Weather_AU (EDA and Pre-Processing).ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1a93da73",
   "metadata": {
    "id": "1a93da73"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "#Visualisation Libraries\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "KB_IRxw8stjf",
   "metadata": {
    "colab": {
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       "headers": [
        [
         "content-type",
         "application/javascript"
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       ],
       "ok": true,
       "status": 200,
       "status_text": ""
      }
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    },
    "id": "KB_IRxw8stjf",
    "outputId": "1669fc5e-90e8-4c0d-fda2-b6bb13333ac1"
   },
   "outputs": [],
   "source": [
    "#from google.colab import files\n",
    "#od = files.upload()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "iAiNQCWJtFT6",
   "metadata": {
    "id": "iAiNQCWJtFT6"
   },
   "outputs": [],
   "source": [
    "#import io\n",
    "#of = pd.read_csv(io.BytesIO(od['weatherAUS.csv']))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5e7aed54",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 661
    },
    "id": "5e7aed54",
    "outputId": "20ca8e68-a46a-43c3-afec-1dfebfeebef1"
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Location</th>\n",
       "      <th>MinTemp</th>\n",
       "      <th>MaxTemp</th>\n",
       "      <th>Rainfall</th>\n",
       "      <th>Evaporation</th>\n",
       "      <th>Sunshine</th>\n",
       "      <th>WindGustDir</th>\n",
       "      <th>WindGustSpeed</th>\n",
       "      <th>WindDir9am</th>\n",
       "      <th>...</th>\n",
       "      <th>Humidity9am</th>\n",
       "      <th>Humidity3pm</th>\n",
       "      <th>Pressure9am</th>\n",
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       "      <th>Cloud3pm</th>\n",
       "      <th>Temp9am</th>\n",
       "      <th>Temp3pm</th>\n",
       "      <th>RainToday</th>\n",
       "      <th>RainTomorrow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2008-12-01</td>\n",
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       "      <td>13.4</td>\n",
       "      <td>22.9</td>\n",
       "      <td>0.6</td>\n",
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       "      <td>1007.1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.9</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2008-12-02</td>\n",
       "      <td>Albury</td>\n",
       "      <td>7.4</td>\n",
       "      <td>25.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WNW</td>\n",
       "      <td>44.0</td>\n",
       "      <td>NNW</td>\n",
       "      <td>...</td>\n",
       "      <td>44.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1010.6</td>\n",
       "      <td>1007.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.2</td>\n",
       "      <td>24.3</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2008-12-03</td>\n",
       "      <td>Albury</td>\n",
       "      <td>12.9</td>\n",
       "      <td>25.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WSW</td>\n",
       "      <td>46.0</td>\n",
       "      <td>W</td>\n",
       "      <td>...</td>\n",
       "      <td>38.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1007.6</td>\n",
       "      <td>1008.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>23.2</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2008-12-04</td>\n",
       "      <td>Albury</td>\n",
       "      <td>9.2</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NE</td>\n",
       "      <td>24.0</td>\n",
       "      <td>SE</td>\n",
       "      <td>...</td>\n",
       "      <td>45.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1017.6</td>\n",
       "      <td>1012.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.1</td>\n",
       "      <td>26.5</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2008-12-05</td>\n",
       "      <td>Albury</td>\n",
       "      <td>17.5</td>\n",
       "      <td>32.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>W</td>\n",
       "      <td>41.0</td>\n",
       "      <td>ENE</td>\n",
       "      <td>...</td>\n",
       "      <td>82.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1010.8</td>\n",
       "      <td>1006.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>29.7</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145455</th>\n",
       "      <td>2017-06-21</td>\n",
       "      <td>Uluru</td>\n",
       "      <td>2.8</td>\n",
       "      <td>23.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>E</td>\n",
       "      <td>31.0</td>\n",
       "      <td>SE</td>\n",
       "      <td>...</td>\n",
       "      <td>51.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1024.6</td>\n",
       "      <td>1020.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.1</td>\n",
       "      <td>22.4</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145456</th>\n",
       "      <td>2017-06-22</td>\n",
       "      <td>Uluru</td>\n",
       "      <td>3.6</td>\n",
       "      <td>25.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NNW</td>\n",
       "      <td>22.0</td>\n",
       "      <td>SE</td>\n",
       "      <td>...</td>\n",
       "      <td>56.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1023.5</td>\n",
       "      <td>1019.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.9</td>\n",
       "      <td>24.5</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145457</th>\n",
       "      <td>2017-06-23</td>\n",
       "      <td>Uluru</td>\n",
       "      <td>5.4</td>\n",
       "      <td>26.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>37.0</td>\n",
       "      <td>SE</td>\n",
       "      <td>...</td>\n",
       "      <td>53.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1021.0</td>\n",
       "      <td>1016.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.5</td>\n",
       "      <td>26.1</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145458</th>\n",
       "      <td>2017-06-24</td>\n",
       "      <td>Uluru</td>\n",
       "      <td>7.8</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SE</td>\n",
       "      <td>28.0</td>\n",
       "      <td>SSE</td>\n",
       "      <td>...</td>\n",
       "      <td>51.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1019.4</td>\n",
       "      <td>1016.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>15.1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145459</th>\n",
       "      <td>2017-06-25</td>\n",
       "      <td>Uluru</td>\n",
       "      <td>14.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ESE</td>\n",
       "      <td>...</td>\n",
       "      <td>62.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>1020.2</td>\n",
       "      <td>1017.9</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>20.9</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>145460 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Date Location  MinTemp  MaxTemp  Rainfall  Evaporation  \\\n",
       "0       2008-12-01   Albury     13.4     22.9       0.6          NaN   \n",
       "1       2008-12-02   Albury      7.4     25.1       0.0          NaN   \n",
       "2       2008-12-03   Albury     12.9     25.7       0.0          NaN   \n",
       "3       2008-12-04   Albury      9.2     28.0       0.0          NaN   \n",
       "4       2008-12-05   Albury     17.5     32.3       1.0          NaN   \n",
       "...            ...      ...      ...      ...       ...          ...   \n",
       "145455  2017-06-21    Uluru      2.8     23.4       0.0          NaN   \n",
       "145456  2017-06-22    Uluru      3.6     25.3       0.0          NaN   \n",
       "145457  2017-06-23    Uluru      5.4     26.9       0.0          NaN   \n",
       "145458  2017-06-24    Uluru      7.8     27.0       0.0          NaN   \n",
       "145459  2017-06-25    Uluru     14.9      NaN       0.0          NaN   \n",
       "\n",
       "        Sunshine WindGustDir  WindGustSpeed WindDir9am  ... Humidity9am  \\\n",
       "0            NaN           W           44.0          W  ...        71.0   \n",
       "1            NaN         WNW           44.0        NNW  ...        44.0   \n",
       "2            NaN         WSW           46.0          W  ...        38.0   \n",
       "3            NaN          NE           24.0         SE  ...        45.0   \n",
       "4            NaN           W           41.0        ENE  ...        82.0   \n",
       "...          ...         ...            ...        ...  ...         ...   \n",
       "145455       NaN           E           31.0         SE  ...        51.0   \n",
       "145456       NaN         NNW           22.0         SE  ...        56.0   \n",
       "145457       NaN           N           37.0         SE  ...        53.0   \n",
       "145458       NaN          SE           28.0        SSE  ...        51.0   \n",
       "145459       NaN         NaN            NaN        ESE  ...        62.0   \n",
       "\n",
       "        Humidity3pm  Pressure9am  Pressure3pm  Cloud9am  Cloud3pm  Temp9am  \\\n",
       "0              22.0       1007.7       1007.1       8.0       NaN     16.9   \n",
       "1              25.0       1010.6       1007.8       NaN       NaN     17.2   \n",
       "2              30.0       1007.6       1008.7       NaN       2.0     21.0   \n",
       "3              16.0       1017.6       1012.8       NaN       NaN     18.1   \n",
       "4              33.0       1010.8       1006.0       7.0       8.0     17.8   \n",
       "...             ...          ...          ...       ...       ...      ...   \n",
       "145455         24.0       1024.6       1020.3       NaN       NaN     10.1   \n",
       "145456         21.0       1023.5       1019.1       NaN       NaN     10.9   \n",
       "145457         24.0       1021.0       1016.8       NaN       NaN     12.5   \n",
       "145458         24.0       1019.4       1016.5       3.0       2.0     15.1   \n",
       "145459         36.0       1020.2       1017.9       8.0       8.0     15.0   \n",
       "\n",
       "        Temp3pm  RainToday  RainTomorrow  \n",
       "0          21.8         No            No  \n",
       "1          24.3         No            No  \n",
       "2          23.2         No            No  \n",
       "3          26.5         No            No  \n",
       "4          29.7         No            No  \n",
       "...         ...        ...           ...  \n",
       "145455     22.4         No            No  \n",
       "145456     24.5         No            No  \n",
       "145457     26.1         No            No  \n",
       "145458     26.0         No            No  \n",
       "145459     20.9         No           NaN  \n",
       "\n",
       "[145460 rows x 23 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Read dataset\n",
    "\n",
    "df = pd.read_csv(\"weatherAUS.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8c9fcfc8",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "8c9fcfc8",
    "outputId": "ccc7ce11-0553-4978-c8cd-3d22692518fc"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(145460, 23)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Dimensions of dfset\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7920e976",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7920e976",
    "outputId": "15d3c1a2-9911-4972-b008-8a5f981ad358"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "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"
     ]
    }
   ],
   "source": [
    "#dfset contains both categorical and continuous values\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e683510b",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "e683510b",
    "outputId": "3a68dc3f-c3e4-4fce-ae83-00111c91e827"
   },
   "outputs": [
    {
     "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')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Columns\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "53f59223",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "53f59223",
    "outputId": "0ec0da7d-3a37-4dfe-ec8b-1f884f914c1e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No.of Categorical values : 7\n",
      "Categorical variables in the dfset are : ['Date', 'Location', 'WindGustDir', 'WindDir9am', 'WindDir3pm', 'RainToday', 'RainTomorrow']\n"
     ]
    }
   ],
   "source": [
    "#Finding categorical variables\n",
    "cat_var = [var for var in df.columns if df[var].dtype=='O']\n",
    "print('No.of Categorical values :',len(cat_var))\n",
    "print('Categorical variables in the dfset are :', cat_var)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0b5cd9aa",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "0b5cd9aa",
    "outputId": "98102395-910e-43a9-a8f8-fd527a79e52d"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Location</th>\n",
       "      <th>WindGustDir</th>\n",
       "      <th>WindDir9am</th>\n",
       "      <th>WindDir3pm</th>\n",
       "      <th>RainToday</th>\n",
       "      <th>RainTomorrow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2008-12-01</td>\n",
       "      <td>Albury</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "      <td>WNW</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2008-12-02</td>\n",
       "      <td>Albury</td>\n",
       "      <td>WNW</td>\n",
       "      <td>NNW</td>\n",
       "      <td>WSW</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2008-12-03</td>\n",
       "      <td>Albury</td>\n",
       "      <td>WSW</td>\n",
       "      <td>W</td>\n",
       "      <td>WSW</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2008-12-04</td>\n",
       "      <td>Albury</td>\n",
       "      <td>NE</td>\n",
       "      <td>SE</td>\n",
       "      <td>E</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2008-12-05</td>\n",
       "      <td>Albury</td>\n",
       "      <td>W</td>\n",
       "      <td>ENE</td>\n",
       "      <td>NW</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date Location WindGustDir WindDir9am WindDir3pm RainToday  \\\n",
       "0  2008-12-01   Albury           W          W        WNW        No   \n",
       "1  2008-12-02   Albury         WNW        NNW        WSW        No   \n",
       "2  2008-12-03   Albury         WSW          W        WSW        No   \n",
       "3  2008-12-04   Albury          NE         SE          E        No   \n",
       "4  2008-12-05   Albury           W        ENE         NW        No   \n",
       "\n",
       "  RainTomorrow  \n",
       "0           No  \n",
       "1           No  \n",
       "2           No  \n",
       "3           No  \n",
       "4           No  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Diaplying categorical variables\n",
    "#RainTomorrow is our Target variable\n",
    "df[cat_var].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "048b7116",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "048b7116",
    "outputId": "dfad19c5-d579-4c98-83ea-faf6f07d98b9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Date  -  3436  labels\n",
      "Location  -  49  labels\n",
      "WindGustDir  -  17  labels\n",
      "WindDir9am  -  17  labels\n",
      "WindDir3pm  -  17  labels\n",
      "RainToday  -  3  labels\n",
      "RainTomorrow  -  3  labels\n"
     ]
    }
   ],
   "source": [
    "#Cardinality of Categorical variables\n",
    "for catvar in cat_var:\n",
    "    print(catvar, ' - ', len(df[catvar].unique()), ' labels')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0946dec2",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "0946dec2",
    "outputId": "5b320f2f-cecd-428b-8a5e-47255b3c9224"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date                0\n",
       "Location            0\n",
       "WindGustDir     10326\n",
       "WindDir9am      10566\n",
       "WindDir3pm       4228\n",
       "RainToday        3261\n",
       "RainTomorrow     3267\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Identifying missing values in Categorical variables\n",
    "# 5 Features - windGustDir, WindDir9am, WindDir3pm, RainToday, RainTomorrow contains null values\n",
    "df[cat_var].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "400b122e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a Data Quality Report for Categorical Features\n",
    "\n",
    "desc = ['The date of observation', 'The common name of the location of the weather station', 'The direction of the strongest wind gust in the 24 hours to midnight', 'Direction of the wind at 9am','Direction of the wind at 3pm','1 if precipitation (mm) in the 24 hours to 9am exceeds 1mm, otherwise 0','The amount of next day rain in mm.']\n",
    "\n",
    "dqr_df_cat = pd.DataFrame(columns = ['Feature', 'Desc.', 'Count', '% of Missing', 'Card.', 'Mode', 'Mode Freq.', 'Mode %', '2nd Mode','2nd Mode Freq.','2nd Mode Perc' , 'Notes'])\n",
    "\n",
    "count = 0\n",
    "for col in cat_var:\n",
    "    vals = df[col].value_counts().index.tolist()\n",
    "    dqr_df_cat.loc[len(dqr_df_cat)] = [df[col].name, desc[count], df[col].count(), ((df.shape[0] - df[col].count()) / df.shape[0])*100, len(df[col].unique()), vals[0] ,df[col].value_counts()[0], df[col].value_counts(normalize = True)[0]*100 , vals[1], df[col].value_counts()[1], df[col].value_counts(normalize = True)[1]*100 , '']\n",
    "    count += 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81b5868a",
   "metadata": {},
   "source": [
    "# Data Quality Report for Categorical Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a3be402e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Feature</th>\n",
       "      <th>Desc.</th>\n",
       "      <th>Count</th>\n",
       "      <th>% of Missing</th>\n",
       "      <th>Card.</th>\n",
       "      <th>Mode</th>\n",
       "      <th>Mode Freq.</th>\n",
       "      <th>Mode %</th>\n",
       "      <th>2nd Mode</th>\n",
       "      <th>2nd Mode Freq.</th>\n",
       "      <th>2nd Mode Perc</th>\n",
       "      <th>Notes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Date</td>\n",
       "      <td>The date of observation</td>\n",
       "      <td>145460</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3436</td>\n",
       "      <td>2013-11-12</td>\n",
       "      <td>49</td>\n",
       "      <td>0.033686</td>\n",
       "      <td>2014-09-01</td>\n",
       "      <td>49</td>\n",
       "      <td>0.033686</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Location</td>\n",
       "      <td>The common name of the location of the weather...</td>\n",
       "      <td>145460</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>49</td>\n",
       "      <td>Canberra</td>\n",
       "      <td>3436</td>\n",
       "      <td>2.362161</td>\n",
       "      <td>Sydney</td>\n",
       "      <td>3344</td>\n",
       "      <td>2.298914</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>WindGustDir</td>\n",
       "      <td>The direction of the strongest wind gust in th...</td>\n",
       "      <td>135134</td>\n",
       "      <td>7.098859</td>\n",
       "      <td>17</td>\n",
       "      <td>W</td>\n",
       "      <td>9915</td>\n",
       "      <td>7.337162</td>\n",
       "      <td>SE</td>\n",
       "      <td>9418</td>\n",
       "      <td>6.969379</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>WindDir9am</td>\n",
       "      <td>Direction of the wind at 9am</td>\n",
       "      <td>134894</td>\n",
       "      <td>7.263853</td>\n",
       "      <td>17</td>\n",
       "      <td>N</td>\n",
       "      <td>11758</td>\n",
       "      <td>8.716474</td>\n",
       "      <td>SE</td>\n",
       "      <td>9287</td>\n",
       "      <td>6.884665</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>WindDir3pm</td>\n",
       "      <td>Direction of the wind at 3pm</td>\n",
       "      <td>141232</td>\n",
       "      <td>2.906641</td>\n",
       "      <td>17</td>\n",
       "      <td>SE</td>\n",
       "      <td>10838</td>\n",
       "      <td>7.673898</td>\n",
       "      <td>W</td>\n",
       "      <td>10110</td>\n",
       "      <td>7.158434</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>RainToday</td>\n",
       "      <td>1 if precipitation (mm) in the 24 hours to 9am...</td>\n",
       "      <td>142199</td>\n",
       "      <td>2.241853</td>\n",
       "      <td>3</td>\n",
       "      <td>No</td>\n",
       "      <td>110319</td>\n",
       "      <td>77.580714</td>\n",
       "      <td>Yes</td>\n",
       "      <td>31880</td>\n",
       "      <td>22.419286</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>RainTomorrow</td>\n",
       "      <td>The amount of next day rain in mm.</td>\n",
       "      <td>142193</td>\n",
       "      <td>2.245978</td>\n",
       "      <td>3</td>\n",
       "      <td>No</td>\n",
       "      <td>110316</td>\n",
       "      <td>77.581878</td>\n",
       "      <td>Yes</td>\n",
       "      <td>31877</td>\n",
       "      <td>22.418122</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Feature                                              Desc.   Count  \\\n",
       "0          Date                            The date of observation  145460   \n",
       "1      Location  The common name of the location of the weather...  145460   \n",
       "2   WindGustDir  The direction of the strongest wind gust in th...  135134   \n",
       "3    WindDir9am                       Direction of the wind at 9am  134894   \n",
       "4    WindDir3pm                       Direction of the wind at 3pm  141232   \n",
       "5     RainToday  1 if precipitation (mm) in the 24 hours to 9am...  142199   \n",
       "6  RainTomorrow                 The amount of next day rain in mm.  142193   \n",
       "\n",
       "   % of Missing Card.        Mode Mode Freq.     Mode %    2nd Mode  \\\n",
       "0      0.000000  3436  2013-11-12         49   0.033686  2014-09-01   \n",
       "1      0.000000    49    Canberra       3436   2.362161      Sydney   \n",
       "2      7.098859    17           W       9915   7.337162          SE   \n",
       "3      7.263853    17           N      11758   8.716474          SE   \n",
       "4      2.906641    17          SE      10838   7.673898           W   \n",
       "5      2.241853     3          No     110319  77.580714         Yes   \n",
       "6      2.245978     3          No     110316  77.581878         Yes   \n",
       "\n",
       "  2nd Mode Freq.  2nd Mode Perc Notes  \n",
       "0             49       0.033686        \n",
       "1           3344       2.298914        \n",
       "2           9418       6.969379        \n",
       "3           9287       6.884665        \n",
       "4          10110       7.158434        \n",
       "5          31880      22.419286        \n",
       "6          31877      22.418122        "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dqr_df_cat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "64aa74c6",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "64aa74c6",
    "outputId": "6735ad79-224a-4d1b-c623-0aa7bcf8f0d2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No.of Continuous values : 16\n",
      "Coninuous variables in the dfset are : ['MinTemp', 'MaxTemp', 'Rainfall', 'Evaporation', 'Sunshine', 'WindGustSpeed', 'WindSpeed9am', 'WindSpeed3pm', 'Humidity9am', 'Humidity3pm', 'Pressure9am', 'Pressure3pm', 'Cloud9am', 'Cloud3pm', 'Temp9am', 'Temp3pm']\n"
     ]
    }
   ],
   "source": [
    "#Finding Continuous variables\n",
    "cont_var = [var for var in df.columns if df[var].dtype!='O']\n",
    "print('No.of Continuous values :',len(cont_var))\n",
    "print('Coninuous variables in the dfset are :', cont_var)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "41e42c9c",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 270
    },
    "id": "41e42c9c",
    "outputId": "9338311d-8439-478b-a4da-aba41cbef834"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MinTemp</th>\n",
       "      <th>MaxTemp</th>\n",
       "      <th>Rainfall</th>\n",
       "      <th>Evaporation</th>\n",
       "      <th>Sunshine</th>\n",
       "      <th>WindGustSpeed</th>\n",
       "      <th>WindSpeed9am</th>\n",
       "      <th>WindSpeed3pm</th>\n",
       "      <th>Humidity9am</th>\n",
       "      <th>Humidity3pm</th>\n",
       "      <th>Pressure9am</th>\n",
       "      <th>Pressure3pm</th>\n",
       "      <th>Cloud9am</th>\n",
       "      <th>Cloud3pm</th>\n",
       "      <th>Temp9am</th>\n",
       "      <th>Temp3pm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13.4</td>\n",
       "      <td>22.9</td>\n",
       "      <td>0.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>44.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1007.7</td>\n",
       "      <td>1007.1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.9</td>\n",
       "      <td>21.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.4</td>\n",
       "      <td>25.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>44.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1010.6</td>\n",
       "      <td>1007.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.2</td>\n",
       "      <td>24.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12.9</td>\n",
       "      <td>25.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>46.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1007.6</td>\n",
       "      <td>1008.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>23.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9.2</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1017.6</td>\n",
       "      <td>1012.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.1</td>\n",
       "      <td>26.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17.5</td>\n",
       "      <td>32.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1010.8</td>\n",
       "      <td>1006.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>29.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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  \n",
       "0       1007.1       8.0       NaN     16.9     21.8  \n",
       "1       1007.8       NaN       NaN     17.2     24.3  \n",
       "2       1008.7       NaN       2.0     21.0     23.2  \n",
       "3       1012.8       NaN       NaN     18.1     26.5  \n",
       "4       1006.0       7.0       8.0     17.8     29.7  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Displying Continuous variables\n",
    "df[cont_var].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d111a16f",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "d111a16f",
    "outputId": "8f72d2ad-7609-40e3-cda1-6010d7e94629"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MinTemp           1485\n",
       "MaxTemp           1261\n",
       "Rainfall          3261\n",
       "Evaporation      62790\n",
       "Sunshine         69835\n",
       "WindGustSpeed    10263\n",
       "WindSpeed9am      1767\n",
       "WindSpeed3pm      3062\n",
       "Humidity9am       2654\n",
       "Humidity3pm       4507\n",
       "Pressure9am      15065\n",
       "Pressure3pm      15028\n",
       "Cloud9am         55888\n",
       "Cloud3pm         59358\n",
       "Temp9am           1767\n",
       "Temp3pm           3609\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Identifying missing values in Continuous variables\n",
    "# 5 Features - windGustDir, WindDir9am, WindDir3pm, RainToday, RainTomorrow contains null values\n",
    "df[cont_var].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "baf78b54",
   "metadata": {},
   "outputs": [],
   "source": [
    "dqr_df_cont = pd.DataFrame(columns = ['Feature', 'Desc.', 'Count', '% of Missing', 'Card.', 'Min.', 'Q1', 'Median', 'Q3', 'Max.', 'Mean', 'Std. Dev.', 'Notes'])\n",
    "\n",
    "desc1 =['The minimum temperature in degrees celsius', 'The maximum temperature in degrees celsius','The amount of rainfall recorded for the day in mm','The so-called Class A pan evaporation (mm) in the 24 hours to 9am','The number of hours of bright sunshine in the day.','The speed (km/h) of the strongest wind gust in the 24 hours to midnight','Wind speed (km/hr) averaged over 10 minutes prior to 9am','Wind speed (km/hr) averaged over 10 minutes prior to 3pm','Humidity (percent) at 9am','Humidity (percent) at 3pm','Atmospheric pressure (hpa) reduced to mean sea level at 9am','Atmospheric pressure (hpa) reduced to mean sea level at 3pm','Fraction of sky obscured by cloud at 9am. This is measured in \"oktas\", which are a unit of eigths.','Fraction of sky obscured by cloud (in \"oktas\": eighths) at 3pm.','Temperature (degrees C) at 9am','Temperature (degrees C) at 3pm']\n",
    "\n",
    "count = 0\n",
    "for col in cont_var:\n",
    "    vals = df[col].value_counts().index.tolist()\n",
    "    dqr_df_cont.loc[len(dqr_df_cont)] = [df[col].name, desc1[count], df[col].count(), ((df.shape[0] - df[col].count()) / df.shape[0])*100, len(df[col].unique()), df[col].min() , df[col].describe()['25%'],df[col].describe()['50%'],df[col].describe()['75%'], df[col].max(), df[col].describe()['mean'], df[col].describe()['std'] , '']\n",
    "    count += 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0377b1bc",
   "metadata": {},
   "source": [
    "# Data Quality Report for Continuous Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "cfcb2acc",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Feature</th>\n",
       "      <th>Desc.</th>\n",
       "      <th>Count</th>\n",
       "      <th>% of Missing</th>\n",
       "      <th>Card.</th>\n",
       "      <th>Min.</th>\n",
       "      <th>Q1</th>\n",
       "      <th>Median</th>\n",
       "      <th>Q3</th>\n",
       "      <th>Max.</th>\n",
       "      <th>Mean</th>\n",
       "      <th>Std. Dev.</th>\n",
       "      <th>Notes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MinTemp</td>\n",
       "      <td>The minimum temperature in degrees celsius</td>\n",
       "      <td>143975</td>\n",
       "      <td>1.020899</td>\n",
       "      <td>390</td>\n",
       "      <td>-8.5</td>\n",
       "      <td>7.6</td>\n",
       "      <td>12.0</td>\n",
       "      <td>16.9</td>\n",
       "      <td>33.9</td>\n",
       "      <td>12.194034</td>\n",
       "      <td>6.398495</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MaxTemp</td>\n",
       "      <td>The maximum temperature in degrees celsius</td>\n",
       "      <td>144199</td>\n",
       "      <td>0.866905</td>\n",
       "      <td>506</td>\n",
       "      <td>-4.8</td>\n",
       "      <td>17.9</td>\n",
       "      <td>22.6</td>\n",
       "      <td>28.2</td>\n",
       "      <td>48.1</td>\n",
       "      <td>23.221348</td>\n",
       "      <td>7.119049</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Rainfall</td>\n",
       "      <td>The amount of rainfall recorded for the day in mm</td>\n",
       "      <td>142199</td>\n",
       "      <td>2.241853</td>\n",
       "      <td>682</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>371.0</td>\n",
       "      <td>2.360918</td>\n",
       "      <td>8.478060</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Evaporation</td>\n",
       "      <td>The so-called Class A pan evaporation (mm) in ...</td>\n",
       "      <td>82670</td>\n",
       "      <td>43.166506</td>\n",
       "      <td>359</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.6</td>\n",
       "      <td>4.8</td>\n",
       "      <td>7.4</td>\n",
       "      <td>145.0</td>\n",
       "      <td>5.468232</td>\n",
       "      <td>4.193704</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Sunshine</td>\n",
       "      <td>The number of hours of bright sunshine in the ...</td>\n",
       "      <td>75625</td>\n",
       "      <td>48.009762</td>\n",
       "      <td>146</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>8.4</td>\n",
       "      <td>10.6</td>\n",
       "      <td>14.5</td>\n",
       "      <td>7.611178</td>\n",
       "      <td>3.785483</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>WindGustSpeed</td>\n",
       "      <td>The speed (km/h) of the strongest wind gust in...</td>\n",
       "      <td>135197</td>\n",
       "      <td>7.055548</td>\n",
       "      <td>68</td>\n",
       "      <td>6.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>40.035230</td>\n",
       "      <td>13.607062</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>WindSpeed9am</td>\n",
       "      <td>Wind speed (km/hr) averaged over 10 minutes pr...</td>\n",
       "      <td>143693</td>\n",
       "      <td>1.214767</td>\n",
       "      <td>44</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>14.043426</td>\n",
       "      <td>8.915375</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>WindSpeed3pm</td>\n",
       "      <td>Wind speed (km/hr) averaged over 10 minutes pr...</td>\n",
       "      <td>142398</td>\n",
       "      <td>2.105046</td>\n",
       "      <td>45</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>18.662657</td>\n",
       "      <td>8.809800</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Humidity9am</td>\n",
       "      <td>Humidity (percent) at 9am</td>\n",
       "      <td>142806</td>\n",
       "      <td>1.824557</td>\n",
       "      <td>102</td>\n",
       "      <td>0.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>68.880831</td>\n",
       "      <td>19.029164</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Humidity3pm</td>\n",
       "      <td>Humidity (percent) at 3pm</td>\n",
       "      <td>140953</td>\n",
       "      <td>3.098446</td>\n",
       "      <td>102</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>51.539116</td>\n",
       "      <td>20.795902</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Pressure9am</td>\n",
       "      <td>Atmospheric pressure (hpa) reduced to mean sea...</td>\n",
       "      <td>130395</td>\n",
       "      <td>10.356799</td>\n",
       "      <td>547</td>\n",
       "      <td>980.5</td>\n",
       "      <td>1012.9</td>\n",
       "      <td>1017.6</td>\n",
       "      <td>1022.4</td>\n",
       "      <td>1041.0</td>\n",
       "      <td>1017.649940</td>\n",
       "      <td>7.106530</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Pressure3pm</td>\n",
       "      <td>Atmospheric pressure (hpa) reduced to mean sea...</td>\n",
       "      <td>130432</td>\n",
       "      <td>10.331363</td>\n",
       "      <td>550</td>\n",
       "      <td>977.1</td>\n",
       "      <td>1010.4</td>\n",
       "      <td>1015.2</td>\n",
       "      <td>1020.0</td>\n",
       "      <td>1039.6</td>\n",
       "      <td>1015.255889</td>\n",
       "      <td>7.037414</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Cloud9am</td>\n",
       "      <td>Fraction of sky obscured by cloud at 9am. This...</td>\n",
       "      <td>89572</td>\n",
       "      <td>38.421559</td>\n",
       "      <td>11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.447461</td>\n",
       "      <td>2.887159</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Cloud3pm</td>\n",
       "      <td>Fraction of sky obscured by cloud (in \"oktas\":...</td>\n",
       "      <td>86102</td>\n",
       "      <td>40.807095</td>\n",
       "      <td>11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.509930</td>\n",
       "      <td>2.720357</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Temp9am</td>\n",
       "      <td>Temperature (degrees C) at 9am</td>\n",
       "      <td>143693</td>\n",
       "      <td>1.214767</td>\n",
       "      <td>442</td>\n",
       "      <td>-7.2</td>\n",
       "      <td>12.3</td>\n",
       "      <td>16.7</td>\n",
       "      <td>21.6</td>\n",
       "      <td>40.2</td>\n",
       "      <td>16.990631</td>\n",
       "      <td>6.488753</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Temp3pm</td>\n",
       "      <td>Temperature (degrees C) at 3pm</td>\n",
       "      <td>141851</td>\n",
       "      <td>2.481094</td>\n",
       "      <td>503</td>\n",
       "      <td>-5.4</td>\n",
       "      <td>16.6</td>\n",
       "      <td>21.1</td>\n",
       "      <td>26.4</td>\n",
       "      <td>46.7</td>\n",
       "      <td>21.683390</td>\n",
       "      <td>6.936650</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Feature                                              Desc.   Count  \\\n",
       "0         MinTemp         The minimum temperature in degrees celsius  143975   \n",
       "1         MaxTemp         The maximum temperature in degrees celsius  144199   \n",
       "2        Rainfall  The amount of rainfall recorded for the day in mm  142199   \n",
       "3     Evaporation  The so-called Class A pan evaporation (mm) in ...   82670   \n",
       "4        Sunshine  The number of hours of bright sunshine in the ...   75625   \n",
       "5   WindGustSpeed  The speed (km/h) of the strongest wind gust in...  135197   \n",
       "6    WindSpeed9am  Wind speed (km/hr) averaged over 10 minutes pr...  143693   \n",
       "7    WindSpeed3pm  Wind speed (km/hr) averaged over 10 minutes pr...  142398   \n",
       "8     Humidity9am                          Humidity (percent) at 9am  142806   \n",
       "9     Humidity3pm                          Humidity (percent) at 3pm  140953   \n",
       "10    Pressure9am  Atmospheric pressure (hpa) reduced to mean sea...  130395   \n",
       "11    Pressure3pm  Atmospheric pressure (hpa) reduced to mean sea...  130432   \n",
       "12       Cloud9am  Fraction of sky obscured by cloud at 9am. This...   89572   \n",
       "13       Cloud3pm  Fraction of sky obscured by cloud (in \"oktas\":...   86102   \n",
       "14        Temp9am                     Temperature (degrees C) at 9am  143693   \n",
       "15        Temp3pm                     Temperature (degrees C) at 3pm  141851   \n",
       "\n",
       "    % of Missing Card.   Min.      Q1  Median      Q3    Max.         Mean  \\\n",
       "0       1.020899   390   -8.5     7.6    12.0    16.9    33.9    12.194034   \n",
       "1       0.866905   506   -4.8    17.9    22.6    28.2    48.1    23.221348   \n",
       "2       2.241853   682    0.0     0.0     0.0     0.8   371.0     2.360918   \n",
       "3      43.166506   359    0.0     2.6     4.8     7.4   145.0     5.468232   \n",
       "4      48.009762   146    0.0     4.8     8.4    10.6    14.5     7.611178   \n",
       "5       7.055548    68    6.0    31.0    39.0    48.0   135.0    40.035230   \n",
       "6       1.214767    44    0.0     7.0    13.0    19.0   130.0    14.043426   \n",
       "7       2.105046    45    0.0    13.0    19.0    24.0    87.0    18.662657   \n",
       "8       1.824557   102    0.0    57.0    70.0    83.0   100.0    68.880831   \n",
       "9       3.098446   102    0.0    37.0    52.0    66.0   100.0    51.539116   \n",
       "10     10.356799   547  980.5  1012.9  1017.6  1022.4  1041.0  1017.649940   \n",
       "11     10.331363   550  977.1  1010.4  1015.2  1020.0  1039.6  1015.255889   \n",
       "12     38.421559    11    0.0     1.0     5.0     7.0     9.0     4.447461   \n",
       "13     40.807095    11    0.0     2.0     5.0     7.0     9.0     4.509930   \n",
       "14      1.214767   442   -7.2    12.3    16.7    21.6    40.2    16.990631   \n",
       "15      2.481094   503   -5.4    16.6    21.1    26.4    46.7    21.683390   \n",
       "\n",
       "    Std. Dev. Notes  \n",
       "0    6.398495        \n",
       "1    7.119049        \n",
       "2    8.478060        \n",
       "3    4.193704        \n",
       "4    3.785483        \n",
       "5   13.607062        \n",
       "6    8.915375        \n",
       "7    8.809800        \n",
       "8   19.029164        \n",
       "9   20.795902        \n",
       "10   7.106530        \n",
       "11   7.037414        \n",
       "12   2.887159        \n",
       "13   2.720357        \n",
       "14   6.488753        \n",
       "15   6.936650        "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dqr_df_cont"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc1e7a0d",
   "metadata": {},
   "source": [
    "# Handling of Missing Values and Outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4ca5f2e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date                0\n",
       "Location            0\n",
       "WindGustDir     10326\n",
       "WindDir9am      10566\n",
       "WindDir3pm       4228\n",
       "RainToday        3261\n",
       "RainTomorrow     3267\n",
       "dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking for Missing Values before Imputation in Categorical Features\n",
    "df[cat_var].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "16b01976",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 661
    },
    "id": "16b01976",
    "outputId": "912d2996-fe9c-4e0d-fd0e-21bbe8dd69ea"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Location</th>\n",
       "      <th>MinTemp</th>\n",
       "      <th>MaxTemp</th>\n",
       "      <th>Rainfall</th>\n",
       "      <th>Evaporation</th>\n",
       "      <th>Sunshine</th>\n",
       "      <th>WindGustDir</th>\n",
       "      <th>WindGustSpeed</th>\n",
       "      <th>WindDir9am</th>\n",
       "      <th>...</th>\n",
       "      <th>Humidity9am</th>\n",
       "      <th>Humidity3pm</th>\n",
       "      <th>Pressure9am</th>\n",
       "      <th>Pressure3pm</th>\n",
       "      <th>Cloud9am</th>\n",
       "      <th>Cloud3pm</th>\n",
       "      <th>Temp9am</th>\n",
       "      <th>Temp3pm</th>\n",
       "      <th>RainToday</th>\n",
       "      <th>RainTomorrow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2008-12-01</td>\n",
       "      <td>Albury</td>\n",
       "      <td>13.4</td>\n",
       "      <td>22.9</td>\n",
       "      <td>0.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>W</td>\n",
       "      <td>44.0</td>\n",
       "      <td>W</td>\n",
       "      <td>...</td>\n",
       "      <td>71.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1007.7</td>\n",
       "      <td>1007.1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.9</td>\n",
       "      <td>21.8</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2008-12-02</td>\n",
       "      <td>Albury</td>\n",
       "      <td>7.4</td>\n",
       "      <td>25.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WNW</td>\n",
       "      <td>44.0</td>\n",
       "      <td>NNW</td>\n",
       "      <td>...</td>\n",
       "      <td>44.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1010.6</td>\n",
       "      <td>1007.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.2</td>\n",
       "      <td>24.3</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2008-12-03</td>\n",
       "      <td>Albury</td>\n",
       "      <td>12.9</td>\n",
       "      <td>25.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WSW</td>\n",
       "      <td>46.0</td>\n",
       "      <td>W</td>\n",
       "      <td>...</td>\n",
       "      <td>38.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1007.6</td>\n",
       "      <td>1008.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>23.2</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2008-12-04</td>\n",
       "      <td>Albury</td>\n",
       "      <td>9.2</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NE</td>\n",
       "      <td>24.0</td>\n",
       "      <td>SE</td>\n",
       "      <td>...</td>\n",
       "      <td>45.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1017.6</td>\n",
       "      <td>1012.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.1</td>\n",
       "      <td>26.5</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2008-12-05</td>\n",
       "      <td>Albury</td>\n",
       "      <td>17.5</td>\n",
       "      <td>32.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>W</td>\n",
       "      <td>41.0</td>\n",
       "      <td>ENE</td>\n",
       "      <td>...</td>\n",
       "      <td>82.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1010.8</td>\n",
       "      <td>1006.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>29.7</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145455</th>\n",
       "      <td>2017-06-21</td>\n",
       "      <td>Uluru</td>\n",
       "      <td>2.8</td>\n",
       "      <td>23.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>E</td>\n",
       "      <td>31.0</td>\n",
       "      <td>SE</td>\n",
       "      <td>...</td>\n",
       "      <td>51.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1024.6</td>\n",
       "      <td>1020.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.1</td>\n",
       "      <td>22.4</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145456</th>\n",
       "      <td>2017-06-22</td>\n",
       "      <td>Uluru</td>\n",
       "      <td>3.6</td>\n",
       "      <td>25.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NNW</td>\n",
       "      <td>22.0</td>\n",
       "      <td>SE</td>\n",
       "      <td>...</td>\n",
       "      <td>56.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1023.5</td>\n",
       "      <td>1019.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.9</td>\n",
       "      <td>24.5</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145457</th>\n",
       "      <td>2017-06-23</td>\n",
       "      <td>Uluru</td>\n",
       "      <td>5.4</td>\n",
       "      <td>26.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>37.0</td>\n",
       "      <td>SE</td>\n",
       "      <td>...</td>\n",
       "      <td>53.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1021.0</td>\n",
       "      <td>1016.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.5</td>\n",
       "      <td>26.1</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145458</th>\n",
       "      <td>2017-06-24</td>\n",
       "      <td>Uluru</td>\n",
       "      <td>7.8</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SE</td>\n",
       "      <td>28.0</td>\n",
       "      <td>SSE</td>\n",
       "      <td>...</td>\n",
       "      <td>51.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1019.4</td>\n",
       "      <td>1016.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>15.1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145459</th>\n",
       "      <td>2017-06-25</td>\n",
       "      <td>Uluru</td>\n",
       "      <td>14.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>W</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ESE</td>\n",
       "      <td>...</td>\n",
       "      <td>62.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>1020.2</td>\n",
       "      <td>1017.9</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>20.9</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>145460 rows × 23 columns</p>\n",
       "</div>"
      ],
      "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           W            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            No  \n",
       "\n",
       "[145460 rows x 23 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Missing value Imputation in Categorical Features using Mode (Mode Imputation)\n",
    "df['WindGustDir']= df['WindGustDir'].fillna( df['WindGustDir'].mode()[0])\n",
    "df['WindDir9am']= df['WindDir9am'].fillna( df['WindDir9am'].mode()[0])\n",
    "df['WindDir3pm']= df['WindDir3pm'].fillna( df['WindDir3pm'].mode()[0])\n",
    "df['RainToday']= df['RainToday'].fillna( df['RainToday'].mode()[0])\n",
    "df['RainTomorrow']= df['RainTomorrow'].fillna( df['RainTomorrow'].mode()[0])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c15f9049",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "c15f9049",
    "outputId": "71534ecc-df28-40a2-8898-41091e236a60"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date            0\n",
       "Location        0\n",
       "WindGustDir     0\n",
       "WindDir9am      0\n",
       "WindDir3pm      0\n",
       "RainToday       0\n",
       "RainTomorrow    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking for Missing Values after Imputation in Categorical Features\n",
    "df[cat_var].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "07c4e291",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MinTemp           1485\n",
       "MaxTemp           1261\n",
       "Rainfall          3261\n",
       "Evaporation      62790\n",
       "Sunshine         69835\n",
       "WindGustSpeed    10263\n",
       "WindSpeed9am      1767\n",
       "WindSpeed3pm      3062\n",
       "Humidity9am       2654\n",
       "Humidity3pm       4507\n",
       "Pressure9am      15065\n",
       "Pressure3pm      15028\n",
       "Cloud9am         55888\n",
       "Cloud3pm         59358\n",
       "Temp9am           1767\n",
       "Temp3pm           3609\n",
       "dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking for Missing Values before Imputation in Continuous Features\n",
    "df[cont_var].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "8ad369eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Imputing Missing Values in Continuous Variables with Median (Median Imputation)\n",
    "\n",
    "for col in cont_var:\n",
    "    col_median=df[col].median()\n",
    "    df[col].fillna(col_median, inplace=True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "cdda6d1e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MinTemp          0\n",
       "MaxTemp          0\n",
       "Rainfall         0\n",
       "Evaporation      0\n",
       "Sunshine         0\n",
       "WindGustSpeed    0\n",
       "WindSpeed9am     0\n",
       "WindSpeed3pm     0\n",
       "Humidity9am      0\n",
       "Humidity3pm      0\n",
       "Pressure9am      0\n",
       "Pressure3pm      0\n",
       "Cloud9am         0\n",
       "Cloud3pm         0\n",
       "Temp9am          0\n",
       "Temp3pm          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking for Missing Values before Imputation in Continuous Features\n",
    "df[cont_var].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "DohVhGOmqOS5",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "DohVhGOmqOS5",
    "outputId": "fd88dbc8-9c55-4c82-8e82-659771e194fc"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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g6Tiqx+geDywHPiZpRil/CbCa6hnvi8r+iIjooX6f2joFeMD298YpswK4xvYO2w8BI8AySbOBWbZvs23gKuC0rrc4IiL20O9EspLdz4EHeJukeyRdLumIEpsDPFIrM1pic8r22HhERPRQ3xKJpGcBrwM+W0KXAL8GLAa2AB9uFW1T3ePE233WaknDkoa3bdvWpNkRETFGP0ckrwbuLkvVY/tx27ts/xL4BLCslBsF5tXqzQUeK/G5beJ7sX2p7aW2lw4NDU1yNyIiprd+JpIzqZ3WKnMeLa8HNpbttcBKSYdIWkg1qX6n7S3Adkknlau1VgE39KbpERHRMqHnkUw2Sc8G/hB4Sy38AUmLqU5PPdzaZ3uTpOuAe4GdwHm2d5U65wJXUD0//sbyioiIHupLIrH9c+CoMbGzxim/BljTJj7M7ic3RkREH/T7qq2IiBhwSSQREdFIEklERDSSRBIREY0kkURERCNJJBER0UgSSURENJJEEhERjSSRREREI0kkERHRSBJJREQ0kkQSERGNJJFEREQjSSQREdFIEklERDSSRBIREY0kkURERCN9SSSSHpa0QdJ6ScMldqSkmyTdX74eUSt/gaQRSZslnVqLLynHGZF0cXl2e0RE9FA/RyS/b3ux7aXl/fnAzbYXATeX90g6DlgJHA8sBz4maUapcwmwGlhUXst72P6IiGBqndpaAVxZtq8ETqvFr7G9w/ZDwAiwTNJsYJbt22wbuKpWJyIieqRficTA1yStk7S6xF5gewtA+fr8Ep8DPFKrO1pic8r22PheJK2WNCxpeNu2bZPYjYiI6Fci+R3bLwFeDZwn6eRxyrab9/A48b2D9qW2l9peOjQ0tP+tjYg9zJ47H0lIYvbc+f1uTvTZzH58qO3Hytetkr4ALAMelzTb9pZy2mprKT4KzKtVnws8VuJz28Qjost+8OgjHPOeLwHwvQtf2+fWRL/1fEQi6TmSntfaBl4FbATWAmeXYmcDN5TttcBKSYdIWkg1qX5nOf21XdJJ5WqtVbU6EdNaRgzRS/0YkbwA+EK5Uncm8Bnb/yTpLuA6SecA3wfOALC9SdJ1wL3ATuA827vKsc4FrgAOA24sr4hpr9GIYcYzaV1J/5/mzGPL6Pcnu3lxkOl5IrH9IPCbbeI/Ak7pUGcNsKZNfBg4YbLbGDGt7fpFTlvFfplKl/9GRMQASiKJmEYydxLd0JertiKiP/Z77qQ2XzLjWYey6+mnutm8GFAZkURMVyVJjLtEXZkvOeY9X2LX00/9x/b+ykjo4JZEEjEAGv0i7pQwakmikdrxO7WtNRI65j1f4gePPtK2TAyunNqKGAATOSU1e+789r+ku30VVq7ymvYyIok4SNT/6u+bCYxO4uCTEUnEAOs4CumXjE6mpSSSiEFTu5IKmLq/uMe0Mw5eObUVMWgma5K82walndFYEklERDSSRBIRvZUJ+YNO5kgipqgpN5E+WTIhf9DJiCRiipoSl/N2W0YnB4Ukkojon9qEfH30lSVVBktObUVMEQftqawDkEf5DpYkkogeqyeM+hMI6788YRr+As19JwOrH89snyfp65Luk7RJ0ttL/H2SHpW0vrxeU6tzgaQRSZslnVqLL5G0oey7WPkujAGwxwKGP/jBvlfgnS5y38nA6seIZCfwbtt3S3oesE7STWXfRbY/VC8s6ThgJXA88KvAP0t6UXlu+yXAauB24CvAcvLc9hgkuYIpDgI9H5HY3mL77rK9HbgPmDNOlRXANbZ32H4IGAGWSZoNzLJ9m20DVwGndbf1ERExVl+v2pK0ADgRuKOE3ibpHkmXSzqixOYA9RnI0RKbU7bHxtt9zmpJw5KGt23bNpldiIiY9vqWSCQ9F7geeIftn1Cdpvo1YDGwBfhwq2ib6h4nvnfQvtT2UttLh4aGmjY9Inqpdq9JLgeemvpy1ZakZ1IlkU/b/jyA7cdr+z8BtGbcRoF5tepzgcdKfG6beMSUk0t7G6jNI0Hmkqaifly1JeAy4D7bH6nFZ9eKvR7YWLbXAislHSJpIbAIuNP2FmC7pJPKMVcBN/SkExH7aVrcpR7TVj9GJL8DnAVskLS+xP4SOFPSYqrTUw8DbwGwvUnSdcC9VFd8nVeu2AI4F7gCOIzqaq1csRVTRkYhXVK736R+H070T88Tie1v0X5+4yvj1FkDrGkTHwZOmLzWRUye3J3dJR0ume50o2d0X+5sj4jBNShPizzIZdHGiBhcuRt+SkgiiWiovlLtzEMOy5InMe3k1FZEQ2PnQnJ6JaabjEgiIqKRJJKIOPjkyYs9lUQSEQef+pMXa0v1J6l0RxJJxAGoT7DHFJfH+XZdJtsjDkBuNhxQue+kKzIiiZigjEIOArnvpCsyIokYx9j1svIXbMTeMiKJGKM+8siqvdNErvJqJIkkYowkj2koV3k1kkQSEVGXpLLfkkgiyER6dJCkMiFJJDFtZS4k9kuSSkdJJHHQ67Q6b5JHHLAOSaX+/TWdEszAJxJJyyVtljQi6fx+tyf6ZyIJY9fTTyV5xOSqJZX699d0GrUMdCKRNAP4v8CrgeOonvt+XH9bFd3QKUkkYcSUNY1GLQOdSIBlwIjtB20/DVwDrOhzm6adifyS77Q90XKdkkQSRgyECYxaJvJzMFUTj2z3uw0HTNLpwHLb/6O8Pwt4me23jSm3Glhd3r4Y2NzThsLRwA97/JmTLX2YGga9D4Pefpi+fTjG9lC7HYO+REq7azX3yoy2LwUu7X5z2pM0bHtpvz5/MqQPU8Og92HQ2w/pQzuDfmprFJhXez8XeKxPbYmImJYGPZHcBSyStFDSs4CVwNo+tykiYloZ6FNbtndKehvwVWAGcLntTX1uVjt9O602idKHqWHQ+zDo7Yf0YS8DPdkeERH9N+intiIios+SSCIiopEkkkkg6e2SNkraJOkdJbZY0u2S1ksalrSsVv6CsqTLZkmn9q3hNR368JuSbpO0QdIXJc2qle97HyRdLmmrpI212JGSbpJ0f/l6xL7aLGlJ6eOIpIvVwyWA96cPko6S9HVJP5X00THHGZQ+/KGkdaWt6yS9st992M/2Lys/0+slfUfS6/vd/v3tQ23//PK99OeN+2A7rwYv4ARgI/BsqosX/hlYBHwNeHUp8xrgG2X7OOA7wCHAQuABYMYU7cNdwO+VMm8G/nYq9QE4GXgJsLEW+wBwftk+H7hwX20G7gR+i+q+pBtb/29TsA/PAX4XeCvw0THHGZQ+nAj8au377tF+92E/2/9sYGbZng1srb0fiP+D2v7rgc8Cf970/yAjkuZ+Hbjd9s9t7wS+Cbye6sbI1l/wh7P7/pYVwDW2d9h+CBihWuqlnzr14cXAraXMTcAby/aU6IPtW4EnxoRXAFeW7SuB02rxvdosaTYwy/Ztrn6SrqrV6br96YPtn9n+FvBUvfCA9eHbtls/C5uAQyUd0s8+7Gf7Wz8jAIdSboAepP8DAEmnAQ9S/R+0YgfchySS5jYCJ5fTDs+mGn3MA94BfFDSI8CHgAtK+TnAI7X6oyXWT536sBF4XSlzBrtv/pyKfWh5ge0tAOXr80u8U5vnlO2x8X7q1IdOBrUPbwS+bXsHU68PHdsv6WWSNgEbgLeWxDLV2g8d+iDpOcB7gL8ZU/6A+5BE0pDt+4ALqf5i/yeq0yc7gXOBd9qeB7wTuKxUmdCyLr00Th/eDJwnaR3wPODpUmXK9WECOrV5EPsy1sD1QdLxVN9zb2mF2hSbkn2wfYft44GXAhdIOpQBaj9VArnI9k/HxA+4D0kkk8D2ZbZfYvtkquHl/cDZwOdLkc+y+9TPlFzWpV0fbH/X9qtsLwGupppXgCnah+LxMkRvDdW3lninNo+W7bHxfurUh04Gqg+S5gJfAFbZrn9PTaU+7PP/oPwB9jOquZ6p1n7o3IeXAR+Q9DDVmZO/VHVj9wH3IYlkEkhqDRnnA2+g+qX7GPB7pcgrqZILVEu4rCznhRdSTWrf2dsW761dH2qxZwD/E/h4KT4l+1CspUrilK831OJ7tbkM+bdLOqlcobKqVqdfOvWhrUHqg6RfAb4MXGD7X1uFp2AfOrV/oaSZZfsYqnnEh6dg+6FDH2y/3PYC2wuAvwf+zvZHG/WhV1cVHMwv4F+Ae6lOCZ1SYr8LrCuxO4AltfJ/RfXX/WZ6eGXHAfTh7cC/ldf7KSshTJU+UCXsLcAvqP6aOgc4CriZKnHfDBy5rzYDS6nmgx4APlrv5xTsw8NUI8aflvLHDVIfqP4g+RmwvvZ6fj/7sJ/tP4tqgno9cDdw2iB+H9XqvY89r9o6oD5kiZSIiGgkp7YiIqKRJJKIiGgkiSQiIhpJIomIiEaSSCIiopEkkogaSbvKyq4bJX22LBkz5Yy3Cm1Er+Xy34gaST+1/dyy/Wlgne2P1PbPsL2rR22Z6d0LBI7d92zgaVePm55Ndf/Pr3YqH9FNGZFEdPYvwLGSXqHqOSCfATZImiHpg5LuknSPpLdAtQyFpFtrI5qXl7JXlPcbJL2zlP2GpKVl++iyXAWS/riMhL4IfE3Sc8qzJu6S9G1JK6DzKrTlGD+V9GFJd0u6WdJQ7TMvKm28T9JLJX1e1fMq/k9v/knjYDSz3w2ImIrKMhivplrEEqq10k6w/ZCk1cCTtl8q6RDgXyV9jWppma/aXiNpBtWzKxYDc2yfUI77KxP4+N8CfsP2E5L+DrjF9ptL3Tsl/bPtn0l6GXA5cAxwVi2xPAe42/a7Jf0v4L3A28q+p22fLOntVMtfLKG6U/4BSRfZ/tGB/YvFdJYRScSeDpO0HhgGvs/uVZvvdPUcE4BXAatKuTuolqJoPQjsv0t6H/CfbW+neubDCyX9g6TlwE8m0IabbLeeLfEq4PzyWd+gGn3Mh46r0AL8Eri2bP8/quV6WtaWrxuATba3uFrG/UH2XNQyYsIyIonY07/bXlwPVOvX8bN6CPgz218dW1nSycB/AT4l6YO2r5L0m8CpwHnAm6iW59/J7j/kDh1zmLGf9Ubbmzs12PZ9klqr0A63K1Lb3lG+/rK23Xqf3wdxQDIiidh/XwXOlfRMAEkvKnMZxwBbbX+CaiTzEklHA8+wfT3w11SPQ4Vq8cUlZfv0fXzWn5XVWJF0YvnadhXaUucZtWP+V+BbzbobMb78BRKx//4RWADcXX7Bb6N6JOkrgL+Q9Auq1XlXUT1h7pNlKX7Y/aTMDwHXSToLuGWcz/pbqqW+7ymf9TDwWqrTVeeXz/ol8Ke2f1jq/Aw4XtUDyZ4E/qhZdyPGl8t/Iw4y9UuYI3ohp7YiIqKRjEgiIqKRjEgiIqKRJJKIiGgkiSQiIhpJIomIiEaSSCIiopH/DxVMsquKts+QAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizing Continuous Features to check for Outliers\n",
    "for x in df[cont_var]:\n",
    "  plt.hist(df[x], bins = 100, edgecolor='black')\n",
    "  plt.xlabel(x)\n",
    "  plt.ylabel(\"Counts\")\n",
    "  plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c0b4cbf3",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "c0b4cbf3",
    "outputId": "d3589ec5-6ce5-49fb-d766-f1265912edfb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        MinTemp   MaxTemp  Rainfall  Evaporation  Sunshine  WindGustSpeed  \\\n",
      "count  145460.0  145460.0  145460.0     145460.0  145460.0       145460.0   \n",
      "mean       12.0      23.0       2.0          5.0       8.0           40.0   \n",
      "std         6.0       7.0       8.0          3.0       3.0           13.0   \n",
      "min        -8.0      -5.0       0.0          0.0       0.0            6.0   \n",
      "25%         8.0      18.0       0.0          4.0       8.0           31.0   \n",
      "50%        12.0      23.0       0.0          5.0       8.0           39.0   \n",
      "75%        17.0      28.0       1.0          5.0       9.0           46.0   \n",
      "max        34.0      48.0     371.0        145.0      14.0          135.0   \n",
      "\n",
      "       WindSpeed9am  WindSpeed3pm  Humidity9am  Humidity3pm  Pressure9am  \\\n",
      "count      145460.0      145460.0     145460.0     145460.0     145460.0   \n",
      "mean           14.0          19.0         69.0         52.0       1018.0   \n",
      "std             9.0           9.0         19.0         20.0          7.0   \n",
      "min             0.0           0.0          0.0          0.0        980.0   \n",
      "25%             7.0          13.0         57.0         37.0       1014.0   \n",
      "50%            13.0          19.0         70.0         52.0       1018.0   \n",
      "75%            19.0          24.0         83.0         65.0       1022.0   \n",
      "max           130.0          87.0        100.0        100.0       1041.0   \n",
      "\n",
      "       Pressure3pm  Cloud9am  Cloud3pm   Temp9am   Temp3pm  \n",
      "count     145460.0  145460.0  145460.0  145460.0  145460.0  \n",
      "mean        1015.0       5.0       5.0      17.0      22.0  \n",
      "std            7.0       2.0       2.0       6.0       7.0  \n",
      "min          977.0       0.0       0.0      -7.0      -5.0  \n",
      "25%         1011.0       3.0       4.0      12.0      17.0  \n",
      "50%         1015.0       5.0       5.0      17.0      21.0  \n",
      "75%         1019.0       6.0       6.0      22.0      26.0  \n",
      "max         1040.0       9.0       9.0      40.0      47.0   2\n"
     ]
    }
   ],
   "source": [
    "# Checking for Outliers\n",
    "print(round(df[cont_var].describe()),2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "2d58efae",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 610
    },
    "id": "2d58efae",
    "outputId": "54e74a7c-3a8d-4035-a0e3-c97f6c89f8d1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'WindSpeed3pm')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualizing outliers\n",
    "plt.figure(figsize=(12,12))\n",
    "plt.subplot(2, 2, 1)\n",
    "fig = df.boxplot(column='Rainfall')\n",
    "fig.set_title('')\n",
    "fig.set_ylabel('Rainfall')\n",
    "\n",
    "plt.subplot(2, 2, 2)\n",
    "fig = df.boxplot(column='WindSpeed9am')\n",
    "fig.set_title('')\n",
    "fig.set_ylabel('WindSpeed9am')\n",
    "\n",
    "plt.subplot(2, 2, 3)\n",
    "fig = df.boxplot(column='Evaporation')\n",
    "fig.set_title('')\n",
    "fig.set_ylabel('Evaporation')\n",
    "\n",
    "plt.subplot(2, 2, 4)\n",
    "fig = df.boxplot(column='WindSpeed3pm')\n",
    "fig.set_title('')\n",
    "fig.set_ylabel('WindSpeed3pm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "db0ea904",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "db0ea904",
    "outputId": "21db380f-3520-430d-f5e5-543b47cbe3c0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 145460 entries, 0 to 145459\n",
      "Data columns (total 4 columns):\n",
      " #   Column        Non-Null Count   Dtype  \n",
      "---  ------        --------------   -----  \n",
      " 0   Rainfall      145460 non-null  float64\n",
      " 1   Evaporation   145460 non-null  float64\n",
      " 2   WindSpeed9am  145460 non-null  float64\n",
      " 3   WindSpeed3pm  145460 non-null  float64\n",
      "dtypes: float64(4)\n",
      "memory usage: 4.4 MB\n"
     ]
    }
   ],
   "source": [
    "df_continuous=df[[\"Rainfall\",\"Evaporation\",\"WindSpeed9am\",\"WindSpeed3pm\"]]\n",
    "df_continuous.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "c6c9ec1e",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "c6c9ec1e",
    "outputId": "ae9550df-93bb-474f-81ab-3404f0c0a225"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rainfall IQR range is: -1.7999999999999998 and 2.4\n",
      "Evaporation IQR range is: 0.39999999999999947 and 8.8\n",
      "WindSpeed9am IQR range is: 0.39999999999999947 and 55.0\n",
      "WindSpeed3pm IQR range is: 0.39999999999999947 and 57.0\n"
     ]
    }
   ],
   "source": [
    "#Identifying the Outliers\n",
    "IQR = df.Rainfall.quantile(0.75) - df.Rainfall.quantile(0.25)\n",
    "Low_bound = df.Rainfall.quantile(0.25) - (IQR * 3)\n",
    "Upper_bound = df.Rainfall.quantile(0.75) + (IQR * 3)\n",
    "print('Rainfall IQR range is: {lowerboundary} and {upperboundary}'.format(lowerboundary=Low_bound, upperboundary=Upper_bound))\n",
    "\n",
    "IQR = df.Evaporation.quantile(0.75) - df.Evaporation.quantile(0.25)\n",
    "Low_bound = df.Evaporation.quantile(0.25) - (IQR * 3)\n",
    "Upper_bound = df.Evaporation.quantile(0.75) + (IQR * 3)\n",
    "print('Evaporation IQR range is: {lowerboundary} and {upperboundary}'.format(lowerboundary=Low_bound, upperboundary=Upper_bound))\n",
    "\n",
    "IQR = df.WindSpeed9am.quantile(0.75) - df.WindSpeed9am.quantile(0.25)\n",
    "Lowe_bound = df.WindSpeed9am.quantile(0.25) - (IQR * 3)\n",
    "Upper_bound = df.WindSpeed9am.quantile(0.75) + (IQR * 3)\n",
    "print('WindSpeed9am IQR range is: {lowerboundary} and {upperboundary}'.format(lowerboundary=Low_bound, upperboundary=Upper_bound))\n",
    "\n",
    "IQR = df.WindSpeed3pm.quantile(0.75) - df.WindSpeed3pm.quantile(0.25)\n",
    "Lower_bound = df.WindSpeed3pm.quantile(0.25) - (IQR * 3)\n",
    "Upper_bound = df.WindSpeed3pm.quantile(0.75) + (IQR * 3)\n",
    "print('WindSpeed3pm IQR range is: {lowerboundary} and {upperboundary}'.format(lowerboundary=Low_bound, upperboundary=Upper_bound))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "qCcIe-Ro2IvN",
   "metadata": {
    "id": "qCcIe-Ro2IvN"
   },
   "outputs": [],
   "source": [
    "# Clamping the Outliers to the Upper and Lower bounds as calculated by IQR method above\n",
    "df['Rainfall'].clip(upper = 3.2, inplace = True)\n",
    "df['Rainfall'].clip(lower = -2.4000000000000004, inplace = True)\n",
    "\n",
    "df['Evaporation'].clip(upper = 21.800000000000004, inplace = True)\n",
    "df['Evaporation'].clip(lower = -11.800000000000002, inplace = True)\n",
    "\n",
    "df['WindSpeed9am'].clip(upper = 55.0, inplace = True)\n",
    "df['WindSpeed9am'].clip(lower = -11.800000000000002, inplace = True)\n",
    "\n",
    "df['WindSpeed3pm'].clip(upper = 57.0, inplace = True)\n",
    "df['WindSpeed3pm'].clip(lower = -11.800000000000002, inplace = True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "xXUec7dl4Zu6",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "xXUec7dl4Zu6",
    "outputId": "87189dc7-3087-4717-b9f0-7202c95a823d"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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IGpB0q6R9a/FFklaXdccq12XG38bHn2iRtIYuR0SMxoQXGNu32l5oeyGwCHgEOLesPqa1zvaFAJJ2AZYBuwJLgOMkTSvbHw+sABaU15KJO5OIiBhJpy+R7QPcbvuuEbZZCpxu+zHbdwADwO6SZgFb215l28ApwP6NZxwREaPS6QKzjCefMQPwMUk3SDpR0rYlNhuoX5sZLLHZZXlofBOSVkjql9S/bt268cs+IiKG1bECI+lZwDuB75fQ8cDLgIXAWuDLrU3b7O4R4psG7ZW2F9tePHPmzKeTdkREjFInWzBvB64r09Fg+37bG23/BfgmsHvZbhCYU9uvD1hT4n1t4tHl6vfKZHRaxOQ12kcmN+FAapfHJM2yvba8fRdwY1k+H/iepK8AL6HqzL/G9kZJ6yXtAVwNHAR8bcKyjy1Wn+sMMt9ZxGTVkQIj6TnAW4GP1ML/Imkh1WWuO1vrbN8k6UzgZmADcKjtjWWfQ4CTqJ5Nc1F5RUREF+hIgbH9CLD9kNgHR9j+KOCoNvF+npzlOZ6G+hT9ERHjodOjyKJLZIqXiBhvKTAREdGIFJiIiGhECkxERDQiBSY6LzM2R0xKnbwPJjqsa0aOlRmbIffEREwmacFMYRk5FhFNSoGJiIhGpMBEREQjUmAiIqIRKTAREdGIFJiIiGhECkxERDQiBSYiIhqRAhMREY1IgYmIiEakwERERCNSYKK7DDPx5ay+uZkQM6LHdKTASLpT0mpJ10vqL7HtJF0i6bbyddva9kdIGpB0q6R9a/FF5TgDko6VpE6cT4yjMvHljodd8JSJOOvzpnXFBJ0RsVmdbMG8yfZC24vL+8OBS20vAC4t75G0C7AM2BVYAhwnaVrZ53hgBbCgvJZMYP4RETGCbrpEthQ4uSyfDOxfi59u+zHbdwADwO6SZgFb215l28AptX0iIqLDOlVgDFws6VpJK0rsRbbXApSvLyzx2UD9mshgic0uy0Pjm5C0QlK/pP5169aN42lER+QBZRE9oVMFZi/brwPeDhwq6Y0jbNuuX8UjxDcN2ittL7a9eObMmWPPNrrLMP00GQgQ0V06UmBsrylfHwDOBXYH7i+XvShfHyibDwJzarv3AWtKvK9NPIaYKr94MxAgortMeIGR9FxJz28tA28DbgTOB5aXzZYD55Xl84FlkmZImk/VmX9NuYy2XtIeZfTYQbV9oia/eCOiE6Z34DNfBJxbRhRPB75n+8eSfgGcKelg4G7gPQC2b5J0JnAzsAE41PbGcqxDgJOArYCLyisiIrrAhBcY278FXtMm/ntgn2H2OQo4qk28H9htvHOMiIinr5uGKUdExCSSAtMjpkpHfURMHp3og4kt0OqoB7jr6P06nE1ExOalBTOJpJUTEd0kLZhJJK2ciOgmacFEREQjUmAiIqIRKTAxOWVCzIiOSx9MTE5lQkxIf1REp6QFExERjUiBiYiIRqTAxJSSe4UiJk76YGJKyb1CERMnLZiIiGhECkxERDQiBSYiIhqRAhMREY1IgYmIiEZMeIGRNEfS5ZJukXSTpE+U+Bck3Svp+vJ6R22fIyQNSLpV0r61+CJJq8u6YyVpos+n0+rDbiMiukknhilvAD5j+zpJzweulXRJWXeM7S/VN5a0C7AM2BV4CfDvkl5ueyNwPLACuAq4EFgCXDRB59EVMuw2IrrVhLdgbK+1fV1ZXg/cAsweYZelwOm2H7N9BzAA7C5pFrC17VW2DZwC7N9s9jGpZELMiEZ1tA9G0jzgtcDVJfQxSTdIOlHStiU2G7intttgic0uy0PjEaNTJsTc8bALuO/eeza/fUSMSccKjKTnAWcDn7T9MNXlrpcBC4G1wJdbm7bZ3SPE233WCkn9kvrXrVv3dFOPySitmYhx15ECI+mZVMXlVNvnANi+3/ZG238BvgnsXjYfBObUdu8D1pR4X5v4JmyvtL3Y9uKZM2eO78nE5JDWTMS468QoMgEnALfY/kotPqu22buAG8vy+cAySTMkzQcWANfYXgusl7RHOeZBwHkTchIREbFZnRhFthfwQWC1pOtL7B+AAyUtpLrMdSfwEQDbN0k6E7iZagTaoWUEGcAhwEnAVlSjx6bUCLKIiG424QXG9k9p339y4Qj7HAUc1SbeD+w2ftlFRMR4yZ38ESPI82MitlyeBxMxgtzIGrHl0oKJGK0MZY4Yk7RgIkarDGWGtGYiRiMtmIiIaEQKTERENCIFJiIiGpEC02UyLLb35P8sor108neZDIvtPfk/i2gvLZiI8ZShzBFPSAsmYjxlKHPEE9KCiYiIRqTAREyA+kCA6TO2ymW0mBJSYDokI4+mltZAgB0Pu4CNf350VA83y/dI9Lr0wXRIRh4F8MSgAIAXz57D2sG7n1iV75HodSkwEZ2UQQExieUS2QSqX/KIGJNhhj/nMlp0s7RgJlAuecQWG6alk++p6GZpwURMFmnlRJfp+QIjaYmkWyUNSDq80/lEdExp5QwdnVYfwXbfffdluHRMmJ4uMJKmAf8HeDuwC3CgpF06mVP+WoyuVitCww2Xzj07MV56usAAuwMDtn9r+8/A6cDSiU6i/gP5lL8WR7jHIaJbjeaendEUoeG2Gev2Qwtb/ojrHbLd6Ry2mKQDgCW2/1t5/0Hgv9j+2JDtVgArytudgd8Dv5vIXMfRDvRu7tDb+Sf3zkjunTE09x1tzxzLAXp9FFm78b6bVEzbK4GVT+wk9dte3GRiTenl3KG380/unZHcO2M8cu/1S2SDwJza+z5gTYdyiYiIml4vML8AFkiaL+lZwDLg/A7nFBER9PglMtsbJH0M+AkwDTjR9k2j2HXl5jfpWr2cO/R2/sm9M5J7Zzzt3Hu6kz8iIrpXr18ii4iILpUCExERjZhyBaaXppaRNEfS5ZJukXSTpE+U+HaSLpF0W/m6badzHY6kaZJ+KemC8r4ncpf0AklnSfp1+fd/fQ/l/qny/XKjpNMkPbubc5d0oqQHJN1Yiw2br6Qjys/vrZL27UzWT+TSLvd/Ld83N0g6V9ILauu6Ovfaur+XZEk71GJjzn1KFZhunFpmMzYAn7H9SmAP4NCS7+HApbYXAJeW993qE8Attfe9kvu/AT+2/QrgNVTn0PW5S5oN/B2w2PZuVINfltHduZ8ELBkSa5tv+f5fBuxa9jmu/Fx3yklsmvslwG62Xw38BjgCeiZ3JM0B3grcXYttUe5TqsDQJVPLjJbttbavK8vrqX7JzabK+eSy2cnA/h1JcDMk9QF/A3yrFu763CVtDbwROAHA9p9t/yc9kHsxHdhK0nTgOVT3hnVt7ravBB4cEh4u36XA6bYfs30HMED1c90R7XK3fbHtDeXtVVT350EP5F4cA/wPnnrT+hblPtUKzGygPkHYYIl1PUnzgNcCVwMvsr0WqiIEvLCDqY3kq1TfqH+pxXoh95cC64Bvl8t735L0XHogd9v3Al+i+utzLfCQ7YvpgdyHGC7fXvsZ/lvgorLc9blLeidwr+1fDVm1RblPtQIzqqlluo2k5wFnA5+0/XCn8xkNSfsBD9i+ttO5bIHpwOuA422/Fvgj3XVJaVilr2IpMB94CfBcSR/obFbjqmd+hiUdSXWZ+9RWqM1mXZO7pOcARwL/2G51m9hmc59qBabnppaR9Eyq4nKq7XNK+H5Js8r6WcADncpvBHsB75R0J9WlyDdL+i69kfsgMGj76vL+LKqC0wu5vwW4w/Y6248D5wB70hu51w2Xb0/8DEtaDuwHvN9P3mzY7bm/jOoPk1+Vn9s+4DpJL2YLc59qBaanppaRJKp+gFtsf6W26nxgeVleDpw30bltju0jbPfZnkf173yZ7Q/QG7nfB9wjaecS2ge4mR7InerS2B6SnlO+f/ah6rvrhdzrhsv3fGCZpBmS5gMLgGs6kN+wJC0BDgPeafuR2qquzt32atsvtD2v/NwOAq8rPw9blrvtKfUC3kE1suN24MhO57OZXP+aqhl6A3B9eb0D2J5qZM1t5et2nc51M+exN3BBWe6J3IGFQH/5t/8BsG0P5f5PwK+BG4HvADO6OXfgNKr+osfLL7WDR8qX6jLO7cCtwNu7MPcBqv6K1s/sN3ol9yHr7wR2eDq5Z6qYiIhoxFS7RBYRERMkBSYiIhqRAhMREY1IgYmIiEakwERERCNSYGJKkXSMpE/W3v9E0rdq778s6R81xpm2JZ0k6YCyPF3S/y4zAV9fXkduYb57S9qz9n5nSf9RjnmLpEafmFg+/4ImPyMmrxSYmGp+TnVnO5KeAexANUNsy57AT2x/8Wl8xv+imqblVbYXAm8AnrmFx9q75NRyLHCM7YWuZtn+2tPIM6JRKTAx1fyMJ39h70p1M+J6SdtKmgG8EniNpK/DEy2TYyX9XNJva60USfq6pJsl/YgyGWOZz+nDwMdtPwrVTNi2v1DWzxvy7JC/l9Ra93fleDdIOr1McPpR4FOlxfIGYBbVTXGUY68u+35I0nmSflye1/H52md8QNI15Rj/tzXNuqS3SVol6TpJ3y9z3rWemfRrST8F3j1u//Ix5UzvdAIRE8n2GkkbJM2lKjSrqGaFfT3wENWd+38estssqlkVXkE1ZcZZwLuAnYFXAS+imkrmRGAn4G5Xj1cYq8OB+bYfk/QC2/8p6RvA/7P9Jagu8QGXSfo5cDHwbVePEoBq+vTdgEeAX5TC90fgfcBeth+XdBzwfkkXAp8D3mL7j5IOAz4t6V+AbwJvproj/YwtOI8IIC2YmJparZhWgVlVe//zNtv/wPZfbN9MVUygel7MabY32l4DXNbugyT919JyuEfVg5xGcgNwapn9eEO7DWx/m6qV9X2qy2dXlZYXwCW2f2/7T1STXP411Vxki6gKzvXl/UupHmC3C/CzEl8O7EhVRO+wfZuraT6+u5mcI4aVAhNTUasf5lVUl8iuomrB7ElVfIZ6rLZcn7a83TxLA8BcSc+HqiCUfpiHqJ4uuYGn/tw9u7b8N1RPXF0EXKvqgWGbsL3G9om2l5bj7TZMPi75nlz6bBba3rlcrhNVQWrFd7F98AjnFTFmKTAxFf2Mair1B0sL5EHgBVRFZtUoj3El1eyy08p08m8CcDV77gnA1yU9G554VPezyn73Ay+UtH1peexXtnkGMMf25VQPaXsB8DxgPfD81oeW/pFnluUXU00KeW9Z/VZVz7LfiuoJkD+jmijyAEmtPqLtJO1IVVT3krRTiT9H0supJsmcL+ll5ZgHjvLfI2IT6YOJqWg11eix7w2JPc/276pZ7jfrXKp+itVUs3NfUVt3JPA/gRslrQf+RPXY3zWlH+SfqZ5MegfVL3SoWjfflbQNVevimNIH80PgLElLgY8DbwP+TdKjZb/P2r6v5PxTqtmTdwK+Z7sfQNLngItLEXscONT2VZI+BJxWu8T2Odu/kbQC+JGk35VjtlpIEWOS2ZQjJoFSLBbb/linc4loySWyiIhoRFowERHRiLRgIiKiESkwERHRiBSYiIhoRApMREQ0IgUmIiIa8f8BB8JXv0/KXqMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizing Continuous Features after clamping the Outliers\n",
    "for x in df[cont_var]:\n",
    "  plt.hist(df[x], bins = 100, edgecolor='black')\n",
    "  plt.xlabel(x)\n",
    "  plt.ylabel(\"Counts\")\n",
    "  plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d74d78c9",
   "metadata": {},
   "source": [
    "# Data Normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "ee993c7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Location</th>\n",
       "      <th>WindGustDir</th>\n",
       "      <th>WindDir9am</th>\n",
       "      <th>WindDir3pm</th>\n",
       "      <th>RainToday</th>\n",
       "      <th>RainTomorrow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2008-12-01</td>\n",
       "      <td>Albury</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "      <td>WNW</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2008-12-02</td>\n",
       "      <td>Albury</td>\n",
       "      <td>WNW</td>\n",
       "      <td>NNW</td>\n",
       "      <td>WSW</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2008-12-03</td>\n",
       "      <td>Albury</td>\n",
       "      <td>WSW</td>\n",
       "      <td>W</td>\n",
       "      <td>WSW</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2008-12-04</td>\n",
       "      <td>Albury</td>\n",
       "      <td>NE</td>\n",
       "      <td>SE</td>\n",
       "      <td>E</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2008-12-05</td>\n",
       "      <td>Albury</td>\n",
       "      <td>W</td>\n",
       "      <td>ENE</td>\n",
       "      <td>NW</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145455</th>\n",
       "      <td>2017-06-21</td>\n",
       "      <td>Uluru</td>\n",
       "      <td>E</td>\n",
       "      <td>SE</td>\n",
       "      <td>ENE</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145456</th>\n",
       "      <td>2017-06-22</td>\n",
       "      <td>Uluru</td>\n",
       "      <td>NNW</td>\n",
       "      <td>SE</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145457</th>\n",
       "      <td>2017-06-23</td>\n",
       "      <td>Uluru</td>\n",
       "      <td>N</td>\n",
       "      <td>SE</td>\n",
       "      <td>WNW</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145458</th>\n",
       "      <td>2017-06-24</td>\n",
       "      <td>Uluru</td>\n",
       "      <td>SE</td>\n",
       "      <td>SSE</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145459</th>\n",
       "      <td>2017-06-25</td>\n",
       "      <td>Uluru</td>\n",
       "      <td>W</td>\n",
       "      <td>ESE</td>\n",
       "      <td>ESE</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>145460 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Date Location WindGustDir WindDir9am WindDir3pm RainToday  \\\n",
       "0       2008-12-01   Albury           W          W        WNW        No   \n",
       "1       2008-12-02   Albury         WNW        NNW        WSW        No   \n",
       "2       2008-12-03   Albury         WSW          W        WSW        No   \n",
       "3       2008-12-04   Albury          NE         SE          E        No   \n",
       "4       2008-12-05   Albury           W        ENE         NW        No   \n",
       "...            ...      ...         ...        ...        ...       ...   \n",
       "145455  2017-06-21    Uluru           E         SE        ENE        No   \n",
       "145456  2017-06-22    Uluru         NNW         SE          N        No   \n",
       "145457  2017-06-23    Uluru           N         SE        WNW        No   \n",
       "145458  2017-06-24    Uluru          SE        SSE          N        No   \n",
       "145459  2017-06-25    Uluru           W        ESE        ESE        No   \n",
       "\n",
       "       RainTomorrow  \n",
       "0                No  \n",
       "1                No  \n",
       "2                No  \n",
       "3                No  \n",
       "4                No  \n",
       "...             ...  \n",
       "145455           No  \n",
       "145456           No  \n",
       "145457           No  \n",
       "145458           No  \n",
       "145459           No  \n",
       "\n",
       "[145460 rows x 7 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[cat_var]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "TM2SRvQ18Hzr",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 270
    },
    "id": "TM2SRvQ18Hzr",
    "outputId": "fd402c2a-ec7d-498a-bdba-cbe42bfbe268"
   },
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MinTemp</th>\n",
       "      <th>MaxTemp</th>\n",
       "      <th>Rainfall</th>\n",
       "      <th>Evaporation</th>\n",
       "      <th>Sunshine</th>\n",
       "      <th>WindGustSpeed</th>\n",
       "      <th>WindSpeed9am</th>\n",
       "      <th>WindSpeed3pm</th>\n",
       "      <th>Humidity9am</th>\n",
       "      <th>Humidity3pm</th>\n",
       "      <th>Pressure9am</th>\n",
       "      <th>Pressure3pm</th>\n",
       "      <th>Cloud9am</th>\n",
       "      <th>Cloud3pm</th>\n",
       "      <th>Temp9am</th>\n",
       "      <th>Temp3pm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.516509</td>\n",
       "      <td>0.523629</td>\n",
       "      <td>0.1875</td>\n",
       "      <td>0.220183</td>\n",
       "      <td>0.57931</td>\n",
       "      <td>0.294574</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.421053</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.449587</td>\n",
       "      <td>0.4800</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.508439</td>\n",
       "      <td>0.522073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.565217</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.220183</td>\n",
       "      <td>0.57931</td>\n",
       "      <td>0.294574</td>\n",
       "      <td>0.072727</td>\n",
       "      <td>0.385965</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.497521</td>\n",
       "      <td>0.4912</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.514768</td>\n",
       "      <td>0.570058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.504717</td>\n",
       "      <td>0.576560</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.220183</td>\n",
       "      <td>0.57931</td>\n",
       "      <td>0.310078</td>\n",
       "      <td>0.345455</td>\n",
       "      <td>0.456140</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.447934</td>\n",
       "      <td>0.5056</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.594937</td>\n",
       "      <td>0.548944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.417453</td>\n",
       "      <td>0.620038</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.220183</td>\n",
       "      <td>0.57931</td>\n",
       "      <td>0.139535</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.157895</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.613223</td>\n",
       "      <td>0.5712</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.533755</td>\n",
       "      <td>0.612284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.613208</td>\n",
       "      <td>0.701323</td>\n",
       "      <td>0.3125</td>\n",
       "      <td>0.220183</td>\n",
       "      <td>0.57931</td>\n",
       "      <td>0.271318</td>\n",
       "      <td>0.127273</td>\n",
       "      <td>0.350877</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.500826</td>\n",
       "      <td>0.4624</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.527426</td>\n",
       "      <td>0.673704</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    MinTemp   MaxTemp  Rainfall  Evaporation  Sunshine  WindGustSpeed  \\\n",
       "0  0.516509  0.523629    0.1875     0.220183   0.57931       0.294574   \n",
       "1  0.375000  0.565217    0.0000     0.220183   0.57931       0.294574   \n",
       "2  0.504717  0.576560    0.0000     0.220183   0.57931       0.310078   \n",
       "3  0.417453  0.620038    0.0000     0.220183   0.57931       0.139535   \n",
       "4  0.613208  0.701323    0.3125     0.220183   0.57931       0.271318   \n",
       "\n",
       "   WindSpeed9am  WindSpeed3pm  Humidity9am  Humidity3pm  Pressure9am  \\\n",
       "0      0.363636      0.421053         0.71         0.22     0.449587   \n",
       "1      0.072727      0.385965         0.44         0.25     0.497521   \n",
       "2      0.345455      0.456140         0.38         0.30     0.447934   \n",
       "3      0.200000      0.157895         0.45         0.16     0.613223   \n",
       "4      0.127273      0.350877         0.82         0.33     0.500826   \n",
       "\n",
       "   Pressure3pm  Cloud9am  Cloud3pm   Temp9am   Temp3pm  \n",
       "0       0.4800  0.888889  0.555556  0.508439  0.522073  \n",
       "1       0.4912  0.555556  0.555556  0.514768  0.570058  \n",
       "2       0.5056  0.555556  0.222222  0.594937  0.548944  \n",
       "3       0.5712  0.555556  0.555556  0.533755  0.612284  \n",
       "4       0.4624  0.777778  0.888889  0.527426  0.673704  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Normalization for contnuous variables\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler = MinMaxScaler()\n",
    "df[cont_var] = scaler.fit_transform(df[cont_var])\n",
    "df[cont_var].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbcd0f1f",
   "metadata": {},
   "source": [
    "# Data Transformations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "6hIIjIdE-9xE",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "6hIIjIdE-9xE",
    "outputId": "35cb183f-4ba1-40cf-a3f6-33172d1e0632"
   },
   "outputs": [],
   "source": [
    "# One-Hot Encoding Location, WindGustDir, WindDir9am and WindDir3pm features\n",
    "df_loc = pd.get_dummies(df.Location, drop_first=True)\n",
    "\n",
    "df_wg = pd.get_dummies(df.WindGustDir, drop_first=True, dummy_na=True)\n",
    "\n",
    "df_wd9 = pd.get_dummies(df.WindDir9am, drop_first=True, dummy_na=True)\n",
    "\n",
    "df_wd3 = pd.get_dummies(df.WindDir3pm, drop_first=True, dummy_na=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "d306f258",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Label Encoding RainToday and RainTomorrow features\n",
    "from sklearn import preprocessing\n",
    "le = preprocessing.LabelEncoder()\n",
    "df['RainToday'] = le.fit_transform(df['RainToday'])\n",
    "df['RainTomorrow'] = le.fit_transform(df['RainTomorrow'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "aba2d0d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(columns=['Location', 'WindGustDir', 'WindDir9am', 'WindDir3pm'])\n",
    "df_enc = pd.concat([df,df_loc, df_wg, df_wd9, df_wd3], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "d7b64fd0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date           0\n",
       "MinTemp        0\n",
       "MaxTemp        0\n",
       "Rainfall       0\n",
       "Evaporation    0\n",
       "              ..\n",
       "SW             0\n",
       "W              0\n",
       "WNW            0\n",
       "WSW            0\n",
       "NaN            0\n",
       "Length: 115, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_enc.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "810be103",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>MinTemp</th>\n",
       "      <th>MaxTemp</th>\n",
       "      <th>Rainfall</th>\n",
       "      <th>Evaporation</th>\n",
       "      <th>Sunshine</th>\n",
       "      <th>WindGustSpeed</th>\n",
       "      <th>WindSpeed9am</th>\n",
       "      <th>WindSpeed3pm</th>\n",
       "      <th>Humidity9am</th>\n",
       "      <th>...</th>\n",
       "      <th>NW</th>\n",
       "      <th>S</th>\n",
       "      <th>SE</th>\n",
       "      <th>SSE</th>\n",
       "      <th>SSW</th>\n",
       "      <th>SW</th>\n",
       "      <th>W</th>\n",
       "      <th>WNW</th>\n",
       "      <th>WSW</th>\n",
       "      <th>NaN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2008-12-01</td>\n",
       "      <td>0.516509</td>\n",
       "      <td>0.523629</td>\n",
       "      <td>0.1875</td>\n",
       "      <td>0.220183</td>\n",
       "      <td>0.57931</td>\n",
       "      <td>0.294574</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.421053</td>\n",
       "      <td>0.71</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2008-12-02</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.565217</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.220183</td>\n",
       "      <td>0.57931</td>\n",
       "      <td>0.294574</td>\n",
       "      <td>0.072727</td>\n",
       "      <td>0.385965</td>\n",
       "      <td>0.44</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2008-12-03</td>\n",
       "      <td>0.504717</td>\n",
       "      <td>0.576560</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.220183</td>\n",
       "      <td>0.57931</td>\n",
       "      <td>0.310078</td>\n",
       "      <td>0.345455</td>\n",
       "      <td>0.456140</td>\n",
       "      <td>0.38</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2008-12-04</td>\n",
       "      <td>0.417453</td>\n",
       "      <td>0.620038</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.220183</td>\n",
       "      <td>0.57931</td>\n",
       "      <td>0.139535</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.157895</td>\n",
       "      <td>0.45</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2008-12-05</td>\n",
       "      <td>0.613208</td>\n",
       "      <td>0.701323</td>\n",
       "      <td>0.3125</td>\n",
       "      <td>0.220183</td>\n",
       "      <td>0.57931</td>\n",
       "      <td>0.271318</td>\n",
       "      <td>0.127273</td>\n",
       "      <td>0.350877</td>\n",
       "      <td>0.82</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145455</th>\n",
       "      <td>2017-06-21</td>\n",
       "      <td>0.266509</td>\n",
       "      <td>0.533081</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.220183</td>\n",
       "      <td>0.57931</td>\n",
       "      <td>0.193798</td>\n",
       "      <td>0.236364</td>\n",
       "      <td>0.192982</td>\n",
       "      <td>0.51</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145456</th>\n",
       "      <td>2017-06-22</td>\n",
       "      <td>0.285377</td>\n",
       "      <td>0.568998</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.220183</td>\n",
       "      <td>0.57931</td>\n",
       "      <td>0.124031</td>\n",
       "      <td>0.236364</td>\n",
       "      <td>0.157895</td>\n",
       "      <td>0.56</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145457</th>\n",
       "      <td>2017-06-23</td>\n",
       "      <td>0.327830</td>\n",
       "      <td>0.599244</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.220183</td>\n",
       "      <td>0.57931</td>\n",
       "      <td>0.240310</td>\n",
       "      <td>0.163636</td>\n",
       "      <td>0.157895</td>\n",
       "      <td>0.53</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145458</th>\n",
       "      <td>2017-06-24</td>\n",
       "      <td>0.384434</td>\n",
       "      <td>0.601134</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.220183</td>\n",
       "      <td>0.57931</td>\n",
       "      <td>0.170543</td>\n",
       "      <td>0.236364</td>\n",
       "      <td>0.122807</td>\n",
       "      <td>0.51</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145459</th>\n",
       "      <td>2017-06-25</td>\n",
       "      <td>0.551887</td>\n",
       "      <td>0.517958</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.220183</td>\n",
       "      <td>0.57931</td>\n",
       "      <td>0.255814</td>\n",
       "      <td>0.309091</td>\n",
       "      <td>0.298246</td>\n",
       "      <td>0.62</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>145460 rows × 115 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Date   MinTemp   MaxTemp  Rainfall  Evaporation  Sunshine  \\\n",
       "0       2008-12-01  0.516509  0.523629    0.1875     0.220183   0.57931   \n",
       "1       2008-12-02  0.375000  0.565217    0.0000     0.220183   0.57931   \n",
       "2       2008-12-03  0.504717  0.576560    0.0000     0.220183   0.57931   \n",
       "3       2008-12-04  0.417453  0.620038    0.0000     0.220183   0.57931   \n",
       "4       2008-12-05  0.613208  0.701323    0.3125     0.220183   0.57931   \n",
       "...            ...       ...       ...       ...          ...       ...   \n",
       "145455  2017-06-21  0.266509  0.533081    0.0000     0.220183   0.57931   \n",
       "145456  2017-06-22  0.285377  0.568998    0.0000     0.220183   0.57931   \n",
       "145457  2017-06-23  0.327830  0.599244    0.0000     0.220183   0.57931   \n",
       "145458  2017-06-24  0.384434  0.601134    0.0000     0.220183   0.57931   \n",
       "145459  2017-06-25  0.551887  0.517958    0.0000     0.220183   0.57931   \n",
       "\n",
       "        WindGustSpeed  WindSpeed9am  WindSpeed3pm  Humidity9am  ...  NW  S  \\\n",
       "0            0.294574      0.363636      0.421053         0.71  ...   0  0   \n",
       "1            0.294574      0.072727      0.385965         0.44  ...   0  0   \n",
       "2            0.310078      0.345455      0.456140         0.38  ...   0  0   \n",
       "3            0.139535      0.200000      0.157895         0.45  ...   0  0   \n",
       "4            0.271318      0.127273      0.350877         0.82  ...   1  0   \n",
       "...               ...           ...           ...          ...  ...  .. ..   \n",
       "145455       0.193798      0.236364      0.192982         0.51  ...   0  0   \n",
       "145456       0.124031      0.236364      0.157895         0.56  ...   0  0   \n",
       "145457       0.240310      0.163636      0.157895         0.53  ...   0  0   \n",
       "145458       0.170543      0.236364      0.122807         0.51  ...   0  0   \n",
       "145459       0.255814      0.309091      0.298246         0.62  ...   0  0   \n",
       "\n",
       "        SE  SSE  SSW  SW  W  WNW  WSW  NaN  \n",
       "0        0    0    0   0  0    1    0    0  \n",
       "1        0    0    0   0  0    0    1    0  \n",
       "2        0    0    0   0  0    0    1    0  \n",
       "3        0    0    0   0  0    0    0    0  \n",
       "4        0    0    0   0  0    0    0    0  \n",
       "...     ..  ...  ...  .. ..  ...  ...  ...  \n",
       "145455   0    0    0   0  0    0    0    0  \n",
       "145456   0    0    0   0  0    0    0    0  \n",
       "145457   0    0    0   0  0    1    0    0  \n",
       "145458   0    0    0   0  0    0    0    0  \n",
       "145459   0    0    0   0  0    0    0    0  \n",
       "\n",
       "[145460 rows x 115 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_enc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "316a93b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(145460, 115)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_enc.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "70b74db4",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_enc.to_csv('Processed_Weather_Data.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a04e260",
   "metadata": {},
   "source": [
    "# Feature Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "94e50c16",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split # To split data\n",
    "from sklearn.ensemble import RandomForestClassifier # Random Forest\n",
    "from sklearn.metrics import confusion_matrix # To calculate the confusion matrix\n",
    "from sklearn.metrics import accuracy_score # To calculate the score\n",
    "from sklearn.feature_selection import SelectKBest # Univariate Feature Selection\n",
    "from sklearn.feature_selection import chi2 # To apply Univariate Feature Selection\n",
    "from sklearn.feature_selection import RFE # Recursive Feature Selection\n",
    "from sklearn.feature_selection import RFECV # Recursive Feature Selection with Cross Validation\n",
    "from sklearn.decomposition import PCA # To apply PCA\n",
    "\n",
    "# To plot inline\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "0570796d",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df['RainTomorrow']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "9c13f0d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df_enc.drop(columns = ['Date','RainTomorrow'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "e017a408",
   "metadata": {},
   "outputs": [],
   "source": [
    "X1 = df.drop(columns = ['Date', 'RainTomorrow'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "81ccfcd6",
   "metadata": {},
   "outputs": [],
   "source": [
    "UnivariateFeatureSelection = SelectKBest(chi2, k=5).fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "3314baaa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(10617.40077202912, 'RainToday'),\n",
       " (10167.672808723586, 'Rainfall'),\n",
       " (2218.641383006436, 'Humidity3pm'),\n",
       " (1286.2896360835502, 'Cloud3pm'),\n",
       " (1076.9953277251475, 'Cloud9am'),\n",
       " (974.2223350144237, 'Sunshine'),\n",
       " (474.42138931083963, 'Humidity9am'),\n",
       " (406.3375938012192, 'Woomera'),\n",
       " (368.4923147289704, 'Portland'),\n",
       " (342.6637355925192, 'AliceSprings'),\n",
       " (277.01192791691005, 'WindGustSpeed'),\n",
       " (214.5973806664779, 'Mildura'),\n",
       " (170.57724557255324, 'Temp3pm'),\n",
       " (156.29995193132044, 'Pressure9am'),\n",
       " (154.82260617994152, 'Cairns'),\n",
       " (145.17956450830053, 'Cobar'),\n",
       " (136.80795112913367, 'Moree'),\n",
       " (133.91461156741556, 'Dartmoor'),\n",
       " (130.87222509445562, 'NorfolkIsland'),\n",
       " (123.82013271606843, 'MountGambier'),\n",
       " (121.70476152701578, 'Pressure3pm'),\n",
       " (120.81671738583185, 'MaxTemp'),\n",
       " (110.80930324966154, 'WindSpeed9am'),\n",
       " (106.8795221066139, 'Albany'),\n",
       " (95.02693493232118, 'Evaporation'),\n",
       " (85.31237984103612, 'CoffsHarbour'),\n",
       " (73.14159030603446, 'WindSpeed3pm'),\n",
       " (67.1206166712695, 'SalmonGums'),\n",
       " (46.3051687270476, 'PearceRAAF'),\n",
       " (45.45004923117924, 'MinTemp'),\n",
       " (44.879186020346395, 'MountGinini'),\n",
       " (43.586570669112, 'NorahHead'),\n",
       " (42.43314070086589, 'Darwin'),\n",
       " (41.65468938109083, 'Townsville'),\n",
       " (39.91074415754259, 'Nhil'),\n",
       " (30.530253272991217, 'Sydney'),\n",
       " (26.145075579387093, 'Canberra'),\n",
       " (25.501262306533178, 'SydneyAirport'),\n",
       " (25.332258742263893, 'Ballarat'),\n",
       " (24.185102576055748, 'Katherine'),\n",
       " (22.753390919107144, 'GoldCoast'),\n",
       " (20.87344975068612, 'Bendigo'),\n",
       " (19.192927391854777, 'Richmond'),\n",
       " (16.58515496550108, 'PerthAirport'),\n",
       " (11.3392293764018, 'BadgerysCreek'),\n",
       " (9.689490843317392, 'Penrith'),\n",
       " (8.825371361431724, 'Nuriootpa'),\n",
       " (8.12812802509508, 'Newcastle'),\n",
       " (7.4342207452259075, 'Melbourne'),\n",
       " (6.869752157863735, 'Hobart'),\n",
       " (5.482854961947822, 'Perth'),\n",
       " (4.4667811921481695, 'Albury'),\n",
       " (3.428180498406441, 'Temp9am'),\n",
       " (2.067550642910657, 'Launceston'),\n",
       " (0.5230376962844427, 'Sale'),\n",
       " (0.15714917950287244, 'Brisbane'),\n",
       " (0.07981726172616806, 'MelbourneAirport'),\n",
       " (nan, nan),\n",
       " (339.82881603992035, 'NNW'),\n",
       " (235.13913510712405, 'ESE'),\n",
       " (195.58574448437216, 'Uluru'),\n",
       " (186.36079470800973, 'SE'),\n",
       " (182.8344513067799, 'NW'),\n",
       " (171.69838660385318, 'ENE'),\n",
       " (nan, nan),\n",
       " (173.88192716772383, 'NW'),\n",
       " (163.77282868079618, 'Walpole'),\n",
       " (158.02497911783541, 'NNW'),\n",
       " (154.18340917193058, 'WNW'),\n",
       " (150.0254264836233, 'NNW'),\n",
       " (148.61129267235944, 'NW'),\n",
       " (146.93742075169766, 'N'),\n",
       " (136.88257480926762, 'ESE'),\n",
       " (136.51065384061667, 'WNW'),\n",
       " (134.74478338420403, 'ESE'),\n",
       " (117.61514994936034, 'W'),\n",
       " (106.04360851694244, 'N'),\n",
       " (100.49985650949954, 'SSE'),\n",
       " (93.64750421812309, 'Witchcliffe'),\n",
       " (92.52367773790947, 'ENE'),\n",
       " (90.88395823326528, 'NNE'),\n",
       " (85.89473254059843, 'ENE'),\n",
       " (82.36105994530033, 'WNW'),\n",
       " (75.98873703380848, 'N'),\n",
       " (67.94849073614412, 'SE'),\n",
       " (63.526942345912396, 'W'),\n",
       " (59.65551954325899, 'SSE'),\n",
       " (56.42313736512493, 'W'),\n",
       " (51.48226467274667, 'NE'),\n",
       " (48.84353946665733, 'NE'),\n",
       " (44.412062863237274, 'SSE'),\n",
       " (36.55494823312357, 'SW'),\n",
       " (31.658600378776285, 'S'),\n",
       " (29.578792517957734, 'WaggaWagga'),\n",
       " (19.403893838243903, 'WSW'),\n",
       " (18.462325352114, 'Tuggeranong'),\n",
       " (18.375523455095326, 'SE'),\n",
       " (16.910319724714892, 'SW'),\n",
       " (15.645251870137553, 'NNE'),\n",
       " (11.994995416218751, 'Watsonia'),\n",
       " (9.739586074762322, 'NE'),\n",
       " (6.133547716976614, 'WSW'),\n",
       " (5.916235209064769, 'S'),\n",
       " (4.209537469581283, 'Wollongong'),\n",
       " (3.19959842071768, 'Williamtown'),\n",
       " (2.7652065392026155, 'SW'),\n",
       " (1.8909840048941864, 'NNE'),\n",
       " (0.24027070263615652, 'SSW'),\n",
       " (0.20394516697684187, 'SSW'),\n",
       " (0.039459726322393154, 'S'),\n",
       " (0.03153629358899302, 'WSW'),\n",
       " (0.001936390165509605, 'SSW'),\n",
       " (nan, nan)]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dic = {key:value for (key, value) in zip(UnivariateFeatureSelection.scores_, X.columns)}\n",
    "sorted(dic.items(), reverse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "0d0b658f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PCA()"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Principal Component Analysis\n",
    "pca = PCA()\n",
    "pca.fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "79881bf2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Variance Ratio')"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting to visualize the best number of elements\n",
    "plt.figure(1, figsize=(9, 8))\n",
    "plt.clf()\n",
    "plt.axes([.2, .2, .7, .7])\n",
    "plt.plot(pca.explained_variance_ratio_, linewidth=2)\n",
    "plt.axis('tight')\n",
    "plt.xlabel('Number of Feautres')\n",
    "plt.ylabel('Variance Ratio')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "cdacdb6a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/vinay/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/validation.py:70: FutureWarning: Pass n_features_to_select=10 as keyword args. From version 1.0 (renaming of 0.25) passing these as positional arguments will result in an error\n",
      "  warnings.warn(f\"Pass {args_msg} as keyword args. From version \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Num Features: 10\n",
      "Selected Features: [False  True False False  True  True False  True False  True  True  True\n",
      " False  True False  True False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False  True False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False]\n",
      "Feature Ranking: [  7   1   2  60   1   1   5   1  43   1   1   1  88   1  21   1  58  32\n",
      "  96  25  48  19  45  70  24  38  64  33  40  13  22   6  11  18  10   9\n",
      "  29  31  36   1  41  28   8  12  35  44  74  86  72  34  46  14  73  37\n",
      "  26   4  65  30  47  42  16  39  87   3  15 101  83  95  63  62  91  94\n",
      "  81  82  84  93  92  76  77 100 102  49  79  52  50  27  54  53  68  78\n",
      "  69  90  55  57  51  56 103  85  97  23  67  61  17  20  89  66  75  98\n",
      "  71  80  59  99 104]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_selection import RFE\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "# Feature extraction\n",
    "model = LogisticRegression(max_iter = 10000)\n",
    "rfe = RFE(model, 10)\n",
    "fit = rfe.fit(X, y)\n",
    "print(\"Num Features: %s\" % (fit.n_features_))\n",
    "print(\"Selected Features: %s\" % (fit.support_))\n",
    "print(\"Feature Ranking: %s\" % (fit.ranking_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "095c8d8c",
   "metadata": {
    "id": "095c8d8c"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Data Correlation Visualization\n",
    "correlation = df.corr()\n",
    "\n",
    "#Heatmap\n",
    "plt.figure(figsize=(16,12))\n",
    "plt.title('Heatmap for Rain in Australia dfset')\n",
    "ax = sns.heatmap(correlation, square=True, annot=True, fmt='.2f', linecolor='white')\n",
    "ax.set_xticklabels(ax.get_xticklabels(), rotation=90)\n",
    "ax.set_yticklabels(ax.get_yticklabels(), rotation=30)           \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "60911b5f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "FDS_Project_Weather_AU.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}