2_따릉이_결과3_다른튜닝.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "2_따릉이_결과3_다른튜닝.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b3cb436glH3D",
        "outputId": "28a82e02-c92c-4f43-84ed-683b09cae34b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "fonts-nanum is already the newest version (20170925-1).\n",
            "The following package was automatically installed and is no longer required:\n",
            "  libnvidia-common-460\n",
            "Use 'sudo apt autoremove' to remove it.\n",
            "0 upgraded, 0 newly installed, 0 to remove and 49 not upgraded.\n",
            "fc-cache: invalid option -- ']'\n",
            "usage: fc-cache [-EfrsvVh] [-y SYSROOT] [--error-on-no-fonts] [--force|--really-force] [--sysroot=SYSROOT] [--system-only] [--verbose] [--version] [--help] [dirs]\n",
            "Build font information caches in [dirs]\n",
            "(all directories in font configuration by default).\n",
            "\n",
            "  -E, --error-on-no-fonts  raise an error if no fonts in a directory\n",
            "  -f, --force              scan directories with apparently valid caches\n",
            "  -r, --really-force       erase all existing caches, then rescan\n",
            "  -s, --system-only        scan system-wide directories only\n",
            "  -y, --sysroot=SYSROOT    prepend SYSROOT to all paths for scanning\n",
            "  -v, --verbose            display status information while busy\n",
            "  -V, --version            display font config version and exit\n",
            "  -h, --help               display this help and exit\n"
          ]
        }
      ],
      "source": [
        "!sudo apt-get install -y fonts-nanum\n",
        "!sudo fc-cache -fv]\n",
        "!rm ~/.cache/matplotlib -rf"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 드라이브 사용 설정\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UiRZl1ihwvhM",
        "outputId": "86bf1462-1531-4709-b5d0-a8d68ce82fd5"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 1. 데이터 살펴보기\n",
        "\n",
        "train.csv : 모델을 학습하는 데 사용하는 데이터  \n",
        "test.csv : 모델을 통하여 따릉이 대여량을 예측하는 데 사용하는 데이터  \n",
        "sample_submission.csv : test데이터의 예측 값을 저장하여 제출하기 위한 데이터"
      ],
      "metadata": {
        "id": "ZnEgNgRvxH21"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 줄마다 마지막에 띄어쓰기 두번하면 줄이 나뉜다!"
      ],
      "metadata": {
        "id": "pJPH4Ag_wlCG"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1.1. 데이터 준비\n",
        "\n",
        "read_csv() 메소드 이용하기"
      ],
      "metadata": {
        "id": "6QZJEXabxjjO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 데이터 로드\n",
        "import pandas as pd\n",
        "\n",
        "train=pd.read_csv('/content/drive/MyDrive/따릉이/train.csv')\n",
        "test = pd.read_csv('/content/drive/MyDrive/따릉이/test.csv')\n",
        "submission = pd.read_csv('/content/drive/MyDrive/따릉이/sample_submission.csv')"
      ],
      "metadata": {
        "id": "-tYSUzYbxANV"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1.2. 데이터 확인\n",
        "\n",
        "head()  \n",
        "tail()  \n",
        "info()  \n",
        "shape"
      ],
      "metadata": {
        "id": "Ch-ziE00zITs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 데이터 상위 5개 행 출력\n",
        "train.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "-xJETrRbyAiE",
        "outputId": "dc92016a-bd23-4d64-a1d8-da694f331daf"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    date_time  wind_direction  sky_condition  precipitation_form  wind_speed  \\\n",
              "0  2018-04-01         207.500          4.000               0.000       3.050   \n",
              "1  2018-04-02         208.317          2.950               0.000       3.278   \n",
              "2  2018-04-03         213.516          2.911               0.000       2.690   \n",
              "3  2018-04-04         143.836          3.692               0.425       3.138   \n",
              "4  2018-04-05          95.905          4.000               0.723       3.186   \n",
              "\n",
              "   humidity  low_temp  high_temp  Precipitation_Probability  number_of_rentals  \n",
              "0    75.000    12.600     21.000                     30.000              22994  \n",
              "1    69.833    12.812     19.000                     19.500              28139  \n",
              "2    74.879    10.312     15.316                     19.113              26817  \n",
              "3    71.849     8.312     12.368                     43.493              26034  \n",
              "4    73.784     5.875     10.421                     63.378               2833  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3e302109-0dbe-4b30-a083-8db1601c0443\">\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>date_time</th>\n",
              "      <th>wind_direction</th>\n",
              "      <th>sky_condition</th>\n",
              "      <th>precipitation_form</th>\n",
              "      <th>wind_speed</th>\n",
              "      <th>humidity</th>\n",
              "      <th>low_temp</th>\n",
              "      <th>high_temp</th>\n",
              "      <th>Precipitation_Probability</th>\n",
              "      <th>number_of_rentals</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2018-04-01</td>\n",
              "      <td>207.500</td>\n",
              "      <td>4.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>3.050</td>\n",
              "      <td>75.000</td>\n",
              "      <td>12.600</td>\n",
              "      <td>21.000</td>\n",
              "      <td>30.000</td>\n",
              "      <td>22994</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2018-04-02</td>\n",
              "      <td>208.317</td>\n",
              "      <td>2.950</td>\n",
              "      <td>0.000</td>\n",
              "      <td>3.278</td>\n",
              "      <td>69.833</td>\n",
              "      <td>12.812</td>\n",
              "      <td>19.000</td>\n",
              "      <td>19.500</td>\n",
              "      <td>28139</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2018-04-03</td>\n",
              "      <td>213.516</td>\n",
              "      <td>2.911</td>\n",
              "      <td>0.000</td>\n",
              "      <td>2.690</td>\n",
              "      <td>74.879</td>\n",
              "      <td>10.312</td>\n",
              "      <td>15.316</td>\n",
              "      <td>19.113</td>\n",
              "      <td>26817</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2018-04-04</td>\n",
              "      <td>143.836</td>\n",
              "      <td>3.692</td>\n",
              "      <td>0.425</td>\n",
              "      <td>3.138</td>\n",
              "      <td>71.849</td>\n",
              "      <td>8.312</td>\n",
              "      <td>12.368</td>\n",
              "      <td>43.493</td>\n",
              "      <td>26034</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2018-04-05</td>\n",
              "      <td>95.905</td>\n",
              "      <td>4.000</td>\n",
              "      <td>0.723</td>\n",
              "      <td>3.186</td>\n",
              "      <td>73.784</td>\n",
              "      <td>5.875</td>\n",
              "      <td>10.421</td>\n",
              "      <td>63.378</td>\n",
              "      <td>2833</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3e302109-0dbe-4b30-a083-8db1601c0443')\"\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",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </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-3e302109-0dbe-4b30-a083-8db1601c0443 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-3e302109-0dbe-4b30-a083-8db1601c0443');\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",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 데이터 하위 5개 행 출력\n",
        "train.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "9zZy79Y5zROp",
        "outputId": "55453a5d-e318-4b70-a118-e8014753db7a"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      date_time  wind_direction  sky_condition  precipitation_form  \\\n",
              "268  2020-06-26         228.662          3.980               0.223   \n",
              "269  2020-06-27         207.770          2.865               0.081   \n",
              "270  2020-06-28         282.568          1.730               0.000   \n",
              "271  2020-06-29         137.027          2.257               0.088   \n",
              "272  2020-06-30         120.797          3.622               0.432   \n",
              "\n",
              "     wind_speed  humidity  low_temp  high_temp  Precipitation_Probability  \\\n",
              "268       2.271    78.378    20.500     27.526                     36.486   \n",
              "269       1.794    78.412    20.812     28.842                     21.081   \n",
              "270       1.820    72.736    21.000     29.053                      7.297   \n",
              "271       2.043    70.473    19.625     26.000                     15.541   \n",
              "272       5.574    77.061    19.125     26.053                     41.284   \n",
              "\n",
              "     number_of_rentals  \n",
              "268              96150  \n",
              "269             107001  \n",
              "270              98568  \n",
              "271              70053  \n",
              "272              38086  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6a22bcb6-c849-44bb-926d-aa490ef3171e\">\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>date_time</th>\n",
              "      <th>wind_direction</th>\n",
              "      <th>sky_condition</th>\n",
              "      <th>precipitation_form</th>\n",
              "      <th>wind_speed</th>\n",
              "      <th>humidity</th>\n",
              "      <th>low_temp</th>\n",
              "      <th>high_temp</th>\n",
              "      <th>Precipitation_Probability</th>\n",
              "      <th>number_of_rentals</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>268</th>\n",
              "      <td>2020-06-26</td>\n",
              "      <td>228.662</td>\n",
              "      <td>3.980</td>\n",
              "      <td>0.223</td>\n",
              "      <td>2.271</td>\n",
              "      <td>78.378</td>\n",
              "      <td>20.500</td>\n",
              "      <td>27.526</td>\n",
              "      <td>36.486</td>\n",
              "      <td>96150</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>269</th>\n",
              "      <td>2020-06-27</td>\n",
              "      <td>207.770</td>\n",
              "      <td>2.865</td>\n",
              "      <td>0.081</td>\n",
              "      <td>1.794</td>\n",
              "      <td>78.412</td>\n",
              "      <td>20.812</td>\n",
              "      <td>28.842</td>\n",
              "      <td>21.081</td>\n",
              "      <td>107001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>270</th>\n",
              "      <td>2020-06-28</td>\n",
              "      <td>282.568</td>\n",
              "      <td>1.730</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.820</td>\n",
              "      <td>72.736</td>\n",
              "      <td>21.000</td>\n",
              "      <td>29.053</td>\n",
              "      <td>7.297</td>\n",
              "      <td>98568</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>271</th>\n",
              "      <td>2020-06-29</td>\n",
              "      <td>137.027</td>\n",
              "      <td>2.257</td>\n",
              "      <td>0.088</td>\n",
              "      <td>2.043</td>\n",
              "      <td>70.473</td>\n",
              "      <td>19.625</td>\n",
              "      <td>26.000</td>\n",
              "      <td>15.541</td>\n",
              "      <td>70053</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>272</th>\n",
              "      <td>2020-06-30</td>\n",
              "      <td>120.797</td>\n",
              "      <td>3.622</td>\n",
              "      <td>0.432</td>\n",
              "      <td>5.574</td>\n",
              "      <td>77.061</td>\n",
              "      <td>19.125</td>\n",
              "      <td>26.053</td>\n",
              "      <td>41.284</td>\n",
              "      <td>38086</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6a22bcb6-c849-44bb-926d-aa490ef3171e')\"\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",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </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-6a22bcb6-c849-44bb-926d-aa490ef3171e 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-6a22bcb6-c849-44bb-926d-aa490ef3171e');\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",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 데이터 결측치 및 변수들의 타입 확인\n",
        "train.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kKGh9UL8zWSF",
        "outputId": "07833738-2c01-41cb-c352-0b42ceac9208"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 273 entries, 0 to 272\n",
            "Data columns (total 10 columns):\n",
            " #   Column                     Non-Null Count  Dtype  \n",
            "---  ------                     --------------  -----  \n",
            " 0   date_time                  273 non-null    object \n",
            " 1   wind_direction             273 non-null    float64\n",
            " 2   sky_condition              273 non-null    float64\n",
            " 3   precipitation_form         273 non-null    float64\n",
            " 4   wind_speed                 273 non-null    float64\n",
            " 5   humidity                   273 non-null    float64\n",
            " 6   low_temp                   273 non-null    float64\n",
            " 7   high_temp                  273 non-null    float64\n",
            " 8   Precipitation_Probability  273 non-null    float64\n",
            " 9   number_of_rentals          273 non-null    int64  \n",
            "dtypes: float64(8), int64(1), object(1)\n",
            "memory usage: 21.5+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 데이터 행, 열 출력\n",
        "train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gZvgZTyUzblK",
        "outputId": "885dbc3a-9ae6-4ece-fc33-6ccb33ecf573"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(273, 10)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1.3. 데이터 통계치 확인"
      ],
      "metadata": {
        "id": "lyduWdwIztXG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "id": "2SfWXxglzoTH",
        "outputId": "6a14c244-cecd-4737-934a-0f546700cef1"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       wind_direction  sky_condition  precipitation_form  wind_speed  \\\n",
              "count      273.000000     273.000000          273.000000  273.000000   \n",
              "mean       202.750967       2.288256            0.100963    2.480963   \n",
              "std         56.659232       0.961775            0.203193    0.884397   \n",
              "min         57.047000       1.000000            0.000000    0.753000   \n",
              "25%        171.541000       1.405000            0.000000    1.820000   \n",
              "50%        209.774000       2.167000            0.000000    2.411000   \n",
              "75%        238.412000       3.000000            0.088000    2.924000   \n",
              "max        321.622000       4.000000            1.000000    5.607000   \n",
              "\n",
              "         humidity    low_temp   high_temp  Precipitation_Probability  \\\n",
              "count  273.000000  273.000000  273.000000                 273.000000   \n",
              "mean    56.745491   13.795249   23.384733                  16.878103   \n",
              "std     12.351268    5.107711    5.204605                  16.643772   \n",
              "min     24.831000    1.938000    9.895000                   0.000000   \n",
              "25%     47.196000    9.938000   19.842000                   4.054000   \n",
              "50%     55.845000   14.375000   24.158000                  12.162000   \n",
              "75%     66.419000   18.000000   27.526000                  22.973000   \n",
              "max     88.885000   22.312000   33.421000                  82.162000   \n",
              "\n",
              "       number_of_rentals  \n",
              "count         273.000000  \n",
              "mean        59574.978022  \n",
              "std         27659.575774  \n",
              "min          1037.000000  \n",
              "25%         36761.000000  \n",
              "50%         63032.000000  \n",
              "75%         81515.000000  \n",
              "max        110377.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-dbd9cadd-192e-4234-a59d-0fe55807577d\">\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>wind_direction</th>\n",
              "      <th>sky_condition</th>\n",
              "      <th>precipitation_form</th>\n",
              "      <th>wind_speed</th>\n",
              "      <th>humidity</th>\n",
              "      <th>low_temp</th>\n",
              "      <th>high_temp</th>\n",
              "      <th>Precipitation_Probability</th>\n",
              "      <th>number_of_rentals</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>273.000000</td>\n",
              "      <td>273.000000</td>\n",
              "      <td>273.000000</td>\n",
              "      <td>273.000000</td>\n",
              "      <td>273.000000</td>\n",
              "      <td>273.000000</td>\n",
              "      <td>273.000000</td>\n",
              "      <td>273.000000</td>\n",
              "      <td>273.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>202.750967</td>\n",
              "      <td>2.288256</td>\n",
              "      <td>0.100963</td>\n",
              "      <td>2.480963</td>\n",
              "      <td>56.745491</td>\n",
              "      <td>13.795249</td>\n",
              "      <td>23.384733</td>\n",
              "      <td>16.878103</td>\n",
              "      <td>59574.978022</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>56.659232</td>\n",
              "      <td>0.961775</td>\n",
              "      <td>0.203193</td>\n",
              "      <td>0.884397</td>\n",
              "      <td>12.351268</td>\n",
              "      <td>5.107711</td>\n",
              "      <td>5.204605</td>\n",
              "      <td>16.643772</td>\n",
              "      <td>27659.575774</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>57.047000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.753000</td>\n",
              "      <td>24.831000</td>\n",
              "      <td>1.938000</td>\n",
              "      <td>9.895000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1037.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>171.541000</td>\n",
              "      <td>1.405000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.820000</td>\n",
              "      <td>47.196000</td>\n",
              "      <td>9.938000</td>\n",
              "      <td>19.842000</td>\n",
              "      <td>4.054000</td>\n",
              "      <td>36761.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>209.774000</td>\n",
              "      <td>2.167000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.411000</td>\n",
              "      <td>55.845000</td>\n",
              "      <td>14.375000</td>\n",
              "      <td>24.158000</td>\n",
              "      <td>12.162000</td>\n",
              "      <td>63032.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>238.412000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.088000</td>\n",
              "      <td>2.924000</td>\n",
              "      <td>66.419000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>27.526000</td>\n",
              "      <td>22.973000</td>\n",
              "      <td>81515.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>321.622000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>5.607000</td>\n",
              "      <td>88.885000</td>\n",
              "      <td>22.312000</td>\n",
              "      <td>33.421000</td>\n",
              "      <td>82.162000</td>\n",
              "      <td>110377.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-dbd9cadd-192e-4234-a59d-0fe55807577d')\"\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",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </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-dbd9cadd-192e-4234-a59d-0fe55807577d 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-dbd9cadd-192e-4234-a59d-0fe55807577d');\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",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 2. 데이터 전처리\n",
        "\n",
        "EDA를 하기 위한 데이터 선행처리"
      ],
      "metadata": {
        "id": "KYbU030fz5xU"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2.1. 'date_time' 열 쪼개기"
      ],
      "metadata": {
        "id": "KFbxwvnIz-le"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train['date_time']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CvxNopzS0GLq",
        "outputId": "3da0fe27-2c2f-4604-c4c1-f1d32b13b01d"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      2018-04-01\n",
              "1      2018-04-02\n",
              "2      2018-04-03\n",
              "3      2018-04-04\n",
              "4      2018-04-05\n",
              "          ...    \n",
              "268    2020-06-26\n",
              "269    2020-06-27\n",
              "270    2020-06-28\n",
              "271    2020-06-29\n",
              "272    2020-06-30\n",
              "Name: date_time, Length: 273, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 이건 _ 일때 가능하다! .으로 이름이 되어있다면 불가능!\n",
        "train.date_time"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8YkVH0LywuqB",
        "outputId": "f22c6300-6360-4bb9-e95e-4f4d65ee20d1"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      2018-04-01\n",
              "1      2018-04-02\n",
              "2      2018-04-03\n",
              "3      2018-04-04\n",
              "4      2018-04-05\n",
              "          ...    \n",
              "268    2020-06-26\n",
              "269    2020-06-27\n",
              "270    2020-06-28\n",
              "271    2020-06-29\n",
              "272    2020-06-30\n",
              "Name: date_time, Length: 273, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### split 분리"
      ],
      "metadata": {
        "id": "JNF0J12-w1K1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train['date_time'][0].split('-')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GApGqaOt0L2f",
        "outputId": "30fcae95-7eaa-40b4-a90f-c283a4e233f9"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['2018', '04', '01']"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train['date_time'][0].split('-')[1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "bq_Qzcuuw6vt",
        "outputId": "6cfc7189-7d02-4aff-8df3-0b992cf19bed"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'04'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "year, month, day = train['date_time'][0].split('-')\n",
        "print('년 : ' + year)\n",
        "print('월 : ' + month)\n",
        "print('일 : ' + day)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HEhOo1yy0WrS",
        "outputId": "626ae896-3f03-4b84-c229-b00e70388cf7"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "년 : 2018\n",
            "월 : 04\n",
            "일 : 01\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train['date_time'].apply(lambda x : x.split('-'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "luhei_nF00Lv",
        "outputId": "98cb26e5-20e2-4230-bc6a-a3229dab6288"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      [2018, 04, 01]\n",
              "1      [2018, 04, 02]\n",
              "2      [2018, 04, 03]\n",
              "3      [2018, 04, 04]\n",
              "4      [2018, 04, 05]\n",
              "            ...      \n",
              "268    [2020, 06, 26]\n",
              "269    [2020, 06, 27]\n",
              "270    [2020, 06, 28]\n",
              "271    [2020, 06, 29]\n",
              "272    [2020, 06, 30]\n",
              "Name: date_time, Length: 273, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train['date_time'].apply(lambda x : x.split('-')[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2S53uIz71N_9",
        "outputId": "b0d2c84c-3a28-431a-e63e-019be475e951"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      2018\n",
              "1      2018\n",
              "2      2018\n",
              "3      2018\n",
              "4      2018\n",
              "       ... \n",
              "268    2020\n",
              "269    2020\n",
              "270    2020\n",
              "271    2020\n",
              "272    2020\n",
              "Name: date_time, Length: 273, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def temp(x):\n",
        "  return x.split('-')[0]\n",
        "\n",
        "train['date_time'].map(temp)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jf6KiczO1ekM",
        "outputId": "fc651775-3471-4afb-bdb8-f1ea379010e6"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      2018\n",
              "1      2018\n",
              "2      2018\n",
              "3      2018\n",
              "4      2018\n",
              "       ... \n",
              "268    2020\n",
              "269    2020\n",
              "270    2020\n",
              "271    2020\n",
              "272    2020\n",
              "Name: date_time, Length: 273, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### map, apply\n",
        "\n",
        "- apply는 데이터프레임에 적용 가능\n",
        "- map은 시리즈에서만 가능\n",
        "- map은 특정열에서만 적용가능"
      ],
      "metadata": {
        "id": "7mLqBW0KxPHR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train.apply(lambda x : x*2) # 다바꾸는거 가능!"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 835
        },
        "id": "cUWmnDC31-NE",
        "outputId": "dd00128d-356b-4f60-b054-d6eee47a6b39"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                date_time  wind_direction  sky_condition  precipitation_form  \\\n",
              "0    2018-04-012018-04-01         415.000          8.000               0.000   \n",
              "1    2018-04-022018-04-02         416.634          5.900               0.000   \n",
              "2    2018-04-032018-04-03         427.032          5.822               0.000   \n",
              "3    2018-04-042018-04-04         287.672          7.384               0.850   \n",
              "4    2018-04-052018-04-05         191.810          8.000               1.446   \n",
              "..                    ...             ...            ...                 ...   \n",
              "268  2020-06-262020-06-26         457.324          7.960               0.446   \n",
              "269  2020-06-272020-06-27         415.540          5.730               0.162   \n",
              "270  2020-06-282020-06-28         565.136          3.460               0.000   \n",
              "271  2020-06-292020-06-29         274.054          4.514               0.176   \n",
              "272  2020-06-302020-06-30         241.594          7.244               0.864   \n",
              "\n",
              "     wind_speed  humidity  low_temp  high_temp  Precipitation_Probability  \\\n",
              "0         6.100   150.000    25.200     42.000                     60.000   \n",
              "1         6.556   139.666    25.624     38.000                     39.000   \n",
              "2         5.380   149.758    20.624     30.632                     38.226   \n",
              "3         6.276   143.698    16.624     24.736                     86.986   \n",
              "4         6.372   147.568    11.750     20.842                    126.756   \n",
              "..          ...       ...       ...        ...                        ...   \n",
              "268       4.542   156.756    41.000     55.052                     72.972   \n",
              "269       3.588   156.824    41.624     57.684                     42.162   \n",
              "270       3.640   145.472    42.000     58.106                     14.594   \n",
              "271       4.086   140.946    39.250     52.000                     31.082   \n",
              "272      11.148   154.122    38.250     52.106                     82.568   \n",
              "\n",
              "     number_of_rentals  \n",
              "0                45988  \n",
              "1                56278  \n",
              "2                53634  \n",
              "3                52068  \n",
              "4                 5666  \n",
              "..                 ...  \n",
              "268             192300  \n",
              "269             214002  \n",
              "270             197136  \n",
              "271             140106  \n",
              "272              76172  \n",
              "\n",
              "[273 rows x 10 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-45cc1f30-2585-45e0-a6e4-c2d9ef9daa1f\">\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>date_time</th>\n",
              "      <th>wind_direction</th>\n",
              "      <th>sky_condition</th>\n",
              "      <th>precipitation_form</th>\n",
              "      <th>wind_speed</th>\n",
              "      <th>humidity</th>\n",
              "      <th>low_temp</th>\n",
              "      <th>high_temp</th>\n",
              "      <th>Precipitation_Probability</th>\n",
              "      <th>number_of_rentals</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2018-04-012018-04-01</td>\n",
              "      <td>415.000</td>\n",
              "      <td>8.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>6.100</td>\n",
              "      <td>150.000</td>\n",
              "      <td>25.200</td>\n",
              "      <td>42.000</td>\n",
              "      <td>60.000</td>\n",
              "      <td>45988</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2018-04-022018-04-02</td>\n",
              "      <td>416.634</td>\n",
              "      <td>5.900</td>\n",
              "      <td>0.000</td>\n",
              "      <td>6.556</td>\n",
              "      <td>139.666</td>\n",
              "      <td>25.624</td>\n",
              "      <td>38.000</td>\n",
              "      <td>39.000</td>\n",
              "      <td>56278</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2018-04-032018-04-03</td>\n",
              "      <td>427.032</td>\n",
              "      <td>5.822</td>\n",
              "      <td>0.000</td>\n",
              "      <td>5.380</td>\n",
              "      <td>149.758</td>\n",
              "      <td>20.624</td>\n",
              "      <td>30.632</td>\n",
              "      <td>38.226</td>\n",
              "      <td>53634</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2018-04-042018-04-04</td>\n",
              "      <td>287.672</td>\n",
              "      <td>7.384</td>\n",
              "      <td>0.850</td>\n",
              "      <td>6.276</td>\n",
              "      <td>143.698</td>\n",
              "      <td>16.624</td>\n",
              "      <td>24.736</td>\n",
              "      <td>86.986</td>\n",
              "      <td>52068</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2018-04-052018-04-05</td>\n",
              "      <td>191.810</td>\n",
              "      <td>8.000</td>\n",
              "      <td>1.446</td>\n",
              "      <td>6.372</td>\n",
              "      <td>147.568</td>\n",
              "      <td>11.750</td>\n",
              "      <td>20.842</td>\n",
              "      <td>126.756</td>\n",
              "      <td>5666</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>268</th>\n",
              "      <td>2020-06-262020-06-26</td>\n",
              "      <td>457.324</td>\n",
              "      <td>7.960</td>\n",
              "      <td>0.446</td>\n",
              "      <td>4.542</td>\n",
              "      <td>156.756</td>\n",
              "      <td>41.000</td>\n",
              "      <td>55.052</td>\n",
              "      <td>72.972</td>\n",
              "      <td>192300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>269</th>\n",
              "      <td>2020-06-272020-06-27</td>\n",
              "      <td>415.540</td>\n",
              "      <td>5.730</td>\n",
              "      <td>0.162</td>\n",
              "      <td>3.588</td>\n",
              "      <td>156.824</td>\n",
              "      <td>41.624</td>\n",
              "      <td>57.684</td>\n",
              "      <td>42.162</td>\n",
              "      <td>214002</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>270</th>\n",
              "      <td>2020-06-282020-06-28</td>\n",
              "      <td>565.136</td>\n",
              "      <td>3.460</td>\n",
              "      <td>0.000</td>\n",
              "      <td>3.640</td>\n",
              "      <td>145.472</td>\n",
              "      <td>42.000</td>\n",
              "      <td>58.106</td>\n",
              "      <td>14.594</td>\n",
              "      <td>197136</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>271</th>\n",
              "      <td>2020-06-292020-06-29</td>\n",
              "      <td>274.054</td>\n",
              "      <td>4.514</td>\n",
              "      <td>0.176</td>\n",
              "      <td>4.086</td>\n",
              "      <td>140.946</td>\n",
              "      <td>39.250</td>\n",
              "      <td>52.000</td>\n",
              "      <td>31.082</td>\n",
              "      <td>140106</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>272</th>\n",
              "      <td>2020-06-302020-06-30</td>\n",
              "      <td>241.594</td>\n",
              "      <td>7.244</td>\n",
              "      <td>0.864</td>\n",
              "      <td>11.148</td>\n",
              "      <td>154.122</td>\n",
              "      <td>38.250</td>\n",
              "      <td>52.106</td>\n",
              "      <td>82.568</td>\n",
              "      <td>76172</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>273 rows × 10 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-45cc1f30-2585-45e0-a6e4-c2d9ef9daa1f')\"\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",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </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-45cc1f30-2585-45e0-a6e4-c2d9ef9daa1f 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-45cc1f30-2585-45e0-a6e4-c2d9ef9daa1f');\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",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# train.map(lambda x : x*2) 불가능"
      ],
      "metadata": {
        "id": "UAl1JQ9Ox17v"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# map은 특정열에서만 적용가능\n",
        "train['date_time'].map(lambda x : x*2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ivKpTFwg2ZQE",
        "outputId": "1a9a5831-49b1-499d-e4f9-3e4e7419d296"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      2018-04-012018-04-01\n",
              "1      2018-04-022018-04-02\n",
              "2      2018-04-032018-04-03\n",
              "3      2018-04-042018-04-04\n",
              "4      2018-04-052018-04-05\n",
              "               ...         \n",
              "268    2020-06-262020-06-26\n",
              "269    2020-06-272020-06-27\n",
              "270    2020-06-282020-06-28\n",
              "271    2020-06-292020-06-29\n",
              "272    2020-06-302020-06-30\n",
              "Name: date_time, Length: 273, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#바꾸길 원하는 것에 더블클릭후 컨트롤 D 누르면 다중선택 가능 그리고 바로 변경 가능\n",
        "\n",
        "train['year'] = train['date_time'].apply(lambda x : x.split('-')[0]) #년\n",
        "train['month'] = train['date_time'].apply(lambda x : x.split('-')[1]) #월\n",
        "train['day'] = train['date_time'].apply(lambda x : x.split('-')[2]) #일\n",
        "\n",
        "# 혹은 \n",
        "#train['year'] = train['date_time'].map(lambda x : x.split('-')[0]) #년\n",
        "#train['month'] = train['date_time'].map(lambda x : x.split('-')[1]) #월\n",
        "#train['day'] = train['date_time'].map(lambda x : x.split('-')[2]) #일"
      ],
      "metadata": {
        "id": "Vwg0uVoX2hkL"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "V7m4rcqk3Esi",
        "outputId": "69577043-4cab-48d7-feb2-2c0ae01b9137"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    date_time  wind_direction  sky_condition  precipitation_form  wind_speed  \\\n",
              "0  2018-04-01         207.500          4.000               0.000       3.050   \n",
              "1  2018-04-02         208.317          2.950               0.000       3.278   \n",
              "2  2018-04-03         213.516          2.911               0.000       2.690   \n",
              "3  2018-04-04         143.836          3.692               0.425       3.138   \n",
              "4  2018-04-05          95.905          4.000               0.723       3.186   \n",
              "\n",
              "   humidity  low_temp  high_temp  Precipitation_Probability  \\\n",
              "0    75.000    12.600     21.000                     30.000   \n",
              "1    69.833    12.812     19.000                     19.500   \n",
              "2    74.879    10.312     15.316                     19.113   \n",
              "3    71.849     8.312     12.368                     43.493   \n",
              "4    73.784     5.875     10.421                     63.378   \n",
              "\n",
              "   number_of_rentals  year month day  \n",
              "0              22994  2018    04  01  \n",
              "1              28139  2018    04  02  \n",
              "2              26817  2018    04  03  \n",
              "3              26034  2018    04  04  \n",
              "4               2833  2018    04  05  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b2aed138-e8c2-4e26-9546-c1f5bd6bd4cd\">\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>date_time</th>\n",
              "      <th>wind_direction</th>\n",
              "      <th>sky_condition</th>\n",
              "      <th>precipitation_form</th>\n",
              "      <th>wind_speed</th>\n",
              "      <th>humidity</th>\n",
              "      <th>low_temp</th>\n",
              "      <th>high_temp</th>\n",
              "      <th>Precipitation_Probability</th>\n",
              "      <th>number_of_rentals</th>\n",
              "      <th>year</th>\n",
              "      <th>month</th>\n",
              "      <th>day</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2018-04-01</td>\n",
              "      <td>207.500</td>\n",
              "      <td>4.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>3.050</td>\n",
              "      <td>75.000</td>\n",
              "      <td>12.600</td>\n",
              "      <td>21.000</td>\n",
              "      <td>30.000</td>\n",
              "      <td>22994</td>\n",
              "      <td>2018</td>\n",
              "      <td>04</td>\n",
              "      <td>01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2018-04-02</td>\n",
              "      <td>208.317</td>\n",
              "      <td>2.950</td>\n",
              "      <td>0.000</td>\n",
              "      <td>3.278</td>\n",
              "      <td>69.833</td>\n",
              "      <td>12.812</td>\n",
              "      <td>19.000</td>\n",
              "      <td>19.500</td>\n",
              "      <td>28139</td>\n",
              "      <td>2018</td>\n",
              "      <td>04</td>\n",
              "      <td>02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2018-04-03</td>\n",
              "      <td>213.516</td>\n",
              "      <td>2.911</td>\n",
              "      <td>0.000</td>\n",
              "      <td>2.690</td>\n",
              "      <td>74.879</td>\n",
              "      <td>10.312</td>\n",
              "      <td>15.316</td>\n",
              "      <td>19.113</td>\n",
              "      <td>26817</td>\n",
              "      <td>2018</td>\n",
              "      <td>04</td>\n",
              "      <td>03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2018-04-04</td>\n",
              "      <td>143.836</td>\n",
              "      <td>3.692</td>\n",
              "      <td>0.425</td>\n",
              "      <td>3.138</td>\n",
              "      <td>71.849</td>\n",
              "      <td>8.312</td>\n",
              "      <td>12.368</td>\n",
              "      <td>43.493</td>\n",
              "      <td>26034</td>\n",
              "      <td>2018</td>\n",
              "      <td>04</td>\n",
              "      <td>04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2018-04-05</td>\n",
              "      <td>95.905</td>\n",
              "      <td>4.000</td>\n",
              "      <td>0.723</td>\n",
              "      <td>3.186</td>\n",
              "      <td>73.784</td>\n",
              "      <td>5.875</td>\n",
              "      <td>10.421</td>\n",
              "      <td>63.378</td>\n",
              "      <td>2833</td>\n",
              "      <td>2018</td>\n",
              "      <td>04</td>\n",
              "      <td>05</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b2aed138-e8c2-4e26-9546-c1f5bd6bd4cd')\"\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",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </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-b2aed138-e8c2-4e26-9546-c1f5bd6bd4cd 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-b2aed138-e8c2-4e26-9546-c1f5bd6bd4cd');\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",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2.2. 요일 정보 추가 (week_day 열 생성)"
      ],
      "metadata": {
        "id": "QM-NGtQz3osb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "week_day = pd.to_datetime(train['date_time']).dt.day_name()\n",
        "week_day"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Na1ENuv73Gjd",
        "outputId": "50209446-4f03-49ed-c6d5-708e8aaf6e54"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0         Sunday\n",
              "1         Monday\n",
              "2        Tuesday\n",
              "3      Wednesday\n",
              "4       Thursday\n",
              "         ...    \n",
              "268       Friday\n",
              "269     Saturday\n",
              "270       Sunday\n",
              "271       Monday\n",
              "272      Tuesday\n",
              "Name: date_time, Length: 273, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train['week_day']= week_day"
      ],
      "metadata": {
        "id": "Zc9o80Rw7IuT"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "DGDKvP_w30JD",
        "outputId": "9224a36e-27d0-40e0-9b47-056abf85d2e5"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    date_time  wind_direction  sky_condition  precipitation_form  wind_speed  \\\n",
              "0  2018-04-01         207.500          4.000               0.000       3.050   \n",
              "1  2018-04-02         208.317          2.950               0.000       3.278   \n",
              "2  2018-04-03         213.516          2.911               0.000       2.690   \n",
              "3  2018-04-04         143.836          3.692               0.425       3.138   \n",
              "4  2018-04-05          95.905          4.000               0.723       3.186   \n",
              "\n",
              "   humidity  low_temp  high_temp  Precipitation_Probability  \\\n",
              "0    75.000    12.600     21.000                     30.000   \n",
              "1    69.833    12.812     19.000                     19.500   \n",
              "2    74.879    10.312     15.316                     19.113   \n",
              "3    71.849     8.312     12.368                     43.493   \n",
              "4    73.784     5.875     10.421                     63.378   \n",
              "\n",
              "   number_of_rentals  year month day   week_day  \n",
              "0              22994  2018    04  01     Sunday  \n",
              "1              28139  2018    04  02     Monday  \n",
              "2              26817  2018    04  03    Tuesday  \n",
              "3              26034  2018    04  04  Wednesday  \n",
              "4               2833  2018    04  05   Thursday  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-723967a6-efcc-4849-82f0-16b6351869e5\">\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>date_time</th>\n",
              "      <th>wind_direction</th>\n",
              "      <th>sky_condition</th>\n",
              "      <th>precipitation_form</th>\n",
              "      <th>wind_speed</th>\n",
              "      <th>humidity</th>\n",
              "      <th>low_temp</th>\n",
              "      <th>high_temp</th>\n",
              "      <th>Precipitation_Probability</th>\n",
              "      <th>number_of_rentals</th>\n",
              "      <th>year</th>\n",
              "      <th>month</th>\n",
              "      <th>day</th>\n",
              "      <th>week_day</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2018-04-01</td>\n",
              "      <td>207.500</td>\n",
              "      <td>4.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>3.050</td>\n",
              "      <td>75.000</td>\n",
              "      <td>12.600</td>\n",
              "      <td>21.000</td>\n",
              "      <td>30.000</td>\n",
              "      <td>22994</td>\n",
              "      <td>2018</td>\n",
              "      <td>04</td>\n",
              "      <td>01</td>\n",
              "      <td>Sunday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2018-04-02</td>\n",
              "      <td>208.317</td>\n",
              "      <td>2.950</td>\n",
              "      <td>0.000</td>\n",
              "      <td>3.278</td>\n",
              "      <td>69.833</td>\n",
              "      <td>12.812</td>\n",
              "      <td>19.000</td>\n",
              "      <td>19.500</td>\n",
              "      <td>28139</td>\n",
              "      <td>2018</td>\n",
              "      <td>04</td>\n",
              "      <td>02</td>\n",
              "      <td>Monday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2018-04-03</td>\n",
              "      <td>213.516</td>\n",
              "      <td>2.911</td>\n",
              "      <td>0.000</td>\n",
              "      <td>2.690</td>\n",
              "      <td>74.879</td>\n",
              "      <td>10.312</td>\n",
              "      <td>15.316</td>\n",
              "      <td>19.113</td>\n",
              "      <td>26817</td>\n",
              "      <td>2018</td>\n",
              "      <td>04</td>\n",
              "      <td>03</td>\n",
              "      <td>Tuesday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2018-04-04</td>\n",
              "      <td>143.836</td>\n",
              "      <td>3.692</td>\n",
              "      <td>0.425</td>\n",
              "      <td>3.138</td>\n",
              "      <td>71.849</td>\n",
              "      <td>8.312</td>\n",
              "      <td>12.368</td>\n",
              "      <td>43.493</td>\n",
              "      <td>26034</td>\n",
              "      <td>2018</td>\n",
              "      <td>04</td>\n",
              "      <td>04</td>\n",
              "      <td>Wednesday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2018-04-05</td>\n",
              "      <td>95.905</td>\n",
              "      <td>4.000</td>\n",
              "      <td>0.723</td>\n",
              "      <td>3.186</td>\n",
              "      <td>73.784</td>\n",
              "      <td>5.875</td>\n",
              "      <td>10.421</td>\n",
              "      <td>63.378</td>\n",
              "      <td>2833</td>\n",
              "      <td>2018</td>\n",
              "      <td>04</td>\n",
              "      <td>05</td>\n",
              "      <td>Thursday</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-723967a6-efcc-4849-82f0-16b6351869e5')\"\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",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </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-723967a6-efcc-4849-82f0-16b6351869e5 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-723967a6-efcc-4849-82f0-16b6351869e5');\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",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2.3. 가공한 변수 타입 변경"
      ],
      "metadata": {
        "id": "0HJg_XYf4G7J"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hTqtztwoybTy",
        "outputId": "b3c33c0e-cf0b-4aed-e635-90e8459a3f99"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 273 entries, 0 to 272\n",
            "Data columns (total 14 columns):\n",
            " #   Column                     Non-Null Count  Dtype  \n",
            "---  ------                     --------------  -----  \n",
            " 0   date_time                  273 non-null    object \n",
            " 1   wind_direction             273 non-null    float64\n",
            " 2   sky_condition              273 non-null    float64\n",
            " 3   precipitation_form         273 non-null    float64\n",
            " 4   wind_speed                 273 non-null    float64\n",
            " 5   humidity                   273 non-null    float64\n",
            " 6   low_temp                   273 non-null    float64\n",
            " 7   high_temp                  273 non-null    float64\n",
            " 8   Precipitation_Probability  273 non-null    float64\n",
            " 9   number_of_rentals          273 non-null    int64  \n",
            " 10  year                       273 non-null    object \n",
            " 11  month                      273 non-null    object \n",
            " 12  day                        273 non-null    object \n",
            " 13  week_day                   273 non-null    object \n",
            "dtypes: float64(8), int64(1), object(5)\n",
            "memory usage: 30.0+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#train['year'] = train['year'].apply(lambda x : int(x)) -> astype 과 같은 방법 같은 결과 도출\n",
        "train['year'] = train['year'].astype('int')"
      ],
      "metadata": {
        "id": "8tPEt83A4cYp"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train['month'] = train['month'].astype('int')\n",
        "train['day'] = train['day'].astype('int')"
      ],
      "metadata": {
        "id": "wlb6rtyj4ufa"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train.week_day.unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q64Ljcde5DqH",
        "outputId": "fac269ec-4a8a-426c-9bfe-16902bf8d773"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Sunday', 'Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday',\n",
              "       'Saturday'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train.loc[train['week_day'] == 'Sunday', 'week_day'] = 0\n",
        "train.loc[train['week_day'] == 'Monday', 'week_day'] = 1\n",
        "train.loc[train['week_day'] == 'Tuesday', 'week_day'] = 2\n",
        "train.loc[train['week_day'] == 'Wednesday', 'week_day'] = 3\n",
        "train.loc[train['week_day'] == 'Thursday', 'week_day'] = 4\n",
        "train.loc[train['week_day'] == 'Friday', 'week_day'] = 5\n",
        "train.loc[train['week_day'] == 'Saturday', 'week_day'] = 6"
      ],
      "metadata": {
        "id": "psdqTvxm5Jmh"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FNWvJIJm1aBJ",
        "outputId": "7ce983ec-eba0-45fc-c2d9-8149e0f497f6"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 273 entries, 0 to 272\n",
            "Data columns (total 14 columns):\n",
            " #   Column                     Non-Null Count  Dtype  \n",
            "---  ------                     --------------  -----  \n",
            " 0   date_time                  273 non-null    object \n",
            " 1   wind_direction             273 non-null    float64\n",
            " 2   sky_condition              273 non-null    float64\n",
            " 3   precipitation_form         273 non-null    float64\n",
            " 4   wind_speed                 273 non-null    float64\n",
            " 5   humidity                   273 non-null    float64\n",
            " 6   low_temp                   273 non-null    float64\n",
            " 7   high_temp                  273 non-null    float64\n",
            " 8   Precipitation_Probability  273 non-null    float64\n",
            " 9   number_of_rentals          273 non-null    int64  \n",
            " 10  year                       273 non-null    int64  \n",
            " 11  month                      273 non-null    int64  \n",
            " 12  day                        273 non-null    int64  \n",
            " 13  week_day                   273 non-null    object \n",
            "dtypes: float64(8), int64(4), object(2)\n",
            "memory usage: 30.0+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train['week_day'] = train['week_day'].astype('int')"
      ],
      "metadata": {
        "id": "RYSBY3GG5mDU"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QletW-TW5uFw",
        "outputId": "3832a33a-e244-4e66-db2b-4ce75bcf7ee9"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 273 entries, 0 to 272\n",
            "Data columns (total 14 columns):\n",
            " #   Column                     Non-Null Count  Dtype  \n",
            "---  ------                     --------------  -----  \n",
            " 0   date_time                  273 non-null    object \n",
            " 1   wind_direction             273 non-null    float64\n",
            " 2   sky_condition              273 non-null    float64\n",
            " 3   precipitation_form         273 non-null    float64\n",
            " 4   wind_speed                 273 non-null    float64\n",
            " 5   humidity                   273 non-null    float64\n",
            " 6   low_temp                   273 non-null    float64\n",
            " 7   high_temp                  273 non-null    float64\n",
            " 8   Precipitation_Probability  273 non-null    float64\n",
            " 9   number_of_rentals          273 non-null    int64  \n",
            " 10  year                       273 non-null    int64  \n",
            " 11  month                      273 non-null    int64  \n",
            " 12  day                        273 non-null    int64  \n",
            " 13  week_day                   273 non-null    int64  \n",
            "dtypes: float64(8), int64(5), object(1)\n",
            "memory usage: 30.0+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 3. EDA\n",
        "\n",
        "가설 설정\n",
        "\n",
        "1. 주중에 비해서 주말에 따릉이 대여량이 증가 할 것이다.\n",
        "2. 날씨가 덥고 습하다면 따릉이 대여량이 감소 할 것이다.\n",
        "3. 날씨가 춥고 바람이 많이 분다면 따릉이 대여량이 감소 할 것이다."
      ],
      "metadata": {
        "id": "_NlOUxT_-XgR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3.1. 시각화"
      ],
      "metadata": {
        "id": "fMAiIgGm-sZu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns \n",
        "\n",
        "%matplotlib inline\n",
        "plt.rcParams['font.size'] = 15\n",
        "plt.style.use('ggplot')\n",
        "plt.rc('font', family='NanumGothic')"
      ],
      "metadata": {
        "id": "65Spy-_05wV_"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 3.1.1. Target"
      ],
      "metadata": {
        "id": "lHquW-hx_HRu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(dpi = 150)\n",
        "\n",
        "x = train['number_of_rentals'].index\n",
        "y = train['number_of_rentals'].values\n",
        "\n",
        "plt.title(\"따릉이 대여량\")\n",
        "plt.xlabel(\"일별\")\n",
        "plt.ylabel(\"일별 따릉이 대여량\")\n",
        "plt.hlines(y=8000, xmin=0, xmax=len(x), color='Orange', linestyle='dotted')\n",
        "plt.plot(x,y)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 549
        },
        "id": "j5Bnh-4x_DXc",
        "outputId": "ef5e777c-9d64-4766-d727-f62412439e52"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* 인사이트\n",
        "\n",
        "1. 시간이 흐를수록 대여량이 증가하고있다 -> 연도별 차이\n",
        "2. 이상치가 존재한다\n",
        "3. 평일에는 출근을 하니까 대여량이 주말보다 많을까? -> 요일별 차이"
      ],
      "metadata": {
        "id": "nyDV0Ikx_zyY"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3.1.2. 연도별 따릉이 대여량\n",
        "\n",
        "인사이트1 : 시간이 흐를수록 대여량이 증가하고있다 "
      ],
      "metadata": {
        "id": "FAMrEzmb_-7B"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "month_day = train['month'].astype(str) + '.' + train['day'].astype(str)\n",
        "\n",
        "plt.figure(figsize=(20,10))\n",
        "sns.scatterplot(x=month_day, y=train['number_of_rentals'], hue=train['year']) # hue: 분화하기\n",
        "plt.xticks(rotation=90)\n",
        "plt.title('연도별 따릉이 대여량 비교', fontsize = 30)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 418
        },
        "id": "urwRR0jP_rxX",
        "outputId": "48a061fc-eb1d-46ee-a115-a686dfceed15"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* 결론\n",
        "\n",
        "Year은 매우 중요한 변수이고,\n",
        "\n",
        "Year을 반영하는 파생 변수를 생성해도 좋겠다!"
      ],
      "metadata": {
        "id": "mqPxKnboA6TB"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3.1.3.  평일과 주말 대여량\n",
        "\n",
        "인사이트 3 : 평일에는 출근을 하니까 대여량이 주말보다 많을까?\n",
        "\n",
        "가설 1"
      ],
      "metadata": {
        "id": "uqXFwdP8BLZC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "weekend = train[(train['week_day'] == 0) | (train['week_day'] == 6)] # 일요일과 토요일 -> 주말을 나타내는 데이터 프레임"
      ],
      "metadata": {
        "id": "jGzvJdVtAoHA"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "weekday = train[(train['week_day'] != 0) & (train['week_day'] != 6)] # 일요일과 토요일 아닌 -> 평일을 나타내는 데이터 프레임"
      ],
      "metadata": {
        "id": "Gg0OC6f0Be35"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "weekend.mean().number_of_rentals"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t4xbcKC9B6lS",
        "outputId": "2d2beffa-b5f1-4acd-f568-44d5760e4073"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "58988.64102564102"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "weekday.mean().number_of_rentals"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MdIwAu0dB_Sh",
        "outputId": "ada2ab0f-14d5-4e43-adc5-dce62b4c4b3c"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "59809.51282051282"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = ['weekday', 'weekend']\n",
        "y = [weekday.mean().number_of_rentals, weekend.mean().number_of_rentals]\n",
        "\n",
        "plt.figure(figsize = (8,5))\n",
        "plt.title(\"따릉이 대여량 평균\")\n",
        "plt.xlabel('평일/주말')\n",
        "plt.ylabel('대여량')\n",
        "plt.bar(x, y)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "id": "OrL9p5e3CBKw",
        "outputId": "84482e2c-e7e9-4aa3-c794-1660893f1003"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
            "  \n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "weekday.number_of_rentals.plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "7nPtjQd0CXbs",
        "outputId": "d2399a79-3ca5-4b36-809b-afa07a0a34ed"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa67cc48f50>"
            ]
          },
          "metadata": {},
          "execution_count": 42
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "weekend.number_of_rentals.plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "MIdj-zs2CfGp",
        "outputId": "ce03fc91-413b-426d-a37d-683742a80bdf"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa67cbd1450>"
            ]
          },
          "metadata": {},
          "execution_count": 43
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* 결론\n",
        "\n",
        "평일과 주말의 따릉이 대여량은 별 차이가 없다고 말할 수 있다!"
      ],
      "metadata": {
        "id": "As9bDD5BCpCI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3.1.4. 요일별 대여량"
      ],
      "metadata": {
        "id": "r0B7qiHtCs0a"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(15,8))\n",
        "\n",
        "sns.barplot(x=train['week_day'], y=train['number_of_rentals'])\n",
        "plt.title('요일별 대여량 비교', fontsize = 30)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 453
        },
        "id": "HXwj9WyiCjzl",
        "outputId": "dca57d9c-ee65-4fbb-87cb-7f9fcb5f4bc6"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(15,8))\n",
        "\n",
        "sns.barplot(x=train['week_day'], y=train['number_of_rentals'], hue=train['year'])\n",
        "plt.title('요일별+연도별 대여량 비교', fontsize = 30)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "id": "H54HVOI0DBFU",
        "outputId": "bee5291d-bbf4-42b5-d501-d2e783c1dcde"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* 결론\n",
        "\n",
        "요일이라는 변수는 따릉이 대여량에 영향을 크게 미치지 않는다\n",
        "\n",
        "따라서 drop을 해줘도 괜찮겠다!"
      ],
      "metadata": {
        "id": "5Ufk_9YVDqbY"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3.1.5 더위+습도 = 더위 점수\n",
        "\n",
        "가설2. 날씨가 덥고 습하다면 따릉이 대여량 감소"
      ],
      "metadata": {
        "id": "pzi5ZdECD-Vh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from seaborn.relational import scatterplot\n",
        "hot_score = train['high_temp'] * train['humidity'] # 높은 온도와 높은 습도 *\n",
        "\n",
        "plt.figure(figsize=(8,5))\n",
        "sns.scatterplot(x=hot_score, y=train['number_of_rentals'], hue=train['year'], s = 100 )\n",
        "plt.xticks(fontsize=15)\n",
        "plt.title('더위 점수와 대여량', fontsize = 30)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 353
        },
        "id": "e7Hmhc_RDcWt",
        "outputId": "927b2c78-b37a-4337-868a-19a624e104aa"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* 결론\n",
        "\n",
        "더위 점수가 따릉이 대여량이 영향을 준다  \n",
        ": 파생변수 '더위 점수'"
      ],
      "metadata": {
        "id": "PFHQdTqTE1cX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3.1.6. 추위 점수\n",
        "\n",
        "가설3 : 날씨가 춥고 바람이 많이 불면 따릉이 대여량이 감소할 것이다."
      ],
      "metadata": {
        "id": "vjfYZer5E-T0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cold_score = train['wind_speed'] / train['low_temp'] # 바람 높고 온도 낮다 /\n",
        "\n",
        "plt.figure(figsize=(8,5))\n",
        "sns.scatterplot(x = cold_score, y=train['number_of_rentals'], hue=train['year'], s = 100 )\n",
        "plt.xticks(fontsize=15)\n",
        "plt.title('추위 점수와 대여량', fontsize = 30)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 353
        },
        "id": "IF73oLsnEQj4",
        "outputId": "c7eb919a-6408-4031-8205-784ee552a4cf"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* 결론 \n",
        "\n",
        "추위 점수가 따릉이 대여량에 영향을 미칠 것이다 (음의 상관관계)\n",
        "\n",
        ": 추위 점수라는 파생변수를 생성"
      ],
      "metadata": {
        "id": "kMu-0g4MFt8i"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3.1.7. 이상치 확인"
      ],
      "metadata": {
        "id": "e0jL_omuGZ_K"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "id": "al1VpTn_5REm",
        "outputId": "02bc514d-d9bd-419c-8804-4d437006f725"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       wind_direction  sky_condition  precipitation_form  wind_speed  \\\n",
              "count      273.000000     273.000000          273.000000  273.000000   \n",
              "mean       202.750967       2.288256            0.100963    2.480963   \n",
              "std         56.659232       0.961775            0.203193    0.884397   \n",
              "min         57.047000       1.000000            0.000000    0.753000   \n",
              "25%        171.541000       1.405000            0.000000    1.820000   \n",
              "50%        209.774000       2.167000            0.000000    2.411000   \n",
              "75%        238.412000       3.000000            0.088000    2.924000   \n",
              "max        321.622000       4.000000            1.000000    5.607000   \n",
              "\n",
              "         humidity    low_temp   high_temp  Precipitation_Probability  \\\n",
              "count  273.000000  273.000000  273.000000                 273.000000   \n",
              "mean    56.745491   13.795249   23.384733                  16.878103   \n",
              "std     12.351268    5.107711    5.204605                  16.643772   \n",
              "min     24.831000    1.938000    9.895000                   0.000000   \n",
              "25%     47.196000    9.938000   19.842000                   4.054000   \n",
              "50%     55.845000   14.375000   24.158000                  12.162000   \n",
              "75%     66.419000   18.000000   27.526000                  22.973000   \n",
              "max     88.885000   22.312000   33.421000                  82.162000   \n",
              "\n",
              "       number_of_rentals         year       month         day    week_day  \n",
              "count         273.000000   273.000000  273.000000  273.000000  273.000000  \n",
              "mean        59574.978022  2019.000000    5.000000   15.670330    3.000000  \n",
              "std         27659.575774     0.817996    0.813489    8.774195    2.003673  \n",
              "min          1037.000000  2018.000000    4.000000    1.000000    0.000000  \n",
              "25%         36761.000000  2018.000000    4.000000    8.000000    1.000000  \n",
              "50%         63032.000000  2019.000000    5.000000   16.000000    3.000000  \n",
              "75%         81515.000000  2020.000000    6.000000   23.000000    5.000000  \n",
              "max        110377.000000  2020.000000    6.000000   31.000000    6.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b3c38719-6581-4c6a-b709-3f9af0f75d09\">\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>wind_direction</th>\n",
              "      <th>sky_condition</th>\n",
              "      <th>precipitation_form</th>\n",
              "      <th>wind_speed</th>\n",
              "      <th>humidity</th>\n",
              "      <th>low_temp</th>\n",
              "      <th>high_temp</th>\n",
              "      <th>Precipitation_Probability</th>\n",
              "      <th>number_of_rentals</th>\n",
              "      <th>year</th>\n",
              "      <th>month</th>\n",
              "      <th>day</th>\n",
              "      <th>week_day</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>273.000000</td>\n",
              "      <td>273.000000</td>\n",
              "      <td>273.000000</td>\n",
              "      <td>273.000000</td>\n",
              "      <td>273.000000</td>\n",
              "      <td>273.000000</td>\n",
              "      <td>273.000000</td>\n",
              "      <td>273.000000</td>\n",
              "      <td>273.000000</td>\n",
              "      <td>273.000000</td>\n",
              "      <td>273.000000</td>\n",
              "      <td>273.000000</td>\n",
              "      <td>273.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>202.750967</td>\n",
              "      <td>2.288256</td>\n",
              "      <td>0.100963</td>\n",
              "      <td>2.480963</td>\n",
              "      <td>56.745491</td>\n",
              "      <td>13.795249</td>\n",
              "      <td>23.384733</td>\n",
              "      <td>16.878103</td>\n",
              "      <td>59574.978022</td>\n",
              "      <td>2019.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>15.670330</td>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>56.659232</td>\n",
              "      <td>0.961775</td>\n",
              "      <td>0.203193</td>\n",
              "      <td>0.884397</td>\n",
              "      <td>12.351268</td>\n",
              "      <td>5.107711</td>\n",
              "      <td>5.204605</td>\n",
              "      <td>16.643772</td>\n",
              "      <td>27659.575774</td>\n",
              "      <td>0.817996</td>\n",
              "      <td>0.813489</td>\n",
              "      <td>8.774195</td>\n",
              "      <td>2.003673</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>57.047000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.753000</td>\n",
              "      <td>24.831000</td>\n",
              "      <td>1.938000</td>\n",
              "      <td>9.895000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1037.000000</td>\n",
              "      <td>2018.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>171.541000</td>\n",
              "      <td>1.405000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.820000</td>\n",
              "      <td>47.196000</td>\n",
              "      <td>9.938000</td>\n",
              "      <td>19.842000</td>\n",
              "      <td>4.054000</td>\n",
              "      <td>36761.000000</td>\n",
              "      <td>2018.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>209.774000</td>\n",
              "      <td>2.167000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.411000</td>\n",
              "      <td>55.845000</td>\n",
              "      <td>14.375000</td>\n",
              "      <td>24.158000</td>\n",
              "      <td>12.162000</td>\n",
              "      <td>63032.000000</td>\n",
              "      <td>2019.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>238.412000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.088000</td>\n",
              "      <td>2.924000</td>\n",
              "      <td>66.419000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>27.526000</td>\n",
              "      <td>22.973000</td>\n",
              "      <td>81515.000000</td>\n",
              "      <td>2020.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>5.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>321.622000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>5.607000</td>\n",
              "      <td>88.885000</td>\n",
              "      <td>22.312000</td>\n",
              "      <td>33.421000</td>\n",
              "      <td>82.162000</td>\n",
              "      <td>110377.000000</td>\n",
              "      <td>2020.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>6.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b3c38719-6581-4c6a-b709-3f9af0f75d09')\"\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",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </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-b3c38719-6581-4c6a-b709-3f9af0f75d09 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-b3c38719-6581-4c6a-b709-3f9af0f75d09');\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",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.style.use(\"ggplot\")\n",
        "\n",
        "feature = train.describe().columns\n",
        "\n",
        "plt.figure(figsize=(30,15))\n",
        "plt.suptitle(\"독립변수별 이상치 확인\", fontsize = 30)\n",
        "\n",
        "for i in range(len(feature)):\n",
        "  plt.subplot(2,7,i+1)\n",
        "  plt.title(feature[i])\n",
        "  plt.boxplot(train[feature[i]])\n",
        "plt.show()\n",
        "\n",
        "# 이상치가 없는 듯하다. 강수형태는 이상치라고 보기에는 애매"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "CDW7CtWRFj1k",
        "outputId": "b06e1974-e39c-4ca9-a7da-bf55f9b955c8"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2160x1080 with 13 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3.1.8. 상관관계\n",
        "\n",
        "가로를 봐주자"
      ],
      "metadata": {
        "id": "JX7E2fn2IQo1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12,10))\n",
        "sns.heatmap(data = train.corr(method='pearson'), annot=True, fmt='.2f', linewidths=.5, cmap='Blues')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 809
        },
        "id": "BuwGiwfOGnCR",
        "outputId": "c2d60de8-feeb-4f1d-ce4e-62570ce67974"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 8722 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 8722 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa67c8178d0>"
            ]
          },
          "metadata": {},
          "execution_count": 50
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x720 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3.2. 가설 검정 및 총 정리\n",
        "\n",
        "3.1.1. : 타겟을 통하여 3가지 인사이트 도출  \n",
        "3.1.2. : 연도별 대여량이 증가 하므로 , Year 관한 변수 하나 생성  \n",
        "3.1.3. : 가설 1 : 평일과 주말의 대여량 차이는 X  \n",
        "3.1.4. : 가설 1 (파생) : 요일별 대여량도 차이가 없다  \n",
        "3.1.5. : 가설 2 : 더위 점수는 대여량이 감소하는 것이아니라 오히려 증가했다  \n",
        "3.1.6. : 가설 3 : 추위 점수는 대여량이 감소하였다.  \n",
        "3.1.7. : 이상치에서는 특이한 이상치 발견 X  \n",
        "3.1.8. : 피어슨 상관계수를 통한 전반적인 피처의 상관관계를 도출  \n",
        "\n",
        ": Year 인플레이션 변수 생성, 더위 점수 생성, 추위 점수 생성, 요일, 날짜, 일 변수 삭제"
      ],
      "metadata": {
        "id": "8XIqX_D3KU10"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 4. Feature Engineering\n",
        "\n",
        "## 4.1. Feature Extraction\n",
        "\n",
        "### 4.1.1. Year inflation"
      ],
      "metadata": {
        "id": "P0uYYc8nL4TR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train[train['year']==2018]['number_of_rentals']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6ffrpGgsIkm1",
        "outputId": "e28be297-6028-4d48-a304-9c24f04b64f1"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0     22994\n",
              "1     28139\n",
              "2     26817\n",
              "3     26034\n",
              "4      2833\n",
              "      ...  \n",
              "86     6391\n",
              "87    41128\n",
              "88    27757\n",
              "89    42765\n",
              "90    26911\n",
              "Name: number_of_rentals, Length: 91, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train[train['year']==2019]['number_of_rentals']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ff6oSiveMJSg",
        "outputId": "f667e5dc-5490-4b9d-8e15-76ea5c688e37"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "91     37660\n",
              "92     42029\n",
              "93     43257\n",
              "94     45681\n",
              "95     45643\n",
              "       ...  \n",
              "177    81274\n",
              "178    79234\n",
              "179    77056\n",
              "180    67346\n",
              "181    74994\n",
              "Name: number_of_rentals, Length: 91, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train[train['year']==2020]['number_of_rentals']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kPKpKtZAMLJU",
        "outputId": "aa980228-cd73-4208-e8ae-423e9340ca66"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "182     70258\n",
              "183     72129\n",
              "184     74856\n",
              "185     66405\n",
              "186     64111\n",
              "        ...  \n",
              "268     96150\n",
              "269    107001\n",
              "270     98568\n",
              "271     70053\n",
              "272     38086\n",
              "Name: number_of_rentals, Length: 91, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "NGLrt956NKZK",
        "outputId": "d09d6c34-e41e-4643-ac74-1e915b6e0bff"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     date_time  wind_direction  sky_condition  precipitation_form  wind_speed  \\\n",
              "0   2021-04-01         108.833          3.000               0.000       2.900   \n",
              "1   2021-04-02         116.717          3.850               0.000       2.662   \n",
              "2   2021-04-03          82.669          4.000               0.565       2.165   \n",
              "3   2021-04-04          44.123          3.466               0.466       3.747   \n",
              "4   2021-04-05         147.791          1.500               0.000       1.560   \n",
              "..         ...             ...            ...                 ...         ...   \n",
              "86  2021-06-26         177.149          3.980               0.223       1.066   \n",
              "87  2021-06-27         138.723          2.777               0.135       1.290   \n",
              "88  2021-06-28         111.095          3.338               1.270       1.692   \n",
              "89  2021-06-29         171.622          3.270               0.595       1.470   \n",
              "90  2021-06-30         181.709          3.270               0.703       1.180   \n",
              "\n",
              "    humidity  low_temp  high_temp  Precipitation_Probability  \n",
              "0     28.333    11.800     20.667                     18.333  \n",
              "1     46.417    12.000     19.000                     28.500  \n",
              "2     77.258     8.875     16.368                     52.847  \n",
              "3     63.288     6.250     17.368                     37.671  \n",
              "4     48.176     7.188     18.684                      4.459  \n",
              "..       ...       ...        ...                        ...  \n",
              "86    74.628    20.312     28.579                     36.486  \n",
              "87    70.236    20.812     29.000                     18.378  \n",
              "88    70.338    21.000     28.789                     35.946  \n",
              "89    70.473    21.000     29.421                     27.770  \n",
              "90    75.203    21.500     30.211                     29.054  \n",
              "\n",
              "[91 rows x 9 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f7901c3c-003f-46aa-b705-d08beec52168\">\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>date_time</th>\n",
              "      <th>wind_direction</th>\n",
              "      <th>sky_condition</th>\n",
              "      <th>precipitation_form</th>\n",
              "      <th>wind_speed</th>\n",
              "      <th>humidity</th>\n",
              "      <th>low_temp</th>\n",
              "      <th>high_temp</th>\n",
              "      <th>Precipitation_Probability</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2021-04-01</td>\n",
              "      <td>108.833</td>\n",
              "      <td>3.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>2.900</td>\n",
              "      <td>28.333</td>\n",
              "      <td>11.800</td>\n",
              "      <td>20.667</td>\n",
              "      <td>18.333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2021-04-02</td>\n",
              "      <td>116.717</td>\n",
              "      <td>3.850</td>\n",
              "      <td>0.000</td>\n",
              "      <td>2.662</td>\n",
              "      <td>46.417</td>\n",
              "      <td>12.000</td>\n",
              "      <td>19.000</td>\n",
              "      <td>28.500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2021-04-03</td>\n",
              "      <td>82.669</td>\n",
              "      <td>4.000</td>\n",
              "      <td>0.565</td>\n",
              "      <td>2.165</td>\n",
              "      <td>77.258</td>\n",
              "      <td>8.875</td>\n",
              "      <td>16.368</td>\n",
              "      <td>52.847</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2021-04-04</td>\n",
              "      <td>44.123</td>\n",
              "      <td>3.466</td>\n",
              "      <td>0.466</td>\n",
              "      <td>3.747</td>\n",
              "      <td>63.288</td>\n",
              "      <td>6.250</td>\n",
              "      <td>17.368</td>\n",
              "      <td>37.671</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2021-04-05</td>\n",
              "      <td>147.791</td>\n",
              "      <td>1.500</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.560</td>\n",
              "      <td>48.176</td>\n",
              "      <td>7.188</td>\n",
              "      <td>18.684</td>\n",
              "      <td>4.459</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>86</th>\n",
              "      <td>2021-06-26</td>\n",
              "      <td>177.149</td>\n",
              "      <td>3.980</td>\n",
              "      <td>0.223</td>\n",
              "      <td>1.066</td>\n",
              "      <td>74.628</td>\n",
              "      <td>20.312</td>\n",
              "      <td>28.579</td>\n",
              "      <td>36.486</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>87</th>\n",
              "      <td>2021-06-27</td>\n",
              "      <td>138.723</td>\n",
              "      <td>2.777</td>\n",
              "      <td>0.135</td>\n",
              "      <td>1.290</td>\n",
              "      <td>70.236</td>\n",
              "      <td>20.812</td>\n",
              "      <td>29.000</td>\n",
              "      <td>18.378</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>88</th>\n",
              "      <td>2021-06-28</td>\n",
              "      <td>111.095</td>\n",
              "      <td>3.338</td>\n",
              "      <td>1.270</td>\n",
              "      <td>1.692</td>\n",
              "      <td>70.338</td>\n",
              "      <td>21.000</td>\n",
              "      <td>28.789</td>\n",
              "      <td>35.946</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>89</th>\n",
              "      <td>2021-06-29</td>\n",
              "      <td>171.622</td>\n",
              "      <td>3.270</td>\n",
              "      <td>0.595</td>\n",
              "      <td>1.470</td>\n",
              "      <td>70.473</td>\n",
              "      <td>21.000</td>\n",
              "      <td>29.421</td>\n",
              "      <td>27.770</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>90</th>\n",
              "      <td>2021-06-30</td>\n",
              "      <td>181.709</td>\n",
              "      <td>3.270</td>\n",
              "      <td>0.703</td>\n",
              "      <td>1.180</td>\n",
              "      <td>75.203</td>\n",
              "      <td>21.500</td>\n",
              "      <td>30.211</td>\n",
              "      <td>29.054</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>91 rows × 9 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f7901c3c-003f-46aa-b705-d08beec52168')\"\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",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </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-f7901c3c-003f-46aa-b705-d08beec52168 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-f7901c3c-003f-46aa-b705-d08beec52168');\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",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 4.1.2. 피쳐 제거"
      ],
      "metadata": {
        "id": "CmDQ3E7gNmBA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train = train.drop(['week_day'], axis = 1)\n",
        "train = train.drop(['day'], axis = 1)\n",
        "train = train.drop(['date_time'], axis = 1)"
      ],
      "metadata": {
        "id": "v2pofASMNYXP"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "4iUlYqR-N0kZ",
        "outputId": "4426b523-ae7c-41e0-eeaf-58750e636063"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   wind_direction  sky_condition  precipitation_form  wind_speed  humidity  \\\n",
              "0         207.500          4.000               0.000       3.050    75.000   \n",
              "1         208.317          2.950               0.000       3.278    69.833   \n",
              "2         213.516          2.911               0.000       2.690    74.879   \n",
              "3         143.836          3.692               0.425       3.138    71.849   \n",
              "4          95.905          4.000               0.723       3.186    73.784   \n",
              "\n",
              "   low_temp  high_temp  Precipitation_Probability  number_of_rentals  year  \\\n",
              "0    12.600     21.000                     30.000              22994  2018   \n",
              "1    12.812     19.000                     19.500              28139  2018   \n",
              "2    10.312     15.316                     19.113              26817  2018   \n",
              "3     8.312     12.368                     43.493              26034  2018   \n",
              "4     5.875     10.421                     63.378               2833  2018   \n",
              "\n",
              "   month  \n",
              "0      4  \n",
              "1      4  \n",
              "2      4  \n",
              "3      4  \n",
              "4      4  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b3ce292a-377f-4d5d-a723-67b10c10c490\">\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>wind_direction</th>\n",
              "      <th>sky_condition</th>\n",
              "      <th>precipitation_form</th>\n",
              "      <th>wind_speed</th>\n",
              "      <th>humidity</th>\n",
              "      <th>low_temp</th>\n",
              "      <th>high_temp</th>\n",
              "      <th>Precipitation_Probability</th>\n",
              "      <th>number_of_rentals</th>\n",
              "      <th>year</th>\n",
              "      <th>month</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>207.500</td>\n",
              "      <td>4.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>3.050</td>\n",
              "      <td>75.000</td>\n",
              "      <td>12.600</td>\n",
              "      <td>21.000</td>\n",
              "      <td>30.000</td>\n",
              "      <td>22994</td>\n",
              "      <td>2018</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>208.317</td>\n",
              "      <td>2.950</td>\n",
              "      <td>0.000</td>\n",
              "      <td>3.278</td>\n",
              "      <td>69.833</td>\n",
              "      <td>12.812</td>\n",
              "      <td>19.000</td>\n",
              "      <td>19.500</td>\n",
              "      <td>28139</td>\n",
              "      <td>2018</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>213.516</td>\n",
              "      <td>2.911</td>\n",
              "      <td>0.000</td>\n",
              "      <td>2.690</td>\n",
              "      <td>74.879</td>\n",
              "      <td>10.312</td>\n",
              "      <td>15.316</td>\n",
              "      <td>19.113</td>\n",
              "      <td>26817</td>\n",
              "      <td>2018</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>143.836</td>\n",
              "      <td>3.692</td>\n",
              "      <td>0.425</td>\n",
              "      <td>3.138</td>\n",
              "      <td>71.849</td>\n",
              "      <td>8.312</td>\n",
              "      <td>12.368</td>\n",
              "      <td>43.493</td>\n",
              "      <td>26034</td>\n",
              "      <td>2018</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>95.905</td>\n",
              "      <td>4.000</td>\n",
              "      <td>0.723</td>\n",
              "      <td>3.186</td>\n",
              "      <td>73.784</td>\n",
              "      <td>5.875</td>\n",
              "      <td>10.421</td>\n",
              "      <td>63.378</td>\n",
              "      <td>2833</td>\n",
              "      <td>2018</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b3ce292a-377f-4d5d-a723-67b10c10c490')\"\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",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </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-b3ce292a-377f-4d5d-a723-67b10c10c490 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-b3ce292a-377f-4d5d-a723-67b10c10c490');\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",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 4.1.3. 더위 및 추위 파생 변수 생성"
      ],
      "metadata": {
        "id": "EKu4TBPKN5gh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train['hot_score'] = train['high_temp']*train['humidity']\n",
        "train['cold_score'] = train['wind_speed']/train['low_temp']"
      ],
      "metadata": {
        "id": "Q_291xYBN2on"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "c3Po1sr8OJWf",
        "outputId": "b9658496-b3c7-4a2d-b260-55713a707884"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   wind_direction  sky_condition  precipitation_form  wind_speed  humidity  \\\n",
              "0         207.500          4.000               0.000       3.050    75.000   \n",
              "1         208.317          2.950               0.000       3.278    69.833   \n",
              "2         213.516          2.911               0.000       2.690    74.879   \n",
              "3         143.836          3.692               0.425       3.138    71.849   \n",
              "4          95.905          4.000               0.723       3.186    73.784   \n",
              "\n",
              "   low_temp  high_temp  Precipitation_Probability  number_of_rentals  year  \\\n",
              "0    12.600     21.000                     30.000              22994  2018   \n",
              "1    12.812     19.000                     19.500              28139  2018   \n",
              "2    10.312     15.316                     19.113              26817  2018   \n",
              "3     8.312     12.368                     43.493              26034  2018   \n",
              "4     5.875     10.421                     63.378               2833  2018   \n",
              "\n",
              "   month    hot_score  cold_score  \n",
              "0      4  1575.000000    0.242063  \n",
              "1      4  1326.827000    0.255854  \n",
              "2      4  1146.846764    0.260861  \n",
              "3      4   888.628432    0.377526  \n",
              "4      4   768.903064    0.542298  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8a9f202a-3fad-4d18-ac71-851f0db96e15\">\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>wind_direction</th>\n",
              "      <th>sky_condition</th>\n",
              "      <th>precipitation_form</th>\n",
              "      <th>wind_speed</th>\n",
              "      <th>humidity</th>\n",
              "      <th>low_temp</th>\n",
              "      <th>high_temp</th>\n",
              "      <th>Precipitation_Probability</th>\n",
              "      <th>number_of_rentals</th>\n",
              "      <th>year</th>\n",
              "      <th>month</th>\n",
              "      <th>hot_score</th>\n",
              "      <th>cold_score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>207.500</td>\n",
              "      <td>4.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>3.050</td>\n",
              "      <td>75.000</td>\n",
              "      <td>12.600</td>\n",
              "      <td>21.000</td>\n",
              "      <td>30.000</td>\n",
              "      <td>22994</td>\n",
              "      <td>2018</td>\n",
              "      <td>4</td>\n",
              "      <td>1575.000000</td>\n",
              "      <td>0.242063</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>208.317</td>\n",
              "      <td>2.950</td>\n",
              "      <td>0.000</td>\n",
              "      <td>3.278</td>\n",
              "      <td>69.833</td>\n",
              "      <td>12.812</td>\n",
              "      <td>19.000</td>\n",
              "      <td>19.500</td>\n",
              "      <td>28139</td>\n",
              "      <td>2018</td>\n",
              "      <td>4</td>\n",
              "      <td>1326.827000</td>\n",
              "      <td>0.255854</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>213.516</td>\n",
              "      <td>2.911</td>\n",
              "      <td>0.000</td>\n",
              "      <td>2.690</td>\n",
              "      <td>74.879</td>\n",
              "      <td>10.312</td>\n",
              "      <td>15.316</td>\n",
              "      <td>19.113</td>\n",
              "      <td>26817</td>\n",
              "      <td>2018</td>\n",
              "      <td>4</td>\n",
              "      <td>1146.846764</td>\n",
              "      <td>0.260861</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>143.836</td>\n",
              "      <td>3.692</td>\n",
              "      <td>0.425</td>\n",
              "      <td>3.138</td>\n",
              "      <td>71.849</td>\n",
              "      <td>8.312</td>\n",
              "      <td>12.368</td>\n",
              "      <td>43.493</td>\n",
              "      <td>26034</td>\n",
              "      <td>2018</td>\n",
              "      <td>4</td>\n",
              "      <td>888.628432</td>\n",
              "      <td>0.377526</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>95.905</td>\n",
              "      <td>4.000</td>\n",
              "      <td>0.723</td>\n",
              "      <td>3.186</td>\n",
              "      <td>73.784</td>\n",
              "      <td>5.875</td>\n",
              "      <td>10.421</td>\n",
              "      <td>63.378</td>\n",
              "      <td>2833</td>\n",
              "      <td>2018</td>\n",
              "      <td>4</td>\n",
              "      <td>768.903064</td>\n",
              "      <td>0.542298</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8a9f202a-3fad-4d18-ac71-851f0db96e15')\"\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",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </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-8a9f202a-3fad-4d18-ac71-851f0db96e15 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-8a9f202a-3fad-4d18-ac71-851f0db96e15');\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",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 4.2. Feature Selection"
      ],
      "metadata": {
        "id": "Tt0ihmqsOOjl"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "axis = 0 : 인덱스(index)\n",
        "\n",
        "axis = 1 : 열(columns)"
      ],
      "metadata": {
        "id": "wlNdSwIfUvPz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#X = train.drop(['number_of_rentals'], axis = 1) 같은 방법\n",
        "\n",
        "X = train[['wind_direction', 'sky_condition', 'precipitation_form', 'wind_speed',\n",
        "       'humidity', 'low_temp', 'high_temp', 'Precipitation_Probability',\n",
        "       'year', 'month', 'hot_score', 'cold_score']]\n",
        "\n",
        "y = train['number_of_rentals']"
      ],
      "metadata": {
        "id": "-_HbIXXPOKcO"
      },
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WKroCEeWOX_H",
        "outputId": "621aeaeb-77bb-46c8-9140-60156b900dc0"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['wind_direction', 'sky_condition', 'precipitation_form', 'wind_speed',\n",
              "       'humidity', 'low_temp', 'high_temp', 'Precipitation_Probability',\n",
              "       'number_of_rentals', 'year', 'month', 'hot_score', 'cold_score'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 5. Modeling"
      ],
      "metadata": {
        "id": "HVzma8fNOq6B"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 5.1. 모델 선택"
      ],
      "metadata": {
        "id": "DWppNlwbOszx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 하이퍼파라미터"
      ],
      "metadata": {
        "id": "qoqbWNi9d8ll"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from sklearn.model_selection import RandomizedSearchCV\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from scipy.stats import randint\n",
        "\n",
        "param_distribs = {\n",
        "    'n_estimators': randint(low=1,high=200),\n",
        "    'max_features':randint(low=1,high=8),\n",
        "    'max_depth':randint(low=1,high=16),\n",
        "    'min_samples_leaf':randint(low=1,high=20),\n",
        "    'min_samples_split':randint(low=1,high=20)\n",
        "}\n",
        "\n",
        "rfr = RandomForestRegressor(random_state=42)\n",
        "grid_cv = RandomizedSearchCV(rfr, param_distributions=param_distribs, n_iter=10,cv=5,\n",
        "                       scoring='neg_mean_squared_error', random_state=42, n_jobs=-1)\n",
        "grid_cv.fit(X,y)\n",
        "\n",
        "cvres=grid_cv.cv_results_\n",
        "for mean_score, params in zip(cvres['mean_test_score'],cvres['params']):\n",
        "  print(np.sqrt(-mean_score), params)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ElKFTW09CNhh",
        "outputId": "50871afb-4cec-4cde-d701-0a81896aa663"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "20289.518990503835 {'max_depth': 7, 'max_features': 4, 'min_samples_leaf': 15, 'min_samples_split': 11, 'n_estimators': 72}\n",
            "18494.31539200906 {'max_depth': 13, 'max_features': 5, 'min_samples_leaf': 7, 'min_samples_split': 19, 'n_estimators': 75}\n",
            "16712.826431295485 {'max_depth': 11, 'max_features': 5, 'min_samples_leaf': 4, 'min_samples_split': 8, 'n_estimators': 152}\n",
            "17509.538003419122 {'max_depth': 3, 'max_features': 6, 'min_samples_leaf': 2, 'min_samples_split': 12, 'n_estimators': 158}\n",
            "21816.634641430384 {'max_depth': 6, 'max_features': 2, 'min_samples_leaf': 1, 'min_samples_split': 12, 'n_estimators': 58}\n",
            "18992.0399716079 {'max_depth': 6, 'max_features': 5, 'min_samples_leaf': 12, 'min_samples_split': 17, 'n_estimators': 59}\n",
            "23974.29244111996 {'max_depth': 15, 'max_features': 2, 'min_samples_leaf': 16, 'min_samples_split': 15, 'n_estimators': 190}\n",
            "19558.734428949487 {'max_depth': 14, 'max_features': 7, 'min_samples_leaf': 19, 'min_samples_split': 12, 'n_estimators': 55}\n",
            "24465.700661103652 {'max_depth': 4, 'max_features': 1, 'min_samples_leaf': 3, 'min_samples_split': 5, 'n_estimators': 51}\n",
            "18702.95105047334 {'max_depth': 7, 'max_features': 5, 'min_samples_leaf': 9, 'min_samples_split': 7, 'n_estimators': 18}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "model = RandomForestRegressor(max_depth=15,max_features=2, min_samples_leaf= 16, min_samples_split= 15, n_estimators= 190)"
      ],
      "metadata": {
        "id": "aktf95vrOZSA"
      },
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 이방법도 가능!\n",
        "\n",
        "#from sklearn.linear_model import LinearRegression\n",
        "\n",
        "#model = LinearRegression()"
      ],
      "metadata": {
        "id": "f30oXOH8RNvS"
      },
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 5.2. 모델 학습"
      ],
      "metadata": {
        "id": "zttn4jbcO2QU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(X,y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jWdKI_D1O1EH",
        "outputId": "ab32052b-d30e-4bb8-e766-dd29810e54c0"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestRegressor(max_depth=15, max_features=2, min_samples_leaf=16,\n",
              "                      min_samples_split=15, n_estimators=190)"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 5.3. 모델 예측"
      ],
      "metadata": {
        "id": "mjvYKYuDO66r"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 5.3.1. test 셋 데이터 처리"
      ],
      "metadata": {
        "id": "YIiR-MeIO-FB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "zpp4MmTmPB-5",
        "outputId": "a564e7a9-e4eb-459b-9c18-8c6386de4e54"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     wind_direction  sky_condition  precipitation_form  wind_speed  humidity  \\\n",
              "0           207.500          4.000               0.000       3.050    75.000   \n",
              "1           208.317          2.950               0.000       3.278    69.833   \n",
              "2           213.516          2.911               0.000       2.690    74.879   \n",
              "3           143.836          3.692               0.425       3.138    71.849   \n",
              "4            95.905          4.000               0.723       3.186    73.784   \n",
              "..              ...            ...                 ...         ...       ...   \n",
              "268         228.662          3.980               0.223       2.271    78.378   \n",
              "269         207.770          2.865               0.081       1.794    78.412   \n",
              "270         282.568          1.730               0.000       1.820    72.736   \n",
              "271         137.027          2.257               0.088       2.043    70.473   \n",
              "272         120.797          3.622               0.432       5.574    77.061   \n",
              "\n",
              "     low_temp  high_temp  Precipitation_Probability  year  month    hot_score  \\\n",
              "0      12.600     21.000                     30.000  2018      4  1575.000000   \n",
              "1      12.812     19.000                     19.500  2018      4  1326.827000   \n",
              "2      10.312     15.316                     19.113  2018      4  1146.846764   \n",
              "3       8.312     12.368                     43.493  2018      4   888.628432   \n",
              "4       5.875     10.421                     63.378  2018      4   768.903064   \n",
              "..        ...        ...                        ...   ...    ...          ...   \n",
              "268    20.500     27.526                     36.486  2020      6  2157.432828   \n",
              "269    20.812     28.842                     21.081  2020      6  2261.558904   \n",
              "270    21.000     29.053                      7.297  2020      6  2113.199008   \n",
              "271    19.625     26.000                     15.541  2020      6  1832.298000   \n",
              "272    19.125     26.053                     41.284  2020      6  2007.670233   \n",
              "\n",
              "     cold_score  \n",
              "0      0.242063  \n",
              "1      0.255854  \n",
              "2      0.260861  \n",
              "3      0.377526  \n",
              "4      0.542298  \n",
              "..          ...  \n",
              "268    0.110780  \n",
              "269    0.086200  \n",
              "270    0.086667  \n",
              "271    0.104102  \n",
              "272    0.291451  \n",
              "\n",
              "[273 rows x 12 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-166af71b-7a57-42a5-86f3-be8a7f56854b\">\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>wind_direction</th>\n",
              "      <th>sky_condition</th>\n",
              "      <th>precipitation_form</th>\n",
              "      <th>wind_speed</th>\n",
              "      <th>humidity</th>\n",
              "      <th>low_temp</th>\n",
              "      <th>high_temp</th>\n",
              "      <th>Precipitation_Probability</th>\n",
              "      <th>year</th>\n",
              "      <th>month</th>\n",
              "      <th>hot_score</th>\n",
              "      <th>cold_score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>207.500</td>\n",
              "      <td>4.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>3.050</td>\n",
              "      <td>75.000</td>\n",
              "      <td>12.600</td>\n",
              "      <td>21.000</td>\n",
              "      <td>30.000</td>\n",
              "      <td>2018</td>\n",
              "      <td>4</td>\n",
              "      <td>1575.000000</td>\n",
              "      <td>0.242063</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>208.317</td>\n",
              "      <td>2.950</td>\n",
              "      <td>0.000</td>\n",
              "      <td>3.278</td>\n",
              "      <td>69.833</td>\n",
              "      <td>12.812</td>\n",
              "      <td>19.000</td>\n",
              "      <td>19.500</td>\n",
              "      <td>2018</td>\n",
              "      <td>4</td>\n",
              "      <td>1326.827000</td>\n",
              "      <td>0.255854</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>213.516</td>\n",
              "      <td>2.911</td>\n",
              "      <td>0.000</td>\n",
              "      <td>2.690</td>\n",
              "      <td>74.879</td>\n",
              "      <td>10.312</td>\n",
              "      <td>15.316</td>\n",
              "      <td>19.113</td>\n",
              "      <td>2018</td>\n",
              "      <td>4</td>\n",
              "      <td>1146.846764</td>\n",
              "      <td>0.260861</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>143.836</td>\n",
              "      <td>3.692</td>\n",
              "      <td>0.425</td>\n",
              "      <td>3.138</td>\n",
              "      <td>71.849</td>\n",
              "      <td>8.312</td>\n",
              "      <td>12.368</td>\n",
              "      <td>43.493</td>\n",
              "      <td>2018</td>\n",
              "      <td>4</td>\n",
              "      <td>888.628432</td>\n",
              "      <td>0.377526</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>95.905</td>\n",
              "      <td>4.000</td>\n",
              "      <td>0.723</td>\n",
              "      <td>3.186</td>\n",
              "      <td>73.784</td>\n",
              "      <td>5.875</td>\n",
              "      <td>10.421</td>\n",
              "      <td>63.378</td>\n",
              "      <td>2018</td>\n",
              "      <td>4</td>\n",
              "      <td>768.903064</td>\n",
              "      <td>0.542298</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>268</th>\n",
              "      <td>228.662</td>\n",
              "      <td>3.980</td>\n",
              "      <td>0.223</td>\n",
              "      <td>2.271</td>\n",
              "      <td>78.378</td>\n",
              "      <td>20.500</td>\n",
              "      <td>27.526</td>\n",
              "      <td>36.486</td>\n",
              "      <td>2020</td>\n",
              "      <td>6</td>\n",
              "      <td>2157.432828</td>\n",
              "      <td>0.110780</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>269</th>\n",
              "      <td>207.770</td>\n",
              "      <td>2.865</td>\n",
              "      <td>0.081</td>\n",
              "      <td>1.794</td>\n",
              "      <td>78.412</td>\n",
              "      <td>20.812</td>\n",
              "      <td>28.842</td>\n",
              "      <td>21.081</td>\n",
              "      <td>2020</td>\n",
              "      <td>6</td>\n",
              "      <td>2261.558904</td>\n",
              "      <td>0.086200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>270</th>\n",
              "      <td>282.568</td>\n",
              "      <td>1.730</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.820</td>\n",
              "      <td>72.736</td>\n",
              "      <td>21.000</td>\n",
              "      <td>29.053</td>\n",
              "      <td>7.297</td>\n",
              "      <td>2020</td>\n",
              "      <td>6</td>\n",
              "      <td>2113.199008</td>\n",
              "      <td>0.086667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>271</th>\n",
              "      <td>137.027</td>\n",
              "      <td>2.257</td>\n",
              "      <td>0.088</td>\n",
              "      <td>2.043</td>\n",
              "      <td>70.473</td>\n",
              "      <td>19.625</td>\n",
              "      <td>26.000</td>\n",
              "      <td>15.541</td>\n",
              "      <td>2020</td>\n",
              "      <td>6</td>\n",
              "      <td>1832.298000</td>\n",
              "      <td>0.104102</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>272</th>\n",
              "      <td>120.797</td>\n",
              "      <td>3.622</td>\n",
              "      <td>0.432</td>\n",
              "      <td>5.574</td>\n",
              "      <td>77.061</td>\n",
              "      <td>19.125</td>\n",
              "      <td>26.053</td>\n",
              "      <td>41.284</td>\n",
              "      <td>2020</td>\n",
              "      <td>6</td>\n",
              "      <td>2007.670233</td>\n",
              "      <td>0.291451</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>273 rows × 12 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-166af71b-7a57-42a5-86f3-be8a7f56854b')\"\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",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </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-166af71b-7a57-42a5-86f3-be8a7f56854b 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-166af71b-7a57-42a5-86f3-be8a7f56854b');\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",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 5.3.1.1.  데이터 전처리 과정에서"
      ],
      "metadata": {
        "id": "VqflR7N4PQLs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# date_time 쪼개기 -> year, month, day\n",
        "test['year'] = test['date_time'].apply(lambda x : x.split('-')[0])\n",
        "test['month'] = test['date_time'].apply(lambda x : x.split('-')[1])\n",
        "test['day'] = test['date_time'].apply(lambda x : x.split('-')[2])\n",
        "\n",
        "# 요일 정보 추가\n",
        "week_day = pd.to_datetime(test['date_time']).dt.day_name()\n",
        "test['week_day'] = week_day\n",
        "\n",
        "# 가공한 변수 타입 변경\n",
        "test['year'] = test['year'].astype('int')\n",
        "test['month'] = test['month'].astype('int')\n",
        "test['day'] = test['day'].astype('int')\n",
        "\n",
        "test.loc[test['week_day'] == 'Sunday', 'week_day'] = 0\n",
        "test.loc[test['week_day'] == 'Monday', 'week_day'] = 1\n",
        "test.loc[test['week_day'] == 'Tuesday', 'week_day'] = 2\n",
        "test.loc[test['week_day'] == 'Wednesday', 'week_day'] = 3\n",
        "test.loc[test['week_day'] == 'Thursday', 'week_day'] = 4\n",
        "test.loc[test['week_day'] == 'Friday', 'week_day'] = 5\n",
        "test.loc[test['week_day'] == 'Saturday', 'week_day'] = 6\n",
        "test['week_day'] = test['week_day'].astype('int')\n",
        "\n",
        "test.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "q0SapVNGPD4b",
        "outputId": "8e7b7f79-da88-4256-9a42-e77fdaa35b35"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    date_time  wind_direction  sky_condition  precipitation_form  wind_speed  \\\n",
              "0  2021-04-01         108.833          3.000               0.000       2.900   \n",
              "1  2021-04-02         116.717          3.850               0.000       2.662   \n",
              "2  2021-04-03          82.669          4.000               0.565       2.165   \n",
              "3  2021-04-04          44.123          3.466               0.466       3.747   \n",
              "4  2021-04-05         147.791          1.500               0.000       1.560   \n",
              "\n",
              "   humidity  low_temp  high_temp  Precipitation_Probability  year  month  day  \\\n",
              "0    28.333    11.800     20.667                     18.333  2021      4    1   \n",
              "1    46.417    12.000     19.000                     28.500  2021      4    2   \n",
              "2    77.258     8.875     16.368                     52.847  2021      4    3   \n",
              "3    63.288     6.250     17.368                     37.671  2021      4    4   \n",
              "4    48.176     7.188     18.684                      4.459  2021      4    5   \n",
              "\n",
              "   week_day  \n",
              "0         4  \n",
              "1         5  \n",
              "2         6  \n",
              "3         0  \n",
              "4         1  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8c8f5c8b-10b8-4d25-a344-ac3880d9f482\">\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>date_time</th>\n",
              "      <th>wind_direction</th>\n",
              "      <th>sky_condition</th>\n",
              "      <th>precipitation_form</th>\n",
              "      <th>wind_speed</th>\n",
              "      <th>humidity</th>\n",
              "      <th>low_temp</th>\n",
              "      <th>high_temp</th>\n",
              "      <th>Precipitation_Probability</th>\n",
              "      <th>year</th>\n",
              "      <th>month</th>\n",
              "      <th>day</th>\n",
              "      <th>week_day</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2021-04-01</td>\n",
              "      <td>108.833</td>\n",
              "      <td>3.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>2.900</td>\n",
              "      <td>28.333</td>\n",
              "      <td>11.800</td>\n",
              "      <td>20.667</td>\n",
              "      <td>18.333</td>\n",
              "      <td>2021</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2021-04-02</td>\n",
              "      <td>116.717</td>\n",
              "      <td>3.850</td>\n",
              "      <td>0.000</td>\n",
              "      <td>2.662</td>\n",
              "      <td>46.417</td>\n",
              "      <td>12.000</td>\n",
              "      <td>19.000</td>\n",
              "      <td>28.500</td>\n",
              "      <td>2021</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2021-04-03</td>\n",
              "      <td>82.669</td>\n",
              "      <td>4.000</td>\n",
              "      <td>0.565</td>\n",
              "      <td>2.165</td>\n",
              "      <td>77.258</td>\n",
              "      <td>8.875</td>\n",
              "      <td>16.368</td>\n",
              "      <td>52.847</td>\n",
              "      <td>2021</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2021-04-04</td>\n",
              "      <td>44.123</td>\n",
              "      <td>3.466</td>\n",
              "      <td>0.466</td>\n",
              "      <td>3.747</td>\n",
              "      <td>63.288</td>\n",
              "      <td>6.250</td>\n",
              "      <td>17.368</td>\n",
              "      <td>37.671</td>\n",
              "      <td>2021</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2021-04-05</td>\n",
              "      <td>147.791</td>\n",
              "      <td>1.500</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.560</td>\n",
              "      <td>48.176</td>\n",
              "      <td>7.188</td>\n",
              "      <td>18.684</td>\n",
              "      <td>4.459</td>\n",
              "      <td>2021</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8c8f5c8b-10b8-4d25-a344-ac3880d9f482')\"\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",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </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-8c8f5c8b-10b8-4d25-a344-ac3880d9f482 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-8c8f5c8b-10b8-4d25-a344-ac3880d9f482');\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",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 5.4.1.2. Feature Extraction 과정"
      ],
      "metadata": {
        "id": "Xni30AtiPcoB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 피쳐 제거\n",
        "test = test.drop(['week_day'],axis = 1)\n",
        "test = test.drop(['day'],axis = 1)\n",
        "test = test.drop(['date_time'],axis = 1)\n",
        "\n",
        "# 더위 점수\n",
        "test['hot_score'] = test['high_temp'] * test['humidity']\n",
        "\n",
        "# 추위 점수\n",
        "test['cold_score'] = test['wind_speed'] / test['low_temp']\n",
        "\n",
        "test.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "x1NJo0xyPaWO",
        "outputId": "1c3ef095-8429-48a2-8036-1e230678a33d"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   wind_direction  sky_condition  precipitation_form  wind_speed  humidity  \\\n",
              "0         108.833          3.000               0.000       2.900    28.333   \n",
              "1         116.717          3.850               0.000       2.662    46.417   \n",
              "2          82.669          4.000               0.565       2.165    77.258   \n",
              "3          44.123          3.466               0.466       3.747    63.288   \n",
              "4         147.791          1.500               0.000       1.560    48.176   \n",
              "\n",
              "   low_temp  high_temp  Precipitation_Probability  year  month    hot_score  \\\n",
              "0    11.800     20.667                     18.333  2021      4   585.558111   \n",
              "1    12.000     19.000                     28.500  2021      4   881.923000   \n",
              "2     8.875     16.368                     52.847  2021      4  1264.558944   \n",
              "3     6.250     17.368                     37.671  2021      4  1099.185984   \n",
              "4     7.188     18.684                      4.459  2021      4   900.120384   \n",
              "\n",
              "   cold_score  \n",
              "0    0.245763  \n",
              "1    0.221833  \n",
              "2    0.243944  \n",
              "3    0.599520  \n",
              "4    0.217028  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6e6c0f51-ca98-4f87-bb0e-4d88e1f31711\">\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>wind_direction</th>\n",
              "      <th>sky_condition</th>\n",
              "      <th>precipitation_form</th>\n",
              "      <th>wind_speed</th>\n",
              "      <th>humidity</th>\n",
              "      <th>low_temp</th>\n",
              "      <th>high_temp</th>\n",
              "      <th>Precipitation_Probability</th>\n",
              "      <th>year</th>\n",
              "      <th>month</th>\n",
              "      <th>hot_score</th>\n",
              "      <th>cold_score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>108.833</td>\n",
              "      <td>3.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>2.900</td>\n",
              "      <td>28.333</td>\n",
              "      <td>11.800</td>\n",
              "      <td>20.667</td>\n",
              "      <td>18.333</td>\n",
              "      <td>2021</td>\n",
              "      <td>4</td>\n",
              "      <td>585.558111</td>\n",
              "      <td>0.245763</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>116.717</td>\n",
              "      <td>3.850</td>\n",
              "      <td>0.000</td>\n",
              "      <td>2.662</td>\n",
              "      <td>46.417</td>\n",
              "      <td>12.000</td>\n",
              "      <td>19.000</td>\n",
              "      <td>28.500</td>\n",
              "      <td>2021</td>\n",
              "      <td>4</td>\n",
              "      <td>881.923000</td>\n",
              "      <td>0.221833</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>82.669</td>\n",
              "      <td>4.000</td>\n",
              "      <td>0.565</td>\n",
              "      <td>2.165</td>\n",
              "      <td>77.258</td>\n",
              "      <td>8.875</td>\n",
              "      <td>16.368</td>\n",
              "      <td>52.847</td>\n",
              "      <td>2021</td>\n",
              "      <td>4</td>\n",
              "      <td>1264.558944</td>\n",
              "      <td>0.243944</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>44.123</td>\n",
              "      <td>3.466</td>\n",
              "      <td>0.466</td>\n",
              "      <td>3.747</td>\n",
              "      <td>63.288</td>\n",
              "      <td>6.250</td>\n",
              "      <td>17.368</td>\n",
              "      <td>37.671</td>\n",
              "      <td>2021</td>\n",
              "      <td>4</td>\n",
              "      <td>1099.185984</td>\n",
              "      <td>0.599520</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>147.791</td>\n",
              "      <td>1.500</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.560</td>\n",
              "      <td>48.176</td>\n",
              "      <td>7.188</td>\n",
              "      <td>18.684</td>\n",
              "      <td>4.459</td>\n",
              "      <td>2021</td>\n",
              "      <td>4</td>\n",
              "      <td>900.120384</td>\n",
              "      <td>0.217028</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6e6c0f51-ca98-4f87-bb0e-4d88e1f31711')\"\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",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </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-6e6c0f51-ca98-4f87-bb0e-4d88e1f31711 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-6e6c0f51-ca98-4f87-bb0e-4d88e1f31711');\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",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "q3_5v8grPjQp",
        "outputId": "353f1b52-47d7-41a7-c691-4267a073ce7c"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     wind_direction  sky_condition  precipitation_form  wind_speed  humidity  \\\n",
              "0           207.500          4.000               0.000       3.050    75.000   \n",
              "1           208.317          2.950               0.000       3.278    69.833   \n",
              "2           213.516          2.911               0.000       2.690    74.879   \n",
              "3           143.836          3.692               0.425       3.138    71.849   \n",
              "4            95.905          4.000               0.723       3.186    73.784   \n",
              "..              ...            ...                 ...         ...       ...   \n",
              "268         228.662          3.980               0.223       2.271    78.378   \n",
              "269         207.770          2.865               0.081       1.794    78.412   \n",
              "270         282.568          1.730               0.000       1.820    72.736   \n",
              "271         137.027          2.257               0.088       2.043    70.473   \n",
              "272         120.797          3.622               0.432       5.574    77.061   \n",
              "\n",
              "     low_temp  high_temp  Precipitation_Probability  year  month    hot_score  \\\n",
              "0      12.600     21.000                     30.000  2018      4  1575.000000   \n",
              "1      12.812     19.000                     19.500  2018      4  1326.827000   \n",
              "2      10.312     15.316                     19.113  2018      4  1146.846764   \n",
              "3       8.312     12.368                     43.493  2018      4   888.628432   \n",
              "4       5.875     10.421                     63.378  2018      4   768.903064   \n",
              "..        ...        ...                        ...   ...    ...          ...   \n",
              "268    20.500     27.526                     36.486  2020      6  2157.432828   \n",
              "269    20.812     28.842                     21.081  2020      6  2261.558904   \n",
              "270    21.000     29.053                      7.297  2020      6  2113.199008   \n",
              "271    19.625     26.000                     15.541  2020      6  1832.298000   \n",
              "272    19.125     26.053                     41.284  2020      6  2007.670233   \n",
              "\n",
              "     cold_score  \n",
              "0      0.242063  \n",
              "1      0.255854  \n",
              "2      0.260861  \n",
              "3      0.377526  \n",
              "4      0.542298  \n",
              "..          ...  \n",
              "268    0.110780  \n",
              "269    0.086200  \n",
              "270    0.086667  \n",
              "271    0.104102  \n",
              "272    0.291451  \n",
              "\n",
              "[273 rows x 12 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-13939607-b332-4e36-a1d3-e0378b90ffdb\">\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>wind_direction</th>\n",
              "      <th>sky_condition</th>\n",
              "      <th>precipitation_form</th>\n",
              "      <th>wind_speed</th>\n",
              "      <th>humidity</th>\n",
              "      <th>low_temp</th>\n",
              "      <th>high_temp</th>\n",
              "      <th>Precipitation_Probability</th>\n",
              "      <th>year</th>\n",
              "      <th>month</th>\n",
              "      <th>hot_score</th>\n",
              "      <th>cold_score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>207.500</td>\n",
              "      <td>4.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>3.050</td>\n",
              "      <td>75.000</td>\n",
              "      <td>12.600</td>\n",
              "      <td>21.000</td>\n",
              "      <td>30.000</td>\n",
              "      <td>2018</td>\n",
              "      <td>4</td>\n",
              "      <td>1575.000000</td>\n",
              "      <td>0.242063</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>208.317</td>\n",
              "      <td>2.950</td>\n",
              "      <td>0.000</td>\n",
              "      <td>3.278</td>\n",
              "      <td>69.833</td>\n",
              "      <td>12.812</td>\n",
              "      <td>19.000</td>\n",
              "      <td>19.500</td>\n",
              "      <td>2018</td>\n",
              "      <td>4</td>\n",
              "      <td>1326.827000</td>\n",
              "      <td>0.255854</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>213.516</td>\n",
              "      <td>2.911</td>\n",
              "      <td>0.000</td>\n",
              "      <td>2.690</td>\n",
              "      <td>74.879</td>\n",
              "      <td>10.312</td>\n",
              "      <td>15.316</td>\n",
              "      <td>19.113</td>\n",
              "      <td>2018</td>\n",
              "      <td>4</td>\n",
              "      <td>1146.846764</td>\n",
              "      <td>0.260861</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>143.836</td>\n",
              "      <td>3.692</td>\n",
              "      <td>0.425</td>\n",
              "      <td>3.138</td>\n",
              "      <td>71.849</td>\n",
              "      <td>8.312</td>\n",
              "      <td>12.368</td>\n",
              "      <td>43.493</td>\n",
              "      <td>2018</td>\n",
              "      <td>4</td>\n",
              "      <td>888.628432</td>\n",
              "      <td>0.377526</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>95.905</td>\n",
              "      <td>4.000</td>\n",
              "      <td>0.723</td>\n",
              "      <td>3.186</td>\n",
              "      <td>73.784</td>\n",
              "      <td>5.875</td>\n",
              "      <td>10.421</td>\n",
              "      <td>63.378</td>\n",
              "      <td>2018</td>\n",
              "      <td>4</td>\n",
              "      <td>768.903064</td>\n",
              "      <td>0.542298</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>268</th>\n",
              "      <td>228.662</td>\n",
              "      <td>3.980</td>\n",
              "      <td>0.223</td>\n",
              "      <td>2.271</td>\n",
              "      <td>78.378</td>\n",
              "      <td>20.500</td>\n",
              "      <td>27.526</td>\n",
              "      <td>36.486</td>\n",
              "      <td>2020</td>\n",
              "      <td>6</td>\n",
              "      <td>2157.432828</td>\n",
              "      <td>0.110780</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>269</th>\n",
              "      <td>207.770</td>\n",
              "      <td>2.865</td>\n",
              "      <td>0.081</td>\n",
              "      <td>1.794</td>\n",
              "      <td>78.412</td>\n",
              "      <td>20.812</td>\n",
              "      <td>28.842</td>\n",
              "      <td>21.081</td>\n",
              "      <td>2020</td>\n",
              "      <td>6</td>\n",
              "      <td>2261.558904</td>\n",
              "      <td>0.086200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>270</th>\n",
              "      <td>282.568</td>\n",
              "      <td>1.730</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.820</td>\n",
              "      <td>72.736</td>\n",
              "      <td>21.000</td>\n",
              "      <td>29.053</td>\n",
              "      <td>7.297</td>\n",
              "      <td>2020</td>\n",
              "      <td>6</td>\n",
              "      <td>2113.199008</td>\n",
              "      <td>0.086667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>271</th>\n",
              "      <td>137.027</td>\n",
              "      <td>2.257</td>\n",
              "      <td>0.088</td>\n",
              "      <td>2.043</td>\n",
              "      <td>70.473</td>\n",
              "      <td>19.625</td>\n",
              "      <td>26.000</td>\n",
              "      <td>15.541</td>\n",
              "      <td>2020</td>\n",
              "      <td>6</td>\n",
              "      <td>1832.298000</td>\n",
              "      <td>0.104102</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>272</th>\n",
              "      <td>120.797</td>\n",
              "      <td>3.622</td>\n",
              "      <td>0.432</td>\n",
              "      <td>5.574</td>\n",
              "      <td>77.061</td>\n",
              "      <td>19.125</td>\n",
              "      <td>26.053</td>\n",
              "      <td>41.284</td>\n",
              "      <td>2020</td>\n",
              "      <td>6</td>\n",
              "      <td>2007.670233</td>\n",
              "      <td>0.291451</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>273 rows × 12 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-13939607-b332-4e36-a1d3-e0378b90ffdb')\"\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",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </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-13939607-b332-4e36-a1d3-e0378b90ffdb 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-13939607-b332-4e36-a1d3-e0378b90ffdb');\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",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "s4lpDHxuPm_Y",
        "outputId": "36d318c3-cc8b-4638-d67f-13e2ba155fdd"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    wind_direction  sky_condition  precipitation_form  wind_speed  humidity  \\\n",
              "0          108.833          3.000               0.000       2.900    28.333   \n",
              "1          116.717          3.850               0.000       2.662    46.417   \n",
              "2           82.669          4.000               0.565       2.165    77.258   \n",
              "3           44.123          3.466               0.466       3.747    63.288   \n",
              "4          147.791          1.500               0.000       1.560    48.176   \n",
              "..             ...            ...                 ...         ...       ...   \n",
              "86         177.149          3.980               0.223       1.066    74.628   \n",
              "87         138.723          2.777               0.135       1.290    70.236   \n",
              "88         111.095          3.338               1.270       1.692    70.338   \n",
              "89         171.622          3.270               0.595       1.470    70.473   \n",
              "90         181.709          3.270               0.703       1.180    75.203   \n",
              "\n",
              "    low_temp  high_temp  Precipitation_Probability  year  month    hot_score  \\\n",
              "0     11.800     20.667                     18.333  2021      4   585.558111   \n",
              "1     12.000     19.000                     28.500  2021      4   881.923000   \n",
              "2      8.875     16.368                     52.847  2021      4  1264.558944   \n",
              "3      6.250     17.368                     37.671  2021      4  1099.185984   \n",
              "4      7.188     18.684                      4.459  2021      4   900.120384   \n",
              "..       ...        ...                        ...   ...    ...          ...   \n",
              "86    20.312     28.579                     36.486  2021      6  2132.793612   \n",
              "87    20.812     29.000                     18.378  2021      6  2036.844000   \n",
              "88    21.000     28.789                     35.946  2021      6  2024.960682   \n",
              "89    21.000     29.421                     27.770  2021      6  2073.386133   \n",
              "90    21.500     30.211                     29.054  2021      6  2271.957833   \n",
              "\n",
              "    cold_score  \n",
              "0     0.245763  \n",
              "1     0.221833  \n",
              "2     0.243944  \n",
              "3     0.599520  \n",
              "4     0.217028  \n",
              "..         ...  \n",
              "86    0.052481  \n",
              "87    0.061983  \n",
              "88    0.080571  \n",
              "89    0.070000  \n",
              "90    0.054884  \n",
              "\n",
              "[91 rows x 12 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7c79761c-0492-4ece-bc0b-8992fb99857b\">\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>wind_direction</th>\n",
              "      <th>sky_condition</th>\n",
              "      <th>precipitation_form</th>\n",
              "      <th>wind_speed</th>\n",
              "      <th>humidity</th>\n",
              "      <th>low_temp</th>\n",
              "      <th>high_temp</th>\n",
              "      <th>Precipitation_Probability</th>\n",
              "      <th>year</th>\n",
              "      <th>month</th>\n",
              "      <th>hot_score</th>\n",
              "      <th>cold_score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>108.833</td>\n",
              "      <td>3.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>2.900</td>\n",
              "      <td>28.333</td>\n",
              "      <td>11.800</td>\n",
              "      <td>20.667</td>\n",
              "      <td>18.333</td>\n",
              "      <td>2021</td>\n",
              "      <td>4</td>\n",
              "      <td>585.558111</td>\n",
              "      <td>0.245763</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>116.717</td>\n",
              "      <td>3.850</td>\n",
              "      <td>0.000</td>\n",
              "      <td>2.662</td>\n",
              "      <td>46.417</td>\n",
              "      <td>12.000</td>\n",
              "      <td>19.000</td>\n",
              "      <td>28.500</td>\n",
              "      <td>2021</td>\n",
              "      <td>4</td>\n",
              "      <td>881.923000</td>\n",
              "      <td>0.221833</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>82.669</td>\n",
              "      <td>4.000</td>\n",
              "      <td>0.565</td>\n",
              "      <td>2.165</td>\n",
              "      <td>77.258</td>\n",
              "      <td>8.875</td>\n",
              "      <td>16.368</td>\n",
              "      <td>52.847</td>\n",
              "      <td>2021</td>\n",
              "      <td>4</td>\n",
              "      <td>1264.558944</td>\n",
              "      <td>0.243944</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>44.123</td>\n",
              "      <td>3.466</td>\n",
              "      <td>0.466</td>\n",
              "      <td>3.747</td>\n",
              "      <td>63.288</td>\n",
              "      <td>6.250</td>\n",
              "      <td>17.368</td>\n",
              "      <td>37.671</td>\n",
              "      <td>2021</td>\n",
              "      <td>4</td>\n",
              "      <td>1099.185984</td>\n",
              "      <td>0.599520</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>147.791</td>\n",
              "      <td>1.500</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.560</td>\n",
              "      <td>48.176</td>\n",
              "      <td>7.188</td>\n",
              "      <td>18.684</td>\n",
              "      <td>4.459</td>\n",
              "      <td>2021</td>\n",
              "      <td>4</td>\n",
              "      <td>900.120384</td>\n",
              "      <td>0.217028</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>86</th>\n",
              "      <td>177.149</td>\n",
              "      <td>3.980</td>\n",
              "      <td>0.223</td>\n",
              "      <td>1.066</td>\n",
              "      <td>74.628</td>\n",
              "      <td>20.312</td>\n",
              "      <td>28.579</td>\n",
              "      <td>36.486</td>\n",
              "      <td>2021</td>\n",
              "      <td>6</td>\n",
              "      <td>2132.793612</td>\n",
              "      <td>0.052481</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>87</th>\n",
              "      <td>138.723</td>\n",
              "      <td>2.777</td>\n",
              "      <td>0.135</td>\n",
              "      <td>1.290</td>\n",
              "      <td>70.236</td>\n",
              "      <td>20.812</td>\n",
              "      <td>29.000</td>\n",
              "      <td>18.378</td>\n",
              "      <td>2021</td>\n",
              "      <td>6</td>\n",
              "      <td>2036.844000</td>\n",
              "      <td>0.061983</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>88</th>\n",
              "      <td>111.095</td>\n",
              "      <td>3.338</td>\n",
              "      <td>1.270</td>\n",
              "      <td>1.692</td>\n",
              "      <td>70.338</td>\n",
              "      <td>21.000</td>\n",
              "      <td>28.789</td>\n",
              "      <td>35.946</td>\n",
              "      <td>2021</td>\n",
              "      <td>6</td>\n",
              "      <td>2024.960682</td>\n",
              "      <td>0.080571</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>89</th>\n",
              "      <td>171.622</td>\n",
              "      <td>3.270</td>\n",
              "      <td>0.595</td>\n",
              "      <td>1.470</td>\n",
              "      <td>70.473</td>\n",
              "      <td>21.000</td>\n",
              "      <td>29.421</td>\n",
              "      <td>27.770</td>\n",
              "      <td>2021</td>\n",
              "      <td>6</td>\n",
              "      <td>2073.386133</td>\n",
              "      <td>0.070000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>90</th>\n",
              "      <td>181.709</td>\n",
              "      <td>3.270</td>\n",
              "      <td>0.703</td>\n",
              "      <td>1.180</td>\n",
              "      <td>75.203</td>\n",
              "      <td>21.500</td>\n",
              "      <td>30.211</td>\n",
              "      <td>29.054</td>\n",
              "      <td>2021</td>\n",
              "      <td>6</td>\n",
              "      <td>2271.957833</td>\n",
              "      <td>0.054884</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>91 rows × 12 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7c79761c-0492-4ece-bc0b-8992fb99857b')\"\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",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </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-7c79761c-0492-4ece-bc0b-8992fb99857b 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-7c79761c-0492-4ece-bc0b-8992fb99857b');\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",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 5.3.2. test셋 예측"
      ],
      "metadata": {
        "id": "EgHs5ew7Pr-W"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Feature Engineering : Year Inflaction : 2020 -> 2021"
      ],
      "metadata": {
        "id": "DT9F1GOfRyle"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x1 = sum(train[train['year']==2018]['number_of_rentals'])\n",
        "\n",
        "x2 = sum(train[train['year']==2019]['number_of_rentals'])\n",
        "\n",
        "x3 = sum(train[train['year']==2020]['number_of_rentals'])\n",
        "\n",
        "x4 = sum(submission['number_of_rentals']) ## 2021"
      ],
      "metadata": {
        "id": "jA4jDUh3P8Y5"
      },
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ab9y-4_rTZaz",
        "outputId": "2ce9e529-d1b8-4d00-d8f4-7877684be2f9"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2860617"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x4"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_JkgJNsySJlF",
        "outputId": "1dba8b73-924c-44e0-88c2-d8815fe760b8"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(x4/x1)\n",
        "print(x4/x2)\n",
        "print(x4/x3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1Aix6kKuSKe4",
        "outputId": "34d8b6d8-d8c6-4cc1-d1c6-42a0aa33e50a"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.0\n",
            "0.0\n",
            "0.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# lr\n",
        "def rental_rate_change(df):\n",
        "    y1 = df[df['year'] == 2018]['number_of_rentals'] * 3.3859145348144577\n",
        "    y2 = df[df['year'] == 2019]['number_of_rentals'] * 1.6157080615278123\n",
        "    y3 = df[df['year'] == 2020]['number_of_rentals'] * 1.3073770268514862\n",
        "    new = pd.concat([y1, y2, y3], axis=0).to_frame()\n",
        "    df['inflaction_rentals'] = new['number_of_rentals']\n",
        "    return True\n",
        "\n",
        "# # concat 문자열 합치기"
      ],
      "metadata": {
        "id": "S66Z-szrSRxF"
      },
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rental_rate_change(train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RS3NohBhShpF",
        "outputId": "cf1711a7-f8af-44dc-9715-6f83f1101410"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "EQqcTy1PSkLM",
        "outputId": "dfbe6b28-4948-45b4-e9d4-c1342b2411ee"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   wind_direction  sky_condition  precipitation_form  wind_speed  humidity  \\\n",
              "0         207.500          4.000               0.000       3.050    75.000   \n",
              "1         208.317          2.950               0.000       3.278    69.833   \n",
              "2         213.516          2.911               0.000       2.690    74.879   \n",
              "3         143.836          3.692               0.425       3.138    71.849   \n",
              "4          95.905          4.000               0.723       3.186    73.784   \n",
              "\n",
              "   low_temp  high_temp  Precipitation_Probability  number_of_rentals  year  \\\n",
              "0    12.600     21.000                     30.000              22994  2018   \n",
              "1    12.812     19.000                     19.500              28139  2018   \n",
              "2    10.312     15.316                     19.113              26817  2018   \n",
              "3     8.312     12.368                     43.493              26034  2018   \n",
              "4     5.875     10.421                     63.378               2833  2018   \n",
              "\n",
              "   month    hot_score  cold_score  inflaction_rentals  \n",
              "0      4  1575.000000    0.242063        77855.718814  \n",
              "1      4  1326.827000    0.255854        95276.249095  \n",
              "2      4  1146.846764    0.260861        90800.070080  \n",
              "3      4   888.628432    0.377526        88148.898999  \n",
              "4      4   768.903064    0.542298         9592.295877  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b4c80f81-57e2-4f26-9ad7-514f17744a56\">\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>wind_direction</th>\n",
              "      <th>sky_condition</th>\n",
              "      <th>precipitation_form</th>\n",
              "      <th>wind_speed</th>\n",
              "      <th>humidity</th>\n",
              "      <th>low_temp</th>\n",
              "      <th>high_temp</th>\n",
              "      <th>Precipitation_Probability</th>\n",
              "      <th>number_of_rentals</th>\n",
              "      <th>year</th>\n",
              "      <th>month</th>\n",
              "      <th>hot_score</th>\n",
              "      <th>cold_score</th>\n",
              "      <th>inflaction_rentals</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>207.500</td>\n",
              "      <td>4.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>3.050</td>\n",
              "      <td>75.000</td>\n",
              "      <td>12.600</td>\n",
              "      <td>21.000</td>\n",
              "      <td>30.000</td>\n",
              "      <td>22994</td>\n",
              "      <td>2018</td>\n",
              "      <td>4</td>\n",
              "      <td>1575.000000</td>\n",
              "      <td>0.242063</td>\n",
              "      <td>77855.718814</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>208.317</td>\n",
              "      <td>2.950</td>\n",
              "      <td>0.000</td>\n",
              "      <td>3.278</td>\n",
              "      <td>69.833</td>\n",
              "      <td>12.812</td>\n",
              "      <td>19.000</td>\n",
              "      <td>19.500</td>\n",
              "      <td>28139</td>\n",
              "      <td>2018</td>\n",
              "      <td>4</td>\n",
              "      <td>1326.827000</td>\n",
              "      <td>0.255854</td>\n",
              "      <td>95276.249095</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>213.516</td>\n",
              "      <td>2.911</td>\n",
              "      <td>0.000</td>\n",
              "      <td>2.690</td>\n",
              "      <td>74.879</td>\n",
              "      <td>10.312</td>\n",
              "      <td>15.316</td>\n",
              "      <td>19.113</td>\n",
              "      <td>26817</td>\n",
              "      <td>2018</td>\n",
              "      <td>4</td>\n",
              "      <td>1146.846764</td>\n",
              "      <td>0.260861</td>\n",
              "      <td>90800.070080</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>143.836</td>\n",
              "      <td>3.692</td>\n",
              "      <td>0.425</td>\n",
              "      <td>3.138</td>\n",
              "      <td>71.849</td>\n",
              "      <td>8.312</td>\n",
              "      <td>12.368</td>\n",
              "      <td>43.493</td>\n",
              "      <td>26034</td>\n",
              "      <td>2018</td>\n",
              "      <td>4</td>\n",
              "      <td>888.628432</td>\n",
              "      <td>0.377526</td>\n",
              "      <td>88148.898999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>95.905</td>\n",
              "      <td>4.000</td>\n",
              "      <td>0.723</td>\n",
              "      <td>3.186</td>\n",
              "      <td>73.784</td>\n",
              "      <td>5.875</td>\n",
              "      <td>10.421</td>\n",
              "      <td>63.378</td>\n",
              "      <td>2833</td>\n",
              "      <td>2018</td>\n",
              "      <td>4</td>\n",
              "      <td>768.903064</td>\n",
              "      <td>0.542298</td>\n",
              "      <td>9592.295877</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b4c80f81-57e2-4f26-9ad7-514f17744a56')\"\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",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </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-b4c80f81-57e2-4f26-9ad7-514f17744a56 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-b4c80f81-57e2-4f26-9ad7-514f17744a56');\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",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = train.drop(['number_of_rentals', 'inflaction_rentals'], axis=1 )\n",
        "y = train['inflaction_rentals']"
      ],
      "metadata": {
        "id": "DuCeZ2TOSlcb"
      },
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = RandomForestRegressor(max_depth=15,max_features=2, min_samples_leaf= 16, min_samples_split= 15, n_estimators= 190)# RandomForestRegressor() 이 방법이 더 좋다."
      ],
      "metadata": {
        "id": "DO3S786RSzne"
      },
      "execution_count": 81,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(X,y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SJK9V_r0S4w4",
        "outputId": "099a7d7f-c6fb-42cd-e1fa-f623d5bd8656"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestRegressor(max_depth=15, max_features=2, min_samples_leaf=16,\n",
              "                      min_samples_split=15, n_estimators=190)"
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predict = model.predict(test)"
      ],
      "metadata": {
        "id": "0xdNVGtAS6ZH"
      },
      "execution_count": 83,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "submission['number_of_rentals'] = predict\n",
        "submission.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "fLf8BeKFS_E2",
        "outputId": "5f5f52e9-33e3-46f6-c497-1fcdc93b0668"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    date_time  number_of_rentals\n",
              "0  2021-04-01       91722.522583\n",
              "1  2021-04-02       88651.023192\n",
              "2  2021-04-03       62927.042547\n",
              "3  2021-04-04       63769.892680\n",
              "4  2021-04-05       95699.410457"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2af850eb-ca8b-4fc9-a28b-ea1a43610dec\">\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>date_time</th>\n",
              "      <th>number_of_rentals</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2021-04-01</td>\n",
              "      <td>91722.522583</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2021-04-02</td>\n",
              "      <td>88651.023192</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2021-04-03</td>\n",
              "      <td>62927.042547</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2021-04-04</td>\n",
              "      <td>63769.892680</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2021-04-05</td>\n",
              "      <td>95699.410457</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2af850eb-ca8b-4fc9-a28b-ea1a43610dec')\"\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",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </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-2af850eb-ca8b-4fc9-a28b-ea1a43610dec 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-2af850eb-ca8b-4fc9-a28b-ea1a43610dec');\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",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "submission.to_csv('submission_4.csv', index = False)"
      ],
      "metadata": {
        "id": "i0Cn9jDwTCmN"
      },
      "execution_count": 85,
      "outputs": []
    }
  ]
}