ADA_project_code_daily.ipynb

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      "cell_type": "markdown",
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        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/chittoorking/Bike-Sharing-Demand-Prediction/blob/main/ADA_project_code_daily.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Mounting to the drive "
      ],
      "metadata": {
        "id": "Ppa6yQL_gyKs"
      }
    },
    {
      "cell_type": "code",
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        "outputId": "ecfdcaf1-d965-4a9a-c19a-58d5bd348218"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yYYx76wsW57E",
        "outputId": "38e260a0-154d-4be4-9927-1fee335c6647"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/MyDrive/Bike-Sharing-Dataset\n"
          ]
        }
      ],
      "source": [
        "#Changing the path to the folder containing the datasets \n",
        "%cd /content/drive/MyDrive/Bike-Sharing-Dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EbyIZHlpXPtp"
      },
      "source": [
        "**we will dig deeper into the data and apply various machine learning regression techniques like Decision Trees, Random Forest and Ada boost regressor. We will find the best hyperparameters by parameter tuning using GridSearchCV for every regressor algorithm.**\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9X45sG_LXXul"
      },
      "source": [
        "**After analysing errors of each model, We will select a model which gives us the least  Mean Squared  Error(MSE) score and best r2_score and build a model using it.**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rjnHsAV9Zhq9"
      },
      "source": [
        "# **Importing necessary libraries**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KkKuTk0VZgQQ"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np \n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns; sns.set_theme()\n",
        "import calendar\n",
        "%matplotlib inline\n",
        "import missingno as msno\n",
        "from datetime import datetime "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2GTKLmvHH68u"
      },
      "source": [
        "# Loading Day wise dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Predication of bike rental count  daily based on the environmental and seasonal settings."
      ],
      "metadata": {
        "id": "wSlQ_NHWiJsj"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "id": "a-dS0HHZZxIz",
        "outputId": "d800f928-00c4-4006-f526-34758be9638f"
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      "outputs": [
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          "data": {
            "text/plain": [
              "   instant      dteday  season  yr  mnth  holiday  weekday  workingday  \\\n",
              "0        1  01-01-2011       1   0     1        0        6           0   \n",
              "1        2  02-01-2011       1   0     1        0        0           0   \n",
              "2        3  03-01-2011       1   0     1        0        1           1   \n",
              "3        4  04-01-2011       1   0     1        0        2           1   \n",
              "4        5  05-01-2011       1   0     1        0        3           1   \n",
              "\n",
              "   weathersit      temp     atemp       hum  windspeed  casual  registered  \\\n",
              "0           2  0.344167  0.363625  0.805833   0.160446     331         654   \n",
              "1           2  0.363478  0.353739  0.696087   0.248539     131         670   \n",
              "2           1  0.196364  0.189405  0.437273   0.248309     120        1229   \n",
              "3           1  0.200000  0.212122  0.590435   0.160296     108        1454   \n",
              "4           1  0.226957  0.229270  0.436957   0.186900      82        1518   \n",
              "\n",
              "    cnt  \n",
              "0   985  \n",
              "1   801  \n",
              "2  1349  \n",
              "3  1562  \n",
              "4  1600  "
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              "      <td>05-01-2011</td>\n",
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          "metadata": {},
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      ],
      "source": [
        "df_daily = pd.read_csv(\"/content/drive/MyDrive/Bike-Sharing-Dataset/day.csv\")\n",
        "df_daily.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z4ESLljBXpEq"
      },
      "source": [
        "# Understanding the data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lq9vhjxGYyfM"
      },
      "source": [
        "\t- instant: record index\n",
        "\t- dteday : date\n",
        "\t- season : season (1:springer, 2:summer, 3:fall, 4:winter)\n",
        "\t- yr : year (0: 2011, 1:2012)\n",
        "\t- mnth : month ( 1 to 12)\n",
        "\t- hr : hour (0 to 23)\n",
        "\t- holiday : weather day is holiday or not (extracted from http://dchr.dc.gov/page/holiday-schedule)\n",
        "\t- weekday : day of the week\n",
        "\t- workingday : if day is neither weekend nor holiday is 1, otherwise is 0.\n",
        "\t+ weathersit : \n",
        "\t\t- 1: Clear, Few clouds, Partly cloudy, Partly cloudy\n",
        "\t\t- 2: Mist + Cloudy, Mist + Broken clouds, Mist + Few clouds, Mist\n",
        "\t\t- 3: Light Snow, Light Rain + Thunderstorm + Scattered clouds, Light Rain + Scattered clouds\n",
        "\t\t- 4: Heavy Rain + Ice Pallets + Thunderstorm + Mist, Snow + Fog\n",
        "\t- temp : Normalized temperature in Celsius. The values are divided to 41 (max)\n",
        "\t- atemp: Normalized feeling temperature in Celsius. The values are divided to 50 (max)\n",
        "\t- hum: Normalized humidity. The values are divided to 100 (max)\n",
        "\t- windspeed: Normalized wind speed. The values are divided to 67 (max)\n",
        "\t- casual: count of casual users\n",
        "\t- registered: count of registered users\n",
        "\t- cnt: count of total rental bikes including both casual and registered\n",
        "\t"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9orzTHqLaj_x"
      },
      "source": [
        "# Data Preprocessing\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lz1zcpyPbM6D"
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      "outputs": [],
      "source": [
        "#Creating a tempDate column\n",
        "df_daily['tempDate'] = df_daily.dteday.apply(lambda x:x.split())"
      ]
    },
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            "text/plain": [
              "   instant      dteday  season  yr  mnth  holiday  weekday  workingday  \\\n",
              "0        1  01-01-2011       1   0     1        0        6           0   \n",
              "1        2  02-01-2011       1   0     1        0        0           0   \n",
              "2        3  03-01-2011       1   0     1        0        1           1   \n",
              "3        4  04-01-2011       1   0     1        0        2           1   \n",
              "4        5  05-01-2011       1   0     1        0        3           1   \n",
              "\n",
              "   weathersit      temp     atemp       hum  windspeed  casual  registered  \\\n",
              "0           2  0.344167  0.363625  0.805833   0.160446     331         654   \n",
              "1           2  0.363478  0.353739  0.696087   0.248539     131         670   \n",
              "2           1  0.196364  0.189405  0.437273   0.248309     120        1229   \n",
              "3           1  0.200000  0.212122  0.590435   0.160296     108        1454   \n",
              "4           1  0.226957  0.229270  0.436957   0.186900      82        1518   \n",
              "\n",
              "    cnt      tempDate  \n",
              "0   985  [01-01-2011]  \n",
              "1   801  [02-01-2011]  \n",
              "2  1349  [03-01-2011]  \n",
              "3  1562  [04-01-2011]  \n",
              "4  1600  [05-01-2011]  "
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              "      <td>801</td>\n",
              "      <td>[02-01-2011]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>03-01-2011</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.196364</td>\n",
              "      <td>0.189405</td>\n",
              "      <td>0.437273</td>\n",
              "      <td>0.248309</td>\n",
              "      <td>120</td>\n",
              "      <td>1229</td>\n",
              "      <td>1349</td>\n",
              "      <td>[03-01-2011]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>04-01-2011</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.200000</td>\n",
              "      <td>0.212122</td>\n",
              "      <td>0.590435</td>\n",
              "      <td>0.160296</td>\n",
              "      <td>108</td>\n",
              "      <td>1454</td>\n",
              "      <td>1562</td>\n",
              "      <td>[04-01-2011]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>05-01-2011</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.226957</td>\n",
              "      <td>0.229270</td>\n",
              "      <td>0.436957</td>\n",
              "      <td>0.186900</td>\n",
              "      <td>82</td>\n",
              "      <td>1518</td>\n",
              "      <td>1600</td>\n",
              "      <td>[05-01-2011]</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "\n",
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              "                                                     [key], {});\n",
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              "\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 108
        }
      ],
      "source": [
        "#Loading the first five columns\n",
        "df_daily.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "r91RPFa7b93t"
      },
      "outputs": [],
      "source": [
        "#splitting the tempDate column into year ,month ,day and finding the weekday\n",
        "df_daily['year'] = df_daily.tempDate.apply(lambda x:x[0].split('-')[2])\n",
        "df_daily['month'] = df_daily.tempDate.apply(lambda x:x[0].split('-')[1])\n",
        "df_daily['day'] = df_daily.tempDate.apply(lambda x:x[0].split('-')[0])\n",
        "df_daily['weekday'] = df_daily.tempDate.apply(lambda x:calendar.day_name[datetime.strptime(x[0],\"%d-%m-%Y\").weekday()])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "mGIHD0Ujcapp",
        "outputId": "e808df3e-7c70-4094-a207-d0cbb4d6a377"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   instant      dteday  season  yr  mnth  holiday    weekday  workingday  \\\n",
              "0        1  01-01-2011       1   0     1        0   Saturday           0   \n",
              "1        2  02-01-2011       1   0     1        0     Sunday           0   \n",
              "2        3  03-01-2011       1   0     1        0     Monday           1   \n",
              "3        4  04-01-2011       1   0     1        0    Tuesday           1   \n",
              "4        5  05-01-2011       1   0     1        0  Wednesday           1   \n",
              "\n",
              "   weathersit      temp     atemp       hum  windspeed  casual  registered  \\\n",
              "0           2  0.344167  0.363625  0.805833   0.160446     331         654   \n",
              "1           2  0.363478  0.353739  0.696087   0.248539     131         670   \n",
              "2           1  0.196364  0.189405  0.437273   0.248309     120        1229   \n",
              "3           1  0.200000  0.212122  0.590435   0.160296     108        1454   \n",
              "4           1  0.226957  0.229270  0.436957   0.186900      82        1518   \n",
              "\n",
              "    cnt      tempDate  year month day  \n",
              "0   985  [01-01-2011]  2011    01  01  \n",
              "1   801  [02-01-2011]  2011    01  02  \n",
              "2  1349  [03-01-2011]  2011    01  03  \n",
              "3  1562  [04-01-2011]  2011    01  04  \n",
              "4  1600  [05-01-2011]  2011    01  05  "
            ],
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              "      <th>year</th>\n",
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              "      <th>day</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
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              "      <td>01-01-2011</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "      <td>Saturday</td>\n",
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              "      <td>[01-01-2011]</td>\n",
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              "      <td>0.363478</td>\n",
              "      <td>0.353739</td>\n",
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              "      <td>131</td>\n",
              "      <td>670</td>\n",
              "      <td>801</td>\n",
              "      <td>[02-01-2011]</td>\n",
              "      <td>2011</td>\n",
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              "      <td>02</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>03-01-2011</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Monday</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.196364</td>\n",
              "      <td>0.189405</td>\n",
              "      <td>0.437273</td>\n",
              "      <td>0.248309</td>\n",
              "      <td>120</td>\n",
              "      <td>1229</td>\n",
              "      <td>1349</td>\n",
              "      <td>[03-01-2011]</td>\n",
              "      <td>2011</td>\n",
              "      <td>01</td>\n",
              "      <td>03</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>04-01-2011</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.200000</td>\n",
              "      <td>0.212122</td>\n",
              "      <td>0.590435</td>\n",
              "      <td>0.160296</td>\n",
              "      <td>108</td>\n",
              "      <td>1454</td>\n",
              "      <td>1562</td>\n",
              "      <td>[04-01-2011]</td>\n",
              "      <td>2011</td>\n",
              "      <td>01</td>\n",
              "      <td>04</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
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              "      <td>05-01-2011</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Wednesday</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.226957</td>\n",
              "      <td>0.229270</td>\n",
              "      <td>0.436957</td>\n",
              "      <td>0.186900</td>\n",
              "      <td>82</td>\n",
              "      <td>1518</td>\n",
              "      <td>1600</td>\n",
              "      <td>[05-01-2011]</td>\n",
              "      <td>2011</td>\n",
              "      <td>01</td>\n",
              "      <td>05</td>\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-065744be-b85f-49d2-b468-a2c78d642dec 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-065744be-b85f-49d2-b468-a2c78d642dec');\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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 110
        }
      ],
      "source": [
        "df_daily.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Anomaly detection**"
      ],
      "metadata": {
        "id": "U4t2AL1UkJNq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12,8))\n",
        "plt.plot(df_daily['dteday'],df_daily['cnt'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 485
        },
        "id": "_dpz3t6KQx-v",
        "outputId": "413aeb35-269b-4a4a-d21c-5736983d4784"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_anomaly = df_daily[np.abs(df_daily[\"cnt\"]-df_daily[\"cnt\"].mean()) >=(2*df_daily[\"cnt\"].std())]"
      ],
      "metadata": {
        "id": "tYRnig_JRzq8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_anomaly"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 362
        },
        "id": "uF6MuRVqR2i6",
        "outputId": "356c5c74-1eb2-4876-8e8f-2c6cb9ac8d45"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     instant      dteday  season  yr  mnth  holiday    weekday  workingday  \\\n",
              "25        26  26-01-2011       1   0     1        0  Wednesday           1   \n",
              "26        27  27-01-2011       1   0     1        0   Thursday           1   \n",
              "64        65  06-03-2011       1   0     3        0     Sunday           0   \n",
              "68        69  10-03-2011       1   0     3        0   Thursday           1   \n",
              "301      302  29-10-2011       4   0    10        0   Saturday           0   \n",
              "623      624  15-09-2012       3   1     9        0   Saturday           0   \n",
              "630      631  22-09-2012       3   1     9        0   Saturday           0   \n",
              "637      638  29-09-2012       4   1     9        0   Saturday           0   \n",
              "667      668  29-10-2012       4   1    10        0     Monday           1   \n",
              "725      726  26-12-2012       1   1    12        0  Wednesday           1   \n",
              "\n",
              "     weathersit      temp     atemp       hum  windspeed  casual  registered  \\\n",
              "25            3  0.217500  0.203600  0.862500   0.293850      34         472   \n",
              "26            1  0.195000  0.219700  0.687500   0.113837      15         416   \n",
              "64            2  0.376522  0.366252  0.948261   0.343287     114         491   \n",
              "68            3  0.389091  0.385668  0.000000   0.261877      46         577   \n",
              "301           3  0.254167  0.227913  0.882500   0.351371      57         570   \n",
              "623           1  0.608333  0.585867  0.501667   0.247521    3160        5554   \n",
              "630           1  0.650000  0.610488  0.646667   0.283583    2512        5883   \n",
              "637           1  0.542500  0.530296  0.542917   0.227604    2589        5966   \n",
              "667           3  0.440000  0.439400  0.880000   0.358200       2          20   \n",
              "725           3  0.243333  0.220333  0.823333   0.316546       9         432   \n",
              "\n",
              "      cnt      tempDate  year month day  \n",
              "25    506  [26-01-2011]  2011    01  26  \n",
              "26    431  [27-01-2011]  2011    01  27  \n",
              "64    605  [06-03-2011]  2011    03  06  \n",
              "68    623  [10-03-2011]  2011    03  10  \n",
              "301   627  [29-10-2011]  2011    10  29  \n",
              "623  8714  [15-09-2012]  2012    09  15  \n",
              "630  8395  [22-09-2012]  2012    09  22  \n",
              "637  8555  [29-09-2012]  2012    09  29  \n",
              "667    22  [29-10-2012]  2012    10  29  \n",
              "725   441  [26-12-2012]  2012    12  26  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f9f93fa7-fc31-4089-9bc3-eb30cb42f4e8\">\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>instant</th>\n",
              "      <th>dteday</th>\n",
              "      <th>season</th>\n",
              "      <th>yr</th>\n",
              "      <th>mnth</th>\n",
              "      <th>holiday</th>\n",
              "      <th>weekday</th>\n",
              "      <th>workingday</th>\n",
              "      <th>weathersit</th>\n",
              "      <th>temp</th>\n",
              "      <th>atemp</th>\n",
              "      <th>hum</th>\n",
              "      <th>windspeed</th>\n",
              "      <th>casual</th>\n",
              "      <th>registered</th>\n",
              "      <th>cnt</th>\n",
              "      <th>tempDate</th>\n",
              "      <th>year</th>\n",
              "      <th>month</th>\n",
              "      <th>day</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>26</td>\n",
              "      <td>26-01-2011</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Wednesday</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0.217500</td>\n",
              "      <td>0.203600</td>\n",
              "      <td>0.862500</td>\n",
              "      <td>0.293850</td>\n",
              "      <td>34</td>\n",
              "      <td>472</td>\n",
              "      <td>506</td>\n",
              "      <td>[26-01-2011]</td>\n",
              "      <td>2011</td>\n",
              "      <td>01</td>\n",
              "      <td>26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>27</td>\n",
              "      <td>27-01-2011</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Thursday</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.195000</td>\n",
              "      <td>0.219700</td>\n",
              "      <td>0.687500</td>\n",
              "      <td>0.113837</td>\n",
              "      <td>15</td>\n",
              "      <td>416</td>\n",
              "      <td>431</td>\n",
              "      <td>[27-01-2011]</td>\n",
              "      <td>2011</td>\n",
              "      <td>01</td>\n",
              "      <td>27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>64</th>\n",
              "      <td>65</td>\n",
              "      <td>06-03-2011</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0.376522</td>\n",
              "      <td>0.366252</td>\n",
              "      <td>0.948261</td>\n",
              "      <td>0.343287</td>\n",
              "      <td>114</td>\n",
              "      <td>491</td>\n",
              "      <td>605</td>\n",
              "      <td>[06-03-2011]</td>\n",
              "      <td>2011</td>\n",
              "      <td>03</td>\n",
              "      <td>06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>68</th>\n",
              "      <td>69</td>\n",
              "      <td>10-03-2011</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>Thursday</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0.389091</td>\n",
              "      <td>0.385668</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.261877</td>\n",
              "      <td>46</td>\n",
              "      <td>577</td>\n",
              "      <td>623</td>\n",
              "      <td>[10-03-2011]</td>\n",
              "      <td>2011</td>\n",
              "      <td>03</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>301</th>\n",
              "      <td>302</td>\n",
              "      <td>29-10-2011</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>Saturday</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0.254167</td>\n",
              "      <td>0.227913</td>\n",
              "      <td>0.882500</td>\n",
              "      <td>0.351371</td>\n",
              "      <td>57</td>\n",
              "      <td>570</td>\n",
              "      <td>627</td>\n",
              "      <td>[29-10-2011]</td>\n",
              "      <td>2011</td>\n",
              "      <td>10</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>623</th>\n",
              "      <td>624</td>\n",
              "      <td>15-09-2012</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>Saturday</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.608333</td>\n",
              "      <td>0.585867</td>\n",
              "      <td>0.501667</td>\n",
              "      <td>0.247521</td>\n",
              "      <td>3160</td>\n",
              "      <td>5554</td>\n",
              "      <td>8714</td>\n",
              "      <td>[15-09-2012]</td>\n",
              "      <td>2012</td>\n",
              "      <td>09</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>630</th>\n",
              "      <td>631</td>\n",
              "      <td>22-09-2012</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>Saturday</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.650000</td>\n",
              "      <td>0.610488</td>\n",
              "      <td>0.646667</td>\n",
              "      <td>0.283583</td>\n",
              "      <td>2512</td>\n",
              "      <td>5883</td>\n",
              "      <td>8395</td>\n",
              "      <td>[22-09-2012]</td>\n",
              "      <td>2012</td>\n",
              "      <td>09</td>\n",
              "      <td>22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>637</th>\n",
              "      <td>638</td>\n",
              "      <td>29-09-2012</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>Saturday</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.542500</td>\n",
              "      <td>0.530296</td>\n",
              "      <td>0.542917</td>\n",
              "      <td>0.227604</td>\n",
              "      <td>2589</td>\n",
              "      <td>5966</td>\n",
              "      <td>8555</td>\n",
              "      <td>[29-09-2012]</td>\n",
              "      <td>2012</td>\n",
              "      <td>09</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>667</th>\n",
              "      <td>668</td>\n",
              "      <td>29-10-2012</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>Monday</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0.440000</td>\n",
              "      <td>0.439400</td>\n",
              "      <td>0.880000</td>\n",
              "      <td>0.358200</td>\n",
              "      <td>2</td>\n",
              "      <td>20</td>\n",
              "      <td>22</td>\n",
              "      <td>[29-10-2012]</td>\n",
              "      <td>2012</td>\n",
              "      <td>10</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>725</th>\n",
              "      <td>726</td>\n",
              "      <td>26-12-2012</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "      <td>0</td>\n",
              "      <td>Wednesday</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0.243333</td>\n",
              "      <td>0.220333</td>\n",
              "      <td>0.823333</td>\n",
              "      <td>0.316546</td>\n",
              "      <td>9</td>\n",
              "      <td>432</td>\n",
              "      <td>441</td>\n",
              "      <td>[26-12-2012]</td>\n",
              "      <td>2012</td>\n",
              "      <td>12</td>\n",
              "      <td>26</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f9f93fa7-fc31-4089-9bc3-eb30cb42f4e8')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f9f93fa7-fc31-4089-9bc3-eb30cb42f4e8 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-f9f93fa7-fc31-4089-9bc3-eb30cb42f4e8');\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": 113
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12,8))\n",
        "plt.plot(df_anomaly['dteday'],df_anomaly['cnt'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 485
        },
        "id": "W_iNeQdaSfGs",
        "outputId": "d643b318-7bc6-4363-e40b-2ec64b0ec893"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Few Reasons why few of the above anamolies might have happened are analyzed**"
      ],
      "metadata": {
        "id": "yK9R2RtTmA1I"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 1.Reasons for having high bike rental demand on 29-09-2012 might be"
      ],
      "metadata": {
        "id": "Tx-abbivmXxt"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "On September 29, 2013, a group of motorcyclists were participating in an annual rally, titled \"Hollywood Stuntz,\" organized by a man named Jamie Lao.[4] Rallies organized by Lao in the past involved performing motorcycle stunts and an unauthorized ride through Times Square, Manhattan. During the 2012 ride a year prior, \"well over a thousand motorcycles, dirt bikes, quads, four-wheel vehicles\" rode through Times Square, according to New York Police Commissioner Raymond Kelly. The group did not have permits to do so.[5][6] Driving through the section of Broadway within Times Square has been illegal since it was pedestrianized in 2009. Kelly reported that the altercation with Lien was not the only problem involving the group on September 29, as over 200 other people had complained to the police about the reckless driving of the bikers on Manhattan's streets that day.\n",
        "\n"
      ],
      "metadata": {
        "id": "W2A6u4PJem1E"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 2.Reasons for having bike rental demand on 2012-10-30 might be\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "gQ32BQvpnGIl"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "query like \"2012-10-30 washington d.c.\" in Google returns related results to \n",
        "Hurricane Sandy."
      ],
      "metadata": {
        "id": "gy8I_v2Plf1o"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pw970GfkdIBB"
      },
      "outputs": [],
      "source": [
        "df_daily = df_daily.drop('tempDate',axis=1) \n",
        "df_daily = df_daily.drop('dteday',axis=1)\n",
        "df_daily = df_daily.drop('instant',axis=1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "liDLZrUGGTKB"
      },
      "source": [
        "# **Missing value analysis**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kR4cc2O3glQe",
        "outputId": "38635ba7-5c6f-4e1e-c283-bebe311a7e92"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "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",
              "year          0\n",
              "month         0\n",
              "day           0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 116
        }
      ],
      "source": [
        "df_daily.isna().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "MdcaDVUUjSAe",
        "outputId": "27f95a0a-27ab-45c1-96e9-e281d3f35519"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           season          yr        mnth     holiday  workingday  weathersit  \\\n",
              "count  731.000000  731.000000  731.000000  731.000000  731.000000  731.000000   \n",
              "mean     2.496580    0.500684    6.519836    0.028728    0.683995    1.395349   \n",
              "std      1.110807    0.500342    3.451913    0.167155    0.465233    0.544894   \n",
              "min      1.000000    0.000000    1.000000    0.000000    0.000000    1.000000   \n",
              "25%      2.000000    0.000000    4.000000    0.000000    0.000000    1.000000   \n",
              "50%      3.000000    1.000000    7.000000    0.000000    1.000000    1.000000   \n",
              "75%      3.000000    1.000000   10.000000    0.000000    1.000000    2.000000   \n",
              "max      4.000000    1.000000   12.000000    1.000000    1.000000    3.000000   \n",
              "\n",
              "             temp       atemp         hum   windspeed       casual  \\\n",
              "count  731.000000  731.000000  731.000000  731.000000   731.000000   \n",
              "mean     0.495385    0.474354    0.627894    0.190486   848.176471   \n",
              "std      0.183051    0.162961    0.142429    0.077498   686.622488   \n",
              "min      0.059130    0.079070    0.000000    0.022392     2.000000   \n",
              "25%      0.337083    0.337842    0.520000    0.134950   315.500000   \n",
              "50%      0.498333    0.486733    0.626667    0.180975   713.000000   \n",
              "75%      0.655417    0.608602    0.730209    0.233214  1096.000000   \n",
              "max      0.861667    0.840896    0.972500    0.507463  3410.000000   \n",
              "\n",
              "        registered          cnt  \n",
              "count   731.000000   731.000000  \n",
              "mean   3656.172367  4504.348837  \n",
              "std    1560.256377  1937.211452  \n",
              "min      20.000000    22.000000  \n",
              "25%    2497.000000  3152.000000  \n",
              "50%    3662.000000  4548.000000  \n",
              "75%    4776.500000  5956.000000  \n",
              "max    6946.000000  8714.000000  "
            ],
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              "\n",
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              "    <div class=\"colab-df-container\">\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>season</th>\n",
              "      <th>yr</th>\n",
              "      <th>mnth</th>\n",
              "      <th>holiday</th>\n",
              "      <th>workingday</th>\n",
              "      <th>weathersit</th>\n",
              "      <th>temp</th>\n",
              "      <th>atemp</th>\n",
              "      <th>hum</th>\n",
              "      <th>windspeed</th>\n",
              "      <th>casual</th>\n",
              "      <th>registered</th>\n",
              "      <th>cnt</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>731.000000</td>\n",
              "      <td>731.000000</td>\n",
              "      <td>731.000000</td>\n",
              "      <td>731.000000</td>\n",
              "      <td>731.000000</td>\n",
              "      <td>731.000000</td>\n",
              "      <td>731.000000</td>\n",
              "      <td>731.000000</td>\n",
              "      <td>731.000000</td>\n",
              "      <td>731.000000</td>\n",
              "      <td>731.000000</td>\n",
              "      <td>731.000000</td>\n",
              "      <td>731.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2.496580</td>\n",
              "      <td>0.500684</td>\n",
              "      <td>6.519836</td>\n",
              "      <td>0.028728</td>\n",
              "      <td>0.683995</td>\n",
              "      <td>1.395349</td>\n",
              "      <td>0.495385</td>\n",
              "      <td>0.474354</td>\n",
              "      <td>0.627894</td>\n",
              "      <td>0.190486</td>\n",
              "      <td>848.176471</td>\n",
              "      <td>3656.172367</td>\n",
              "      <td>4504.348837</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.110807</td>\n",
              "      <td>0.500342</td>\n",
              "      <td>3.451913</td>\n",
              "      <td>0.167155</td>\n",
              "      <td>0.465233</td>\n",
              "      <td>0.544894</td>\n",
              "      <td>0.183051</td>\n",
              "      <td>0.162961</td>\n",
              "      <td>0.142429</td>\n",
              "      <td>0.077498</td>\n",
              "      <td>686.622488</td>\n",
              "      <td>1560.256377</td>\n",
              "      <td>1937.211452</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.059130</td>\n",
              "      <td>0.079070</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.022392</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>22.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.337083</td>\n",
              "      <td>0.337842</td>\n",
              "      <td>0.520000</td>\n",
              "      <td>0.134950</td>\n",
              "      <td>315.500000</td>\n",
              "      <td>2497.000000</td>\n",
              "      <td>3152.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.498333</td>\n",
              "      <td>0.486733</td>\n",
              "      <td>0.626667</td>\n",
              "      <td>0.180975</td>\n",
              "      <td>713.000000</td>\n",
              "      <td>3662.000000</td>\n",
              "      <td>4548.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.655417</td>\n",
              "      <td>0.608602</td>\n",
              "      <td>0.730209</td>\n",
              "      <td>0.233214</td>\n",
              "      <td>1096.000000</td>\n",
              "      <td>4776.500000</td>\n",
              "      <td>5956.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>4.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.861667</td>\n",
              "      <td>0.840896</td>\n",
              "      <td>0.972500</td>\n",
              "      <td>0.507463</td>\n",
              "      <td>3410.000000</td>\n",
              "      <td>6946.000000</td>\n",
              "      <td>8714.000000</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 117
        }
      ],
      "source": [
        "#summary statistics about the dataframe such as the mean, maximum and minimum values. \n",
        "#At the top of the table is a row called counts\n",
        "df_daily.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yyArN8hBhpE6",
        "outputId": "a70bc91a-ca49-4a2d-d70e-cdb62fd6d36d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 731 entries, 0 to 730\n",
            "Data columns (total 17 columns):\n",
            " #   Column      Non-Null Count  Dtype  \n",
            "---  ------      --------------  -----  \n",
            " 0   season      731 non-null    int64  \n",
            " 1   yr          731 non-null    int64  \n",
            " 2   mnth        731 non-null    int64  \n",
            " 3   holiday     731 non-null    int64  \n",
            " 4   weekday     731 non-null    object \n",
            " 5   workingday  731 non-null    int64  \n",
            " 6   weathersit  731 non-null    int64  \n",
            " 7   temp        731 non-null    float64\n",
            " 8   atemp       731 non-null    float64\n",
            " 9   hum         731 non-null    float64\n",
            " 10  windspeed   731 non-null    float64\n",
            " 11  casual      731 non-null    int64  \n",
            " 12  registered  731 non-null    int64  \n",
            " 13  cnt         731 non-null    int64  \n",
            " 14  year        731 non-null    object \n",
            " 15  month       731 non-null    object \n",
            " 16  day         731 non-null    object \n",
            "dtypes: float64(4), int64(9), object(4)\n",
            "memory usage: 97.2+ KB\n"
          ]
        }
      ],
      "source": [
        "#we have a more concise summary of the state of the data and the extent of data missingness \n",
        "#We can also look at the datatypes of each column\n",
        "df_daily.info()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#The newly created columns year,month and day are created as Object datatype\n",
        "#We need to convert those columns to integer type\n",
        "df_daily['year']=df_daily['year'].astype('str').astype(int);\n",
        "df_daily['month']=df_daily['month'].astype('str').astype(int);\n",
        "df_daily['day']=df_daily['day'].astype('str').astype(int);"
      ],
      "metadata": {
        "id": "l83LcBOxq_KP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Adik2bvd3IyJ",
        "outputId": "ae187ccd-a566-4ac9-f52f-fa83aeb805c5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 731 entries, 0 to 730\n",
            "Data columns (total 17 columns):\n",
            " #   Column      Non-Null Count  Dtype  \n",
            "---  ------      --------------  -----  \n",
            " 0   season      731 non-null    int64  \n",
            " 1   yr          731 non-null    int64  \n",
            " 2   mnth        731 non-null    int64  \n",
            " 3   holiday     731 non-null    int64  \n",
            " 4   weekday     731 non-null    object \n",
            " 5   workingday  731 non-null    int64  \n",
            " 6   weathersit  731 non-null    int64  \n",
            " 7   temp        731 non-null    float64\n",
            " 8   atemp       731 non-null    float64\n",
            " 9   hum         731 non-null    float64\n",
            " 10  windspeed   731 non-null    float64\n",
            " 11  casual      731 non-null    int64  \n",
            " 12  registered  731 non-null    int64  \n",
            " 13  cnt         731 non-null    int64  \n",
            " 14  year        731 non-null    int64  \n",
            " 15  month       731 non-null    int64  \n",
            " 16  day         731 non-null    int64  \n",
            "dtypes: float64(4), int64(12), object(1)\n",
            "memory usage: 97.2+ KB\n"
          ]
        }
      ],
      "source": [
        "df_daily.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "oShoN3mUopG4",
        "outputId": "1cb9936d-6256-440b-e78f-91d8ff4374a7"
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f94065892d0>"
            ]
          },
          "metadata": {},
          "execution_count": 121
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x360 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "\n",
        "msno.matrix(df_daily,figsize=(12,5))"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Missingno is an excellent and simple to use Python library that provides a series of visualisations \n",
        "to understand the presence and distribution of missing data within a pandas dataframe"
      ],
      "metadata": {
        "id": "rmLbwWhjteI0"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "The matrix plot is a great tool if you are working with depth-related data or time-series data. It provides a colour fill for each column. When data is present, the plot is shaded in grey (or your colour of choice), and when it is absent the plot is displayed in white.\n"
      ],
      "metadata": {
        "id": "otogImINtYXj"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xi0bn40CeVmV"
      },
      "source": [
        "# **Exploratory Data Analytics on Day dataset**"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_daily.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "53Cs7tju9uqx",
        "outputId": "8034195e-e209-45c4-fc2d-f73b1a672f54"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(731, 17)"
            ]
          },
          "metadata": {},
          "execution_count": 122
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_daily.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "NL3xD_P-IjQX",
        "outputId": "e280e2d6-3108-48b2-a39b-a622179df21f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   season  yr  mnth  holiday    weekday  workingday  weathersit      temp  \\\n",
              "0       1   0     1        0   Saturday           0           2  0.344167   \n",
              "1       1   0     1        0     Sunday           0           2  0.363478   \n",
              "2       1   0     1        0     Monday           1           1  0.196364   \n",
              "3       1   0     1        0    Tuesday           1           1  0.200000   \n",
              "4       1   0     1        0  Wednesday           1           1  0.226957   \n",
              "\n",
              "      atemp       hum  windspeed  casual  registered   cnt  year  month  day  \n",
              "0  0.363625  0.805833   0.160446     331         654   985  2011      1    1  \n",
              "1  0.353739  0.696087   0.248539     131         670   801  2011      1    2  \n",
              "2  0.189405  0.437273   0.248309     120        1229  1349  2011      1    3  \n",
              "3  0.212122  0.590435   0.160296     108        1454  1562  2011      1    4  \n",
              "4  0.229270  0.436957   0.186900      82        1518  1600  2011      1    5  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f785f74d-0dc7-496c-baba-3defaf1a6ea5\">\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",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>season</th>\n",
              "      <th>yr</th>\n",
              "      <th>mnth</th>\n",
              "      <th>holiday</th>\n",
              "      <th>weekday</th>\n",
              "      <th>workingday</th>\n",
              "      <th>weathersit</th>\n",
              "      <th>temp</th>\n",
              "      <th>atemp</th>\n",
              "      <th>hum</th>\n",
              "      <th>windspeed</th>\n",
              "      <th>casual</th>\n",
              "      <th>registered</th>\n",
              "      <th>cnt</th>\n",
              "      <th>year</th>\n",
              "      <th>month</th>\n",
              "      <th>day</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Saturday</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0.344167</td>\n",
              "      <td>0.363625</td>\n",
              "      <td>0.805833</td>\n",
              "      <td>0.160446</td>\n",
              "      <td>331</td>\n",
              "      <td>654</td>\n",
              "      <td>985</td>\n",
              "      <td>2011</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0.363478</td>\n",
              "      <td>0.353739</td>\n",
              "      <td>0.696087</td>\n",
              "      <td>0.248539</td>\n",
              "      <td>131</td>\n",
              "      <td>670</td>\n",
              "      <td>801</td>\n",
              "      <td>2011</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Monday</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.196364</td>\n",
              "      <td>0.189405</td>\n",
              "      <td>0.437273</td>\n",
              "      <td>0.248309</td>\n",
              "      <td>120</td>\n",
              "      <td>1229</td>\n",
              "      <td>1349</td>\n",
              "      <td>2011</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.200000</td>\n",
              "      <td>0.212122</td>\n",
              "      <td>0.590435</td>\n",
              "      <td>0.160296</td>\n",
              "      <td>108</td>\n",
              "      <td>1454</td>\n",
              "      <td>1562</td>\n",
              "      <td>2011</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Wednesday</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.226957</td>\n",
              "      <td>0.229270</td>\n",
              "      <td>0.436957</td>\n",
              "      <td>0.186900</td>\n",
              "      <td>82</td>\n",
              "      <td>1518</td>\n",
              "      <td>1600</td>\n",
              "      <td>2011</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f785f74d-0dc7-496c-baba-3defaf1a6ea5')\"\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-f785f74d-0dc7-496c-baba-3defaf1a6ea5 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-f785f74d-0dc7-496c-baba-3defaf1a6ea5');\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": 123
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 721
        },
        "id": "AUfJe4Zmd979",
        "outputId": "4a5bc70f-2978-485b-da0d-08ebe1030f6b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1584x864 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "fig = plt.figure(figsize=[22,12])\n",
        "ax1 = fig.add_subplot(2,2,1)\n",
        "my_labels =['2011','2012']\n",
        "ax1 = sns.barplot(x='yr',y='cnt',data=df_daily.groupby('yr')['cnt'].mean().reset_index())\n",
        "ax1.set_xticks([0,1])\n",
        "ax1.set_xticklabels(['2011','2012'])\n",
        "#month count\n",
        "ax2 = fig.add_subplot(2,2,2)\n",
        "ax2 = sns.barplot(x='mnth',y='cnt',data=df_daily.groupby('mnth')['cnt'].mean().reset_index())\n",
        "\n",
        "#day countS\n",
        "indexes=['Monday','Tuesday','Wednesday','Thursday','Friday','Saturday','Sunday']\n",
        "\n",
        "ax3 = fig.add_subplot(2,2,3)\n",
        "ax3 = sns.barplot(x='weekday',y='cnt',data=df_daily.groupby('weekday')['cnt'].mean().reindex(indexes).reset_index())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 737
        },
        "id": "nQ-2fMRagGbc",
        "outputId": "dad97f54-ae0e-4bd3-d8c5-d583174a1b5f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Text(0, 0, 'Clear'), Text(0, 0, 'Mist'), Text(0, 0, 'Snow')]"
            ]
          },
          "metadata": {},
          "execution_count": 125
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1584x864 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "fig = plt.figure(figsize=[22,12])\n",
        "ax1 = fig.add_subplot(2,2,1)\n",
        "ax1 = sns.barplot(x='season',y='cnt',data=df_daily.groupby('season')['cnt'].mean().reset_index())\n",
        "ax1.set_xticks([0,1,2,3])\n",
        "ax1.set_xticklabels(['Spring','Summer','Fall','Winter'])\n",
        "\n",
        "#holiday based count\n",
        "ax2 = fig.add_subplot(2,2,2)\n",
        "ax2 = sns.barplot(x='holiday',y='cnt',data=df_daily.groupby('holiday')['cnt'].mean().reset_index())\n",
        "ax2.set_xticks([0,1])\n",
        "ax2.set_xticklabels(['No Holiday','Holiday'])\n",
        "#working day based count\n",
        "ax3 = fig.add_subplot(2,2,3)\n",
        "ax3 = sns.barplot(x='workingday',y='cnt',data=df_daily.groupby('workingday')['cnt'].mean().reset_index())\n",
        "ax3.set_xticks([0,1])\n",
        "ax3.set_xticklabels(['No working','Working'])\n",
        "#weather situation based count\n",
        "ax4 = fig.add_subplot(2,2,4)\n",
        "ax4 = sns.barplot(x='weathersit',y='cnt',data=df_daily.groupby('weathersit')['cnt'].mean().reset_index())\n",
        "ax4.set_xticks([0,1,2])\n",
        "ax4.set_xticklabels(['Clear','Mist','Snow'])"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#workingday based count\n",
        "data=df_daily.groupby('workingday')['cnt'].mean()\n",
        "mylabels = ['Non-working day','workingday']\n",
        "fig = plt.figure(figsize=[25,15])\n",
        "ax1 = fig.add_subplot(2,2,1)\n",
        "ax1 = plt.pie(data, labels = mylabels, autopct='%1.1f%%')\n",
        "plt.show()\n",
        "\n",
        "#holiday based count\n",
        "data=df_daily.groupby('holiday')['cnt'].mean()\n",
        "mylabels = ['Not a holiday','holiday']\n",
        "fig = plt.figure(figsize=[25,15])\n",
        "ax2 = fig.add_subplot(2,2,2)\n",
        "ax2 = plt.pie(data, labels = mylabels, autopct='%1.1f%%')\n",
        "plt.show()\n",
        "\n",
        "#season based count\n",
        "data=df_daily.groupby('season')['cnt'].mean()\n",
        "mylabels = ['spring','summer','fall','winter']\n",
        "fig = plt.figure(figsize=[25,15])\n",
        "ax3 = fig.add_subplot(2,2,3)\n",
        "ax3 = plt.pie(data, labels = mylabels, autopct='%1.1f%%')\n",
        "plt.show()\n",
        "\n",
        "#weatherlist based count\n",
        "data=df_daily.groupby('weathersit')['cnt'].mean()\n",
        "mylabels = ['clear','Mist','Snow']\n",
        "fig = plt.figure(figsize=[25,15])\n",
        "ax4 = fig.add_subplot(2,2,4)\n",
        "ax4 = plt.pie(data, labels = mylabels, autopct='%1.1f%%')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "1SYxhhY-1hv1",
        "outputId": "2bb6ef78-0071-41e6-9d07-30048dd56061"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x1080 with 1 Axes>"
            ],
            "image/png": 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TkyMGlS6L6qFLAHSKzhQELIG+UZG07IWrNtYbtz60MuK4/DsnOhxFlvDFyydbZ5w8cGdUD80EUC86U7FjCfS+ylTaef1fr+2ovfuJdRr/uomObsEFY+0Pzhy5O6KpMwA0iM5TzFgCvas0adlLn126fdS9T60Piw5DVEg+eeE4++KzT9oZ0dTTATSKzlOsZNEBilg0adkLX35jz0gWAFH3/emZDaFnXt02JGU5rwKoFJ2nWLEEekc4adnPrthQN+GOR9doosMQFap7n1offu617cOSlvMKgArReYoRS6DnqUnL/vv6bc3Tb31oVYRH24hOzN1PvBV+fvmOEUnLXgKgXHSeYsMS6FlyynLu37q3bc7//PH1qMfLgBH1iD88ti780qrdJyUteyGAqOg8xYQl0HOkVNq5fU9j5we+94dlUcflSqBEPenOv6/VVm6sH5+07KcAhETnKRYsgR5iZZyfNLWlPvHtO1+JpTkRjKjH+T5w64Mr9U07W2YkLfvP4ParR/AvsQdkbPfG9kTmizfeviSWsDjbnai3uJ6PH93zenRPQ+cFqbRzJwCuvnuCWAInyHa8axMp+zvfuG0x1wIi6gNp28V3fvtqtKktNT+dcX4oOk+hYwmcANf1Pm5lnFu/+Zsl0aY2S3QcosBIWA5uuv2VWMJyvuJ5/hWi8xQyzhg+fpOstLP0v29bHN2+r110FqJAGj6wFL/40sykHlZPB/Cm6DyFiHsCx6c8lXb++ZtH1kRYAETibN/Xjtv/tiaSSjvPgXMIjgtLoPvkpGX/bdGq3RUvreIlIYlEW7Rqt/TCil0VSct+FNymdZsqOkChSdvuzfubkmf8/h9ruRxEnvN9DzsX/xqqXora0z6NZOPbaFj/NAAfkqJhwOTLEY5Vvet56fZ9qFv7KDwnDUgShp79RQAS9q64F47VhvJhZ6B8+JkAgLq1j6Bs2OnQywb37Zujg/zhsTe1MUPLZwytKfm+Fla/IzpPIWFrds+5tuPd9IO7l8V4TYD817ptCcLx6ndu1735DwyYcgWGzfoKSmsno/nthe96ju+52PfGn1Ez6VIMn/01DDnjGkiygmSDiUjFCAyb9RW0714FAEi374Xv+yyAPOB6Pn5492uxjON9DcCFovMUEpbAsRtiZZxHf3Lv6xGeCZT/7FQrOus2omzoaQfd7znp3J8WVL30Xc9LNGyCVjoQWukgAIASjkGSZEiyAt/NwPf+MxO80XwOVcZ5vfguqDtaOtL44d2vRayM8xcAI0XnKRQsgWOjJS372Yf/vSm6djOXNS8EDW89if7jLkTXuUQ1kz6KPa/fg63P/w/ad69Cv5Fz3vU8O9EIQMLu1+7Cjpd/hebNiwAA0arRsJMt2PXKb1A+4ix07n8LWmktVL2sb94QHZMN25tx39ProynLeQ5cY+iYsASOQcpy7li/rXnEIy+8zTGUAtBZtx6KFodefvBhmtZti1F72qdx0rk3o3TIdDSsf/Jdz/V9D6nmbRg45QoMOesL6Ny/DsnGtyHJCgZOvRLDZn0ZJQMnoWXbElSMnIX6t57E3hX3o3P/W3319ugonlqyTVm+Yf+gpGXfD84oPiqWwFG4nvfJhGV//BcPrOCnigKRat6BRN16bF34E+x740EkG7dgz+v3IN2+F5F+QwEAJYNOgdWy413PVfUyRCtPghKOQVbCiFWPhdW256Cfad2+FKWDpyHVshNKSMfAafPRsvXlPnlvdGx+/fDqSEt7ep7teDeIzpLvWALvbZJte3d+7w9Lo0muCVQw+o+7ACedezNOmnsTBk6Zj2jVSAyafhVc20KmM3u52mTD2wcNGh8Q6z8G6fZ98NwMfM9FqnkrwvGadx53M0kk6jegdPBU+K6NAx80PZf/P/JJ2nbx3T8sjdmO+2MAM0XnyWecMXxkZam0s/72R9YM5HyAwpVs3IKWrS+h9rRPo2PfOjRt+hcACUoogppTPopwrBKd+9+C1bYbVcY8AED77lVo3vwiIAGx/mPRf/xF77xe/VtPIF4zAdGqkfBcG3uX3wvHakfZsNPRb8RZgt4lHcm0sdW48apTm/SwOhJAm+g8+YglcARJy7536Zv7Pvarv7yhi85CRMfvho9Nsc6cNPDhqB76lOgs+YiHgw5vpuv5l//hsTdZAEQF7q7H39Qd1/8ogFmis+QjlsC7aam08+Bv/ro6wmsDEBW+hOXgtr++EU2lnQcB8IPdIVgCh0hn3P+3fltT5atv7hMdhYh6yLJ1+/Hm5sYKK+P8QHSWfMMSONh4z/e/cttfV/N0UKIic9vfVkc9D9cDOEV0lnzCEvgPOWnZD9339HqNy0IQFZ/WjjTuevxNPXd9Yk78zGEJ5Diud83+puSoZ17dxr8ToiL179d3Sjv2dwzNOO5XRWfJF9zgZdW6rveLWx9cGeMZs0TF7ZcPrYx5nv9dACeJzpIPWAIAEpZ91+Mvbw3vrOsQHYWIetn+piT+8i9TS6TsB8C1hVgCAD6UTNkz//JvMyQ6CBH1jX+8tEVpbrcmuZ73KdFZRAt6CZRZaefuWx9aFbMd7+g/TURFwfN8/OKBFTHb8f4PQM1Rn1DEAl0CqbRzy5I1e6NvbW0SHYWI+ti2ve149tXtetKybxGdRaQgl8AoAAvufmIdZxASBdTDz28KSZJ0GbLbg0AKbAkkLPvH/1i0We1M2aKjEJEgiZSNfyzarCZS9k9EZxElqCUwVgIufvzlLZwwQhRwj720RZUkXARgvOgsIgSyBBIp+6ePvvB2iBeKIaJU2sHDz28KJ1L2L0RnESGIJTBRknDek0u2cS+AiAAATy/Zpvi+PwfAFNFZ+lrgSiCRsn/28PObwqk09wKIKCttu3jwuY1aImXfKjpLXwtaCZziA3OeXrJNER2EiPLLP5fukB3XmwHgdNFZ+lKgSiCRsm/5y782amnbFR2FiPKM43r40zMbIomU/b+is/SlIJXANM/zz3z21e1Bes9E1A0Ll++U0rZ7MoDZorP0lcBsEBMp+9YHn9uoZ7g8BBEdgev5uPept2K5vYFALC4XlBI43XG9U59btiMo75eIjtNLq3YjYdmjAZwnOktfCMRGMZGyf3n/sxsijsu9ACJ6b54P3PPEW7FEyv4lArA3EIQSONV2vFMWLt9Z9P+YRNQzXn1zL9K2OwzAdNFZelvRl0DSsr/0xOItmuPykmFEdGx8H3hy8VY9adk3iM7S24q9BEpURf7I86/v5LwAIuqW55fvVFRFvhRAmegsvanYS+CKtZsb3ZaOtOgcRFRgWjvSWL2pwfN9/0rRWXpTUZdAImV/9aklW+OicxBRYXpi8dZY0nK+hiIeIC7mEpjsuN6QN8x60TmIqECt3dyAtO0OAHCa6Cy9pWhLIGk51z/z6rawx/FgIjpOuQHiSDEPEBdrCUQVRbriX6/t4HLRRHRC/v36DllV5A+jSAeIi7UEPrpxe7PX2GqJzkFEBa6tM4M3NtW7vu8vEJ2lNxRlCXSmMl97cjEHhImoZxTzAHExlsAE+Bi1fEOd6BxEVCTe3NwIK+NUA5ghOktPK7oSsNLOF55dul31OCJMRD3kwABxwrK/LDpLTyu2EtAlSfrkc8t2hEQHIaLi8vzynXJIkS9BkQ0QF1sJfGDr3ja/rjkpOgcRFZm2zgw27mjJAJgnOktPKqoSSKTsy19csatEdA4iKk6vrNlTmkjZl4vO0ZOKqQRkVZHnrdzIAWEi6h3LN9RBVeV5AIpmUcpiKoFpbZ1p1LekROcgoiLV0JJCS7vlo4iWkSiaErAd7wOvvrlXF52DiIrbK2v26hnb/aDoHD2laErAyjgfff2tOp4VRES96vX1+0MZxyuacYFiKYHqsKqM2LC9SXQOIipyG3e0QJWlWgCDRWfpCcVSAvPWbWm0eQlJIuptnudjpVnvALhIdJaeUBQl0JnMfPTVN/dyrSAi6hOvrt0b60hmPi46R08ohhJQwyFl7ooNvHgMEfWNVRvroYeVMwBERGc5UcVQAjMa21JuczuXjSaivtGZsrFjf0cawGzRWU5UwZdAxnY/+OrafQXfxkRUWJas3hNPpZ2PiM5xooqhBC5bvn4/ryBGRH1q+fo6GcAlKPBrDBR6CdSqqly7cUeL6BxEFDA76zpgO14UwEjRWU5EoZfA2Ru2NWd47QAiEuHtXS0OgKmic5yIgi4B23Gnrd/WHBOdg4iCaeP25njGdgt6HaGCLoFU2jl76962gn4PRFS4Nu9uk62MO1N0jhNR0BvQcEgZv3VPq+gYRBRQW3a3Qgsr41HAg8OFXAID4ENvbOX8ACISo6UjDcfxFBTwOkKFXAKTd+zvYAMQkVDb9rZlUMCDwwVbAp7nT9m4ozkqOgcRBdv6bc1x1/Wmic5xvAq2BBIp++wtu1t5/QAiEmrL7lYlYTmzROc4XgVbArIsTd6yp010DCIKuC172hBW5UmicxyvQi2BmBZWqvfUd4rOQUQBV9echCQhCqBadJbjUaglcPL+pkTC5UxhIsoDuZNUpojOcTwKtQQmb9rZGhYdgogIADZsa456nl+Qg8MFWQKJlH3Gpp0tXD6aiPLC27taQomUXZAzhwuyBDzPP23bXg4KE1F+2LG/A5IsjRed43gUZAloYWXYrroO0TGIiAAALR0WQqpcKTrH8SjEEojJshTqSNqicxARAQDaExmEFDkCoODGKguxBGo7EhkuF0FEecP3gaRlWwBqRGfproIsgaZ2yxUdgoioq9bOtA1goOgc3VWQJdDQkizE3ERUxJrbLIAl0Cdq9zcleXooEeWVxrZUCMAA0Tm6q+BKwEo74wdURtVTx9VgQCUXESWi/FDfkop4nj9IdI7uUkUH6C7VS0+YPkT1pw8bDSUSlyRFhefYcG3Hy9iun8x4fmvSVZo7bKmxPYW65iT2NSawu64T+5o64Xmi3wERFaPmNktKpZ0RsUhhLW5ccCUg+a5S/9TtkrVjXfYORYUSLYMSL5eVWBnUWD9UxcpQU1rpqtVVvnJShaREB8hKJC5Jahiek4HrOJ5tu34y7fltlic3d2TkpjYL9S25wmjoxJ66DjgsDCI6Ri0dabieP1R0ju4quBKArFS5yS6zhV0HbkcT3I6mQ39SOcxzocTKoMTKZCVWDiVWjn6xMvQvqfSUyipPHdZPUmL9ZTlSIskhDb5rw7Vt33ZcL5n2/HbLk5s7bbmpzUJDSypbGPWd2F3fgQwbgyjQWtotSFLhDQwXXAlIilruJY9ztrDnwu1ohtvRfOgjMg4dH5FkKNFSKLEySYmVK0qsHGWxMlSUVHhjy6o8ZXCFpMSGSEqkRJbDOnzXgevYvm07npXx/HbLk5o6HaW5PVsY+5sS2NPQiZ37O2FlnOPLT0R5q7nDgqrIVaJzdFchloDuWYne/0W+BzfRCjfRCmBH10cOWxhyJA4lVi6psXJFiZWhJFaO8pJ+3pjS/p46sEJSYoOyhaFF4HsuvOwehm9lXK/D8qTmhNulMJLY09CBXfs70GmxMIgKQWtHGuGQUobs9qFgDg0UWgkokGTVd/NsyQjfg5dsh5dsh92ws+sj7y4MSJAjMSixcin7VSZHY+UoiffzR5ZWeeqYCijxGkmJjJJlPQrf8+A5tu/Yjm/ZntdhuVJLwpOb2y2poTWFuuYE9jQksGt/B9oSmT5800TUle14sB3XUcJqBYBG0XmOVaGVQASuYwMorOH3g/jwUp3wUp2wG3d3fUDCYcYxZC2aLYx4uaTEyuVIrAzxeD9/RGmVq4yshHpKf0mOjJQVPXu6rGtnfMdx/bTtep2Wh5aEqzZ3pNHYmj1Tak9DJ3bVdaK5nStvEPW0RMrO6GG1CiyBXhP1XdtBQZdA93jpJLx0Enbz3q53H7YwpHAkt4dRJqmxclmLl6E21g9DSytddXiVr0zoJyvRYZKixyRIMjwnA8d2vLTj+QnLRUvSk5s70lJTrjD2Niawq64D9S2pPnu/RIXMdX0fBbZ9KrwScOyCOdbW1/xMCk4mBadlH9IHP/TuwghpyJ4hlT1TSo2VYUCs3B9cVuUqgyt9dWyFrESHSHIkJkmyAs/Onlqbzp1a25L0lOaOjNTYlkJ9Uwp7mzqxu64DdS1JzsWgwMpd8ragtqsFFRbZEro88BgAABytSURBVODicT3At9NwWuvgtNZ1vfvwexhqOHdqbXYuhhIrR3WsDINKq1xlYJWvjO4nK9GBUnbyXig7F8N2vIzj+sm067emPLm5I3tqbX1zEvsaO7G7oRP7Gjo5F4OKiut5Pgpsu1pQYQFEPSfNq8v3Md/JwGlrgNPWcOhD756L8a7Je+XZyXslla7av8pXRlRISqzmncl7vmPDdexcYXh+W6rrXIzkf+ZiNHTCYWNQnnNd7gn0Nt138uzMIDpYdyfvRUuzexjxQybvVVR66tAKSYkNy07eC2vZwshN3ktlPL8t5UnNnY7S3J5CfXN2Lsbu+k7squPkPRKDh4N6ny9JkugM1FM8F25nC9zOFuCgo1Ldm7ynllZ5Su2ByXtxWQ5H/jN5z3E9K+P67ancXIy2VHYuRnMSe+o7sYezvakHZY8GcWC4N7mQCm7hU+oJ3Zm8BwlytKRLYeQm78X7+aNL+7vZyXsD35m8lx0KIeoRZbmvglF4JSCzBOho/C6T93Z1fUBC4f2fpwIy6FM/bdNrR7eLztEdhbZFdSXuCRBRnsodri6o44uFtkV1Icvcdyei/JT9kMoS6EUcEyCivCUpKgAU1KqPhbZFdSWJewJElJ+ksC4BOM617sUotBJISmqo0DITUUDIYV0BS6BXtUphvaDOwSWi4JDUsAqWQK/qkBQ1xHEBIspHkhoOgSXQqzzfdSxZi4rOQUR0EEkJAYAPHLqIb34rtBIAXCch6zHRKYiIDiJpEfiuU1AFABRgCfie1yHr3BMgovwihyOA5yZF5+iugisB+F6brHFPgIjyixIrg+8571o+N98VXglAalIiJaJDEBEdRI1XAL6/R3SO7iq4EpDU0Ga1tFJ0DCKigygl/SAp6nbRObqr4EpADutb1LL+vLIMEeUVtaTSlbXoVtE5uqvgSgDALrXfAEt0CCKirtTyagvAXtE5uqsgSyBUVl1Qq/QRUfFTS6scsAT6xG6lpB+XjiCivKKUVEhgCfSJvbIW1bl0BBHlEzVWHgGw66g/mGcKcUtq+47drpRUiM5BRAQAUEoq4PueBaBVdJbuKsQSgO9ktoUrBomOQUQEAAhV1sJ3MttE5zgeBVkCkhJaFaoaLDoGEREAIFw5CJIkrxWd43gUZAnIWuSNcM1wniZKRHkhVDUkI+ux1aJzHI+CLAEA67UBIwputT4iKk5azfAUAFN0juNRsCUQqhikiw5BRAQAoYpBClgCfaoekuzK0VLROYgo4KSQBlmP6wA4MNyHfN9ObwlXDRGdg4gCLlwzHF4mtRWAIzrL8SjUEoCkqMu1ASNExyCigNMGjoIkK6+KznG8CrYEZC3ysj50QqfoHEQUbPqQcQlZiywWneN4FWwJAHhNH2xIokMQUbDptaM9ACtE5zhehVwCm2QtIiuxMtE5iCigpHAESqxcA7BedJbjVcgl4HmZ9Bpt0GjROYgooLSBI+FlUm+jQAeFgcIuAchaZKFWa7iicxBRMGkDR/mSEirY8QCgwEtAUtRXI8MnJkTnIKJgio6c3CGH9RdF5zgRBV0CAF4LVw/XISuicxBR0EgytNoxGgCWgEBNvpvZrQ0aJToHEQWMNnAkfNfZB6BBdJYTUeglAEkJPRE9aTLHBYioT0WGn+xJsvKM6BwnquBLQA5pz0THnMZxASLqU9HR0zvlsP5P0TlOVMGXAIAl4cpaXdKionMQUVAoKrQBJ+kAXhYd5UQVQwmkPNtaHRk2UXQOIgoIvXYMPDu9FUCb6CwnqhhKALIW+3t05BReZIaI+kR01DRbDmmPic7RE4qiBCRZ/ld09HRbdA4iCob4hJlpSQ09IjpHTyiKEgCwWtaimVBlregcRFTkQpW1kPVYBsAq0Vl6QrGUgA/4j8bGnu6JDkJExS1mzPDg+38H4IvO0hOKpQQghyN/iU+cxesLEFGvik+c2SlrkYdF5+gpRVMCABarZdWKWtpfdA4iKlJKSQXUfgNUAC+JztJTiqkEbHjOY7FxZxTFLhoR5Z/o6FPhO/ZzAIrmRJRiKgHIWvRPJafM4SEhIuoVJZPmdCh67AHROXpSUZUAgBfV8hpJLasWnYOIioxa2h/h6mEygKdFZ+lJxVYCNnzv4ZJJswv2Kj9ElJ/ik85x4bkPAyiqianFVgKQw5E7S6bOSwO8Bj0R9ZzSKeelZC3ye9E5elrRlQCAVbIaqteHTRCdg4iKhDZoNGQt0g7gddFZeloxloAvhSO3lU49Lyk6CBEVh5LJ51qSGv4timSCWFfFWAKQZPmB6OhTFS4vTUQnTFERn3A2JEW9T3SU3lCUJQCgwXftF+Pjzyq61iaivhUzTofvOW8C2Ck6S28o1hKAosduKzv1Qs4ZIKITUn7GJR2KHv+Z6By9pWhLAMBzall/Sxs0WnQOIipQ4ephCFUMcgA8LjpLbynmEnAlNfzT8jM/zAFiIjouZaddbEmy8msARTv3qJhLAJKs3BU5aTKUkkrRUYiowMiROGLjz4Kkhu4UnaU3FXUJAGiH799bdtpFGdFBiKiwlE45z4XnPg6gTnSW3lTsJQA5rN9aOnWeJ4V00VGIqFDICspO/2Ba1qI/FR2ltxV9CQDY6nvuyyWTZvN0USI6JvGJswBJXgdgtegsvS0IJQBFj/2o/KyPJCEF4u0S0YmQZFScc0VC0WM3io7SF4KyVVwihbRN8Qlni85BRHkuNv5MSGF9M4BForP0haCUgK/osa9XzFmQ4N4AER2ZhIrZ8zsVPfZNFOE6QYcTpC3ii1JYN+MTZ4rOQUR5KjZ2BhQ9thPAv0Rn6StBKoHc3sB87g0Q0WFVzJnfKQdoLwAIVgkAwItSSN8YnzhLdA4iyjPRMadCiZbvQ5FdPvJoglYC4N4AEb2LJKPy/VcnZD36VQRoLwAIYAkAWCSH9LdKJs8N1D80ER1ZyeS5vqKXrEfA9gKAYJYAZD16bcX7PmHxojNEJIV0VMxZYMl69FoEbC8ACGgJAFglSdJj/c6+jGsKEQVc+ZkftiVZfhbAStFZRAhqCUDWol8rnXa+o5bXiI5CRIIo8X4om/EBR9aiXxWdRZTAlgCAfZIk/7zy/VfzegNEAVUxZ4EF3/8dgB2is4gS5BKApIZ+Hhl+clIfMk50FCLqY+EBIxAbd4Yth/Xvi84iUqBLAEBKDutfqrrw8zxllChIJBnVH/xSQlJDXwLQKjqOSNzyAX9RSyo2lE6/wBMdhIj6Rum08z21tGqDJMn3ic4iGksA8GUtOr9i9pVppbRKdBYi6mVKSQUq5sxPy1r0kwjgKaGHYglkbYIs/7z64usSooMQUe+qOv9zSUjSbQA2iM6SD1gCObIa/rE2aHR9bNyZoqMQUS+JjJyKyPCJbXJI+57oLPmCJfAfGVmLXFl14TVJWY+LzkJEPUwK6eh/8XVJORz5FICU6Dz5giVwsGWSrDxYed7VluggRNSzquZ92pJD2pMI0LUCjgVL4BByOPK1mHF6R3TUNNFRiKiHREZNRWzcWR2yFrlGdJZ8I/l+4AfHD2emayWe2/3bL0bcRJvoLHQEVz1qIhqSIUsSFBn49UWjsHh7Gx5YU49dbWn86sKRGFMVOeLzXc/Hl57egqpoCN+fOwwA8LPFu7C9xcKMwSX41NQBAIA/r63HsHIdZw4t7ZP3RT1LjpRgyLW/SSqR+IUAXhKdJ99wT+DwFkuK+pvqS77Ms4Xy3E/PG4HbPzAKv75oFABgWLmG78weiok1R18h9vGNTRhapr1ze1uLBU2RcecHR2NTUwqJjIvmpI2NjSkWQAHr/4Hrk5Ki3g0WwGGxBI5ADmk3a4NG7eQkssIytFzH4C4b9iNpSNh4fXcH5o3u9859igSkXQ+e78PxfMgScP/qenzilOrejEy9KH7yOX5k6IQ6Oax/Q3SWfMUSODJb1qKXVMxZYIWqBovOQochScDNz2/HF5/ajGc2NXfrub9bvg+fmTYAsvSf+4aW6yjTVXzxqS2YMbgUezsy8OBjVOWRDylR/lLL+qNq3mctWYt8GABP9jgCVXSAPPe2pIZuqLnsm7/a/YevxOA6ovNQF7ecfxKqoiG0phx86/ntGFKm4eSa2FGf99rudpTrKkZXRrB2f+dBj33+1IHvfP/dF3bgS6cPwp/X1mNbi4UpA+O4YExFj78P6gWKigGXfyshKer3AawRHSefcU/gKCRZuVuNl79UNe+/+Ekiz1RFQwCA8oiKM4eUwGw8tlO/19cnsWx3O6561MRPX96NNfs78fPFuw76maU72zG6QkfK8bCvI4NvnTMUS3a0w3J4dLAQVM37L0stq3pZUkO3iM6S71gCR+fLWvSK+ISZjfFJ7+OpVHnCsj0kbfed71ft68Tw8qOPBQDA1VMH4IHLxuK+jxi4cdZgnDIgjm/MHPLO447n47ENTbhsYn9kHA9S7pDRgbECym/xibP8+ISzm2Qt+nFwbaCj4uGgY9Muh/V5Ved/5vVM/fZYZv9W0XkCr8Vy8MNFOwFkT/WcPaIM02tL8MrOdtz5+l60WS6++8J2nNQvgv95/3A0JW38auke/HDu8KO+9pMbm3DuyHLoqowR/XSkHR/XPvE2pteWIB5Wevmd0YkI9R+CqguuSclh/QIA7aLzFALOE+gG3/cuc5Pt9+3+3ZejXqpDdBwi6kIKRzDkc79KKiX9viDJSuCXiD5WPBzUDZIkPyKH9N/XXPaNJC9CQ5RPJFRfckNK1mOPsAC6h1uybpLD+n9rNcPXVMxZkBGdhYiy+s25MhMZNmETl4XoPpZA9zmyFv1g6bR5LfGTz+GxNCLB4pPm+GXTL2yWtej7wfkA3cYSOD6Nclh/X9UF1yT0oRNEZyEKLH3oBFTN+69OOay/D0CD6DyFiCVw/NbLIe1DAy6/MRWqrBWdhShwQhWDMODyG1NyWP8weJWw48YSODELJTV8/cAF308qsXLRWYgCQ46UYOCC7yUlNfwlAAtF5ylkLIETJCnqPbIWvWXggu8npLAuOg5R0ZNCGgZe8f8Sshb7naSod4nOU+hYAj1ADmnfU0sqHx3wsZuTUDj/jqi3SEoIAz7+7WSoYuCTclj/uug8xYAl0DN8WYt8RqsZvmjAR29MQuasUqIeJ8mouewbSa1m+IuyFvkEAC7k1ANYAj3HkbXoh/XBxtKaS7+e4mQyop4kofpDX07pQ8aukLXopQC4pG8P4ZaqZ2VkLXpxZPjEldWX3JACpKM/g4iOqurCa9LRkVPekrXoBQA4UbMHsQR6niVr0XnRUVPf7P+B6ywWAdGJqZj7yUx8/NlbZC06F0BSdJ5iwxLoHUlZi86NGTM2Vp3/2bToMESFqvL9V2dKp5y3Q9Yis8BVQXsFVxHtXWVeOrUksfHVUQ1P/1aHz3EsomMjoer8z6bjE2dtkbXITADdu34oHTOWQO8r8dLJf6e2rZ1U94//jcDjeBbRe5Jk9L/oC1Zs7IxNshadBaBNdKRixhLoG7qXTj6R3rflrP0P/zjqOxzXIjosWUXNh7+SioyYtEbWoucB4IU7ehlLoO+oXjr1kN2058K9D30/5qc5vkXUlaSGMeDym5LaoFGvyFr0g+CKoH2CJdC3ZC+TutNpb56/9/5vx7wkx7mIAECOlmLgld9NhMqrn5W16JUAbNGZgoJnB/UtTw5HPq+WVv7f4P+6NcnVR4mAUGUtBn/2l8lQxcDbZC16OVgAfYp7AoL4nvsp387cUffozyOpbWtFxyESQh86AQMuvzElhbTrJFn5o+g8QcQSEGuWZ6efbF54X7x95XPcK6NAiZ98jl91/uc65bD+IQAviM4TVCwB8UZ6mdQLHWsX1TT96x6Ncwmo+EnoN+tjdtmMD7TIYX0OgPWiEwUZSyA/lHvp5NPpfVsm73/k51GeOUTFStaiqL70a0m9dszm3CmgdaIzBR1LIH+EvHTqdi+dnL//rz+OZuq2i85D1KPC1cMw4GM3J2U9+oAcjnwRXAguL7AE8ozve1f4duaupn//Ue9Y/TzHCago5I7/p6RQ+LOSJD8kOg/9B0sgP4310qlnk5tX1DQ8fWfEt7kGHRUoWUXVvM+k4xNnNsrhyPkA1omORAdjCeSvmJdO/dFNtl20/+EfR+2mPaLzEHWL2m8Aai77RiJU1v+V3Pn/XAMoD7EE8pvku85/+a7zq6Z/3RPpWLOQFyegglAy+Vyv8v1XW5KsfFtSQ78CwA1NnmIJFIaTvXTq79aeTYManvh11E20is5DdFhytBTVH/xSUh88do+sRT4M4C3Rmei9sQQKR9iz0z+E636x4enbI4mNy0TnITpIZORUVF9yQ0pS1N/KYf0mABzMKgAsgcIzw8ukHkluWV3Z+MydEc9KiM5DASeFI6g891NWfMLZHXJYvwzAy6Iz0bFjCRSmqJdJ/a/v2Asanro9mnx7heg8FFDRMaeh/0XXJiVFfUzWol8AB38LDkugsM3xMqn7rV0b+zU+87uo094gOg8FhFJSgf4XXpvQh4xrkbXIAgAvic5Ex4clUPg0z8ncBM/7Rusrj4Rblz2p8BKW1GskGaXTL/AqZl+ZhiT/Ug6Ffwge+y9oLIHicZJnJe9xrY7pDU/+Jmbt5Jpc1LO0gSPR/+LrE2pZ1UZZiy4AsFF0JjpxLIHiIgG41MtYv09uXhlpWviniNveKDoTFTiltAqV516VjI6alpHU0FclSb4PAJe7LRIsgeJU4tnp7wC4vn3lP5XWJY+EPa5MSt0khSPod9ZH7NJTL3QA/EoOaT8G0Ck6F/UslkBxq/XSyZ8B0qUtix/W2lY8K8PleAEdhSSjZPK5fsX7FliSJD0pa9GvAdgtOhb1DpZAMExwrcRtvmPPaH7+3mjnW0vAWfz0LpKM2LgzUDFnQULR4+tlPfp5AKtEx6LexRIIlnO8dPION9k+tPnFB+OJjcvAK5kRJBnx8Weh35z5nYoW3S7rsf8G8Bz4SSEQWALBIwE437OSv3CtzmEtix6Kd65/hWUQRJKM+ISZqJhzZUIKRzYr2Y3/8+DGP1BYAsElAXi/ayV+4jv22NYlf4t0rHlB8h1e7KnYSWoY8Qkz0W/WxxJSWN+U2/i/AG78A4klQABwpmslfgjgjPYVz4TaV/1LdTuaRWeiHqaUVKBs+gV26bTzHd/3X1f02PcBLAI3/oHGEqCuxnvp1FchK1emtq3x2pY9EbN2cdJZodMGjUb5GR9KRkZOleC7D8jhyC0ANonORfmBJUCHU+p73lW+bX3TTXaUtS79R6xz3WLJty3RuegYSSENsbGno/z0SzrU8uqUpIZ/JsnK3eACb3QIlgC9FwnA+1wr8U1JUWcmNi7zO1YvjGSXpOD/m3ykDRqN0qnnWbHxZ8F37NeUSPx/ATwFwBWdjfITS4COVa3vufP9jHWt7zrVHWteCHWsfTHEax+Lp5b2R/zkc9zSqeelZC3aIamhOyRF/SMA/uPQUbEE6Hic4mWsqwFc5bQ3Ku1v/Due3PiaxKWs+45aXoOYMcOPn3xOR6hikALPfVjWIncBWAbuplE3sAToRCgA3uelk5+BrFzkdjT7nW8tiSY2va5k9m8Vna3ohPoPRXzsGW584sykUlLhw3Mfk7Xon5E9vZPn9tJxYQlQT1EAnOHZ6cvguZf7nluW2LhMTmxcpls714PzD7pPCuvQh45HdMTkTGzcGRlZi1iA9LAc1h8G8Cp4nJ96AEuAeovhe+6HvHTySjmsj8s07EolN6+Kpba/qaT3bGIpHI6sQBs0GtGTTnGjo0/tDPcfHPEy1jo5HH1cUpSnALwBHuqhHsYSoL4QB3Cm52TO9e30RXJIH51p2JlKbl4Zt3ZtlNP7t8BLBW+FYjlaCm3QKOiDRnv6sJM7tUEjdd/J7JSU8BNyKPwcgCUAuAY49SqWAIlQAuAsz86833fS75ND+lgvnXTT+7Z41u6N8fS+LVJ6/1Z4yXbROXuMrMcR7j8U2qBRvj50fEKvHSPJWlTOftLXX5DU0DIAiwE0ic5KwcISoHwgAxgFYJpnZ2b4TnqmHNLHe3bas1v22Zn6nZrduEu3m/fBbtoLu7Ue+XkdZQlqaRVCVbUIVdZCqxmeClcPy4QqBmqSEoJnW9slRV0shyOLAbwO4G3wCl0kGEuA8pUMYCiA0QDGeBlrou9kJkmKOlIKaVVusj3ldjS7TkeT7LQ3ak57U9jtbIXb2QI30Qo32QYvnYJvn+A10CUZshaFrEWyf+oxKPF+UEsqoJZVZ9Ty6nSorL+nxPupsh6L+I7d7juZzZISekPWImsAbED2Wrz7wOP5lIdYAlSIwgBGAKgFMADAQM/JDPbtzAj4Xi1kpUZS1HJJVnTIsuo7tu07tuO7tuc7Gc93bB++D0hydk40JEiSBEiSBEmCJKuSFNYVORQOQVZV37UtuG7S99xO+H47gD2SGtoqh/WtyE7I2p37cy+AE2wdor7FEqBipwIoBRAFEOnyp9/lyzvkzwyya+y0A0iAn+CpiLEEiIgCTBYdgIiIxGEJEBEFGEuAiCjAWAJERAHGEiAiCjCWABFRgLEEiIgCjCVAx8UwjHsNw7j+MPdPNwzjwR7+XcMNw2jsydckoixVdAAqPIZhKEd6zDTNFQDm92EcIjoBLIGAMAzjGgCTTNO8zjCM0wC8BuA00zSXG4ZxB4DVAHYC+AmyVwlrAHCNaZqbDcOYDeDXAFYCmALg24e89hwA/wfgSgBVAG4xTXO6YRjDAawA8DsAFyK7ZMNnTNNcknve9QBuANAK4BkA15mmWZV77DoAX0F26Yanu/wuNXe7EtnlH17P5cwYhrEOwNWmaS7P/exXAYw1TfNzPfO3SFR8eDgoOBYCmJv7fi6ApYfcXgPgfgDzTdOcBOAhAF0P60wA8HvTNCebpvnUgTsNw5gP4OcALjBNc91hfm8lgKWmaU4B8AMAP8s9bxKAmwCcaZrmqQDKu7zmJAA3AzjLNM2pudc4wAVwpWma0wFMRLawPp177DcAvpB7DQnAtQBuP6a/HaKAYgkEhGmamwFEDMMYjOxG/1sA5hqGMQSABqAawBrTNNfnnvJHAJMNwyjJ3X7bNM2lh7zs1chuaOeaprnnCL+6s0tpLAMwMvf9bADPmKbZkLt9T5fnzAbwtGmadbnbv+/ymAzg64ZhrAawFsD7AEzOPXY/gHmGYVQAmAegzjTNNUfIRURgCQTNCwAuBlBjmuYiAAMBXJS7/2gOd/3HNcgu5TzuPZ7XdWllFyd+CPJKAGcDmGma5skA7gCgA4Bpmglk92CuBnAduBdAdFQsgWBZCOBGAK/kbr+Su70Q2U/ppxiGMTb32FUA3jBNs+M9Xm8VgEsBPGgYxjndzPISgAsMw6jq8vsOWATgQsMwqnO3P9PlsXIAjaZpdhiGUYZsKXR1O4AvA5gG4NFuZiIKHJZAsLwAYBiyG33k/hwG4IXcYZlPAHjIMIy1ABbkvt6TaZprkd27uMswjHnHGiR3mObnAJYahrESgIPsGv4HXvPHAF7JPdba5al/AlBiGMZGAE8ie13erq+7Ddkred1jmmbmWPMQBRWvJ0DCGIZRcmBPwzCM7wEYZZrmUYvnKK9ZimwJnPoe4xRElMNTREmknxqGcRayl4vcCuCETuU0DOPzyJ6+eisLgOjYcE+AiCjAOCZARBRgLAEiogBjCRARBRhLgIgowFgCREQBxhIgIgowlgARUYCxBIiIAowlQEQUYCwBIqIAYwkQEQUYS4CIKMBYAkREAcYSICIKMJYAEVGAsQSIiAKMJUBEFGAsASKiAGMJEBEFGEuAiCjAWAJERAHGEiAiCjCWABFRgLEEiIgC7P8DjTFDCXioFDsAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x1080 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x1080 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x1080 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EH5ZYekeDwd7"
      },
      "source": [
        "# **Outlier detection**"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_daily.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "qVJtxruB7sbp",
        "outputId": "e9f7a3f3-f147-4386-a5e9-bca7f8f4fe59"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   season  yr  mnth  holiday    weekday  workingday  weathersit      temp  \\\n",
              "0       1   0     1        0   Saturday           0           2  0.344167   \n",
              "1       1   0     1        0     Sunday           0           2  0.363478   \n",
              "2       1   0     1        0     Monday           1           1  0.196364   \n",
              "3       1   0     1        0    Tuesday           1           1  0.200000   \n",
              "4       1   0     1        0  Wednesday           1           1  0.226957   \n",
              "\n",
              "      atemp       hum  windspeed  casual  registered   cnt  year  month  day  \n",
              "0  0.363625  0.805833   0.160446     331         654   985  2011      1    1  \n",
              "1  0.353739  0.696087   0.248539     131         670   801  2011      1    2  \n",
              "2  0.189405  0.437273   0.248309     120        1229  1349  2011      1    3  \n",
              "3  0.212122  0.590435   0.160296     108        1454  1562  2011      1    4  \n",
              "4  0.229270  0.436957   0.186900      82        1518  1600  2011      1    5  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-36ed94f4-8f12-4cfc-916b-bf2141e56c7d\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>season</th>\n",
              "      <th>yr</th>\n",
              "      <th>mnth</th>\n",
              "      <th>holiday</th>\n",
              "      <th>weekday</th>\n",
              "      <th>workingday</th>\n",
              "      <th>weathersit</th>\n",
              "      <th>temp</th>\n",
              "      <th>atemp</th>\n",
              "      <th>hum</th>\n",
              "      <th>windspeed</th>\n",
              "      <th>casual</th>\n",
              "      <th>registered</th>\n",
              "      <th>cnt</th>\n",
              "      <th>year</th>\n",
              "      <th>month</th>\n",
              "      <th>day</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Saturday</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0.344167</td>\n",
              "      <td>0.363625</td>\n",
              "      <td>0.805833</td>\n",
              "      <td>0.160446</td>\n",
              "      <td>331</td>\n",
              "      <td>654</td>\n",
              "      <td>985</td>\n",
              "      <td>2011</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0.363478</td>\n",
              "      <td>0.353739</td>\n",
              "      <td>0.696087</td>\n",
              "      <td>0.248539</td>\n",
              "      <td>131</td>\n",
              "      <td>670</td>\n",
              "      <td>801</td>\n",
              "      <td>2011</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Monday</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.196364</td>\n",
              "      <td>0.189405</td>\n",
              "      <td>0.437273</td>\n",
              "      <td>0.248309</td>\n",
              "      <td>120</td>\n",
              "      <td>1229</td>\n",
              "      <td>1349</td>\n",
              "      <td>2011</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.200000</td>\n",
              "      <td>0.212122</td>\n",
              "      <td>0.590435</td>\n",
              "      <td>0.160296</td>\n",
              "      <td>108</td>\n",
              "      <td>1454</td>\n",
              "      <td>1562</td>\n",
              "      <td>2011</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Wednesday</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.226957</td>\n",
              "      <td>0.229270</td>\n",
              "      <td>0.436957</td>\n",
              "      <td>0.186900</td>\n",
              "      <td>82</td>\n",
              "      <td>1518</td>\n",
              "      <td>1600</td>\n",
              "      <td>2011</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-36ed94f4-8f12-4cfc-916b-bf2141e56c7d')\"\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-36ed94f4-8f12-4cfc-916b-bf2141e56c7d 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-36ed94f4-8f12-4cfc-916b-bf2141e56c7d');\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": 127
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, axes = plt.subplots(nrows=2,ncols=4)\n",
        "fig.set_size_inches(25, 10)\n",
        "sns.boxplot(data=df_daily,y=\"cnt\",orient=\"v\",ax=axes[0][0])\n",
        "sns.boxplot(data=df_daily,y=\"cnt\",x=\"season\",orient=\"v\",ax=axes[0][1])\n",
        "sns.boxplot(data=df_daily,y=\"cnt\",x=\"workingday\",orient=\"v\",ax=axes[0][2])\n",
        "sns.boxplot(data=df_daily,y=\"cnt\",x=\"holiday\",orient=\"v\",ax=axes[0][3])\n",
        "sns.boxplot(data=df_daily,y=\"cnt\",x=\"weekday\",orient=\"v\",ax=axes[1][0])\n",
        "sns.boxplot(data=df_daily,y=\"cnt\",x=\"weathersit\",orient=\"v\",ax=axes[1][1])\n",
        "sns.boxplot(data=df_daily,y=\"cnt\",x=\"year\",orient=\"v\",ax=axes[1][2])\n",
        "sns.boxplot(data=df_daily,y=\"cnt\",x=\"month\",orient=\"v\",ax=axes[1][3])\n",
        "\n",
        "axes[0][0].set(ylabel='Count',title=\"Box Plot On Count\")\n",
        "axes[0][1].set(xlabel='Season', ylabel='Count',title=\"Box Plot On Count Across Season\")\n",
        "axes[0][2].set(xlabel='Working Day', ylabel='Count',title=\"Box Plot On Count Across Working Day\")\n",
        "axes[0][3].set(ylabel='Count',title=\"Box Plot On holiday\")\n",
        "axes[1][0].set(xlabel='Season', ylabel='Count',title=\"Box Plot On Count Across weekday\")\n",
        "axes[1][1].set(xlabel='Working Day', ylabel='Count',title=\"Box Plot On Count Across weathersit\")\n",
        "axes[1][2].set(xlabel='Season', ylabel='Count',title=\"Box Plot On Count Across year\")\n",
        "axes[1][3].set(xlabel='Working Day', ylabel='Count',title=\"Box Plot On Count Across month\")\n",
        "\n",
        "plt.subplots_adjust(bottom=2,top=3)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 802
        },
        "id": "9aGkAIvoF44t",
        "outputId": "998b17fa-7046-471b-e193-89dba6ce3122"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x720 with 8 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bcSK5SK0rfYt"
      },
      "outputs": [],
      "source": [
        "trainWithtoutOutlier = df_daily[np.abs(df_daily[\"year\"]-df_daily[\"cnt\"].mean()) <=(1.5*df_daily[\"cnt\"].std())]\n",
        "trainWithtoutOutlier = df_daily[np.abs(df_daily[\"season\"]-df_daily[\"season\"].mean()) <=(1.5*df_daily[\"season\"].std())]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_bd3eq53rqzy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "267c8700-6b6c-4984-e275-004541b2845d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape Of The Before Ouliers:  (731, 17)\n",
            "Shape Of The After Ouliers:  (731, 17)\n"
          ]
        }
      ],
      "source": [
        "print (\"Shape Of The Before Ouliers: \",df_daily.shape)\n",
        "print (\"Shape Of The After Ouliers: \",trainWithtoutOutlier.shape)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#There are not any outliers"
      ],
      "metadata": {
        "id": "bkwM1JM3Gg9q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QVX_b1xfr2C9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 924
        },
        "outputId": "d31ba425-2f60-47b1-b0d5-2afdae09a3b8"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2160x864 with 8 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "fig, axes = plt.subplots(nrows=2,ncols=4)\n",
        "fig.set_size_inches(30, 12)\n",
        "sns.boxplot(data=trainWithtoutOutlier,y=\"cnt\",orient=\"v\",ax=axes[0][0])\n",
        "sns.boxplot(data=trainWithtoutOutlier,y=\"cnt\",x=\"season\",orient=\"v\",ax=axes[0][1])\n",
        "sns.boxplot(data=trainWithtoutOutlier,y=\"cnt\",x=\"workingday\",orient=\"v\",ax=axes[0][2])\n",
        "sns.boxplot(data=trainWithtoutOutlier,y=\"cnt\",x=\"holiday\",orient=\"v\",ax=axes[0][3])\n",
        "sns.boxplot(data=trainWithtoutOutlier,y=\"cnt\",x=\"weekday\",orient=\"v\",ax=axes[1][0])\n",
        "sns.boxplot(data=trainWithtoutOutlier,y=\"cnt\",x=\"weathersit\",orient=\"v\",ax=axes[1][1])\n",
        "sns.boxplot(data=trainWithtoutOutlier,y=\"cnt\",x=\"year\",orient=\"v\",ax=axes[1][2])\n",
        "sns.boxplot(data=trainWithtoutOutlier,y=\"cnt\",x=\"month\",orient=\"v\",ax=axes[1][3])\n",
        "\n",
        "axes[0][0].set(ylabel='Count',title=\"Box Plot On Count\")\n",
        "axes[0][1].set(xlabel='Season', ylabel='Count',title=\"Box Plot On Count Across Season\")\n",
        "axes[0][2].set(xlabel='Working Day', ylabel='Count',title=\"Box Plot On Count Across Working Day\")\n",
        "axes[0][3].set(ylabel='Count',title=\"Box Plot On holiday\")\n",
        "axes[1][0].set(xlabel='Season', ylabel='Count',title=\"Box Plot On Count Across weekday\")\n",
        "axes[1][1].set(xlabel='Working Day', ylabel='Count',title=\"Box Plot On Count Across weathersit\")\n",
        "axes[1][2].set(xlabel='Season', ylabel='Count',title=\"Box Plot On Count Across year\")\n",
        "axes[1][3].set(xlabel='Working Day', ylabel='Count',title=\"Box Plot On Count Across month\")\n",
        "\n",
        "plt.subplots_adjust(bottom=2,top=3)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ylm4ivevsuGW"
      },
      "source": [
        "# *Correlation Analysis*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wUjbGGINsFb2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 552
        },
        "outputId": "034ce9b5-4d0d-4195-9c4e-56fc07a268e2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              season        yr      mnth   holiday  workingday  weathersit  \\\n",
              "season      1.000000 -0.001844  0.831440 -0.010537    0.012485    0.019211   \n",
              "yr         -0.001844  1.000000 -0.001792  0.007954   -0.002013   -0.048727   \n",
              "mnth        0.831440 -0.001792  1.000000  0.019191   -0.005901    0.043528   \n",
              "holiday    -0.010537  0.007954  0.019191  1.000000   -0.253023   -0.034627   \n",
              "workingday  0.012485 -0.002013 -0.005901 -0.253023    1.000000    0.061200   \n",
              "weathersit  0.019211 -0.048727  0.043528 -0.034627    0.061200    1.000000   \n",
              "temp        0.334315  0.047604  0.220205 -0.028556    0.052660   -0.120602   \n",
              "atemp       0.342876  0.046106  0.227459 -0.032507    0.052182   -0.121583   \n",
              "hum         0.205445 -0.110651  0.222204 -0.015937    0.024327    0.591045   \n",
              "windspeed  -0.229046 -0.011817 -0.207502  0.006292   -0.018796    0.039511   \n",
              "casual      0.210399  0.248546  0.123006  0.054274   -0.518044   -0.247353   \n",
              "registered  0.411623  0.594248  0.293488 -0.108745    0.303907   -0.260388   \n",
              "cnt         0.406100  0.566710  0.279977 -0.068348    0.061156   -0.297391   \n",
              "year       -0.001844  1.000000 -0.001792  0.007954   -0.002013   -0.048727   \n",
              "month       0.831440 -0.001792  1.000000  0.019191   -0.005901    0.043528   \n",
              "day        -0.000861  0.002061  0.009157 -0.010710    0.009907   -0.023824   \n",
              "\n",
              "                temp     atemp       hum  windspeed    casual  registered  \\\n",
              "season      0.334315  0.342876  0.205445  -0.229046  0.210399    0.411623   \n",
              "yr          0.047604  0.046106 -0.110651  -0.011817  0.248546    0.594248   \n",
              "mnth        0.220205  0.227459  0.222204  -0.207502  0.123006    0.293488   \n",
              "holiday    -0.028556 -0.032507 -0.015937   0.006292  0.054274   -0.108745   \n",
              "workingday  0.052660  0.052182  0.024327  -0.018796 -0.518044    0.303907   \n",
              "weathersit -0.120602 -0.121583  0.591045   0.039511 -0.247353   -0.260388   \n",
              "temp        1.000000  0.991702  0.126963  -0.157944  0.543285    0.540012   \n",
              "atemp       0.991702  1.000000  0.139988  -0.183643  0.543864    0.544192   \n",
              "hum         0.126963  0.139988  1.000000  -0.248489 -0.077008   -0.091089   \n",
              "windspeed  -0.157944 -0.183643 -0.248489   1.000000 -0.167613   -0.217449   \n",
              "casual      0.543285  0.543864 -0.077008  -0.167613  1.000000    0.395282   \n",
              "registered  0.540012  0.544192 -0.091089  -0.217449  0.395282    1.000000   \n",
              "cnt         0.627494  0.631066 -0.100659  -0.234545  0.672804    0.945517   \n",
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              "\n",
              "                 cnt      year     month       day  \n",
              "season      0.406100 -0.001844  0.831440 -0.000861  \n",
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              "                                                     [key], {});\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 133
        }
      ],
      "source": [
        "df_daily.corr()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2LzeEbfWG4uh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 501
        },
        "outputId": "6135bcb6-717a-468f-a0bc-a5c00407645f"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x576 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "#Create the correlation matrix\n",
        "correMtr=df_daily[[\"temp\",\"atemp\",\"hum\",\"windspeed\",\"casual\",\"registered\",\"cnt\"]].corr()\n",
        "mask=np.array(correMtr)\n",
        "mask[np.tril_indices_from(mask)]=False\n",
        "#Heat map for correlation matrix of attributes\n",
        "fig,ax=plt.subplots(figsize=(15,8))\n",
        "sns.heatmap(correMtr,mask=mask,vmax=0.8,square=True,annot=True,ax=ax)\n",
        "ax.set_title('Correlation matrix of attributes')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2Q_5jrYouGuZ"
      },
      "outputs": [],
      "source": [
        "# Dropping season column as it is highly correlated to‘mnth’ column\n",
        "df_daily.drop('season',axis=1,inplace=True) \n",
        "# Dropping atemp column as it is highly correlated to ‘temp’ column\n",
        "df_daily.drop('atemp',axis=1,inplace=True) \n",
        "#Dropping year and month as they are present twice\n",
        "df_daily.drop('year',axis=1,inplace=True) \n",
        "df_daily.drop('month',axis=1,inplace=True) "
      ]
    },
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              "   yr  mnth  holiday    weekday  workingday  weathersit      temp       hum  \\\n",
              "0   0     1        0   Saturday           0           2  0.344167  0.805833   \n",
              "1   0     1        0     Sunday           0           2  0.363478  0.696087   \n",
              "2   0     1        0     Monday           1           1  0.196364  0.437273   \n",
              "3   0     1        0    Tuesday           1           1  0.200000  0.590435   \n",
              "4   0     1        0  Wednesday           1           1  0.226957  0.436957   \n",
              "\n",
              "   windspeed  casual  registered   cnt  day  \n",
              "0   0.160446     331         654   985    1  \n",
              "1   0.248539     131         670   801    2  \n",
              "2   0.248309     120        1229  1349    3  \n",
              "3   0.160296     108        1454  1562    4  \n",
              "4   0.186900      82        1518  1600    5  "
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          "metadata": {},
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      ],
      "source": [
        "df_daily.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LGJ9PkrdwVYE"
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      "source": [
        "#Replacing categorical variables with actual values in weathersit\n",
        "df_daily['weathersit'] = df_daily['weathersit'].replace({1:'clear',2:'mist',3:'snow',4:'rain'})\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "s1rkUA6IvChy"
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      "source": [
        "# One hot encoding on categorical columns.\n",
        "df_daily=pd.get_dummies(df_daily,columns=['weathersit','weekday'],drop_first=True)"
      ]
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              "   yr  mnth  holiday  workingday      temp       hum  windspeed  casual  \\\n",
              "0   0     1        0           0  0.344167  0.805833   0.160446     331   \n",
              "1   0     1        0           0  0.363478  0.696087   0.248539     131   \n",
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              "\n",
              "   registered   cnt  day  weathersit_springer  weathersit_summer  \\\n",
              "0         654   985    1                    0                  1   \n",
              "1         670   801    2                    0                  1   \n",
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              "4        1518  1600    5                    1                  0   \n",
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              "   weekday_Monday  weekday_Saturday  weekday_Sunday  weekday_Thursday  \\\n",
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              "2               1                 0               0                 0   \n",
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              "4               0                 0               0                 0   \n",
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              "   weekday_Tuesday  weekday_Wednesday  \n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      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-faba085b-09cd-4576-8944-2799bc9b4468 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-faba085b-09cd-4576-8944-2799bc9b4468');\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": 139
        }
      ],
      "source": [
        "df_daily.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4EJ31P91_SGX"
      },
      "source": [
        "We have segregated the data based on ‘workingday’ column as the demand changes significantly on a working day when compared to a non-working day.\n",
        "We have built two models one to predict ‘casual’ and the other to predict ‘registered’. The predicted ‘count’ is the sum of predicted ‘casual’ and predicted ‘registered’. Here, We are only showing the code to predict ‘registered’ on a working day. Same techniques can be applied to predict ‘casual’ on a working day and ‘casual’, ‘registered’ on a non-working day. You can check the whole code in the end.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Building model for registered bike rentals"
      ],
      "metadata": {
        "id": "ZrQQUhKGFduW"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pC_kOJYt_Lnj"
      },
      "outputs": [],
      "source": [
        "# Segregating data based on ‘workingday’ column\n",
        "work_day = df_daily[df_daily['workingday']==1] \n",
        "non_work_day = df_daily[df_daily['workingday']==0]"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Model for working days**"
      ],
      "metadata": {
        "id": "pRP7jP66i_46"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Model for registered\n",
        "x = work_day.drop(['casual','registered','cnt','workingday'],axis = 1)\n",
        "y = work_day.registered\n",
        "# Dividing the data into train and test\n",
        "from sklearn.model_selection import train_test_split\n",
        "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size = 0.30 ,random_state = 2)"
      ],
      "metadata": {
        "id": "yYNuqgPsiT8M"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8LfbHgm0_6XJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "93790d91-2041-4f7a-bca2-a40b118707d2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MSE for the data: 526682.5021514217\n",
            "r2_score for the data 0.7269987856902445\n"
          ]
        }
      ],
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "model1=LinearRegression()\n",
        "result = model1.fit(x_train,y_train)\n",
        "y_pred = result.predict(x_test)\n",
        "\n",
        "from sklearn.metrics import mean_squared_error\n",
        "mse=mean_squared_error(y_pred,y_test)\n",
        "print('MSE for the data:',mse)\n",
        "from sklearn.metrics import r2_score\n",
        "r2 = r2_score(y_pred,y_test)\n",
        "print('r2_score for the data',r2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ebDNTj-E_-2V"
      },
      "outputs": [],
      "source": [
        "# Finding best parameters for decision tree\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "dt = DecisionTreeRegressor(random_state=0)\n",
        "dt_params = {'max_depth':np.arange(1,50,1),'min_samples_leaf':np.arange(2,15)}\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "gs_dt = GridSearchCV(dt,dt_params,cv=10)\n",
        "gs_dt.fit(x_train,y_train)\n",
        "a = gs_dt.best_params_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "e7HVc4kkC90h",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8905a7d3-6a1b-4e9b-9438-e50b920f9049"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MSE for the data: 524849.3332481482\n",
            "r2_score for the data 0.7789279082224284\n"
          ]
        }
      ],
      "source": [
        "# Training with best parameters\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "dtr=DecisionTreeRegressor(max_depth=a['max_depth'],min_samples_leaf= a['min_samples_leaf'])\n",
        "model = dtr.fit(x_train,y_train)\n",
        "y_pred = model.predict(x_test)\n",
        "mse=mean_squared_error(y_pred,y_test)\n",
        "print('MSE for the data:',mse)\n",
        "r2 = r2_score(y_pred,y_test)\n",
        "print('r2_score for the data',r2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "w8FIFEywE1sC"
      },
      "outputs": [],
      "source": [
        "# Finding best parameters for RandomForestRegressor\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "rf = RandomForestRegressor(random_state=0)\n",
        "rf_params = {'n_estimators':np.arange(25,150,25),'max_depth':np.arange(1,11,2),'min_samples_leaf':np.arange(2,15,3)}\n",
        "\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "gs_rf = GridSearchCV(rf,rf_params,cv=10)\n",
        "gs_rf.fit(x_train,y_train)\n",
        "b = gs_rf.best_params_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "O6AV8DwMFair",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "69b8e5c4-4a04-4dc0-da7b-287db2853fdc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MSE for the data: 390773.1989712338\n",
            "r2_score for the data 0.8176904220786967\n"
          ]
        }
      ],
      "source": [
        "# Fitting the model with best params\n",
        "RF = RandomForestRegressor(n_estimators=b['n_estimators'],max_depth=b['max_depth'],min_samples_leaf=b['min_samples_leaf'],random_state=0)\n",
        "model = RF.fit(x_train,y_train)\n",
        "y_pred = model.predict(x_test)\n",
        "mse=mean_squared_error(y_pred,y_test)\n",
        "print('MSE for the data:',mse)\n",
        "r2 = r2_score(y_pred,y_test)\n",
        "print('r2_score for the data',r2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "j-mk0T7dFh3l"
      },
      "outputs": [],
      "source": [
        "# Finding best parameters for AdaBoostRegressor\n",
        "from sklearn.ensemble import AdaBoostRegressor\n",
        "ar = AdaBoostRegressor(base_estimator=RF,random_state=0)\n",
        "ar_params = {'n_estimators':np.arange(25,100,25)}\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "gs_ar = GridSearchCV(ar,ar_params,cv=5)\n",
        "gs_ar.fit(x_train,y_train)\n",
        "c = gs_ar.best_params_\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lCL1jBG7F4sj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "38809308-acc3-4cbb-f9de-2b2ec0ac8537"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MSE for the data: 383203.2035494365\n",
            "r2_score for the data 0.8184584802180302\n"
          ]
        }
      ],
      "source": [
        "# Fitting the model with best params\n",
        "ab_rf = AdaBoostRegressor(base_estimator=RF,n_estimators=c['n_estimators'],random_state=0)\n",
        "model = ab_rf.fit(x_train,y_train)\n",
        "y_pred = model.predict(x_test)\n",
        "mse=mean_squared_error(y_pred,y_test)\n",
        "print('MSE for the data:',mse)\n",
        "r2 = r2_score(y_pred,y_test)\n",
        "print('r2_score for the data',r2)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#We got best r2_score and least mean squared error with AdaBoostRegressor model \n",
        "#We will select it if we are deploying it in production."
      ],
      "metadata": {
        "id": "SK7okrF9LeMA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Model for Non-working days**"
      ],
      "metadata": {
        "id": "k09GfBNGj7uW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Model for registered\n",
        "x = non_work_day.drop(['casual','registered','cnt','workingday'],axis = 1)\n",
        "y = non_work_day.registered\n",
        "# Dividing the data into train and test\n",
        "from sklearn.model_selection import train_test_split\n",
        "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size = 0.30 ,random_state = 2)"
      ],
      "metadata": {
        "id": "FACAHW_vi9Bz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "model1=LinearRegression()\n",
        "result = model1.fit(x_train,y_train)\n",
        "y_pred = result.predict(x_test)\n",
        "\n",
        "from sklearn.metrics import mean_squared_error\n",
        "mse=mean_squared_error(y_pred,y_test)\n",
        "print('MSE for the data:',mse)\n",
        "from sklearn.metrics import r2_score\n",
        "r2 = r2_score(y_pred,y_test)\n",
        "print('r2_score for the data',r2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wTRv4TbIwrmk",
        "outputId": "c6edab0d-5629-49ab-ed07-96360f84c569"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MSE for the data: 522103.7914015562\n",
            "r2_score for the data 0.5752410756446696\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Finding best parameters for decision tree\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "dt = DecisionTreeRegressor(random_state=0)\n",
        "dt_params = {'max_depth':np.arange(1,50,1),'min_samples_leaf':np.arange(2,15)}\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "gs_dt = GridSearchCV(dt,dt_params,cv=10)\n",
        "gs_dt.fit(x_train,y_train)\n",
        "a = gs_dt.best_params_"
      ],
      "metadata": {
        "id": "2BcNBE2Okgvi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Training with best parameters\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "dtr=DecisionTreeRegressor(max_depth=a['max_depth'],min_samples_leaf= a['min_samples_leaf'])\n",
        "model = dtr.fit(x_train,y_train)\n",
        "y_pred = model.predict(x_test)\n",
        "mse=mean_squared_error(y_pred,y_test)\n",
        "print('MSE for the data:',mse)\n",
        "r2 = r2_score(y_pred,y_test)\n",
        "print('r2_score for the data',r2)"
      ],
      "metadata": {
        "id": "yZtg2-UoktsU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fb22d919-2431-4d0c-dd3d-644668a7d3b9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MSE for the data: 642302.231948495\n",
            "r2_score for the data 0.48588217139634626\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Finding best parameters for RandomForestRegressor\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "rf = RandomForestRegressor(random_state=0)\n",
        "rf_params = {'n_estimators':np.arange(25,150,25),'max_depth':np.arange(1,11,2),'min_samples_leaf':np.arange(2,15,3)}\n",
        "\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "gs_rf = GridSearchCV(rf,rf_params,cv=10)\n",
        "gs_rf.fit(x_train,y_train)\n",
        "b = gs_rf.best_params_"
      ],
      "metadata": {
        "id": "Qcg0YF3Rk3ny"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Fitting the model with best params\n",
        "RF = RandomForestRegressor(n_estimators=b['n_estimators'],max_depth=b['max_depth'],min_samples_leaf=b['min_samples_leaf'],random_state=0)\n",
        "model = RF.fit(x_train,y_train)\n",
        "y_pred = model.predict(x_test)\n",
        "mse=mean_squared_error(y_pred,y_test)\n",
        "print('MSE for the data:',mse)\n",
        "r2 = r2_score(y_pred,y_test)\n",
        "print('r2_score for the data',r2)"
      ],
      "metadata": {
        "id": "cP0U4TUik84Z",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f80c8cd1-6b5a-4270-dd76-dce451416b4b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MSE for the data: 311973.24900839065\n",
            "r2_score for the data 0.7258216884136692\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Finding best parameters for AdaBoostRegressor\n",
        "from sklearn.ensemble import AdaBoostRegressor\n",
        "ar = AdaBoostRegressor(base_estimator=RF,random_state=0)\n",
        "ar_params = {'n_estimators':np.arange(25,100,25)}\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "gs_ar = GridSearchCV(ar,ar_params,cv=5)\n",
        "gs_ar.fit(x_train,y_train)\n",
        "c = gs_ar.best_params_"
      ],
      "metadata": {
        "id": "bRM2-Ku-lQeF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Fitting the model with best params\n",
        "ab_rf = AdaBoostRegressor(base_estimator=RF,n_estimators=c['n_estimators'],random_state=0)\n",
        "model = ab_rf.fit(x_train,y_train)\n",
        "y_pred = model.predict(x_test)\n",
        "mse=mean_squared_error(y_pred,y_test)\n",
        "print('MSE for the data:',mse)\n",
        "r2 = r2_score(y_pred,y_test)\n",
        "print('r2_score for the data',r2)"
      ],
      "metadata": {
        "id": "c9VVf-hBpkkP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "03448061-2ca8-4c21-9607-a4b27dcd13f1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MSE for the data: 299575.12113376\n",
            "r2_score for the data 0.719147220517912\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Building model for casual bike rentals"
      ],
      "metadata": {
        "id": "g1FXoUvlLOXo"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sRHc8b8X1IZ1"
      },
      "outputs": [],
      "source": [
        "# Segregating data based on ‘workingday’ column\n",
        "work_day = df_daily[df_daily['workingday']==1] \n",
        "non_work_day = df_daily[df_daily['workingday']==0]"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Model for working days**"
      ],
      "metadata": {
        "id": "evT4yuN4r7-S"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x = work_day.drop(['casual','registered','cnt','workingday'],axis = 1)\n",
        "y = work_day.casual\n",
        "# Dividing the data into train and test\n",
        "from sklearn.model_selection import train_test_split\n",
        "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size = 0.30 ,random_state = 2)"
      ],
      "metadata": {
        "id": "fpQWxWzJrFwf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "A1nqkhmW1axj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "886226ec-1cb2-4e94-d8b0-16f5ea245d57"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MSE for the data: 42942.58157980533\n",
            "r2_score for the data 0.5933670414985597\n"
          ]
        }
      ],
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "model1=LinearRegression()\n",
        "result = model1.fit(x_train,y_train)\n",
        "y_pred = result.predict(x_test)\n",
        "from sklearn.metrics import mean_squared_error\n",
        "mse=mean_squared_error(y_pred,y_test)\n",
        "print('MSE for the data:',mse)\n",
        "from sklearn.metrics import r2_score\n",
        "r2 = r2_score(y_pred,y_test)\n",
        "print('r2_score for the data',r2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jyw5yjHB3qmw"
      },
      "outputs": [],
      "source": [
        "# Finding best parameters for decision tree\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "dt = DecisionTreeRegressor(random_state=0)\n",
        "dt_params = {'max_depth':np.arange(1,50,1),'min_samples_leaf':np.arange(2,15)}\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "gs_dt = GridSearchCV(dt,dt_params,cv=10)\n",
        "gs_dt.fit(x_train,y_train)\n",
        "a = gs_dt.best_params_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iptqLtgq3yPS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6f5ab6e2-cc0c-4bc1-c6ef-829c43545fe6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MSE for the data: 57563.74079981994\n",
            "r2_score for the data 0.5447970448884745\n"
          ]
        }
      ],
      "source": [
        "# Training with best parameters\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "dtr=DecisionTreeRegressor(max_depth=a['max_depth'],min_samples_leaf= a['min_samples_leaf'])\n",
        "model = dtr.fit(x_train,y_train)\n",
        "y_pred = model.predict(x_test)\n",
        "mse=mean_squared_error(y_pred,y_test)\n",
        "print('MSE for the data:',mse)\n",
        "r2 = r2_score(y_pred,y_test)\n",
        "print('r2_score for the data',r2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "noM5ycRQ35DB"
      },
      "outputs": [],
      "source": [
        "# Finding best parameters for RandomForestRegressor\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "rf = RandomForestRegressor(random_state=0)\n",
        "rf_params = {'n_estimators':np.arange(25,150,25),'max_depth':np.arange(1,11,2),'min_samples_leaf':np.arange(2,15,3)}\n",
        "\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "gs_rf = GridSearchCV(rf,rf_params,cv=10)\n",
        "gs_rf.fit(x_train,y_train)\n",
        "b = gs_rf.best_params_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HL24xVxk3-hY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "db10ac9c-dca7-4423-8a2c-96d8e1089a81"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MSE for the data: 43918.39289579461\n",
            "r2_score for the data 0.6276223635699258\n"
          ]
        }
      ],
      "source": [
        "# Fitting the model with best params\n",
        "RF = RandomForestRegressor(n_estimators=b['n_estimators'],max_depth=b['max_depth'],min_samples_leaf=b['min_samples_leaf'],random_state=0)\n",
        "model = RF.fit(x_train,y_train)\n",
        "y_pred = model.predict(x_test)\n",
        "mse=mean_squared_error(y_pred,y_test)\n",
        "print('MSE for the data:',mse)\n",
        "r2 = r2_score(y_pred,y_test)\n",
        "print('r2_score for the data',r2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mhD4RU054Fkf"
      },
      "outputs": [],
      "source": [
        "# Finding best parameters for AdaBoostRegressor\n",
        "from sklearn.ensemble import AdaBoostRegressor\n",
        "ar = AdaBoostRegressor(base_estimator=RF,random_state=0)\n",
        "ar_params = {'n_estimators':np.arange(25,100,25)}\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "gs_ar = GridSearchCV(ar,ar_params,cv=5)\n",
        "gs_ar.fit(x_train,y_train)\n",
        "c = gs_ar.best_params_"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Fitting the model with best params\n",
        "ab_rf = AdaBoostRegressor(base_estimator=RF,n_estimators=c['n_estimators'],random_state=0)\n",
        "model = ab_rf.fit(x_train,y_train)\n",
        "y_pred = model.predict(x_test)\n",
        "mse=mean_squared_error(y_pred,y_test)\n",
        "print('MSE for the data:',mse)\n",
        "r2 = r2_score(y_pred,y_test)\n",
        "print('r2_score for the data',r2)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CyGsKM1aN3-T",
        "outputId": "4cf8cbdd-b71b-4386-c1f3-ef3002e9734e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MSE for the data: 41621.95893782781\n",
            "r2_score for the data 0.6292062784675707\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#In this case also AdaBoostRegressor is giving the best output for casual bike rentals\n",
        "#We will use this model if we are deploying it in production\n",
        "#Note :In the above case the r2_score we got is only 0.63 \n",
        "#Since casual users are more random than the registered the model is not that great at predicting \n",
        "#Hence in this case 0.63 can be considered as a good r2_score"
      ],
      "metadata": {
        "id": "57GmGLrBOiMn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "  **Model for Non-working days**"
      ],
      "metadata": {
        "id": "JT5ISkrRsHo7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x = non_work_day.drop(['casual','registered','cnt','workingday'],axis = 1)\n",
        "y = non_work_day.casual\n",
        "# Dividing the data into train and test\n",
        "from sklearn.model_selection import train_test_split\n",
        "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size = 0.30 ,random_state = 2)"
      ],
      "metadata": {
        "id": "gYI8gPyhsGRG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "model1=LinearRegression()\n",
        "result = model1.fit(x_train,y_train)\n",
        "y_pred = result.predict(x_test)\n",
        "from sklearn.metrics import mean_squared_error\n",
        "mse=mean_squared_error(y_pred,y_test)\n",
        "print('MSE for the data:',mse)\n",
        "from sklearn.metrics import r2_score\n",
        "r2 = r2_score(y_pred,y_test)\n",
        "print('r2_score for the data',r2)"
      ],
      "metadata": {
        "id": "uPc48f_tslNo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9fdf2cb0-2ea8-4a3e-8117-7ff697c79b4e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MSE for the data: 245165.23756501125\n",
            "r2_score for the data 0.5634671086080872\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Finding best parameters for decision tree\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "dt = DecisionTreeRegressor(random_state=0)\n",
        "dt_params = {'max_depth':np.arange(1,50,1),'min_samples_leaf':np.arange(2,15)}\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "gs_dt = GridSearchCV(dt,dt_params,cv=10)\n",
        "gs_dt.fit(x_train,y_train)\n",
        "a = gs_dt.best_params_"
      ],
      "metadata": {
        "id": "fyMUZ3swsroT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Training with best parameters\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "dtr=DecisionTreeRegressor(max_depth=a['max_depth'],min_samples_leaf= a['min_samples_leaf'])\n",
        "model = dtr.fit(x_train,y_train)\n",
        "y_pred = model.predict(x_test)\n",
        "mse=mean_squared_error(y_pred,y_test)\n",
        "print('MSE for the data:',mse)\n",
        "r2 = r2_score(y_pred,y_test)\n",
        "print('r2_score for the data',r2)"
      ],
      "metadata": {
        "id": "FFgnDVyfsxcf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f306f3fd-7a11-44e9-ee9f-08f952c250f0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MSE for the data: 191122.1190290945\n",
            "r2_score for the data 0.7238177993533758\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Finding best parameters for RandomForestRegressor\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "rf = RandomForestRegressor(random_state=0)\n",
        "rf_params = {'n_estimators':np.arange(25,150,25),'max_depth':np.arange(1,11,2),'min_samples_leaf':np.arange(2,15,3)}\n",
        "\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "gs_rf = GridSearchCV(rf,rf_params,cv=10)\n",
        "gs_rf.fit(x_train,y_train)\n",
        "b = gs_rf.best_params_"
      ],
      "metadata": {
        "id": "a6eNC9k1s2T9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Fitting the model with best params\n",
        "RF = RandomForestRegressor(n_estimators=b['n_estimators'],max_depth=b['max_depth'],min_samples_leaf=b['min_samples_leaf'],random_state=0)\n",
        "model = RF.fit(x_train,y_train)\n",
        "y_pred = model.predict(x_test)\n",
        "mse=mean_squared_error(y_pred,y_test)\n",
        "print('MSE for the data:',mse)\n",
        "r2 = r2_score(y_pred,y_test)\n",
        "print('r2_score for the data',r2)"
      ],
      "metadata": {
        "id": "6-cOdOEUs7pZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6f83f3d4-1c9b-4abd-b3ae-144ff0d1cc09"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MSE for the data: 146910.12462449734\n",
            "r2_score for the data 0.7755580184431531\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Finding best parameters for AdaBoostRegressor\n",
        "from sklearn.ensemble import AdaBoostRegressor\n",
        "ar = AdaBoostRegressor(base_estimator=RF,random_state=0)\n",
        "ar_params = {'n_estimators':np.arange(25,100,25)}\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "gs_ar = GridSearchCV(ar,ar_params,cv=5)\n",
        "gs_ar.fit(x_train,y_train)\n",
        "c = gs_ar.best_params_"
      ],
      "metadata": {
        "id": "zUHJZl_EtD_g"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Fitting the model with best params\n",
        "ab_rf = AdaBoostRegressor(base_estimator=RF,n_estimators=c['n_estimators'],random_state=0)\n",
        "model = ab_rf.fit(x_train,y_train)\n",
        "y_pred = model.predict(x_test)\n",
        "mse=mean_squared_error(y_pred,y_test)\n",
        "print('MSE for the data:',mse)\n",
        "r2 = r2_score(y_pred,y_test)\n",
        "print('r2_score for the data',r2)"
      ],
      "metadata": {
        "id": "SH6GrT0ntIUR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ecbe7b04-47bf-4008-8c40-53b746dbfa26"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MSE for the data: 143677.75532339312\n",
            "r2_score for the data 0.7356875112590213\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i3gMPobQ7kD6"
      },
      "source": [
        "# Model Building for cnt "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "49yL813o7jKs"
      },
      "outputs": [],
      "source": [
        "# Segregating data based on ‘workingday’ column\n",
        "work_day = df_daily[df_daily['workingday']==1] \n",
        "non_work_day = df_daily[df_daily['workingday']==0]"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Model for working days**"
      ],
      "metadata": {
        "id": "P8zWG5RVtWsN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x = work_day.drop(['casual','registered','cnt','workingday'],axis = 1)\n",
        "y = work_day.cnt\n",
        "# Dividing the data into train and test\n",
        "from sklearn.model_selection import train_test_split\n",
        "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size = 0.30 ,random_state = 2)"
      ],
      "metadata": {
        "id": "PYYHXA55tV7-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JOc2a3Ur-YdO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "308a2b63-6664-44a3-dc09-21751d3fa03e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MSE for the data: 640249.1401510865\n",
            "r2_score for the data 0.7718907251327742\n"
          ]
        }
      ],
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "model1=LinearRegression()\n",
        "result = model1.fit(x_train,y_train)\n",
        "y_pred = result.predict(x_test)\n",
        "from sklearn.metrics import mean_squared_error\n",
        "mse=mean_squared_error(y_pred,y_test)\n",
        "print('MSE for the data:',mse)\n",
        "from sklearn.metrics import r2_score\n",
        "r2 = r2_score(y_pred,y_test)\n",
        "print('r2_score for the data',r2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2mU4_qQL2RCy"
      },
      "outputs": [],
      "source": [
        "# Finding best parameters for decision tree\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "dt = DecisionTreeRegressor(random_state=0)\n",
        "dt_params = {'max_depth':np.arange(1,50,1),'min_samples_leaf':np.arange(2,15)}\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "gs_dt = GridSearchCV(dt,dt_params,cv=10)\n",
        "gs_dt.fit(x_train,y_train)\n",
        "a = gs_dt.best_params_"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Training with best parameters\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "dtr=DecisionTreeRegressor(max_depth=a['max_depth'],min_samples_leaf= a['min_samples_leaf'])\n",
        "model = dtr.fit(x_train,y_train)\n",
        "y_pred = model.predict(x_test)\n",
        "mse=mean_squared_error(y_pred,y_test)\n",
        "print('MSE for the data:',mse)\n",
        "r2 = r2_score(y_pred,y_test)\n",
        "print('r2_score for the data',r2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MiJPA1ANQzPt",
        "outputId": "360000c4-9d68-47d6-f4fd-9c31ce5a1c16"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MSE for the data: 620736.561061285\n",
            "r2_score for the data 0.8090308997622665\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Finding best parameters for RandomForestRegressor\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "rf = RandomForestRegressor(random_state=0)\n",
        "rf_params = {'n_estimators':np.arange(25,150,25),'max_depth':np.arange(1,11,2),'min_samples_leaf':np.arange(2,15,3)}\n",
        "\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "gs_rf = GridSearchCV(rf,rf_params,cv=10)\n",
        "gs_rf.fit(x_train,y_train)\n",
        "b = gs_rf.best_params_"
      ],
      "metadata": {
        "id": "f28M-aAbQ87m"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Fitting the model with best params\n",
        "RF = RandomForestRegressor(n_estimators=b['n_estimators'],max_depth=b['max_depth'],min_samples_leaf=b['min_samples_leaf'],random_state=0)\n",
        "model = RF.fit(x_train,y_train)\n",
        "y_pred = model.predict(x_test)\n",
        "mse=mean_squared_error(y_pred,y_test)\n",
        "print('MSE for the data:',mse)\n",
        "r2 = r2_score(y_pred,y_test)\n",
        "print('r2_score for the data',r2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o1ojiutzRD44",
        "outputId": "83073227-1513-4ad2-abea-11d13d100189"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MSE for the data: 452803.61124522984\n",
            "r2_score for the data 0.858826384478959\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Finding best parameters for AdaBoostRegressor\n",
        "from sklearn.ensemble import AdaBoostRegressor\n",
        "ar = AdaBoostRegressor(base_estimator=RF,random_state=0)\n",
        "ar_params = {'n_estimators':np.arange(25,100,25)}\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "gs_ar = GridSearchCV(ar,ar_params,cv=5)\n",
        "gs_ar.fit(x_train,y_train)\n",
        "c = gs_ar.best_params_"
      ],
      "metadata": {
        "id": "iAQO6IuFRI8c"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Fitting the model with best params\n",
        "ab_rf = AdaBoostRegressor(base_estimator=RF,n_estimators=c['n_estimators'],random_state=0)\n",
        "model = ab_rf.fit(x_train,y_train)\n",
        "y_pred = model.predict(x_test)\n",
        "mse=mean_squared_error(y_pred,y_test)\n",
        "print('MSE for the data:',mse)\n",
        "r2 = r2_score(y_pred,y_test)\n",
        "print('r2_score for the data',r2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dpdcDgRhRN9L",
        "outputId": "38822068-6c3a-4912-d250-bc6940623956"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MSE for the data: 469047.1839846259\n",
            "r2_score for the data 0.8499280918731321\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Again AdaBoostRregressor is the best model"
      ],
      "metadata": {
        "id": "ZU2h3hssS5mF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Model for Non-working days**"
      ],
      "metadata": {
        "id": "C46n5QsbtjYF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x = non_work_day.drop(['casual','registered','cnt','workingday'],axis = 1)\n",
        "y = non_work_day.cnt\n",
        "# Dividing the data into train and test\n",
        "from sklearn.model_selection import train_test_split\n",
        "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size = 0.30 ,random_state = 2)"
      ],
      "metadata": {
        "id": "8CzJNGlMti51"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "model1=LinearRegression()\n",
        "result = model1.fit(x_train,y_train)\n",
        "y_pred = result.predict(x_test)\n",
        "from sklearn.metrics import mean_squared_error\n",
        "mse=mean_squared_error(y_pred,y_test)\n",
        "print('MSE for the data:',mse)\n",
        "from sklearn.metrics import r2_score\n",
        "r2 = r2_score(y_pred,y_test)\n",
        "print('r2_score for the data',r2)"
      ],
      "metadata": {
        "id": "MvoW9F6quG1D",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d2ca987a-eda8-43a7-c0d7-acaadab8c7dd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MSE for the data: 1083602.5635630684\n",
            "r2_score for the data 0.6661323871775375\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Finding best parameters for decision tree\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "dt = DecisionTreeRegressor(random_state=0)\n",
        "dt_params = {'max_depth':np.arange(1,50,1),'min_samples_leaf':np.arange(2,15)}\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "gs_dt = GridSearchCV(dt,dt_params,cv=10)\n",
        "gs_dt.fit(x_train,y_train)\n",
        "a = gs_dt.best_params_"
      ],
      "metadata": {
        "id": "TY_I8M8suQkC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Training with best parameters\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "dtr=DecisionTreeRegressor(max_depth=a['max_depth'],min_samples_leaf= a['min_samples_leaf'])\n",
        "model = dtr.fit(x_train,y_train)\n",
        "y_pred = model.predict(x_test)\n",
        "mse=mean_squared_error(y_pred,y_test)\n",
        "print('MSE for the data:',mse)\n",
        "r2 = r2_score(y_pred,y_test)\n",
        "print('r2_score for the data',r2)"
      ],
      "metadata": {
        "id": "LOz2geuGuXQH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bea4bcc6-98e6-48b8-b3f0-006d4596f874"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MSE for the data: 927796.1974305003\n",
            "r2_score for the data 0.7157184504443861\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Finding best parameters for RandomForestRegressor\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "rf = RandomForestRegressor(random_state=0)\n",
        "rf_params = {'n_estimators':np.arange(25,150,25),'max_depth':np.arange(1,11,2),'min_samples_leaf':np.arange(2,15,3)}\n",
        "\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "gs_rf = GridSearchCV(rf,rf_params,cv=10)\n",
        "gs_rf.fit(x_train,y_train)\n",
        "b = gs_rf.best_params_"
      ],
      "metadata": {
        "id": "yhdtJJMYudtF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Fitting the model with best params\n",
        "RF = RandomForestRegressor(n_estimators=b['n_estimators'],max_depth=b['max_depth'],min_samples_leaf=b['min_samples_leaf'],random_state=0)\n",
        "model = RF.fit(x_train,y_train)\n",
        "y_pred = model.predict(x_test)\n",
        "mse=mean_squared_error(y_pred,y_test)\n",
        "print('MSE for the data:',mse)\n",
        "r2 = r2_score(y_pred,y_test)\n",
        "print('r2_score for the data',r2)"
      ],
      "metadata": {
        "id": "evTvbj-Suibz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "35932608-67e8-40e7-e92f-2323eacc359a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MSE for the data: 653843.5491918524\n",
            "r2_score for the data 0.7964308956686346\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Finding best parameters for AdaBoostRegressor\n",
        "from sklearn.ensemble import AdaBoostRegressor\n",
        "ar = AdaBoostRegressor(base_estimator=RF,random_state=0)\n",
        "ar_params = {'n_estimators':np.arange(25,100,25)}\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "gs_ar = GridSearchCV(ar,ar_params,cv=5)\n",
        "gs_ar.fit(x_train,y_train)\n",
        "c = gs_ar.best_params_"
      ],
      "metadata": {
        "id": "mzdlgWW2uoMl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Fitting the model with best params\n",
        "ab_rf = AdaBoostRegressor(base_estimator=RF,n_estimators=c['n_estimators'],random_state=0)\n",
        "model = ab_rf.fit(x_train,y_train)\n",
        "y_pred = model.predict(x_test)\n",
        "mse=mean_squared_error(y_pred,y_test)\n",
        "print('MSE for the data:',mse)\n",
        "r2 = r2_score(y_pred,y_test)\n",
        "print('r2_score for the data',r2)"
      ],
      "metadata": {
        "id": "hdTp4wOvutHA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "49714311-3b98-4626-9c59-4b23551ff4fc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MSE for the data: 525744.3981968537\n",
            "r2_score for the data 0.8282466521747489\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Generalized model for deployment with respect to cnt"
      ],
      "metadata": {
        "id": "SDD4pBJySt-H"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x = df_daily.drop(['casual','registered','cnt'],axis = 1)\n",
        "y = df_daily.cnt\n",
        "x.head(50)"
      ],
      "metadata": {
        "id": "zgTgMRUYS2lQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "d314f2a4-370c-46c9-9176-eb2b49c5e7a7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    yr  mnth  holiday  workingday      temp       hum  windspeed  day  \\\n",
              "0    0     1        0           0  0.344167  0.805833   0.160446    1   \n",
              "1    0     1        0           0  0.363478  0.696087   0.248539    2   \n",
              "2    0     1        0           1  0.196364  0.437273   0.248309    3   \n",
              "3    0     1        0           1  0.200000  0.590435   0.160296    4   \n",
              "4    0     1        0           1  0.226957  0.436957   0.186900    5   \n",
              "5    0     1        0           1  0.204348  0.518261   0.089565    6   \n",
              "6    0     1        0           1  0.196522  0.498696   0.168726    7   \n",
              "7    0     1        0           0  0.165000  0.535833   0.266804    8   \n",
              "8    0     1        0           0  0.138333  0.434167   0.361950    9   \n",
              "9    0     1        0           1  0.150833  0.482917   0.223267   10   \n",
              "10   0     1        0           1  0.169091  0.686364   0.122132   11   \n",
              "11   0     1        0           1  0.172727  0.599545   0.304627   12   \n",
              "12   0     1        0           1  0.165000  0.470417   0.301000   13   \n",
              "13   0     1        0           1  0.160870  0.537826   0.126548   14   \n",
              "14   0     1        0           0  0.233333  0.498750   0.157963   15   \n",
              "15   0     1        0           0  0.231667  0.483750   0.188433   16   \n",
              "16   0     1        1           0  0.175833  0.537500   0.194017   17   \n",
              "17   0     1        0           1  0.216667  0.861667   0.146775   18   \n",
              "18   0     1        0           1  0.292174  0.741739   0.208317   19   \n",
              "19   0     1        0           1  0.261667  0.538333   0.195904   20   \n",
              "20   0     1        0           1  0.177500  0.457083   0.353242   21   \n",
              "21   0     1        0           0  0.059130  0.400000   0.171970   22   \n",
              "22   0     1        0           0  0.096522  0.436522   0.246600   23   \n",
              "23   0     1        0           1  0.097391  0.491739   0.158330   24   \n",
              "24   0     1        0           1  0.223478  0.616957   0.129796   25   \n",
              "25   0     1        0           1  0.217500  0.862500   0.293850   26   \n",
              "26   0     1        0           1  0.195000  0.687500   0.113837   27   \n",
              "27   0     1        0           1  0.203478  0.793043   0.123300   28   \n",
              "28   0     1        0           0  0.196522  0.651739   0.145365   29   \n",
              "29   0     1        0           0  0.216522  0.722174   0.073983   30   \n",
              "30   0     1        0           1  0.180833  0.603750   0.187192   31   \n",
              "31   0     2        0           1  0.192174  0.829565   0.053213    1   \n",
              "32   0     2        0           1  0.260000  0.775417   0.264308    2   \n",
              "33   0     2        0           1  0.186957  0.437826   0.277752    3   \n",
              "34   0     2        0           1  0.211304  0.585217   0.127839    4   \n",
              "35   0     2        0           0  0.233333  0.929167   0.161079    5   \n",
              "36   0     2        0           0  0.285833  0.568333   0.141800    6   \n",
              "37   0     2        0           1  0.271667  0.738333   0.045408    7   \n",
              "38   0     2        0           1  0.220833  0.537917   0.361950    8   \n",
              "39   0     2        0           1  0.134783  0.494783   0.188839    9   \n",
              "40   0     2        0           1  0.144348  0.437391   0.221935   10   \n",
              "41   0     2        0           1  0.189091  0.506364   0.108550   11   \n",
              "42   0     2        0           0  0.222500  0.544167   0.203367   12   \n",
              "43   0     2        0           0  0.316522  0.457391   0.260883   13   \n",
              "44   0     2        0           1  0.415000  0.375833   0.417908   14   \n",
              "45   0     2        0           1  0.266087  0.314348   0.291374   15   \n",
              "46   0     2        0           1  0.318261  0.423478   0.251791   16   \n",
              "47   0     2        0           1  0.435833  0.505000   0.230104   17   \n",
              "48   0     2        0           1  0.521667  0.516667   0.264925   18   \n",
              "49   0     2        0           0  0.399167  0.187917   0.507463   19   \n",
              "\n",
              "    weathersit_springer  weathersit_summer  weekday_Monday  weekday_Saturday  \\\n",
              "0                     0                  1               0                 1   \n",
              "1                     0                  1               0                 0   \n",
              "2                     1                  0               1                 0   \n",
              "3                     1                  0               0                 0   \n",
              "4                     1                  0               0                 0   \n",
              "5                     1                  0               0                 0   \n",
              "6                     0                  1               0                 0   \n",
              "7                     0                  1               0                 1   \n",
              "8                     1                  0               0                 0   \n",
              "9                     1                  0               1                 0   \n",
              "10                    0                  1               0                 0   \n",
              "11                    1                  0               0                 0   \n",
              "12                    1                  0               0                 0   \n",
              "13                    1                  0               0                 0   \n",
              "14                    0                  1               0                 1   \n",
              "15                    1                  0               0                 0   \n",
              "16                    0                  1               1                 0   \n",
              "17                    0                  1               0                 0   \n",
              "18                    0                  1               0                 0   \n",
              "19                    0                  1               0                 0   \n",
              "20                    1                  0               0                 0   \n",
              "21                    1                  0               0                 1   \n",
              "22                    1                  0               0                 0   \n",
              "23                    1                  0               1                 0   \n",
              "24                    0                  1               0                 0   \n",
              "25                    0                  0               0                 0   \n",
              "26                    1                  0               0                 0   \n",
              "27                    0                  1               0                 0   \n",
              "28                    1                  0               0                 1   \n",
              "29                    1                  0               0                 0   \n",
              "30                    0                  1               1                 0   \n",
              "31                    0                  1               0                 0   \n",
              "32                    0                  1               0                 0   \n",
              "33                    1                  0               0                 0   \n",
              "34                    0                  1               0                 0   \n",
              "35                    0                  1               0                 1   \n",
              "36                    1                  0               0                 0   \n",
              "37                    1                  0               1                 0   \n",
              "38                    1                  0               0                 0   \n",
              "39                    0                  1               0                 0   \n",
              "40                    1                  0               0                 0   \n",
              "41                    1                  0               0                 0   \n",
              "42                    1                  0               0                 1   \n",
              "43                    1                  0               0                 0   \n",
              "44                    1                  0               1                 0   \n",
              "45                    1                  0               0                 0   \n",
              "46                    1                  0               0                 0   \n",
              "47                    1                  0               0                 0   \n",
              "48                    1                  0               0                 0   \n",
              "49                    1                  0               0                 1   \n",
              "\n",
              "    weekday_Sunday  weekday_Thursday  weekday_Tuesday  weekday_Wednesday  \n",
              "0                0                 0                0                  0  \n",
              "1                1                 0                0                  0  \n",
              "2                0                 0                0                  0  \n",
              "3                0                 0                1                  0  \n",
              "4                0                 0                0                  1  \n",
              "5                0                 1                0                  0  \n",
              "6                0                 0                0                  0  \n",
              "7                0                 0                0                  0  \n",
              "8                1                 0                0                  0  \n",
              "9                0                 0                0                  0  \n",
              "10               0                 0                1                  0  \n",
              "11               0                 0                0                  1  \n",
              "12               0                 1                0                  0  \n",
              "13               0                 0                0                  0  \n",
              "14               0                 0                0                  0  \n",
              "15               1                 0                0                  0  \n",
              "16               0                 0                0                  0  \n",
              "17               0                 0                1                  0  \n",
              "18               0                 0                0                  1  \n",
              "19               0                 1                0                  0  \n",
              "20               0                 0                0                  0  \n",
              "21               0                 0                0                  0  \n",
              "22               1                 0                0                  0  \n",
              "23               0                 0                0                  0  \n",
              "24               0                 0                1                  0  \n",
              "25               0                 0                0                  1  \n",
              "26               0                 1                0                  0  \n",
              "27               0                 0                0                  0  \n",
              "28               0                 0                0                  0  \n",
              "29               1                 0                0                  0  \n",
              "30               0                 0                0                  0  \n",
              "31               0                 0                1                  0  \n",
              "32               0                 0                0                  1  \n",
              "33               0                 1                0                  0  \n",
              "34               0                 0                0                  0  \n",
              "35               0                 0                0                  0  \n",
              "36               1                 0                0                  0  \n",
              "37               0                 0                0                  0  \n",
              "38               0                 0                1                  0  \n",
              "39               0                 0                0                  1  \n",
              "40               0                 1                0                  0  \n",
              "41               0                 0                0                  0  \n",
              "42               0                 0                0                  0  \n",
              "43               1                 0                0                  0  \n",
              "44               0                 0                0                  0  \n",
              "45               0                 0                1                  0  \n",
              "46               0                 0                0                  1  \n",
              "47               0                 1                0                  0  \n",
              "48               0                 0                0                  0  \n",
              "49               0                 0                0                  0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-046c974c-0e24-4aa5-9c7e-7b350d2601c1\">\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>yr</th>\n",
              "      <th>mnth</th>\n",
              "      <th>holiday</th>\n",
              "      <th>workingday</th>\n",
              "      <th>temp</th>\n",
              "      <th>hum</th>\n",
              "      <th>windspeed</th>\n",
              "      <th>day</th>\n",
              "      <th>weathersit_springer</th>\n",
              "      <th>weathersit_summer</th>\n",
              "      <th>weekday_Monday</th>\n",
              "      <th>weekday_Saturday</th>\n",
              "      <th>weekday_Sunday</th>\n",
              "      <th>weekday_Thursday</th>\n",
              "      <th>weekday_Tuesday</th>\n",
              "      <th>weekday_Wednesday</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.344167</td>\n",
              "      <td>0.805833</td>\n",
              "      <td>0.160446</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>0.363478</td>\n",
              "      <td>0.696087</td>\n",
              "      <td>0.248539</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.196364</td>\n",
              "      <td>0.437273</td>\n",
              "      <td>0.248309</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.200000</td>\n",
              "      <td>0.590435</td>\n",
              "      <td>0.160296</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>1</td>\n",
              "      <td>0.226957</td>\n",
              "      <td>0.436957</td>\n",
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              "      <td>5</td>\n",
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              "      <td>0.204348</td>\n",
              "      <td>0.518261</td>\n",
              "      <td>0.089565</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.196522</td>\n",
              "      <td>0.498696</td>\n",
              "      <td>0.168726</td>\n",
              "      <td>7</td>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.165000</td>\n",
              "      <td>0.535833</td>\n",
              "      <td>0.266804</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.138333</td>\n",
              "      <td>0.434167</td>\n",
              "      <td>0.361950</td>\n",
              "      <td>9</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.150833</td>\n",
              "      <td>0.482917</td>\n",
              "      <td>0.223267</td>\n",
              "      <td>10</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.169091</td>\n",
              "      <td>0.686364</td>\n",
              "      <td>0.122132</td>\n",
              "      <td>11</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.172727</td>\n",
              "      <td>0.599545</td>\n",
              "      <td>0.304627</td>\n",
              "      <td>12</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.165000</td>\n",
              "      <td>0.470417</td>\n",
              "      <td>0.301000</td>\n",
              "      <td>13</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.160870</td>\n",
              "      <td>0.537826</td>\n",
              "      <td>0.126548</td>\n",
              "      <td>14</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.233333</td>\n",
              "      <td>0.498750</td>\n",
              "      <td>0.157963</td>\n",
              "      <td>15</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.231667</td>\n",
              "      <td>0.483750</td>\n",
              "      <td>0.188433</td>\n",
              "      <td>16</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.175833</td>\n",
              "      <td>0.537500</td>\n",
              "      <td>0.194017</td>\n",
              "      <td>17</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.216667</td>\n",
              "      <td>0.861667</td>\n",
              "      <td>0.146775</td>\n",
              "      <td>18</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.292174</td>\n",
              "      <td>0.741739</td>\n",
              "      <td>0.208317</td>\n",
              "      <td>19</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.261667</td>\n",
              "      <td>0.538333</td>\n",
              "      <td>0.195904</td>\n",
              "      <td>20</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.177500</td>\n",
              "      <td>0.457083</td>\n",
              "      <td>0.353242</td>\n",
              "      <td>21</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.059130</td>\n",
              "      <td>0.400000</td>\n",
              "      <td>0.171970</td>\n",
              "      <td>22</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.096522</td>\n",
              "      <td>0.436522</td>\n",
              "      <td>0.246600</td>\n",
              "      <td>23</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.097391</td>\n",
              "      <td>0.491739</td>\n",
              "      <td>0.158330</td>\n",
              "      <td>24</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.223478</td>\n",
              "      <td>0.616957</td>\n",
              "      <td>0.129796</td>\n",
              "      <td>25</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.217500</td>\n",
              "      <td>0.862500</td>\n",
              "      <td>0.293850</td>\n",
              "      <td>26</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.195000</td>\n",
              "      <td>0.687500</td>\n",
              "      <td>0.113837</td>\n",
              "      <td>27</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.203478</td>\n",
              "      <td>0.793043</td>\n",
              "      <td>0.123300</td>\n",
              "      <td>28</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.196522</td>\n",
              "      <td>0.651739</td>\n",
              "      <td>0.145365</td>\n",
              "      <td>29</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.216522</td>\n",
              "      <td>0.722174</td>\n",
              "      <td>0.073983</td>\n",
              "      <td>30</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.180833</td>\n",
              "      <td>0.603750</td>\n",
              "      <td>0.187192</td>\n",
              "      <td>31</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.192174</td>\n",
              "      <td>0.829565</td>\n",
              "      <td>0.053213</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.260000</td>\n",
              "      <td>0.775417</td>\n",
              "      <td>0.264308</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.186957</td>\n",
              "      <td>0.437826</td>\n",
              "      <td>0.277752</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.211304</td>\n",
              "      <td>0.585217</td>\n",
              "      <td>0.127839</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.233333</td>\n",
              "      <td>0.929167</td>\n",
              "      <td>0.161079</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.285833</td>\n",
              "      <td>0.568333</td>\n",
              "      <td>0.141800</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.271667</td>\n",
              "      <td>0.738333</td>\n",
              "      <td>0.045408</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.220833</td>\n",
              "      <td>0.537917</td>\n",
              "      <td>0.361950</td>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.134783</td>\n",
              "      <td>0.494783</td>\n",
              "      <td>0.188839</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.144348</td>\n",
              "      <td>0.437391</td>\n",
              "      <td>0.221935</td>\n",
              "      <td>10</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.189091</td>\n",
              "      <td>0.506364</td>\n",
              "      <td>0.108550</td>\n",
              "      <td>11</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.222500</td>\n",
              "      <td>0.544167</td>\n",
              "      <td>0.203367</td>\n",
              "      <td>12</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.316522</td>\n",
              "      <td>0.457391</td>\n",
              "      <td>0.260883</td>\n",
              "      <td>13</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.415000</td>\n",
              "      <td>0.375833</td>\n",
              "      <td>0.417908</td>\n",
              "      <td>14</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.266087</td>\n",
              "      <td>0.314348</td>\n",
              "      <td>0.291374</td>\n",
              "      <td>15</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.318261</td>\n",
              "      <td>0.423478</td>\n",
              "      <td>0.251791</td>\n",
              "      <td>16</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.435833</td>\n",
              "      <td>0.505000</td>\n",
              "      <td>0.230104</td>\n",
              "      <td>17</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.521667</td>\n",
              "      <td>0.516667</td>\n",
              "      <td>0.264925</td>\n",
              "      <td>18</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.399167</td>\n",
              "      <td>0.187917</td>\n",
              "      <td>0.507463</td>\n",
              "      <td>19</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  <style>\n",
              "    .colab-df-container {\n",
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              "      gap: 12px;\n",
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              "\n",
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              "        buttonEl.style.display =\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",
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              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Dividing the data into train and test\n",
        "from sklearn.model_selection import train_test_split\n",
        "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size = 0.30 ,random_state = 2)"
      ],
      "metadata": {
        "id": "0tMd2laWJkhs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "model1=LinearRegression()\n",
        "result = model1.fit(x_train,y_train)\n",
        "y_pred = result.predict(x_test)\n",
        "from sklearn.metrics import mean_squared_error\n",
        "mse=mean_squared_error(y_pred,y_test)\n",
        "print('MSE for the data:',mse)\n",
        "from sklearn.metrics import r2_score\n",
        "r2 = r2_score(y_pred,y_test)\n",
        "print('r2_score for the data',r2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oaZ-S6TPTe-f",
        "outputId": "05660057-3657-4ff4-dc84-482d32dc6d07"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MSE for the data: 799383.1653506244\n",
            "r2_score for the data 0.7268478591679416\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Finding best parameters for decision tree\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "dt = DecisionTreeRegressor(random_state=0)\n",
        "dt_params = {'max_depth':np.arange(1,50,1),'min_samples_leaf':np.arange(2,15)}\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "gs_dt = GridSearchCV(dt,dt_params,cv=10)\n",
        "gs_dt.fit(x_train,y_train)\n",
        "a = gs_dt.best_params_"
      ],
      "metadata": {
        "id": "yXS8kqjDTm4Y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Training with best parameters\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "dtr=DecisionTreeRegressor(max_depth=a['max_depth'],min_samples_leaf= a['min_samples_leaf'])\n",
        "model = dtr.fit(x_train,y_train)\n",
        "y_pred = model.predict(x_test)\n",
        "mse=mean_squared_error(y_pred,y_test)\n",
        "print('MSE for the data:',mse)\n",
        "r2 = r2_score(y_pred,y_test)\n",
        "print('r2_score for the data',r2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1YEvwxQ1TsWT",
        "outputId": "01110d24-2d75-421b-c73f-1e728660dbe0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MSE for the data: 617441.7654921587\n",
            "r2_score for the data 0.8055195185581923\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Finding best parameters for RandomForestRegressor\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "rf = RandomForestRegressor(random_state=0)\n",
        "rf_params = {'n_estimators':np.arange(25,150,25),'max_depth':np.arange(1,11,2),'min_samples_leaf':np.arange(2,15,3)}\n",
        "\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "gs_rf = GridSearchCV(rf,rf_params,cv=10)\n",
        "gs_rf.fit(x_train,y_train)\n",
        "b = gs_rf.best_params_"
      ],
      "metadata": {
        "id": "ZjOfHpoMT1Nz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Fitting the model with best params\n",
        "RF = RandomForestRegressor(n_estimators=b['n_estimators'],max_depth=b['max_depth'],min_samples_leaf=b['min_samples_leaf'],random_state=0)\n",
        "model_best = RF.fit(x_train,y_train)\n",
        "y_pred = model.predict(x_test)\n",
        "mse=mean_squared_error(y_pred,y_test)\n",
        "print('MSE for the data:',mse)\n",
        "r2 = r2_score(y_pred,y_test)\n",
        "print('r2_score for the data',r2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ibucbzYsT6RU",
        "outputId": "15ca5b2e-4b2b-4eca-e6e4-cee2b06cf725"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MSE for the data: 617441.7654921587\n",
            "r2_score for the data 0.8055195185581923\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Finding best parameters for AdaBoostRegressor\n",
        "from sklearn.ensemble import AdaBoostRegressor\n",
        "ar = AdaBoostRegressor(base_estimator=RF,random_state=0)\n",
        "ar_params = {'n_estimators':np.arange(25,100,25)}\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "gs_ar = GridSearchCV(ar,ar_params,cv=5)\n",
        "gs_ar.fit(x_train,y_train)\n",
        "c = gs_ar.best_params_"
      ],
      "metadata": {
        "id": "VREpi9igUFNC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Fitting the model with best params\n",
        "ab_rf = AdaBoostRegressor(base_estimator=RF,n_estimators=c['n_estimators'],random_state=0)\n",
        "model = ab_rf.fit(x_train,y_train)\n",
        "y_pred = model.predict(x_test)\n",
        "mse=mean_squared_error(y_pred,y_test)\n",
        "print('MSE for the data:',mse)\n",
        "r2 = r2_score(y_pred,y_test)\n",
        "print('r2_score for the data',r2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C7AvMylqUMHn",
        "outputId": "138e5fa2-f1fe-4e9e-ba8e-76c080d601a0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MSE for the data: 463245.88189044665\n",
            "r2_score for the data 0.8489198551714614\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#In this case RandomForestRregressor is giving the best r2_score \n",
        "#We will try to make a pickle file for it and deploy it in production"
      ],
      "metadata": {
        "id": "WINRgD4mV4np"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "In order to deploy a model to some app we need pickle file .Here we create the pickle file for the generalized model with best r2_score with respect to count ."
      ],
      "metadata": {
        "id": "6Mf2CEbcUw_5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle \n",
        "pickle_out = open(\"day_model_best\", mode = \"wb\") \n",
        "pickle.dump(model_best, pickle_out) \n",
        "pickle_out.close()"
      ],
      "metadata": {
        "id": "dt7M-AgVUv_v"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "name": "ADA_project_code_daily.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
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