ML_10_Logistic_Regression_Implementation.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "ML_10_Logistic_Regression_Implementation.ipynb",
      "provenance": [],
      "mount_file_id": "1pZwfTXHeJOQw5pwJy8taXPTHwtE_f5P7",
      "authorship_tag": "ABX9TyPpHg7hxGYPOco1su+bTjCk",
      "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/sandeepbansode/Machine_Learning_Data_Science/blob/main/ML_10_Logistic_Regression_Implementation.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 1. Introduction to Logistic Regression\n",
        "\n",
        "When data scientists may come across a new classification problem, the first algorithm that may come across their mind is Logistic Regression. It is a supervised learning classification algorithm which is used to predict observations to a discrete set of classes. Practically, it is used to classify observations into different categories. Hence, its output is discrete in nature. Logistic Regression is also called Logit Regression. It is one of the most simple, straightforward and versatile classification algorithms which is used to solve classification problems."
      ],
      "metadata": {
        "id": "M0R3h4DNc7XB"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 2. Logistic Regression intuition\n",
        "\n",
        "In statistics, the Logistic Regression model is a widely used statistical model which is primarily used for classification purposes. It means that given a set of observations, Logistic Regression algorithm helps us to classify these observations into two or more discrete classes. So, the target variable is discrete in nature.\n",
        "\n",
        "The Logistic Regression algorithm works as follows -\n",
        "\n",
        "## Implement linear equation\n",
        "Logistic Regression algorithm works by implementing a linear equation with independent or explanatory variables to predict a response value. For example, we consider the example of number of hours studied and probability of passing the exam. Here, number of hours studied is the explanatory variable and it is denoted by x1. Probability of passing the exam is the response or target variable and it is denoted by z.\n",
        "\n",
        "If we have one explanatory variable (x1) and one response variable (z), then the linear equation would be given mathematically with the following equation-\n",
        "\n",
        "z = β0 + β1x1    \n",
        "\n",
        "Here, the coefficients β0 and β1 are the parameters of the model.\n",
        "\n",
        "If there are multiple explanatory variables, then the above equation can be extended to\n",
        "\n",
        "z = β0 + β1x1+ β2x2+……..+ βnxn\n",
        "\n",
        "Here, the coefficients β0, β1, β2 and βn are the parameters of the model.\n",
        "\n",
        "So, the predicted response value is given by the above equations and is denoted by z."
      ],
      "metadata": {
        "id": "yLwdftcXdC4q"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Sigmoid Function\n",
        "This predicted response value, denoted by z is then converted into a probability value that lie between 0 and 1. We use the sigmoid function in order to map predicted values to probability values. This sigmoid function then maps any real value into a probability value between 0 and 1.\n",
        "\n",
        "In machine learning, sigmoid function is used to map predictions to probabilities. The sigmoid function has an S shaped curve. It is also called sigmoid curve.\n",
        "\n",
        "A Sigmoid function is a special case of the Logistic function. It is given by the following mathematical formula.\n",
        "\n",
        "Graphically, we can represent sigmoid function with the following graph."
      ],
      "metadata": {
        "id": "s1ETehL4dIB3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![image.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "ljOM5wPcdNDK"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Decision boundary\n",
        "The sigmoid function returns a probability value between 0 and 1. This probability value is then mapped to a discrete class which is either “0” or “1”. In order to map this probability value to a discrete class (pass/fail, yes/no, true/false), we select a threshold value. This threshold value is called Decision boundary. Above this threshold value, we will map the probability values into class 1 and below which we will map values into class 0.\n",
        "\n",
        "Mathematically, it can be expressed as follows:-\n",
        "\n",
        "p ≥ 0.5 => class = 1\n",
        "\n",
        "p < 0.5 => class = 0\n",
        "\n",
        "Generally, the decision boundary is set to 0.5. So, if the probability value is 0.8 (> 0.5), we will map this observation to class 1. Similarly, if the probability value is 0.2 (< 0.5), we will map this observation to class 0. This is represented in the graph below-"
      ],
      "metadata": {
        "id": "emYndNUydUbZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![image.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "nR2f0STOdUYG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Making predictions\n",
        "Now, we know about sigmoid function and decision boundary in logistic regression. We can use our knowledge of sigmoid function and decision boundary to write a prediction function. A prediction function in logistic regression returns the probability of the observation being positive, Yes or True. We call this as class 1 and it is denoted by P(class = 1). If the probability inches closer to one, then we will be more confident about our model that the observation is in class 1, otherwise it is in class 0."
      ],
      "metadata": {
        "id": "xZMqgfoodUU8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 3. Assumptions of Logistic Regression\n",
        "\n",
        "The Logistic Regression model requires several key assumptions. These are as follows:-\n",
        "\n",
        "1. Logistic Regression model requires the dependent variable to be binary, multinomial or ordinal in nature.\n",
        "\n",
        "2. It requires the observations to be independent of each other. So, the observations should not come from repeated measurements.\n",
        "\n",
        "3. Logistic Regression algorithm requires little or no multicollinearity among the independent variables. It means that the independent variables should not be too highly correlated with each other.\n",
        "\n",
        "4. Logistic Regression model assumes linearity of independent variables and log odds.\n",
        "\n",
        "5. The success of Logistic Regression model depends on the sample sizes. Typically, it requires a large sample size to achieve the high accuracy."
      ],
      "metadata": {
        "id": "w-Qdtw41dUSB"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 4. Types of Logistic Regression\n",
        "\n",
        "Logistic Regression model can be classified into three groups based on the target variable categories. These three groups are described below:-\n",
        "\n",
        "1. Binary Logistic Regression\n",
        "\n",
        "In Binary Logistic Regression, the target variable has two possible categories. The common examples of categories are yes or no, good or bad, true or false, spam or no spam and pass or fail.\n",
        "\n",
        "2. Multinomial Logistic Regression\n",
        "\n",
        "In Multinomial Logistic Regression, the target variable has three or more categories which are not in any particular order. So, there are three or more nominal categories. The examples include the type of categories of fruits - apple, mango, orange and banana.\n",
        "\n",
        "3. Ordinal Logistic Regression\n",
        "\n",
        "In Ordinal Logistic Regression, the target variable has three or more ordinal categories. So, there is intrinsic order involved with the categories. For example, the student performance can be categorized as poor, average, good and excellent."
      ],
      "metadata": {
        "id": "AVSXcegddxQF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Problem Statement\n",
        "\n",
        "This dataset contains about 10 years of daily weather observations from numerous Australian weather stations.\n",
        "\n",
        "The target RainTomorrow means: Did it rain the next day? Yes or No.\n",
        "\n",
        "Note: You should exclude the variable Risk-MM when training your binary classification model. If you don't exclude it, you will leak the answers to your model and reduce its predictability."
      ],
      "metadata": {
        "id": "gJO-RaYpdxMv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np # linear algebra\n",
        "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
        "import matplotlib.pyplot as plt # data visualization\n",
        "import seaborn as sns # statistical data visualization\n",
        "%matplotlib inline\n",
        "\n",
        "import warnings\n",
        "\n",
        "warnings.filterwarnings('ignore')"
      ],
      "metadata": {
        "id": "0SlNRmyyc_J7"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Reading the file\n",
        "df = pd.read_csv(\"/content/drive/MyDrive/Data Science Files/weatherAUS.csv\")"
      ],
      "metadata": {
        "id": "bGPFYPYLd8Ag"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Exploratory data analysis "
      ],
      "metadata": {
        "id": "l9_FZ4hserl1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# view dimensions of dataset\n",
        "# We can see that there are 142193 instances and 24 variables in the data set.\n",
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ABNQE8q1e1Vo",
        "outputId": "958521ba-93cf-4474-c416-680fb2cb84ea"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(145460, 23)"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#preview dataset\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 661
        },
        "id": "rc0JU_gFeEMO",
        "outputId": "7ea84a34-2c38-41f5-daae-9cf76cd1e2e9"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              Date Location  MinTemp  MaxTemp  Rainfall  Evaporation  \\\n",
              "0       2008-12-01   Albury     13.4     22.9       0.6          NaN   \n",
              "1       2008-12-02   Albury      7.4     25.1       0.0          NaN   \n",
              "2       2008-12-03   Albury     12.9     25.7       0.0          NaN   \n",
              "3       2008-12-04   Albury      9.2     28.0       0.0          NaN   \n",
              "4       2008-12-05   Albury     17.5     32.3       1.0          NaN   \n",
              "...            ...      ...      ...      ...       ...          ...   \n",
              "145455  2017-06-21    Uluru      2.8     23.4       0.0          NaN   \n",
              "145456  2017-06-22    Uluru      3.6     25.3       0.0          NaN   \n",
              "145457  2017-06-23    Uluru      5.4     26.9       0.0          NaN   \n",
              "145458  2017-06-24    Uluru      7.8     27.0       0.0          NaN   \n",
              "145459  2017-06-25    Uluru     14.9      NaN       0.0          NaN   \n",
              "\n",
              "        Sunshine WindGustDir  WindGustSpeed WindDir9am  ... Humidity9am  \\\n",
              "0            NaN           W           44.0          W  ...        71.0   \n",
              "1            NaN         WNW           44.0        NNW  ...        44.0   \n",
              "2            NaN         WSW           46.0          W  ...        38.0   \n",
              "3            NaN          NE           24.0         SE  ...        45.0   \n",
              "4            NaN           W           41.0        ENE  ...        82.0   \n",
              "...          ...         ...            ...        ...  ...         ...   \n",
              "145455       NaN           E           31.0         SE  ...        51.0   \n",
              "145456       NaN         NNW           22.0         SE  ...        56.0   \n",
              "145457       NaN           N           37.0         SE  ...        53.0   \n",
              "145458       NaN          SE           28.0        SSE  ...        51.0   \n",
              "145459       NaN         NaN            NaN        ESE  ...        62.0   \n",
              "\n",
              "        Humidity3pm  Pressure9am  Pressure3pm  Cloud9am  Cloud3pm  Temp9am  \\\n",
              "0              22.0       1007.7       1007.1       8.0       NaN     16.9   \n",
              "1              25.0       1010.6       1007.8       NaN       NaN     17.2   \n",
              "2              30.0       1007.6       1008.7       NaN       2.0     21.0   \n",
              "3              16.0       1017.6       1012.8       NaN       NaN     18.1   \n",
              "4              33.0       1010.8       1006.0       7.0       8.0     17.8   \n",
              "...             ...          ...          ...       ...       ...      ...   \n",
              "145455         24.0       1024.6       1020.3       NaN       NaN     10.1   \n",
              "145456         21.0       1023.5       1019.1       NaN       NaN     10.9   \n",
              "145457         24.0       1021.0       1016.8       NaN       NaN     12.5   \n",
              "145458         24.0       1019.4       1016.5       3.0       2.0     15.1   \n",
              "145459         36.0       1020.2       1017.9       8.0       8.0     15.0   \n",
              "\n",
              "        Temp3pm  RainToday  RainTomorrow  \n",
              "0          21.8         No            No  \n",
              "1          24.3         No            No  \n",
              "2          23.2         No            No  \n",
              "3          26.5         No            No  \n",
              "4          29.7         No            No  \n",
              "...         ...        ...           ...  \n",
              "145455     22.4         No            No  \n",
              "145456     24.5         No            No  \n",
              "145457     26.1         No            No  \n",
              "145458     26.0         No            No  \n",
              "145459     20.9         No           NaN  \n",
              "\n",
              "[145460 rows x 23 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c31f4efe-0961-4c5d-bb45-7ca03e8afb98\">\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>Location</th>\n",
              "      <th>MinTemp</th>\n",
              "      <th>MaxTemp</th>\n",
              "      <th>Rainfall</th>\n",
              "      <th>Evaporation</th>\n",
              "      <th>Sunshine</th>\n",
              "      <th>WindGustDir</th>\n",
              "      <th>WindGustSpeed</th>\n",
              "      <th>WindDir9am</th>\n",
              "      <th>...</th>\n",
              "      <th>Humidity9am</th>\n",
              "      <th>Humidity3pm</th>\n",
              "      <th>Pressure9am</th>\n",
              "      <th>Pressure3pm</th>\n",
              "      <th>Cloud9am</th>\n",
              "      <th>Cloud3pm</th>\n",
              "      <th>Temp9am</th>\n",
              "      <th>Temp3pm</th>\n",
              "      <th>RainToday</th>\n",
              "      <th>RainTomorrow</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2008-12-01</td>\n",
              "      <td>Albury</td>\n",
              "      <td>13.4</td>\n",
              "      <td>22.9</td>\n",
              "      <td>0.6</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>W</td>\n",
              "      <td>44.0</td>\n",
              "      <td>W</td>\n",
              "      <td>...</td>\n",
              "      <td>71.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1007.7</td>\n",
              "      <td>1007.1</td>\n",
              "      <td>8.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>16.9</td>\n",
              "      <td>21.8</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2008-12-02</td>\n",
              "      <td>Albury</td>\n",
              "      <td>7.4</td>\n",
              "      <td>25.1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>WNW</td>\n",
              "      <td>44.0</td>\n",
              "      <td>NNW</td>\n",
              "      <td>...</td>\n",
              "      <td>44.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>1010.6</td>\n",
              "      <td>1007.8</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>17.2</td>\n",
              "      <td>24.3</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2008-12-03</td>\n",
              "      <td>Albury</td>\n",
              "      <td>12.9</td>\n",
              "      <td>25.7</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>WSW</td>\n",
              "      <td>46.0</td>\n",
              "      <td>W</td>\n",
              "      <td>...</td>\n",
              "      <td>38.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>1007.6</td>\n",
              "      <td>1008.7</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>23.2</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2008-12-04</td>\n",
              "      <td>Albury</td>\n",
              "      <td>9.2</td>\n",
              "      <td>28.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NE</td>\n",
              "      <td>24.0</td>\n",
              "      <td>SE</td>\n",
              "      <td>...</td>\n",
              "      <td>45.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>1017.6</td>\n",
              "      <td>1012.8</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>18.1</td>\n",
              "      <td>26.5</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2008-12-05</td>\n",
              "      <td>Albury</td>\n",
              "      <td>17.5</td>\n",
              "      <td>32.3</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>W</td>\n",
              "      <td>41.0</td>\n",
              "      <td>ENE</td>\n",
              "      <td>...</td>\n",
              "      <td>82.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>1010.8</td>\n",
              "      <td>1006.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>17.8</td>\n",
              "      <td>29.7</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>145455</th>\n",
              "      <td>2017-06-21</td>\n",
              "      <td>Uluru</td>\n",
              "      <td>2.8</td>\n",
              "      <td>23.4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>E</td>\n",
              "      <td>31.0</td>\n",
              "      <td>SE</td>\n",
              "      <td>...</td>\n",
              "      <td>51.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>1024.6</td>\n",
              "      <td>1020.3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>10.1</td>\n",
              "      <td>22.4</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>145456</th>\n",
              "      <td>2017-06-22</td>\n",
              "      <td>Uluru</td>\n",
              "      <td>3.6</td>\n",
              "      <td>25.3</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NNW</td>\n",
              "      <td>22.0</td>\n",
              "      <td>SE</td>\n",
              "      <td>...</td>\n",
              "      <td>56.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>1023.5</td>\n",
              "      <td>1019.1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>10.9</td>\n",
              "      <td>24.5</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>145457</th>\n",
              "      <td>2017-06-23</td>\n",
              "      <td>Uluru</td>\n",
              "      <td>5.4</td>\n",
              "      <td>26.9</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>N</td>\n",
              "      <td>37.0</td>\n",
              "      <td>SE</td>\n",
              "      <td>...</td>\n",
              "      <td>53.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>1021.0</td>\n",
              "      <td>1016.8</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12.5</td>\n",
              "      <td>26.1</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>145458</th>\n",
              "      <td>2017-06-24</td>\n",
              "      <td>Uluru</td>\n",
              "      <td>7.8</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>SE</td>\n",
              "      <td>28.0</td>\n",
              "      <td>SSE</td>\n",
              "      <td>...</td>\n",
              "      <td>51.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>1019.4</td>\n",
              "      <td>1016.5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>15.1</td>\n",
              "      <td>26.0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>145459</th>\n",
              "      <td>2017-06-25</td>\n",
              "      <td>Uluru</td>\n",
              "      <td>14.9</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>ESE</td>\n",
              "      <td>...</td>\n",
              "      <td>62.0</td>\n",
              "      <td>36.0</td>\n",
              "      <td>1020.2</td>\n",
              "      <td>1017.9</td>\n",
              "      <td>8.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>20.9</td>\n",
              "      <td>No</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>145460 rows × 23 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c31f4efe-0961-4c5d-bb45-7ca03e8afb98')\"\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-c31f4efe-0961-4c5d-bb45-7ca03e8afb98 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-c31f4efe-0961-4c5d-bb45-7ca03e8afb98');\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": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JjDqC5nyeURN",
        "outputId": "8d9b422c-d273-4791-c06b-549a9e39a9f6"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Date', 'Location', 'MinTemp', 'MaxTemp', 'Rainfall', 'Evaporation',\n",
              "       'Sunshine', 'WindGustDir', 'WindGustSpeed', 'WindDir9am', 'WindDir3pm',\n",
              "       'WindSpeed9am', 'WindSpeed3pm', 'Humidity9am', 'Humidity3pm',\n",
              "       'Pressure9am', 'Pressure3pm', 'Cloud9am', 'Cloud3pm', 'Temp9am',\n",
              "       'Temp3pm', 'RainToday', 'RainTomorrow'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#smmary of dataset\n",
        "\n",
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "woURBCWyfInP",
        "outputId": "606539bf-e824-4a96-b535-78c1c047104f"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 145460 entries, 0 to 145459\n",
            "Data columns (total 23 columns):\n",
            " #   Column         Non-Null Count   Dtype  \n",
            "---  ------         --------------   -----  \n",
            " 0   Date           145460 non-null  object \n",
            " 1   Location       145460 non-null  object \n",
            " 2   MinTemp        143975 non-null  float64\n",
            " 3   MaxTemp        144199 non-null  float64\n",
            " 4   Rainfall       142199 non-null  float64\n",
            " 5   Evaporation    82670 non-null   float64\n",
            " 6   Sunshine       75625 non-null   float64\n",
            " 7   WindGustDir    135134 non-null  object \n",
            " 8   WindGustSpeed  135197 non-null  float64\n",
            " 9   WindDir9am     134894 non-null  object \n",
            " 10  WindDir3pm     141232 non-null  object \n",
            " 11  WindSpeed9am   143693 non-null  float64\n",
            " 12  WindSpeed3pm   142398 non-null  float64\n",
            " 13  Humidity9am    142806 non-null  float64\n",
            " 14  Humidity3pm    140953 non-null  float64\n",
            " 15  Pressure9am    130395 non-null  float64\n",
            " 16  Pressure3pm    130432 non-null  float64\n",
            " 17  Cloud9am       89572 non-null   float64\n",
            " 18  Cloud3pm       86102 non-null   float64\n",
            " 19  Temp9am        143693 non-null  float64\n",
            " 20  Temp3pm        141851 non-null  float64\n",
            " 21  RainToday      142199 non-null  object \n",
            " 22  RainTomorrow   142193 non-null  object \n",
            "dtypes: float64(16), object(7)\n",
            "memory usage: 25.5+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#checking null values in dataset\n",
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XZWFTrLAeWqS",
        "outputId": "0828f617-47d0-4b4a-d59e-b045a5cebcca"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Date                 0\n",
              "Location             0\n",
              "MinTemp           1485\n",
              "MaxTemp           1261\n",
              "Rainfall          3261\n",
              "Evaporation      62790\n",
              "Sunshine         69835\n",
              "WindGustDir      10326\n",
              "WindGustSpeed    10263\n",
              "WindDir9am       10566\n",
              "WindDir3pm        4228\n",
              "WindSpeed9am      1767\n",
              "WindSpeed3pm      3062\n",
              "Humidity9am       2654\n",
              "Humidity3pm       4507\n",
              "Pressure9am      15065\n",
              "Pressure3pm      15028\n",
              "Cloud9am         55888\n",
              "Cloud3pm         59358\n",
              "Temp9am           1767\n",
              "Temp3pm           3609\n",
              "RainToday         3261\n",
              "RainTomorrow      3267\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Dropping the null values in dependent variable\n",
        "df1 = df[df['RainTomorrow'].notna()]"
      ],
      "metadata": {
        "id": "_k89VUCrehAS"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df1.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_bXHjjIffzxj",
        "outputId": "383a2ea4-a285-4bb0-95d7-ac9a30b6a96f"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 142193 entries, 0 to 145458\n",
            "Data columns (total 23 columns):\n",
            " #   Column         Non-Null Count   Dtype  \n",
            "---  ------         --------------   -----  \n",
            " 0   Date           142193 non-null  object \n",
            " 1   Location       142193 non-null  object \n",
            " 2   MinTemp        141556 non-null  float64\n",
            " 3   MaxTemp        141871 non-null  float64\n",
            " 4   Rainfall       140787 non-null  float64\n",
            " 5   Evaporation    81350 non-null   float64\n",
            " 6   Sunshine       74377 non-null   float64\n",
            " 7   WindGustDir    132863 non-null  object \n",
            " 8   WindGustSpeed  132923 non-null  float64\n",
            " 9   WindDir9am     132180 non-null  object \n",
            " 10  WindDir3pm     138415 non-null  object \n",
            " 11  WindSpeed9am   140845 non-null  float64\n",
            " 12  WindSpeed3pm   139563 non-null  float64\n",
            " 13  Humidity9am    140419 non-null  float64\n",
            " 14  Humidity3pm    138583 non-null  float64\n",
            " 15  Pressure9am    128179 non-null  float64\n",
            " 16  Pressure3pm    128212 non-null  float64\n",
            " 17  Cloud9am       88536 non-null   float64\n",
            " 18  Cloud3pm       85099 non-null   float64\n",
            " 19  Temp9am        141289 non-null  float64\n",
            " 20  Temp3pm        139467 non-null  float64\n",
            " 21  RainToday      140787 non-null  object \n",
            " 22  RainTomorrow   142193 non-null  object \n",
            "dtypes: float64(16), object(7)\n",
            "memory usage: 26.0+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#filling the null values in numerical variable\n",
        "df1 = df.fillna(df.median())"
      ],
      "metadata": {
        "id": "WKHFiv1hf1fq"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df1.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BpkxkUo-hopE",
        "outputId": "3492746c-c423-4159-d77d-d64097edd8aa"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Date                 0\n",
              "Location             0\n",
              "MinTemp              0\n",
              "MaxTemp              0\n",
              "Rainfall             0\n",
              "Evaporation          0\n",
              "Sunshine             0\n",
              "WindGustDir      10326\n",
              "WindGustSpeed        0\n",
              "WindDir9am       10566\n",
              "WindDir3pm        4228\n",
              "WindSpeed9am         0\n",
              "WindSpeed3pm         0\n",
              "Humidity9am          0\n",
              "Humidity3pm          0\n",
              "Pressure9am          0\n",
              "Pressure3pm          0\n",
              "Cloud9am             0\n",
              "Cloud3pm             0\n",
              "Temp9am              0\n",
              "Temp3pm              0\n",
              "RainToday         3261\n",
              "RainTomorrow      3267\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#replacing null values in categorical variables with mode value\n",
        "df1['WindGustDir'].fillna(df1['WindGustDir'].mode()[0], inplace=True)\n",
        "df1['WindDir9am'].fillna(df1['WindDir9am'].mode()[0], inplace=True)\n",
        "df1['WindDir3pm'].fillna(df1['WindDir3pm'].mode()[0], inplace=True)\n",
        "df1['RainToday'].fillna(df1['RainToday'].mode()[0], inplace=True)"
      ],
      "metadata": {
        "id": "k0qYh5Tfh5wx"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 661
        },
        "id": "_7_TLukBjZkK",
        "outputId": "02d355b8-221f-4f1f-85f0-ffc74d3d708c"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              Date Location  MinTemp  MaxTemp  Rainfall  Evaporation  \\\n",
              "0       2008-12-01   Albury     13.4     22.9       0.6          4.8   \n",
              "1       2008-12-02   Albury      7.4     25.1       0.0          4.8   \n",
              "2       2008-12-03   Albury     12.9     25.7       0.0          4.8   \n",
              "3       2008-12-04   Albury      9.2     28.0       0.0          4.8   \n",
              "4       2008-12-05   Albury     17.5     32.3       1.0          4.8   \n",
              "...            ...      ...      ...      ...       ...          ...   \n",
              "145455  2017-06-21    Uluru      2.8     23.4       0.0          4.8   \n",
              "145456  2017-06-22    Uluru      3.6     25.3       0.0          4.8   \n",
              "145457  2017-06-23    Uluru      5.4     26.9       0.0          4.8   \n",
              "145458  2017-06-24    Uluru      7.8     27.0       0.0          4.8   \n",
              "145459  2017-06-25    Uluru     14.9     22.6       0.0          4.8   \n",
              "\n",
              "        Sunshine WindGustDir  WindGustSpeed WindDir9am  ... Humidity9am  \\\n",
              "0            8.4           W           44.0          W  ...        71.0   \n",
              "1            8.4         WNW           44.0        NNW  ...        44.0   \n",
              "2            8.4         WSW           46.0          W  ...        38.0   \n",
              "3            8.4          NE           24.0         SE  ...        45.0   \n",
              "4            8.4           W           41.0        ENE  ...        82.0   \n",
              "...          ...         ...            ...        ...  ...         ...   \n",
              "145455       8.4           E           31.0         SE  ...        51.0   \n",
              "145456       8.4         NNW           22.0         SE  ...        56.0   \n",
              "145457       8.4           N           37.0         SE  ...        53.0   \n",
              "145458       8.4          SE           28.0        SSE  ...        51.0   \n",
              "145459       8.4           W           39.0        ESE  ...        62.0   \n",
              "\n",
              "        Humidity3pm  Pressure9am  Pressure3pm  Cloud9am  Cloud3pm  Temp9am  \\\n",
              "0              22.0       1007.7       1007.1       8.0       5.0     16.9   \n",
              "1              25.0       1010.6       1007.8       5.0       5.0     17.2   \n",
              "2              30.0       1007.6       1008.7       5.0       2.0     21.0   \n",
              "3              16.0       1017.6       1012.8       5.0       5.0     18.1   \n",
              "4              33.0       1010.8       1006.0       7.0       8.0     17.8   \n",
              "...             ...          ...          ...       ...       ...      ...   \n",
              "145455         24.0       1024.6       1020.3       5.0       5.0     10.1   \n",
              "145456         21.0       1023.5       1019.1       5.0       5.0     10.9   \n",
              "145457         24.0       1021.0       1016.8       5.0       5.0     12.5   \n",
              "145458         24.0       1019.4       1016.5       3.0       2.0     15.1   \n",
              "145459         36.0       1020.2       1017.9       8.0       8.0     15.0   \n",
              "\n",
              "        Temp3pm  RainToday  RainTomorrow  \n",
              "0          21.8         No            No  \n",
              "1          24.3         No            No  \n",
              "2          23.2         No            No  \n",
              "3          26.5         No            No  \n",
              "4          29.7         No            No  \n",
              "...         ...        ...           ...  \n",
              "145455     22.4         No            No  \n",
              "145456     24.5         No            No  \n",
              "145457     26.1         No            No  \n",
              "145458     26.0         No            No  \n",
              "145459     20.9         No           NaN  \n",
              "\n",
              "[145460 rows x 23 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-13124b52-170c-4cb3-894a-df45ca7436e3\">\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>Location</th>\n",
              "      <th>MinTemp</th>\n",
              "      <th>MaxTemp</th>\n",
              "      <th>Rainfall</th>\n",
              "      <th>Evaporation</th>\n",
              "      <th>Sunshine</th>\n",
              "      <th>WindGustDir</th>\n",
              "      <th>WindGustSpeed</th>\n",
              "      <th>WindDir9am</th>\n",
              "      <th>...</th>\n",
              "      <th>Humidity9am</th>\n",
              "      <th>Humidity3pm</th>\n",
              "      <th>Pressure9am</th>\n",
              "      <th>Pressure3pm</th>\n",
              "      <th>Cloud9am</th>\n",
              "      <th>Cloud3pm</th>\n",
              "      <th>Temp9am</th>\n",
              "      <th>Temp3pm</th>\n",
              "      <th>RainToday</th>\n",
              "      <th>RainTomorrow</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2008-12-01</td>\n",
              "      <td>Albury</td>\n",
              "      <td>13.4</td>\n",
              "      <td>22.9</td>\n",
              "      <td>0.6</td>\n",
              "      <td>4.8</td>\n",
              "      <td>8.4</td>\n",
              "      <td>W</td>\n",
              "      <td>44.0</td>\n",
              "      <td>W</td>\n",
              "      <td>...</td>\n",
              "      <td>71.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1007.7</td>\n",
              "      <td>1007.1</td>\n",
              "      <td>8.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>16.9</td>\n",
              "      <td>21.8</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2008-12-02</td>\n",
              "      <td>Albury</td>\n",
              "      <td>7.4</td>\n",
              "      <td>25.1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.8</td>\n",
              "      <td>8.4</td>\n",
              "      <td>WNW</td>\n",
              "      <td>44.0</td>\n",
              "      <td>NNW</td>\n",
              "      <td>...</td>\n",
              "      <td>44.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>1010.6</td>\n",
              "      <td>1007.8</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>17.2</td>\n",
              "      <td>24.3</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2008-12-03</td>\n",
              "      <td>Albury</td>\n",
              "      <td>12.9</td>\n",
              "      <td>25.7</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.8</td>\n",
              "      <td>8.4</td>\n",
              "      <td>WSW</td>\n",
              "      <td>46.0</td>\n",
              "      <td>W</td>\n",
              "      <td>...</td>\n",
              "      <td>38.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>1007.6</td>\n",
              "      <td>1008.7</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>23.2</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2008-12-04</td>\n",
              "      <td>Albury</td>\n",
              "      <td>9.2</td>\n",
              "      <td>28.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.8</td>\n",
              "      <td>8.4</td>\n",
              "      <td>NE</td>\n",
              "      <td>24.0</td>\n",
              "      <td>SE</td>\n",
              "      <td>...</td>\n",
              "      <td>45.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>1017.6</td>\n",
              "      <td>1012.8</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>18.1</td>\n",
              "      <td>26.5</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2008-12-05</td>\n",
              "      <td>Albury</td>\n",
              "      <td>17.5</td>\n",
              "      <td>32.3</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.8</td>\n",
              "      <td>8.4</td>\n",
              "      <td>W</td>\n",
              "      <td>41.0</td>\n",
              "      <td>ENE</td>\n",
              "      <td>...</td>\n",
              "      <td>82.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>1010.8</td>\n",
              "      <td>1006.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>17.8</td>\n",
              "      <td>29.7</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>145455</th>\n",
              "      <td>2017-06-21</td>\n",
              "      <td>Uluru</td>\n",
              "      <td>2.8</td>\n",
              "      <td>23.4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.8</td>\n",
              "      <td>8.4</td>\n",
              "      <td>E</td>\n",
              "      <td>31.0</td>\n",
              "      <td>SE</td>\n",
              "      <td>...</td>\n",
              "      <td>51.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>1024.6</td>\n",
              "      <td>1020.3</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>10.1</td>\n",
              "      <td>22.4</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>145456</th>\n",
              "      <td>2017-06-22</td>\n",
              "      <td>Uluru</td>\n",
              "      <td>3.6</td>\n",
              "      <td>25.3</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.8</td>\n",
              "      <td>8.4</td>\n",
              "      <td>NNW</td>\n",
              "      <td>22.0</td>\n",
              "      <td>SE</td>\n",
              "      <td>...</td>\n",
              "      <td>56.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>1023.5</td>\n",
              "      <td>1019.1</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>10.9</td>\n",
              "      <td>24.5</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>145457</th>\n",
              "      <td>2017-06-23</td>\n",
              "      <td>Uluru</td>\n",
              "      <td>5.4</td>\n",
              "      <td>26.9</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.8</td>\n",
              "      <td>8.4</td>\n",
              "      <td>N</td>\n",
              "      <td>37.0</td>\n",
              "      <td>SE</td>\n",
              "      <td>...</td>\n",
              "      <td>53.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>1021.0</td>\n",
              "      <td>1016.8</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>12.5</td>\n",
              "      <td>26.1</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>145458</th>\n",
              "      <td>2017-06-24</td>\n",
              "      <td>Uluru</td>\n",
              "      <td>7.8</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.8</td>\n",
              "      <td>8.4</td>\n",
              "      <td>SE</td>\n",
              "      <td>28.0</td>\n",
              "      <td>SSE</td>\n",
              "      <td>...</td>\n",
              "      <td>51.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>1019.4</td>\n",
              "      <td>1016.5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>15.1</td>\n",
              "      <td>26.0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>145459</th>\n",
              "      <td>2017-06-25</td>\n",
              "      <td>Uluru</td>\n",
              "      <td>14.9</td>\n",
              "      <td>22.6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.8</td>\n",
              "      <td>8.4</td>\n",
              "      <td>W</td>\n",
              "      <td>39.0</td>\n",
              "      <td>ESE</td>\n",
              "      <td>...</td>\n",
              "      <td>62.0</td>\n",
              "      <td>36.0</td>\n",
              "      <td>1020.2</td>\n",
              "      <td>1017.9</td>\n",
              "      <td>8.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>20.9</td>\n",
              "      <td>No</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>145460 rows × 23 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-13124b52-170c-4cb3-894a-df45ca7436e3')\"\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-13124b52-170c-4cb3-894a-df45ca7436e3 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-13124b52-170c-4cb3-894a-df45ca7436e3');\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": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#checking info after replacing null vlues\n",
        "df1.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TgSj_CEvkUuh",
        "outputId": "eef24a75-050d-4a1b-b241-ae0a53e4a0e0"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 145460 entries, 0 to 145459\n",
            "Data columns (total 23 columns):\n",
            " #   Column         Non-Null Count   Dtype  \n",
            "---  ------         --------------   -----  \n",
            " 0   Date           145460 non-null  object \n",
            " 1   Location       145460 non-null  object \n",
            " 2   MinTemp        145460 non-null  float64\n",
            " 3   MaxTemp        145460 non-null  float64\n",
            " 4   Rainfall       145460 non-null  float64\n",
            " 5   Evaporation    145460 non-null  float64\n",
            " 6   Sunshine       145460 non-null  float64\n",
            " 7   WindGustDir    145460 non-null  object \n",
            " 8   WindGustSpeed  145460 non-null  float64\n",
            " 9   WindDir9am     145460 non-null  object \n",
            " 10  WindDir3pm     145460 non-null  object \n",
            " 11  WindSpeed9am   145460 non-null  float64\n",
            " 12  WindSpeed3pm   145460 non-null  float64\n",
            " 13  Humidity9am    145460 non-null  float64\n",
            " 14  Humidity3pm    145460 non-null  float64\n",
            " 15  Pressure9am    145460 non-null  float64\n",
            " 16  Pressure3pm    145460 non-null  float64\n",
            " 17  Cloud9am       145460 non-null  float64\n",
            " 18  Cloud3pm       145460 non-null  float64\n",
            " 19  Temp9am        145460 non-null  float64\n",
            " 20  Temp3pm        145460 non-null  float64\n",
            " 21  RainToday      145460 non-null  object \n",
            " 22  RainTomorrow   142193 non-null  object \n",
            "dtypes: float64(16), object(7)\n",
            "memory usage: 25.5+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#checking null values\n",
        "df1.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UW6ZRMqVlCbh",
        "outputId": "b16c1b78-837f-4709-924f-50137ccc4bea"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Date                0\n",
              "Location            0\n",
              "MinTemp             0\n",
              "MaxTemp             0\n",
              "Rainfall            0\n",
              "Evaporation         0\n",
              "Sunshine            0\n",
              "WindGustDir         0\n",
              "WindGustSpeed       0\n",
              "WindDir9am          0\n",
              "WindDir3pm          0\n",
              "WindSpeed9am        0\n",
              "WindSpeed3pm        0\n",
              "Humidity9am         0\n",
              "Humidity3pm         0\n",
              "Pressure9am         0\n",
              "Pressure3pm         0\n",
              "Cloud9am            0\n",
              "Cloud3pm            0\n",
              "Temp9am             0\n",
              "Temp3pm             0\n",
              "RainToday           0\n",
              "RainTomorrow     3267\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Types of variables\n",
        "In this section, we segregate the dataset into categorical and numerical variables. There are a mixture of categorical and numerical variables in the dataset. Categorical variables have data type object. Numerical variables have data type float64.\n",
        "\n",
        "First of all, we will find categorical variables."
      ],
      "metadata": {
        "id": "msejJTfaXz3h"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# find categorical variables\n",
        "\n",
        "categorical = [var for var in df1.columns if df[var].dtype=='O']\n",
        "\n",
        "print('There are {} categorical variables\\n'.format(len(categorical)))\n",
        "\n",
        "print('The categorical variables are :', categorical)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bUdv-LNWXZlS",
        "outputId": "271d5ca3-a2eb-46d7-c731-b411e1473ff9"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "There are 7 categorical variables\n",
            "\n",
            "The categorical variables are : ['Date', 'Location', 'WindGustDir', 'WindDir9am', 'WindDir3pm', 'RainToday', 'RainTomorrow']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# view the categorical variables\n",
        "\n",
        "df1[categorical].head()"
      ],
      "metadata": {
        "id": "pnvBXv2NlXl7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "2ad8863d-8603-4456-fab2-605048a6c24f"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Date Location WindGustDir WindDir9am WindDir3pm RainToday  \\\n",
              "0  2008-12-01   Albury           W          W        WNW        No   \n",
              "1  2008-12-02   Albury         WNW        NNW        WSW        No   \n",
              "2  2008-12-03   Albury         WSW          W        WSW        No   \n",
              "3  2008-12-04   Albury          NE         SE          E        No   \n",
              "4  2008-12-05   Albury           W        ENE         NW        No   \n",
              "\n",
              "  RainTomorrow  \n",
              "0           No  \n",
              "1           No  \n",
              "2           No  \n",
              "3           No  \n",
              "4           No  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f434c086-b6a4-4a25-a19c-38783d6720dd\">\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>Location</th>\n",
              "      <th>WindGustDir</th>\n",
              "      <th>WindDir9am</th>\n",
              "      <th>WindDir3pm</th>\n",
              "      <th>RainToday</th>\n",
              "      <th>RainTomorrow</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2008-12-01</td>\n",
              "      <td>Albury</td>\n",
              "      <td>W</td>\n",
              "      <td>W</td>\n",
              "      <td>WNW</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2008-12-02</td>\n",
              "      <td>Albury</td>\n",
              "      <td>WNW</td>\n",
              "      <td>NNW</td>\n",
              "      <td>WSW</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2008-12-03</td>\n",
              "      <td>Albury</td>\n",
              "      <td>WSW</td>\n",
              "      <td>W</td>\n",
              "      <td>WSW</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2008-12-04</td>\n",
              "      <td>Albury</td>\n",
              "      <td>NE</td>\n",
              "      <td>SE</td>\n",
              "      <td>E</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2008-12-05</td>\n",
              "      <td>Albury</td>\n",
              "      <td>W</td>\n",
              "      <td>ENE</td>\n",
              "      <td>NW</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f434c086-b6a4-4a25-a19c-38783d6720dd')\"\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-f434c086-b6a4-4a25-a19c-38783d6720dd 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-f434c086-b6a4-4a25-a19c-38783d6720dd');\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": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Import label encoder\n",
        "from sklearn import preprocessing\n",
        "  \n",
        "# label_encoder object knows how to understand word labels.\n",
        "label_encoder = preprocessing.LabelEncoder()\n",
        "  \n",
        "# Encode labels in column 'Location'.'RainTomorrow','Date','WindDir9am',\t'WindDir3pm',\t'RainToday',\t'RainTomorrow'\n",
        "df1['Location']= label_encoder.fit_transform(df1['Location'])\n",
        "df1['WindGustDir']= label_encoder.fit_transform(df1['WindGustDir'])\n",
        "df1['WindDir9am']= label_encoder.fit_transform(df1['WindDir9am'])\n",
        "df1['WindDir3pm']= label_encoder.fit_transform(df1['WindDir3pm'])\n",
        "df1['RainToday']= label_encoder.fit_transform(df1['RainToday'])\n",
        "df1['RainTomorrow']= label_encoder.fit_transform(df1['RainTomorrow'])\n",
        "df1['Date']= label_encoder.fit_transform(df1['Date'])\n",
        "  \n",
        "print(df1['Location'].unique())\n",
        "\n",
        "print('\\n\\n',df1['WindGustDir'].unique())\n",
        "\n",
        "print('\\n\\n',df1['WindDir9am'].unique())\n",
        "\n",
        "print('\\n\\n',df1['WindDir3pm'].unique())\n",
        "\n",
        "print('\\n\\n',df1['RainToday'].unique())\n",
        "\n",
        "print('\\n\\n',df1['RainTomorrow'].unique())\n",
        "\n",
        "print('\\n\\n',df1['Date'].unique())\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "94Ze2Q3LXukq",
        "outputId": "b07842e4-59b1-49ba-d443-4bafe78ead36"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[ 2  4 10 11 21 24 26 27 30 34 37 38 42 45 47  9 40 23  5  6 35 19 18 20\n",
            " 25 33 44 12  7  8 14 39  0 22 28 48  1 46 29 32 31 36 43 15 17  3 13 16\n",
            " 41]\n",
            "\n",
            "\n",
            " [13 14 15  4  6  3  5 12  1 10  8  7  9  2  0 11]\n",
            "\n",
            "\n",
            " [13  6  9  1 12 10  8  4  3 11 15  2  0  7 14  5]\n",
            "\n",
            "\n",
            " [14 15  0  7 13 10  2  1  6 11 12  9  3  8  5  4]\n",
            "\n",
            "\n",
            " [0 1]\n",
            "\n",
            "\n",
            " [0 1 2]\n",
            "\n",
            "\n",
            " [396 397 398 ...  89  90  91]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1[categorical].head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "cYNHujKJX-RK",
        "outputId": "f7a78572-4a0e-4fb8-ef89-f250c0bfb1d3"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Date  Location  WindGustDir  WindDir9am  WindDir3pm  RainToday  \\\n",
              "0   396         2           13          13          14          0   \n",
              "1   397         2           14           6          15          0   \n",
              "2   398         2           15          13          15          0   \n",
              "3   399         2            4           9           0          0   \n",
              "4   400         2           13           1           7          0   \n",
              "\n",
              "   RainTomorrow  \n",
              "0             0  \n",
              "1             0  \n",
              "2             0  \n",
              "3             0  \n",
              "4             0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-99e06b3b-a2b8-4508-b505-fd3ab5766bbc\">\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>Location</th>\n",
              "      <th>WindGustDir</th>\n",
              "      <th>WindDir9am</th>\n",
              "      <th>WindDir3pm</th>\n",
              "      <th>RainToday</th>\n",
              "      <th>RainTomorrow</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>396</td>\n",
              "      <td>2</td>\n",
              "      <td>13</td>\n",
              "      <td>13</td>\n",
              "      <td>14</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>397</td>\n",
              "      <td>2</td>\n",
              "      <td>14</td>\n",
              "      <td>6</td>\n",
              "      <td>15</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>398</td>\n",
              "      <td>2</td>\n",
              "      <td>15</td>\n",
              "      <td>13</td>\n",
              "      <td>15</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>399</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>400</td>\n",
              "      <td>2</td>\n",
              "      <td>13</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-99e06b3b-a2b8-4508-b505-fd3ab5766bbc')\"\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-99e06b3b-a2b8-4508-b505-fd3ab5766bbc 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-99e06b3b-a2b8-4508-b505-fd3ab5766bbc');\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": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Summary of categorical variables\n",
        "There is a date variable. It is denoted by Date column.\n",
        "\n",
        "There are 6 categorical variables. These are given by Location, WindGustDir, WindDir9am, WindDir3pm, RainToday and RainTomorrow.\n",
        "\n",
        "There are two binary categorical variables - RainToday and RainTomorrow.\n",
        "\n",
        "RainTomorrow is the target variable."
      ],
      "metadata": {
        "id": "oEqCHkbAaS8q"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Explore problems within categorical variables\n",
        "First, we will explore the categorical variables.\n",
        "\n",
        "Missing values in categorical variables"
      ],
      "metadata": {
        "id": "q5HeURmNaXG0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# check missing values in categorical variables\n",
        "\n",
        "df[categorical].isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wznrJ3xVZyQp",
        "outputId": "e5224c0e-73a1-4d08-c98b-5be3ccb6deb2"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Date            0\n",
              "Location        0\n",
              "WindGustDir     0\n",
              "WindDir9am      0\n",
              "WindDir3pm      0\n",
              "RainToday       0\n",
              "RainTomorrow    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# print categorical variables containing missing values\n",
        "\n",
        "cat1 = [var for var in categorical if df[var].isnull().sum()!=0]\n",
        "\n",
        "print(df[cat1].isnull().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V_xiWqrBacfE",
        "outputId": "91896dc9-69e2-4908-ca1c-092f3820e5e5"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Series([], dtype: float64)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        " # view frequency of categorical variables\n",
        "\n",
        "for var in categorical: \n",
        "    \n",
        "    print(df[var].value_counts())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0kwtlRkmahKK",
        "outputId": "2d725010-c677-4222-9433-91d429edc2df"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2114    49\n",
            "2407    49\n",
            "2398    49\n",
            "2399    49\n",
            "2400    49\n",
            "        ..\n",
            "28       1\n",
            "27       1\n",
            "26       1\n",
            "25       1\n",
            "91       1\n",
            "Name: Date, Length: 3436, dtype: int64\n",
            "9     3436\n",
            "37    3344\n",
            "13    3193\n",
            "18    3193\n",
            "7     3193\n",
            "0     3193\n",
            "31    3193\n",
            "15    3193\n",
            "1     3040\n",
            "22    3040\n",
            "5     3040\n",
            "39    3040\n",
            "14    3040\n",
            "8     3040\n",
            "17    3040\n",
            "3     3040\n",
            "6     3040\n",
            "2     3040\n",
            "23    3040\n",
            "47    3040\n",
            "24    3039\n",
            "40    3039\n",
            "30    3039\n",
            "48    3009\n",
            "28    3009\n",
            "10    3009\n",
            "11    3009\n",
            "21    3009\n",
            "35    3009\n",
            "32    3009\n",
            "29    3009\n",
            "46    3009\n",
            "4     3009\n",
            "20    3009\n",
            "27    3009\n",
            "19    3009\n",
            "34    3009\n",
            "38    3009\n",
            "42    3009\n",
            "45    3009\n",
            "12    3009\n",
            "44    3009\n",
            "33    3009\n",
            "43    3006\n",
            "26    3004\n",
            "36    3001\n",
            "16    1578\n",
            "25    1578\n",
            "41    1578\n",
            "Name: Location, dtype: int64\n",
            "16    10326\n",
            "13     9915\n",
            "9      9418\n",
            "3      9313\n",
            "10     9216\n",
            "0      9181\n",
            "8      9168\n",
            "15     9069\n",
            "12     8967\n",
            "11     8736\n",
            "14     8252\n",
            "7      8122\n",
            "1      8104\n",
            "2      7372\n",
            "4      7133\n",
            "6      6620\n",
            "5      6548\n",
            "Name: WindGustDir, dtype: int64\n",
            "3     11758\n",
            "16    10566\n",
            "9      9287\n",
            "0      9176\n",
            "10     9112\n",
            "7      8749\n",
            "8      8659\n",
            "13     8459\n",
            "12     8423\n",
            "5      8129\n",
            "6      7980\n",
            "1      7836\n",
            "4      7671\n",
            "2      7630\n",
            "11     7587\n",
            "14     7414\n",
            "15     7024\n",
            "Name: WindDir9am, dtype: int64\n",
            "9     10838\n",
            "13    10110\n",
            "8      9926\n",
            "15     9518\n",
            "10     9399\n",
            "12     9354\n",
            "3      8890\n",
            "14     8874\n",
            "7      8610\n",
            "2      8505\n",
            "0      8472\n",
            "4      8263\n",
            "11     8156\n",
            "6      7870\n",
            "1      7857\n",
            "5      6590\n",
            "16     4228\n",
            "Name: WindDir3pm, dtype: int64\n",
            "0    110319\n",
            "1     31880\n",
            "2      3261\n",
            "Name: RainToday, dtype: int64\n",
            "0    110316\n",
            "1     31877\n",
            "2      3267\n",
            "Name: RainTomorrow, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Number of labels: cardinality\n",
        "The number of labels within a categorical variable is known as cardinality. A high number of labels within a variable is known as high cardinality. High cardinality may pose some serious problems in the machine learning model. So, we will check for high cardinality."
      ],
      "metadata": {
        "id": "RxGzQzzjavza"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# check for cardinality in categorical variables\n",
        "\n",
        "for var in categorical:\n",
        "    \n",
        "    print(var, ' contains ', len(df[var].unique()), ' labels')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ejl_A-RTapPJ",
        "outputId": "c1c9a552-1b87-4e45-fe67-835fe43958c6"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Date  contains  3436  labels\n",
            "Location  contains  49  labels\n",
            "WindGustDir  contains  17  labels\n",
            "WindDir9am  contains  17  labels\n",
            "WindDir3pm  contains  17  labels\n",
            "RainToday  contains  3  labels\n",
            "RainTomorrow  contains  3  labels\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can see that there is a Date variable which needs to be preprocessed. I will do preprocessing in the following section.\n",
        "\n",
        "All the other variables contain relatively smaller number of variables."
      ],
      "metadata": {
        "id": "qFIfbv8fa4fp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Declare feature vector and target variable "
      ],
      "metadata": {
        "id": "olBIhZpGbZTp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = df1.drop(['RainTomorrow'], axis=1)\n",
        "\n",
        "y = df1['RainTomorrow']"
      ],
      "metadata": {
        "id": "6jwgfbFgazvW"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# split X and y into training and testing sets\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)"
      ],
      "metadata": {
        "id": "E-m4GiiibfLu"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# check the shape of X_train and X_test\n",
        "\n",
        "X_train.shape, X_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eE6v9iprbnwi",
        "outputId": "7e490dce-c518-456a-ea3b-593925d907e3"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((116368, 22), (29092, 22))"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Feature Eng."
      ],
      "metadata": {
        "id": "b2KPDICSbzT6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# check data types in X_train\n",
        "\n",
        "X_train.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-rmdmvT5bsEx",
        "outputId": "cc0b7c65-a004-496f-8d09-0748a5b683fa"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Date               int64\n",
              "Location           int64\n",
              "MinTemp          float64\n",
              "MaxTemp          float64\n",
              "Rainfall         float64\n",
              "Evaporation      float64\n",
              "Sunshine         float64\n",
              "WindGustDir        int64\n",
              "WindGustSpeed    float64\n",
              "WindDir9am         int64\n",
              "WindDir3pm         int64\n",
              "WindSpeed9am     float64\n",
              "WindSpeed3pm     float64\n",
              "Humidity9am      float64\n",
              "Humidity3pm      float64\n",
              "Pressure9am      float64\n",
              "Pressure3pm      float64\n",
              "Cloud9am         float64\n",
              "Cloud3pm         float64\n",
              "Temp9am          float64\n",
              "Temp3pm          float64\n",
              "RainToday          int64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# display categorical variables\n",
        "\n",
        "categorical = [col for col in X_train.columns if X_train[col].dtypes == 'O']\n",
        "\n",
        "categorical"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DnX7G7vBb2_7",
        "outputId": "dd13a112-2aed-40f8-fce9-79d82d4253ed"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# display numerical variables\n",
        "\n",
        "numerical = [col for col in X_train.columns if X_train[col].dtypes != 'O']\n",
        "\n",
        "numerical"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yh_qPiadb9gV",
        "outputId": "7fb7c202-1335-4676-a063-acfadca4117c"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Date',\n",
              " 'Location',\n",
              " 'MinTemp',\n",
              " 'MaxTemp',\n",
              " 'Rainfall',\n",
              " 'Evaporation',\n",
              " 'Sunshine',\n",
              " 'WindGustDir',\n",
              " 'WindGustSpeed',\n",
              " 'WindDir9am',\n",
              " 'WindDir3pm',\n",
              " 'WindSpeed9am',\n",
              " 'WindSpeed3pm',\n",
              " 'Humidity9am',\n",
              " 'Humidity3pm',\n",
              " 'Pressure9am',\n",
              " 'Pressure3pm',\n",
              " 'Cloud9am',\n",
              " 'Cloud3pm',\n",
              " 'Temp9am',\n",
              " 'Temp3pm',\n",
              " 'RainToday']"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Engineering missing values in numerical variables\n",
        "\n",
        "# check missing values in numerical variables in X_train\n",
        "\n",
        "X_train[numerical].isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xw3ZZplscAmP",
        "outputId": "7225f90c-d493-4a96-9a16-edbe8c3aef08"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Date             0\n",
              "Location         0\n",
              "MinTemp          0\n",
              "MaxTemp          0\n",
              "Rainfall         0\n",
              "Evaporation      0\n",
              "Sunshine         0\n",
              "WindGustDir      0\n",
              "WindGustSpeed    0\n",
              "WindDir9am       0\n",
              "WindDir3pm       0\n",
              "WindSpeed9am     0\n",
              "WindSpeed3pm     0\n",
              "Humidity9am      0\n",
              "Humidity3pm      0\n",
              "Pressure9am      0\n",
              "Pressure3pm      0\n",
              "Cloud9am         0\n",
              "Cloud3pm         0\n",
              "Temp9am          0\n",
              "Temp3pm          0\n",
              "RainToday        0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jgy0ArircHfk",
        "outputId": "c97b72a2-e3a8-493f-8019-080325a27a93"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Engineering missing values in categorical variables¶\n",
        "\n"
      ],
      "metadata": {
        "id": "g8S-uoGTcSq4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# print percentage of missing values in the categorical variables in training set\n",
        "\n",
        "X_train[categorical].isnull().mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ywB_RyedcOzN",
        "outputId": "24b02a3e-1c04-4be4-94d7-d0fdc4c1c24b"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Series([], dtype: float64)"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# print categorical variables with missing data\n",
        "\n",
        "for col in categorical:\n",
        "    if X_train[col].isnull().mean()>0:\n",
        "        print(col, (X_train[col].isnull().mean()))"
      ],
      "metadata": {
        "id": "SMBz6QCjcVsY"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# check missing values in categorical variables in X_train\n",
        "\n",
        "X_train[categorical].isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eKDFwvAMcYyB",
        "outputId": "1dff3ddf-0f58-4dfa-aa5b-f78b6f8ed2d1"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Series([], dtype: float64)"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# check missing values in categorical variables in X_test\n",
        "\n",
        "X_test[categorical].isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1q6CYr3bcdj-",
        "outputId": "5a2738b2-58d0-463d-afb6-a9a6bd25c44a"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Series([], dtype: float64)"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# As a final check, we will check for missing values in X_train and X_test.\n",
        "\n",
        "# check missing values in X_train\n",
        "\n",
        "X_train.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-hxv5tyacgm6",
        "outputId": "e26389de-a72e-47fa-e5b0-f7e138d96156"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Date             0\n",
              "Location         0\n",
              "MinTemp          0\n",
              "MaxTemp          0\n",
              "Rainfall         0\n",
              "Evaporation      0\n",
              "Sunshine         0\n",
              "WindGustDir      0\n",
              "WindGustSpeed    0\n",
              "WindDir9am       0\n",
              "WindDir3pm       0\n",
              "WindSpeed9am     0\n",
              "WindSpeed3pm     0\n",
              "Humidity9am      0\n",
              "Humidity3pm      0\n",
              "Pressure9am      0\n",
              "Pressure3pm      0\n",
              "Cloud9am         0\n",
              "Cloud3pm         0\n",
              "Temp9am          0\n",
              "Temp3pm          0\n",
              "RainToday        0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# check missing values in X_test\n",
        "\n",
        "X_test.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n7b_Ym9scnqx",
        "outputId": "cf58b388-c7b2-42e4-a74d-7b2e1217b239"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Date             0\n",
              "Location         0\n",
              "MinTemp          0\n",
              "MaxTemp          0\n",
              "Rainfall         0\n",
              "Evaporation      0\n",
              "Sunshine         0\n",
              "WindGustDir      0\n",
              "WindGustSpeed    0\n",
              "WindDir9am       0\n",
              "WindDir3pm       0\n",
              "WindSpeed9am     0\n",
              "WindSpeed3pm     0\n",
              "Humidity9am      0\n",
              "Humidity3pm      0\n",
              "Pressure9am      0\n",
              "Pressure3pm      0\n",
              "Cloud9am         0\n",
              "Cloud3pm         0\n",
              "Temp9am          0\n",
              "Temp3pm          0\n",
              "RainToday        0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.head()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "ALKtEPxtcriP",
        "outputId": "d623dbd9-0a1a-4485-f0c8-94a7ebe8615c"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Date  Location  MinTemp  MaxTemp  Rainfall  Evaporation  Sunshine  \\\n",
              "22926   2234        27     18.8     23.7       0.2          5.0       7.3   \n",
              "80735   3173        44      9.3     24.0       0.2          1.6      10.9   \n",
              "121764  1369        31     10.9     22.2       1.4          1.2       9.6   \n",
              "139821   953        13     19.3     29.9       0.0          9.2      11.0   \n",
              "1867    2263         2     15.7     17.6      12.4          4.8       8.4   \n",
              "\n",
              "        WindGustDir  WindGustSpeed  WindDir9am  ...  WindSpeed3pm  \\\n",
              "22926             2           52.0           2  ...          28.0   \n",
              "80735             4           48.0           6  ...          24.0   \n",
              "121764           12           26.0           3  ...          11.0   \n",
              "139821            2           43.0           2  ...          17.0   \n",
              "1867              0           20.0           2  ...          13.0   \n",
              "\n",
              "        Humidity9am  Humidity3pm  Pressure9am  Pressure3pm  Cloud9am  \\\n",
              "22926          74.0         73.0       1016.6       1013.9       5.0   \n",
              "80735          74.0         55.0       1018.3       1014.6       3.0   \n",
              "121764         85.0         47.0       1017.6       1014.9       1.0   \n",
              "139821         44.0         37.0       1015.9       1012.1       1.0   \n",
              "1867          100.0        100.0       1015.2       1010.5       8.0   \n",
              "\n",
              "        Cloud3pm  Temp9am  Temp3pm  RainToday  \n",
              "22926        7.0     21.4     22.2          0  \n",
              "80735        5.0     14.3     23.2          0  \n",
              "121764       2.0     16.6     21.5          1  \n",
              "139821       1.0     23.2     29.1          0  \n",
              "1867         8.0     16.5     17.3          1  \n",
              "\n",
              "[5 rows x 22 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ec00e944-a848-417d-9985-726c69d596a7\">\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>Location</th>\n",
              "      <th>MinTemp</th>\n",
              "      <th>MaxTemp</th>\n",
              "      <th>Rainfall</th>\n",
              "      <th>Evaporation</th>\n",
              "      <th>Sunshine</th>\n",
              "      <th>WindGustDir</th>\n",
              "      <th>WindGustSpeed</th>\n",
              "      <th>WindDir9am</th>\n",
              "      <th>...</th>\n",
              "      <th>WindSpeed3pm</th>\n",
              "      <th>Humidity9am</th>\n",
              "      <th>Humidity3pm</th>\n",
              "      <th>Pressure9am</th>\n",
              "      <th>Pressure3pm</th>\n",
              "      <th>Cloud9am</th>\n",
              "      <th>Cloud3pm</th>\n",
              "      <th>Temp9am</th>\n",
              "      <th>Temp3pm</th>\n",
              "      <th>RainToday</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>22926</th>\n",
              "      <td>2234</td>\n",
              "      <td>27</td>\n",
              "      <td>18.8</td>\n",
              "      <td>23.7</td>\n",
              "      <td>0.2</td>\n",
              "      <td>5.0</td>\n",
              "      <td>7.3</td>\n",
              "      <td>2</td>\n",
              "      <td>52.0</td>\n",
              "      <td>2</td>\n",
              "      <td>...</td>\n",
              "      <td>28.0</td>\n",
              "      <td>74.0</td>\n",
              "      <td>73.0</td>\n",
              "      <td>1016.6</td>\n",
              "      <td>1013.9</td>\n",
              "      <td>5.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>21.4</td>\n",
              "      <td>22.2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80735</th>\n",
              "      <td>3173</td>\n",
              "      <td>44</td>\n",
              "      <td>9.3</td>\n",
              "      <td>24.0</td>\n",
              "      <td>0.2</td>\n",
              "      <td>1.6</td>\n",
              "      <td>10.9</td>\n",
              "      <td>4</td>\n",
              "      <td>48.0</td>\n",
              "      <td>6</td>\n",
              "      <td>...</td>\n",
              "      <td>24.0</td>\n",
              "      <td>74.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>1018.3</td>\n",
              "      <td>1014.6</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>14.3</td>\n",
              "      <td>23.2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>121764</th>\n",
              "      <td>1369</td>\n",
              "      <td>31</td>\n",
              "      <td>10.9</td>\n",
              "      <td>22.2</td>\n",
              "      <td>1.4</td>\n",
              "      <td>1.2</td>\n",
              "      <td>9.6</td>\n",
              "      <td>12</td>\n",
              "      <td>26.0</td>\n",
              "      <td>3</td>\n",
              "      <td>...</td>\n",
              "      <td>11.0</td>\n",
              "      <td>85.0</td>\n",
              "      <td>47.0</td>\n",
              "      <td>1017.6</td>\n",
              "      <td>1014.9</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>16.6</td>\n",
              "      <td>21.5</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>139821</th>\n",
              "      <td>953</td>\n",
              "      <td>13</td>\n",
              "      <td>19.3</td>\n",
              "      <td>29.9</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9.2</td>\n",
              "      <td>11.0</td>\n",
              "      <td>2</td>\n",
              "      <td>43.0</td>\n",
              "      <td>2</td>\n",
              "      <td>...</td>\n",
              "      <td>17.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>37.0</td>\n",
              "      <td>1015.9</td>\n",
              "      <td>1012.1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>23.2</td>\n",
              "      <td>29.1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1867</th>\n",
              "      <td>2263</td>\n",
              "      <td>2</td>\n",
              "      <td>15.7</td>\n",
              "      <td>17.6</td>\n",
              "      <td>12.4</td>\n",
              "      <td>4.8</td>\n",
              "      <td>8.4</td>\n",
              "      <td>0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>2</td>\n",
              "      <td>...</td>\n",
              "      <td>13.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>1015.2</td>\n",
              "      <td>1010.5</td>\n",
              "      <td>8.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>16.5</td>\n",
              "      <td>17.3</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 22 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ec00e944-a848-417d-9985-726c69d596a7')\"\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-ec00e944-a848-417d-9985-726c69d596a7 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-ec00e944-a848-417d-9985-726c69d596a7');\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": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_test.head()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "HgK2LN0fc4R1",
        "outputId": "7eff29be-8616-45e3-e8a2-993480229999"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Date  Location  MinTemp  MaxTemp  Rainfall  Evaporation  Sunshine  \\\n",
              "138175  2500         3     21.9     39.4       1.6         11.2      11.5   \n",
              "38638   2536        42     20.5     37.5       0.0          9.2       8.4   \n",
              "124058   654        36      5.1     17.2       0.2          4.8       8.4   \n",
              "99214   3137         0     11.9     16.8       1.0          4.8       8.4   \n",
              "25097   1366        30      7.5     21.3       0.0          4.8       8.4   \n",
              "\n",
              "        WindGustDir  WindGustSpeed  WindDir9am  ...  WindSpeed3pm  \\\n",
              "138175            6           57.0           4  ...          33.0   \n",
              "38638            14           59.0           1  ...          20.0   \n",
              "124058           13           50.0          14  ...          22.0   \n",
              "99214            12           28.0          14  ...          13.0   \n",
              "25097             4           15.0          12  ...           7.0   \n",
              "\n",
              "        Humidity9am  Humidity3pm  Pressure9am  Pressure3pm  Cloud9am  \\\n",
              "138175         50.0         26.0       1008.5       1004.1       2.0   \n",
              "38638          47.0         22.0       1012.9       1006.5       4.0   \n",
              "124058         68.0         51.0       1017.6       1015.2       5.0   \n",
              "99214          80.0         79.0       1018.5       1017.7       5.0   \n",
              "25097          88.0         52.0       1017.6       1015.2       5.0   \n",
              "\n",
              "        Cloud3pm  Temp9am  Temp3pm  RainToday  \n",
              "138175       6.0     29.8     35.4          1  \n",
              "38638        7.0     27.3     35.7          0  \n",
              "124058       5.0     10.1     16.1          0  \n",
              "99214        5.0     14.3     15.2          0  \n",
              "25097        5.0     13.7     21.1          0  \n",
              "\n",
              "[5 rows x 22 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-45a9f244-2a37-4fbd-87d3-230bd2f71f0b\">\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>Location</th>\n",
              "      <th>MinTemp</th>\n",
              "      <th>MaxTemp</th>\n",
              "      <th>Rainfall</th>\n",
              "      <th>Evaporation</th>\n",
              "      <th>Sunshine</th>\n",
              "      <th>WindGustDir</th>\n",
              "      <th>WindGustSpeed</th>\n",
              "      <th>WindDir9am</th>\n",
              "      <th>...</th>\n",
              "      <th>WindSpeed3pm</th>\n",
              "      <th>Humidity9am</th>\n",
              "      <th>Humidity3pm</th>\n",
              "      <th>Pressure9am</th>\n",
              "      <th>Pressure3pm</th>\n",
              "      <th>Cloud9am</th>\n",
              "      <th>Cloud3pm</th>\n",
              "      <th>Temp9am</th>\n",
              "      <th>Temp3pm</th>\n",
              "      <th>RainToday</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>138175</th>\n",
              "      <td>2500</td>\n",
              "      <td>3</td>\n",
              "      <td>21.9</td>\n",
              "      <td>39.4</td>\n",
              "      <td>1.6</td>\n",
              "      <td>11.2</td>\n",
              "      <td>11.5</td>\n",
              "      <td>6</td>\n",
              "      <td>57.0</td>\n",
              "      <td>4</td>\n",
              "      <td>...</td>\n",
              "      <td>33.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>1008.5</td>\n",
              "      <td>1004.1</td>\n",
              "      <td>2.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>29.8</td>\n",
              "      <td>35.4</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38638</th>\n",
              "      <td>2536</td>\n",
              "      <td>42</td>\n",
              "      <td>20.5</td>\n",
              "      <td>37.5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9.2</td>\n",
              "      <td>8.4</td>\n",
              "      <td>14</td>\n",
              "      <td>59.0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>20.0</td>\n",
              "      <td>47.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1012.9</td>\n",
              "      <td>1006.5</td>\n",
              "      <td>4.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>27.3</td>\n",
              "      <td>35.7</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>124058</th>\n",
              "      <td>654</td>\n",
              "      <td>36</td>\n",
              "      <td>5.1</td>\n",
              "      <td>17.2</td>\n",
              "      <td>0.2</td>\n",
              "      <td>4.8</td>\n",
              "      <td>8.4</td>\n",
              "      <td>13</td>\n",
              "      <td>50.0</td>\n",
              "      <td>14</td>\n",
              "      <td>...</td>\n",
              "      <td>22.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>51.0</td>\n",
              "      <td>1017.6</td>\n",
              "      <td>1015.2</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>10.1</td>\n",
              "      <td>16.1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99214</th>\n",
              "      <td>3137</td>\n",
              "      <td>0</td>\n",
              "      <td>11.9</td>\n",
              "      <td>16.8</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.8</td>\n",
              "      <td>8.4</td>\n",
              "      <td>12</td>\n",
              "      <td>28.0</td>\n",
              "      <td>14</td>\n",
              "      <td>...</td>\n",
              "      <td>13.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>79.0</td>\n",
              "      <td>1018.5</td>\n",
              "      <td>1017.7</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>14.3</td>\n",
              "      <td>15.2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25097</th>\n",
              "      <td>1366</td>\n",
              "      <td>30</td>\n",
              "      <td>7.5</td>\n",
              "      <td>21.3</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.8</td>\n",
              "      <td>8.4</td>\n",
              "      <td>4</td>\n",
              "      <td>15.0</td>\n",
              "      <td>12</td>\n",
              "      <td>...</td>\n",
              "      <td>7.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>52.0</td>\n",
              "      <td>1017.6</td>\n",
              "      <td>1015.2</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>13.7</td>\n",
              "      <td>21.1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 22 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-45a9f244-2a37-4fbd-87d3-230bd2f71f0b')\"\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-45a9f244-2a37-4fbd-87d3-230bd2f71f0b 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-45a9f244-2a37-4fbd-87d3-230bd2f71f0b');\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": 54
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Feature Scaling "
      ],
      "metadata": {
        "id": "eABktBVtdNQT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 393
        },
        "id": "KRTnmpnbc_38",
        "outputId": "a7892c1d-b6e8-4706-83bc-8ef05828eb0a"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                Date       Location        MinTemp        MaxTemp  \\\n",
              "count  116368.000000  116368.000000  116368.000000  116368.000000   \n",
              "mean     1927.424017      23.811288      12.190189      23.203107   \n",
              "std       885.227909      14.225133       6.366893       7.085408   \n",
              "min         0.000000       0.000000      -8.500000      -4.800000   \n",
              "25%      1166.000000      11.000000       7.700000      18.000000   \n",
              "50%      1949.000000      24.000000      12.000000      22.600000   \n",
              "75%      2692.000000      36.000000      16.800000      28.200000   \n",
              "max      3435.000000      48.000000      31.900000      48.100000   \n",
              "\n",
              "            Rainfall    Evaporation       Sunshine    WindGustDir  \\\n",
              "count  116368.000000  116368.000000  116368.000000  116368.000000   \n",
              "mean        2.321101       5.174856       7.982476       8.096504   \n",
              "std         8.454648       3.184452       2.761639       4.691228   \n",
              "min         0.000000       0.000000       0.000000       0.000000   \n",
              "25%         0.000000       4.000000       8.200000       4.000000   \n",
              "50%         0.000000       4.800000       8.400000       9.000000   \n",
              "75%         0.600000       5.200000       8.600000      13.000000   \n",
              "max       367.600000     145.000000      14.500000      15.000000   \n",
              "\n",
              "       WindGustSpeed     WindDir9am  ...   WindSpeed3pm    Humidity9am  \\\n",
              "count  116368.000000  116368.000000  ...  116368.000000  116368.000000   \n",
              "mean       39.982091       7.003180  ...      18.691384      68.950691   \n",
              "std        13.127953       4.511328  ...       8.720419      18.811437   \n",
              "min         6.000000       0.000000  ...       0.000000       0.000000   \n",
              "25%        31.000000       3.000000  ...      13.000000      57.000000   \n",
              "50%        39.000000       7.000000  ...      19.000000      70.000000   \n",
              "75%        46.000000      11.000000  ...      24.000000      83.000000   \n",
              "max       135.000000      15.000000  ...      83.000000     100.000000   \n",
              "\n",
              "         Humidity3pm    Pressure9am    Pressure3pm       Cloud9am  \\\n",
              "count  116368.000000  116368.000000  116368.000000  116368.000000   \n",
              "mean       51.605828    1017.639891    1015.244946       4.664092   \n",
              "std        20.439999       6.728234       6.661517       2.280687   \n",
              "min         0.000000     980.500000     977.100000       0.000000   \n",
              "25%        37.000000    1013.500000    1011.100000       3.000000   \n",
              "50%        52.000000    1017.600000    1015.200000       5.000000   \n",
              "75%        65.000000    1021.800000    1019.400000       6.000000   \n",
              "max       100.000000    1041.000000    1039.600000       9.000000   \n",
              "\n",
              "            Cloud3pm        Temp9am        Temp3pm      RainToday  \n",
              "count  116368.000000  116368.000000  116368.000000  116368.000000  \n",
              "mean        4.710728      16.979454      21.657195       0.219648  \n",
              "std         2.106040       6.449641       6.848293       0.414010  \n",
              "min         0.000000      -7.200000      -5.400000       0.000000  \n",
              "25%         4.000000      12.300000      16.700000       0.000000  \n",
              "50%         5.000000      16.700000      21.100000       0.000000  \n",
              "75%         6.000000      21.500000      26.200000       0.000000  \n",
              "max         8.000000      40.200000      46.700000       1.000000  \n",
              "\n",
              "[8 rows x 22 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ec96d3d4-5b21-4d22-9b6b-e905ebe05f30\">\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>Location</th>\n",
              "      <th>MinTemp</th>\n",
              "      <th>MaxTemp</th>\n",
              "      <th>Rainfall</th>\n",
              "      <th>Evaporation</th>\n",
              "      <th>Sunshine</th>\n",
              "      <th>WindGustDir</th>\n",
              "      <th>WindGustSpeed</th>\n",
              "      <th>WindDir9am</th>\n",
              "      <th>...</th>\n",
              "      <th>WindSpeed3pm</th>\n",
              "      <th>Humidity9am</th>\n",
              "      <th>Humidity3pm</th>\n",
              "      <th>Pressure9am</th>\n",
              "      <th>Pressure3pm</th>\n",
              "      <th>Cloud9am</th>\n",
              "      <th>Cloud3pm</th>\n",
              "      <th>Temp9am</th>\n",
              "      <th>Temp3pm</th>\n",
              "      <th>RainToday</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>116368.000000</td>\n",
              "      <td>116368.000000</td>\n",
              "      <td>116368.000000</td>\n",
              "      <td>116368.000000</td>\n",
              "      <td>116368.000000</td>\n",
              "      <td>116368.000000</td>\n",
              "      <td>116368.000000</td>\n",
              "      <td>116368.000000</td>\n",
              "      <td>116368.000000</td>\n",
              "      <td>116368.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>116368.000000</td>\n",
              "      <td>116368.000000</td>\n",
              "      <td>116368.000000</td>\n",
              "      <td>116368.000000</td>\n",
              "      <td>116368.000000</td>\n",
              "      <td>116368.000000</td>\n",
              "      <td>116368.000000</td>\n",
              "      <td>116368.000000</td>\n",
              "      <td>116368.000000</td>\n",
              "      <td>116368.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1927.424017</td>\n",
              "      <td>23.811288</td>\n",
              "      <td>12.190189</td>\n",
              "      <td>23.203107</td>\n",
              "      <td>2.321101</td>\n",
              "      <td>5.174856</td>\n",
              "      <td>7.982476</td>\n",
              "      <td>8.096504</td>\n",
              "      <td>39.982091</td>\n",
              "      <td>7.003180</td>\n",
              "      <td>...</td>\n",
              "      <td>18.691384</td>\n",
              "      <td>68.950691</td>\n",
              "      <td>51.605828</td>\n",
              "      <td>1017.639891</td>\n",
              "      <td>1015.244946</td>\n",
              "      <td>4.664092</td>\n",
              "      <td>4.710728</td>\n",
              "      <td>16.979454</td>\n",
              "      <td>21.657195</td>\n",
              "      <td>0.219648</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>885.227909</td>\n",
              "      <td>14.225133</td>\n",
              "      <td>6.366893</td>\n",
              "      <td>7.085408</td>\n",
              "      <td>8.454648</td>\n",
              "      <td>3.184452</td>\n",
              "      <td>2.761639</td>\n",
              "      <td>4.691228</td>\n",
              "      <td>13.127953</td>\n",
              "      <td>4.511328</td>\n",
              "      <td>...</td>\n",
              "      <td>8.720419</td>\n",
              "      <td>18.811437</td>\n",
              "      <td>20.439999</td>\n",
              "      <td>6.728234</td>\n",
              "      <td>6.661517</td>\n",
              "      <td>2.280687</td>\n",
              "      <td>2.106040</td>\n",
              "      <td>6.449641</td>\n",
              "      <td>6.848293</td>\n",
              "      <td>0.414010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-8.500000</td>\n",
              "      <td>-4.800000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>980.500000</td>\n",
              "      <td>977.100000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-7.200000</td>\n",
              "      <td>-5.400000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1166.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>7.700000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>8.200000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>57.000000</td>\n",
              "      <td>37.000000</td>\n",
              "      <td>1013.500000</td>\n",
              "      <td>1011.100000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>12.300000</td>\n",
              "      <td>16.700000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1949.000000</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>22.600000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.800000</td>\n",
              "      <td>8.400000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>39.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>19.000000</td>\n",
              "      <td>70.000000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>1017.600000</td>\n",
              "      <td>1015.200000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>16.700000</td>\n",
              "      <td>21.100000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2692.000000</td>\n",
              "      <td>36.000000</td>\n",
              "      <td>16.800000</td>\n",
              "      <td>28.200000</td>\n",
              "      <td>0.600000</td>\n",
              "      <td>5.200000</td>\n",
              "      <td>8.600000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>46.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>83.000000</td>\n",
              "      <td>65.000000</td>\n",
              "      <td>1021.800000</td>\n",
              "      <td>1019.400000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>21.500000</td>\n",
              "      <td>26.200000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>3435.000000</td>\n",
              "      <td>48.000000</td>\n",
              "      <td>31.900000</td>\n",
              "      <td>48.100000</td>\n",
              "      <td>367.600000</td>\n",
              "      <td>145.000000</td>\n",
              "      <td>14.500000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>135.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>83.000000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>1041.000000</td>\n",
              "      <td>1039.600000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>40.200000</td>\n",
              "      <td>46.700000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 22 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ec96d3d4-5b21-4d22-9b6b-e905ebe05f30')\"\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-ec96d3d4-5b21-4d22-9b6b-e905ebe05f30 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-ec96d3d4-5b21-4d22-9b6b-e905ebe05f30');\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": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cols = X_train.columns\n"
      ],
      "metadata": {
        "id": "P8Y9yvrsdP6k"
      },
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "scaler = MinMaxScaler()\n",
        "\n",
        "X_train = scaler.fit_transform(X_train)\n",
        "\n",
        "X_test = scaler.transform(X_test)"
      ],
      "metadata": {
        "id": "5Urtn7VhdgUy"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train = pd.DataFrame(X_train, columns=[cols])\n"
      ],
      "metadata": {
        "id": "PqZMOELvdi_W"
      },
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_test = pd.DataFrame(X_test, columns=[cols])\n"
      ],
      "metadata": {
        "id": "T_5CF0WwdlhB"
      },
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.describe()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 393
        },
        "id": "Ea8Aa5tRdnMH",
        "outputId": "82277ec9-c4db-45ea-bac4-38dccf090643"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                Date       Location        MinTemp        MaxTemp  \\\n",
              "count  116368.000000  116368.000000  116368.000000  116368.000000   \n",
              "mean        0.561113       0.496069       0.512133       0.529359   \n",
              "std         0.257708       0.296357       0.157596       0.133940   \n",
              "min         0.000000       0.000000       0.000000       0.000000   \n",
              "25%         0.339447       0.229167       0.400990       0.431002   \n",
              "50%         0.567394       0.500000       0.507426       0.517958   \n",
              "75%         0.783697       0.750000       0.626238       0.623819   \n",
              "max         1.000000       1.000000       1.000000       1.000000   \n",
              "\n",
              "            Rainfall    Evaporation       Sunshine    WindGustDir  \\\n",
              "count  116368.000000  116368.000000  116368.000000  116368.000000   \n",
              "mean        0.006314       0.035689       0.550516       0.539767   \n",
              "std         0.023000       0.021962       0.190458       0.312749   \n",
              "min         0.000000       0.000000       0.000000       0.000000   \n",
              "25%         0.000000       0.027586       0.565517       0.266667   \n",
              "50%         0.000000       0.033103       0.579310       0.600000   \n",
              "75%         0.001632       0.035862       0.593103       0.866667   \n",
              "max         1.000000       1.000000       1.000000       1.000000   \n",
              "\n",
              "       WindGustSpeed     WindDir9am  ...   WindSpeed3pm    Humidity9am  \\\n",
              "count  116368.000000  116368.000000  ...  116368.000000  116368.000000   \n",
              "mean        0.263427       0.466879  ...       0.225197       0.689507   \n",
              "std         0.101767       0.300755  ...       0.105065       0.188114   \n",
              "min         0.000000       0.000000  ...       0.000000       0.000000   \n",
              "25%         0.193798       0.200000  ...       0.156627       0.570000   \n",
              "50%         0.255814       0.466667  ...       0.228916       0.700000   \n",
              "75%         0.310078       0.733333  ...       0.289157       0.830000   \n",
              "max         1.000000       1.000000  ...       1.000000       1.000000   \n",
              "\n",
              "         Humidity3pm    Pressure9am    Pressure3pm       Cloud9am  \\\n",
              "count  116368.000000  116368.000000  116368.000000  116368.000000   \n",
              "mean        0.516058       0.613882       0.610319       0.518232   \n",
              "std         0.204400       0.111210       0.106584       0.253410   \n",
              "min         0.000000       0.000000       0.000000       0.000000   \n",
              "25%         0.370000       0.545455       0.544000       0.333333   \n",
              "50%         0.520000       0.613223       0.609600       0.555556   \n",
              "75%         0.650000       0.682645       0.676800       0.666667   \n",
              "max         1.000000       1.000000       1.000000       1.000000   \n",
              "\n",
              "            Cloud3pm        Temp9am        Temp3pm      RainToday  \n",
              "count  116368.000000  116368.000000  116368.000000  116368.000000  \n",
              "mean        0.588841       0.510115       0.519332       0.219648  \n",
              "std         0.263255       0.136068       0.131445       0.414010  \n",
              "min         0.000000       0.000000       0.000000       0.000000  \n",
              "25%         0.500000       0.411392       0.424184       0.000000  \n",
              "50%         0.625000       0.504219       0.508637       0.000000  \n",
              "75%         0.750000       0.605485       0.606526       0.000000  \n",
              "max         1.000000       1.000000       1.000000       1.000000  \n",
              "\n",
              "[8 rows x 22 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-22aac904-4fab-4c18-9e3f-6e084a6da846\">\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 tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Location</th>\n",
              "      <th>MinTemp</th>\n",
              "      <th>MaxTemp</th>\n",
              "      <th>Rainfall</th>\n",
              "      <th>Evaporation</th>\n",
              "      <th>Sunshine</th>\n",
              "      <th>WindGustDir</th>\n",
              "      <th>WindGustSpeed</th>\n",
              "      <th>WindDir9am</th>\n",
              "      <th>...</th>\n",
              "      <th>WindSpeed3pm</th>\n",
              "      <th>Humidity9am</th>\n",
              "      <th>Humidity3pm</th>\n",
              "      <th>Pressure9am</th>\n",
              "      <th>Pressure3pm</th>\n",
              "      <th>Cloud9am</th>\n",
              "      <th>Cloud3pm</th>\n",
              "      <th>Temp9am</th>\n",
              "      <th>Temp3pm</th>\n",
              "      <th>RainToday</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>116368.000000</td>\n",
              "      <td>116368.000000</td>\n",
              "      <td>116368.000000</td>\n",
              "      <td>116368.000000</td>\n",
              "      <td>116368.000000</td>\n",
              "      <td>116368.000000</td>\n",
              "      <td>116368.000000</td>\n",
              "      <td>116368.000000</td>\n",
              "      <td>116368.000000</td>\n",
              "      <td>116368.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>116368.000000</td>\n",
              "      <td>116368.000000</td>\n",
              "      <td>116368.000000</td>\n",
              "      <td>116368.000000</td>\n",
              "      <td>116368.000000</td>\n",
              "      <td>116368.000000</td>\n",
              "      <td>116368.000000</td>\n",
              "      <td>116368.000000</td>\n",
              "      <td>116368.000000</td>\n",
              "      <td>116368.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.561113</td>\n",
              "      <td>0.496069</td>\n",
              "      <td>0.512133</td>\n",
              "      <td>0.529359</td>\n",
              "      <td>0.006314</td>\n",
              "      <td>0.035689</td>\n",
              "      <td>0.550516</td>\n",
              "      <td>0.539767</td>\n",
              "      <td>0.263427</td>\n",
              "      <td>0.466879</td>\n",
              "      <td>...</td>\n",
              "      <td>0.225197</td>\n",
              "      <td>0.689507</td>\n",
              "      <td>0.516058</td>\n",
              "      <td>0.613882</td>\n",
              "      <td>0.610319</td>\n",
              "      <td>0.518232</td>\n",
              "      <td>0.588841</td>\n",
              "      <td>0.510115</td>\n",
              "      <td>0.519332</td>\n",
              "      <td>0.219648</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.257708</td>\n",
              "      <td>0.296357</td>\n",
              "      <td>0.157596</td>\n",
              "      <td>0.133940</td>\n",
              "      <td>0.023000</td>\n",
              "      <td>0.021962</td>\n",
              "      <td>0.190458</td>\n",
              "      <td>0.312749</td>\n",
              "      <td>0.101767</td>\n",
              "      <td>0.300755</td>\n",
              "      <td>...</td>\n",
              "      <td>0.105065</td>\n",
              "      <td>0.188114</td>\n",
              "      <td>0.204400</td>\n",
              "      <td>0.111210</td>\n",
              "      <td>0.106584</td>\n",
              "      <td>0.253410</td>\n",
              "      <td>0.263255</td>\n",
              "      <td>0.136068</td>\n",
              "      <td>0.131445</td>\n",
              "      <td>0.414010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.339447</td>\n",
              "      <td>0.229167</td>\n",
              "      <td>0.400990</td>\n",
              "      <td>0.431002</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.027586</td>\n",
              "      <td>0.565517</td>\n",
              "      <td>0.266667</td>\n",
              "      <td>0.193798</td>\n",
              "      <td>0.200000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.156627</td>\n",
              "      <td>0.570000</td>\n",
              "      <td>0.370000</td>\n",
              "      <td>0.545455</td>\n",
              "      <td>0.544000</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.411392</td>\n",
              "      <td>0.424184</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.567394</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.507426</td>\n",
              "      <td>0.517958</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.033103</td>\n",
              "      <td>0.579310</td>\n",
              "      <td>0.600000</td>\n",
              "      <td>0.255814</td>\n",
              "      <td>0.466667</td>\n",
              "      <td>...</td>\n",
              "      <td>0.228916</td>\n",
              "      <td>0.700000</td>\n",
              "      <td>0.520000</td>\n",
              "      <td>0.613223</td>\n",
              "      <td>0.609600</td>\n",
              "      <td>0.555556</td>\n",
              "      <td>0.625000</td>\n",
              "      <td>0.504219</td>\n",
              "      <td>0.508637</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.783697</td>\n",
              "      <td>0.750000</td>\n",
              "      <td>0.626238</td>\n",
              "      <td>0.623819</td>\n",
              "      <td>0.001632</td>\n",
              "      <td>0.035862</td>\n",
              "      <td>0.593103</td>\n",
              "      <td>0.866667</td>\n",
              "      <td>0.310078</td>\n",
              "      <td>0.733333</td>\n",
              "      <td>...</td>\n",
              "      <td>0.289157</td>\n",
              "      <td>0.830000</td>\n",
              "      <td>0.650000</td>\n",
              "      <td>0.682645</td>\n",
              "      <td>0.676800</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.750000</td>\n",
              "      <td>0.605485</td>\n",
              "      <td>0.606526</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 22 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-22aac904-4fab-4c18-9e3f-6e084a6da846')\"\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-22aac904-4fab-4c18-9e3f-6e084a6da846 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-22aac904-4fab-4c18-9e3f-6e084a6da846');\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": 60
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We now have X_train dataset ready to be fed into the Logistic Regression classifier. I will do it as follows."
      ],
      "metadata": {
        "id": "3kW499ledr4V"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model training "
      ],
      "metadata": {
        "id": "Xp3SAdYRdwrc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# train a logistic regression model on the training set\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "\n",
        "# instantiate the model\n",
        "logreg = LogisticRegression(solver='liblinear', random_state=0)\n",
        "\n",
        "\n",
        "# fit the model\n",
        "logreg.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cI8eSC4sdoop",
        "outputId": "0ac9e68b-e6dd-4749-8073-4df6e233f38c"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(random_state=0, solver='liblinear')"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Predict results "
      ],
      "metadata": {
        "id": "wpIi4MaGd4LR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_test = logreg.predict(X_test)\n",
        "\n",
        "y_pred_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hkYGpyeBd0SV",
        "outputId": "fc2c87e3-220b-4640-f1bc-7217e9cf74bb"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, ..., 0, 0, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# predict_proba method\n",
        "predict_proba method gives the probabilities for the target variable(0 and 1) in this case, in array form.\n",
        "\n",
        "0 is for probability of no rain and 1 is for probability of rain."
      ],
      "metadata": {
        "id": "AkvwGxuueBJI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# probability of getting output as 0 - no rain\n",
        "\n",
        "logreg.predict_proba(X_test)[:,0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u7lhwqCKd620",
        "outputId": "414695a7-9ccf-4d83-8241-d0478b629711"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.82853638, 0.73337425, 0.831236  , ..., 0.57635762, 0.686995  ,\n",
              "       0.9486096 ])"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# probability of getting output as 1 - rain\n",
        "\n",
        "logreg.predict_proba(X_test)[:,1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Nse9jk7oeETL",
        "outputId": "31e7b8ab-b75b-451c-bd3a-86b598b6eb36"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.16493756, 0.19291954, 0.13487763, ..., 0.41220779, 0.27235237,\n",
              "       0.02876936])"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Check accuracy score"
      ],
      "metadata": {
        "id": "g1e_WZvVeKBE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "print('Model accuracy score: {0:0.4f}'. format(accuracy_score(y_test, y_pred_test)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3RlGEEaveHX3",
        "outputId": "28a485c4-88de-4a2d-c5b5-69af1413fbd0"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model accuracy score: 0.8242\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here, y_test are the true class labels and y_pred_test are the predicted class labels in the test-set.\n",
        "\n"
      ],
      "metadata": {
        "id": "yc1_XeSMeQyJ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Compare the train-set and test-set accuracy\n",
        "Now, I will compare the train-set and test-set accuracy to check for overfitting."
      ],
      "metadata": {
        "id": "MWeVScA1eTm7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_train = logreg.predict(X_train)\n",
        "\n",
        "y_pred_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LywwUbBFeM_D",
        "outputId": "a8ca1d7a-9c41-4d63-f6f5-3cd2e32d24ac"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 0, 0, ..., 0, 0, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('Training-set accuracy score: {0:0.4f}'. format(accuracy_score(y_train, y_pred_train)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZkLNK0GmeWe-",
        "outputId": "78ae3f0d-a054-45f6-cb40-696896daff07"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training-set accuracy score: 0.8233\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Check for overfitting and underfitting"
      ],
      "metadata": {
        "id": "0qtqaGdcecsC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# print the scores on training and test set\n",
        "\n",
        "print('Training set score: {:.4f}'.format(logreg.score(X_train, y_train)))\n",
        "\n",
        "print('Test set score: {:.4f}'.format(logreg.score(X_test, y_test)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G5d2dSSYeZPW",
        "outputId": "63a54d52-35c0-4a3a-d068-e128d20ad265"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training set score: 0.8233\n",
            "Test set score: 0.8242\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Confusion matrix \n",
        "Table of Contents\n",
        "\n",
        "A confusion matrix is a tool for summarizing the performance of a classification algorithm. A confusion matrix will give us a clear picture of classification model performance and the types of errors produced by the model. It gives us a summary of correct and incorrect predictions broken down by each category. The summary is represented in a tabular form.\n",
        "\n",
        "Four types of outcomes are possible while evaluating a classification model performance. These four outcomes are described below:-\n",
        "\n",
        "True Positives (TP) – True Positives occur when we predict an observation belongs to a certain class and the observation actually belongs to that class.\n",
        "\n",
        "True Negatives (TN) – True Negatives occur when we predict an observation does not belong to a certain class and the observation actually does not belong to that class.\n",
        "\n",
        "False Positives (FP) – False Positives occur when we predict an observation belongs to a certain class but the observation actually does not belong to that class. This type of error is called Type I error.\n",
        "\n",
        "False Negatives (FN) – False Negatives occur when we predict an observation does not belong to a certain class but the observation actually belongs to that class. This is a very serious error and it is called Type II error.\n",
        "\n",
        "These four outcomes are summarized in a confusion matrix given below."
      ],
      "metadata": {
        "id": "UbdWgCgjfWy0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Print the Confusion Matrix and slice it into four pieces\n",
        "\n",
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "cm = confusion_matrix(y_test, y_pred_test)\n",
        "\n",
        "print('Confusion matrix\\n\\n', cm)\n",
        "\n",
        "print('\\nTrue Positives(TP) = ', cm[0,0])\n",
        "\n",
        "print('\\nTrue Negatives(TN) = ', cm[1,1])\n",
        "\n",
        "print('\\nFalse Positives(FP) = ', cm[0,1])\n",
        "\n",
        "print('\\nFalse Negatives(FN) = ', cm[1,0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VPU0sKxuefQc",
        "outputId": "af9933df-7100-4c13-8f33-d370f8580066"
      },
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion matrix\n",
            "\n",
            " [[20896  1186     0]\n",
            " [ 3284  3082     0]\n",
            " [  571    73     0]]\n",
            "\n",
            "True Positives(TP) =  20896\n",
            "\n",
            "True Negatives(TN) =  3082\n",
            "\n",
            "False Positives(FP) =  1186\n",
            "\n",
            "False Negatives(FN) =  3284\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The confusion matrix shows 20892 + 3285 = 24177 correct predictions and 3087 + 1175 = 4262 incorrect predictions.\n",
        "\n",
        "In this case, we have\n",
        "\n",
        "True Positives (Actual Positive:1 and Predict Positive:1) - 20892\n",
        "\n",
        "True Negatives (Actual Negative:0 and Predict Negative:0) - 3285\n",
        "\n",
        "False Positives (Actual Negative:0 but Predict Positive:1) - 1175 (Type I error)\n",
        "\n",
        "False Negatives (Actual Positive:1 but Predict Negative:0) - 3087 (Type II error)"
      ],
      "metadata": {
        "id": "4hUb8lAKfulA"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Classification metrices"
      ],
      "metadata": {
        "id": "cN-YBVFMgE1R"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Classification Report\n",
        "Classification report is another way to evaluate the classification model performance. It displays the precision, recall, f1 and support scores for the model. I have described these terms in later.\n",
        "\n",
        "We can print a classification report as follows:-"
      ],
      "metadata": {
        "id": "-yW6Mg9mgHUP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report\n",
        "\n",
        "print(classification_report(y_test, y_pred_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R73rsuzdfj2X",
        "outputId": "a852702b-326f-451e-a985-d7035bea904b"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.84      0.95      0.89     22082\n",
            "           1       0.71      0.48      0.58      6366\n",
            "           2       0.00      0.00      0.00       644\n",
            "\n",
            "    accuracy                           0.82     29092\n",
            "   macro avg       0.52      0.48      0.49     29092\n",
            "weighted avg       0.80      0.82      0.80     29092\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Classification accuracy¶\n"
      ],
      "metadata": {
        "id": "jgLOITAVgNE1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "TP = cm[0,0]\n",
        "TN = cm[1,1]\n",
        "FP = cm[0,1]\n",
        "FN = cm[1,0]"
      ],
      "metadata": {
        "id": "IKVKY8tUfxxn"
      },
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# print classification accuracy\n",
        "\n",
        "classification_accuracy = (TP + TN) / float(TP + TN + FP + FN)\n",
        "\n",
        "print('Classification accuracy : {0:0.4f}'.format(classification_accuracy))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "87PaYunfgPlQ",
        "outputId": "b1818277-c029-42b5-8f8d-44756c77d35f"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classification accuracy : 0.8429\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Classification error"
      ],
      "metadata": {
        "id": "R63jjS9NgUo_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# print classification error\n",
        "\n",
        "classification_error = (FP + FN) / float(TP + TN + FP + FN)\n",
        "\n",
        "print('Classification error : {0:0.4f}'.format(classification_error))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mATw_MLLgR4o",
        "outputId": "2fba26bd-d6e6-455d-a972-0504347f3970"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classification error : 0.1571\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Precision\n",
        "Precision can be defined as the percentage of correctly predicted positive outcomes out of all the predicted positive outcomes. It can be given as the ratio of true positives (TP) to the sum of true and false positives (TP + FP).\n",
        "\n",
        "So, Precision identifies the proportion of correctly predicted positive outcome. It is more concerned with the positive class than the negative class.\n",
        "\n",
        "Mathematically, precision can be defined as the ratio of TP to (TP + FP)."
      ],
      "metadata": {
        "id": "0fixyi8QgbWD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# print precision score\n",
        "\n",
        "precision = TP / float(TP + FP)\n",
        "\n",
        "\n",
        "print('Precision : {0:0.4f}'.format(precision))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p-UJQgNbgXPY",
        "outputId": "774693d6-d66e-4c03-9596-0ee3bf9914d4"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Precision : 0.9463\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Recall\n",
        "Recall can be defined as the percentage of correctly predicted positive outcomes out of all the actual positive outcomes. It can be given as the ratio of true positives (TP) to the sum of true positives and false negatives (TP + FN). Recall is also called Sensitivity.\n",
        "\n",
        "Recall identifies the proportion of correctly predicted actual positives.\n",
        "\n",
        "Mathematically, recall can be given as the ratio of TP to (TP + FN)."
      ],
      "metadata": {
        "id": "um-AVMEWggGs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "recall = TP / float(TP + FN)\n",
        "\n",
        "print('Recall or Sensitivity : {0:0.4f}'.format(recall))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YsC-DiDFgZhs",
        "outputId": "6aed31bf-7903-4103-88d3-18c93f55cc6a"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Recall or Sensitivity : 0.8642\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# True Positive Rate\n",
        "True Positive Rate is synonymous with Recall."
      ],
      "metadata": {
        "id": "SgCqGysegk-g"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "true_positive_rate = TP / float(TP + FN)\n",
        "\n",
        "\n",
        "print('True Positive Rate : {0:0.4f}'.format(true_positive_rate))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lTMriXktgjDl",
        "outputId": "bb90698a-f676-489d-c902-a6520c2cdb6e"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "True Positive Rate : 0.8642\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# False Positive Rate"
      ],
      "metadata": {
        "id": "3c-D47b2gqtt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "false_positive_rate = FP / float(FP + TN)\n",
        "\n",
        "\n",
        "print('False Positive Rate : {0:0.4f}'.format(false_positive_rate))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "77mbdAuvgoSK",
        "outputId": "2deb593e-e79b-4182-885c-a138f5e438f9"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "False Positive Rate : 0.2779\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Specificity"
      ],
      "metadata": {
        "id": "7c8mIcxMgwHN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "specificity = TN / (TN + FP)\n",
        "\n",
        "print('Specificity : {0:0.4f}'.format(specificity))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PYK_WVsxgtG0",
        "outputId": "ace5b7dc-3a81-477f-e061-41407dbdb513"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Specificity : 0.7221\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# f1-score\n",
        "f1-score is the weighted harmonic mean of precision and recall. The best possible f1-score would be 1.0 and the worst would be 0.0. f1-score is the harmonic mean of precision and recall. So, f1-score is always lower than accuracy measures as they embed precision and recall into their computation. The weighted average of f1-score should be used to compare classifier models, not global accuracy."
      ],
      "metadata": {
        "id": "L6uEkOu3g2Uv"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Support\n",
        "Support is the actual number of occurrences of the class in our dataset."
      ],
      "metadata": {
        "id": "OvA5ua0Ci3nw"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Adjusting the threshold level "
      ],
      "metadata": {
        "id": "ZqeI7X5ai6_M"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# print the first 10 predicted probabilities of two classes- 0 and 1\n",
        "\n",
        "y_pred_prob = logreg.predict_proba(X_test)[0:10]\n",
        "\n",
        "y_pred_prob"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oyIjOEuUgylO",
        "outputId": "b1d80f09-ed8d-40c2-c5ab-6cc4129c72da"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.82853638, 0.16493756, 0.00652606],\n",
              "       [0.73337425, 0.19291954, 0.07370621],\n",
              "       [0.831236  , 0.13487763, 0.03388638],\n",
              "       [0.73884767, 0.233182  , 0.02797033],\n",
              "       [0.93344644, 0.05512822, 0.01142534],\n",
              "       [0.96105764, 0.02947328, 0.00946908],\n",
              "       [0.5753477 , 0.38667952, 0.03797278],\n",
              "       [0.51566996, 0.47446862, 0.00986142],\n",
              "       [0.66682827, 0.28718093, 0.04599079],\n",
              "       [0.85941312, 0.11722868, 0.0233582 ]])"
            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Observations\n",
        "In each row, the numbers sum to 1.\n",
        "\n",
        "* There are 2 columns which correspond to 2 classes - 0 and 1.\n",
        "\n",
        "Class 0 - predicted probability that there is no rain tomorrow.\n",
        "\n",
        "Class 1 - predicted probability that there is rain tomorrow.\n",
        "\n",
        "* Importance of predicted probabilities\n",
        "\n",
        "We can rank the observations by probability of rain or no rain.\n",
        "\n",
        "* predict_proba process\n",
        "\n",
        "Predicts the probabilities\n",
        "\n",
        "Choose the class with the highest probability\n",
        "\n",
        "* Classification threshold level: \n",
        "\n",
        "There is a classification threshold level of 0.5.\n",
        "\n",
        "Class 1 - probability of rain is predicted if probability > 0.5.\n",
        "\n",
        "Class 0 - probability of no rain is predicted if probability < 0.5."
      ],
      "metadata": {
        "id": "0RErOW4-jAba"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# store the probabilities in dataframe\n",
        "\n",
        "y_pred_prob_df = pd.DataFrame(data=y_pred_prob, columns=['Prob of - No rain tomorrow (0)', 'Prob of - Rain tomorrow (1)','2'])\n",
        "\n",
        "y_pred_prob_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "yRrju1rgi9Yn",
        "outputId": "2ee0e615-071e-45c3-d05e-ea430100172e"
      },
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Prob of - No rain tomorrow (0)  Prob of - Rain tomorrow (1)         2\n",
              "0                        0.828536                     0.164938  0.006526\n",
              "1                        0.733374                     0.192920  0.073706\n",
              "2                        0.831236                     0.134878  0.033886\n",
              "3                        0.738848                     0.233182  0.027970\n",
              "4                        0.933446                     0.055128  0.011425\n",
              "5                        0.961058                     0.029473  0.009469\n",
              "6                        0.575348                     0.386680  0.037973\n",
              "7                        0.515670                     0.474469  0.009861\n",
              "8                        0.666828                     0.287181  0.045991\n",
              "9                        0.859413                     0.117229  0.023358"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-07a0f6f8-363f-475c-96c4-b2272dc0a228\">\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>Prob of - No rain tomorrow (0)</th>\n",
              "      <th>Prob of - Rain tomorrow (1)</th>\n",
              "      <th>2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.828536</td>\n",
              "      <td>0.164938</td>\n",
              "      <td>0.006526</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.733374</td>\n",
              "      <td>0.192920</td>\n",
              "      <td>0.073706</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.831236</td>\n",
              "      <td>0.134878</td>\n",
              "      <td>0.033886</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.738848</td>\n",
              "      <td>0.233182</td>\n",
              "      <td>0.027970</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.933446</td>\n",
              "      <td>0.055128</td>\n",
              "      <td>0.011425</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.961058</td>\n",
              "      <td>0.029473</td>\n",
              "      <td>0.009469</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.575348</td>\n",
              "      <td>0.386680</td>\n",
              "      <td>0.037973</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.515670</td>\n",
              "      <td>0.474469</td>\n",
              "      <td>0.009861</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.666828</td>\n",
              "      <td>0.287181</td>\n",
              "      <td>0.045991</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.859413</td>\n",
              "      <td>0.117229</td>\n",
              "      <td>0.023358</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-07a0f6f8-363f-475c-96c4-b2272dc0a228')\"\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-07a0f6f8-363f-475c-96c4-b2272dc0a228 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-07a0f6f8-363f-475c-96c4-b2272dc0a228');\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": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "V4Yk4gmVjESB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        ""
      ],
      "metadata": {
        "id": "KwWcxfnDmbPE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        ""
      ],
      "metadata": {
        "id": "p87WrDtimbLs"
      }
    }
  ]
}