데이터분석_5일차_전력사용량_예측_워크샵22.ipynb

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      "source": [
        "<a href=\"https://colab.research.google.com/github/younghoonNa/pythonDT/blob/main/%E1%84%83%E1%85%A6%E1%84%8B%E1%85%B5%E1%84%90%E1%85%A5%E1%84%87%E1%85%AE%E1%86%AB%E1%84%89%E1%85%A5%E1%86%A8_5%E1%84%8B%E1%85%B5%E1%86%AF%E1%84%8E%E1%85%A1_%E1%84%8C%E1%85%A5%E1%86%AB%E1%84%85%E1%85%A7%E1%86%A8%E1%84%89%E1%85%A1%E1%84%8B%E1%85%AD%E1%86%BC%E1%84%85%E1%85%A3%E1%86%BC_%E1%84%8B%E1%85%A8%E1%84%8E%E1%85%B3%E1%86%A8_%E1%84%8B%E1%85%AF%E1%84%8F%E1%85%B3%E1%84%89%E1%85%A3%E1%86%B822.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "725c5e53"
      },
      "source": [
        "# 환경 설정"
      ],
      "id": "725c5e53"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0383dfd5",
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        },
        "outputId": "334320b4-2ba4-4d50-8e47-e52960298bd7"
      },
      "source": [
        "!wget http://49.247.133.7/ws2.zip\n",
        "!unzip ws2.zip"
      ],
      "id": "0383dfd5",
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2021-12-31 08:15:38--  http://49.247.133.7/ws2.zip\n",
            "Connecting to 49.247.133.7:80... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 1452903 (1.4M) [application/zip]\n",
            "Saving to: ‘ws2.zip’\n",
            "\n",
            "ws2.zip             100%[===================>]   1.38M  1.61MB/s    in 0.9s    \n",
            "\n",
            "2021-12-31 08:15:39 (1.61 MB/s) - ‘ws2.zip’ saved [1452903/1452903]\n",
            "\n",
            "Archive:  ws2.zip\n",
            "  inflating: submission.csv          \n",
            "  inflating: test.csv                \n",
            "  inflating: train.csv               \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d0701563",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "644c5bbb-fbfb-4c22-c635-4beea450f89e"
      },
      "source": [
        "!pip install jaen"
      ],
      "id": "d0701563",
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting jaen\n",
            "  Downloading JAEN-0.0.1-py3-none-any.whl (2.1 kB)\n",
            "Installing collected packages: jaen\n",
            "Successfully installed jaen-0.0.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e79ea331"
      },
      "source": [
        "# 실전프로젝트 환경 설정\n",
        "from JAEN.project import Project\n",
        "pjt = Project('전력 사용량 예측',\n",
        "              '한림대-데이터분석과정_전공자',                    \n",
        "              '1차수 A반',                    \n",
        "              'yh07280@naver.com')             "
      ],
      "id": "e79ea331",
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9d10ce4f"
      },
      "source": [
        "# 모듈 import"
      ],
      "id": "9d10ce4f"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "61caf2ae"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import mean_absolute_error"
      ],
      "id": "61caf2ae",
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "574f0b63"
      },
      "source": [
        "# 데이터 로드\n",
        "2개의 데이터 셋트를 나누어 제공합니다.\n",
        "\n",
        "1. train: 학습용 데이터 세트. 정답 값이라고 할 수 있는 `label` 값이 같이 제공됩니다.\n",
        "2. test: 예측용 데이터 세트. 정답 값이 빠진 `feature` 들만 제공됩니다. 우리는 이 `feature`를 활용하여 예측을 진행합니다.\n",
        "3. submission: 정답 제출용 데이터 세트. 주어진 형식에 맞춰 정답 값을 기입하고 제출합니다.|"
      ],
      "id": "574f0b63"
    },
    {
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        "id": "e6c3ae8a",
        "outputId": "1a8824dc-ca74-4297-9882-705d1c45937d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        }
      },
      "source": [
        "# train 데이터셋 로드 (train.csv)\n",
        "train = pd.read_csv('train.csv')\n",
        "train\n",
        "# 60개 건물 X 61일 X 24시간 = 87840"
      ],
      "id": "e6c3ae8a",
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
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            "text/plain": [
              "       num            date_time  전력사용량(kWh)  ...  일조(hr)  비전기냉방설비운영  태양광보유\n",
              "0        1  2020-06-01 00:00:00    8179.056  ...     0.0        0.0    0.0\n",
              "1        1  2020-06-01 01:00:00    8135.640  ...     0.0        0.0    0.0\n",
              "2        1  2020-06-01 02:00:00    8107.128  ...     0.0        0.0    0.0\n",
              "3        1  2020-06-01 03:00:00    8048.808  ...     0.0        0.0    0.0\n",
              "4        1  2020-06-01 04:00:00    8043.624  ...     0.0        0.0    0.0\n",
              "...    ...                  ...         ...  ...     ...        ...    ...\n",
              "87835   60  2020-07-31 19:00:00    3603.744  ...     0.0        1.0    1.0\n",
              "87836   60  2020-07-31 20:00:00    3688.848  ...     0.0        1.0    1.0\n",
              "87837   60  2020-07-31 21:00:00    3765.312  ...     0.0        1.0    1.0\n",
              "87838   60  2020-07-31 22:00:00    3833.568  ...     0.0        1.0    1.0\n",
              "87839   60  2020-07-31 23:00:00    3309.120  ...     0.0        1.0    1.0\n",
              "\n",
              "[87840 rows x 10 columns]"
            ]
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          "metadata": {},
          "execution_count": 5
        }
      ]
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    {
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      "metadata": {
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        "outputId": "9ea47a7c-39c9-4100-8a94-913a5df31a9d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
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      },
      "source": [
        "# test 데이터셋 로드 (test.csv)\n",
        "test = pd.read_csv('test.csv')\n",
        "test\n",
        "\n",
        "# 60개 건물 X 24일 X 24시간 = 34560"
      ],
      "id": "172c64ab",
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>num</th>\n",
              "      <th>date_time</th>\n",
              "      <th>기온(°C)</th>\n",
              "      <th>풍속(m/s)</th>\n",
              "      <th>습도(%)</th>\n",
              "      <th>강수량(mm)</th>\n",
              "      <th>일조(hr)</th>\n",
              "      <th>비전기냉방설비운영</th>\n",
              "      <th>태양광보유</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>2020-08-01 00:00:00</td>\n",
              "      <td>25.0</td>\n",
              "      <td>3.4</td>\n",
              "      <td>90.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>2020-08-01 01:00:00</td>\n",
              "      <td>24.6</td>\n",
              "      <td>0.5</td>\n",
              "      <td>90.0</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>2020-08-01 02:00:00</td>\n",
              "      <td>24.6</td>\n",
              "      <td>0.5</td>\n",
              "      <td>91.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>2020-08-01 03:00:00</td>\n",
              "      <td>24.7</td>\n",
              "      <td>1.2</td>\n",
              "      <td>93.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>2020-08-01 04:00:00</td>\n",
              "      <td>24.7</td>\n",
              "      <td>0.8</td>\n",
              "      <td>94.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</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>34555</th>\n",
              "      <td>60</td>\n",
              "      <td>2020-08-24 19:00:00</td>\n",
              "      <td>27.8</td>\n",
              "      <td>2.3</td>\n",
              "      <td>68.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.7</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34556</th>\n",
              "      <td>60</td>\n",
              "      <td>2020-08-24 20:00:00</td>\n",
              "      <td>27.3</td>\n",
              "      <td>1.2</td>\n",
              "      <td>71.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34557</th>\n",
              "      <td>60</td>\n",
              "      <td>2020-08-24 21:00:00</td>\n",
              "      <td>27.3</td>\n",
              "      <td>1.8</td>\n",
              "      <td>71.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34558</th>\n",
              "      <td>60</td>\n",
              "      <td>2020-08-24 22:00:00</td>\n",
              "      <td>27.1</td>\n",
              "      <td>1.8</td>\n",
              "      <td>74.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34559</th>\n",
              "      <td>60</td>\n",
              "      <td>2020-08-24 23:00:00</td>\n",
              "      <td>27.1</td>\n",
              "      <td>2.6</td>\n",
              "      <td>75.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>34560 rows × 9 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a9ff3190-f895-423f-80ed-7faacbb75581')\"\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 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a9ff3190-f895-423f-80ed-7faacbb75581 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-a9ff3190-f895-423f-80ed-7faacbb75581');\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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              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "       num            date_time  기온(°C)  ...  일조(hr)  비전기냉방설비운영  태양광보유\n",
              "0        1  2020-08-01 00:00:00    25.0  ...     0.0        0.0    0.0\n",
              "1        1  2020-08-01 01:00:00    24.6  ...     0.0        0.0    0.0\n",
              "2        1  2020-08-01 02:00:00    24.6  ...     0.0        0.0    0.0\n",
              "3        1  2020-08-01 03:00:00    24.7  ...     0.0        0.0    0.0\n",
              "4        1  2020-08-01 04:00:00    24.7  ...     0.0        0.0    0.0\n",
              "...    ...                  ...     ...  ...     ...        ...    ...\n",
              "34555   60  2020-08-24 19:00:00    27.8  ...     0.7        1.0    1.0\n",
              "34556   60  2020-08-24 20:00:00    27.3  ...     0.0        1.0    1.0\n",
              "34557   60  2020-08-24 21:00:00    27.3  ...     0.0        1.0    1.0\n",
              "34558   60  2020-08-24 22:00:00    27.1  ...     0.0        1.0    1.0\n",
              "34559   60  2020-08-24 23:00:00    27.1  ...     0.0        1.0    1.0\n",
              "\n",
              "[34560 rows x 9 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "107cbc89",
        "outputId": "6fe37639-0c71-4a51-9504-33c45fd53c35",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# train column 별 데이터 개수, 타입 확인\n",
        "train.info()"
      ],
      "id": "107cbc89",
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 87840 entries, 0 to 87839\n",
            "Data columns (total 10 columns):\n",
            " #   Column      Non-Null Count  Dtype  \n",
            "---  ------      --------------  -----  \n",
            " 0   num         87840 non-null  int64  \n",
            " 1   date_time   87840 non-null  object \n",
            " 2   전력사용량(kWh)  87840 non-null  float64\n",
            " 3   기온(°C)      87840 non-null  float64\n",
            " 4   풍속(m/s)     87840 non-null  float64\n",
            " 5   습도(%)       87840 non-null  float64\n",
            " 6   강수량(mm)     87840 non-null  float64\n",
            " 7   일조(hr)      87840 non-null  float64\n",
            " 8   비전기냉방설비운영   87840 non-null  float64\n",
            " 9   태양광보유       87840 non-null  float64\n",
            "dtypes: float64(8), int64(1), object(1)\n",
            "memory usage: 6.7+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6a5f7ce4",
        "outputId": "719f9cfd-5ef0-4734-94fe-704576dc3ac5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# test column 별 데이터 개수, 타입 확인\n",
        "test.info()"
      ],
      "id": "6a5f7ce4",
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 34560 entries, 0 to 34559\n",
            "Data columns (total 9 columns):\n",
            " #   Column     Non-Null Count  Dtype  \n",
            "---  ------     --------------  -----  \n",
            " 0   num        34560 non-null  int64  \n",
            " 1   date_time  34560 non-null  object \n",
            " 2   기온(°C)     34560 non-null  float64\n",
            " 3   풍속(m/s)    34560 non-null  float64\n",
            " 4   습도(%)      34560 non-null  float64\n",
            " 5   강수량(mm)    34560 non-null  float64\n",
            " 6   일조(hr)     34560 non-null  float64\n",
            " 7   비전기냉방설비운영  34560 non-null  float64\n",
            " 8   태양광보유      34560 non-null  float64\n",
            "dtypes: float64(7), int64(1), object(1)\n",
            "memory usage: 2.4+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d3469184",
        "outputId": "9add9e43-8710-412e-a1e6-7d7f8d7cbbbc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# train 결측치 확인 \n",
        "train.isna().sum(axis=0)"
      ],
      "id": "d3469184",
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "num           0\n",
              "date_time     0\n",
              "전력사용량(kWh)    0\n",
              "기온(°C)        0\n",
              "풍속(m/s)       0\n",
              "습도(%)         0\n",
              "강수량(mm)       0\n",
              "일조(hr)        0\n",
              "비전기냉방설비운영     0\n",
              "태양광보유         0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0d7faa69",
        "outputId": "ec122259-934e-4afb-fe24-0b5cedfc3413",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# test 결측치 확인\n",
        "test.isna().sum(axis=0)"
      ],
      "id": "0d7faa69",
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "num          0\n",
              "date_time    0\n",
              "기온(°C)       0\n",
              "풍속(m/s)      0\n",
              "습도(%)        0\n",
              "강수량(mm)      0\n",
              "일조(hr)       0\n",
              "비전기냉방설비운영    0\n",
              "태양광보유        0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "23e511bf",
        "outputId": "3a89ed93-c86d-48d4-963f-898809e32a91",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# 본인의 판단에 따라 훈련에 사용할 feature와 사용하지 않을 feature를 선택합니다.\n",
        "features = [\n",
        "    'num',\n",
        "    '기온(°C)',\n",
        "    '풍속(m/s)',\n",
        "    '습도(%)',\n",
        "    '강수량(mm)',\n",
        "    '일조(hr)',\n",
        "    '비전기냉방설비운영',\n",
        "    '태양광보유',\n",
        "]\n",
        "features"
      ],
      "id": "23e511bf",
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['num',\n",
              " '기온(°C)',\n",
              " '풍속(m/s)',\n",
              " '습도(%)',\n",
              " '강수량(mm)',\n",
              " '일조(hr)',\n",
              " '비전기냉방설비운영',\n",
              " '태양광보유']"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# X = train.drop(['전력사용량(kWh)', 'date_time'], axis = 1)\n",
        "X = train[features]\n",
        "# features의 컬럼을 X로 설정한다.\n",
        "X"
      ],
      "metadata": {
        "id": "bgvNL2Q5ZTWV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "9ff2819c-588c-4598-d319-1e990ec90b99"
      },
      "id": "bgvNL2Q5ZTWV",
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>num</th>\n",
              "      <th>기온(°C)</th>\n",
              "      <th>풍속(m/s)</th>\n",
              "      <th>습도(%)</th>\n",
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              "      <th>일조(hr)</th>\n",
              "      <th>비전기냉방설비운영</th>\n",
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              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>17.6</td>\n",
              "      <td>2.5</td>\n",
              "      <td>92.0</td>\n",
              "      <td>0.8</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>17.7</td>\n",
              "      <td>2.9</td>\n",
              "      <td>91.0</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>17.5</td>\n",
              "      <td>3.2</td>\n",
              "      <td>91.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>17.1</td>\n",
              "      <td>3.2</td>\n",
              "      <td>91.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>17.0</td>\n",
              "      <td>3.3</td>\n",
              "      <td>92.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>87835</th>\n",
              "      <td>60</td>\n",
              "      <td>25.0</td>\n",
              "      <td>2.7</td>\n",
              "      <td>95.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>87836</th>\n",
              "      <td>60</td>\n",
              "      <td>24.9</td>\n",
              "      <td>2.5</td>\n",
              "      <td>96.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>87837</th>\n",
              "      <td>60</td>\n",
              "      <td>24.9</td>\n",
              "      <td>1.9</td>\n",
              "      <td>96.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>87838</th>\n",
              "      <td>60</td>\n",
              "      <td>24.9</td>\n",
              "      <td>2.8</td>\n",
              "      <td>97.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>87839</th>\n",
              "      <td>60</td>\n",
              "      <td>24.9</td>\n",
              "      <td>2.2</td>\n",
              "      <td>97.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>87840 rows × 8 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5f3bef48-28dd-49ab-a5f2-6756738c40c9')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-5f3bef48-28dd-49ab-a5f2-6756738c40c9');\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",
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              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "       num  기온(°C)  풍속(m/s)  습도(%)  강수량(mm)  일조(hr)  비전기냉방설비운영  태양광보유\n",
              "0        1    17.6      2.5   92.0      0.8     0.0        0.0    0.0\n",
              "1        1    17.7      2.9   91.0      0.3     0.0        0.0    0.0\n",
              "2        1    17.5      3.2   91.0      0.0     0.0        0.0    0.0\n",
              "3        1    17.1      3.2   91.0      0.0     0.0        0.0    0.0\n",
              "4        1    17.0      3.3   92.0      0.0     0.0        0.0    0.0\n",
              "...    ...     ...      ...    ...      ...     ...        ...    ...\n",
              "87835   60    25.0      2.7   95.0      0.0     0.0        1.0    1.0\n",
              "87836   60    24.9      2.5   96.0      0.0     0.0        1.0    1.0\n",
              "87837   60    24.9      1.9   96.0      0.0     0.0        1.0    1.0\n",
              "87838   60    24.9      2.8   97.0      0.0     0.0        1.0    1.0\n",
              "87839   60    24.9      2.2   97.0      0.0     0.0        1.0    1.0\n",
              "\n",
              "[87840 rows x 8 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 전력사용량(kWh) 값을 Y값으로 설정한다.\n",
        "y = train['전력사용량(kWh)']\n",
        "y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s27JaTNsZ5qo",
        "outputId": "3698c2cd-6744-476e-e1a2-98bfc39fb5fb"
      },
      "id": "s27JaTNsZ5qo",
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        8179.056\n",
              "1        8135.640\n",
              "2        8107.128\n",
              "3        8048.808\n",
              "4        8043.624\n",
              "           ...   \n",
              "87835    3603.744\n",
              "87836    3688.848\n",
              "87837    3765.312\n",
              "87838    3833.568\n",
              "87839    3309.120\n",
              "Name: 전력사용량(kWh), Length: 87840, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 표준화 적용\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "scaler = StandardScaler() # 변환기 생성\n",
        "scaler.fit(X) # 각 컬럼의 평균과 표준편차를 알아내는 작업\n",
        "x_scaled = scaler.transform(X) # 표준화 적용\n",
        "pd.DataFrame(x_scaled).describe().round(2)"
      ],
      "metadata": {
        "id": "bwiEzwoJljT2"
      },
      "id": "bwiEzwoJljT2",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split  \n",
        "x_train, x_test, y_train, y_test = train_test_split(X, y, random_state = 0, test_size = 0.1)"
      ],
      "metadata": {
        "id": "eJ_Hx5c3gUif"
      },
      "id": "eJ_Hx5c3gUif",
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "07523c2b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4cbe125b-03a4-450f-8da3-ccf506f7062d"
      },
      "source": [
        "# RandomForestRegressor 사용\n",
        "  # n_estimators=500,random_state=123적용\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "model = RandomForestRegressor(max_depth=10,random_state=23).fit(x_train, y_train)\n",
        "model.score(x_train, y_train), model.score(x_test, y_test)"
      ],
      "id": "07523c2b",
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.8649968887575398, 0.8508758788567085)"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import xgboost as xgb\n",
        "model = xgb.XGBRegressor(n_estimators=1000,random_state=23)\n",
        "model.fit(x_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ukMw9S1nhZHB",
        "outputId": "f729b525-3b64-4d88-95b0-2433d392b8aa"
      },
      "id": "ukMw9S1nhZHB",
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[08:24:03] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBRegressor(n_estimators=1000, random_state=23)"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# svm_confirmed = svm_search.best_estimator_\n",
        "from sklearn.svm import SVR\n",
        "svm_rbf_clf =  SVR(kernel=\"rbf\", gamma = 10, C = 100, epsilon=0.01)\n",
        "svm_rbf_clf.fit(x_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tq3--XZilQ4E",
        "outputId": "9189b9f6-d755-4143-dce2-06c697c74156"
      },
      "id": "Tq3--XZilQ4E",
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVR(C=100, epsilon=0.01, gamma=10)"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import KNeighborsRegressor\n",
        "model = KNeighborsRegressor(n_neighbors=5)\n",
        "model.fit(x_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l0a29bm2iSs_",
        "outputId": "094c6301-bbe3-45f5-cde7-220d55a8421d"
      },
      "id": "l0a29bm2iSs_",
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsRegressor()"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.score(x_train, y_train), model.score(x_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yosZY5fDhqwv",
        "outputId": "8947512e-be37-4f43-c891-afb43291ce64"
      },
      "id": "yosZY5fDhqwv",
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.8784390288159912, 0.8691922124254559)"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "wBlVUztib-rw",
        "outputId": "92059614-4717-4bba-998c-e7a0ef2694de"
      },
      "id": "wBlVUztib-rw",
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>num</th>\n",
              "      <th>기온(°C)</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
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              "      <td>3.4</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
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              "      <td>90.0</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>24.6</td>\n",
              "      <td>0.5</td>\n",
              "      <td>91.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>24.7</td>\n",
              "      <td>1.2</td>\n",
              "      <td>93.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>24.7</td>\n",
              "      <td>0.8</td>\n",
              "      <td>94.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34555</th>\n",
              "      <td>60</td>\n",
              "      <td>27.8</td>\n",
              "      <td>2.3</td>\n",
              "      <td>68.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.7</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34556</th>\n",
              "      <td>60</td>\n",
              "      <td>27.3</td>\n",
              "      <td>1.2</td>\n",
              "      <td>71.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
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              "    <tr>\n",
              "      <th>34557</th>\n",
              "      <td>60</td>\n",
              "      <td>27.3</td>\n",
              "      <td>1.8</td>\n",
              "      <td>71.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34558</th>\n",
              "      <td>60</td>\n",
              "      <td>27.1</td>\n",
              "      <td>1.8</td>\n",
              "      <td>74.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34559</th>\n",
              "      <td>60</td>\n",
              "      <td>27.1</td>\n",
              "      <td>2.6</td>\n",
              "      <td>75.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>34560 rows × 8 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-439288d2-b63f-468f-99ba-e4d7137332ce')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-439288d2-b63f-468f-99ba-e4d7137332ce 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-439288d2-b63f-468f-99ba-e4d7137332ce');\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",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "       num  기온(°C)  풍속(m/s)  습도(%)  강수량(mm)  일조(hr)  비전기냉방설비운영  태양광보유\n",
              "0        1    25.0      3.4   90.0      1.0     0.0        0.0    0.0\n",
              "1        1    24.6      0.5   90.0      0.2     0.0        0.0    0.0\n",
              "2        1    24.6      0.5   91.0      0.0     0.0        0.0    0.0\n",
              "3        1    24.7      1.2   93.0      0.0     0.0        0.0    0.0\n",
              "4        1    24.7      0.8   94.0      0.0     0.0        0.0    0.0\n",
              "...    ...     ...      ...    ...      ...     ...        ...    ...\n",
              "34555   60    27.8      2.3   68.0      0.0     0.7        1.0    1.0\n",
              "34556   60    27.3      1.2   71.0      0.0     0.0        1.0    1.0\n",
              "34557   60    27.3      1.8   71.0      0.0     0.0        1.0    1.0\n",
              "34558   60    27.1      1.8   74.0      0.0     0.0        1.0    1.0\n",
              "34559   60    27.1      2.6   75.0      0.0     0.0        1.0    1.0\n",
              "\n",
              "[34560 rows x 8 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_x = test.drop(['date_time'], axis = 1)\n",
        "test_x"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 329
        },
        "id": "t8b_KZPRb0gN",
        "outputId": "30babdde-c7c1-4cb9-8d93-35dbad380ac3"
      },
      "id": "t8b_KZPRb0gN",
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-33-9abbbf0d2673>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtest\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'date_time'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\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\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[1;32m   4172\u001b[0m             \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4173\u001b[0m             \u001b[0minplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minplace\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4174\u001b[0;31m             \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   4175\u001b[0m         )\n\u001b[1;32m   4176\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;36mdrop\u001b[0;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[1;32m   3887\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;32min\u001b[0m \u001b[0maxes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\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   3888\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3889\u001b[0;31m                 \u001b[0mobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_drop_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\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   3890\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3891\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0minplace\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;36m_drop_axis\u001b[0;34m(self, labels, axis, level, errors)\u001b[0m\n\u001b[1;32m   3921\u001b[0m                 \u001b[0mnew_axis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\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[1;32m   3922\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-> 3923\u001b[0;31m                 \u001b[0mnew_axis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\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   3924\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0maxis_name\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mnew_axis\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   3925\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, errors)\u001b[0m\n\u001b[1;32m   5285\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0many\u001b[0m\u001b[0;34m(\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   5286\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0merrors\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m\"ignore\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5287\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"{labels[mask]} not found in axis\"\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   5288\u001b[0m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m~\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5289\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdelete\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: \"['date_time'] not found in axis\""
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "50c378d5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c37d4882-ab1c-4fe8-ea6b-b0108c33c8c5"
      },
      "source": [
        "# test 데이터셋에 대한 예측\n",
        "fin = model.predict(test_x)\n",
        "fin"
      ],
      "id": "50c378d5",
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([8314.219 , 8718.772 , 8649.264 , ..., 3544.191 , 3588.4988,\n",
              "       3593.99  ], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "20b8d400",
        "outputId": "3376edc1-c420-4a93-a807-8127e33b55fa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        }
      },
      "source": [
        "# submission 데이터 로드 (submission.csv)\n",
        "submission = pd.read_csv('submission.csv')\n",
        "submission['전력사용량(kWh)'] = fin\n",
        "# 예측 결과 대입\n",
        "submission"
      ],
      "id": "20b8d400",
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-90fd00b6-0cd8-4836-b259-432d7386a5b1\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>num</th>\n",
              "      <th>date_time</th>\n",
              "      <th>전력사용량(kWh)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>2020-08-01 00:00:00</td>\n",
              "      <td>8314.218750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>2020-08-01 01:00:00</td>\n",
              "      <td>8718.772461</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>2020-08-01 02:00:00</td>\n",
              "      <td>8649.263672</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>2020-08-01 03:00:00</td>\n",
              "      <td>8632.441406</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>2020-08-01 04:00:00</td>\n",
              "      <td>8651.513672</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34555</th>\n",
              "      <td>60</td>\n",
              "      <td>2020-08-24 19:00:00</td>\n",
              "      <td>3810.382080</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34556</th>\n",
              "      <td>60</td>\n",
              "      <td>2020-08-24 20:00:00</td>\n",
              "      <td>3460.931152</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34557</th>\n",
              "      <td>60</td>\n",
              "      <td>2020-08-24 21:00:00</td>\n",
              "      <td>3544.190918</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34558</th>\n",
              "      <td>60</td>\n",
              "      <td>2020-08-24 22:00:00</td>\n",
              "      <td>3588.498779</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34559</th>\n",
              "      <td>60</td>\n",
              "      <td>2020-08-24 23:00:00</td>\n",
              "      <td>3593.989990</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>34560 rows × 3 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-90fd00b6-0cd8-4836-b259-432d7386a5b1')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "    .colab-df-convert:hover {\n",
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              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-90fd00b6-0cd8-4836-b259-432d7386a5b1 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-90fd00b6-0cd8-4836-b259-432d7386a5b1');\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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              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "       num            date_time   전력사용량(kWh)\n",
              "0        1  2020-08-01 00:00:00  8314.218750\n",
              "1        1  2020-08-01 01:00:00  8718.772461\n",
              "2        1  2020-08-01 02:00:00  8649.263672\n",
              "3        1  2020-08-01 03:00:00  8632.441406\n",
              "4        1  2020-08-01 04:00:00  8651.513672\n",
              "...    ...                  ...          ...\n",
              "34555   60  2020-08-24 19:00:00  3810.382080\n",
              "34556   60  2020-08-24 20:00:00  3460.931152\n",
              "34557   60  2020-08-24 21:00:00  3544.190918\n",
              "34558   60  2020-08-24 22:00:00  3588.498779\n",
              "34559   60  2020-08-24 23:00:00  3593.989990\n",
              "\n",
              "[34560 rows x 3 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('MAE:', mean_absolute_error(svm_rbf_pred, submission))\n",
        "print('MSE:',mean_squared_error(svm_rbf_pred, y_test_price))"
      ],
      "metadata": {
        "id": "-HzOKMTvmAYQ"
      },
      "id": "-HzOKMTvmAYQ",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "19adb2c4"
      },
      "source": [
        "# 데이터 전처리, 모델 튜닝 등 성능을 더 높여보세요\n",
        "- 제출 결과가 낮을수록 좋은 모델입니다."
      ],
      "id": "19adb2c4"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7810dda6",
        "outputId": "274add2a-a384-40c0-b8a7-cb0957cb819d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 346
        }
      },
      "source": [
        "# 결과 제출 \n",
        "pjt.submit(submission)"
      ],
      "id": "7810dda6",
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "파일을 저장하였습니다. 파일명: submission-08-24-49.csv\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-54-0be2fd4a4e9a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# 결과 제출\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mpjt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubmit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msubmission\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/JAEN/project.py\u001b[0m in \u001b[0;36msubmit\u001b[0;34m(self, submission)\u001b[0m\n\u001b[1;32m     34\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0msubmit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msubmission\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     35\u001b[0m         \u001b[0mfilename\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__make_submission\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msubmission\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 36\u001b[0;31m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__project_submission\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\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     37\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     38\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0msubmit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msubmission_file\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[0;32m/usr/local/lib/python3.7/dist-packages/JAEN/project.py\u001b[0m in \u001b[0;36m__project_submission\u001b[0;34m(self, file_name)\u001b[0m\n\u001b[1;32m     27\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;34m'msg'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     28\u001b[0m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mjson\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloads\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 29\u001b[0;31m             \u001b[0mmessage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'제출 여부 :{}\\n오늘 제출 횟수 : {}\\n제출 결과:{}'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'msg'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'trial'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'score'\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     30\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     31\u001b[0m             \u001b[0mmessage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'trial'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_squared_log_error\n",
        "P = model.predict(X)\n",
        "mean_squared_log_error(Y, P, squared=False)"
      ],
      "metadata": {
        "id": "cW8NV_LBew9t"
      },
      "id": "cW8NV_LBew9t",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 본인의 판단에 따라 훈련에 사용할 feature와 사용하지 않을 feature를 선택합니다.\n",
        "features = [\n",
        "    'num',\n",
        "    '기온(°C)',\n",
        "    '풍속(m/s)',\n",
        "    '습도(%)',\n",
        "    '강수량(mm)',\n",
        "    '일조(hr)',\n",
        "    '비전기냉방설비운영',\n",
        "    '태양광보유',\n",
        "    'month',\n",
        "    'weekday',\n",
        "    'hour'\n",
        "]\n",
        "\n",
        "X = train[features]\n",
        "model = RandomForestRegressor(n_estimators=100, random_state=123).fit(X, Y)\n",
        "pred = model.predict(test[features])\n",
        "submission['전력사용량(kWh)'] = pred\n",
        "pjt.submit(submission) # RMSLE"
      ],
      "metadata": {
        "id": "vMR9A_rxe3Eu"
      },
      "id": "vMR9A_rxe3Eu",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e142e981",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "b0c855c5-b3ed-4ae8-9f62-b390dfdd97c2"
      },
      "source": [
        "train"
      ],
      "id": "e142e981",
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-dc0d1431-5a29-4d89-9caa-16be0ee5b857\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>num</th>\n",
              "      <th>date_time</th>\n",
              "      <th>전력사용량(kWh)</th>\n",
              "      <th>기온(°C)</th>\n",
              "      <th>풍속(m/s)</th>\n",
              "      <th>습도(%)</th>\n",
              "      <th>강수량(mm)</th>\n",
              "      <th>일조(hr)</th>\n",
              "      <th>비전기냉방설비운영</th>\n",
              "      <th>태양광보유</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
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              "      <td>2020-06-01 00:00:00</td>\n",
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              "      <td>17.6</td>\n",
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              "      <td>1</td>\n",
              "      <td>2020-06-01 01:00:00</td>\n",
              "      <td>8135.640</td>\n",
              "      <td>17.7</td>\n",
              "      <td>2.9</td>\n",
              "      <td>91.0</td>\n",
              "      <td>0.3</td>\n",
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              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>2020-06-01 02:00:00</td>\n",
              "      <td>8107.128</td>\n",
              "      <td>17.5</td>\n",
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              "      <th>3</th>\n",
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              "      <td>2020-06-01 03:00:00</td>\n",
              "      <td>8048.808</td>\n",
              "      <td>17.1</td>\n",
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              "      <td>2020-06-01 04:00:00</td>\n",
              "      <td>8043.624</td>\n",
              "      <td>17.0</td>\n",
              "      <td>3.3</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>87835</th>\n",
              "      <td>60</td>\n",
              "      <td>2020-07-31 19:00:00</td>\n",
              "      <td>3603.744</td>\n",
              "      <td>25.0</td>\n",
              "      <td>2.7</td>\n",
              "      <td>95.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>87836</th>\n",
              "      <td>60</td>\n",
              "      <td>2020-07-31 20:00:00</td>\n",
              "      <td>3688.848</td>\n",
              "      <td>24.9</td>\n",
              "      <td>2.5</td>\n",
              "      <td>96.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>87837</th>\n",
              "      <td>60</td>\n",
              "      <td>2020-07-31 21:00:00</td>\n",
              "      <td>3765.312</td>\n",
              "      <td>24.9</td>\n",
              "      <td>1.9</td>\n",
              "      <td>96.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>87838</th>\n",
              "      <td>60</td>\n",
              "      <td>2020-07-31 22:00:00</td>\n",
              "      <td>3833.568</td>\n",
              "      <td>24.9</td>\n",
              "      <td>2.8</td>\n",
              "      <td>97.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>87839</th>\n",
              "      <td>60</td>\n",
              "      <td>2020-07-31 23:00:00</td>\n",
              "      <td>3309.120</td>\n",
              "      <td>24.9</td>\n",
              "      <td>2.2</td>\n",
              "      <td>97.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>87840 rows × 10 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-dc0d1431-5a29-4d89-9caa-16be0ee5b857')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-dc0d1431-5a29-4d89-9caa-16be0ee5b857 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-dc0d1431-5a29-4d89-9caa-16be0ee5b857');\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",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "       num            date_time  전력사용량(kWh)  ...  일조(hr)  비전기냉방설비운영  태양광보유\n",
              "0        1  2020-06-01 00:00:00    8179.056  ...     0.0        0.0    0.0\n",
              "1        1  2020-06-01 01:00:00    8135.640  ...     0.0        0.0    0.0\n",
              "2        1  2020-06-01 02:00:00    8107.128  ...     0.0        0.0    0.0\n",
              "3        1  2020-06-01 03:00:00    8048.808  ...     0.0        0.0    0.0\n",
              "4        1  2020-06-01 04:00:00    8043.624  ...     0.0        0.0    0.0\n",
              "...    ...                  ...         ...  ...     ...        ...    ...\n",
              "87835   60  2020-07-31 19:00:00    3603.744  ...     0.0        1.0    1.0\n",
              "87836   60  2020-07-31 20:00:00    3688.848  ...     0.0        1.0    1.0\n",
              "87837   60  2020-07-31 21:00:00    3765.312  ...     0.0        1.0    1.0\n",
              "87838   60  2020-07-31 22:00:00    3833.568  ...     0.0        1.0    1.0\n",
              "87839   60  2020-07-31 23:00:00    3309.120  ...     0.0        1.0    1.0\n",
              "\n",
              "[87840 rows x 10 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train['date_time'] = pd.to_datetime(train['date_time'])\n",
        "train['date_time']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RNOHJq3lrG9W",
        "outputId": "1a14dd9f-e35e-4c3e-93b3-dbb103e27c52"
      },
      "id": "RNOHJq3lrG9W",
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       2020-06-01 00:00:00\n",
              "1       2020-06-01 01:00:00\n",
              "2       2020-06-01 02:00:00\n",
              "3       2020-06-01 03:00:00\n",
              "4       2020-06-01 04:00:00\n",
              "                ...        \n",
              "87835   2020-07-31 19:00:00\n",
              "87836   2020-07-31 20:00:00\n",
              "87837   2020-07-31 21:00:00\n",
              "87838   2020-07-31 22:00:00\n",
              "87839   2020-07-31 23:00:00\n",
              "Name: date_time, Length: 87840, dtype: datetime64[ns]"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train['date_time'] = pd.to_datetime(train['date_time'])\n",
        "train['month'] = train['date_time'].dt.month\n",
        "train['weekday'] = train['date_time'].dt.weekday\n",
        "train['hour'] = train['date_time'].dt.hour\n",
        "\n",
        "test['date_time'] = pd.to_datetime(test['date_time'])\n",
        "test['month'] = test['date_time'].dt.month\n",
        "test['weekday'] = test['date_time'].dt.weekday\n",
        "test['hour'] = test['date_time'].dt.hour"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 346
        },
        "id": "8rRtsZsprRgt",
        "outputId": "24410441-68bc-4174-c448-27a829c48e62"
      },
      "id": "8rRtsZsprRgt",
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-63-98c6f9583324>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mtrain\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'hour'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'date_time'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhour\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mtest\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'month'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtest\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'date_time'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmonth\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0mtest\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'weekday'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtest\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'date_time'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweekday\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mtest\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'hour'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtest\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'date_time'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhour\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;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   5135\u001b[0m             \u001b[0;32mor\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_accessors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5136\u001b[0m         ):\n\u001b[0;32m-> 5137\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\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   5138\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   5139\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\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[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/accessor.py\u001b[0m in \u001b[0;36m__get__\u001b[0;34m(self, obj, cls)\u001b[0m\n\u001b[1;32m    185\u001b[0m             \u001b[0;31m# we're accessing the attribute of the class, i.e., Dataset.geo\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    186\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_accessor\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 187\u001b[0;31m         \u001b[0maccessor_obj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_accessor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\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    188\u001b[0m         \u001b[0;31m# Replace the property with the accessor object. Inspired by:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    189\u001b[0m         \u001b[0;31m# https://www.pydanny.com/cached-property.html\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/indexes/accessors.py\u001b[0m in \u001b[0;36m__new__\u001b[0;34m(cls, data)\u001b[0m\n\u001b[1;32m    478\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mPeriodProperties\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    479\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 480\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Can only use .dt accessor with datetimelike values\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m: Can only use .dt accessor with datetimelike values"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "BVakAJbbrYHN"
      },
      "id": "BVakAJbbrYHN",
      "execution_count": null,
      "outputs": []
    }
  ]
}