ML_10_Logistic_Regression_Implementation_1.ipynb

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      "name": "ML_10_Logistic_Regression_Implementation_1.ipynb",
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      "cell_type": "markdown",
      "metadata": {
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      "source": [
        "<a href=\"https://colab.research.google.com/github/sandeepbansode/Machine_Learning_Data_Science/blob/main/ML_10_Logistic_Regression_Implementation_1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
      "source": [
        "# **Problem Statement**\n",
        "This dataset contains about 10 years of daily weather observations from numerous Australian weather stations.\n",
        "\n",
        "The target RainTomorrow means: Did it rain the next day? Yes or No.\n",
        "\n",
        "Note: You should exclude the variable Risk-MM when training your binary classification model. If you don't exclude it, you will leak the answers to your model and reduce its predictability."
      ],
      "metadata": {
        "id": "xsHFuI7qDkoL"
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    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "6qW2v9YVs4pI"
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      "outputs": [],
      "source": [
        "import numpy as np # linear algebra\n",
        "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
        "import matplotlib.pyplot as plt # data visualization\n",
        "import seaborn as sns # statistical data visualization\n",
        "%matplotlib inline\n",
        "\n",
        "import warnings\n",
        "\n",
        "warnings.filterwarnings('ignore')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Reading the file\n",
        "df = pd.read_csv(\"/content/drive/MyDrive/Data Science Files/weatherAUS.csv\")"
      ],
      "metadata": {
        "id": "GOa-2APrEMTz"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Exploratory data analysis "
      ],
      "metadata": {
        "id": "fMbSSgTMEoBT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# view dimensions of dataset\n",
        "# We can see that there are 142193 instances and 24 variables in the data set.\n",
        "df.shape"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
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      "execution_count": 18,
      "outputs": [
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    {
      "cell_type": "code",
      "source": [
        "#preview Dataset\n",
        "df"
      ],
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        "id": "-DjwYkE6EdW9",
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              Date Location  MinTemp  MaxTemp  Rainfall  Evaporation  \\\n",
              "0       2008-12-01   Albury     13.4     22.9       0.6          NaN   \n",
              "1       2008-12-02   Albury      7.4     25.1       0.0          NaN   \n",
              "2       2008-12-03   Albury     12.9     25.7       0.0          NaN   \n",
              "3       2008-12-04   Albury      9.2     28.0       0.0          NaN   \n",
              "4       2008-12-05   Albury     17.5     32.3       1.0          NaN   \n",
              "...            ...      ...      ...      ...       ...          ...   \n",
              "145455  2017-06-21    Uluru      2.8     23.4       0.0          NaN   \n",
              "145456  2017-06-22    Uluru      3.6     25.3       0.0          NaN   \n",
              "145457  2017-06-23    Uluru      5.4     26.9       0.0          NaN   \n",
              "145458  2017-06-24    Uluru      7.8     27.0       0.0          NaN   \n",
              "145459  2017-06-25    Uluru     14.9      NaN       0.0          NaN   \n",
              "\n",
              "        Sunshine WindGustDir  WindGustSpeed WindDir9am  ... Humidity9am  \\\n",
              "0            NaN           W           44.0          W  ...        71.0   \n",
              "1            NaN         WNW           44.0        NNW  ...        44.0   \n",
              "2            NaN         WSW           46.0          W  ...        38.0   \n",
              "3            NaN          NE           24.0         SE  ...        45.0   \n",
              "4            NaN           W           41.0        ENE  ...        82.0   \n",
              "...          ...         ...            ...        ...  ...         ...   \n",
              "145455       NaN           E           31.0         SE  ...        51.0   \n",
              "145456       NaN         NNW           22.0         SE  ...        56.0   \n",
              "145457       NaN           N           37.0         SE  ...        53.0   \n",
              "145458       NaN          SE           28.0        SSE  ...        51.0   \n",
              "145459       NaN         NaN            NaN        ESE  ...        62.0   \n",
              "\n",
              "        Humidity3pm  Pressure9am  Pressure3pm  Cloud9am  Cloud3pm  Temp9am  \\\n",
              "0              22.0       1007.7       1007.1       8.0       NaN     16.9   \n",
              "1              25.0       1010.6       1007.8       NaN       NaN     17.2   \n",
              "2              30.0       1007.6       1008.7       NaN       2.0     21.0   \n",
              "3              16.0       1017.6       1012.8       NaN       NaN     18.1   \n",
              "4              33.0       1010.8       1006.0       7.0       8.0     17.8   \n",
              "...             ...          ...          ...       ...       ...      ...   \n",
              "145455         24.0       1024.6       1020.3       NaN       NaN     10.1   \n",
              "145456         21.0       1023.5       1019.1       NaN       NaN     10.9   \n",
              "145457         24.0       1021.0       1016.8       NaN       NaN     12.5   \n",
              "145458         24.0       1019.4       1016.5       3.0       2.0     15.1   \n",
              "145459         36.0       1020.2       1017.9       8.0       8.0     15.0   \n",
              "\n",
              "        Temp3pm  RainToday  RainTomorrow  \n",
              "0          21.8         No            No  \n",
              "1          24.3         No            No  \n",
              "2          23.2         No            No  \n",
              "3          26.5         No            No  \n",
              "4          29.7         No            No  \n",
              "...         ...        ...           ...  \n",
              "145455     22.4         No            No  \n",
              "145456     24.5         No            No  \n",
              "145457     26.1         No            No  \n",
              "145458     26.0         No            No  \n",
              "145459     20.9         No           NaN  \n",
              "\n",
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              "      <th>Date</th>\n",
              "      <th>Location</th>\n",
              "      <th>MinTemp</th>\n",
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              "      <th>0</th>\n",
              "      <td>2008-12-01</td>\n",
              "      <td>Albury</td>\n",
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              "      <th>1</th>\n",
              "      <td>2008-12-02</td>\n",
              "      <td>Albury</td>\n",
              "      <td>7.4</td>\n",
              "      <td>25.1</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>44.0</td>\n",
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              "      <td>17.2</td>\n",
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              "      <td>No</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2008-12-03</td>\n",
              "      <td>Albury</td>\n",
              "      <td>12.9</td>\n",
              "      <td>25.7</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>WSW</td>\n",
              "      <td>46.0</td>\n",
              "      <td>W</td>\n",
              "      <td>...</td>\n",
              "      <td>38.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>1007.6</td>\n",
              "      <td>1008.7</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>23.2</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2008-12-04</td>\n",
              "      <td>Albury</td>\n",
              "      <td>9.2</td>\n",
              "      <td>28.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NE</td>\n",
              "      <td>24.0</td>\n",
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              "      <td>16.0</td>\n",
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              "      <td>18.1</td>\n",
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              "      <td>No</td>\n",
              "      <td>No</td>\n",
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              "      <th>4</th>\n",
              "      <td>2008-12-05</td>\n",
              "      <td>Albury</td>\n",
              "      <td>17.5</td>\n",
              "      <td>32.3</td>\n",
              "      <td>1.0</td>\n",
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              "      <td>W</td>\n",
              "      <td>41.0</td>\n",
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              "      <td>82.0</td>\n",
              "      <td>33.0</td>\n",
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              "      <td>2017-06-21</td>\n",
              "      <td>Uluru</td>\n",
              "      <td>2.8</td>\n",
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              "      <td>2017-06-22</td>\n",
              "      <td>Uluru</td>\n",
              "      <td>3.6</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>2017-06-24</td>\n",
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              "    [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-a1dbe3e8-3ecd-4394-982f-4bebdacfdd96 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-a1dbe3e8-3ecd-4394-982f-4bebdacfdd96');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SKrrm_6WE2VC",
        "outputId": "1608e350-60d1-4f4b-c905-74646a1abf71"
      },
      "execution_count": 20,
      "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": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#smmary of dataset\n",
        "\n",
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9LhsC772E9QG",
        "outputId": "70fa8fb5-4024-4eb6-8f4b-c3390911a786"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 145460 entries, 0 to 145459\n",
            "Data columns (total 23 columns):\n",
            " #   Column         Non-Null Count   Dtype  \n",
            "---  ------         --------------   -----  \n",
            " 0   Date           145460 non-null  object \n",
            " 1   Location       145460 non-null  object \n",
            " 2   MinTemp        143975 non-null  float64\n",
            " 3   MaxTemp        144199 non-null  float64\n",
            " 4   Rainfall       142199 non-null  float64\n",
            " 5   Evaporation    82670 non-null   float64\n",
            " 6   Sunshine       75625 non-null   float64\n",
            " 7   WindGustDir    135134 non-null  object \n",
            " 8   WindGustSpeed  135197 non-null  float64\n",
            " 9   WindDir9am     134894 non-null  object \n",
            " 10  WindDir3pm     141232 non-null  object \n",
            " 11  WindSpeed9am   143693 non-null  float64\n",
            " 12  WindSpeed3pm   142398 non-null  float64\n",
            " 13  Humidity9am    142806 non-null  float64\n",
            " 14  Humidity3pm    140953 non-null  float64\n",
            " 15  Pressure9am    130395 non-null  float64\n",
            " 16  Pressure3pm    130432 non-null  float64\n",
            " 17  Cloud9am       89572 non-null   float64\n",
            " 18  Cloud3pm       86102 non-null   float64\n",
            " 19  Temp9am        143693 non-null  float64\n",
            " 20  Temp3pm        141851 non-null  float64\n",
            " 21  RainToday      142199 non-null  object \n",
            " 22  RainTomorrow   142193 non-null  object \n",
            "dtypes: float64(16), object(7)\n",
            "memory usage: 25.5+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#checking null values in dataset\n",
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kOrVGJWeFAEu",
        "outputId": "a66a8472-b41c-4ff7-ec73-c6b36ca9a861"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Date                 0\n",
              "Location             0\n",
              "MinTemp           1485\n",
              "MaxTemp           1261\n",
              "Rainfall          3261\n",
              "Evaporation      62790\n",
              "Sunshine         69835\n",
              "WindGustDir      10326\n",
              "WindGustSpeed    10263\n",
              "WindDir9am       10566\n",
              "WindDir3pm        4228\n",
              "WindSpeed9am      1767\n",
              "WindSpeed3pm      3062\n",
              "Humidity9am       2654\n",
              "Humidity3pm       4507\n",
              "Pressure9am      15065\n",
              "Pressure3pm      15028\n",
              "Cloud9am         55888\n",
              "Cloud3pm         59358\n",
              "Temp9am           1767\n",
              "Temp3pm           3609\n",
              "RainToday         3261\n",
              "RainTomorrow      3267\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Feature Engineering of Date Variable\n",
        "df['Date'].dtypes\n",
        "\n",
        "# parse the dates, currently coded as strings, into datetime format\n",
        "\n",
        "df['Date'] = pd.to_datetime(df['Date'])"
      ],
      "metadata": {
        "id": "ALuK5C_z4la5"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# extract year from date\n",
        "\n",
        "df['Year'] = df['Date'].dt.year\n",
        "\n",
        "print(df['Year'].head(),'\\n\\n')\n",
        "\n",
        "# extract month from date\n",
        "\n",
        "df['Month'] = df['Date'].dt.month\n",
        "\n",
        "print(df['Month'].head(),'\\n\\n')\n",
        "\n",
        "# extract day from date\n",
        "\n",
        "df['Day'] = df['Date'].dt.day\n",
        "\n",
        "print(df['Day'].head(),'\\n\\n')\n",
        "\n",
        "# again view the summary of dataset\n",
        "\n",
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "30hdFvX44yWn",
        "outputId": "5fa38c69-eabf-4a02-caea-7a7cf25b6f77"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0    2008\n",
            "1    2008\n",
            "2    2008\n",
            "3    2008\n",
            "4    2008\n",
            "Name: Year, dtype: int64 \n",
            "\n",
            "\n",
            "0    12\n",
            "1    12\n",
            "2    12\n",
            "3    12\n",
            "4    12\n",
            "Name: Month, dtype: int64 \n",
            "\n",
            "\n",
            "0    1\n",
            "1    2\n",
            "2    3\n",
            "3    4\n",
            "4    5\n",
            "Name: Day, dtype: int64 \n",
            "\n",
            "\n",
            "<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  int64         \n",
            " 24  Month          145460 non-null  int64         \n",
            " 25  Day            145460 non-null  int64         \n",
            "dtypes: datetime64[ns](1), float64(16), int64(3), object(6)\n",
            "memory usage: 28.9+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# drop the original Date variable\n",
        "\n",
        "df.drop('Date', axis=1, inplace = True)\n",
        "\n",
        "df.head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "AC8zZfir48lr",
        "outputId": "3023f6fb-18f1-4713-b620-4e96d77aa940"
      },
      "execution_count": 25,
      "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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              "\n",
              "  <div id=\"df-26157552-f755-4737-9482-855100fc9f82\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th></th>\n",
              "      <th>Location</th>\n",
              "      <th>MinTemp</th>\n",
              "      <th>MaxTemp</th>\n",
              "      <th>Rainfall</th>\n",
              "      <th>Evaporation</th>\n",
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              "      <th>WindGustSpeed</th>\n",
              "      <th>WindDir9am</th>\n",
              "      <th>WindDir3pm</th>\n",
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              "      <th>RainToday</th>\n",
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              "      <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",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
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              "          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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              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Dropping the null values in dependent variable"
      ],
      "metadata": {
        "id": "0yrbYa6OFRIy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Dropping the null values in dependent variable\n",
        "df1 = df[df['RainTomorrow'].notna()]"
      ],
      "metadata": {
        "id": "f8hzygs0FIXx"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "SF6KCgKvFQYU",
        "outputId": "dbe8f2b7-ecf4-481b-af58-55abfe64b68d"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Location  MinTemp  MaxTemp  Rainfall  Evaporation  Sunshine  \\\n",
              "0        Albury     13.4     22.9       0.6          NaN       NaN   \n",
              "1        Albury      7.4     25.1       0.0          NaN       NaN   \n",
              "2        Albury     12.9     25.7       0.0          NaN       NaN   \n",
              "3        Albury      9.2     28.0       0.0          NaN       NaN   \n",
              "4        Albury     17.5     32.3       1.0          NaN       NaN   \n",
              "...         ...      ...      ...       ...          ...       ...   \n",
              "145454    Uluru      3.5     21.8       0.0          NaN       NaN   \n",
              "145455    Uluru      2.8     23.4       0.0          NaN       NaN   \n",
              "145456    Uluru      3.6     25.3       0.0          NaN       NaN   \n",
              "145457    Uluru      5.4     26.9       0.0          NaN       NaN   \n",
              "145458    Uluru      7.8     27.0       0.0          NaN       NaN   \n",
              "\n",
              "       WindGustDir  WindGustSpeed WindDir9am WindDir3pm  ...  Pressure3pm  \\\n",
              "0                W           44.0          W        WNW  ...       1007.1   \n",
              "1              WNW           44.0        NNW        WSW  ...       1007.8   \n",
              "2              WSW           46.0          W        WSW  ...       1008.7   \n",
              "3               NE           24.0         SE          E  ...       1012.8   \n",
              "4                W           41.0        ENE         NW  ...       1006.0   \n",
              "...            ...            ...        ...        ...  ...          ...   \n",
              "145454           E           31.0        ESE          E  ...       1021.2   \n",
              "145455           E           31.0         SE        ENE  ...       1020.3   \n",
              "145456         NNW           22.0         SE          N  ...       1019.1   \n",
              "145457           N           37.0         SE        WNW  ...       1016.8   \n",
              "145458          SE           28.0        SSE          N  ...       1016.5   \n",
              "\n",
              "        Cloud9am  Cloud3pm  Temp9am  Temp3pm  RainToday  RainTomorrow  Year  \\\n",
              "0            8.0       NaN     16.9     21.8         No            No  2008   \n",
              "1            NaN       NaN     17.2     24.3         No            No  2008   \n",
              "2            NaN       2.0     21.0     23.2         No            No  2008   \n",
              "3            NaN       NaN     18.1     26.5         No            No  2008   \n",
              "4            7.0       8.0     17.8     29.7         No            No  2008   \n",
              "...          ...       ...      ...      ...        ...           ...   ...   \n",
              "145454       NaN       NaN      9.4     20.9         No            No  2017   \n",
              "145455       NaN       NaN     10.1     22.4         No            No  2017   \n",
              "145456       NaN       NaN     10.9     24.5         No            No  2017   \n",
              "145457       NaN       NaN     12.5     26.1         No            No  2017   \n",
              "145458       3.0       2.0     15.1     26.0         No            No  2017   \n",
              "\n",
              "        Month  Day  \n",
              "0          12    1  \n",
              "1          12    2  \n",
              "2          12    3  \n",
              "3          12    4  \n",
              "4          12    5  \n",
              "...       ...  ...  \n",
              "145454      6   20  \n",
              "145455      6   21  \n",
              "145456      6   22  \n",
              "145457      6   23  \n",
              "145458      6   24  \n",
              "\n",
              "[142193 rows x 25 columns]"
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              "      <td>1008.7</td>\n",
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              "      <td>3</td>\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#preview data\n",
        "df1.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h-ciTLdJF92r",
        "outputId": "7ec27004-91a1-4a7e-e357-419decf4ea6a"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 142193 entries, 0 to 145458\n",
            "Data columns (total 25 columns):\n",
            " #   Column         Non-Null Count   Dtype  \n",
            "---  ------         --------------   -----  \n",
            " 0   Location       142193 non-null  object \n",
            " 1   MinTemp        141556 non-null  float64\n",
            " 2   MaxTemp        141871 non-null  float64\n",
            " 3   Rainfall       140787 non-null  float64\n",
            " 4   Evaporation    81350 non-null   float64\n",
            " 5   Sunshine       74377 non-null   float64\n",
            " 6   WindGustDir    132863 non-null  object \n",
            " 7   WindGustSpeed  132923 non-null  float64\n",
            " 8   WindDir9am     132180 non-null  object \n",
            " 9   WindDir3pm     138415 non-null  object \n",
            " 10  WindSpeed9am   140845 non-null  float64\n",
            " 11  WindSpeed3pm   139563 non-null  float64\n",
            " 12  Humidity9am    140419 non-null  float64\n",
            " 13  Humidity3pm    138583 non-null  float64\n",
            " 14  Pressure9am    128179 non-null  float64\n",
            " 15  Pressure3pm    128212 non-null  float64\n",
            " 16  Cloud9am       88536 non-null   float64\n",
            " 17  Cloud3pm       85099 non-null   float64\n",
            " 18  Temp9am        141289 non-null  float64\n",
            " 19  Temp3pm        139467 non-null  float64\n",
            " 20  RainToday      140787 non-null  object \n",
            " 21  RainTomorrow   142193 non-null  object \n",
            " 22  Year           142193 non-null  int64  \n",
            " 23  Month          142193 non-null  int64  \n",
            " 24  Day            142193 non-null  int64  \n",
            "dtypes: float64(16), int64(3), object(6)\n",
            "memory usage: 28.2+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "as we see in our data, there are 7 variables are categorical variables"
      ],
      "metadata": {
        "id": "ek4kBDKqGIKv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# find categorical variables\n",
        "\n",
        "categorical = [var for var in df1.columns if df1[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": "iQW71UQCGgKH",
        "outputId": "ea93977b-9229-4dea-b180-794c3cbc4aa7"
      },
      "execution_count": 29,
      "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": "code",
      "source": [
        "# view the categorical variables\n",
        "\n",
        "df1[categorical].head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "cI-MJAcBHWyj",
        "outputId": "365c35ec-69d9-45dc-dfef-8bdfaa7676e6"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Location WindGustDir WindDir9am WindDir3pm RainToday RainTomorrow\n",
              "0   Albury           W          W        WNW        No           No\n",
              "1   Albury         WNW        NNW        WSW        No           No\n",
              "2   Albury         WSW          W        WSW        No           No\n",
              "3   Albury          NE         SE          E        No           No\n",
              "4   Albury           W        ENE         NW        No           No"
            ],
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              "\n",
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            ]
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# find numerical variables\n",
        "\n",
        "numerical = [var for var in df1.columns if df1[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": "WrR9hY8XG5Pr",
        "outputId": "102ec58f-3fdc-4737-c8f4-8cf41490469b"
      },
      "execution_count": 31,
      "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",
        "df1[numerical].head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 250
        },
        "id": "-wue3-DqHi6R",
        "outputId": "188b1a08-fd8b-4834-92f9-e8cd913e220c"
      },
      "execution_count": 32,
      "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  "
            ],
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              "    .colab-df-convert {\n",
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              "    .colab-df-convert:hover {\n",
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              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
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              "                                                     [key], {});\n",
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              "\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#checking null values in dataframe\n",
        "df1.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CjeZM2YeFXAr",
        "outputId": "393cbc65-7a83-4654-b1d7-9361ff5eef69"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Location             0\n",
              "MinTemp            637\n",
              "MaxTemp            322\n",
              "Rainfall          1406\n",
              "Evaporation      60843\n",
              "Sunshine         67816\n",
              "WindGustDir       9330\n",
              "WindGustSpeed     9270\n",
              "WindDir9am       10013\n",
              "WindDir3pm        3778\n",
              "WindSpeed9am      1348\n",
              "WindSpeed3pm      2630\n",
              "Humidity9am       1774\n",
              "Humidity3pm       3610\n",
              "Pressure9am      14014\n",
              "Pressure3pm      13981\n",
              "Cloud9am         53657\n",
              "Cloud3pm         57094\n",
              "Temp9am            904\n",
              "Temp3pm           2726\n",
              "RainToday         1406\n",
              "RainTomorrow         0\n",
              "Year                 0\n",
              "Month                0\n",
              "Day                  0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Filling the null values**"
      ],
      "metadata": {
        "id": "42vdja4tH7bQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#filling the null values in numerical variable with median\n",
        "df1 = df1.fillna(df1.median())"
      ],
      "metadata": {
        "id": "8EeSqj57H6_Q"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df1.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G7OvlPz4F3my",
        "outputId": "8527ba03-a53e-4a82-a3c0-4d56a812fc0f"
      },
      "execution_count": 35,
      "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       9330\n",
              "WindGustSpeed        0\n",
              "WindDir9am       10013\n",
              "WindDir3pm        3778\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         1406\n",
              "RainTomorrow         0\n",
              "Year                 0\n",
              "Month                0\n",
              "Day                  0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#replacing null values in categorical variables with mode value\n",
        "df1['WindGustDir'].fillna(df1['WindGustDir'].mode()[0], inplace=True)\n",
        "df1['WindDir9am'].fillna(df1['WindDir9am'].mode()[0], inplace=True)\n",
        "df1['WindDir3pm'].fillna(df1['WindDir3pm'].mode()[0], inplace=True)\n",
        "df1['RainToday'].fillna(df1['RainToday'].mode()[0], inplace=True)\n",
        "df1['RainTomorrow'].fillna(df1['RainTomorrow'].mode()[0], inplace=True)"
      ],
      "metadata": {
        "id": "a_AHOHpcIjz5"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df1.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6F3tpL6pIxLj",
        "outputId": "dbd968ed-909b-4d75-e1b8-41e2130c54dd"
      },
      "execution_count": 37,
      "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",
              "RainTomorrow     0\n",
              "Year             0\n",
              "Month            0\n",
              "Day              0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#**Lablel Encoding**\n",
        "Label Encoding refers to converting the labels into a numeric form so as to convert them into the machine-readable form. Machine learning algorithms can then decide in a better way how those labels must be operated. It is an important pre-processing step for the structured dataset in supervised learning."
      ],
      "metadata": {
        "id": "L6lPip9kJfLr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# view the categorical variables\n",
        "\n",
        "df1[categorical].head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "ACTWU2yyI13r",
        "outputId": "d506ac7f-455a-4f41-df92-9d7c46d60077"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Location WindGustDir WindDir9am WindDir3pm RainToday RainTomorrow\n",
              "0   Albury           W          W        WNW        No           No\n",
              "1   Albury         WNW        NNW        WSW        No           No\n",
              "2   Albury         WSW          W        WSW        No           No\n",
              "3   Albury          NE         SE          E        No           No\n",
              "4   Albury           W        ENE         NW        No           No"
            ],
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              "\n",
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              "      <th>Location</th>\n",
              "      <th>WindGustDir</th>\n",
              "      <th>WindDir9am</th>\n",
              "      <th>WindDir3pm</th>\n",
              "      <th>RainToday</th>\n",
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              "      <th>0</th>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Albury</td>\n",
              "      <td>WNW</td>\n",
              "      <td>NNW</td>\n",
              "      <td>WSW</td>\n",
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              "    <tr>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
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              "      <td>SE</td>\n",
              "      <td>E</td>\n",
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              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Albury</td>\n",
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              "      <td>ENE</td>\n",
              "      <td>NW</td>\n",
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              "    .colab-df-convert {\n",
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              "\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\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",
              "            '<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",
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            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Import label encoder\n",
        "from sklearn import preprocessing\n",
        "  \n",
        "# label_encoder object knows how to understand word labels.\n",
        "label_encoder = preprocessing.LabelEncoder()\n",
        "  \n",
        "# Encode labels in column 'Location'.'RainTomorrow','Date','WindDir9am',\t'WindDir3pm',\t'RainToday',\t'RainTomorrow'\n",
        "df1['Location']= label_encoder.fit_transform(df1['Location'])\n",
        "df1['WindGustDir']= label_encoder.fit_transform(df1['WindGustDir'])\n",
        "df1['WindDir9am']= label_encoder.fit_transform(df1['WindDir9am'])\n",
        "df1['WindDir3pm']= label_encoder.fit_transform(df1['WindDir3pm'])\n",
        "df1['RainToday']= label_encoder.fit_transform(df1['RainToday'])\n",
        "df1['RainTomorrow']= label_encoder.fit_transform(df1['RainTomorrow'])"
      ],
      "metadata": {
        "id": "P9C-F-_PJrVU"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#preview the data after label encoding \n",
        "df1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "kZNpwzVDLsxv",
        "outputId": "be3215e9-dd64-4ce6-ff45-f96e094f90b9"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Location  MinTemp  MaxTemp  Rainfall  Evaporation  Sunshine  \\\n",
              "0              2     13.4     22.9       0.6          4.8       8.5   \n",
              "1              2      7.4     25.1       0.0          4.8       8.5   \n",
              "2              2     12.9     25.7       0.0          4.8       8.5   \n",
              "3              2      9.2     28.0       0.0          4.8       8.5   \n",
              "4              2     17.5     32.3       1.0          4.8       8.5   \n",
              "...          ...      ...      ...       ...          ...       ...   \n",
              "145454        41      3.5     21.8       0.0          4.8       8.5   \n",
              "145455        41      2.8     23.4       0.0          4.8       8.5   \n",
              "145456        41      3.6     25.3       0.0          4.8       8.5   \n",
              "145457        41      5.4     26.9       0.0          4.8       8.5   \n",
              "145458        41      7.8     27.0       0.0          4.8       8.5   \n",
              "\n",
              "        WindGustDir  WindGustSpeed  WindDir9am  WindDir3pm  ...  Pressure3pm  \\\n",
              "0                13           44.0          13          14  ...       1007.1   \n",
              "1                14           44.0           6          15  ...       1007.8   \n",
              "2                15           46.0          13          15  ...       1008.7   \n",
              "3                 4           24.0           9           0  ...       1012.8   \n",
              "4                13           41.0           1           7  ...       1006.0   \n",
              "...             ...            ...         ...         ...  ...          ...   \n",
              "145454            0           31.0           2           0  ...       1021.2   \n",
              "145455            0           31.0           9           1  ...       1020.3   \n",
              "145456            6           22.0           9           3  ...       1019.1   \n",
              "145457            3           37.0           9          14  ...       1016.8   \n",
              "145458            9           28.0          10           3  ...       1016.5   \n",
              "\n",
              "        Cloud9am  Cloud3pm  Temp9am  Temp3pm  RainToday  RainTomorrow  Year  \\\n",
              "0            8.0       5.0     16.9     21.8          0             0  2008   \n",
              "1            5.0       5.0     17.2     24.3          0             0  2008   \n",
              "2            5.0       2.0     21.0     23.2          0             0  2008   \n",
              "3            5.0       5.0     18.1     26.5          0             0  2008   \n",
              "4            7.0       8.0     17.8     29.7          0             0  2008   \n",
              "...          ...       ...      ...      ...        ...           ...   ...   \n",
              "145454       5.0       5.0      9.4     20.9          0             0  2017   \n",
              "145455       5.0       5.0     10.1     22.4          0             0  2017   \n",
              "145456       5.0       5.0     10.9     24.5          0             0  2017   \n",
              "145457       5.0       5.0     12.5     26.1          0             0  2017   \n",
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              "\n",
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          "metadata": {},
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      ]
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    {
      "cell_type": "code",
      "source": [
        "df1.info()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "muu1OU3JLu3V",
        "outputId": "ad5b22ba-7dcb-4d8c-a88f-4e39030f5585"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 142193 entries, 0 to 145458\n",
            "Data columns (total 25 columns):\n",
            " #   Column         Non-Null Count   Dtype  \n",
            "---  ------         --------------   -----  \n",
            " 0   Location       142193 non-null  int64  \n",
            " 1   MinTemp        142193 non-null  float64\n",
            " 2   MaxTemp        142193 non-null  float64\n",
            " 3   Rainfall       142193 non-null  float64\n",
            " 4   Evaporation    142193 non-null  float64\n",
            " 5   Sunshine       142193 non-null  float64\n",
            " 6   WindGustDir    142193 non-null  int64  \n",
            " 7   WindGustSpeed  142193 non-null  float64\n",
            " 8   WindDir9am     142193 non-null  int64  \n",
            " 9   WindDir3pm     142193 non-null  int64  \n",
            " 10  WindSpeed9am   142193 non-null  float64\n",
            " 11  WindSpeed3pm   142193 non-null  float64\n",
            " 12  Humidity9am    142193 non-null  float64\n",
            " 13  Humidity3pm    142193 non-null  float64\n",
            " 14  Pressure9am    142193 non-null  float64\n",
            " 15  Pressure3pm    142193 non-null  float64\n",
            " 16  Cloud9am       142193 non-null  float64\n",
            " 17  Cloud3pm       142193 non-null  float64\n",
            " 18  Temp9am        142193 non-null  float64\n",
            " 19  Temp3pm        142193 non-null  float64\n",
            " 20  RainToday      142193 non-null  int64  \n",
            " 21  RainTomorrow   142193 non-null  int64  \n",
            " 22  Year           142193 non-null  int64  \n",
            " 23  Month          142193 non-null  int64  \n",
            " 24  Day            142193 non-null  int64  \n",
            "dtypes: float64(16), int64(9)\n",
            "memory usage: 28.2 MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "as we can see their all categorical variables converted into numerical after Label encoding"
      ],
      "metadata": {
        "id": "etowCeTKMX2z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#here we can there is no null record in our data\n",
        "df1.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Bcv9AUUjMSeH",
        "outputId": "1529198e-17c1-43a7-c494-b63435f55f16"
      },
      "execution_count": 42,
      "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",
              "RainTomorrow     0\n",
              "Year             0\n",
              "Month            0\n",
              "Day              0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Model Building**"
      ],
      "metadata": {
        "id": "UtJjSwWH12pD"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Declare feature vector and target variable"
      ],
      "metadata": {
        "id": "iH7XMvtw5xvx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = df1.drop(['RainTomorrow'], axis=1)\n",
        "\n",
        "y = df1['RainTomorrow']\n",
        "\n",
        "\n",
        "# split X and y into training and testing sets\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)\n",
        "\n",
        "# check the shape of X_train and X_test\n",
        "print(X_train.shape, X_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3j1aq4RN1B5N",
        "outputId": "b021ae24-732d-4443-f712-a16d4773c0c0"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(113754, 24) (28439, 24)\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/"
        },
        "id": "0X0jLQAD52Y0",
        "outputId": "f24dc607-6e4f-4165-969f-bbaad7e3af6d"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(random_state=0, solver='liblinear')"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Predict results**"
      ],
      "metadata": {
        "id": "fulWyJ0l9eZT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_test = logreg.predict(X_test)\n",
        "\n",
        "y_pred_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tBOUb8NF8y3j",
        "outputId": "cf700dbf-4f74-45b4-ce89-126eed3a6753"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, ..., 0, 0, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Check accuracy score**"
      ],
      "metadata": {
        "id": "8P8yerjb-Vi5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "print('Model accuracy score: {0:0.4f}'. format(accuracy_score(y_test, y_pred_test)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wdbw1HhE99dk",
        "outputId": "eae69100-27ff-4031-f284-41f18d58df12"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model accuracy score: 0.8444\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Print the Confusion Matrix and slice it into four pieces\n",
        "\n",
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "cm = confusion_matrix(y_test, y_pred_test)\n",
        "\n",
        "print('Confusion matrix\\n\\n', cm)\n",
        "\n",
        "print('\\nTrue Positives(TP) = ', cm[0,0])\n",
        "\n",
        "print('\\nTrue Negatives(TN) = ', cm[1,1])\n",
        "\n",
        "print('\\nFalse Positives(FP) = ', cm[0,1])\n",
        "\n",
        "print('\\nFalse Negatives(FN) = ', cm[1,0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VntDd2iI-U4L",
        "outputId": "6b0e32a4-a33d-4e59-8992-907e8265acdf"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion matrix\n",
            "\n",
            " [[20874  1193]\n",
            " [ 3232  3140]]\n",
            "\n",
            "True Positives(TP) =  20874\n",
            "\n",
            "True Negatives(TN) =  3140\n",
            "\n",
            "False Positives(FP) =  1193\n",
            "\n",
            "False Negatives(FN) =  3232\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# visualize confusion matrix with seaborn heatmap\n",
        "\n",
        "cm_matrix = pd.DataFrame(data=cm, columns=['Actual Positive:1', 'Actual Negative:0'], \n",
        "                                 index=['Predict Positive:1', 'Predict Negative:0'])\n",
        "\n",
        "sns.heatmap(cm_matrix, annot=True, fmt='d', cmap='YlGnBu')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "ABUm0J_--d53",
        "outputId": "10bc6697-0dd2-4055-a7de-998c09af1991"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc93c1d8950>"
            ]
          },
          "metadata": {},
          "execution_count": 48
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report\n",
        "\n",
        "print(classification_report(y_test, y_pred_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xTWxOnR8-pRG",
        "outputId": "2a050d08-d976-42a4-bac2-45e718668b1a"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.87      0.95      0.90     22067\n",
            "           1       0.72      0.49      0.59      6372\n",
            "\n",
            "    accuracy                           0.84     28439\n",
            "   macro avg       0.80      0.72      0.75     28439\n",
            "weighted avg       0.83      0.84      0.83     28439\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Adjusting the threshold level"
      ],
      "metadata": {
        "id": "WphoM5Hy_ajC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# print the first 10 predicted probabilities of two classes- 0 and 1\n",
        "\n",
        "y_pred_prob = logreg.predict_proba(X_test)[0:10]\n",
        "\n",
        "y_pred_prob"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1E9uyEtQ-3zR",
        "outputId": "cb09a79b-f5e5-4e6e-bb06-f5cc07645001"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.88620387, 0.11379613],\n",
              "       [0.8909896 , 0.1090104 ],\n",
              "       [0.8460829 , 0.1539171 ],\n",
              "       [0.9897594 , 0.0102406 ],\n",
              "       [0.95327054, 0.04672946],\n",
              "       [0.96485024, 0.03514976],\n",
              "       [0.20790001, 0.79209999],\n",
              "       [0.21220004, 0.78779996],\n",
              "       [0.86271451, 0.13728549],\n",
              "       [0.88871074, 0.11128926]])"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Observations\n",
        "In each row, the numbers sum to 1.\n",
        "\n",
        "* There are 2 columns which correspond to 2 classes - 0 and 1.\n",
        "\n",
        "Class 0 - predicted probability that there is no rain tomorrow.\n",
        "\n",
        "Class 1 - predicted probability that there is rain tomorrow.\n",
        "\n",
        "* Importance of predicted probabilities\n",
        "\n",
        "We can rank the observations by probability of rain or no rain.\n",
        "\n",
        "* predict_proba process\n",
        "\n",
        "Predicts the probabilities\n",
        "\n",
        "Choose the class with the highest probability\n",
        "\n",
        "* Classification threshold level: \n",
        "\n",
        "There is a classification threshold level of 0.5.\n",
        "\n",
        "Class 1 - probability of rain is predicted if probability > 0.5.\n",
        "\n",
        "Class 0 - probability of no rain is predicted if probability < 0.5.\n"
      ],
      "metadata": {
        "id": "1ypv6R2P_np-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# store the probabilities in dataframe\n",
        "\n",
        "y_pred_prob_df1 = pd.DataFrame(data=y_pred_prob, columns=['Prob of - No rain tomorrow (0)', 'Prob of - Rain tomorrow (1)'])\n",
        "\n",
        "y_pred_prob_df1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
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        "id": "_usKxJ7w_ZD1",
        "outputId": "d80a9941-9b86-46c7-9f42-0b00d105159d"
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      "execution_count": 52,
      "outputs": [
        {
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            "text/plain": [
              "   Prob of - No rain tomorrow (0)  Prob of - Rain tomorrow (1)\n",
              "0                        0.886204                     0.113796\n",
              "1                        0.890990                     0.109010\n",
              "2                        0.846083                     0.153917\n",
              "3                        0.989759                     0.010241\n",
              "4                        0.953271                     0.046729\n",
              "5                        0.964850                     0.035150\n",
              "6                        0.207900                     0.792100\n",
              "7                        0.212200                     0.787800\n",
              "8                        0.862715                     0.137285\n",
              "9                        0.888711                     0.111289"
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              "      <th>9</th>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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    },
    {
      "cell_type": "markdown",
      "source": [
        "#**Now Creating new dataset DF2**"
      ],
      "metadata": {
        "id": "BPccalXoAKf7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df2=df[df['RainTomorrow'].isna()]"
      ],
      "metadata": {
        "id": "UjNSdsryMEak"
      },
      "execution_count": 62,
      "outputs": []
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      "cell_type": "code",
      "source": [
        "df2"
      ],
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          "height": 488
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        "id": "183SwBsvAVQH",
        "outputId": "c40ddc83-07bd-428b-cdf6-c3522e4e4ca6"
      },
      "execution_count": 63,
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              "       Location  MinTemp  MaxTemp  Rainfall  Evaporation  Sunshine  \\\n",
              "14       Albury      8.4     24.6       0.0          NaN       NaN   \n",
              "283      Albury      2.6      NaN       0.0          NaN       NaN   \n",
              "435      Albury     22.1     35.1       0.0          NaN       NaN   \n",
              "437      Albury     21.5     35.0       0.0          NaN       NaN   \n",
              "443      Albury     15.5     30.6       0.0          NaN       NaN   \n",
              "...         ...      ...      ...       ...          ...       ...   \n",
              "144955    Uluru     22.5     37.7       0.0          NaN       NaN   \n",
              "145091    Uluru     12.3     19.1       0.0          NaN       NaN   \n",
              "145092    Uluru     14.2     18.7       NaN          NaN       NaN   \n",
              "145335    Uluru     13.5      NaN       0.0          NaN       NaN   \n",
              "145459    Uluru     14.9      NaN       0.0          NaN       NaN   \n",
              "\n",
              "       WindGustDir  WindGustSpeed WindDir9am WindDir3pm  ...  Pressure3pm  \\\n",
              "14             NaN            NaN          S        WNW  ...       1008.7   \n",
              "283            NaN            NaN          E        WNW  ...       1017.6   \n",
              "435            NaN            NaN        ENE         SW  ...       1017.8   \n",
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              "...            ...            ...        ...        ...  ...          ...   \n",
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              "145092         NaN            NaN          W        NaN  ...       1014.6   \n",
              "145335         NaN            NaN        ENE         SW  ...       1008.4   \n",
              "145459         NaN            NaN        ESE        ESE  ...       1017.9   \n",
              "\n",
              "        Cloud9am  Cloud3pm  Temp9am  Temp3pm  RainToday  RainTomorrow  Year  \\\n",
              "14           NaN       NaN     15.9     23.5         No           NaN  2008   \n",
              "283          NaN       6.0      9.7     14.3         No           NaN  2009   \n",
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              "437          NaN       8.0     24.2     33.1         No           NaN  2010   \n",
              "443          NaN       NaN     19.4     30.0         No           NaN  2010   \n",
              "...          ...       ...      ...      ...        ...           ...   ...   \n",
              "144955       NaN       NaN     28.8     37.2         No           NaN  2016   \n",
              "145091       8.0       8.0     15.6     18.9         No           NaN  2016   \n",
              "145092       8.0       8.0     16.6     17.5        NaN           NaN  2016   \n",
              "145335       NaN       NaN     25.2     34.4         No           NaN  2017   \n",
              "145459       8.0       8.0     15.0     20.9         No           NaN  2017   \n",
              "\n",
              "        Month  Day  \n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>144955</th>\n",
              "      <td>Uluru</td>\n",
              "      <td>22.5</td>\n",
              "      <td>37.7</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>SE</td>\n",
              "      <td>50.0</td>\n",
              "      <td>SE</td>\n",
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              "      <td>1009.6</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>28.8</td>\n",
              "      <td>37.2</td>\n",
              "      <td>No</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2016</td>\n",
              "      <td>2</td>\n",
              "      <td>7</td>\n",
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              "    <tr>\n",
              "      <th>145091</th>\n",
              "      <td>Uluru</td>\n",
              "      <td>12.3</td>\n",
              "      <td>19.1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>1014.1</td>\n",
              "      <td>8.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>15.6</td>\n",
              "      <td>18.9</td>\n",
              "      <td>No</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2016</td>\n",
              "      <td>6</td>\n",
              "      <td>22</td>\n",
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              "    <tr>\n",
              "      <th>145092</th>\n",
              "      <td>Uluru</td>\n",
              "      <td>14.2</td>\n",
              "      <td>18.7</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>W</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>1014.6</td>\n",
              "      <td>8.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>16.6</td>\n",
              "      <td>17.5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2016</td>\n",
              "      <td>6</td>\n",
              "      <td>23</td>\n",
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              "    <tr>\n",
              "      <th>145335</th>\n",
              "      <td>Uluru</td>\n",
              "      <td>13.5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>1008.4</td>\n",
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              "      <td>25.2</td>\n",
              "      <td>34.4</td>\n",
              "      <td>No</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2017</td>\n",
              "      <td>2</td>\n",
              "      <td>21</td>\n",
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              "    <tr>\n",
              "      <th>145459</th>\n",
              "      <td>Uluru</td>\n",
              "      <td>14.9</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>ESE</td>\n",
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              "      <td>1017.9</td>\n",
              "      <td>8.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>20.9</td>\n",
              "      <td>No</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2017</td>\n",
              "      <td>6</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3267 rows × 25 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0fb107a1-4f0a-457b-a844-bea98720ea1f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
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              "                                                     [key], {});\n",
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              "\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",
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "l5uCtuOqLkaV",
        "outputId": "a074dbe3-4d4a-48ac-e93c-9ae14c3a0922"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    Location  MinTemp  MaxTemp  Rainfall  Evaporation  Sunshine WindGustDir  \\\n",
              "14    Albury      8.4     24.6       0.0          NaN       NaN         NaN   \n",
              "283   Albury      2.6      NaN       0.0          NaN       NaN         NaN   \n",
              "435   Albury     22.1     35.1       0.0          NaN       NaN         NaN   \n",
              "437   Albury     21.5     35.0       0.0          NaN       NaN         NaN   \n",
              "443   Albury     15.5     30.6       0.0          NaN       NaN         NaN   \n",
              "\n",
              "     WindGustSpeed WindDir9am WindDir3pm  ...  Pressure3pm  Cloud9am  \\\n",
              "14             NaN          S        WNW  ...       1008.7       NaN   \n",
              "283            NaN          E        WNW  ...       1017.6       NaN   \n",
              "435            NaN        ENE         SW  ...       1017.8       NaN   \n",
              "437            NaN        SSE          E  ...       1010.3       NaN   \n",
              "443            NaN         SE        SSW  ...       1013.8       NaN   \n",
              "\n",
              "     Cloud3pm  Temp9am  Temp3pm  RainToday  RainTomorrow  Year  Month  Day  \n",
              "14        NaN     15.9     23.5         No           NaN  2008     12   15  \n",
              "283       6.0      9.7     14.3         No           NaN  2009      9   10  \n",
              "435       1.0     26.9     33.5         No           NaN  2010      2    9  \n",
              "437       8.0     24.2     33.1         No           NaN  2010      2   11  \n",
              "443       NaN     19.4     30.0         No           NaN  2010      2   17  \n",
              "\n",
              "[5 rows x 25 columns]"
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              "<p>5 rows × 25 columns</p>\n",
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              "\n",
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              "\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",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a85cebf4-066d-499d-8fc6-a9bf5141e369 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-a85cebf4-066d-499d-8fc6-a9bf5141e369');\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",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OxssLgTWAb2Z",
        "outputId": "09c2298a-ada6-46b9-a35f-03a97c37e46e"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 3267 entries, 14 to 145459\n",
            "Data columns (total 25 columns):\n",
            " #   Column         Non-Null Count  Dtype  \n",
            "---  ------         --------------  -----  \n",
            " 0   Location       3267 non-null   object \n",
            " 1   MinTemp        2419 non-null   float64\n",
            " 2   MaxTemp        2328 non-null   float64\n",
            " 3   Rainfall       1412 non-null   float64\n",
            " 4   Evaporation    1320 non-null   float64\n",
            " 5   Sunshine       1248 non-null   float64\n",
            " 6   WindGustDir    2271 non-null   object \n",
            " 7   WindGustSpeed  2274 non-null   float64\n",
            " 8   WindDir9am     2714 non-null   object \n",
            " 9   WindDir3pm     2817 non-null   object \n",
            " 10  WindSpeed9am   2848 non-null   float64\n",
            " 11  WindSpeed3pm   2835 non-null   float64\n",
            " 12  Humidity9am    2387 non-null   float64\n",
            " 13  Humidity3pm    2370 non-null   float64\n",
            " 14  Pressure9am    2216 non-null   float64\n",
            " 15  Pressure3pm    2220 non-null   float64\n",
            " 16  Cloud9am       1036 non-null   float64\n",
            " 17  Cloud3pm       1003 non-null   float64\n",
            " 18  Temp9am        2404 non-null   float64\n",
            " 19  Temp3pm        2384 non-null   float64\n",
            " 20  RainToday      1412 non-null   object \n",
            " 21  RainTomorrow   0 non-null      object \n",
            " 22  Year           3267 non-null   int64  \n",
            " 23  Month          3267 non-null   int64  \n",
            " 24  Day            3267 non-null   int64  \n",
            "dtypes: float64(16), int64(3), object(6)\n",
            "memory usage: 663.6+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0cfalMkZBGPX",
        "outputId": "d7bee0a3-1262-4fcf-fa28-c25be9cf4d73"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Location            0\n",
              "MinTemp           848\n",
              "MaxTemp           939\n",
              "Rainfall         1855\n",
              "Evaporation      1947\n",
              "Sunshine         2019\n",
              "WindGustDir       996\n",
              "WindGustSpeed     993\n",
              "WindDir9am        553\n",
              "WindDir3pm        450\n",
              "WindSpeed9am      419\n",
              "WindSpeed3pm      432\n",
              "Humidity9am       880\n",
              "Humidity3pm       897\n",
              "Pressure9am      1051\n",
              "Pressure3pm      1047\n",
              "Cloud9am         2231\n",
              "Cloud3pm         2264\n",
              "Temp9am           863\n",
              "Temp3pm           883\n",
              "RainToday        1855\n",
              "RainTomorrow     3267\n",
              "Year                0\n",
              "Month               0\n",
              "Day                 0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Import label encoder\n",
        "from sklearn import preprocessing\n",
        "  \n",
        "# label_encoder object knows how to understand word labels.\n",
        "label_encoder = preprocessing.LabelEncoder()\n",
        "  \n",
        "# Encode labels in column 'Location'.'RainTomorrow','Date','WindDir9am',\t'WindDir3pm',\t'RainToday',\t'RainTomorrow'\n",
        "df2['Location']= label_encoder.fit_transform(df2['Location'])\n",
        "df2['WindGustDir']= label_encoder.fit_transform(df2['WindGustDir'])\n",
        "df2['WindDir9am']= label_encoder.fit_transform(df2['WindDir9am'])\n",
        "df2['WindDir3pm']= label_encoder.fit_transform(df2['WindDir3pm'])\n",
        "df2['RainToday']= label_encoder.fit_transform(df2['RainToday'])\n",
        "df2['RainTomorrow']= label_encoder.fit_transform(df2['RainTomorrow'])"
      ],
      "metadata": {
        "id": "5DmEV20JRhNW"
      },
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#now split the data in x and y"
      ],
      "metadata": {
        "id": "pb8RRlFiBSCf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.utils.validation import indexable\n",
        "X = df2.drop(['RainTomorrow'], axis=1)\n",
        "\n",
        "y = df2['RainTomorrow']\n",
        "\n",
        "print(X.shape)\n",
        "print(y.shape)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BCw5fg7SBPx8",
        "outputId": "10980057-fe56-4aca-92c1-b65173e6a44d"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(3267, 24)\n",
            "(3267,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "4C-jAivTBdUQ",
        "outputId": "db7aeada-b218-4591-c92f-4e785e6a7e5c"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Location  MinTemp  MaxTemp  Rainfall  Evaporation  Sunshine  \\\n",
              "14             2      8.4     24.6       0.0          NaN       NaN   \n",
              "283            2      2.6      NaN       0.0          NaN       NaN   \n",
              "435            2     22.1     35.1       0.0          NaN       NaN   \n",
              "437            2     21.5     35.0       0.0          NaN       NaN   \n",
              "443            2     15.5     30.6       0.0          NaN       NaN   \n",
              "...          ...      ...      ...       ...          ...       ...   \n",
              "144955        38     22.5     37.7       0.0          NaN       NaN   \n",
              "145091        38     12.3     19.1       0.0          NaN       NaN   \n",
              "145092        38     14.2     18.7       NaN          NaN       NaN   \n",
              "145335        38     13.5      NaN       0.0          NaN       NaN   \n",
              "145459        38     14.9      NaN       0.0          NaN       NaN   \n",
              "\n",
              "        WindGustDir  WindGustSpeed  WindDir9am  WindDir3pm  ...  Pressure9am  \\\n",
              "14               16            NaN           8          14  ...       1009.7   \n",
              "283              16            NaN           0          14  ...       1017.8   \n",
              "435              16            NaN           1          12  ...       1020.9   \n",
              "437              16            NaN          10           0  ...       1015.8   \n",
              "443              16            NaN           9          11  ...       1015.9   \n",
              "...             ...            ...         ...         ...  ...          ...   \n",
              "144955            9           50.0           9           2  ...       1012.6   \n",
              "145091           16            NaN          16          16  ...       1016.8   \n",
              "145092           16            NaN          13          16  ...       1015.2   \n",
              "145335           16            NaN           1          12  ...       1013.7   \n",
              "145459           16            NaN           2           2  ...       1020.2   \n",
              "\n",
              "        Pressure3pm  Cloud9am  Cloud3pm  Temp9am  Temp3pm  RainToday  Year  \\\n",
              "14           1008.7       NaN       NaN     15.9     23.5          0  2008   \n",
              "283          1017.6       NaN       6.0      9.7     14.3          0  2009   \n",
              "435          1017.8       NaN       1.0     26.9     33.5          0  2010   \n",
              "437          1010.3       NaN       8.0     24.2     33.1          0  2010   \n",
              "443          1013.8       NaN       NaN     19.4     30.0          0  2010   \n",
              "...             ...       ...       ...      ...      ...        ...   ...   \n",
              "144955       1009.6       NaN       NaN     28.8     37.2          0  2016   \n",
              "145091       1014.1       8.0       8.0     15.6     18.9          0  2016   \n",
              "145092       1014.6       8.0       8.0     16.6     17.5          2  2016   \n",
              "145335       1008.4       NaN       NaN     25.2     34.4          0  2017   \n",
              "145459       1017.9       8.0       8.0     15.0     20.9          0  2017   \n",
              "\n",
              "        Month  Day  \n",
              "14         12   15  \n",
              "283         9   10  \n",
              "435         2    9  \n",
              "437         2   11  \n",
              "443         2   17  \n",
              "...       ...  ...  \n",
              "144955      2    7  \n",
              "145091      6   22  \n",
              "145092      6   23  \n",
              "145335      2   21  \n",
              "145459      6   25  \n",
              "\n",
              "[3267 rows x 24 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-bc3e0d67-7c4e-4485-b3c9-3d5b5858259a\">\n",
              "    <div 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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              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Location</th>\n",
              "      <th>MinTemp</th>\n",
              "      <th>MaxTemp</th>\n",
              "      <th>Rainfall</th>\n",
              "      <th>Evaporation</th>\n",
              "      <th>Sunshine</th>\n",
              "      <th>WindGustDir</th>\n",
              "      <th>WindGustSpeed</th>\n",
              "      <th>WindDir9am</th>\n",
              "      <th>WindDir3pm</th>\n",
              "      <th>...</th>\n",
              "      <th>Pressure9am</th>\n",
              "      <th>Pressure3pm</th>\n",
              "      <th>Cloud9am</th>\n",
              "      <th>Cloud3pm</th>\n",
              "      <th>Temp9am</th>\n",
              "      <th>Temp3pm</th>\n",
              "      <th>RainToday</th>\n",
              "      <th>Year</th>\n",
              "      <th>Month</th>\n",
              "      <th>Day</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>2</td>\n",
              "      <td>8.4</td>\n",
              "      <td>24.6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>16</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8</td>\n",
              "      <td>14</td>\n",
              "      <td>...</td>\n",
              "      <td>1009.7</td>\n",
              "      <td>1008.7</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>15.9</td>\n",
              "      <td>23.5</td>\n",
              "      <td>0</td>\n",
              "      <td>2008</td>\n",
              "      <td>12</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>283</th>\n",
              "      <td>2</td>\n",
              "      <td>2.6</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>16</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>14</td>\n",
              "      <td>...</td>\n",
              "      <td>1017.8</td>\n",
              "      <td>1017.6</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6.0</td>\n",
              "      <td>9.7</td>\n",
              "      <td>14.3</td>\n",
              "      <td>0</td>\n",
              "      <td>2009</td>\n",
              "      <td>9</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>435</th>\n",
              "      <td>2</td>\n",
              "      <td>22.1</td>\n",
              "      <td>35.1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>16</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "      <td>...</td>\n",
              "      <td>1020.9</td>\n",
              "      <td>1017.8</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>26.9</td>\n",
              "      <td>33.5</td>\n",
              "      <td>0</td>\n",
              "      <td>2010</td>\n",
              "      <td>2</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>437</th>\n",
              "      <td>2</td>\n",
              "      <td>21.5</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>16</td>\n",
              "      <td>NaN</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>1015.8</td>\n",
              "      <td>1010.3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8.0</td>\n",
              "      <td>24.2</td>\n",
              "      <td>33.1</td>\n",
              "      <td>0</td>\n",
              "      <td>2010</td>\n",
              "      <td>2</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>443</th>\n",
              "      <td>2</td>\n",
              "      <td>15.5</td>\n",
              "      <td>30.6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>16</td>\n",
              "      <td>NaN</td>\n",
              "      <td>9</td>\n",
              "      <td>11</td>\n",
              "      <td>...</td>\n",
              "      <td>1015.9</td>\n",
              "      <td>1013.8</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>19.4</td>\n",
              "      <td>30.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2010</td>\n",
              "      <td>2</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
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              "      <th>144955</th>\n",
              "      <td>38</td>\n",
              "      <td>22.5</td>\n",
              "      <td>37.7</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>9</td>\n",
              "      <td>50.0</td>\n",
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              "      <td>2</td>\n",
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              "      <td>1012.6</td>\n",
              "      <td>1009.6</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>37.2</td>\n",
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              "      <td>7</td>\n",
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              "    <tr>\n",
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              "      <td>38</td>\n",
              "      <td>12.3</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>16</td>\n",
              "      <td>NaN</td>\n",
              "      <td>16</td>\n",
              "      <td>16</td>\n",
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              "      <td>1014.1</td>\n",
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              "      <td>15.6</td>\n",
              "      <td>18.9</td>\n",
              "      <td>0</td>\n",
              "      <td>2016</td>\n",
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              "      <td>14.2</td>\n",
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              "      <td>16</td>\n",
              "      <td>NaN</td>\n",
              "      <td>13</td>\n",
              "      <td>16</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>16</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "      <td>...</td>\n",
              "      <td>1013.7</td>\n",
              "      <td>1008.4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>25.2</td>\n",
              "      <td>34.4</td>\n",
              "      <td>0</td>\n",
              "      <td>2017</td>\n",
              "      <td>2</td>\n",
              "      <td>21</td>\n",
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              "    <tr>\n",
              "      <th>145459</th>\n",
              "      <td>38</td>\n",
              "      <td>14.9</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>16</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>...</td>\n",
              "      <td>1020.2</td>\n",
              "      <td>1017.9</td>\n",
              "      <td>8.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>20.9</td>\n",
              "      <td>0</td>\n",
              "      <td>2017</td>\n",
              "      <td>6</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3267 rows × 24 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bc3e0d67-7c4e-4485-b3c9-3d5b5858259a')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
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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",
              "    [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-bc3e0d67-7c4e-4485-b3c9-3d5b5858259a 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-bc3e0d67-7c4e-4485-b3c9-3d5b5858259a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rqGRhKwlB6Wt",
        "outputId": "0ec49bb2-ed9b-480b-df8f-4859772936e9"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Location            0\n",
              "MinTemp           848\n",
              "MaxTemp           939\n",
              "Rainfall         1855\n",
              "Evaporation      1947\n",
              "Sunshine         2019\n",
              "WindGustDir         0\n",
              "WindGustSpeed     993\n",
              "WindDir9am          0\n",
              "WindDir3pm          0\n",
              "WindSpeed9am      419\n",
              "WindSpeed3pm      432\n",
              "Humidity9am       880\n",
              "Humidity3pm       897\n",
              "Pressure9am      1051\n",
              "Pressure3pm      1047\n",
              "Cloud9am         2231\n",
              "Cloud3pm         2264\n",
              "Temp9am           863\n",
              "Temp3pm           883\n",
              "RainToday           0\n",
              "Year                0\n",
              "Month               0\n",
              "Day                 0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#now categorial variables\n",
        "#replacing null values in categorical variables with mode value\n",
        "X['WindGustDir'].fillna(X['WindGustDir'].mode()[0], inplace=True)\n",
        "X['WindDir9am'].fillna(X['WindDir9am'].mode()[0], inplace=True)\n",
        "X['WindDir3pm'].fillna(X['WindDir3pm'].mode()[0], inplace=True)\n",
        "X['RainToday'].fillna(X['RainToday'].mode()[0], inplace=True)"
      ],
      "metadata": {
        "id": "4K0-CzUZklRS"
      },
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HnarmPXsklN7",
        "outputId": "84fd0dba-ebd8-4d43-b663-45bfb7b511de"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Location            0\n",
              "MinTemp           848\n",
              "MaxTemp           939\n",
              "Rainfall         1855\n",
              "Evaporation      1947\n",
              "Sunshine         2019\n",
              "WindGustDir         0\n",
              "WindGustSpeed     993\n",
              "WindDir9am          0\n",
              "WindDir3pm          0\n",
              "WindSpeed9am      419\n",
              "WindSpeed3pm      432\n",
              "Humidity9am       880\n",
              "Humidity3pm       897\n",
              "Pressure9am      1051\n",
              "Pressure3pm      1047\n",
              "Cloud9am         2231\n",
              "Cloud3pm         2264\n",
              "Temp9am           863\n",
              "Temp3pm           883\n",
              "RainToday           0\n",
              "Year                0\n",
              "Month               0\n",
              "Day                 0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#filling the null values in numerical variable with median\n",
        "X = X.fillna(X.median())"
      ],
      "metadata": {
        "id": "QGagGDvTCPfC"
      },
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(y.isna().sum())\n",
        "X.isna().sum()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7OpmfuPzCzUR",
        "outputId": "76ae9925-5a34-4c29-cd07-51b2676058a9"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0\n"
          ]
        },
        {
          "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": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "TpXZlrihEMtZ",
        "outputId": "b1ca4e22-e833-46eb-861c-a47344540e2f"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Location  MinTemp  MaxTemp  Rainfall  Evaporation  Sunshine  WindGustDir  \\\n",
              "14          2      8.4     24.6       0.0          4.4       7.0           16   \n",
              "283         2      2.6     22.4       0.0          4.4       7.0           16   \n",
              "435         2     22.1     35.1       0.0          4.4       7.0           16   \n",
              "437         2     21.5     35.0       0.0          4.4       7.0           16   \n",
              "443         2     15.5     30.6       0.0          4.4       7.0           16   \n",
              "\n",
              "     WindGustSpeed  WindDir9am  WindDir3pm  ...  Pressure9am  Pressure3pm  \\\n",
              "14            41.0           8          14  ...       1009.7       1008.7   \n",
              "283           41.0           0          14  ...       1017.8       1017.6   \n",
              "435           41.0           1          12  ...       1020.9       1017.8   \n",
              "437           41.0          10           0  ...       1015.8       1010.3   \n",
              "443           41.0           9          11  ...       1015.9       1013.8   \n",
              "\n",
              "     Cloud9am  Cloud3pm  Temp9am  Temp3pm  RainToday  Year  Month  Day  \n",
              "14        7.0       6.0     15.9     23.5          0  2008     12   15  \n",
              "283       7.0       6.0      9.7     14.3          0  2009      9   10  \n",
              "435       7.0       1.0     26.9     33.5          0  2010      2    9  \n",
              "437       7.0       8.0     24.2     33.1          0  2010      2   11  \n",
              "443       7.0       6.0     19.4     30.0          0  2010      2   17  \n",
              "\n",
              "[5 rows x 24 columns]"
            ],
            "text/html": [
              "\n",
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
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              "        vertical-align: middle;\n",
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              "      <th></th>\n",
              "      <th>Location</th>\n",
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              "      <th>WindDir9am</th>\n",
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              "      <th>...</th>\n",
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              "      <th>Cloud3pm</th>\n",
              "      <th>Temp9am</th>\n",
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              "      <th>Day</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>2</td>\n",
              "      <td>8.4</td>\n",
              "      <td>24.6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.4</td>\n",
              "      <td>7.0</td>\n",
              "      <td>16</td>\n",
              "      <td>41.0</td>\n",
              "      <td>8</td>\n",
              "      <td>14</td>\n",
              "      <td>...</td>\n",
              "      <td>1009.7</td>\n",
              "      <td>1008.7</td>\n",
              "      <td>7.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>15.9</td>\n",
              "      <td>23.5</td>\n",
              "      <td>0</td>\n",
              "      <td>2008</td>\n",
              "      <td>12</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>283</th>\n",
              "      <td>2</td>\n",
              "      <td>2.6</td>\n",
              "      <td>22.4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.4</td>\n",
              "      <td>7.0</td>\n",
              "      <td>16</td>\n",
              "      <td>41.0</td>\n",
              "      <td>0</td>\n",
              "      <td>14</td>\n",
              "      <td>...</td>\n",
              "      <td>1017.8</td>\n",
              "      <td>1017.6</td>\n",
              "      <td>7.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>9.7</td>\n",
              "      <td>14.3</td>\n",
              "      <td>0</td>\n",
              "      <td>2009</td>\n",
              "      <td>9</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>435</th>\n",
              "      <td>2</td>\n",
              "      <td>22.1</td>\n",
              "      <td>35.1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.4</td>\n",
              "      <td>7.0</td>\n",
              "      <td>16</td>\n",
              "      <td>41.0</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "      <td>...</td>\n",
              "      <td>1020.9</td>\n",
              "      <td>1017.8</td>\n",
              "      <td>7.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>26.9</td>\n",
              "      <td>33.5</td>\n",
              "      <td>0</td>\n",
              "      <td>2010</td>\n",
              "      <td>2</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>437</th>\n",
              "      <td>2</td>\n",
              "      <td>21.5</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.4</td>\n",
              "      <td>7.0</td>\n",
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              "      <td>41.0</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>1015.8</td>\n",
              "      <td>1010.3</td>\n",
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              "      <td>33.1</td>\n",
              "      <td>0</td>\n",
              "      <td>2010</td>\n",
              "      <td>2</td>\n",
              "      <td>11</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>9</td>\n",
              "      <td>11</td>\n",
              "      <td>...</td>\n",
              "      <td>1015.9</td>\n",
              "      <td>1013.8</td>\n",
              "      <td>7.0</td>\n",
              "      <td>6.0</td>\n",
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              "      <td>30.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2010</td>\n",
              "      <td>2</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 24 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e80ce62f-2326-4aa4-bd3d-ec8d6dbb89ae')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e80ce62f-2326-4aa4-bd3d-ec8d6dbb89ae 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-e80ce62f-2326-4aa4-bd3d-ec8d6dbb89ae');\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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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#now predict results on past traning model building"
      ],
      "metadata": {
        "id": "pxE8ESVZDsNM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_df2 = logreg.predict(X)\n",
        "\n",
        "y_pred_df2"
      ],
      "metadata": {
        "id": "T5zAw4FAGjAz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ef20648e-9958-43c7-adcf-64e6f0d01600"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, ..., 1, 0, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "print('Model accuracy score: {0:0.4f}'. format(accuracy_score(y, y_pred_df2)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VLy1mRV6DmTd",
        "outputId": "ccfe3b02-4684-429a-d93a-af51914f0261"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model accuracy score: 0.7606\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report\n",
        "\n",
        "print(classification_report(y, y_pred_df2))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HZTNhvOSGyy1",
        "outputId": "dc905361-fa1c-4854-cb0c-38e096f4ab8b"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      0.76      0.86      3267\n",
            "           1       0.00      0.00      0.00         0\n",
            "\n",
            "    accuracy                           0.76      3267\n",
            "   macro avg       0.50      0.38      0.43      3267\n",
            "weighted avg       1.00      0.76      0.86      3267\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2['y_pred_df2'] = y_pred_df2"
      ],
      "metadata": {
        "id": "tIEAWdj3Vg5n"
      },
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df2.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "e6PqZZA1V6f-",
        "outputId": "33ba9832-552c-458b-8890-2e90ab8f0548"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Location  MinTemp  MaxTemp  Rainfall  Evaporation  Sunshine  WindGustDir  \\\n",
              "14          2      8.4     24.6       0.0          NaN       NaN           16   \n",
              "283         2      2.6      NaN       0.0          NaN       NaN           16   \n",
              "435         2     22.1     35.1       0.0          NaN       NaN           16   \n",
              "437         2     21.5     35.0       0.0          NaN       NaN           16   \n",
              "443         2     15.5     30.6       0.0          NaN       NaN           16   \n",
              "\n",
              "     WindGustSpeed  WindDir9am  WindDir3pm  ...  Cloud9am  Cloud3pm  Temp9am  \\\n",
              "14             NaN           8          14  ...       NaN       NaN     15.9   \n",
              "283            NaN           0          14  ...       NaN       6.0      9.7   \n",
              "435            NaN           1          12  ...       NaN       1.0     26.9   \n",
              "437            NaN          10           0  ...       NaN       8.0     24.2   \n",
              "443            NaN           9          11  ...       NaN       NaN     19.4   \n",
              "\n",
              "     Temp3pm  RainToday  RainTomorrow  Year  Month  Day  y_pred_df2  \n",
              "14      23.5          0             0  2008     12   15           0  \n",
              "283     14.3          0             0  2009      9   10           0  \n",
              "435     33.5          0             0  2010      2    9           0  \n",
              "437     33.1          0             0  2010      2   11           0  \n",
              "443     30.0          0             0  2010      2   17           0  \n",
              "\n",
              "[5 rows x 26 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-93a26ca4-13f3-4919-9788-db2f8b9a41fd\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\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>Location</th>\n",
              "      <th>MinTemp</th>\n",
              "      <th>MaxTemp</th>\n",
              "      <th>Rainfall</th>\n",
              "      <th>Evaporation</th>\n",
              "      <th>Sunshine</th>\n",
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              "      <th>WindGustSpeed</th>\n",
              "      <th>WindDir9am</th>\n",
              "      <th>WindDir3pm</th>\n",
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              "      <th>Day</th>\n",
              "      <th>y_pred_df2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>2</td>\n",
              "      <td>8.4</td>\n",
              "      <td>24.6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>16</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8</td>\n",
              "      <td>14</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>15.9</td>\n",
              "      <td>23.5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2008</td>\n",
              "      <td>12</td>\n",
              "      <td>15</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>283</th>\n",
              "      <td>2</td>\n",
              "      <td>2.6</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>16</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>14</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6.0</td>\n",
              "      <td>9.7</td>\n",
              "      <td>14.3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2009</td>\n",
              "      <td>9</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>435</th>\n",
              "      <td>2</td>\n",
              "      <td>22.1</td>\n",
              "      <td>35.1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>16</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>26.9</td>\n",
              "      <td>33.5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2010</td>\n",
              "      <td>2</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>437</th>\n",
              "      <td>2</td>\n",
              "      <td>21.5</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>16</td>\n",
              "      <td>NaN</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8.0</td>\n",
              "      <td>24.2</td>\n",
              "      <td>33.1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2010</td>\n",
              "      <td>2</td>\n",
              "      <td>11</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>443</th>\n",
              "      <td>2</td>\n",
              "      <td>15.5</td>\n",
              "      <td>30.6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>16</td>\n",
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              "      <td>9</td>\n",
              "      <td>11</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>19.4</td>\n",
              "      <td>30.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2010</td>\n",
              "      <td>2</td>\n",
              "      <td>17</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 26 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-93a26ca4-13f3-4919-9788-db2f8b9a41fd')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-93a26ca4-13f3-4919-9788-db2f8b9a41fd 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-93a26ca4-13f3-4919-9788-db2f8b9a41fd');\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",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "Qr3FUkDsjYV8"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}