main.ipynb

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      "name": "Untitled0.ipynb",
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      "include_colab_link": true
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      "name": "python3",
      "display_name": "Python 3"
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      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/AmanCSE-1/Smart-Weather-Prediction-using-ML/blob/main/main.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ld4OVVrw51cN"
      },
      "source": [
        "<em><b>Objective :</b> Weather forecasting is simply the prediction of future weather based on different parameters of the past like temperature, humidity, dew, wind speed and direction, precipitation, Haze and contents of air, Solar and terrestrial radiation etc. Weather forecast is an essential factor affecting people’s lives. Weather forecast notices are important because they can be used to prevent destruction of life and environment.</em>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-a_2U0egrJQN"
      },
      "source": [
        "# **Importing Libraries and Loading Dataset**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YoxBFSVXK1So"
      },
      "source": [
        "# Importing the fundamental libraries of data science\n",
        "\n",
        "import pandas as pd                       # For Data Analysis and Preprocessing \n",
        "import numpy as np                        # For mathematical operations on data \n",
        "import matplotlib.pyplot as plt           # For Visualization and setting graph elements\n",
        "import seaborn as sns                     # Consists of easy and interactive statistical graphics "
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "JBhT8yFuVsUH",
        "outputId": "dcb6e7cc-da78-4f9f-c873-c2388c5f5f9d"
      },
      "source": [
        "url = \"https://raw.githubusercontent.com/AmanCSE-1/Smart-Weather-Prediction-using-ML/main/India%20Weather%20History.csv\"\n",
        "df = pd.read_csv(url)                     # Reading the dataset\n",
        "\n",
        "df.head()                                 # Printing the first 5 rows of the dataset"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    }\n",
              "\n",
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              "    }\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Formatted Date</th>\n",
              "      <th>Summary</th>\n",
              "      <th>Precip Type</th>\n",
              "      <th>Temperature (C)</th>\n",
              "      <th>Apparent Temperature (C)</th>\n",
              "      <th>Humidity</th>\n",
              "      <th>Wind Speed (km/h)</th>\n",
              "      <th>Wind Bearing (degrees)</th>\n",
              "      <th>Visibility (km)</th>\n",
              "      <th>Loud Cover</th>\n",
              "      <th>Pressure (millibars)</th>\n",
              "      <th>Daily Summary</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2006-04-01 00:00:00.000 +0200</td>\n",
              "      <td>Partly Cloudy</td>\n",
              "      <td>rain</td>\n",
              "      <td>9.472222</td>\n",
              "      <td>7.388889</td>\n",
              "      <td>0.89</td>\n",
              "      <td>14.1197</td>\n",
              "      <td>251.0</td>\n",
              "      <td>15.8263</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1015.13</td>\n",
              "      <td>Partly cloudy throughout the day.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2006-04-01 01:00:00.000 +0200</td>\n",
              "      <td>Partly Cloudy</td>\n",
              "      <td>rain</td>\n",
              "      <td>9.355556</td>\n",
              "      <td>7.227778</td>\n",
              "      <td>0.86</td>\n",
              "      <td>14.2646</td>\n",
              "      <td>259.0</td>\n",
              "      <td>15.8263</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1015.63</td>\n",
              "      <td>Partly cloudy throughout the day.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2006-04-01 02:00:00.000 +0200</td>\n",
              "      <td>Mostly Cloudy</td>\n",
              "      <td>rain</td>\n",
              "      <td>9.377778</td>\n",
              "      <td>9.377778</td>\n",
              "      <td>0.89</td>\n",
              "      <td>3.9284</td>\n",
              "      <td>204.0</td>\n",
              "      <td>14.9569</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1015.94</td>\n",
              "      <td>Partly cloudy throughout the day.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2006-04-01 03:00:00.000 +0200</td>\n",
              "      <td>Partly Cloudy</td>\n",
              "      <td>rain</td>\n",
              "      <td>8.288889</td>\n",
              "      <td>5.944444</td>\n",
              "      <td>0.83</td>\n",
              "      <td>14.1036</td>\n",
              "      <td>269.0</td>\n",
              "      <td>15.8263</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1016.41</td>\n",
              "      <td>Partly cloudy throughout the day.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2006-04-01 04:00:00.000 +0200</td>\n",
              "      <td>Mostly Cloudy</td>\n",
              "      <td>rain</td>\n",
              "      <td>8.755556</td>\n",
              "      <td>6.977778</td>\n",
              "      <td>0.83</td>\n",
              "      <td>11.0446</td>\n",
              "      <td>259.0</td>\n",
              "      <td>15.8263</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1016.51</td>\n",
              "      <td>Partly cloudy throughout the day.</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  Formatted Date  ...                      Daily Summary\n",
              "0  2006-04-01 00:00:00.000 +0200  ...  Partly cloudy throughout the day.\n",
              "1  2006-04-01 01:00:00.000 +0200  ...  Partly cloudy throughout the day.\n",
              "2  2006-04-01 02:00:00.000 +0200  ...  Partly cloudy throughout the day.\n",
              "3  2006-04-01 03:00:00.000 +0200  ...  Partly cloudy throughout the day.\n",
              "4  2006-04-01 04:00:00.000 +0200  ...  Partly cloudy throughout the day.\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3cK9hePDVxoG",
        "outputId": "1e0bd14b-b5cf-436f-e863-5007c8b383e9"
      },
      "source": [
        "df.shape                          # Returns the number of rows and number of columns of dataset"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(96453, 12)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WYUcEqSBWgCV",
        "outputId": "9b6c0e09-5bd1-457f-a590-8b7bc32347d9"
      },
      "source": [
        "df.columns                        # Returns the name of columns of dataset"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Formatted Date', 'Summary', 'Precip Type', 'Temperature (C)',\n",
              "       'Apparent Temperature (C)', 'Humidity', 'Wind Speed (km/h)',\n",
              "       'Wind Bearing (degrees)', 'Visibility (km)', 'Loud Cover',\n",
              "       'Pressure (millibars)', 'Daily Summary'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y8XdZ8xhq__T"
      },
      "source": [
        "# **Exploratory Data Analysis**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S5GPaNwMq-rx",
        "outputId": "7432a382-28e6-452d-c7ea-29cd624d9b35"
      },
      "source": [
        "df.info()           # to get a consice summary of the dataframe"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 96453 entries, 0 to 96452\n",
            "Data columns (total 12 columns):\n",
            " #   Column                    Non-Null Count  Dtype  \n",
            "---  ------                    --------------  -----  \n",
            " 0   Formatted Date            96453 non-null  object \n",
            " 1   Summary                   96453 non-null  object \n",
            " 2   Precip Type               95936 non-null  object \n",
            " 3   Temperature (C)           96453 non-null  float64\n",
            " 4   Apparent Temperature (C)  96453 non-null  float64\n",
            " 5   Humidity                  96453 non-null  float64\n",
            " 6   Wind Speed (km/h)         96453 non-null  float64\n",
            " 7   Wind Bearing (degrees)    96453 non-null  float64\n",
            " 8   Visibility (km)           96453 non-null  float64\n",
            " 9   Loud Cover                96453 non-null  float64\n",
            " 10  Pressure (millibars)      96453 non-null  float64\n",
            " 11  Daily Summary             96453 non-null  object \n",
            "dtypes: float64(8), object(4)\n",
            "memory usage: 8.8+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ClRw8p9pEH0l"
      },
      "source": [
        "We observe that 'Formatted Date' column has datatype 'Object'. This doesn't helps us, as python has not recognised it as datetime type. \n",
        "So, we will change the datatype of 'Formatted Date' column."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-f90TPBhCHwA",
        "outputId": "074abb33-67c0-4b8c-dfa4-ca6d31c61a65"
      },
      "source": [
        "# Converts arguement to datetime \n",
        "df['Formatted Date'] = pd.to_datetime(df['Formatted Date'], utc=True)\n",
        "\n",
        "# Checking the datatype of the column by using dtype attribute\n",
        "df['Formatted Date'].dtype"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "datetime64[ns, UTC]"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bE8LY4RhQCif"
      },
      "source": [
        "import datetime as dt\n",
        "\n",
        "# Extracting Date (yyyy-mm-dd) from 'Formatted Date' column\n",
        "df['Date'] = df['Formatted Date'].dt.date\n",
        "df['Date'] = pd.to_datetime(df['Date'])\n",
        "\n",
        "# Extracting Time (hh) from 'Formatted Date' column (Note: We are only extracting hour because all observations have {mm-ss} as {00-00})\n",
        "df['Time'] = df['Formatted Date'].dt.hour\n",
        "\n",
        "df.drop(columns='Formatted Date', inplace=True)                 # Now, we drop the Formatted Date column"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I85JozjtT-rr"
      },
      "source": [
        "# We want to place the recently created columns-'Date' and 'Time' as first two columns in our DataFrame\n",
        "# So, first we pop them out and insert them into 0th and 1th position respectively\n",
        "\n",
        "first_column = df.pop('Date')\n",
        "second_column = df.pop('Time')\n",
        "\n",
        "df.insert(0, 'Date', first_column)\n",
        "df.insert(1, 'Time', second_column)"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "Q6E9fhcYQwi3",
        "outputId": "445e9130-63a5-4898-d3c6-3065e4d67acd"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<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>Summary</th>\n",
              "      <th>Precip Type</th>\n",
              "      <th>Temperature (C)</th>\n",
              "      <th>Apparent Temperature (C)</th>\n",
              "      <th>Humidity</th>\n",
              "      <th>Wind Speed (km/h)</th>\n",
              "      <th>Wind Bearing (degrees)</th>\n",
              "      <th>Visibility (km)</th>\n",
              "      <th>Loud Cover</th>\n",
              "      <th>Pressure (millibars)</th>\n",
              "      <th>Daily Summary</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2006-03-31</td>\n",
              "      <td>22</td>\n",
              "      <td>Partly Cloudy</td>\n",
              "      <td>rain</td>\n",
              "      <td>9.472222</td>\n",
              "      <td>7.388889</td>\n",
              "      <td>0.89</td>\n",
              "      <td>14.1197</td>\n",
              "      <td>251.0</td>\n",
              "      <td>15.8263</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1015.13</td>\n",
              "      <td>Partly cloudy throughout the day.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2006-03-31</td>\n",
              "      <td>23</td>\n",
              "      <td>Partly Cloudy</td>\n",
              "      <td>rain</td>\n",
              "      <td>9.355556</td>\n",
              "      <td>7.227778</td>\n",
              "      <td>0.86</td>\n",
              "      <td>14.2646</td>\n",
              "      <td>259.0</td>\n",
              "      <td>15.8263</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1015.63</td>\n",
              "      <td>Partly cloudy throughout the day.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2006-04-01</td>\n",
              "      <td>0</td>\n",
              "      <td>Mostly Cloudy</td>\n",
              "      <td>rain</td>\n",
              "      <td>9.377778</td>\n",
              "      <td>9.377778</td>\n",
              "      <td>0.89</td>\n",
              "      <td>3.9284</td>\n",
              "      <td>204.0</td>\n",
              "      <td>14.9569</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1015.94</td>\n",
              "      <td>Partly cloudy throughout the day.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2006-04-01</td>\n",
              "      <td>1</td>\n",
              "      <td>Partly Cloudy</td>\n",
              "      <td>rain</td>\n",
              "      <td>8.288889</td>\n",
              "      <td>5.944444</td>\n",
              "      <td>0.83</td>\n",
              "      <td>14.1036</td>\n",
              "      <td>269.0</td>\n",
              "      <td>15.8263</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1016.41</td>\n",
              "      <td>Partly cloudy throughout the day.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2006-04-01</td>\n",
              "      <td>2</td>\n",
              "      <td>Mostly Cloudy</td>\n",
              "      <td>rain</td>\n",
              "      <td>8.755556</td>\n",
              "      <td>6.977778</td>\n",
              "      <td>0.83</td>\n",
              "      <td>11.0446</td>\n",
              "      <td>259.0</td>\n",
              "      <td>15.8263</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1016.51</td>\n",
              "      <td>Partly cloudy throughout the day.</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Date  Time  ... Pressure (millibars)                      Daily Summary\n",
              "0 2006-03-31    22  ...              1015.13  Partly cloudy throughout the day.\n",
              "1 2006-03-31    23  ...              1015.63  Partly cloudy throughout the day.\n",
              "2 2006-04-01     0  ...              1015.94  Partly cloudy throughout the day.\n",
              "3 2006-04-01     1  ...              1016.41  Partly cloudy throughout the day.\n",
              "4 2006-04-01     2  ...              1016.51  Partly cloudy throughout the day.\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "-gCkfckgrZ5K",
        "outputId": "80f3b69f-3e76-447c-8a1f-f0eb1876ba95"
      },
      "source": [
        "df.describe()         # Used to describe various statistical measures for quantitative columns\n",
        "                      # Mean, Median(Q2) describes the characteristic of distribution of column\n",
        "                      # 25% (Q1),  50% (Q2) and 75 (Q3) are Quartiles of the features.\n",
        "                      # std is Standard Deviation i.e. depicts spread  of the distribution\n",
        "                      # min and max are known to everybody :)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Time</th>\n",
              "      <th>Temperature (C)</th>\n",
              "      <th>Apparent Temperature (C)</th>\n",
              "      <th>Humidity</th>\n",
              "      <th>Wind Speed (km/h)</th>\n",
              "      <th>Wind Bearing (degrees)</th>\n",
              "      <th>Visibility (km)</th>\n",
              "      <th>Loud Cover</th>\n",
              "      <th>Pressure (millibars)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>96453.000000</td>\n",
              "      <td>96453.000000</td>\n",
              "      <td>96453.000000</td>\n",
              "      <td>96453.000000</td>\n",
              "      <td>96453.000000</td>\n",
              "      <td>96453.000000</td>\n",
              "      <td>96453.000000</td>\n",
              "      <td>96453.0</td>\n",
              "      <td>96453.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>11.499891</td>\n",
              "      <td>11.932678</td>\n",
              "      <td>10.855029</td>\n",
              "      <td>0.734899</td>\n",
              "      <td>10.810640</td>\n",
              "      <td>187.509232</td>\n",
              "      <td>10.347325</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1003.235956</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>6.922049</td>\n",
              "      <td>9.551546</td>\n",
              "      <td>10.696847</td>\n",
              "      <td>0.195473</td>\n",
              "      <td>6.913571</td>\n",
              "      <td>107.383428</td>\n",
              "      <td>4.192123</td>\n",
              "      <td>0.0</td>\n",
              "      <td>116.969906</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>-21.822222</td>\n",
              "      <td>-27.716667</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>6.000000</td>\n",
              "      <td>4.688889</td>\n",
              "      <td>2.311111</td>\n",
              "      <td>0.600000</td>\n",
              "      <td>5.828200</td>\n",
              "      <td>116.000000</td>\n",
              "      <td>8.339800</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1011.900000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>11.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>0.780000</td>\n",
              "      <td>9.965900</td>\n",
              "      <td>180.000000</td>\n",
              "      <td>10.046400</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1016.450000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>17.000000</td>\n",
              "      <td>18.838889</td>\n",
              "      <td>18.838889</td>\n",
              "      <td>0.890000</td>\n",
              "      <td>14.135800</td>\n",
              "      <td>290.000000</td>\n",
              "      <td>14.812000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1021.090000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>23.000000</td>\n",
              "      <td>39.905556</td>\n",
              "      <td>39.344444</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>63.852600</td>\n",
              "      <td>359.000000</td>\n",
              "      <td>16.100000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1046.380000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               Time  Temperature (C)  ...  Loud Cover  Pressure (millibars)\n",
              "count  96453.000000     96453.000000  ...     96453.0          96453.000000\n",
              "mean      11.499891        11.932678  ...         0.0           1003.235956\n",
              "std        6.922049         9.551546  ...         0.0            116.969906\n",
              "min        0.000000       -21.822222  ...         0.0              0.000000\n",
              "25%        6.000000         4.688889  ...         0.0           1011.900000\n",
              "50%       11.000000        12.000000  ...         0.0           1016.450000\n",
              "75%       17.000000        18.838889  ...         0.0           1021.090000\n",
              "max       23.000000        39.905556  ...         0.0           1046.380000\n",
              "\n",
              "[8 rows x 9 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t949Uc8_rbpg",
        "outputId": "a97b3785-f307-447a-a3c4-84c0c933b13a"
      },
      "source": [
        "df.isna().sum()             # Returns the sum of rows that contains null values for each column"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Date                          0\n",
              "Time                          0\n",
              "Summary                       0\n",
              "Precip Type                 517\n",
              "Temperature (C)               0\n",
              "Apparent Temperature (C)      0\n",
              "Humidity                      0\n",
              "Wind Speed (km/h)             0\n",
              "Wind Bearing (degrees)        0\n",
              "Visibility (km)               0\n",
              "Loud Cover                    0\n",
              "Pressure (millibars)          0\n",
              "Daily Summary                 0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yQzTDMVCq-xu"
      },
      "source": [
        "# Dropping the 'Loud Cover' Column as it contains all rows with value '0'.\n",
        "# It will not add any value to the model\n",
        "\n",
        "df.drop('Loud Cover', axis=1, inplace=True)"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TVTqWptx8RnJ"
      },
      "source": [
        "We will predict Summary, Temperature and Precip Type features for our project.\n",
        "\n",
        "*   Summary is Categorical variable; so it would require classification algorithms.\n",
        "*   Temperature is a quantitative variable; hence we would use multiple regression.\n",
        "*   Precip Type is also a categorical variable; so we will use classification algorithm\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CCZUtMewR4QZ"
      },
      "source": [
        "**EDA on Temperature Feature**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F_DP6e9XRDx5"
      },
      "source": [
        "month = df['Date'].dt.month\n",
        "\n",
        "monthly_avg = df.groupby(month)['Temperature (C)'].mean()"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WagwuqlQRMJY"
      },
      "source": [
        "year = df['Date'].dt.year\n",
        "\n",
        "annual_avg = df.groupby(year)['Temperature (C)'].mean()"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "pu4XxL6jRufB",
        "outputId": "e76b3bcf-9d60-4895-bebe-932b8b920e8e"
      },
      "source": [
        "plt.subplots_adjust(left=0, right=2, bottom=0, top=1, wspace=0.3, hspace=0.1)\n",
        "\n",
        "plt.subplot(1, 2, 1)\n",
        "month_abb = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']\n",
        "sns.lineplot(x=month_abb, y=monthly_avg.values, color='blue', linestyle=\"-\")\n",
        "plt.xlabel('Months')\n",
        "plt.ylabel(\"Average Temperature in \\N{DEGREE SIGN}C\")\n",
        "plt.title('Variation of Mean Monthly Temperature')\n",
        "\n",
        "plt.subplot(1, 2, 2)\n",
        "sns.lineplot(x=annual_avg.index, y=annual_avg.values, color='orange', linestyle=\"solid\")\n",
        "plt.xlim([2006, 2016])\n",
        "plt.xlabel('Year')\n",
        "plt.ylabel(\"Average Temperature in \\N{DEGREE SIGN}C\")\n",
        "plt.title('Variation of Mean Annual Temperature')\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "ciesFXFYF_Jt",
        "outputId": "89732e31-cb46-4dee-ba5c-e2b1dc4aa08a"
      },
      "source": [
        "# To visualize the relation the relation between two columns, we will plot scatterplot\n",
        "# Here, our predictor variable for Linear Regression model is Temperature\n",
        "\n",
        "# used to adjust the subplot size and spacing between them\n",
        "plt.subplots_adjust(left=0, right=2, bottom=0, top=1, wspace=0.3, hspace=0.1)\n",
        "\n",
        "# First subplot - Relation between Temperature and Humidity\n",
        "plt.subplot(1,2,1)\n",
        "plt.title('Relation between Temperature and Humidity')\n",
        "sns.scatterplot(data=df.sample(1000, random_state=1), x='Temperature (C)', y='Humidity')\n",
        "\n",
        "# Second subplot - Relation between Temperature and Apparent Temperature\n",
        "plt.subplot(1,2,2)\n",
        "plt.title('Relation between Temperature and Apparent Temperature')\n",
        "sns.scatterplot(data=df.sample(1000, random_state=1), x='Temperature (C)', y='Apparent Temperature (C)')\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qLniUBYi6yKS",
        "outputId": "10585711-7d43-4c16-b2d7-a23ec88e1ab5"
      },
      "source": [
        "# To begin with, we will check the number of different categories in Summary feature\n",
        "\n",
        "df['Summary'].nunique()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "27"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DjSTB3q461lZ",
        "outputId": "d53ab818-0462-4167-adc2-57d84c2f2425"
      },
      "source": [
        "#  Since, we get 27 categories, lets first check the occurence of each of the categories; \n",
        "#  to if there are categories who have least occurence (they may be outliers/noise) '''\n",
        "\n",
        "df['Summary'].value_counts()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Partly Cloudy                          31733\n",
              "Mostly Cloudy                          28094\n",
              "Overcast                               16597\n",
              "Clear                                  10890\n",
              "Foggy                                   7148\n",
              "Breezy and Overcast                      528\n",
              "Breezy and Mostly Cloudy                 516\n",
              "Breezy and Partly Cloudy                 386\n",
              "Dry and Partly Cloudy                     86\n",
              "Windy and Partly Cloudy                   67\n",
              "Light Rain                                63\n",
              "Breezy                                    54\n",
              "Windy and Overcast                        45\n",
              "Humid and Mostly Cloudy                   40\n",
              "Drizzle                                   39\n",
              "Windy and Mostly Cloudy                   35\n",
              "Breezy and Foggy                          35\n",
              "Dry                                       34\n",
              "Humid and Partly Cloudy                   17\n",
              "Dry and Mostly Cloudy                     14\n",
              "Rain                                      10\n",
              "Windy                                      8\n",
              "Humid and Overcast                         7\n",
              "Windy and Foggy                            4\n",
              "Breezy and Dry                             1\n",
              "Windy and Dry                              1\n",
              "Dangerously Windy and Partly Cloudy        1\n",
              "Name: Summary, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iSupOf7y64rT",
        "outputId": "88522695-b6a0-43ea-85c1-80481441ead1"
      },
      "source": [
        "df['Precip Type'].nunique()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "KoxGtO346_QF",
        "outputId": "6adf8009-5a23-44fb-a0c4-6a2b1a4612c3"
      },
      "source": [
        "plt.figure(figsize=(8,6))\n",
        "precip = sns.countplot(x=df['Precip Type'])\n",
        "plt.title('Graph of occurence of values of Precip Type')\n",
        "\n",
        "for p in precip.patches:\n",
        "  txt = str(round(p.get_height()/df.shape[0]*100, 2)) + '%'\n",
        "  txt_x = p.get_x() + p.get_width()/2 -0.07\n",
        "  txt_y = p.get_height()+400\n",
        "  precip.text(txt_x, txt_y, txt, size=12)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "2X8vRONSCe5O",
        "outputId": "e0d06e2c-e322-4d5f-dcf2-68d4c10aa952"
      },
      "source": [
        "df.groupby('Precip Type').mean()"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Time</th>\n",
              "      <th>Temperature (C)</th>\n",
              "      <th>Apparent Temperature (C)</th>\n",
              "      <th>Humidity</th>\n",
              "      <th>Wind Speed (km/h)</th>\n",
              "      <th>Wind Bearing (degrees)</th>\n",
              "      <th>Visibility (km)</th>\n",
              "      <th>Pressure (millibars)</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Precip Type</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>rain</th>\n",
              "      <td>11.623651</td>\n",
              "      <td>13.852989</td>\n",
              "      <td>13.013425</td>\n",
              "      <td>0.718699</td>\n",
              "      <td>10.971219</td>\n",
              "      <td>189.123170</td>\n",
              "      <td>10.830711</td>\n",
              "      <td>1002.764548</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>snow</th>\n",
              "      <td>10.497573</td>\n",
              "      <td>-3.270885</td>\n",
              "      <td>-6.249847</td>\n",
              "      <td>0.863263</td>\n",
              "      <td>9.481998</td>\n",
              "      <td>174.754294</td>\n",
              "      <td>6.636570</td>\n",
              "      <td>1006.216970</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  Time  Temperature (C)  ...  Visibility (km)  Pressure (millibars)\n",
              "Precip Type                              ...                                       \n",
              "rain         11.623651        13.852989  ...        10.830711           1002.764548\n",
              "snow         10.497573        -3.270885  ...         6.636570           1006.216970\n",
              "\n",
              "[2 rows x 8 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hjj9GeUuAZc0"
      },
      "source": [
        "# **Data Preprocessing**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_CKKTLz2At6u"
      },
      "source": [
        "First we will perform preprocess the main response variable-'Summary' Feature."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y1FF5oyyApng"
      },
      "source": [
        "**Summary Feature**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ShuHRIZZ7Cot",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "83e7c3a6-b1f2-4668-e16d-3269d4e609bc"
      },
      "source": [
        "plt.figure(figsize=(14,7))\n",
        "\n",
        "df['Summary'].value_counts().plot(kind='bar')\n",
        "\n",
        "plt.ylabel('Count')\n",
        "plt.xlabel('Weather Summary')\n",
        "plt.title('Visualization of Weather Summary Feature')\n",
        "plt.xticks(rotation=80)\n",
        "plt.show()"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1008x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zDTk1UZrBNgU"
      },
      "source": [
        "By the above visualization plot, we can infer that most of the data lies in the first five categories of Weather Summary Feature. The other categories have infinitesimal occurences. Since, this is a qualitative feature, we must clean this rare occuring categories from out dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "724KAe5ABOYP",
        "outputId": "43678502-7247-45af-ac5b-c3596b2efc4f"
      },
      "source": [
        "# We will first estimate their percentage of occurence within the dataset.\n",
        "\n",
        "# The below code uses \"Boolean Masking\" technique of python.\n",
        "# Here, we will not consider the rows have ['Partly Cloudy', 'Mostly Cloudy', 'Overcast', 'Foggy' and 'Clear']\n",
        "df[(df['Summary']!='Partly Cloudy') & (df['Summary']!='Mostly Cloudy') &\n",
        "     (df['Summary']!='Overcast') & (df['Summary']!='Foggy') & (df['Summary']!='Clear')].shape[0]/df.shape[0]*100"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2.0642178055633313"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Okm06icyEJfS"
      },
      "source": [
        "Only 2% of our data not lies in the top five categories of Summary Feature. \n",
        "\n",
        "But, instead of dropping them all, we observe that there are categories which are intersection between two weather summary. Like, 'Breezy and Partly Cloudy' is the data recorded when weather was both Breezy and Partly Cloudy. Instead of dropping this observations (rows), we will add their occurence to 'Partly Cloudy'\n",
        "\n",
        "Similarly, with other categories as well."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GakxXjrJllQw"
      },
      "source": [
        "# storing the index for unclean data for preprocessing\n",
        "uncleanData_index = df[(df['Summary']!='Partly Cloudy') & (df['Summary']!='Mostly Cloudy') &\n",
        "     (df['Summary']!='Overcast') & (df['Summary']!='Foggy') & (df['Summary']!='Clear')].index"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MyS9XjaOBWhD"
      },
      "source": [
        "# Creating a copy of dataset\n",
        "df_preprocessed = df.copy()\n",
        "\n",
        "# Iterating through the unclean_data index\n",
        "for row in uncleanData_index:\n",
        "\n",
        "      # Using string matching technique\n",
        "      if 'Partly Cloudy' in df_preprocessed['Summary'][row]:\n",
        "          df_preprocessed.loc[row, 'Summary'] = 'Partly Cloudy'\n",
        "          \n",
        "      elif 'Mostly Cloudy' in df_preprocessed['Summary'][row]:\n",
        "          df_preprocessed.loc[row, 'Summary'] = 'Mostly Cloudy'\n",
        "          \n",
        "      elif 'Overcast' in df_preprocessed['Summary'][row]:\n",
        "          df_preprocessed.loc[row, 'Summary'] = 'Overcast'\n",
        "      \n",
        "      elif 'Foggy' in df_preprocessed['Summary'][row]:\n",
        "          df_preprocessed.loc[row, 'Summary'] = 'Foggy'"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WSfb45KcBw4H",
        "outputId": "6e89d15f-c21a-4a4d-8bba-89e94916404d"
      },
      "source": [
        "# We check that preprocessed data has retained all the rows of the original dataset \n",
        "\n",
        "df_preprocessed.shape"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(96453, 12)"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xT-3Ope6cMUt",
        "outputId": "e3293896-63e3-4768-cd38-dd8d54f2fa46"
      },
      "source": [
        "df_preprocessed['Summary'].nunique()"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "13"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R4SYavZdl8Xk",
        "outputId": "bf069e1e-6dcd-4619-d019-5d2c30961063"
      },
      "source": [
        "df_preprocessed['Summary'].value_counts()"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Partly Cloudy     32290\n",
              "Mostly Cloudy     28699\n",
              "Overcast          17177\n",
              "Clear             10890\n",
              "Foggy              7187\n",
              "Light Rain           63\n",
              "Breezy               54\n",
              "Drizzle              39\n",
              "Dry                  34\n",
              "Rain                 10\n",
              "Windy                 8\n",
              "Breezy and Dry        1\n",
              "Windy and Dry         1\n",
              "Name: Summary, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4Vo_9JF6mbrL"
      },
      "source": [
        "uncleanData_index = df_preprocessed[(df_preprocessed['Summary']!='Partly Cloudy') & (df_preprocessed['Summary']!='Mostly Cloudy') &\n",
        "     (df_preprocessed['Summary']!='Overcast') & (df_preprocessed['Summary']!='Foggy') & (df_preprocessed['Summary']!='Clear')].index"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FVotBUm9mBZ6",
        "outputId": "73a9e5c9-bfbb-4dd1-b110-d319ec718eea"
      },
      "source": [
        "uncleanData_index.shape[0]/df.shape[0]*100"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.21772262138036141"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dg9fBdddmMDj"
      },
      "source": [
        "df_preprocessed.drop(index=uncleanData_index, inplace=True)"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "4Cb9tCmIm3Cl",
        "outputId": "58cf0012-caf4-4a56-eccb-3da7c596cec6"
      },
      "source": [
        "plt.figure(figsize=(14,8))\n",
        "\n",
        "df_preprocessed['Summary'].value_counts().plot(kind='bar')\n",
        "\n",
        "plt.ylabel('Count')\n",
        "plt.xlabel('Weather Summary')\n",
        "plt.title('Summary Feature after Preprocessing')\n",
        "plt.xticks(rotation=0)\n",
        "plt.show()"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1008x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7XcfNaZNkFrL"
      },
      "source": [
        "**Studying Coorelation between Features**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "iPAkX9SbkMCV",
        "outputId": "61ee2db3-46b3-4df0-e118-f1c16b5f5237"
      },
      "source": [
        "plt.figure(figsize=(12,6))\n",
        "sns.heatmap(data=df_preprocessed.corr(), annot=True, fmt='.2f', cmap='Blues', xticklabels=False)\n",
        "plt.show()"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XYxj1soukeMR"
      },
      "source": [
        "Temperature has very strong positive correlation with 'Apparent Temprature'<br />&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;\n",
        "                moderate negative correlation with 'Humidity'<br />&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;\n",
        "                moderate positive correaltion with 'Visibility'<br />&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;\n",
        "                no correlation with 'Wind Speed', 'Wind Bearing' and 'Pressure'"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F4H-RQFNC4_O"
      },
      "source": [
        "**Precip Type Feature**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RVwtaZP8C_VC",
        "outputId": "34ca68da-3142-4e1f-cb28-ed92bccb78df"
      },
      "source": [
        "df_preprocessed.isna().sum()"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Date                          0\n",
              "Time                          0\n",
              "Summary                       0\n",
              "Precip Type                 517\n",
              "Temperature (C)               0\n",
              "Apparent Temperature (C)      0\n",
              "Humidity                      0\n",
              "Wind Speed (km/h)             0\n",
              "Wind Bearing (degrees)        0\n",
              "Visibility (km)               0\n",
              "Pressure (millibars)          0\n",
              "Daily Summary                 0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DI2O9hwgDEcM",
        "outputId": "0c9e092a-5993-4355-93c1-9dbe36df547e"
      },
      "source": [
        "precip_na_index = df_preprocessed.loc[pd.isna(df_preprocessed['Precip Type']), :].index\n",
        "\n",
        "for row in precip_na_index:\n",
        "    if df_preprocessed.loc[row, 'Temperature (C)']<=0:\n",
        "        df_preprocessed.loc[row, 'Precip Type'] = 'snow'\n",
        "    else :\n",
        "        df_preprocessed.loc[row, 'Precip Type'] = 'rain'\n",
        "\n",
        "df_preprocessed['Precip Type'].isna().sum()"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xH85sHDQVwbq"
      },
      "source": [
        "# Data Modelling"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sC5kg_KLVzhD"
      },
      "source": [
        "from sklearn.preprocessing import LabelBinarizer, LabelEncoder\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import r2_score, mean_squared_error, confusion_matrix, accuracy_score, classification_report\n",
        "import joblib"
      ],
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jDhPw5c7V9Rp"
      },
      "source": [
        "**Linear Regression - Predicting Temperature**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8xwrQgGFV7BA"
      },
      "source": [
        "## Model Selection - Splitting Training and Test Dataset\n",
        "x = df_preprocessed.loc[:, ['Apparent Temperature (C)', 'Humidity']]\n",
        "y = df_preprocessed.loc[:, 'Temperature (C)']\n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=1)"
      ],
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0szO24W4WHJV"
      },
      "source": [
        "## Building the Model\n",
        "from sklearn.linear_model import LinearRegression\n",
        "\n",
        "lrmodel = LinearRegression()\n",
        "\n",
        "lrmodel.fit(x_train, y_train)\n",
        "temp_predict = lrmodel.predict(x_test)"
      ],
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0VwDAZT3WQoW",
        "outputId": "d855b86d-f7ab-44c3-9379-594fed2d9998"
      },
      "source": [
        "## Linear regression Model Evaluation\n",
        "r2_value = r2_score(temp_predict, y_test) \n",
        "print(\"R-Squared Score of the Linear Regression Model is\", round(r2_value, 4))\n",
        "\n",
        "mean_sq_error = mean_squared_error(temp_predict, y_test)\n",
        "print(\"Mean Square Error of the Linear Regression Model is\", round(mean_sq_error, 2))"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R-Squared Score of the Linear Regression Model is 0.9871\n",
            "Mean Square Error of the Linear Regression Model is 1.16\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6j0pKf1LWtWl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 432
        },
        "outputId": "f93d78d4-3f26-4ed8-f0ca-5eac446dcbe4"
      },
      "source": [
        "## Visualizing the Prediction\n",
        "from yellowbrick.regressor import PredictionError\n",
        "\n",
        "visualizer = PredictionError(lrmodel).fit(x_train, y_train)\n",
        "visualizer.score(x_test.sample(500, random_state=1), y_test.sample(500, random_state=1))\n",
        "\n",
        "visualizer.poof()"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:144: FutureWarning: The sklearn.metrics.classification module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.metrics. Anything that cannot be imported from sklearn.metrics is now part of the private API.\n",
            "  warnings.warn(message, FutureWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rImavsMuWuH-"
      },
      "source": [
        "#user_input_temp = int(input('Enter the temperature in celcius : '))\n",
        "#user_input_humid = float(input('Enter the humidity : '))\n",
        "\n",
        "#result1 = lrmodel.predict([[user_input_temp, user_input_humid]])\n",
        "#print(\"Expected Tempearture is {}\\N{DEGREE SIGN}C\".format(round(result1[0], 2)))"
      ],
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ga1hojJ1iT0O"
      },
      "source": [
        "**Logistic Regression - Predicting the Precipitation Type**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DpNXukumilRQ",
        "outputId": "e7a0f527-a500-4226-cd53-1f5ac5bac08b"
      },
      "source": [
        "## Encoding the Categorical Variables\n",
        "lb = LabelBinarizer()\n",
        "\n",
        "df_preprocessed['Precip Type Binary'] = lb.fit_transform(df_preprocessed['Precip Type'])\n",
        "df_preprocessed['Precip Type Binary'].value_counts()"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    85543\n",
              "1    10700\n",
              "Name: Precip Type Binary, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r-mezvLpis8q"
      },
      "source": [
        "## Splitting Dataset \n",
        "x = df_preprocessed.loc[:, ['Temperature (C)', 'Humidity', 'Visibility (km)']]\n",
        "y = df_preprocessed.loc[:, 'Precip Type Binary']\n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=1)"
      ],
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fzgmHnFAi4zr",
        "outputId": "e17959e6-767b-4504-f1d8-627b950b112c"
      },
      "source": [
        "## Building the Model\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "logmodel = LogisticRegression()\n",
        "logmodel.fit(x_train, y_train)\n",
        "precip_predict = logmodel.predict(x_test)\n",
        "\n",
        "\n",
        "## Logistic Regression Model Evaluation\n",
        "print(\"R-Squared Score of the Logistic Regression Model is\", round(r2_score(precip_predict, y_test), 4))\n",
        "\n",
        "print(\"Accuracy is\", round(accuracy_score(y_test, precip_predict), 4))"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R-Squared Score of the Logistic Regression Model is 0.9799\n",
            "Accuracy is 0.998\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        },
        "id": "ROw06y58jI1v",
        "outputId": "09d75f74-bdb4-4343-cb51-a6976ccc258c"
      },
      "source": [
        "plt.figure(figsize=(6, 4))\n",
        "cf_matrix = confusion_matrix(y_test, precip_predict)\n",
        "\n",
        "sns.heatmap(cf_matrix, annot=True, cmap='Blues', fmt='0')\n",
        "plt.xlabel('Predicted Class')\n",
        "plt.ylabel('True Class')\n",
        "\n",
        "plt.title('Confusion Matrix for Logistic Regression Model')\n",
        "plt.show()"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X_fip3Z3jWdV",
        "outputId": "2b4f9b7c-da51-4d4a-8b2d-375f6c35eb25"
      },
      "source": [
        "print(classification_report(y_test, precip_predict))"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00     17096\n",
            "           1       0.98      1.00      0.99      2153\n",
            "\n",
            "    accuracy                           1.00     19249\n",
            "   macro avg       0.99      1.00      0.99     19249\n",
            "weighted avg       1.00      1.00      1.00     19249\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m74SpA7xjP32",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "460082e7-5db2-436c-eccc-94edcffb0a64"
      },
      "source": [
        "user_input_temp = int(input('Enter the temperature in celcius : '))\n",
        "user_input_humid = float(input('Enter the humidity : '))\n",
        "user_input_visiblity = float(input('Enter the Visibility : '))\n",
        "\n",
        "\n",
        "result2 = logmodel.predict([[user_input_temp, user_input_humid, user_input_visiblity]])\n",
        "result2 = lb.inverse_transform(result2)\n",
        "print(\"Expected Precipitation Type is\", result2[0])"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Enter the temperature in celcius : 27\n",
            "Enter the humidity : 0.85\n",
            "Enter the Visibility : 6\n",
            "Expected Precipitation Type is rain\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "irbdcIBx4d2y"
      },
      "source": [
        "**Classification Model - Predicting Summary Feature**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pd8IWHCC4hib"
      },
      "source": [
        "df_preprocessed['Month'] = df_preprocessed['Date'].dt.month"
      ],
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QIZGS-Uc4fUF"
      },
      "source": [
        "le = LabelEncoder()\n",
        "\n",
        "df_preprocessed['Summary Encoded'] = le.fit_transform(df_preprocessed.loc[:, 'Summary'])\n",
        "\n",
        "x = df_preprocessed.loc[:, ['Temperature (C)', 'Humidity', 'Visibility (km)', 'Pressure (millibars)', 'Wind Speed (km/h)', 'Month']]\n",
        "y = df_preprocessed.loc[:, 'Summary Encoded']"
      ],
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GGWtq08L4-TN",
        "outputId": "e9837ad0-0aff-42ca-a3a0-4514c7640a9b"
      },
      "source": [
        "from imblearn.over_sampling import SMOTE\n",
        "\n",
        "smote = SMOTE()\n",
        "\n",
        "x, y = smote.fit_resample(x, y) \n",
        "\n",
        "x.shape[0], y.shape[0]"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/externals/six.py:31: FutureWarning: The module is deprecated in version 0.21 and will be removed in version 0.23 since we've dropped support for Python 2.7. Please rely on the official version of six (https://pypi.org/project/six/).\n",
            "  \"(https://pypi.org/project/six/).\", FutureWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:144: FutureWarning: The sklearn.neighbors.base module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.neighbors. Anything that cannot be imported from sklearn.neighbors is now part of the private API.\n",
            "  warnings.warn(message, FutureWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.\n",
            "  warnings.warn(msg, category=FutureWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.\n",
            "  warnings.warn(msg, category=FutureWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.\n",
            "  warnings.warn(msg, category=FutureWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.\n",
            "  warnings.warn(msg, category=FutureWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(161450, 161450)"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OkrrgWm15BPe"
      },
      "source": [
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=1)"
      ],
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tcyXSgwM5T1m"
      },
      "source": [
        "**Naive Bayes Classifier**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HM2AkcK-5Rhs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6ed130dd-6208-47eb-c37a-fb395b51827e"
      },
      "source": [
        "from sklearn.naive_bayes import GaussianNB\n",
        "\n",
        "classifier  = GaussianNB()\n",
        "\n",
        "classifier.fit(x_train, y_train)\n",
        "summary_predict = classifier.predict(x_test)\n",
        "\n",
        "print(\"Accuracy of Naive Bayes Algorithm is {}\".format(round(accuracy_score(y_test, summary_predict), 2)*100))"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy of Naive Bayes Algorithm is 50.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "txpehvl0vfGc"
      },
      "source": [
        "**K-Nearest Neighbors**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FadQRL2NumlL",
        "outputId": "096a889c-a0f0-481b-9352-88793f51c7d6"
      },
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "\n",
        "knn = KNeighborsClassifier(n_neighbors=25)\n",
        "knn.fit(x_train, y_train)\n",
        "summary_predict = knn.predict(x_test)\n",
        "\n",
        "print(\"Accuracy of KNN Algorithm is \" + '%.2f' %(round(accuracy_score(y_test, summary_predict), 2)*100))"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy of KNN Algorithm is 62.00\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BxGVdu6Wvlmu"
      },
      "source": [
        "**Random Forest**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6xHeLbPEveHI",
        "outputId": "616c8977-2b0c-4396-d4bd-ec4232853a48"
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "rforest = RandomForestClassifier(max_depth=55, random_state=1)\n",
        "\n",
        "rforest.fit(x_train, y_train)\n",
        "summary_predict = rforest.predict(x_test)\n",
        "\n",
        "print(\"Accuracy of Random Forest Algorithm is\", round(accuracy_score(y_test, summary_predict), 2)*100)"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy of Random Forest Algorithm is 76.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FP5AONSuvtYK",
        "outputId": "764f14d6-7718-4ee4-e929-5847d9b2b7d3"
      },
      "source": [
        "user_input = [27, 0.85, 6, 1003, 14, 4]\n",
        "\n",
        "result3 = rforest.predict([user_input])\n",
        "result3"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([4])"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pqqh3_94v2qs",
        "outputId": "5af8c732-1aed-4829-9d91-6b48d4dffa57"
      },
      "source": [
        "print(le.inverse_transform(result3))"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Partly Cloudy']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "npsLSBAgwIo0",
        "outputId": "80dc80e3-742b-4837-81fa-75f06db7ca5b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 376
        }
      },
      "source": [
        "plt.title('Confusion Matrix for Random Forest Classifier')\n",
        "sns.heatmap(confusion_matrix(y_test, summary_predict), annot=True, fmt='0', cmap='Blues')\n",
        "plt.xlabel('Predicted Class')\n",
        "plt.ylabel('True Class')\n",
        "plt.show()"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x396 with 2 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OPiPek6pwJuz",
        "outputId": "70d15223-e193-437e-90f8-abf95b05f367",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "print(classification_report(y_test, summary_predict))"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.79      0.86      0.82      6467\n",
            "           1       1.00      1.00      1.00      6447\n",
            "           2       0.60      0.53      0.56      6464\n",
            "           3       0.76      0.83      0.79      6427\n",
            "           4       0.64      0.60      0.62      6485\n",
            "\n",
            "    accuracy                           0.76     32290\n",
            "   macro avg       0.76      0.76      0.76     32290\n",
            "weighted avg       0.76      0.76      0.76     32290\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ANBQLWlgwQDM"
      },
      "source": [
        "<b>Summary</b><br>\n",
        "<li>We have performed Exploratory Data Analysis and shortlised key features.<br>\n",
        "&emsp; We have performed Data Preprocessing on unclean observations.</li>\n",
        "<li>Then, we built Linear Regression Model to predict Tempearture.</li>\n",
        "<li>We built Logistic Regression Model to predict precipitation type.</li>\n",
        "<li>Then, we built Random Forest Model to predict Summary.</li>\n",
        "<li>Website is developed by using FLASK Framework to make project accessible and easy-to-use for end users.</li>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jv5L7_9_wZT3"
      },
      "source": [
        "<b>Conclusion :</b> Thus, we have successfully completed Project \"Smart Weather Forecasting using ML\"."
      ]
    }
  ]
}