notebook.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H9wADwK78DCz"
      },
      "source": [
        "# Proyek Analisis Data: **bike-sharing-dataset**\n",
        "- **Nama:** **Bagus Erwanto**\n",
        "- **Email:** **bagus251001@gmail.com**\n",
        "- **ID Dicoding:** **bagus_erwanto**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eE0raob58DC0"
      },
      "source": [
        "## Menentukan Pertanyaan Bisnis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GmQeQ5YF8DC0"
      },
      "source": [
        "- Pertanyaan 1: Bagaimana pengaruh cuaca terhadap jumlah peminjaman sepeda?\n",
        "- Pertanyaan 2: Apakah ada perbedaan signifikan antara jumlah peminjaman sepeda pada hari kerja dan hari libur?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H-z4QGlO8DC1"
      },
      "source": [
        "## Import Semua Packages/Library yang Digunakan"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "FVYwaObI8DC1"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import altair as alt\n",
        "import seaborn as sns\n",
        "import zipfile\n",
        "from google.colab import files"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Upload the dataset"
      ],
      "metadata": {
        "id": "rfVHSAqev6iA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "uploaded = files.upload()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "4_9uBpJMwCyD",
        "outputId": "4fec9864-8ff6-4cce-9766-0eafc9e6a8d8"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-9398c9a2-8809-4ebc-a662-1dde8dc1163f\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-9398c9a2-8809-4ebc-a662-1dde8dc1163f\">\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>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving Bike-sharing-dataset.zip to Bike-sharing-dataset.zip\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Extract the contents of the zip file"
      ],
      "metadata": {
        "id": "XGsg8mb0vHeg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "with zipfile.ZipFile(\"Bike-sharing-dataset.zip\", \"r\") as zip_ref:\n",
        "    zip_ref.extractall()"
      ],
      "metadata": {
        "id": "KGMSTkNFvOF9"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h_Sh51Xy8DC1"
      },
      "source": [
        "## Data Wrangling"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sXU2GBYu8DC1"
      },
      "source": [
        "### Gathering Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "zjCBk1BI8DC1"
      },
      "outputs": [],
      "source": [
        "df_hour = pd.read_csv('hour.csv')\n",
        "df_day = pd.read_csv('day.csv')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FHSiqaZp8DC1"
      },
      "source": [
        "### Assessing Data"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###hour"
      ],
      "metadata": {
        "id": "Qc0oVdu8YCaw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(df_hour.info())\n",
        "print(df_hour.describe())"
      ],
      "metadata": {
        "id": "ax-3tEjc9Cj1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3a2d1d92-ef86-4a21-bc9c-6d13bc05f020"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 17379 entries, 0 to 17378\n",
            "Data columns (total 17 columns):\n",
            " #   Column      Non-Null Count  Dtype  \n",
            "---  ------      --------------  -----  \n",
            " 0   instant     17379 non-null  int64  \n",
            " 1   dteday      17379 non-null  object \n",
            " 2   season      17379 non-null  int64  \n",
            " 3   yr          17379 non-null  int64  \n",
            " 4   mnth        17379 non-null  int64  \n",
            " 5   hr          17379 non-null  int64  \n",
            " 6   holiday     17379 non-null  int64  \n",
            " 7   weekday     17379 non-null  int64  \n",
            " 8   workingday  17379 non-null  int64  \n",
            " 9   weathersit  17379 non-null  int64  \n",
            " 10  temp        17379 non-null  float64\n",
            " 11  atemp       17379 non-null  float64\n",
            " 12  hum         17379 non-null  float64\n",
            " 13  windspeed   17379 non-null  float64\n",
            " 14  casual      17379 non-null  int64  \n",
            " 15  registered  17379 non-null  int64  \n",
            " 16  cnt         17379 non-null  int64  \n",
            "dtypes: float64(4), int64(12), object(1)\n",
            "memory usage: 2.3+ MB\n",
            "None\n",
            "          instant        season            yr          mnth            hr  \\\n",
            "count  17379.0000  17379.000000  17379.000000  17379.000000  17379.000000   \n",
            "mean    8690.0000      2.501640      0.502561      6.537775     11.546752   \n",
            "std     5017.0295      1.106918      0.500008      3.438776      6.914405   \n",
            "min        1.0000      1.000000      0.000000      1.000000      0.000000   \n",
            "25%     4345.5000      2.000000      0.000000      4.000000      6.000000   \n",
            "50%     8690.0000      3.000000      1.000000      7.000000     12.000000   \n",
            "75%    13034.5000      3.000000      1.000000     10.000000     18.000000   \n",
            "max    17379.0000      4.000000      1.000000     12.000000     23.000000   \n",
            "\n",
            "            holiday       weekday    workingday    weathersit          temp  \\\n",
            "count  17379.000000  17379.000000  17379.000000  17379.000000  17379.000000   \n",
            "mean       0.028770      3.003683      0.682721      1.425283      0.496987   \n",
            "std        0.167165      2.005771      0.465431      0.639357      0.192556   \n",
            "min        0.000000      0.000000      0.000000      1.000000      0.020000   \n",
            "25%        0.000000      1.000000      0.000000      1.000000      0.340000   \n",
            "50%        0.000000      3.000000      1.000000      1.000000      0.500000   \n",
            "75%        0.000000      5.000000      1.000000      2.000000      0.660000   \n",
            "max        1.000000      6.000000      1.000000      4.000000      1.000000   \n",
            "\n",
            "              atemp           hum     windspeed        casual    registered  \\\n",
            "count  17379.000000  17379.000000  17379.000000  17379.000000  17379.000000   \n",
            "mean       0.475775      0.627229      0.190098     35.676218    153.786869   \n",
            "std        0.171850      0.192930      0.122340     49.305030    151.357286   \n",
            "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
            "25%        0.333300      0.480000      0.104500      4.000000     34.000000   \n",
            "50%        0.484800      0.630000      0.194000     17.000000    115.000000   \n",
            "75%        0.621200      0.780000      0.253700     48.000000    220.000000   \n",
            "max        1.000000      1.000000      0.850700    367.000000    886.000000   \n",
            "\n",
            "                cnt  \n",
            "count  17379.000000  \n",
            "mean     189.463088  \n",
            "std      181.387599  \n",
            "min        1.000000  \n",
            "25%       40.000000  \n",
            "50%      142.000000  \n",
            "75%      281.000000  \n",
            "max      977.000000  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###day"
      ],
      "metadata": {
        "id": "TD1PKJ4FYI3s"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(df_day.info())\n",
        "print(df_day.describe())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u6OC5kNbYK30",
        "outputId": "7abf5285-b025-4bf5-eb79-0ee4bb68506d"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 731 entries, 0 to 730\n",
            "Data columns (total 16 columns):\n",
            " #   Column      Non-Null Count  Dtype  \n",
            "---  ------      --------------  -----  \n",
            " 0   instant     731 non-null    int64  \n",
            " 1   dteday      731 non-null    object \n",
            " 2   season      731 non-null    int64  \n",
            " 3   yr          731 non-null    int64  \n",
            " 4   mnth        731 non-null    int64  \n",
            " 5   holiday     731 non-null    int64  \n",
            " 6   weekday     731 non-null    int64  \n",
            " 7   workingday  731 non-null    int64  \n",
            " 8   weathersit  731 non-null    int64  \n",
            " 9   temp        731 non-null    float64\n",
            " 10  atemp       731 non-null    float64\n",
            " 11  hum         731 non-null    float64\n",
            " 12  windspeed   731 non-null    float64\n",
            " 13  casual      731 non-null    int64  \n",
            " 14  registered  731 non-null    int64  \n",
            " 15  cnt         731 non-null    int64  \n",
            "dtypes: float64(4), int64(11), object(1)\n",
            "memory usage: 91.5+ KB\n",
            "None\n",
            "          instant      season          yr        mnth     holiday     weekday  \\\n",
            "count  731.000000  731.000000  731.000000  731.000000  731.000000  731.000000   \n",
            "mean   366.000000    2.496580    0.500684    6.519836    0.028728    2.997264   \n",
            "std    211.165812    1.110807    0.500342    3.451913    0.167155    2.004787   \n",
            "min      1.000000    1.000000    0.000000    1.000000    0.000000    0.000000   \n",
            "25%    183.500000    2.000000    0.000000    4.000000    0.000000    1.000000   \n",
            "50%    366.000000    3.000000    1.000000    7.000000    0.000000    3.000000   \n",
            "75%    548.500000    3.000000    1.000000   10.000000    0.000000    5.000000   \n",
            "max    731.000000    4.000000    1.000000   12.000000    1.000000    6.000000   \n",
            "\n",
            "       workingday  weathersit        temp       atemp         hum   windspeed  \\\n",
            "count  731.000000  731.000000  731.000000  731.000000  731.000000  731.000000   \n",
            "mean     0.683995    1.395349    0.495385    0.474354    0.627894    0.190486   \n",
            "std      0.465233    0.544894    0.183051    0.162961    0.142429    0.077498   \n",
            "min      0.000000    1.000000    0.059130    0.079070    0.000000    0.022392   \n",
            "25%      0.000000    1.000000    0.337083    0.337842    0.520000    0.134950   \n",
            "50%      1.000000    1.000000    0.498333    0.486733    0.626667    0.180975   \n",
            "75%      1.000000    2.000000    0.655417    0.608602    0.730209    0.233214   \n",
            "max      1.000000    3.000000    0.861667    0.840896    0.972500    0.507463   \n",
            "\n",
            "            casual   registered          cnt  \n",
            "count   731.000000   731.000000   731.000000  \n",
            "mean    848.176471  3656.172367  4504.348837  \n",
            "std     686.622488  1560.256377  1937.211452  \n",
            "min       2.000000    20.000000    22.000000  \n",
            "25%     315.500000  2497.000000  3152.000000  \n",
            "50%     713.000000  3662.000000  4548.000000  \n",
            "75%    1096.000000  4776.500000  5956.000000  \n",
            "max    3410.000000  6946.000000  8714.000000  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EhN5R4hr8DC1"
      },
      "source": [
        "### Cleaning Data"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df_hour.isnull().sum())\n",
        "print(df_day.isnull().sum())"
      ],
      "metadata": {
        "id": "jVnYpprE9Evz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e410bbe5-e24d-4cad-c8de-29096d85f6d8"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "instant       0\n",
            "dteday        0\n",
            "season        0\n",
            "yr            0\n",
            "mnth          0\n",
            "hr            0\n",
            "holiday       0\n",
            "weekday       0\n",
            "workingday    0\n",
            "weathersit    0\n",
            "temp          0\n",
            "atemp         0\n",
            "hum           0\n",
            "windspeed     0\n",
            "casual        0\n",
            "registered    0\n",
            "cnt           0\n",
            "dtype: int64\n",
            "instant       0\n",
            "dteday        0\n",
            "season        0\n",
            "yr            0\n",
            "mnth          0\n",
            "holiday       0\n",
            "weekday       0\n",
            "workingday    0\n",
            "weathersit    0\n",
            "temp          0\n",
            "atemp         0\n",
            "hum           0\n",
            "windspeed     0\n",
            "casual        0\n",
            "registered    0\n",
            "cnt           0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gp-Y6wU38DC1"
      },
      "source": [
        "## Exploratory Data Analysis (EDA)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MW7WF2kr8DC1"
      },
      "source": [
        "## Explore weather situation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "e9CQCZjk8DC2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 570
        },
        "outputId": "9efacaf5-959e-4b35-b38d-45d750f1ef54"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(12,6))\n",
        "sns.boxplot(x='weathersit', y='cnt', data=df_hour)\n",
        "plt.title('Jumlah Peminjaman Sepeda berdasarkan Kondisi Cuaca', fontsize=16)\n",
        "plt.xlabel('Kondisi Cuaca', fontsize=12)\n",
        "plt.ylabel('Jumlah Peminjaman', fontsize=12)\n",
        "plt.xticks([0, 1, 2, 3], ['Clear', 'Mist', 'Light Snow/Rain', 'Heavy Rain/Snow'], fontsize=10)\n",
        "plt.yticks(fontsize=10)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Explore working day vs non-working day"
      ],
      "metadata": {
        "id": "ocqQVdNUworv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10,6))\n",
        "sns.boxplot(x='workingday', y='cnt', data=df_hour)\n",
        "plt.title('Jumlah Peminjaman Sepeda: Hari Kerja vs Hari Libur', fontsize=16)\n",
        "plt.xlabel('Hari Kerja (1) vs Hari Libur (0)', fontsize=12)\n",
        "plt.ylabel('Jumlah Peminjaman', fontsize=12)\n",
        "plt.xticks([0, 1], ['Hari Libur', 'Hari Kerja'], fontsize=10)\n",
        "plt.yticks(fontsize=10)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 570
        },
        "id": "3jLUasy5wrqV",
        "outputId": "f8e8d5c8-a87c-425a-8b64-293fcdb4dd36"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zsyZjqak8DC2"
      },
      "source": [
        "## Visualization & Explanatory Analysis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HZxOiQ6n8DC2"
      },
      "source": [
        "### Pertanyaan 1: Bagaimana pengaruh cuaca terhadap jumlah peminjaman sepeda?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "1swJUdAD8DC2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "outputId": "1d3e0fa7-2fdf-4dbf-ec0f-d4989d417e2e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(12,6))\n",
        "sns.barplot(x='weathersit', y='cnt', data=df_hour)\n",
        "plt.title('Rata-rata Jumlah Peminjaman Sepeda berdasarkan Kondisi Cuaca')\n",
        "plt.xlabel('Kondisi Cuaca')\n",
        "plt.ylabel('Rata-rata Jumlah Peminjaman')\n",
        "plt.xticks([0, 1, 2, 3], ['Clear', 'Mist', 'Light Snow/Rain', 'Heavy Rain/Snow'], fontsize=10)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DgHI7CiU8DC2"
      },
      "source": [
        "### Pertanyaan 2: Apakah ada perbedaan signifikan antara jumlah peminjaman sepeda pada hari kerja dan hari libur?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "Go0lCsvO8DC2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "outputId": "705443f7-0420-4e2d-c233-858624a28c15"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(10,6))\n",
        "sns.violinplot(x='workingday', y='cnt', data=df_hour)\n",
        "plt.title('Distribusi Jumlah Peminjaman Sepeda: Hari Kerja vs Hari Libur')\n",
        "plt.xlabel('Hari Kerja (1) vs Hari Libur (0)')\n",
        "plt.ylabel('Jumlah Peminjaman')\n",
        "plt.xticks([0, 1], ['Hari Libur', 'Hari Kerja'], fontsize=10)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "hourly_avg = df_hour.groupby('hr')['cnt'].mean().reset_index()\n",
        "\n",
        "plt.figure(figsize=(12, 6))\n",
        "sns.lineplot(x='hr', y='cnt', data=hourly_avg)\n",
        "plt.title('Tren Peminjaman Sepeda Sepanjang Hari', fontsize=16)\n",
        "plt.xlabel('Jam', fontsize=12)\n",
        "plt.ylabel('Rata-rata Jumlah Peminjaman', fontsize=12)\n",
        "plt.xticks(range(0, 24), fontsize=10)\n",
        "plt.yticks(fontsize=10)\n",
        "plt.grid(True, linestyle='--', alpha=0.7)\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 570
        },
        "id": "NwOec8v-ZwNv",
        "outputId": "7694a719-c5f1-4d3c-cc08-89fde9c7fd1f"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Advanced Analysis"
      ],
      "metadata": {
        "id": "BCYm3VWpSz4E"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##RFM Analysis"
      ],
      "metadata": {
        "id": "sWwl5LvzT3AW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_hour['dteday'] = pd.to_datetime(df_hour['dteday'])\n",
        "df_hour['datetime'] = df_hour['dteday'] + pd.to_timedelta(df_hour['hr'], unit='h')\n",
        "\n",
        "today = df_hour['datetime'].max() + pd.Timedelta(hours=1)\n",
        "rfm = df_hour.groupby('hr').agg({\n",
        "    'datetime': lambda x: (today - x.max()).total_seconds() / 3600,\n",
        "    'instant': 'count',\n",
        "    'cnt': 'sum'\n",
        "})\n",
        "\n",
        "rfm.columns = ['Recency', 'Frequency', 'Monetary']\n",
        "\n",
        "def normalize(x):\n",
        "    if x.max() == x.min():\n",
        "        return x * 0\n",
        "    return (x - x.min()) / (x.max() - x.min())\n",
        "\n",
        "rfm_normalized = rfm[['Recency', 'Frequency', 'Monetary']].apply(normalize)\n",
        "\n",
        "rfm_normalized['Recency'] = 1 - rfm_normalized['Recency']\n",
        "\n",
        "rfm['RFM_Score'] = (rfm_normalized['Recency'] * 0.2 +\n",
        "                    rfm_normalized['Frequency'] * 0.4 +\n",
        "                    rfm_normalized['Monetary'] * 0.4)\n",
        "\n",
        "rfm = rfm.reset_index()\n",
        "\n",
        "chart = alt.Chart(rfm).mark_circle().encode(\n",
        "    x='Recency',\n",
        "    y='Frequency',\n",
        "    size=alt.Size('Monetary', scale=alt.Scale(range=[100, 1000])),\n",
        "    color=alt.Color('RFM_Score:Q', scale=alt.Scale(scheme='viridis')),\n",
        "    tooltip=['hr', 'Recency', 'Frequency', 'Monetary', 'RFM_Score']\n",
        ").properties(\n",
        "    width=600,\n",
        "    height=400,\n",
        "    title='RFM Analysis by Hour'\n",
        ").interactive()\n",
        "\n",
        "text = alt.Chart(rfm).mark_text(dx=10, dy=-10).encode(\n",
        "    x='Recency',\n",
        "    y='Frequency',\n",
        "    text='hr:N'\n",
        ")\n",
        "\n",
        "# Combine chart and text\n",
        "final_chart = chart + text\n",
        "\n",
        "# Display the chart\n",
        "final_chart.display()\n",
        "\n",
        "# Display statistics\n",
        "print(rfm.describe())\n",
        "print(\"\\nRFM Score Range:\", rfm['RFM_Score'].min(), \"-\", rfm['RFM_Score'].max())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 684
        },
        "id": "yg9Oma3YUBxD",
        "outputId": "77643d8a-d53c-483f-ff52-df9e3e5f51ce"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "\n",
              "    function maybeLoadScript(lib, version) {\n",
              "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
              "      return (VEGA_DEBUG[key] == version) ?\n",
              "        Promise.resolve(paths[lib]) :\n",
              "        new Promise(function(resolve, reject) {\n",
              "          var s = document.createElement('script');\n",
              "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
              "          s.async = true;\n",
              "          s.onload = () => {\n",
              "            VEGA_DEBUG[key] = version;\n",
              "            return resolve(paths[lib]);\n",
              "          };\n",
              "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
              "          s.src = paths[lib];\n",
              "        });\n",
              "    }\n",
              "\n",
              "    function showError(err) {\n",
              "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
              "      throw err;\n",
              "    }\n",
              "\n",
              "    function displayChart(vegaEmbed) {\n",
              "      vegaEmbed(outputDiv, spec, embedOpt)\n",
              "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
              "    }\n",
              "\n",
              "    if(typeof define === \"function\" && define.amd) {\n",
              "      requirejs.config({paths});\n",
              "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
              "    } else {\n",
              "      maybeLoadScript(\"vega\", \"5\")\n",
              "        .then(() => maybeLoadScript(\"vega-lite\", \"4.17.0\"))\n",
              "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
              "        .catch(showError)\n",
              "        .then(() => displayChart(vegaEmbed));\n",
              "    }\n",
              "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"layer\": [{\"mark\": \"circle\", \"encoding\": {\"color\": {\"field\": \"RFM_Score\", \"scale\": {\"scheme\": \"viridis\"}, \"type\": \"quantitative\"}, \"size\": {\"field\": \"Monetary\", \"scale\": {\"range\": [100, 1000]}, \"type\": \"quantitative\"}, \"tooltip\": [{\"field\": \"hr\", \"type\": \"quantitative\"}, {\"field\": \"Recency\", \"type\": \"quantitative\"}, {\"field\": \"Frequency\", \"type\": \"quantitative\"}, {\"field\": \"Monetary\", \"type\": \"quantitative\"}, {\"field\": \"RFM_Score\", \"type\": \"quantitative\"}], \"x\": {\"field\": \"Recency\", \"type\": \"quantitative\"}, \"y\": {\"field\": \"Frequency\", \"type\": \"quantitative\"}}, \"height\": 400, \"selection\": {\"selector002\": {\"type\": \"interval\", \"bind\": \"scales\", \"encodings\": [\"x\", \"y\"]}}, \"title\": \"RFM Analysis by Hour\", \"width\": 600}, {\"mark\": {\"type\": \"text\", \"dx\": 10, \"dy\": -10}, \"encoding\": {\"text\": {\"field\": \"hr\", \"type\": \"nominal\"}, \"x\": {\"field\": \"Recency\", \"type\": \"quantitative\"}, \"y\": {\"field\": \"Frequency\", \"type\": \"quantitative\"}}}], \"data\": {\"name\": \"data-37efc672270699105bd2983a3df015dd\"}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.17.0.json\", \"datasets\": {\"data-37efc672270699105bd2983a3df015dd\": [{\"hr\": 0, \"Recency\": 24.0, \"Frequency\": 726, \"Monetary\": 39130, \"RFM_Score\": 0.39327045786351755}, {\"hr\": 1, \"Recency\": 23.0, \"Frequency\": 724, \"Monetary\": 24164, \"RFM_Score\": 0.3597157924514053}, {\"hr\": 2, \"Recency\": 22.0, \"Frequency\": 715, \"Monetary\": 16352, \"RFM_Score\": 0.249920718428956}, {\"hr\": 3, \"Recency\": 21.0, \"Frequency\": 697, \"Monetary\": 8174, \"RFM_Score\": 0.03059434449882919}, {\"hr\": 4, \"Recency\": 20.0, \"Frequency\": 697, \"Monetary\": 4428, \"RFM_Score\": 0.034782608695652174}, {\"hr\": 5, \"Recency\": 19.0, \"Frequency\": 717, \"Monetary\": 14261, \"RFM_Score\": 0.29773409592026956}, {\"hr\": 6, \"Recency\": 18.0, \"Frequency\": 725, \"Monetary\": 55132, \"RFM_Score\": 0.4525776228566313}, {\"hr\": 7, \"Recency\": 17.0, \"Frequency\": 727, \"Monetary\": 154171, \"RFM_Score\": 0.6046847323383774}, {\"hr\": 8, \"Recency\": 16.0, \"Frequency\": 727, \"Monetary\": 261001, \"RFM_Score\": 0.7419239663576}, {\"hr\": 9, \"Recency\": 15.0, \"Frequency\": 727, \"Monetary\": 159438, \"RFM_Score\": 0.6284135733854381}, {\"hr\": 10, \"Recency\": 14.0, \"Frequency\": 727, \"Monetary\": 126257, \"RFM_Score\": 0.59718406793313}, {\"hr\": 11, \"Recency\": 13.0, \"Frequency\": 727, \"Monetary\": 151320, \"RFM_Score\": 0.6360368650269063}, {\"hr\": 12, \"Recency\": 12.0, \"Frequency\": 728, \"Monetary\": 184414, \"RFM_Score\": 0.6966742038852505}, {\"hr\": 13, \"Recency\": 11.0, \"Frequency\": 729, \"Monetary\": 184919, \"RFM_Score\": 0.7180987112472283}, {\"hr\": 14, \"Recency\": 10.0, \"Frequency\": 729, \"Monetary\": 175652, \"RFM_Score\": 0.7156438123309936}, {\"hr\": 15, \"Recency\": 9.0, \"Frequency\": 729, \"Monetary\": 183149, \"RFM_Score\": 0.7333602567270755}, {\"hr\": 16, \"Recency\": 8.0, \"Frequency\": 730, \"Monetary\": 227748, \"RFM_Score\": 0.8078410282272831}, {\"hr\": 17, \"Recency\": 7.0, \"Frequency\": 730, \"Monetary\": 336860, \"RFM_Score\": 0.9478260869565218}, {\"hr\": 18, \"Recency\": 6.0, \"Frequency\": 728, \"Monetary\": 309772, \"RFM_Score\": 0.8996855814327476}, {\"hr\": 19, \"Recency\": 5.0, \"Frequency\": 728, \"Monetary\": 226789, \"RFM_Score\": 0.8085316403063766}, {\"hr\": 20, \"Recency\": 4.0, \"Frequency\": 728, \"Monetary\": 164550, \"RFM_Score\": 0.7423379918112807}, {\"hr\": 21, \"Recency\": 3.0, \"Frequency\": 728, \"Monetary\": 125445, \"RFM_Score\": 0.7039804120460302}, {\"hr\": 22, \"Recency\": 2.0, \"Frequency\": 728, \"Monetary\": 95612, \"RFM_Score\": 0.6767793996389161}, {\"hr\": 23, \"Recency\": 1.0, \"Frequency\": 728, \"Monetary\": 63941, \"RFM_Score\": 0.6473668071191776}]}}, {\"mode\": \"vega-lite\"});\n",
              "</script>"
            ],
            "text/plain": [
              "alt.LayerChart(...)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              hr    Recency   Frequency       Monetary  RFM_Score\n",
            "count  24.000000  24.000000   24.000000      24.000000  24.000000\n",
            "mean   11.500000  12.500000  724.125000  137194.958333   0.588540\n",
            "std     7.071068   7.071068    9.080857   95894.380180   0.245843\n",
            "min     0.000000   1.000000  697.000000    4428.000000   0.030594\n",
            "25%     5.750000   6.750000  725.750000   51131.500000   0.437751\n",
            "50%    11.500000  12.500000  727.500000  152745.500000   0.662073\n",
            "75%    17.250000  18.250000  728.000000  184540.250000   0.735501\n",
            "max    23.000000  24.000000  730.000000  336860.000000   0.947826\n",
            "\n",
            "RFM Score Range: 0.03059434449882919 - 0.9478260869565218\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZTcyR48Y8DC2"
      },
      "source": [
        "## Conclusion\n",
        "### Conclution pertanyaan 1 : Bagaimana pengaruh cuaca terhadap jumlah peminjaman sepeda?\n",
        "  - Berdasarkan visualisasi, terlihat bahwa kondisi cuaca mempengaruhi jumlah peminjaman sepeda.\n",
        "  - Cuaca yang lebih baik (kategori 1 dan 2) cenderung menghasilkan jumlah peminjaman yang lebih tinggi\n",
        "  dibandingkan dengan cuaca yang lebih buruk (kategori 3 dan 4\n",
        "  \n",
        "### Conclution pertanyaan 2 : Apakah ada perbedaan signifikan antara jumlah peminjaman sepeda pada hari kerja dan hari libur?\n",
        "  - Terdapat perbedaan dalam distribusi jumlah peminjaman sepeda antara hari kerja dan hari libur.\n",
        "  - Hari kerja cenderung memiliki jumlah peminjaman yang lebih tinggi dan konsisten, sementara hari libur memiliki variasi yang lebih besar dalam jumlah peminjaman.\n",
        "\n",
        "### Conclution RFM Analysis\n",
        "Berdasarkan analisis RFM:\n",
        "- Recency: Nilai berkisar dari 1.00 hingga 24.00 jam.\n",
        "- Frequency: Nilai berkisar dari 697 hingga 730 penyewaan.\n",
        "- Monetary: Nilai berkisar dari 4428.00 hingga 336860.00 total penyewaan sepeda.\n",
        "- Skor RFM tertinggi adalah 0.95, menunjukkan jam yang paling berharga.\n",
        "- Skor RFM terendah adalah 0.03, yang mungkin memerlukan strategi perbaikan khusus.\n",
        "\n",
        "\n",
        "Wawasan tambahan:\n",
        "- Jam paling berharga adalah 17:00, dengan Skor RFM 0.95.\n",
        "- Jam paling kurang berharga adalah 3:00, dengan Skor RFM 0.03.\n",
        "- Jam-jam dengan skor RFM tinggi mungkin merupakan target yang baik untuk promosi atau peningkatan ketersediaan layanan.\n",
        "- Jam-jam dengan skor RFM rendah mungkin memerlukan strategi untuk meningkatkan penyewaan sepeda, seperti diskon khusus di luar jam sibuk."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_hour = pd.read_csv('hour.csv')\n",
        "\n",
        "main_data = df_hour.rename(columns={\n",
        "    'instant': 'record_id',\n",
        "    'dteday': 'date',\n",
        "    'season': 'season',\n",
        "    'yr': 'year',\n",
        "    'mnth': 'month',\n",
        "    'hr': 'hour',\n",
        "    'holiday': 'holiday',\n",
        "    'workingday': 'workingday',\n",
        "    'weathersit': 'weather_situation',\n",
        "    'temp': 'temperature',\n",
        "    'atemp': 'feels_like_temperature',\n",
        "    'hum': 'humidity',\n",
        "    'windspeed': 'windspeed',\n",
        "    'cnt': 'total_count'\n",
        "})\n",
        "\n",
        "main_data.to_csv('main_data.csv', index=False)\n",
        "\n",
        "print(\"Shape of main_data:\", main_data.shape)\n",
        "print(\"Columns in main_data:\", main_data.columns.tolist())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FUfoD52HOW28",
        "outputId": "66cb4861-596d-4400-bc21-cb51f343e08f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape of main_data: (17379, 17)\n",
            "Columns in main_data: ['record_id', 'date', 'season', 'year', 'month', 'hour', 'holiday', 'weekday', 'workingday', 'weather_situation', 'temperature', 'feels_like_temperature', 'humidity', 'windspeed', 'casual', 'registered', 'total_count']\n"
          ]
        }
      ]
    }
  ],
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