_real_estate_apartment.ipynb

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              "        계약일  물건금액(만원)  건물면적(㎡)   층  건축년도\n",
              "0  20240529    120000   114.70  21  1999\n",
              "1  20240528     98200    84.79   4  2005\n",
              "2  20240527    104500    82.19   2  2003\n",
              "3  20240527     76500    59.40  15  1998\n",
              "4  20240526    124200    59.91  18  2017"
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          },
          "metadata": {},
          "execution_count": 95
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      ],
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# 데이터 불러오기\n",
        "data = pd.read_csv('../content/서울시 부동산 실거래가 정보_아파트 - 서울시 부동산 실거래가 정보 (1).csv')\n",
        "\n",
        "# 데이터 확인\n",
        "data.head()\n",
        "\n"
      ]
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        "data.dropna(inplace=True)\n",
        "\n",
        "data.info()\n",
        "\n",
        "# 기술 통계 요약\n",
        "data.describe()"
      ],
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          "height": 515
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        "id": "7pK0anSLRMp6",
        "outputId": "71677c1d-53cb-477c-ea16-8a0edc896375"
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      "execution_count": 96,
      "outputs": [
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 2627 entries, 0 to 2626\n",
            "Data columns (total 5 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   계약일       2627 non-null   int64  \n",
            " 1   물건금액(만원)  2627 non-null   int64  \n",
            " 2   건물면적(㎡)   2627 non-null   float64\n",
            " 3   층         2627 non-null   int64  \n",
            " 4   건축년도      2627 non-null   int64  \n",
            "dtypes: float64(1), int64(4)\n",
            "memory usage: 102.7 KB\n"
          ]
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              "                계약일       물건금액(만원)      건물면적(㎡)            층         건축년도\n",
              "count  2.627000e+03    2627.000000  2627.000000  2627.000000  2627.000000\n",
              "mean   2.023364e+07  103508.628093    73.970760    10.960411  2002.933384\n",
              "std    4.513898e+03   58611.253956    33.483216     6.662518    13.711967\n",
              "min    2.022122e+07    8000.000000    10.780000     1.000000  1971.000000\n",
              "25%    2.023060e+07   72000.000000    59.700000     6.000000  1996.000000\n",
              "50%    2.023083e+07   95800.000000    75.800000    10.000000  2002.000000\n",
              "75%    2.024021e+07  125000.000000    84.980000    15.000000  2015.000000\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>계약일</th>\n",
              "      <th>물건금액(만원)</th>\n",
              "      <th>건물면적(㎡)</th>\n",
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              "      <th>count</th>\n",
              "      <td>2.627000e+03</td>\n",
              "      <td>2627.000000</td>\n",
              "      <td>2627.000000</td>\n",
              "      <td>2627.000000</td>\n",
              "      <td>2627.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2.023364e+07</td>\n",
              "      <td>103508.628093</td>\n",
              "      <td>73.970760</td>\n",
              "      <td>10.960411</td>\n",
              "      <td>2002.933384</td>\n",
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              "      <th>std</th>\n",
              "      <td>4.513898e+03</td>\n",
              "      <td>58611.253956</td>\n",
              "      <td>33.483216</td>\n",
              "      <td>6.662518</td>\n",
              "      <td>13.711967</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>2.022122e+07</td>\n",
              "      <td>8000.000000</td>\n",
              "      <td>10.780000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1971.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2.023060e+07</td>\n",
              "      <td>72000.000000</td>\n",
              "      <td>59.700000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>1996.000000</td>\n",
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              "      <th>50%</th>\n",
              "      <td>2.023083e+07</td>\n",
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              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2.024021e+07</td>\n",
              "      <td>125000.000000</td>\n",
              "      <td>84.980000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>2015.000000</td>\n",
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              "      <td>480000.000000</td>\n",
              "      <td>244.910000</td>\n",
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          "metadata": {},
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 특정 열의 값이 0인 행 삭제\n",
        "column_name_to_check = '토지면적(㎡)'\n",
        "data = data[data[column_name_to_check] != null]\n",
        "\n",
        "data.info()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "jftL-V-GRauf",
        "outputId": "e47fe72f-7b71-421c-c14e-3bbe6ca6a647"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "           계약일  물건금액(만원)  건물면적(㎡)     층    건축년도  단독다가구    아파트  연립다세대   오피스텔\n",
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              "24  20240522.0  207000.0  123.270   4.0  1974.0  False   True  False  False\n",
              "25  20240522.0   40000.0   44.460   3.0  2014.0  False  False   True  False\n",
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              "28  20240521.0  123300.0   59.700   7.0  2017.0  False   True  False  False\n",
              "29  20240521.0  197000.0   79.240   8.0  1971.0  False   True  False  False\n",
              "30  20240521.0  109000.0   84.908   8.0  2002.0  False   True  False  False\n",
              "31  20240521.0  207000.0   95.800   5.0  1977.0  False   True  False  False\n",
              "32  20240521.0  187000.0  115.580  13.0  2017.0  False   True  False  False\n",
              "33  20240521.0  285000.0  149.520  10.0  1975.0  False   True  False  False\n",
              "34  20240520.0   94000.0   68.280   9.0  2010.0  False   True  False  False\n",
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              "36  20240519.0  119200.0  131.340  14.0  1999.0  False   True  False  False\n",
              "37  20240519.0   73000.0   59.980   8.0  1996.0  False   True  False  False\n",
              "38  20240519.0   21000.0   29.780   5.0  2011.0  False  False   True  False\n",
              "39  20240519.0  117500.0   59.950  12.0  2017.0  False   True  False  False\n",
              "40  20240518.0  119000.0   59.910   8.0  2020.0  False   True  False  False\n",
              "41  20240518.0  106000.0   79.777  16.0  2002.0  False   True  False  False\n",
              "42  20240518.0  103500.0   59.897  20.0  2020.0  False   True  False  False\n",
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              "45  20240518.0  124000.0   84.980  10.0  2015.0  False   True  False  False\n",
              "46  20240518.0  142000.0   84.980  10.0  2020.0  False   True  False  False\n",
              "48  20240517.0   54000.0   84.980   8.0  2001.0  False   True  False  False\n",
              "49  20240517.0   34000.0   32.890  14.0  1991.0  False  False  False   True\n",
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              "      <td>82.190</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2003.0</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>20240527.0</td>\n",
              "      <td>11800.0</td>\n",
              "      <td>18.180</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2014.0</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>20240527.0</td>\n",
              "      <td>76500.0</td>\n",
              "      <td>59.400</td>\n",
              "      <td>15.0</td>\n",
              "      <td>1998.0</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>20240526.0</td>\n",
              "      <td>124200.0</td>\n",
              "      <td>59.910</td>\n",
              "      <td>18.0</td>\n",
              "      <td>2017.0</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>20240525.0</td>\n",
              "      <td>290000.0</td>\n",
              "      <td>151.740</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1975.0</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>20240524.0</td>\n",
              "      <td>29000.0</td>\n",
              "      <td>70.690</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2000.0</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>20240524.0</td>\n",
              "      <td>39000.0</td>\n",
              "      <td>32.620</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1999.0</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>20240524.0</td>\n",
              "      <td>71000.0</td>\n",
              "      <td>60.480</td>\n",
              "      <td>9.0</td>\n",
              "      <td>1984.0</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>20240524.0</td>\n",
              "      <td>118000.0</td>\n",
              "      <td>59.910</td>\n",
              "      <td>29.0</td>\n",
              "      <td>2020.0</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>20240523.0</td>\n",
              "      <td>9500.0</td>\n",
              "      <td>20.420</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2015.0</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>20240523.0</td>\n",
              "      <td>152000.0</td>\n",
              "      <td>84.650</td>\n",
              "      <td>10.0</td>\n",
              "      <td>2017.0</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>20240523.0</td>\n",
              "      <td>190000.0</td>\n",
              "      <td>95.500</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1975.0</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>20240523.0</td>\n",
              "      <td>163000.0</td>\n",
              "      <td>84.980</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2020.0</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>20240522.0</td>\n",
              "      <td>50050.0</td>\n",
              "      <td>59.840</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2000.0</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>20240522.0</td>\n",
              "      <td>24250.0</td>\n",
              "      <td>29.880</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2017.0</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>20240522.0</td>\n",
              "      <td>480000.0</td>\n",
              "      <td>139.310</td>\n",
              "      <td>10.0</td>\n",
              "      <td>1976.0</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>20240522.0</td>\n",
              "      <td>20700.0</td>\n",
              "      <td>18.302</td>\n",
              "      <td>26.0</td>\n",
              "      <td>2020.0</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>20240522.0</td>\n",
              "      <td>103500.0</td>\n",
              "      <td>82.770</td>\n",
              "      <td>12.0</td>\n",
              "      <td>1994.0</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>20240522.0</td>\n",
              "      <td>26000.0</td>\n",
              "      <td>24.500</td>\n",
              "      <td>8.0</td>\n",
              "      <td>2019.0</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>20240522.0</td>\n",
              "      <td>207000.0</td>\n",
              "      <td>123.270</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1974.0</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>20240522.0</td>\n",
              "      <td>40000.0</td>\n",
              "      <td>44.460</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2014.0</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>20240522.0</td>\n",
              "      <td>196000.0</td>\n",
              "      <td>79.240</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1971.0</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>20240521.0</td>\n",
              "      <td>69000.0</td>\n",
              "      <td>59.400</td>\n",
              "      <td>6.0</td>\n",
              "      <td>1996.0</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>20240521.0</td>\n",
              "      <td>123300.0</td>\n",
              "      <td>59.700</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2017.0</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>20240521.0</td>\n",
              "      <td>197000.0</td>\n",
              "      <td>79.240</td>\n",
              "      <td>8.0</td>\n",
              "      <td>1971.0</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>20240521.0</td>\n",
              "      <td>109000.0</td>\n",
              "      <td>84.908</td>\n",
              "      <td>8.0</td>\n",
              "      <td>2002.0</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>20240521.0</td>\n",
              "      <td>207000.0</td>\n",
              "      <td>95.800</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1977.0</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>20240521.0</td>\n",
              "      <td>187000.0</td>\n",
              "      <td>115.580</td>\n",
              "      <td>13.0</td>\n",
              "      <td>2017.0</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>20240521.0</td>\n",
              "      <td>285000.0</td>\n",
              "      <td>149.520</td>\n",
              "      <td>10.0</td>\n",
              "      <td>1975.0</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>20240520.0</td>\n",
              "      <td>94000.0</td>\n",
              "      <td>68.280</td>\n",
              "      <td>9.0</td>\n",
              "      <td>2010.0</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>20240520.0</td>\n",
              "      <td>13000.0</td>\n",
              "      <td>16.880</td>\n",
              "      <td>11.0</td>\n",
              "      <td>2013.0</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>20240519.0</td>\n",
              "      <td>119200.0</td>\n",
              "      <td>131.340</td>\n",
              "      <td>14.0</td>\n",
              "      <td>1999.0</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>20240519.0</td>\n",
              "      <td>73000.0</td>\n",
              "      <td>59.980</td>\n",
              "      <td>8.0</td>\n",
              "      <td>1996.0</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>20240519.0</td>\n",
              "      <td>21000.0</td>\n",
              "      <td>29.780</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2011.0</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>20240519.0</td>\n",
              "      <td>117500.0</td>\n",
              "      <td>59.950</td>\n",
              "      <td>12.0</td>\n",
              "      <td>2017.0</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>20240518.0</td>\n",
              "      <td>119000.0</td>\n",
              "      <td>59.910</td>\n",
              "      <td>8.0</td>\n",
              "      <td>2020.0</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>20240518.0</td>\n",
              "      <td>106000.0</td>\n",
              "      <td>79.777</td>\n",
              "      <td>16.0</td>\n",
              "      <td>2002.0</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>20240518.0</td>\n",
              "      <td>103500.0</td>\n",
              "      <td>59.897</td>\n",
              "      <td>20.0</td>\n",
              "      <td>2020.0</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>20240518.0</td>\n",
              "      <td>64000.0</td>\n",
              "      <td>50.550</td>\n",
              "      <td>14.0</td>\n",
              "      <td>1997.0</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>20240518.0</td>\n",
              "      <td>163000.0</td>\n",
              "      <td>84.940</td>\n",
              "      <td>10.0</td>\n",
              "      <td>2003.0</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>20240518.0</td>\n",
              "      <td>124000.0</td>\n",
              "      <td>84.980</td>\n",
              "      <td>10.0</td>\n",
              "      <td>2015.0</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>20240518.0</td>\n",
              "      <td>142000.0</td>\n",
              "      <td>84.980</td>\n",
              "      <td>10.0</td>\n",
              "      <td>2020.0</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>20240517.0</td>\n",
              "      <td>54000.0</td>\n",
              "      <td>84.980</td>\n",
              "      <td>8.0</td>\n",
              "      <td>2001.0</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>20240517.0</td>\n",
              "      <td>34000.0</td>\n",
              "      <td>32.890</td>\n",
              "      <td>14.0</td>\n",
              "      <td>1991.0</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>20240517.0</td>\n",
              "      <td>12000.0</td>\n",
              "      <td>16.690</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2012.0</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0e84375c-9389-4085-be59-691fb3eaba85')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
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              "        document.querySelector('#df-0e84375c-9389-4085-be59-691fb3eaba85 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-0e84375c-9389-4085-be59-691fb3eaba85');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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            }
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv('../content/11 - 11.csv (1).csv')\n",
        "data.info()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qxktqDVKhr1P",
        "outputId": "f8991e22-ffab-4762-f0ae-d9d9a134f3f2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1604 entries, 0 to 1603\n",
            "Data columns (total 8 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   계약일       1604 non-null   int64  \n",
            " 1   물건금액(만원)  1604 non-null   int64  \n",
            " 2   건물면적(㎡)   1604 non-null   float64\n",
            " 3   토지면적(㎡)   1604 non-null   float64\n",
            " 4   층         1604 non-null   int64  \n",
            " 5   건축년도      1604 non-null   int64  \n",
            " 6   건물용도      1604 non-null   object \n",
            " 7   신고구분      1604 non-null   object \n",
            "dtypes: float64(2), int64(4), object(2)\n",
            "memory usage: 100.4+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "one_hot_encoded_data = pd.get_dummies(data['건물용도'])\n",
        "#one_hot_encoded_data = pd.get_dummies(data['신고구분'])\n"
      ],
      "metadata": {
        "id": "HEi-ZWbckwWx"
      },
      "execution_count": 75,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 새로운 열을 데이터프레임에 삽입\n",
        "#data = pd.concat([data, one_hot_encoded_data], axis=1)\n",
        "\n",
        "# 기존 열 삭제\n",
        "data.drop('건물용도', axis=1, inplace=True)\n",
        "#data.drop('직거래', axis=1, inplace=True)\n",
        "\n",
        "data.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "PNqRLpk_k2bg",
        "outputId": "737eedd9-36d7-47ba-8ac7-8d39fd40745b"
      },
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        계약일  물건금액(만원)  건물면적(㎡)   층  건축년도\n",
              "0  20240530     40000   26.050   5  2020\n",
              "1  20240530     19500   30.610   5  2010\n",
              "2  20240530     30000   27.670   6  2015\n",
              "3  20240527     11800   18.180   7  2014\n",
              "4  20240524     29000   70.690   3  2000\n",
              "5  20240523      9500   20.420   4  2015\n",
              "6  20240522     24250   29.880   7  2017\n",
              "7  20240522     20700   18.302  26  2020\n",
              "8  20240522     26000   24.500   8  2019\n",
              "9  20240522     40000   44.460   3  2014"
            ],
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-d6a2bd5c-0e47-438b-97fa-38d72dd9e216');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-ec33f1ec-26cc-40ce-8402-7252a049ba1b\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ec33f1ec-26cc-40ce-8402-7252a049ba1b')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ec33f1ec-26cc-40ce-8402-7252a049ba1b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 1690,\n  \"fields\": [\n    {\n      \"column\": \"\\uacc4\\uc57d\\uc77c\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4392,\n        \"min\": 20221223,\n        \"max\": 20240530,\n        \"num_unique_values\": 424,\n        \"samples\": [\n          20231203,\n          20230621,\n          20231028\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"\\ubb3c\\uac74\\uae08\\uc561(\\ub9cc\\uc6d0)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 24594,\n        \"min\": 5000,\n        \"max\": 280000,\n        \"num_unique_values\": 580,\n        \"samples\": [\n          27825,\n          26100,\n          61700\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"\\uac74\\ubb3c\\uba74\\uc801(\\u33a1)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19.56122571530726,\n        \"min\": 11.93,\n        \"max\": 165.77,\n        \"num_unique_values\": 814,\n        \"samples\": [\n          29.77,\n          23.37,\n          76.99\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"\\uce35\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": -1,\n        \"max\": 38,\n        \"num_unique_values\": 33,\n        \"samples\": [\n          23,\n          12,\n          19\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"\\uac74\\ucd95\\ub144\\ub3c4\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9,\n        \"min\": 1967,\n        \"max\": 2024,\n        \"num_unique_values\": 42,\n        \"samples\": [\n          1998,\n          2004,\n          2011\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from sklearn.metrics import r2_score\n",
        "from sklearn.metrics import mean_absolute_percentage_error\n",
        "from sklearn.metrics import mean_absolute_error\n",
        "\n",
        "\n",
        "\n",
        "# 특성과 타겟 변수 선택\n",
        "X = data.drop('물건금액(만원)', axis=1)  # 타겟 변수인 Price를 제외한 모든 열을 특성으로 사용\n",
        "y = data['물건금액(만원)']\n",
        "\n",
        "# 학습 데이터와 테스트 데이터로 분할\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# 랜덤 포레스트 모델 생성 및 학습\n",
        "model = RandomForestRegressor(n_estimators=100, random_state=42)\n",
        "model.fit(X_train, y_train)\n",
        "\n",
        "# 테스트 데이터에 대한 예측\n",
        "predictions = model.predict(X_test)\n",
        "\n",
        "# 평가\n",
        "mape = mean_absolute_percentage_error(y_test, predictions)\n",
        "print(\"mape:\", mape)\n",
        "\n",
        "mse = mean_squared_error(y_test, predictions)\n",
        "print(\"mse:\", mse)\n",
        "\n",
        "r2 = r2_score(y_test, predictions)\n",
        "print(\"r2:\", r2)\n",
        "\n",
        "mae = mean_absolute_error(y_test, predictions)\n",
        "print(\"mae:\", mae)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_PYw8DxQlqtj",
        "outputId": "f3ac131b-d1cd-4d86-8791-8efd9af508dc"
      },
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "mape: 0.08103585569264392\n",
            "mse: 197455393.33117533\n",
            "r2: 0.9432245023274491\n",
            "mae: 7853.003933719428\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import joblib\n",
        "\n",
        "joblib.dump(model, 'random_forest_model.pkl')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FErpjmj4nzFb",
        "outputId": "34cf98d6-faa3-48f8-8421-0209b8fc1695"
      },
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['random_forest_model.pkl']"
            ]
          },
          "metadata": {},
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "\n",
        "\n",
        "# 특성과 타겟 변수 선택\n",
        "y = data['물건금액(만원)']\n",
        "\n",
        "# 데이터 정규화\n",
        "scaler = StandardScaler()\n",
        "X_scaled = scaler.fit_transform(X)\n",
        "\n",
        "# 학습 데이터와 테스트 데이터로 분할\n",
        "X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# 딥러닝 모델 구축\n",
        "model = Sequential()\n",
        "model.add(Dense(64, activation='relu', input_dim=X_train.shape[1]))\n",
        "model.add(Dense(32, activation='relu'))\n",
        "model.add(Dense(1, activation='linear'))\n",
        "\n",
        "# 모델 컴파일\n",
        "model.compile(optimizer='adam', loss='mean_squared_error')\n",
        "\n",
        "# 모델 학습\n",
        "model.fit(X_train, y_train, epochs=100, batch_size=32, verbose=1)\n",
        "\n",
        "# 모델 평가\n",
        "mse = model.evaluate(X_test, y_test)\n",
        "print(\"평균 제곱 오차 (MSE):\", mse)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8V_SbFMkn2Yq",
        "outputId": "00234525-6dd1-4b59-93f0-533ac111bb92"
      },
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "108/108 [==============================] - 1s 3ms/step - loss: 9287119872.0000\n",
            "Epoch 2/100\n",
            "108/108 [==============================] - 0s 3ms/step - loss: 9271866368.0000\n",
            "Epoch 3/100\n",
            "108/108 [==============================] - 0s 3ms/step - loss: 9208172544.0000\n",
            "Epoch 4/100\n",
            "108/108 [==============================] - 0s 3ms/step - loss: 9057764352.0000\n",
            "Epoch 5/100\n",
            "108/108 [==============================] - 0s 3ms/step - loss: 8793413632.0000\n",
            "Epoch 6/100\n",
            "108/108 [==============================] - 0s 3ms/step - loss: 8397882368.0000\n",
            "Epoch 7/100\n",
            "108/108 [==============================] - 0s 3ms/step - loss: 7866447360.0000\n",
            "Epoch 8/100\n",
            "108/108 [==============================] - 0s 3ms/step - loss: 7211991040.0000\n",
            "Epoch 9/100\n",
            "108/108 [==============================] - 0s 3ms/step - loss: 6455329280.0000\n",
            "Epoch 10/100\n",
            "108/108 [==============================] - 0s 3ms/step - loss: 5637543424.0000\n",
            "Epoch 11/100\n",
            "108/108 [==============================] - 0s 2ms/step - loss: 4810439680.0000\n",
            "Epoch 12/100\n",
            "108/108 [==============================] - 0s 2ms/step - loss: 4021402368.0000\n",
            "Epoch 13/100\n",
            "108/108 [==============================] - 0s 2ms/step - loss: 3316167168.0000\n",
            "Epoch 14/100\n",
            "108/108 [==============================] - 0s 2ms/step - loss: 2725091840.0000\n",
            "Epoch 15/100\n",
            "108/108 [==============================] - 0s 2ms/step - loss: 2265962496.0000\n",
            "Epoch 16/100\n",
            "108/108 [==============================] - 0s 2ms/step - loss: 1929490560.0000\n",
            "Epoch 17/100\n",
            "108/108 [==============================] - 0s 2ms/step - loss: 1700144000.0000\n",
            "Epoch 18/100\n",
            "108/108 [==============================] - 0s 2ms/step - loss: 1543885824.0000\n",
            "Epoch 19/100\n",
            "108/108 [==============================] - 0s 2ms/step - loss: 1438256000.0000\n",
            "Epoch 20/100\n",
            "108/108 [==============================] - 0s 2ms/step - loss: 1363623040.0000\n",
            "Epoch 21/100\n",
            "108/108 [==============================] - 0s 2ms/step - loss: 1305537024.0000\n",
            "Epoch 22/100\n",
            "108/108 [==============================] - 0s 2ms/step - loss: 1257190656.0000\n",
            "Epoch 23/100\n",
            "108/108 [==============================] - 0s 2ms/step - loss: 1215069568.0000\n",
            "Epoch 24/100\n",
            "108/108 [==============================] - 0s 2ms/step - loss: 1176617728.0000\n",
            "Epoch 25/100\n",
            "108/108 [==============================] - 0s 2ms/step - loss: 1140366336.0000\n",
            "Epoch 26/100\n",
            "108/108 [==============================] - 0s 2ms/step - loss: 1106874496.0000\n",
            "Epoch 27/100\n",
            "108/108 [==============================] - 0s 2ms/step - loss: 1075438592.0000\n",
            "Epoch 28/100\n",
            "108/108 [==============================] - 0s 2ms/step - loss: 1046087808.0000\n",
            "Epoch 29/100\n",
            "108/108 [==============================] - 0s 2ms/step - loss: 1018665664.0000\n",
            "Epoch 30/100\n",
            "108/108 [==============================] - 0s 2ms/step - loss: 992789056.0000\n",
            "Epoch 31/100\n",
            "108/108 [==============================] - 0s 2ms/step - loss: 968897344.0000\n",
            "Epoch 32/100\n",
            "108/108 [==============================] - 0s 2ms/step - loss: 946548736.0000\n",
            "Epoch 33/100\n",
            "108/108 [==============================] - 0s 2ms/step - loss: 925951104.0000\n",
            "Epoch 34/100\n",
            "108/108 [==============================] - 0s 2ms/step - loss: 907068480.0000\n",
            "Epoch 35/100\n",
            "108/108 [==============================] - 0s 2ms/step - loss: 889795776.0000\n",
            "Epoch 36/100\n",
            "108/108 [==============================] - 0s 2ms/step - loss: 874312000.0000\n",
            "Epoch 37/100\n",
            "108/108 [==============================] - 0s 2ms/step - loss: 860062016.0000\n",
            "Epoch 38/100\n",
            "108/108 [==============================] - 0s 2ms/step - loss: 846798336.0000\n",
            "Epoch 39/100\n",
            "108/108 [==============================] - 0s 2ms/step - loss: 834992000.0000\n",
            "Epoch 40/100\n",
            "108/108 [==============================] - 0s 2ms/step - loss: 824133888.0000\n",
            "Epoch 41/100\n",
            "108/108 [==============================] - 0s 2ms/step - loss: 814788160.0000\n",
            "Epoch 42/100\n",
            "108/108 [==============================] - 0s 2ms/step - loss: 805634176.0000\n",
            "Epoch 43/100\n",
            "108/108 [==============================] - 0s 2ms/step - loss: 797650816.0000\n",
            "Epoch 44/100\n",
            "108/108 [==============================] - 0s 2ms/step - loss: 790599936.0000\n",
            "Epoch 45/100\n",
            "108/108 [==============================] - 0s 2ms/step - loss: 784434880.0000\n",
            "Epoch 46/100\n",
            "108/108 [==============================] - 0s 2ms/step - loss: 778338368.0000\n",
            "Epoch 47/100\n",
            "108/108 [==============================] - 0s 2ms/step - loss: 772916544.0000\n",
            "Epoch 48/100\n",
            "108/108 [==============================] - 0s 2ms/step - loss: 768153920.0000\n",
            "Epoch 49/100\n",
            "108/108 [==============================] - 0s 2ms/step - loss: 763901632.0000\n",
            "Epoch 50/100\n",
            "108/108 [==============================] - 0s 2ms/step - loss: 759673600.0000\n",
            "Epoch 51/100\n",
            "108/108 [==============================] - 0s 2ms/step - loss: 755821312.0000\n",
            "Epoch 52/100\n",
            "108/108 [==============================] - 0s 2ms/step - loss: 752731264.0000\n",
            "Epoch 53/100\n",
            "108/108 [==============================] - 0s 2ms/step - loss: 749135424.0000\n",
            "Epoch 54/100\n",
            "108/108 [==============================] - 0s 2ms/step - loss: 746397312.0000\n",
            "Epoch 55/100\n",
            "108/108 [==============================] - 0s 2ms/step - loss: 743661184.0000\n",
            "Epoch 56/100\n",
            "108/108 [==============================] - 0s 2ms/step - loss: 740837760.0000\n",
            "Epoch 57/100\n",
            "108/108 [==============================] - 0s 3ms/step - loss: 738844352.0000\n",
            "Epoch 58/100\n",
            "108/108 [==============================] - 0s 3ms/step - loss: 736318976.0000\n",
            "Epoch 59/100\n",
            "108/108 [==============================] - 0s 3ms/step - loss: 734074624.0000\n",
            "Epoch 60/100\n",
            "108/108 [==============================] - 0s 3ms/step - loss: 731671360.0000\n",
            "Epoch 61/100\n",
            "108/108 [==============================] - 0s 4ms/step - loss: 729949504.0000\n",
            "Epoch 62/100\n",
            "108/108 [==============================] - 0s 3ms/step - loss: 728063232.0000\n",
            "Epoch 63/100\n",
            "108/108 [==============================] - 0s 3ms/step - loss: 725976256.0000\n",
            "Epoch 64/100\n",
            "108/108 [==============================] - 0s 3ms/step - loss: 724520000.0000\n",
            "Epoch 65/100\n",
            "108/108 [==============================] - 0s 3ms/step - loss: 722832640.0000\n",
            "Epoch 66/100\n",
            "108/108 [==============================] - 0s 3ms/step - loss: 721239168.0000\n",
            "Epoch 67/100\n",
            "108/108 [==============================] - 0s 3ms/step - loss: 720028032.0000\n",
            "Epoch 68/100\n",
            "108/108 [==============================] - 0s 3ms/step - loss: 718626816.0000\n",
            "Epoch 69/100\n",
            "108/108 [==============================] - 0s 3ms/step - loss: 717332928.0000\n",
            "Epoch 70/100\n",
            "108/108 [==============================] - 0s 3ms/step - loss: 716252224.0000\n",
            "Epoch 71/100\n",
            "108/108 [==============================] - 0s 3ms/step - loss: 714908800.0000\n",
            "Epoch 72/100\n",
            "108/108 [==============================] - 0s 4ms/step - loss: 713614912.0000\n",
            "Epoch 73/100\n",
            "108/108 [==============================] - 0s 3ms/step - loss: 712895552.0000\n",
            "Epoch 74/100\n",
            "108/108 [==============================] - 0s 2ms/step - loss: 712034880.0000\n",
            "Epoch 75/100\n",
            "108/108 [==============================] - 0s 2ms/step - loss: 711343808.0000\n",
            "Epoch 76/100\n",
            "108/108 [==============================] - 0s 2ms/step - loss: 710249920.0000\n",
            "Epoch 77/100\n",
            "108/108 [==============================] - 0s 2ms/step - loss: 709516480.0000\n",
            "Epoch 78/100\n",
            "108/108 [==============================] - 0s 2ms/step - loss: 708659712.0000\n",
            "Epoch 79/100\n",
            "108/108 [==============================] - 0s 2ms/step - loss: 708008768.0000\n",
            "Epoch 80/100\n",
            "108/108 [==============================] - 0s 2ms/step - loss: 707448576.0000\n",
            "Epoch 81/100\n",
            "108/108 [==============================] - 0s 2ms/step - loss: 706502528.0000\n",
            "Epoch 82/100\n",
            "108/108 [==============================] - 0s 2ms/step - loss: 706095552.0000\n",
            "Epoch 83/100\n",
            "108/108 [==============================] - 0s 2ms/step - loss: 705494912.0000\n",
            "Epoch 84/100\n",
            "108/108 [==============================] - 0s 2ms/step - loss: 705514816.0000\n",
            "Epoch 85/100\n",
            "108/108 [==============================] - 0s 2ms/step - loss: 704761088.0000\n",
            "Epoch 86/100\n",
            "108/108 [==============================] - 0s 2ms/step - loss: 703804160.0000\n",
            "Epoch 87/100\n",
            "108/108 [==============================] - 0s 2ms/step - loss: 703573184.0000\n",
            "Epoch 88/100\n",
            "108/108 [==============================] - 0s 2ms/step - loss: 703562048.0000\n",
            "Epoch 89/100\n",
            "108/108 [==============================] - 0s 2ms/step - loss: 703127424.0000\n",
            "Epoch 90/100\n",
            "108/108 [==============================] - 0s 2ms/step - loss: 702001664.0000\n",
            "Epoch 91/100\n",
            "108/108 [==============================] - 0s 2ms/step - loss: 701649856.0000\n",
            "Epoch 92/100\n",
            "108/108 [==============================] - 0s 2ms/step - loss: 701612608.0000\n",
            "Epoch 93/100\n",
            "108/108 [==============================] - 0s 2ms/step - loss: 701191680.0000\n",
            "Epoch 94/100\n",
            "108/108 [==============================] - 0s 2ms/step - loss: 701002816.0000\n",
            "Epoch 95/100\n",
            "108/108 [==============================] - 0s 2ms/step - loss: 700622464.0000\n",
            "Epoch 96/100\n",
            "108/108 [==============================] - 0s 2ms/step - loss: 700003392.0000\n",
            "Epoch 97/100\n",
            "108/108 [==============================] - 0s 2ms/step - loss: 699579904.0000\n",
            "Epoch 98/100\n",
            "108/108 [==============================] - 0s 2ms/step - loss: 699308160.0000\n",
            "Epoch 99/100\n",
            "108/108 [==============================] - 0s 2ms/step - loss: 699029760.0000\n",
            "Epoch 100/100\n",
            "108/108 [==============================] - 0s 2ms/step - loss: 698845760.0000\n",
            "28/28 [==============================] - 0s 2ms/step - loss: 777880256.0000\n",
            "평균 제곱 오차 (MSE): 777880256.0\n"
          ]
        }
      ]
    }
  ]
}