잘 되는 업체의 사정율 확인"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "태양종합건설 주식회사      28\n",
       "신우건설 주식회사        28\n",
       "남광건설 주식회사        27\n",
       "신영종합건설(주)        25\n",
       "주식회사 우리종합건설      25\n",
       "일진건설 주식회사        25\n",
       "동우건설 주식회사        24\n",
       "삼진건설(주)          24\n",
       "주식회사 혜성종합건설      23\n",
       "보광종합건설 주식회사      23\n",
       "우일종합건설(주)        22\n",
       "효창건설(주)          22\n",
       "청암종합건설 주식회사      22\n",
       "영진건설(주)          22\n",
       "대연종합건설 주식회사      22\n",
       "거성종합건설(주)        21\n",
       "세움종합건설 주식회사      21\n",
       "세원종합건설 주식회사      21\n",
       "우진종합건설(주)        20\n",
       "두마종합건설주식회사       20\n",
       "주식회사 부림종합건설      20\n",
       "주식회사 덕성건설        20\n",
       "우림종합건설(주)        20\n",
       "서림종합건설 주식회사      20\n",
       "유창건설 주식회사        20\n",
       "(주)동일종합건설        20\n",
       "대림종합건설 주식회사      20\n",
       "예림종합건설(주)        20\n",
       "청진건설주식회사         20\n",
       "주식회사 태화건설        19\n",
       "대창건설(주)          19\n",
       "성원건설(주)          19\n",
       "승원종합건설(주)        19\n",
       "제이와이건설 주식회사      19\n",
       "금종종합건설 주식회사      19\n",
       "(주)거성종합건설        19\n",
       "주식회사 에스엠건설       19\n",
       "성인종합건설 주식회사      19\n",
       "대호종합건설(주)        19\n",
       "대원건설(주)          18\n",
       "주식회사 다우종합건설      18\n",
       "주식회사 일성건설        18\n",
       "대영건설 주식회사        18\n",
       "대일종합건설 주식회사      18\n",
       "제이티건설(주)         18\n",
       "(주)삼구건설          18\n",
       "태창건설합자회사         18\n",
       "주식회사 조영건설        18\n",
       "제이케이종합건설 주식회사    18\n",
       "주식회사 백율          17\n",
       "Name: 업체명, dtype: int64"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['업체명'].value_counts().head(50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 시각화 함수화"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "def visualize_pct_time(data):\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    d = data.groupby('year')['사정율'].mean()\n",
    "    sns.barplot(x=d.index, y=d)\n",
    "    plt.ylim(d.min() - 0.02, d.max() + 0.02)\n",
    "    plt.title('평균 사정율 (연도별)', fontsize=18)\n",
    "    plt.show()\n",
    "    print(d)\n",
    "    print('===='*10)\n",
    "    \n",
    "    plt.figure(figsize=(10, 6))\n",
    "    d = data.groupby('quarter')['사정율'].mean()\n",
    "    sns.barplot(x=d.index, y=d)\n",
    "    plt.ylim(d.min() - 0.02, d.max() + 0.02)\n",
    "    plt.title('평균 사정율 (분기별)', fontsize=18)\n",
    "    plt.show()\n",
    "    print(d)\n",
    "    print('===='*10)\n",
    "    \n",
    "    plt.figure(figsize=(10, 6))\n",
    "    d = data.groupby('month')['사정율'].mean()\n",
    "    sns.barplot(x=d.index, y=d)\n",
    "    plt.ylim(d.min() - 0.02, d.max() + 0.02)\n",
    "    plt.title('평균 사정율 (월별)', fontsize=18)\n",
    "    plt.show()\n",
    "    print(d)\n",
    "    print('===='*10)\n",
    "    \n",
    "    plt.figure(figsize=(10, 6))\n",
    "    d = data.groupby('day')['사정율'].mean()\n",
    "    sns.barplot(x=d.index, y=d)\n",
    "    plt.ylim(d.min() - 0.02, d.max() + 0.02)\n",
    "    plt.title('평균 사정율 (일자별)', fontsize=18)\n",
    "    plt.show()\n",
    "    print(d)\n",
    "    print('===='*10)\n",
    "    \n",
    "    plt.figure(figsize=(10, 6))\n",
    "    d = data.groupby('dayofweek')['사정율'].mean()\n",
    "    sns.barplot(x=d.index, y=d)\n",
    "    plt.ylim(d.min() - 0.02, d.max() + 0.02)\n",
    "    plt.title('평균 사정율 (요일별)', fontsize=18)\n",
    "    plt.show()\n",
    "    print(d)\n",
    "    print('===='*10)\n",
    "    \n",
    "    plt.figure(figsize=(10, 6))\n",
    "    d = data.groupby('hour')['사정율'].mean()\n",
    "    sns.barplot(x=d.index, y=d)\n",
    "    plt.ylim(d.min() - 0.02, d.max() + 0.02)\n",
    "    plt.title('평균 사정율 (시간별)', fontsize=18)\n",
    "    plt.show()\n",
    "    print(d)\n",
    "    print('===='*10)\n",
    "    \n",
    "    plt.figure(figsize=(10, 6))\n",
    "    d = data.groupby('minute')['사정율'].mean()\n",
    "    sns.barplot(x=d.index, y=d)\n",
    "    plt.ylim(d.min() - 0.02, d.max() + 0.02)\n",
    "    plt.title('평균 사정율 (분별)', fontsize=18)\n",
    "    plt.show()\n",
    "    print(d)\n",
    "    print('===='*10)\n",
    "    \n",
    "    plt.figure(figsize=(10, 6))\n",
    "    d = data.groupby('weekofyear')['사정율'].mean()\n",
    "    sns.barplot(x=d.index, y=d)\n",
    "    plt.ylim(d.min() - 0.02, d.max() + 0.02)\n",
    "    plt.title('평균 사정율 (주차별)', fontsize=18)\n",
    "    plt.show()\n",
    "    print(d)\n",
    "    print('===='*10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "IPython.OutputArea.auto_scroll_threshold = 9999;"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%javascript\n",
    "IPython.OutputArea.auto_scroll_threshold = 9999;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### top 50개 업체 분석"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "태양종합건설 주식회사      28\n",
       "신우건설 주식회사        28\n",
       "남광건설 주식회사        27\n",
       "신영종합건설(주)        25\n",
       "주식회사 우리종합건설      25\n",
       "일진건설 주식회사        25\n",
       "동우건설 주식회사        24\n",
       "삼진건설(주)          24\n",
       "주식회사 혜성종합건설      23\n",
       "보광종합건설 주식회사      23\n",
       "우일종합건설(주)        22\n",
       "효창건설(주)          22\n",
       "청암종합건설 주식회사      22\n",
       "영진건설(주)          22\n",
       "대연종합건설 주식회사      22\n",
       "거성종합건설(주)        21\n",
       "세움종합건설 주식회사      21\n",
       "세원종합건설 주식회사      21\n",
       "우진종합건설(주)        20\n",
       "두마종합건설주식회사       20\n",
       "주식회사 부림종합건설      20\n",
       "주식회사 덕성건설        20\n",
       "우림종합건설(주)        20\n",
       "서림종합건설 주식회사      20\n",
       "유창건설 주식회사        20\n",
       "(주)동일종합건설        20\n",
       "대림종합건설 주식회사      20\n",
       "예림종합건설(주)        20\n",
       "청진건설주식회사         20\n",
       "주식회사 태화건설        19\n",
       "대창건설(주)          19\n",
       "성원건설(주)          19\n",
       "승원종합건설(주)        19\n",
       "제이와이건설 주식회사      19\n",
       "금종종합건설 주식회사      19\n",
       "(주)거성종합건설        19\n",
       "주식회사 에스엠건설       19\n",
       "성인종합건설 주식회사      19\n",
       "대호종합건설(주)        19\n",
       "대원건설(주)          18\n",
       "주식회사 다우종합건설      18\n",
       "주식회사 일성건설        18\n",
       "대영건설 주식회사        18\n",
       "대일종합건설 주식회사      18\n",
       "제이티건설(주)         18\n",
       "(주)삼구건설          18\n",
       "태창건설합자회사         18\n",
       "주식회사 조영건설        18\n",
       "제이케이종합건설 주식회사    18\n",
       "주식회사 백율          17\n",
       "Name: 업체명, dtype: int64"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['업체명'].value_counts().head(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['태양종합건설 주식회사', '신우건설 주식회사', '남광건설 주식회사', '신영종합건설(주)', '주식회사 우리종합건설']"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "company = list(df['업체명'].value_counts().head(50).keys())\n",
    "company[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "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>업체명</th>\n",
       "      <th>기초금액</th>\n",
       "      <th>예가</th>\n",
       "      <th>사정율</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "      <th>minute</th>\n",
       "      <th>dayofweek</th>\n",
       "      <th>...</th>\n",
       "      <th>부산</th>\n",
       "      <th>제주</th>\n",
       "      <th>대구</th>\n",
       "      <th>인천</th>\n",
       "      <th>대전</th>\n",
       "      <th>광주</th>\n",
       "      <th>울산</th>\n",
       "      <th>전국</th>\n",
       "      <th>세종</th>\n",
       "      <th>근접율</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>제이티건설(주)</td>\n",
       "      <td>166200000.0</td>\n",
       "      <td>99.407501</td>\n",
       "      <td>99.456497</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.048996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>(주)동일종합건설</td>\n",
       "      <td>131254000.0</td>\n",
       "      <td>100.563698</td>\n",
       "      <td>100.712997</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.149300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>대림종합건설 주식회사</td>\n",
       "      <td>152384992.0</td>\n",
       "      <td>100.215202</td>\n",
       "      <td>100.254097</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.038895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>(주)동일종합건설</td>\n",
       "      <td>140848000.0</td>\n",
       "      <td>99.618202</td>\n",
       "      <td>99.675301</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.057098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>우림종합건설(주)</td>\n",
       "      <td>99250000.0</td>\n",
       "      <td>99.945999</td>\n",
       "      <td>99.966797</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.020798</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            업체명         기초금액          예가         사정율  year  month  day  hour  \\\n",
       "0      제이티건설(주)  166200000.0   99.407501   99.456497  2020      3   13    16   \n",
       "7     (주)동일종합건설  131254000.0  100.563698  100.712997  2020      3   13    15   \n",
       "19  대림종합건설 주식회사  152384992.0  100.215202  100.254097  2020      3   13    11   \n",
       "20    (주)동일종합건설  140848000.0   99.618202   99.675301  2020      3   12    17   \n",
       "48    우림종합건설(주)   99250000.0   99.945999   99.966797  2020      3   11    14   \n",
       "\n",
       "    minute  dayofweek  ...   부산   제주   대구   인천   대전   광주   울산   전국   세종  \\\n",
       "0        0          4  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "7        0          4  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "19       0          4  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "20       0          3  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "48       0          2  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "\n",
       "         근접율  \n",
       "0   0.048996  \n",
       "7   0.149300  \n",
       "19  0.038895  \n",
       "20  0.057098  \n",
       "48  0.020798  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['업체명'].isin(company)].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1032, 32)"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['업체명'].isin(company)].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 상위 50개 업체에 대한 사정율 분석"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "year\n",
      "2018     99.614723\n",
      "2019     99.650284\n",
      "2020    100.047600\n",
      "Name: 사정율, dtype: float32\n",
      "========================================\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "quarter\n",
      "1    99.524040\n",
      "2    99.793167\n",
      "3    99.495934\n",
      "4    99.726967\n",
      "Name: 사정율, dtype: float32\n",
      "========================================\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "month\n",
      "1     100.058823\n",
      "2      98.448105\n",
      "3     100.153893\n",
      "4      99.295822\n",
      "5      99.994537\n",
      "6     100.129677\n",
      "7      99.948189\n",
      "8     100.118813\n",
      "9      96.680954\n",
      "10     99.025192\n",
      "11    100.005043\n",
      "12     99.979912\n",
      "Name: 사정율, dtype: float32\n",
      "========================================\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "day\n",
      "1     101.271019\n",
      "2     100.231270\n",
      "3      97.381348\n",
      "4      99.821388\n",
      "5      99.954758\n",
      "6      99.833641\n",
      "7     100.318314\n",
      "8     100.020660\n",
      "9      99.737701\n",
      "10     99.918396\n",
      "11     98.235985\n",
      "12    100.185211\n",
      "13    100.478363\n",
      "14     99.994896\n",
      "15     99.834633\n",
      "16    100.165291\n",
      "17    100.220901\n",
      "18     99.927635\n",
      "19     99.911423\n",
      "20    100.103706\n",
      "21     97.082840\n",
      "22    100.683266\n",
      "23    100.495117\n",
      "24     99.625702\n",
      "25     99.886864\n",
      "26    100.223473\n",
      "27     99.914177\n",
      "28     99.872894\n",
      "29     99.967613\n",
      "30     96.702568\n",
      "31     99.965637\n",
      "Name: 사정율, dtype: float32\n",
      "========================================\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dayofweek\n",
      "0     99.562622\n",
      "1     99.574066\n",
      "2     99.974068\n",
      "3     99.014107\n",
      "4    100.241432\n",
      "Name: 사정율, dtype: float32\n",
      "========================================\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hour\n",
      "10     50.258099\n",
      "11    100.028793\n",
      "12     98.877197\n",
      "13     99.950279\n",
      "14     97.659386\n",
      "15     99.175568\n",
      "16     99.841110\n",
      "17    100.710976\n",
      "Name: 사정율, dtype: float32\n",
      "========================================\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "minute\n",
      "0      99.656639\n",
      "2      99.977829\n",
      "5      99.748566\n",
      "7     100.364998\n",
      "8     100.026352\n",
      "10     99.964233\n",
      "19    100.351303\n",
      "20    100.712151\n",
      "25     99.957802\n",
      "30     99.941765\n",
      "40    100.157654\n",
      "50    100.236298\n",
      "Name: 사정율, dtype: float32\n",
      "========================================\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "weekofyear\n",
      "1      99.661919\n",
      "2     100.014099\n",
      "3     100.747063\n",
      "4      99.940147\n",
      "5     100.009071\n",
      "6      99.955833\n",
      "7      99.823471\n",
      "8      95.927040\n",
      "9      99.859200\n",
      "10    100.142670\n",
      "11    100.001343\n",
      "12    100.518356\n",
      "13     99.893852\n",
      "14    100.370247\n",
      "15     99.850792\n",
      "16    100.627945\n",
      "17     99.982849\n",
      "18     95.889160\n",
      "19     99.727051\n",
      "20    100.228340\n",
      "21    100.135078\n",
      "22    100.518684\n",
      "23     99.685753\n",
      "24    100.148605\n",
      "25     99.961456\n",
      "26     99.614449\n",
      "27     99.932823\n",
      "28    100.017334\n",
      "29     99.884766\n",
      "30     99.870316\n",
      "31    100.464653\n",
      "32     99.721504\n",
      "33     99.657547\n",
      "34    100.685196\n",
      "35     99.553894\n",
      "36     75.060875\n",
      "37    100.103607\n",
      "38    100.163940\n",
      "39    100.151520\n",
      "40     99.986008\n",
      "41     96.657692\n",
      "42     99.729042\n",
      "43    100.439697\n",
      "44     99.698433\n",
      "45     99.980774\n",
      "46    100.067871\n",
      "47    100.138771\n",
      "48     99.932625\n",
      "49     99.713791\n",
      "50     99.992912\n",
      "51    100.159637\n",
      "52    100.053780\n",
      "Name: 사정율, dtype: float32\n",
      "========================================\n"
     ]
    }
   ],
   "source": [
    "visualize_pct_time(df.loc[df['업체명'].isin(company)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>업체명</th>\n",
       "      <th>기초금액</th>\n",
       "      <th>예가</th>\n",
       "      <th>사정율</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "      <th>minute</th>\n",
       "      <th>dayofweek</th>\n",
       "      <th>...</th>\n",
       "      <th>부산</th>\n",
       "      <th>제주</th>\n",
       "      <th>대구</th>\n",
       "      <th>인천</th>\n",
       "      <th>대전</th>\n",
       "      <th>광주</th>\n",
       "      <th>울산</th>\n",
       "      <th>전국</th>\n",
       "      <th>세종</th>\n",
       "      <th>근접율</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>제이티건설(주)</td>\n",
       "      <td>166200000.0</td>\n",
       "      <td>99.407501</td>\n",
       "      <td>99.456497</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.048996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>통일종합건설 주식회사</td>\n",
       "      <td>49000000.0</td>\n",
       "      <td>99.273003</td>\n",
       "      <td>99.513802</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.240799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>성문건설 주식회사</td>\n",
       "      <td>35000000.0</td>\n",
       "      <td>99.120697</td>\n",
       "      <td>99.411400</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.290703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(주)덕산종합건설</td>\n",
       "      <td>27000000.0</td>\n",
       "      <td>99.877800</td>\n",
       "      <td>99.988602</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.110802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(유)아라야</td>\n",
       "      <td>64460000.0</td>\n",
       "      <td>99.681801</td>\n",
       "      <td>99.684502</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.002701</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           업체명         기초금액         예가        사정율  year  month  day  hour  \\\n",
       "0     제이티건설(주)  166200000.0  99.407501  99.456497  2020      3   13    16   \n",
       "1  통일종합건설 주식회사   49000000.0  99.273003  99.513802  2020      3   13    16   \n",
       "2    성문건설 주식회사   35000000.0  99.120697  99.411400  2020      3   13    16   \n",
       "3    (주)덕산종합건설   27000000.0  99.877800  99.988602  2020      3   13    16   \n",
       "4       (유)아라야   64460000.0  99.681801  99.684502  2020      3   13    16   \n",
       "\n",
       "   minute  dayofweek  ...   부산   제주   대구   인천   대전   광주   울산   전국   세종  \\\n",
       "0       0          4  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "1       0          4  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "2       0          4  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "3       0          4  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "4       0          4  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "\n",
       "        근접율  \n",
       "0  0.048996  \n",
       "1  0.240799  \n",
       "2  0.290703  \n",
       "3  0.110802  \n",
       "4  0.002701  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9. 예측"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 사정율이 중요한 feature라 판단\n",
    "* 따라서, 사정율이 0인 컬럼은 제외합니다"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = df.loc[df['사정율'] > 85]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(31595, 32)"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 업체명은 drop 합니다"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = train_data.drop('업체명', 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 지역은 서울, 전국을 포함하는 지표만 추립니다"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = train_data.loc[(train_data['서울'] == 1) | (train_data['전국'] == 1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [],
   "source": [
    "area_drop_cols = ['전남', '경북', '경기', '경남', '강원', '전북', '충남', '충북', '부산', '제주', '대구',\n",
    "       '인천', '대전', '광주', '울산', '세종']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = train_data.drop(area_drop_cols, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 예가, 사정율, 근접율은 사후에 나오는 지표 이므로 drop 합니다"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_feature = train_data.drop(['예가', '사정율', '근접율'], 1)\n",
    "train_label = train_data['예가']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 최종 Feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "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>기초금액</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "      <th>minute</th>\n",
       "      <th>dayofweek</th>\n",
       "      <th>weekofyear</th>\n",
       "      <th>dayofyear</th>\n",
       "      <th>quarter</th>\n",
       "      <th>서울</th>\n",
       "      <th>전국</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>4.674210e+08</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>66</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219</th>\n",
       "      <td>1.154583e+09</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>66</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>6.420660e+08</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>65</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275</th>\n",
       "      <td>1.330120e+09</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>65</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>305</th>\n",
       "      <td>9.960500e+08</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>64</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             기초금액  year  month  day  hour  minute  dayofweek  weekofyear  \\\n",
       "167  4.674210e+08  2020      3    6    15       0          4          10   \n",
       "219  1.154583e+09  2020      3    6    11       0          4          10   \n",
       "235  6.420660e+08  2020      3    5    15       0          3          10   \n",
       "275  1.330120e+09  2020      3    5    11       0          3          10   \n",
       "305  9.960500e+08  2020      3    4    15       0          2          10   \n",
       "\n",
       "     dayofyear  quarter   서울   전국  \n",
       "167         66        1  1.0  0.0  \n",
       "219         66        1  1.0  0.0  \n",
       "235         65        1  1.0  0.0  \n",
       "275         65        1  1.0  0.0  \n",
       "305         64        1  1.0  0.0  "
      ]
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_feature.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 모델링"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import KFold\n",
    "from xgboost import XGBRegressor\n",
    "from lightgbm import LGBMRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [],
   "source": [
    "SEED = 123"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\tvalidation_0-mae:97.43237\tvalidation_1-mae:97.48483\n",
      "Multiple eval metrics have been passed: 'validation_1-mae' will be used for early stopping.\n",
      "\n",
      "Will train until validation_1-mae hasn't improved in 30 rounds.\n",
      "[200]\tvalidation_0-mae:1.72026\tvalidation_1-mae:1.77291\n",
      "[400]\tvalidation_0-mae:0.53526\tvalidation_1-mae:0.61973\n",
      "Stopping. Best iteration:\n",
      "[390]\tvalidation_0-mae:0.53786\tvalidation_1-mae:0.61945\n",
      "\n",
      "[0]\tvalidation_0-mae:97.45020\tvalidation_1-mae:97.41235\n",
      "Multiple eval metrics have been passed: 'validation_1-mae' will be used for early stopping.\n",
      "\n",
      "Will train until validation_1-mae hasn't improved in 30 rounds.\n",
      "[200]\tvalidation_0-mae:1.72187\tvalidation_1-mae:1.68547\n",
      "Stopping. Best iteration:\n",
      "[349]\tvalidation_0-mae:0.55724\tvalidation_1-mae:0.57244\n",
      "\n",
      "[0]\tvalidation_0-mae:97.43646\tvalidation_1-mae:97.46812\n",
      "Multiple eval metrics have been passed: 'validation_1-mae' will be used for early stopping.\n",
      "\n",
      "Will train until validation_1-mae hasn't improved in 30 rounds.\n",
      "[200]\tvalidation_0-mae:1.72254\tvalidation_1-mae:1.75116\n",
      "[400]\tvalidation_0-mae:0.54700\tvalidation_1-mae:0.58112\n",
      "Stopping. Best iteration:\n",
      "[420]\tvalidation_0-mae:0.54295\tvalidation_1-mae:0.58088\n",
      "\n",
      "[0]\tvalidation_0-mae:97.44897\tvalidation_1-mae:97.41762\n",
      "Multiple eval metrics have been passed: 'validation_1-mae' will be used for early stopping.\n",
      "\n",
      "Will train until validation_1-mae hasn't improved in 30 rounds.\n",
      "[200]\tvalidation_0-mae:1.72082\tvalidation_1-mae:1.69088\n",
      "Stopping. Best iteration:\n",
      "[369]\tvalidation_0-mae:0.54636\tvalidation_1-mae:0.59549\n",
      "\n",
      "[0]\tvalidation_0-mae:97.44544\tvalidation_1-mae:97.43135\n",
      "Multiple eval metrics have been passed: 'validation_1-mae' will be used for early stopping.\n",
      "\n",
      "Will train until validation_1-mae hasn't improved in 30 rounds.\n",
      "[200]\tvalidation_0-mae:1.72041\tvalidation_1-mae:1.70751\n",
      "[400]\tvalidation_0-mae:0.54088\tvalidation_1-mae:0.59959\n",
      "Stopping. Best iteration:\n",
      "[378]\tvalidation_0-mae:0.54601\tvalidation_1-mae:0.59915\n",
      "\n",
      "avg score: 0.593481\n"
     ]
    }
   ],
   "source": [
    "# 코드 비공개 처리"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>quarter</td>\n",
       "      <td>0.212947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>기초금액</td>\n",
       "      <td>0.090076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>dayofyear</td>\n",
       "      <td>0.089709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>전국</td>\n",
       "      <td>0.078249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>weekofyear</td>\n",
       "      <td>0.075134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>minute</td>\n",
       "      <td>0.074531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>dayofweek</td>\n",
       "      <td>0.070992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>day</td>\n",
       "      <td>0.070871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>서울</td>\n",
       "      <td>0.069562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>hour</td>\n",
       "      <td>0.059260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>year</td>\n",
       "      <td>0.055194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>month</td>\n",
       "      <td>0.053476</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             0         1\n",
       "9      quarter  0.212947\n",
       "0         기초금액  0.090076\n",
       "8    dayofyear  0.089709\n",
       "11          전국  0.078249\n",
       "7   weekofyear  0.075134\n",
       "5       minute  0.074531\n",
       "6    dayofweek  0.070992\n",
       "3          day  0.070871\n",
       "10          서울  0.069562\n",
       "4         hour  0.059260\n",
       "1         year  0.055194\n",
       "2        month  0.053476"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(list(zip(train_feature.columns, xgb_model.feature_importances_))).sort_values(by=1, ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_xgb(train_features, train_labels, test_features, num_split=3):\n",
    "    # 코드 비공개 처리\n",
    "    return preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_lgbm(train_feature, train_label, test_feature, num_split=5):\n",
    "    # 코드 비공개 처리\n",
    "    return preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_xgb(train_feature, train_label, test_feature, num_split=3):\n",
    "    # 코드 비공개 처리\n",
    "    return preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1790, 12), (597, 12))"
      ]
     },
     "execution_count": 334,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "x_train, x_valid, y_train, y_valid = train_test_split(train_feature, train_label, random_state=321)\n",
    "x_train.shape, x_valid.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training until validation scores don't improve for 30 rounds\n",
      "Early stopping, best iteration is:\n",
      "[1]\ttraining's l1: 0.596837\ttraining's l2: 0.544079\tvalid_1's l1: 0.599506\tvalid_1's l2: 0.56646\n",
      "Training until validation scores don't improve for 30 rounds\n",
      "Early stopping, best iteration is:\n",
      "[9]\ttraining's l1: 0.608888\ttraining's l2: 0.56613\tvalid_1's l1: 0.541242\tvalid_1's l2: 0.460266\n",
      "Training until validation scores don't improve for 30 rounds\n",
      "Early stopping, best iteration is:\n",
      "[1]\ttraining's l1: 0.590583\ttraining's l2: 0.540623\tvalid_1's l1: 0.624393\tvalid_1's l2: 0.580429\n",
      "Training until validation scores don't improve for 30 rounds\n",
      "Early stopping, best iteration is:\n",
      "[1]\ttraining's l1: 0.593366\ttraining's l2: 0.541869\tvalid_1's l1: 0.613335\tvalid_1's l2: 0.57537\n",
      "Training until validation scores don't improve for 30 rounds\n",
      "Early stopping, best iteration is:\n",
      "[19]\ttraining's l1: 0.58905\ttraining's l2: 0.536167\tvalid_1's l1: 0.609964\tvalid_1's l2: 0.563332\n",
      "avg score: 0.5976880338001499\n",
      "[0]\tvalidation_0-mae:97.44695\tvalidation_1-mae:97.39553\n",
      "Multiple eval metrics have been passed: 'validation_1-mae' will be used for early stopping.\n",
      "\n",
      "Will train until validation_1-mae hasn't improved in 20 rounds.\n",
      "[200]\tvalidation_0-mae:1.72761\tvalidation_1-mae:1.67668\n",
      "Stopping. Best iteration:\n",
      "[366]\tvalidation_0-mae:0.54420\tvalidation_1-mae:0.58611\n",
      "\n",
      "[0]\tvalidation_0-mae:97.43077\tvalidation_1-mae:97.42883\n",
      "Multiple eval metrics have been passed: 'validation_1-mae' will be used for early stopping.\n",
      "\n",
      "Will train until validation_1-mae hasn't improved in 20 rounds.\n",
      "[200]\tvalidation_0-mae:1.72430\tvalidation_1-mae:1.72252\n",
      "Stopping. Best iteration:\n",
      "[349]\tvalidation_0-mae:0.55145\tvalidation_1-mae:0.61090\n",
      "\n",
      "[0]\tvalidation_0-mae:97.41282\tvalidation_1-mae:97.46614\n",
      "Multiple eval metrics have been passed: 'validation_1-mae' will be used for early stopping.\n",
      "\n",
      "Will train until validation_1-mae hasn't improved in 20 rounds.\n",
      "[200]\tvalidation_0-mae:1.72471\tvalidation_1-mae:1.77892\n",
      "[400]\tvalidation_0-mae:0.52633\tvalidation_1-mae:0.62074\n",
      "Stopping. Best iteration:\n",
      "[400]\tvalidation_0-mae:0.52633\tvalidation_1-mae:0.62074\n",
      "\n",
      "avg score: 0.6059176666666666\n"
     ]
    }
   ],
   "source": [
    "lgbm_pred = train_lgbm(x_train, y_train, x_valid)\n",
    "xgb_pred = train_xgb(x_train, y_train, x_valid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = lgbm_pred * 0.6 + xgb_pred * 0.4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5554264117899838"
      ]
     },
     "execution_count": 337,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "abs(pred - y_valid).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 검증 (시뮬레이션)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "metadata": {},
   "outputs": [],
   "source": [
    "validation_df = df.iloc[y_valid.index][['예가', '사정율']].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>예가</th>\n",
       "      <th>사정율</th>\n",
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       "    <tr>\n",
       "      <th>16649</th>\n",
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       "    <tr>\n",
       "      <th>26280</th>\n",
       "      <td>99.882004</td>\n",
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      ],
      "text/plain": [
       "               예가         사정율\n",
       "14548   98.969002   98.977898\n",
       "5612    99.151497   99.167297\n",
       "16649  100.463997  100.474098\n",
       "26280   99.882004   99.883202\n",
       "17308   99.960197   99.964798"
      ]
     },
     "execution_count": 339,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "validation_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14548     98.977898\n",
       "5612      99.167297\n",
       "16649    100.474098\n",
       "26280     99.883202\n",
       "17308     99.964798\n",
       "Name: 사정율, dtype: float32"
      ]
     },
     "execution_count": 340,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[y_valid.index]['사정율'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "metadata": {},
   "outputs": [],
   "source": [
    "validation_df['pred'] = pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>14548</th>\n",
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       "      <td>99.907663</td>\n",
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       "    <tr>\n",
       "      <th>5612</th>\n",
       "      <td>99.151497</td>\n",
       "      <td>99.167297</td>\n",
       "      <td>99.847656</td>\n",
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       "      <th>16649</th>\n",
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       "      <td>100.474098</td>\n",
       "      <td>99.921612</td>\n",
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       "    <tr>\n",
       "      <th>26280</th>\n",
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       "      <td>99.883202</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               예가         사정율       pred\n",
       "14548   98.969002   98.977898  99.907663\n",
       "5612    99.151497   99.167297  99.847656\n",
       "16649  100.463997  100.474098  99.921612\n",
       "26280   99.882004   99.883202  99.940660\n",
       "17308   99.960197   99.964798  99.905172"
      ]
     },
     "execution_count": 342,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "validation_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 괴리율 = 예측값 - 예가 (0보다 작으면 무조건 탈락)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 343,
   "metadata": {},
   "outputs": [],
   "source": [
    "validation_df['괴리율'] = validation_df['pred'] - validation_df['예가']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "metadata": {},
   "outputs": [],
   "source": [
    "validation_df['선정여부'] = (validation_df['pred'] < validation_df['사정율']) & (validation_df['pred'] > validation_df['예가'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 345,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>예가</th>\n",
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       "      <th>pred</th>\n",
       "      <th>괴리율</th>\n",
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       "  <tbody>\n",
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       "      <th>7468</th>\n",
       "      <td>99.897202</td>\n",
       "      <td>99.933403</td>\n",
       "      <td>99.897466</td>\n",
       "      <td>0.000264</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>6025</th>\n",
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       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>21792</th>\n",
       "      <td>99.869003</td>\n",
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       "      <td>0.004141</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>17479</th>\n",
       "      <td>99.917999</td>\n",
       "      <td>99.919998</td>\n",
       "      <td>99.924551</td>\n",
       "      <td>0.006552</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26677</th>\n",
       "      <td>99.924004</td>\n",
       "      <td>99.925003</td>\n",
       "      <td>99.938099</td>\n",
       "      <td>0.014095</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              예가        사정율       pred       괴리율   선정여부\n",
       "7468   99.897202  99.933403  99.897466  0.000264   True\n",
       "6025   99.857002  99.860001  99.857646  0.000643   True\n",
       "21792  99.869003  99.874496  99.873144  0.004141   True\n",
       "17479  99.917999  99.919998  99.924551  0.006552  False\n",
       "26677  99.924004  99.925003  99.938099  0.014095  False"
      ]
     },
     "execution_count": 345,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "validation_df.loc[validation_df['괴리율'] >= 0].sort_values(by='괴리율').head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 선정된 지표 확인"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>22744</th>\n",
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       "      <th>23983</th>\n",
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       "      <td>105.500000</td>\n",
       "      <td>99.884492</td>\n",
       "      <td>0.212792</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>21792</th>\n",
       "      <td>99.869003</td>\n",
       "      <td>99.874496</td>\n",
       "      <td>99.873144</td>\n",
       "      <td>0.004141</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30818</th>\n",
       "      <td>99.890999</td>\n",
       "      <td>99.925003</td>\n",
       "      <td>99.905421</td>\n",
       "      <td>0.014422</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7468</th>\n",
       "      <td>99.897202</td>\n",
       "      <td>99.933403</td>\n",
       "      <td>99.897466</td>\n",
       "      <td>0.000264</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21491</th>\n",
       "      <td>99.849701</td>\n",
       "      <td>99.899803</td>\n",
       "      <td>99.897837</td>\n",
       "      <td>0.048136</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              예가         사정율       pred       괴리율  선정여부\n",
       "22744  99.438499  100.062599  99.869264  0.430764  True\n",
       "10751  97.873703  100.247398  99.918239  2.044536  True\n",
       "26726  99.441704  101.254898  99.859676  0.417972  True\n",
       "6025   99.857002   99.860001  99.857646  0.000643  True\n",
       "23983  99.671700  105.500000  99.884492  0.212792  True\n",
       "21792  99.869003   99.874496  99.873144  0.004141  True\n",
       "30818  99.890999   99.925003  99.905421  0.014422  True\n",
       "7468   99.897202   99.933403  99.897466  0.000264  True\n",
       "21491  99.849701   99.899803  99.897837  0.048136  True"
      ]
     },
     "execution_count": 346,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "validation_df.loc[validation_df['선정여부'] ==True]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 347,
   "metadata": {},
   "outputs": [],
   "source": [
    "idx = validation_df.loc[validation_df['선정여부'] ==True].index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 선정된 지표 (전체데이터)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        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>업체명</th>\n",
       "      <th>기초금액</th>\n",
       "      <th>예가</th>\n",
       "      <th>사정율</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "      <th>minute</th>\n",
       "      <th>dayofweek</th>\n",
       "      <th>...</th>\n",
       "      <th>부산</th>\n",
       "      <th>제주</th>\n",
       "      <th>대구</th>\n",
       "      <th>인천</th>\n",
       "      <th>대전</th>\n",
       "      <th>광주</th>\n",
       "      <th>울산</th>\n",
       "      <th>전국</th>\n",
       "      <th>세종</th>\n",
       "      <th>근접율</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>22744</th>\n",
       "      <td>(주)아랑존디</td>\n",
       "      <td>4.840000e+07</td>\n",
       "      <td>99.438499</td>\n",
       "      <td>100.062599</td>\n",
       "      <td>2018</td>\n",
       "      <td>10</td>\n",
       "      <td>19</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.624100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10751</th>\n",
       "      <td>(주)지투원</td>\n",
       "      <td>1.732107e+09</td>\n",
       "      <td>97.873703</td>\n",
       "      <td>100.247398</td>\n",
       "      <td>2019</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.373695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26726</th>\n",
       "      <td>계룡건설산업 주식회사</td>\n",
       "      <td>2.322301e+10</td>\n",
       "      <td>99.441704</td>\n",
       "      <td>101.254898</td>\n",
       "      <td>2018</td>\n",
       "      <td>6</td>\n",
       "      <td>26</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.813194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6025</th>\n",
       "      <td>세한종합건설 주식회사</td>\n",
       "      <td>1.335007e+10</td>\n",
       "      <td>99.857002</td>\n",
       "      <td>99.860001</td>\n",
       "      <td>2019</td>\n",
       "      <td>11</td>\n",
       "      <td>14</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.002998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23983</th>\n",
       "      <td>중미건설합자회사</td>\n",
       "      <td>1.501838e+10</td>\n",
       "      <td>99.671700</td>\n",
       "      <td>105.500000</td>\n",
       "      <td>2018</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.828300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21792</th>\n",
       "      <td>새움綜合建設 株式會社</td>\n",
       "      <td>1.856688e+09</td>\n",
       "      <td>99.869003</td>\n",
       "      <td>99.874496</td>\n",
       "      <td>2018</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.005493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30818</th>\n",
       "      <td>(주)이공</td>\n",
       "      <td>9.640700e+08</td>\n",
       "      <td>99.890999</td>\n",
       "      <td>99.925003</td>\n",
       "      <td>2018</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.034004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7468</th>\n",
       "      <td>주식회사 예진건설</td>\n",
       "      <td>1.083887e+10</td>\n",
       "      <td>99.897202</td>\n",
       "      <td>99.933403</td>\n",
       "      <td>2019</td>\n",
       "      <td>10</td>\n",
       "      <td>22</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.036201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21491</th>\n",
       "      <td>갑인종합건설주식회사</td>\n",
       "      <td>3.914170e+09</td>\n",
       "      <td>99.849701</td>\n",
       "      <td>99.899803</td>\n",
       "      <td>2018</td>\n",
       "      <td>11</td>\n",
       "      <td>14</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.050102</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               업체명          기초금액         예가         사정율  year  month  day  \\\n",
       "22744      (주)아랑존디  4.840000e+07  99.438499  100.062599  2018     10   19   \n",
       "10751       (주)지투원  1.732107e+09  97.873703  100.247398  2019      7    9   \n",
       "26726  계룡건설산업 주식회사  2.322301e+10  99.441704  101.254898  2018      6   26   \n",
       "6025   세한종합건설 주식회사  1.335007e+10  99.857002   99.860001  2019     11   14   \n",
       "23983     중미건설합자회사  1.501838e+10  99.671700  105.500000  2018      9   10   \n",
       "21792  새움綜合建設 株式會社  1.856688e+09  99.869003   99.874496  2018     11    8   \n",
       "30818        (주)이공  9.640700e+08  99.890999   99.925003  2018      4    6   \n",
       "7468     주식회사 예진건설  1.083887e+10  99.897202   99.933403  2019     10   22   \n",
       "21491   갑인종합건설주식회사  3.914170e+09  99.849701   99.899803  2018     11   14   \n",
       "\n",
       "       hour  minute  dayofweek  ...   부산   제주   대구   인천   대전   광주   울산   전국  \\\n",
       "22744    11       0          4  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0   \n",
       "10751    11       0          1  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "26726    11       0          1  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0   \n",
       "6025     11       0          3  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0   \n",
       "23983    17       0          0  ...  0.0  0.0  0.0  0.0  0.0  1.0  0.0  1.0   \n",
       "21792    11       0          3  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "30818    11       0          4  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "7468     11       0          1  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0   \n",
       "21491    11       0          2  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0   \n",
       "\n",
       "        세종       근접율  \n",
       "22744  0.0  0.624100  \n",
       "10751  0.0  2.373695  \n",
       "26726  0.0  1.813194  \n",
       "6025   0.0  0.002998  \n",
       "23983  0.0  5.828300  \n",
       "21792  0.0  0.005493  \n",
       "30818  0.0  0.034004  \n",
       "7468   0.0  0.036201  \n",
       "21491  0.0  0.050102  \n",
       "\n",
       "[9 rows x 32 columns]"
      ]
     },
     "execution_count": 348,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[idx]"
   ]
  }
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