{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyMsX1QjcLD/3U9XHJjtu20/",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ekramasif/Basic-Machine-Learning/blob/main/NeuralProphet/WeatherForecast.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pystan==2.19.1.1 prophet"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B5E0M6aff1Dt",
        "outputId": "b3dd9fbb-0576-465e-9dd6-29d63b7a777f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: pystan==2.19.1.1 in /usr/local/lib/python3.8/dist-packages (2.19.1.1)\n",
            "Requirement already satisfied: prophet in /usr/local/lib/python3.8/dist-packages (1.1.2)\n",
            "Requirement already satisfied: numpy>=1.7 in /usr/local/lib/python3.8/dist-packages (from pystan==2.19.1.1) (1.21.6)\n",
            "Requirement already satisfied: Cython!=0.25.1,>=0.22 in /usr/local/lib/python3.8/dist-packages (from pystan==2.19.1.1) (0.29.33)\n",
            "Requirement already satisfied: convertdate>=2.1.2 in /usr/local/lib/python3.8/dist-packages (from prophet) (2.4.0)\n",
            "Collecting cmdstanpy>=1.0.4\n",
            "  Downloading cmdstanpy-1.1.0-py3-none-any.whl (83 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m83.2/83.2 KB\u001b[0m \u001b[31m4.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: pandas>=1.0.4 in /usr/local/lib/python3.8/dist-packages (from prophet) (1.3.5)\n",
            "Requirement already satisfied: LunarCalendar>=0.0.9 in /usr/local/lib/python3.8/dist-packages (from prophet) (0.0.9)\n",
            "Requirement already satisfied: matplotlib>=2.0.0 in /usr/local/lib/python3.8/dist-packages (from prophet) (3.2.2)\n",
            "Requirement already satisfied: holidays>=0.14.2 in /usr/local/lib/python3.8/dist-packages (from prophet) (0.19)\n",
            "Requirement already satisfied: python-dateutil>=2.8.0 in /usr/local/lib/python3.8/dist-packages (from prophet) (2.8.2)\n",
            "Requirement already satisfied: tqdm>=4.36.1 in /usr/local/lib/python3.8/dist-packages (from prophet) (4.64.1)\n",
            "Requirement already satisfied: pymeeus<=1,>=0.3.13 in /usr/local/lib/python3.8/dist-packages (from convertdate>=2.1.2->prophet) (0.5.12)\n",
            "Requirement already satisfied: korean-lunar-calendar in /usr/local/lib/python3.8/dist-packages (from holidays>=0.14.2->prophet) (0.3.1)\n",
            "Requirement already satisfied: hijri-converter in /usr/local/lib/python3.8/dist-packages (from holidays>=0.14.2->prophet) (2.2.4)\n",
            "Requirement already satisfied: ephem>=3.7.5.3 in /usr/local/lib/python3.8/dist-packages (from LunarCalendar>=0.0.9->prophet) (4.1.4)\n",
            "Requirement already satisfied: pytz in /usr/local/lib/python3.8/dist-packages (from LunarCalendar>=0.0.9->prophet) (2022.7.1)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.8/dist-packages (from matplotlib>=2.0.0->prophet) (1.4.4)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.8/dist-packages (from matplotlib>=2.0.0->prophet) (0.11.0)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.8/dist-packages (from matplotlib>=2.0.0->prophet) (3.0.9)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.8/dist-packages (from python-dateutil>=2.8.0->prophet) (1.15.0)\n",
            "Installing collected packages: cmdstanpy\n",
            "  Attempting uninstall: cmdstanpy\n",
            "    Found existing installation: cmdstanpy 0.9.5\n",
            "    Uninstalling cmdstanpy-0.9.5:\n",
            "      Successfully uninstalled cmdstanpy-0.9.5\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "fbprophet 0.7.1 requires cmdstanpy==0.9.5, but you have cmdstanpy 1.1.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed cmdstanpy-1.1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "He1R8jCfRe-U"
      },
      "outputs": [],
      "source": [
        "import numpy as np  \n",
        "import pandas as pd \n",
        "import tensorflow as tf\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import tensorflow as tf\n",
        "import os\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from prophet import Prophet\n",
        "from prophet.plot import add_changepoints_to_plot, plot_plotly, plot_components_plotly\n",
        "import warnings\n",
        "import plotly.graph_objects as go\n",
        "import plotly.express as px\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "sns.set_theme()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv('/content/Final dataset - CHITTAGONG 2023-01-20 to 2023-02-18.csv')\n",
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "r_ZODB8qRh22",
        "outputId": "50de248b-78f3-423c-ef8b-c64e89a71185"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Date     Time  Temparature(c)  Humidity%\n",
              "0  2023-01-20  0:00:00            15.2      71.49\n",
              "1  2023-01-20  1:00:00            16.9      48.94\n",
              "2  2023-01-20  2:00:00            16.3      50.14\n",
              "3  2023-01-20  3:00:00            14.2      71.55\n",
              "4  2023-01-20  4:00:00            15.6      52.07"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-bb21ba90-6eca-45bf-ab60-4299c232ed8b\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Time</th>\n",
              "      <th>Temparature(c)</th>\n",
              "      <th>Humidity%</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-01-20</td>\n",
              "      <td>0:00:00</td>\n",
              "      <td>15.2</td>\n",
              "      <td>71.49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2023-01-20</td>\n",
              "      <td>1:00:00</td>\n",
              "      <td>16.9</td>\n",
              "      <td>48.94</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2023-01-20</td>\n",
              "      <td>2:00:00</td>\n",
              "      <td>16.3</td>\n",
              "      <td>50.14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2023-01-20</td>\n",
              "      <td>3:00:00</td>\n",
              "      <td>14.2</td>\n",
              "      <td>71.55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2023-01-20</td>\n",
              "      <td>4:00:00</td>\n",
              "      <td>15.6</td>\n",
              "      <td>52.07</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bb21ba90-6eca-45bf-ab60-4299c232ed8b')\"\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-bb21ba90-6eca-45bf-ab60-4299c232ed8b 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-bb21ba90-6eca-45bf-ab60-4299c232ed8b');\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": 125
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Prepare data for Prophet\n",
        "days = int(7)\n",
        "predicted_data = pd.DataFrame()  "
      ],
      "metadata": {
        "id": "ZqTlxD2JR1Au"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Prophet is a time series forecasting library developed by Facebook that allows you to make predictions based on historical time series data. Once you create an instance of the Prophet class, you can use its methods to fit the model to your data, make predictions, and visualize the results."
      ],
      "metadata": {
        "id": "ElTKiK5arXU9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model1 = Prophet()"
      ],
      "metadata": {
        "id": "Opika7etm8c5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_for_humidity=data[['Date', 'Humidity%']]\n",
        "df_for_humidity['Date']=pd.to_datetime(df_for_humidity['Date'])\n",
        "df_for_humidity.rename(columns = {'Date':'ds'}, inplace = True)\n",
        "df_for_humidity.rename(columns = {'Humidity%':'y'}, inplace = True)\n",
        "model1.fit(df_for_humidity)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jCUi2a9WnAoQ",
        "outputId": "51065b22-0226-4ee2-eca5-c0801c630dc9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:prophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.\n",
            "INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<prophet.forecaster.Prophet at 0x7f32e77d5040>"
            ]
          },
          "metadata": {},
          "execution_count": 128
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "future = model1.make_future_dataframe(periods = days)\n",
        "future.tail(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "SGUpK48fn0ix",
        "outputId": "33094691-0881-47e8-eb6a-26e34c738fe2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           ds\n",
              "32 2023-02-21\n",
              "33 2023-02-22\n",
              "34 2023-02-23\n",
              "35 2023-02-24\n",
              "36 2023-02-25"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-17b3a7c4-eaf3-48fd-83e8-fdf3f4a6ee52\">\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>ds</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>2023-02-21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>2023-02-22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>2023-02-23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>2023-02-24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>2023-02-25</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-17b3a7c4-eaf3-48fd-83e8-fdf3f4a6ee52')\"\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-17b3a7c4-eaf3-48fd-83e8-fdf3f4a6ee52 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-17b3a7c4-eaf3-48fd-83e8-fdf3f4a6ee52');\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": 129
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "forecast = model1.predict(future)\n",
        "predicted_data['Date'] = forecast['ds']\n",
        "predicted_data['Humidity%'] = forecast['yhat']\n",
        "# forecast = forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']]\n",
        "forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']].tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "7-oPusERoBev",
        "outputId": "8ae61019-b249-4329-f10d-30a3592b6a1b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           ds       yhat  yhat_lower  yhat_upper\n",
              "32 2023-02-21  51.327467   29.332195   72.490708\n",
              "33 2023-02-22  55.204802   32.915952   73.969156\n",
              "34 2023-02-23  54.219886   32.279221   75.889967\n",
              "35 2023-02-24  56.362918   33.955412   77.824773\n",
              "36 2023-02-25  53.215721   31.932644   73.995633"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-53062e62-b0f6-4d0f-8276-29652a1a8429\">\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>ds</th>\n",
              "      <th>yhat</th>\n",
              "      <th>yhat_lower</th>\n",
              "      <th>yhat_upper</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>2023-02-21</td>\n",
              "      <td>51.327467</td>\n",
              "      <td>29.332195</td>\n",
              "      <td>72.490708</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>2023-02-22</td>\n",
              "      <td>55.204802</td>\n",
              "      <td>32.915952</td>\n",
              "      <td>73.969156</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>2023-02-23</td>\n",
              "      <td>54.219886</td>\n",
              "      <td>32.279221</td>\n",
              "      <td>75.889967</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>2023-02-24</td>\n",
              "      <td>56.362918</td>\n",
              "      <td>33.955412</td>\n",
              "      <td>77.824773</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>2023-02-25</td>\n",
              "      <td>53.215721</td>\n",
              "      <td>31.932644</td>\n",
              "      <td>73.995633</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-53062e62-b0f6-4d0f-8276-29652a1a8429')\"\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-53062e62-b0f6-4d0f-8276-29652a1a8429 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-53062e62-b0f6-4d0f-8276-29652a1a8429');\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": 130
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig = model1.plot(forecast,figsize=(26, 6))\n",
        "# fig = m.plot(forecast,figsize=(20, 12))\n",
        "a = add_changepoints_to_plot(fig.gca(), model1, forecast)\n",
        "plt.xlabel('Date')\n",
        "plt.ylabel('Humidity')\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "id": "ONLPZFvWqgyx",
        "outputId": "7ca81f6a-63b9-43b5-d7d1-6be050a2d634"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1872x432 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data1 = pd.read_csv('/content/Final dataset - CHITTAGONG 2023-01-20 to 2023-02-18.csv')\n",
        "data1.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "NbyF4yg8wmmS",
        "outputId": "c390445e-48e3-4184-a092-d41de02eabc9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Date     Time  Temparature(c)  Humidity%\n",
              "0  2023-01-20  0:00:00            15.2      71.49\n",
              "1  2023-01-20  1:00:00            16.9      48.94\n",
              "2  2023-01-20  2:00:00            16.3      50.14\n",
              "3  2023-01-20  3:00:00            14.2      71.55\n",
              "4  2023-01-20  4:00:00            15.6      52.07"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-de522859-8b4b-47f0-aba9-9627feafdbf4\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Time</th>\n",
              "      <th>Temparature(c)</th>\n",
              "      <th>Humidity%</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-01-20</td>\n",
              "      <td>0:00:00</td>\n",
              "      <td>15.2</td>\n",
              "      <td>71.49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2023-01-20</td>\n",
              "      <td>1:00:00</td>\n",
              "      <td>16.9</td>\n",
              "      <td>48.94</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2023-01-20</td>\n",
              "      <td>2:00:00</td>\n",
              "      <td>16.3</td>\n",
              "      <td>50.14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2023-01-20</td>\n",
              "      <td>3:00:00</td>\n",
              "      <td>14.2</td>\n",
              "      <td>71.55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2023-01-20</td>\n",
              "      <td>4:00:00</td>\n",
              "      <td>15.6</td>\n",
              "      <td>52.07</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-de522859-8b4b-47f0-aba9-9627feafdbf4')\"\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-de522859-8b4b-47f0-aba9-9627feafdbf4 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-de522859-8b4b-47f0-aba9-9627feafdbf4');\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": 133
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model2 = Prophet()"
      ],
      "metadata": {
        "id": "a0AkcGMMscV6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_for_temp=data1[['Date', 'Temparature(c)']]\n"
      ],
      "metadata": {
        "id": "oqJPT_Kzs2bQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_for_temp"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "f6xtOcCBwwRg",
        "outputId": "bd356b41-9b0d-4e11-a3d9-5fb535a2de29"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Date  Temparature(c)\n",
              "0    2023-01-20            15.2\n",
              "1    2023-01-20            16.9\n",
              "2    2023-01-20            16.3\n",
              "3    2023-01-20            14.2\n",
              "4    2023-01-20            15.6\n",
              "..          ...             ...\n",
              "715  2023-02-18            23.7\n",
              "716  2023-02-18            23.7\n",
              "717  2023-02-18            23.7\n",
              "718  2023-02-18            22.8\n",
              "719  2023-02-18            21.9\n",
              "\n",
              "[720 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-adb593bd-d1d9-403c-87cb-7451a503dd19\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Temparature(c)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-01-20</td>\n",
              "      <td>15.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2023-01-20</td>\n",
              "      <td>16.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2023-01-20</td>\n",
              "      <td>16.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2023-01-20</td>\n",
              "      <td>14.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2023-01-20</td>\n",
              "      <td>15.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>715</th>\n",
              "      <td>2023-02-18</td>\n",
              "      <td>23.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>716</th>\n",
              "      <td>2023-02-18</td>\n",
              "      <td>23.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>717</th>\n",
              "      <td>2023-02-18</td>\n",
              "      <td>23.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>718</th>\n",
              "      <td>2023-02-18</td>\n",
              "      <td>22.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>719</th>\n",
              "      <td>2023-02-18</td>\n",
              "      <td>21.9</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>720 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-adb593bd-d1d9-403c-87cb-7451a503dd19')\"\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-adb593bd-d1d9-403c-87cb-7451a503dd19 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-adb593bd-d1d9-403c-87cb-7451a503dd19');\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": 136
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_for_temp['Date']=pd.to_datetime(df_for_temp['Date'])\n",
        "df_for_temp.rename(columns = {'Date':'ds'}, inplace = True)\n",
        "df_for_temp.rename(columns = {'Temparature(c)': 'y'}, inplace = True)\n"
      ],
      "metadata": {
        "id": "tqeCtfSXw2ZI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_for_temp"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "h67n38xow7cY",
        "outputId": "b1060d9a-9da7-4667-a046-c65419549357"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            ds     y\n",
              "0   2023-01-20  15.2\n",
              "1   2023-01-20  16.9\n",
              "2   2023-01-20  16.3\n",
              "3   2023-01-20  14.2\n",
              "4   2023-01-20  15.6\n",
              "..         ...   ...\n",
              "715 2023-02-18  23.7\n",
              "716 2023-02-18  23.7\n",
              "717 2023-02-18  23.7\n",
              "718 2023-02-18  22.8\n",
              "719 2023-02-18  21.9\n",
              "\n",
              "[720 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-bf0c7da4-92dc-4ff5-b13c-f30e43913bdc\">\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>ds</th>\n",
              "      <th>y</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-01-20</td>\n",
              "      <td>15.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2023-01-20</td>\n",
              "      <td>16.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2023-01-20</td>\n",
              "      <td>16.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2023-01-20</td>\n",
              "      <td>14.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2023-01-20</td>\n",
              "      <td>15.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>715</th>\n",
              "      <td>2023-02-18</td>\n",
              "      <td>23.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>716</th>\n",
              "      <td>2023-02-18</td>\n",
              "      <td>23.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>717</th>\n",
              "      <td>2023-02-18</td>\n",
              "      <td>23.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>718</th>\n",
              "      <td>2023-02-18</td>\n",
              "      <td>22.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>719</th>\n",
              "      <td>2023-02-18</td>\n",
              "      <td>21.9</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>720 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bf0c7da4-92dc-4ff5-b13c-f30e43913bdc')\"\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-bf0c7da4-92dc-4ff5-b13c-f30e43913bdc 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-bf0c7da4-92dc-4ff5-b13c-f30e43913bdc');\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": 138
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model2.fit(df_for_temp)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Nez1lJc_xk3S",
        "outputId": "35a7a61f-d02c-484b-f181-fa2b5dcb7674"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:prophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.\n",
            "INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<prophet.forecaster.Prophet at 0x7f32e5e17bb0>"
            ]
          },
          "metadata": {},
          "execution_count": 139
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "future = model2.make_future_dataframe(periods = days)\n",
        "future.tail(5)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "lnROrMjdxgAh",
        "outputId": "fa7cf1e8-1b22-4da1-e770-a377bfc39351"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           ds\n",
              "32 2023-02-21\n",
              "33 2023-02-22\n",
              "34 2023-02-23\n",
              "35 2023-02-24\n",
              "36 2023-02-25"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-205b6c72-8193-442c-96ae-9f3e85131d4d\">\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>ds</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>2023-02-21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>2023-02-22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>2023-02-23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>2023-02-24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>2023-02-25</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-205b6c72-8193-442c-96ae-9f3e85131d4d')\"\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-205b6c72-8193-442c-96ae-9f3e85131d4d 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-205b6c72-8193-442c-96ae-9f3e85131d4d');\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": 140
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "forecast = model2.predict(future)\n",
        "predicted_data['Temparature(c)'] = forecast['yhat']\n",
        "# forecast = forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']]\n",
        "forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']].tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "5qiUdtNvxxnx",
        "outputId": "a33a6706-3caf-4fcf-ba9f-bcf983900156"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           ds       yhat  yhat_lower  yhat_upper\n",
              "32 2023-02-21  21.268647   17.082549   25.543922\n",
              "33 2023-02-22  21.520523   17.092971   25.461118\n",
              "34 2023-02-23  21.861993   17.218035   26.199335\n",
              "35 2023-02-24  21.044969   16.837732   25.517934\n",
              "36 2023-02-25  21.416377   16.881256   25.831405"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7d1f3a16-386d-4bf8-958e-08c90ed1d8e5\">\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>ds</th>\n",
              "      <th>yhat</th>\n",
              "      <th>yhat_lower</th>\n",
              "      <th>yhat_upper</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>2023-02-21</td>\n",
              "      <td>21.268647</td>\n",
              "      <td>17.082549</td>\n",
              "      <td>25.543922</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>2023-02-22</td>\n",
              "      <td>21.520523</td>\n",
              "      <td>17.092971</td>\n",
              "      <td>25.461118</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>2023-02-23</td>\n",
              "      <td>21.861993</td>\n",
              "      <td>17.218035</td>\n",
              "      <td>26.199335</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>2023-02-24</td>\n",
              "      <td>21.044969</td>\n",
              "      <td>16.837732</td>\n",
              "      <td>25.517934</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>2023-02-25</td>\n",
              "      <td>21.416377</td>\n",
              "      <td>16.881256</td>\n",
              "      <td>25.831405</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7d1f3a16-386d-4bf8-958e-08c90ed1d8e5')\"\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-7d1f3a16-386d-4bf8-958e-08c90ed1d8e5 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-7d1f3a16-386d-4bf8-958e-08c90ed1d8e5');\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": 141
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig = model2.plot(forecast,figsize=(26, 6))\n",
        "# fig = m.plot(forecast,figsize=(20, 12))\n",
        "a = add_changepoints_to_plot(fig.gca(), model2, forecast)\n",
        "plt.xlabel('Date')\n",
        "plt.ylabel('Temperature')\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "id": "xhxSHlBrx0D6",
        "outputId": "ce96111a-abed-4995-c353-dd81fc9f4600"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1872x432 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predicted_data.iloc[-7:,:]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "hAl2BoWux8ef",
        "outputId": "e955337f-92c2-4b3a-a230-c6a0dcfb5939"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Date  Humidity%  Temparature(c)\n",
              "30 2023-02-19  59.496735       21.509351\n",
              "31 2023-02-20  53.614904       21.717620\n",
              "32 2023-02-21  51.327467       21.268647\n",
              "33 2023-02-22  55.204802       21.520523\n",
              "34 2023-02-23  54.219886       21.861993\n",
              "35 2023-02-24  56.362918       21.044969\n",
              "36 2023-02-25  53.215721       21.416377"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-37f370f4-09fb-4e6c-aee5-58fa27fa2172\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Humidity%</th>\n",
              "      <th>Temparature(c)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>2023-02-19</td>\n",
              "      <td>59.496735</td>\n",
              "      <td>21.509351</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>2023-02-20</td>\n",
              "      <td>53.614904</td>\n",
              "      <td>21.717620</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>2023-02-21</td>\n",
              "      <td>51.327467</td>\n",
              "      <td>21.268647</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>2023-02-22</td>\n",
              "      <td>55.204802</td>\n",
              "      <td>21.520523</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>2023-02-23</td>\n",
              "      <td>54.219886</td>\n",
              "      <td>21.861993</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>2023-02-24</td>\n",
              "      <td>56.362918</td>\n",
              "      <td>21.044969</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>2023-02-25</td>\n",
              "      <td>53.215721</td>\n",
              "      <td>21.416377</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-37f370f4-09fb-4e6c-aee5-58fa27fa2172')\"\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-37f370f4-09fb-4e6c-aee5-58fa27fa2172 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-37f370f4-09fb-4e6c-aee5-58fa27fa2172');\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": 144
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import linear_model\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import mean_squared_error, r2_score\n",
        "\n",
        "ls = linear_model.LinearRegression()\n",
        "\n",
        "\n",
        "\n",
        "X = data[\"Humidity%\"].values.reshape(-1,1)\n",
        "y = data[\"Temparature(c)\"].values.reshape(-1,1)\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, \n",
        "                                                    y, \n",
        "                                                    test_size=0.33, \n",
        "                                                    shuffle=True, random_state=0)\n",
        "print(\"Linear Regression\")\n",
        "ls.fit(X_train, y_train)\n",
        "print(\"alpha = \",ls.coef_[0])\n",
        "print(\"beta = \",ls.intercept_)\n",
        "print(\"\\n\\nCalculating some regression quality metrics\")\n",
        "y_pred = ls.predict(X_test)\n",
        "print(\"MSE = \",mean_squared_error(y_test, y_pred))\n",
        "print(\"R2 = \",r2_score(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1vWg3h_SR9e9",
        "outputId": "90e151d9-65a8-458b-c5ec-20f02f440659"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Linear Regression\n",
            "alpha =  [-0.10560347]\n",
            "beta =  [28.49799402]\n",
            "\n",
            "\n",
            "Calculating some regression quality metrics\n",
            "MSE =  8.87122800689842\n",
            "R2 =  0.2617854287631435\n"
          ]
        }
      ]
    }
  ]
}