주차수요_예측_AI_경진대회.ipynb

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      "name": "주차수요 예측 AI 경진대회.ipynb",
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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/jiin124/2021_2_SAI_ML/blob/main/%EC%A3%BC%EC%B0%A8%EC%88%98%EC%9A%94_%EC%98%88%EC%B8%A1_AI_%EA%B2%BD%EC%A7%84%EB%8C%80%ED%9A%8C.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BWvG4lUlSMzh"
      },
      "source": [
        "# 주차수요 예측 AI 경진대회"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eal6_3BwR_pw"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score,roc_auc_score\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from xgboost import XGBRegressor\n",
        "from lightgbm import LGBMRegressor"
      ],
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X2Yimy5AuLfg"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.rc('font', family='NanumBarunGothic') "
      ],
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CDjNm8qke3B0"
      },
      "source": [
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ],
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        },
        "id": "a8caGCVSZkbe",
        "outputId": "f05aa4dc-0548-4366-99ec-147de7333b11"
      },
      "source": [
        "train=pd.read_csv(\"train.csv\")\n",
        "train.head()"
      ],
      "execution_count": 213,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>단지코드</th>\n",
              "      <th>총세대수</th>\n",
              "      <th>임대건물구분</th>\n",
              "      <th>지역</th>\n",
              "      <th>공급유형</th>\n",
              "      <th>전용면적</th>\n",
              "      <th>전용면적별세대수</th>\n",
              "      <th>공가수</th>\n",
              "      <th>자격유형</th>\n",
              "      <th>임대보증금</th>\n",
              "      <th>임대료</th>\n",
              "      <th>도보 10분거리 내 지하철역 수(환승노선 수 반영)</th>\n",
              "      <th>도보 10분거리 내 버스정류장 수</th>\n",
              "      <th>단지내주차면수</th>\n",
              "      <th>등록차량수</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>C2483</td>\n",
              "      <td>900</td>\n",
              "      <td>아파트</td>\n",
              "      <td>경상북도</td>\n",
              "      <td>국민임대</td>\n",
              "      <td>39.72</td>\n",
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              "      <td>3.0</td>\n",
              "      <td>1425.0</td>\n",
              "      <td>1015.0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>C2483</td>\n",
              "      <td>900</td>\n",
              "      <td>아파트</td>\n",
              "      <td>경상북도</td>\n",
              "      <td>국민임대</td>\n",
              "      <td>39.72</td>\n",
              "      <td>15</td>\n",
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              "      <td>A</td>\n",
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              "      <td>1425.0</td>\n",
              "      <td>1015.0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>C2483</td>\n",
              "      <td>900</td>\n",
              "      <td>아파트</td>\n",
              "      <td>경상북도</td>\n",
              "      <td>국민임대</td>\n",
              "      <td>51.93</td>\n",
              "      <td>385</td>\n",
              "      <td>38.0</td>\n",
              "      <td>A</td>\n",
              "      <td>27304000</td>\n",
              "      <td>184330</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1425.0</td>\n",
              "      <td>1015.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>C2483</td>\n",
              "      <td>900</td>\n",
              "      <td>아파트</td>\n",
              "      <td>경상북도</td>\n",
              "      <td>국민임대</td>\n",
              "      <td>51.93</td>\n",
              "      <td>15</td>\n",
              "      <td>38.0</td>\n",
              "      <td>A</td>\n",
              "      <td>27304000</td>\n",
              "      <td>184330</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1425.0</td>\n",
              "      <td>1015.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>C2483</td>\n",
              "      <td>900</td>\n",
              "      <td>아파트</td>\n",
              "      <td>경상북도</td>\n",
              "      <td>국민임대</td>\n",
              "      <td>51.93</td>\n",
              "      <td>41</td>\n",
              "      <td>38.0</td>\n",
              "      <td>A</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>1425.0</td>\n",
              "      <td>1015.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            "text/plain": [
              "    단지코드  총세대수 임대건물구분  ... 도보 10분거리 내 버스정류장 수 단지내주차면수   등록차량수\n",
              "0  C2483   900    아파트  ...                3.0  1425.0  1015.0\n",
              "1  C2483   900    아파트  ...                3.0  1425.0  1015.0\n",
              "2  C2483   900    아파트  ...                3.0  1425.0  1015.0\n",
              "3  C2483   900    아파트  ...                3.0  1425.0  1015.0\n",
              "4  C2483   900    아파트  ...                3.0  1425.0  1015.0\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 213
        }
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      },
      "source": [
        "train.info()"
      ],
      "execution_count": 214,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 2952 entries, 0 to 2951\n",
            "Data columns (total 15 columns):\n",
            " #   Column                        Non-Null Count  Dtype  \n",
            "---  ------                        --------------  -----  \n",
            " 0   단지코드                          2952 non-null   object \n",
            " 1   총세대수                          2952 non-null   int64  \n",
            " 2   임대건물구분                        2952 non-null   object \n",
            " 3   지역                            2952 non-null   object \n",
            " 4   공급유형                          2952 non-null   object \n",
            " 5   전용면적                          2952 non-null   float64\n",
            " 6   전용면적별세대수                      2952 non-null   int64  \n",
            " 7   공가수                           2952 non-null   float64\n",
            " 8   자격유형                          2952 non-null   object \n",
            " 9   임대보증금                         2383 non-null   object \n",
            " 10  임대료                           2383 non-null   object \n",
            " 11  도보 10분거리 내 지하철역 수(환승노선 수 반영)  2741 non-null   float64\n",
            " 12  도보 10분거리 내 버스정류장 수            2948 non-null   float64\n",
            " 13  단지내주차면수                       2952 non-null   float64\n",
            " 14  등록차량수                         2952 non-null   float64\n",
            "dtypes: float64(6), int64(2), object(7)\n",
            "memory usage: 346.1+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ehzcwaqQc1Ee"
      },
      "source": [
        "왜 임대료하고 임대보증금이 objest 형인지 알수없는 부분... 왜? 내가 형식을 바꿔줘야 겠다고 생각함"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c3QkQRCzZunc",
        "outputId": "fb87ba51-6623-4f0e-9eb7-8f9045e23712"
      },
      "source": [
        "train.isna().sum()"
      ],
      "execution_count": 215,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "단지코드                              0\n",
              "총세대수                              0\n",
              "임대건물구분                            0\n",
              "지역                                0\n",
              "공급유형                              0\n",
              "전용면적                              0\n",
              "전용면적별세대수                          0\n",
              "공가수                               0\n",
              "자격유형                              0\n",
              "임대보증금                           569\n",
              "임대료                             569\n",
              "도보 10분거리 내 지하철역 수(환승노선 수 반영)    211\n",
              "도보 10분거리 내 버스정류장 수                4\n",
              "단지내주차면수                           0\n",
              "등록차량수                             0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 215
        }
      ]
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      "metadata": {
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          "height": 312
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        "id": "Vl5rR3q3Z5fO",
        "outputId": "3afdbd53-0183-48cc-ed34-3af65f87805a"
      },
      "source": [
        "train.describe()"
      ],
      "execution_count": 216,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>총세대수</th>\n",
              "      <th>전용면적</th>\n",
              "      <th>전용면적별세대수</th>\n",
              "      <th>공가수</th>\n",
              "      <th>도보 10분거리 내 지하철역 수(환승노선 수 반영)</th>\n",
              "      <th>도보 10분거리 내 버스정류장 수</th>\n",
              "      <th>단지내주차면수</th>\n",
              "      <th>등록차량수</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>2952.000000</td>\n",
              "      <td>2952.000000</td>\n",
              "      <td>2952.000000</td>\n",
              "      <td>2952.000000</td>\n",
              "      <td>2741.000000</td>\n",
              "      <td>2948.000000</td>\n",
              "      <td>2952.000000</td>\n",
              "      <td>2952.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>886.661247</td>\n",
              "      <td>44.757215</td>\n",
              "      <td>102.747967</td>\n",
              "      <td>12.921070</td>\n",
              "      <td>0.176578</td>\n",
              "      <td>3.695726</td>\n",
              "      <td>601.668360</td>\n",
              "      <td>559.768293</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>513.540168</td>\n",
              "      <td>31.874280</td>\n",
              "      <td>132.640159</td>\n",
              "      <td>10.778831</td>\n",
              "      <td>0.427408</td>\n",
              "      <td>2.644665</td>\n",
              "      <td>396.407072</td>\n",
              "      <td>433.375027</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>26.000000</td>\n",
              "      <td>12.620000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>13.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>513.500000</td>\n",
              "      <td>32.100000</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>279.250000</td>\n",
              "      <td>220.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>779.000000</td>\n",
              "      <td>39.930000</td>\n",
              "      <td>60.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>517.000000</td>\n",
              "      <td>487.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1106.000000</td>\n",
              "      <td>51.562500</td>\n",
              "      <td>144.000000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>823.000000</td>\n",
              "      <td>770.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2568.000000</td>\n",
              "      <td>583.400000</td>\n",
              "      <td>1865.000000</td>\n",
              "      <td>55.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>1798.000000</td>\n",
              "      <td>2550.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              총세대수         전용면적  ...      단지내주차면수        등록차량수\n",
              "count  2952.000000  2952.000000  ...  2952.000000  2952.000000\n",
              "mean    886.661247    44.757215  ...   601.668360   559.768293\n",
              "std     513.540168    31.874280  ...   396.407072   433.375027\n",
              "min      26.000000    12.620000  ...    13.000000    13.000000\n",
              "25%     513.500000    32.100000  ...   279.250000   220.000000\n",
              "50%     779.000000    39.930000  ...   517.000000   487.000000\n",
              "75%    1106.000000    51.562500  ...   823.000000   770.000000\n",
              "max    2568.000000   583.400000  ...  1798.000000  2550.000000\n",
              "\n",
              "[8 rows x 8 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 216
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304
        },
        "id": "pO0IjUvdaIK6",
        "outputId": "59058bc9-1865-47ea-b2a9-c4ebbd140b7a"
      },
      "source": [
        "test=pd.read_csv(\"test.csv\")\n",
        "test.head()"
      ],
      "execution_count": 217,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>단지코드</th>\n",
              "      <th>총세대수</th>\n",
              "      <th>임대건물구분</th>\n",
              "      <th>지역</th>\n",
              "      <th>공급유형</th>\n",
              "      <th>전용면적</th>\n",
              "      <th>전용면적별세대수</th>\n",
              "      <th>공가수</th>\n",
              "      <th>자격유형</th>\n",
              "      <th>임대보증금</th>\n",
              "      <th>임대료</th>\n",
              "      <th>도보 10분거리 내 지하철역 수(환승노선 수 반영)</th>\n",
              "      <th>도보 10분거리 내 버스정류장 수</th>\n",
              "      <th>단지내주차면수</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>C1072</td>\n",
              "      <td>754</td>\n",
              "      <td>아파트</td>\n",
              "      <td>경기도</td>\n",
              "      <td>국민임대</td>\n",
              "      <td>39.79</td>\n",
              "      <td>116</td>\n",
              "      <td>14.0</td>\n",
              "      <td>H</td>\n",
              "      <td>22830000</td>\n",
              "      <td>189840</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>683.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>C1072</td>\n",
              "      <td>754</td>\n",
              "      <td>아파트</td>\n",
              "      <td>경기도</td>\n",
              "      <td>국민임대</td>\n",
              "      <td>46.81</td>\n",
              "      <td>30</td>\n",
              "      <td>14.0</td>\n",
              "      <td>A</td>\n",
              "      <td>36048000</td>\n",
              "      <td>249930</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>683.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>C1072</td>\n",
              "      <td>754</td>\n",
              "      <td>아파트</td>\n",
              "      <td>경기도</td>\n",
              "      <td>국민임대</td>\n",
              "      <td>46.90</td>\n",
              "      <td>112</td>\n",
              "      <td>14.0</td>\n",
              "      <td>H</td>\n",
              "      <td>36048000</td>\n",
              "      <td>249930</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>683.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>C1072</td>\n",
              "      <td>754</td>\n",
              "      <td>아파트</td>\n",
              "      <td>경기도</td>\n",
              "      <td>국민임대</td>\n",
              "      <td>46.90</td>\n",
              "      <td>120</td>\n",
              "      <td>14.0</td>\n",
              "      <td>H</td>\n",
              "      <td>36048000</td>\n",
              "      <td>249930</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>683.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>C1072</td>\n",
              "      <td>754</td>\n",
              "      <td>아파트</td>\n",
              "      <td>경기도</td>\n",
              "      <td>국민임대</td>\n",
              "      <td>51.46</td>\n",
              "      <td>60</td>\n",
              "      <td>14.0</td>\n",
              "      <td>H</td>\n",
              "      <td>43497000</td>\n",
              "      <td>296780</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>683.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    단지코드  총세대수  ... 도보 10분거리 내 버스정류장 수 단지내주차면수\n",
              "0  C1072   754  ...                2.0   683.0\n",
              "1  C1072   754  ...                2.0   683.0\n",
              "2  C1072   754  ...                2.0   683.0\n",
              "3  C1072   754  ...                2.0   683.0\n",
              "4  C1072   754  ...                2.0   683.0\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 217
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TTNs9RG-aWBP",
        "outputId": "b335f39f-30ae-411a-a681-6d4df4bba92b"
      },
      "source": [
        "test.info()"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1022 entries, 0 to 1021\n",
            "Data columns (total 14 columns):\n",
            " #   Column                        Non-Null Count  Dtype  \n",
            "---  ------                        --------------  -----  \n",
            " 0   단지코드                          1022 non-null   object \n",
            " 1   총세대수                          1022 non-null   int64  \n",
            " 2   임대건물구분                        1022 non-null   object \n",
            " 3   지역                            1022 non-null   object \n",
            " 4   공급유형                          1022 non-null   object \n",
            " 5   전용면적                          1022 non-null   float64\n",
            " 6   전용면적별세대수                      1022 non-null   int64  \n",
            " 7   공가수                           1022 non-null   float64\n",
            " 8   자격유형                          1020 non-null   object \n",
            " 9   임대보증금                         842 non-null    object \n",
            " 10  임대료                           842 non-null    object \n",
            " 11  도보 10분거리 내 지하철역 수(환승노선 수 반영)  980 non-null    float64\n",
            " 12  도보 10분거리 내 버스정류장 수            1022 non-null   float64\n",
            " 13  단지내주차면수                       1022 non-null   float64\n",
            "dtypes: float64(5), int64(2), object(7)\n",
            "memory usage: 111.9+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S1sN3FATaZa2",
        "outputId": "367dc2f2-2b24-47c4-8312-09bc5f9421bb"
      },
      "source": [
        "test.isna().sum()"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "단지코드                              0\n",
              "총세대수                              0\n",
              "임대건물구분                            0\n",
              "지역                                0\n",
              "공급유형                              0\n",
              "전용면적                              0\n",
              "전용면적별세대수                          0\n",
              "공가수                               0\n",
              "자격유형                              2\n",
              "임대보증금                           180\n",
              "임대료                             180\n",
              "도보 10분거리 내 지하철역 수(환승노선 수 반영)     42\n",
              "도보 10분거리 내 버스정류장 수                0\n",
              "단지내주차면수                           0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nFuExlJWboQN"
      },
      "source": [
        "# EDA"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bAoN1tKUamdo",
        "outputId": "d75bb35a-e008-44c2-f662-57781115e6a2"
      },
      "source": [
        "train.columns"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['단지코드', '총세대수', '임대건물구분', '지역', '공급유형', '전용면적', '전용면적별세대수', '공가수',\n",
              "       '자격유형', '임대보증금', '임대료', '도보 10분거리 내 지하철역 수(환승노선 수 반영)',\n",
              "       '도보 10분거리 내 버스정류장 수', '단지내주차면수', '등록차량수'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XYEZxHO8bstx",
        "outputId": "868699c9-aaa0-4749-a43d-5af563fae7ad"
      },
      "source": [
        "train.info()"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 2952 entries, 0 to 2951\n",
            "Data columns (total 15 columns):\n",
            " #   Column                        Non-Null Count  Dtype  \n",
            "---  ------                        --------------  -----  \n",
            " 0   단지코드                          2952 non-null   object \n",
            " 1   총세대수                          2952 non-null   int64  \n",
            " 2   임대건물구분                        2952 non-null   object \n",
            " 3   지역                            2952 non-null   object \n",
            " 4   공급유형                          2952 non-null   object \n",
            " 5   전용면적                          2952 non-null   float64\n",
            " 6   전용면적별세대수                      2952 non-null   int64  \n",
            " 7   공가수                           2952 non-null   float64\n",
            " 8   자격유형                          2952 non-null   object \n",
            " 9   임대보증금                         2383 non-null   object \n",
            " 10  임대료                           2383 non-null   object \n",
            " 11  도보 10분거리 내 지하철역 수(환승노선 수 반영)  2741 non-null   float64\n",
            " 12  도보 10분거리 내 버스정류장 수            2948 non-null   float64\n",
            " 13  단지내주차면수                       2952 non-null   float64\n",
            " 14  등록차량수                         2952 non-null   float64\n",
            "dtypes: float64(6), int64(2), object(7)\n",
            "memory usage: 346.1+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 178
        },
        "id": "76MFzs0nb0-q",
        "outputId": "41ef763e-d548-4a4d-bf78-98cbc076c70d"
      },
      "source": [
        "train.head(2)"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>단지코드</th>\n",
              "      <th>총세대수</th>\n",
              "      <th>임대건물구분</th>\n",
              "      <th>지역</th>\n",
              "      <th>공급유형</th>\n",
              "      <th>전용면적</th>\n",
              "      <th>전용면적별세대수</th>\n",
              "      <th>공가수</th>\n",
              "      <th>자격유형</th>\n",
              "      <th>임대보증금</th>\n",
              "      <th>임대료</th>\n",
              "      <th>도보 10분거리 내 지하철역 수(환승노선 수 반영)</th>\n",
              "      <th>도보 10분거리 내 버스정류장 수</th>\n",
              "      <th>단지내주차면수</th>\n",
              "      <th>등록차량수</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>C2483</td>\n",
              "      <td>900</td>\n",
              "      <td>아파트</td>\n",
              "      <td>경상북도</td>\n",
              "      <td>국민임대</td>\n",
              "      <td>39.72</td>\n",
              "      <td>134</td>\n",
              "      <td>38.0</td>\n",
              "      <td>A</td>\n",
              "      <td>15667000</td>\n",
              "      <td>103680</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1425.0</td>\n",
              "      <td>1015.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>C2483</td>\n",
              "      <td>900</td>\n",
              "      <td>아파트</td>\n",
              "      <td>경상북도</td>\n",
              "      <td>국민임대</td>\n",
              "      <td>39.72</td>\n",
              "      <td>15</td>\n",
              "      <td>38.0</td>\n",
              "      <td>A</td>\n",
              "      <td>15667000</td>\n",
              "      <td>103680</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1425.0</td>\n",
              "      <td>1015.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    단지코드  총세대수 임대건물구분  ... 도보 10분거리 내 버스정류장 수 단지내주차면수   등록차량수\n",
              "0  C2483   900    아파트  ...                3.0  1425.0  1015.0\n",
              "1  C2483   900    아파트  ...                3.0  1425.0  1015.0\n",
              "\n",
              "[2 rows x 15 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        },
        "id": "tviNfq12bwgB",
        "outputId": "49ad4d7e-86b5-4816-cd12-9e9df4fb20c0"
      },
      "source": [
        "train['임대보증금']=train['임대보증금'].astype(float)\n",
        "test['임대보증금']=test['임대보증금'].astype(float)\n",
        "\n",
        "train['임대료']=train['임대료'].astype(float)\n",
        "test['임대료']=test['임대료'].astype(float)"
      ],
      "execution_count": 218,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-218-e332ef03704a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtrain\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'임대보증금'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'임대보증금'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mtest\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'임대보증금'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtest\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'임대보증금'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mtrain\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'임대료'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'임대료'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mtest\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'임대료'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtest\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'임대료'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m   5546\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5547\u001b[0m             \u001b[0;31m# else, only a single dtype is given\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5548\u001b[0;31m             \u001b[0mnew_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mgr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5549\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_constructor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__finalize__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"astype\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5550\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m    602\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"raise\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    603\u001b[0m     ) -> \"BlockManager\":\n\u001b[0;32m--> 604\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"astype\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    605\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    606\u001b[0m     def convert(\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, f, align_keys, **kwargs)\u001b[0m\n\u001b[1;32m    407\u001b[0m                 \u001b[0mapplied\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    408\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 409\u001b[0;31m                 \u001b[0mapplied\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    410\u001b[0m             \u001b[0mresult_blocks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_extend_blocks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mapplied\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult_blocks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    411\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/internals/blocks.py\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m    593\u001b[0m             \u001b[0mvals1d\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    594\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 595\u001b[0;31m                 \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mastype_nansafe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvals1d\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    596\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mValueError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    597\u001b[0m                 \u001b[0;31m# e.g. astype_nansafe can fail on object-dtype of strings\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/dtypes/cast.py\u001b[0m in \u001b[0;36mastype_nansafe\u001b[0;34m(arr, dtype, copy, skipna)\u001b[0m\n\u001b[1;32m    995\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mcopy\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mis_object_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mis_object_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    996\u001b[0m         \u001b[0;31m# Explicit copy, or required since NumPy can't view from / to object.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 997\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0marr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    998\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    999\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0marr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mview\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: could not convert string to float: '-'"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UsSq0K0Ic-bI"
      },
      "source": [
        "엥.. 머선일.. -이게 왜 있는데\n",
        "'-' 이거 뭔데. 뭔가 숨어있나보다"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CZhvJNMzcrnW"
      },
      "source": [
        "train.loc[train.임대보증금=='-', '임대보증금'] = np.nan\n",
        "test.loc[test.임대보증금=='-', '임대보증금'] = np.nan\n",
        "train['임대보증금'] = train['임대보증금'].astype(float)\n",
        "test['임대보증금'] = test['임대보증금'].astype(float)\n",
        "train.loc[train.임대료=='-', '임대료'] = np.nan\n",
        "test.loc[test.임대료=='-', '임대료'] = np.nan\n",
        "train['임대료'] = train['임대료'].astype(float)\n",
        "test['임대료'] = test['임대료'].astype(float)"
      ],
      "execution_count": 219,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2Ko64Bq-dIEw",
        "outputId": "e0bff6b6-277c-4545-925a-1fd7ae514c4e"
      },
      "source": [
        "train.info()"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 2952 entries, 0 to 2951\n",
            "Data columns (total 15 columns):\n",
            " #   Column                        Non-Null Count  Dtype  \n",
            "---  ------                        --------------  -----  \n",
            " 0   단지코드                          2952 non-null   object \n",
            " 1   총세대수                          2952 non-null   int64  \n",
            " 2   임대건물구분                        2952 non-null   object \n",
            " 3   지역                            2952 non-null   object \n",
            " 4   공급유형                          2952 non-null   object \n",
            " 5   전용면적                          2952 non-null   float64\n",
            " 6   전용면적별세대수                      2952 non-null   int64  \n",
            " 7   공가수                           2952 non-null   float64\n",
            " 8   자격유형                          2952 non-null   object \n",
            " 9   임대보증금                         2371 non-null   float64\n",
            " 10  임대료                           2362 non-null   float64\n",
            " 11  도보 10분거리 내 지하철역 수(환승노선 수 반영)  2741 non-null   float64\n",
            " 12  도보 10분거리 내 버스정류장 수            2948 non-null   float64\n",
            " 13  단지내주차면수                       2952 non-null   float64\n",
            " 14  등록차량수                         2952 non-null   float64\n",
            "dtypes: float64(8), int64(2), object(5)\n",
            "memory usage: 346.1+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AELEslu3dNGP"
      },
      "source": [
        "good"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RVNMRKUXdMBs",
        "outputId": "600a1525-2aa8-4bf8-d346-080f07b1c73b"
      },
      "source": [
        "train['단지코드'].value_counts()"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "C1439    48\n",
              "C2132    47\n",
              "C1859    40\n",
              "C1790    33\n",
              "C2597    32\n",
              "         ..\n",
              "C2490     2\n",
              "C2188     1\n",
              "C2340     1\n",
              "C2520     1\n",
              "C2437     1\n",
              "Name: 단지코드, Length: 423, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B0vvC8ICdT0H"
      },
      "source": [
        "주어진 단지가 423개는 있나봄. 나중에 얘네끼리 묶어주던가 해야겠다. 어차피 같은 단지니까"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "yZ19Akc9dxG1",
        "outputId": "db8f176f-4742-447c-e00c-201ca3db47fb"
      },
      "source": [
        "\n",
        "\n",
        "sns.histplot(data=train,x='총세대수')"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb77231a250>"
            ]
          },
          "metadata": {},
          "execution_count": 79
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r43oELlRf-Z5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "4dbaa1ce-abad-4db1-92e1-e58b8dadbf80"
      },
      "source": [
        "sns.scatterplot(data=train,x='총세대수',y=\"등록차량수\")"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb772cf3bd0>"
            ]
          },
          "metadata": {},
          "execution_count": 64
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tkQUrvN9e8iC"
      },
      "source": [
        "아니 왜 한글 안되는데.. 뭘 더 해줘야하는데\n",
        "원하는대로 해줬자나!~~~~~~~~~~~~~~~~`\n",
        "여튼 500에서 1000사이가 가장 많다. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3rSPTrgGdRDV",
        "outputId": "81adc61b-d597-4cd9-935c-0953fcf4d6c3"
      },
      "source": [
        "train['임대건물구분'].value_counts()"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "아파트    2390\n",
              "상가      562\n",
              "Name: 임대건물구분, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yxFrX6jddhGt",
        "outputId": "74ec9ad3-fca4-4326-a7f8-d718330d7952"
      },
      "source": [
        "train['지역'].value_counts()"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "경기도        621\n",
              "경상남도       369\n",
              "부산광역시      328\n",
              "대전광역시      266\n",
              "강원도        187\n",
              "충청북도       163\n",
              "충청남도       158\n",
              "전라남도       151\n",
              "광주광역시      141\n",
              "경상북도       119\n",
              "전라북도       109\n",
              "대구광역시      102\n",
              "제주특별자치도     91\n",
              "서울특별시       64\n",
              "세종특별자치시     47\n",
              "울산광역시       36\n",
              "Name: 지역, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Gnngm63Tdmpw",
        "outputId": "03ed05e0-d33d-4b9b-9020-0fa4e244e637"
      },
      "source": [
        "train['공급유형'].value_counts()"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "국민임대         1758\n",
              "임대상가          562\n",
              "행복주택          213\n",
              "공공임대(10년)     205\n",
              "영구임대          152\n",
              "공공임대(50년)      31\n",
              "공공임대(분납)       12\n",
              "장기전세            9\n",
              "공공분양            7\n",
              "공공임대(5년)        3\n",
              "Name: 공급유형, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "zLDeVPGFdqpd",
        "outputId": "17e4fafe-2bbb-4cb1-ef35-c847180d3bec"
      },
      "source": [
        "sns.histplot(data=train,x='전용면적')"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb772c7f690>"
            ]
          },
          "metadata": {},
          "execution_count": 68
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D1RujCkMfkNu"
      },
      "source": [
        "굉장히 꼬리가 긴 데이터..이건 나중에 StandardScaler로 처리해버리던지 log로 바꿔버리던지 해야지 "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "gqY7lGwCx9oN",
        "outputId": "6ee829d7-de02-4992-bcde-e93b8b6a141b"
      },
      "source": [
        "sns.boxplot(data=train['전용면적'])"
      ],
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb7702ec6d0>"
            ]
          },
          "metadata": {},
          "execution_count": 93
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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j1LUB7O+qao+77meddVZ6zykAYNj2Kbj3L5H5eHqXvfzGgMum7qhPNXWHfdD5Wwf8fAD2werVq3Pqqafu1n/qqadm9erVI6gIYGnY1x33Q9K7tvzIJI9OeulSS/Ke/pzL+32/3z/+Zr/d7Zr0qlqV5OAk322tbU+S1tojSf5nkkP641M9v9/uds08APNj3bp1WbFixZPHK1asyLp160ZXEMASsK/B/bEkfzTDz1/153y5fzxxGc3N/fZV05zv1VPmZMrxXNYAME9WrlyZN7/5h0/hfctb3pKVK91qBDCflu/L4v6NqL8y3VhVvTe9G1avaq394aShK5NckOScqrpy4lnuVfWM/PCJNFNf3nRZkjcleXdVXT/xLPf+S5fOTu9/IK7cl+8CwNyceuqp2bFjx5O/AzC/9im4743W2qaqemeSDye5o6quTfJ4krVJDss0N7m21r5aVb+X5Nwkd1fVdUkOTPLvkzwzyX+Y/DInAObf2NhYTjvttFGXAbBkLHhwT5LW2keqanOS85Oclt4lO19PcmFr7aoZ1pxXVf8jvR32M5PsSnJXkv/SWvt/F6RwAAAYkXkL7q219yZ57x7Gb0xy4xzPuSHJhn0oCwAAOmlYL2ACAADmkeAOAAAdILgDAEAHCO4AANABgjsAAHSA4A4AAB0guAMAQAcI7gAA0AGCOwAAdIDgDgAAHSC4AwBABwjuAADQAYI7AAB0gOAOAAAdILgDAEAHCO4AANABgjsAAHSA4A4AAB0guAMAQAcI7gAA0AGCOwAAdIDgDgAAHSC4AwBABwjuAADQAYI7AAB0gOAOAAAdILgDAEAHCO4AANABgjsAAHSA4A4AAB0guAMAQAcI7gAA0AGCOwAAdIDgDgAAHSC4AwBABwjuAADQAYI7AAB0gOAOAAAdILgDAEAHCO4AANABy0ddAADdND4+nk996lNJkje84Q0ZGxsbcUUAi5vgDsBeuf7663PFFVckSVasWJG1a9eOuCKAxc2lMgDM2datW3PVVVc9ebxhw4Zs3bp1hBUBLH6COwBztmHDhmzbtu3J423btmXDhg2jKwhgCRDcAZiT++67LzfccMNu/TfccEM2bdo0gooAlgbBHYCBtdayfv367Nq1a7exXbt2Zf369WmtjaAygMVPcAdgYLfeemvuuOOOGcdvv/32fO1rX1vAigCWDsEdgIGMj4/n0ksvnXXe+vXrMz4+vgAVASwtgjsAAHSA4A7AQMbGxvL2t7991nlnnXWWlzEBzAPBHYCBHXfccVmzZs2M48ccc0yOPfbYBawIYOkQ3AEYWFXl7LPPzrJlu//1sWzZspx11lmpqhFUBrD4Ce4AzMnq1atzyimn7NZ/yimnZPXq1SOoCGBpENwBmLN169blkEMOefL4kEMOybp160ZXEMASMJTgXlX/uaq+VFXfqaodVfVgVf1VVb2nqn5khjXHV9VN/bk7quruqnpHVR2wh885uao2VtVDVbWtqm6rqtOH8R0AGNzKlStz+uk//ON33bp1Wbly5QgrAlj8lg/pPL+e5K4kX0zyT0kOTnJskvcmObOqjm2tfWdiclW9Nsmnkzya5NokDyZ5TZKLk7wkyS9O/YCqOifJR5I8kOTqJI8nWZtkQ1X9VGvt/CF9FwAGcOqpp2bHjh1P/g7A/KphvJq6qp7SWnt0mv7fSfKuJJe21s7q9z09ybeSHJrkJa21OybOkeTmJMcleUNr7ZpJ5zkiyTeSPJLk6Nba5n7/M5LcnuR5SY5vrd26D9/hzqOOOuqoO++8c29PAQAAszr66KNz11133dVaO3ou64Zyqcx0ob3v/+63z5/UtzbJjya5ZiK0TzrHhf3DqQ8KfkuSg5JcMhHa+2u+n+T9/cO37VXxAADQAfN9c+pr+u3dk/pO7Lefn2b+LUm2Jzm+qg4acM3npswBAIBFZ1jXuCdJqur8JIekdxnMmiT/e3qh/QOTpr2w3947dX1rbWdVbUryE0mem+SeAdZsqapHkhxWVStaa9tnqXGma2FetKd1AAAwSkMN7knOT/KcScefT7KutfbPk/oO7bcPzXCOif7JjycYZM3B/Xl7DO4AANBFQw3urbUfS5Kqek6S49Pbaf+rqjq5tXbXMD9rb810E0B/J/6oBS4HAAAGMi/XuLfW/rG19pkkr0zyI0k+Pml4Ytf80N0W/sv+rXuxZqYdeQAA6LR5vTm1tfb3Sb6e5Ceq6ln97m/22xdMnV9Vy5OsTrIzyX2Thva0ZlV6l8l8d7br2wEAoKvm+6kySfLj/faJfntzv33VNHNflmRFkq+21h6b1L+nNa+eMgcAABadfQ7uVfWCqtrtEpaqWtZ/AdOz0wvi3+8PXZfk/iSvr6o1k+Y/Jcn7+oeXTjndlUkeS3JO/2VME2uekd4LnpLksn39LgAAsL8axs2pJyX53ar6cpJNSR5I78kyL0/vkY7fS3LGxOTW2sNVdUZ6AX5jVV2T5MEkp6T32Mfrklw7+QNaa5uq6p1JPpzkjqq6Nsnj6b3M6bAkH9qXt6YCAMD+bhjB/U+T/K/pPbP9Z9J7jOMj6T1z/RNJPtxae3Dygtba9VX18iTvTvK6JE9J8q0k5/bnt6kf0lr7SFVtTu+Rk6el968FX09yYWvtqiF8DwAA2G/tc3Bvrf1NknP2Yt1X0tutn8uaG5PcONfPAgCArluIm1MBAIB9JLgDAEAHCO4AANABgjsAAHSA4A4AAB0guAMAQAcI7gAA0AGCOwAAdIDgDgAAHSC4AwBABwjuAADQAYI7AAB0gOAOAAAdILgDAEAHCO4AANABgjsAAHSA4A4AAB0guAMAQAcI7gDsle3bt+fcc8/Nueeem+3bt4+6HIBFT3AHYK+8//3vz1133ZW77rorv/u7vzvqcgAWPcEdgDn79re/nS9/+ctPHv/FX/xFvv3tb4+wIoDFT3AHYM7e8573DNQHwPAI7gDMyS233JJNmzbt1r9p06Z/sQsPwHAJ7gAMbNeuXbnoootmHP/gBz+YXbt2LWBFAEuH4A7AwDZs2JCHH354xvGHH344V1111QJWBLB0CO4ADGT79u355Cc/Oeu8P/7jP/Z4SIB5ILgDAEAHCO4ADGTFihV54xvfOOu8X/qlX8qKFSsWoCKApUVwB2Bg69atywEHHDDj+AEHHJDTTz99ASsCWDoEdwAGtnPnzjzxxBMzjj/xxBPZuXPnAlYEsHQI7gAM7Jd/+ZdnnfOmN71pASoBWHoEdwAG8p3vfCf/9E//NOu8f/zHf8x3vvOdBagIYGkR3AEAoAMEdwAGcvjhh+fZz372rPOe85zn5PDDD1+AigCWFsEdgIFdffXVs875xCc+sQCVACw9gjsAAzvwwAPz0pe+dMbxl770pTnwwAMXsCKApUNwB2BOfvu3f3uvxgDYN4I7AHN2/vnnD9QHwPAI7gDM2cknn5yxsbEnj8fGxnLyySePsCKAxU9wB2CvXHHFFTnggANywAEHuCEVYAEsH3UBAHTT4Ycfni996UujLgNgybDjDgAAHSC4AwBABwjuAADQAYI7AAB0gOAOAAAdILgDAEAHCO4AANABgjsAAHSA4A4AAB0guAMAQAcI7gAA0AGCOwAAdIDgDgAAHbDPwb2qfqSqfqWqPlNV36qqHVX1UFV9uareWlXTfkZVHV9VN1XVg/01d1fVO6rqgD181slVtbF//m1VdVtVnb6v3wEAAPZ3y4dwjl9McmmSLUn+LMm3kzwnyb9L8odJXl1Vv9haaxMLquq1ST6d5NEk1yZ5MMlrklyc5CX9c/4LVXVOko8keSDJ1UkeT7I2yYaq+qnW2vlD+C4AALBfGkZwvzfJKUn+W2tt10RnVb0ryV8meV16If7T/f6nJ7k8yRNJTmit3dHv/40kNydZW1Wvb61dM+lcRyS5KL2Av6a1trnf/1tJbk9yXlV9urV26xC+DwAA7Hf2+VKZ1trNrbUbJ4f2fv/3klzWPzxh0tDaJD+a5JqJ0N6f/2iSC/uHb5/yMW9JclCSSyZCe3/N95O8v3/4tn37JgAAsP+a75tTx/vtzkl9J/bbz08z/5Yk25McX1UHDbjmc1PmAADAojOMS2WmVVXLk5zWP5wcuF/Yb++duqa1trOqNiX5iSTPTXLPAGu2VNUjSQ6rqhWtte2z1HXnDEMv2tM6AAAYpfnccf9Akp9MclNr7U8m9R/abx+aYd1E/8q9WHPoDOMAANBp87LjXlW/luS8JN9I8qb5+Iy91Vo7err+/k78UQtcDgAADGToO+79xzb+QZKvJ/n51tqDU6bMtjs+0b91L9bMtCMPAACdNtTgXlXvSO9Z63+TXmj/3jTTvtlvXzDN+uVJVqd3M+t9A65ZleTgJN+d7fp2AADoqqEF96r6P9N7gdJ/Ty+0/9MMU2/ut6+aZuxlSVYk+Wpr7bEB17x6yhwAAFh0hhLc+y9P+kCSO5P8Qmvt/j1Mvy7J/UleX1VrJp3jKUne1z+8dMqaK5M8luSc/suYJtY8I8m7+oeXBQAAFql9vjm1qk5P8lvpvQn1L5L8WlVNnba5tbYhSVprD1fVGekF+I1VdU16b0Q9Jb3HPl6X5NrJi1trm6rqnUk+nOSOqro2yePpvczpsCQf8tZUAAAWs2E8VWZ1vz0gyTtmmPPnSTZMHLTWrq+qlyd5d5LXJXlKkm8lOTfJh1trbeoJWmsfqarNSc5P7/nwy9K7AfbC1tpVQ/geAACw39rn4N5ae2+S9+7Fuq8kOWmOa25McuNcPwsAALpuPl/ABAAADIngDgAAHSC4AwBABwjuAADQAYI7AAB0gOAOAAAdILgDAEAHCO4AANABgjsAAHSA4A4AAB0guAMAQAcI7gAA0AGCOwAAdIDgDgAAHSC4AwBABwjuAADQAYI7AAB0gOAOAAAdILgDAEAHCO4AANABgjsAAHSA4A4AAB0guAMAQAcI7gAA0AGCOwAAdIDgDgAAHSC4A7BX7r///px88sk5+eSTc//994+6HIBFT3AHYK+ceeaZ2bZtW7Zt25Zf/dVfHXU5AIue4A7AnN1222158MEHnzx+4IEHctttt42wIoDFT3AHYM4uvPDCgfoAGB7BHYA5ueyyyzI+Pr5b//j4eD72sY+NoCKApUFwB2BgO3fuzDXXXDPj+Cc/+cns3LlzASsCWDoEdwAGdu65584657zzzluASgCWHsEdgIHcf//9ufvuu2ed99d//dceDwkwDwR3AADoAMEdgIE861nPyotf/OJZ5/30T/90nvWsZy1ARQBLi+AOwMB+7/d+b9Y5H/rQhxagEoClR3AHYGDLly/PG9/4xhnH3/jGN2b58uULWBHA0iG4AzAnk9+YOtXWrVsXsBKApUVwB2Bg4+Pj+fznPz/j+E033TTty5kA2HeCOwADO+OMM2adc+aZZy5AJQBLj+AOwEC2bNmSzZs3zzpv06ZN2bJly/wXBLDECO4AANABgjsAA1m1alWOOOKIWeetXr06q1atmv+CAJYYwR2AgV1++eWzzvnYxz62AJUALD2COwADGxsby0knnTTj+EknnZSxsbEFrAhg6RDcAZiTCy64IMuW7f7Xx7Jly3LBBReMoCKApUFwB2DOLrzwwoH6ABgewR2AOTvqqKMG6gNgeAR3AOZsw4YNA/UBMDyCOwBzct999+WGG27Yrf+GG27Ipk2bRlARwNIguAMwsNZa1q9fn127du02tmvXrqxfvz6ttRFUBrD4Ce4ADOzWW2/NHXfcMeP47bffnq997WsLWBHA0iG4AzCQ8fHxXHrppbPOW79+fcbHxxegIoClZSjBvarWVtVHquovqurhqmpVdfUsa46vqpuq6sGq2lFVd1fVO6rqgD2sObmqNlbVQ1W1rapuq6rTh/EdAABgfzasHfcLk5yT5F8n+Z+zTa6q1ya5JcnLknwmySVJDkxycZJrZlhzTpIbk/xkkquTXJ7kx5NsqKqL9v0rALAnY2Njefvb3z7rvLPOOsvbUwHmwbCC+68neUGSpyfZ45/qVfX09EL3E0lOaK29tbX2zvRC/61J1lbV66esOSLJRUkeTLKmtXZ2a+3Xk7w4yd8lOa+qjhvSdwFgBscdd1zWrFkz4/gxxxyTY489dgErAlg6hhLcW2t/1jmd/o4AAAjQSURBVFr72zbYowTWJvnRJNe01p68w6m19mh6O/fJ7uH/LUkOSnJJa23zpDXfT/L+/uHb9rJ8AAZUVTn77LOzbNnuf30sW7YsZ511VqpqBJUBLH6juDn1xH77+WnGbkmyPcnxVXXQgGs+N2UOAPNo9erVOeWUU3brP+WUU7J69eoRVASwNIwiuL+w3947daC1tjPJpiTLkzx3wDVbkjyS5LCqWjHbh1fVndP9JHnRHL8HwJK1bt26HHLIIU8eH3LIIVm3bt3oCgJYAkYR3A/ttw/NMD7Rv3Iv1hw6wzgAQ7Ry5cqcfvoPH+q1bt26rFy5cg8rANhXy0ddwEJrrR09XX9/1/2oBS4HoLNOPfXU7Nix48nfAZhfowjus+2OT/RvnbLmWf2xB/awZqYdeQCGbGxsLKeddtqoywBYMkYR3L+ZZE16j4+8c/JAVS1PsjrJziT3TVnzrP6aW6esWZXk4CTfba1tn7+yAZjsNa95TX7wgx8kSZ72tKflxhtvHHFFAIvbKK5xv7nfvmqasZclWZHkq621xwZc8+opcwBYABOhfervAMyPUQT365Lcn+T1VfXkWzyq6ilJ3tc/vHTKmiuTPJbknP7LmCbWPCPJu/qHl81TvQBMccIJJwzUB8DwDOVSmao6NcnEnUk/1m+Pq6oN/d/vb62dnySttYer6oz0AvzGqromvTeinpLeYx+vS3Lt5PO31jZV1TuTfDjJHVV1bZLH03uZ02FJPtRa+xeX0AAAwGIyrGvc/3WS06f0PTc/fBb73yc5f2KgtXZ9Vb08ybuTvC7JU5J8K8m5ST483RtYW2sfqarN/fOclt6/Fnw9yYWttauG9D0AmMWedtZPOOGEbNy4ccFqAVhKhhLcW2vvTfLeOa75SpKT5rjmxiTufgIYkc9+9rMDzXnta1+7ANUALC2juMYdgI66+OKLhzIHgLkT3AEYyGte85p5mQvAYAR3AAYyze1HQ5kLwGAEdwAG8pnPfGZe5gIwmFG8ORXoIM/oZi5e8YpXjLoE9hOeMgTDY8cdAAA6QHAHAIAOcKkMMBD/3M1kM1065b8TgPljxx2AObv++usH6gNgeAR3AOZs5cqVA/UBMDyCOwB75Ytf/OKoSwBYUgR3APbK2NjYqEsAWFIEdwAA6ADBHQAAOkBwBwCADhDcAQCgAwR3AADoAMEdAAA6QHAHAIAOENwBAKADBHcAAOgAwR0AADpAcAcAgA4Q3AEAoAOWj7oA9j8nnHDCqEsAOsifHcCgNm7cOOoSOsmOOwAAdIDgDgAAHeBSGfboB8e8ZdQlAACLwNNuv2LUJXSeHXcAAOgAwR0AADpAcAcAgA4Q3AEAoAPcnMoeuZEEAGD/YMcdAAA6QHAHAIAOcKkMe+Q57gDAMLj8dt/ZcQcAgA6w484e+b9jAID9gx13AADoAMEdAAA6wKUy7Gbjxo2jLgHoiBNOOOHJ3/3ZATC/7LgDAEAHCO4AANABgjsAAHSA4A4AAB0guAMAQAcI7gAA0AGCOwAAdIDgDgAAHSC4AwBABwjuAADQActHXQDQDZNfbQ/T8d8I09m4ceOoS4BFw447AAB0gOAOAAAd4FIZYCD+uRsARqtTO+5VdVhVXVFV/1BVj1XV5qr6/ap6xqhrAwCA+dSZHfeqel6SryZ5dpLPJvlGkp9N8h+TvKqqXtJae2CEJQIAwLzp0o77+vRC+6+11k5trf2n1tqJSS5O8sIkvzPS6gAAYB51Irj3d9tfmWRzko9OGX5PkkeSvKmqDl7g0gAAYEF0Irgn+fl++4XW2q7JA621HyT5SpIVSY5d6MIAAGAhdOUa9xf223tnGP/b9HbkX5DkS3s6UVXdOcPQi/auNAAAmH9d2XE/tN8+NMP4RP/KBagFAAAWXFd23IemtXb0dP39nfijFrgcAAAYSFd23Cd21A+dYXyif+sC1AIAAAuuK8H9m/32BTOMP7/fznQNPAAAdFpXgvuf9dtXVtW/qLmqnpbkJUm2J/naQhcGAAALoRPBvbX2d0m+kOSIJGdPGf7NJAcn+URr7ZEFLg0AABZEl25OPSvJV5N8uKp+Ick9SX4uvWe835vk3SOsDQAA5lUndtyTJ3fd1yTZkF5gPy/J85L8QZJjW2sPjK46AACYX13acU9r7TtJ3jzqOgAAYKF1ZscdAACWsmqtjbqG/UJVPfDUpz71mUceeeSoSwEAYBG75557smPHjgdbaz8yl3WCe19VbUry9CSbR1wKQJe8qN9+Y6RVAHTLEUkebq2tnssiwR2AvVZVdyZJa+3oUdcCsNi5xh0AADpAcAcAgA4Q3AEAoAMEdwAA6ADBHQAAOsBTZQAAoAPsuAMAQAcI7gAA0AGCOwAAdIDgDgAAHSC4AwBABwjuAADQAYI7AAB0gOAOwJxV1WFVdUVV/UNVPVZVm6vq96vqGaOuDWCx8gImAOakqp6X5KtJnp3ks0m+keRnk/x8km8meUlr7YHRVQiwONlxB2Cu1qcX2n+ttXZqa+0/tdZOTHJxkhcm+Z2RVgewSNlxB2Bg/d32byXZnOR5rbVdk8aelmRLkkry7NbaIyMpEmCRsuMOwFz8fL/9wuTQniSttR8k+UqSFUmOXejCABY7wR2AuXhhv713hvG/7bcvWIBaAJYUwR2AuTi03z40w/hE/8oFqAVgSRHcAQCgAwR3AOZiYkf90BnGJ/q3LkAtAEuK4A7AXHyz3850Dfvz++1M18ADsJc8DhKAgXkcJMDo2HEHYGCttb9L8oUkRyQ5e8rwbyY5OMknhHaA4bPjDsCc9Hfdv5re21M/m+SeJD+X3jPe701yfGvtgdFVCLA4Ce4AzFlVHZ7kt5K8KsmPpHeJzGeS/GZr7fujrA1gsRLcAQCgA1zjDgAAHSC4AwBABwjuAADQAYI7AAB0gOAOAAAdILgDAEAHCO4AANABgjsAAHSA4A4AAB0guAMAQAcI7gAA0AGCOwAAdIDgDgAAHSC4AwBABwjuAADQAYI7AAB0gOAOAAAd8P8DpUlWTWS0vqsAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "image/png": {
              "width": 375,
              "height": 248
            },
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "78r_qUVffi61",
        "outputId": "250b1268-9871-4468-945a-becb396b1f9c"
      },
      "source": [
        "sns.histplot(data=train,x='공가수')"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb772c7f490>"
            ]
          },
          "metadata": {},
          "execution_count": 69
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vk9FaW6tfwxZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "3682ea05-d442-4ccc-dc05-9516453e75b3"
      },
      "source": [
        "sns.histplot(data=train,x='전용면적별세대수')"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb7728e4c90>"
            ]
          },
          "metadata": {},
          "execution_count": 70
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "4yvABXepyDJM",
        "outputId": "9a76e709-fc99-4b1e-80fb-56e57fd85491"
      },
      "source": [
        "sns.boxplot(data=train['전용면적별세대수'])"
      ],
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb7702d1f90>"
            ]
          },
          "metadata": {},
          "execution_count": 94
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "image/png": {
              "width": 381,
              "height": 248
            },
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zYaLruC-qgwl",
        "outputId": "39717bd8-f7f2-49d0-abe9-e2a7db1a4b72"
      },
      "source": [
        "train['자격유형'].value_counts()"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "A    1801\n",
              "D     569\n",
              "H     155\n",
              "J     114\n",
              "C      95\n",
              "I      49\n",
              "E      37\n",
              "K      33\n",
              "L      33\n",
              "N      30\n",
              "B      21\n",
              "G       9\n",
              "F       3\n",
              "M       2\n",
              "O       1\n",
              "Name: 자격유형, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "llZ2nmjHLxMk",
        "outputId": "9df259e5-fcfd-4282-f1dc-cd0d715f2879"
      },
      "source": [
        "test['자격유형'].value_counts()"
      ],
      "execution_count": 168,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "A    572\n",
              "D    180\n",
              "H     92\n",
              "J     84\n",
              "C     34\n",
              "K     16\n",
              "L     12\n",
              "N     10\n",
              "E     10\n",
              "I      7\n",
              "M      2\n",
              "G      1\n",
              "Name: 자격유형, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 168
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "9GjTVx5sqoja",
        "outputId": "7b9e8c9c-4f19-4068-ef02-a9c92ce68b0b"
      },
      "source": [
        "sns.histplot(data=train,x='임대보증금')"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb772740490>"
            ]
          },
          "metadata": {},
          "execution_count": 72
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "PXb72_ZwrXHR",
        "outputId": "17e22789-a8a6-42ef-8ca9-aeea61608a04"
      },
      "source": [
        "sns.histplot(data=train,x='임대료')"
      ],
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb772609250>"
            ]
          },
          "metadata": {},
          "execution_count": 73
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "Geah7CYDraxk",
        "outputId": "822c64a3-62ac-47f0-fbae-4e7a87e5cd8b"
      },
      "source": [
        "sns.histplot(data=train,x='도보 10분거리 내 지하철역 수(환승노선 수 반영)')"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb772a33b50>"
            ]
          },
          "metadata": {},
          "execution_count": 74
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q__TpCJmrjIV"
      },
      "source": [
        "컬럼명이 너무 길고 빡쳐서 안되겠다. 바꿔주기로함"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q_R5NjaBrhim"
      },
      "source": [
        "train=train.rename(columns={'도보 10분거리 내 지하철역 수(환승노선 수 반영)':'지하철'})\n",
        "train=train.rename(columns={'도보 10분거리 내 버스정류장 수':'버스'})\n",
        "test=test.rename(columns={'도보 10분거리 내 지하철역 수(환승노선 수 반영)':'지하철'})\n",
        "test=test.rename(columns={'도보 10분거리 내 버스정류장 수':'버스'})"
      ],
      "execution_count": 220,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bLi8LF50sNpF",
        "outputId": "e64482c3-4e02-4c70-818e-a534bb8d7c16"
      },
      "source": [
        "train.columns"
      ],
      "execution_count": 125,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['단지코드', '총세대수', '임대건물구분', '지역', '공급유형', '전용면적', '전용면적별세대수', '공가수',\n",
              "       '자격유형', '임대보증금', '임대료', '지하철', '버스', '단지내주차면수', '등록차량수'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 125
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "5oEVVGlXsQ-3",
        "outputId": "08be64df-a8fa-48bc-f29f-877b71e24385"
      },
      "source": [
        "sns.histplot(data=train,x='버스')"
      ],
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb77b6ea4d0>"
            ]
          },
          "metadata": {},
          "execution_count": 80
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "4JTs8Nb-secy",
        "outputId": "399ac601-07e0-4763-ec02-380bbf8658f4"
      },
      "source": [
        "sns.histplot(data=train,x='단지내주차면수')"
      ],
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb77211e3d0>"
            ]
          },
          "metadata": {},
          "execution_count": 81
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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gSYfTIu+iMhQk6XBa5F1Udh9JkjqGgiSpY/eRpOmwyPvijxSGgqTpMGJf/DT1wx9J7D6SJHUMBUlSx1CQJHUMBUlSZ+pCIcl5SR5LsiXJ1ZOuR5KWkqkKhSRHA/8FeBdwGnBJktMmW5UkLR1TFQrAWcCWqnqyqv4GuAlYO+GaJGnJSFVNuoZOkvcC51XVb7TX7wP+cVVdObTMemB9e/lTwGMj/pgTgb8+DOWOw2KpdbHUCdbah8VSJ1jrPj9eVcvnmrHoTl6rqg3AhkNdP8mmqlpzGEvqzWKpdbHUCdbah8VSJ1jrQkxb99F2YHbo9crWJkkag2kLhb8ATk1ySpLXAhcDd0y4JklaMqaq+6iq9iS5EvgScDRwfVU9fJh/zCF3PU3AYql1sdQJ1tqHxVInWOtBTdWBZknSZE1b95EkaYIMBUlSZ0mFwjRdQiPJbJK7kzyS5OEkH2jt1yTZnmRze5w/tM6HWu2PJXnnmOt9KsmDraZNre2EJHcmebw9H9/ak+STrdYHkpw5php/ami7bU7yYpKrpmWbJrk+ya4kDw21jbwNk6xryz+eZN0Ya/2PSb7V6rktyXGtfXWS/zO0fT81tM4/av9utrTPc1jvonOAOkf+fY/ju+EAtd48VOdTSTa39oltU6pqSTwYHLh+Angj8Frgm8BpE6znJODMNn0s8FcMLu1xDfCv51j+tFbzMcAp7bMcPcZ6nwJO3K/tPwBXt+mrgY+26fOB/wEEOBu4d0K/72eBH5+WbQr8PHAm8NChbkPgBODJ9nx8mz5+TLW+A1jWpj86VOvq4eX2e59vtPrTPs+7xlDnSL/vcX03zFXrfvM/BvzbSW/TpbSnMFWX0KiqHVV1f5v+PvAoMDPPKmuBm6rqpar6NrCFwWeapLXAxja9EbhgqP2GGrgHOC7JSWOu7Vzgiap6ep5lxrpNq+prwPNz1DDKNnwncGdVPV9VLwB3AueNo9aq+nJV7Wkv72FwHtEBtXrfUFX31ODb7AZe+Xy91TmPA/2+x/LdMF+t7a/9C4HPzfce49imSykUZoCtQ6+3Mf+X8NgkWQ2cAdzbmq5su+jX7+tOYPL1F/DlJPdlcKkRgBVVtaNNPwusaNOTrhUG57gM/webxm0Ko2/DaagZ4NcZ/JW6zylJ/jLJV5O8tbXNMKhvn3HWOsrvexq26VuBnVX1+FDbRLbpUgqFqZTk9cDngauq6kXgWuAngNOBHQx2KafBW6rqTAZXsL0iyc8Pz2x/tUzF+OYMTnx8N/DHrWlat+kPmaZtOJ8kHwb2ADe2ph3Aqqo6A/hN4I+SvGFS9bFIft/7uYQf/iNmYtt0KYXC1F1CI8lrGATCjVV1K0BV7ayql6tqL/AZXunOmGj9VbW9Pe8Cbmt17dzXLdSed01DrQyC6/6q2gnTu02bUbfhRGtO8s+AXwR+rYUYrTvmuTZ9H4P++Te1uoa7mMZS6yH8vie9TZcBvwzcvK9tktt0KYXCVF1Co/UhXgc8WlUfH2of7nt/D7BvpMIdwMVJjklyCnAqgwNO46j1dUmO3TfN4IDjQ62mfaNf1gG3D9V6aRtBczbwvaEuknH4ob+6pnGbDhl1G34JeEeS41u3yDtaW++SnAf8G+DdVfWDofblGdwLhSRvZLAdn2z1vpjk7Pbv/dKhz9dnnaP+vif93fBPgG9VVdctNNFteriPsE/zg8GIjr9ikLofnnAtb2HQVfAAsLk9zgf+EHiwtd8BnDS0zodb7Y9xmEccHKTWNzIYkfFN4OF92w74MeAu4HHgK8AJrT0Mbpb0RPssa8ZY6+uA54AfHWqbim3KIKh2AP+PQV/wZYeyDRn0529pj/ePsdYtDPre9/17/VRb9lfav4vNwP3ALw29zxoGX8pPAH9Au4pCz3WO/Psex3fDXLW29s8C/2K/ZSe2Tb3MhSSps5S6jyRJB2EoSJI6hoIkqWMoSJI6hoIkqWMoSJI6U3U7TmlSklzD4MqT+y74tozBRd/mamMS7VV1zaF8NmkUhoL0iour6rsAGdwr4KoDtB1o2XG0S72y+0iS1DEUJEkdQ0GS1DEUJEkdQ0GS1DEUJEkdh6RKA7uAG5Lsba+PAr54gDYm2C71yvspSJI6dh9JkjqGgiSpYyhIkjqGgiSpYyhIkjr/H0k1HNawwJP9AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "id": "ZQeKO8saslRd",
        "outputId": "86cf165b-5628-4297-e7b1-defcf4823909"
      },
      "source": [
        "corr=train.corr()\n",
        "corr"
      ],
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>총세대수</th>\n",
              "      <th>전용면적</th>\n",
              "      <th>전용면적별세대수</th>\n",
              "      <th>공가수</th>\n",
              "      <th>임대보증금</th>\n",
              "      <th>임대료</th>\n",
              "      <th>지하철</th>\n",
              "      <th>버스</th>\n",
              "      <th>단지내주차면수</th>\n",
              "      <th>등록차량수</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>총세대수</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.042271</td>\n",
              "      <td>0.098205</td>\n",
              "      <td>0.068192</td>\n",
              "      <td>0.075493</td>\n",
              "      <td>0.099606</td>\n",
              "      <td>0.213843</td>\n",
              "      <td>-0.001446</td>\n",
              "      <td>0.501488</td>\n",
              "      <td>0.316703</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>전용면적</th>\n",
              "      <td>0.042271</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.035479</td>\n",
              "      <td>-0.089201</td>\n",
              "      <td>0.490950</td>\n",
              "      <td>0.740591</td>\n",
              "      <td>-0.013111</td>\n",
              "      <td>-0.007866</td>\n",
              "      <td>0.071260</td>\n",
              "      <td>0.115453</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>전용면적별세대수</th>\n",
              "      <td>0.098205</td>\n",
              "      <td>-0.035479</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.166472</td>\n",
              "      <td>-0.099252</td>\n",
              "      <td>-0.057759</td>\n",
              "      <td>-0.056680</td>\n",
              "      <td>0.047112</td>\n",
              "      <td>0.281989</td>\n",
              "      <td>0.247085</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>공가수</th>\n",
              "      <td>0.068192</td>\n",
              "      <td>-0.089201</td>\n",
              "      <td>0.166472</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.030728</td>\n",
              "      <td>-0.194322</td>\n",
              "      <td>-0.093334</td>\n",
              "      <td>0.043285</td>\n",
              "      <td>0.289819</td>\n",
              "      <td>0.118121</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>임대보증금</th>\n",
              "      <td>0.075493</td>\n",
              "      <td>0.490950</td>\n",
              "      <td>-0.099252</td>\n",
              "      <td>0.030728</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.557144</td>\n",
              "      <td>0.027113</td>\n",
              "      <td>0.110757</td>\n",
              "      <td>0.270697</td>\n",
              "      <td>0.311469</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>임대료</th>\n",
              "      <td>0.099606</td>\n",
              "      <td>0.740591</td>\n",
              "      <td>-0.057759</td>\n",
              "      <td>-0.194322</td>\n",
              "      <td>0.557144</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.071571</td>\n",
              "      <td>0.044654</td>\n",
              "      <td>0.279785</td>\n",
              "      <td>0.416536</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>지하철</th>\n",
              "      <td>0.213843</td>\n",
              "      <td>-0.013111</td>\n",
              "      <td>-0.056680</td>\n",
              "      <td>-0.093334</td>\n",
              "      <td>0.027113</td>\n",
              "      <td>0.071571</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.098380</td>\n",
              "      <td>-0.055544</td>\n",
              "      <td>-0.108592</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>버스</th>\n",
              "      <td>-0.001446</td>\n",
              "      <td>-0.007866</td>\n",
              "      <td>0.047112</td>\n",
              "      <td>0.043285</td>\n",
              "      <td>0.110757</td>\n",
              "      <td>0.044654</td>\n",
              "      <td>0.098380</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.070696</td>\n",
              "      <td>0.103752</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>단지내주차면수</th>\n",
              "      <td>0.501488</td>\n",
              "      <td>0.071260</td>\n",
              "      <td>0.281989</td>\n",
              "      <td>0.289819</td>\n",
              "      <td>0.270697</td>\n",
              "      <td>0.279785</td>\n",
              "      <td>-0.055544</td>\n",
              "      <td>0.070696</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.859216</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>등록차량수</th>\n",
              "      <td>0.316703</td>\n",
              "      <td>0.115453</td>\n",
              "      <td>0.247085</td>\n",
              "      <td>0.118121</td>\n",
              "      <td>0.311469</td>\n",
              "      <td>0.416536</td>\n",
              "      <td>-0.108592</td>\n",
              "      <td>0.103752</td>\n",
              "      <td>0.859216</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              총세대수      전용면적  전용면적별세대수  ...        버스   단지내주차면수     등록차량수\n",
              "총세대수      1.000000  0.042271  0.098205  ... -0.001446  0.501488  0.316703\n",
              "전용면적      0.042271  1.000000 -0.035479  ... -0.007866  0.071260  0.115453\n",
              "전용면적별세대수  0.098205 -0.035479  1.000000  ...  0.047112  0.281989  0.247085\n",
              "공가수       0.068192 -0.089201  0.166472  ...  0.043285  0.289819  0.118121\n",
              "임대보증금     0.075493  0.490950 -0.099252  ...  0.110757  0.270697  0.311469\n",
              "임대료       0.099606  0.740591 -0.057759  ...  0.044654  0.279785  0.416536\n",
              "지하철       0.213843 -0.013111 -0.056680  ...  0.098380 -0.055544 -0.108592\n",
              "버스       -0.001446 -0.007866  0.047112  ...  1.000000  0.070696  0.103752\n",
              "단지내주차면수   0.501488  0.071260  0.281989  ...  0.070696  1.000000  0.859216\n",
              "등록차량수     0.316703  0.115453  0.247085  ...  0.103752  0.859216  1.000000\n",
              "\n",
              "[10 rows x 10 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6sH168Y5vcSI"
      },
      "source": [
        "히트맵그렸더니 한글이 안보여서 어쩔수 없이 시각화는 안하고 이렇게 표로 확인 함. 등록 차량 수랑 관계가 있어보이는 피처값은 총세대수, 임대료, 임대보증금, 단지내주차면수. 단지내주차면수가 가장 상관성이 크다."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "HjC6DkgWt-DW",
        "outputId": "e36b0a6a-adee-4ec4-b31c-2abf8fcd55b3"
      },
      "source": [
        "sns.jointplot(data=train,x=\"단지내주차면수\",y=\"등록차량수\")"
      ],
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.JointGrid at 0x7fb7704b1290>"
            ]
          },
          "metadata": {},
          "execution_count": 90
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x432 with 3 Axes>"
            ]
          },
          "metadata": {
            "image/png": {
              "width": 434,
              "height": 423
            },
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "31sh4Eyiw_jE"
      },
      "source": [
        "# 전처리"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8qYcSVb5xHEk"
      },
      "source": [
        "데이터를 어떻게 문석할까 생각을 해봤는데 지역끼리 묶을지, 단지끼리 묶을지 고민을 많이 했다. 아무래도 새로운 데이터를 만들어서 분석에 이용해야할 것 같기도..?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f_SYU_u90tdD",
        "outputId": "997f8c4e-c0ae-455e-f54a-012c91964f4b"
      },
      "source": [
        "train.columns"
      ],
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['단지코드', '총세대수', '임대건물구분', '지역', '공급유형', '전용면적', '전용면적별세대수', '공가수',\n",
              "       '자격유형', '임대보증금', '임대료', '지하철', '버스', '단지내주차면수', '등록차량수'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "cvSWjXrb4VUA",
        "outputId": "ccb52e88-aeba-4379-f127-339b79c750ee"
      },
      "source": [
        "train.groupby('단지코드').mean()"
      ],
      "execution_count": 221,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>총세대수</th>\n",
              "      <th>전용면적</th>\n",
              "      <th>전용면적별세대수</th>\n",
              "      <th>공가수</th>\n",
              "      <th>임대보증금</th>\n",
              "      <th>임대료</th>\n",
              "      <th>지하철</th>\n",
              "      <th>버스</th>\n",
              "      <th>단지내주차면수</th>\n",
              "      <th>등록차량수</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>단지코드</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>C1000</th>\n",
              "      <td>566.0</td>\n",
              "      <td>45.218571</td>\n",
              "      <td>80.857143</td>\n",
              "      <td>10.0</td>\n",
              "      <td>1.320357e+07</td>\n",
              "      <td>121064.285714</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>438.0</td>\n",
              "      <td>481.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C1004</th>\n",
              "      <td>521.0</td>\n",
              "      <td>39.136842</td>\n",
              "      <td>27.421053</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.705000e+06</td>\n",
              "      <td>104695.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
              "      <td>153.0</td>\n",
              "      <td>93.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C1005</th>\n",
              "      <td>1144.0</td>\n",
              "      <td>54.360000</td>\n",
              "      <td>381.333333</td>\n",
              "      <td>16.0</td>\n",
              "      <td>1.710767e+07</td>\n",
              "      <td>222046.666667</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8.0</td>\n",
              "      <td>950.0</td>\n",
              "      <td>376.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C1013</th>\n",
              "      <td>1308.0</td>\n",
              "      <td>46.142000</td>\n",
              "      <td>261.600000</td>\n",
              "      <td>16.0</td>\n",
              "      <td>2.492480e+07</td>\n",
              "      <td>134726.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>1119.0</td>\n",
              "      <td>1665.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C1014</th>\n",
              "      <td>996.0</td>\n",
              "      <td>49.232857</td>\n",
              "      <td>142.285714</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.389400e+07</td>\n",
              "      <td>160488.571429</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>823.0</td>\n",
              "      <td>708.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C2663</th>\n",
              "      <td>772.0</td>\n",
              "      <td>41.005000</td>\n",
              "      <td>128.666667</td>\n",
              "      <td>14.0</td>\n",
              "      <td>3.025867e+07</td>\n",
              "      <td>269070.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>700.0</td>\n",
              "      <td>652.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C2666</th>\n",
              "      <td>1084.0</td>\n",
              "      <td>43.258333</td>\n",
              "      <td>180.666667</td>\n",
              "      <td>23.0</td>\n",
              "      <td>1.889050e+07</td>\n",
              "      <td>133145.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>837.0</td>\n",
              "      <td>701.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C2670</th>\n",
              "      <td>648.0</td>\n",
              "      <td>40.918000</td>\n",
              "      <td>129.600000</td>\n",
              "      <td>9.0</td>\n",
              "      <td>1.635000e+07</td>\n",
              "      <td>115188.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>600.0</td>\n",
              "      <td>435.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C2680</th>\n",
              "      <td>758.0</td>\n",
              "      <td>43.350000</td>\n",
              "      <td>252.666667</td>\n",
              "      <td>19.0</td>\n",
              "      <td>2.360633e+07</td>\n",
              "      <td>129816.666667</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>564.0</td>\n",
              "      <td>590.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C2692</th>\n",
              "      <td>499.0</td>\n",
              "      <td>43.570000</td>\n",
              "      <td>99.800000</td>\n",
              "      <td>19.0</td>\n",
              "      <td>1.579360e+07</td>\n",
              "      <td>117848.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>409.0</td>\n",
              "      <td>437.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>423 rows × 10 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         총세대수       전용면적    전용면적별세대수   공가수  ...  지하철   버스  단지내주차면수   등록차량수\n",
              "단지코드                                        ...                           \n",
              "C1000   566.0  45.218571   80.857143  10.0  ...  0.0  1.0    438.0   481.0\n",
              "C1004   521.0  39.136842   27.421053   3.0  ...  NaN  2.0    153.0    93.0\n",
              "C1005  1144.0  54.360000  381.333333  16.0  ...  NaN  8.0    950.0   376.0\n",
              "C1013  1308.0  46.142000  261.600000  16.0  ...  0.0  6.0   1119.0  1665.0\n",
              "C1014   996.0  49.232857  142.285714   5.0  ...  0.0  2.0    823.0   708.0\n",
              "...       ...        ...         ...   ...  ...  ...  ...      ...     ...\n",
              "C2663   772.0  41.005000  128.666667  14.0  ...  0.0  3.0    700.0   652.0\n",
              "C2666  1084.0  43.258333  180.666667  23.0  ...  0.0  1.0    837.0   701.0\n",
              "C2670   648.0  40.918000  129.600000   9.0  ...  0.0  2.0    600.0   435.0\n",
              "C2680   758.0  43.350000  252.666667  19.0  ...  0.0  7.0    564.0   590.0\n",
              "C2692   499.0  43.570000   99.800000  19.0  ...  0.0  2.0    409.0   437.0\n",
              "\n",
              "[423 rows x 10 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 221
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304
        },
        "id": "5glg-DRp1Qf5",
        "outputId": "78fcbeea-b857-4bba-a4e0-a476ad805022"
      },
      "source": [
        "train.head()"
      ],
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>단지코드</th>\n",
              "      <th>총세대수</th>\n",
              "      <th>임대건물구분</th>\n",
              "      <th>지역</th>\n",
              "      <th>공급유형</th>\n",
              "      <th>전용면적</th>\n",
              "      <th>전용면적별세대수</th>\n",
              "      <th>공가수</th>\n",
              "      <th>자격유형</th>\n",
              "      <th>임대보증금</th>\n",
              "      <th>임대료</th>\n",
              "      <th>지하철</th>\n",
              "      <th>버스</th>\n",
              "      <th>단지내주차면수</th>\n",
              "      <th>등록차량수</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>C2483</td>\n",
              "      <td>900</td>\n",
              "      <td>아파트</td>\n",
              "      <td>경상북도</td>\n",
              "      <td>국민임대</td>\n",
              "      <td>39.72</td>\n",
              "      <td>134</td>\n",
              "      <td>38.0</td>\n",
              "      <td>A</td>\n",
              "      <td>15667000.0</td>\n",
              "      <td>103680.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1425.0</td>\n",
              "      <td>1015.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>C2483</td>\n",
              "      <td>900</td>\n",
              "      <td>아파트</td>\n",
              "      <td>경상북도</td>\n",
              "      <td>국민임대</td>\n",
              "      <td>39.72</td>\n",
              "      <td>15</td>\n",
              "      <td>38.0</td>\n",
              "      <td>A</td>\n",
              "      <td>15667000.0</td>\n",
              "      <td>103680.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1425.0</td>\n",
              "      <td>1015.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>C2483</td>\n",
              "      <td>900</td>\n",
              "      <td>아파트</td>\n",
              "      <td>경상북도</td>\n",
              "      <td>국민임대</td>\n",
              "      <td>51.93</td>\n",
              "      <td>385</td>\n",
              "      <td>38.0</td>\n",
              "      <td>A</td>\n",
              "      <td>27304000.0</td>\n",
              "      <td>184330.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1425.0</td>\n",
              "      <td>1015.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>C2483</td>\n",
              "      <td>900</td>\n",
              "      <td>아파트</td>\n",
              "      <td>경상북도</td>\n",
              "      <td>국민임대</td>\n",
              "      <td>51.93</td>\n",
              "      <td>15</td>\n",
              "      <td>38.0</td>\n",
              "      <td>A</td>\n",
              "      <td>27304000.0</td>\n",
              "      <td>184330.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1425.0</td>\n",
              "      <td>1015.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>C2483</td>\n",
              "      <td>900</td>\n",
              "      <td>아파트</td>\n",
              "      <td>경상북도</td>\n",
              "      <td>국민임대</td>\n",
              "      <td>51.93</td>\n",
              "      <td>41</td>\n",
              "      <td>38.0</td>\n",
              "      <td>A</td>\n",
              "      <td>27304000.0</td>\n",
              "      <td>184330.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1425.0</td>\n",
              "      <td>1015.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    단지코드  총세대수 임대건물구분    지역  공급유형  ...       임대료  지하철   버스 단지내주차면수   등록차량수\n",
              "0  C2483   900    아파트  경상북도  국민임대  ...  103680.0  0.0  3.0  1425.0  1015.0\n",
              "1  C2483   900    아파트  경상북도  국민임대  ...  103680.0  0.0  3.0  1425.0  1015.0\n",
              "2  C2483   900    아파트  경상북도  국민임대  ...  184330.0  0.0  3.0  1425.0  1015.0\n",
              "3  C2483   900    아파트  경상북도  국민임대  ...  184330.0  0.0  3.0  1425.0  1015.0\n",
              "4  C2483   900    아파트  경상북도  국민임대  ...  184330.0  0.0  3.0  1425.0  1015.0\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k1kUlq4S8CKn"
      },
      "source": [
        "## 지역명은 숫자로 인코딩"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z9iaCAiX8A_1"
      },
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "encoding=LabelEncoder()\n",
        "\n",
        "train['지역']=encoding.fit_transform(train['지역'])\n",
        "test['지역']=encoding.fit_transform(test['지역'])\n",
        "\n"
      ],
      "execution_count": 222,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FMjGGT05A2di"
      },
      "source": [
        "## Outlier 제거\n",
        "\n",
        "EDA과정에서 본 것중에 outlier가 엄청 큰것들이 있었음. 그걸 제거해버리겠다"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CT1zNloGJkUl",
        "outputId": "810f81a0-dce1-4249-eacc-779d8cc57f24"
      },
      "source": [
        "train.isna().sum()"
      ],
      "execution_count": 157,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "단지코드          0\n",
              "총세대수          0\n",
              "임대건물구분        0\n",
              "지역            0\n",
              "공급유형          0\n",
              "전용면적          0\n",
              "전용면적별세대수      0\n",
              "공가수           0\n",
              "자격유형          0\n",
              "임대보증금       569\n",
              "임대료         569\n",
              "지하철         211\n",
              "버스            4\n",
              "단지내주차면수       0\n",
              "등록차량수         0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 157
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dvMYI3GKAq3R"
      },
      "source": [
        "train['임대료']=train['임대료'].fillna(0)\n",
        "test['임대료']=test['임대료'].fillna(0)\n",
        "\n",
        "train['임대보증금']=train['임대보증금'].fillna(0)\n",
        "test['임대보증금']=test['임대보증금'].fillna(0)\n",
        "\n",
        "train['지하철']=train['지하철'].fillna(0)\n",
        "test['지하철']=test['지하철'].fillna(0)\n",
        "\n",
        "train['버스']=train['버스'].fillna(0)\n",
        "test['버스']=test['버스'].fillna(0)\n"
      ],
      "execution_count": 223,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wAYcC-rNLhn3",
        "outputId": "f4ea37e9-fa40-49be-dfeb-aa39b42bf2c5"
      },
      "source": [
        "test.isna().sum()"
      ],
      "execution_count": 180,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "단지코드        0\n",
              "총세대수        0\n",
              "임대건물구분      0\n",
              "지역          0\n",
              "공급유형        0\n",
              "전용면적        0\n",
              "전용면적별세대수    0\n",
              "공가수         0\n",
              "자격유형        0\n",
              "임대보증금       0\n",
              "임대료         0\n",
              "지하철         0\n",
              "버스          0\n",
              "단지내주차면수     0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 180
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CM_RSCbkL9n4"
      },
      "source": [
        "test['자격유형']=test['자격유형'].fillna('A')"
      ],
      "execution_count": 224,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mBq1mMpYBCGb"
      },
      "source": [
        "## log또는 standardscaler 적용"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ewo5acOCBEjX",
        "outputId": "331650e2-f253-4133-c00d-f86480a63a35"
      },
      "source": [
        "train.columns"
      ],
      "execution_count": 181,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['단지코드', '총세대수', '임대건물구분', '지역', '공급유형', '전용면적', '전용면적별세대수', '공가수',\n",
              "       '자격유형', '임대보증금', '임대료', '지하철', '버스', '단지내주차면수', '등록차량수'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 181
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5utbaME4Eg6e"
      },
      "source": [
        "unique_cols = ['총세대수', '지역', '공가수', \n",
        "               '지하철',\n",
        "               '버스',\n",
        "               '단지내주차면수', '등록차량수']\n",
        "train_agg = train.set_index('단지코드')[unique_cols].drop_duplicates()\n",
        "test_agg = test.set_index('단지코드')[[col for col in unique_cols if col!='등록차량수']].drop_duplicates()"
      ],
      "execution_count": 225,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "OBMlKZ9SEhda",
        "outputId": "571eb588-65bf-4bd9-c277-a0f55b02c904"
      },
      "source": [
        "train_agg"
      ],
      "execution_count": 183,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
              "\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>총세대수</th>\n",
              "      <th>지역</th>\n",
              "      <th>공가수</th>\n",
              "      <th>지하철</th>\n",
              "      <th>버스</th>\n",
              "      <th>단지내주차면수</th>\n",
              "      <th>등록차량수</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>단지코드</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>C2483</th>\n",
              "      <td>900</td>\n",
              "      <td>3</td>\n",
              "      <td>38.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1425.0</td>\n",
              "      <td>1015.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C2515</th>\n",
              "      <td>545</td>\n",
              "      <td>2</td>\n",
              "      <td>17.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>624.0</td>\n",
              "      <td>205.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C1407</th>\n",
              "      <td>1216</td>\n",
              "      <td>6</td>\n",
              "      <td>13.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1285.0</td>\n",
              "      <td>1064.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C1945</th>\n",
              "      <td>755</td>\n",
              "      <td>1</td>\n",
              "      <td>6.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>734.0</td>\n",
              "      <td>730.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C1470</th>\n",
              "      <td>696</td>\n",
              "      <td>12</td>\n",
              "      <td>14.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>645.0</td>\n",
              "      <td>553.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C2586</th>\n",
              "      <td>90</td>\n",
              "      <td>13</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>57.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C2035</th>\n",
              "      <td>492</td>\n",
              "      <td>0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>521.0</td>\n",
              "      <td>246.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C2020</th>\n",
              "      <td>40</td>\n",
              "      <td>7</td>\n",
              "      <td>7.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>19.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C2437</th>\n",
              "      <td>90</td>\n",
              "      <td>15</td>\n",
              "      <td>12.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>16.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C2532</th>\n",
              "      <td>239</td>\n",
              "      <td>0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>166.0</td>\n",
              "      <td>146.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>423 rows × 7 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       총세대수  지역   공가수  지하철   버스  단지내주차면수   등록차량수\n",
              "단지코드                                            \n",
              "C2483   900   3  38.0  0.0  3.0   1425.0  1015.0\n",
              "C2515   545   2  17.0  0.0  3.0    624.0   205.0\n",
              "C1407  1216   6  13.0  1.0  1.0   1285.0  1064.0\n",
              "C1945   755   1   6.0  1.0  3.0    734.0   730.0\n",
              "C1470   696  12  14.0  0.0  2.0    645.0   553.0\n",
              "...     ...  ..   ...  ...  ...      ...     ...\n",
              "C2586    90  13   7.0  0.0  3.0     66.0    57.0\n",
              "C2035   492   0  24.0  0.0  1.0    521.0   246.0\n",
              "C2020    40   7   7.0  1.0  2.0     25.0    19.0\n",
              "C2437    90  15  12.0  0.0  1.0     30.0    16.0\n",
              "C2532   239   0   7.0  0.0  1.0    166.0   146.0\n",
              "\n",
              "[423 rows x 7 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 183
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JsbWtokCFuuw"
      },
      "source": [
        "### '전용면적별세대수''임대보증금', '임대료' 컬럼 만들기"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "08h9k2NcME79",
        "outputId": "e57dfa86-faf0-40cc-cae5-485498aef83d"
      },
      "source": [
        "train.info()"
      ],
      "execution_count": 184,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 2952 entries, 0 to 2951\n",
            "Data columns (total 15 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   단지코드      2952 non-null   object \n",
            " 1   총세대수      2952 non-null   int64  \n",
            " 2   임대건물구분    2952 non-null   object \n",
            " 3   지역        2952 non-null   int64  \n",
            " 4   공급유형      2952 non-null   object \n",
            " 5   전용면적      2952 non-null   float64\n",
            " 6   전용면적별세대수  2952 non-null   int64  \n",
            " 7   공가수       2952 non-null   float64\n",
            " 8   자격유형      2952 non-null   object \n",
            " 9   임대보증금     2952 non-null   float64\n",
            " 10  임대료       2952 non-null   float64\n",
            " 11  지하철       2952 non-null   float64\n",
            " 12  버스        2952 non-null   float64\n",
            " 13  단지내주차면수   2952 non-null   float64\n",
            " 14  등록차량수     2952 non-null   float64\n",
            "dtypes: float64(8), int64(3), object(4)\n",
            "memory usage: 346.1+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o8VHq0JHMIWM",
        "outputId": "22050f4f-1339-4769-b5e4-d04e4c4a3f4f"
      },
      "source": [
        "train['임대료'].astype(int)\n",
        "test['임대료'].astype(int)\n",
        "\n",
        "train['임대보증금'].astype(int)\n",
        "test['임대보증금'].astype(int)"
      ],
      "execution_count": 185,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       22830000\n",
              "1       36048000\n",
              "2       36048000\n",
              "3       36048000\n",
              "4       43497000\n",
              "          ...   \n",
              "1017           0\n",
              "1018     6872000\n",
              "1019     6872000\n",
              "1020    13410000\n",
              "1021    18689000\n",
              "Name: 임대보증금, Length: 1022, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 185
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kaw5jU4QEuZw"
      },
      "source": [
        "trainm = train.groupby(['단지코드']).mean() \n",
        "testm = test.groupby(['단지코드']).mean()"
      ],
      "execution_count": 226,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "Q0mZcEYfJUTe",
        "outputId": "000c2a60-3936-408e-e07b-a6a32e82400c"
      },
      "source": [
        "trainm"
      ],
      "execution_count": 187,
      "outputs": [
        {
          "output_type": "execute_result",
          "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",
              "    .dataframe thead th {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>총세대수</th>\n",
              "      <th>지역</th>\n",
              "      <th>전용면적</th>\n",
              "      <th>전용면적별세대수</th>\n",
              "      <th>공가수</th>\n",
              "      <th>임대보증금</th>\n",
              "      <th>임대료</th>\n",
              "      <th>지하철</th>\n",
              "      <th>버스</th>\n",
              "      <th>단지내주차면수</th>\n",
              "      <th>등록차량수</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>단지코드</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>C1000</th>\n",
              "      <td>566.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>45.218571</td>\n",
              "      <td>80.857143</td>\n",
              "      <td>10.0</td>\n",
              "      <td>1.320357e+07</td>\n",
              "      <td>121064.285714</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>438.0</td>\n",
              "      <td>481.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C1004</th>\n",
              "      <td>521.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>39.136842</td>\n",
              "      <td>27.421053</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.952632e+05</td>\n",
              "      <td>11020.526316</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>153.0</td>\n",
              "      <td>93.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C1005</th>\n",
              "      <td>1144.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>54.360000</td>\n",
              "      <td>381.333333</td>\n",
              "      <td>16.0</td>\n",
              "      <td>1.710767e+07</td>\n",
              "      <td>222046.666667</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>950.0</td>\n",
              "      <td>376.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C1013</th>\n",
              "      <td>1308.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>46.142000</td>\n",
              "      <td>261.600000</td>\n",
              "      <td>16.0</td>\n",
              "      <td>2.492480e+07</td>\n",
              "      <td>134726.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>1119.0</td>\n",
              "      <td>1665.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C1014</th>\n",
              "      <td>996.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>49.232857</td>\n",
              "      <td>142.285714</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.389400e+07</td>\n",
              "      <td>160488.571429</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>823.0</td>\n",
              "      <td>708.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C2663</th>\n",
              "      <td>772.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>41.005000</td>\n",
              "      <td>128.666667</td>\n",
              "      <td>14.0</td>\n",
              "      <td>3.025867e+07</td>\n",
              "      <td>269070.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>700.0</td>\n",
              "      <td>652.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C2666</th>\n",
              "      <td>1084.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>43.258333</td>\n",
              "      <td>180.666667</td>\n",
              "      <td>23.0</td>\n",
              "      <td>1.889050e+07</td>\n",
              "      <td>133145.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>837.0</td>\n",
              "      <td>701.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C2670</th>\n",
              "      <td>648.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>40.918000</td>\n",
              "      <td>129.600000</td>\n",
              "      <td>9.0</td>\n",
              "      <td>1.635000e+07</td>\n",
              "      <td>115188.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>600.0</td>\n",
              "      <td>435.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C2680</th>\n",
              "      <td>758.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>43.350000</td>\n",
              "      <td>252.666667</td>\n",
              "      <td>19.0</td>\n",
              "      <td>2.360633e+07</td>\n",
              "      <td>129816.666667</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>564.0</td>\n",
              "      <td>590.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C2692</th>\n",
              "      <td>499.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>43.570000</td>\n",
              "      <td>99.800000</td>\n",
              "      <td>19.0</td>\n",
              "      <td>1.579360e+07</td>\n",
              "      <td>117848.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>409.0</td>\n",
              "      <td>437.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>423 rows × 11 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         총세대수    지역       전용면적    전용면적별세대수  ...  지하철   버스  단지내주차면수   등록차량수\n",
              "단지코드                                        ...                           \n",
              "C1000   566.0  15.0  45.218571   80.857143  ...  0.0  1.0    438.0   481.0\n",
              "C1004   521.0  14.0  39.136842   27.421053  ...  0.0  2.0    153.0    93.0\n",
              "C1005  1144.0  14.0  54.360000  381.333333  ...  0.0  8.0    950.0   376.0\n",
              "C1013  1308.0   4.0  46.142000  261.600000  ...  0.0  6.0   1119.0  1665.0\n",
              "C1014   996.0  12.0  49.232857  142.285714  ...  0.0  2.0    823.0   708.0\n",
              "...       ...   ...        ...         ...  ...  ...  ...      ...     ...\n",
              "C2663   772.0   1.0  41.005000  128.666667  ...  0.0  3.0    700.0   652.0\n",
              "C2666  1084.0   7.0  43.258333  180.666667  ...  0.0  1.0    837.0   701.0\n",
              "C2670   648.0  12.0  40.918000  129.600000  ...  0.0  2.0    600.0   435.0\n",
              "C2680   758.0   2.0  43.350000  252.666667  ...  0.0  7.0    564.0   590.0\n",
              "C2692   499.0   3.0  43.570000   99.800000  ...  0.0  2.0    409.0   437.0\n",
              "\n",
              "[423 rows x 11 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 187
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tKVrh6NZFpad"
      },
      "source": [
        "train_agg['임대료']=trainm['임대료']\n",
        "test_agg['임대료']=testm['임대료']\n",
        "\n",
        "train_agg['임대보증금']=trainm['임대보증금']\n",
        "test_agg['임대보증금']=testm['임대보증금']\n",
        "\n",
        "train_agg['전용면적별세대수']=trainm['전용면적별세대수']\n",
        "test_agg['전용면적별세대수']=testm['전용면적별세대수']"
      ],
      "execution_count": 227,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 234
        },
        "id": "O8hMn_TFIgXF",
        "outputId": "12abc429-1e9b-4cfb-a465-98f56eee9b74"
      },
      "source": [
        "train_agg.head()"
      ],
      "execution_count": 189,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>총세대수</th>\n",
              "      <th>지역</th>\n",
              "      <th>공가수</th>\n",
              "      <th>지하철</th>\n",
              "      <th>버스</th>\n",
              "      <th>단지내주차면수</th>\n",
              "      <th>등록차량수</th>\n",
              "      <th>임대료</th>\n",
              "      <th>임대보증금</th>\n",
              "      <th>전용면적별세대수</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>단지코드</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>C2483</th>\n",
              "      <td>900</td>\n",
              "      <td>3</td>\n",
              "      <td>38.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1425.0</td>\n",
              "      <td>1015.0</td>\n",
              "      <td>167910.000000</td>\n",
              "      <td>2.477638e+07</td>\n",
              "      <td>112.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C2515</th>\n",
              "      <td>545</td>\n",
              "      <td>2</td>\n",
              "      <td>17.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>624.0</td>\n",
              "      <td>205.0</td>\n",
              "      <td>139557.142857</td>\n",
              "      <td>1.678714e+07</td>\n",
              "      <td>77.857143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C1407</th>\n",
              "      <td>1216</td>\n",
              "      <td>6</td>\n",
              "      <td>13.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1285.0</td>\n",
              "      <td>1064.0</td>\n",
              "      <td>176730.000000</td>\n",
              "      <td>2.851055e+07</td>\n",
              "      <td>110.545455</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C1945</th>\n",
              "      <td>755</td>\n",
              "      <td>1</td>\n",
              "      <td>6.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>734.0</td>\n",
              "      <td>730.0</td>\n",
              "      <td>189106.666667</td>\n",
              "      <td>2.265167e+07</td>\n",
              "      <td>125.833333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C1470</th>\n",
              "      <td>696</td>\n",
              "      <td>12</td>\n",
              "      <td>14.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>645.0</td>\n",
              "      <td>553.0</td>\n",
              "      <td>120150.000000</td>\n",
              "      <td>1.772275e+07</td>\n",
              "      <td>174.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       총세대수  지역   공가수  지하철  ...   등록차량수            임대료         임대보증금    전용면적별세대수\n",
              "단지코드                        ...                                                 \n",
              "C2483   900   3  38.0  0.0  ...  1015.0  167910.000000  2.477638e+07  112.500000\n",
              "C2515   545   2  17.0  0.0  ...   205.0  139557.142857  1.678714e+07   77.857143\n",
              "C1407  1216   6  13.0  1.0  ...  1064.0  176730.000000  2.851055e+07  110.545455\n",
              "C1945   755   1   6.0  1.0  ...   730.0  189106.666667  2.265167e+07  125.833333\n",
              "C1470   696  12  14.0  0.0  ...   553.0  120150.000000  1.772275e+07  174.000000\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 189
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 234
        },
        "id": "IRe9D0lIM6xp",
        "outputId": "7761aa8a-61f2-4ac4-a009-a24f44e85c4e"
      },
      "source": [
        "test_agg.head()"
      ],
      "execution_count": 190,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>총세대수</th>\n",
              "      <th>지역</th>\n",
              "      <th>공가수</th>\n",
              "      <th>지하철</th>\n",
              "      <th>버스</th>\n",
              "      <th>단지내주차면수</th>\n",
              "      <th>임대료</th>\n",
              "      <th>임대보증금</th>\n",
              "      <th>전용면적별세대수</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>단지코드</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>C1072</th>\n",
              "      <td>754</td>\n",
              "      <td>1</td>\n",
              "      <td>14.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>683.0</td>\n",
              "      <td>265843.750000</td>\n",
              "      <td>3.812025e+07</td>\n",
              "      <td>94.250000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C1128</th>\n",
              "      <td>1354</td>\n",
              "      <td>1</td>\n",
              "      <td>9.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1216.0</td>\n",
              "      <td>245516.666667</td>\n",
              "      <td>3.412500e+07</td>\n",
              "      <td>150.444444</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C1456</th>\n",
              "      <td>619</td>\n",
              "      <td>7</td>\n",
              "      <td>18.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>547.0</td>\n",
              "      <td>223624.444444</td>\n",
              "      <td>3.396522e+07</td>\n",
              "      <td>68.777778</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C1840</th>\n",
              "      <td>593</td>\n",
              "      <td>11</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>543.0</td>\n",
              "      <td>123150.000000</td>\n",
              "      <td>1.892425e+07</td>\n",
              "      <td>148.250000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C1332</th>\n",
              "      <td>1297</td>\n",
              "      <td>1</td>\n",
              "      <td>11.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1112.0</td>\n",
              "      <td>292880.000000</td>\n",
              "      <td>4.133450e+07</td>\n",
              "      <td>162.125000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       총세대수  지역   공가수  지하철  ...  단지내주차면수            임대료         임대보증금    전용면적별세대수\n",
              "단지코드                        ...                                                  \n",
              "C1072   754   1  14.0  0.0  ...    683.0  265843.750000  3.812025e+07   94.250000\n",
              "C1128  1354   1   9.0  0.0  ...   1216.0  245516.666667  3.412500e+07  150.444444\n",
              "C1456   619   7  18.0  0.0  ...    547.0  223624.444444  3.396522e+07   68.777778\n",
              "C1840   593  11   7.0  0.0  ...    543.0  123150.000000  1.892425e+07  148.250000\n",
              "C1332  1297   1  11.0  0.0  ...   1112.0  292880.000000  4.133450e+07  162.125000\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 190
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D7WH3pfZNHZi"
      },
      "source": [
        "### log 적용"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9QOp-3TmM-7L"
      },
      "source": [
        "def get_preprocessed_df(df=None):\n",
        "  df_copy=df.copy()\n",
        "  log1=np.log1p(df_copy['임대료'])\n",
        "  log2=np.log1p(df_copy['임대보증금'])\n",
        "  df_copy.insert(0,'임대료_s',log1)\n",
        "  df_copy.insert(0,'임대보증금_s',log2)\n",
        "  df_copy.drop(['임대료','임대보증금'],axis=1,inplace=True)\n",
        "  return df_copy"
      ],
      "execution_count": 228,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MNxmuzs2OE5h"
      },
      "source": [
        "train_agg=get_preprocessed_df(train_agg)\n"
      ],
      "execution_count": 229,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ok03Sz_jPd8x"
      },
      "source": [
        "test_agg=get_preprocessed_df(test_agg)"
      ],
      "execution_count": 230,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9knEZYDROJis"
      },
      "source": [
        "X_train = train_agg.drop('등록차량수',axis=1)\n",
        "y_train = train_agg['등록차량수']\n",
        "X_test=test_agg"
      ],
      "execution_count": 235,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "sZQDaDqxPCPB",
        "outputId": "8399d9b8-f20f-427c-8cfa-e3c31c062eb5"
      },
      "source": [
        "X_train"
      ],
      "execution_count": 236,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>임대보증금_s</th>\n",
              "      <th>임대료_s</th>\n",
              "      <th>총세대수</th>\n",
              "      <th>지역</th>\n",
              "      <th>공가수</th>\n",
              "      <th>지하철</th>\n",
              "      <th>버스</th>\n",
              "      <th>단지내주차면수</th>\n",
              "      <th>전용면적별세대수</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>단지코드</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>C2483</th>\n",
              "      <td>17.025401</td>\n",
              "      <td>12.031189</td>\n",
              "      <td>900</td>\n",
              "      <td>3</td>\n",
              "      <td>38.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1425.0</td>\n",
              "      <td>112.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C2515</th>\n",
              "      <td>16.636124</td>\n",
              "      <td>11.846237</td>\n",
              "      <td>545</td>\n",
              "      <td>2</td>\n",
              "      <td>17.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>624.0</td>\n",
              "      <td>77.857143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C1407</th>\n",
              "      <td>17.165785</td>\n",
              "      <td>12.082384</td>\n",
              "      <td>1216</td>\n",
              "      <td>6</td>\n",
              "      <td>13.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1285.0</td>\n",
              "      <td>110.545455</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C1945</th>\n",
              "      <td>16.935744</td>\n",
              "      <td>12.150072</td>\n",
              "      <td>755</td>\n",
              "      <td>1</td>\n",
              "      <td>6.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>734.0</td>\n",
              "      <td>125.833333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C1470</th>\n",
              "      <td>16.690360</td>\n",
              "      <td>11.696505</td>\n",
              "      <td>696</td>\n",
              "      <td>12</td>\n",
              "      <td>14.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>645.0</td>\n",
              "      <td>174.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C2586</th>\n",
              "      <td>17.079742</td>\n",
              "      <td>11.618915</td>\n",
              "      <td>90</td>\n",
              "      <td>13</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>18.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C2035</th>\n",
              "      <td>16.571080</td>\n",
              "      <td>11.975407</td>\n",
              "      <td>492</td>\n",
              "      <td>0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>521.0</td>\n",
              "      <td>98.400000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C2020</th>\n",
              "      <td>17.152466</td>\n",
              "      <td>11.820246</td>\n",
              "      <td>40</td>\n",
              "      <td>7</td>\n",
              "      <td>7.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>13.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C2437</th>\n",
              "      <td>16.152111</td>\n",
              "      <td>11.585534</td>\n",
              "      <td>90</td>\n",
              "      <td>15</td>\n",
              "      <td>12.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>90.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C2532</th>\n",
              "      <td>16.399973</td>\n",
              "      <td>11.813210</td>\n",
              "      <td>239</td>\n",
              "      <td>0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>166.0</td>\n",
              "      <td>39.833333</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>423 rows × 9 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         임대보증금_s      임대료_s  총세대수  지역   공가수  지하철   버스  단지내주차면수    전용면적별세대수\n",
              "단지코드                                                                      \n",
              "C2483  17.025401  12.031189   900   3  38.0  0.0  3.0   1425.0  112.500000\n",
              "C2515  16.636124  11.846237   545   2  17.0  0.0  3.0    624.0   77.857143\n",
              "C1407  17.165785  12.082384  1216   6  13.0  1.0  1.0   1285.0  110.545455\n",
              "C1945  16.935744  12.150072   755   1   6.0  1.0  3.0    734.0  125.833333\n",
              "C1470  16.690360  11.696505   696  12  14.0  0.0  2.0    645.0  174.000000\n",
              "...          ...        ...   ...  ..   ...  ...  ...      ...         ...\n",
              "C2586  17.079742  11.618915    90  13   7.0  0.0  3.0     66.0   18.000000\n",
              "C2035  16.571080  11.975407   492   0  24.0  0.0  1.0    521.0   98.400000\n",
              "C2020  17.152466  11.820246    40   7   7.0  1.0  2.0     25.0   13.333333\n",
              "C2437  16.152111  11.585534    90  15  12.0  0.0  1.0     30.0   90.000000\n",
              "C2532  16.399973  11.813210   239   0   7.0  0.0  1.0    166.0   39.833333\n",
              "\n",
              "[423 rows x 9 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 236
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A83hv-FnPoXE",
        "outputId": "64b25eb3-ca4d-4d4e-b371-7f36fb5d08fa"
      },
      "source": [
        "y_train.head()"
      ],
      "execution_count": 237,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "단지코드\n",
              "C2483    1015.0\n",
              "C2515     205.0\n",
              "C1407    1064.0\n",
              "C1945     730.0\n",
              "C1470     553.0\n",
              "Name: 등록차량수, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 237
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "aD9n6Q_uRPko",
        "outputId": "58697d3e-6285-4120-f221-fe8f73db037b"
      },
      "source": [
        "X_test"
      ],
      "execution_count": 238,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>임대보증금_s</th>\n",
              "      <th>임대료_s</th>\n",
              "      <th>총세대수</th>\n",
              "      <th>지역</th>\n",
              "      <th>공가수</th>\n",
              "      <th>지하철</th>\n",
              "      <th>버스</th>\n",
              "      <th>단지내주차면수</th>\n",
              "      <th>전용면적별세대수</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>단지코드</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>C1072</th>\n",
              "      <td>17.456256</td>\n",
              "      <td>12.490668</td>\n",
              "      <td>754</td>\n",
              "      <td>1</td>\n",
              "      <td>14.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>683.0</td>\n",
              "      <td>94.250000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C1128</th>\n",
              "      <td>17.345541</td>\n",
              "      <td>12.411124</td>\n",
              "      <td>1354</td>\n",
              "      <td>1</td>\n",
              "      <td>9.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1216.0</td>\n",
              "      <td>150.444444</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C1456</th>\n",
              "      <td>17.340848</td>\n",
              "      <td>12.317728</td>\n",
              "      <td>619</td>\n",
              "      <td>7</td>\n",
              "      <td>18.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>547.0</td>\n",
              "      <td>68.777778</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C1840</th>\n",
              "      <td>16.755955</td>\n",
              "      <td>11.721167</td>\n",
              "      <td>593</td>\n",
              "      <td>11</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>543.0</td>\n",
              "      <td>148.250000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C1332</th>\n",
              "      <td>17.537208</td>\n",
              "      <td>12.587522</td>\n",
              "      <td>1297</td>\n",
              "      <td>1</td>\n",
              "      <td>11.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1112.0</td>\n",
              "      <td>162.125000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C2456</th>\n",
              "      <td>16.319300</td>\n",
              "      <td>11.951719</td>\n",
              "      <td>349</td>\n",
              "      <td>12</td>\n",
              "      <td>17.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>270.0</td>\n",
              "      <td>86.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C1266</th>\n",
              "      <td>16.479623</td>\n",
              "      <td>12.205467</td>\n",
              "      <td>596</td>\n",
              "      <td>14</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>593.0</td>\n",
              "      <td>118.200000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C2152</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>120</td>\n",
              "      <td>0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>60.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C1267</th>\n",
              "      <td>15.931087</td>\n",
              "      <td>11.345089</td>\n",
              "      <td>675</td>\n",
              "      <td>2</td>\n",
              "      <td>38.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>467.0</td>\n",
              "      <td>60.909091</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C2189</th>\n",
              "      <td>16.254439</td>\n",
              "      <td>11.782769</td>\n",
              "      <td>382</td>\n",
              "      <td>11</td>\n",
              "      <td>45.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>300.0</td>\n",
              "      <td>94.500000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>150 rows × 9 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         임대보증금_s      임대료_s  총세대수  지역   공가수  지하철    버스  단지내주차면수    전용면적별세대수\n",
              "단지코드                                                                       \n",
              "C1072  17.456256  12.490668   754   1  14.0  0.0   2.0    683.0   94.250000\n",
              "C1128  17.345541  12.411124  1354   1   9.0  0.0   3.0   1216.0  150.444444\n",
              "C1456  17.340848  12.317728   619   7  18.0  0.0  16.0    547.0   68.777778\n",
              "C1840  16.755955  11.721167   593  11   7.0  0.0   3.0    543.0  148.250000\n",
              "C1332  17.537208  12.587522  1297   1  11.0  0.0   2.0   1112.0  162.125000\n",
              "...          ...        ...   ...  ..   ...  ...   ...      ...         ...\n",
              "C2456  16.319300  11.951719   349  12  17.0  0.0   4.0    270.0   86.500000\n",
              "C1266  16.479623  12.205467   596  14  35.0  0.0   1.0    593.0  118.200000\n",
              "C2152   0.000000   0.000000   120   0   9.0  0.0   1.0     40.0   60.000000\n",
              "C1267  15.931087  11.345089   675   2  38.0  0.0   1.0    467.0   60.909091\n",
              "C2189  16.254439  11.782769   382  11  45.0  0.0   2.0    300.0   94.500000\n",
              "\n",
              "[150 rows x 9 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 238
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j2lSWn-vUHve",
        "outputId": "35876dfd-c4d7-45c6-e82d-5a5ce8b0bc97"
      },
      "source": [
        "rf_clf=RandomForestRegressor(n_jobs=-1,random_state=0)\n",
        "rf_clf.fit(X_train,y_train)\n",
        "rf_pred=rf_clf.predict(X_test)\n",
        "\n",
        "lr_clf=LogisticRegression(n_jobs=-1,random_state=0)\n",
        "lr_clf.fit(X_train,y_train)\n",
        "lr_pred=lr_clf.predict(X_test)\n",
        "\n",
        "xgb_clf=XGBRegressor(n_jobs=-1,random_state=0)\n",
        "xgb_clf.fit(X_train,y_train)\n",
        "xgb_pred=xgb_clf.predict(X_test)\n",
        "\n",
        "lgbm_clf=LGBMRegressor(n_jobs=-1,random_state=0)\n",
        "lgbm_clf.fit(X_train,y_train)\n",
        "lgbm_pred=lgbm_clf.predict(X_test)\n",
        "\n"
      ],
      "execution_count": 243,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[18:21:22] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3KlTRpdzVQjs"
      },
      "source": [
        "submission=pd.read_csv(\"sample_submission.csv\")"
      ],
      "execution_count": 241,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-Yx1L2K1VadY"
      },
      "source": [
        "rf_sub=submission.copy()\n",
        "lr_sub=submission.copy()\n",
        "xgb_sub=submission.copy()\n",
        "lgbm_sub=submission.copy()"
      ],
      "execution_count": 242,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E9RUXPDfVomu"
      },
      "source": [
        "rf_sub['num']=rf_pred\n",
        "lr_sub['num']=lr_pred\n",
        "xgb_sub['num']=xgb_pred\n",
        "lgbm_sub['num']=lgbm_pred"
      ],
      "execution_count": 244,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6JPDT7SbV2vN"
      },
      "source": [
        "rf_sub.to_csv('rf.csv', index=False)\n",
        "lr_sub.to_csv('lr.csv', index=False)\n",
        "xgb_sub.to_csv('xgb.csv', index=False)\n",
        "lgbm_sub.to_csv('lgbm.csv', index=False)"
      ],
      "execution_count": 245,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "STZw5IoEWCYY",
        "outputId": "8a523021-198d-4d6e-b27e-18136734edff"
      },
      "source": [
        "rf_clf.feature_importances_"
      ],
      "execution_count": 249,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.0387312 , 0.05720176, 0.03499823, 0.01793811, 0.05564183,\n",
              "       0.00232199, 0.01753195, 0.75184863, 0.02378631])"
            ]
          },
          "metadata": {},
          "execution_count": 249
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "oaGiOy5VY-mn",
        "outputId": "3a3d9df1-5662-4ec3-c5e8-87d5e6d53690"
      },
      "source": [
        "sns.barplot(x=rf_clf.feature_importances_,y=X_train.columns)"
      ],
      "execution_count": 255,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb76d12ea50>"
            ]
          },
          "metadata": {},
          "execution_count": 255
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "image/png": {
              "width": 404,
              "height": 248
            },
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TUpyA9drZQi6"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}