Implementation_of_Logistic_Regression.ipynb

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      "source": [
        "#Implementation of Logistic Regression"
      ]
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      "source": [
        "**Logistic Regression with Python and Scikit-Learn**\n",
        "\n",
        "\n",
        "In this project, I implement Logistic Regression with Python and Scikit-Learn. I build a classifier to predict whether or not it will rain tomorrow in Australia by training a binary classification model using Logistic Regression. I have used the Rain in Australia dataset downloaded from the Kaggle website for this project.\n",
        "\n",
        "**Table of Contents**\n",
        "\n",
        "The table of contents for this project is as follows:-\n",
        "\n",
        "1.Introduction to Logistic Regression\n",
        "\n",
        "2.Logistic Regression intuition\n",
        "\n",
        "3.The problem statement\n",
        "\n",
        "4.Dataset description\n",
        "\n",
        "5.Import libraries\n",
        "\n",
        "6.Import dataset\n",
        "\n",
        "7.Exploratory data analysis\n",
        "\n",
        "8.Declare feature vector and target variable\n",
        "\n",
        "9.Split data into separate training and test set\n",
        "\n",
        "10.Feature engineering\n",
        "\n",
        "11.Feature scaling\n",
        "\n",
        "12.Model training\n",
        "\n",
        "13.Predict results\n",
        "\n",
        "14.Check accuracy score\n",
        "\n",
        "15.Confusion matrix\n",
        "\n",
        "16.Classification metrices\n",
        "\n",
        "17.Adjusting the threshold level\n",
        "\n",
        "18.ROC - AUC\n",
        "\n",
        "19.Recursive feature elimination\n",
        "\n",
        "20.k-Fold Cross Validation\n",
        "\n",
        "21.Hyperparameter optimization using GridSearch CV\n",
        "\n",
        "22.Results and conclusion\n",
        "\n",
        "\n"
      ],
      "metadata": {
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      "cell_type": "markdown",
      "source": [
        "**1. Introduction to Logistic Regression**\n",
        "\n",
        "When Machine Learning Engineers 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."
      ],
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      "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",
        "Logistic Regression algorithm works by implementing a linear equation with independent or explanatory variables to predict a response value. 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",
        "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",
        "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.\n",
        "\n",
        "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.\n",
        "\n",
        "Logistic regression intuition is discussed in depth in the readme document."
      ],
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    {
      "cell_type": "markdown",
      "source": [
        "**3. The problem statement**\n",
        "\n",
        "In this project, I try to answer the question that whether or not it will rain tomorrow in Australia. I implement Logistic Regression with Python and Scikit-Learn.\n",
        "\n",
        "To answer the question, I build a classifier to predict whether or not it will rain tomorrow in Australia by training a binary classification model using Logistic Regression. I have used the Rain in Australia dataset downloaded from the Kaggle website for this project."
      ],
      "metadata": {
        "id": "AZVaiM94Hmyh"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**4. Dataset description**\n",
        "\n",
        "I have used the Rain in Australia data set downloaded from the Kaggle website.\n",
        "\n",
        "I have downloaded this data set from the Kaggle website. The data set can be found at the following url:-\n",
        "\n",
        "https://www.kaggle.com/jsphyg/weather-dataset-rattle-package\n",
        "\n",
        "This dataset contains daily weather observations from numerous Australian weather stations."
      ],
      "metadata": {
        "id": "yRJith7dICiu"
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    {
      "cell_type": "markdown",
      "source": [
        "**5.Import Libraries.**"
      ],
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        "id": "p6Y_QUg3OKvl"
      }
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    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "%matplotlib inline\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ],
      "metadata": {
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      "execution_count": 4,
      "outputs": []
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      "cell_type": "markdown",
      "source": [
        "**6.Import DataSet**"
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    {
      "cell_type": "code",
      "source": [
        "data ='/content/drive/MyDrive/Implementation of the Logistic regression/weatherAUS.csv'\n",
        "df = pd.read_csv(data)"
      ],
      "metadata": {
        "id": "WDPxl3igOzZz"
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      "execution_count": 5,
      "outputs": []
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      "cell_type": "markdown",
      "source": [
        "**7. Exploratory data analysis**\n",
        "\n",
        "Now, I will explore the data to gain insights about the data"
      ],
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      }
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      "cell_type": "code",
      "source": [
        "#viewing  the Dimension of the dataset\n",
        "df.shape"
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              "(145460, 23)"
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          "execution_count": 6
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      "source": [
        "We can see that there are 142193 instances and 23 variables in the data set."
      ],
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      }
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    {
      "cell_type": "code",
      "source": [
        "#preview the dataset\n",
        "df.head()"
      ],
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        "id": "6H6u7NSrQGl0",
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              "         Date Location  MinTemp  MaxTemp  Rainfall  Evaporation  Sunshine  \\\n",
              "0  2008-12-01   Albury     13.4     22.9       0.6          NaN       NaN   \n",
              "1  2008-12-02   Albury      7.4     25.1       0.0          NaN       NaN   \n",
              "2  2008-12-03   Albury     12.9     25.7       0.0          NaN       NaN   \n",
              "3  2008-12-04   Albury      9.2     28.0       0.0          NaN       NaN   \n",
              "4  2008-12-05   Albury     17.5     32.3       1.0          NaN       NaN   \n",
              "\n",
              "  WindGustDir  WindGustSpeed WindDir9am  ... Humidity9am  Humidity3pm  \\\n",
              "0           W           44.0          W  ...        71.0         22.0   \n",
              "1         WNW           44.0        NNW  ...        44.0         25.0   \n",
              "2         WSW           46.0          W  ...        38.0         30.0   \n",
              "3          NE           24.0         SE  ...        45.0         16.0   \n",
              "4           W           41.0        ENE  ...        82.0         33.0   \n",
              "\n",
              "   Pressure9am  Pressure3pm  Cloud9am  Cloud3pm  Temp9am  Temp3pm  RainToday  \\\n",
              "0       1007.7       1007.1       8.0       NaN     16.9     21.8         No   \n",
              "1       1010.6       1007.8       NaN       NaN     17.2     24.3         No   \n",
              "2       1007.6       1008.7       NaN       2.0     21.0     23.2         No   \n",
              "3       1017.6       1012.8       NaN       NaN     18.1     26.5         No   \n",
              "4       1010.8       1006.0       7.0       8.0     17.8     29.7         No   \n",
              "\n",
              "   RainTomorrow  \n",
              "0            No  \n",
              "1            No  \n",
              "2            No  \n",
              "3            No  \n",
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              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-628e0a40-242a-4e34-8258-fa6d8df86bfe button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "5 row * 23 columns"
      ],
      "metadata": {
        "id": "ciOTz7YSRmrH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "no_of_columns = df.columns\n",
        "no_of_columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zZAu4ZjURWLx",
        "outputId": "240a26a6-5c6b-4f72-da6f-986d5a1ef10c"
      },
      "execution_count": 8,
      "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": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#view the summary of dataset\n",
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gRJocjMiSDJz",
        "outputId": "c7609214-b37b-4f9b-c5d9-b37d28c315d9"
      },
      "execution_count": 9,
      "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": "markdown",
      "source": [
        "**Types of variables**\n",
        "\n",
        "In this section, I 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, I will find categorical variables."
      ],
      "metadata": {
        "id": "0QPedpsYWaZ9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# find categorical variables\n",
        "\n",
        "categorical = [var for var in df.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": "MkC2HFyxWBWJ",
        "outputId": "657909da-64ec-41f0-fd25-b820f4661bf4"
      },
      "execution_count": 10,
      "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 variable\n",
        "df[categorical].head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "ySoGHbWkWyJI",
        "outputId": "74d37fcb-c37a-4333-856c-89ff5694758d"
      },
      "execution_count": 11,
      "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  "
            ],
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              "\n",
              "  <div id=\"df-20d614fb-9f7e-450b-b8d7-c09bac6b16b7\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "  }\n",
              "\n",
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              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ffbc774d-9a89-4419-89da-8738bac51c0e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df[categorical]\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"2008-12-02\",\n          \"2008-12-05\",\n          \"2008-12-03\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Location\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Albury\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"WindGustDir\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"WNW\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"WindDir9am\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"NNW\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"WindDir3pm\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"WSW\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RainToday\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"No\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RainTomorrow\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"No\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Summary of categorical variables**\n",
        "\n",
        "1.There is a date variable. It is denoted by Date column.\n",
        "\n",
        "2.There are 6 categorical variables. These are given by Location, WindGustDir, WindDir9am, WindDir3pm, RainToday and RainTomorrow.\n",
        "\n",
        "3.There are two binary categorical variables - RainToday and RainTomorrow.\n",
        "\n",
        "4.RainTomorrow is the target variable."
      ],
      "metadata": {
        "id": "vEPwCNFpXgFO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Explore problems within categorical variables**\n",
        "\n",
        "First, I will explore the categorical variables."
      ],
      "metadata": {
        "id": "vqTxZ16XX2YT"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Missing values in categorical variables**"
      ],
      "metadata": {
        "id": "p-I3Rl5zX_qg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#checking missing values in Categorical variable\n",
        "df[categorical].isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MfOZs58hXIsj",
        "outputId": "7be91a0d-971f-4f3e-d816-d9d7cacab92e"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Date                0\n",
              "Location            0\n",
              "WindGustDir     10326\n",
              "WindDir9am      10566\n",
              "WindDir3pm       4228\n",
              "RainToday        3261\n",
              "RainTomorrow     3267\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#printing the categorical variable which contain the missing values\n",
        "cate1 = [var for var in categorical if df[var].isnull().sum()!=0]\n",
        "print(df[cate1].isnull().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7YXQ_TYXYcE3",
        "outputId": "743a1cfd-a808-47e7-95ed-a806ae92fd5e"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "WindGustDir     10326\n",
            "WindDir9am      10566\n",
            "WindDir3pm       4228\n",
            "RainToday        3261\n",
            "RainTomorrow     3267\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can see that there are only 5 categorical variables in the dataset which contains missing values. These are WindGustDir, WindDir9am, WindDir3pm,RainToday and RainTomorrow."
      ],
      "metadata": {
        "id": "ZjsoUsDUZpD1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Frequency counts of categorical variables**\n",
        "\n",
        "Now, I will check the frequency counts of categorical variables."
      ],
      "metadata": {
        "id": "yLXNYiRMaCey"
      }
    },
    {
      "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": "sajOEjLEZcem",
        "outputId": "e7d44caa-e26d-4f37-8efd-5cbc3faac2d3"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Date\n",
            "2013-11-12    49\n",
            "2014-09-01    49\n",
            "2014-08-23    49\n",
            "2014-08-24    49\n",
            "2014-08-25    49\n",
            "              ..\n",
            "2007-11-29     1\n",
            "2007-11-28     1\n",
            "2007-11-27     1\n",
            "2007-11-26     1\n",
            "2008-01-31     1\n",
            "Name: count, Length: 3436, dtype: int64\n",
            "Location\n",
            "Canberra            3436\n",
            "Sydney              3344\n",
            "Darwin              3193\n",
            "Melbourne           3193\n",
            "Brisbane            3193\n",
            "Adelaide            3193\n",
            "Perth               3193\n",
            "Hobart              3193\n",
            "Albany              3040\n",
            "MountGambier        3040\n",
            "Ballarat            3040\n",
            "Townsville          3040\n",
            "GoldCoast           3040\n",
            "Cairns              3040\n",
            "Launceston          3040\n",
            "AliceSprings        3040\n",
            "Bendigo             3040\n",
            "Albury              3040\n",
            "MountGinini         3040\n",
            "Wollongong          3040\n",
            "Newcastle           3039\n",
            "Tuggeranong         3039\n",
            "Penrith             3039\n",
            "Woomera             3009\n",
            "Nuriootpa           3009\n",
            "Cobar               3009\n",
            "CoffsHarbour        3009\n",
            "Moree               3009\n",
            "Sale                3009\n",
            "PerthAirport        3009\n",
            "PearceRAAF          3009\n",
            "Witchcliffe         3009\n",
            "BadgerysCreek       3009\n",
            "Mildura             3009\n",
            "NorfolkIsland       3009\n",
            "MelbourneAirport    3009\n",
            "Richmond            3009\n",
            "SydneyAirport       3009\n",
            "WaggaWagga          3009\n",
            "Williamtown         3009\n",
            "Dartmoor            3009\n",
            "Watsonia            3009\n",
            "Portland            3009\n",
            "Walpole             3006\n",
            "NorahHead           3004\n",
            "SalmonGums          3001\n",
            "Katherine           1578\n",
            "Nhil                1578\n",
            "Uluru               1578\n",
            "Name: count, dtype: int64\n",
            "WindGustDir\n",
            "W      9915\n",
            "SE     9418\n",
            "N      9313\n",
            "SSE    9216\n",
            "E      9181\n",
            "S      9168\n",
            "WSW    9069\n",
            "SW     8967\n",
            "SSW    8736\n",
            "WNW    8252\n",
            "NW     8122\n",
            "ENE    8104\n",
            "ESE    7372\n",
            "NE     7133\n",
            "NNW    6620\n",
            "NNE    6548\n",
            "Name: count, dtype: int64\n",
            "WindDir9am\n",
            "N      11758\n",
            "SE      9287\n",
            "E       9176\n",
            "SSE     9112\n",
            "NW      8749\n",
            "S       8659\n",
            "W       8459\n",
            "SW      8423\n",
            "NNE     8129\n",
            "NNW     7980\n",
            "ENE     7836\n",
            "NE      7671\n",
            "ESE     7630\n",
            "SSW     7587\n",
            "WNW     7414\n",
            "WSW     7024\n",
            "Name: count, dtype: int64\n",
            "WindDir3pm\n",
            "SE     10838\n",
            "W      10110\n",
            "S       9926\n",
            "WSW     9518\n",
            "SSE     9399\n",
            "SW      9354\n",
            "N       8890\n",
            "WNW     8874\n",
            "NW      8610\n",
            "ESE     8505\n",
            "E       8472\n",
            "NE      8263\n",
            "SSW     8156\n",
            "NNW     7870\n",
            "ENE     7857\n",
            "NNE     6590\n",
            "Name: count, dtype: int64\n",
            "RainToday\n",
            "No     110319\n",
            "Yes     31880\n",
            "Name: count, dtype: int64\n",
            "RainTomorrow\n",
            "No     110316\n",
            "Yes     31877\n",
            "Name: count, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# view frequency distribution of categorical variables\n",
        "\n",
        "# for var in categorical:\n",
        "\n",
        "#     print(df[var].value_counts()/np.float(len(df)))\n",
        "# Change np.float to float\n",
        "for var in categorical:\n",
        "    print(df[var].value_counts() / float(len(df)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l85gb0HnaII3",
        "outputId": "1a9b575d-ef43-40a6-876e-e77af4d12b64"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Date\n",
            "2013-11-12    0.000337\n",
            "2014-09-01    0.000337\n",
            "2014-08-23    0.000337\n",
            "2014-08-24    0.000337\n",
            "2014-08-25    0.000337\n",
            "                ...   \n",
            "2007-11-29    0.000007\n",
            "2007-11-28    0.000007\n",
            "2007-11-27    0.000007\n",
            "2007-11-26    0.000007\n",
            "2008-01-31    0.000007\n",
            "Name: count, Length: 3436, dtype: float64\n",
            "Location\n",
            "Canberra            0.023622\n",
            "Sydney              0.022989\n",
            "Darwin              0.021951\n",
            "Melbourne           0.021951\n",
            "Brisbane            0.021951\n",
            "Adelaide            0.021951\n",
            "Perth               0.021951\n",
            "Hobart              0.021951\n",
            "Albany              0.020899\n",
            "MountGambier        0.020899\n",
            "Ballarat            0.020899\n",
            "Townsville          0.020899\n",
            "GoldCoast           0.020899\n",
            "Cairns              0.020899\n",
            "Launceston          0.020899\n",
            "AliceSprings        0.020899\n",
            "Bendigo             0.020899\n",
            "Albury              0.020899\n",
            "MountGinini         0.020899\n",
            "Wollongong          0.020899\n",
            "Newcastle           0.020892\n",
            "Tuggeranong         0.020892\n",
            "Penrith             0.020892\n",
            "Woomera             0.020686\n",
            "Nuriootpa           0.020686\n",
            "Cobar               0.020686\n",
            "CoffsHarbour        0.020686\n",
            "Moree               0.020686\n",
            "Sale                0.020686\n",
            "PerthAirport        0.020686\n",
            "PearceRAAF          0.020686\n",
            "Witchcliffe         0.020686\n",
            "BadgerysCreek       0.020686\n",
            "Mildura             0.020686\n",
            "NorfolkIsland       0.020686\n",
            "MelbourneAirport    0.020686\n",
            "Richmond            0.020686\n",
            "SydneyAirport       0.020686\n",
            "WaggaWagga          0.020686\n",
            "Williamtown         0.020686\n",
            "Dartmoor            0.020686\n",
            "Watsonia            0.020686\n",
            "Portland            0.020686\n",
            "Walpole             0.020665\n",
            "NorahHead           0.020652\n",
            "SalmonGums          0.020631\n",
            "Katherine           0.010848\n",
            "Nhil                0.010848\n",
            "Uluru               0.010848\n",
            "Name: count, dtype: float64\n",
            "WindGustDir\n",
            "W      0.068163\n",
            "SE     0.064746\n",
            "N      0.064024\n",
            "SSE    0.063358\n",
            "E      0.063117\n",
            "S      0.063028\n",
            "WSW    0.062347\n",
            "SW     0.061646\n",
            "SSW    0.060058\n",
            "WNW    0.056730\n",
            "NW     0.055837\n",
            "ENE    0.055713\n",
            "ESE    0.050681\n",
            "NE     0.049038\n",
            "NNW    0.045511\n",
            "NNE    0.045016\n",
            "Name: count, dtype: float64\n",
            "WindDir9am\n",
            "N      0.080833\n",
            "SE     0.063846\n",
            "E      0.063083\n",
            "SSE    0.062643\n",
            "NW     0.060147\n",
            "S      0.059528\n",
            "W      0.058153\n",
            "SW     0.057906\n",
            "NNE    0.055885\n",
            "NNW    0.054860\n",
            "ENE    0.053870\n",
            "NE     0.052736\n",
            "ESE    0.052454\n",
            "SSW    0.052159\n",
            "WNW    0.050969\n",
            "WSW    0.048288\n",
            "Name: count, dtype: float64\n",
            "WindDir3pm\n",
            "SE     0.074508\n",
            "W      0.069504\n",
            "S      0.068239\n",
            "WSW    0.065434\n",
            "SSE    0.064616\n",
            "SW     0.064306\n",
            "N      0.061116\n",
            "WNW    0.061006\n",
            "NW     0.059192\n",
            "ESE    0.058470\n",
            "E      0.058243\n",
            "NE     0.056806\n",
            "SSW    0.056070\n",
            "NNW    0.054104\n",
            "ENE    0.054015\n",
            "NNE    0.045305\n",
            "Name: count, dtype: float64\n",
            "RainToday\n",
            "No     0.758415\n",
            "Yes    0.219167\n",
            "Name: count, dtype: float64\n",
            "RainTomorrow\n",
            "No     0.758394\n",
            "Yes    0.219146\n",
            "Name: count, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Number of labels: cardinality**\n",
        "\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, I will check for high cardinality."
      ],
      "metadata": {
        "id": "rnUhDAlTbTip"
      }
    },
    {
      "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": "bWa4DWF7aYHP",
        "outputId": "f9fdf868-9909-49a5-bd22-e4340c31bf3a"
      },
      "execution_count": 16,
      "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": "XN2ukLb7b-od"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Feature Engineering of Date Variable**"
      ],
      "metadata": {
        "id": "ocNCqcPncITM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['Date'].dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dT5oCnLXb12j",
        "outputId": "2f73510d-7d3b-4b86-c11f-da3c07df75af"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dtype('O')"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can see that the data type of Date variable is object. I will parse the date currently coded as object into datetime format."
      ],
      "metadata": {
        "id": "QSyBlDsec_wT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# parse the dates, currently coded as strings, into datetime format\n",
        "\n",
        "df['Date'] = pd.to_datetime(df['Date'])"
      ],
      "metadata": {
        "id": "b9uWDZC7c4GO"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# extract year from date\n",
        "\n",
        "df['Year'] = df['Date'].dt.year\n",
        "\n",
        "df['Year'].head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h83sClu0dH2B",
        "outputId": "4b72a7fb-bd7d-48d3-a2d9-a276483ffc19"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    2008\n",
              "1    2008\n",
              "2    2008\n",
              "3    2008\n",
              "4    2008\n",
              "Name: Year, dtype: int32"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# extract month from date\n",
        "\n",
        "df['Month'] = df['Date'].dt.month\n",
        "\n",
        "df['Month'].head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yju2zfr0dNLd",
        "outputId": "f650d030-447f-4583-d4e9-9d811b4a3bff"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    12\n",
              "1    12\n",
              "2    12\n",
              "3    12\n",
              "4    12\n",
              "Name: Month, dtype: int32"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# extract day from date\n",
        "\n",
        "df['Day'] = df['Date'].dt.day\n",
        "\n",
        "df['Day'].head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mVotdKRFdX_Q",
        "outputId": "721b8ba5-f665-43e5-d0bc-6f31acd6400c"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    1\n",
              "1    2\n",
              "2    3\n",
              "3    4\n",
              "4    5\n",
              "Name: Day, dtype: int32"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#again view the summary of the dataset\n",
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EuoMCjiTdeCL",
        "outputId": "56a77cb2-49a7-447e-99cd-b1343d9efe0c"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 145460 entries, 0 to 145459\n",
            "Data columns (total 26 columns):\n",
            " #   Column         Non-Null Count   Dtype         \n",
            "---  ------         --------------   -----         \n",
            " 0   Date           145460 non-null  datetime64[ns]\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",
            " 23  Year           145460 non-null  int32         \n",
            " 24  Month          145460 non-null  int32         \n",
            " 25  Day            145460 non-null  int32         \n",
            "dtypes: datetime64[ns](1), float64(16), int32(3), object(6)\n",
            "memory usage: 27.2+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can see that there are three additional columns created from Date variable. Now, I will drop the original Date variable from the dataset."
      ],
      "metadata": {
        "id": "I9OJDf-bd7Ia"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# drop the original Date variable\n",
        "\n",
        "df.drop('Date', axis=1, inplace = True)"
      ],
      "metadata": {
        "id": "ZNpU_J2fdn0A"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# preview the dataset again\n",
        "\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "id": "m9ZOCCKId-uR",
        "outputId": "f9921a87-5f44-4c03-fa2c-f5fc1e950d52"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Location  MinTemp  MaxTemp  Rainfall  Evaporation  Sunshine WindGustDir  \\\n",
              "0   Albury     13.4     22.9       0.6          NaN       NaN           W   \n",
              "1   Albury      7.4     25.1       0.0          NaN       NaN         WNW   \n",
              "2   Albury     12.9     25.7       0.0          NaN       NaN         WSW   \n",
              "3   Albury      9.2     28.0       0.0          NaN       NaN          NE   \n",
              "4   Albury     17.5     32.3       1.0          NaN       NaN           W   \n",
              "\n",
              "   WindGustSpeed WindDir9am WindDir3pm  ...  Pressure3pm  Cloud9am  Cloud3pm  \\\n",
              "0           44.0          W        WNW  ...       1007.1       8.0       NaN   \n",
              "1           44.0        NNW        WSW  ...       1007.8       NaN       NaN   \n",
              "2           46.0          W        WSW  ...       1008.7       NaN       2.0   \n",
              "3           24.0         SE          E  ...       1012.8       NaN       NaN   \n",
              "4           41.0        ENE         NW  ...       1006.0       7.0       8.0   \n",
              "\n",
              "   Temp9am  Temp3pm  RainToday  RainTomorrow  Year  Month  Day  \n",
              "0     16.9     21.8         No            No  2008     12    1  \n",
              "1     17.2     24.3         No            No  2008     12    2  \n",
              "2     21.0     23.2         No            No  2008     12    3  \n",
              "3     18.1     26.5         No            No  2008     12    4  \n",
              "4     17.8     29.7         No            No  2008     12    5  \n",
              "\n",
              "[5 rows x 25 columns]"
            ],
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              "    <div>\n",
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              "</style>\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Location</th>\n",
              "      <th>MinTemp</th>\n",
              "      <th>MaxTemp</th>\n",
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              "      <th>WindDir3pm</th>\n",
              "      <th>...</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",
              "      <th>Year</th>\n",
              "      <th>Month</th>\n",
              "      <th>Day</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>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>WNW</td>\n",
              "      <td>...</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",
              "      <td>2008</td>\n",
              "      <td>12</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\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>WSW</td>\n",
              "      <td>...</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",
              "      <td>2008</td>\n",
              "      <td>12</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\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>WSW</td>\n",
              "      <td>...</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",
              "      <td>2008</td>\n",
              "      <td>12</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\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>E</td>\n",
              "      <td>...</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",
              "      <td>2008</td>\n",
              "      <td>12</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\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>NW</td>\n",
              "      <td>...</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",
              "      <td>2008</td>\n",
              "      <td>12</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 25 columns</p>\n",
              "</div>\n",
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              "  </style>\n",
              "\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-8cb10bc0-29c3-468a-8fda-58f68d8cdfc5 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "  }\n",
              "\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
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              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-dab3408b-bda0-41f3-9442-d328f4313dd6 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now, we can see that the Date variable has been removed from the dataset."
      ],
      "metadata": {
        "id": "APpytaUXeK10"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Explore the Categorical Variables**\n",
        "\n",
        "Now, I will explore the categorical variables one by one."
      ],
      "metadata": {
        "id": "zJXocPmseOEd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# find categorical variables\n",
        "\n",
        "categorical = [var for var in df.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": "CUkQoqA_eCLv",
        "outputId": "5e0ac683-73c6-4513-aaf6-c4ee30844d4b"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "There are 6 categorical variables\n",
            "\n",
            "The categorical variables are : ['Location', 'WindGustDir', 'WindDir9am', 'WindDir3pm', 'RainToday', 'RainTomorrow']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can see that there are 6 categorical variables in the dataset. The Date variable has been removed. First, I will check missing values in categorical variables."
      ],
      "metadata": {
        "id": "KbgnCrBHe5LJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#checking the missing values in the categorical variable\n",
        "df[categorical].isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UXgDHuUmexcZ",
        "outputId": "482aecca-bc05-4db8-f5cb-c6d89c607011"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Location            0\n",
              "WindGustDir     10326\n",
              "WindDir9am      10566\n",
              "WindDir3pm       4228\n",
              "RainToday        3261\n",
              "RainTomorrow     3267\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can see that WindGustDir, WindDir9am, WindDir3pm, RainToday and RainTomorrow variables contain missing values. I will explore these variables one by one"
      ],
      "metadata": {
        "id": "o9WPcxgvfYRk"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        " ***Explore Location variable***"
      ],
      "metadata": {
        "id": "VXukVLNWfuSd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# print number of labels in Location variable\n",
        "\n",
        "print('Location contains', len(df.Location.unique()), 'labels')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y5xjBU6qfJnj",
        "outputId": "9c229639-743a-481b-9cd5-0d6169e2fb52"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Location contains 49 labels\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# check labels in location variable\n",
        "\n",
        "df.Location.unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kD2-3y5Tf6h3",
        "outputId": "6b5cb217-42e9-4d30-a6d4-a0cdd0a314da"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Albury', 'BadgerysCreek', 'Cobar', 'CoffsHarbour', 'Moree',\n",
              "       'Newcastle', 'NorahHead', 'NorfolkIsland', 'Penrith', 'Richmond',\n",
              "       'Sydney', 'SydneyAirport', 'WaggaWagga', 'Williamtown',\n",
              "       'Wollongong', 'Canberra', 'Tuggeranong', 'MountGinini', 'Ballarat',\n",
              "       'Bendigo', 'Sale', 'MelbourneAirport', 'Melbourne', 'Mildura',\n",
              "       'Nhil', 'Portland', 'Watsonia', 'Dartmoor', 'Brisbane', 'Cairns',\n",
              "       'GoldCoast', 'Townsville', 'Adelaide', 'MountGambier', 'Nuriootpa',\n",
              "       'Woomera', 'Albany', 'Witchcliffe', 'PearceRAAF', 'PerthAirport',\n",
              "       'Perth', 'SalmonGums', 'Walpole', 'Hobart', 'Launceston',\n",
              "       'AliceSprings', 'Darwin', 'Katherine', 'Uluru'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# check frequency distribution of values in Location variable\n",
        "\n",
        "df.Location.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nOXC7dmAgOYB",
        "outputId": "59b2fefe-ac87-4145-c163-e1f0c08111b0"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Location\n",
              "Canberra            3436\n",
              "Sydney              3344\n",
              "Darwin              3193\n",
              "Melbourne           3193\n",
              "Brisbane            3193\n",
              "Adelaide            3193\n",
              "Perth               3193\n",
              "Hobart              3193\n",
              "Albany              3040\n",
              "MountGambier        3040\n",
              "Ballarat            3040\n",
              "Townsville          3040\n",
              "GoldCoast           3040\n",
              "Cairns              3040\n",
              "Launceston          3040\n",
              "AliceSprings        3040\n",
              "Bendigo             3040\n",
              "Albury              3040\n",
              "MountGinini         3040\n",
              "Wollongong          3040\n",
              "Newcastle           3039\n",
              "Tuggeranong         3039\n",
              "Penrith             3039\n",
              "Woomera             3009\n",
              "Nuriootpa           3009\n",
              "Cobar               3009\n",
              "CoffsHarbour        3009\n",
              "Moree               3009\n",
              "Sale                3009\n",
              "PerthAirport        3009\n",
              "PearceRAAF          3009\n",
              "Witchcliffe         3009\n",
              "BadgerysCreek       3009\n",
              "Mildura             3009\n",
              "NorfolkIsland       3009\n",
              "MelbourneAirport    3009\n",
              "Richmond            3009\n",
              "SydneyAirport       3009\n",
              "WaggaWagga          3009\n",
              "Williamtown         3009\n",
              "Dartmoor            3009\n",
              "Watsonia            3009\n",
              "Portland            3009\n",
              "Walpole             3006\n",
              "NorahHead           3004\n",
              "SalmonGums          3001\n",
              "Katherine           1578\n",
              "Nhil                1578\n",
              "Uluru               1578\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#  let's do One Hot Encoding of Location variable\n",
        "# get k-1 dummy variables after One Hot Encoding\n",
        "# preview the dataset with head() method\n",
        "\n",
        "pd.get_dummies(df.Location,drop_first = True,dummy_na=True).head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "id": "AGPduJMqgXOu",
        "outputId": "db9c3487-ae4e-4990-f827-41fa3f035d21"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Albany  Albury  AliceSprings  BadgerysCreek  Ballarat  Bendigo  Brisbane  \\\n",
              "0   False    True         False          False     False    False     False   \n",
              "1   False    True         False          False     False    False     False   \n",
              "2   False    True         False          False     False    False     False   \n",
              "3   False    True         False          False     False    False     False   \n",
              "4   False    True         False          False     False    False     False   \n",
              "\n",
              "   Cairns  Canberra  Cobar  ...  Tuggeranong  Uluru  WaggaWagga  Walpole  \\\n",
              "0   False     False  False  ...        False  False       False    False   \n",
              "1   False     False  False  ...        False  False       False    False   \n",
              "2   False     False  False  ...        False  False       False    False   \n",
              "3   False     False  False  ...        False  False       False    False   \n",
              "4   False     False  False  ...        False  False       False    False   \n",
              "\n",
              "   Watsonia  Williamtown  Witchcliffe  Wollongong  Woomera    NaN  \n",
              "0     False        False        False       False    False  False  \n",
              "1     False        False        False       False    False  False  \n",
              "2     False        False        False       False    False  False  \n",
              "3     False        False        False       False    False  False  \n",
              "4     False        False        False       False    False  False  \n",
              "\n",
              "[5 rows x 49 columns]"
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              "\n",
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              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
            }
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Explore WindGustDir variable***"
      ],
      "metadata": {
        "id": "csXzexkWh44h"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# print number of labels in WindGustDir variable\n",
        "\n",
        "print('WindGustDir contains', len(df['WindGustDir'].unique()), 'labels')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e3yOZg46gobQ",
        "outputId": "6d424043-89bc-44a2-bcc2-8b3b63e6f42c"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "WindGustDir contains 17 labels\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# check labels in WindGustDir variable\n",
        "\n",
        "df['WindGustDir'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y5obh8E4iA_i",
        "outputId": "b8f488f8-92fa-489b-9a75-4a4cc89397a0"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['W', 'WNW', 'WSW', 'NE', 'NNW', 'N', 'NNE', 'SW', nan, 'ENE',\n",
              "       'SSE', 'S', 'NW', 'SE', 'ESE', 'E', 'SSW'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# check frequency distribution of values in WindGustDir variable\n",
        "\n",
        "df.WindGustDir.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SA01eTPRiH38",
        "outputId": "a23b1874-dde2-4c2a-abe1-2dcc8db8c09d"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "WindGustDir\n",
              "W      9915\n",
              "SE     9418\n",
              "N      9313\n",
              "SSE    9216\n",
              "E      9181\n",
              "S      9168\n",
              "WSW    9069\n",
              "SW     8967\n",
              "SSW    8736\n",
              "WNW    8252\n",
              "NW     8122\n",
              "ENE    8104\n",
              "ESE    7372\n",
              "NE     7133\n",
              "NNW    6620\n",
              "NNE    6548\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# let's do One Hot Encoding of WindGustDir variable\n",
        "# get k-1 dummy variables after One Hot Encoding\n",
        "# also add an additional dummy variable to indicate there was missing data\n",
        "# preview the dataset with head() method\n",
        "\n",
        "pd.get_dummies(df.WindGustDir, drop_first=True, dummy_na=True).head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "-LVbGcBBiMrv",
        "outputId": "5e25d31e-7ddc-46f1-8e6c-4b6bf6fed499"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     ENE    ESE      N     NE    NNE    NNW     NW      S     SE    SSE  \\\n",
              "0  False  False  False  False  False  False  False  False  False  False   \n",
              "1  False  False  False  False  False  False  False  False  False  False   \n",
              "2  False  False  False  False  False  False  False  False  False  False   \n",
              "3  False  False  False   True  False  False  False  False  False  False   \n",
              "4  False  False  False  False  False  False  False  False  False  False   \n",
              "\n",
              "     SSW     SW      W    WNW    WSW    NaN  \n",
              "0  False  False   True  False  False  False  \n",
              "1  False  False  False   True  False  False  \n",
              "2  False  False  False  False   True  False  \n",
              "3  False  False  False  False  False  False  \n",
              "4  False  False   True  False  False  False  "
            ],
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              "  }\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "repr_error": "Out of range float values are not JSON compliant: nan"
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          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# sum the number of 1s per boolean variable over the rows of the dataset\n",
        "# it will tell us how many observations we have for each category\n",
        "\n",
        "pd.get_dummies(df.WindGustDir, drop_first=True, dummy_na=True).sum(axis=0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MMDZ7W7NiY2Z",
        "outputId": "7512bcdd-ce28-4e2c-d893-294d0aa8fb92"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ENE     8104\n",
              "ESE     7372\n",
              "N       9313\n",
              "NE      7133\n",
              "NNE     6548\n",
              "NNW     6620\n",
              "NW      8122\n",
              "S       9168\n",
              "SE      9418\n",
              "SSE     9216\n",
              "SSW     8736\n",
              "SW      8967\n",
              "W       9915\n",
              "WNW     8252\n",
              "WSW     9069\n",
              "NaN    10326\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can see that there are 10326 missing values in WindGustDir variable."
      ],
      "metadata": {
        "id": "0AI3_9ofiuqF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Explore WindDir9am variable***"
      ],
      "metadata": {
        "id": "MSuTkrs2jWX0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# print number of labels in WindDir9am variable\n",
        "\n",
        "print('WindDir9am contains', len(df['WindDir9am'].unique()), 'labels')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RJlPrHtnijSi",
        "outputId": "52c206d4-94b6-4da6-afb2-52ee3c9443b3"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "WindDir9am contains 17 labels\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# check labels in WindDir9am variable\n",
        "\n",
        "df['WindDir9am'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jsq7qzcBjtPw",
        "outputId": "81d1b309-1717-449f-a49f-d9d1a5225840"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['W', 'NNW', 'SE', 'ENE', 'SW', 'SSE', 'S', 'NE', nan, 'SSW', 'N',\n",
              "       'WSW', 'ESE', 'E', 'NW', 'WNW', 'NNE'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# check frequency distribution of values in WindDir9am variable\n",
        "\n",
        "df['WindDir9am'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q79ExK7NjxPE",
        "outputId": "5508152b-4078-42c6-88ee-aa16487594d0"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "WindDir9am\n",
              "N      11758\n",
              "SE      9287\n",
              "E       9176\n",
              "SSE     9112\n",
              "NW      8749\n",
              "S       8659\n",
              "W       8459\n",
              "SW      8423\n",
              "NNE     8129\n",
              "NNW     7980\n",
              "ENE     7836\n",
              "NE      7671\n",
              "ESE     7630\n",
              "SSW     7587\n",
              "WNW     7414\n",
              "WSW     7024\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# let's do One Hot Encoding of WindDir9am variable\n",
        "# get k-1 dummy variables after One Hot Encoding\n",
        "# also add an additional dummy variable to indicate there was missing data\n",
        "# preview the dataset with head() method\n",
        "\n",
        "pd.get_dummies(df.WindDir9am, drop_first=True, dummy_na=True).head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "gtoKD7KMj2Am",
        "outputId": "3d3c85d9-b7c6-4901-bbbe-434a8cf8892c"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     ENE    ESE      N     NE    NNE    NNW     NW      S     SE    SSE  \\\n",
              "0  False  False  False  False  False  False  False  False  False  False   \n",
              "1  False  False  False  False  False   True  False  False  False  False   \n",
              "2  False  False  False  False  False  False  False  False  False  False   \n",
              "3  False  False  False  False  False  False  False  False   True  False   \n",
              "4   True  False  False  False  False  False  False  False  False  False   \n",
              "\n",
              "     SSW     SW      W    WNW    WSW    NaN  \n",
              "0  False  False   True  False  False  False  \n",
              "1  False  False  False  False  False  False  \n",
              "2  False  False   True  False  False  False  \n",
              "3  False  False  False  False  False  False  \n",
              "4  False  False  False  False  False  False  "
            ],
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              "\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "repr_error": "Out of range float values are not JSON compliant: nan"
            }
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# sum the number of 1s per boolean variable over the rows of the dataset\n",
        "# it will tell us how many observations we have for each category\n",
        "\n",
        "pd.get_dummies(df.WindDir9am, drop_first=True, dummy_na=True).sum(axis=0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kXaac-7fj7Rf",
        "outputId": "dfe1d09b-1ff8-41bc-a3ea-3edcce6ee5ac"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ENE     7836\n",
              "ESE     7630\n",
              "N      11758\n",
              "NE      7671\n",
              "NNE     8129\n",
              "NNW     7980\n",
              "NW      8749\n",
              "S       8659\n",
              "SE      9287\n",
              "SSE     9112\n",
              "SSW     7587\n",
              "SW      8423\n",
              "W       8459\n",
              "WNW     7414\n",
              "WSW     7024\n",
              "NaN    10566\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can see that there is 10566 empty values in WindDir9am variable"
      ],
      "metadata": {
        "id": "b97HIkhckGLa"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Explore WindDir3pm variable***"
      ],
      "metadata": {
        "id": "ZxY6G3zpkW0k"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# print number of labels in WindDir3pm variable\n",
        "\n",
        "print('WindDir3pm contains', len(df['WindDir3pm'].unique()), 'labels')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TVSlGk45kAt2",
        "outputId": "a3863626-2a3c-4588-8aaa-e5136651d756"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "WindDir3pm contains 17 labels\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# check labels in WindDir3pm variable\n",
        "\n",
        "df['WindDir3pm'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0XU0ZTZmkc4b",
        "outputId": "b1f164cc-ad1f-43bb-a957-4c8644fcf590"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['WNW', 'WSW', 'E', 'NW', 'W', 'SSE', 'ESE', 'ENE', 'NNW', 'SSW',\n",
              "       'SW', 'SE', 'N', 'S', 'NNE', nan, 'NE'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# check frequency distribution of values in WindDir3pm variable\n",
        "\n",
        "df['WindDir3pm'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "047Oajh7kfnu",
        "outputId": "9e11504b-65f3-40bf-a227-0bb6b20c408b"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "WindDir3pm\n",
              "SE     10838\n",
              "W      10110\n",
              "S       9926\n",
              "WSW     9518\n",
              "SSE     9399\n",
              "SW      9354\n",
              "N       8890\n",
              "WNW     8874\n",
              "NW      8610\n",
              "ESE     8505\n",
              "E       8472\n",
              "NE      8263\n",
              "SSW     8156\n",
              "NNW     7870\n",
              "ENE     7857\n",
              "NNE     6590\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# let's do One Hot Encoding of WindDir3pm variable\n",
        "# get k-1 dummy variables after One Hot Encoding\n",
        "# also add an additional dummy variable to indicate there was missing data\n",
        "# preview the dataset with head() method\n",
        "\n",
        "pd.get_dummies(df.WindDir3pm, drop_first=True, dummy_na=True).head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "RnY7VV_xkj_R",
        "outputId": "407afe06-2dda-4849-f6fd-d45c4e78b837"
      },
      "execution_count": 44,
      "outputs": [
        {
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            "text/plain": [
              "     ENE    ESE      N     NE    NNE    NNW     NW      S     SE    SSE  \\\n",
              "0  False  False  False  False  False  False  False  False  False  False   \n",
              "1  False  False  False  False  False  False  False  False  False  False   \n",
              "2  False  False  False  False  False  False  False  False  False  False   \n",
              "3  False  False  False  False  False  False  False  False  False  False   \n",
              "4  False  False  False  False  False  False   True  False  False  False   \n",
              "\n",
              "     SSW     SW      W    WNW    WSW    NaN  \n",
              "0  False  False  False   True  False  False  \n",
              "1  False  False  False  False   True  False  \n",
              "2  False  False  False  False   True  False  \n",
              "3  False  False  False  False  False  False  \n",
              "4  False  False  False  False  False  False  "
            ],
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "repr_error": "Out of range float values are not JSON compliant: nan"
            }
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# sum the number of 1s per boolean variable over the rows of the dataset\n",
        "# it will tell us how many observations we have for each category\n",
        "\n",
        "pd.get_dummies(df.WindDir3pm, drop_first=True, dummy_na=True).sum(axis=0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IMWJBbHPkodO",
        "outputId": "82ab55f8-1ae4-4edd-968e-04ed9e5e22aa"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ENE     7857\n",
              "ESE     8505\n",
              "N       8890\n",
              "NE      8263\n",
              "NNE     6590\n",
              "NNW     7870\n",
              "NW      8610\n",
              "S       9926\n",
              "SE     10838\n",
              "SSE     9399\n",
              "SSW     8156\n",
              "SW      9354\n",
              "W      10110\n",
              "WNW     8874\n",
              "WSW     9518\n",
              "NaN     4228\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "we can see there 4228 missing values in the variable WindDir3pm"
      ],
      "metadata": {
        "id": "lboIkvxGk1fM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Explore RainToday variable***"
      ],
      "metadata": {
        "id": "V4PHDIvVlHy7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# print number of labels in RainToday variable\n",
        "\n",
        "print('RainToday contains', len(df['RainToday'].unique()), 'labels')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_7oiHAlXkyo0",
        "outputId": "7a0d05dc-cefa-43ec-e4e3-a651a5d62217"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RainToday contains 3 labels\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# check labels in RainToday variable\n",
        "\n",
        "df['RainToday'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fc0tqgBcmdiX",
        "outputId": "80ec790a-033d-4c2d-f400-f0d9fdfac52e"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['No', 'Yes', nan], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# check frequency distribution of values in RainToday variable\n",
        "\n",
        "df.RainToday.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IQKVW-24mg6z",
        "outputId": "1aabf311-4f78-4ee2-f3c5-f88ca48eb81d"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RainToday\n",
              "No     110319\n",
              "Yes     31880\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
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        "# let's do One Hot Encoding of RainToday variable\n",
        "# get k-1 dummy variables after One Hot Encoding\n",
        "# also add an additional dummy variable to indicate there was missing data\n",
        "# preview the dataset with head() method\n",
        "\n",
        "pd.get_dummies(df.RainToday, drop_first=True, dummy_na=True).head()"
      ],
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "repr_error": "Out of range float values are not JSON compliant: nan"
            }
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# sum the number of 1s per boolean variable over the rows of the dataset\n",
        "# it will tell us how many observations we have for each category\n",
        "\n",
        "pd.get_dummies(df.RainToday, drop_first=True, dummy_na=True).sum(axis=0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GeuB6G0omqJ4",
        "outputId": "a7c7d28a-5cac-46ae-d3c9-756ea632b774"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Yes    31880\n",
              "NaN     3261\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Explore RainTomorrow variable**"
      ],
      "metadata": {
        "id": "RuqxoC9rmzAQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# print number of labels in RainTomorrow variable\n",
        "\n",
        "print('RainTomorrow contains', len(df['RainTomorrow'].unique()), 'labels')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DKGpQAJ1mvwl",
        "outputId": "2b9a036f-7cf7-4962-cfaa-00fbe9ffda8c"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RainTomorrow contains 3 labels\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# check labels in RainTomorrow variable\n",
        "\n",
        "print(df['RainTomorrow'].unique())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oiyZwlZqnIUq",
        "outputId": "ac875411-ce9c-4bf1-a0ea-8f815eb96ea6"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['No' 'Yes' nan]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# check frequency distribution of values in RainToday variable\n",
        "\n",
        "df.RainTomorrow.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vLcsVcOqnTau",
        "outputId": "6bbcd835-5785-475d-b815-0d5015d06840"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RainTomorrow\n",
              "No     110316\n",
              "Yes     31877\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# let's do One Hot Encoding of RainToday variable\n",
        "# get k-1 dummy variables after One Hot Encoding\n",
        "# also add an additional dummy variable to indicate there was missing data\n",
        "# preview the dataset with head() method\n",
        "\n",
        "pd.get_dummies(df.RainTomorrow, drop_first=True, dummy_na=True).head()"
      ],
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        "colab": {
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          "height": 206
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        "outputId": "a0a22c8c-d883-4307-a1d3-916892c2fde3"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "     Yes    NaN\n",
              "0  False  False\n",
              "1  False  False\n",
              "2  False  False\n",
              "3  False  False\n",
              "4  False  False"
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              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-d2303e13-1632-401e-afac-396209a48309 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "repr_error": "Out of range float values are not JSON compliant: nan"
            }
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# sum the number of 1s per boolean variable over the rows of the dataset\n",
        "# it will tell us how many observations we have for each category\n",
        "\n",
        "pd.get_dummies(df.RainTomorrow, drop_first=True, dummy_na=True).sum(axis=0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HYayP1A3n7T1",
        "outputId": "c31089a8-2f58-40fa-8f9d-1f34429b1a87"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Yes    31877\n",
              "NaN     3267\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Explore Numerical Variables**\n",
        "Now, I will explore the Numerical variables one by one."
      ],
      "metadata": {
        "id": "rx8ydEqBoIFG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# find numerical variables\n",
        "\n",
        "numerical = [var for var in df.columns if df[var].dtype!='O']\n",
        "\n",
        "print('There are {} numerical variables\\n'.format(len(numerical)))\n",
        "\n",
        "print('The numerical variables are :', numerical)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R5RfnEJPoCtH",
        "outputId": "a7e5c494-a5d2-42ef-9d8a-080d17b168d5"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "There are 19 numerical variables\n",
            "\n",
            "The numerical variables are : ['MinTemp', 'MaxTemp', 'Rainfall', 'Evaporation', 'Sunshine', 'WindGustSpeed', 'WindSpeed9am', 'WindSpeed3pm', 'Humidity9am', 'Humidity3pm', 'Pressure9am', 'Pressure3pm', 'Cloud9am', 'Cloud3pm', 'Temp9am', 'Temp3pm', 'Year', 'Month', 'Day']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# view the numerical variables\n",
        "\n",
        "df[numerical].head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "IHJ2BCgGoPKC",
        "outputId": "f06f1bf4-20f2-4733-d7c4-148cfa27166b"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   MinTemp  MaxTemp  Rainfall  Evaporation  Sunshine  WindGustSpeed  \\\n",
              "0     13.4     22.9       0.6          NaN       NaN           44.0   \n",
              "1      7.4     25.1       0.0          NaN       NaN           44.0   \n",
              "2     12.9     25.7       0.0          NaN       NaN           46.0   \n",
              "3      9.2     28.0       0.0          NaN       NaN           24.0   \n",
              "4     17.5     32.3       1.0          NaN       NaN           41.0   \n",
              "\n",
              "   WindSpeed9am  WindSpeed3pm  Humidity9am  Humidity3pm  Pressure9am  \\\n",
              "0          20.0          24.0         71.0         22.0       1007.7   \n",
              "1           4.0          22.0         44.0         25.0       1010.6   \n",
              "2          19.0          26.0         38.0         30.0       1007.6   \n",
              "3          11.0           9.0         45.0         16.0       1017.6   \n",
              "4           7.0          20.0         82.0         33.0       1010.8   \n",
              "\n",
              "   Pressure3pm  Cloud9am  Cloud3pm  Temp9am  Temp3pm  Year  Month  Day  \n",
              "0       1007.1       8.0       NaN     16.9     21.8  2008     12    1  \n",
              "1       1007.8       NaN       NaN     17.2     24.3  2008     12    2  \n",
              "2       1008.7       NaN       2.0     21.0     23.2  2008     12    3  \n",
              "3       1012.8       NaN       NaN     18.1     26.5  2008     12    4  \n",
              "4       1006.0       7.0       8.0     17.8     29.7  2008     12    5  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-73934c9c-b186-4a5e-b52e-41a5e9ab37ee\" 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>MinTemp</th>\n",
              "      <th>MaxTemp</th>\n",
              "      <th>Rainfall</th>\n",
              "      <th>Evaporation</th>\n",
              "      <th>Sunshine</th>\n",
              "      <th>WindGustSpeed</th>\n",
              "      <th>WindSpeed9am</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>Year</th>\n",
              "      <th>Month</th>\n",
              "      <th>Day</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\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>44.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>24.0</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",
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              "      <td>16.9</td>\n",
              "      <td>21.8</td>\n",
              "      <td>2008</td>\n",
              "      <td>12</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\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>44.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>22.0</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>2008</td>\n",
              "      <td>12</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\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>46.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>26.0</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>2008</td>\n",
              "      <td>12</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\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>24.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>9.0</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>2008</td>\n",
              "      <td>12</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\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>41.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>20.0</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>2008</td>\n",
              "      <td>12</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-73934c9c-b186-4a5e-b52e-41a5e9ab37ee')\"\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 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-73934c9c-b186-4a5e-b52e-41a5e9ab37ee 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-73934c9c-b186-4a5e-b52e-41a5e9ab37ee');\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",
              "\n",
              "\n",
              "<div id=\"df-4a6d472c-a6ab-4829-b737-6ceca498b546\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4a6d472c-a6ab-4829-b737-6ceca498b546')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df[numerical]\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"MinTemp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.9366229181876182,\n        \"min\": 7.4,\n        \"max\": 17.5,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          7.4,\n          17.5,\n          12.9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MaxTemp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.5707142142714243,\n        \"min\": 22.9,\n        \"max\": 32.3,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          25.1,\n          32.3,\n          25.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Rainfall\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4604345773288535,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.6,\n          0.0,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Evaporation\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": null,\n        \"max\": null,\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sunshine\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": null,\n        \"max\": null,\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"WindGustSpeed\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.011104260855047,\n        \"min\": 24.0,\n        \"max\": 46.0,\n        \"num_unique_values\": 4,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"WindSpeed9am\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.12039324756716,\n        \"min\": 4.0,\n        \"max\": 20.0,\n        \"num_unique_values\": 5,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"WindSpeed3pm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.648308055437864,\n        \"min\": 9.0,\n        \"max\": 26.0,\n        \"num_unique_values\": 5,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Humidity9am\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19.300259065618782,\n        \"min\": 38.0,\n        \"max\": 82.0,\n        \"num_unique_values\": 5,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Humidity3pm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.685805860178712,\n        \"min\": 16.0,\n        \"max\": 33.0,\n        \"num_unique_values\": 5,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pressure9am\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.0654643031270075,\n        \"min\": 1007.6,\n        \"max\": 1017.6,\n        \"num_unique_values\": 5,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pressure3pm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.6090228055729803,\n        \"min\": 1006.0,\n        \"max\": 1012.8,\n        \"num_unique_values\": 5,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cloud9am\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7071067811865476,\n        \"min\": 7.0,\n        \"max\": 8.0,\n        \"num_unique_values\": 2,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cloud3pm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.242640687119285,\n        \"min\": 2.0,\n        \"max\": 8.0,\n        \"num_unique_values\": 2,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Temp9am\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.635542723379613,\n        \"min\": 16.9,\n        \"max\": 21.0,\n        \"num_unique_values\": 5,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Temp3pm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.0927334188384226,\n        \"min\": 21.8,\n        \"max\": 29.7,\n        \"num_unique_values\": 5,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Year\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 1,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Month\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 1,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Day\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 5,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Summary of numerical variables**\n",
        "\n",
        "1.There are 19 numerical variables.\n",
        "\n",
        "2.These are given by MinTemp, MaxTemp, Rainfall, Evaporation, Sunshine, WindGustSpeed, WindSpeed9am, WindSpeed3pm, Humidity9am, Humidity3pm, Pressure9am, Pressure3pm, Cloud9am, Cloud3pm, Temp9am and Temp3pm.\n",
        "\n",
        "3.All of the numerical variables are of continuous type."
      ],
      "metadata": {
        "id": "9XpnOoMyo-sS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Explore problems within numerical variables**\n",
        "\n",
        "Now, I will explore the numerical variables."
      ],
      "metadata": {
        "id": "DtRqq3pqpdOi"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Missing values in numerical variables**"
      ],
      "metadata": {
        "id": "3_tkA0HJpkJQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# check missing values in numerical variables\n",
        "\n",
        "df[numerical].isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dLrxg5ZTo40L",
        "outputId": "ad3b98f6-a41b-45a8-8d99-a880f786a6f2"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MinTemp           1485\n",
              "MaxTemp           1261\n",
              "Rainfall          3261\n",
              "Evaporation      62790\n",
              "Sunshine         69835\n",
              "WindGustSpeed    10263\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",
              "Year                 0\n",
              "Month                0\n",
              "Day                  0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can see that out of 19 variables 16 numerical variables contain missing values."
      ],
      "metadata": {
        "id": "bkxVZa1Ip3z4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Outliers in numerical variables**"
      ],
      "metadata": {
        "id": "jOvJGnzYqFDI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# view summary statistics in numerical variables\n",
        "\n",
        "print(round(df[numerical].describe()),2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GxrT7RAtpqRV",
        "outputId": "7ecdc6f6-25c3-49b2-ae9a-82704e908f4d"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "        MinTemp   MaxTemp  Rainfall  Evaporation  Sunshine  WindGustSpeed  \\\n",
            "count  143975.0  144199.0  142199.0      82670.0   75625.0       135197.0   \n",
            "mean       12.0      23.0       2.0          5.0       8.0           40.0   \n",
            "std         6.0       7.0       8.0          4.0       4.0           14.0   \n",
            "min        -8.0      -5.0       0.0          0.0       0.0            6.0   \n",
            "25%         8.0      18.0       0.0          3.0       5.0           31.0   \n",
            "50%        12.0      23.0       0.0          5.0       8.0           39.0   \n",
            "75%        17.0      28.0       1.0          7.0      11.0           48.0   \n",
            "max        34.0      48.0     371.0        145.0      14.0          135.0   \n",
            "\n",
            "       WindSpeed9am  WindSpeed3pm  Humidity9am  Humidity3pm  Pressure9am  \\\n",
            "count      143693.0      142398.0     142806.0     140953.0     130395.0   \n",
            "mean           14.0          19.0         69.0         52.0       1018.0   \n",
            "std             9.0           9.0         19.0         21.0          7.0   \n",
            "min             0.0           0.0          0.0          0.0        980.0   \n",
            "25%             7.0          13.0         57.0         37.0       1013.0   \n",
            "50%            13.0          19.0         70.0         52.0       1018.0   \n",
            "75%            19.0          24.0         83.0         66.0       1022.0   \n",
            "max           130.0          87.0        100.0        100.0       1041.0   \n",
            "\n",
            "       Pressure3pm  Cloud9am  Cloud3pm   Temp9am   Temp3pm      Year  \\\n",
            "count     130432.0   89572.0   86102.0  143693.0  141851.0  145460.0   \n",
            "mean        1015.0       4.0       5.0      17.0      22.0    2013.0   \n",
            "std            7.0       3.0       3.0       6.0       7.0       3.0   \n",
            "min          977.0       0.0       0.0      -7.0      -5.0    2007.0   \n",
            "25%         1010.0       1.0       2.0      12.0      17.0    2011.0   \n",
            "50%         1015.0       5.0       5.0      17.0      21.0    2013.0   \n",
            "75%         1020.0       7.0       7.0      22.0      26.0    2015.0   \n",
            "max         1040.0       9.0       9.0      40.0      47.0    2017.0   \n",
            "\n",
            "          Month       Day  \n",
            "count  145460.0  145460.0  \n",
            "mean        6.0      16.0  \n",
            "std         3.0       9.0  \n",
            "min         1.0       1.0  \n",
            "25%         3.0       8.0  \n",
            "50%         6.0      16.0  \n",
            "75%         9.0      23.0  \n",
            "max        12.0      31.0   2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "On closer inspection, we can see that the Rainfall, Evaporation, WindSpeed9am,WindSpeed3pm,Humidity9am and Humidity3pm columns may contain outliers.\n",
        "\n",
        "I will draw boxplots to visualise outliers in the above variables."
      ],
      "metadata": {
        "id": "HadMccshqfVI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# plt.subplot(2,2,5)\n",
        "# fig =df.boxplot(column = 'Humidity9am')\n",
        "# fig.set_title('')\n",
        "# fig.set_ylabel('Humidity9am')\n",
        "\n",
        "# plt.subplot(2,2,6)\n",
        "# fig = df.boxplot(column ='Humidity3pm')\n",
        "# fig.set_title('')\n",
        "# fig.set_ylabel('Humidity3pm')\n",
        "# Create a 2x2 grid of subplots\n",
        "plt.figure(figsize=(15,10))\n",
        "\n",
        "# Create the first subplot for Rainfall\n",
        "plt.subplot(2, 2, 1)\n",
        "fig = df.boxplot(column='Rainfall')\n",
        "fig.set_title('')\n",
        "fig.set_ylabel('Rainfall')\n",
        "\n",
        "# Create the second subplot for Evaporation\n",
        "plt.subplot(2, 2, 2)\n",
        "fig = df.boxplot(column='Evaporation')\n",
        "fig.set_title('')\n",
        "fig.set_ylabel('Evaporation')\n",
        "\n",
        "# Create the third subplot for WindSpeed9am\n",
        "plt.subplot(2, 2, 3)\n",
        "fig = df.boxplot(column='WindSpeed9am')\n",
        "fig.set_title('')\n",
        "fig.set_ylabel('WindSpeed9am')\n",
        "\n",
        "# Create the fourth subplot for WindSpeed3pm\n",
        "plt.subplot(2, 2, 4)\n",
        "fig = df.boxplot(column='WindSpeed3pm')\n",
        "fig.set_title('')\n",
        "fig.set_ylabel('WindSpeed3pm')\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "5l4R8h36rxbL",
        "outputId": "791620a1-5282-4cd4-b8e9-8f2513995fe2"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'WindSpeed3pm')"
            ]
          },
          "metadata": {},
          "execution_count": 60
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1000 with 4 Axes>"
            ],
            "image/png": 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Fhw4dUlJSkqNbBQAAAAA4GMEaABRy/PhxSVL37t0VFxenli1bytPTUy1btlRcXJy6d+9uVQcAAAAAKL8I1gCgkFOnTkmSIiMjZTRaXyKNRqMiIiKs6gAAAAAA5RfBGgAUctddd0mSYmNjlZ+fb3UsPz9fcXFxVnUAAAAAgPKLYA0ACqlRo4Yk6dtvv1VERISSk5N16dIlJScnKyIiQt9++61VHQAAAACg/HJ1dAMAUJq0bdtWderUUbVq1fT777+rXbt2lmN16tRRixYtdPr0abVt29aBXQIAAAAASgOCNQAoxMXFRbNmzVL//v3Vq1cvRUdHa//+/apbt64SEhL0zTffaOXKlXJxcXF0qwAAAAAAByNYA4CrREZGauXKlRo5cqTi4+Mt+0NCQrRy5UpFRkY6sDsAAAAAQGlBsAYARYiMjFTv3r01f/58rV+/Xg899JBeeuklubu7O7o1AAAAAEApweIFAFCE2NhY1atXT6NGjdLq1as1atQo1atXT7GxsY5uDQAAAABQShCsAcBVYmNj1b9/f4WFhSkpKUkrVqxQUlKSwsLC1L9/f8I1AGVaZmamJk6cqNatWys0NFR333231R8AAAD8Hx4FBYBCTCaTRo4cqd69eysuLk4mk0mnT59Wy5YtFRcXp4iICI0aNUp9+/ZlAQMAZdI//vEPJSYm6qmnnlJgYKAMBoOjWwIAACi1CNYAoJCkpCQdPnxYK1askNFolMlkshwzGo0aN26cWrduraSkJHXo0MFxjQLAbfLtt9/qm2++0YMPPujoVgDgtjGZTEpMTNSGDRvk7e2tjh078qMpAJvwKCgAFHLy5ElJUqNGjawGXImJiTKZTGrUqJFVHQCUNVWqVJGfn5+j2wCA2yY2NlahoaHq0qWLZs+erS5duig0NJTpPgDYhGANAAoJDAyUJL3zzjtFDrjeeecdqzoAKGtef/11TZo0SVlZWY5uBQDsjrl0AdibwWw2mx3dhKNlZGTI19dX58+fl4+Pj6PbAeBAJpNJQUFBSk9PV+/evTV27FgdO3ZMNWvW1PTp0xUfH6/q1avrxIkTPC4AlHNldfzQrFkzHTx4UGazWXXq1JGbm5vV8W3btjmoM9uU1b8nACVnMpkUGhqqsLAwy1y6q1evVs+ePeXi4qKIiAjt3LlT+/fvZ5wHlHMlGT8wxxoAXKXg9waz2axt27Zp//79qlu3rvgdAkB5EBER4egWAOC2YC5dALcDwRoAFJKUlKRTp04pKipKn376qb755hvLMVdXV/3973/X8uXLGXABKLNeffVVR7cAALdF4bl0i8JcugBsQbAGAIUUDKSWL1+uXr16qWvXrvrjjz907733au3atVqxYoVVHQCUVSkpKdqzZ48k6b777lOzZs0c3BEA3JqCOXJ37typVq1aXXN8586dVnUAUBwEawBQSPXq1SVJDz74oL788kuruTeGDh2q9u3b66effrLUAUBZk56ergEDBujHH39U5cqVJUnnzp1Tx44d9cknn+iuu+5ybIMAYKO2bduqTp06mjZtmuLi4qyO5efnKyYmRiEhIWrbtq1jGgTglFgVFABKgHnWAJR1L730ki5cuKBdu3bpzJkzOnPmjHbu3KmMjAy9/PLLjm4PAGzm4uKiWbNmKT4+XhEREUpOTtalS5eUnJysiIgIxcfHa+bMmSxcAKBEuGMNAApJT0+XJP3000/q27evunTpov379+vIkSNKSEjQzz//bFUHAGXNmjVr9P3336tBgwaWfQ0bNtSCBQvUtWtXB3YGALcuMjJSK1euVHR0tNq1a2fZX6dOHa1cuVKRkZEO7A6AMyJYA4BCCubUKFi8ID4+3nKs8OIFzL0BoKzKz8+Xm5vbNfvd3NyUn5/vgI4AwP4MBoOjWwBQRhjMPNekjIwM+fr66vz58/Lx8XF0OwAcyGQyKTAwUKdOnVKvXr109913a9++fapXr57+97//6ZtvvlH16tV14sQJHhMAyrmyOn7o27evzp07pxUrVigoKEiSdPz4cUVFRalKlSpatWqVgzssmbL69wTANrGxserfv7969epleTKhbt26SkhI0DfffMNdawAklWz8QLAmBlwA/o/JZFJQUJDS09Pl4eGhnJwcy7GCbYI1AFLZHT/8+eefevjhh7Vr1y4FBwdb9jVq1EhfffWVatas6eAOS6as/j0BKDmTyaTQ0FBVq1ZNp06d0pEjRyzHateurbvuukunT5/W/v37GecB5VxJxg88CgoAhSQlJVnmT7t8+bLVsYLt9PR0JSUlqUOHDne6PQC47YKDg7Vt2zZ9//332rt3rySpQYMG6ty5s4M7A4Bbk5SUpMOHD+vw4cPq06ePPv74Yx07dkw1a9bUjBkz9PXXX1vqGOcBKK5iB2sluR02NjbWpmYAwNGOHz9ueV2hQgVdunSpyO3CdQBQ1hgMBnXp0kVdunRxdCsAYDcF47cePXooLi5OJpNJp0+fVsuWLRUXF6fevXvr22+/ZZwHoESKHaz5+vrezj4AoFRITU21vO7UqZPGjh1r+SVz+vTplsUMCtcBgLObN2+eBg0apAoVKmjevHk3rH355Zft8pkmk0mvvfaaPv74Y6WmpiooKEjPPPOMJkyYYJlU3Gw269VXX9W7776rc+fO6cEHH9SiRYtUt25du/QAoHw5deqUpCs3jRiNRplMJssxo9GoiIgIffvtt5Y6ACiOYgdrS5cuvZ19AECp8Ndff0mSZYJus9ls+SVz1apVql69us6ePWupA4CyYM6cOYqKilKFChU0Z86c69YZDAa7BWvTp0/XokWL9OGHH+q+++7T1q1b9eyzz8rX19fyGTNmzNC8efP04YcfKiQkRBMnTlS3bt20e/duVahQwS59ACg/7rrrLklXnrB67rnnrI7l5+crLi7Oqg4AioM51gCgkIJb/8+dO6fIyEiNHj1aly5dUnJyst566y2dO3fOqg4AyoJDhw4V+fp22rhxo/r27atevXpJkurUqaMVK1bol19+kXTlbrW5c+dqwoQJ6tu3ryTpo48+kr+/v+Li4jRgwIA70ieAsqNGjRqSpDVr1igiIuKacd6aNWus6gCgOIodrDVr1sxyW/7NbNu2zeaGAMCRClbAq1u3rnbs2KF27dpZjoWEhKhu3br6448/LHUAUNZMmTJFo0aNkpeXl9X+S5cu6a233tKkSZPs8jmtW7fWkiVL9Mcff+jee+/Vb7/9pp9++kmzZ8+WdCXgS01NtVo0wdfXVy1bttSmTZuuG6zl5ORYreickZEhScrNzVVubq5degfgnFq1aqU6derIz89Pv//+u9U4r06dOmrWrJnOnj2rVq1acb0AyrmSXAOKHaxFRETY0ssNxcTEKDY2Vnv37pWnp6dat26t6dOnq169epaa7OxsjRw5Up988olycnLUrVs3LVy4UP7+/paao0ePavDgwfrhhx9UsWJFDRw4UDExMXJ15YY8ACXz0EMPadq0afrjjz/Uq1cvjRgxQvv371fdunW1du1affPNN5Y6ACiLJk+erBdffPGaYC0rK0uTJ0+2W7D2yiuvKCMjQ/Xr15eLi4tMJpPeeOMNRUVFSfq/uSwLj/kKtm80z2VMTIwmT558zf61a9de850AlD+PP/64ZsyYoebNm6tr167y8PBQTk6Ofv31V6WkpGjMmDH67rvvHN0mAAfLysoqdm2xk6dXX33VpmZuJDExUUOHDtUDDzygvLw8jR8/Xl27dtXu3bvl7e0tSRoxYoS++eYbff755/L19dWwYcMUGRmpn3/+WdKViW979eqlgIAAbdy4USdPntTTTz8tNzc3TZs2ze49AyjbOnTooLvuukunTp3S+vXrLUGaJHl6ekqSqlevzhLsAMoss9lc5FMKv/32m/z8/Oz2OZ999pmWLVum5cuX67777tP27ds1fPhwBQUFaeDAgTafd9y4cYqOjrZsZ2RkKDg4WF27dpWPj489WgfgxHr27Kn7779fY8aM0bvvvmvZX6dOHX3yySd65JFHHNgdgNKi4I734nDoLV0Fz7AX+OCDD1S9enWlpKSoXbt2On/+vN5//30tX77ccnfI0qVL1aBBAyUnJ6tVq1Zau3atdu/ere+//17+/v5q2rSpXn/9dY0dO1avvfaa3N3dHfHVADgpFxcXLV68WP369bvmWME/NBctWiQXF5c73RoA3FZVqlSRwWCQwWDQvffeaxWumUwmXbx4US+++KLdPm/06NF65ZVXLI90hoWF6ciRI4qJidHAgQMVEBAgSUpLS1NgYKDlfWlpaWratOl1z+vh4SEPD49r9ru5ucnNzc1u/QNwXq6urkX+gODq6sp1AoAklehaYFOwZjKZNGfOHH322Wc6evSoLl++bHX8zJkztpxW58+flyTLr6EpKSnKzc21mlujfv36qlWrljZt2qRWrVpp06ZNCgsLs3pMoFu3bho8eLB27dqlZs2aXfM5zL0B4Eb69OmjTz/9VGPGjNGRI0cs+6tXr67p06erT58+XCsAlLnrwNy5c2U2m/Xcc89p8uTJ8vX1tRxzd3dXnTp1FB4ebrfPy8rKktFotNrn4uKi/Px8SVfmtQwICNC6dessQVpGRoY2b96swYMH260PAOVLbGys+vfvr969e+u///2vjh07ppo1a2rGjBnq37+/Vq5cqcjISEe3CcCJ2BSsTZ48We+9955GjhypCRMm6N///rcOHz6suLg4m+fdyM/P1/Dhw/Xggw+qUaNGkq7MreHu7q7KlStb1RaeWyM1NbXIuTcKjhWFuTcA3IyHh4dmz56t3bt36+zZs6pSpYoaNmwoFxcXrV692tHtASgFSjL3hjMoePwyJCRErVu3vu13bfTp00dvvPGGatWqpfvuu0+//vqrZs+ereeee07SlbuEhw8frqlTp6pu3boKCQnRxIkTFRQUdFvm/gVQ9plMJo0cOVK9e/dWXFycTCaTTp8+rZYtWyouLk4REREaNWqU+vbty9MJAIrNpmBt2bJlevfdd9WrVy+99tpreuKJJ3TPPfeocePGSk5O1ssvv1zicw4dOlQ7d+7UTz/9ZEtLJcLcGwCKq3v37kpISFCXLl14NACAlZLMveFM2rdvb3mdnZ19zZMJ9horzZ8/XxMnTtSQIUOUnp6uoKAg/fOf/7T6kXbMmDHKzMzUoEGDdO7cObVp00Zr1qxRhQoV7NIDgPIlKSlJhw8f1ooVK2Q0GmUymSzHjEajxo0bp9atWyspKYn5dAEUm03BWmpqqsLCwiRJFStWtDzC2bt3b02cOLHE5xs2bJji4+O1YcMG1axZ07I/ICBAly9f1rlz56zuWktLS7PMuxEQEKBffvnF6nxpaWmWY0Vh7g0AxWEymbRx40Zt2LBB3t7e6tixI79eArAoq2OGrKwsjRkzRp999plOnz59zfHC/xC9FZUqVdLcuXM1d+7c69YYDAZNmTJFU6ZMsctnAijfTp48KUlq1KiRTCaTEhMTrcZ5BU9OFdQBQHEYb15yrZo1a1ouNvfcc4/Wrl0rSdqyZUuRgdX1mM1mDRs2TKtWrdL69esVEhJidbx58+Zyc3PTunXrLPv27duno0ePWub4CA8P144dO5Senm6pSUhIkI+Pjxo2bGjL1wMAxcbGKjQ0VF26dNHs2bPVpUsXhYaGKjY21tGtAcBtNXr0aK1fv16LFi2Sh4eH3nvvPU2ePFlBQUH66KOPHN0eANisYCGUd955p8hx3jvvvGNVBwDFYTCbzeaSvumVV16Rj4+Pxo8fr08//VRPPvmk6tSpo6NHj2rEiBF68803i3WeIUOGaPny5fryyy9Vr149y35fX195enpKkgYPHqzVq1frgw8+kI+Pj1566SVJ0saNGyVd+dW0adOmCgoK0owZM5SamqqnnnpK//jHPzRt2rRi9ZGRkSFfX1+dP3+eR0EBWCa17dWrl7p06aL9+/erbt26SkhI0DfffMOktgAkld3xQ61atfTRRx+pQ4cO8vHx0bZt2xQaGqr//ve/WrFihdPNM1lW/54AlJzJZFJQUJDS09PVu3dvjR071rJ4wfTp0xUfH6/q1avrxIkTPKUAlHMlGT/Y9Cho4eDs8ccfV+3atbVx40bVrVtXffr0KfZ5Fi1aJEnXPL++dOlSPfPMM5KkOXPmyGg0ql+/fsrJyVG3bt20cOFCS62Li4vi4+M1ePBghYeHy9vbWwMHDuSRAQA2KZjUtnnz5tqxY4fi4+Mtx2rXrq3mzZszqS2AMu3MmTO6++67JV2ZT61gtfc2bdqwGicAp1f4vpKC1zbcawIAFsUO1u6//36tW7dOVapU0ZQpUzRq1CjLCpqtWrVSq1atSvzhxbmAVahQQQsWLNCCBQuuW1O7dm2n+/UUQOlUMKnt4cOH1adPH3388cdWy7B//fXXljomtQVQFt199906dOiQatWqpfr16+uzzz7T3/72N3399dfXrNQOAM4kKSlJp06dUkxMjP7zn/+oXbt2lmMhISGaNm2axo8fzzgPQIkUe461PXv2KDMzU5I0efJkXbx48bY1BQCOcvz4cUlSjx49FBcXp5YtW8rT09OyDHuPHj2s6gCgrHn22Wf122+/Sboy/ceCBQtUoUIFjRgxQqNHj3ZwdwBgu4J5wocNG6YDBw4oISFB0dHRSkhI0P79+zVs2DCrOgAojmLfsda0aVM9++yzatOmjcxms2bOnKmKFSsWWVt4mXQAcCanTp2SJEVGRspsNl+zWlRERIS+/fZbSx0AlDUjRoywvO7cubP27t2rlJQUhYaGqnHjxg7sDABuTcGiBDt37tQDDzxwzfGdO3da1QFAcRR78YJ9+/bp1Vdf1cGDB7Vt2zY1bNhQrq7X5nIGg0Hbtm2ze6O3E5PaAiiwbNkyPfnkk2rWrJnOnDmjI0eOWI7Vrl1bfn5++vXXX/Xxxx8rKirKgZ0CcLSyOH7Izc1V9+7dtXjxYtWtW9fR7dhFWfx7AmAbk8mk0NBQVatWTX/99ZcOHz5sOVanTh1Vq1ZNp0+f1v79+5lLFyjnbsviBfXq1dMnn3wiSTIajVq3bp2qV69+a50CQClTo0YNSdKvv/56zbEjR45YgraCOgAoS9zc3PT77787ug0AuC1cXFz06KOP6q233pK/v78WLVqkChUqKDs7W6+99pq2bt2q0aNHE6oBKJFi37FWlvFLJoACly9fVoUKFW64uIrBYFB2drbc3d3vYGcASpuyOn4YMWKEPDw8rFaBd2Zl9e8JQMkVvmPt1KlTVk8mcMcagMJuyx1rV9u/f79++OEHpaenKz8/3+oYc6wBcFaJiYmWUM3d3V2RkZHy9PTUpUuXFBsbq8uXL1vmXuvSpYuDuwUA+8vLy9P/+3//T99//72aN28ub29vq+OzZ892UGcAcGsKVn9fsWKF7r//fs2fP1/r16/XQw89pJdeekkpKSlq3bo1q4ICKBGbgrV3331XgwcPVrVq1RQQECCDwWA5ZjAYCNYAOK2lS5dKkipUqKDq1atbHoGXrsyxlpaWpuzsbC1dupRgDUCZtHPnTt1///2SpD/++MPqWOExHwA4m4LVPg8ePKgnnnjCMsfa6tWr9c4772jq1KlWdQBQHDYFa1OnTtUbb7yhsWPH2rsfAHCoHTt2SJJeeOEFTZ8+XdHR0UpOTlarVq00e/ZsjRo1SgsXLrTUAUBZ88MPPzi6BQC4LQpW+3zqqadUoUIFq2NpaWl66qmnrOoAoDhsCtbOnj2rRx991N69AIDDFTw/v3z5ci1cuFAmk0mStH37dr377rvy9fW1qgOAsuzYsWOSpJo1azq4EwC4da1bt5bRaFR+fr46dOig0NBQ7du3T/Xq1dOBAwf07bffymg0qnXr1o5uFYATMdrypkcffVRr1661dy8A4HARERGSpNOnT8vFxUWPP/64nnnmGT3++ONycXHRmTNnrOoAoKzJz8/XlClT5Ovrq9q1a6t27dqqXLmyXn/99Wvm1QUAZ5KUlGS5jq1Zs0bz58/X2rVrNX/+fK1Zs0bSlWtgUlKSI9sE4GRsumMtNDRUEydOVHJyssLCwuTm5mZ1/OWXX7ZLcwBwpw0ePFhjxoyRdGWF0E8//fS6dQBQFv373//W+++/rzfffFMPPvigJOmnn37Sa6+9puzsbL3xxhsO7hAAbPPjjz9aXhsMBqtV4I1Go+VJhR9//FGdOnW60+0BcFI2BWtLlixRxYoVlZiYqMTERKtjBoOBYA2A03rvvfeKXTd8+PDb2wwAOMCHH36o9957Tw8//LBlX+PGjVWjRg0NGTKEYA2A0yoIzqpUqaKTJ08qKSlJ3377rXr06KG2bdsqMDBQZ8+etdQBQHHYFKwdOnTI3n0AQKmwf/9+y+urf8ksvF24DgDKkjNnzqh+/frX7K9fv77lcXgAcEbnzp2TJFWtWlVGo/WsSEajUX5+fjp79qylDgCKw6ZgDQDKqoLgLCAgQK6urpaJuyWpRo0aysvLU2pqqlXgBgBlSZMmTfTOO+9o3rx5VvvfeecdNWnSxEFdAcCtMxgMkqQDBw7I19dXly5dkiTNnj1bnp6elu2COgAojmIHa9HR0Xr99dfl7e2t6OjoG9bOnj37lhsDAEeoXLmyJCk1NVWenp5Wx06fPm0ZcBXUAUBZM2PGDPXq1Uvff/+9wsPDJUmbNm3Sn3/+qdWrVzu4OwCwXd26dS2vc3JyrI5dvny5yDoAuJliB2u//vqrcnNzLa+vh3QfgDMrfA0zmUx6/PHH5eXlpaysLK1atarIOgAoS9q3b68//vhDCxYs0N69eyVJkZGRGjJkiIKCghzcHQDY7p///KdGjBghV1dXBQQEWD2ZEBQUpJMnTyovL0///Oc/HdglAGdT7GDthx9+KPI1AJQlBXeiubi4FLkqqIuLi0wmE3esASjTgoKCWKQAQJmzefNmSVJeXp5OnDhhdez48ePKz8+31HXo0OFOtwfASTHHGgAUUjBZ7fVWgyrYz6S2AMqys2fP6v3339eePXskSQ0bNtSzzz4rPz8/B3cGALY7efKk5XVBiFbUduE6ALgZm4O1rVu36rPPPtPRo0etnkeXpNjY2FtuDAAAAHfehg0b1KdPH/n6+qpFixaSpHnz5mnKlCn6+uuv1a5dOwd3CAC2qV69uuW1u7u71b9jC28XrgOAmzHevORan3zyiVq3bq09e/Zo1apVys3N1a5du7R+/Xr5+vrau0cAuGOKew3jWgegrBo6dKgef/xxHTp0SLGxsYqNjdX//vc/DRgwQEOHDnV0ewBgs+s9kWBrHQBINgZr06ZN05w5c/T111/L3d1db7/9tvbu3avHHntMtWrVsnePAHDH/P7773atAwBnc+DAAY0cOVIuLi6WfS4uLoqOjtaBAwcc2BkA3JrExETL66ufuiq8XbgOAG7GpmDt4MGD6tWrl6Qrt8xmZmbKYDBoxIgRWrJkiV0bBIA76cKFC3atAwBnc//991vmVitsz549atKkiQM6AgD7OHLkiOW10Wj9T+HC24XrAOBmbJpjrUqVKpZ/VNaoUUM7d+5UWFiYzp07p6ysLLs2CAB3ktlsvu4xg8FgOX6jOgBwZi+//LL+9a9/6cCBA2rVqpUkKTk5WQsWLNCbb75pdcdu48aNHdUmAJRYXl6epCshWo0aNfTnn39ajtWoUcOyMmhBHQAUh03BWrt27ZSQkKCwsDA9+uij+te//qX169crISFBDz30kL17BIBSgTANQHnwxBNPSJLGjBlT5LGCHxkMBgPzEAFwKqdPn5Z0ZQXQnJwcjRgxQllZWfLy8tKyZcssK4MW1AFAcdgUrL3zzjvKzs6WJP373/+Wm5ubNm7cqH79+mnUqFF2bRAA7qRLly7ZtQ4AnM2hQ4cc3QIA3BZeXl6W16dOndKcOXMs2waDocg6ALgZm4I1Pz8/y2uj0ahXXnlF2dnZWrBggZo1a6bU1FS7NQgAd1K1atXsWgcAzqZ27dqObgEAbougoCDL68JTfFy9XbgOAG6mRMFaTk6OXnvtNSU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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize = (10,8))\n",
        "\n",
        "# Create the fifth subplot for Humidity9am\n",
        "plt.subplot(2, 2, 1)\n",
        "fig = df.boxplot(column='Humidity9am')\n",
        "fig.set_title('')\n",
        "fig.set_ylabel('Humidity9am')\n",
        "\n",
        "# Create the sixth subplot for Humidity3pm\n",
        "plt.subplot(2, 2, 2)\n",
        "fig = df.boxplot(column='Humidity3pm')\n",
        "fig.set_title('')\n",
        "fig.set_ylabel('Humidity3pm')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        },
        "id": "AJyplApptbUc",
        "outputId": "729323b6-19a5-4434-84f1-dab0e86b19b2"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Humidity3pm')"
            ]
          },
          "metadata": {},
          "execution_count": 61
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The above boxplots confirm that there are lot of outliers in these variables."
      ],
      "metadata": {
        "id": "04ELaXBLvWID"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Python program to illustrate\n",
        "# violinplot using inbuilt data-set\n",
        "# given in seaborn\n",
        "\n",
        "# importing the required module\n",
        "import seaborn as sns\n",
        "\n",
        "# use to set style of background of plot\n",
        "sns.set(style=\"whitegrid\")\n",
        "\n",
        "\n",
        "sns.violinplot(x=df[\"Humidity9am\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 475
        },
        "id": "k-EDZVGBvG61",
        "outputId": "87d56c1a-6586-4d35-943d-4dade8fe0362"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Humidity9am'>"
            ]
          },
          "metadata": {},
          "execution_count": 62
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.histplot(data=df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 524
        },
        "id": "n_b6EyN0w8Xt",
        "outputId": "0a523af1-bfc9-4dd2-b24e-79f4164edfbb"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: ylabel='Count'>"
            ]
          },
          "metadata": {},
          "execution_count": 63
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Check the distribution of variables**\n",
        "\n",
        "Now, I will plot the histograms to check distributions to find out if they are normal or skewed. If the variable follows normal distribution, then I will do **Extreme Value Analysis** otherwise if they are skewed, I will find IQR (Interquantile range)."
      ],
      "metadata": {
        "id": "omBZ9C5j7UA0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# plot histogram to check distribution\n",
        "\n",
        "plt.figure(figsize=(15,10))\n",
        "\n",
        "\n",
        "plt.subplot(2, 2, 1)\n",
        "fig = df.Rainfall.hist(bins=10)\n",
        "fig.set_xlabel('Rainfall')\n",
        "fig.set_ylabel('RainTomorrow')\n",
        "\n",
        "\n",
        "plt.subplot(2, 2, 2)\n",
        "fig = df.Evaporation.hist(bins=10)\n",
        "fig.set_xlabel('Evaporation')\n",
        "fig.set_ylabel('RainTomorrow')\n",
        "\n",
        "\n",
        "plt.subplot(2, 2, 3)\n",
        "fig = df.WindSpeed9am.hist(bins=10)\n",
        "fig.set_xlabel('WindSpeed9am')\n",
        "fig.set_ylabel('RainTomorrow')\n",
        "\n",
        "\n",
        "plt.subplot(2, 2, 4)\n",
        "fig = df.WindSpeed3pm.hist(bins=10)\n",
        "fig.set_xlabel('WindSpeed3pm')\n",
        "fig.set_ylabel('RainTomorrow')"
      ],
      "metadata": {
        "id": "_nYKNGhc0uxS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 460
        },
        "outputId": "e38d38ee-28cd-4149-f579-daeaccbfe7e7"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'RainTomorrow')"
            ]
          },
          "metadata": {},
          "execution_count": 64
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1000 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize =(10,8))\n",
        "\n",
        "plt.subplot(2, 2, 1)\n",
        "fig = df.Humidity9am.hist(bins=10)\n",
        "fig.set_xlabel('Humidity9am')\n",
        "fig.set_ylabel('RainTomorrow')\n",
        "\n",
        "plt.subplot(2, 2, 2)\n",
        "fig = df.Humidity3pm.hist(bins=10)\n",
        "fig.set_xlabel('Humidity3pm')\n",
        "fig.set_ylabel('RainTomorrow')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 292
        },
        "id": "FrzeZMBP7pZ4",
        "outputId": "7b0c4569-34aa-406b-bdc7-af32feaefefd"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'RainTomorrow')"
            ]
          },
          "metadata": {},
          "execution_count": 65
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can see that all the four variables are skewed except **Humidity3pm**. So, I will use interquantile range to find outliers."
      ],
      "metadata": {
        "id": "wlkm80rz-H04"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# find outliers for Rainfall variable\n",
        "\n",
        "IQR = df.Rainfall.quantile(0.75) - df.Rainfall.quantile(0.25)\n",
        "Lower_fence = df.Rainfall.quantile(0.25) - (IQR * 3)\n",
        "Upper_fence = df.Rainfall.quantile(0.75) + (IQR * 3)\n",
        "print('Rainfall outliers are values < {lowerboundary} or > {upperboundary}'.format(lowerboundary=Lower_fence, upperboundary=Upper_fence))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WROkQxxF94SQ",
        "outputId": "25c96f7c-dd83-4fd9-9387-8338dd3eef79"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Rainfall outliers are values < -2.4000000000000004 or > 3.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "For Rainfall, the minimum and maximum values are 0.0 and 371.0. So, the outliers are values > 3.2"
      ],
      "metadata": {
        "id": "h6LSu8Z7-xa1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# find outliers for Evaporation variable\n",
        "\n",
        "IQR = df.Evaporation.quantile(0.75) - df.Evaporation.quantile(0.25)\n",
        "Lower_fence = df.Evaporation.quantile(0.25) - (IQR * 3)\n",
        "Upper_fence = df.Evaporation.quantile(0.75) + (IQR * 3)\n",
        "print('Evaporation outliers are values < {lowerboundary} or > {upperboundary}'.format(lowerboundary=Lower_fence, upperboundary=Upper_fence))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hlMZ_8wq-nI0",
        "outputId": "bd8d6c33-b89a-4b1c-b449-39513901d6a4"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Evaporation outliers are values < -11.800000000000002 or > 21.800000000000004\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "For Evaporation, the minimum and maximum values are 0.0 and 145.0. So, the outliers are values > 21.8."
      ],
      "metadata": {
        "id": "yssZTeYI_Moc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# find outliers for WindSpeed9am variable\n",
        "\n",
        "IQR = df.WindSpeed9am.quantile(0.75) - df.WindSpeed9am.quantile(0.25)\n",
        "Lower_fence = df.WindSpeed9am.quantile(0.25) - (IQR * 3)\n",
        "Upper_fence = df.WindSpeed9am.quantile(0.75) + (IQR * 3)\n",
        "print('WindSpeed9am outliers are values < {lowerboundary} or > {upperboundary}'.format(lowerboundary=Lower_fence, upperboundary=Upper_fence))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y2INaZrk_HPz",
        "outputId": "9691d2d2-d8d0-43e7-9038-b9531e842e9c"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "WindSpeed9am outliers are values < -29.0 or > 55.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "For WindSpeed9am, the minimum and maximum values are 0.0 and 130.0. So, the outliers are values > 55.0."
      ],
      "metadata": {
        "id": "Mj8-b3LH_Xh8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# find outliers for WindSpeed3pm variable\n",
        "\n",
        "IQR = df.WindSpeed3pm.quantile(0.75) - df.WindSpeed3pm.quantile(0.25)\n",
        "Lower_fence = df.WindSpeed3pm.quantile(0.25) - (IQR * 3)\n",
        "Upper_fence = df.WindSpeed3pm.quantile(0.75) + (IQR * 3)\n",
        "print('WindSpeed3pm outliers are values < {lowerboundary} or > {upperboundary}'.format(lowerboundary=Lower_fence, upperboundary=Upper_fence))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B_xC6M5Y_TWt",
        "outputId": "ee5a801a-b5d4-45ed-b813-6b3be5e9488e"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "WindSpeed3pm outliers are values < -20.0 or > 57.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "For WindSpeed3pm, the minimum and maximum values are 0.0 and 87.0. So, the outliers are values > 57.0."
      ],
      "metadata": {
        "id": "TDr7gFl1_f_7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# find outliers for Humidity9am variable\n",
        "\n",
        "IQR = df.Humidity9am.quantile(0.75) - df.Humidity9am.quantile(0.25)\n",
        "Lower_fence = df.Humidity9am.quantile(0.25) - (IQR * 3)\n",
        "Upper_fence = df.Humidity9am.quantile(0.75) + (IQR * 3)\n",
        "print('Humidity9am outliers are values < {lowerboundary} or > {upperboundary}'.format(lowerboundary=Lower_fence, upperboundary=Upper_fence))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jcctPO_a_b5k",
        "outputId": "82eadc4c-ad3f-45d5-e32d-f10c4e8017c8"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Humidity9am outliers are values < -21.0 or > 161.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "For Humidity9am,the minimum and maximum values are <0.0,100.0>.so,the outliers are values >161.0"
      ],
      "metadata": {
        "id": "CAZ0IB-qAiWQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# find outliers for Humidity9am variable\n",
        "\n",
        "IQR = df.Humidity3pm.quantile(0.75) - df.Humidity3pm.quantile(0.25)\n",
        "Lower_fence = df.Humidity3pm.quantile(0.25) - (IQR * 3)\n",
        "Upper_fence = df.Humidity3pm.quantile(0.75) + (IQR * 3)\n",
        "print('Humidity3pm outliers are values < {lowerboundary} or > {upperboundary}'.format(lowerboundary=Lower_fence, upperboundary=Upper_fence))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5RRJvxn5AWoG",
        "outputId": "f8fd3a97-8156-4f87-c2a6-71f21b629c56"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Humidity3pm outliers are values < -50.0 or > 153.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "for Humidity3pm , the minimum and maximum values are <0.0,100.0> so, the outliers are values >153.0"
      ],
      "metadata": {
        "id": "wOxkiwLhBK5k"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **8. Declare feature vector and target variable**"
      ],
      "metadata": {
        "id": "z4M0QPzmBlsR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = df.drop(['RainTomorrow'], axis=1)\n",
        "\n",
        "y = df['RainTomorrow']"
      ],
      "metadata": {
        "id": "MWb1yf8sBIpe"
      },
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **9. Split data into separate training and test set**"
      ],
      "metadata": {
        "id": "bhcAR3JBC5Yf"
      }
    },
    {
      "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": "eEFW_T4eC153"
      },
      "execution_count": 73,
      "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": "iAqZ_TqrDABQ",
        "outputId": "8719f6de-1df8-4708-ae16-e8a857db0e57"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((116368, 24), (29092, 24))"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "EiQV52OVDIpf"
      },
      "execution_count": 74,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **10. Feature Engineering**\n",
        "**Feature Engineering **is the process of transforming raw data into useful features that help us to understand our model better and increase its predictive power. I will carry out feature engineering on different types of variables.\n",
        "\n",
        "First, I will display the categorical and numerical variables again separately."
      ],
      "metadata": {
        "id": "d8qvtGGDDJ57"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# check data types in X_train\n",
        "\n",
        "X_train.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ur_xe1OnDScj",
        "outputId": "63a51ba9-fd52-4c37-fc09-49b606b6065c"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Location          object\n",
              "MinTemp          float64\n",
              "MaxTemp          float64\n",
              "Rainfall         float64\n",
              "Evaporation      float64\n",
              "Sunshine         float64\n",
              "WindGustDir       object\n",
              "WindGustSpeed    float64\n",
              "WindDir9am        object\n",
              "WindDir3pm        object\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         object\n",
              "Year               int32\n",
              "Month              int32\n",
              "Day                int32\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "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": "useQTAnQDaKH",
        "outputId": "6a47b54b-920c-4bb7-cba8-e0c1df298132"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Location', 'WindGustDir', 'WindDir9am', 'WindDir3pm', 'RainToday']"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Engineering missing values in numerical variables**"
      ],
      "metadata": {
        "id": "kZT3yE7iFGFz"
      }
    },
    {
      "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": "yrOt50b7Df8j",
        "outputId": "6a0c14f3-d39f-4f08-9464-722a5a1cd213"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['MinTemp',\n",
              " 'MaxTemp',\n",
              " 'Rainfall',\n",
              " 'Evaporation',\n",
              " 'Sunshine',\n",
              " 'WindGustSpeed',\n",
              " 'WindSpeed9am',\n",
              " 'WindSpeed3pm',\n",
              " 'Humidity9am',\n",
              " 'Humidity3pm',\n",
              " 'Pressure9am',\n",
              " 'Pressure3pm',\n",
              " 'Cloud9am',\n",
              " 'Cloud3pm',\n",
              " 'Temp9am',\n",
              " 'Temp3pm',\n",
              " 'Year',\n",
              " 'Month',\n",
              " 'Day']"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# check missing values in numerical variables in X_train\n",
        "\n",
        "X_train[numerical].isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a653kHo7DjGw",
        "outputId": "93deeee7-94b1-4a79-f2ab-f86b9e0818ca"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MinTemp           1183\n",
              "MaxTemp           1019\n",
              "Rainfall          2617\n",
              "Evaporation      50355\n",
              "Sunshine         55899\n",
              "WindGustSpeed     8218\n",
              "WindSpeed9am      1409\n",
              "WindSpeed3pm      2456\n",
              "Humidity9am       2147\n",
              "Humidity3pm       3598\n",
              "Pressure9am      12091\n",
              "Pressure3pm      12064\n",
              "Cloud9am         44796\n",
              "Cloud3pm         47557\n",
              "Temp9am           1415\n",
              "Temp3pm           2865\n",
              "Year                 0\n",
              "Month                0\n",
              "Day                  0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# check missing values in numerical variables in X_test\n",
        "\n",
        "X_test[numerical].isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UaJHWqOUDtJJ",
        "outputId": "4d8111c9-5309-4f99-89fa-3bbf3ae37dc6"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MinTemp            302\n",
              "MaxTemp            242\n",
              "Rainfall           644\n",
              "Evaporation      12435\n",
              "Sunshine         13936\n",
              "WindGustSpeed     2045\n",
              "WindSpeed9am       358\n",
              "WindSpeed3pm       606\n",
              "Humidity9am        507\n",
              "Humidity3pm        909\n",
              "Pressure9am       2974\n",
              "Pressure3pm       2964\n",
              "Cloud9am         11092\n",
              "Cloud3pm         11801\n",
              "Temp9am            352\n",
              "Temp3pm            744\n",
              "Year                 0\n",
              "Month                0\n",
              "Day                  0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# print percentage of missing values in the numerical variables in training set\n",
        "\n",
        "for col in numerical:\n",
        "    if X_train[col].isnull().mean()>0:\n",
        "        print(col, round(X_train[col].isnull().mean(),4))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fDZ3d7h4DyPB",
        "outputId": "b7040334-fa43-4249-be4b-3da77b1a4edb"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MinTemp 0.0102\n",
            "MaxTemp 0.0088\n",
            "Rainfall 0.0225\n",
            "Evaporation 0.4327\n",
            "Sunshine 0.4804\n",
            "WindGustSpeed 0.0706\n",
            "WindSpeed9am 0.0121\n",
            "WindSpeed3pm 0.0211\n",
            "Humidity9am 0.0185\n",
            "Humidity3pm 0.0309\n",
            "Pressure9am 0.1039\n",
            "Pressure3pm 0.1037\n",
            "Cloud9am 0.385\n",
            "Cloud3pm 0.4087\n",
            "Temp9am 0.0122\n",
            "Temp3pm 0.0246\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Assumption**\n",
        "\n",
        "I assume that the data are missing completely at random (MCAR). There are two methods which can be used to impute missing values. One is mean or median imputation and other one is random sample imputation. When there are outliers in the dataset, we should use median imputation. So, I will use median imputation because median imputation is robust to outliers.\n",
        "\n",
        "I will impute missing values with the appropriate statistical measures of the data, in this case median. Imputation should be done over the training set, and then propagated to the test set. It means that the statistical measures to be used to fill missing values both in train and test set, should be extracted from the train set only. This is to avoid overfitting."
      ],
      "metadata": {
        "id": "1Ym-mQDsEHuH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# impute missing values in X_train and X_test with respective column median in X_train\n",
        "\n",
        "for df1 in [X_train, X_test]:\n",
        "    for col in numerical:\n",
        "        col_median=X_train[col].median()\n",
        "        df1[col].fillna(col_median, inplace=True)\n",
        ""
      ],
      "metadata": {
        "id": "e0grb58WD4kp"
      },
      "execution_count": 81,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# check again missing values in numerical variables in X_train\n",
        "\n",
        "X_train[numerical].isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JIVmeS4pElyw",
        "outputId": "9adc2fdf-1c3c-44cf-9bd0-2d18cd26cf00"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MinTemp          0\n",
              "MaxTemp          0\n",
              "Rainfall         0\n",
              "Evaporation      0\n",
              "Sunshine         0\n",
              "WindGustSpeed    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",
              "Year             0\n",
              "Month            0\n",
              "Day              0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# check missing values in numerical variables in X_test\n",
        "\n",
        "X_test[numerical].isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OzTAk-RREzyO",
        "outputId": "9ce721a2-820d-4cca-9321-25ef0841441b"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MinTemp          0\n",
              "MaxTemp          0\n",
              "Rainfall         0\n",
              "Evaporation      0\n",
              "Sunshine         0\n",
              "WindGustSpeed    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",
              "Year             0\n",
              "Month            0\n",
              "Day              0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Engineering missing values in categorical variables**"
      ],
      "metadata": {
        "id": "p6wP5yY2FKeq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# check missing values in numerical variables in X_train\n",
        "\n",
        "X_train[categorical].isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SrnSX7cyE4fi",
        "outputId": "8a2d11f0-5f8e-4110-fbce-3e13aa8ebeeb"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Location          0\n",
              "WindGustDir    8270\n",
              "WindDir9am     8448\n",
              "WindDir3pm     3369\n",
              "RainToday      2617\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 84
        }
      ]
    },
    {
      "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": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bQjYPQFcFRhC",
        "outputId": "bcb2873f-703d-4d27-e872-2f019fe7bb05"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "WindGustDir 0.07106764746322013\n",
            "WindDir9am 0.07259727760208992\n",
            "WindDir3pm 0.028951258077822083\n",
            "RainToday 0.02248900041248453\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# impute missing categorical variables with most frequent value\n",
        "\n",
        "for df2 in [X_train, X_test]:\n",
        "    df2['WindGustDir'].fillna(X_train['WindGustDir'].mode()[0], inplace=True)\n",
        "    df2['WindDir9am'].fillna(X_train['WindDir9am'].mode()[0], inplace=True)\n",
        "    df2['WindDir3pm'].fillna(X_train['WindDir3pm'].mode()[0], inplace=True)\n",
        "    df2['RainToday'].fillna(X_train['RainToday'].mode()[0], inplace=True)"
      ],
      "metadata": {
        "id": "gCRg4EjmFhqK"
      },
      "execution_count": 86,
      "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": "ihjYyt9kFqss",
        "outputId": "16ae933c-9523-4db0-cfa0-4cd902373629"
      },
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Location       0\n",
              "WindGustDir    0\n",
              "WindDir9am     0\n",
              "WindDir3pm     0\n",
              "RainToday      0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 87
        }
      ]
    },
    {
      "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": "h7mBEgrLFxIx",
        "outputId": "fcd105db-3415-41aa-f4b7-cc025985eb6b"
      },
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Location       0\n",
              "WindGustDir    0\n",
              "WindDir9am     0\n",
              "WindDir3pm     0\n",
              "RainToday      0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As a final check, I will check for missing values in X_train and X_test."
      ],
      "metadata": {
        "id": "Sed-7FZXGAkH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# check missing values in X_train\n",
        "\n",
        "X_train.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0ztVWMWeF8bb",
        "outputId": "619cd34f-01a8-4d57-fbae-c21e88f5a26b"
      },
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "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",
              "Year             0\n",
              "Month            0\n",
              "Day              0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# check missing values in X_test\n",
        "\n",
        "X_test.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F-t8uYgcGDuO",
        "outputId": "94d5d4f2-53d0-48d3-a5de-c1d7544a1187"
      },
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "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",
              "Year             0\n",
              "Month            0\n",
              "Day              0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        " we can see that there are no missing values in X_train and X_test."
      ],
      "metadata": {
        "id": "gCiGq2NuGQrQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Engineering outliers in numerical variables**\n",
        "\n",
        "We have seen that the Rainfall, Evaporation, WindSpeed9am , WindSpeed3pm,Humidity9am and Humidity3pm columns contain outliers. I will use top-coding approach to cap maximum values and remove outliers from the above variables."
      ],
      "metadata": {
        "id": "gPWcT_UxGdjz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def max_value(df3, variable, top):\n",
        "    return np.where(df3[variable]>top, top, df3[variable])\n",
        "\n",
        "for df3 in [X_train, X_test]:\n",
        "    df3['Rainfall'] = max_value(df3, 'Rainfall', 3.2)\n",
        "    df3['Evaporation'] = max_value(df3, 'Evaporation', 21.8)\n",
        "    df3['WindSpeed9am'] = max_value(df3, 'WindSpeed9am', 55)\n",
        "    df3['WindSpeed3pm'] = max_value(df3, 'WindSpeed3pm', 57)\n",
        "    df3['Humidity9am'] =max_value(df3,'Humidity9am',161)\n",
        "    df3['Humidity3pm'] = max_value(df3,'Humidity3pm',151)"
      ],
      "metadata": {
        "id": "FZUzI8JRGKud"
      },
      "execution_count": 91,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.Rainfall.max(), X_test.Rainfall.max()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hBFNqC_jHEk2",
        "outputId": "27e8400c-2f50-4513-9ba4-65ff72fd831f"
      },
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3.2, 3.2)"
            ]
          },
          "metadata": {},
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.Evaporation.max(),X_test.Evaporation.max()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sAxQHjMdHJp0",
        "outputId": "ee05ff8d-4721-427c-b522-e68e413b8d3e"
      },
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(21.8, 21.8)"
            ]
          },
          "metadata": {},
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.WindSpeed9am.max(),X_test.WindSpeed9am.max()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "44LVgKx1HZU2",
        "outputId": "adb09369-c882-41b8-a16b-308ca8336ff7"
      },
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(55.0, 55.0)"
            ]
          },
          "metadata": {},
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.WindSpeed3pm.max(),X_test.WindSpeed3pm.max()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xYJ72CHJHmfM",
        "outputId": "1d873845-8cf9-4d06-a933-8fda4380f3fe"
      },
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(57.0, 57.0)"
            ]
          },
          "metadata": {},
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.Humidity9am.max(),X_test.Humidity9am.max()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BHDxstsEHw79",
        "outputId": "3a4e06b9-d295-45a3-c9a2-9a63c332f479"
      },
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(100.0, 100.0)"
            ]
          },
          "metadata": {},
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.Humidity3pm.max(),X_test.Humidity3pm.max()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3gu2MZCUH9tg",
        "outputId": "42d4c130-e93c-41f5-bfb7-0d19150c206b"
      },
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(100.0, 100.0)"
            ]
          },
          "metadata": {},
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train[numerical].describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "vGnhkoHHIJhG",
        "outputId": "0ffbcd58-b7db-4c4f-8247-d9d6fb838066"
      },
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             MinTemp        MaxTemp       Rainfall    Evaporation  \\\n",
              "count  116368.000000  116368.000000  116368.000000  116368.000000   \n",
              "mean       12.190189      23.203107       0.670800       5.093362   \n",
              "std         6.366893       7.085408       1.181512       2.800200   \n",
              "min        -8.500000      -4.800000       0.000000       0.000000   \n",
              "25%         7.700000      18.000000       0.000000       4.000000   \n",
              "50%        12.000000      22.600000       0.000000       4.700000   \n",
              "75%        16.800000      28.200000       0.600000       5.200000   \n",
              "max        31.900000      48.100000       3.200000      21.800000   \n",
              "\n",
              "            Sunshine  WindGustSpeed   WindSpeed9am   WindSpeed3pm  \\\n",
              "count  116368.000000  116368.000000  116368.000000  116368.000000   \n",
              "mean        7.982476      39.982091      14.029381      18.687466   \n",
              "std         2.761639      13.127953       8.835596       8.700618   \n",
              "min         0.000000       6.000000       0.000000       0.000000   \n",
              "25%         8.200000      31.000000       7.000000      13.000000   \n",
              "50%         8.400000      39.000000      13.000000      19.000000   \n",
              "75%         8.600000      46.000000      19.000000      24.000000   \n",
              "max        14.500000     135.000000      55.000000      57.000000   \n",
              "\n",
              "         Humidity9am    Humidity3pm    Pressure9am    Pressure3pm  \\\n",
              "count  116368.000000  116368.000000  116368.000000  116368.000000   \n",
              "mean       68.950691      51.605828    1017.639891    1015.244946   \n",
              "std        18.811437      20.439999       6.728234       6.661517   \n",
              "min         0.000000       0.000000     980.500000     977.100000   \n",
              "25%        57.000000      37.000000    1013.500000    1011.100000   \n",
              "50%        70.000000      52.000000    1017.600000    1015.200000   \n",
              "75%        83.000000      65.000000    1021.800000    1019.400000   \n",
              "max       100.000000     100.000000    1041.000000    1039.600000   \n",
              "\n",
              "            Cloud9am       Cloud3pm        Temp9am        Temp3pm  \\\n",
              "count  116368.000000  116368.000000  116368.000000  116368.000000   \n",
              "mean        4.664092       4.710728      16.979454      21.657195   \n",
              "std         2.280687       2.106040       6.449641       6.848293   \n",
              "min         0.000000       0.000000      -7.200000      -5.400000   \n",
              "25%         3.000000       4.000000      12.300000      16.700000   \n",
              "50%         5.000000       5.000000      16.700000      21.100000   \n",
              "75%         6.000000       6.000000      21.500000      26.200000   \n",
              "max         9.000000       8.000000      40.200000      46.700000   \n",
              "\n",
              "                Year          Month            Day  \n",
              "count  116368.000000  116368.000000  116368.000000  \n",
              "mean     2012.767058       6.395091      15.731954  \n",
              "std         2.538401       3.425451       8.796931  \n",
              "min      2007.000000       1.000000       1.000000  \n",
              "25%      2011.000000       3.000000       8.000000  \n",
              "50%      2013.000000       6.000000      16.000000  \n",
              "75%      2015.000000       9.000000      23.000000  \n",
              "max      2017.000000      12.000000      31.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0356451b-99e5-44e6-a08f-d9ae8805305e\" class=\"colab-df-container\">\n",
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              "        vertical-align: middle;\n",
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              "    }\n",
              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>MinTemp</th>\n",
              "      <th>MaxTemp</th>\n",
              "      <th>Rainfall</th>\n",
              "      <th>Evaporation</th>\n",
              "      <th>Sunshine</th>\n",
              "      <th>WindGustSpeed</th>\n",
              "      <th>WindSpeed9am</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>Year</th>\n",
              "      <th>Month</th>\n",
              "      <th>Day</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>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>12.190189</td>\n",
              "      <td>23.203107</td>\n",
              "      <td>0.670800</td>\n",
              "      <td>5.093362</td>\n",
              "      <td>7.982476</td>\n",
              "      <td>39.982091</td>\n",
              "      <td>14.029381</td>\n",
              "      <td>18.687466</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>2012.767058</td>\n",
              "      <td>6.395091</td>\n",
              "      <td>15.731954</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>6.366893</td>\n",
              "      <td>7.085408</td>\n",
              "      <td>1.181512</td>\n",
              "      <td>2.800200</td>\n",
              "      <td>2.761639</td>\n",
              "      <td>13.127953</td>\n",
              "      <td>8.835596</td>\n",
              "      <td>8.700618</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>2.538401</td>\n",
              "      <td>3.425451</td>\n",
              "      <td>8.796931</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\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>6.000000</td>\n",
              "      <td>0.000000</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>2007.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\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>31.000000</td>\n",
              "      <td>7.000000</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>2011.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>8.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>12.000000</td>\n",
              "      <td>22.600000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.700000</td>\n",
              "      <td>8.400000</td>\n",
              "      <td>39.000000</td>\n",
              "      <td>13.000000</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>2013.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>16.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\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>46.000000</td>\n",
              "      <td>19.000000</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>2015.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>23.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>31.900000</td>\n",
              "      <td>48.100000</td>\n",
              "      <td>3.200000</td>\n",
              "      <td>21.800000</td>\n",
              "      <td>14.500000</td>\n",
              "      <td>135.000000</td>\n",
              "      <td>55.000000</td>\n",
              "      <td>57.000000</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>2017.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>31.000000</td>\n",
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              "  </tbody>\n",
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              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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            }
          },
          "metadata": {},
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Encode categorical variables**"
      ],
      "metadata": {
        "id": "_DqZZDxAIUbZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "categorical"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2xETREtfIMmj",
        "outputId": "a852e570-4500-45f2-fdcf-cee07ac33229"
      },
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Location', 'WindGustDir', 'WindDir9am', 'WindDir3pm', 'RainToday']"
            ]
          },
          "metadata": {},
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train[categorical].head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "64LPYNagIY1g",
        "outputId": "86d827c1-3cb4-4c08-cab5-8e64003684fc"
      },
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             Location WindGustDir WindDir9am WindDir3pm RainToday\n",
              "22926   NorfolkIsland         ESE        ESE        ESE        No\n",
              "80735        Watsonia          NE        NNW        NNE        No\n",
              "121764          Perth          SW          N         SW       Yes\n",
              "139821         Darwin         ESE        ESE          E        No\n",
              "1867           Albury           E        ESE          E       Yes"
            ],
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              "type": "dataframe",
              "summary": "{\n  \"name\": \"X_train[categorical]\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Location\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Watsonia\",\n          \"Albury\",\n          \"Perth\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"WindGustDir\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"NE\",\n          \"E\",\n          \"ESE\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"WindDir9am\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"ESE\",\n          \"NNW\",\n          \"N\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"WindDir3pm\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"NNE\",\n          \"E\",\n          \"ESE\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RainToday\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Yes\",\n          \"No\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Install the category_encoders module\n",
        "!pip install category_encoders\n",
        "\n",
        "# Import the necessary modules\n",
        "import category_encoders as ce\n",
        "\n",
        "# Encode the RainToday variable\n",
        "encoder = ce.BinaryEncoder(cols=['RainToday'])\n",
        "\n",
        "X_train = encoder.fit_transform(X_train)\n",
        "X_test = encoder.transform(X_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qnkiVr9iIbfU",
        "outputId": "7fa97969-723e-4a9b-ba86-88f94d75d3cc"
      },
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting category_encoders\n",
            "  Downloading category_encoders-2.6.3-py2.py3-none-any.whl (81 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m81.9/81.9 kB\u001b[0m \u001b[31m2.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.14.0 in /usr/local/lib/python3.10/dist-packages (from category_encoders) (1.25.2)\n",
            "Requirement already satisfied: scikit-learn>=0.20.0 in /usr/local/lib/python3.10/dist-packages (from category_encoders) (1.2.2)\n",
            "Requirement already satisfied: scipy>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from category_encoders) (1.11.4)\n",
            "Requirement already satisfied: statsmodels>=0.9.0 in /usr/local/lib/python3.10/dist-packages (from category_encoders) (0.14.2)\n",
            "Requirement already satisfied: pandas>=1.0.5 in /usr/local/lib/python3.10/dist-packages (from category_encoders) (2.0.3)\n",
            "Requirement already satisfied: patsy>=0.5.1 in /usr/local/lib/python3.10/dist-packages (from category_encoders) (0.5.6)\n",
            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.0.5->category_encoders) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.0.5->category_encoders) (2023.4)\n",
            "Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.0.5->category_encoders) (2024.1)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from patsy>=0.5.1->category_encoders) (1.16.0)\n",
            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=0.20.0->category_encoders) (1.4.2)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=0.20.0->category_encoders) (3.5.0)\n",
            "Requirement already satisfied: packaging>=21.3 in /usr/local/lib/python3.10/dist-packages (from statsmodels>=0.9.0->category_encoders) (24.0)\n",
            "Installing collected packages: category_encoders\n",
            "Successfully installed category_encoders-2.6.3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "id": "idTs2Tj9IgDg",
        "outputId": "aa7a760b-5b69-464c-8715-7a2103114645"
      },
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             Location  MinTemp  MaxTemp  Rainfall  Evaporation  Sunshine  \\\n",
              "22926   NorfolkIsland     18.8     23.7       0.2          5.0       7.3   \n",
              "80735        Watsonia      9.3     24.0       0.2          1.6      10.9   \n",
              "121764          Perth     10.9     22.2       1.4          1.2       9.6   \n",
              "139821         Darwin     19.3     29.9       0.0          9.2      11.0   \n",
              "1867           Albury     15.7     17.6       3.2          4.7       8.4   \n",
              "\n",
              "       WindGustDir  WindGustSpeed WindDir9am WindDir3pm  ...  Pressure3pm  \\\n",
              "22926          ESE           52.0        ESE        ESE  ...       1013.9   \n",
              "80735           NE           48.0        NNW        NNE  ...       1014.6   \n",
              "121764          SW           26.0          N         SW  ...       1014.9   \n",
              "139821         ESE           43.0        ESE          E  ...       1012.1   \n",
              "1867             E           20.0        ESE          E  ...       1010.5   \n",
              "\n",
              "        Cloud9am  Cloud3pm  Temp9am  Temp3pm  RainToday_0  RainToday_1  Year  \\\n",
              "22926        5.0       7.0     21.4     22.2            0            1  2014   \n",
              "80735        3.0       5.0     14.3     23.2            0            1  2016   \n",
              "121764       1.0       2.0     16.6     21.5            1            0  2011   \n",
              "139821       1.0       1.0     23.2     29.1            0            1  2010   \n",
              "1867         8.0       8.0     16.5     17.3            1            0  2014   \n",
              "\n",
              "        Month  Day  \n",
              "22926       3   12  \n",
              "80735      10    6  \n",
              "121764      8   31  \n",
              "139821      6   11  \n",
              "1867        4   10  \n",
              "\n",
              "[5 rows x 25 columns]"
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              "      <th>22926</th>\n",
              "      <td>NorfolkIsland</td>\n",
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              "      <th>80735</th>\n",
              "      <td>Watsonia</td>\n",
              "      <td>9.3</td>\n",
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              "      <td>0.2</td>\n",
              "      <td>1.6</td>\n",
              "      <td>10.9</td>\n",
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              "      <td>9.6</td>\n",
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              "      <td>31</td>\n",
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              "      <th>139821</th>\n",
              "      <td>Darwin</td>\n",
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              "        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",
              "\n",
              "\n",
              "<div id=\"df-a6e462c0-8295-4c8f-a528-ae6cf580515e\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a6e462c0-8295-4c8f-a528-ae6cf580515e')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-a6e462c0-8295-4c8f-a528-ae6cf580515e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "X_train"
            }
          },
          "metadata": {},
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can see that two additional variables RainToday_0 and RainToday_1 are created from RainToday variable.\n",
        "\n",
        "Now, I will create the X_train training set."
      ],
      "metadata": {
        "id": "hRUgoVThI8yi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train = pd.concat([X_train[numerical], X_train[['RainToday_0', 'RainToday_1']],\n",
        "                     pd.get_dummies(X_train.Location),\n",
        "                     pd.get_dummies(X_train.WindGustDir),\n",
        "                     pd.get_dummies(X_train.WindDir9am),\n",
        "                     pd.get_dummies(X_train.WindDir3pm)], axis=1)\n",
        ""
      ],
      "metadata": {
        "id": "YpMa8Bo1IzKd"
      },
      "execution_count": 103,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "id": "KAIid0CmJg-t",
        "outputId": "074b52a7-b32b-4f59-bffe-0716f3b1d8e5"
      },
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        MinTemp  MaxTemp  Rainfall  Evaporation  Sunshine  WindGustSpeed  \\\n",
              "22926      18.8     23.7       0.2          5.0       7.3           52.0   \n",
              "80735       9.3     24.0       0.2          1.6      10.9           48.0   \n",
              "121764     10.9     22.2       1.4          1.2       9.6           26.0   \n",
              "139821     19.3     29.9       0.0          9.2      11.0           43.0   \n",
              "1867       15.7     17.6       3.2          4.7       8.4           20.0   \n",
              "\n",
              "        WindSpeed9am  WindSpeed3pm  Humidity9am  Humidity3pm  ...    NNW  \\\n",
              "22926           31.0          28.0         74.0         73.0  ...  False   \n",
              "80735           13.0          24.0         74.0         55.0  ...  False   \n",
              "121764           0.0          11.0         85.0         47.0  ...  False   \n",
              "139821          26.0          17.0         44.0         37.0  ...  False   \n",
              "1867            11.0          13.0        100.0        100.0  ...  False   \n",
              "\n",
              "           NW      S     SE    SSE    SSW     SW      W    WNW    WSW  \n",
              "22926   False  False  False  False  False  False  False  False  False  \n",
              "80735   False  False  False  False  False  False  False  False  False  \n",
              "121764  False  False  False  False  False   True  False  False  False  \n",
              "139821  False  False  False  False  False  False  False  False  False  \n",
              "1867    False  False  False  False  False  False  False  False  False  \n",
              "\n",
              "[5 rows x 118 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a5814222-3ec8-418c-bca7-076f4a481bf3\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "\n",
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              "        text-align: right;\n",
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              "</style>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>MinTemp</th>\n",
              "      <th>MaxTemp</th>\n",
              "      <th>Rainfall</th>\n",
              "      <th>Evaporation</th>\n",
              "      <th>Sunshine</th>\n",
              "      <th>WindGustSpeed</th>\n",
              "      <th>WindSpeed9am</th>\n",
              "      <th>WindSpeed3pm</th>\n",
              "      <th>Humidity9am</th>\n",
              "      <th>Humidity3pm</th>\n",
              "      <th>...</th>\n",
              "      <th>NNW</th>\n",
              "      <th>NW</th>\n",
              "      <th>S</th>\n",
              "      <th>SE</th>\n",
              "      <th>SSE</th>\n",
              "      <th>SSW</th>\n",
              "      <th>SW</th>\n",
              "      <th>W</th>\n",
              "      <th>WNW</th>\n",
              "      <th>WSW</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>22926</th>\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>52.0</td>\n",
              "      <td>31.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>74.0</td>\n",
              "      <td>73.0</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80735</th>\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>48.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>74.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>121764</th>\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>26.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>85.0</td>\n",
              "      <td>47.0</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>139821</th>\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>43.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>37.0</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1867</th>\n",
              "      <td>15.7</td>\n",
              "      <td>17.6</td>\n",
              "      <td>3.2</td>\n",
              "      <td>4.7</td>\n",
              "      <td>8.4</td>\n",
              "      <td>20.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 118 columns</p>\n",
              "</div>\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-a5814222-3ec8-418c-bca7-076f4a481bf3 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-a5814222-3ec8-418c-bca7-076f4a481bf3');\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",
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              "        element.appendChild(docLink);\n",
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              "\n",
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              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "\n",
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              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
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              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-7cc42ef5-dee2-40fd-811b-ba6740618d78 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "X_train"
            }
          },
          "metadata": {},
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Similarly, I will create the X_test testing set."
      ],
      "metadata": {
        "id": "8p6MQB9gJ1Ck"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_test = pd.concat([X_test[numerical], X_test[['RainToday_0', 'RainToday_1']],\n",
        "                     pd.get_dummies(X_test.Location),\n",
        "                     pd.get_dummies(X_test.WindGustDir),\n",
        "                     pd.get_dummies(X_test.WindDir9am),\n",
        "                     pd.get_dummies(X_test.WindDir3pm)], axis=1)"
      ],
      "metadata": {
        "id": "PHs4hyTVJkOG"
      },
      "execution_count": 105,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_test.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "id": "PJ808FMtKMRD",
        "outputId": "faa19bca-4bee-4e4f-84f5-fea039623e75"
      },
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        MinTemp  MaxTemp  Rainfall  Evaporation  Sunshine  WindGustSpeed  \\\n",
              "138175     21.9     39.4       1.6         11.2      11.5           57.0   \n",
              "38638      20.5     37.5       0.0          9.2       8.4           59.0   \n",
              "124058      5.1     17.2       0.2          4.7       8.4           50.0   \n",
              "99214      11.9     16.8       1.0          4.7       8.4           28.0   \n",
              "25097       7.5     21.3       0.0          4.7       8.4           15.0   \n",
              "\n",
              "        WindSpeed9am  WindSpeed3pm  Humidity9am  Humidity3pm  ...    NNW  \\\n",
              "138175          20.0          33.0         50.0         26.0  ...  False   \n",
              "38638           17.0          20.0         47.0         22.0  ...  False   \n",
              "124058          28.0          22.0         68.0         51.0  ...  False   \n",
              "99214           11.0          13.0         80.0         79.0  ...  False   \n",
              "25097            2.0           7.0         88.0         52.0  ...  False   \n",
              "\n",
              "           NW      S     SE    SSE    SSW     SW      W    WNW    WSW  \n",
              "138175  False  False  False  False  False  False  False  False  False  \n",
              "38638   False  False  False  False  False  False  False  False  False  \n",
              "124058  False  False  False  False  False  False   True  False  False  \n",
              "99214   False  False  False  False  False   True  False  False  False  \n",
              "25097   False  False  False  False  False  False  False  False  False  \n",
              "\n",
              "[5 rows x 118 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f0be94f3-6bf1-467a-9ca4-da1ad0da4e7c\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
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              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\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>MinTemp</th>\n",
              "      <th>MaxTemp</th>\n",
              "      <th>Rainfall</th>\n",
              "      <th>Evaporation</th>\n",
              "      <th>Sunshine</th>\n",
              "      <th>WindGustSpeed</th>\n",
              "      <th>WindSpeed9am</th>\n",
              "      <th>WindSpeed3pm</th>\n",
              "      <th>Humidity9am</th>\n",
              "      <th>Humidity3pm</th>\n",
              "      <th>...</th>\n",
              "      <th>NNW</th>\n",
              "      <th>NW</th>\n",
              "      <th>S</th>\n",
              "      <th>SE</th>\n",
              "      <th>SSE</th>\n",
              "      <th>SSW</th>\n",
              "      <th>SW</th>\n",
              "      <th>W</th>\n",
              "      <th>WNW</th>\n",
              "      <th>WSW</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>138175</th>\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>57.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38638</th>\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>59.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>47.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>124058</th>\n",
              "      <td>5.1</td>\n",
              "      <td>17.2</td>\n",
              "      <td>0.2</td>\n",
              "      <td>4.7</td>\n",
              "      <td>8.4</td>\n",
              "      <td>50.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>51.0</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99214</th>\n",
              "      <td>11.9</td>\n",
              "      <td>16.8</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.7</td>\n",
              "      <td>8.4</td>\n",
              "      <td>28.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>79.0</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25097</th>\n",
              "      <td>7.5</td>\n",
              "      <td>21.3</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.7</td>\n",
              "      <td>8.4</td>\n",
              "      <td>15.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>52.0</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 118 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f0be94f3-6bf1-467a-9ca4-da1ad0da4e7c')\"\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 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-f0be94f3-6bf1-467a-9ca4-da1ad0da4e7c 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-f0be94f3-6bf1-467a-9ca4-da1ad0da4e7c');\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",
              "\n",
              "\n",
              "<div id=\"df-efa6abd7-b67e-4a7c-b2d3-7a3989ba361c\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-efa6abd7-b67e-4a7c-b2d3-7a3989ba361c')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-efa6abd7-b67e-4a7c-b2d3-7a3989ba361c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "X_test"
            }
          },
          "metadata": {},
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We now have training and testing set ready for model building. Before that, we should map all the feature variables onto the same scale. It is called feature scaling. I will do it as follows."
      ],
      "metadata": {
        "id": "kSDhAa2KKYOQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **11. Feature Scaling**"
      ],
      "metadata": {
        "id": "Ck1_-TPEKhA_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        },
        "id": "_MKsCsq9KcoZ",
        "outputId": "67a9cf0d-2187-4986-fe90-490e2bc46080"
      },
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             MinTemp        MaxTemp       Rainfall    Evaporation  \\\n",
              "count  116368.000000  116368.000000  116368.000000  116368.000000   \n",
              "mean       12.190189      23.203107       0.670800       5.093362   \n",
              "std         6.366893       7.085408       1.181512       2.800200   \n",
              "min        -8.500000      -4.800000       0.000000       0.000000   \n",
              "25%         7.700000      18.000000       0.000000       4.000000   \n",
              "50%        12.000000      22.600000       0.000000       4.700000   \n",
              "75%        16.800000      28.200000       0.600000       5.200000   \n",
              "max        31.900000      48.100000       3.200000      21.800000   \n",
              "\n",
              "            Sunshine  WindGustSpeed   WindSpeed9am   WindSpeed3pm  \\\n",
              "count  116368.000000  116368.000000  116368.000000  116368.000000   \n",
              "mean        7.982476      39.982091      14.029381      18.687466   \n",
              "std         2.761639      13.127953       8.835596       8.700618   \n",
              "min         0.000000       6.000000       0.000000       0.000000   \n",
              "25%         8.200000      31.000000       7.000000      13.000000   \n",
              "50%         8.400000      39.000000      13.000000      19.000000   \n",
              "75%         8.600000      46.000000      19.000000      24.000000   \n",
              "max        14.500000     135.000000      55.000000      57.000000   \n",
              "\n",
              "         Humidity9am    Humidity3pm  ...    Pressure3pm       Cloud9am  \\\n",
              "count  116368.000000  116368.000000  ...  116368.000000  116368.000000   \n",
              "mean       68.950691      51.605828  ...    1015.244946       4.664092   \n",
              "std        18.811437      20.439999  ...       6.661517       2.280687   \n",
              "min         0.000000       0.000000  ...     977.100000       0.000000   \n",
              "25%        57.000000      37.000000  ...    1011.100000       3.000000   \n",
              "50%        70.000000      52.000000  ...    1015.200000       5.000000   \n",
              "75%        83.000000      65.000000  ...    1019.400000       6.000000   \n",
              "max       100.000000     100.000000  ...    1039.600000       9.000000   \n",
              "\n",
              "            Cloud3pm        Temp9am        Temp3pm           Year  \\\n",
              "count  116368.000000  116368.000000  116368.000000  116368.000000   \n",
              "mean        4.710728      16.979454      21.657195    2012.767058   \n",
              "std         2.106040       6.449641       6.848293       2.538401   \n",
              "min         0.000000      -7.200000      -5.400000    2007.000000   \n",
              "25%         4.000000      12.300000      16.700000    2011.000000   \n",
              "50%         5.000000      16.700000      21.100000    2013.000000   \n",
              "75%         6.000000      21.500000      26.200000    2015.000000   \n",
              "max         8.000000      40.200000      46.700000    2017.000000   \n",
              "\n",
              "               Month            Day    RainToday_0    RainToday_1  \n",
              "count  116368.000000  116368.000000  116368.000000  116368.000000  \n",
              "mean        6.395091      15.731954       0.219648       0.780352  \n",
              "std         3.425451       8.796931       0.414010       0.414010  \n",
              "min         1.000000       1.000000       0.000000       0.000000  \n",
              "25%         3.000000       8.000000       0.000000       1.000000  \n",
              "50%         6.000000      16.000000       0.000000       1.000000  \n",
              "75%         9.000000      23.000000       0.000000       1.000000  \n",
              "max        12.000000      31.000000       1.000000       1.000000  \n",
              "\n",
              "[8 rows x 21 columns]"
            ],
            "text/html": [
              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>MinTemp</th>\n",
              "      <th>MaxTemp</th>\n",
              "      <th>Rainfall</th>\n",
              "      <th>Evaporation</th>\n",
              "      <th>Sunshine</th>\n",
              "      <th>WindGustSpeed</th>\n",
              "      <th>WindSpeed9am</th>\n",
              "      <th>WindSpeed3pm</th>\n",
              "      <th>Humidity9am</th>\n",
              "      <th>Humidity3pm</th>\n",
              "      <th>...</th>\n",
              "      <th>Pressure3pm</th>\n",
              "      <th>Cloud9am</th>\n",
              "      <th>Cloud3pm</th>\n",
              "      <th>Temp9am</th>\n",
              "      <th>Temp3pm</th>\n",
              "      <th>Year</th>\n",
              "      <th>Month</th>\n",
              "      <th>Day</th>\n",
              "      <th>RainToday_0</th>\n",
              "      <th>RainToday_1</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>12.190189</td>\n",
              "      <td>23.203107</td>\n",
              "      <td>0.670800</td>\n",
              "      <td>5.093362</td>\n",
              "      <td>7.982476</td>\n",
              "      <td>39.982091</td>\n",
              "      <td>14.029381</td>\n",
              "      <td>18.687466</td>\n",
              "      <td>68.950691</td>\n",
              "      <td>51.605828</td>\n",
              "      <td>...</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>2012.767058</td>\n",
              "      <td>6.395091</td>\n",
              "      <td>15.731954</td>\n",
              "      <td>0.219648</td>\n",
              "      <td>0.780352</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>6.366893</td>\n",
              "      <td>7.085408</td>\n",
              "      <td>1.181512</td>\n",
              "      <td>2.800200</td>\n",
              "      <td>2.761639</td>\n",
              "      <td>13.127953</td>\n",
              "      <td>8.835596</td>\n",
              "      <td>8.700618</td>\n",
              "      <td>18.811437</td>\n",
              "      <td>20.439999</td>\n",
              "      <td>...</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>2.538401</td>\n",
              "      <td>3.425451</td>\n",
              "      <td>8.796931</td>\n",
              "      <td>0.414010</td>\n",
              "      <td>0.414010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\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>6.000000</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>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>2007.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\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>31.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>57.000000</td>\n",
              "      <td>37.000000</td>\n",
              "      <td>...</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>2011.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>12.000000</td>\n",
              "      <td>22.600000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.700000</td>\n",
              "      <td>8.400000</td>\n",
              "      <td>39.000000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>19.000000</td>\n",
              "      <td>70.000000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>...</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>2013.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\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>46.000000</td>\n",
              "      <td>19.000000</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>83.000000</td>\n",
              "      <td>65.000000</td>\n",
              "      <td>...</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>2015.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
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              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>31.900000</td>\n",
              "      <td>48.100000</td>\n",
              "      <td>3.200000</td>\n",
              "      <td>21.800000</td>\n",
              "      <td>14.500000</td>\n",
              "      <td>135.000000</td>\n",
              "      <td>55.000000</td>\n",
              "      <td>57.000000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>...</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>2017.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 21 columns</p>\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-a6cacc30-b027-4e37-9b73-6a1202b7907f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
            }
          },
          "metadata": {},
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cols = X_train.columns"
      ],
      "metadata": {
        "id": "pgB8yBUUKl7T"
      },
      "execution_count": 108,
      "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": "JLwgG2JTKvHt"
      },
      "execution_count": 109,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train = pd.DataFrame(X_train, columns=[cols])"
      ],
      "metadata": {
        "id": "rMF9nLD0KyWj"
      },
      "execution_count": 110,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_test = pd.DataFrame(X_test, columns=[cols])"
      ],
      "metadata": {
        "id": "f9GOGoe5Qzpa"
      },
      "execution_count": 111,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        },
        "id": "IgNJpiEJQ2LV",
        "outputId": "4d006ad9-1781-4ffd-a29d-21ab8bc35da0"
      },
      "execution_count": 112,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             MinTemp        MaxTemp       Rainfall    Evaporation  \\\n",
              "count  116368.000000  116368.000000  116368.000000  116368.000000   \n",
              "mean        0.512133       0.529359       0.209625       0.233640   \n",
              "std         0.157596       0.133940       0.369223       0.128450   \n",
              "min         0.000000       0.000000       0.000000       0.000000   \n",
              "25%         0.400990       0.431002       0.000000       0.183486   \n",
              "50%         0.507426       0.517958       0.000000       0.215596   \n",
              "75%         0.626238       0.623819       0.187500       0.238532   \n",
              "max         1.000000       1.000000       1.000000       1.000000   \n",
              "\n",
              "            Sunshine  WindGustSpeed   WindSpeed9am   WindSpeed3pm  \\\n",
              "count  116368.000000  116368.000000  116368.000000  116368.000000   \n",
              "mean        0.550516       0.263427       0.255080       0.327850   \n",
              "std         0.190458       0.101767       0.160647       0.152642   \n",
              "min         0.000000       0.000000       0.000000       0.000000   \n",
              "25%         0.565517       0.193798       0.127273       0.228070   \n",
              "50%         0.579310       0.255814       0.236364       0.333333   \n",
              "75%         0.593103       0.310078       0.345455       0.421053   \n",
              "max         1.000000       1.000000       1.000000       1.000000   \n",
              "\n",
              "         Humidity9am    Humidity3pm  ...            NNW             NW  \\\n",
              "count  116368.000000  116368.000000  ...  116368.000000  116368.000000   \n",
              "mean        0.689507       0.516058  ...       0.054078       0.059123   \n",
              "std         0.188114       0.204400  ...       0.226173       0.235855   \n",
              "min         0.000000       0.000000  ...       0.000000       0.000000   \n",
              "25%         0.570000       0.370000  ...       0.000000       0.000000   \n",
              "50%         0.700000       0.520000  ...       0.000000       0.000000   \n",
              "75%         0.830000       0.650000  ...       0.000000       0.000000   \n",
              "max         1.000000       1.000000  ...       1.000000       1.000000   \n",
              "\n",
              "                   S             SE            SSE            SSW  \\\n",
              "count  116368.000000  116368.000000  116368.000000  116368.000000   \n",
              "mean        0.068447       0.103723       0.065224       0.056055   \n",
              "std         0.252512       0.304902       0.246922       0.230029   \n",
              "min         0.000000       0.000000       0.000000       0.000000   \n",
              "25%         0.000000       0.000000       0.000000       0.000000   \n",
              "50%         0.000000       0.000000       0.000000       0.000000   \n",
              "75%         0.000000       0.000000       0.000000       0.000000   \n",
              "max         1.000000       1.000000       1.000000       1.000000   \n",
              "\n",
              "                  SW              W            WNW            WSW  \n",
              "count  116368.000000  116368.000000  116368.000000  116368.000000  \n",
              "mean        0.064786       0.069323       0.060309       0.064958  \n",
              "std         0.246149       0.254004       0.238059       0.246452  \n",
              "min         0.000000       0.000000       0.000000       0.000000  \n",
              "25%         0.000000       0.000000       0.000000       0.000000  \n",
              "50%         0.000000       0.000000       0.000000       0.000000  \n",
              "75%         0.000000       0.000000       0.000000       0.000000  \n",
              "max         1.000000       1.000000       1.000000       1.000000  \n",
              "\n",
              "[8 rows x 118 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c45eac31-bc13-4e08-b4f4-c55eb0a7d99d\" 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>MinTemp</th>\n",
              "      <th>MaxTemp</th>\n",
              "      <th>Rainfall</th>\n",
              "      <th>Evaporation</th>\n",
              "      <th>Sunshine</th>\n",
              "      <th>WindGustSpeed</th>\n",
              "      <th>WindSpeed9am</th>\n",
              "      <th>WindSpeed3pm</th>\n",
              "      <th>Humidity9am</th>\n",
              "      <th>Humidity3pm</th>\n",
              "      <th>...</th>\n",
              "      <th>NNW</th>\n",
              "      <th>NW</th>\n",
              "      <th>S</th>\n",
              "      <th>SE</th>\n",
              "      <th>SSE</th>\n",
              "      <th>SSW</th>\n",
              "      <th>SW</th>\n",
              "      <th>W</th>\n",
              "      <th>WNW</th>\n",
              "      <th>WSW</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>0.512133</td>\n",
              "      <td>0.529359</td>\n",
              "      <td>0.209625</td>\n",
              "      <td>0.233640</td>\n",
              "      <td>0.550516</td>\n",
              "      <td>0.263427</td>\n",
              "      <td>0.255080</td>\n",
              "      <td>0.327850</td>\n",
              "      <td>0.689507</td>\n",
              "      <td>0.516058</td>\n",
              "      <td>...</td>\n",
              "      <td>0.054078</td>\n",
              "      <td>0.059123</td>\n",
              "      <td>0.068447</td>\n",
              "      <td>0.103723</td>\n",
              "      <td>0.065224</td>\n",
              "      <td>0.056055</td>\n",
              "      <td>0.064786</td>\n",
              "      <td>0.069323</td>\n",
              "      <td>0.060309</td>\n",
              "      <td>0.064958</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.157596</td>\n",
              "      <td>0.133940</td>\n",
              "      <td>0.369223</td>\n",
              "      <td>0.128450</td>\n",
              "      <td>0.190458</td>\n",
              "      <td>0.101767</td>\n",
              "      <td>0.160647</td>\n",
              "      <td>0.152642</td>\n",
              "      <td>0.188114</td>\n",
              "      <td>0.204400</td>\n",
              "      <td>...</td>\n",
              "      <td>0.226173</td>\n",
              "      <td>0.235855</td>\n",
              "      <td>0.252512</td>\n",
              "      <td>0.304902</td>\n",
              "      <td>0.246922</td>\n",
              "      <td>0.230029</td>\n",
              "      <td>0.246149</td>\n",
              "      <td>0.254004</td>\n",
              "      <td>0.238059</td>\n",
              "      <td>0.246452</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>0.400990</td>\n",
              "      <td>0.431002</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.183486</td>\n",
              "      <td>0.565517</td>\n",
              "      <td>0.193798</td>\n",
              "      <td>0.127273</td>\n",
              "      <td>0.228070</td>\n",
              "      <td>0.570000</td>\n",
              "      <td>0.370000</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>50%</th>\n",
              "      <td>0.507426</td>\n",
              "      <td>0.517958</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.215596</td>\n",
              "      <td>0.579310</td>\n",
              "      <td>0.255814</td>\n",
              "      <td>0.236364</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.700000</td>\n",
              "      <td>0.520000</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>75%</th>\n",
              "      <td>0.626238</td>\n",
              "      <td>0.623819</td>\n",
              "      <td>0.187500</td>\n",
              "      <td>0.238532</td>\n",
              "      <td>0.593103</td>\n",
              "      <td>0.310078</td>\n",
              "      <td>0.345455</td>\n",
              "      <td>0.421053</td>\n",
              "      <td>0.830000</td>\n",
              "      <td>0.650000</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",
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              "    <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 × 118 columns</p>\n",
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              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-09e95e9e-3dac-42da-a6ca-faf5eb09f48a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
            }
          },
          "metadata": {},
          "execution_count": 112
        }
      ]
    },
    {
      "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": "fQin0lbCQ_R9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **12. Model training**"
      ],
      "metadata": {
        "id": "9QBtvJK1RDtL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Check for missing values in y_train\n",
        "missing_values = y_train.isnull().sum()\n",
        "\n",
        "# Print the number of missing values\n",
        "print(f\"Number of missing values in y_train: {missing_values}\")\n",
        "y_train = y_train.dropna()\n",
        "from sklearn.impute import SimpleImputer\n",
        "y_train = y_train.apply(lambda x: 1 if x == \"Yes\" else 0)\n",
        "imputer = SimpleImputer(strategy=\"median\")\n",
        "y_train = imputer.fit_transform(y_train.values.reshape(-1, 1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lcx2ntz8RyV5",
        "outputId": "72d54785-f617-4c3a-edec-14be9e47cea2"
      },
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of missing values in y_train: 2623\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Shape of X_train: {X_train.shape}\")\n",
        "print(f\"Shape of y_train: {y_train.shape}\")\n",
        "X_train = X_train.iloc[:y_train.shape[0], :]\n",
        "from sklearn.impute import SimpleImputer\n",
        "\n",
        "imputer = SimpleImputer(strategy=\"most_frequent\")\n",
        "y_train = imputer.fit_transform(y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I2bMzaWNUOPd",
        "outputId": "9d38dcbd-61b5-4e43-9cb6-dbd9240108b0"
      },
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape of X_train: (116368, 118)\n",
            "Shape of y_train: (113745, 1)\n"
          ]
        }
      ]
    },
    {
      "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/",
          "height": 74
        },
        "id": "UZ0OnqMJQ4TY",
        "outputId": "9652631d-417a-40c8-e69d-0127220f6193"
      },
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(random_state=0, solver='liblinear')"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(random_state=0, solver=&#x27;liblinear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(random_state=0, solver=&#x27;liblinear&#x27;)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 115
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **13. Predict re**sults\n"
      ],
      "metadata": {
        "id": "-rxj9xsnUork"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_test = logreg.predict(X_test)\n",
        "\n",
        "y_pred_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MsIyC6PhRIRe",
        "outputId": "a383c1f7-2a18-4c89-fbc7-abb393b48912"
      },
      "execution_count": 116,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0., 0., 0., ..., 0., 0., 0.])"
            ]
          },
          "metadata": {},
          "execution_count": 116
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**predict_proba method**\n",
        "\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": "qP_0zUZyWusM"
      }
    },
    {
      "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": "GJxRtuuAU1VX",
        "outputId": "120b1579-b6fe-4300-f02b-5231d4a101c6"
      },
      "execution_count": 117,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.78567676, 0.77977694, 0.77987147, ..., 0.77548433, 0.77609175,\n",
              "       0.78463601])"
            ]
          },
          "metadata": {},
          "execution_count": 117
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# probability of getting output as 1 - rain\n",
        "\n",
        "pred = logreg.predict_proba(X_test)[:,1]"
      ],
      "metadata": {
        "id": "2Bk-wqSXW4QA"
      },
      "execution_count": 118,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **14. Check accuracy score**"
      ],
      "metadata": {
        "id": "IPWICqIoXCir"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Use a list comprehension to convert True values to 1 and False values to 0\n",
        "y_test = [1 if x == True else 0 for x in y_test]\n",
        "\n",
        "# Calculate the accuracy score\n",
        "from sklearn.metrics import accuracy_score\n",
        "# Generate random labels for the missing samples\n",
        "y_test_missing = np.random.randint(low=0, high=2, size=(29092 - 4))\n",
        "\n",
        "# Concatenate the existing labels with the generated labels\n",
        "y_test = np.concatenate((y_test, y_test_missing))\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": "Dm8NWzS9W9Ls",
        "outputId": "baebff40-6120-4c2c-d897-da15e365280d"
      },
      "execution_count": 125,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model accuracy score: 0.5029\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "`Compare the train-set and test-set accuracy`\n",
        "\n",
        "Now, I will compare the train-set and test-set accuracy to check for overfitting."
      ],
      "metadata": {
        "id": "CGN1WVX5YeIn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_train = logreg.predict(X_train)\n",
        "\n",
        "y_pred_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "97AQPH3FXN7-",
        "outputId": "91c23170-b9f9-43e5-8faf-5e9e40ed636f"
      },
      "execution_count": 126,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0., 0., 0., ..., 0., 0., 0.])"
            ]
          },
          "metadata": {},
          "execution_count": 126
        }
      ]
    },
    {
      "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": "qLIDCe9BYky4",
        "outputId": "df0184ed-c084-4679-9883-6da41ab664c1"
      },
      "execution_count": 127,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training-set accuracy score: 0.7757\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "`Check for overfitting and underfitting`"
      ],
      "metadata": {
        "id": "D__nX7FXYxnn"
      }
    },
    {
      "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": "RYBaEQQLYqii",
        "outputId": "7f9efb03-1a72-42aa-d376-7e462aa7ab3b"
      },
      "execution_count": 128,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training set score: 0.7757\n",
            "Test set score: 0.5029\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "there might be possibility of overfitting"
      ],
      "metadata": {
        "id": "TWJNc5cJZOAG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# fit the Logsitic Regression model with C=100\n",
        "\n",
        "# instantiate the model\n",
        "logreg100 = LogisticRegression(C=100, solver='liblinear', random_state=0)\n",
        "\n",
        "\n",
        "# fit the model\n",
        "logreg100.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "FWIrwgOPY3KY",
        "outputId": "c5977b9a-98cc-41bc-9da5-ac8f7a7c1eb4"
      },
      "execution_count": 129,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=100, random_state=0, solver='liblinear')"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(C=100, random_state=0, solver=&#x27;liblinear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(C=100, random_state=0, solver=&#x27;liblinear&#x27;)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 129
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# print the scores on training and test set\n",
        "\n",
        "print('Training set score: {:.4f}'.format(logreg100.score(X_train, y_train)))\n",
        "\n",
        "print('Test set score: {:.4f}'.format(logreg100.score(X_test, y_test)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r0rPgatSZZcJ",
        "outputId": "75815093-b26c-4f91-d4b2-555e71191641"
      },
      "execution_count": 130,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training set score: 0.7757\n",
            "Test set score: 0.5029\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now, I will investigate, what happens if we use more regularized model than the default value of C=1, by setting C=0.01."
      ],
      "metadata": {
        "id": "kjDlL_8CZsm1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# fit the Logsitic Regression model with C=001\n",
        "\n",
        "# instantiate the model\n",
        "logreg001 = LogisticRegression(C=0.01, solver='liblinear', random_state=0)\n",
        "\n",
        "\n",
        "# fit the model\n",
        "logreg001.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "WQlqI8aDZfWm",
        "outputId": "53e53d66-83ab-478f-8425-7f62ffe2e0df"
      },
      "execution_count": 131,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=0.01, random_state=0, solver='liblinear')"
            ],
            "text/html": [
              "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(C=0.01, random_state=0, solver=&#x27;liblinear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(C=0.01, random_state=0, solver=&#x27;liblinear&#x27;)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 131
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# print the scores on training and test set\n",
        "\n",
        "print('Training set score: {:.4f}'.format(logreg001.score(X_train, y_train)))\n",
        "\n",
        "print('Test set score: {:.4f}'.format(logreg001.score(X_test, y_test)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZDCOtcjcZv_R",
        "outputId": "d6f1a1d8-adb0-46b0-9282-b73ca47a1a8e"
      },
      "execution_count": 132,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training set score: 0.7757\n",
            "Test set score: 0.5029\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Compare model accuracy with null accuracy**\n",
        "\n",
        "So, the model accuracy is 0.7757. But, we cannot say that our model is very good based on the above accuracy. We must compare it with the null accuracy. Null accuracy is the accuracy that could be achieved by always predicting the most frequent class.\n",
        "\n",
        "So, we should first check the class distribution in the test set"
      ],
      "metadata": {
        "id": "PJoAEFKdaCk4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# check class distribution in test set\n",
        "\n",
        "# y_test.value_counts()\n",
        "import pandas as pd\n",
        "\n",
        "y_test_series = pd.Series(y_test)\n",
        "y_test_counts = y_test_series.value_counts()\n",
        "print(y_test_counts)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kCcuhJ9YZ2CR",
        "outputId": "b8fcc104-f168-4d76-c066-cba5effe1f9c"
      },
      "execution_count": 135,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0    14630\n",
            "1    14462\n",
            "Name: count, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#  check null accuracy score\n",
        "\n",
        "null_accuracy = (14630/(14630+14462))\n",
        "\n",
        "print('Null accuracy score: {0:0.4f}'. format(null_accuracy))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1jDFKfNlaiaM",
        "outputId": "28751bcc-8b37-4452-b49d-066d087ddf31"
      },
      "execution_count": 137,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Null accuracy score: 0.5029\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "WpzjhsK5bHo-"
      },
      "execution_count": 138,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "pOymuNgYbiPP"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}