Logistic Regression Classifier.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing the libraries "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import matplotlib.pyplot as plt \n",
    "import pandas as pd \n",
    "import seaborn as sns \n",
    "\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "from sklearn.metrics import roc_auc_score,recall_score, precision_score, f1_score\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report, average_precision_score\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load and Explore Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Location</th>\n",
       "      <th>MinTemp</th>\n",
       "      <th>MaxTemp</th>\n",
       "      <th>Rainfall</th>\n",
       "      <th>Evaporation</th>\n",
       "      <th>Sunshine</th>\n",
       "      <th>WindGustDir</th>\n",
       "      <th>WindGustSpeed</th>\n",
       "      <th>WindDir9am</th>\n",
       "      <th>...</th>\n",
       "      <th>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>RISK_MM</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>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>0.0</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>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>0.0</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>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>0.0</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>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>1.0</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>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>0.2</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date Location  MinTemp  MaxTemp  Rainfall  Evaporation  Sunshine  \\\n",
       "0  2008-12-01   Albury     13.4     22.9       0.6          NaN       NaN   \n",
       "1  2008-12-02   Albury      7.4     25.1       0.0          NaN       NaN   \n",
       "2  2008-12-03   Albury     12.9     25.7       0.0          NaN       NaN   \n",
       "3  2008-12-04   Albury      9.2     28.0       0.0          NaN       NaN   \n",
       "4  2008-12-05   Albury     17.5     32.3       1.0          NaN       NaN   \n",
       "\n",
       "  WindGustDir  WindGustSpeed WindDir9am  ... Humidity3pm  Pressure9am  \\\n",
       "0           W           44.0          W  ...        22.0       1007.7   \n",
       "1         WNW           44.0        NNW  ...        25.0       1010.6   \n",
       "2         WSW           46.0          W  ...        30.0       1007.6   \n",
       "3          NE           24.0         SE  ...        16.0       1017.6   \n",
       "4           W           41.0        ENE  ...        33.0       1010.8   \n",
       "\n",
       "   Pressure3pm  Cloud9am  Cloud3pm  Temp9am  Temp3pm  RainToday  RISK_MM  \\\n",
       "0       1007.1       8.0       NaN     16.9     21.8         No      0.0   \n",
       "1       1007.8       NaN       NaN     17.2     24.3         No      0.0   \n",
       "2       1008.7       NaN       2.0     21.0     23.2         No      0.0   \n",
       "3       1012.8       NaN       NaN     18.1     26.5         No      1.0   \n",
       "4       1006.0       7.0       8.0     17.8     29.7         No      0.2   \n",
       "\n",
       "   RainTomorrow  \n",
       "0            No  \n",
       "1            No  \n",
       "2            No  \n",
       "3            No  \n",
       "4            No  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset=pd.read_csv('weatherAUS.csv') \n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>MinTemp</th>\n",
       "      <th>MaxTemp</th>\n",
       "      <th>Rainfall</th>\n",
       "      <th>Evaporation</th>\n",
       "      <th>Sunshine</th>\n",
       "      <th>WindGustSpeed</th>\n",
       "      <th>WindSpeed9am</th>\n",
       "      <th>WindSpeed3pm</th>\n",
       "      <th>Humidity9am</th>\n",
       "      <th>Humidity3pm</th>\n",
       "      <th>Pressure9am</th>\n",
       "      <th>Pressure3pm</th>\n",
       "      <th>Cloud9am</th>\n",
       "      <th>Cloud3pm</th>\n",
       "      <th>Temp9am</th>\n",
       "      <th>Temp3pm</th>\n",
       "      <th>RISK_MM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>141556.000000</td>\n",
       "      <td>141871.000000</td>\n",
       "      <td>140787.000000</td>\n",
       "      <td>81350.000000</td>\n",
       "      <td>74377.000000</td>\n",
       "      <td>132923.000000</td>\n",
       "      <td>140845.000000</td>\n",
       "      <td>139563.000000</td>\n",
       "      <td>140419.000000</td>\n",
       "      <td>138583.000000</td>\n",
       "      <td>128179.000000</td>\n",
       "      <td>128212.000000</td>\n",
       "      <td>88536.000000</td>\n",
       "      <td>85099.000000</td>\n",
       "      <td>141289.000000</td>\n",
       "      <td>139467.000000</td>\n",
       "      <td>142193.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>12.186400</td>\n",
       "      <td>23.226784</td>\n",
       "      <td>2.349974</td>\n",
       "      <td>5.469824</td>\n",
       "      <td>7.624853</td>\n",
       "      <td>39.984292</td>\n",
       "      <td>14.001988</td>\n",
       "      <td>18.637576</td>\n",
       "      <td>68.843810</td>\n",
       "      <td>51.482606</td>\n",
       "      <td>1017.653758</td>\n",
       "      <td>1015.258204</td>\n",
       "      <td>4.437189</td>\n",
       "      <td>4.503167</td>\n",
       "      <td>16.987509</td>\n",
       "      <td>21.687235</td>\n",
       "      <td>2.360682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.403283</td>\n",
       "      <td>7.117618</td>\n",
       "      <td>8.465173</td>\n",
       "      <td>4.188537</td>\n",
       "      <td>3.781525</td>\n",
       "      <td>13.588801</td>\n",
       "      <td>8.893337</td>\n",
       "      <td>8.803345</td>\n",
       "      <td>19.051293</td>\n",
       "      <td>20.797772</td>\n",
       "      <td>7.105476</td>\n",
       "      <td>7.036677</td>\n",
       "      <td>2.887016</td>\n",
       "      <td>2.720633</td>\n",
       "      <td>6.492838</td>\n",
       "      <td>6.937594</td>\n",
       "      <td>8.477969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-8.500000</td>\n",
       "      <td>-4.800000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>980.500000</td>\n",
       "      <td>977.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-7.200000</td>\n",
       "      <td>-5.400000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>7.600000</td>\n",
       "      <td>17.900000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>4.900000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>1012.900000</td>\n",
       "      <td>1010.400000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>12.300000</td>\n",
       "      <td>16.600000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>12.000000</td>\n",
       "      <td>22.600000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.800000</td>\n",
       "      <td>8.500000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>1017.600000</td>\n",
       "      <td>1015.200000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>16.700000</td>\n",
       "      <td>21.100000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>16.800000</td>\n",
       "      <td>28.200000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>7.400000</td>\n",
       "      <td>10.600000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>83.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>1022.400000</td>\n",
       "      <td>1020.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>21.600000</td>\n",
       "      <td>26.400000</td>\n",
       "      <td>0.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>33.900000</td>\n",
       "      <td>48.100000</td>\n",
       "      <td>371.000000</td>\n",
       "      <td>145.000000</td>\n",
       "      <td>14.500000</td>\n",
       "      <td>135.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>1041.000000</td>\n",
       "      <td>1039.600000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>40.200000</td>\n",
       "      <td>46.700000</td>\n",
       "      <td>371.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "             MinTemp        MaxTemp       Rainfall   Evaporation  \\\n",
       "count  141556.000000  141871.000000  140787.000000  81350.000000   \n",
       "mean       12.186400      23.226784       2.349974      5.469824   \n",
       "std         6.403283       7.117618       8.465173      4.188537   \n",
       "min        -8.500000      -4.800000       0.000000      0.000000   \n",
       "25%         7.600000      17.900000       0.000000      2.600000   \n",
       "50%        12.000000      22.600000       0.000000      4.800000   \n",
       "75%        16.800000      28.200000       0.800000      7.400000   \n",
       "max        33.900000      48.100000     371.000000    145.000000   \n",
       "\n",
       "           Sunshine  WindGustSpeed   WindSpeed9am   WindSpeed3pm  \\\n",
       "count  74377.000000  132923.000000  140845.000000  139563.000000   \n",
       "mean       7.624853      39.984292      14.001988      18.637576   \n",
       "std        3.781525      13.588801       8.893337       8.803345   \n",
       "min        0.000000       6.000000       0.000000       0.000000   \n",
       "25%        4.900000      31.000000       7.000000      13.000000   \n",
       "50%        8.500000      39.000000      13.000000      19.000000   \n",
       "75%       10.600000      48.000000      19.000000      24.000000   \n",
       "max       14.500000     135.000000     130.000000      87.000000   \n",
       "\n",
       "         Humidity9am    Humidity3pm    Pressure9am    Pressure3pm  \\\n",
       "count  140419.000000  138583.000000  128179.000000  128212.000000   \n",
       "mean       68.843810      51.482606    1017.653758    1015.258204   \n",
       "std        19.051293      20.797772       7.105476       7.036677   \n",
       "min         0.000000       0.000000     980.500000     977.100000   \n",
       "25%        57.000000      37.000000    1012.900000    1010.400000   \n",
       "50%        70.000000      52.000000    1017.600000    1015.200000   \n",
       "75%        83.000000      66.000000    1022.400000    1020.000000   \n",
       "max       100.000000     100.000000    1041.000000    1039.600000   \n",
       "\n",
       "           Cloud9am      Cloud3pm        Temp9am        Temp3pm        RISK_MM  \n",
       "count  88536.000000  85099.000000  141289.000000  139467.000000  142193.000000  \n",
       "mean       4.437189      4.503167      16.987509      21.687235       2.360682  \n",
       "std        2.887016      2.720633       6.492838       6.937594       8.477969  \n",
       "min        0.000000      0.000000      -7.200000      -5.400000       0.000000  \n",
       "25%        1.000000      2.000000      12.300000      16.600000       0.000000  \n",
       "50%        5.000000      5.000000      16.700000      21.100000       0.000000  \n",
       "75%        7.000000      7.000000      21.600000      26.400000       0.800000  \n",
       "max        9.000000      9.000000      40.200000      46.700000     371.000000  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 7 categorical variables : \n",
      " ['Date', 'Location', 'WindGustDir', 'WindDir9am', 'WindDir3pm', 'RainToday', 'RainTomorrow']\n"
     ]
    }
   ],
   "source": [
    "# find categorical variables\n",
    "categorical = [var for var in dataset.columns if dataset[var].dtype=='O']\n",
    "print('There are {} categorical variables : \\n'.format(len(categorical)), categorical)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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",
       "  </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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# view the categorical variables\n",
    "dataset[categorical].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WindGustDir     9330\n",
      "WindDir9am     10013\n",
      "WindDir3pm      3778\n",
      "RainToday       1406\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# check and print categorical variables containing missing values\n",
    "\n",
    "nullCategorical = [var for var in categorical if dataset[var].isnull().sum()!=0]\n",
    "print(dataset[nullCategorical].isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Number of labels: cardinality\n",
    "    \n",
    "The number of labels within a categorical variable is known as cardinality. A high number of labels within a variable is known as high cardinality. High cardinality may pose some serious problems in the machine learning model. So, I will check for high cardinality."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Date  contains  3436  labels\n",
      "Location  contains  49  labels\n",
      "WindGustDir  contains  17  labels\n",
      "WindDir9am  contains  17  labels\n",
      "WindDir3pm  contains  17  labels\n",
      "RainToday  contains  3  labels\n",
      "RainTomorrow  contains  2  labels\n"
     ]
    }
   ],
   "source": [
    "# check for cardinality in categorical variables\n",
    "for var in categorical:\n",
    "    print(var, ' contains ', len(dataset[var].unique()), ' labels')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('O')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['Date'].dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# parse the dates, currently coded as strings, into datetime format\n",
    "dataset['Date'] = pd.to_datetime(dataset['Date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('<M8[ns]')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['Date'].dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# extract year from date\n",
    "dataset['Year'] = dataset['Date'].dt.year\n",
    "# extract month from date\n",
    "dataset['Month'] = dataset['Date'].dt.month\n",
    "# extract day from date\n",
    "dataset['Day'] = dataset['Date'].dt.day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 142193 entries, 0 to 142192\n",
      "Data columns (total 27 columns):\n",
      " #   Column         Non-Null Count   Dtype         \n",
      "---  ------         --------------   -----         \n",
      " 0   Date           142193 non-null  datetime64[ns]\n",
      " 1   Location       142193 non-null  object        \n",
      " 2   MinTemp        141556 non-null  float64       \n",
      " 3   MaxTemp        141871 non-null  float64       \n",
      " 4   Rainfall       140787 non-null  float64       \n",
      " 5   Evaporation    81350 non-null   float64       \n",
      " 6   Sunshine       74377 non-null   float64       \n",
      " 7   WindGustDir    132863 non-null  object        \n",
      " 8   WindGustSpeed  132923 non-null  float64       \n",
      " 9   WindDir9am     132180 non-null  object        \n",
      " 10  WindDir3pm     138415 non-null  object        \n",
      " 11  WindSpeed9am   140845 non-null  float64       \n",
      " 12  WindSpeed3pm   139563 non-null  float64       \n",
      " 13  Humidity9am    140419 non-null  float64       \n",
      " 14  Humidity3pm    138583 non-null  float64       \n",
      " 15  Pressure9am    128179 non-null  float64       \n",
      " 16  Pressure3pm    128212 non-null  float64       \n",
      " 17  Cloud9am       88536 non-null   float64       \n",
      " 18  Cloud3pm       85099 non-null   float64       \n",
      " 19  Temp9am        141289 non-null  float64       \n",
      " 20  Temp3pm        139467 non-null  float64       \n",
      " 21  RainToday      140787 non-null  object        \n",
      " 22  RISK_MM        142193 non-null  float64       \n",
      " 23  RainTomorrow   142193 non-null  object        \n",
      " 24  Year           142193 non-null  int64         \n",
      " 25  Month          142193 non-null  int64         \n",
      " 26  Day            142193 non-null  int64         \n",
      "dtypes: datetime64[ns](1), float64(17), int64(3), object(6)\n",
      "memory usage: 29.3+ MB\n"
     ]
    }
   ],
   "source": [
    "dataset.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>Location</th>\n",
       "      <th>MinTemp</th>\n",
       "      <th>MaxTemp</th>\n",
       "      <th>Rainfall</th>\n",
       "      <th>Evaporation</th>\n",
       "      <th>Sunshine</th>\n",
       "      <th>WindGustDir</th>\n",
       "      <th>WindGustSpeed</th>\n",
       "      <th>WindDir9am</th>\n",
       "      <th>WindDir3pm</th>\n",
       "      <th>...</th>\n",
       "      <th>Cloud9am</th>\n",
       "      <th>Cloud3pm</th>\n",
       "      <th>Temp9am</th>\n",
       "      <th>Temp3pm</th>\n",
       "      <th>RainToday</th>\n",
       "      <th>RISK_MM</th>\n",
       "      <th>RainTomorrow</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Albury</td>\n",
       "      <td>13.4</td>\n",
       "      <td>22.9</td>\n",
       "      <td>0.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>W</td>\n",
       "      <td>44.0</td>\n",
       "      <td>W</td>\n",
       "      <td>WNW</td>\n",
       "      <td>...</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.9</td>\n",
       "      <td>21.8</td>\n",
       "      <td>No</td>\n",
       "      <td>0.0</td>\n",
       "      <td>No</td>\n",
       "      <td>2008</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albury</td>\n",
       "      <td>7.4</td>\n",
       "      <td>25.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WNW</td>\n",
       "      <td>44.0</td>\n",
       "      <td>NNW</td>\n",
       "      <td>WSW</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.2</td>\n",
       "      <td>24.3</td>\n",
       "      <td>No</td>\n",
       "      <td>0.0</td>\n",
       "      <td>No</td>\n",
       "      <td>2008</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Albury</td>\n",
       "      <td>12.9</td>\n",
       "      <td>25.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WSW</td>\n",
       "      <td>46.0</td>\n",
       "      <td>W</td>\n",
       "      <td>WSW</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>23.2</td>\n",
       "      <td>No</td>\n",
       "      <td>0.0</td>\n",
       "      <td>No</td>\n",
       "      <td>2008</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Albury</td>\n",
       "      <td>9.2</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NE</td>\n",
       "      <td>24.0</td>\n",
       "      <td>SE</td>\n",
       "      <td>E</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.1</td>\n",
       "      <td>26.5</td>\n",
       "      <td>No</td>\n",
       "      <td>1.0</td>\n",
       "      <td>No</td>\n",
       "      <td>2008</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Albury</td>\n",
       "      <td>17.5</td>\n",
       "      <td>32.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>W</td>\n",
       "      <td>41.0</td>\n",
       "      <td>ENE</td>\n",
       "      <td>NW</td>\n",
       "      <td>...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>29.7</td>\n",
       "      <td>No</td>\n",
       "      <td>0.2</td>\n",
       "      <td>No</td>\n",
       "      <td>2008</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Location  MinTemp  MaxTemp  Rainfall  Evaporation  Sunshine WindGustDir  \\\n",
       "0   Albury     13.4     22.9       0.6          NaN       NaN           W   \n",
       "1   Albury      7.4     25.1       0.0          NaN       NaN         WNW   \n",
       "2   Albury     12.9     25.7       0.0          NaN       NaN         WSW   \n",
       "3   Albury      9.2     28.0       0.0          NaN       NaN          NE   \n",
       "4   Albury     17.5     32.3       1.0          NaN       NaN           W   \n",
       "\n",
       "   WindGustSpeed WindDir9am WindDir3pm  ...  Cloud9am  Cloud3pm  Temp9am  \\\n",
       "0           44.0          W        WNW  ...       8.0       NaN     16.9   \n",
       "1           44.0        NNW        WSW  ...       NaN       NaN     17.2   \n",
       "2           46.0          W        WSW  ...       NaN       2.0     21.0   \n",
       "3           24.0         SE          E  ...       NaN       NaN     18.1   \n",
       "4           41.0        ENE         NW  ...       7.0       8.0     17.8   \n",
       "\n",
       "   Temp3pm  RainToday  RISK_MM  RainTomorrow  Year  Month  Day  \n",
       "0     21.8         No      0.0            No  2008     12    1  \n",
       "1     24.3         No      0.0            No  2008     12    2  \n",
       "2     23.2         No      0.0            No  2008     12    3  \n",
       "3     26.5         No      1.0            No  2008     12    4  \n",
       "4     29.7         No      0.2            No  2008     12    5  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop the original Date variable\n",
    "dataset.drop('Date', axis=1, inplace = True)\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explore Categorical Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Albury', 'BadgerysCreek', 'Cobar', 'CoffsHarbour', 'Moree',\n",
       "       'Newcastle', 'NorahHead', 'NorfolkIsland', 'Penrith', 'Richmond',\n",
       "       'Sydney', 'SydneyAirport', 'WaggaWagga', 'Williamtown',\n",
       "       'Wollongong', 'Canberra', 'Tuggeranong', 'MountGinini', 'Ballarat',\n",
       "       'Bendigo', 'Sale', 'MelbourneAirport', 'Melbourne', 'Mildura',\n",
       "       'Nhil', 'Portland', 'Watsonia', 'Dartmoor', 'Brisbane', 'Cairns',\n",
       "       'GoldCoast', 'Townsville', 'Adelaide', 'MountGambier', 'Nuriootpa',\n",
       "       'Woomera', 'Albany', 'Witchcliffe', 'PearceRAAF', 'PerthAirport',\n",
       "       'Perth', 'SalmonGums', 'Walpole', 'Hobart', 'Launceston',\n",
       "       'AliceSprings', 'Darwin', 'Katherine', 'Uluru'], dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Explore Location variable\n",
    "dataset.Location.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Canberra            3418\n",
       "Sydney              3337\n",
       "Perth               3193\n",
       "Darwin              3192\n",
       "Hobart              3188\n",
       "Brisbane            3161\n",
       "Adelaide            3090\n",
       "Bendigo             3034\n",
       "Townsville          3033\n",
       "AliceSprings        3031\n",
       "MountGambier        3030\n",
       "Ballarat            3028\n",
       "Launceston          3028\n",
       "Albany              3016\n",
       "Albury              3011\n",
       "PerthAirport        3009\n",
       "MelbourneAirport    3009\n",
       "Mildura             3007\n",
       "SydneyAirport       3005\n",
       "Nuriootpa           3002\n",
       "Sale                3000\n",
       "Watsonia            2999\n",
       "Tuggeranong         2998\n",
       "Portland            2996\n",
       "Woomera             2990\n",
       "Cairns              2988\n",
       "Cobar               2988\n",
       "Wollongong          2983\n",
       "GoldCoast           2980\n",
       "WaggaWagga          2976\n",
       "NorfolkIsland       2964\n",
       "Penrith             2964\n",
       "Newcastle           2955\n",
       "SalmonGums          2955\n",
       "CoffsHarbour        2953\n",
       "Witchcliffe         2952\n",
       "Richmond            2951\n",
       "Dartmoor            2943\n",
       "NorahHead           2929\n",
       "BadgerysCreek       2928\n",
       "MountGinini         2907\n",
       "Moree               2854\n",
       "Walpole             2819\n",
       "PearceRAAF          2762\n",
       "Williamtown         2553\n",
       "Melbourne           2435\n",
       "Nhil                1569\n",
       "Katherine           1559\n",
       "Uluru               1521\n",
       "Name: Location, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check frequency distribution of values in Location variable\n",
    "dataset.Location.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Albany</th>\n",
       "      <th>Albury</th>\n",
       "      <th>AliceSprings</th>\n",
       "      <th>BadgerysCreek</th>\n",
       "      <th>Ballarat</th>\n",
       "      <th>Bendigo</th>\n",
       "      <th>Brisbane</th>\n",
       "      <th>Cairns</th>\n",
       "      <th>Canberra</th>\n",
       "      <th>Cobar</th>\n",
       "      <th>...</th>\n",
       "      <th>Townsville</th>\n",
       "      <th>Tuggeranong</th>\n",
       "      <th>Uluru</th>\n",
       "      <th>WaggaWagga</th>\n",
       "      <th>Walpole</th>\n",
       "      <th>Watsonia</th>\n",
       "      <th>Williamtown</th>\n",
       "      <th>Witchcliffe</th>\n",
       "      <th>Wollongong</th>\n",
       "      <th>Woomera</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</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>...</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>1</th>\n",
       "      <td>0</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>...</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>2</th>\n",
       "      <td>0</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>...</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>3</th>\n",
       "      <td>0</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>...</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>0</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>...</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>5 rows × 48 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Albany  Albury  AliceSprings  BadgerysCreek  Ballarat  Bendigo  Brisbane  \\\n",
       "0       0       1             0              0         0        0         0   \n",
       "1       0       1             0              0         0        0         0   \n",
       "2       0       1             0              0         0        0         0   \n",
       "3       0       1             0              0         0        0         0   \n",
       "4       0       1             0              0         0        0         0   \n",
       "\n",
       "   Cairns  Canberra  Cobar  ...  Townsville  Tuggeranong  Uluru  WaggaWagga  \\\n",
       "0       0         0      0  ...           0            0      0           0   \n",
       "1       0         0      0  ...           0            0      0           0   \n",
       "2       0         0      0  ...           0            0      0           0   \n",
       "3       0         0      0  ...           0            0      0           0   \n",
       "4       0         0      0  ...           0            0      0           0   \n",
       "\n",
       "   Walpole  Watsonia  Williamtown  Witchcliffe  Wollongong  Woomera  \n",
       "0        0         0            0            0           0        0  \n",
       "1        0         0            0            0           0        0  \n",
       "2        0         0            0            0           0        0  \n",
       "3        0         0            0            0           0        0  \n",
       "4        0         0            0            0           0        0  \n",
       "\n",
       "[5 rows x 48 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's do One Hot Encoding of Location variable\n",
    "# get k-1 dummy variables after One Hot Encoding \n",
    "pd.get_dummies(dataset.Location, drop_first=True).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['W', 'WNW', 'WSW', 'NE', 'NNW', 'N', 'NNE', 'SW', 'ENE', 'SSE',\n",
       "       'S', 'NW', 'SE', 'ESE', nan, 'E', 'SSW'], dtype=object)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Explore WindGustDir variable\n",
    "dataset.WindGustDir.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "W      9780\n",
       "SE     9309\n",
       "E      9071\n",
       "N      9033\n",
       "SSE    8993\n",
       "S      8949\n",
       "WSW    8901\n",
       "SW     8797\n",
       "SSW    8610\n",
       "WNW    8066\n",
       "NW     8003\n",
       "ENE    7992\n",
       "ESE    7305\n",
       "NE     7060\n",
       "NNW    6561\n",
       "NNE    6433\n",
       "Name: WindGustDir, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check frequency distribution of values in WindGustDir variable\n",
    "dataset.WindGustDir.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ENE</th>\n",
       "      <th>ESE</th>\n",
       "      <th>N</th>\n",
       "      <th>NE</th>\n",
       "      <th>NNE</th>\n",
       "      <th>NNW</th>\n",
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       "      <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",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <th>3</th>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ENE  ESE  N  NE  NNE  NNW  NW  S  SE  SSE  SSW  SW  W  WNW  WSW  NaN\n",
       "0    0    0  0   0    0    0   0  0   0    0    0   0  1    0    0    0\n",
       "1    0    0  0   0    0    0   0  0   0    0    0   0  0    1    0    0\n",
       "2    0    0  0   0    0    0   0  0   0    0    0   0  0    0    1    0\n",
       "3    0    0  0   1    0    0   0  0   0    0    0   0  0    0    0    0\n",
       "4    0    0  0   0    0    0   0  0   0    0    0   0  1    0    0    0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's do One Hot Encoding of WindGustDir variable\n",
    "# get k-1 dummy variables after One Hot Encoding \n",
    "# also add an additional dummy variable to indicate there was missing data\n",
    "pd.get_dummies(dataset.WindGustDir, drop_first=True, dummy_na=True).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ENE    7992\n",
       "ESE    7305\n",
       "N      9033\n",
       "NE     7060\n",
       "NNE    6433\n",
       "NNW    6561\n",
       "NW     8003\n",
       "S      8949\n",
       "SE     9309\n",
       "SSE    8993\n",
       "SSW    8610\n",
       "SW     8797\n",
       "W      9780\n",
       "WNW    8066\n",
       "WSW    8901\n",
       "NaN    9330\n",
       "dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sum the number of 1s per boolean variable over the rows of the dataset --> it will tell us how many observations we have for each category\n",
    "pd.get_dummies(dataset.WindGustDir, drop_first=True, dummy_na=True).sum(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['W', 'NNW', 'SE', 'ENE', 'SW', 'SSE', 'S', 'NE', nan, 'SSW', 'N',\n",
       "       'WSW', 'ESE', 'E', 'NW', 'WNW', 'NNE'], dtype=object)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Explore WindDir9am variable\n",
    "dataset.WindDir9am.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "N      11393\n",
       "SE      9162\n",
       "E       9024\n",
       "SSE     8966\n",
       "NW      8552\n",
       "S       8493\n",
       "W       8260\n",
       "SW      8237\n",
       "NNE     7948\n",
       "NNW     7840\n",
       "ENE     7735\n",
       "ESE     7558\n",
       "NE      7527\n",
       "SSW     7448\n",
       "WNW     7194\n",
       "WSW     6843\n",
       "Name: WindDir9am, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.WindDir9am.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ENE  ESE  N  NE  NNE  NNW  NW  S  SE  SSE  SSW  SW  W  WNW  WSW  NaN\n",
       "0    0    0  0   0    0    0   0  0   0    0    0   0  1    0    0    0\n",
       "1    0    0  0   0    0    1   0  0   0    0    0   0  0    0    0    0\n",
       "2    0    0  0   0    0    0   0  0   0    0    0   0  1    0    0    0\n",
       "3    0    0  0   0    0    0   0  0   1    0    0   0  0    0    0    0\n",
       "4    1    0  0   0    0    0   0  0   0    0    0   0  0    0    0    0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.get_dummies(dataset.WindDir9am, drop_first=True, dummy_na=True).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ENE     7735\n",
       "ESE     7558\n",
       "N      11393\n",
       "NE      7527\n",
       "NNE     7948\n",
       "NNW     7840\n",
       "NW      8552\n",
       "S       8493\n",
       "SE      9162\n",
       "SSE     8966\n",
       "SSW     7448\n",
       "SW      8237\n",
       "W       8260\n",
       "WNW     7194\n",
       "WSW     6843\n",
       "NaN    10013\n",
       "dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sum the number of 1s per boolean variable over the rows of the dataset -- it will tell us how many observations we have for each category\n",
    "\n",
    "pd.get_dummies(dataset.WindDir9am, drop_first=True, dummy_na=True).sum(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['WNW', 'WSW', 'E', 'NW', 'W', 'SSE', 'ESE', 'ENE', 'NNW', 'SSW',\n",
       "       'SW', 'SE', 'N', 'S', 'NNE', nan, 'NE'], dtype=object)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Explore WindDir3pm variable\n",
    "dataset['WindDir3pm'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SE     10663\n",
       "W       9911\n",
       "S       9598\n",
       "WSW     9329\n",
       "SW      9182\n",
       "SSE     9142\n",
       "N       8667\n",
       "WNW     8656\n",
       "NW      8468\n",
       "ESE     8382\n",
       "E       8342\n",
       "NE      8164\n",
       "SSW     8010\n",
       "NNW     7733\n",
       "ENE     7724\n",
       "NNE     6444\n",
       "Name: WindDir3pm, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['WindDir3pm'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>ENE</th>\n",
       "      <th>ESE</th>\n",
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       "      <th>NNE</th>\n",
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       "      <th>2</th>\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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>3</th>\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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       "      <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>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",
       "      <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",
       "</div>"
      ],
      "text/plain": [
       "   ENE  ESE  N  NE  NNE  NNW  NW  S  SE  SSE  SSW  SW  W  WNW  WSW  NaN\n",
       "0    0    0  0   0    0    0   0  0   0    0    0   0  0    1    0    0\n",
       "1    0    0  0   0    0    0   0  0   0    0    0   0  0    0    1    0\n",
       "2    0    0  0   0    0    0   0  0   0    0    0   0  0    0    1    0\n",
       "3    0    0  0   0    0    0   0  0   0    0    0   0  0    0    0    0\n",
       "4    0    0  0   0    0    0   1  0   0    0    0   0  0    0    0    0"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.get_dummies(dataset.WindDir3pm, drop_first=True, dummy_na=True).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ENE     7724\n",
       "ESE     8382\n",
       "N       8667\n",
       "NE      8164\n",
       "NNE     6444\n",
       "NNW     7733\n",
       "NW      8468\n",
       "S       9598\n",
       "SE     10663\n",
       "SSE     9142\n",
       "SSW     8010\n",
       "SW      9182\n",
       "W       9911\n",
       "WNW     8656\n",
       "WSW     9329\n",
       "NaN     3778\n",
       "dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.get_dummies(dataset.WindDir3pm, drop_first=True, dummy_na=True).sum(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['No', 'Yes', nan], dtype=object)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Explore RainToday variable\n",
    "dataset['RainToday'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No     109332\n",
       "Yes     31455\n",
       "Name: RainToday, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.RainToday.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Yes</th>\n",
       "      <th>NaN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Yes  NaN\n",
       "0    0    0\n",
       "1    0    0\n",
       "2    0    0\n",
       "3    0    0\n",
       "4    0    0"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.get_dummies(dataset.RainToday, drop_first=True, dummy_na=True).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Yes    31455\n",
       "NaN     1406\n",
       "dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.get_dummies(dataset.RainToday, drop_first=True, dummy_na=True).sum(axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explore Numerical Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 20 numerical variables : \n",
      " ['MinTemp', 'MaxTemp', 'Rainfall', 'Evaporation', 'Sunshine', 'WindGustSpeed', 'WindSpeed9am', 'WindSpeed3pm', 'Humidity9am', 'Humidity3pm', 'Pressure9am', 'Pressure3pm', 'Cloud9am', 'Cloud3pm', 'Temp9am', 'Temp3pm', 'RISK_MM', 'Year', 'Month', 'Day']\n"
     ]
    }
   ],
   "source": [
    "# find numerical variables\n",
    "numerical = [var for var in dataset.columns if dataset[var].dtype!='O']\n",
    "print('There are {} numerical variables : \\n'.format(len(numerical)), numerical)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MinTemp</th>\n",
       "      <th>MaxTemp</th>\n",
       "      <th>Rainfall</th>\n",
       "      <th>Evaporation</th>\n",
       "      <th>Sunshine</th>\n",
       "      <th>WindGustSpeed</th>\n",
       "      <th>WindSpeed9am</th>\n",
       "      <th>WindSpeed3pm</th>\n",
       "      <th>Humidity9am</th>\n",
       "      <th>Humidity3pm</th>\n",
       "      <th>Pressure9am</th>\n",
       "      <th>Pressure3pm</th>\n",
       "      <th>Cloud9am</th>\n",
       "      <th>Cloud3pm</th>\n",
       "      <th>Temp9am</th>\n",
       "      <th>Temp3pm</th>\n",
       "      <th>RISK_MM</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13.4</td>\n",
       "      <td>22.9</td>\n",
       "      <td>0.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>44.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1007.7</td>\n",
       "      <td>1007.1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.9</td>\n",
       "      <td>21.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2008</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.4</td>\n",
       "      <td>25.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>44.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1010.6</td>\n",
       "      <td>1007.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.2</td>\n",
       "      <td>24.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2008</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12.9</td>\n",
       "      <td>25.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>46.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1007.6</td>\n",
       "      <td>1008.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>23.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2008</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9.2</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1017.6</td>\n",
       "      <td>1012.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.1</td>\n",
       "      <td>26.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2008</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17.5</td>\n",
       "      <td>32.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1010.8</td>\n",
       "      <td>1006.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>29.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>2008</td>\n",
       "      <td>12</td>\n",
       "      <td>5</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  RISK_MM  Year  Month  \\\n",
       "0       1007.1       8.0       NaN     16.9     21.8      0.0  2008     12   \n",
       "1       1007.8       NaN       NaN     17.2     24.3      0.0  2008     12   \n",
       "2       1008.7       NaN       2.0     21.0     23.2      0.0  2008     12   \n",
       "3       1012.8       NaN       NaN     18.1     26.5      1.0  2008     12   \n",
       "4       1006.0       7.0       8.0     17.8     29.7      0.2  2008     12   \n",
       "\n",
       "   Day  \n",
       "0    1  \n",
       "1    2  \n",
       "2    3  \n",
       "3    4  \n",
       "4    5  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# view the numerical variables\n",
    "dataset[numerical].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MinTemp            637\n",
       "MaxTemp            322\n",
       "Rainfall          1406\n",
       "Evaporation      60843\n",
       "Sunshine         67816\n",
       "WindGustSpeed     9270\n",
       "WindSpeed9am      1348\n",
       "WindSpeed3pm      2630\n",
       "Humidity9am       1774\n",
       "Humidity3pm       3610\n",
       "Pressure9am      14014\n",
       "Pressure3pm      13981\n",
       "Cloud9am         53657\n",
       "Cloud3pm         57094\n",
       "Temp9am            904\n",
       "Temp3pm           2726\n",
       "RISK_MM              0\n",
       "Year                 0\n",
       "Month                0\n",
       "Day                  0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check missing values in numerical variables\n",
    "dataset[numerical].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        MinTemp   MaxTemp  Rainfall  Evaporation  Sunshine  WindGustSpeed  \\\n",
      "count  141556.0  141871.0  140787.0      81350.0   74377.0       132923.0   \n",
      "mean       12.0      23.0       2.0          5.0       8.0           40.0   \n",
      "std         6.0       7.0       8.0          4.0       4.0           14.0   \n",
      "min        -8.0      -5.0       0.0          0.0       0.0            6.0   \n",
      "25%         8.0      18.0       0.0          3.0       5.0           31.0   \n",
      "50%        12.0      23.0       0.0          5.0       8.0           39.0   \n",
      "75%        17.0      28.0       1.0          7.0      11.0           48.0   \n",
      "max        34.0      48.0     371.0        145.0      14.0          135.0   \n",
      "\n",
      "       WindSpeed9am  WindSpeed3pm  Humidity9am  Humidity3pm  Pressure9am  \\\n",
      "count      140845.0      139563.0     140419.0     138583.0     128179.0   \n",
      "mean           14.0          19.0         69.0         51.0       1018.0   \n",
      "std             9.0           9.0         19.0         21.0          7.0   \n",
      "min             0.0           0.0          0.0          0.0        980.0   \n",
      "25%             7.0          13.0         57.0         37.0       1013.0   \n",
      "50%            13.0          19.0         70.0         52.0       1018.0   \n",
      "75%            19.0          24.0         83.0         66.0       1022.0   \n",
      "max           130.0          87.0        100.0        100.0       1041.0   \n",
      "\n",
      "       Pressure3pm  Cloud9am  Cloud3pm   Temp9am   Temp3pm   RISK_MM  \\\n",
      "count     128212.0   88536.0   85099.0  141289.0  139467.0  142193.0   \n",
      "mean        1015.0       4.0       5.0      17.0      22.0       2.0   \n",
      "std            7.0       3.0       3.0       6.0       7.0       8.0   \n",
      "min          977.0       0.0       0.0      -7.0      -5.0       0.0   \n",
      "25%         1010.0       1.0       2.0      12.0      17.0       0.0   \n",
      "50%         1015.0       5.0       5.0      17.0      21.0       0.0   \n",
      "75%         1020.0       7.0       7.0      22.0      26.0       1.0   \n",
      "max         1040.0       9.0       9.0      40.0      47.0     371.0   \n",
      "\n",
      "           Year     Month       Day  \n",
      "count  142193.0  142193.0  142193.0  \n",
      "mean     2013.0       6.0      16.0  \n",
      "std         3.0       3.0       9.0  \n",
      "min      2007.0       1.0       1.0  \n",
      "25%      2011.0       3.0       8.0  \n",
      "50%      2013.0       6.0      16.0  \n",
      "75%      2015.0       9.0      23.0  \n",
      "max      2017.0      12.0      31.0   2\n"
     ]
    }
   ],
   "source": [
    "# view summary statistics in numerical variables to check for outliers\n",
    "print(round(dataset[numerical].describe()),2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'WindSpeed3pm')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x1080 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot box plot to check outliers\n",
    "plt.figure(figsize=(10,15))\n",
    "\n",
    "plt.subplot(2, 2, 1)\n",
    "fig = sns.boxplot(y=dataset['Rainfall'])\n",
    "fig.set_ylabel('Rainfall')\n",
    "\n",
    "plt.subplot(2, 2, 2)\n",
    "fig = sns.boxplot(y=dataset[\"Evaporation\"])\n",
    "fig.set_ylabel('Evaporation')\n",
    "\n",
    "plt.subplot(2, 2, 3)\n",
    "fig = sns.boxplot(y=dataset['WindSpeed9am'])\n",
    "fig.set_ylabel('WindSpeed9am')\n",
    "\n",
    "plt.subplot(2, 2, 4)\n",
    "fig = sns.boxplot(y=dataset['WindSpeed3pm'])\n",
    "fig.set_ylabel('WindSpeed3pm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'RainTomorrow')"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x1080 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot histogram to check distribution\n",
    "plt.figure(figsize=(10,15))\n",
    "\n",
    "plt.subplot(2, 2, 1)\n",
    "fig = dataset.Rainfall.hist(bins=10)\n",
    "fig.set_xlabel('Rainfall')\n",
    "fig.set_ylabel('RainTomorrow')\n",
    "\n",
    "plt.subplot(2, 2, 2)\n",
    "fig = dataset.Evaporation.hist(bins=10)\n",
    "fig.set_xlabel('Evaporation')\n",
    "fig.set_ylabel('RainTomorrow')\n",
    "\n",
    "plt.subplot(2, 2, 3)\n",
    "fig = dataset.WindSpeed9am.hist(bins=10)\n",
    "fig.set_xlabel('WindSpeed9am')\n",
    "fig.set_ylabel('RainTomorrow')\n",
    "\n",
    "plt.subplot(2, 2, 4)\n",
    "fig = dataset.WindSpeed3pm.hist(bins=10)\n",
    "fig.set_xlabel('WindSpeed3pm')\n",
    "fig.set_ylabel('RainTomorrow')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Outliers are values < -2.4000000000000004 or > 3.2\n"
     ]
    }
   ],
   "source": [
    "# find outliers for Rainfall variable\n",
    "\n",
    "IQR = dataset.Rainfall.quantile(0.75) - dataset.Rainfall.quantile(0.25)\n",
    "Rainfall_Lower_fence = dataset.Rainfall.quantile(0.25) - (IQR * 3)\n",
    "Rainfall_Upper_fence = dataset.Rainfall.quantile(0.75) + (IQR * 3)\n",
    "print('Outliers are values < {lowerboundary} or > {upperboundary}'.format(lowerboundary=Rainfall_Lower_fence, upperboundary=Rainfall_Upper_fence))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of outliers are 20462\n"
     ]
    }
   ],
   "source": [
    "print('Number of outliers are {}'. format(dataset[(dataset.Rainfall> Rainfall_Upper_fence) | (dataset.Rainfall< Rainfall_Lower_fence)]['Rainfall'].count()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Outliers are values < -11.800000000000002 or > 21.800000000000004\n"
     ]
    }
   ],
   "source": [
    "# find outliers for Evaporation variable\n",
    "\n",
    "IQR = dataset.Evaporation.quantile(0.75) - dataset.Evaporation.quantile(0.25)\n",
    "Evaporation_Lower_fence = dataset.Evaporation.quantile(0.25) - (IQR * 3)\n",
    "Evaporation_Upper_fence = dataset.Evaporation.quantile(0.75) + (IQR * 3)\n",
    "print('Outliers are values < {lowerboundary} or > {upperboundary}'.format(lowerboundary=Evaporation_Lower_fence, upperboundary=Evaporation_Upper_fence))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of outliers are 471\n"
     ]
    }
   ],
   "source": [
    "print('Number of outliers are {}'. format(dataset[(dataset.Evaporation> Evaporation_Upper_fence) | (dataset.Evaporation< Evaporation_Lower_fence)]['Evaporation'].count()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Outliers are values < -29.0 or > 55.0\n"
     ]
    }
   ],
   "source": [
    "# find outliers for WindSpeed9am variable\n",
    "\n",
    "IQR = dataset.WindSpeed9am.quantile(0.75) - dataset.WindSpeed9am.quantile(0.25)\n",
    "WindSpeed9am_Lower_fence = dataset.WindSpeed9am.quantile(0.25) - (IQR * 3)\n",
    "WindSpeed9am_Upper_fence = dataset.WindSpeed9am.quantile(0.75) + (IQR * 3)\n",
    "print('Outliers are values < {lowerboundary} or > {upperboundary}'.format(lowerboundary=WindSpeed9am_Lower_fence, upperboundary=WindSpeed9am_Upper_fence))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of outliers are 107\n"
     ]
    }
   ],
   "source": [
    "print('Number of outliers are {}'. format(dataset[(dataset.WindSpeed9am> WindSpeed9am_Upper_fence) | (dataset.WindSpeed9am< WindSpeed9am_Lower_fence)]['WindSpeed9am'].count()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Outliers are values < -20.0 or > 57.0\n"
     ]
    }
   ],
   "source": [
    "# find outliers for WindSpeed3pm variable\n",
    "\n",
    "IQR = dataset.WindSpeed3pm.quantile(0.75) - dataset.WindSpeed3pm.quantile(0.25)\n",
    "WindSpeed3pm_Lower_fence = dataset.WindSpeed3pm.quantile(0.25) - (IQR * 3)\n",
    "WindSpeed3pm_Upper_fence = dataset.WindSpeed3pm.quantile(0.75) + (IQR * 3)\n",
    "print('Outliers are values < {lowerboundary} or > {upperboundary}'.format(lowerboundary=WindSpeed3pm_Lower_fence, upperboundary=WindSpeed3pm_Upper_fence))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of outliers are 139563\n"
     ]
    }
   ],
   "source": [
    "print('Number of outliers are {}'. format(dataset[(dataset.WindSpeed3pm> WindSpeed3pm_Lower_fence) | (dataset.WindSpeed3pm< WindSpeed3pm_Upper_fence)]['WindSpeed3pm'].count()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "def max_value(dataset, variable, top):\n",
    "    return np.where(dataset[variable]>top, top, dataset[variable])\n",
    "\n",
    "\n",
    "dataset['Rainfall'] = max_value(dataset, 'Rainfall', Rainfall_Upper_fence)\n",
    "dataset['Evaporation'] = max_value(dataset, 'Evaporation', Evaporation_Upper_fence)\n",
    "dataset['WindSpeed9am'] = max_value(dataset, 'WindSpeed9am', WindSpeed9am_Upper_fence)\n",
    "dataset['WindSpeed3pm'] = max_value(dataset, 'WindSpeed3pm', 57)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of outliers are 0\n"
     ]
    }
   ],
   "source": [
    "print('Number of outliers are {}'. format(dataset[(dataset.Rainfall> Rainfall_Upper_fence) | (dataset.Rainfall< Rainfall_Lower_fence)]['Rainfall'].count()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of outliers are 0\n"
     ]
    }
   ],
   "source": [
    "print('Number of outliers are {}'. format(dataset[(dataset.Evaporation> Evaporation_Upper_fence) | (dataset.Evaporation< Evaporation_Lower_fence)]['Evaporation'].count()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of outliers are 0\n"
     ]
    }
   ],
   "source": [
    "print('Number of outliers are {}'. format(dataset[(dataset.WindSpeed9am> WindSpeed9am_Upper_fence) | (dataset.WindSpeed9am< WindSpeed9am_Lower_fence)]['WindSpeed9am'].count()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of outliers are 139563\n"
     ]
    }
   ],
   "source": [
    "print('Number of outliers are {}'. format(dataset[(dataset.WindSpeed3pm> WindSpeed3pm_Lower_fence) | (dataset.WindSpeed3pm< WindSpeed3pm_Upper_fence)]['WindSpeed3pm'].count()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replace NaN with default values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MinTemp            637\n",
      "MaxTemp            322\n",
      "Rainfall          1406\n",
      "Evaporation      60843\n",
      "Sunshine         67816\n",
      "WindGustDir       9330\n",
      "WindGustSpeed     9270\n",
      "WindDir9am       10013\n",
      "WindDir3pm        3778\n",
      "WindSpeed9am      1348\n",
      "WindSpeed3pm      2630\n",
      "Humidity9am       1774\n",
      "Humidity3pm       3610\n",
      "Pressure9am      14014\n",
      "Pressure3pm      13981\n",
      "Cloud9am         53657\n",
      "Cloud3pm         57094\n",
      "Temp9am            904\n",
      "Temp3pm           2726\n",
      "RainToday         1406\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "nullValues = [var for var in dataset.columns if dataset[var].isnull().sum()!=0]\n",
    "print(dataset[nullValues].isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical = [var for var in nullValues if dataset[var].dtype=='O']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Location  MinTemp  MaxTemp  Rainfall  Evaporation  Sunshine WindGustDir  \\\n",
      "0   Albury     13.4     22.9       0.6          NaN       NaN           W   \n",
      "1   Albury      7.4     25.1       0.0          NaN       NaN         WNW   \n",
      "2   Albury     12.9     25.7       0.0          NaN       NaN         WSW   \n",
      "3   Albury      9.2     28.0       0.0          NaN       NaN          NE   \n",
      "4   Albury     17.5     32.3       1.0          NaN       NaN           W   \n",
      "\n",
      "   WindGustSpeed WindDir9am WindDir3pm  ...  Cloud9am  Cloud3pm  Temp9am  \\\n",
      "0           44.0          W        WNW  ...       8.0       NaN     16.9   \n",
      "1           44.0        NNW        WSW  ...       NaN       NaN     17.2   \n",
      "2           46.0          W        WSW  ...       NaN       2.0     21.0   \n",
      "3           24.0         SE          E  ...       NaN       NaN     18.1   \n",
      "4           41.0        ENE         NW  ...       7.0       8.0     17.8   \n",
      "\n",
      "   Temp3pm  RainToday  RISK_MM  RainTomorrow  Year  Month  Day  \n",
      "0     21.8         No      0.0            No  2008     12    1  \n",
      "1     24.3         No      0.0            No  2008     12    2  \n",
      "2     23.2         No      0.0            No  2008     12    3  \n",
      "3     26.5         No      1.0            No  2008     12    4  \n",
      "4     29.7         No      0.2            No  2008     12    5  \n",
      "\n",
      "[5 rows x 26 columns]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.impute import SimpleImputer \n",
    "categoricalImputer = SimpleImputer(missing_values=np.nan,strategy='constant') \n",
    "categoricalImputer.fit(dataset[categorical]) \n",
    "dataset[categorical]=categoricalImputer.transform(dataset[categorical])\n",
    "print(dataset.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "numerical = [var for var in dataset.columns if dataset[var].dtype!='O']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Location  MinTemp  MaxTemp  Rainfall  Evaporation  Sunshine WindGustDir  \\\n",
      "0   Albury     13.4     22.9       0.6     5.404478  7.624853           W   \n",
      "1   Albury      7.4     25.1       0.0     5.404478  7.624853         WNW   \n",
      "2   Albury     12.9     25.7       0.0     5.404478  7.624853         WSW   \n",
      "3   Albury      9.2     28.0       0.0     5.404478  7.624853          NE   \n",
      "4   Albury     17.5     32.3       1.0     5.404478  7.624853           W   \n",
      "\n",
      "   WindGustSpeed WindDir9am WindDir3pm  ...  Cloud9am  Cloud3pm  Temp9am  \\\n",
      "0           44.0          W        WNW  ...  8.000000  4.503167     16.9   \n",
      "1           44.0        NNW        WSW  ...  4.437189  4.503167     17.2   \n",
      "2           46.0          W        WSW  ...  4.437189  2.000000     21.0   \n",
      "3           24.0         SE          E  ...  4.437189  4.503167     18.1   \n",
      "4           41.0        ENE         NW  ...  7.000000  8.000000     17.8   \n",
      "\n",
      "   Temp3pm  RainToday  RISK_MM  RainTomorrow    Year  Month  Day  \n",
      "0     21.8         No      0.0            No  2008.0   12.0  1.0  \n",
      "1     24.3         No      0.0            No  2008.0   12.0  2.0  \n",
      "2     23.2         No      0.0            No  2008.0   12.0  3.0  \n",
      "3     26.5         No      1.0            No  2008.0   12.0  4.0  \n",
      "4     29.7         No      0.2            No  2008.0   12.0  5.0  \n",
      "\n",
      "[5 rows x 26 columns]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.impute import SimpleImputer \n",
    "numericalImputer = SimpleImputer(missing_values=np.nan,strategy='mean') \n",
    "numericalImputer.fit(dataset[numerical]) \n",
    "dataset[numerical]=numericalImputer.transform(dataset[numerical])\n",
    "print(dataset.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Split data for model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = dataset.drop(['RainTomorrow'], axis=1)  # get all row data expect RainTomorrow\n",
    "y = dataset['RainTomorrow']  # get the RainTomorrow column depentant variable data for all rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Location  MinTemp  MaxTemp  Rainfall  Evaporation  Sunshine WindGustDir  \\\n",
      "0   Albury     13.4     22.9       0.6     5.404478  7.624853           W   \n",
      "1   Albury      7.4     25.1       0.0     5.404478  7.624853         WNW   \n",
      "2   Albury     12.9     25.7       0.0     5.404478  7.624853         WSW   \n",
      "3   Albury      9.2     28.0       0.0     5.404478  7.624853          NE   \n",
      "4   Albury     17.5     32.3       1.0     5.404478  7.624853           W   \n",
      "\n",
      "   WindGustSpeed WindDir9am WindDir3pm  ...  Pressure3pm  Cloud9am  Cloud3pm  \\\n",
      "0           44.0          W        WNW  ...       1007.1  8.000000  4.503167   \n",
      "1           44.0        NNW        WSW  ...       1007.8  4.437189  4.503167   \n",
      "2           46.0          W        WSW  ...       1008.7  4.437189  2.000000   \n",
      "3           24.0         SE          E  ...       1012.8  4.437189  4.503167   \n",
      "4           41.0        ENE         NW  ...       1006.0  7.000000  8.000000   \n",
      "\n",
      "   Temp9am  Temp3pm  RainToday  RISK_MM    Year  Month  Day  \n",
      "0     16.9     21.8         No      0.0  2008.0   12.0  1.0  \n",
      "1     17.2     24.3         No      0.0  2008.0   12.0  2.0  \n",
      "2     21.0     23.2         No      0.0  2008.0   12.0  3.0  \n",
      "3     18.1     26.5         No      1.0  2008.0   12.0  4.0  \n",
      "4     17.8     29.7         No      0.2  2008.0   12.0  5.0  \n",
      "\n",
      "[5 rows x 25 columns]\n"
     ]
    }
   ],
   "source": [
    "print(x.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0     No\n",
      "1     No\n",
      "2     No\n",
      "3     No\n",
      "4     No\n",
      "5     No\n",
      "6     No\n",
      "7     No\n",
      "8    Yes\n",
      "9     No\n",
      "Name: RainTomorrow, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(y[:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Encoding categorical data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "#encoding independent variable "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   MinTemp  MaxTemp  Rainfall  Evaporation  Sunshine  WindGustSpeed  \\\n",
      "0     13.4     22.9       0.6     5.404478  7.624853           44.0   \n",
      "1      7.4     25.1       0.0     5.404478  7.624853           44.0   \n",
      "2     12.9     25.7       0.0     5.404478  7.624853           46.0   \n",
      "3      9.2     28.0       0.0     5.404478  7.624853           24.0   \n",
      "4     17.5     32.3       1.0     5.404478  7.624853           41.0   \n",
      "\n",
      "   WindSpeed9am  WindSpeed3pm  Humidity9am  Humidity3pm  ...  WindDir3pm_SSE  \\\n",
      "0          20.0          24.0         71.0         22.0  ...               0   \n",
      "1           4.0          22.0         44.0         25.0  ...               0   \n",
      "2          19.0          26.0         38.0         30.0  ...               0   \n",
      "3          11.0           9.0         45.0         16.0  ...               0   \n",
      "4           7.0          20.0         82.0         33.0  ...               0   \n",
      "\n",
      "   WindDir3pm_SSW  WindDir3pm_SW  WindDir3pm_W  WindDir3pm_WNW  \\\n",
      "0               0              0             0               1   \n",
      "1               0              0             0               0   \n",
      "2               0              0             0               0   \n",
      "3               0              0             0               0   \n",
      "4               0              0             0               0   \n",
      "\n",
      "   WindDir3pm_WSW  WindDir3pm_missing_value  RainToday_No  RainToday_Yes  \\\n",
      "0               0                         0             1              0   \n",
      "1               1                         0             1              0   \n",
      "2               1                         0             1              0   \n",
      "3               0                         0             1              0   \n",
      "4               0                         0             1              0   \n",
      "\n",
      "   RainToday_missing_value  \n",
      "0                        0  \n",
      "1                        0  \n",
      "2                        0  \n",
      "3                        0  \n",
      "4                        0  \n",
      "\n",
      "[5 rows x 123 columns]\n"
     ]
    }
   ],
   "source": [
    "x = pd.get_dummies(x)\n",
    "print(x.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Encoding dependent variable "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 0 0 0 0 0 1 0]\n"
     ]
    }
   ],
   "source": [
    "# use LabelEncoder to replace purchased (dependent variable) with 0 and 1 \n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "y= LabelEncoder().fit_transform(y)\n",
    "print(y[:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Splitting the dataset into training and test set  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.3,random_state = 0) # func returns train and test data. It takes dataset and then split size test_size =0.3 means 30% data is for test and rest for training and random_state \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       MinTemp  MaxTemp  Rainfall  Evaporation  Sunshine  WindGustSpeed  \\\n",
      "95444     13.0     21.0       3.2     5.404478  6.000000      31.000000   \n",
      "27932     19.5     22.8       3.2     5.404478  7.624853      35.000000   \n",
      "15097     21.0     33.4       0.0     5.404478  7.624853      39.984292   \n",
      "94607     10.3     19.8       3.2     2.800000  5.500000      54.000000   \n",
      "99888     15.5     25.3       0.2     5.404478  7.624853      31.000000   \n",
      "\n",
      "       WindSpeed9am  WindSpeed3pm  Humidity9am  Humidity3pm  ...  \\\n",
      "95444           6.0          17.0         89.0         70.0  ...   \n",
      "27932          13.0           0.0         99.0         83.0  ...   \n",
      "15097           4.0          19.0         26.0         17.0  ...   \n",
      "94607           9.0          20.0         78.0         50.0  ...   \n",
      "99888           4.0          20.0         80.0         77.0  ...   \n",
      "\n",
      "       WindDir3pm_SSE  WindDir3pm_SSW  WindDir3pm_SW  WindDir3pm_W  \\\n",
      "95444               0               0              1             0   \n",
      "27932               0               0              0             0   \n",
      "15097               0               0              0             0   \n",
      "94607               0               0              0             0   \n",
      "99888               0               0              1             0   \n",
      "\n",
      "       WindDir3pm_WNW  WindDir3pm_WSW  WindDir3pm_missing_value  RainToday_No  \\\n",
      "95444               0               0                         0             0   \n",
      "27932               0               0                         1             0   \n",
      "15097               0               0                         0             1   \n",
      "94607               0               0                         0             0   \n",
      "99888               0               0                         0             1   \n",
      "\n",
      "       RainToday_Yes  RainToday_missing_value  \n",
      "95444              1                        0  \n",
      "27932              1                        0  \n",
      "15097              0                        0  \n",
      "94607              1                        0  \n",
      "99888              0                        0  \n",
      "\n",
      "[5 rows x 123 columns]\n"
     ]
    }
   ],
   "source": [
    "print(x_train.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        MinTemp  MaxTemp  Rainfall  Evaporation   Sunshine  WindGustSpeed  \\\n",
      "86232      17.4     29.0       0.0     3.600000  11.100000           33.0   \n",
      "57576       6.8     14.4       0.8     0.800000   7.624853           46.0   \n",
      "124071     10.1     15.4       3.2     5.404478   7.624853           31.0   \n",
      "117955     14.4     33.4       0.0     8.000000  11.600000           41.0   \n",
      "133468      6.8     14.3       3.2     0.200000   7.300000           28.0   \n",
      "\n",
      "        WindSpeed9am  WindSpeed3pm  Humidity9am  Humidity3pm  ...  \\\n",
      "86232           11.0          19.0         63.0         61.0  ...   \n",
      "57576           17.0          22.0         80.0         55.0  ...   \n",
      "124071          13.0           9.0         70.0         61.0  ...   \n",
      "117955           9.0          17.0         40.0         23.0  ...   \n",
      "133468          15.0          13.0         92.0         47.0  ...   \n",
      "\n",
      "        WindDir3pm_SSE  WindDir3pm_SSW  WindDir3pm_SW  WindDir3pm_W  \\\n",
      "86232                0               0              0             0   \n",
      "57576                0               0              0             0   \n",
      "124071               1               0              0             0   \n",
      "117955               0               0              1             0   \n",
      "133468               0               0              0             0   \n",
      "\n",
      "        WindDir3pm_WNW  WindDir3pm_WSW  WindDir3pm_missing_value  \\\n",
      "86232                0               0                         0   \n",
      "57576                0               0                         0   \n",
      "124071               0               0                         0   \n",
      "117955               0               0                         0   \n",
      "133468               0               0                         0   \n",
      "\n",
      "        RainToday_No  RainToday_Yes  RainToday_missing_value  \n",
      "86232              1              0                        0  \n",
      "57576              1              0                        0  \n",
      "124071             0              1                        0  \n",
      "117955             1              0                        0  \n",
      "133468             0              1                        0  \n",
      "\n",
      "[5 rows x 123 columns]\n"
     ]
    }
   ],
   "source": [
    "print(x_test.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 1 0 1 0 0 0 0 0 0]\n"
     ]
    }
   ],
   "source": [
    "print(y_train[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 0 0 0 1 1 0 0]\n"
     ]
    }
   ],
   "source": [
    "print(y_test[:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature scaling "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.50356295 0.48771267 1.         ... 0.         1.         0.        ]\n",
      " [0.65795724 0.52173913 1.         ... 0.         1.         0.        ]\n",
      " [0.6935867  0.7221172  0.         ... 1.         0.         0.        ]\n",
      " ...\n",
      " [0.74109264 0.80718336 0.         ... 1.         0.         0.        ]\n",
      " [0.66508314 0.68241966 0.         ... 1.         0.         0.        ]\n",
      " [0.59857482 0.66162571 0.         ... 1.         0.         0.        ]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler = MinMaxScaler()\n",
    "x_train= scaler.fit_transform(x_train) \n",
    "x_test = scaler.transform(x_test)\n",
    "print(x_train[:10,:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.60807601 0.6389414  0.         ... 1.         0.         0.        ]\n",
      " [0.35629454 0.36294896 0.25       ... 1.         0.         0.        ]\n",
      " [0.43467933 0.38185255 1.         ... 0.         1.         0.        ]\n",
      " ...\n",
      " [0.52256532 0.47069943 0.         ... 1.         0.         0.        ]\n",
      " [0.67458432 0.59546314 0.125      ... 1.         0.         0.        ]\n",
      " [0.39667458 0.47258979 0.         ... 1.         0.         0.        ]]\n"
     ]
    }
   ],
   "source": [
    "print(x_test[:10,:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Build Model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(random_state=0, solver='liblinear')"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "classifier = LogisticRegression(solver='liblinear', random_state=0)\n",
    "classifier.fit(x_train,y_train) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "#predicting the test set results\n",
    "y_pred = classifier.predict(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluate Model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[32426   610]\n",
      " [ 3445  6177]]\n"
     ]
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test,y_pred)\n",
    "print(cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.90      0.98      0.94     33036\n",
      "           1       0.91      0.64      0.75      9622\n",
      "\n",
      "    accuracy                           0.90     42658\n",
      "   macro avg       0.91      0.81      0.85     42658\n",
      "weighted avg       0.91      0.90      0.90     42658\n",
      "\n"
     ]
    }
   ],
   "source": [
    "cr = classification_report(y_test,y_pred)\n",
    "print(cr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9049416287683436"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6650264570537755\n"
     ]
    }
   ],
   "source": [
    "average_precision= average_precision_score(y_test,y_pred)\n",
    "print(average_precision)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6419663271669092"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9101222926182407"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precision_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7528795173380463"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import precision_recall_curve\n",
    "from sklearn.metrics import plot_precision_recall_curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '2-class Precision-Recall curve: AP=0.67')"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "disp = plot_precision_recall_curve(classifier, x_test, y_test)\n",
    "disp.ax_.set_title('2-class Precision-Recall curve: '\n",
    "                   'AP={0:0.2f}'.format(average_precision))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}