데이터분석_8일차_프로젝트_자전거_대여량_예측(나영훈).ipynb

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      "name": "데이터분석 8일차 프로젝트 자전거 대여량 예측(나영훈).ipynb",
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        "<a href=\"https://colab.research.google.com/github/younghoonNa/pythonDT/blob/main/%EB%8D%B0%EC%9D%B4%ED%84%B0%EB%B6%84%EC%84%9D_8%EC%9D%BC%EC%B0%A8_%ED%94%84%EB%A1%9C%EC%A0%9D%ED%8A%B8_%EC%9E%90%EC%A0%84%EA%B1%B0_%EB%8C%80%EC%97%AC%EB%9F%89_%EC%98%88%EC%B8%A1(%EB%82%98%EC%98%81%ED%9B%88).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "markdown",
      "metadata": {
        "id": "3369b0e7"
      },
      "source": [
        "## 자전거 대여량 예측\n",
        "\n",
        "워싱턴 D.C 소재의 자전거 대여 스타트업 Capital Bikeshare의 데이터를 활용하여, 특정 시간대에 얼마나 많은 사람들이 자전거를 대여하는지 예측하는 것이 목표입니다.\n",
        "\n",
        "사람들이 자전거를 대여하는데는 많은 요소가 관여되어 있을 겁니다. 가령 **시간(새벽보다 낮에 많이 빌리겠죠)**, **날씨(비가 오면 자전거를 대여하지 않을 겁니다), 근무일(근무 시간에는 자전거를 대여하지 않겠죠)** 등. 이런 모든 요소를 조합하여 워싱턴 D.C의 자전거 교통량을 예측해주세요.\n",
        "\n",
        "## 데이터 설명\n",
        "\n",
        "#### 파일 구조\n",
        "- train.csv\n",
        "    - 학습을 위하여 활용되는 데이터 셋입니다.\n",
        "- test.csv\n",
        "    - 평가를 위하여 제공되는 데이터 셋입니다.\n",
        "- submission.csv\n",
        "    - test.csv 파일을 예측한 결과를 submission 파일에 작성한 후 제출합니다.\n",
        "    \n",
        "#### 컬럼 구조\n",
        "  * **datetime** - 시간. 연-월-일 시:분:초 로 표현합니다. (가령 2011-01-01 00:00:00은 2011년 1월 1일 0시 0분 0초)\n",
        "  * **season** - 계절. 봄(1), 여름(2), 가을(3), 겨울(4) 순으로 표현합니다.\n",
        "  * **holiday** - 공휴일. 1이면 공휴일이며, 0이면 공휴일이 아닙니다.\n",
        "  * **workingday** - 근무일. 1이면 근무일이며, 0이면 근무일이 아닙니다.\n",
        "  * **weather** - 날씨. 1 ~ 4 사이의 값을 가지며, 구체적으로는 다음과 같습니다.\n",
        "    * 1: 아주 깨끗한 날씨입니다. 또는 아주 약간의 구름이 끼어있습니다.\n",
        "    * 2: 약간의 안개와 구름이 끼어있는 날씨입니다.\n",
        "    * 3: 약간의 눈, 비가 오거나 천둥이 칩니다.\n",
        "    * 4: 아주 많은 비가 오거나 우박이 내립니다.\n",
        "  * **temp** - 온도. 섭씨(Celsius)로 적혀있습니다.\n",
        "  * **atemp** - 체감 온도. 마찬가지로 섭씨(Celsius)로 적혀있습니다.\n",
        "  * **humidity** - 습도.\n",
        "  * **windspeed** - 풍속.\n",
        "  * **casual** - 비회원(non-registered)의 자전거 대여량.\n",
        "  * **registered** - 회원(registered)의 자전거 대여량.\n",
        "  * **count** - 총 자전거 대여랑. 비회원(casual) + 회원(registered)과 동일합니다.\n",
        "  \n",
        "  ## 프로젝트 구성\n",
        "* 데이터 로드 (load data, train.csv, test.csv)\n",
        "* 데이터 전처리 및 특성 공학 (train.csv, test.csv)\n",
        "* 모델 생성 및 검증 (train.csv)\n",
        "* 평가 데이터 예측 (test.csv)\n",
        "* 결과 제출 및 점수 확인\n",
        "\n",
        "## 제출 이력 및 순위 조회\n",
        "[평가시스템](http://manage.jaen.kr)에 접속하여 확인 가능\n",
        "> 로그인 -> 프로젝트 -> 자전거 대여량 예측 -> 리더보드\n",
        "\n",
        "### 접속 정보\n",
        "- ID : `본인이메일`\n",
        "- PWD : `hl1227`"
      ],
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "62dc5783"
      },
      "source": [
        "## 환경 설정"
      ],
      "id": "62dc5783"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "05162753-f193-4159-ba56-c6a0c3ead860",
        "outputId": "e5c02e03-5229-4d2c-f58d-42948784848b"
      },
      "source": [
        "!wget http://49.247.133.7/hl_pjt.zip\n",
        "!unzip hl_pjt.zip"
      ],
      "id": "05162753-f193-4159-ba56-c6a0c3ead860",
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2022-01-05 07:04:40--  http://49.247.133.7/hl_pjt.zip\n",
            "Connecting to 49.247.133.7:80... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 194889 (190K) [application/zip]\n",
            "Saving to: ‘hl_pjt.zip’\n",
            "\n",
            "hl_pjt.zip          100%[===================>] 190.32K  --.-KB/s    in 0.1s    \n",
            "\n",
            "2022-01-05 07:04:41 (1.44 MB/s) - ‘hl_pjt.zip’ saved [194889/194889]\n",
            "\n",
            "Archive:  hl_pjt.zip\n",
            "  inflating: submission.csv          \n",
            "  inflating: test.csv                \n",
            "  inflating: train.csv               \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d81362e8-9e3a-4755-bc06-eb816e90f3ba",
        "outputId": "69f9aee8-2c11-4dab-c0b5-b0dda1f1edbe"
      },
      "source": [
        "!pip install jaen"
      ],
      "id": "d81362e8-9e3a-4755-bc06-eb816e90f3ba",
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting jaen\n",
            "  Downloading JAEN-0.0.1-py3-none-any.whl (2.1 kB)\n",
            "Installing collected packages: jaen\n",
            "Successfully installed jaen-0.0.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "922cec6a-cbb2-4f6a-b401-f49bcf76175d"
      },
      "source": [
        "from JAEN.project import Project\n",
        "pjt = Project('자전거 대여량 예측', \n",
        "              '한림대-데이터분석과정_전공자',        \n",
        "              '1차수 A반',       \n",
        "              'yh07280@naver.com')"
      ],
      "id": "922cec6a-cbb2-4f6a-b401-f49bcf76175d",
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7e986263"
      },
      "source": [
        "# 성능 평가 함수\n",
        "from sklearn.metrics import mean_squared_log_error\n",
        "def rmsle(true, pred):\n",
        "    return np.sqrt(mean_squared_log_error(true, pred)) "
      ],
      "id": "7e986263",
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8df096ac"
      },
      "source": [
        "## 모듈 import \n",
        "\n",
        "필요한 모듈을 import 합니다. 필요에 따라 추가 패키지를 load 할 수 있습니다."
      ],
      "id": "8df096ac"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dcd3cbaf"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt"
      ],
      "id": "dcd3cbaf",
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b5e4ff9e"
      },
      "source": [
        "2개의 데이터 셋트를 나누어 제공합니다.\n",
        "\n",
        "1. train: 학습용 데이터 세트. 정답 값이라고 할 수 있는 `label` 값이 같이 제공됩니다.\n",
        "2. test: 예측용 데이터 세트. 정답 값이 빠진 `feature` 들만 제공됩니다. 우리는 이 `feature`를 활용하여 예측을 진행합니다.\n",
        "3. submission: 정답 제출용 데이터 세트. 주어진 형식에 맞춰 정답 값을 기입하고 제출합니다."
      ],
      "id": "b5e4ff9e"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "0eeeffb0",
        "outputId": "6588b5e5-c23b-437e-b6b6-098c85ea67df"
      },
      "source": [
        "# train 데이터셋 로드 (train.csv)\n",
        "train = pd.read_csv('train.csv')\n",
        "train.head()"
      ],
      "id": "0eeeffb0",
      "execution_count": 72,
      "outputs": [
        {
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              "              datetime  season  holiday  ...  casual  registered  count\n",
              "0  2011-01-01 00:00:00       1        0  ...       3          13     16\n",
              "1  2011-01-01 01:00:00       1        0  ...       8          32     40\n",
              "2  2011-01-01 02:00:00       1        0  ...       5          27     32\n",
              "3  2011-01-01 03:00:00       1        0  ...       3          10     13\n",
              "4  2011-01-01 04:00:00       1        0  ...       0           1      1\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#datetime 열이 문자열이므로 먼저 처리해주고 넘어가기. > train 데이터\n",
        "#quarter -> 뭔가 성능이 향상 되는거 같진 않음.. 분기랑 계절이랑 겹쳐서 그런가..\n",
        "train['datetime'] = pd.to_datetime(train['datetime'])\n",
        "train['hour'] = train['datetime'].dt.hour\n",
        "train['year'] = train['datetime'].dt.year\n",
        "train['dayofweek'] = train['datetime'].dt.dayofweek"
      ],
      "metadata": {
        "id": "W_xh9Tjbzz9I"
      },
      "id": "W_xh9Tjbzz9I",
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#peak라는 속성 추가 공휴일일 때, 아닐 때 구분해서 / kaggle 사이클_데이터_셋 1등_모델 참조\n",
        "train['peak'] = train[['hour', 'workingday']].apply(lambda x: (0, 1)[(x['workingday'] == 1 and \\\n",
        "    ( x['hour'] == 8 or 17 <= x['hour'] <= 18 or 12 <= x['hour'] <= 13)) or \\\n",
        "      (x['workingday'] == 0 and  10 <= x['hour'] <= 19)], axis = 1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WcOWuD-O2Za8",
        "outputId": "00eb25f9-de8a-435d-9f33-43e1f3760183"
      },
      "id": "WcOWuD-O2Za8",
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: DeprecationWarning: In future, it will be an error for 'np.bool_' scalars to be interpreted as an index\n",
            "  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#drop을 통해 datetime 이제 제거\n",
        "train = train.drop('datetime' ,axis=1)\n",
        "train.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
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        "id": "whrN0wjT3qhL",
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      },
      "id": "whrN0wjT3qhL",
      "execution_count": 75,
      "outputs": [
        {
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          "data": {
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   season  holiday  workingday  weather  ...  hour  year  dayofweek  peak\n",
              "0       1        0           0        1  ...     0  2011          5     0\n",
              "1       1        0           0        1  ...     1  2011          5     0\n",
              "2       1        0           0        1  ...     2  2011          5     0\n",
              "3       1        0           0        1  ...     3  2011          5     0\n",
              "4       1        0           0        1  ...     4  2011          5     0\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "a2b7ff5f",
        "outputId": "1c7836bb-1755-4311-f146-1e934791263f"
      },
      "source": [
        "# test 데이터셋 로드 (test.csv)\n",
        "test = pd.read_csv('test.csv')\n",
        "test.head()"
      ],
      "id": "a2b7ff5f",
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "\n",
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              "      <th></th>\n",
              "      <th>datetime</th>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2011-01-20 02:00:00</td>\n",
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              "      <td>2011-01-20 03:00:00</td>\n",
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              "      <td>11.0014</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2011-01-20 04:00:00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>56</td>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
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              "    .colab-df-container {\n",
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              "\n",
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              "\n",
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              "\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",
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              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-e851c796-a095-466a-81b4-c11467cca0f2 button.colab-df-convert');\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e851c796-a095-466a-81b4-c11467cca0f2');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "              datetime  season  holiday  ...   atemp  humidity  windspeed\n",
              "0  2011-01-20 00:00:00       1        0  ...  11.365        56    26.0027\n",
              "1  2011-01-20 01:00:00       1        0  ...  13.635        56     0.0000\n",
              "2  2011-01-20 02:00:00       1        0  ...  13.635        56     0.0000\n",
              "3  2011-01-20 03:00:00       1        0  ...  12.880        56    11.0014\n",
              "4  2011-01-20 04:00:00       1        0  ...  12.880        56    11.0014\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#datetime 열이 문자열이므로 먼저 처리해주고 넘어가기. > test 데이터\n",
        "test['datetime'] = pd.to_datetime(test['datetime'])\n",
        "test['hour'] = test['datetime'].dt.hour\n",
        "test['year'] = test['datetime'].dt.year\n",
        "test['dayofweek'] = test['datetime'].dt.dayofweek"
      ],
      "metadata": {
        "id": "acNntkEOz1an"
      },
      "id": "acNntkEOz1an",
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#peak라는 속성 추가 공휴일일 때, 아닐 때 구분해서 / kaggle 참조\n",
        "test['peak'] = test[['hour', 'workingday']].apply(lambda x: (0, 1)[(x['workingday'] == 1 and \\\n",
        "    ( x['hour'] == 8 or 17 <= x['hour'] <= 18 or 12 <= x['hour'] <= 13)) or \\\n",
        "      (x['workingday'] == 0 and  10 <= x['hour'] <= 19)], axis = 1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e1khW5lh28n7",
        "outputId": "93e9f2b0-79a6-4242-b2af-c2c93c67fcf2"
      },
      "id": "e1khW5lh28n7",
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: DeprecationWarning: In future, it will be an error for 'np.bool_' scalars to be interpreted as an index\n",
            "  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#drop을 통해 datetime 이제 제거\n",
        "test = test.drop('datetime' ,axis=1)\n",
        "test.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "OGyp8Dta4NrT",
        "outputId": "5eefc64d-9bd3-4643-ab17-a646f4ffa94a"
      },
      "id": "OGyp8Dta4NrT",
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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            ],
            "text/plain": [
              "   season  holiday  workingday  weather  ...  hour  year  dayofweek  peak\n",
              "0       1        0           1        1  ...     0  2011          3     0\n",
              "1       1        0           1        1  ...     1  2011          3     0\n",
              "2       1        0           1        1  ...     2  2011          3     0\n",
              "3       1        0           1        1  ...     3  2011          3     0\n",
              "4       1        0           1        1  ...     4  2011          3     0\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e3fd6cad-4668-4016-b7c5-c73071e15a4e",
        "outputId": "f97ddea7-8655-4e90-e30a-86cc33f5f39a"
      },
      "source": [
        "# train column 별 데이터 개수, 타입 확인\n",
        "train.info()"
      ],
      "id": "e3fd6cad-4668-4016-b7c5-c73071e15a4e",
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 10886 entries, 0 to 10885\n",
            "Data columns (total 14 columns):\n",
            " #   Column      Non-Null Count  Dtype  \n",
            "---  ------      --------------  -----  \n",
            " 0   season      10886 non-null  int64  \n",
            " 1   holiday     10886 non-null  int64  \n",
            " 2   workingday  10886 non-null  int64  \n",
            " 3   weather     10886 non-null  int64  \n",
            " 4   temp        10886 non-null  float64\n",
            " 5   atemp       10886 non-null  float64\n",
            " 6   humidity    10886 non-null  int64  \n",
            " 7   windspeed   10886 non-null  float64\n",
            " 8   casual      10886 non-null  int64  \n",
            " 9   registered  10886 non-null  int64  \n",
            " 10  count       10886 non-null  int64  \n",
            " 11  hour        10886 non-null  int64  \n",
            " 12  dayofweek   10886 non-null  int64  \n",
            " 13  peak        10886 non-null  int64  \n",
            "dtypes: float64(3), int64(11)\n",
            "memory usage: 1.2 MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c16d30d8-8346-4d2d-b3af-308f8ae31661",
        "outputId": "f8d9e26f-736f-4ba6-b07e-5fbe6b498903"
      },
      "source": [
        "# test column 별 데이터 개수, 타입 확인\n",
        "test.info()"
      ],
      "id": "c16d30d8-8346-4d2d-b3af-308f8ae31661",
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 6493 entries, 0 to 6492\n",
            "Data columns (total 11 columns):\n",
            " #   Column      Non-Null Count  Dtype  \n",
            "---  ------      --------------  -----  \n",
            " 0   season      6493 non-null   int64  \n",
            " 1   holiday     6493 non-null   int64  \n",
            " 2   workingday  6493 non-null   int64  \n",
            " 3   weather     6493 non-null   int64  \n",
            " 4   temp        6493 non-null   float64\n",
            " 5   atemp       6493 non-null   float64\n",
            " 6   humidity    6493 non-null   int64  \n",
            " 7   windspeed   6493 non-null   float64\n",
            " 8   hour        6493 non-null   int64  \n",
            " 9   dayofweek   6493 non-null   int64  \n",
            " 10  peak        6493 non-null   int64  \n",
            "dtypes: float64(3), int64(8)\n",
            "memory usage: 558.1 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "16298f34-b662-4fe2-8a10-61d240e00d8c",
        "outputId": "9aa1551f-9f08-47ed-b492-c796f6aa1b4a"
      },
      "source": [
        "# train 결측치 확인 \n",
        "train.isna().sum()"
      ],
      "id": "16298f34-b662-4fe2-8a10-61d240e00d8c",
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "season        0\n",
              "holiday       0\n",
              "workingday    0\n",
              "weather       0\n",
              "temp          0\n",
              "atemp         0\n",
              "humidity      0\n",
              "windspeed     0\n",
              "casual        0\n",
              "registered    0\n",
              "count         0\n",
              "hour          0\n",
              "dayofweek     0\n",
              "peak          0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "867fdefb-b850-4aff-9bf8-62e740534f0a",
        "outputId": "a177700c-76eb-4898-a5bf-ea8fcf2e58d0"
      },
      "source": [
        "# test 결측치 확인\n",
        "test.isna().sum()"
      ],
      "id": "867fdefb-b850-4aff-9bf8-62e740534f0a",
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "season        0\n",
              "holiday       0\n",
              "workingday    0\n",
              "weather       0\n",
              "temp          0\n",
              "atemp         0\n",
              "humidity      0\n",
              "windspeed     0\n",
              "hour          0\n",
              "dayofweek     0\n",
              "peak          0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#season은 모하는 친구일까?\n",
        "train['season'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fytydOeZ5ipE",
        "outputId": "a9ae76cc-e342-4519-b448-9d0b857cc913"
      },
      "id": "fytydOeZ5ipE",
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4    2734\n",
              "3    2733\n",
              "2    2733\n",
              "1    2686\n",
              "Name: season, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train['count'].plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "gqDxeZfkDuSe",
        "outputId": "9a215209-7829-4230-d7ca-d5e531fdccf5"
      },
      "id": "gqDxeZfkDuSe",
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff32e000f50>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.subplots(figsize=(15,15))\n",
        "sns.heatmap(train.corr(), mask=np.zeros_like(train.corr(), dtype=bool),\n",
        "            square=True, annot=True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 850
        },
        "id": "L8mofC5n--up",
        "outputId": "0c098cf4-c662-4e48-bfa7-a0a22e43a0ed"
      },
      "id": "L8mofC5n--up",
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x1080 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "id": "IAQcPG2R_NeY",
        "outputId": "869354b3-a36e-44b2-b924-51be592bf464"
      },
      "id": "IAQcPG2R_NeY",
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-28cc2928-6e79-4c2a-b84f-8fd5dfb5b6d2\">\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>season</th>\n",
              "      <th>holiday</th>\n",
              "      <th>workingday</th>\n",
              "      <th>weather</th>\n",
              "      <th>temp</th>\n",
              "      <th>atemp</th>\n",
              "      <th>humidity</th>\n",
              "      <th>windspeed</th>\n",
              "      <th>casual</th>\n",
              "      <th>registered</th>\n",
              "      <th>count</th>\n",
              "      <th>hour</th>\n",
              "      <th>dayofweek</th>\n",
              "      <th>peak</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>10886.000000</td>\n",
              "      <td>10886.000000</td>\n",
              "      <td>10886.000000</td>\n",
              "      <td>10886.000000</td>\n",
              "      <td>10886.00000</td>\n",
              "      <td>10886.000000</td>\n",
              "      <td>10886.000000</td>\n",
              "      <td>10886.000000</td>\n",
              "      <td>10886.000000</td>\n",
              "      <td>10886.000000</td>\n",
              "      <td>10886.000000</td>\n",
              "      <td>10886.000000</td>\n",
              "      <td>10886.000000</td>\n",
              "      <td>10886.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2.506614</td>\n",
              "      <td>0.028569</td>\n",
              "      <td>0.680875</td>\n",
              "      <td>1.418427</td>\n",
              "      <td>20.23086</td>\n",
              "      <td>23.655084</td>\n",
              "      <td>61.886460</td>\n",
              "      <td>12.799395</td>\n",
              "      <td>36.021955</td>\n",
              "      <td>155.552177</td>\n",
              "      <td>191.574132</td>\n",
              "      <td>11.541613</td>\n",
              "      <td>3.013963</td>\n",
              "      <td>0.275951</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.116174</td>\n",
              "      <td>0.166599</td>\n",
              "      <td>0.466159</td>\n",
              "      <td>0.633839</td>\n",
              "      <td>7.79159</td>\n",
              "      <td>8.474601</td>\n",
              "      <td>19.245033</td>\n",
              "      <td>8.164537</td>\n",
              "      <td>49.960477</td>\n",
              "      <td>151.039033</td>\n",
              "      <td>181.144454</td>\n",
              "      <td>6.915838</td>\n",
              "      <td>2.004585</td>\n",
              "      <td>0.447013</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.82000</td>\n",
              "      <td>0.760000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>13.94000</td>\n",
              "      <td>16.665000</td>\n",
              "      <td>47.000000</td>\n",
              "      <td>7.001500</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>36.000000</td>\n",
              "      <td>42.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>20.50000</td>\n",
              "      <td>24.240000</td>\n",
              "      <td>62.000000</td>\n",
              "      <td>12.998000</td>\n",
              "      <td>17.000000</td>\n",
              "      <td>118.000000</td>\n",
              "      <td>145.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>4.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>26.24000</td>\n",
              "      <td>31.060000</td>\n",
              "      <td>77.000000</td>\n",
              "      <td>16.997900</td>\n",
              "      <td>49.000000</td>\n",
              "      <td>222.000000</td>\n",
              "      <td>284.000000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>4.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>41.00000</td>\n",
              "      <td>45.455000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>56.996900</td>\n",
              "      <td>367.000000</td>\n",
              "      <td>886.000000</td>\n",
              "      <td>977.000000</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-28cc2928-6e79-4c2a-b84f-8fd5dfb5b6d2')\"\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-28cc2928-6e79-4c2a-b84f-8fd5dfb5b6d2 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-28cc2928-6e79-4c2a-b84f-8fd5dfb5b6d2');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "             season       holiday  ...     dayofweek          peak\n",
              "count  10886.000000  10886.000000  ...  10886.000000  10886.000000\n",
              "mean       2.506614      0.028569  ...      3.013963      0.275951\n",
              "std        1.116174      0.166599  ...      2.004585      0.447013\n",
              "min        1.000000      0.000000  ...      0.000000      0.000000\n",
              "25%        2.000000      0.000000  ...      1.000000      0.000000\n",
              "50%        3.000000      0.000000  ...      3.000000      0.000000\n",
              "75%        4.000000      0.000000  ...      5.000000      1.000000\n",
              "max        4.000000      1.000000  ...      6.000000      1.000000\n",
              "\n",
              "[8 rows x 14 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "abe13ec8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1ff31895-1af8-4c7a-f779-2e8447bbd94d"
      },
      "source": [
        "# 학습에 사용할 특성 선택\n",
        "# 추가 month\tweekday\thour\tyear / \tquarter -> 제거\n",
        "features = [\n",
        "    'hour',\n",
        "    'dayofweek',\n",
        "    'year',\n",
        "    'peak',\n",
        "    'season',\n",
        "    'holiday',\n",
        "    'workingday',\n",
        "    'weather',\n",
        "    'temp',\n",
        "    'atemp',\n",
        "    'humidity',\n",
        "    'windspeed',\n",
        "]\n",
        "features"
      ],
      "id": "abe13ec8",
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['hour',\n",
              " 'dayofweek',\n",
              " 'year',\n",
              " 'peak',\n",
              " 'season',\n",
              " 'holiday',\n",
              " 'workingday',\n",
              " 'weather',\n",
              " 'temp',\n",
              " 'atemp',\n",
              " 'humidity',\n",
              " 'windspeed']"
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "1c7ce15d",
        "outputId": "ce46ed8a-1a7e-45e3-87ac-429a9a77b3ae"
      },
      "source": [
        "# features의 컬럼을 X로 설정\n",
        "X = train[features]\n",
        "X"
      ],
      "id": "1c7ce15d",
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-5ca6af60-6727-4368-a9b5-94db6f53faac\">\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>hour</th>\n",
              "      <th>dayofweek</th>\n",
              "      <th>year</th>\n",
              "      <th>peak</th>\n",
              "      <th>season</th>\n",
              "      <th>holiday</th>\n",
              "      <th>workingday</th>\n",
              "      <th>weather</th>\n",
              "      <th>temp</th>\n",
              "      <th>atemp</th>\n",
              "      <th>humidity</th>\n",
              "      <th>windspeed</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>2011</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>9.84</td>\n",
              "      <td>14.395</td>\n",
              "      <td>81</td>\n",
              "      <td>0.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>2011</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>9.02</td>\n",
              "      <td>13.635</td>\n",
              "      <td>80</td>\n",
              "      <td>0.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>2011</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>9.02</td>\n",
              "      <td>13.635</td>\n",
              "      <td>80</td>\n",
              "      <td>0.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>2011</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>9.84</td>\n",
              "      <td>14.395</td>\n",
              "      <td>75</td>\n",
              "      <td>0.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>2011</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>9.84</td>\n",
              "      <td>14.395</td>\n",
              "      <td>75</td>\n",
              "      <td>0.0000</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>10881</th>\n",
              "      <td>19</td>\n",
              "      <td>2</td>\n",
              "      <td>2012</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>15.58</td>\n",
              "      <td>19.695</td>\n",
              "      <td>50</td>\n",
              "      <td>26.0027</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10882</th>\n",
              "      <td>20</td>\n",
              "      <td>2</td>\n",
              "      <td>2012</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>14.76</td>\n",
              "      <td>17.425</td>\n",
              "      <td>57</td>\n",
              "      <td>15.0013</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10883</th>\n",
              "      <td>21</td>\n",
              "      <td>2</td>\n",
              "      <td>2012</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>13.94</td>\n",
              "      <td>15.910</td>\n",
              "      <td>61</td>\n",
              "      <td>15.0013</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10884</th>\n",
              "      <td>22</td>\n",
              "      <td>2</td>\n",
              "      <td>2012</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>13.94</td>\n",
              "      <td>17.425</td>\n",
              "      <td>61</td>\n",
              "      <td>6.0032</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10885</th>\n",
              "      <td>23</td>\n",
              "      <td>2</td>\n",
              "      <td>2012</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>13.12</td>\n",
              "      <td>16.665</td>\n",
              "      <td>66</td>\n",
              "      <td>8.9981</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10886 rows × 12 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5ca6af60-6727-4368-a9b5-94db6f53faac')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "    <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",
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              "      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",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-5ca6af60-6727-4368-a9b5-94db6f53faac 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-5ca6af60-6727-4368-a9b5-94db6f53faac');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "       hour  dayofweek  year  peak  ...   temp   atemp  humidity  windspeed\n",
              "0         0          5  2011     0  ...   9.84  14.395        81     0.0000\n",
              "1         1          5  2011     0  ...   9.02  13.635        80     0.0000\n",
              "2         2          5  2011     0  ...   9.02  13.635        80     0.0000\n",
              "3         3          5  2011     0  ...   9.84  14.395        75     0.0000\n",
              "4         4          5  2011     0  ...   9.84  14.395        75     0.0000\n",
              "...     ...        ...   ...   ...  ...    ...     ...       ...        ...\n",
              "10881    19          2  2012     0  ...  15.58  19.695        50    26.0027\n",
              "10882    20          2  2012     0  ...  14.76  17.425        57    15.0013\n",
              "10883    21          2  2012     0  ...  13.94  15.910        61    15.0013\n",
              "10884    22          2  2012     0  ...  13.94  17.425        61     6.0032\n",
              "10885    23          2  2012     0  ...  13.12  16.665        66     8.9981\n",
              "\n",
              "[10886 rows x 12 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2874be94-02ff-48c9-949c-52390a17d233",
        "outputId": "eee69000-3b98-4941-cdb5-884f45f5bf93"
      },
      "source": [
        "# count 컬럼의 값을 Y값으로 설정\n",
        "Y = np.log1p(train['count'])\n",
        "Y"
      ],
      "id": "2874be94-02ff-48c9-949c-52390a17d233",
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        2.833213\n",
              "1        3.713572\n",
              "2        3.496508\n",
              "3        2.639057\n",
              "4        0.693147\n",
              "           ...   \n",
              "10881    5.820083\n",
              "10882    5.488938\n",
              "10883    5.129899\n",
              "10884    4.867534\n",
              "10885    4.488636\n",
              "Name: count, Length: 10886, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 에러 발생 -> 원인은 np.log1p를 쓰지 않았기 때문...\n",
        "# Y_registered = np.log(train['registered'])\n",
        "# Y_registered"
      ],
      "metadata": {
        "id": "aRjoz_IPzGk6"
      },
      "id": "aRjoz_IPzGk6",
      "execution_count": 122,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 이거도 log 취해주고 exp로 안풀어줘서.. 선언은 했지만 사용 못함.\n",
        "# Y_casual = np.log(train['casual']+1)\n",
        "# Y_casual"
      ],
      "metadata": {
        "id": "u03cO3wJzTbX"
      },
      "id": "u03cO3wJzTbX",
      "execution_count": 123,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "4085a987-4ebb-406f-b6d5-62846a370268",
        "outputId": "252245f6-692d-4a85-a292-14cd9a8ad659"
      },
      "source": [
        "# seaborn 라이브러리의 histplot 함수를 이용하여 Y값 출력\n",
        "sns.histplot(Y)"
      ],
      "id": "4085a987-4ebb-406f-b6d5-62846a370268",
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff325153d10>"
            ]
          },
          "metadata": {},
          "execution_count": 29
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 어떤 모델이 가장 좋을까? \n",
        "\n",
        "1. 랜덤 포리스트\n",
        "2. 배깅\n",
        "3. LGBM"
      ],
      "metadata": {
        "id": "0hg3Pf85PDPe"
      },
      "id": "0hg3Pf85PDPe"
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.neighbors import KNeighborsRegressor\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "#결정트리는 PASS\n",
        "from sklearn.ensemble import GradientBoostingRegressor\n",
        "from sklearn.ensemble import BaggingRegressor\n",
        "from xgboost import XGBRegressor\n",
        "from lightgbm import LGBMRegressor\n",
        "from sklearn.ensemble import AdaBoostRegressor\n",
        "from sklearn.svm import SVR\n",
        "\n",
        "models=[RandomForestRegressor(), KNeighborsRegressor(), BaggingRegressor(), GradientBoostingRegressor(), XGBRegressor(), LGBMRegressor(),\n",
        "        AdaBoostRegressor(), LogisticRegression(), LinearRegression(), SVR(kernel='poly'), SVR(kernel='rbf'),\n",
        "        ]\n",
        "     \n",
        "model_names=['RandomForestRegressor', 'KNeighborsRegressor', 'BaggingRegressor', 'GradientBoostingRegressor', 'XGBRegressor', 'LGBMRegressor',\n",
        "             'AdaBoostRegressor', 'LogisticRegression', 'LinearRegression', 'SVR_poly', 'SVR_rbf',\n",
        "          ]\n",
        "rmsle_list=[]\n",
        "d={}\n",
        "for model in range (len(models)):\n",
        "    clf=models[model]\n",
        "    clf.fit(x_train,y_train)\n",
        "    test_pred=clf.predict(x_test)\n",
        "\n",
        "    k=0\n",
        "    for i in test_pred:\n",
        "      if i<0:\n",
        "        test_pred[k] = 0\n",
        "      k+=1\n",
        "\n",
        "    rmsle_list.append(rmsle(test_pred,y_test))\n",
        "d={'Modelling Algo':model_names,'RMSLE':rmsle_list}   \n",
        "d\n",
        "    "
      ],
      "metadata": {
        "id": "1vkVmLpmPCdq"
      },
      "id": "1vkVmLpmPCdq",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        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)"
      ],
      "metadata": {
        "id": "dBJ1NM0GPQcm"
      },
      "id": "dBJ1NM0GPQcm"
    },
    {
      "cell_type": "markdown",
      "source": [
        "<font color=red> 랜덤포리스트&배깅 -> grid search 했는데도 효과 비슷.. </font>"
      ],
      "metadata": {
        "id": "m5IvjN6lPoOX"
      },
      "id": "m5IvjN6lPoOX"
    },
    {
      "cell_type": "markdown",
      "source": [
        "<font color = blue > 그렇다면 LGBM 튜닝을 해보자! </font>"
      ],
      "metadata": {
        "id": "Y02IW6-_QDeR"
      },
      "id": "Y02IW6-_QDeR"
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 1. RandomForest"
      ],
      "metadata": {
        "id": "I5qPLyYwO2Hj"
      },
      "id": "I5qPLyYwO2Hj"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a670319b-2381-480d-ae86-9612da7f1ae0"
      },
      "source": [
        "# # train 및 test 데이터 분할\n",
        "#   # random_state = 0 지정\n",
        "# from sklearn.model_selection import train_test_split\n",
        "# x_train, x_test, y_train_ca, y_test_ca = train_test_split(X,Y_casual, random_state=0, test_size=0.05)"
      ],
      "id": "a670319b-2381-480d-ae86-9612da7f1ae0",
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# # train 및 test 데이터 분할\n",
        "#   # random_state = 0 지정\n",
        "# from sklearn.model_selection import train_test_split\n",
        "# x_train, x_test, y_train_r, y_test_r = train_test_split(X,Y_registered, random_state=0, test_size=0.05)"
      ],
      "metadata": {
        "id": "tXykweqb3dpX"
      },
      "id": "tXykweqb3dpX",
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# RandomForestRegressor를 사용\n",
        "  # random_state=123 지정\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "model_rf_ca = RandomForestRegressor(random_state = 23).fit(X, Y_casual)\n",
        "model_rf_ca.score(X, Y_casual)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HLtm6V5o3XDX",
        "outputId": "19ccbc2d-bafa-47a0-8e1c-d4b209082805"
      },
      "id": "HLtm6V5o3XDX",
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9834711138417321"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2a4f01bd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "15dc1742-8bf4-4b03-e3a3-ed594859868d"
      },
      "source": [
        "# RandomForestRegressor를 사용\n",
        "  # random_state=123 지정\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "model_rf_r = RandomForestRegressor(random_state = 23).fit(X, Y_registered)\n",
        "model_rf_r.score(X, Y_registered)"
      ],
      "id": "2a4f01bd",
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9891037763632801"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 2. 강사님 추천 XGB"
      ],
      "metadata": {
        "id": "noCB6tgaQSpM"
      },
      "id": "noCB6tgaQSpM"
    },
    {
      "cell_type": "code",
      "source": [
        "import xgboost as xgb\n",
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "xgbr = xgb.XGBRegressor() \n",
        "xgbr.fit(X, Y)\n",
        "\n",
        "scores_xgbr = cross_val_score(xgbr, X, Y ,cv=10)\n",
        "\n",
        "print(\"score: %.2f\" % scores_xgbr.mean())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ixbnOiLJCdDn",
        "outputId": "a2917616-903d-4842-d747-40d58de19cd1"
      },
      "id": "ixbnOiLJCdDn",
      "execution_count": 124,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[08:01:03] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[08:01:03] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[08:01:04] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[08:01:04] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[08:01:05] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[08:01:05] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[08:01:06] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[08:01:06] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[08:01:07] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[08:01:07] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[08:01:08] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "score: 0.91\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 3. 내가 튜닝해본 LGBM"
      ],
      "metadata": {
        "id": "awBPzP66Qmbv"
      },
      "id": "awBPzP66Qmbv"
    },
    {
      "cell_type": "code",
      "source": [
        "from lightgbm import LGBMRegressor\n",
        "lgbm = LGBMRegressor(n_estimators = 2000)\n",
        "lgbm_c = LGBMRegressor(n_estimators = 2000)\n",
        "lgbm_r = LGBMRegressor(n_estimators = 2000)\n",
        "# exp1을 쓰는걸 왜 까먹었을까.. \n",
        "# 나중에 count ->  casual\tregistered 분할해서 만들고 더하기 까지 해보기"
      ],
      "metadata": {
        "id": "krkAl46LKuga"
      },
      "id": "krkAl46LKuga",
      "execution_count": 143,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lgbm.fit(X,Y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hN6BOvqaK9ld",
        "outputId": "c80119f3-15c7-4419-d313-1c463c23c2d8"
      },
      "id": "hN6BOvqaK9ld",
      "execution_count": 144,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LGBMRegressor(n_estimators=2000)"
            ]
          },
          "metadata": {},
          "execution_count": 144
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 에러 발생 -> 원인은 np.log1p를 쓰지 않았기 때문...\n",
        "Y_registered = np.log1p(train['registered'])\n",
        "Y_registered"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oJMRybGXYVvs",
        "outputId": "b2b0b440-a9e5-4977-a1b9-88ca7e7ff0c7"
      },
      "id": "oJMRybGXYVvs",
      "execution_count": 145,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        2.639057\n",
              "1        3.496508\n",
              "2        3.332205\n",
              "3        2.397895\n",
              "4        0.693147\n",
              "           ...   \n",
              "10881    5.799093\n",
              "10882    5.446737\n",
              "10883    5.105945\n",
              "10884    4.770685\n",
              "10885    4.442651\n",
              "Name: registered, Length: 10886, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 145
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 이거도 log 취해주고 exp로 안풀어줘서.. 선언은 했지만 사용 못함.\n",
        "Y_casual = np.log1p(train['casual']+1)\n",
        "Y_casual"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hkaGt3lLYYXc",
        "outputId": "5dffcd1e-fcba-475b-e121-18e80b2bb5ab"
      },
      "id": "hkaGt3lLYYXc",
      "execution_count": 146,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        1.609438\n",
              "1        2.302585\n",
              "2        1.945910\n",
              "3        1.609438\n",
              "4        0.693147\n",
              "           ...   \n",
              "10881    2.197225\n",
              "10882    2.484907\n",
              "10883    1.791759\n",
              "10884    2.639057\n",
              "10885    1.791759\n",
              "Name: casual, Length: 10886, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 146
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lgbm_c.fit(X,Y_casual)\n",
        "lgbm_r.fit(X,Y_registered)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IlTpuR2fYiR1",
        "outputId": "90099ece-2665-4f77-fe0d-f8d7e0a65f09"
      },
      "id": "IlTpuR2fYiR1",
      "execution_count": 148,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LGBMRegressor(n_estimators=2000)"
            ]
          },
          "metadata": {},
          "execution_count": 148
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### <font color = red> 랜덤포리스트 - train/test 분할 할 때 썼던 잔해.. </font> "
      ],
      "metadata": {
        "id": "PE7fo-a3Q_Ce"
      },
      "id": "PE7fo-a3Q_Ce"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fd240674-5aa1-4340-a3b8-ac69fa51a345"
      },
      "source": [
        "# x_train에 대한 예측값 저장 \n",
        "p_train_ca = model_rf_ca.predict(x_train)\n",
        "p_train_r = model_rf_r.predict(x_train)"
      ],
      "id": "fd240674-5aa1-4340-a3b8-ac69fa51a345",
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rmsle(Y, p_train_r)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WFD9vvUT4EO7",
        "outputId": "9acfb1a6-34f9-4097-f005-c11f0c8224ec"
      },
      "id": "WFD9vvUT4EO7",
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.1472208483998753"
            ]
          },
          "metadata": {},
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5fed7a3c-a791-439d-88c1-93ceddd8366e"
      },
      "source": [
        "# x_test에 대한 예측값 저장 \n",
        "p_test_ca = model_rf_ca.predict(X)\n",
        "p_test_r = model_rf_r.predict(X)"
      ],
      "id": "5fed7a3c-a791-439d-88c1-93ceddd8366e",
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f9e58541-c703-4816-98e1-06d7ee9a861b",
        "outputId": "32279a41-54f1-4690-8963-686e39f6dce9"
      },
      "source": [
        "rmsle(Y, p_test_ca + p_test_r)"
      ],
      "id": "f9e58541-c703-4816-98e1-06d7ee9a861b",
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.36404751880047365"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Submission 업로드\n"
      ],
      "metadata": {
        "id": "IvodM_uzQ3qK"
      },
      "id": "IvodM_uzQ3qK"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "1c1a5168-859d-4a6c-8979-52dc8a5c652a",
        "outputId": "a683a121-4b96-4f30-b685-560bee36dda9"
      },
      "source": [
        "# test 데이터 프레임을 대상으로 features 선택 (행, 열 확인)\n",
        "test.head()"
      ],
      "id": "1c1a5168-859d-4a6c-8979-52dc8a5c652a",
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-2e4152a2-47f7-48cf-8cf3-4ef9c821de20\">\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>season</th>\n",
              "      <th>holiday</th>\n",
              "      <th>workingday</th>\n",
              "      <th>weather</th>\n",
              "      <th>temp</th>\n",
              "      <th>atemp</th>\n",
              "      <th>humidity</th>\n",
              "      <th>windspeed</th>\n",
              "      <th>hour</th>\n",
              "      <th>\byear</th>\n",
              "      <th>dayofweek</th>\n",
              "      <th>peak</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>10.66</td>\n",
              "      <td>11.365</td>\n",
              "      <td>56</td>\n",
              "      <td>26.0027</td>\n",
              "      <td>0</td>\n",
              "      <td>2011</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>10.66</td>\n",
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              "      <td>56</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>1</td>\n",
              "      <td>2011</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>10.66</td>\n",
              "      <td>13.635</td>\n",
              "      <td>56</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>2</td>\n",
              "      <td>2011</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>10.66</td>\n",
              "      <td>12.880</td>\n",
              "      <td>56</td>\n",
              "      <td>11.0014</td>\n",
              "      <td>3</td>\n",
              "      <td>2011</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>10.66</td>\n",
              "      <td>12.880</td>\n",
              "      <td>56</td>\n",
              "      <td>11.0014</td>\n",
              "      <td>4</td>\n",
              "      <td>2011</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2e4152a2-47f7-48cf-8cf3-4ef9c821de20')\"\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-2e4152a2-47f7-48cf-8cf3-4ef9c821de20 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-2e4152a2-47f7-48cf-8cf3-4ef9c821de20');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   season  holiday  workingday  weather  ...  hour  \byear  dayofweek  peak\n",
              "0       1        0           1        1  ...     0   2011          3     0\n",
              "1       1        0           1        1  ...     1   2011          3     0\n",
              "2       1        0           1        1  ...     2   2011          3     0\n",
              "3       1        0           1        1  ...     3   2011          3     0\n",
              "4       1        0           1        1  ...     4   2011          3     0\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "uhNhullNo7KP",
        "outputId": "075fef93-aece-454d-8ee3-da5d91808f62"
      },
      "source": [
        "# submission 데이터셋 로드 (submission.csv)\n",
        "submission = pd.read_csv('submission.csv')\n",
        "submission"
      ],
      "id": "uhNhullNo7KP",
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>datetime</th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2011-01-20 00:00:00</td>\n",
              "      <td>0</td>\n",
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              "      <td>2011-01-20 01:00:00</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2011-01-20 02:00:00</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2011-01-20 03:00:00</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2011-01-20 04:00:00</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "      <th>6488</th>\n",
              "      <td>2012-12-31 19:00:00</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6489</th>\n",
              "      <td>2012-12-31 20:00:00</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6490</th>\n",
              "      <td>2012-12-31 21:00:00</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6491</th>\n",
              "      <td>2012-12-31 22:00:00</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6492</th>\n",
              "      <td>2012-12-31 23:00:00</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>6493 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7d181357-bbd1-49e1-bb79-ed2279363af9')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      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-7d181357-bbd1-49e1-bb79-ed2279363af9 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-7d181357-bbd1-49e1-bb79-ed2279363af9');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                 datetime  count\n",
              "0     2011-01-20 00:00:00      0\n",
              "1     2011-01-20 01:00:00      0\n",
              "2     2011-01-20 02:00:00      0\n",
              "3     2011-01-20 03:00:00      0\n",
              "4     2011-01-20 04:00:00      0\n",
              "...                   ...    ...\n",
              "6488  2012-12-31 19:00:00      0\n",
              "6489  2012-12-31 20:00:00      0\n",
              "6490  2012-12-31 21:00:00      0\n",
              "6491  2012-12-31 22:00:00      0\n",
              "6492  2012-12-31 23:00:00      0\n",
              "\n",
              "[6493 rows x 2 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## LGBM 확인"
      ],
      "metadata": {
        "id": "-OLfBYmTRTel"
      },
      "id": "-OLfBYmTRTel"
    },
    {
      "cell_type": "code",
      "source": [
        "pred = lgbm.predict(test[features])\n",
        "submission['count'] =  np.expm1(pred)\n",
        "submission.loc[submission['count']<0, 'count'] = 0\n",
        "submission[submission['count']<0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49
        },
        "id": "OE-l1ewnLXAv",
        "outputId": "5f99395b-38f9-4cac-8573-a65c6a528700"
      },
      "id": "OE-l1ewnLXAv",
      "execution_count": 137,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-7c5e5e15-2315-463e-bd94-c5ec80dba940\">\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>datetime</th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7c5e5e15-2315-463e-bd94-c5ec80dba940')\"\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-7c5e5e15-2315-463e-bd94-c5ec80dba940 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-7c5e5e15-2315-463e-bd94-c5ec80dba940');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [datetime, count]\n",
              "Index: []"
            ]
          },
          "metadata": {},
          "execution_count": 137
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lgbm_c.fit(X,Y_casual)\n",
        "lgbm_r.fit(X,Y_registered)\n",
        "\n",
        "pred_c = lgbm_c.predict(test[features])\n",
        "pred_r = lgbm_r.predict(test[features])\n",
        "\n",
        "\n",
        "submission['count'] =  np.expm1(pred_c) +  np.expm1(pred_r) "
      ],
      "metadata": {
        "id": "1nNUEbRnY1jC"
      },
      "id": "1nNUEbRnY1jC",
      "execution_count": 150,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#케글에 업로드하며 수치 확인하기 위해 csv 확인으로 변환.\n",
        "submission.to_csv('submission_lstm.csv', index=False)"
      ],
      "metadata": {
        "id": "rlINw19pRY8F"
      },
      "id": "rlINw19pRY8F",
      "execution_count": 151,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "submission"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "DPtIWsCERrL8",
        "outputId": "a3428954-7273-494d-9af3-13b57d3f9723"
      },
      "id": "DPtIWsCERrL8",
      "execution_count": 152,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
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              "      <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>datetime</th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>2011-01-20 00:00:00</td>\n",
              "      <td>16.594259</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2011-01-20 01:00:00</td>\n",
              "      <td>4.558288</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2011-01-20 02:00:00</td>\n",
              "      <td>3.807847</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2011-01-20 03:00:00</td>\n",
              "      <td>3.144839</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2011-01-20 04:00:00</td>\n",
              "      <td>2.390077</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "      <td>209.540733</td>\n",
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              "    <tr>\n",
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              "      <td>125.679871</td>\n",
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              "      <th>6490</th>\n",
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              "      <th>6491</th>\n",
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              "    <tr>\n",
              "      <th>6492</th>\n",
              "      <td>2012-12-31 23:00:00</td>\n",
              "      <td>38.014377</td>\n",
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              "  </tbody>\n",
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              "<p>6493 rows × 2 columns</p>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cc325f62-fb35-4474-a85b-cb7cea56ffba')\"\n",
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              "    .colab-df-convert {\n",
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              "    }\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-cc325f62-fb35-4474-a85b-cb7cea56ffba 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-cc325f62-fb35-4474-a85b-cb7cea56ffba');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                 datetime       count\n",
              "0     2011-01-20 00:00:00   16.594259\n",
              "1     2011-01-20 01:00:00    4.558288\n",
              "2     2011-01-20 02:00:00    3.807847\n",
              "3     2011-01-20 03:00:00    3.144839\n",
              "4     2011-01-20 04:00:00    2.390077\n",
              "...                   ...         ...\n",
              "6488  2012-12-31 19:00:00  209.540733\n",
              "6489  2012-12-31 20:00:00  125.679871\n",
              "6490  2012-12-31 21:00:00  102.386849\n",
              "6491  2012-12-31 22:00:00   86.786816\n",
              "6492  2012-12-31 23:00:00   38.014377\n",
              "\n",
              "[6493 rows x 2 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 152
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## XGB 확인"
      ],
      "metadata": {
        "id": "-1npt2uWRhm6"
      },
      "id": "-1npt2uWRhm6"
    },
    {
      "cell_type": "code",
      "source": [
        "# test 데이터 프레임을 예측한 결과 대입 XGB\n",
        "submission['count'] = xgbr.predict(test[features])\n",
        "submission.loc[submission['count']<0, 'count'] = 0\n",
        "submission[submission['count']<0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49
        },
        "id": "apunRfdODtx_",
        "outputId": "caab6347-20d7-4d62-ae3d-da87e4476b4a"
      },
      "id": "apunRfdODtx_",
      "execution_count": 130,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-2f307b1c-7268-4c29-b10b-c7c1bb8bb9f8\">\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>datetime</th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2f307b1c-7268-4c29-b10b-c7c1bb8bb9f8')\"\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-2f307b1c-7268-4c29-b10b-c7c1bb8bb9f8 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-2f307b1c-7268-4c29-b10b-c7c1bb8bb9f8');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [datetime, count]\n",
              "Index: []"
            ]
          },
          "metadata": {},
          "execution_count": 130
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#케글에 업로드하며 수치 확인하기 위해 csv 확인으로 변환.\n",
        "submission.to_csv('submission_xgb.csv', index=False)"
      ],
      "metadata": {
        "id": "d1Pm92lVRm83"
      },
      "id": "d1Pm92lVRm83",
      "execution_count": 131,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "submission"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "EJcxOTN_Erxy",
        "outputId": "dadf9ecf-2461-41bd-e0e7-e2f685ce1578"
      },
      "id": "EJcxOTN_Erxy",
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-806b0dc5-5375-46d9-b4f3-8fc0c96c80d1\">\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>datetime</th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2011-01-20 00:00:00</td>\n",
              "      <td>19.759269</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2011-01-20 01:00:00</td>\n",
              "      <td>4.462512</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2011-01-20 02:00:00</td>\n",
              "      <td>3.954145</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2011-01-20 03:00:00</td>\n",
              "      <td>3.014751</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2011-01-20 04:00:00</td>\n",
              "      <td>1.287687</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6488</th>\n",
              "      <td>2012-12-31 19:00:00</td>\n",
              "      <td>216.774997</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6489</th>\n",
              "      <td>2012-12-31 20:00:00</td>\n",
              "      <td>119.804123</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6490</th>\n",
              "      <td>2012-12-31 21:00:00</td>\n",
              "      <td>87.649766</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6491</th>\n",
              "      <td>2012-12-31 22:00:00</td>\n",
              "      <td>89.607728</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6492</th>\n",
              "      <td>2012-12-31 23:00:00</td>\n",
              "      <td>34.911313</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>6493 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-806b0dc5-5375-46d9-b4f3-8fc0c96c80d1')\"\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-806b0dc5-5375-46d9-b4f3-8fc0c96c80d1 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-806b0dc5-5375-46d9-b4f3-8fc0c96c80d1');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                 datetime       count\n",
              "0     2011-01-20 00:00:00   19.759269\n",
              "1     2011-01-20 01:00:00    4.462512\n",
              "2     2011-01-20 02:00:00    3.954145\n",
              "3     2011-01-20 03:00:00    3.014751\n",
              "4     2011-01-20 04:00:00    1.287687\n",
              "...                   ...         ...\n",
              "6488  2012-12-31 19:00:00  216.774997\n",
              "6489  2012-12-31 20:00:00  119.804123\n",
              "6490  2012-12-31 21:00:00   87.649766\n",
              "6491  2012-12-31 22:00:00   89.607728\n",
              "6492  2012-12-31 23:00:00   34.911313\n",
              "\n",
              "[6493 rows x 2 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 109
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## casual\tregistered 나눠서 더하려고 했었는데.. 왜 안됐을까..\n",
        "\n",
        "-> 원인 : log 취해주고 exp1로 안풀어줬으니까.. "
      ],
      "metadata": {
        "id": "Trb9owQnRtZ7"
      },
      "id": "Trb9owQnRtZ7"
    },
    {
      "cell_type": "code",
      "source": [
        "# test 데이터 프레임을 예측한 결과 대입\n",
        "submission['count'] = model_rf_ca.predict(test) + model_rf_r.predict(test)\n",
        "submission[submission['count']<0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "id": "9O55n1wPq-4w",
        "outputId": "09d4b8d2-e22d-4d5b-e940-caf4c4e91725"
      },
      "id": "9O55n1wPq-4w",
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/base.py:488: FutureWarning: The feature names should match those that were passed during fit. Starting version 1.2, an error will be raised.\n",
            "Feature names unseen at fit time:\n",
            "- \byear\n",
            "Feature names seen at fit time, yet now missing:\n",
            "- year\n",
            "\n",
            "  warnings.warn(message, FutureWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/base.py:488: FutureWarning: The feature names should match those that were passed during fit. Starting version 1.2, an error will be raised.\n",
            "Feature names unseen at fit time:\n",
            "- \byear\n",
            "Feature names seen at fit time, yet now missing:\n",
            "- year\n",
            "\n",
            "  warnings.warn(message, FutureWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-37b73ac2-aac1-427b-8c45-1096ea9ce96c\">\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>datetime</th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-37b73ac2-aac1-427b-8c45-1096ea9ce96c')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-37b73ac2-aac1-427b-8c45-1096ea9ce96c 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-37b73ac2-aac1-427b-8c45-1096ea9ce96c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [datetime, count]\n",
              "Index: []"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "outputs_hidden": true
        },
        "id": "46851629"
      },
      "source": [
        "# # test 데이터 프레임을 예측한 결과 대입\n",
        "# submission['count'] = model_rf.predict(test[features])\n",
        "# submission[submission['count']<0]"
      ],
      "id": "46851629",
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3d6cde3f"
      },
      "source": [
        "# 순서대로 평가가 진행됩니다. 실행은 1회만 해주세요.\n",
        "\n",
        "- 느리다고 중지 후 다시 평가 코드를 실행하는 경우 제출 과정에서 패널티가 발생할 수 있습니다. (제출 횟수 이슈 발생 가능)"
      ],
      "id": "3d6cde3f"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5f3c044f",
        "outputId": "006a31e4-5431-4737-802a-76b9d59a00cd"
      },
      "source": [
        "# #shift + space로 무작정실행하다가 에러 나는 경우 방지하기 위해 우선 주석처리\n",
        "pjt.submit(submission) # 제출 파일 생성 및 결과 제출"
      ],
      "id": "5f3c044f",
      "execution_count": 153,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "파일을 저장하였습니다. 파일명: submission-08-39-22.csv\n",
            "제출 여부 :success\n",
            "오늘 제출 횟수 : 7\n",
            "제출 결과:0.39329\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "42c178d1"
      },
      "source": [
        "# 축하드립니다. 모델의 성능을 개선하여 더 좋은 점수를 획득해보세요!\n",
        "* 데이터 전처리 (preprocessing)를 통하여, 결측치 처리\n",
        "* 데이터 시각화를 통하여 이상치 처리 및 특성 처리\n",
        "* 학습 특성 추가 사용\n",
        "* **모델 앙상블(model ensemble)**을 통해 예측 성능 향상"
      ],
      "id": "42c178d1"
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 1번째 train, test 비율 늘리기 0.3 -> 우선 0.05/0.1까지 낮추기 마지막 제출 때는 X 통째로 학습\n",
        "\n",
        "-> 학습시 데이터는 많으면 많을 수록 좋다."
      ],
      "metadata": {
        "id": "PU28LWaE8LWU"
      },
      "id": "PU28LWaE8LWU"
    },
    {
      "cell_type": "code",
      "source": [
        "# train 및 test 데이터 분할\n",
        "  # random_state = 0 지정\n",
        "from sklearn.model_selection import train_test_split\n",
        "x_train, x_test, y_train, y_test = train_test_split(X,Y, random_state=0)"
      ],
      "metadata": {
        "id": "b19fsEt18WYu"
      },
      "id": "b19fsEt18WYu",
      "execution_count": 146,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from xgboost import XGBRegressor\n",
        "model = XGBRegressor().fit(x_train, y_train)\n",
        "p_test = model.predict(x_test)\n",
        "rmsle(p_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "ku_H4n4eEDQD",
        "outputId": "920a9f62-31fa-473b-806a-37beb9404783"
      },
      "id": "ku_H4n4eEDQD",
      "execution_count": 147,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[05:18:22] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-147-df1cd1562323>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mXGBRegressor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mp_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mrmsle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mp_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m<ipython-input-4-149cdfb73835>\u001b[0m in \u001b[0;36mrmsle\u001b[0;34m(true, pred)\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetrics\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmean_squared_log_error\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mrmsle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmean_squared_log_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_regression.py\u001b[0m in \u001b[0;36mmean_squared_log_error\u001b[0;34m(y_true, y_pred, sample_weight, multioutput, squared)\u001b[0m\n\u001b[1;32m    505\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0many\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0my_pred\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0many\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    506\u001b[0m         raise ValueError(\n\u001b[0;32m--> 507\u001b[0;31m             \u001b[0;34m\"Mean Squared Logarithmic Error cannot be used when \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    508\u001b[0m             \u001b[0;34m\"targets contain negative values.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    509\u001b[0m         )\n",
            "\u001b[0;31mValueError\u001b[0m: Mean Squared Logarithmic Error cannot be used when targets contain negative values."
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5a93f6d7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "defd1e26-879c-4f20-874a-7c3e139b7cdb"
      },
      "source": [
        ""
      ],
      "id": "5a93f6d7",
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[02:56:49] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Modelling Algo': ['RandomForestRegressor',\n",
              "  'KNeighborsRegressor',\n",
              "  'BaggingRegressor',\n",
              "  'GradientBoostingRegressor',\n",
              "  'XGBRegressor',\n",
              "  'LGBMRegressor',\n",
              "  'AdaBoostRegressor',\n",
              "  'LogisticRegression',\n",
              "  'LinearRegression',\n",
              "  'SVR_poly',\n",
              "  'SVR_rbf'],\n",
              " 'RMSLE': [0.3325649080759767,\n",
              "  0.884637574920973,\n",
              "  0.3487260210604861,\n",
              "  0.7153430276304868,\n",
              "  0.7367693034268152,\n",
              "  0.46085043275966964,\n",
              "  1.0589051168497343,\n",
              "  2.671482614709335,\n",
              "  1.2880868933943133,\n",
              "  1.470329498767334,\n",
              "  1.4718211026328198]}"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# train 및 test 데이터 분할\n",
        "  # random_state = 0 지정\n",
        "from sklearn.model_selection import train_test_split\n",
        "x_train, x_test, y_train, y_test = train_test_split(X,Y, random_state=0, test_size = 0.05)"
      ],
      "metadata": {
        "id": "_trX2A197WiR"
      },
      "id": "_trX2A197WiR",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.ensemble import BaggingRegressor\n",
        "from sklearn.neighbors import KNeighborsRegressor\n",
        "\n",
        "from sklearn.svm import SVR\n",
        "\n",
        "models=[RandomForestRegressor(), AdaBoostRegressor(), BaggingRegressor(),SVR(),KNeighborsRegressor()]\n",
        "model_names=['RandomForestRegressor', 'AdaBoostRegressor', 'BaggingRegressor','SVR','KNeighborsRegressor']\n",
        "rmsle=[]\n",
        "d={}\n",
        "for model in range (len(models)):\n",
        "    clf=models[model]\n",
        "    clf.fit(x_train,y_train)\n",
        "    test_pred=clf.predict(x_test)\n",
        "    \n",
        "    k=0\n",
        "    for i in test_pred:\n",
        "      if i<0:\n",
        "        test_pred[k] = 0\n",
        "      k+=1\n",
        "    rmsle.append(np.sqrt(mean_squared_log_error(test_pred,y_test)))\n",
        "d={'Modelling Algo':model_names,'RMSLE':rmsle}   \n",
        "d\n",
        "    "
      ],
      "metadata": {
        "id": "HrVr_gu78fnO"
      },
      "id": "HrVr_gu78fnO",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 또 다른 모델 \n",
        "\n",
        "-> PolynomialFeatures 도전 해보려 했지만.. 시간부족.. "
      ],
      "metadata": {
        "id": "Ooe5qwaR_Coe"
      },
      "id": "Ooe5qwaR_Coe"
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "P = PolynomialFeatures(degree = 3)"
      ],
      "metadata": {
        "id": "zhbYtHXF8gX1"
      },
      "id": "zhbYtHXF8gX1",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## season과 weather의 경우 원래는 범주형 데이터인데 int형으로 나온다 -> get_dummies를 사용한다면? \n",
        "\n",
        "### 47801 (전) / 47648\n",
        "\n",
        "#### -> 효과 별로... 실행 X"
      ],
      "metadata": {
        "id": "bi0EW8xyjfcX"
      },
      "id": "bi0EW8xyjfcX"
    },
    {
      "cell_type": "code",
      "source": [
        "train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "sHvUNcpOj0xE",
        "outputId": "94b900da-a9a7-458d-f23d-67fc0d87f1b1"
      },
      "id": "sHvUNcpOj0xE",
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-6e09c5d8-2eb5-4d46-a4a2-b8800036d78d\">\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>holiday</th>\n",
              "      <th>workingday</th>\n",
              "      <th>temp</th>\n",
              "      <th>atemp</th>\n",
              "      <th>humidity</th>\n",
              "      <th>windspeed</th>\n",
              "      <th>casual</th>\n",
              "      <th>registered</th>\n",
              "      <th>count</th>\n",
              "      <th>month</th>\n",
              "      <th>hour</th>\n",
              "      <th>year</th>\n",
              "      <th>day</th>\n",
              "      <th>dayofweek</th>\n",
              "      <th>weather_1</th>\n",
              "      <th>weather_2</th>\n",
              "      <th>weather_3</th>\n",
              "      <th>weather_4</th>\n",
              "      <th>weather_1</th>\n",
              "      <th>weather_2</th>\n",
              "      <th>weather_3</th>\n",
              "      <th>weather_4</th>\n",
              "      <th>season_1</th>\n",
              "      <th>season_2</th>\n",
              "      <th>season_3</th>\n",
              "      <th>season_4</th>\n",
              "      <th>weather_1</th>\n",
              "      <th>weather_2</th>\n",
              "      <th>weather_3</th>\n",
              "      <th>weather_4</th>\n",
              "      <th>season_1</th>\n",
              "      <th>season_2</th>\n",
              "      <th>season_3</th>\n",
              "      <th>season_4</th>\n",
              "      <th>weather_1</th>\n",
              "      <th>weather_2</th>\n",
              "      <th>weather_3</th>\n",
              "      <th>weather_4</th>\n",
              "      <th>season_1</th>\n",
              "      <th>season_2</th>\n",
              "      <th>season_3</th>\n",
              "      <th>season_4</th>\n",
              "      <th>weather_1</th>\n",
              "      <th>weather_2</th>\n",
              "      <th>weather_3</th>\n",
              "      <th>weather_4</th>\n",
              "      <th>season_1</th>\n",
              "      <th>season_2</th>\n",
              "      <th>season_3</th>\n",
              "      <th>season_4</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>9.84</td>\n",
              "      <td>14.395</td>\n",
              "      <td>81</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>3</td>\n",
              "      <td>13</td>\n",
              "      <td>16</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2011</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>9.02</td>\n",
              "      <td>13.635</td>\n",
              "      <td>80</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>8</td>\n",
              "      <td>32</td>\n",
              "      <td>40</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2011</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>9.02</td>\n",
              "      <td>13.635</td>\n",
              "      <td>80</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>5</td>\n",
              "      <td>27</td>\n",
              "      <td>32</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2011</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>9.84</td>\n",
              "      <td>14.395</td>\n",
              "      <td>75</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>3</td>\n",
              "      <td>10</td>\n",
              "      <td>13</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2011</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>9.84</td>\n",
              "      <td>14.395</td>\n",
              "      <td>75</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>2011</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <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",
              "      <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",
              "      <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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10881</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>15.58</td>\n",
              "      <td>19.695</td>\n",
              "      <td>50</td>\n",
              "      <td>26.0027</td>\n",
              "      <td>7</td>\n",
              "      <td>329</td>\n",
              "      <td>336</td>\n",
              "      <td>12</td>\n",
              "      <td>19</td>\n",
              "      <td>2012</td>\n",
              "      <td>19</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10882</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>14.76</td>\n",
              "      <td>17.425</td>\n",
              "      <td>57</td>\n",
              "      <td>15.0013</td>\n",
              "      <td>10</td>\n",
              "      <td>231</td>\n",
              "      <td>241</td>\n",
              "      <td>12</td>\n",
              "      <td>20</td>\n",
              "      <td>2012</td>\n",
              "      <td>19</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10883</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>13.94</td>\n",
              "      <td>15.910</td>\n",
              "      <td>61</td>\n",
              "      <td>15.0013</td>\n",
              "      <td>4</td>\n",
              "      <td>164</td>\n",
              "      <td>168</td>\n",
              "      <td>12</td>\n",
              "      <td>21</td>\n",
              "      <td>2012</td>\n",
              "      <td>19</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10884</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>13.94</td>\n",
              "      <td>17.425</td>\n",
              "      <td>61</td>\n",
              "      <td>6.0032</td>\n",
              "      <td>12</td>\n",
              "      <td>117</td>\n",
              "      <td>129</td>\n",
              "      <td>12</td>\n",
              "      <td>22</td>\n",
              "      <td>2012</td>\n",
              "      <td>19</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10885</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>13.12</td>\n",
              "      <td>16.665</td>\n",
              "      <td>66</td>\n",
              "      <td>8.9981</td>\n",
              "      <td>4</td>\n",
              "      <td>84</td>\n",
              "      <td>88</td>\n",
              "      <td>12</td>\n",
              "      <td>23</td>\n",
              "      <td>2012</td>\n",
              "      <td>19</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10886 rows × 50 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6e09c5d8-2eb5-4d46-a4a2-b8800036d78d')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "       holiday  workingday   temp  ...  season_2  season_3  season_4\n",
              "0            0           0   9.84  ...         0         0         0\n",
              "1            0           0   9.02  ...         0         0         0\n",
              "2            0           0   9.02  ...         0         0         0\n",
              "3            0           0   9.84  ...         0         0         0\n",
              "4            0           0   9.84  ...         0         0         0\n",
              "...        ...         ...    ...  ...       ...       ...       ...\n",
              "10881        0           1  15.58  ...         0         0         1\n",
              "10882        0           1  14.76  ...         0         0         1\n",
              "10883        0           1  13.94  ...         0         0         1\n",
              "10884        0           1  13.94  ...         0         0         1\n",
              "10885        0           1  13.12  ...         0         0         1\n",
              "\n",
              "[10886 rows x 50 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#범주형 데이터 이지만 int형으로 표시된 season, weather를 get_dummies 사용\n",
        "weather_dummmies = pd.get_dummies(train['weather'], prefix='weather')\n",
        "season_dummmies = pd.get_dummies(train['season'], prefix='season')\n",
        "train = pd.concat((train, weather_dummmies), axis=1)\n",
        "train = pd.concat((train, season_dummmies), axis=1)\n",
        "train = train.drop(['weather', 'season'], axis = 1)\n",
        "train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "RccNo_EHjfHX",
        "outputId": "703b1476-3078-4211-9b2f-f40daad0090b"
      },
      "id": "RccNo_EHjfHX",
      "execution_count": 148,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
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              "      <th>holiday</th>\n",
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              "      <th>temp</th>\n",
              "      <th>atemp</th>\n",
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              "      <th>weather_3</th>\n",
              "      <th>weather_4</th>\n",
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              "      <th>10882</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>14.76</td>\n",
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              "      <td>57</td>\n",
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              "      <td>10</td>\n",
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              "      <td>12</td>\n",
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              "      <td>2012</td>\n",
              "      <td>19</td>\n",
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              "      <th>10883</th>\n",
              "      <td>0</td>\n",
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              "      <td>15.910</td>\n",
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              "      <td>15.0013</td>\n",
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              "      <td>164</td>\n",
              "      <td>168</td>\n",
              "      <td>12</td>\n",
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              "      <td>2012</td>\n",
              "      <td>19</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
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              "      <td>17.425</td>\n",
              "      <td>61</td>\n",
              "      <td>6.0032</td>\n",
              "      <td>12</td>\n",
              "      <td>117</td>\n",
              "      <td>129</td>\n",
              "      <td>12</td>\n",
              "      <td>22</td>\n",
              "      <td>2012</td>\n",
              "      <td>19</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
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              "      <th>10885</th>\n",
              "      <td>0</td>\n",
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              "      <td>2012</td>\n",
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              "<p>10886 rows × 22 columns</p>\n",
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              "\n",
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              "\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",
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              "  "
            ],
            "text/plain": [
              "       holiday  workingday   temp  ...  season_2  season_3  season_4\n",
              "0            0           0   9.84  ...         0         0         0\n",
              "1            0           0   9.02  ...         0         0         0\n",
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              "3            0           0   9.84  ...         0         0         0\n",
              "4            0           0   9.84  ...         0         0         0\n",
              "...        ...         ...    ...  ...       ...       ...       ...\n",
              "10881        0           1  15.58  ...         0         0         1\n",
              "10882        0           1  14.76  ...         0         0         1\n",
              "10883        0           1  13.94  ...         0         0         1\n",
              "10884        0           1  13.94  ...         0         0         1\n",
              "10885        0           1  13.12  ...         0         0         1\n",
              "\n",
              "[10886 rows x 22 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 148
        }
      ]
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    {
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      "execution_count": 149,
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            ],
            "text/plain": [
              "      season  holiday  workingday  weather  ...  hour  year  day  dayofweek\n",
              "0          1        0           1        1  ...     0  2011   20          3\n",
              "1          1        0           1        1  ...     1  2011   20          3\n",
              "2          1        0           1        1  ...     2  2011   20          3\n",
              "3          1        0           1        1  ...     3  2011   20          3\n",
              "4          1        0           1        1  ...     4  2011   20          3\n",
              "...      ...      ...         ...      ...  ...   ...   ...  ...        ...\n",
              "6488       1        0           1        2  ...    19  2012   31          0\n",
              "6489       1        0           1        2  ...    20  2012   31          0\n",
              "6490       1        0           1        1  ...    21  2012   31          0\n",
              "6491       1        0           1        1  ...    22  2012   31          0\n",
              "6492       1        0           1        1  ...    23  2012   31          0\n",
              "\n",
              "[6493 rows x 13 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 149
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#범주형 데이터 이지만 int형으로 표시된 season, weather를 get_dummies 사용\n",
        "weather_dummmies = pd.get_dummies(test['weather'], prefix='weather')\n",
        "season_dummmies = pd.get_dummies(test['season'], prefix='season')\n",
        "test = pd.concat((test, weather_dummmies), axis=1)\n",
        "test = pd.concat((test, season_dummmies), axis=1)\n",
        "test = test.drop(['weather', 'season'], axis = 1)\n",
        "test"
      ],
      "metadata": {
        "colab": {
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          "height": 0
        },
        "id": "aMqbOvVWj0Qn",
        "outputId": "653f348b-4417-41c8-d09f-2589e31be8ce"
      },
      "id": "aMqbOvVWj0Qn",
      "execution_count": 150,
      "outputs": [
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              "      <td>2</td>\n",
              "      <td>2011</td>\n",
              "      <td>20</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>10.66</td>\n",
              "      <td>12.880</td>\n",
              "      <td>56</td>\n",
              "      <td>11.0014</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2011</td>\n",
              "      <td>20</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>10.66</td>\n",
              "      <td>12.880</td>\n",
              "      <td>56</td>\n",
              "      <td>11.0014</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>2011</td>\n",
              "      <td>20</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>6488</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>10.66</td>\n",
              "      <td>12.880</td>\n",
              "      <td>60</td>\n",
              "      <td>11.0014</td>\n",
              "      <td>12</td>\n",
              "      <td>19</td>\n",
              "      <td>2012</td>\n",
              "      <td>31</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6489</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>10.66</td>\n",
              "      <td>12.880</td>\n",
              "      <td>60</td>\n",
              "      <td>11.0014</td>\n",
              "      <td>12</td>\n",
              "      <td>20</td>\n",
              "      <td>2012</td>\n",
              "      <td>31</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6490</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>10.66</td>\n",
              "      <td>12.880</td>\n",
              "      <td>60</td>\n",
              "      <td>11.0014</td>\n",
              "      <td>12</td>\n",
              "      <td>21</td>\n",
              "      <td>2012</td>\n",
              "      <td>31</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6491</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>10.66</td>\n",
              "      <td>13.635</td>\n",
              "      <td>56</td>\n",
              "      <td>8.9981</td>\n",
              "      <td>12</td>\n",
              "      <td>22</td>\n",
              "      <td>2012</td>\n",
              "      <td>31</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6492</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>10.66</td>\n",
              "      <td>13.635</td>\n",
              "      <td>65</td>\n",
              "      <td>8.9981</td>\n",
              "      <td>12</td>\n",
              "      <td>23</td>\n",
              "      <td>2012</td>\n",
              "      <td>31</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>6493 rows × 19 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-30bffaa1-d0fe-45c8-ba35-80f1ac8d761b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "    }\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",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      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",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-30bffaa1-d0fe-45c8-ba35-80f1ac8d761b 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-30bffaa1-d0fe-45c8-ba35-80f1ac8d761b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "      holiday  workingday   temp  ...  season_2  season_3  season_4\n",
              "0           0           1  10.66  ...         0         0         0\n",
              "1           0           1  10.66  ...         0         0         0\n",
              "2           0           1  10.66  ...         0         0         0\n",
              "3           0           1  10.66  ...         0         0         0\n",
              "4           0           1  10.66  ...         0         0         0\n",
              "...       ...         ...    ...  ...       ...       ...       ...\n",
              "6488        0           1  10.66  ...         0         0         0\n",
              "6489        0           1  10.66  ...         0         0         0\n",
              "6490        0           1  10.66  ...         0         0         0\n",
              "6491        0           1  10.66  ...         0         0         0\n",
              "6492        0           1  10.66  ...         0         0         0\n",
              "\n",
              "[6493 rows x 19 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 150
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 학습에 사용할 특성 선택\n",
        "# 추가 month hour\tyear day, dayofweekend\n",
        "features = [\n",
        "    'month',\n",
        "    'hour',\n",
        "    'year',\n",
        "    'day',\n",
        "    'weather_1',\n",
        "    'weather_2',\n",
        "    'weather_3',\n",
        "    'weather_4',\n",
        "    'season_1',\n",
        "    'season_2',\n",
        "    'season_3',\n",
        "   \t'season_4',\t\n",
        "    'dayofweek',\n",
        "    'holiday',\n",
        "    'workingday',\n",
        "    'temp',\n",
        "    'atemp',\n",
        "    'humidity',\n",
        "    'windspeed',\n",
        "]\n",
        "features"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1iuBeCdPkIl2",
        "outputId": "8fb7df5e-8235-4693-84e6-56fcbdefbcee"
      },
      "id": "1iuBeCdPkIl2",
      "execution_count": 151,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['month',\n",
              " 'hour',\n",
              " 'year',\n",
              " 'day',\n",
              " 'weather_1',\n",
              " 'weather_2',\n",
              " 'weather_3',\n",
              " 'weather_4',\n",
              " 'season_1',\n",
              " 'season_2',\n",
              " 'season_3',\n",
              " 'season_4',\n",
              " 'dayofweek',\n",
              " 'holiday',\n",
              " 'workingday',\n",
              " 'temp',\n",
              " 'atemp',\n",
              " 'humidity',\n",
              " 'windspeed']"
            ]
          },
          "metadata": {},
          "execution_count": 151
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = train[features]\n",
        "X"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "Pj356B22ksgR",
        "outputId": "9980fd49-0cae-40df-d1d6-cc779ccfa479"
      },
      "id": "Pj356B22ksgR",
      "execution_count": 152,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
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              "      <th>season_3</th>\n",
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              "      <th>workingday</th>\n",
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              "      <th>atemp</th>\n",
              "      <th>humidity</th>\n",
              "      <th>windspeed</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <td>1</td>\n",
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              "      <td>1</td>\n",
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              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>15.58</td>\n",
              "      <td>19.695</td>\n",
              "      <td>50</td>\n",
              "      <td>26.0027</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10882</th>\n",
              "      <td>12</td>\n",
              "      <td>20</td>\n",
              "      <td>2012</td>\n",
              "      <td>19</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>14.76</td>\n",
              "      <td>17.425</td>\n",
              "      <td>57</td>\n",
              "      <td>15.0013</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10883</th>\n",
              "      <td>12</td>\n",
              "      <td>21</td>\n",
              "      <td>2012</td>\n",
              "      <td>19</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>13.94</td>\n",
              "      <td>15.910</td>\n",
              "      <td>61</td>\n",
              "      <td>15.0013</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10884</th>\n",
              "      <td>12</td>\n",
              "      <td>22</td>\n",
              "      <td>2012</td>\n",
              "      <td>19</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>13.94</td>\n",
              "      <td>17.425</td>\n",
              "      <td>61</td>\n",
              "      <td>6.0032</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10885</th>\n",
              "      <td>12</td>\n",
              "      <td>23</td>\n",
              "      <td>2012</td>\n",
              "      <td>19</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>13.12</td>\n",
              "      <td>16.665</td>\n",
              "      <td>66</td>\n",
              "      <td>8.9981</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10886 rows × 19 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0c32d9ed-a6b0-4c8d-90fc-d13c952329cc')\"\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-0c32d9ed-a6b0-4c8d-90fc-d13c952329cc 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-0c32d9ed-a6b0-4c8d-90fc-d13c952329cc');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "       month  hour  year  day  ...   temp   atemp  humidity  windspeed\n",
              "0          1     0  2011    1  ...   9.84  14.395        81     0.0000\n",
              "1          1     1  2011    1  ...   9.02  13.635        80     0.0000\n",
              "2          1     2  2011    1  ...   9.02  13.635        80     0.0000\n",
              "3          1     3  2011    1  ...   9.84  14.395        75     0.0000\n",
              "4          1     4  2011    1  ...   9.84  14.395        75     0.0000\n",
              "...      ...   ...   ...  ...  ...    ...     ...       ...        ...\n",
              "10881     12    19  2012   19  ...  15.58  19.695        50    26.0027\n",
              "10882     12    20  2012   19  ...  14.76  17.425        57    15.0013\n",
              "10883     12    21  2012   19  ...  13.94  15.910        61    15.0013\n",
              "10884     12    22  2012   19  ...  13.94  17.425        61     6.0032\n",
              "10885     12    23  2012   19  ...  13.12  16.665        66     8.9981\n",
              "\n",
              "[10886 rows x 19 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 152
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cfgAnnoYkuzj",
        "outputId": "4b801a84-5b60-40e3-d6c9-6cc676a3de89"
      },
      "id": "cfgAnnoYkuzj",
      "execution_count": 153,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0         16\n",
              "1         40\n",
              "2         32\n",
              "3         13\n",
              "4          1\n",
              "        ... \n",
              "10881    336\n",
              "10882    241\n",
              "10883    168\n",
              "10884    129\n",
              "10885     88\n",
              "Name: count, Length: 10886, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 153
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# train 및 test 데이터 분할\n",
        "  # random_state = 0 지정\n",
        "from sklearn.model_selection import train_test_split\n",
        "x_train, x_test, y_train, y_test = train_test_split(X,Y, random_state=0, test_size=0.05)"
      ],
      "metadata": {
        "id": "7Fw7av-sk20M"
      },
      "id": "7Fw7av-sk20M",
      "execution_count": 84,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# RandomForestRegressor를 사용\n",
        "  # random_state=123 지정\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "model_rf = RandomForestRegressor(random_state = 123).fit(x_train, y_train)\n",
        "model_rf.score(x_train, y_train), model_rf.score(x_test, y_test)\n",
        "# ?? score 왜그러니.. -> 날짜 데이터 추가해서 극복"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mJNC1mZ6lCSo",
        "outputId": "aff7f4f5-989c-460b-90b8-8407710271f0"
      },
      "id": "mJNC1mZ6lCSo",
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.9932456097854895, 0.9494461208031826)"
            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 스케일링 차이 확인 -> 트리라서 별 차이 없음."
      ],
      "metadata": {
        "id": "DE4akVO7meQR"
      },
      "id": "DE4akVO7meQR"
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "scaler = StandardScaler()\n"
      ],
      "metadata": {
        "id": "AhugXic3m5OL"
      },
      "id": "AhugXic3m5OL",
      "execution_count": 158,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_scaled = scaler.fit_transform(X)"
      ],
      "metadata": {
        "id": "4Dul9CS2rR2T"
      },
      "id": "4Dul9CS2rR2T",
      "execution_count": 161,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# train 및 test 데이터 분할\n",
        "  # random_state = 0 지정\n",
        "from sklearn.model_selection import train_test_split\n",
        "x_train, x_test, y_train, y_test = train_test_split(X,Y, random_state=0, test_size=0.05)"
      ],
      "metadata": {
        "id": "c47VwrRNpeUy"
      },
      "id": "c47VwrRNpeUy",
      "execution_count": 162,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# RandomForestRegressor를 사용\n",
        "  # random_state=123 지정\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "model_rf = RandomForestRegressor(random_state = 123).fit(x_train, y_train)\n",
        "model_rf.score(x_train, y_train), model_rf.score(x_test, y_test)\n",
        "# ?? score 왜그러니.. -> 날짜 데이터 추가해서 극복"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nasLpu47pQHE",
        "outputId": "c0078392-e1f0-43ab-8b48-7ed55d58ff37"
      },
      "id": "nasLpu47pQHE",
      "execution_count": 163,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.9932456097854895, 0.9494461208031826)"
            ]
          },
          "metadata": {},
          "execution_count": 163
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 시계열부분 프로프넷 호기심에 사용"
      ],
      "metadata": {
        "id": "KYr3gGpur39u"
      },
      "id": "KYr3gGpur39u"
    },
    {
      "cell_type": "code",
      "source": [
        "# train 데이터셋 로드 (train.csv)\n",
        "train = pd.read_csv('train.csv')\n",
        "train.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "AEGcN40LsmyW",
        "outputId": "a62e2aaa-7161-44e2-b3a7-0a1fa2af1479"
      },
      "id": "AEGcN40LsmyW",
      "execution_count": 169,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-1fce5e76-a665-4b42-8b4c-565f57aeaef6\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>datetime</th>\n",
              "      <th>season</th>\n",
              "      <th>holiday</th>\n",
              "      <th>workingday</th>\n",
              "      <th>weather</th>\n",
              "      <th>temp</th>\n",
              "      <th>atemp</th>\n",
              "      <th>humidity</th>\n",
              "      <th>windspeed</th>\n",
              "      <th>casual</th>\n",
              "      <th>registered</th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2011-01-01 00:00:00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>9.84</td>\n",
              "      <td>14.395</td>\n",
              "      <td>81</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3</td>\n",
              "      <td>13</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2011-01-01 01:00:00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>9.02</td>\n",
              "      <td>13.635</td>\n",
              "      <td>80</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8</td>\n",
              "      <td>32</td>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2011-01-01 02:00:00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>80</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5</td>\n",
              "      <td>27</td>\n",
              "      <td>32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2011-01-01 03:00:00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>9.84</td>\n",
              "      <td>14.395</td>\n",
              "      <td>75</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3</td>\n",
              "      <td>10</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2011-01-01 04:00:00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>9.84</td>\n",
              "      <td>14.395</td>\n",
              "      <td>75</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1fce5e76-a665-4b42-8b4c-565f57aeaef6')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
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              "          document.querySelector('#df-1fce5e76-a665-4b42-8b4c-565f57aeaef6 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-1fce5e76-a665-4b42-8b4c-565f57aeaef6');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "              datetime  season  holiday  ...  casual  registered  count\n",
              "0  2011-01-01 00:00:00       1        0  ...       3          13     16\n",
              "1  2011-01-01 01:00:00       1        0  ...       8          32     40\n",
              "2  2011-01-01 02:00:00       1        0  ...       5          27     32\n",
              "3  2011-01-01 03:00:00       1        0  ...       3          10     13\n",
              "4  2011-01-01 04:00:00       1        0  ...       0           1      1\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 169
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = train.loc[:, ('datetime', 'count')]\n",
        "df.columns = ['ds', 'y']\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "3thmdJAVs1FA",
        "outputId": "3fdb6b3f-0ddd-4267-a9eb-916c907afb66"
      },
      "id": "3thmdJAVs1FA",
      "execution_count": 210,
      "outputs": [
        {
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          "data": {
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              "\n",
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              "      <th>10885</th>\n",
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              "<p>10886 rows × 2 columns</p>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b5d83bab-25c8-4d9b-8a6b-e615766bcc7c')\"\n",
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              "          const element = document.querySelector('#df-b5d83bab-25c8-4d9b-8a6b-e615766bcc7c');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "                        ds    y\n",
              "0      2011-01-01 00:00:00   16\n",
              "1      2011-01-01 01:00:00   40\n",
              "2      2011-01-01 02:00:00   32\n",
              "3      2011-01-01 03:00:00   13\n",
              "4      2011-01-01 04:00:00    1\n",
              "...                    ...  ...\n",
              "10881  2012-12-19 19:00:00  336\n",
              "10882  2012-12-19 20:00:00  241\n",
              "10883  2012-12-19 21:00:00  168\n",
              "10884  2012-12-19 22:00:00  129\n",
              "10885  2012-12-19 23:00:00   88\n",
              "\n",
              "[10886 rows x 2 columns]"
            ]
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          "metadata": {},
          "execution_count": 210
        }
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    },
    {
      "cell_type": "code",
      "source": [
        "# train 데이터셋 로드 (train.csv)\n",
        "test = pd.read_csv('test.csv')\n",
        "test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "Dv_wJHbbss9J",
        "outputId": "05db9fef-788f-4cca-f653-819917c6afa8"
      },
      "id": "Dv_wJHbbss9J",
      "execution_count": 211,
      "outputs": [
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1be45184-3a2b-41b5-b6ad-4a4d0a737e13')\"\n",
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              "\n",
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              "          const element = document.querySelector('#df-1be45184-3a2b-41b5-b6ad-4a4d0a737e13');\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                 datetime  season  holiday  ...   atemp  humidity  windspeed\n",
              "0     2011-01-20 00:00:00       1        0  ...  11.365        56    26.0027\n",
              "1     2011-01-20 01:00:00       1        0  ...  13.635        56     0.0000\n",
              "2     2011-01-20 02:00:00       1        0  ...  13.635        56     0.0000\n",
              "3     2011-01-20 03:00:00       1        0  ...  12.880        56    11.0014\n",
              "4     2011-01-20 04:00:00       1        0  ...  12.880        56    11.0014\n",
              "...                   ...     ...      ...  ...     ...       ...        ...\n",
              "6488  2012-12-31 19:00:00       1        0  ...  12.880        60    11.0014\n",
              "6489  2012-12-31 20:00:00       1        0  ...  12.880        60    11.0014\n",
              "6490  2012-12-31 21:00:00       1        0  ...  12.880        60    11.0014\n",
              "6491  2012-12-31 22:00:00       1        0  ...  13.635        56     8.9981\n",
              "6492  2012-12-31 23:00:00       1        0  ...  13.635        65     8.9981\n",
              "\n",
              "[6493 rows x 9 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 211
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from fbprophet import Prophet\n",
        "Prophet_model = Prophet()\n",
        "Prophet_model.fit(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fcjZWm7EsMjq",
        "outputId": "92d02415-5507-4f66-9546-910987bdac40"
      },
      "id": "fcjZWm7EsMjq",
      "execution_count": 212,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:fbprophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<fbprophet.forecaster.Prophet at 0x7fd615a78610>"
            ]
          },
          "metadata": {},
          "execution_count": 212
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "submission.columns = ['ds', 'y']\n",
        "df_fin = submission\n",
        "df_fin['y'] = 0\n",
        "df_fin"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "owcFn7Kwupgm",
        "outputId": "db29a487-059e-47c3-96d2-2e8854acca61"
      },
      "id": "owcFn7Kwupgm",
      "execution_count": 213,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-a4a5e1c1-1108-4664-9261-e21caf5c4e2e\">\n",
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              "</table>\n",
              "<p>6493 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a4a5e1c1-1108-4664-9261-e21caf5c4e2e')\"\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "          const element = document.querySelector('#df-a4a5e1c1-1108-4664-9261-e21caf5c4e2e');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "  "
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            "text/plain": [
              "                       ds  y\n",
              "0     2011-01-20 00:00:00  0\n",
              "1     2011-01-20 01:00:00  0\n",
              "2     2011-01-20 02:00:00  0\n",
              "3     2011-01-20 03:00:00  0\n",
              "4     2011-01-20 04:00:00  0\n",
              "...                   ... ..\n",
              "6488  2012-12-31 19:00:00  0\n",
              "6489  2012-12-31 20:00:00  0\n",
              "6490  2012-12-31 21:00:00  0\n",
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              "6492  2012-12-31 23:00:00  0\n",
              "\n",
              "[6493 rows x 2 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 213
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_fin['y'] = Prophet_model.predict(df_fin)['trend']\n",
        "df_fin"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "W6tMTfFMuvwy",
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      "id": "W6tMTfFMuvwy",
      "execution_count": 214,
      "outputs": [
        {
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          "data": {
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              "\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "\n",
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              "\n",
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              "          const element = document.querySelector('#df-bda86441-446f-428c-ab3c-02681f7dd2fd');\n",
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              "            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",
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              "  "
            ],
            "text/plain": [
              "                       ds           y\n",
              "0     2011-01-20 00:00:00   48.655387\n",
              "1     2011-01-20 01:00:00   48.691912\n",
              "2     2011-01-20 02:00:00   48.728436\n",
              "3     2011-01-20 03:00:00   48.764961\n",
              "4     2011-01-20 04:00:00   48.801486\n",
              "...                   ...         ...\n",
              "6488  2012-12-31 19:00:00  217.615515\n",
              "6489  2012-12-31 20:00:00  217.594217\n",
              "6490  2012-12-31 21:00:00  217.572920\n",
              "6491  2012-12-31 22:00:00  217.551622\n",
              "6492  2012-12-31 23:00:00  217.530324\n",
              "\n",
              "[6493 rows x 2 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 214
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_fin.columns = ['datetime', 'count']"
      ],
      "metadata": {
        "id": "-7Zw8jnpwP2Y"
      },
      "id": "-7Zw8jnpwP2Y",
      "execution_count": 215,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## log를 씌워서 XGBoost / LGBM 써보자 -> 위에랑 합체\n",
        "\n",
        "-> exp1로 로그를 풀어서 제출하는 걸 까먹지 않았다면.. 더 많은 걸 했을텐데.."
      ],
      "metadata": {
        "id": "2lcSoXUKw_bE"
      },
      "id": "2lcSoXUKw_bE"
    },
    {
      "cell_type": "code",
      "source": [
        "train.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "id": "j8mM6_Fw0QXA",
        "outputId": "19d25a88-97f1-49d2-8ebb-a4349a16e605"
      },
      "id": "j8mM6_Fw0QXA",
      "execution_count": 222,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-43513185-af6d-4eae-80f4-4c14c727c759\">\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>datetime</th>\n",
              "      <th>season</th>\n",
              "      <th>holiday</th>\n",
              "      <th>workingday</th>\n",
              "      <th>weather</th>\n",
              "      <th>temp</th>\n",
              "      <th>atemp</th>\n",
              "      <th>humidity</th>\n",
              "      <th>windspeed</th>\n",
              "      <th>casual</th>\n",
              "      <th>registered</th>\n",
              "      <th>count</th>\n",
              "      <th>ds</th>\n",
              "      <th>casual_log</th>\n",
              "      <th>registered_log</th>\n",
              "      <th>count_log</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2011-01-01 00:00:00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>9.84</td>\n",
              "      <td>14.395</td>\n",
              "      <td>81</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3</td>\n",
              "      <td>13</td>\n",
              "      <td>16</td>\n",
              "      <td>2011-01-01 00:00:00</td>\n",
              "      <td>1.386294</td>\n",
              "      <td>2.639057</td>\n",
              "      <td>2.833213</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2011-01-01 01:00:00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>9.02</td>\n",
              "      <td>13.635</td>\n",
              "      <td>80</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8</td>\n",
              "      <td>32</td>\n",
              "      <td>40</td>\n",
              "      <td>2011-01-01 01:00:00</td>\n",
              "      <td>2.197225</td>\n",
              "      <td>3.496508</td>\n",
              "      <td>3.713572</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2011-01-01 02:00:00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>9.02</td>\n",
              "      <td>13.635</td>\n",
              "      <td>80</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5</td>\n",
              "      <td>27</td>\n",
              "      <td>32</td>\n",
              "      <td>2011-01-01 02:00:00</td>\n",
              "      <td>1.791759</td>\n",
              "      <td>3.332205</td>\n",
              "      <td>3.496508</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2011-01-01 03:00:00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>9.84</td>\n",
              "      <td>14.395</td>\n",
              "      <td>75</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3</td>\n",
              "      <td>10</td>\n",
              "      <td>13</td>\n",
              "      <td>2011-01-01 03:00:00</td>\n",
              "      <td>1.386294</td>\n",
              "      <td>2.397895</td>\n",
              "      <td>2.639057</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2011-01-01 04:00:00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>9.84</td>\n",
              "      <td>14.395</td>\n",
              "      <td>75</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2011-01-01 04:00:00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.693147</td>\n",
              "      <td>0.693147</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-43513185-af6d-4eae-80f4-4c14c727c759')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      height: 32px;\n",
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              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-43513185-af6d-4eae-80f4-4c14c727c759');\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",
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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "              datetime  season  holiday  ...  casual_log  registered_log  count_log\n",
              "0  2011-01-01 00:00:00       1        0  ...    1.386294        2.639057   2.833213\n",
              "1  2011-01-01 01:00:00       1        0  ...    2.197225        3.496508   3.713572\n",
              "2  2011-01-01 02:00:00       1        0  ...    1.791759        3.332205   3.496508\n",
              "3  2011-01-01 03:00:00       1        0  ...    1.386294        2.397895   2.639057\n",
              "4  2011-01-01 04:00:00       1        0  ...    0.000000        0.693147   0.693147\n",
              "\n",
              "[5 rows x 16 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 222
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "klBJ-d7Uz8lY"
      },
      "id": "klBJ-d7Uz8lY",
      "execution_count": null,
      "outputs": []
    }
  ]
}