캐글 필사 3. 자전거_수요_예측_Practice_for_AIFFEL_김건희.ipynb

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    "colab": {
      "name": "캐글 필사 3. 자전거 수요 예측 Practice for AIFFEL_김건희",
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    "language_info": {
      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# 1. 자전거 대여 수요 예측 경진대회 EDA"
      ],
      "metadata": {
        "id": "KBWk_1PWAq7N"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1.1. 데이터 둘러보기"
      ],
      "metadata": {
        "id": "xgEZBgEDAqSu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "이번에는 캐글에서 직접 작성하지 않고 코랩과 연동시킨 후 데이터를 다운받고 진행코자 한다."
      ],
      "metadata": {
        "id": "bE22t37ZAyk7"
      }
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    {
      "cell_type": "code",
      "execution_count": null,
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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 333
        },
        "id": "Ih0Six8u813v",
        "outputId": "ad9e2068-a71c-4623-f029-4f24934c1891"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: kaggle in /usr/local/lib/python3.7/dist-packages (1.5.12)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from kaggle) (4.64.0)\n",
            "Requirement already satisfied: python-slugify in /usr/local/lib/python3.7/dist-packages (from kaggle) (6.1.2)\n",
            "Requirement already satisfied: urllib3 in /usr/local/lib/python3.7/dist-packages (from kaggle) (1.24.3)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.7/dist-packages (from kaggle) (2022.6.15)\n",
            "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.7/dist-packages (from kaggle) (1.15.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from kaggle) (2.23.0)\n",
            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.7/dist-packages (from kaggle) (2.8.2)\n",
            "Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.7/dist-packages (from python-slugify->kaggle) (1.3)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->kaggle) (3.0.4)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->kaggle) (2.10)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-b190563f-ddb0-4085-a7f4-7be11d1a35b2\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-b190563f-ddb0-4085-a7f4-7be11d1a35b2\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving kaggle.json to kaggle.json\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'kaggle.json': b'{\"username\":\"xpelqpdj0422\",\"key\":\"2edebe6c02209895e8d7e00e69b72f16\"}'}"
            ]
          },
          "metadata": {},
          "execution_count": 1
        }
      ],
      "source": [
        "# 토큰 파일 업로드를 먼저 진행. kaggle.json이라는 파일이 업로드가 되도록 한다.\n",
        "!pip install kaggle\n",
        "from google.colab import files\n",
        "files.upload()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 파일이 잘 업로드 되었는지 확인을 하기 위해서 다음 코드를 실행해 본다.\n",
        "# ls -1ha kaggle.json\n",
        "# 이 때 파일명이 kaggle.json이라고 뜬다면 성공이다.\n",
        "# 처음 시도할 때는 kaggle.json이라고 잘 뜨지만 이후에 중복 업로드가 될 경우는 이 코드가 오류가 날 수도 있다.\n",
        "# 오류가 나더라도 이하의 코드를 진행하는 데는 지장이 없긴 하다."
      ],
      "metadata": {
        "id": "tmLqqosn9PSO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Kaggle API를 사용하기 위해 json파일을 ~/.kaggle로 이동시키는 작업이 필요하다.\n",
        "!mkdir -p ~/.kaggle\n",
        "!cp kaggle.json ~/.kaggle/\n",
        "\n",
        "# Permission Warning이 발생하지 않도록 해줍니다.\n",
        "!chmod 600 ~/.kaggle/kaggle.json\n",
        "\n",
        "# 내가 참가한 대회 리스트 확인\n",
        "!kaggle competitions list"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JCg3XUFK9ReX",
        "outputId": "46317515-6c03-48d5-fd72-ddbaf7f236a2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ref                                            deadline             category            reward  teamCount  userHasEntered  \n",
            "---------------------------------------------  -------------------  ---------------  ---------  ---------  --------------  \n",
            "contradictory-my-dear-watson                   2030-07-01 23:59:00  Getting Started     Prizes         53           False  \n",
            "gan-getting-started                            2030-07-01 23:59:00  Getting Started     Prizes         98           False  \n",
            "store-sales-time-series-forecasting            2030-06-30 23:59:00  Getting Started  Knowledge        617           False  \n",
            "tpu-getting-started                            2030-06-03 23:59:00  Getting Started  Knowledge        125           False  \n",
            "digit-recognizer                               2030-01-01 00:00:00  Getting Started  Knowledge       1251           False  \n",
            "titanic                                        2030-01-01 00:00:00  Getting Started  Knowledge      15237           False  \n",
            "house-prices-advanced-regression-techniques    2030-01-01 00:00:00  Getting Started  Knowledge       3981           False  \n",
            "connectx                                       2030-01-01 00:00:00  Getting Started  Knowledge        202           False  \n",
            "nlp-getting-started                            2030-01-01 00:00:00  Getting Started  Knowledge        809           False  \n",
            "spaceship-titanic                              2030-01-01 00:00:00  Getting Started  Knowledge       2358           False  \n",
            "competitive-data-science-predict-future-sales  2022-12-31 23:59:00  Playground           Kudos      15145           False  \n",
            "google-universal-image-embedding               2022-10-10 23:59:00  Research           $50,000        251           False  \n",
            "jpx-tokyo-stock-exchange-prediction            2022-10-07 23:59:00  Featured           $63,000       1372           False  \n",
            "mayo-clinic-strip-ai                           2022-10-05 23:59:00  Research           $10,000        179           False  \n",
            "hubmap-organ-segmentation                      2022-09-22 23:59:00  Research           $60,000        461           False  \n",
            "amex-default-prediction                        2022-08-24 23:59:00  Featured          $100,000       3170           False  \n",
            "feedback-prize-effectiveness                   2022-08-23 23:59:00  Featured           $55,000       1043           False  \n",
            "AI4Code                                        2022-08-11 23:59:00  Featured          $150,000        965           False  \n",
            "tabular-playground-series-jul-2022             2022-07-31 23:59:00  Playground            Swag        990           False  \n",
            "smartphone-decimeter-2022                      2022-07-29 23:59:00  Research           $10,000        553           False  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! kaggle competitions download -c bike-sharing-demand     # API를 복사 붙여넣기 하여 파일 다운로드 진행"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HUsFN6429adM",
        "outputId": "c40cf669-6b0b-4be9-a9ca-e158c4bc9cc3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading bike-sharing-demand.zip to /content\n",
            "\r  0% 0.00/189k [00:00<?, ?B/s]\n",
            "\r100% 189k/189k [00:00<00:00, 62.6MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!ls           # 압축 파일의 목록 확인"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FXBcl25jAXt8",
        "outputId": "1877a62c-86f6-4e08-ff39-0d4b4e2f3e67"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "bike-sharing-demand.zip  drive\tkaggle.json  sample_data\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!unzip bike-sharing-demand.zip               # 해당 파일의 압축을 푸는 과정(unzip)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oN7cSJVFAbTa",
        "outputId": "60a04eaf-24ef-4cf9-c7de-a3c69da3ad65"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  bike-sharing-demand.zip\n",
            "  inflating: sampleSubmission.csv    \n",
            "  inflating: test.csv                \n",
            "  inflating: train.csv               \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 이제 파일 다운이 완료되었으니 본격적으로 numpy와 pandas를 들여오고 train과 test, sampleSubmission 자료를 지정한다.\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "train = pd.read_csv('./train.csv')\n",
        "test = pd.read_csv('./test.csv')\n",
        "submission = pd.read_csv('./sampleSubmission.csv')\n",
        "train.shape, test.shape, submission.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8gHIx1kyAeoV",
        "outputId": "dd958c92-0fc9-443a-bcfe-f96a641b5d04"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((10886, 12), (6493, 9), (6493, 2))"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "id": "nljGaLmnRYyl",
        "outputId": "b6ca09a8-4c6a-4ac6-a893-8f04881171ee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "              datetime  season  holiday  workingday  weather  temp   atemp  \\\n",
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              "4  2011-01-01 04:00:00       1        0           0        1  9.84  14.395   \n",
              "\n",
              "   humidity  windspeed  casual  registered  count  \n",
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              "2        80        0.0       5          27     32  \n",
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              "              datetime  season  holiday  workingday  weather   temp   atemp  \\\n",
              "0  2011-01-20 00:00:00       1        0           1        1  10.66  11.365   \n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d8075e1d-89c6-49b9-afb5-595ff5361477');\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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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 9
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      ]
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    {
      "cell_type": "code",
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        "submission.head()"
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        "outputId": "cf58b4c8-dc6b-4ae9-e7cd-646fa07792fe"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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",
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              "      <td>2011-01-20 03:00:00</td>\n",
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          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "캐글과 코랩 연동과정은 https://jaaamj.tistory.com/141 사이트를 참고하였다.  \n",
        "아니면 파일을 직접 다운로드하여 구글 드라이브에 저장 후 경로 복사를 할 수도 있다.  \n",
        "저는 캐글과 코랩을 연동시켜서 파일을 불러오는데 성공했다!!!"
      ],
      "metadata": {
        "id": "847tY4ee9NiD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train.info()        # train의 자료형태에 관한 정보 확인"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wTf858dOSpr_",
        "outputId": "b9547edc-4559-43ab-fec7-1596be500263"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 10886 entries, 0 to 10885\n",
            "Data columns (total 12 columns):\n",
            " #   Column      Non-Null Count  Dtype  \n",
            "---  ------      --------------  -----  \n",
            " 0   datetime    10886 non-null  object \n",
            " 1   season      10886 non-null  int64  \n",
            " 2   holiday     10886 non-null  int64  \n",
            " 3   workingday  10886 non-null  int64  \n",
            " 4   weather     10886 non-null  int64  \n",
            " 5   temp        10886 non-null  float64\n",
            " 6   atemp       10886 non-null  float64\n",
            " 7   humidity    10886 non-null  int64  \n",
            " 8   windspeed   10886 non-null  float64\n",
            " 9   casual      10886 non-null  int64  \n",
            " 10  registered  10886 non-null  int64  \n",
            " 11  count       10886 non-null  int64  \n",
            "dtypes: float64(3), int64(8), object(1)\n",
            "memory usage: 1020.7+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test.info()       # test의 자료형태에 관한 정보 확인"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jIb1sRu3Sq_D",
        "outputId": "1c1aaf44-176d-4ef9-920f-87f179fce9e2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 6493 entries, 0 to 6492\n",
            "Data columns (total 9 columns):\n",
            " #   Column      Non-Null Count  Dtype  \n",
            "---  ------      --------------  -----  \n",
            " 0   datetime    6493 non-null   object \n",
            " 1   season      6493 non-null   int64  \n",
            " 2   holiday     6493 non-null   int64  \n",
            " 3   workingday  6493 non-null   int64  \n",
            " 4   weather     6493 non-null   int64  \n",
            " 5   temp        6493 non-null   float64\n",
            " 6   atemp       6493 non-null   float64\n",
            " 7   humidity    6493 non-null   int64  \n",
            " 8   windspeed   6493 non-null   float64\n",
            "dtypes: float64(3), int64(5), object(1)\n",
            "memory usage: 456.7+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1.2. 더 효과적인 분석을 위한 피처 엔지니어링"
      ],
      "metadata": {
        "id": "2Wlla2KIS2qg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(train['datetime'][100])                # datetime 100번째 인덱스 출력\n",
        "print(train['datetime'][100].split())        # 공백 기준으로 문자열 나누기\n",
        "print(train['datetime'][100].split()[0])     # 날짜와 시간으로 나뉘어진 문자열 중 날짜 출력\n",
        "print(train['datetime'][100].split()[1])     # 날짜와 시간으로 나뉘어진 문자열 중 시간 출력"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8rwt26MoS2MU",
        "outputId": "7e443b69-1579-4c74-847e-0c98874a4c64"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2011-01-05 09:00:00\n",
            "['2011-01-05', '09:00:00']\n",
            "2011-01-05\n",
            "09:00:00\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(train['datetime'][100].split()[1])                    # 시간\n",
        "print(train['datetime'][100].split()[1].split(':'))         # \":\" 기준으로 문자열 나누기(시간, 분, 초로 분할)\n",
        "print(train['datetime'][100].split()[1].split(':')[0])      # 시, 분, 초로 나뉘어진 문자열 중 시간 출력\n",
        "print(train['datetime'][100].split()[1].split(':')[1])      # 시, 분, 초로 나뉘어진 문자열 중 분 출력\n",
        "print(train['datetime'][100].split()[1].split(':')[2])      # 시, 분, 초로 나뉘어진 문자열 중 초 출력"
      ],
      "metadata": {
        "id": "oyeRmkfoSsu8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6bc0be07-e3c7-43c7-96e7-f180151daf18"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "09:00:00\n",
            "['09', '00', '00']\n",
            "09\n",
            "00\n",
            "00\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train['date'] = train['datetime'].apply(lambda x: x.split()[0]) # 날짜 피처 생성\n",
        "\n",
        "# 연도, 월, 일, 시, 분, 초 피처를 차례로 생성. lambda 함수를 사용하여 간단하게 표현.\n",
        "train['year'] = train['datetime'].apply(lambda x: x.split()[0].split('-')[0]) \n",
        "train['month'] = train['datetime'].apply(lambda x: x.split()[0].split('-')[1])\n",
        "train['day'] = train['datetime'].apply(lambda x: x.split()[0].split('-')[2]) \n",
        "train['hour'] = train['datetime'].apply(lambda x: x.split()[1].split(':')[0])\n",
        "train['minute'] = train['datetime'].apply(lambda x: x.split()[1].split(':')[1])\n",
        "train['second'] = train['datetime'].apply(lambda x: x.split()[1].split(':')[2])"
      ],
      "metadata": {
        "id": "MP7ZcnFHVoDc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from datetime import datetime                 # datatime 라이브러리 임포트\n",
        "import calendar\n",
        "\n",
        "print(train['date'][100])     # 날짜\n",
        "print(datetime.strptime(train['date'][100], '%Y-%m-%d'))                               # datetime 타입으로 변경\n",
        "print(datetime.strptime(train['date'][100], '%Y-%m-%d').weekday())                     # 정수로 요일 반환\n",
        "print(calendar.day_name[datetime.strptime(train['date'][100], '%Y-%m-%d').weekday()])  # 문자열로 요일 반환"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oYJKFO_XVyCK",
        "outputId": "fa1c9b47-82b0-483f-a524-0c2420758aff"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2011-01-05\n",
            "2011-01-05 00:00:00\n",
            "2\n",
            "Wednesday\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train['weekday'] = train['date'].apply(\n",
        "    lambda dateString: \n",
        "    calendar.day_name[datetime.strptime(dateString,\"%Y-%m-%d\").weekday()])"
      ],
      "metadata": {
        "id": "My6mu8sPV8v-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train['season'] = train['season'].map({1: 'Spring',\n",
        "                                       2: 'Summer',\n",
        "                                       3: 'Fall',\n",
        "                                       4: 'Winter' })\n",
        "train['weather'] = train['weather'].map({1: 'Clear',\n",
        "                                         2: 'Mist, Few clouds',\n",
        "                                         3: 'Light Snow, Rain, Thunder',\n",
        "                                         4: 'Heavy Snow, Rain, Thunder'})"
      ],
      "metadata": {
        "id": "DeQuSB6fWGI6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "시즌(계절)의 경우 1은 spring으로, 2는 summer로, 3은 fall로, 4는 winter로 mapping하고,  \n",
        "\n",
        "날씨의 경우는 1은 clear로, 2는 Mist, Few clouds로, 3은 Light snow, Rain, Thunder로, 4는 'Heavy "
      ],
      "metadata": {
        "id": "OvYi1G4LzFDd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train.head()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "id": "vc4X5DzIyldm",
        "outputId": "f25efb84-697c-4eef-b162-2a4c02c58a73"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              datetime  season  holiday  workingday weather  temp   atemp  \\\n",
              "0  2011-01-01 00:00:00  Spring        0           0   Clear  9.84  14.395   \n",
              "1  2011-01-01 01:00:00  Spring        0           0   Clear  9.02  13.635   \n",
              "2  2011-01-01 02:00:00  Spring        0           0   Clear  9.02  13.635   \n",
              "3  2011-01-01 03:00:00  Spring        0           0   Clear  9.84  14.395   \n",
              "4  2011-01-01 04:00:00  Spring        0           0   Clear  9.84  14.395   \n",
              "\n",
              "   humidity  windspeed  casual  registered  count        date  year month day  \\\n",
              "0        81        0.0       3          13     16  2011-01-01  2011    01  01   \n",
              "1        80        0.0       8          32     40  2011-01-01  2011    01  01   \n",
              "2        80        0.0       5          27     32  2011-01-01  2011    01  01   \n",
              "3        75        0.0       3          10     13  2011-01-01  2011    01  01   \n",
              "4        75        0.0       0           1      1  2011-01-01  2011    01  01   \n",
              "\n",
              "  hour minute second   weekday  \n",
              "0   00     00     00  Saturday  \n",
              "1   01     00     00  Saturday  \n",
              "2   02     00     00  Saturday  \n",
              "3   03     00     00  Saturday  \n",
              "4   04     00     00  Saturday  "
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              "      <th>minute</th>\n",
              "      <th>second</th>\n",
              "      <th>weekday</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2011-01-01 00:00:00</td>\n",
              "      <td>Spring</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Clear</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</td>\n",
              "      <td>2011</td>\n",
              "      <td>01</td>\n",
              "      <td>01</td>\n",
              "      <td>00</td>\n",
              "      <td>00</td>\n",
              "      <td>00</td>\n",
              "      <td>Saturday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2011-01-01 01:00:00</td>\n",
              "      <td>Spring</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Clear</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</td>\n",
              "      <td>2011</td>\n",
              "      <td>01</td>\n",
              "      <td>01</td>\n",
              "      <td>01</td>\n",
              "      <td>00</td>\n",
              "      <td>00</td>\n",
              "      <td>Saturday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2011-01-01 02:00:00</td>\n",
              "      <td>Spring</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Clear</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</td>\n",
              "      <td>2011</td>\n",
              "      <td>01</td>\n",
              "      <td>01</td>\n",
              "      <td>02</td>\n",
              "      <td>00</td>\n",
              "      <td>00</td>\n",
              "      <td>Saturday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2011-01-01 03:00:00</td>\n",
              "      <td>Spring</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Clear</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</td>\n",
              "      <td>2011</td>\n",
              "      <td>01</td>\n",
              "      <td>01</td>\n",
              "      <td>03</td>\n",
              "      <td>00</td>\n",
              "      <td>00</td>\n",
              "      <td>Saturday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2011-01-01 04:00:00</td>\n",
              "      <td>Spring</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Clear</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</td>\n",
              "      <td>2011</td>\n",
              "      <td>01</td>\n",
              "      <td>01</td>\n",
              "      <td>04</td>\n",
              "      <td>00</td>\n",
              "      <td>00</td>\n",
              "      <td>Saturday</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b4e7daf2-06b9-4842-a348-6882cd71efc5')\"\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-b4e7daf2-06b9-4842-a348-6882cd71efc5 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-b4e7daf2-06b9-4842-a348-6882cd71efc5');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1.3. 데이터의 시각화"
      ],
      "metadata": {
        "id": "tNE_8ADC0_id"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 1.3.1. 분포도(displot)"
      ],
      "metadata": {
        "id": "Me2buHjS7q9N"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "import matplotlib as mpl\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ],
      "metadata": {
        "id": "fnKv41y00-by"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mpl.rc('font', size=15)                 # 폰트 크기를 15로 설정\n",
        "sns.displot([train['count']]);          # 분포도 출력"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "id": "MGaFJ1u81Nsv",
        "outputId": "5c72d024-0f01-42ec-8169-d9a1e0d262cc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 456.5x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.displot(np.log(train['count']));"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 358
        },
        "id": "O4ltD9s613gM",
        "outputId": "e098e974-24c2-4214-c890-415e76a86f47"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 1.3.2. 막대그래프(barplot)"
      ],
      "metadata": {
        "id": "S-gb5Bh67vT7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 스텝 1 : m행 n열 Figure 준비\n",
        "mpl.rc('font', size=14)                           # 폰트 크기 설정\n",
        "mpl.rc('axes', titlesize=15)                      # 각 축의 제목 크기 설정\n",
        "figure, axes = plt.subplots(nrows=3, ncols=2)     # 3행 2열 Figure 생성\n",
        "plt.tight_layout()                                # 그래프 사이에 여백 확보\n",
        "figure.set_size_inches(10,9)                      # Figure 크기를 10 X 9인치로 설정\n",
        "\n",
        "# 스텝 2 : 각 축에 서브플롯 할당\n",
        "# 각 축에 연도, 월, 일, 시간, 분, 초별 평균 대여 수량 막대 그래프 할당\n",
        "sns.barplot(x='year', y='count', data=train, ax=axes[0,0])\n",
        "sns.barplot(x='month', y='count', data=train, ax=axes[0,1])\n",
        "sns.barplot(x='day', y='count', data=train, ax=axes[1,0])\n",
        "sns.barplot(x='hour', y='count', data=train, ax=axes[1,1])\n",
        "sns.barplot(x='minute', y='count', data=train, ax=axes[2, 0])\n",
        "sns.barplot(x='second', y='count', data=train, ax=axes[2, 1])\n",
        "\n",
        "# 스텝 3 : 세부 설정(서브플롯에 제목 달기)\n",
        "axes[0,0].set(title='Rental amounts by year')\n",
        "axes[0,1].set(title='Rental amounts by month')\n",
        "axes[1,0].set(title='Rental amounts by day')\n",
        "axes[1,1].set(title='Rental amounts by hour')\n",
        "axes[2,0].set(title='Rental amounts by minute')\n",
        "axes[2,1].set(title='Rental amounts by second')\n",
        "\n",
        "# 1행에 위치한 서브플롯들의 x축 라벨 90도 회전.\n",
        "# 글자가 겹치는 부분을 깔끔하게 정리해 주는 보조적인 기능\n",
        "axes[1, 0].tick_params(axis='x', labelrotation=90)\n",
        "axes[1, 1].tick_params(axis='x', labelrotation=90)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 608
        },
        "id": "R3jhygo61-ZG",
        "outputId": "9b1361a4-85c3-4f49-b70c-548543590d4a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x648 with 6 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 1.3.3. 박스플롯(boxplot)"
      ],
      "metadata": {
        "id": "LOwoJlAH736x"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 스텝 1 : 2행 2열 Figure 준비\n",
        "figure, axes = plt.subplots(nrows=2, ncols=2)   # 2행 2열 Figiure 생성\n",
        "plt.tight_layout()\n",
        "figure.set_size_inches(10, 10)\n",
        "\n",
        "# 스텝 2 : 서브플롯 할당\n",
        "# 계절(season), 날씨(weather), 공휴일(holiday), 근무일별(workingday) 대여 수량 박스플롯\n",
        "sns.boxplot(x='season', y='count', data=train, ax=axes[0,0])\n",
        "sns.boxplot(x='weather', y='count', data=train, ax=axes[0,1])\n",
        "sns.boxplot(x='holiday', y='count', data=train, ax=axes[1,0])\n",
        "sns.boxplot(x='workingday', y='count', data=train, ax=axes[1, 1])\n",
        "\n",
        "# 스텝 3 : 세부 설정(서브플롯에 제목 달기)\n",
        "axes[0,0].set(title='Bow Plot on Count Across Season')\n",
        "axes[0,1].set(title='Bow Plot on Count Across Weather')\n",
        "axes[1,0].set(title='Bow Plot on Count Across Holiday')\n",
        "axes[1, 1].set(title='Box Plot On Count Across Working Day')\n",
        "\n",
        "# 3-2 : x축 라벨 겹침 해결\n",
        "axes[0, 1].tick_params('x', labelrotation=10) # 반시계방향으로 10도 회전"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 666
        },
        "id": "9PASXW0su7wW",
        "outputId": "02047ee2-cda2-428d-a95b-d54a23b0607e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* 박스 플롯의 해석방법을 정리해볼 필요가 있어서 확인해 보고자 한다.\n",
        "* 박스 플롯에 대한 설명 블로그 : https://codedragon.tistory.com/7012\n",
        "\n",
        "1) whisker : 상자의 좌우 또는 상하로 뻗어나간 선\n",
        "\n",
        "2) 박스 내부의 가로선 : 중앙값\n",
        "\n",
        "3) lower whisker : 최솟값 혹은 '중앙값 - 1.5 X IQR'보다 큰 데이터 중 가장 작은 값\n",
        "\n",
        "4) upper whisker : 최댓값 혹은 '중앙값 + 1.5 X IQR'보다 큰 데이터 중 가장 큰 값\n",
        "\n",
        "5) IQR : Inter Quartile Range\n",
        " 제3사분위수 - 제1사분위수\n",
        "\n",
        "6) 점 : 이상치 혹은 특이점\n",
        " lover whisker보다 작은 데이터 또는 upper whisker보다 큰 데이터가 여기에 해당한다.\n",
        "\n",
        "제1사분위수, 제3사분위수와 중앙값에 대한 내용이 정리된 블로그 : https://mycodepia.tistory.com/18\n"
      ],
      "metadata": {
        "id": "7VNCF2H_33Wd"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 1.3.4. 포인트플롯(pointplot)"
      ],
      "metadata": {
        "id": "S0e3Y3fa78Tu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 스텝 1 : m행 n열 Figure 준비\n",
        "mpl.rc('font', size=11)\n",
        "figure, axes = plt.subplots(nrows=5) # 5행 1열\n",
        "figure.set_size_inches(12, 18)\n",
        "\n",
        "# 스텝 2 : 서브플롯 할당\n",
        "# 근무일, 공휴일, 요일, 계절, 날씨에 따른 시간대별 평균 대여 수량 포인트플롯\n",
        "sns.pointplot(x='hour', y='count', data=train, hue='workingday', ax=axes[0])\n",
        "sns.pointplot(x='hour', y='count', data=train, hue='holiday', ax=axes[1])\n",
        "sns.pointplot(x='hour', y='count', data=train, hue='weekday', ax=axes[2])\n",
        "sns.pointplot(x='hour', y='count', data=train, hue='season', ax=axes[3])\n",
        "sns.pointplot(x='hour', y='count', data=train, hue='weather', ax=axes[4]);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "phnLb0ea2bfr",
        "outputId": "55765a65-9f00-4d29-ef58-d75d2140825f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x1296 with 5 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "참고로 workingday의 0은 주말, 1은 평일을 의미  \n",
        "holiday의 0과 1은 미국 기준 기념일인 경우에 1을, 나머지의 경우 0으로 표시한다.  \n",
        "(7월 4일 독립기념일 등등)"
      ],
      "metadata": {
        "id": "89q8NluD0--P"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 1.3.5. 회귀선을 포함한 산점도 그래프(regplot)\n",
        "\n",
        "산점도를 그릴 때는 보통 plt.scatter로 나타내지만 여기에서는 산점도에 회귀선 혹은 추세선을 포함하기 때문에 sns.regplot을 사용해 산점도에 추세선을 표시할 수 있다.\n",
        "\n"
      ],
      "metadata": {
        "id": "L1LFtP4D8KwO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 스텝 1 : m행 n열 Figure 준비\n",
        "mpl.rc('font', size=15)\n",
        "figure, axes = plt.subplots(nrows=2, ncols=2) # 2행 2열\n",
        "plt.tight_layout()\n",
        "figure.set_size_inches(7, 6)\n",
        "\n",
        "# 스텝 2 : 서브플롯 할당\n",
        "# 온도(temp), 체감 온도(atemp), 풍속(windspeed), 습도 별 대여 수량 산점도 그래프\n",
        "sns.regplot(x='temp', y='count', data=train, ax=axes[0, 0], \n",
        "            scatter_kws={'alpha': 0.2}, line_kws={'color': 'blue'})\n",
        "sns.regplot(x='atemp', y='count', data=train, ax=axes[0, 1], \n",
        "            scatter_kws={'alpha': 0.2}, line_kws={'color': 'blue'})\n",
        "sns.regplot(x='windspeed', y='count', data=train, ax=axes[1, 0], \n",
        "            scatter_kws={'alpha': 0.2}, line_kws={'color': 'blue'})\n",
        "sns.regplot(x='humidity', y='count', data=train, ax=axes[1, 1], \n",
        "            scatter_kws={'alpha': 0.2}, line_kws={'color': 'blue'});\n",
        "\n",
        "# alpha 값으로 산점도 내 점의 투명도를 조절할 수 있다.\n",
        "# 0으로 갈수록 산점도 내 점이 투명해지고 1로 갈수록 선명해진다.(따라서 0과 1 사이의 값만 넣을 수 있다.)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 416
        },
        "id": "ybjDJVy08I2H",
        "outputId": "f989b55c-ae81-474f-84ee-216985eb477b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 504x432 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 1.3.6. 히트맵(heatmap)"
      ],
      "metadata": {
        "id": "MWMg1VkD-2qy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train[['temp', 'atemp', 'humidity', 'windspeed', 'count']].corr()   # corr을 통해 5가지 특성 간의 상관관계를 확인해 볼 수 있다."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "_WR-Axhu_UYO",
        "outputId": "5b6b73c4-1d98-4a4c-f77d-d2814826461e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               temp     atemp  humidity  windspeed     count\n",
              "temp       1.000000  0.984948 -0.064949  -0.017852  0.394454\n",
              "atemp      0.984948  1.000000 -0.043536  -0.057473  0.389784\n",
              "humidity  -0.064949 -0.043536  1.000000  -0.318607 -0.317371\n",
              "windspeed -0.017852 -0.057473 -0.318607   1.000000  0.101369\n",
              "count      0.394454  0.389784 -0.317371   0.101369  1.000000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c9636214-9db3-45c2-a3e0-3c4cc17cfa5c\">\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>temp</th>\n",
              "      <th>atemp</th>\n",
              "      <th>humidity</th>\n",
              "      <th>windspeed</th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>temp</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.984948</td>\n",
              "      <td>-0.064949</td>\n",
              "      <td>-0.017852</td>\n",
              "      <td>0.394454</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>atemp</th>\n",
              "      <td>0.984948</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.043536</td>\n",
              "      <td>-0.057473</td>\n",
              "      <td>0.389784</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>humidity</th>\n",
              "      <td>-0.064949</td>\n",
              "      <td>-0.043536</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.318607</td>\n",
              "      <td>-0.317371</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>windspeed</th>\n",
              "      <td>-0.017852</td>\n",
              "      <td>-0.057473</td>\n",
              "      <td>-0.318607</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.101369</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>0.394454</td>\n",
              "      <td>0.389784</td>\n",
              "      <td>-0.317371</td>\n",
              "      <td>0.101369</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c9636214-9db3-45c2-a3e0-3c4cc17cfa5c')\"\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-c9636214-9db3-45c2-a3e0-3c4cc17cfa5c 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-c9636214-9db3-45c2-a3e0-3c4cc17cfa5c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "corrMat = train[['temp', 'atemp', 'humidity', 'windspeed', 'count']].corr() \n",
        "fig, ax = plt.subplots()\n",
        "fig.set_size_inches(10, 10)\n",
        "sns.heatmap(corrMat, annot=True)    # 상관관계 히트맵 그리기\n",
        "ax.set(title='Heatmap of Numerical Data');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 614
        },
        "id": "8NJiNYF9AFha",
        "outputId": "778a4a28-fedc-485a-aa5f-d61572b0088c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 2. 자전거 대여 수요 예측 경진대회 베이스라인 모델"
      ],
      "metadata": {
        "id": "k3dQN7XX8B1C"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "# 데이터 경로\n",
        "train = pd.read_csv('./train.csv')\n",
        "test = pd.read_csv('./test.csv')\n",
        "submission = pd.read_csv('./sampleSubmission.csv')\n",
        "train.shape, test.shape"
      ],
      "metadata": {
        "id": "ertP8vW0Ba-O",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c720897a-a49c-4fd8-f202-dada7f98e94e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((10886, 12), (6493, 9))"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 이상치 제거. 훈련 데이터에서 weather가 4가 아닌 데이터만 추출\n",
        "train = train[train['weather'] != 4]"
      ],
      "metadata": {
        "id": "YQVSotU0AtsK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 데이터 합치기. train + test\n",
        "all_data_temp = pd.concat([train, test])\n",
        "all_data_temp"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 835
        },
        "id": "5ueVmx83A5C1",
        "outputId": "4cbe6dbd-0f5e-48d4-a600-385872a8c7be"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 datetime  season  holiday  workingday  weather   temp  \\\n",
              "0     2011-01-01 00:00:00       1        0           0        1   9.84   \n",
              "1     2011-01-01 01:00:00       1        0           0        1   9.02   \n",
              "2     2011-01-01 02:00:00       1        0           0        1   9.02   \n",
              "3     2011-01-01 03:00:00       1        0           0        1   9.84   \n",
              "4     2011-01-01 04:00:00       1        0           0        1   9.84   \n",
              "...                   ...     ...      ...         ...      ...    ...   \n",
              "6488  2012-12-31 19:00:00       1        0           1        2  10.66   \n",
              "6489  2012-12-31 20:00:00       1        0           1        2  10.66   \n",
              "6490  2012-12-31 21:00:00       1        0           1        1  10.66   \n",
              "6491  2012-12-31 22:00:00       1        0           1        1  10.66   \n",
              "6492  2012-12-31 23:00:00       1        0           1        1  10.66   \n",
              "\n",
              "       atemp  humidity  windspeed  casual  registered  count  \n",
              "0     14.395        81     0.0000     3.0        13.0   16.0  \n",
              "1     13.635        80     0.0000     8.0        32.0   40.0  \n",
              "2     13.635        80     0.0000     5.0        27.0   32.0  \n",
              "3     14.395        75     0.0000     3.0        10.0   13.0  \n",
              "4     14.395        75     0.0000     0.0         1.0    1.0  \n",
              "...      ...       ...        ...     ...         ...    ...  \n",
              "6488  12.880        60    11.0014     NaN         NaN    NaN  \n",
              "6489  12.880        60    11.0014     NaN         NaN    NaN  \n",
              "6490  12.880        60    11.0014     NaN         NaN    NaN  \n",
              "6491  13.635        56     8.9981     NaN         NaN    NaN  \n",
              "6492  13.635        65     8.9981     NaN         NaN    NaN  \n",
              "\n",
              "[17378 rows x 12 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0f96f3d6-af9a-44a9-892b-6d95dccb499e\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "      <th>datetime</th>\n",
              "      <th>season</th>\n",
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              "      <th>workingday</th>\n",
              "      <th>weather</th>\n",
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              "      <th>atemp</th>\n",
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              "      <th>windspeed</th>\n",
              "      <th>casual</th>\n",
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              "      <th>count</th>\n",
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              "  <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>0.0000</td>\n",
              "      <td>3.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>16.0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2011-01-01 01:00:00</td>\n",
              "      <td>1</td>\n",
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              "      <td>9.02</td>\n",
              "      <td>13.635</td>\n",
              "      <td>80</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>8.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>40.0</td>\n",
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              "    <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",
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              "      <td>9.02</td>\n",
              "      <td>13.635</td>\n",
              "      <td>80</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>5.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>32.0</td>\n",
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              "    <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",
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              "      <td>10.0</td>\n",
              "      <td>13.0</td>\n",
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              "    <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",
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              "      <td>9.84</td>\n",
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              "      <td>0.0000</td>\n",
              "      <td>0.0</td>\n",
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              "      <th>6488</th>\n",
              "      <td>2012-12-31 19:00:00</td>\n",
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              "      <td>NaN</td>\n",
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              "      <th>6489</th>\n",
              "      <td>2012-12-31 20:00:00</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>6490</th>\n",
              "      <td>2012-12-31 21:00:00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <th>6491</th>\n",
              "      <td>2012-12-31 22:00:00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "      <td>8.9981</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>6492</th>\n",
              "      <td>2012-12-31 23:00:00</td>\n",
              "      <td>1</td>\n",
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              "      <td>65</td>\n",
              "      <td>8.9981</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "<p>17378 rows × 12 columns</p>\n",
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          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "all_data = pd.concat([train, test], ignore_index=True)"
      ],
      "metadata": {
        "id": "aDEkmyY8BD33"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 파생 변수(피처) 추가\n",
        "\n",
        "from datetime import datetime\n",
        "\n",
        "all_data['date'] = all_data['datetime'].apply(lambda x: x.split()[0])   # 날짜 피처 생성\n",
        "all_data['year'] = all_data['datetime'].apply(lambda x: x.split()[0].split('-')[0])    # 연도 피처 생성\n",
        "all_data['month'] = all_data['datetime'].apply(lambda x: x.split()[0].split('-')[1])   # 월 피처 생성\n",
        "all_data['hour'] = all_data['datetime'].apply(lambda x: x.split()[1].split(':')[0])    # 시 피처 생성\n",
        "all_data[\"weekday\"] = all_data['date'].apply(lambda dateString : datetime.strptime(dateString,\"%Y-%m-%d\").weekday())   # 요일 피처 생성\n",
        "\n",
        "all_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 835
        },
        "id": "G8gf0uM0B3kH",
        "outputId": "66d4aeb1-c102-4c7e-da3b-0c0627cd159b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  datetime  season  holiday  workingday  weather   temp  \\\n",
              "0      2011-01-01 00:00:00       1        0           0        1   9.84   \n",
              "1      2011-01-01 01:00:00       1        0           0        1   9.02   \n",
              "2      2011-01-01 02:00:00       1        0           0        1   9.02   \n",
              "3      2011-01-01 03:00:00       1        0           0        1   9.84   \n",
              "4      2011-01-01 04:00:00       1        0           0        1   9.84   \n",
              "...                    ...     ...      ...         ...      ...    ...   \n",
              "17373  2012-12-31 19:00:00       1        0           1        2  10.66   \n",
              "17374  2012-12-31 20:00:00       1        0           1        2  10.66   \n",
              "17375  2012-12-31 21:00:00       1        0           1        1  10.66   \n",
              "17376  2012-12-31 22:00:00       1        0           1        1  10.66   \n",
              "17377  2012-12-31 23:00:00       1        0           1        1  10.66   \n",
              "\n",
              "        atemp  humidity  windspeed  casual  registered  count        date  \\\n",
              "0      14.395        81     0.0000     3.0        13.0   16.0  2011-01-01   \n",
              "1      13.635        80     0.0000     8.0        32.0   40.0  2011-01-01   \n",
              "2      13.635        80     0.0000     5.0        27.0   32.0  2011-01-01   \n",
              "3      14.395        75     0.0000     3.0        10.0   13.0  2011-01-01   \n",
              "4      14.395        75     0.0000     0.0         1.0    1.0  2011-01-01   \n",
              "...       ...       ...        ...     ...         ...    ...         ...   \n",
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              "17375  12.880        60    11.0014     NaN         NaN    NaN  2012-12-31   \n",
              "17376  13.635        56     8.9981     NaN         NaN    NaN  2012-12-31   \n",
              "17377  13.635        65     8.9981     NaN         NaN    NaN  2012-12-31   \n",
              "\n",
              "       year month hour  weekday  \n",
              "0      2011    01   00        5  \n",
              "1      2011    01   01        5  \n",
              "2      2011    01   02        5  \n",
              "3      2011    01   03        5  \n",
              "4      2011    01   04        5  \n",
              "...     ...   ...  ...      ...  \n",
              "17373  2012    12   19        0  \n",
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              "17375  2012    12   21        0  \n",
              "17376  2012    12   22        0  \n",
              "17377  2012    12   23        0  \n",
              "\n",
              "[17378 rows x 17 columns]"
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>17373</th>\n",
              "      <td>2012-12-31 19:00:00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>10.66</td>\n",
              "      <td>12.880</td>\n",
              "      <td>60</td>\n",
              "      <td>11.0014</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>2012</td>\n",
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              "      <td>2012-12-31 20:00:00</td>\n",
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              "      <td>10.66</td>\n",
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              "      <td>11.0014</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>12</td>\n",
              "      <td>20</td>\n",
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              "    <tr>\n",
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              "      <td>2012-12-31 21:00:00</td>\n",
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              "      <td>12.880</td>\n",
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              "      <td>11.0014</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2012-12-31</td>\n",
              "      <td>2012</td>\n",
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              "      <td>21</td>\n",
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              "      <td>8.9981</td>\n",
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              "      <td>1</td>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2fb9cd39-24d9-484a-a54c-1b797bb9c6bd')\"\n",
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              "\n",
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              "\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 필요 없는 피처 제거\n",
        "\n",
        "drop_features = ['casual', 'holiday', 'registered', 'datetime', 'date', 'windspeed']\n",
        "\n",
        "all_data = all_data.drop(drop_features, axis=1)\n",
        "all_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "AoSMWd4_D_2f",
        "outputId": "788ce57d-3bfd-46bc-9f9f-513ce3d819ef"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       season  workingday  weather   temp   atemp  humidity  count  year  \\\n",
              "0           1           0        1   9.84  14.395        81   16.0  2011   \n",
              "1           1           0        1   9.02  13.635        80   40.0  2011   \n",
              "2           1           0        1   9.02  13.635        80   32.0  2011   \n",
              "3           1           0        1   9.84  14.395        75   13.0  2011   \n",
              "4           1           0        1   9.84  14.395        75    1.0  2011   \n",
              "...       ...         ...      ...    ...     ...       ...    ...   ...   \n",
              "17373       1           1        2  10.66  12.880        60    NaN  2012   \n",
              "17374       1           1        2  10.66  12.880        60    NaN  2012   \n",
              "17375       1           1        1  10.66  12.880        60    NaN  2012   \n",
              "17376       1           1        1  10.66  13.635        56    NaN  2012   \n",
              "17377       1           1        1  10.66  13.635        65    NaN  2012   \n",
              "\n",
              "      month hour  weekday  \n",
              "0        01   00        5  \n",
              "1        01   01        5  \n",
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              "3        01   03        5  \n",
              "4        01   04        5  \n",
              "...     ...  ...      ...  \n",
              "17373    12   19        0  \n",
              "17374    12   20        0  \n",
              "17375    12   21        0  \n",
              "17376    12   22        0  \n",
              "17377    12   23        0  \n",
              "\n",
              "[17378 rows x 11 columns]"
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 데이터 나누기\n",
        "X_train = all_data[~pd.isnull(all_data['count'])]\n",
        "X_test = all_data[pd.isnull(all_data['count'])]\n",
        "\n",
        "# 타깃값 count 제거\n",
        "X_train = X_train.drop(['count'], axis=1)\n",
        "X_test = X_test.drop(['count'], axis=1)\n",
        "\n",
        "y = train['count'] # 타깃값\n"
      ],
      "metadata": {
        "id": "Xm_j0pCMFGWC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "TDqnk8rlVB2N",
        "outputId": "c6d000da-85ad-4129-90fc-c24c7b78e97f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   season  workingday  weather  temp   atemp  humidity  year month hour  \\\n",
              "0       1           0        1  9.84  14.395        81  2011    01   00   \n",
              "1       1           0        1  9.02  13.635        80  2011    01   01   \n",
              "2       1           0        1  9.02  13.635        80  2011    01   02   \n",
              "3       1           0        1  9.84  14.395        75  2011    01   03   \n",
              "4       1           0        1  9.84  14.395        75  2011    01   04   \n",
              "\n",
              "   weekday  \n",
              "0        5  \n",
              "1        5  \n",
              "2        5  \n",
              "3        5  \n",
              "4        5  "
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              "\n",
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              "  </style>\n",
              "\n",
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              "          const element = document.querySelector('#df-f1e0deef-65b6-4dcb-a7c3-cf82f3cf41cf');\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",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 평가지표 계산 함수 작성\n",
        "import numpy as np\n",
        "\n",
        "def rmsle(y_true, y_pred, convertExp=True):\n",
        "  # 지수변환\n",
        "  if convertExp:\n",
        "    y_true = np.exp(y_true)\n",
        "    y_pred = np.exp(y_pred)\n",
        "\n",
        "  # 로그변환 후 결측값을 0으로 변환\n",
        "  log_true = np.nan_to_num(np.log(y_true+1))\n",
        "  log_pred = np.nan_to_num(np.log(y_pred+1))\n",
        "\n",
        "  # RMSLE 계산\n",
        "  output = np.sqrt(np.mean((log_true - log_pred)**2))\n",
        "  return output"
      ],
      "metadata": {
        "id": "W_mk7l2rTxFR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2.3. 모델 훈련"
      ],
      "metadata": {
        "id": "Kpm9ukMEU8rG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression    # Linear 회귀모델 불러들임\n",
        "\n",
        "linear_reg_model = LinearRegression()"
      ],
      "metadata": {
        "id": "IsjrKdcYX5cl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "log_y = np.log(y)                     # 타깃값 로그변환              \n",
        "linear_reg_model.fit(X_train, log_y)  # 모델 훈련\n",
        "print(lr.intercept_) "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vM4KUPp8YD4u",
        "outputId": "b56ab2d9-e66f-4ddd-828e-8eb7cbc93408"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 2.4. 모델 성능 검증"
      ],
      "metadata": {
        "id": "nQnYjBbcYazM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "preds = linear_reg_model.predict(X_train)"
      ],
      "metadata": {
        "id": "qejPowARYZ4c"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f'선형회귀의 RMSLE 값 : {rmsle(log_y, preds, True):.4f}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cviu4jKOYkph",
        "outputId": "59dd995c-8379-4664-f1ac-f22a9f6db80b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "선형회귀의 RMSLE 값 : 1.0187\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "RMSLE는 Root Mean Square Logarithmic Error로 로그를 취한 양의 각각을 제곱한 값의 평균치를 나타낸 것이다.이를 이해하려면 RMSE를 먼저 아는 것이 중요하다.\n",
        "\n",
        "RMSE는 예측값과 실제값의 차(더하기/빼기) 각각을 제곱(square)한 값을 평균(mean)을 내서, 루트(root)를 씌운 것이다. 제곱하고 다시 루트를 씌웠으니, 그 값의 단위는 예측값과 실제값의 차이의 단위와 같다. 예측하는 대상이 \"미터\" 단위의 길이라면, RMSE 값의 단위도 \"미터\" 일 것이다.\n",
        "\n",
        "RMSLE 는 어떤 양의 log 값이다. 생각하기 어려운 log 를 없애려면, 역함수인 exp 를 취하면 된다. exp(RMSLE) 는 앞 문단에서 설명한 XX와 같은 단위이다. 다시 말해보자, exp(RMSLE) 는 실제값과 예측값의 비율이다.\n",
        "\n",
        "RMSLE의 정의 참조 블로그 : https://daewonyoon.tistory.com/281"
      ],
      "metadata": {
        "id": "X6SHI3U2Y3CP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2.5. 예측 및 결과 제출"
      ],
      "metadata": {
        "id": "MRZTZDF5Y6-m"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "linearreg_preds = linear_reg_model.predict(X_test)  # 테스트 데이터로 예측\n",
        "\n",
        "submission['count'] = np.exp(linearreg_preds)       # 지수변환\n",
        "submission.to_csv('first_submission.csv', index=False)  # 파일로 저장 \n",
        "submission.head()                                        "
      ],
      "metadata": {
        "id": "__tOlKVgY-E5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "8e92accb-44f6-4124-c1a6-03c997338101"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              datetime      count\n",
              "0  2011-01-20 00:00:00  10.521027\n",
              "1  2011-01-20 01:00:00  12.569849\n",
              "2  2011-01-20 02:00:00  13.937106\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-1cb5c196-7e52-40b9-9366-282ce7a71f4f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "여기까지 정상적으로 했다면 'Output/kaggle/working'에 first_submission.csv가 생성되었을 것이다. 이 파일을 다운로드 받아서 직접 대회에 제출하고 평가 점수를 살펴볼 수도 있다고 한다."
      ],
      "metadata": {
        "id": "Ecq2C8d0ZbnX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 3. 자전거 대여 수요 예측 경진대회 모델 성능 개선"
      ],
      "metadata": {
        "id": "urOWdSrQbFUO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3.1 선행 작업 및 피처 엔지니어링"
      ],
      "metadata": {
        "id": "NijfOamxbTMm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 판다스 임포트 및 데이터 경로 불러들이기\n",
        "import pandas as pd\n",
        "\n",
        "train = pd.read_csv('./train.csv')\n",
        "test = pd.read_csv('./test.csv')\n",
        "submission = pd.read_csv('./sampleSubmission.csv')"
      ],
      "metadata": {
        "id": "fi373mZ3bKDb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 이상치 제거\n",
        "train = train[train['weather'] != 4]"
      ],
      "metadata": {
        "id": "VP_7jD4xbXWO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 데이터 합치기\n",
        "all_data = pd.concat([train, test], ignore_index=True)\n",
        "all_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 835
        },
        "id": "IMSdRjA-bsYz",
        "outputId": "50dc1880-f501-4938-afdf-b8dd79c41079"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  datetime  season  holiday  workingday  weather   temp  \\\n",
              "0      2011-01-01 00:00:00       1        0           0        1   9.84   \n",
              "1      2011-01-01 01:00:00       1        0           0        1   9.02   \n",
              "2      2011-01-01 02:00:00       1        0           0        1   9.02   \n",
              "3      2011-01-01 03:00:00       1        0           0        1   9.84   \n",
              "4      2011-01-01 04:00:00       1        0           0        1   9.84   \n",
              "...                    ...     ...      ...         ...      ...    ...   \n",
              "17373  2012-12-31 19:00:00       1        0           1        2  10.66   \n",
              "17374  2012-12-31 20:00:00       1        0           1        2  10.66   \n",
              "17375  2012-12-31 21:00:00       1        0           1        1  10.66   \n",
              "17376  2012-12-31 22:00:00       1        0           1        1  10.66   \n",
              "17377  2012-12-31 23:00:00       1        0           1        1  10.66   \n",
              "\n",
              "        atemp  humidity  windspeed  casual  registered  count  \n",
              "0      14.395        81     0.0000     3.0        13.0   16.0  \n",
              "1      13.635        80     0.0000     8.0        32.0   40.0  \n",
              "2      13.635        80     0.0000     5.0        27.0   32.0  \n",
              "3      14.395        75     0.0000     3.0        10.0   13.0  \n",
              "4      14.395        75     0.0000     0.0         1.0    1.0  \n",
              "...       ...       ...        ...     ...         ...    ...  \n",
              "17373  12.880        60    11.0014     NaN         NaN    NaN  \n",
              "17374  12.880        60    11.0014     NaN         NaN    NaN  \n",
              "17375  12.880        60    11.0014     NaN         NaN    NaN  \n",
              "17376  13.635        56     8.9981     NaN         NaN    NaN  \n",
              "17377  13.635        65     8.9981     NaN         NaN    NaN  \n",
              "\n",
              "[17378 rows x 12 columns]"
            ],
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              "      <div>\n",
              "<style scoped>\n",
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              "      <th></th>\n",
              "      <th>datetime</th>\n",
              "      <th>season</th>\n",
              "      <th>holiday</th>\n",
              "      <th>workingday</th>\n",
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              "      <th>atemp</th>\n",
              "      <th>humidity</th>\n",
              "      <th>windspeed</th>\n",
              "      <th>casual</th>\n",
              "      <th>registered</th>\n",
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              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>2011-01-01 00:00:00</td>\n",
              "      <td>1</td>\n",
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              "      <td>13.0</td>\n",
              "      <td>16.0</td>\n",
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              "      <th>1</th>\n",
              "      <td>2011-01-01 01:00:00</td>\n",
              "      <td>1</td>\n",
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              "      <th>2</th>\n",
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              "      <td>27.0</td>\n",
              "      <td>32.0</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>0.0000</td>\n",
              "      <td>3.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>13.0</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>0.0000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
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              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>17373</th>\n",
              "      <td>2012-12-31 19:00:00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>10.66</td>\n",
              "      <td>12.880</td>\n",
              "      <td>60</td>\n",
              "      <td>11.0014</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17374</th>\n",
              "      <td>2012-12-31 20:00:00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>10.66</td>\n",
              "      <td>12.880</td>\n",
              "      <td>60</td>\n",
              "      <td>11.0014</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17375</th>\n",
              "      <td>2012-12-31 21:00:00</td>\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>60</td>\n",
              "      <td>11.0014</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17376</th>\n",
              "      <td>2012-12-31 22:00:00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>17377</th>\n",
              "      <td>2012-12-31 23:00:00</td>\n",
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              "      <td>1</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>17378 rows × 12 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cd93dbee-c822-4735-be30-9b576828941c')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      fill: #1967D2;\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",
              "      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",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-cd93dbee-c822-4735-be30-9b576828941c 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-cd93dbee-c822-4735-be30-9b576828941c');\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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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 파생 변수(피처) 추가\n",
        "from datetime import datetime\n",
        "\n",
        "# 날짜, 연도, 월, 시간, 요일 피처 각각 생성\n",
        "all_data['date'] = all_data['datetime'].apply(lambda x: x.split()[0])\n",
        "all_data['year'] = all_data['datetime'].apply(lambda x: x.split()[0].split('-')[0])\n",
        "all_data['month'] = all_data['datetime'].apply(lambda x: x.split()[0].split('-')[1])\n",
        "all_data['hour'] = all_data['datetime'].apply(lambda x: x.split()[1].split(':')[0])\n",
        "all_data[\"weekday\"] = all_data['date'].apply(lambda dateString : datetime.strptime(dateString,\"%Y-%m-%d\").weekday())"
      ],
      "metadata": {
        "id": "ni-7O1wSb4ie"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 필요없는 피처 제거\n",
        "drop_features = ['casual', 'registered', 'datetime', 'date', 'windspeed', 'month']\n",
        "\n",
        "all_data = all_data.drop(drop_features, axis=1)"
      ],
      "metadata": {
        "id": "7MY8imldcYnZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 데이터 나누기\n",
        "X_train = all_data[~pd.isnull(all_data['count'])]\n",
        "X_test = all_data[pd.isnull(all_data['count'])]\n",
        "\n",
        "X_train = X_train.drop(['count'], axis=1)\n",
        "X_test = X_test.drop(['count'], axis=1)\n",
        "\n",
        "y = train['count']"
      ],
      "metadata": {
        "id": "Zmf7pOH3cp-b"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 평가지표 계산 함수 작성\n",
        "import numpy as np\n",
        "\n",
        "def rmsle(y_true, y_pred, convertExp=True):\n",
        "    # 지수변환\n",
        "    if convertExp:\n",
        "        y_true = np.exp(y_true)\n",
        "        y_pred = np.exp(y_pred)\n",
        "        \n",
        "    # 로그변환 후 결측값을 0으로 변환\n",
        "    log_true = np.nan_to_num(np.log(y_true+1))\n",
        "    log_pred = np.nan_to_num(np.log(y_pred+1))\n",
        "    \n",
        "    # RMSLE 계산\n",
        "    output = np.sqrt(np.mean((log_true - log_pred)**2))\n",
        "    return output"
      ],
      "metadata": {
        "id": "OIzxw7Adcxa-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        ""
      ],
      "metadata": {
        "id": "p0SfUNYcbSPC"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3.2. 성능 개선 I : 릿지 회귀 모델"
      ],
      "metadata": {
        "id": "qOj8GjKfUsJ-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 3.2.1. 하이퍼 파라미터 최적화(모델 훈련)"
      ],
      "metadata": {
        "id": "enT_Tf19bEEz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 모델 생성(릿지 모델)\n",
        "from sklearn.linear_model import Ridge\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn import metrics\n",
        "\n",
        "ridge_model = Ridge()"
      ],
      "metadata": {
        "id": "sFwnLmZ0YXbg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 그리드서치 객체 생성\n",
        "# 하이퍼 파라미터 값 목록\n",
        "ridge_params = {'max_iter' : [3000], 'alpha':[0.1, 1, 2, 3, 4, 10, 30, 100, 200, 300, 400, 800, 900, 1000]}\n",
        "\n",
        "# 교차 검증용 평가 함수(RMSLE 점수 계산)\n",
        "rmsle_scorer = metrics.make_scorer(rmsle, greater_is_better=False)\n",
        "\n",
        "# 그리드서치(with 릿지) 객체 생성\n",
        "gridsearch_ridge_model = GridSearchCV(estimator=ridge_model,     # 릿지 모델\n",
        "                                      param_grid=ridge_params,   # 값 목록\n",
        "                                      scoring=rmsle_scorer,       # 평가지표\n",
        "                                      cv=5)                      # 교차검증 분할 수"
      ],
      "metadata": {
        "id": "oWOtVRm2f3aC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 그리드서치 수행\n",
        "log_y = np.log(y)\n",
        "gridsearch_ridge_model.fit(X_train, log_y)   # 훈련(그리드서치)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x6mw3zkEgzCV",
        "outputId": "2dc21fbf-3d58-4b61-e5ac-5e9d7e7a3972"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=5, estimator=Ridge(),\n",
              "             param_grid={'alpha': [0.1, 1, 2, 3, 4, 10, 30, 100, 200, 300, 400,\n",
              "                                   800, 900, 1000],\n",
              "                         'max_iter': [3000]},\n",
              "             scoring=make_scorer(rmsle, greater_is_better=False))"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('최적 하이퍼파라미터 :', gridsearch_ridge_model.best_params_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ftUV78rfiAq3",
        "outputId": "bb376125-3b3a-472e-d916-6a6980d4904e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "최적 하이퍼파라미터 : {'alpha': 0.1, 'max_iter': 3000}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 3.2.2. 성능 검증"
      ],
      "metadata": {
        "id": "h31jYpsyUwdz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "preds = gridsearch_ridge_model.best_estimator_.predict(X_train)\n",
        "print(f'릿지 회귀 RMSLE 값 : {rmsle(log_y, preds, True):.4f}') "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v3Kqz3DRUrko",
        "outputId": "e1e80827-b484-42d9-e815-7c5f27a409fc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "릿지 회귀 RMSLE 값 : 1.0205\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3.3. 성능 개선 II : 라쏘 회귀 모델"
      ],
      "metadata": {
        "id": "L4TYXE99iZec"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 3.3.1. 하이퍼 파라미터 최적화(모델 훈련)"
      ],
      "metadata": {
        "id": "59hzZT-uinjs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import Lasso\n",
        "\n",
        "# 모델 생성\n",
        "lasso_model = Lasso()\n",
        "# 하이퍼파라미터 값 목록\n",
        "lasso_alpha = 1/np.array([0.1, 1, 2, 3, 4, 10, 30, 100, 200, 300, 400, 800, 900, 1000])\n",
        "lasso_params = {'max_iter':[3000], 'alpha':lasso_alpha}\n",
        "# 그리드서치(with 라쏘) 객체 생성\n",
        "gridsearch_lasso_model = GridSearchCV(estimator=lasso_model,\n",
        "                                      param_grid=lasso_params,\n",
        "                                      scoring=rmsle_scorer,\n",
        "                                      cv=5)\n",
        "# 그리드서치 수행\n",
        "log_y = np.log(y)\n",
        "gridsearch_lasso_model.fit(X_train, log_y)\n",
        "\n",
        "print('최적 하이퍼파라미터 :', gridsearch_lasso_model.best_params_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Rh3YBEtJieum",
        "outputId": "758d5e11-82c6-47a5-a0cd-007047a659f9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "최적 하이퍼파라미터 : {'alpha': 0.00125, 'max_iter': 3000}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 3.3.2. 성능 검증"
      ],
      "metadata": {
        "id": "ks8CZtbPishH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "preds = gridsearch_lasso_model.best_estimator_.predict(X_train)\n",
        "print(f'라쏘 회귀 RMSLE 값 : {rmsle(log_y, preds, True):.4f}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yn3RHtmEilyc",
        "outputId": "bd50d5f1-2aed-4fb6-bbc3-05fdc21837ba"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "라쏘 회귀 RMSLE 값 : 1.0205\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3.4. 성능 개선 III : 랜덤 포레스트 회귀 모델"
      ],
      "metadata": {
        "id": "om8lLCJfjG4l"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 3.4.1. 하이퍼 파라미터 최적화 및 모델 훈련"
      ],
      "metadata": {
        "id": "xMn1r_ljjM5V"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestRegressor\n",
        "\n",
        "randomforest_model = RandomForestRegressor()\n",
        "\n",
        "rf_params = {'random_state':[42], 'n_estimators':[100,120,140]}\n",
        "gridsearch_random_forest_model = GridSearchCV(estimator=randomforest_model,\n",
        "                                              param_grid=rf_params,\n",
        "                                              scoring=rmsle_scorer,\n",
        "                                              cv=5)\n",
        "\n",
        "log_y = np.log(y)\n",
        "gridsearch_random_forest_model.fit(X_train, log_y)\n",
        "\n",
        "print('최적 하이퍼파라미터 :', gridsearch_random_forest_model.best_params_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xcPiQ5fajAOK",
        "outputId": "d45b3ddc-3193-4c76-ae1a-3f8b04fcedf1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "최적 하이퍼파라미터 : {'n_estimators': 140, 'random_state': 42}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 3.4.2. 모델 성능 검증"
      ],
      "metadata": {
        "id": "3r8RTxXIjFrH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "preds = gridsearch_random_forest_model.best_estimator_.predict(X_train)\n",
        "print(f'랜덤 포레스트 회귀 RMSLE 값 : {rmsle(log_y, preds, True):.4f}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B-y5pl8KkOiV",
        "outputId": "4f29f6bd-e659-4087-8371-45f287a91944"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "랜덤 포레스트 회귀 RMSLE 값 : 0.1127\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 3.4.3. 예측 및 결과 제출"
      ],
      "metadata": {
        "id": "3_QhwgV5kWeX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "randomforest_preds = gridsearch_random_forest_model.best_estimator_.predict(X_test)\n",
        "\n",
        "figure, axes = plt.subplots(ncols=2)\n",
        "figure.set_size_inches(10,4)\n",
        "\n",
        "sns.histplot(y, bins=50, ax=axes[0])\n",
        "axes[0].set_title('Train Data Distribution')\n",
        "sns.histplot(np.exp(randomforest_preds), bins=50, ax=axes[1])\n",
        "axes[1].set_title('Predicted Test Data Distribution');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 307
        },
        "id": "p1C796KYkcjl",
        "outputId": "148ab609-148f-4235-b21c-36c01dc1c52f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "submission['count'] = np.exp(randomforest_preds)\n",
        "submission.to_csv('second_submission.csv', index=False)\n",
        "submission.head()"
      ],
      "metadata": {
        "id": "RMtMPcbQlHjv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "8707fffd-c4c4-402f-c67a-70cdd512b887"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              datetime      count\n",
              "0  2011-01-20 00:00:00  12.127016\n",
              "1  2011-01-20 01:00:00   4.981274\n",
              "2  2011-01-20 02:00:00   3.209022\n",
              "3  2011-01-20 03:00:00   3.503956\n",
              "4  2011-01-20 04:00:00   2.680269"
            ],
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              "\n",
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "      <th>datetime</th>\n",
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              "      <th>0</th>\n",
              "      <td>2011-01-20 00:00:00</td>\n",
              "      <td>12.127016</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2011-01-20 01:00:00</td>\n",
              "      <td>4.981274</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2011-01-20 02:00:00</td>\n",
              "      <td>3.209022</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2011-01-20 03:00:00</td>\n",
              "      <td>3.503956</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2011-01-20 04:00:00</td>\n",
              "      <td>2.680269</td>\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-61899568-4c62-42b6-a0b5-9b3c4ee88954');\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",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "여기까지 정상적으로 했다면 'Output/kaggle/working'에 second_submission.csv가 생성되었을 것이다. 이 파일을 다운로드 받아서 직접 대회에 제출하고 평가 점수를 살펴볼 수도 있다고 한다. 모델 개선을 했기 때문에 더 높은 점수를 기대해도 좋을 거 같다."
      ],
      "metadata": {
        "id": "blNVyvAklVuL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 4. 회고\n",
        "\n",
        "이번주는 필사 후 저장된 파일을 직접 캐글에 제출하여 점수를 확인해 보면서 자신이 어느 수준의 모델링 능력이 있는지 알아보는 시간이었던 거 같다. 점수를 더 올리기 위해서는 아래와 같은 Tip을 참고하면 좋다고 한다.\n",
        "\n",
        "1. 성능향상을 위한 추가적인 EDA 고민\n",
        "2. feature에 대한 분석, 생성, 제거작업 추가진행\n",
        "3. 모델 종류에 대한 고민\n",
        "4. 모델 최적화에 대한 고민\n",
        "\n",
        "* 1번과 2번 항목, 즉 성능향상을 위한 EDA 고민 및 feature에 대한 분석, 생성, 제거작업은 이전에 캐글 필사 과제였던 다이타닉 과제를 비롯하여 몇 차례 연습을 한 덕분에 많이 익숙해 질 수 있었다.  \n",
        "* 그러나 3번과 4번 항목, 즉 모델 종류와 최적화에 대한 내용은 아직 숙지가 되지 않았고 따라서 어느 모델이 성능이 더 뛰어날지에 대한 고민이 필요해 보였다.   \n",
        "* 이번 주 과제 결과를 보면 랜덤 포레스트 회귀모델이 가장 성능이 좋은 모델이라고 하겠지만 자료들이 품고 있는 feature에 따라서 다르기 때문에 모델이라는 것은 상대적인 개념이라고 할 수 있다. 그에 따라서 적절한 모델이 무엇일까를 계속 공부해야 겠다고 생각했다. "
      ],
      "metadata": {
        "id": "y31DqdYnliFS"
      }
    }
  ]
}