Data Preprocessing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "raised-warrior",
   "metadata": {},
   "source": [
    "# Rainfall Prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "novel-sculpture",
   "metadata": {},
   "source": [
    "## Importing the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "million-cookbook",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "from sklearn import preprocessing\n",
    "import scipy.stats as stat"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ignored-shanghai",
   "metadata": {},
   "source": [
    "## Loading the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "raised-laser",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>WindDir3pm</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>RainToday</th>\n",
       "      <th>RainTomorrow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2008-12-01</td>\n",
       "      <td>Albury</td>\n",
       "      <td>13.4</td>\n",
       "      <td>22.9</td>\n",
       "      <td>0.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>W</td>\n",
       "      <td>44.0</td>\n",
       "      <td>W</td>\n",
       "      <td>WNW</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>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2008-12-02</td>\n",
       "      <td>Albury</td>\n",
       "      <td>7.4</td>\n",
       "      <td>25.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WNW</td>\n",
       "      <td>44.0</td>\n",
       "      <td>NNW</td>\n",
       "      <td>WSW</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>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2008-12-03</td>\n",
       "      <td>Albury</td>\n",
       "      <td>12.9</td>\n",
       "      <td>25.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WSW</td>\n",
       "      <td>46.0</td>\n",
       "      <td>W</td>\n",
       "      <td>WSW</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>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2008-12-04</td>\n",
       "      <td>Albury</td>\n",
       "      <td>9.2</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NE</td>\n",
       "      <td>24.0</td>\n",
       "      <td>SE</td>\n",
       "      <td>E</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>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2008-12-05</td>\n",
       "      <td>Albury</td>\n",
       "      <td>17.5</td>\n",
       "      <td>32.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>W</td>\n",
       "      <td>41.0</td>\n",
       "      <td>ENE</td>\n",
       "      <td>NW</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>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\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 WindDir3pm  WindSpeed9am  \\\n",
       "0           W           44.0          W        WNW          20.0   \n",
       "1         WNW           44.0        NNW        WSW           4.0   \n",
       "2         WSW           46.0          W        WSW          19.0   \n",
       "3          NE           24.0         SE          E          11.0   \n",
       "4           W           41.0        ENE         NW           7.0   \n",
       "\n",
       "   WindSpeed3pm  Humidity9am  Humidity3pm  Pressure9am  Pressure3pm  Cloud9am  \\\n",
       "0          24.0         71.0         22.0       1007.7       1007.1       8.0   \n",
       "1          22.0         44.0         25.0       1010.6       1007.8       NaN   \n",
       "2          26.0         38.0         30.0       1007.6       1008.7       NaN   \n",
       "3           9.0         45.0         16.0       1017.6       1012.8       NaN   \n",
       "4          20.0         82.0         33.0       1010.8       1006.0       7.0   \n",
       "\n",
       "   Cloud3pm  Temp9am  Temp3pm RainToday RainTomorrow  \n",
       "0       NaN     16.9     21.8        No           No  \n",
       "1       NaN     17.2     24.3        No           No  \n",
       "2       2.0     21.0     23.2        No           No  \n",
       "3       NaN     18.1     26.5        No           No  \n",
       "4       8.0     17.8     29.7        No           No  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"weatherAUS.CSV\")\n",
    "pd.set_option(\"display.max_columns\", None)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "defined-essex",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(145460, 23)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "broad-swiss",
   "metadata": {},
   "source": [
    "## EDA & Feature Engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "caring-width",
   "metadata": {},
   "source": [
    "### Identifying Numerical and Categorical features of the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "superior-feedback",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Numerical Features Count 16\n",
      "Discrete Features Count 2\n",
      "Continuous Features Count 14\n",
      "Categorical Features Count 7\n"
     ]
    }
   ],
   "source": [
    "numerical_feature = [feature for feature in df.columns if df[feature].dtypes != 'O']\n",
    "discrete_feature = [feature for feature in numerical_feature if len(df[feature].unique()) < 25]\n",
    "continuous_feature = [feature for feature in numerical_feature if feature not in discrete_feature]\n",
    "categorical_feature = [feature for feature in df.columns if feature not in numerical_feature]\n",
    "\n",
    "print(\"Numerical Features Count {}\".format(len(numerical_feature)))\n",
    "print(\"Discrete Features Count {}\".format(len(discrete_feature)))\n",
    "print(\"Continuous Features Count {}\".format(len(continuous_feature)))\n",
    "print(\"Categorical Features Count {}\".format(len(categorical_feature)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "minute-palestinian",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['MinTemp', 'MaxTemp', 'Rainfall', 'Evaporation', 'Sunshine', 'WindGustSpeed', 'WindSpeed9am', 'WindSpeed3pm', 'Humidity9am', 'Humidity3pm', 'Pressure9am', 'Pressure3pm', 'Cloud9am', 'Cloud3pm', 'Temp9am', 'Temp3pm']\n"
     ]
    }
   ],
   "source": [
    "print(numerical_feature)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "equal-update",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Cloud9am', 'Cloud3pm']\n"
     ]
    }
   ],
   "source": [
    "print(discrete_feature)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "varied-constant",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['MinTemp', 'MaxTemp', 'Rainfall', 'Evaporation', 'Sunshine', 'WindGustSpeed', 'WindSpeed9am', 'WindSpeed3pm', 'Humidity9am', 'Humidity3pm', 'Pressure9am', 'Pressure3pm', 'Temp9am', 'Temp3pm']\n"
     ]
    }
   ],
   "source": [
    "print(continuous_feature)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "transsexual-ordering",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Date', 'Location', 'WindGustDir', 'WindDir9am', 'WindDir3pm', 'RainToday', 'RainTomorrow']\n"
     ]
    }
   ],
   "source": [
    "print(categorical_feature)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "consecutive-branch",
   "metadata": {},
   "source": [
    "### Handling Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "introductory-homework",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date                 0\n",
       "Location             0\n",
       "MinTemp           1485\n",
       "MaxTemp           1261\n",
       "Rainfall          3261\n",
       "Evaporation      62790\n",
       "Sunshine         69835\n",
       "WindGustDir      10326\n",
       "WindGustSpeed    10263\n",
       "WindDir9am       10566\n",
       "WindDir3pm        4228\n",
       "WindSpeed9am      1767\n",
       "WindSpeed3pm      3062\n",
       "Humidity9am       2654\n",
       "Humidity3pm       4507\n",
       "Pressure9am      15065\n",
       "Pressure3pm      15028\n",
       "Cloud9am         55888\n",
       "Cloud3pm         59358\n",
       "Temp9am           1767\n",
       "Temp3pm           3609\n",
       "RainToday         3261\n",
       "RainTomorrow      3267\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "neutral-clearance",
   "metadata": {},
   "source": [
    "### Using Random sample imputation for handling missing values of features 'Evaporation' and 'Sunshine'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "monthly-trick",
   "metadata": {},
   "outputs": [],
   "source": [
    "def randomsampleimputation(df,feature):\n",
    "    df[feature] = df[feature]\n",
    "    random_sample = df[feature].dropna().sample(df[feature].isnull().sum(), random_state = 0)\n",
    "    random_sample.index = df[df[feature].isnull()].index\n",
    "    df.loc[df[feature].isnull(), feature] = random_sample    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "plain-medicine",
   "metadata": {},
   "outputs": [],
   "source": [
    "randomsampleimputation(df, \"Evaporation\")\n",
    "randomsampleimputation(df, \"Sunshine\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "radical-motorcycle",
   "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>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>WindDir3pm</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>RainToday</th>\n",
       "      <th>RainTomorrow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2008-12-01</td>\n",
       "      <td>Albury</td>\n",
       "      <td>13.4</td>\n",
       "      <td>22.9</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>8.3</td>\n",
       "      <td>W</td>\n",
       "      <td>44.0</td>\n",
       "      <td>W</td>\n",
       "      <td>WNW</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>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2008-12-02</td>\n",
       "      <td>Albury</td>\n",
       "      <td>7.4</td>\n",
       "      <td>25.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>10.0</td>\n",
       "      <td>WNW</td>\n",
       "      <td>44.0</td>\n",
       "      <td>NNW</td>\n",
       "      <td>WSW</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>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2008-12-03</td>\n",
       "      <td>Albury</td>\n",
       "      <td>12.9</td>\n",
       "      <td>25.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.6</td>\n",
       "      <td>4.4</td>\n",
       "      <td>WSW</td>\n",
       "      <td>46.0</td>\n",
       "      <td>W</td>\n",
       "      <td>WSW</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>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2008-12-04</td>\n",
       "      <td>Albury</td>\n",
       "      <td>9.2</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.4</td>\n",
       "      <td>8.9</td>\n",
       "      <td>NE</td>\n",
       "      <td>24.0</td>\n",
       "      <td>SE</td>\n",
       "      <td>E</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>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2008-12-05</td>\n",
       "      <td>Albury</td>\n",
       "      <td>17.5</td>\n",
       "      <td>32.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>W</td>\n",
       "      <td>41.0</td>\n",
       "      <td>ENE</td>\n",
       "      <td>NW</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>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145455</th>\n",
       "      <td>2017-06-21</td>\n",
       "      <td>Uluru</td>\n",
       "      <td>2.8</td>\n",
       "      <td>23.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>7.8</td>\n",
       "      <td>E</td>\n",
       "      <td>31.0</td>\n",
       "      <td>SE</td>\n",
       "      <td>ENE</td>\n",
       "      <td>13.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1024.6</td>\n",
       "      <td>1020.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.1</td>\n",
       "      <td>22.4</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145456</th>\n",
       "      <td>2017-06-22</td>\n",
       "      <td>Uluru</td>\n",
       "      <td>3.6</td>\n",
       "      <td>25.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.6</td>\n",
       "      <td>13.5</td>\n",
       "      <td>NNW</td>\n",
       "      <td>22.0</td>\n",
       "      <td>SE</td>\n",
       "      <td>N</td>\n",
       "      <td>13.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1023.5</td>\n",
       "      <td>1019.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.9</td>\n",
       "      <td>24.5</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145457</th>\n",
       "      <td>2017-06-23</td>\n",
       "      <td>Uluru</td>\n",
       "      <td>5.4</td>\n",
       "      <td>26.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.8</td>\n",
       "      <td>11.0</td>\n",
       "      <td>N</td>\n",
       "      <td>37.0</td>\n",
       "      <td>SE</td>\n",
       "      <td>WNW</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1021.0</td>\n",
       "      <td>1016.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.5</td>\n",
       "      <td>26.1</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145458</th>\n",
       "      <td>2017-06-24</td>\n",
       "      <td>Uluru</td>\n",
       "      <td>7.8</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.6</td>\n",
       "      <td>13.2</td>\n",
       "      <td>SE</td>\n",
       "      <td>28.0</td>\n",
       "      <td>SSE</td>\n",
       "      <td>N</td>\n",
       "      <td>13.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1019.4</td>\n",
       "      <td>1016.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>15.1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145459</th>\n",
       "      <td>2017-06-25</td>\n",
       "      <td>Uluru</td>\n",
       "      <td>14.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ESE</td>\n",
       "      <td>ESE</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>1020.2</td>\n",
       "      <td>1017.9</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>20.9</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>145460 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Date Location  MinTemp  MaxTemp  Rainfall  Evaporation  \\\n",
       "0       2008-12-01   Albury     13.4     22.9       0.6          2.4   \n",
       "1       2008-12-02   Albury      7.4     25.1       0.0          3.6   \n",
       "2       2008-12-03   Albury     12.9     25.7       0.0          2.6   \n",
       "3       2008-12-04   Albury      9.2     28.0       0.0         18.4   \n",
       "4       2008-12-05   Albury     17.5     32.3       1.0          5.4   \n",
       "...            ...      ...      ...      ...       ...          ...   \n",
       "145455  2017-06-21    Uluru      2.8     23.4       0.0          1.4   \n",
       "145456  2017-06-22    Uluru      3.6     25.3       0.0          7.6   \n",
       "145457  2017-06-23    Uluru      5.4     26.9       0.0          6.8   \n",
       "145458  2017-06-24    Uluru      7.8     27.0       0.0          2.6   \n",
       "145459  2017-06-25    Uluru     14.9      NaN       0.0          1.4   \n",
       "\n",
       "        Sunshine WindGustDir  WindGustSpeed WindDir9am WindDir3pm  \\\n",
       "0            8.3           W           44.0          W        WNW   \n",
       "1           10.0         WNW           44.0        NNW        WSW   \n",
       "2            4.4         WSW           46.0          W        WSW   \n",
       "3            8.9          NE           24.0         SE          E   \n",
       "4            3.0           W           41.0        ENE         NW   \n",
       "...          ...         ...            ...        ...        ...   \n",
       "145455       7.8           E           31.0         SE        ENE   \n",
       "145456      13.5         NNW           22.0         SE          N   \n",
       "145457      11.0           N           37.0         SE        WNW   \n",
       "145458      13.2          SE           28.0        SSE          N   \n",
       "145459       0.7         NaN            NaN        ESE        ESE   \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",
       "145455          13.0          11.0         51.0         24.0       1024.6   \n",
       "145456          13.0           9.0         56.0         21.0       1023.5   \n",
       "145457           9.0           9.0         53.0         24.0       1021.0   \n",
       "145458          13.0           7.0         51.0         24.0       1019.4   \n",
       "145459          17.0          17.0         62.0         36.0       1020.2   \n",
       "\n",
       "        Pressure3pm  Cloud9am  Cloud3pm  Temp9am  Temp3pm RainToday  \\\n",
       "0            1007.1       8.0       NaN     16.9     21.8        No   \n",
       "1            1007.8       NaN       NaN     17.2     24.3        No   \n",
       "2            1008.7       NaN       2.0     21.0     23.2        No   \n",
       "3            1012.8       NaN       NaN     18.1     26.5        No   \n",
       "4            1006.0       7.0       8.0     17.8     29.7        No   \n",
       "...             ...       ...       ...      ...      ...       ...   \n",
       "145455       1020.3       NaN       NaN     10.1     22.4        No   \n",
       "145456       1019.1       NaN       NaN     10.9     24.5        No   \n",
       "145457       1016.8       NaN       NaN     12.5     26.1        No   \n",
       "145458       1016.5       3.0       2.0     15.1     26.0        No   \n",
       "145459       1017.9       8.0       8.0     15.0     20.9        No   \n",
       "\n",
       "       RainTomorrow  \n",
       "0                No  \n",
       "1                No  \n",
       "2                No  \n",
       "3                No  \n",
       "4                No  \n",
       "...             ...  \n",
       "145455           No  \n",
       "145456           No  \n",
       "145457           No  \n",
       "145458           No  \n",
       "145459          NaN  \n",
       "\n",
       "[145460 rows x 23 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "entitled-mistake",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date                 0\n",
       "Location             0\n",
       "MinTemp           1485\n",
       "MaxTemp           1261\n",
       "Rainfall          3261\n",
       "Evaporation          0\n",
       "Sunshine             0\n",
       "WindGustDir      10326\n",
       "WindGustSpeed    10263\n",
       "WindDir9am       10566\n",
       "WindDir3pm        4228\n",
       "WindSpeed9am      1767\n",
       "WindSpeed3pm      3062\n",
       "Humidity9am       2654\n",
       "Humidity3pm       4507\n",
       "Pressure9am      15065\n",
       "Pressure3pm      15028\n",
       "Cloud9am         55888\n",
       "Cloud3pm         59358\n",
       "Temp9am           1767\n",
       "Temp3pm           3609\n",
       "RainToday         3261\n",
       "RainTomorrow      3267\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "continued-blame",
   "metadata": {},
   "source": [
    "### Analyzing each continuous feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "norman-airfare",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\SWARNAVA\\anaconda3\\envs\\rain\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x1080 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\SWARNAVA\\anaconda3\\envs\\rain\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x1080 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\SWARNAVA\\anaconda3\\envs\\rain\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x1080 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\SWARNAVA\\anaconda3\\envs\\rain\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x1080 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\SWARNAVA\\anaconda3\\envs\\rain\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x1080 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\SWARNAVA\\anaconda3\\envs\\rain\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x1080 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\SWARNAVA\\anaconda3\\envs\\rain\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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5BwBff2DL6LYnd/QzkMywpKOZxmOoIIIEEcVBFYSIlI0qiDIqriA6WxO0NET57TNPGN22Yn4rc1saeHJHP+1NcZriUX65aR8ASzqaiZiRiEUmVUGMZLI0hE1M+WVPRUSmSgmijDJFfRDz2hJ8/HdWsWJ+2+g2M+P8sIpYtXAWi+c08djW4Ea3pR3BjXRN8eiEE0Q256SzPtrEpApCRMpFCaKMiiuIsVyQTxAnBgki/6G+JEwQjfEowxOc0TW/SFEiFiEWMbI5n9Q0HSIiY1EfRBllsjkSYR/C0Vy0ch5mcM6S2aMf8B0tDbQmgr+OxniU4QlO2JfK5BNElPyichlVESJSBkoQZZTJOa2R8SuIFfPbuPuDr+TkeS3s6gvuoM5XDwBN8chRlyYtlAqbohrikdEFi9IaySQiZVDRJiYzu9zMNppZl5ldV2J/wsxuC/c/ZGbLivYvNbNBM/twJeMsl3TWDxvFdDQr5rcSiRhLwgn8lhYmiIaJ90GMVhDRyOgcUEoQIlIOFUsQZhYFbgReB6wCrjazVUWHvRvodfcVwA3A9UX7Pwf8qFIxllsmm5tQH0Sh/AyvS+Y0jW4LmpgmlyAa4pHR91YTk4iUQyUriPOBLnff7O4jwHeANUXHrAG+ET6+HbjUwnkqzOyNwBZgQwVjLKt0buIVRN7JnS2c2N7IS8OOa4CWRIxUJjehjuqRgj6I/AiqtIa6ikgZVDJBLAK2FTzfHm4reYy7Z4A+YK6ZtQJ/Dny6gvGVXSabG12PeqJaEjEe+Nilh91Ml29uemH/wXHPT2WCJJIf5hrEoQpCRKauVoe5fgq4wd0Hj3aQmV1rZuvMbF13d/f0RHYUmUn0QRzN0o5mYhFj876D7O5P8qP1u/Axhq4eGsUUGZ1mXH0QIlIOlRzFtANYUvB8cbit1DHbzSwGtAP7gQuAK8zs74HZQM7Mku7+hcKT3f0m4CaA1atXV/VrczbnZN1HP6SnIh6NsKSjmc37Btk7kOTZPYPs7k+OrhWxdf8QLYkoc1sTo01MDbHCTmpVECIydZWsIB4GVprZcjNrAK4C1hYdsxa4Jnx8BXCPBy5292Xuvgz4J+BvipNDrck39cQj5bmkJ89rYdeBIDkA7O4LFgJyd6666UH+9kfPjL6vEUwMGFcFISJlVLEEEfYpvA+4E3ga+K67bzCzz5jZG8LDvkrQ59AFfAg4YihsvUiGN7aVo4IAWN7ZQmEdkF8pbuOeAXb2JdkZrkA3Eq4FYWajyUnzMYlIOVT0Rjl3vwO4o2jbJwseJ4Erx3mNT1UkuDIbrSDK0AcBsHROMPX3yvltrN/RN1pB3P9sMLHf/sGR8H2Dqb7hUHJSJ7WIlIPupC6T0QpikqOYxhKLRvjAq0+lOR7lqZ397O5PAXDfc0Fn/P6DwfNgNblgeg/dKCci5aQEUSblriAAZjXGAWhrirGnP0kyneXXW3qIRoyegyPkch6uRx057L3VSS0i5VCrw1zrTrn7IArNaoyzuy/Jw8/3kMrkuHjlPHIOB4bTo+tRF763bpQTkXJQgiiT/KR55awg8mY1xdnTn2Td872YweWnBwsQ9RxMMTySHV3HOmJG1Ex9ECJSFkoQZZLMlLcPolB7Y4zd/Ume3NHHis7W0TutuwdGODCUZk5zfPTYWNTUByEiZTGhBGFmP53ItuNZpSuIoZEsv36+hzMXt9PR2gBAV/cgI9kcs5sbRo/VqnIiUi5H7aQ2s0agGZhnZnOA/NfjWRw5r9Jx7WgVxK0PbS15ztsuWDqh1853Vg8kM5y5qJ25LQkAnth2AIA5hyUII6MKQkTKYLxRTH8CfAA4EXiEQwmiH6jpO5unW7LCFUTeWYvbmdMcxwx+s/0AAHNaCpuYIqRzqiBEZOqOmiDc/fPA583s/e7+L9MUU82YzDf//KR5lRnFFPw1RQxWLWwnFo0wuynOc3uDaThmN6mCEJHym9B9EO7+L2b2cmBZ4Tnu/s0KxVV3Kt0HAXDqgjaaGoIRS3NbE/QOpWmMR0a3AcQiEXVSi0hZTChBmNktwCnA40B+FRsHlCBClawg4tEIC2YlOHfp7NFtHS1B1VDY/xAca+qkFpGymOid1KuBVT7WogRCMh3Mqhq18icIgG//0YXMaz2UDPKPj0wQEYZH0hWJQUSOLxNNEE8CJwC7KhhLXUtlcsSihlUoQayY33rY8/xIpsJ7IECd1CJSPhNNEPOAp8zs10Aqv9Hd3zD2KceXZDo7uib0dMg3Mc0uriAi6qQWkfKYaIL4VCWDmAlS6dzogj3T4WhNTOqDEJFymOgopnsrHUi9S2ayZVmPeqKWhNNtdLYlDtse11QbIlImE51qY8DM+sOfpJllzay/0sHVk+muIF55aic/+dArj0gQsWhEk/WJSFlMKEG4e5u7z3L3WUAT8GbgXysaWZ1JZqa3D8LMjui4hmCYbdadrDqqRWSKJv2J5oEfAK8tfzj1a7oriLHk16XOT/0hInKsJnqj3JsKnkYI7otIViSiOjXdfRBjySepVCZHS2Kcg0VEjmKio5heX/A4AzwPrCl7NHUslc4Rr8BaEJOVn+pDFYSITNVERzH9YaUDqXfJTJaWhuov8R1TghCRMpnoKKbFZvZ9M9sb/nzPzBZXOrh6Uit9EPn1KPJrZIuIHKuJfuX9OnArcGX4/O3htssqEVQ9Sh3DKKaxphOfitEmpowqCBGZmol+onW6+9fdPRP+3Ax0jneSmV1uZhvNrMvMriuxP2Fmt4X7HzKzZeH2883s8fDnN2b2e5P5paqhViqIfAxqYhKRqZpogthvZm83s2j483Zg/9FOMLMocCPwOmAVcLWZrSo67N1Ar7uvAG4Arg+3PwmsdvdzgMuBfzOz6jfwH0XtjGIKYkipiUlEpmiin2jvAt4C7CaY0fUK4J3jnHM+0OXum919BPgOR458WgN8I3x8O3CpmZm7D7l7JtzeSLD2RM3K5px01iuyFsRkNcSCv9LBVGacI0VEjm6iCeIzwDXu3unu8wkSxqfHOWcRsK3g+fZwW8ljwoTQB8wFMLMLzGwDsB54T0HCGGVm15rZOjNb193dPcFfZWKS6Sxf/HkXz+87OO6xqbC9Pz6Nd1KPpTEerC6nBCEiUzXRT7Sz3L03/8Tde4BzKxPS6Hs85O6nAy8FPmZmjSWOucndV7v76s7OcbtEJuWF/UNs6x1ma8/QuMfmm3NqoYJoDCuIgaQWDRKRqZlogoiY2Zz8EzPrYPwRUDuAJQXPF4fbSh4T9jG0U9S34e5PA4PAGROMtSx29wc3ig9PoLM3P2KoEutRT1ZDLIIBg0lVECIyNRPt+P1H4EEz+4/w+ZXAX49zzsPASjNbTpAIrgLeVnTMWuAa4EGCfo173N3Dc7a5e8bMTgJeTHD39rTZ3TcMTDBB5CuIGriT2sxIxCP0K0GIyBRN9E7qb5rZOuBV4aY3uftT45yTMbP3AXcCUeBr7r7BzD4DrHP3tcBXgVvMrAvoIUgiABcB15lZGsgB73X3fZP95aZid1+wcN7wyPgJIjUNFcRk7plIxKLqgxCRKZvw0NEwIRw1KZQ45w7gjqJtnyx4nOTQzXeFx9wC3DKZ9yq33f1BBTGR+wmSNdQHAdAYj6iJSUSmrPqN5jVqV9/E+yBS6drpg4CgghhIqZNaRKamNj7RatDufIKYQBNTMlM7fRCgCkJEykMJYgyTGcVUkxWEEoSITFFtfKLVmGQ6y4GhNBELHrsf/UbuWqwgBtRJLSJTpARRQr55aV5rgpzDSPbo8xolR2qrgmiMRXWjnIhMWW18otWYfPPSCe3Bzdvj9UNs3neQeNRoa6qN+QQT8QjJdI70OIlNRORolCBKyFcQC2aFCWKcfoindvWzcn7bpNeDqJRELJiP6aCamURkCmrjE63G5CuIBW0TSxBP7+rntIWzKh7XROUn7FNHtYhMhRJECbv7krQ1xmhvjgOH+hhK6R5I0T2QYtWJtZMgEqMT9ilBiMixU4IooW84zZzmBprCb+LDR1l85+ld/QCctrBtWmKbiEMVhDqqReTYKUGUkExnaYxHChLE2BXEU2GCWFVTTUxaNEhEpk4JooRkOksiFiURD6bOPtoopqd39XNieyOzmxumL8Bx5Dup1cQkIlOhBFFCMp2jMR4hEk6dfbQKotY6qOFQBaGb5URkKpQgSkhlsqPt+E3x6Jgzug6NZOjaO8jpNdRBDYcqCM3HJCJToQRRQjKdGx0J1NQQHbOJ6ckd/eQczl4yexqjG188akQjpk5qEZkSJYgSkpksiYIKYqwmpt9sOwDAWYtnT1NkE2NmtDXG1EktIlOiBFFCKp2jMTZ+gnh8+wEWzW6isy0xneFNSGsipk5qEZkSJYgSUpksifihJqaxbpR7YvsBzl7SPp2hTVhbY1wJQkSmRAmihGRBBdHSEOPgSIZc0ZTf+wdTbOsZ5uwaa17Ka0vE1AchIlOiBFFCMIopuDTtzXFyfuQ9BU/s6ANqr4M6r60xxs6+4QmtqS0iUooSRJFszklnfXSo6Oym4Aa4A0Mjhx33zK4BgJob4pr3pvMWs61nmPf/+2NkNO23iBwDJYgi+W/c+Qpidjhh34Hhw5trdvcNM6sxRltjfHoDnKDfOWshf/H6Vdz91B5+uH5XtcMRkTqkBFEkFS4fmr9RbnZTkAD6hg5PELv6kqMLCtWqd7xsGU3xKI+Hw3FFRCZDCaJIvoLI3yiXiEdpikc5MHx4E9Pu/iQntDdNe3yTEY0Ypy1sY8PO/mqHIiJ1qKIJwswuN7ONZtZlZteV2J8ws9vC/Q+Z2bJw+2Vm9oiZrQ//fFUl4yx0qIkpOrptdnOcA0PFTUxJFs6q7QoC4IxF7Ty1s59czsc/WESkQMUShJlFgRuB1wGrgKvNbFXRYe8Get19BXADcH24fR/wenc/E7gGuKVScRY71MR06NK0N8XpK+iDSGdzdA+mar6JCeCME9sZTGV4oWcIAHfHXclCRMZXyQrifKDL3Te7+wjwHWBN0TFrgG+Ej28HLjUzc/fH3H1nuH0D0GRm03K78qEmpsMriN6CUUx7B1K4UxcJIr/S3YadwbDcf/35Jl79uXurGZKI1IlKJohFwLaC59vDbSWPcfcM0AfMLTrmzcCj7p4qfgMzu9bM1pnZuu7u7rIEnQxXj0sUVBCzmxpIpnOjyWN33zBQHwni1AVtxKPGkzuCfoj/enwHm7oP0nNwZJwzReR4F6t2AEdjZqcTNDu9ptR+d78JuAlg9erVZWk3SWWO7IPIr03dN5ymMR5ld1+QqxbWQYJoiEV40QltbNjZx/beIZ7dMwjA5u5BOlo6Ro+79aGtJc9/2wVLpyVOEak9lawgdgBLCp4vDreVPMbMYkA7sD98vhj4PvAOd99UwTgPM1pBxAoriPBeiLCjeldYQSycVdujmPLOWzqHX2/p4dsFSWBz98EqRiQi9aCSCeJhYKWZLTezBuAqYG3RMWsJOqEBrgDucXc3s9nAD4Hr3P2XFYzxCKUqiPxyovl+iN19SRrjEWY11XQBNupPXnkKZvDFn29i0ewmGmIRNu0brHZYIlLjKpYgwj6F9wF3Ak8D33X3DWb2GTN7Q3jYV4G5ZtYFfAjID4V9H7AC+KSZPR7+zK9UrIVKDXNta4zR3hTn0a29uDu7+5MsbG/CzKYjpClbNLuJ916yAoBLT5vPsrnNbNqrCkJEjq6iX4Hd/Q7gjqJtnyx4nASuLHHeXwF/VcnYxpIf5lrYxBQx49IXz+c/H9vBhp397O5LckId3ANR6NpXnMyuviRvv/Ak9vaneHbvQLVDEpEapzupi5SqIADOXTqHzrYEP1y/i2f3DNTFCKZCjfEof/umMzl1QRsnd7awdf8QaU3iJyJHoQRRJN9J3Rg7/NJEI8abzl1EIhahP5lhxfzWaoRXFid3tpLJOdvCm+dEREqpj17WaZTKZIlGjFj0yNx50twWPvDqU3nlizrpbK29ZUYn6uTOFgA2dR/k5M76TXQiUllKEEWC1eSOXljdu7E8N+VVyynzgqSwqXuQy1hQ5WhEpFapialIMp09ov9hpmlvjrNgVoJn96ijWkTGpgRRJJXJHTaCaaY6dUGbEoSIHNXM/yScpOOhggB40YI2ntszSFbTgIvIGJQgiiTTORLHQ4I4oY1UJscL+3XDnIiUpgRRJJXJHhdNTC86oQ1AzUwiMiaNYiqSSucOWyyoXo03O+uK+a2YwTO7B7j8jIXTGZqI1In6/yQss2Tm+OiDaG6IsbSjWRWEiIxJFUSRVHpmj2IqrCxaGmI8sGk/f377E5wyv5XWhP45iMghM/eT8BgdLxUEwKI5TRwYSnPbum389Ok91Q5HRGqMvjIWSaazNMaOjwRx8Yp5rOhs5a6ndtO1V+tDiMjhVEEUSWVyh61HPZPFohGWdDTz4hNmsf/gyOiCSCIioARxhOPlRrlCp4Qz024Kq4iRTI5ndvVXMyQRqQFKEAXcfUKT9c00C9oStCZibOoOEsQdT+7im796gT39ySpHJiLVdHx9Eo5jJFxA53i4k7qQmbFifitdewfZvG+Qh7f0ALC9V+tFiBzPlCAK5BcLmsnDXMdy7pLZDKezfOX+LSTiERKxCNt6h6sdlohUkUYxFUiNsdzo8WDlgjb+7FUr+cnTezht4Swe23rgsAris3du5LFtvfyPFfP4o4tOpuE4TKIixxsliALHcwUBMH9WI2+74CQA9g6kuP+5bpLpLPFohJsfeJ6cO7/s2s+Zi9q5eGVnlaMVkUo7Pj8JxzAcVhBNDcdfBVFsyZwmcg7/dPez3HD3swymMqNJ4fZHtlc5OhGZDkoQBQZTaQBNOQEsntMMwLbe4dEpwc9c1E4sYvQM6n4JkeNBRROEmV1uZhvNrMvMriuxP2Fmt4X7HzKzZeH2uWb2MzMbNLMvVDLGQoOpoIJoa1SCmNUUp70pzrN7BnihZ4j2pjhzmuPMaW6gRzfUiRwXKpYgzCwK3Ai8DlgFXG1mq4oOezfQ6+4rgBuA68PtSeATwIcrFV8pg8kMAC2qIAC4cHkHz+0d5Old/SztaMbM6GhpoOegEoTI8aCSFcT5QJe7b3b3EeA7wJqiY9YA3wgf3w5cambm7gfd/RcEiWLaHEwFCUJNTIGXnTKPtsYY6ayzbG7Q5NTR2sD+gyO4a6lSkZmukgliEbCt4Pn2cFvJY9w9A/QBcyf6BmZ2rZmtM7N13d3dUwwXBsIE0ZaIT/m1ZoKGWITLTluAASd3BtNxdDQ3MJLJqYoQOQ7U9Vdld78JuAlg9erVU/5Km68gWhIaxZS3elkHKxe00d4UJM25LQ0AvNAzxNzWRDVDE5EKq2QFsQNYUvB8cbit5DFmFgPagf0VjOmoBlMZGuMRYlEN7iqUTw4Ac8IEsa1H03CIzHSV/CR8GFhpZsvNrAG4ClhbdMxa4Jrw8RXAPV7Fxu3BVEb9D+PoyFcQ+5UgRGa6in0aunvGzN4H3AlEga+5+wYz+wywzt3XAl8FbjGzLqCHIIkAYGbPA7OABjN7I/Aad3+qUvFCMIpJCeLo4tEIsxpjbFUFITLjVfTT0N3vAO4o2vbJgsdJ4Moxzl1WydhKGUxlaNU9EOPqaGkYvXlORGYuNbYXGExlaGlQghjPvNYEW/YpQYjMdEoQBQaTGd1FPQHzWhPsGxyhbzhd7VBEpIKUIAocHMnoLuoJmBcOb31eVYTIjKYEUUCd1BMzrzUYybR532CVIxGRSlKCKKBO6onpaG0gYrClWxWEyEymBBFKZ3OkMjla1Uk9rlgkwpKOZjapiUlkRlOCCI1O1KcKYkJOnteiCkJkhlOCCA1oqu9JWT6vlS37DpLLaVZXkZlKCSJ0cCQ/k6sSxESc3NnCcDrLnoFpnZFdRKaREkRIiwVNzsnzWgB4YntflSMRkUpRgggNqA9iUs47aQ5LOpr4hzs3MpLJVTscEakAJYjQwZSamCajMR7lU68/na69g3ztl1uqHY6IVIASREhNTJN36WkLeM2qBXz2zo384rl91Q5HRMpMCSI0qCamY/LZt5zNivmtvOdbj7Bhp/ojRGYSJYhQPkFoNtfJmdUY5+Y/PJ9ZjTHe+fWHuXPDbv7vD9bzxPYD1Q5NRKZICSJ0MJWhuSFKNGLVDqXunNDeyM3vOp9UOsuf3PII3/rVVq6+6Vc80KVmJ5F6pgQR0nKjU3PqgjZu/eML+fs3n8XPP3wJi+c080ffXMeOA8PVDk1EjpE+EUMDmsl1Um59aOsR2952wVLOWNQOwFeuWc1rbriPv/ivJ/nyO1ZjpspMpN7oEzH0wv4hFs5urHYYda04aVzyok5+9ORuPnL7E7z5vMW87JS5VYpMRI6FmpiAkUyOjbsHRr/9Snm8/JR5vOSkOdyxfhdXf/lXvO/WR9k3mKp2WCIyQaoggGf3DDCSzXHGiUoQ5RSNGG8+bzFvOm8RX75vM/9yTxe/6NrHey85BcPYO5BieThlR6G3XbC0CtGKSDElCBgdv3+mKoiKaIxHef+lK7n8jBP48+89wd/c8czovhWdrbx61QKWdjQfcd5IJsee/mAywMVzmtSPITLNlCCAJ3f005aIlfyQkqkr7Jt403mLufTFC2iIRXhs2wHu3biXL927iUQsQjRinNLZGi7elOXL92+heyBoklpzzon81RvPoK0xXq1fQ+S4owQBrN/Rx+mLZhHRPRAVFzFjTkuwpvVFK+bx0mVzePj5Xg4MjZBM59i4u5+/WLsBgGVzm7n43EXsPzjC2sd3su75Xv7yjadz/vK5NMV1z4pIpVU0QZjZ5cDngSjwFXf/u6L9CeCbwEuA/cBb3f35cN/HgHcDWeDP3P3OSsSYyeZ4elc/f3DhSZV4eRlHIhblohXzRp/n3BkayZJKZ+loaRhtVnrxCW385Ok9vOvmdQBEDBbMauSiFfO4bNUClnQ0c2J7E7OaYmqKEimTiiUIM4sCNwKXAduBh81srbs/VXDYu4Fed19hZlcB1wNvNbNVwFXA6cCJwE/M7FR3z5Y7zq7uQVKZHGcuVv9DLYiY0ZqIHXFPyklzW7jmZct4YkcfB1MZkulsUFn8Zif/8cj20eMSsQizmuKk0lmG01kaY1HmtjawYFYjTQ1RErEIzQ0xFsxqpL0pTjQSvGe+GukdSjOUymAGc1sTLGxvZGF7EwvbG5nT0oC744A7ULiYnkE+LxmMJqng8aHfzSz4M/hByUxqWiUriPOBLnffDGBm3wHWAIUJYg3wqfDx7cAXLPgfswb4jrungC1m1hW+3oPlDnJWY5wPvHolLzlpTrlfWsosFo1w3tLD/57S2Ry7+pL0DafpG04zkEyTTGeJRiIkYhFGsjkGkxn29CfJ5JxM1kllsvQPZ8j6kculGhCPRnCcdHZ6llPNJ4r8n/WWMsbKcUf7TcY+R47F5Wcs5B/fcnbZX7eSCWIRsK3g+XbggrGOcfeMmfUBc8Ptvyo6d1HxG5jZtcC14dNBM9t4rMF+sPTmeUC9TSikmKdHPcYM9Rm3Yh7HU8Dn3nrMp4/Zvl7XndTufhNwU6Ve38zWufvqSr1+JSjm6VGPMUN9xq2Yq6eSd1LvAJYUPF8cbit5jJnFgHaCzuqJnCsiIhVUyQTxMLDSzJabWQNBp/PaomPWAteEj68A7nF3D7dfZWYJM1sOrAR+XcFYRUSkSMWamMI+hfcBdxIMc/2au28ws88A69x9LfBV4JawE7qHIIkQHvddgqa1DPCnlRjBNAEVa76qIMU8PeoxZqjPuBVzlZiXGMkhIiKi2VxFRKQkJQgRESlJCWIMZna5mW00sy4zu67a8ZRiZkvM7Gdm9pSZbTCz/xVu7zCzu83sufDPmrsL0MyiZvaYmf13+Hy5mT0UXu/bwoENNcPMZpvZ7Wb2jJk9bWYvq/XrbGYfDP9dPGlm/25mjbV2nc3sa2a218yeLNhW8rpa4J/D2J8ws/NqKOZ/CP9tPGFm3zez2QX7PhbGvNHMXluNmI+VEkQJBdOEvA5YBVwdTv9RazLA/3b3VcCFwJ+GcV4H/NTdVwI/DZ/Xmv8FPF3w/HrgBndfAfQSTMNSSz4P/NjdXwycTRB7zV5nM1sE/Bmw2t3PIBgokp/Oppau883A5UXbxrquryMY0biS4AbZL05TjMVu5siY7wbOcPezgGeBjwEUTRt0OfCv4edLXVCCKG10mhB3HwHy04TUFHff5e6Pho8HCD60FhHE+o3wsG8Ab6xKgGMws8XA7wBfCZ8b8CqC6VagxmI2s3bgFQSj7nD3EXc/QI1fZ4JRik3hPUbNwC5q7Dq7+30EIxgLjXVd1wDf9MCvgNlmtnBaAi1QKmZ3v8vdM+HTXxHcuwUF0wa5+xYgP21QXVCCKK3UNCFHTPVRS8xsGXAu8BCwwN13hbt2AwuqFdcY/gn4KJALn88FDhT8B6u1670c6Aa+HjaLfcXMWqjh6+zuO4DPAlsJEkMf8Ai1fZ3zxrqu9fL/8l3Aj8LH9RJzSUoQM4CZtQLfAz7g7v2F+8IbD2tmLLOZ/S6w190fqXYskxADzgO+6O7nAgcpak6qwes8h+Db63KCGZFbOLJZpObV2nUdj5l9nKDp99vVjqUclCBKq5upPswsTpAcvu3u/xlu3pMvvcM/91YrvhL+B/AGM3ueoOnuVQTt+7PDphCoveu9Hdju7g+Fz28nSBi1fJ1fDWxx9253TwP/SXDta/k65411XWv6/6WZvRP4XeD3/dANZjUd83iUIEqbyDQhVRe23X8VeNrdP1ewq3AKk2uA/5ru2Mbi7h9z98Xuvozgut7j7r8P/IxguhWovZh3A9vM7EXhpksJ7vKv2etM0LR0oZk1h/9O8jHX7HUuMNZ1XQu8IxzNdCHQV9AUVVUWLI72UeAN7j5UsKu+pw1yd/2U+AF+m2A0wibg49WOZ4wYLyIov58AHg9/fpugTf+nwHPAT4COasc6RvyXAP8dPj6Z4D9OF/AfQKLa8RXFeg6wLrzWPwDm1Pp1Bj4NPAM8CdwCJGrtOgP/TtBHkiao1N491nUlWC7ixvD/5HqCEVq1EnMXQV9D/v/hlwqO/3gY80bgddX+dzGZH021ISIiJamJSURESlKCEBGRkpQgRESkJCUIEREpSQlCRERKUoKQGcPMbjCzDxQ8v9PMvlLw/B/N7JOTnZ3XzG42syvCx78bTrfxm3AW3T8p2y9Q+r0/ZWYfDh+fbWYPmtl6M/t/Zjarku8togQhM8kvgZcDmFkEmEcwi2bey4G73P3vjuXFw7vWbwJe7+5nE8x99fOpBDxJXwGuc/czge8DH5nG95bjkBKEzCQPAC8LH59OcIPYgJnNMbMEcBpwlpl9AUYrg382swfMbHNBlWBm9oVw/v6fAPPD12wjmJdpP4AHM3RuLHitL5nZOjN7NpxzKr/uxT+Y2cPhWgGjFYeZfaRg+6cLtn88fI1fAPm7twFOBe4LH98NvDk8fpmZ3W9mj4Y/+SR5iZnda2b/Ff5+f2dmv29mvw6rkFPKctVlxoqNf4hIfXD3nWaWMbOlBNXCgwQzZ76MYDbT9cBI0WkLCe5IfzHBtAi3A79H8MG8imAm0aeAr7l7j5mtBV4ws58C/w38u7vnZ6VdRjCV8ynAz8xsBfAOgikhXhomqV+a2V0cWtfgfII7hNea2SsIJgK8iuDO7RjwKMEsrAAbCCbg+wFwJYfm+NkLXObuSTNbSXCn7+pw39kEibEH2Ax8xd3Pt2BxqfcDH5jEJZbjjBKEzDQPECSHlwOfI0gQLydIEL8scfwPwg/4p8wsP630Kwg++LPATjO7J3+wu/+RmZ1JMBneh4HLgHeGu78bvtZzZraZIOm8hqBqyc9/1E6QGF4T/jwWbm8Nt7cB3/dwPp8wIeW9C/hnM/sEQTLLJ7s48AUzOwfIElQaeQ97OF+RmW0C7gq3rwd+q9QFFMlTgpCZJt8PcSZBE9M24H8D/cDXgY6i41MFj20ib+Du64H1ZnYLsIVDCaJ43hoPX/P97n5n4Q4Llp78W3f/t6LtHzjK+z5DkFQws1MJFl0C+CCwh6BaiADJgtMKf79cwfMc+v8v41AfhMw0DxBMudzj7ll37wFmEzQzPTDB17gPeGvYf7CQ8Ju2mbWa2SUFx50DvFDw/Eozi4Rt+ycTTM52J/A/ww5uzOxUCxYbuhN4lwVreWBmi8xsfvjebzSzJjNrA16ff/Fwf74D/v8CXwp3tQO7wurlDwiWFxWZMn2DkJlmPcHopVuLtrW6+75g5utxfZ9gnYqnCKbNfjDcbsBHzezfgGGC/oJ3Fpy3lWCm1FnAe8I+ga8Q9E08Gk673Q280d3vMrPTgAfDmAaBt7v7o2Z2G/Abgr6Fhwte/2oz+9Pw8X8SVEQA/wp8z8zeAfw4jEtkyjSbq0gZmNnNBFOX3z7esSL1Qk1MIiJSkioIEREpSRWEiIiUpAQhIiIlKUGIiEhJShAiIlKSEoSIiJT0/wFzkPzQRYMp4wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x1080 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\SWARNAVA\\anaconda3\\envs\\rain\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x1080 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\SWARNAVA\\anaconda3\\envs\\rain\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x1080 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\SWARNAVA\\anaconda3\\envs\\rain\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x1080 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\SWARNAVA\\anaconda3\\envs\\rain\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x1080 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\SWARNAVA\\anaconda3\\envs\\rain\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x1080 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\SWARNAVA\\anaconda3\\envs\\rain\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x1080 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\SWARNAVA\\anaconda3\\envs\\rain\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x1080 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for feature in continuous_feature:\n",
    "    data = df.copy()\n",
    "    sns.distplot(df[feature])\n",
    "    plt.xlabel(feature)\n",
    "    plt.ylabel('count')\n",
    "    plt.title(feature)\n",
    "    plt.figure(figsize = (15,15))\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "stock-credits",
   "metadata": {},
   "source": [
    "### Replacing the null values of the remaining continuous features by median"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "offshore-surname",
   "metadata": {},
   "outputs": [],
   "source": [
    "for feature in continuous_feature:\n",
    "    if(df[feature].isnull().sum()*100/len(df))>0:\n",
    "        df[feature] = df[feature].fillna(df[feature].median())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "instrumental-crowd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date                 0\n",
       "Location             0\n",
       "MinTemp              0\n",
       "MaxTemp              0\n",
       "Rainfall             0\n",
       "Evaporation          0\n",
       "Sunshine             0\n",
       "WindGustDir      10326\n",
       "WindGustSpeed        0\n",
       "WindDir9am       10566\n",
       "WindDir3pm        4228\n",
       "WindSpeed9am         0\n",
       "WindSpeed3pm         0\n",
       "Humidity9am          0\n",
       "Humidity3pm          0\n",
       "Pressure9am          0\n",
       "Pressure3pm          0\n",
       "Cloud9am         55888\n",
       "Cloud3pm         59358\n",
       "Temp9am              0\n",
       "Temp3pm              0\n",
       "RainToday         3261\n",
       "RainTomorrow      3267\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fuzzy-ticket",
   "metadata": {},
   "source": [
    "So, we have handled all the continuous features!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "electric-atmosphere",
   "metadata": {},
   "source": [
    "### Replacing the null values of the discrete features by mode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "negative-gazette",
   "metadata": {},
   "outputs": [],
   "source": [
    "def mode_nan(df,variable):\n",
    "    mode=df[variable].value_counts().index[0]\n",
    "    df[variable].fillna(mode,inplace=True)\n",
    "mode_nan(df,\"Cloud9am\")\n",
    "mode_nan(df,\"Cloud3pm\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "personalized-franklin",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date                 0\n",
       "Location             0\n",
       "MinTemp              0\n",
       "MaxTemp              0\n",
       "Rainfall             0\n",
       "Evaporation          0\n",
       "Sunshine             0\n",
       "WindGustDir      10326\n",
       "WindGustSpeed        0\n",
       "WindDir9am       10566\n",
       "WindDir3pm        4228\n",
       "WindSpeed9am         0\n",
       "WindSpeed3pm         0\n",
       "Humidity9am          0\n",
       "Humidity3pm          0\n",
       "Pressure9am          0\n",
       "Pressure3pm          0\n",
       "Cloud9am             0\n",
       "Cloud3pm             0\n",
       "Temp9am              0\n",
       "Temp3pm              0\n",
       "RainToday         3261\n",
       "RainTomorrow      3267\n",
       "dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "surprised-resistance",
   "metadata": {},
   "source": [
    "### Handling categorical features using One Hot Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "possible-personal",
   "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>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>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>RainToday</th>\n",
       "      <th>RainTomorrow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2008-12-01</td>\n",
       "      <td>Albury</td>\n",
       "      <td>13.4</td>\n",
       "      <td>22.9</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>8.3</td>\n",
       "      <td>W</td>\n",
       "      <td>44.0</td>\n",
       "      <td>W</td>\n",
       "      <td>WNW</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>7.0</td>\n",
       "      <td>16.9</td>\n",
       "      <td>21.8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2008-12-02</td>\n",
       "      <td>Albury</td>\n",
       "      <td>7.4</td>\n",
       "      <td>25.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>10.0</td>\n",
       "      <td>WNW</td>\n",
       "      <td>44.0</td>\n",
       "      <td>NNW</td>\n",
       "      <td>WSW</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>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>17.2</td>\n",
       "      <td>24.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>2.6</td>\n",
       "      <td>4.4</td>\n",
       "      <td>WSW</td>\n",
       "      <td>46.0</td>\n",
       "      <td>W</td>\n",
       "      <td>WSW</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>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>23.2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>18.4</td>\n",
       "      <td>8.9</td>\n",
       "      <td>NE</td>\n",
       "      <td>24.0</td>\n",
       "      <td>SE</td>\n",
       "      <td>E</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>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>18.1</td>\n",
       "      <td>26.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2008-12-05</td>\n",
       "      <td>Albury</td>\n",
       "      <td>17.5</td>\n",
       "      <td>32.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>W</td>\n",
       "      <td>41.0</td>\n",
       "      <td>ENE</td>\n",
       "      <td>NW</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</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145455</th>\n",
       "      <td>2017-06-21</td>\n",
       "      <td>Uluru</td>\n",
       "      <td>2.8</td>\n",
       "      <td>23.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>7.8</td>\n",
       "      <td>E</td>\n",
       "      <td>31.0</td>\n",
       "      <td>SE</td>\n",
       "      <td>ENE</td>\n",
       "      <td>13.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1024.6</td>\n",
       "      <td>1020.3</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10.1</td>\n",
       "      <td>22.4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145456</th>\n",
       "      <td>2017-06-22</td>\n",
       "      <td>Uluru</td>\n",
       "      <td>3.6</td>\n",
       "      <td>25.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.6</td>\n",
       "      <td>13.5</td>\n",
       "      <td>NNW</td>\n",
       "      <td>22.0</td>\n",
       "      <td>SE</td>\n",
       "      <td>N</td>\n",
       "      <td>13.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1023.5</td>\n",
       "      <td>1019.1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10.9</td>\n",
       "      <td>24.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145457</th>\n",
       "      <td>2017-06-23</td>\n",
       "      <td>Uluru</td>\n",
       "      <td>5.4</td>\n",
       "      <td>26.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.8</td>\n",
       "      <td>11.0</td>\n",
       "      <td>N</td>\n",
       "      <td>37.0</td>\n",
       "      <td>SE</td>\n",
       "      <td>WNW</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1021.0</td>\n",
       "      <td>1016.8</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>12.5</td>\n",
       "      <td>26.1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145458</th>\n",
       "      <td>2017-06-24</td>\n",
       "      <td>Uluru</td>\n",
       "      <td>7.8</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.6</td>\n",
       "      <td>13.2</td>\n",
       "      <td>SE</td>\n",
       "      <td>28.0</td>\n",
       "      <td>SSE</td>\n",
       "      <td>N</td>\n",
       "      <td>13.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1019.4</td>\n",
       "      <td>1016.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>15.1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145459</th>\n",
       "      <td>2017-06-25</td>\n",
       "      <td>Uluru</td>\n",
       "      <td>14.9</td>\n",
       "      <td>22.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39.0</td>\n",
       "      <td>ESE</td>\n",
       "      <td>ESE</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>1020.2</td>\n",
       "      <td>1017.9</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>20.9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>145460 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Date Location  MinTemp  MaxTemp  Rainfall  Evaporation  \\\n",
       "0       2008-12-01   Albury     13.4     22.9       0.6          2.4   \n",
       "1       2008-12-02   Albury      7.4     25.1       0.0          3.6   \n",
       "2       2008-12-03   Albury     12.9     25.7       0.0          2.6   \n",
       "3       2008-12-04   Albury      9.2     28.0       0.0         18.4   \n",
       "4       2008-12-05   Albury     17.5     32.3       1.0          5.4   \n",
       "...            ...      ...      ...      ...       ...          ...   \n",
       "145455  2017-06-21    Uluru      2.8     23.4       0.0          1.4   \n",
       "145456  2017-06-22    Uluru      3.6     25.3       0.0          7.6   \n",
       "145457  2017-06-23    Uluru      5.4     26.9       0.0          6.8   \n",
       "145458  2017-06-24    Uluru      7.8     27.0       0.0          2.6   \n",
       "145459  2017-06-25    Uluru     14.9     22.6       0.0          1.4   \n",
       "\n",
       "        Sunshine WindGustDir  WindGustSpeed WindDir9am WindDir3pm  \\\n",
       "0            8.3           W           44.0          W        WNW   \n",
       "1           10.0         WNW           44.0        NNW        WSW   \n",
       "2            4.4         WSW           46.0          W        WSW   \n",
       "3            8.9          NE           24.0         SE          E   \n",
       "4            3.0           W           41.0        ENE         NW   \n",
       "...          ...         ...            ...        ...        ...   \n",
       "145455       7.8           E           31.0         SE        ENE   \n",
       "145456      13.5         NNW           22.0         SE          N   \n",
       "145457      11.0           N           37.0         SE        WNW   \n",
       "145458      13.2          SE           28.0        SSE          N   \n",
       "145459       0.7         NaN           39.0        ESE        ESE   \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",
       "145455          13.0          11.0         51.0         24.0       1024.6   \n",
       "145456          13.0           9.0         56.0         21.0       1023.5   \n",
       "145457           9.0           9.0         53.0         24.0       1021.0   \n",
       "145458          13.0           7.0         51.0         24.0       1019.4   \n",
       "145459          17.0          17.0         62.0         36.0       1020.2   \n",
       "\n",
       "        Pressure3pm  Cloud9am  Cloud3pm  Temp9am  Temp3pm  RainToday  \\\n",
       "0            1007.1       8.0       7.0     16.9     21.8          0   \n",
       "1            1007.8       7.0       7.0     17.2     24.3          0   \n",
       "2            1008.7       7.0       2.0     21.0     23.2          0   \n",
       "3            1012.8       7.0       7.0     18.1     26.5          0   \n",
       "4            1006.0       7.0       8.0     17.8     29.7          0   \n",
       "...             ...       ...       ...      ...      ...        ...   \n",
       "145455       1020.3       7.0       7.0     10.1     22.4          0   \n",
       "145456       1019.1       7.0       7.0     10.9     24.5          0   \n",
       "145457       1016.8       7.0       7.0     12.5     26.1          0   \n",
       "145458       1016.5       3.0       2.0     15.1     26.0          0   \n",
       "145459       1017.9       8.0       8.0     15.0     20.9          0   \n",
       "\n",
       "        RainTomorrow  \n",
       "0                  0  \n",
       "1                  0  \n",
       "2                  0  \n",
       "3                  0  \n",
       "4                  0  \n",
       "...              ...  \n",
       "145455             0  \n",
       "145456             0  \n",
       "145457             0  \n",
       "145458             0  \n",
       "145459             0  \n",
       "\n",
       "[145460 rows x 23 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"RainToday\"] = pd.get_dummies(df[\"RainToday\"], drop_first = True)\n",
    "df[\"RainTomorrow\"] = pd.get_dummies(df[\"RainTomorrow\"], drop_first = True)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "tracked-intersection",
   "metadata": {},
   "source": [
    "### Grouping categorical features by 'RainTomorrow'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "yellow-breed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Date Index(['2007-11-01', '2007-12-15', '2008-02-03', '2008-01-31', '2008-01-30',\n",
      "       '2008-01-19', '2008-01-18', '2008-01-16', '2008-01-12', '2007-11-30',\n",
      "       ...\n",
      "       '2008-05-08', '2008-05-09', '2008-01-03', '2008-01-02', '2008-05-10',\n",
      "       '2008-05-11', '2008-05-12', '2008-05-13', '2008-05-14', '2007-12-25'],\n",
      "      dtype='object', name='Date', length=3436)\n",
      "Location Index(['Portland', 'Walpole', 'Cairns', 'Dartmoor', 'NorfolkIsland',\n",
      "       'MountGambier', 'Albany', 'Witchcliffe', 'CoffsHarbour', 'MountGinini',\n",
      "       'NorahHead', 'Darwin', 'Sydney', 'SydneyAirport', 'Ballarat',\n",
      "       'GoldCoast', 'Watsonia', 'Newcastle', 'Hobart', 'Wollongong',\n",
      "       'Williamtown', 'Launceston', 'Brisbane', 'MelbourneAirport', 'Adelaide',\n",
      "       'Sale', 'Albury', 'Perth', 'Melbourne', 'Nuriootpa', 'Penrith',\n",
      "       'BadgerysCreek', 'PerthAirport', 'Tuggeranong', 'Richmond', 'Bendigo',\n",
      "       'Canberra', 'WaggaWagga', 'Townsville', 'Katherine', 'PearceRAAF',\n",
      "       'SalmonGums', 'Nhil', 'Moree', 'Cobar', 'Mildura', 'AliceSprings',\n",
      "       'Uluru', 'Woomera'],\n",
      "      dtype='object', name='Location')\n",
      "WindGustDir Index(['NNW', 'NW', 'WNW', 'N', 'W', 'WSW', 'NNE', 'S', 'SSW', 'SW', 'SSE',\n",
      "       'NE', 'SE', 'ESE', 'ENE', 'E'],\n",
      "      dtype='object', name='WindGustDir')\n",
      "WindDir9am Index(['NNW', 'N', 'NW', 'NNE', 'WNW', 'W', 'WSW', 'SW', 'SSW', 'NE', 'S',\n",
      "       'SSE', 'ENE', 'SE', 'ESE', 'E'],\n",
      "      dtype='object', name='WindDir9am')\n",
      "WindDir3pm Index(['NW', 'NNW', 'N', 'WNW', 'W', 'NNE', 'WSW', 'SSW', 'S', 'SW', 'SE',\n",
      "       'NE', 'SSE', 'ENE', 'E', 'ESE'],\n",
      "      dtype='object', name='WindDir3pm')\n",
      "RainToday UInt64Index([1, 0], dtype='uint64', name='RainToday')\n",
      "RainTomorrow UInt64Index([1, 0], dtype='uint64', name='RainTomorrow')\n"
     ]
    }
   ],
   "source": [
    "for feature in categorical_feature:\n",
    "    print(feature, (df.groupby([feature])[\"RainTomorrow\"].mean().sort_values(ascending = False)).index)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "brutal-engineering",
   "metadata": {},
   "source": [
    "### Replacing the null values of the  categorical features \"WindGustDir\", \"WindDir9am\" and \"WindDir3pm\"using Label Encoding,to avoid curse of dimensionality "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "abroad-richmond",
   "metadata": {},
   "outputs": [],
   "source": [
    "windgustdir = {'NNW':0, 'NW':1, 'WNW':2, 'N':3, 'W':4, 'WSW':5, 'NNE':6, 'S':7, 'SSW':8, 'SW':9, 'SSE':10,\n",
    "       'NE':11, 'SE':12, 'ESE':13, 'ENE':14, 'E':15}\n",
    "winddir9am = {'NNW':0, 'N':1, 'NW':2, 'NNE':3, 'WNW':4, 'W':5, 'WSW':6, 'SW':7, 'SSW':8, 'NE':9, 'S':10,\n",
    "       'SSE':11, 'ENE':12, 'SE':13, 'ESE':14, 'E':15}\n",
    "winddir3pm = {'NW':0, 'NNW':1, 'N':2, 'WNW':3, 'W':4, 'NNE':5, 'WSW':6, 'SSW':7, 'S':8, 'SW':9, 'SE':10,\n",
    "       'NE':11, 'SSE':12, 'ENE':13, 'E':14, 'ESE':15}\n",
    "df[\"WindGustDir\"] = df[\"WindGustDir\"].map(windgustdir)\n",
    "df[\"WindDir9am\"] = df[\"WindDir9am\"].map(winddir9am)\n",
    "df[\"WindDir3pm\"] = df[\"WindDir3pm\"].map(winddir3pm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "several-savings",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"WindGustDir\"] = df[\"WindGustDir\"].fillna(df[\"WindGustDir\"].value_counts().index[0])\n",
    "df[\"WindDir9am\"] = df[\"WindDir9am\"].fillna(df[\"WindDir9am\"].value_counts().index[0])\n",
    "df[\"WindDir3pm\"] = df[\"WindDir3pm\"].fillna(df[\"WindDir3pm\"].value_counts().index[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "super-intention",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date             0\n",
       "Location         0\n",
       "MinTemp          0\n",
       "MaxTemp          0\n",
       "Rainfall         0\n",
       "Evaporation      0\n",
       "Sunshine         0\n",
       "WindGustDir      0\n",
       "WindGustSpeed    0\n",
       "WindDir9am       0\n",
       "WindDir3pm       0\n",
       "WindSpeed9am     0\n",
       "WindSpeed3pm     0\n",
       "Humidity9am      0\n",
       "Humidity3pm      0\n",
       "Pressure9am      0\n",
       "Pressure3pm      0\n",
       "Cloud9am         0\n",
       "Cloud3pm         0\n",
       "Temp9am          0\n",
       "Temp3pm          0\n",
       "RainToday        0\n",
       "RainTomorrow     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "competent-marsh",
   "metadata": {},
   "source": [
    "### Performing Label Encoding on \"Location\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "lasting-right",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = df.groupby([\"Location\"])[\"RainTomorrow\"].value_counts().sort_values().unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "british-finding",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>RainTomorrow</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Location</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Adelaide</th>\n",
       "      <td>2505</td>\n",
       "      <td>688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albany</th>\n",
       "      <td>2138</td>\n",
       "      <td>902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albury</th>\n",
       "      <td>2422</td>\n",
       "      <td>618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AliceSprings</th>\n",
       "      <td>2796</td>\n",
       "      <td>244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BadgerysCreek</th>\n",
       "      <td>2426</td>\n",
       "      <td>583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ballarat</th>\n",
       "      <td>2259</td>\n",
       "      <td>781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bendigo</th>\n",
       "      <td>2478</td>\n",
       "      <td>562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brisbane</th>\n",
       "      <td>2484</td>\n",
       "      <td>709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cairns</th>\n",
       "      <td>2090</td>\n",
       "      <td>950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canberra</th>\n",
       "      <td>2807</td>\n",
       "      <td>629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cobar</th>\n",
       "      <td>2623</td>\n",
       "      <td>386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CoffsHarbour</th>\n",
       "      <td>2140</td>\n",
       "      <td>869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dartmoor</th>\n",
       "      <td>2087</td>\n",
       "      <td>922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Darwin</th>\n",
       "      <td>2341</td>\n",
       "      <td>852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GoldCoast</th>\n",
       "      <td>2265</td>\n",
       "      <td>775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hobart</th>\n",
       "      <td>2432</td>\n",
       "      <td>761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Katherine</th>\n",
       "      <td>1313</td>\n",
       "      <td>265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Launceston</th>\n",
       "      <td>2341</td>\n",
       "      <td>699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Melbourne</th>\n",
       "      <td>2557</td>\n",
       "      <td>636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MelbourneAirport</th>\n",
       "      <td>2356</td>\n",
       "      <td>653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mildura</th>\n",
       "      <td>2682</td>\n",
       "      <td>327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Moree</th>\n",
       "      <td>2615</td>\n",
       "      <td>394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MountGambier</th>\n",
       "      <td>2120</td>\n",
       "      <td>920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MountGinini</th>\n",
       "      <td>2221</td>\n",
       "      <td>819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Newcastle</th>\n",
       "      <td>2308</td>\n",
       "      <td>731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nhil</th>\n",
       "      <td>1336</td>\n",
       "      <td>242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NorahHead</th>\n",
       "      <td>2196</td>\n",
       "      <td>808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NorfolkIsland</th>\n",
       "      <td>2090</td>\n",
       "      <td>919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nuriootpa</th>\n",
       "      <td>2417</td>\n",
       "      <td>592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PearceRAAF</th>\n",
       "      <td>2504</td>\n",
       "      <td>505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Penrith</th>\n",
       "      <td>2444</td>\n",
       "      <td>595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Perth</th>\n",
       "      <td>2548</td>\n",
       "      <td>645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PerthAirport</th>\n",
       "      <td>2442</td>\n",
       "      <td>567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Portland</th>\n",
       "      <td>1914</td>\n",
       "      <td>1095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Richmond</th>\n",
       "      <td>2449</td>\n",
       "      <td>560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sale</th>\n",
       "      <td>2366</td>\n",
       "      <td>643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SalmonGums</th>\n",
       "      <td>2529</td>\n",
       "      <td>472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sydney</th>\n",
       "      <td>2479</td>\n",
       "      <td>865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SydneyAirport</th>\n",
       "      <td>2235</td>\n",
       "      <td>774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Townsville</th>\n",
       "      <td>2521</td>\n",
       "      <td>519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tuggeranong</th>\n",
       "      <td>2471</td>\n",
       "      <td>568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uluru</th>\n",
       "      <td>1462</td>\n",
       "      <td>116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WaggaWagga</th>\n",
       "      <td>2473</td>\n",
       "      <td>536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Walpole</th>\n",
       "      <td>2057</td>\n",
       "      <td>949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Watsonia</th>\n",
       "      <td>2271</td>\n",
       "      <td>738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Williamtown</th>\n",
       "      <td>2309</td>\n",
       "      <td>700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Witchcliffe</th>\n",
       "      <td>2130</td>\n",
       "      <td>879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wollongong</th>\n",
       "      <td>2327</td>\n",
       "      <td>713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Woomera</th>\n",
       "      <td>2807</td>\n",
       "      <td>202</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "RainTomorrow         0     1\n",
       "Location                    \n",
       "Adelaide          2505   688\n",
       "Albany            2138   902\n",
       "Albury            2422   618\n",
       "AliceSprings      2796   244\n",
       "BadgerysCreek     2426   583\n",
       "Ballarat          2259   781\n",
       "Bendigo           2478   562\n",
       "Brisbane          2484   709\n",
       "Cairns            2090   950\n",
       "Canberra          2807   629\n",
       "Cobar             2623   386\n",
       "CoffsHarbour      2140   869\n",
       "Dartmoor          2087   922\n",
       "Darwin            2341   852\n",
       "GoldCoast         2265   775\n",
       "Hobart            2432   761\n",
       "Katherine         1313   265\n",
       "Launceston        2341   699\n",
       "Melbourne         2557   636\n",
       "MelbourneAirport  2356   653\n",
       "Mildura           2682   327\n",
       "Moree             2615   394\n",
       "MountGambier      2120   920\n",
       "MountGinini       2221   819\n",
       "Newcastle         2308   731\n",
       "Nhil              1336   242\n",
       "NorahHead         2196   808\n",
       "NorfolkIsland     2090   919\n",
       "Nuriootpa         2417   592\n",
       "PearceRAAF        2504   505\n",
       "Penrith           2444   595\n",
       "Perth             2548   645\n",
       "PerthAirport      2442   567\n",
       "Portland          1914  1095\n",
       "Richmond          2449   560\n",
       "Sale              2366   643\n",
       "SalmonGums        2529   472\n",
       "Sydney            2479   865\n",
       "SydneyAirport     2235   774\n",
       "Townsville        2521   519\n",
       "Tuggeranong       2471   568\n",
       "Uluru             1462   116\n",
       "WaggaWagga        2473   536\n",
       "Walpole           2057   949\n",
       "Watsonia          2271   738\n",
       "Williamtown       2309   700\n",
       "Witchcliffe       2130   879\n",
       "Wollongong        2327   713\n",
       "Woomera           2807   202"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "pacific-genome",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Location\n",
       "Portland            1095\n",
       "Cairns               950\n",
       "Walpole              949\n",
       "Dartmoor             922\n",
       "MountGambier         920\n",
       "NorfolkIsland        919\n",
       "Albany               902\n",
       "Witchcliffe          879\n",
       "CoffsHarbour         869\n",
       "Sydney               865\n",
       "Darwin               852\n",
       "MountGinini          819\n",
       "NorahHead            808\n",
       "Ballarat             781\n",
       "GoldCoast            775\n",
       "SydneyAirport        774\n",
       "Hobart               761\n",
       "Watsonia             738\n",
       "Newcastle            731\n",
       "Wollongong           713\n",
       "Brisbane             709\n",
       "Williamtown          700\n",
       "Launceston           699\n",
       "Adelaide             688\n",
       "MelbourneAirport     653\n",
       "Perth                645\n",
       "Sale                 643\n",
       "Melbourne            636\n",
       "Canberra             629\n",
       "Albury               618\n",
       "Penrith              595\n",
       "Nuriootpa            592\n",
       "BadgerysCreek        583\n",
       "Tuggeranong          568\n",
       "PerthAirport         567\n",
       "Bendigo              562\n",
       "Richmond             560\n",
       "WaggaWagga           536\n",
       "Townsville           519\n",
       "PearceRAAF           505\n",
       "SalmonGums           472\n",
       "Moree                394\n",
       "Cobar                386\n",
       "Mildura              327\n",
       "Katherine            265\n",
       "AliceSprings         244\n",
       "Nhil                 242\n",
       "Woomera              202\n",
       "Uluru                116\n",
       "Name: 1, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1[1].sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "confirmed-burning",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Portland', 'Cairns', 'Walpole', 'Dartmoor', 'MountGambier',\n",
       "       'NorfolkIsland', 'Albany', 'Witchcliffe', 'CoffsHarbour', 'Sydney',\n",
       "       'Darwin', 'MountGinini', 'NorahHead', 'Ballarat', 'GoldCoast',\n",
       "       'SydneyAirport', 'Hobart', 'Watsonia', 'Newcastle', 'Wollongong',\n",
       "       'Brisbane', 'Williamtown', 'Launceston', 'Adelaide', 'MelbourneAirport',\n",
       "       'Perth', 'Sale', 'Melbourne', 'Canberra', 'Albury', 'Penrith',\n",
       "       'Nuriootpa', 'BadgerysCreek', 'Tuggeranong', 'PerthAirport', 'Bendigo',\n",
       "       'Richmond', 'WaggaWagga', 'Townsville', 'PearceRAAF', 'SalmonGums',\n",
       "       'Moree', 'Cobar', 'Mildura', 'Katherine', 'AliceSprings', 'Nhil',\n",
       "       'Woomera', 'Uluru'],\n",
       "      dtype='object', name='Location')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1[1].sort_values(ascending = False).index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "hindu-management",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "49"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df1[1].sort_values(ascending = False).index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "minute-samba",
   "metadata": {},
   "outputs": [],
   "source": [
    "location = {'Portland':1, 'Cairns':2, 'Walpole':3, 'Dartmoor':4, 'MountGambier':5,\n",
    "       'NorfolkIsland':6, 'Albany':7, 'Witchcliffe':8, 'CoffsHarbour':9, 'Sydney':10,\n",
    "       'Darwin':11, 'MountGinini':12, 'NorahHead':13, 'Ballarat':14, 'GoldCoast':15,\n",
    "       'SydneyAirport':16, 'Hobart':17, 'Watsonia':18, 'Newcastle':19, 'Wollongong':20,\n",
    "       'Brisbane':21, 'Williamtown':22, 'Launceston':23, 'Adelaide':24, 'MelbourneAirport':25,\n",
    "       'Perth':26, 'Sale':27, 'Melbourne':28, 'Canberra':29, 'Albury':30, 'Penrith':31,\n",
    "       'Nuriootpa':32, 'BadgerysCreek':33, 'Tuggeranong':34, 'PerthAirport':35, 'Bendigo':36,\n",
    "       'Richmond':37, 'WaggaWagga':38, 'Townsville':39, 'PearceRAAF':40, 'SalmonGums':41,\n",
    "       'Moree':42, 'Cobar':43, 'Mildura':44, 'Katherine':45, 'AliceSprings':46, 'Nhil':47,\n",
    "       'Woomera':48, 'Uluru':49}\n",
    "df[\"Location\"] = df[\"Location\"].map(location)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "representative-factory",
   "metadata": {},
   "source": [
    "### Spliting the \"Date\" feature into \"Date_month\" and \"Date_day\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "sporting-lesbian",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Date\"] = pd.to_datetime(df[\"Date\"], format = \"%Y-%m-%dT\", errors = \"coerce\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "traditional-result",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Date_month\"] = df[\"Date\"].dt.month\n",
    "df[\"Date_day\"] = df[\"Date\"].dt.day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "native-lebanon",
   "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>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>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>RainToday</th>\n",
       "      <th>RainTomorrow</th>\n",
       "      <th>Date_month</th>\n",
       "      <th>Date_day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2008-12-01</td>\n",
       "      <td>30</td>\n",
       "      <td>13.4</td>\n",
       "      <td>22.9</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>8.3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</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>7.0</td>\n",
       "      <td>16.9</td>\n",
       "      <td>21.8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2008-12-02</td>\n",
       "      <td>30</td>\n",
       "      <td>7.4</td>\n",
       "      <td>25.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</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>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>17.2</td>\n",
       "      <td>24.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2008-12-03</td>\n",
       "      <td>30</td>\n",
       "      <td>12.9</td>\n",
       "      <td>25.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.6</td>\n",
       "      <td>4.4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</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>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>23.2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2008-12-04</td>\n",
       "      <td>30</td>\n",
       "      <td>9.2</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.4</td>\n",
       "      <td>8.9</td>\n",
       "      <td>11.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>14.0</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>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>18.1</td>\n",
       "      <td>26.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2008-12-05</td>\n",
       "      <td>30</td>\n",
       "      <td>17.5</td>\n",
       "      <td>32.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</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</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145455</th>\n",
       "      <td>2017-06-21</td>\n",
       "      <td>49</td>\n",
       "      <td>2.8</td>\n",
       "      <td>23.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>7.8</td>\n",
       "      <td>15.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1024.6</td>\n",
       "      <td>1020.3</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10.1</td>\n",
       "      <td>22.4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145456</th>\n",
       "      <td>2017-06-22</td>\n",
       "      <td>49</td>\n",
       "      <td>3.6</td>\n",
       "      <td>25.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.6</td>\n",
       "      <td>13.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1023.5</td>\n",
       "      <td>1019.1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10.9</td>\n",
       "      <td>24.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145457</th>\n",
       "      <td>2017-06-23</td>\n",
       "      <td>49</td>\n",
       "      <td>5.4</td>\n",
       "      <td>26.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.8</td>\n",
       "      <td>11.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1021.0</td>\n",
       "      <td>1016.8</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>12.5</td>\n",
       "      <td>26.1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145458</th>\n",
       "      <td>2017-06-24</td>\n",
       "      <td>49</td>\n",
       "      <td>7.8</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.6</td>\n",
       "      <td>13.2</td>\n",
       "      <td>12.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1019.4</td>\n",
       "      <td>1016.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>15.1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145459</th>\n",
       "      <td>2017-06-25</td>\n",
       "      <td>49</td>\n",
       "      <td>14.9</td>\n",
       "      <td>22.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.7</td>\n",
       "      <td>4.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>1020.2</td>\n",
       "      <td>1017.9</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>20.9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>145460 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Date  Location  MinTemp  MaxTemp  Rainfall  Evaporation  \\\n",
       "0      2008-12-01        30     13.4     22.9       0.6          2.4   \n",
       "1      2008-12-02        30      7.4     25.1       0.0          3.6   \n",
       "2      2008-12-03        30     12.9     25.7       0.0          2.6   \n",
       "3      2008-12-04        30      9.2     28.0       0.0         18.4   \n",
       "4      2008-12-05        30     17.5     32.3       1.0          5.4   \n",
       "...           ...       ...      ...      ...       ...          ...   \n",
       "145455 2017-06-21        49      2.8     23.4       0.0          1.4   \n",
       "145456 2017-06-22        49      3.6     25.3       0.0          7.6   \n",
       "145457 2017-06-23        49      5.4     26.9       0.0          6.8   \n",
       "145458 2017-06-24        49      7.8     27.0       0.0          2.6   \n",
       "145459 2017-06-25        49     14.9     22.6       0.0          1.4   \n",
       "\n",
       "        Sunshine  WindGustDir  WindGustSpeed  WindDir9am  WindDir3pm  \\\n",
       "0            8.3          4.0           44.0         5.0         3.0   \n",
       "1           10.0          2.0           44.0         0.0         6.0   \n",
       "2            4.4          5.0           46.0         5.0         6.0   \n",
       "3            8.9         11.0           24.0        13.0        14.0   \n",
       "4            3.0          4.0           41.0        12.0         0.0   \n",
       "...          ...          ...            ...         ...         ...   \n",
       "145455       7.8         15.0           31.0        13.0        13.0   \n",
       "145456      13.5          0.0           22.0        13.0         2.0   \n",
       "145457      11.0          3.0           37.0        13.0         3.0   \n",
       "145458      13.2         12.0           28.0        11.0         2.0   \n",
       "145459       0.7          4.0           39.0        14.0        15.0   \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",
       "145455          13.0          11.0         51.0         24.0       1024.6   \n",
       "145456          13.0           9.0         56.0         21.0       1023.5   \n",
       "145457           9.0           9.0         53.0         24.0       1021.0   \n",
       "145458          13.0           7.0         51.0         24.0       1019.4   \n",
       "145459          17.0          17.0         62.0         36.0       1020.2   \n",
       "\n",
       "        Pressure3pm  Cloud9am  Cloud3pm  Temp9am  Temp3pm  RainToday  \\\n",
       "0            1007.1       8.0       7.0     16.9     21.8          0   \n",
       "1            1007.8       7.0       7.0     17.2     24.3          0   \n",
       "2            1008.7       7.0       2.0     21.0     23.2          0   \n",
       "3            1012.8       7.0       7.0     18.1     26.5          0   \n",
       "4            1006.0       7.0       8.0     17.8     29.7          0   \n",
       "...             ...       ...       ...      ...      ...        ...   \n",
       "145455       1020.3       7.0       7.0     10.1     22.4          0   \n",
       "145456       1019.1       7.0       7.0     10.9     24.5          0   \n",
       "145457       1016.8       7.0       7.0     12.5     26.1          0   \n",
       "145458       1016.5       3.0       2.0     15.1     26.0          0   \n",
       "145459       1017.9       8.0       8.0     15.0     20.9          0   \n",
       "\n",
       "        RainTomorrow  Date_month  Date_day  \n",
       "0                  0          12         1  \n",
       "1                  0          12         2  \n",
       "2                  0          12         3  \n",
       "3                  0          12         4  \n",
       "4                  0          12         5  \n",
       "...              ...         ...       ...  \n",
       "145455             0           6        21  \n",
       "145456             0           6        22  \n",
       "145457             0           6        23  \n",
       "145458             0           6        24  \n",
       "145459             0           6        25  \n",
       "\n",
       "[145460 rows x 25 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fatty-definition",
   "metadata": {},
   "source": [
    "### Outliers Treatment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "proper-visibility",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\SWARNAVA\\anaconda3\\envs\\rain\\lib\\site-packages\\seaborn\\_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='RainTomorrow', ylabel='count'>"
      ]
     },
     "execution_count": 34,
     "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": [
    "sns.countplot(df[\"RainTomorrow\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "active-toolbox",
   "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>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>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>RainToday</th>\n",
       "      <th>RainTomorrow</th>\n",
       "      <th>Date_month</th>\n",
       "      <th>Date_day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2008-12-01</td>\n",
       "      <td>30</td>\n",
       "      <td>13.4</td>\n",
       "      <td>22.9</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>8.3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</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>7.0</td>\n",
       "      <td>16.9</td>\n",
       "      <td>21.8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2008-12-02</td>\n",
       "      <td>30</td>\n",
       "      <td>7.4</td>\n",
       "      <td>25.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</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>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>17.2</td>\n",
       "      <td>24.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2008-12-03</td>\n",
       "      <td>30</td>\n",
       "      <td>12.9</td>\n",
       "      <td>25.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.6</td>\n",
       "      <td>4.4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</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>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>23.2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2008-12-04</td>\n",
       "      <td>30</td>\n",
       "      <td>9.2</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.4</td>\n",
       "      <td>8.9</td>\n",
       "      <td>11.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>14.0</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>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>18.1</td>\n",
       "      <td>26.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2008-12-05</td>\n",
       "      <td>30</td>\n",
       "      <td>17.5</td>\n",
       "      <td>32.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</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</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145455</th>\n",
       "      <td>2017-06-21</td>\n",
       "      <td>49</td>\n",
       "      <td>2.8</td>\n",
       "      <td>23.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>7.8</td>\n",
       "      <td>15.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1024.6</td>\n",
       "      <td>1020.3</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10.1</td>\n",
       "      <td>22.4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145456</th>\n",
       "      <td>2017-06-22</td>\n",
       "      <td>49</td>\n",
       "      <td>3.6</td>\n",
       "      <td>25.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.6</td>\n",
       "      <td>13.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1023.5</td>\n",
       "      <td>1019.1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10.9</td>\n",
       "      <td>24.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145457</th>\n",
       "      <td>2017-06-23</td>\n",
       "      <td>49</td>\n",
       "      <td>5.4</td>\n",
       "      <td>26.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.8</td>\n",
       "      <td>11.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1021.0</td>\n",
       "      <td>1016.8</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>12.5</td>\n",
       "      <td>26.1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145458</th>\n",
       "      <td>2017-06-24</td>\n",
       "      <td>49</td>\n",
       "      <td>7.8</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.6</td>\n",
       "      <td>13.2</td>\n",
       "      <td>12.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1019.4</td>\n",
       "      <td>1016.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>15.1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145459</th>\n",
       "      <td>2017-06-25</td>\n",
       "      <td>49</td>\n",
       "      <td>14.9</td>\n",
       "      <td>22.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.7</td>\n",
       "      <td>4.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>1020.2</td>\n",
       "      <td>1017.9</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>20.9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>145460 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Date  Location  MinTemp  MaxTemp  Rainfall  Evaporation  \\\n",
       "0      2008-12-01        30     13.4     22.9       0.6          2.4   \n",
       "1      2008-12-02        30      7.4     25.1       0.0          3.6   \n",
       "2      2008-12-03        30     12.9     25.7       0.0          2.6   \n",
       "3      2008-12-04        30      9.2     28.0       0.0         18.4   \n",
       "4      2008-12-05        30     17.5     32.3       1.0          5.4   \n",
       "...           ...       ...      ...      ...       ...          ...   \n",
       "145455 2017-06-21        49      2.8     23.4       0.0          1.4   \n",
       "145456 2017-06-22        49      3.6     25.3       0.0          7.6   \n",
       "145457 2017-06-23        49      5.4     26.9       0.0          6.8   \n",
       "145458 2017-06-24        49      7.8     27.0       0.0          2.6   \n",
       "145459 2017-06-25        49     14.9     22.6       0.0          1.4   \n",
       "\n",
       "        Sunshine  WindGustDir  WindGustSpeed  WindDir9am  WindDir3pm  \\\n",
       "0            8.3          4.0           44.0         5.0         3.0   \n",
       "1           10.0          2.0           44.0         0.0         6.0   \n",
       "2            4.4          5.0           46.0         5.0         6.0   \n",
       "3            8.9         11.0           24.0        13.0        14.0   \n",
       "4            3.0          4.0           41.0        12.0         0.0   \n",
       "...          ...          ...            ...         ...         ...   \n",
       "145455       7.8         15.0           31.0        13.0        13.0   \n",
       "145456      13.5          0.0           22.0        13.0         2.0   \n",
       "145457      11.0          3.0           37.0        13.0         3.0   \n",
       "145458      13.2         12.0           28.0        11.0         2.0   \n",
       "145459       0.7          4.0           39.0        14.0        15.0   \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",
       "145455          13.0          11.0         51.0         24.0       1024.6   \n",
       "145456          13.0           9.0         56.0         21.0       1023.5   \n",
       "145457           9.0           9.0         53.0         24.0       1021.0   \n",
       "145458          13.0           7.0         51.0         24.0       1019.4   \n",
       "145459          17.0          17.0         62.0         36.0       1020.2   \n",
       "\n",
       "        Pressure3pm  Cloud9am  Cloud3pm  Temp9am  Temp3pm  RainToday  \\\n",
       "0            1007.1       8.0       7.0     16.9     21.8          0   \n",
       "1            1007.8       7.0       7.0     17.2     24.3          0   \n",
       "2            1008.7       7.0       2.0     21.0     23.2          0   \n",
       "3            1012.8       7.0       7.0     18.1     26.5          0   \n",
       "4            1006.0       7.0       8.0     17.8     29.7          0   \n",
       "...             ...       ...       ...      ...      ...        ...   \n",
       "145455       1020.3       7.0       7.0     10.1     22.4          0   \n",
       "145456       1019.1       7.0       7.0     10.9     24.5          0   \n",
       "145457       1016.8       7.0       7.0     12.5     26.1          0   \n",
       "145458       1016.5       3.0       2.0     15.1     26.0          0   \n",
       "145459       1017.9       8.0       8.0     15.0     20.9          0   \n",
       "\n",
       "        RainTomorrow  Date_month  Date_day  \n",
       "0                  0          12         1  \n",
       "1                  0          12         2  \n",
       "2                  0          12         3  \n",
       "3                  0          12         4  \n",
       "4                  0          12         5  \n",
       "...              ...         ...       ...  \n",
       "145455             0           6        21  \n",
       "145456             0           6        22  \n",
       "145457             0           6        23  \n",
       "145458             0           6        24  \n",
       "145459             0           6        25  \n",
       "\n",
       "[145460 rows x 25 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "departmental-walnut",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\SWARNAVA\\anaconda3\\envs\\rain\\lib\\site-packages\\seaborn\\_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n",
      "C:\\Users\\SWARNAVA\\anaconda3\\envs\\rain\\lib\\site-packages\\seaborn\\_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n",
      "C:\\Users\\SWARNAVA\\anaconda3\\envs\\rain\\lib\\site-packages\\seaborn\\_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n",
      "C:\\Users\\SWARNAVA\\anaconda3\\envs\\rain\\lib\\site-packages\\seaborn\\_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n",
      "C:\\Users\\SWARNAVA\\anaconda3\\envs\\rain\\lib\\site-packages\\seaborn\\_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n",
      "C:\\Users\\SWARNAVA\\anaconda3\\envs\\rain\\lib\\site-packages\\seaborn\\_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n",
      "C:\\Users\\SWARNAVA\\anaconda3\\envs\\rain\\lib\\site-packages\\seaborn\\_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n",
      "C:\\Users\\SWARNAVA\\anaconda3\\envs\\rain\\lib\\site-packages\\seaborn\\_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n",
      "C:\\Users\\SWARNAVA\\anaconda3\\envs\\rain\\lib\\site-packages\\seaborn\\_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n",
      "C:\\Users\\SWARNAVA\\anaconda3\\envs\\rain\\lib\\site-packages\\seaborn\\_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n",
      "C:\\Users\\SWARNAVA\\anaconda3\\envs\\rain\\lib\\site-packages\\seaborn\\_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n",
      "C:\\Users\\SWARNAVA\\anaconda3\\envs\\rain\\lib\\site-packages\\seaborn\\_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n",
      "C:\\Users\\SWARNAVA\\anaconda3\\envs\\rain\\lib\\site-packages\\seaborn\\_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n",
      "C:\\Users\\SWARNAVA\\anaconda3\\envs\\rain\\lib\\site-packages\\seaborn\\_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ya6+99usxJgAAgH3la+6xWmu9MMkLk+S888671f217YEDB5Ikz3ve87Y8EuBUdNFFF+XyKz+27WHAKeOrd7xLzr3P3X1uA7doP+7Vdo4VAABASVgBAACUhBUAAEBJWAEAAJSEFQAAQElYAQAAlIQVAABASVgBAACUhBUAAEBJWAEAAJSEFQAAQElYAQAAlIQVAABASVgBAACUhBUAAEBJWAEAAJSEFQAAQElYAQAAlIQVAABASVgBAACUhBUAAEBJWAEAAJSEFQAAQElYAQAAlIQVAABASVgBAACUhBUAAEBJWAEAAJSEFQAAQElYAQAAlIQVAABASVgBAACUhBUAAEBJWAEAAJSEFQAAQElYAQAAlIQVAABASVgBAACUhBUAAEBJWAEAAJSEFQAAQElYAQAAlIQVAABASVgBAACUhBUAAEBJWAEAAJSEFQAAQElYAQAAlIQVAABASVgBAACUhBUAAEBJWAEAAJSEFQAAQElYAQAAlIQVAABASVgBAACUhBUAAEBJWAEAAJSEFQAAQElYAQAAlIQVAABASVgBAACUhBUAAEBJWAEAAJSEFQAAQElYAQAAlIQVAABASVgBAACUhBUAAEBJWAEAAJSEFQAAQElYAQAAlIQVAABASVgBAACUhBUAAEBJWAEAAJSEFQAAQElYAQAAlIQVAABASVgBAACUhBUAAEBJWAEAAJSEFQAAQElYAQAAlIQVAABASVgBAACUhBUAAEBJWAEAAJSEFQAAQElYAQAAlIQVAABASVgBAACUhBUAAEBJWAEAAJSEFQAAQElYAQAAlIQVAABASVgBAACUhBUAAEBJWAEAAJSEFQAAQElYAQAAlIQVAABASVgBAACUhBUAAEBJWAEAAJSEFQAAQElYAQAAlIQVAABASVgBAACUhBUAAEBJWAEAAJSEFQAAQElYAQAAlIQVAABASVgBAACUhBUAAEBJWAEAAJSEFQAAQElYAQAAlIQVAABASVgBAACUhBUAAEBJWAEAAJSEFQAAQElYAQAAlIQVAABASVgBAACUhBUAAEBJWAEAAJSEFQAAQElYAQAAlIQVAABASVgBAACUhBUAAEBJWAEAAJSEFQAAQElYAQAAlIQVAABASVgBAACUhBUAAEBJWAEAAJSEFQAAQElYAQAAlIQVAABASVgBAACUhBUAAEBJWAEAAJSEFQAAQElYAQAAlIQVAABASVgBAACUhBUAAEBJWAEAAJSEFQAAQElYAQAAlIQVAABASVgBAACUhBUAAEBJWAEAAJSEFQAAQOn0bQ+gde655257CAAAwB7aj9/x931YXXjhhdseAgAAsIf243d8hwICAACUhBUAAEBJWAEAAJSEFQAAQElYAQAAlIQVAABASVgBAACUhBUAAEBJWAEAAJSEFQAAQElYAQAAlIQVAABASVgBAACUhBUAAEBJWAEAAJSEFQAAQElYAQAAlIQVAABASVgBAACUhBUAAEBJWAEAAJSEFQAAQElYAQAAlIQVAABASVgBAACUhBUAAEBJWAEAAJSEFQAAQElYAQAAlIQVAABASVgBAACUhBUAAEBJWAEAAJSEFQAAQElYAQAAlIQVAABASVgBAACUhBUAAEBJWAEAAJSEFQAAQElYAQAAlIQVAABASVgBAACUhBUAAEBJWAEAAJSEFQAAQElYAQAAlIQVAABASVgBAACUhBUAAEBJWAEAAJSEFQAAQElYAQAAlIQVAABASVgBAACUhBUAAEBJWAEAAJSEFQAAQElYAQAAlIQVAABASVgBAACUhBUAAEBJWAEAAJSEFQAAQElYAQAAlIQVAABASVgBAACUhBUAAEBJWAEAAJSEFQAAQElYAQAAlIQVAABASVgBAACUhBUAAEBJWAEAAJSEFQAAQElYAQAAlIQVAABASVgBAACUhBUAAEBJWAEAAJSEFQAAQElYAQAAlIQVAABASVgBAACUhBUAAEBJWAEAAJSEFQAAQElYAQAAlIQVAABASVgBAACUhBUAAEBJWAEAAJSEFQAAQElYAQAAlIQVAABASVgBAACUhBUAAEBJWAEAAJSEFQAAQElYAQAAlIQVAABASVgBAACUhBUAAEBJWAEAAJSEFQAAQElYAQAAlIQVAABASVgBAACUhBUAAEBJWAEAAJSEFQAAQElYAQAAlIQVAABASVgBAACUhBUAAEBJWAEAAJSEFQAAQElYAQAAlIQVAABASVgBAACUhBUAAEBJWAEAAJSEFQAAQElYAQAAlIQVAABASVgBAACUhBUAAEBJWAEAAJSEFQAAQElYAQAAlIQVAABASVgBAACUhBUAAEBJWAEAAJSEFQAAQElYAQAAlIQVAABASVgBAACUhBUAAEBJWAEAAJSEFQAAQElYAQAAlIQVAABASVgBAACUhBUAAEBJWAEAAJSEFQAAQElYAQAAlIQVAABASVgBAACUhBUAAEBJWAEAAJSEFQAAQElYAQAAlIQVAABASVgBAACUhBUAAEBJWAEAAJSEFQAAQOn0bQ8A4NbutBuvyxnv+71tD4PCaTd+Mkn8Od4KnHbjdUnuvu1hAOw5YQVwC84999xtD4E9cPXVX06SHDjgC/323d3/V8ApSVgB3IILL7xw20MAAPYB51gBAACUhBUAAEBJWAEAAJSEFQAAQElYAQAAlIQVAABASVgBAACUhBUAAEBJWAEAAJSEFQAAQElYAQAAlIQVAABASVgBAACUhBUAAEBJWAEAAJSEFQAAQElYAQAAlIQVAABASVgBAACUhBUAAEBJWAEAAJSEFQAAQElYAQAAlIQVAABASVgBAACUhBUAAEBJWAEAAJSEFQAAQElYAQAAlIQVAABASVgBAACUhBUAAEBJWAEAAJSEFQAAQElYAQAAlIQVAABASVgBAACUhBUAAEBJWAEAAJSEFQAAQElYAQAAlIQVAABASVgBAACUhBUAAEBJWAEAAJSEFQAAQElYAQAAlIQVAABASVgBAACUhBUAAEBJWAEAAJSEFQAAQElYAQAAlIQVAABASVgBAACUhBUAAEBJWAEAAJSEFQAAQElYAQAAlIQVAABASVgBAACUhBUAAEBJWAEAAJSEFQAAQElYAQAAlIQVAABASVgBAACUhBUAAEBJWAEAAJSEFQAAQElYAQAAlIQVAABASVgBAACUhBUAAEBJWAEAAJSEFQAAQElYAQAAlIQVAABASVgBAACUhBUAAEBp1lrHvvDMtUk+dPKGc8LOSfKJbQ+CU5b1i5PJ+sXJZP3iZLJ+cbLdWtexb1lr3e3ImccVVrdWM3PZWuu8bY+DU5P1i5PJ+sXJZP3iZLJ+cbLtt3XMoYAAAAAlYQUAAFA6VcLqhdseAKc06xcnk/WLk8n6xclk/eJk21fr2ClxjhUAAMA2nSp7rAAAALZGWAEAAJT2dVjNzCNn5n/NzKGZ+eltj4f9bWbuNTNvmpn3zMyfzMxFm/lnz8zvz8z7N7+/cdtjZf+amdNm5h0z8/rN9L1n5m2b7di/m5k7bHuM7F8zc9bMvHpm3jcz752Zh9mGsVdm5ic3n49XzMyrZuaOtmGcqJl58cx8fGau2DXvZrdXs+NXNuvZu2bmO7Y38qPbt2E1M6cl+bUkj0rygCRPmJkHbHdU7HNfTvJP1loPSHJ+kqdt1qmfTvLGtdb9krxxMw0n6qIk7901/a+S/PJa69wkn0ryj7YyKk4Vz0vyn9Za90/ywOysa7Zh1GbmQJIfT3LeWuvbkpyW5AdiG8aJe0mSRx4x72jbq0clud/m50eSPP/rNMbjsm/DKslDkxxaa1251vpikt9O8tgtj4l9bK11zVrrjza3P5OdLyQHsrNevXSz2EuTPG4rA2Tfm5l7JvneJC/aTE+S707y6s0i1i9O2Mx8Q5LvSvIbSbLW+uJa6/rYhrF3Tk9yxsycnuTMJNfENowTtNZ6S5Lrjph9tO3VY5O8bO14a5KzZuYvfl0Gehz2c1gdSPLhXdNXbeZBbWYOJnlwkrclufta65rNXR9NcvdtjYt9798keUaSr26m75rk+rXWlzfTtmM07p3k2iS/uTnc9EUzc6fYhrEH1lpXJ/nFJH+WnaC6IcnlsQ1jbx1te7Uvvvfv57CCk2Jm7pzk3yf5ibXWp3fft3b+fQL/RgHHbWYek+Tja63Ltz0WTlmnJ/mOJM9faz04yedyxGF/tmGcqM25Lo/NTsDfI8md8v8fxgV7Zj9ur/ZzWF2d5F67pu+5mQcnbGZun52oesVa6zWb2R87vLt58/vj2xof+9pfS/J9M/PB7By6/N3ZOR/mrM1hNYntGJ2rkly11nrbZvrV2Qkt2zD2wvck+cBa69q11peSvCY72zXbMPbS0bZX++J7/34Oq7cnud/majR3yM4JlJdueUzsY5vzXX4jyXvXWr+0665Lk/zg5vYPJnnd13ts7H9rrX+61rrnWutgdrZX/2Wt9cQkb0ry+M1i1i9O2Frro0k+PDN/eTPrbyR5T2zD2Bt/luT8mTlz83l5eP2yDWMvHW17dWmSp2yuDnh+kht2HTJ4qzE7e9n2p5l5dHbOWTgtyYvXWs/Z7ojYz2bm4Un+IMm78//OgXlmds6z+p0kfynJh5L8/bXWkSdbwjGbmQuS/NRa6zEzc5/s7ME6O8k7kjxprfWFLQ6PfWxmHpSdi6PcIcmVSZ6anb9EtQ2jNjP/PMk/yM5VdN+R5Iezc56LbRjHbWZeleSCJOck+ViSn0vy2tzM9moT87+ancNPb0zy1LXWZVsY9i3a12EFAABwa7CfDwUEAAC4VRBWAAAAJWEFAABQElYAAAAlYQUAAFASVgAck5n57BHTPzQzv7pHz/2jM/OUm5l/cGau2Nw+b2Z+ZXP7gpn5q8fwvBfPzLtm5p0z84aZucdejBcAjiSsANi6tdYL1lov+xrLXLbW+vHN5AVJvmZYJXnuWuvb11oPSvL6JD9bDRQAjkJYAVCbmZfMzON3TX928/uCmXnzzLxuZq6cmV+YmSfOzP+cmXfPzH03yz17Zn5qc/shM/PHM/PHSZ626zkvmJnXz8zBJD+a5Cc3e6K+c2Y+MDO33yx3l8PTa61P7xrmnZKsXa/38pn5HzPz/pn5x8czXgA40unbHgAA+8YZM/POXdNnJ7n0GB73wCTfmuS6JFcmedFa66Ezc1GSC5P8xBHL/2aSp6+13jIzzz3yydZaH5yZFyT57FrrF5NkZv5rku9N8tokP5DkNWutL23ue06SpyS5Iclf3/VU357k/OwE1ztm5ndPcLwAYI8VAMfsprXWgw7/5NgPq3v7WuuatdYXkvxpkjds5r87ycHdC87MWUnOWmu9ZTPr5cf4Gi9K8tTN7admJ86SJGutZ6217pXkFUmevusxr1tr3bTW+kSSNyV56PGOFwAOE1YA7IUvZ/OZMjO3S3KHXfd9Ydftr+6a/mr26MiJtdYfJjk4MxckOW2tdcXNLPaKJH9v98OOfJrN75M+XgBOPcIKgL3wwSQP2dz+viS3P5EnWWtdn+T6mXn4ZtYTj7LoZ5L8hSPmvSzJK7Nrb9XM3G/X/Y9N8r7d0zNzx5m5a3YuhvH2ExkzACTCCoC98etJHrG54MTDknyueK6nJvm1zflcc5Rl/kOS7z988YrNvFck+cYkr9q13C/MzBUz864kfyvJRbvue1d2DgF8a5KL11ofKcYMwG3crHXkkRAAsP9srkr42LXWk49h2Wdn18UvAKDlWHEA9r2ZuSTJo5I8ettjAeC2yR4rAACAknOsAAAASsIKAACgJKwAAABKwgoAAKAkrAAAAEr/B4iUDKG3Ykg2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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k/e9/f84888y5ZZs2bcrNN9+cJDnuuONy5513zm0nSU499dR85CMfSZKceOKJ+eQnP5n169fn7rvvTpKsW7du7u8EAMDDe9vb3pY3vvGNSZKTTz45H/vYx5J8bZ512mmn5cMf/nCSZPPmzdm2bVsOO+ywXHTRRXnta1+bpz71qXPzuEsuuSTvec97ctlll+XWW2/NlVdemQsvvDDXXXddHv/4x+fVr351/v7v/z4veclL8vznPz9vfOMbs2XLlrz+9a/PGCM/+7M/m2uuuWbusV/5lV/J1NRUtm7dmqrKK1/5ymzYsCE7d+7MK17xiowxctVVV+1xnjy7bPa5L3rRi/Krv/qrDzu33hv7s85SqzHGXj9506ZNY3ZCvlxcffXVuf7663POOedkjDF3++KLL97n9WfXWbjsggsuyPbt27NmzZo88MADmZqaSpJs3759bjtVlZtuumlu/Xe84x1Jkqmpqbz5zW9OkrntzI+U3Zl9neXs9NNPnwtMYPFs2bIlt3zyM/nytz1jqYeyrBx52w1J4vuyD4687YY8+cRj/N8NB9GWLVuSJLfeeut+rV9VWThXr6okyTnnnJN3vetdXzdnPPfcc/Pe9753l7nm/O0s3Ob69etz5plnzs1dzz333Fx88cW7zGdnlyV7njvPn/vu2LHjYefWe2N/1jlYquqWMcamhctX9KmAO3fuzI033pgxRrZt25Zt27ZljJEbb7wxO3fu3Kf1Z9dZuOzmm2+eC6jZHXf79u27RFWSuajbuXNntm3bNrd8+/btmZ6ezvT09Nw6DxdV819nOZuenl7qIQAALHuf/vSn93vd3R0AGWNkjLHbqEqSd77znV8315y/nYXbvPvuu/Oud71r7v62bdsyPT2dG2+8cZdlu5snz182a/v27Q87t97fOfpKsKJPBXzLW96Shx56KEly//33zy1/8MEHc9111z1i3c5ff3adMcYuyy6//PK9Hs/VV1+dT3ziE7uMJUmuuuqqvd7GSnHPPffM/RYGWDzT09NZdd/en1kAe7LqK/+U6em7/N8NB9H09HTuueeeRdn2nn4R/+CDD+7ztuavc//99+eqq67aZT57//3373aePLts4dx34eML19mfOfpyO2q1O494KmBVvSDJC5LkhBNOePKOHTsOxrj2yjOe8Yzce++9u33sqKOOyg033LBP6x911FFJssdt7o2jjjqqtf5Kcvrppy/1EOBRb3p6OnfdN3LPGT+x1ENZVpwKuO/Wfei38w2HlWtk4SBazLA62HY3T36kufOe1tmfOfojrXMw7elUwEc8YjXGeEOSNyQz11gtwtj229Oe9rTccMMNeeCBB+bONx1jZM2aNXn605++T+vPrjPG2GXZEUcc8Yin7s2qqjztaU+bO8d01u6uyXo0cJ4+LL7Za6yg66EjvjEbXWMFB9WWLVv2+/qqpVJVecITnpAdO3bscl3W7ubJs8uuv/76rzvFcE9z6/2do68EK/oaq/PPPz+rVs38FdauXZs1a2Y6cfXq1Xne8563T+vPrrNw2b6cCnjJJZfk/PPPz9q1a3dZvnXr1mzdunWvt7MSrFu3bqmHAACw7B1zzDGLst3Zee9Cq1ev3udtzV9n7dq12bp16y7z2bVr1+52njy7bOHcd+HjC9d5JPuzznKwosNqw4YNOeuss1JV2bx5czZv3pyqyllnnbVXb8s4f/3ZdRYu27Rp09wRp9kdeGpqam7ZrKrKs571rGzYsCGbN2+eWz41NZWNGzdm48aNc+usX7/+Yce1p38oy4lTSQAAHtmxxx673+vOnpG1cFlV5eyzz97tnPGZz3zm1801529n4TbXr1+fs88+e+7+5s2bs3Hjxpx11lm7LNvdPHn+sllTU1MPO7fe3zn6SrCiwyqZKdpv//Zvnyvi2dv7s/6elm3dujXr1q3LpZdemnXr1s0dgVq3bl2e+9znJpk5WjV//ZNOOilHHnnkLkeqZte54oorcsIJJyRJzjvvvF2+Hn744bn00ktz+OGHp6py5plnJknOOOOMJDP/OF/wghfMbXN2OyeddFKS7LJOMvM5Vkl2WbZp09dOCT3uuON22U4y8zlWs0488cQku8ago1UAAHvv+c9//tztk08+ee727DzrtNNOm1s2+wv6ww47LJdccklWrVq1yzzu4osvnpunvvzlL0+SXHjhhTnyyCNz0kkn5XnPe14uv/zyrFq1KhdeeGFWrVqVF77whTnyyCNzxBFH5IUvfOEuj11xxRU5//zzc8opp+TUU0+dm/+ef/75OfXUU3PKKac87Dx54XO3bt36iHPrvbE/6yy1Ff85Vhx8s+8o5Tx9WHw+x2r3vHnFvvM5VnDwmTM9Oj0qP8cKAABgORBWAAAATcIKAACgSVgBAAA0CSsAAIAmYQUAANAkrAAAAJqEFQAAQJOwAgAAaBJWAAAATcIKAACgSVgBAAA0CSsAAIAmYQUAANAkrAAAAJqEFQAAQJOwAgAAaBJWAAAATcIKAACgSVgBAAA0CSsAAIAmYQUAANAkrAAAAJqEFQAAQJOwAgAAaBJWAAAATcIKAACgSVgBAAA0CSsAAIAmYQUAANAkrAAAAJqEFQAAQJOwAgAAaBJWAAAATcIKAACgSVgBAAA0CSsAAIAmYQUAANAkrAAAAJqEFQAAQJOwAgAAaBJWAAAATcIKAACgSVgBAAA0CSsAAIAmYQUAANAkrAAAAJqEFQAAQJOwAgAAaBJWAAAATcIKAACgSVgBAAA0CSsAAIAmYQUAANAkrAAAAJqEFQAAQJOwAgAAaBJWAAAATcIKAACgSVgBAAA0CSsAAIAmYQUAANAkrAAAAJqEFQAAQJOwAgAAaBJWAAAATcIKAACgSVgBAAA0CSsAAIAmYQUAANAkrAAAAJqEFQAAQJOwAgAAaBJWAAAATcIKAACgSVgBAAA0CSsAAIAmYQUAANAkrAAAAJqEFQAAQJOwAgAAaBJWAAAATcIKAACgSVgBAAA0CSsAAIAmYQUAANAkrAAAAJqEFQAAQJOwAgAAaBJWAAAATcIKAACgSVgBAAA0CSsAAIAmYQUAANAkrAAAAJqEFQAAQJOwAgAAaBJWAAAATcIKAACgSVgBAAA0CSsAAIAmYQUAANAkrAAAAJqEFQAAQJOwAgAAaBJWAAAATcIKAACgSVgBAAA0CSsAAIAmYQUAANAkrAAAAJqEFQAAQJOwAgAAaBJWAAAATcIKAACgSVgBAAA0CSsAAIAmYQUAANAkrAAAAJqEFQAAQJOwAgAAaBJWAAAATcIKAACgSVgBAAA0CSsAAIAmYQUAANAkrAAAAJqEFQAAQJOwAgAAaBJWAAAATcIKAACgSVgBAAA0CSsAAIAmYQUAANAkrAAAAJqEFQAAQJOwAgAAaBJWAAAATcIKAACgSVgBAAA0CSsAAIAmYQUAANAkrAAAAJqEFQAAQJOwAgAAaBJWAAAATcIKAACgSVgBAAA0CSsAAIAmYQUAANAkrAAAAJqEFQAAQJOwAgAAaBJWAAAATcIKAACgSVgBAAA0CSsAAICmNUs9AFaejRs3LvUQAACWPXOmQ4uwYp9ddNFFSz0EAIBlz5zp0OJUQAAAgCZhBQAA0CSsAAAAmoQVAABAk7ACAABoElYAAABNwgoAAKBJWAEAADQJKwAAgCZhBQAA0CSsAAAAmoQVAABAk7ACAABoElYAAABNwgoAAKBJWAEAADQJKwAAgCZhBQAA0CSsAAAAmoQVAABAk7ACAABoElYAAABNwgoAAKBJWAEAADQJKwAAgCZhBQAA0CSsAAAAmoQVAABAk7ACAABoElYAAABNwgoAAKBJWAEAADQJKwAAgCZhBQAA0CSsAAAAmoQVAABAk7ACAABoElYAAABNwgoAAKBJWAEAADQJKwAAgCZhBQAA0CSsAAAAmoQVAABAk7ACAABoElYAAABNwgoAAKBJWAEAADQJKwAAgCZhBQAA0CSsAAAAmoQVAABAk7ACAABoElYAAABNwgoAAKBJWAEAADQJKwAAgCZhBQAA0CSsAAAAmoQVAABAk7ACAABoElYAAABNwgoAAKBJWAEAADQJKwAAgCZhBQAA0CSsAAAAmoQVAABAk7ACAABoElYAAABNwgoAAKBJWAEAADQJKwAAgCZhBQAA0CSsAAAAmoQVAABAk7ACAABoElYAAABNwgoAAKBJWAEAADQJKwAAgCZhBQAA0CSsAAAAmoQVAABAk7ACAABoElYAAABNwgoAAKBJWAEAADQJKwAAgCZhBQAA0CSsAAAAmoQVAABAk7ACAABoElYAAABNwgoAAKBJWAEAADQJKwAAgCZhBQAA0CSsAAAAmoQVAABAk7ACAABoElYAAABNwgoAAKBJWAEAADQJKwAAgCZhBQAA0CSsAAAAmoQVAABAk7ACAABoElYAAABNwgoAAKBJWAEAADQJKwAAgCZhBQAA0CSsAAAAmoQVAABAk7ACAABoElYAAABNwgoAAKBJWAEAADQJKwAAgCZhBQAA0CSsAAAAmoQVAABAk7ACAABoElYAAABNwgoAAKBJWAEAADQJKwAAgCZhBQAA0CSsAAAAmoQVAABAk7ACAABoElYAAABNwgoAAKBJWAEAADQJKwAAgCZhBQAA0CSsAAAAmoQVAABAk7ACAABoElYAAABNwgoAAKBJWAEAADQJKwAAgCZhBQAA0CSsAAAAmoQVAABAk7ACAABoElYAAABNwgoAAKBJWAEAADQJKwAAgCZhBQAA0CSsAAAAmoQVAABAk7ACAABoElYAAABNwgoAAKBJWAEAADStWeoBAPDwVt/7+Rx52w1LPYxlZfW9O5PE92UfrL7380mOWephADxqCSuAZWzjxo1LPYRl6Y47HkiSHH+8UNh7x9ifABaRsAJYxi666KKlHgIAsBdcYwUAANAkrAAAAJqEFQAAQJOwAgAAaBJWAAAATcIKAACgSVgBAAA0CSsAAIAmYQUAANAkrAAAAJqEFQAAQJOwAgAAaBJWAAAATcIKAACgSVgBAAA0CSsAAIAmYQUAANAkrAAAAJqEFQAAQJOwAgAAaBJWAAAATcIKAACgSVgBAAA0CSsAAIAmYQUAANAkrAAAAJqEFQAAQJOwAgAAaBJWAAAATcIKAACgSVgBAAA0CSsAAIAmYQUAANAkrAAAAJqEFQAAQJOwAgAAaBJWAAAATcIKAACgSVgBAAA0CSsAAIAmYQUAANAkrAAAAJqEFQAAQJOwAgAAaBJWAAAATcIKAACgSVgBAAA0CSsAAIAmYQUAANAkrAAAAJqEFQAAQJOwAgAAaBJWAAAATcIKAACgSVgBAAA0CSsAAIAmYQUAANAkrAAAAJqEFQAAQJOwAgAAaBJWAAAATcIKAACgSVgBAAA0CSsAAIAmYQUAANAkrAAAAJqEFQAAQJOwAgAAaBJWAAAATcIKAACgSVgBAAA0CSsAAIAmYQUAANAkrAAAAJqEFQAAQJOwAgAAaBJWAAAATcIKAACgSVgBAAA0CSsAAICmGmPs/ZOr/jHJjsUbDuToJJ9b6kFwSLMPspTsfyw1+yBLaaXsf08YYzx24cJ9CitYbFV18xhj01KPg0OXfZClZP9jqdkHWUorff9zKiAAAECTsAIAAGgSViw3b1jqAXDIsw+ylOx/LDX7IEtpRe9/rrECAABocsQKAACgSVgBAAA0CSuWjao6q6o+VlXTVfXSpR4Pj35V9aaq+mxV/e28ZY+pqndX1ScmX79lKcfIo1dVPb6qbqqqj1TVh6tqy2S5fZBFV1VHVNVfVtWtk/3visnyf15VfzH5Wfw7VXXYUo+VR7eqWl1VH6yqd07ur9h9UFixLFTV6iS/nmRzklOT/NuqOnVpR8Uh4M1Jzlqw7KVJ3jvGeGKS907uw2J4IMmLxhinJvneJD83+X/PPsjB8NUkZ44xTk9yRpKzqup7k/xSkteOMTYm+UKSn166IXKI2JLko/Pur9h9UFixXHx3kukxxifHGPcl+e0k5y7xmHiUG2P8aZLPL1h8bpK3TG6/JcmPHswxcegYY9w5xvirye27MjOxOD72QQ6CMePuyd21kz8jyZlJfm+y3P7HoqqqxyU5O8l/n9yvrOB9UFixXByf5PZ59/9hsgwOtmPGGHdObn86yTFLORgODVU1leQ7k/xF7IMcJJNTsD6U5LNJ3p3k75J8cYzxwOQpfhaz2F6X5CVJHprc35AVvA8KK4A9GDOfR+EzKVhUVbU+yduTvHCM8U/zH7MPspjGGA+OMc5I8rjMnDnybUs7Ig4lVfXMJJ8dY9yy1GM5UNYs9QBg4o4kj593/3GTZXCwfaaqjhtj3FlVx2XmN7mwKKpqbWai6m1jjN+fLLYPclCNMb5YVTcl+b4k31xVayZHDPwsZjE9Jck5VfWMJEck+cYk12QF74OOWLFc/N8kT5y8E8xhSX4iyTuWeEwcmt6R5PzJ7fOT/NESjoVHscm1BL+Z5KNjjKvnPWQfZNFV1WOr6psnt49M8vTMXOd3U5LnTJ5m/2PRjDFeNsZ43BhjKjPzvveNMX4qK3gfrJmzDGDpTX5j8bokq5O8aYzxqqUdEY92VfVbSZ6a5Ogkn0lyWZI/TPK7SU5IsiPJj48xFr7BBbRV1fcn+bMkf5OvXV/w8sxcZ2UfZFFV1Xdk5o0BVmfmF+2/O8a4sqpOzMwbSD0myQeTPHeM8dWlGymHgqp6apIXjzGeuZL3QWEFAADQ5FRAAACAJmEFAADQJKwAAACahBUAAECTsAIAAGjyAcEAHDBVtSHJeyd3j03yYJJ/nNz/7jHGfQfwtc5M8pokhyW5JclPTz5QEgAOOm+3DsCiqKrLk9w9xnjNImx7VWY+4+mHxxgfr6ork+wYY/zmgX4tANgbTgUEYFFV1ZOr6n9V1S1V9T+r6rjJ8j+pqtdW1c1V9dGq+q6q+v2q+kRVXTV5zlRV3VZVb5s85/eq6qgkG5LcN8b4+ORl3p3kvMk6311VH6iqD1bV/6mqkyfLL6iqP6yqd1fV9qr6T1V1yeR5f15Vjzn43x0AHi2EFQCLqZJcm+Q5Y4wnJ3lTklfNe/y+McamJK9P8kdJfi7Jk5JcMDmtMElOTvIbY4xTkvxTkv+Y5HNJ1lTVpslznpPk8ZPbtyX5gTHGdyb5r0l+Yd7rPSnJv07yXZNx3Dt53geSPO+A/a0BOOS4xgqAxXR4ZmLm3VWVJKuT3Dnv8XdMvv5Nkg+PMe5Mkqr6ZGZC6YtJbh9jvH/yvLcm+fkxxmuq6ieSvLaqDk/yx5m5nitJvinJW6rqiUlGkrXzXu+mMcZdSe6qqi8luX7e63/HgfkrA3AoElYALKbKTDB93x4e/+rk60Pzbs/en/0ZtfBi4JEkY4wPJPmBJKmqH0ly0uTxV2YmoJ5dVVNJ/mQ3r7fwNee/HgDsM6cCArCYvprksVX1fUlSVWur6rR93MYJs+sn+ckk/3uyrW+dfD08yX/JzOmEycwRqzsmty/Y/6EDwN4TVgAspocyc/3TL1XVrUk+lORf7uM2Ppbk56rqo0m+Jcl/myz/z5Nlf53k+jHG+ybLfznJq6vqg3EUCoCDxNutA7BsTU7le+cY40lLPRYAeDiOWAEAADQ5YgUAANDkiBUAAECTsAIAAGgSVgAAAE3CCgAAoElYAQAANP1/U0kCopgzVaoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x1080 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for feature in continuous_feature:\n",
    "    data=df.copy()\n",
    "    sns.boxplot(data[feature])\n",
    "    plt.title(feature)\n",
    "    plt.figure(figsize=(15,15))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "devoted-polyester",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MinTemp\n",
      "MaxTemp\n",
      "Rainfall\n",
      "Evaporation\n",
      "Sunshine\n",
      "WindGustSpeed\n",
      "WindSpeed9am\n",
      "WindSpeed3pm\n",
      "Humidity9am\n",
      "Humidity3pm\n",
      "Pressure9am\n",
      "Pressure3pm\n",
      "Temp9am\n",
      "Temp3pm\n"
     ]
    }
   ],
   "source": [
    "for feature in continuous_feature:\n",
    "    print(feature)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "failing-latter",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-5.950000000000002 30.450000000000003\n"
     ]
    }
   ],
   "source": [
    "IQR=df.MinTemp.quantile(0.75)-df.MinTemp.quantile(0.25)\n",
    "lower_bridge=df.MinTemp.quantile(0.25)-(IQR*1.5)\n",
    "upper_bridge=df.MinTemp.quantile(0.75)+(IQR*1.5)\n",
    "print(lower_bridge, upper_bridge)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "composed-processing",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[df['MinTemp']>=30.45,'MinTemp']=30.45\n",
    "df.loc[df['MinTemp']<=-5.95,'MinTemp']=-5.95"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "cultural-ability",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.700000000000001 43.5\n"
     ]
    }
   ],
   "source": [
    "IQR=df.MaxTemp.quantile(0.75)-df.MaxTemp.quantile(0.25)\n",
    "lower_bridge=df.MaxTemp.quantile(0.25)-(IQR*1.5)\n",
    "upper_bridge=df.MaxTemp.quantile(0.75)+(IQR*1.5)\n",
    "print(lower_bridge, upper_bridge)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "embedded-causing",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[df['MaxTemp']>=43.5,'MaxTemp']=43.5\n",
    "df.loc[df['MaxTemp']<=2.7,'MaxTemp']=2.7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "natural-spain",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.8999999999999999 1.5\n"
     ]
    }
   ],
   "source": [
    "IQR=df.Rainfall.quantile(0.75)-df.Rainfall.quantile(0.25)\n",
    "lower_bridge=df.Rainfall.quantile(0.25)-(IQR*1.5)\n",
    "upper_bridge=df.Rainfall.quantile(0.75)+(IQR*1.5)\n",
    "print(lower_bridge, upper_bridge)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "sexual-bicycle",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[df['Rainfall']>=1.5,'Rainfall']=1.5\n",
    "df.loc[df['Rainfall']<=-0.89,'Rainfall']=-0.89"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "pressing-vault",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-4.600000000000001 14.600000000000001\n"
     ]
    }
   ],
   "source": [
    "IQR=df.Evaporation.quantile(0.75)-df.Evaporation.quantile(0.25)\n",
    "lower_bridge=df.Evaporation.quantile(0.25)-(IQR*1.5)\n",
    "upper_bridge=df.Evaporation.quantile(0.75)+(IQR*1.5)\n",
    "print(lower_bridge, upper_bridge)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "continued-civilization",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[df['Evaporation']>=14.6,'Evaporation']=14.6\n",
    "df.loc[df['Evaporation']<=-4.6,'Evaporation']=-4.6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "stopped-litigation",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8.5 68.5\n"
     ]
    }
   ],
   "source": [
    "IQR=df.WindGustSpeed.quantile(0.75)-df.WindGustSpeed.quantile(0.25)\n",
    "lower_bridge=df.WindGustSpeed.quantile(0.25)-(IQR*1.5)\n",
    "upper_bridge=df.WindGustSpeed.quantile(0.75)+(IQR*1.5)\n",
    "print(lower_bridge, upper_bridge)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "authorized-ordinance",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[df['WindGustSpeed']>=68.5,'WindGustSpeed']=68.5\n",
    "df.loc[df['WindGustSpeed']<=8.5,'WindGustSpeed']=8.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "conventional-aside",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-11.0 37.0\n"
     ]
    }
   ],
   "source": [
    "IQR=df.WindSpeed9am.quantile(0.75)-df.WindSpeed9am.quantile(0.25)\n",
    "lower_bridge=df.WindSpeed9am.quantile(0.25)-(IQR*1.5)\n",
    "upper_bridge=df.WindSpeed9am.quantile(0.75)+(IQR*1.5)\n",
    "print(lower_bridge, upper_bridge)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "judicial-designation",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[df['WindSpeed9am']>=37,'WindSpeed9am']=37\n",
    "df.loc[df['WindSpeed9am']<=-11,'WindSpeed9am']=-11"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "cooked-electron",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-3.5 40.5\n"
     ]
    }
   ],
   "source": [
    "IQR=df.WindSpeed3pm.quantile(0.75)-df.WindSpeed3pm.quantile(0.25)\n",
    "lower_bridge=df.WindSpeed3pm.quantile(0.25)-(IQR*1.5)\n",
    "upper_bridge=df.WindSpeed3pm.quantile(0.75)+(IQR*1.5)\n",
    "print(lower_bridge, upper_bridge)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "cosmetic-morocco",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[df['WindSpeed3pm']>40.5,'WindSpeed3pm']=40.5\n",
    "df.loc[df['WindSpeed3pm']<=-3.5,'WindSpeed3pm']=-3.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "opposed-identification",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18.0 122.0\n"
     ]
    }
   ],
   "source": [
    "IQR=df.Humidity9am.quantile(0.75)-df.Humidity9am.quantile(0.25)\n",
    "lower_bridge=df.Humidity9am.quantile(0.25)-(IQR*1.5)\n",
    "upper_bridge=df.Humidity9am.quantile(0.75)+(IQR*1.5)\n",
    "print(lower_bridge, upper_bridge)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "southeast-drain",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[df['Humidity9am']>=122,'Humidity9am']=122\n",
    "df.loc[df['Humidity9am']<=18,'Humidity9am']=18"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "adopted-entrepreneur",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1001.0500000000001 1034.25\n"
     ]
    }
   ],
   "source": [
    "IQR=df.Pressure9am.quantile(0.75)-df.Pressure9am.quantile(0.25)\n",
    "lower_bridge=df.Pressure9am.quantile(0.25)-(IQR*1.5)\n",
    "upper_bridge=df.Pressure9am.quantile(0.75)+(IQR*1.5)\n",
    "print(lower_bridge, upper_bridge)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "vietnamese-subdivision",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[df['Pressure9am']>=1034.25,'Pressure9am']=1034.25\n",
    "df.loc[df['Pressure9am']<=1001.05,'Pressure9am']=1001.05"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "marine-forward",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "998.6500000000001 1031.85\n"
     ]
    }
   ],
   "source": [
    "IQR=df.Pressure3pm.quantile(0.75)-df.Pressure3pm.quantile(0.25)\n",
    "lower_bridge=df.Pressure3pm.quantile(0.25)-(IQR*1.5)\n",
    "upper_bridge=df.Pressure3pm.quantile(0.75)+(IQR*1.5)\n",
    "print(lower_bridge, upper_bridge)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "seven-kentucky",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[df['Pressure3pm']>=1031.85,'Pressure3pm']=1031.85\n",
    "df.loc[df['Pressure3pm']<=998.65,'Pressure3pm']=998.65"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "tutorial-fitness",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-1.4999999999999982 35.3\n"
     ]
    }
   ],
   "source": [
    "IQR=df.Temp9am.quantile(0.75)-df.Temp9am.quantile(0.25)\n",
    "lower_bridge=df.Temp9am.quantile(0.25)-(IQR*1.5)\n",
    "upper_bridge=df.Temp9am.quantile(0.75)+(IQR*1.5)\n",
    "print(lower_bridge, upper_bridge)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "royal-robertson",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[df['Temp9am']>=35.3,'Temp9am']=35.3\n",
    "df.loc[df['Temp9am']<=-1.49,'Temp9am']=-1.49"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "spare-brazil",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.4499999999999993 40.45\n"
     ]
    }
   ],
   "source": [
    "IQR=df.Temp3pm.quantile(0.75)-df.Temp3pm.quantile(0.25)\n",
    "lower_bridge=df.Temp3pm.quantile(0.25)-(IQR*1.5)\n",
    "upper_bridge=df.Temp3pm.quantile(0.75)+(IQR*1.5)\n",
    "print(lower_bridge, upper_bridge)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "dominican-musical",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[df['Temp3pm']>=40.45,'Temp3pm']=40.45\n",
    "df.loc[df['Temp3pm']<=2.45,'Temp3pm']=2.45"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "unexpected-international",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\SWARNAVA\\anaconda3\\envs\\rain\\lib\\site-packages\\seaborn\\_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n",
      "C:\\Users\\SWARNAVA\\anaconda3\\envs\\rain\\lib\\site-packages\\seaborn\\_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n",
      "C:\\Users\\SWARNAVA\\anaconda3\\envs\\rain\\lib\\site-packages\\seaborn\\_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n",
      "C:\\Users\\SWARNAVA\\anaconda3\\envs\\rain\\lib\\site-packages\\seaborn\\_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n",
      "C:\\Users\\SWARNAVA\\anaconda3\\envs\\rain\\lib\\site-packages\\seaborn\\_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n",
      "C:\\Users\\SWARNAVA\\anaconda3\\envs\\rain\\lib\\site-packages\\seaborn\\_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n",
      "C:\\Users\\SWARNAVA\\anaconda3\\envs\\rain\\lib\\site-packages\\seaborn\\_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n",
      "C:\\Users\\SWARNAVA\\anaconda3\\envs\\rain\\lib\\site-packages\\seaborn\\_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n",
      "C:\\Users\\SWARNAVA\\anaconda3\\envs\\rain\\lib\\site-packages\\seaborn\\_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n",
      "C:\\Users\\SWARNAVA\\anaconda3\\envs\\rain\\lib\\site-packages\\seaborn\\_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n",
      "C:\\Users\\SWARNAVA\\anaconda3\\envs\\rain\\lib\\site-packages\\seaborn\\_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n",
      "C:\\Users\\SWARNAVA\\anaconda3\\envs\\rain\\lib\\site-packages\\seaborn\\_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n",
      "C:\\Users\\SWARNAVA\\anaconda3\\envs\\rain\\lib\\site-packages\\seaborn\\_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n",
      "C:\\Users\\SWARNAVA\\anaconda3\\envs\\rain\\lib\\site-packages\\seaborn\\_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AAICSsAIAACgJKwAAgJKwAgAAKAkrAACAkrACAAAoCSsAAICSsAIAACgJKwAAgJKwAgAAKAkrAACAkrACAAAoCSsAAICSsAIAACgJKwAAgJKwAgAAKAkrAACAkrACAAAoCSsAAICSsAIAACgJKwAAgJKwAgAAKAkrAACAkrACAAAoCSsAAICSsAIAACgJKwAAgJKwAgAAKAkrAACAkrACAAAoCSsAAICSsAIAACgJKwAAgJKwAgAAKAkrAACAkrACAAAoCSsAAICSsAIAACgJKwAAgJKwAgAAKAkrAACAkrACAAAoCSsAAICSsAIAACgJKwAAgJKwAgAAKAkrAACAkrACAAAoCSsAAICSsAIAACgJKwAAgJKwAgAAKAkrAACAkrACAAAoCSsAAICSsAIAACgJKwAAgJKwAgAAKAkrAACAkrACAAAoCSsAAICSsAIAACgJKwAAgJKwAgAAKAkrAACAkrACAAAoCSsAAICSsAIAACgJKwAAgJKwAgAAKAkrAACAkrACAAAoCSsAAICSsAIAACgJKwAAgJKwAgAAKAkrAACAkrACAAAoCSsAAICSsAIAACgJKwAAgJKwAgAAKAkrAACAkrACAAAoCSsAAICSsAIAACgJKwAAgJKwAgAAKAkrAACAkrACAAAoCSsAAICSsAIAACgJKwAAgJKwAgAAKAkrAACAkrACAAAoCSsAAICSsAIAACgJKwAAgJKwAgAAKJ3Y9QCtyy67LJ9Zd+56DAAA4ICcPn161yNctCMfVg94wAPyJ5/+412PAQAAHJAzZ87seoSL5lRAAACAkrACAAAoCSsAAICSsAIAACgJKwAAgJKwAgAAKAkrAACAkrACAAAoCSsAAICSsAIAACgJKwAAgJKwAgAAKAkrAACAkrACAAAoCSsAAICSsAIAACgJKwAAgJKwAgAAKAkrAACAkrACAAAoCSsAAICSsAIAACgJKwAAgJKwAgAAKAkrAACAkrACAAAoCSsAAICSsAIAACgJKwAAgJKwAgAAKAkrAACAkrACAAAoCSsAAICSsAIAACgJKwAAgJKwAgAAKAkrAACAkrACAAAoCSsAAICSsAIAACgJKwAAgJKwAgAAKAkrAACAkrACAAAoCSsAAICSsAIAACgJKwAAgJKwAgAAKAkrAACAkrACAAAoCSsAAICSsAIAACgJKwAAgJKwAgAAKAkrAACAkrACAAAoCSsAAICSsAIAACgJKwAAgJKwAgAAKAkrAACAkrACAAAoCSsAAICSsAIAACgJKwAAgJKwAgAAKAkrAACAkrACAAAoCSsAAICSsAIAACgJKwAAgJKwAgAAKAkrAACAkrACAAAoCSsAAICSsAIAACgJKwAAgJKwAgAAKAkrAACAkrACAAAoCSsAAICSsAIAACgJKwAAgJKwAgAAKAkrAACAkrACAAAoCSsAAICSsAIAACgJKwAAgJKwAgAAKAkrAACAkrACAAAoCSsAAICSsAIAACgJKwAAgJKwAgAAKAkrAACAkrACAAAoCSsAAICSsAIAACgJKwAAgJKwAgAAKAkrAACAkrACAAAoCSsAAICSsAIAACgJKwAAgJKwAgAAKAkrAACAkrACAAAoCSsAAICSsAIAACgJKwAAgJKwAgAAKAkrAACAkrACAAAoCSsAAICSsAIAACgJKwAAgJKwAgAAKAkrAACAkrACAAAoCSsAAICSsAIAACgJKwAAgJKwAgAAKAkrAACAkrACAAAoCSsAAICSsAIAACgJKwAAgJKwAgAAKAkrAACAkrACAAAoCSsAAICSsAIAACgJKwAAgJKwAgAAKAkrAACAkrACAAAoCSsAAICSsAIAACgJKwAAgJKwAgAAKAkrAACAkrACAAAoCSsAAICSsAIAACgJKwAAgJKwAgAAKAkrAACAkrACAAAoCSsAAICSsAIAACgJKwAAgJKwAgAAKAkrAACAkrACAAAoCSsAAICSsAIAACid2PUAB+Izd+SK975+11MAXJTLb/t4koftegwA4AAc+bC66qqrcu7cuVx9tT9OgKPmYTl9+vSuhwAADsCRD6tXvOIVux4BAAA45lxjBQAAUBJWAAAAJWEFAABQElYAAAAlYQUAAFASVgAAACVhBQAAUBJWAAAAJWEFAABQElYAAAAlYQUAAFASVgAAACVhBQAAUBJWAAAAJWEFAABQElYAAAAlYQUAAFASVgAAACVhBQAAUBJWAAAAJWEFAABQElYAAAAlYQUAAFASVgAAACVhBQAAUBJWAAAAJWEFAABQElYAAAAlYQUAAFASVgAAACVhBQAAUBJWAAAAJWEFAABQElYAAAAlYQUAAFASVgAAACVhBQAAUBJWAAAAJWEFAABQElYAAAAlYQUAAFASVgAAACVhBQAAUBJWAAAAJWEFAABQElYAAAAlYQUAAFASVgAAACVhBQAAUBJWAAAAJWEFAABQElYAAAAlYQUAAFASVgAAACVhBQAAUBJWAAAAJWEFAABQElYAAAAlYQUAAFASVgAAACVhBQAAUBJWAAAAJWEFAABQElYAAAAlYQUAAFASVgAAACVhBQAAUBJWAAAAJWEFAABQElYAAAAlYQUAAFASVgAAACVhBQAAUBJWAAAAJWEFAABQElYAAAAlYQUAAFASVgAAACVhBQAAUBJWAAAApVlr7X/jmVuTvP/SjfM5O5nko7segv+PNTmcrMvhY00OJ+ty+FiTw8eaHE7W5dJ75FrrC+5550WF1WE1Mzeuta7d9Rz8KWtyOFmXw8eaHE7W5fCxJoePNTmcrMvuOBUQAACgJKwAAABK95ewevmuB+CzWJPDybocPtbkcLIuh481OXysyeFkXXbkfnGNFQAAwC7dX45YAQAA7IywAgAAKB2ZsJqZp83Mb87M2Zn57nt5/AEz8wubx98+M6d2MOaxs491+Qcz856ZedfM/LeZeeQu5jxOLrQmd9vu62dmzYy3ZN2C/azLzHzj5ufl12fm57Y943Gzj99fj5iZN83MTZvfYc/YxZzHycy8cmZumZl338fjMzM/tlmzd83MX9r2jMfRPtblOZv1+N8z85aZ+fJtz3jcXGhN7rbdV8zMHTPzDdua7Tg7EmE1M5cneVmSpyd5dJJnz8yj77HZc5P8wVrrdJJ/neTF253y+NnnutyU5Nq11uOSvDbJD293yuNln2uSmbkyyQuSvH27Ex5P+1mXmbkmyfck+atrrS9L8ve3Pedxss+flX+S5D+stZ6Q5JuS/MR2pzyWrk/ytPM8/vQk12z+fVuSn9zCTFx4XX43yVettR6b5PvjzRO24fqcf03u+j334iS/tI2BOCJhleSJSc6utd631vrjJD+f5Fn32OZZSX52c/u1SZ4yM7PFGY+jC67LWutNa63bNh++LcnDtzzjcbOfn5Vk74nvxUk+vc3hjrH9rMvfTfKytdYfJMla65Ytz3jc7GdNVpIHb24/JMmHtjjfsbTWenOSj59nk2cledXa87YkD52Zq7Yz3fF1oXVZa73lrt9d8Vy/Ffv4WUmSM0n+YxLPJ1tyVMLq6iS/f7ePb97cd6/brLXuSPKJJJ+/lemOr/2sy909N8kbLulEXHBNNqfOfNFa6xe3Odgxt5+flS9N8qUz86sz87aZOe8rkdT2syYvSvLNM3Nzktdn748Udutin3fYPs/1h8DMXJ3k6+Ko7lad2PUAHA8z881Jrk3yVbue5TibmcuS/Ksk1+14FD7bieyd3vSk7L3a++aZeexa69wuhzrmnp3k+rXWj8zMX0nyb2fmMWutO3c9GBxGM/Pk7IXVX9v1LORHk/yjtdadTuDanqMSVh9M8kV3+/jhm/vubZubZ+ZE9k7b+Nh2xju29rMumZmnJvnH2Tv/+vYtzXZcXWhNrkzymCS/svlF+4VJbpiZZ661btzalMfPfn5Wbk7y9rXWnyT53Zn5reyF1q9tZ8RjZz9r8txsrmFYa711Zh6Y5GScVrNL+3reYftm5nFJXpHk6Wstf3/t3rVJfn7zXH8yyTNm5o611ut2OtX93FE5FfDXklwzM188M38mexcR33CPbW5I8nc2t78hyX9f/vfjS+2C6zIzT0jyb5I80zUjW3HeNVlrfWKtdXKtdWqtdSp758KLqktvP7/DXpe9o1WZmZPZOzXwfVuc8bjZz5p8IMlTkmRm/mKSBya5datTck83JPmWzbsD/uUkn1hrfXjXQx13M/OIJP8pyd9ea/3WruchWWt98d2e61+b5DtE1aV3JI5YrbXumJnvTPLGJJcneeVa69dn5p8luXGtdUOSn8neaRpns3cx3zftbuLjYZ/r8i+SPCjJazavmnxgrfXMnQ19P7fPNWHL9rkub0zy1TPzniSfSfIPvep76exzTV6Y5Kdn5ruy90YW13nB7tKamX+fvRcYTm6ubfu+JJ+XJGutn8retW7PSHI2yW1JvnU3kx4v+1iX783ede0/sXmuv2Ot5b/yuIT2sSbswHiOAAAA6ByVUwEBAAAOLWEFAABQElYAAAAlYQUAAFASVgAAACVhBcAlNzOfmZl3zsy7Z+Y/z8xDL7D9tTPzY/vY7/Nn5jdm5tXn2eZJM/NfNrevm5kfv+hvAAAuQFgBsA2fWms9fq31mOz9X4N/73wbr7VuXGs9fx/7/Y4kf32t9ZyDGBIAPlfCCoBte2uSq5NkZp44M2+dmZtm5i0z8xc299/9KNOLZuaVM/MrM/O+mXn+5v6fSvIlSd4wM991X/sCgG04sesBADg+ZubyJE9J8jObu96b5CvXWnfMzFOT/GCSr7+XT31UkicnuTLJb87MT661vn1mnpbkyWutj87Mg/e5LwA4cMIKgG24Ymbemb0jVb+R5Jc39z8kyc/OzDVJVpLPu4/P/8W11u1Jbp+ZW5I8LMnN99hmv/sCgAPnVEAAtuFTa63HJ3lkksmfXmP1/UnetLn26muSPPA+Pv/2u93+TO79hcH97gsADpywAmBr1lq3JXl+khfOzInsHWX64Obh68rdH+S+AOCiCCsAtmqtdVOSdyV5dpIfTvLPZ+am9KenH+S+AOCizFpr1zMAAAAcaY5YAQAAlIQVAABASVgBAACUhBUAAEBJWAEAAJSEFQAAQElYAQAAlP4fQnGM1Sagh70AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ATcIKAACgSVgBAAA0CSsAAIAmYQUAANAkrAAAAJqEFQAAQJOwAgAAaBJWAAAATcIKAACgSVgBAAA0CSsAAIAmYQUAANAkrAAAAJqEFQAAQJOwAgAAaBJWAAAATcIKAACgSVgBAAA0CSsAAIAmYQUAANAkrAAAAJqEFQAAQJOwAgAAaBJWAAAATcIKAACgSVgBAAA0CSsAAIAmYQUAANAkrAAAAJqEFQAAQJOwAgAAaBJWAAAATcIKAACgSVgBAAA0CSsAAIAmYQUAANAkrAAAAJqEFQAAQJOwAgAAaBJWAAAATcIKAACgSVgBAAA0CSsAAIAmYQUAANAkrAAAAJqEFQAAQJOwAgAAaBJWAAAATcIKAACgSVgBAAA0CSsAAIAmYQUAANAkrAAAAJqEFQAAQJOwAgAAaBJWAAAATcIKAACgSVgBAAA0CSsAAIAmYQUAANAkrAAAAJqEFQAAQJOwAgAAaBJWAAAATZsXPYCuHTt25Jprrslxxx236KEATUtLS9nnh2PRw4A1Z59rv5elpau8FwLrxtLSUvbff/9FD+MW+Uf3WFXVc6vq7Ko6+/LLL98TYwIAAFhT/tE9VmOM1yd5fZI87GEP2+t+lbxly5YkyUknnbTgkQBdxx13XM75yqWLHgasOTfsd8dsu88h3guBdWMt7oF3jhUAAECTsAIAAGgSVgAAAE3CCgAAoElYAQAANAkrAACAJmEFAADQJKwAAACahBUAAECTsAIAAGgSVgAAAE3CCgAAoElYAQAANAkrAACAJmEFAADQJKwAAACahBUAAECTsAIAAGgSVgAAAE3CCgAAoElYAQAANAkrAACAJmEFAADQJKwAAACahBUAAECTsAIAAGgSVgAAAE3CCgAAoElYAQAANAkrAACAJmEFAADQJKwAAACahBUAAECTsAIAAGgSVgAAAE3CCgAAoElYAQAANAkrAACAJmEFAADQJKwAAACahBUAAECTsAIAAGgSVgAAAE3CCgAAoElYAQAANAkrAACAJmEFAADQJKwAAACahBUAAECTsAIAAGgSVgAAAE3CCgAAoElYAQAANAkrAACAJmEFAADQJKwAAACahBUAAECTsAIAAGgSVgAAAE3CCgAAoElYAQAANAkrAACAJmEFAADQJKwAAACahBUAAECTsAIAAGgSVgAAAE3CCgAAoElYAQAANAkrAACAJmEFAADQJKwAAACahBUAAECTsAIAAGgSVgAAAE3CCgAAoElYAQAANAkrAACAJmEFAADQJKwAAACahBUAAECTsAIAAGgSVgAAAE3CCgAAoElYAQAANAkrAACAJmEFAADQJKwAAACahBUAAECTsAIAAGgSVgAAAE3CCgAAoElYAQAANAkrAACAJmEFAADQJKwAAACahBUAAECTsAIAAGgSVgAAAE3CCgAAoElYAQAANAkrAACAJmEFAADQJKwAAACahBUAAECTsAIAAGgSVgAAAE3CCgAAoElYAQAANAkrAACAJmEFAADQJKwAAACahBUAAECTsAIAAGgSVgAAAE3CCgAAoElYAQAANAkrAACAJmEFAADQJKwAAACahBUAAECTsAIAAGgSVgAAAE3CCgAAoElYAQAANAkrAACAJmEFAADQJKwAAACahBUAAECTsAIAAGgSVgAAAE3CCgAAoElYAQAANAkrAACAJmEFAADQJKwAAACahBUAAECTsAIAAGgSVgAAAE3CCgAAoElYAQAANAkrAACAJmEFAADQJKwAAACahBUAAECTsAIAAGgSVgAAAE3CCgAAoElYAQAANAkrAACAJmEFAADQJKwAAACahBUAAECTsAIAAGgSVgAAAE3CCgAAoElYAQAANAkrAACAJmEFAADQJKwAAACahBUAAEDT5kUPoGvbtm2LHgIAALCC1uJn/DUfVtu3b1/0EAAAgBW0Fj/jOxQQAACgSVgBAAA0CSsAAIAmYQUAANAkrAAAAJqEFQAAQJOwAgAAaBJWAAAATcIKAACgSVgBAAA0CSsAAIAmYQUAANAkrAAAAJqEFQAAQJOwAgAAaBJWAAAATcIKAACgSVgBAAA0CSsAAIAmYQUAANAkrAAAAJqEFQAAQJOwAgAAaBJWAAAATcIKAACgSVgBAAA0CSsAAIAmYQUAANAkrAAAAJqEFQAAQJOwAgAAaBJWAAAATcIKAACgSVgBAAA0CSsAAIAmYQUAANAkrAAAAJqEFQAAQJOwAgAAaBJWAAAATcIKAACgSVgBAAA0CSsAAIAmYQUAANAkrAAAAJqEFQAAQJOwAgAAaBJWAAAATcIKAACgSVgBAAA0CSsAAIAmYQUAANAkrAAAAJqEFQAAQJOwAgAAaBJWAAAATcIKAACgSVgBAAA0CSsAAIAmYQUAANAkrAAAAJqEFQAAQJOwAgAAaBJWAAAATcIKAACgSVgBAAA0CSsAAIAmYQUAANAkrAAAAJqEFQAAQJOwAgAAaBJWAAAATcIKAACgSVgBAAA0CSsAAIAmYQUAANAkrAAAAJqEFQAAQJOwAgAAaBJWAAAATcIKAACgSVgBAAA0CSsAAIAmYQUAANAkrAAAAJqEFQAAQJOwAgAAaBJWAAAATcIKAACgSVgBAAA0CSsAAIAmYQUAANAkrAAAAJqEFQAAQJOwAgAAaBJWAAAATcIKAACgSVgBAAA0CSsAAIAmYQUAANAkrAAAAJqEFQAAQJOwAgAAaBJWAAAATcIKAACgSVgBAAA0CSsAAIAmYQUAANAkrAAAAJqEFQAAQJOwAgAAaBJWAAAATcIKAACgSVgBAAA0CSsAAIAmYQUAANAkrAAAAJqEFQAAQJOwAgAAaBJWAAAATcIKAACgSVgBAAA0CSsAAIAmYQUAANAkrAAAAJqEFQAAQJOwAgAAaBJWAAAATcIKAACgSVgBAAA0CSsAAIAmYQUAANAkrAAAAJqEFQAAQJOwAgAAaBJWAAAATcIKAACgSVgBAAA0CSsAAIAmYQUAANAkrAAAAJqEFQAAQJOwAgAAaBJWAAAATcIKAACgSVgBAAA0CSsAAIAmYQUAANAkrAAAAJqEFQAAQJOwAgAAaBJWAAAATcIKAACgSVgBAAA0CSsAAIAmYQUAANAkrAAAAJqEFQAAQJOwAgAAaBJWAAAATZsXPQCAeZuuvjL7f/EDix4Gt9Kmq7+VJP4O97BNV1+Z5JBFDwNgQxNWwF5j27Ztix4CTTt2XJ8k2bLFh/w96xD//QAsmLAC9hrbt29f9BAAAG4V51gBAAA0CSsAAIAmYQUAANAkrAAAAJqEFQAAQJOwAgAAaBJWAAAATcIKAACgSVgBAAA0CSsAAIAmYQUAANAkrAAAAJqEFQAAQJOwAgAAaBJWAAAATcIKAACgSVgBAAA0CSsAAIAmYQUAANAkrAAAAJqEFQAAQJOwAgAAaBJWAAAATcIKAACgSVgBAAA0CSsAAIAmYQUAANAkrAAAAJqEFQAAQJOwAgAAaBJWAAAATcIKAACgSVgBAAA0CSsAAIAmYQUAANAkrAAAAJqEFQAAQJOwAgAAaBJWAAAATcIKAACgSVgBAAA0CSsAAIAmYQUAANAkrAAAAJqEFQAAQJOwAgAAaBJWAAAATcIKAACgSVgBAAA0CSsAAIAmYQUAANAkrAAAAJqEFQAAQJOwAgAAaBJWAAAATcIKAACgSVgBAAA0CSsAAIAmYQUAANAkrAAAAJqEFQAAQJOwAgAAaBJWAAAATcIKAACgSVgBAAA0CSsAAIAmYQUAANAkrAAAAJqEFQAAQJOwAgAAaBJWAAAATcIKAACgSVgBAAA0CSsAAIAmYQUAANAkrAAAAJqEFQAAQJOwAgAAaBJWAAAATcIKAACgqcYYN//GVZcnuWj1hnOrHZTkikUPYgMy74th3hfDvC+Ged/zzPlimPfFMO+Lsdbn/V5jjIOXL7xFYbW3qqqzxxgPW/Q4NhrzvhjmfTHM+2KY9z3PnC+GeV8M874Y63XeHQoIAADQJKwAAACa1ktYvX7RA9igzPtimPfFMO+LYd73PHO+GOZ9Mcz7YqzLeV8X51gBAAAs0nrZYwUAALAwwgoAAKBpTYdVVR1TVRdW1VJVnbDo8WwUVfW1qjq/qs6rqrMXPZ71qqreVFWXVdUFc8sOrKoPVdWXpj/vvMgxrke7mfcTq2rHtM2fV1VPWOQY16OqukdVnV5Vn6+q/11Vx03LbfOr6Cbm3Ta/iqpqv6r6VFV9dpr3l03L711VZ02fa95RVbdZ9FjXk5uY91Oq6qtz2/sRCx7qulNVm6rq3Kp6/3R9XW7razasqmpTktcl+bkkD0zyjKp64GJHtaE8ZoxxxHr8Nwj2IqckOWbZshOSfHiMcb8kH56us7JOyf8/70nymmmbP2KM8YE9PKaN4Pok/3GM8cAkRyb5nen/6bb51bW7eU9s86vpuiSPHWM8JMkRSY6pqiOTvCqzed+W5NtJ/u3ihrgu7W7ek+QFc9v7eYsa4Dp2XJIvzF1fl9v6mg2rJI9IsjTG+MoY44dJ/irJsQseE6yYMcbHkly5bPGxSf5iuvwXSZ68J8e0Eexm3lllY4xLxhifmS5fldkb8JbY5lfVTcw7q2jMfH+6uu/0M5I8Nsm7puW29xV2E/POKqqqQ5P8fJI3TNcr63RbX8thtSXJ1+eufyPeDPaUkeS0qjqnqp676MFsMIeMMS6ZLn8zySGLHMwG87tV9bnpUEGHo62iqtqa5KFJzoptfo9ZNu+JbX5VTYdGnZfksiQfSvLlJN8ZY1w/3cTnmlWwfN7HGDu391dM2/trquq2ixvhuvTaJL+X5Ibp+l2yTrf1tRxWLM5RY4x/ltlhmL9TVY9e9IA2ojH7txL8pm3P+LMk983s0JFLkvzRQkezjlXVAUneneT5Y4zvza+zza+eXcy7bX6VjTF+NMY4IsmhmR2Fc9hiR7QxLJ/3qnpwkhdlNv8PT3JgkhcuboTrS1U9McllY4xzFj2WPWEth9WOJPeYu37otIxVNsbYMf15WZL3ZPaGwJ5xaVXdLUmmPy9b8Hg2hDHGpdOb8Q1J/lts86uiqvbN7MP9W8cY/3NabJtfZbuad9v8njPG+E6S05P8dJI7VdXmaZXPNatobt6PmQ6JHWOM65K8Obb3lfQzSX6hqr6W2Wk7j01yUtbptr6Ww+rTSe43favIbZI8PcmpCx7TuldVt6+qO+y8nOTxSS646Xuxgk5N8qzp8rOSvG+BY9kwdn6wn/xibPMrbjrm/o1JvjDG+OO5Vbb5VbS7ebfNr66qOriq7jRd3j/J4zI7v+30JE+ZbmZ7X2G7mfcvzv3ypjI718f2vkLGGC8aYxw6xtia2Wf1j4wxfi3rdFuv2ZEVa9P09a+vTbIpyZvGGK9Y7IjWv6q6T2Z7qZJkc5K3mffVUVVvT3J0koOSXJrkD5K8N8k7k9wzyUVJnjbG8EULK2g38350ZodEjSRfS/Jbc+f9sAKq6qgkH09yfn5yHP6LMzvfxza/Sm5i3p8R2/yqqarDMzthf1Nmv+R+5xjjP03vsX+V2eFo5yZ55rQXhRVwE/P+kSQHJ6kk5yV53tyXXLBCquroJMePMZ64Xrf1NR1WAAAAe4O1fCggAADAXkFYAQAANAkrAACAJmEFAADQJKwAAACahBUAN1JVr6mq589d/9uqesPc9T+qqt+vqhNu4eOeUlVPmS4/sarOrarPVtXnq+q3VuwF7Pq5T6yq46fLL6+qz1XVeVV1WlXdfTWfG4CNQVgBsNwnkzwySapqn8z+Ta8Hza1/ZJLTxhivvDUPXlX7Jnl9kieNMR6S5KFJ/ldnwLfQH44xDh9jHJHk/Ul+fw8+NwDrlLACYLkzkvz0dPlBSS5IclVV3bmqbpvkp5IcXlV/mvx4T9SfVNUZVfWVub1SVVV/WlUXVtXfJbnr9Jh3yOwfGP9WkowxrhtjXDj3WH9eVWdX1f+pqidOyzdV1R9W1aenvU0/3sNVVS+YW/6yueUvmR7jE0kesHP5GON7c6/19pn9I7g792q9parOrKovVdVvTsuPrqqPVtX7ptf3yqr6tar6VFWdX1X3XZFZB2BN27zoAQCwdxljXFxV11fVPTPbO3Vmki2ZxdZ3k5yf5IfL7na3JEclOSzJqUneleQXMwuaByY5JMnnk7xpjHFlVZ2a5KKq+nBme43ePsa4YXqsrUkekeS+SU6vqm1JfiPJd8cYD5/i7pNVdVqS+00/j0hSSU6tqkcn+UGSpyc5IrP3us8kOWfnYKvqFTsfM8lj5l7H4UmOzCy4zq2qv5mWPySzoLwyyVeSvGGM8YiqOi7J9iTPv7nzC8D6ZI8VALtyRmZRtTOszpy7/sld3P69Y4wbxhifzyyikuTRmQXTj8YYFyf5yM4bjzH+XZJ/leRTSY5P8qa5x3rn9FhfyixiDkvy+CS/UVXnJTkryV0yC6rHTz/nZhZPh03LH5XkPWOMq6c9VKfOD3aM8ZIxxj2SvDXJ786tet8Y45oxxhVJTs8s2JLk02OMS8YY1yX5cpLTpuXnZxaCAGxwwgqAXdl5ntU/zexQwL/PbI/VIzOLruWum7tcN+cJxhjnjzFek+RxSX55ftXym06PuX2MccT0c+8xxmnT8v8yt3zbGOONN+f5J2+9Gc+d3Pj13TB3/YY4+gOACCsAdu2MJE9McuW0x+nKJHfKLK52FVa78rEkvzKdH3W3TIfcVdUBVXX03O2OSHLR3PWnVtU+07lL90lyYZK/TfLb0xdfpKruX1W3n5Y/p6oOmJZvqaq7Ts/95Krav6rukORJOx+8qu4391zHJvni/PWq2q+q7pLk6CSfvpmvFYANzm/ZANiV8zP7NsC3LVt2wBjjiqqbtVPqPUkem9m5Vf+Q2eGEyWwv0+9V1X9Nck1m50M9e+5+/5DZIYJ3TPK8Mca109e9b03ymZo9+eVJnjzGOK2qfirJmdOYvp/kmWOMz1TVO5J8NslluXEgvbKqHpDZ3qaLkjxvbt3nMjsE8KAkL5/ON7v/zXmxAGxsNcbyox4AYDGq6pQk7x9jvGsBz31iku+PMV69p58bgLXPoYAAAABN9lgBAAA02WMFAADQJKwAAACahBUAAECTsAIAAGgSVgAAAE3/D00Xq6gSMGTeAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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sAAAASsIKAACgJKwAAABKwgoAAKAkrAAAAErCCgAAoCSsAAAASsIKAACgJKwAAABKwgoAAKAkrAAAAErCCgAAoCSsAAAASsIKAACgJKwAAABKZ+96AK1HP/rRux4CAABwhnPECgAAoHRah9WxR6scvQIAAHbhtD8V8FhPf/rTdz0E4BA7cuRI7vSXa9fDgJPqTn/xnhw58l6/Y4GT5siRI7nLXe6y62EcyG0esZqZp8zMVTNz1dve9raTMSYAAIDTym0esVpr/USSn0iSSy+99JT/M+2znvWsXQ8BOMSe/vSn55VveOuuhwEn1YfOvXsueuC9/Y4FTprT8Qj5af0aKwAAgFPBaR1WV1555Uf9HAAA4GQ4rcMKAADgVHDah9WVV16Ziy++OBdffPGuhwIAAJyhTvuwAgAA2DVhBQAAUBJWAAAAJWEFAABQElYAAAAlYQUAAFASVgAAACVhBQAAUBJWAAAAJWEFAABQElYAAAAlYQUAAFASVgAAACVhBQAAUBJWAAAAJWEFAABQElYAAAAlYQUAAFASVgAAACVhBQAAUBJWAAAAJWEFAABQElYAAAAlYQUAAFASVgAAACVhBQAAUBJWAAAAJWEFAABQElYAAAAlYQUAAFASVgAAACVhBQAAUBJWAAAAJWEFAABQElYAAAAlYQUAAFASVgAAACVhBQAAUBJWAAAAJWEFAABQElYAAAAlYQUAAFASVgAAACVhBQAAUBJWAAAAJWEFAABQElYAAAAlYQUAAFASVgAAACVhBQAAUBJWAAAAJWEFAABQElYAAAAlYQUAAFASVgAAACVhBQAAUBJWAAAAJWEFAABQElYAAAAlYQUAAFASVgAAACVhBQAAUBJWAAAAJWEFAABQElYAAAAlYQUAAFASVgAAACVhBQAAUBJWAAAAJWEFAABQElYAAAAlYQUAAFASVgAAACVhBQAAUBJWAAAAJWEFAABQElYAAAAlYQUAAFASVgAAACVhBQAAUBJWAAAAJWEFAABQElYAAAAlYQUAAFASVgAAACVhBQAAUBJWAAAAJWEFAABQElYAAAAlYQUAAFASVgAAACVhBQAAUBJWAAAAJWEFAABQElYAAAAlYQUAAFASVgAAACVhBQAAUBJWAAAAJWEFAABQElYAAAAlYQUAAFASVgAAACVhBQAAUBJWAAAAJWEFAABQElYAAAAlYQUAAFASVgAAACVhBQAAUBJWAAAAJWEFAABQElYAAAAlYQUAAFASVgAAACVhBQAAUBJWAAAAJWEFAABQElYAAAAlYQUAAFASVgAAACVhBQAAUBJWAAAAJWEFAABQElYAAAAlYQUAAFASVgAAACVhBQAAUBJWAAAAJWEFAABQElYAAAAlYQUAAFASVgAAACVhBQAAUBJWAAAAJWEFAABQElYAAAAlYQUAAFASVgAAACVhBQAAUBJWAAAAJWEFAABQElYAAAAlYQUAAFASVgAAACVhBQAAUBJWAAAAJWEFAABQElYAAAAlYQUAAFASVgAAACVhBQAAUBJWAAAAJWEFAABQElYAAAAlYQUAAFASVgAAAKWzdz2AE+Giiy7a9RAAAIAT5HR8fn8owuqyyy7b9RAAAIAT5HR8fu9UQAAAgJKwAgAAKAkrAACAkrACAAAoCSsAAICSsAIAACgJKwAAgJKwAgAAKAkrAACAkrACAAAoCSsAAICSsAIAACgJKwAAgJKwAgAAKAkrAACAkrACAAAoCSsAAICSsAIAACgJKwAAgJKwAgAAKAkrAACAkrACAAAoCSsAAICSsAIAACgJKwAAgJKwAgAAKAkrAACAkrACAAAoCSsAAICSsAIAACgJKwAAgJKwAgAAKAkrAACAkrACAAAoCSsAAICSsAIAACgJKwAAgJKwAgAAKAkrAACAkrACAAAoCSsAAICSsAIAACgJKwAAgJKwAgAAKAkrAACAkrACAAAoCSsAAICSsAIAACgJKwAAgJKwAgAAKAkrAACAkrACAAAoCSsAAICSsAIAACgJKwAAgJKwAgAAKAkrAACAkrACAAAoCSsAAICSsAIAACgJKwAAgJKwAgAAKAkrAACAkrACAAAoCSsAAICSsAIAACgJKwAAgJKwAgAAKAkrAACAkrACAAAoCSsAAICSsAIAACgJKwAAgJKwAgAAKAkrAACAkrACAAAoCSsAAICSsAIAACgJKwAAgJKwAgAAKAkrAACAkrACAAAoCSsAAICSsAIAACgJKwAAgJKwAgAAKAkrAACAkrACAAAoCSsAAICSsAIAACgJKwAAgJKwAgAAKAkrAACAkrACAAAoCSsAAICSsAIAACgJKwAAgJKwAgAAKAkrAACAkrACAAAoCSsAAICSsAIAACgJKwAAgJKwAgAAKAkrAACAkrACAAAoCSsAAICSsAIAACgJKwAAgJKwAgAAKAkrAACAkrACAAAoCSsAAICSsAIAACgJKwAAgJKwAgAAKAkrAACAkrACAAAoCSsAAICSsAIAACgJKwAAgJKwAgAAKAkrAACAkrACAAAoCSsAAICSsAIAACgJKwAAgJKwAgAAKAkrAACAkrACAAAoCSsAAICSsAIAACgJKwAAgJKwAgAAKAkrAACAkrACAAAoCSsAAICSsAIAACgJKwAAgJKwAgAAKAkrAACAkrACAAAoCSsAAICSsAIAACgJKwAAgJKwAgAAKAkrAACAkrACAAAoCSsAAICSsAIAACgJKwAAgJKwAgAAKAkrAACAkrACAAAoCSsAAICSsAIAACgJKwAAgJKwAgAAKAkrAACAkrACAAAonb3rAQCcbs56/425y7W/uuthnFHOev87ksT3fUfOev+NSe6962EAnNKEFcABXHTRRbsewhnp+us/mCS54AJP7nfj3n72AW6DsAI4gMsuu2zXQwAATkFeYwUAAFASVgAAACVhBQAAUBJWAAAAJWEFAABQElYAAAAlYQUAAFASVgAAACVhBQAAUBJWAAAAJWEFAABQElYAAAAlYQUAAFASVgAAACVhBQAAUBJWAAAAJWEFAABQElYAAAAlYQUAAFASVgAAACVhBQAAUBJWAAAAJWEFAABQElYAAAAlYQUAAFASVgAAACVhBQAAUBJWAAAAJWEFAABQElYAAAAlYQUAAFASVgAAACVhBQAAUBJWAAAAJWEFAABQElYAAAAlYQUAAFASVgAAACVhBQAAUBJWAAAAJWEFAABQElYAAAAlYQUAAFASVgAAACVhBQAAUBJWAAAAJWEFAABQElYAAAAlYQUAAFASVgAAACVhBQAAUBJWAAAAJWEFAABQElYAAAAlYQUAAFASVgAAACVhBQAAUBJWAAAAJWEFAABQElYAAAAlYQUAAFASVgAAACVhBQAAUBJWAAAAJWEFAABQElYAAAAlYQUAAFASVgAAACVhBQAAUBJWAAAAJWEFAABQElYAAAAlYQUAAFASVgAAACVhBQAAUBJWAAAAJWEFAABQElYAAAAlYQUAAFASVgAAAKVZa93+K8+8Lcmf3nHDOS2dn+Ttux4ENfN4eJjLw8E8Hh7m8vAwl4eDeew9YK11r2NXHiis+Egzc9Va69Jdj4OOeTw8zOXhYB4PD3N5eJjLw8E83nGcCggAAFASVgAAACVh1fuJXQ+AE8I8Hh7m8nAwj4eHuTw8zOXhYB7vIF5jBQAAUHLECgAAoCSsAAAASsLqGDPznJm5YWZevW/dPWfmxTPzuu3jJ2zrZ2Z+ZGaOzMyrZuaSfbf52u36r5uZr93FYznTncC5vGVmrt4uL9jFYzmTHXAeP2Nmfn9mPjAz33LMdh43M6/d5vgZJ/txcELn8rqZuWbbJ6862Y+DA8/lP9n+Xb1mZn5vZi7edxv75Q6dwHm0T+7YAefy8dtcXj0zV83Mo/bdxvPXgrD6SFckedwx656R5CVrrQclecn2eZJ8UZIHbZenJPmxZO8HOcl3Jnlkkkck+c6jP8ycVFeknMvNzWuth26Xv3/HDplbcUVu/zzemOSbkvzg/ivPzFlJ/lP25vnBSb5qZh58B46ZW3dFyrnc5wu2fdL/xbIbV+T2z+Ubk3z+Wuuzknx3thfO2y9PCVeknMd97JO7dUVu/1y+JMnFa62HJnlSkmcnnr+eCMLqGGut38reL/T9Hp/kudvyc5M8Yd/6n157XprkHjNznySPTfLitdaNa613JnlxPvKHnTvYCZpLduwg87jWumGt9Yokf3XM9R+R5Mha6w1rrb9M8gvbNjiJTtBccgo44Fz+3va7MElemuSTt2X75Y6doHnkFHDAubxpffjd6+6a5Oiy568lYXX73Hut9eZt+S1J7r0tX5DkTfuu9+fbuuOtZ/cOOpdJcu52qPylM/OEkzNMbsPx5vF47JOnroPOZbL3JOBFM/PKmXnKHTc0Duj2zOWTk/zatmy/PDUddB4T++Sp6rhzOTNfNjPXJvmV7B21SuyTtbN3PYDTzVprzYz3qD8EDjCXD1hrXT8zD0zymzNzzVrr9Xf0+Lh97JOHxwHm8lHbPvlJSV48M9duf63lFHFrczkzX5C9J+SPuvVbcao5wDzaJ09xx87lWuv5SZ4/M5+XvVM7v3BngztEHLG6fd569LSw7eMN2/rrk9xv3/U+eVt3vPXs3kHnMmutox/fkOTKJA87WYPluI43j8djnzx1HXQu9++TNyR5fvZOKWP3jjuXM/PZ2Xsdx+PXWu/YVtsvT00HnUf75KnrNv993QL4gTNzfuyTNWF1+7wgydF3RvnaJL+8b/0TZ8/nJHn3dsj1hUkeMzOfsL3o7zHbOnbvQHO5zeE5SbL9o/O5SV5zsgfNRzjePB7PK5I8aGY+ZWY+LslXbttg9w40lzNz15n5+KPL2fv39dUf7TacNLc6lzNz/yT/I8nXrLX+ZN/17ZenpgPNo33ylHa8ubxoZmZbviTJOUneEc9fe2stl32XJD+f5M3Ze8H0n2fvcPcnZu8dVF6X5DeS3HO77mTvHY1en+SaJJfu286TkhzZLl+/68d1Jl5OxFwm+Vvb53+4fXzyrh/XmXY54Dz+9e0670nyrm357tvXvjjJn2xz/O27flxn4uVEzGWSB2774x8m+SNzeVrM5bOTvDPJ1dvlqn3bsV+e5vNonzw1Lgecy2/d5urqJL+fvVM5j27H89fiMts3EQAAgI+RUwEBAABKwgoAAKAkrAAAAErCCgAAoCSsAAAASsIKgAOZmVtm5uqZefXM/LeZOW/XY7o1M/OIbZxXz8wfzsyX7XpMABxe3m4dgAOZmZvWWnfbln8uySvXWs/c9/Wz11ofPEljOe59bcH3l2utD87MfbL3/+zc92SNDYAziyNWADR+O8lFM/PomfntmXlBktfMzFkz8x9m5hUz86qZ+YYkmZn7zMxv7Tvi9be3616xfX7NzPzL7bpXzsyl2/L5M3Pdtvx1M/OCmfnNJC+ZmbvOzHNm5uUz8wcz8/gkWWu9f19EnZtkbbe/cGaunZmfm5k/npnnHT3qNjPXzcz3beO7amYumZkXzszrZ+apJ+/bCsDp5uxdDwCA09PMnJ3ki5L8+rbqkiQPWWu9cWaekuTda62Hz8w5SX53Zl6U5MuTvHCt9T0zc1aS85I8NMkFa62HbNu9x+24+0uSfPZa68aZ+d4kv7nWetJ225fPzG+std43M49M8pwkD0jyNdvRqyT59CRPXmv97sw8J8nTkvzgtu0/W2s9dGZ+KMkVST43e2H26iT/+WP8dgFwyDliBcBB3WVmrk5yVZI/S/JT2/qXr7XeuC0/JskTt+u9LMknJnlQklck+fqZ+a4kn7XWem+SNyR54MxcPjOPS/Ke2zGGF6+1btx3X8/Y7uvK7EXQ/ZNkrfWytdZnJnl4km+bmXO327xprfW72/LPJnnUvm2/YPt4TZKXrbXeu9Z6W5IP3M7oA+AM5IgVAAd181rroftXbEeB3rd/VZLL1lovPPbGM/N5Sb4kyRUz88y11k/PzMVJHpvkqUm+IsmTknwwH/4D4LnHbObY+/oHa63XHm/Aa60/npmbkjwkyduznRa4/yr7lj+wffzQvuWjn/u9CcCtcsQKgDvCC5P885m5c5LMzKdtr4V6QJK3rrV+Msmzk1wyM+cnudNa678n+Y7sneaXJNcl+Rvb8j+8jfu6bLa6m5mHbR8/ZTtdMdv9fsa2zSS5/8z8zW35Hyf5nfLxAnCG85c3AO4Iz05yYZL/vQXP25I8Icmjk/zrmfmrJDcleWKSC5L8l5k5+se+b9s+/mCSX9xer/UrH+W+vjvJDyd51baNNyb50uyd3veM7b4+lORpa623z8zdkrw2yTdur696TZIfOwGPGYAzmLdbB+CMMjMXJvmfR98sAwBOBKcCAgAAlByxAgAAKDliBQAAUBJWAAAAJWEFAABQElYAAAAlYQUAAFD6v3S+CIfQ9M3XAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x1080 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for feature in continuous_feature:\n",
    "    data=df.copy()\n",
    "    sns.boxplot(data[feature])\n",
    "    plt.title(feature)\n",
    "    plt.figure(figsize=(15,15))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "small-violence",
   "metadata": {},
   "source": [
    "### Feature Transformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "ranking-psychiatry",
   "metadata": {},
   "outputs": [],
   "source": [
    "def qq_plots(df, variable):\n",
    "    plt.figure(figsize=(15,6))\n",
    "    plt.subplot(1, 2, 1)\n",
    "    df[variable].hist()\n",
    "    plt.subplot(1, 2, 2)\n",
    "    stats.probplot(df[variable], dist=\"norm\", plot=plt)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "false-confirmation",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MinTemp\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MaxTemp\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rainfall\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaporation\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sunshine\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WindGustSpeed\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WindSpeed9am\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WindSpeed3pm\n"
     ]
    },
    {
     "data": {
      "image/png": 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mU1lB87e/NcGrYJInSZIkqXlkwne+A/vuC+9/P9xxB7z73fWOqqGY5EmSJElqDitWwBFHFFMk7L8/3HTTkJwioTsmeZIkSZIanxU0q2bhFUmSJEmNrbKC5kUXwRe+UO+IGppJniRJkqTGdeedsPfe8I9/wA03wMc+Vu+IGp7DNSVJkiQ1piuvhPHjYd11iwqaJnhVMcmTJEmS1FgqK2i+731W0OwhkzxJkiRJjWPFCvjSl1ZV0Lz5Zito9pBJniRJkqTG0FZBc8YMOOkkK2j2koVXJEmSJNXfI48UCZ4VNPvMJE+SJElSfVlBs185XFOSJElS/bRV0Bw+3Aqa/cQkT5IkSdLAa6ugud9+RQXN+fOtoNlPTPIkSZIkDazKCpr77WcFzX5mkidJkiRp4FhBs+YsvCJJkiRpYFhBc0CY5EmSJEmqPStoDhiHa0qSJEmqrauuWlVB8/bbTfBqzCRPkiRJGuIiarUkx8fpvPqZffnt39/Hm5fMJ96zbQ3vV7tl2LDic801V//cdNNiWWMNGDUKvvKV4rNt+7LLVj3nyy7rfF9/crimJEkNIiKGAQuAZZk5KSK2AWYBbwLuBj6bma/UM0ZJg09Eba67Jis4l6M4ghlczn4cwkz+QfMWWHn11eJz5crVP//611XHPPIIXHDB6ttTp67anjoVXnrp9fsOPrh/Y7UnT5KkxnEM8PuK7W8DZ2bm24FngcPqEpUk9dCGPM91TOQIZnAKJ3Egs5o6weuLl16Cb3yjWNoSvPb7+ptJniRJDSAitgAmAheW2wHsDlxRHjITmFKX4CSpB7biEW5jFz7KLXyBi/gGp5BDPO149NFi6Wxff3O4piRJjeF7wAnABuX2m4DnMrO13F4KbF6HuCSpamO5i2vZi3X4BxO4gZuxwArAVlsVn4880vm+/jS0U2pJkhpAREwCnsrMu3t5/tSIWBARC55++ul+jk6SqrMPV/ErPsLfGc6HuN0Er7TuunDyycWy7rod7+tvJnmSJNXfLsDeEbGEotDK7sBZwEYR0TbqZgtgWUcnZ+aMzBybmWNHjBgxEPFKalIdVY3su2Qap3MF+3If72Mc8/k92/bHhRvKGmXmNGzY6p9velOxRMDWW8OXv1x8tm3PmFEUVjn44GK9o339zeGakiTVWWaeBJwEEBHjgeMz8+CI+B9gX4rE7xDgmnrFKKn59Sahy+zmgBUr4Kijimxlv/344MyZPDV8aBZYqUZbsldr9uRJktS4TgS+FhGLKd7Ru6jO8UjSKs8/D5MmFQneSSfBrFnFZOequ26TvIhYJyLujIj7ImJhRPx72b5NRMyPiMUR8bOIeEPZvna5vbjcP6riWieV7X+MiE9VtE8o2xZHxPQafJ2SJDWFzGzJzEnl+sOZuVNmvj0z98vMl+sdnyQBRQWRXXaBm2+Giy6CU05ZNZ5RdVfNn8TLwO6Z+T5ge2BCROxM53P3HAY8W7afWR5HRGwLHAi8B5gAnB8Rw8qJX88D9gC2BQ4qj5UkSZLUaO66C8aNg6VL4YYb4AtfqHdEaqfbJC8Ly8vNtcol6XzunsnlNuX+j5Vz/UwGZmXmy5n5Z2AxsFO5LC5/W/kKxXsHk/v6hUmSJEnqZ1ddBR/5SDEs8/bb4WNW0GxEVRVeKXvb7gbeTtHr9r90PnfP5sBjAJnZGhHPU7xHsDlwR8VlK895rF37uE7imApMBRg5ciQtLS3VhN+p5cuX9/kaA6VZYm2WOKH6WKeNae32mFobObyIo9Gf7WD8828ExipJ6g+ZPSu+slrRlUw44ww44QTYaSe45hoYObLfY1T/qCrJy8yVwPYRsRFwNfCuWgbVRRwzgBkAY8eOzfHjx/fpei0tLfT1GgOlWWJtljih+lgPnX5d7YPpxrQxrZzxwJosOXh8vUPp0mD8828ExipJ6i/dVsvsyIoVcPTR8IMfwH77wcyZFlhpcD16OzIznwNuAT5I53P3LAO2BCj3vxH4a2V7u3M6a5ckSZLUSx3NidfjefLaKmj+4AdW0Gwi1VTXHFH24BERw4FPAL+nSPb2LQ+rnLtndrlNuf/mzMyy/cCy+uY2wGjgTuAuYHRZrfMNFMVZZvfD1yZJkiQNST0ZltnpsZUVNC+80AqaTaSa4ZqbATPL9/LWAC7PzDkRsQiYFRH/CfyOVXP3XAT8uJzT5xmKpI3MXBgRlwOLgFbgyHIYKBFxFHAjMAy4ODMX9ttXKEmSJKln7roL9toL/vGPooKmBVaaSrdJXmbeD7y/g/aHKSpjtm//B7BfJ9c6GTi5g/a5wNwq4pUkSZJUS1dfDQcfXBRWuflm2NbZzZqN/a2SJEmSiqosp58On/kMvPe9cMcdJnhNyiRPkiRJGupWrIAvfxm+/nXYd1+45RanSGhiJnmSJEnSINOTqRLyuYoKmtOnW0FzEKhqnjxJkiRJzaWqRO+RR2DXSfCHPxQVNA87rOZxqfZM8iRJkqQm1ZOpEqBd4mcFzUHL4ZqSJElSE+ppgrfaOVdfDR/5SDEs8/bbTfAGGZM8SZIkaciwguZQYJInSZIkDQHDaOUCygqan/mMFTQHMZM8SZIkaZDbgBeYwyS+RFlB82c/s4LmIGaSJ0mSJA1iW/EIt7ELH+MmDuNC+K//gjVMAwYzq2tKkiRJTSiz++IrY7mLa9mLdfgHe3A9v8yPD0xwqitTeEmSJKlJZXaxXHU1dw3/CG8ZNZyNFt5ugjeEmORJkiRJDS6i+oVMOOMMK2gOYSZ5kiRJUgPryXx4w2jl+2t8GY4/3gqaQ5hJniRJkjQIWEFTbUzyJEmSpCa3JY++VkHzi/zQCppDnH/ykiRJUhPbkQXMZxxb8hh7cD0X8cV6h6Q6M8mTJEmSmtQUruZWPsw/WIcPcTs3YQVNmeRJkiRJDS2zw1a+xhlcyWe4n/eyM3fwe7bt4ngNJSZ5kiRJUoNbbf67Fa3kEV/mDI5njX0/w84v3cKTOfK1/dKa9Q5AkiRJUnVTJWzAC1zO/kzgRjjxRDjlFAus6HX8GyFJkiTVWTUJ3pY8ym/Y9bUKmvHtU03w1CF78iRJkqQGtyMLuJa9WJeX2IPrLbCiLpn6S5IkSQ2srYLmy6xtBU1VxSRPkqQ6i4h1IuLOiLgvIhZGxL+X7dtExPyIWBwRP4uIN9Q7Vkl9E9Hx0rHVK2iOYz6LeM9AhqsmZZInSVL9vQzsnpnvA7YHJkTEzsC3gTMz8+3As8Bh9QtRUl9V895dm2G0cj5f4QyO50o+w0e5hacYWbvgNKiY5EmSVGdZWF5urlUuCewOXFG2zwSmDHx0kgbaBrzAHCbxZb7PqZzIAfyMfzD8dcc5XYI6Y+EVSZIaQEQMA+4G3g6cB/wv8FxmtpaHLAU27+TcqcBUgK222qr2wUqqmS15lDlMYlsW8UV+yEV80WROPWZPniRJDSAzV2bm9sAWwE7Au3pw7ozMHJuZY0eMGFGrECXV2I4sYD7j2JpHmMANXMQX6x2SmpRJniRJDSQznwNuAT4IbBQRbaNutgCW1SsuSbU1mZ9bQVP9xiRPkqQ6i4gREbFRuT4c+ATwe4pkb9/ysEOAa+oSoKR+0fGwy+Q4vstVfJoHGPO6CpoO1VRvdJvkRcSWEXFLRCwqyzofU7b/W0Qsi4h7y2XPinNOKss9/zEiPlXRPqFsWxwR0yvaLREtSRrKNgNuiYj7gbuAeZk5BzgR+FpELAbeBFxUxxgl9YPMimVFK/mlr/BdprHGZz7NuL/dwpM5crVjpN6opvBKKzAtM++JiA2AuyNiXrnvzMw8vfLgiNgWOBB4D/BW4JcR8Y5y93kUv51cCtwVEbMzcxGrSkTPiojvU5SIvqCvX5wkSc0gM+8H3t9B+8MU7+dJaiLVTJWwAS9wOfszgRvhxBPhlFNgDQfZqX90+zcpM5/IzHvK9Rcpho90WN2rNBmYlZkvZ+afgcUU36B2AhZn5sOZ+QowC5gcEYEloiVJkjQIVJPgbcmj/IZd+Ti/5HBmEN8+1QRP/apHUyhExCiK3zTOB3YBjoqIzwELKHr7nqVIAO+oOK2y5PNj7drHUQw/6XGJ6JEjR9LS0tKT8F9n+fLlfb7GQGmWWJslTqg+1mljWrs9ptZGDi/iaPRnOxj//BuBsUrS4LEjC7iWvViXl5jADRZYUU1UneRFxPrAlcCxmflCRFwAfItistZvAWcAX6hJlKXMnAHMABg7dmyOHz++T9draWmhr9cYKM0Sa7PECdXHeuj062ofTDemjWnljAfWZMnB4+sdSpcG459/IzBWSRocJvNzfsI/8xRv5uP8crUCK1J/qqpfOCLWokjwLsvMqwAy88lyTp9XgR+y6p2BZcCWFae3lXzurP2vWCJaktRkImKNiNiw3nFIagZdV9CU+ls11TWDoprX7zPzuxXtm1Uctg/wYLk+GzgwItaOiG2A0cCdFNXCRpeVNN9AUZxldmYmloiWJDWBiPhJRGwYEetRfN9bFBFfr3dckhrXMFo5n6KC5lV8mo9yC08xst5haZCrpidvF+CzwO7tpks4LSIeKMs9fxQ4DiAzFwKXA4uAG4Ajyx6/VuAo4EaK4i2Xl8eCJaIlSc1h28x8gaJA2PXANhTfIyUJWH3agw14gWvZiy/zfU7lRPbncv7Oul2eI/WHbt/Jy8zfAB3VCZrbxTknAyd30D63o/MsES1JahJrla8wTAHOzcwVEeGPZ5JWkwk8+ihMmgSLFsEFM5h++OFM7/ZMqX9Yq1WSpOr9AFgCrAfcGhFbAy/UNSJJdRHR+TI2FvDE1uN4/oFH+MTK64mph9c7XA0xJnmSJFUpM8/OzM0zc88sPELxyoKkIaSrufAm83Nu5cO8zNp8iNv5JZ/o9hypv/VonjxpII2q8dQF08a0NsT0CJKaR0SMBE4B3pqZe0TEtsAH8V1ySSTHcSanczx38QH2ZrYFVlQ39uRJklS9SygKiL213P4TcGy9gpHUGIbRynkcaQVNNQyTPEmSqrdpZl4OvApQVo5eWd+QJNVTWwXNr3AB3+aETitoSgPJ4ZqSJFXvbxHxJiABImJn4Pn6hiSpXrbkUeYwiW1ZxOHM4EIssKLGYJInSVL1vgbMBt4WEbcBI4B96xuSpIGWCTvG3cxhEuvyEntw/WsFVro6RxooJnmSJFUpM++JiI8A76SYQ/aPmbmizmFJGmjXXMPd6/4zjBgB1/2See95T70jklZjkidJUpUi4nPtmnaICDLz0roEJKmmXj/tQXIs3+MMphUVNB+ZzVPbrSqwYm+dGoVJniRJ1ftAxfo6wMeAewCTPGmQaZ/gDaOVs/kqX+ECruAzfI5LX1dgJcJET43BJE+SpCpl5tGV2xGxETCrPtFIGigb8AI/4wD24Aa+zQmcxH+RFqlXAzPJkySp9/4GbFPvICTVzhY8xnVMtIKmmopJniRJVYqIaymnT6CYa3Zb4PL6RSSplnbgbq5lL9bjb1VV0JQahUmeJEnVO71ivRV4JDOX1isYSbWzN9fwE/6ZpxnBJ5jHIqygqeZhkidJUpUy81f1jkFSjWXC977HNTGNO3MsezObJ3lL1adKjcAkT5KkbkTEi6waprnaLiAzc8MBDklSLbS2wle/ChdcAJ/5DDtdeil/WXfd7s+TGoxlgSRJ6kZmbpCZG3awbGCCJzW3iGLZMF7g+rX2ggsu4NucwBpXXk6st+5r+9sWqRnYkydJUg9FxJsp5skDIDMfrWM4knqpLWnrSQVN58JTMzDJkySpShGxN3AG8FbgKWBr4PdgRQapWVlBU4ORwzUlSaret4CdgT9l5jbAx4A76huSpN7am2u4lQ/zCm/gQ9xugqdBwyRPkqTqrcjMvwJrRMQamXkLMLbeQUnqoUw480yuZh8W8h525g6nSNCg4nBNSZKq91xErA/cClwWEU8Bf6tzTJJ6orUVjjkGzj+fq/k0n+XH/B0raGpwsSdPkqRuRMR+EbEOMBl4CTgOuAH4X2CvesYmqQdeeAH23hvOPx++/nU+s/J/epzgWXRFzcCePEmSuvfPwHnAjcBPgRszc2Z9Q5LUI489BhMnwqJFMGMGHF5U0DRp02BkT54kSd3IzH2AtwO/BI4GlkbE9yPiI/1x/YjYMiJuiYhFEbEwIo4p2zeJiHkR8VD5uXF/3E8aSiJgx7ibx7cax/MPPMInVl5PTD38dfPfOReeBhOTPEmSqpCZL2TmzMzcA9gO+B1wdkQ81g+XbwWmZea2FNU7j4yIbYHpwE2ZORq4qdyWVKWI1Sto7sJtVVXQNNFTszPJkySpB8retE8DBwCbAFf09ZqZ+URm3lOuv0gx997mFO8Atg0LnQlM6eu9pCEjk2NZvYLmQrard1TSgDDJkySpGxGxfkR8NiLmAosopk34FrBVZh7Xz/caBbwfmA+MzMwnyl1/AUZ2cs7UiFgQEQuefvrp/gxHak6trXDUUZzJ17iafRhPC0/ylnpHJQ0YC69IktS9JRTVNM+nKLqyohY3KadnuBI4NjNfiIoxY5mZEdFhiYjMnAHMABg7dqxlJDQkdDakcgNeYBYHsifXcxpfZzqnkvZraIgxyZMkqXtbZubfa3mDiFiLIsG7LDOvKpufjIjNMvOJiNgMeKqWMUjNorMEbwseYw6TeA8LmcoP+CFTBzYwqUH4aw1JkroxAAleABcBv8/M71bsmg0cUq4fAlxTyzikZrYDdzOfcYxiCXsyt08JntMqqNl1m+T1tKxzFM6OiMURcX9E7FBxrUPK4x+KiEMq2neMiAfKc86OsKaRJGlI2QX4LLB7RNxbLnsCpwKfiIiHgI+X25LaaV9Bcx6f7PaczM4XqdlVM1yzrazzPRGxAXB3RMwDDqUo63xqREynKOt8IrAHMLpcxgEXAOMiYhPgmxQvq2d5ndmZ+Wx5zOEUL5nPBSYA1/fflylJUuPKzN8Anf2C82MDGYvUXJJjOIvv8jUWMJa9mW2BFYkqkryyqtcT5fqLEVFZ1nl8edhMoIUiyZsMXJqZCdwRERuV7xGMB+Zl5jMAZaI4ISJagA0z846y/VKKEtEmeZKkhhAR11L8grJDmbn3AIYjCRhGK2dxDEdyPlfyaT7Lj/k769Y7LKkh9KjwSpVlnTcHKieGXVq2ddW+tIN2SZIaxenl56eBtwD/XW4fBDxZl4ikISxfeJG5Gx7Q6wqaDsnUYFd1ktfbss79KSKmQvEW7ciRI2lpaenT9ZYvX97nawyUZom1P+OcNqa1X67TmZHDa3+P/tIWa6P/HWiWv6dgrLXSTLH2RGb+CiAizsjMsRW7ro2IBXUKSxqaHnsMJk1iz2EL4fwfcMLUqZxQ75ikBlNVktfDss7LgC0rTt+ibFvGquGdbe0tZfsWHRz/Ou3nARo/fnxHh1WtpaWFvl5joDRLrP0Z56HTr+uX63Rm2phWznigOWYRaYt1ycHj6x1Kl5rl7ykYa600U6y9tF5E/FNmPgwQEdsA69U5JmnQ6awE3w7czbXsxXr8jf2Yy7wjPglHrNpvD51U6PYn3CrKOp/K6mWdZwNHRcQsisIrz5eJ4I3AKW1VOIFPAidl5jMR8UJE7EwxDPRzwDn98LWpF0b1MbGaNqa15smZJNXRcUBLRDxMUShla1b7EVNSX3WW4O3FbH7KQfwfm7ILt7GQ7To810RPqq4nr62s8wMRcW/Z9q8Uyd3lEXEY8Aiwf7lvLrAnsBh4Cfg8QJnMfQu4qzzuP9qKsABfAS4BhlMUXLHoiiSp4WTmDRExGnhX2fSHzHy5njFJg58VNKWeqqa6Zo/KOpdVNY/s5FoXAxd30L4AOvh1jCRJDSQi1gW+BmydmYdHxOiIeGdmzql3bNJgNIxWvsexHMV5VtCUeqD6MkSSJOlHwCvAB8vtZcB/1i8cafBanxeZzd4cxXmcxtfZj/8xwZOqZJInSVL13paZpwErADLzJTof7SKpl7bgMX7DrnySXzCVH3Aip/VoigRpqGuO0oKSJDWGVyJiOOXE6BHxNsB38qR+lHffw+M7TmJ9lrMnc5nHJ6s/16IrEmBPniRJPfFN4AZgy4i4DLgJnKJL6jezZ8Nuu/HWrdZiwwdu5xf5STKpepFUMMmTJKkKEbEGsDHwaeBQ4KfA2MxsqWNYUlOKaL8kx8b3eHXyFO586T285dH5xJjtVjtGUvUcrilJUhUy89WIOCEzLwecEFTqpfYJW2UFzavYh3/hvzsssOIceFL17MmTJKl6v4yI4yNiy4jYpG2pd1BSs6qsoPkdjmdfrrCCptQP7MmTJKl6B5SflfPBJvBPdYhFamqbs5TrmMh7WMgRfJ8ZHFHvkKRBwyRPkqQqZeY29Y5BGgzezz3MoaigOZHr+AWfqndI0qDicE1JkqoUEetGxP8XETPK7dERManecUnNZC9m82t2YwVr8SFuN8GTasAkT5Kk6v0IeAX4ULm9DPjP+oUjNZFMOOssZscUFrEt45jPQrbr0emSqmOSJ0lS9d6WmacBKwAy8yXA4u5Sd1pb4eij4dhjYcoUPvC3X/GXfItz4Ek1YpInSVL1XomI4RTFVoiItwEv1zckqXG8fv472CBe5Lq1JsN5RQXNNa6+glhv3dcdJ6n/WHhFkqTqfRO4AdgyIi4DdqGYGF0a8jpK1DZnKXOYxHY82G0FTefBk/qPSZ7UREZNb5z5l5ecOrHeIUgDLjPnRcQ9wM4UwzSPycz/q3NYUkOygqZUPyZ5kiR1IyJ2aNf0RPm5VURslZn3DHRMUiObxLXM4kD+j035ELf3qMCKpL4zyZMkqXtnlJ/rAGOB+yh68t4LLAA+WKe4pAaTfJWzOZPjuJsd2YtreZK31Dsoacix8IokSd3IzI9m5kcpevB2yMyxmbkj8H6KaRSkIW8YrZzD0ZzFsfycKXyEX5ngSXViT54kSdV7Z2Y+0LaRmQ9GxLvrGZDUEF58kdY9D4S5c/kOx3Mi3yZ72Jdg0RWp/9iTJ0lS9R6IiAsjYny5/BC4v95BSXW1dCnsthvceCN8//t8Pb/Dq7lGj+bAM8GT+pdJniRJ1TsUWAgcUy6LgM/XMyBpIHQ0/10E7BD3sGzLcbxw38N8auV1xJeOcP47qQE4XFOSpCpExDDg+vLdvDPrHY80UDpL1CoraO7CbTzImE7Pt6dOGlj25EmSVIXMXAm8GhFvrHcsUn0lX+UsrmEyi9iWcczvNMGTVB/25EmSVL3lFO/lzQP+1taYmV+tX0jSwBlGK2dyHEdzLlexD//Cf/N31q13WJLaMcmTJKl6V5VLv4uIi4FJwFOZuV3ZtgnwM2AUsATYPzOfrcX9pe6sz4vM4kAm0vsKmpIGhv8yJUmq3s+Au8vlZ5k5MzNn9tO1LwEmtGubDtyUmaOBm8ptacBtzlJ+zW58ihs5gu9zAt8xwZMamP86JUnqRkSsGRGnAUuBmcClwGMRcVpErNUf98jMW4Fn2jVPLu9H+TmlP+4l9UTefQ/zGcc/8TATuY4ZHNGz8y26Ig04kzxJkrr3HWATYJvM3DEzdwDeBmwEnF7D+47MzCfK9b8AIzs6KCKmRsSCiFjw9NNP1zAcDTnXXgu77cbmW63Jhvffxo35Kee/k5qASZ4kSd2bBByemS+2NWTmC8CXgT0HIoDMTKDDH5kzc0Zmjs3MsSNGjBiIcNTkOpv3rnL5apzNq3tP5q6XtmWzR+8g3jvG+e+kJmGSJ0lS97JMsto3rqSTxKufPBkRmwGUn0/V8F4aIrpL0IbRytkczdkcw8+Zwnha+Aub9fg6kurHJE+SpO4tiojPtW+MiH8B/lDD+84GDinXDwGuqeG9JNbnRa5hMkdzLt/hePblCl5ivXqHJamHnEJBkqTuHQlcFRFfoKisCTAWGA7s0x83iIifAuOBTSNiKfBN4FTg8og4DHgE2L8/7iV1ZHOWModJbMeDfIkL+AFfqndIknqp2ySvk3l7/g04HGh7u/tfM3Nuue8k4DBgJfDVzLyxbJ8AnAUMAy7MzFPL9m2AWcCbKL5xfjYzX+mvL1CSpL7KzGXAuIjYHXhP2Tw3M2/qx3sc1Mmuj/XXPaTOvJ97uJa92IAXmch1/IJP1TskSX1QzXDNS3j9vD0AZ2bm9uXSluBtCxxI8Q1wAnB+RAyLiGHAecAewLbAQeWxAN8ur/V24FmKBFGSpIaTmTdn5jnl0m8JnlRPk7iWW/kwKxnGLtxmgicNAt0meZ3M29OZycCszHw5M/8MLAZ2KpfFmflw2Us3C5gcEQHsDlxRnu8cQJIkSTVUWULoaM7m50zh97ybccznQcb06jqSGktf3sk7qnwJfQEwLTOfBTYH7qg4ZmnZBvBYu/ZxFEM0n8vM1g6Of52ImApMBRg5ciQtLS19CB+WL1/e52sMlIGKddqY1u4P6sLI4X2/xkAx1r7p6O+j/6Zqw1gl9bdc0QrHHQfnngv77MMHfvxjnljPAivSYNHbJO8C4FsUZaO/BZwBfKG/gupMZs4AZgCMHTs2x48f36frtbS00NdrDJSBivXQ6df16fxpY1o544HmqOdjrH2z5ODxr2vz31RtGKukalQ7pcH6vMgsDmQiczmdaZx49bd5df1h9sxJg0ivfmrMzCfb1iPih8CccnMZsGXFoVuUbXTS/ldgo4hYs+zNqzxekiRJVag2weuqgmaEQzClwaJX8+S1Tcxa2gd4sFyfDRwYEWuXVTNHA3cCdwGjI2KbiHgDRXGW2eXEsrcA+5bnOweQJElSDWzP75jPOP6Jh5nIdU6RIA1i1Uyh0NG8PeMjYnuK4ZpLgCMAMnNhRFwOLAJagSMzc2V5naOAGymmULg4MxeWtzgRmBUR/wn8Driov744SZIkFRU0f8pBPMMm7MJtPSqwIqn5dJvkdTJvT6eJWGaeDJzcQftcYG4H7Q9TVN+UJElSqdohmN05mrM5k+O4hx3Ym9n8hc26P0lSU+vVcE1JkiTVTn8keMNo5WyO5myOYTZ7M54WEzxpiDDJkyRJGmTW50V+zhSO5lxOZxr7cgUv0fUUCRZdkQYPkzxJkqRBZHOW8mt2YwI38CUu4Pg8nZVZTJHQ1SJp8DDJkyRJGiSsoCkJTPIkSZIGhUlcy6/ZjZUMYxdu4xd8qt4hSaoTkzxJkqQG09Phk0dzNj9nCr/n3Yxj/mtTJDgMUxqaTPIkSZIaUHfv0GVCtq4kj/4qZ3MMw6bszQeWt/BEbuZ7dtIQZ5InSZI0ADbeuJgaoS/Lal58ESZPhnPOgWnT4IorYL2uK2hKGhq6nQxdkiRJfbPxxvDcc32/TkTZQ7d0KUyaBA8+CBdcAF+ywIqkVUzyJEmSaqw/ErzX/O53RYL34oswZw5MmNCPF5c0GJjkSeqVUdOve13btDGtHNpBey0tOXXigN5PkuppInNgtwNhk03gtttgzJh6hySpAflOniRJUhM4mrO5hsnwrnfB/PkmeJI6ZZInSZJUYxtt1Ptz12AlZ1FU0JzN3vCrX8Fmm/VbbJIGH5M8SZKkGnv22d4leuuxnGuYzFc5h9OZxj6tVtCU1D2TPEmSpAHw7LNVzn3Xtjy2lOXb78akYTfA+edzfJ4Ow4bV+8uQ1AQsvCJJktRHr5vDrkqdTlhuBU1JfWBPniRJUh/0NsHr9Nw5c2C33Ypeu9tuM8GT1GMmeZIkSY3i7LNhshU0JfWNSZ4kSVK9rVwJX/0qHHMM7G0FTUl9Y5InSZJUT8uXw5QpcM45MG0aXGEFTUl9Y+EVSZKkOtmcpbDbXvDAA3D++fDlL9c7JEmDgEmeJElSH2T2rvjK9vyO3711EvyvFTQl9S+TPEmSpD7qdCqEzsyZAwceCMM2gd/8Bt773prEJWlo8p08SZIaXERMiIg/RsTiiJhey3tddhmMGgVrrFF8XnZZ5/u+8pVV25tuWiwRsOaaq3+usUbxOdSWTp1zzuoVNE3wJPUze/IkSWpgETEMOA/4BLAUuCsiZmfmov6+12WXwdSp8NJLxfYjjxTbbdrvu+CCVfv++tdV6ytXrv7Z416uQSKi3de+ciUcd9yqJO+yyyywIqkmTPIkSWpsOwGLM/NhgIiYBUwG+j3J+8Y3ViVxbV56qWhvW1cvLV8OBx1UDNP82tfgtNOKyc4lqQZM8iRJamybA49VbC8FxlUeEBFTgakAW221Va9v9OijPWtXlZYtg0mT4P77raApaUD4Tp4kSU0uM2dk5tjMHDtixIheX6ez/HCrrTrfp27cey+MGweLFxe9eCZ4kgaASZ4kSY1tGbBlxfYWZVu/O/lkWHfd1dvWXbdo72ifujaRObDrrkXlmdtugz32qHdIkoYIkzxJkhrbXcDoiNgmIt4AHAjMrsWNDj4YZsyArbcuioZsvXWxffDBHe/78pdXbb/pTcUCq141a/vszRxyze4ozmHOGlbQlFQfvpMnSVIDy8zWiDgKuBEYBlycmQtrdb+2hK6n+1SqrKC5lxU0JdVHtz15EXFxRDwVEQ9WtG0SEfMi4qHyc+OyPSLi7HIen/sjYoeKcw4pj38oIg6paN8xIh4ozzk7Yij+vk+SpM5l5tzMfEdmvi0zT653POrE8uUwZUqR4H3ta3DllSZ4kuqimuGalwAT2rVNB27KzNHATeU2wB7A6HKZClwARVIIfJOiGthOwDfbEsPymMMrzmt/L0mSpMa2bBl8+MMwd25RQfOMM5wiQVLddJvkZeatwDPtmicDM8v1mcCUivZLs3AHsFFEbAZ8CpiXmc9k5rPAPGBCuW/DzLwjMxO4tOJakiRJja+tguZDD1lBU1JD6O07eSMz84ly/S/AyHK9o7l8Nu+mfWkH7R2qnAdo5MiRtLS09DL8wvLly/t8jYEyULFOG9Pap/NHDu/7NQaKsfa/esTZ238X/vuvjWaKVeoXc+bAgQfCxhsXFTQtsCKpAfS58EpmZkRkfwRTxb1mADMAxo4dm+PHj+/T9VpaWujrNQbKQMV66PTr+nT+tDGtnPFAc9TzMdb+V484lxw8vlfn+e+/NpopVqnPzjkHjj0Wtt8err0W3vrWekckSUDvp1B4shxqSfn5VNne2Vw+XbVv0UG7JElSY1q5Eo45Br76VdhrL7j1VhM8SQ2lt79ynw0cApxafl5T0X5URMyiKLLyfGY+ERE3AqdUFFv5JHBSZj4TES9ExM7AfOBzwDm9jKmpjeqmB23amNY+97JJkqQ+Wr4cDjqoGKb5ta/BaadZYEVSw+k2yYuInwLjgU0jYilFlcxTgcsj4jDgEWD/8vC5wJ7AYuAl4PMAZTL3LYoJXQH+IzPbirl8haKC53Dg+nKRJElqLMuWFT13991XVNC0wIqkBtVtkpeZB3Wy62MdHJvAkZ1c52Lg4g7aFwDbdReHJElS3dx7L0yaBM8/X/Ti7bFHvSOSpE719p08SZKkoeG662DXXSGiqKBpgiepwZnkSZIkdebcc2HvveGd74T5850iQVJTMMmTJElqr62C5tFHW0FTUtMxyZMkSaq0fDlMmQJnnw3HHQdXXgnrrVfvqCSpao0/u7IkSdJAsYKmpEHAJE+SJAmsoClp0DDJk9TURk2/rlfnTRvTyqG9PLczS06d2K/XkzSArrsODjgANt4YfvMbeN/76h2RJPWa7+RJkqShrX0FTRM8SU3OJE+SJA1NlRU0J02ygqakQcMkT5IkDT3Ll8M++6yqoHnVVVbQlDRo+E6eJEkaWioraJ53HnzlK/WOSJL6lUmeJEkaOqygKWkIcLimJEkaGq67DnbdFSKKCpomeJIGKZM8SZI0+FlBU9IQYpInSZIGr5Ur4dhjraApaUgxyZMkSYNTWwXNs86ygqakIcXCK5IkafCxgqakIcwkT5IkDS733QcTJ1pBU9KQ5XBNSZI0eMydawVNSUOeSZ4kSRoczjuvGKL5jndYQVPSkGaSJ0mSmltbBc2jjrKCpiRhkidJkppZZQXNY4+1gqYkYeEVSZLUrB5/vOi5s4KmJK3GnjxJkuooIvaLiIUR8WpEjG2376SIWBwRf4yIT9UrxoZ0330wbhw89BBce60JniRVMMmTJKm+HgQ+Ddxa2RgR2wIHAu8BJgDnR8SwgQ+vAbVV0ISiguaee9Y3HklqMCZ5kiTVUWb+PjP/2MGuycCszHw5M/8MLAZ2GtjoGpAVNCWpWyZ5kiQ1ps2Bxyq2l5ZtrxMRUyNiQUQsePrppwckuAFnBU1JqppJniRJNRYRv4yIBztYJvfH9TNzRmaOzcyxI0aM6I9LNhYraEpSj1hdU5KkGsvMj/fitGXAlhXbW5RtQ0tlBc1zz4Ujj6x3RJLU8OzJkySpMc0GDoyItSNiG2A0cGedYxpY7StomuBJUlVM8iRJqqOI2CcilgIfBK6LiBsBMnMhcDmwCLgBODIzV9Yv0gFmBU1J6rU+JXkRsSQiHoiIeyNiQdm2SUTMi4iHys+Ny/aIiLPL+X7uj4gdKq5zSHn8QxFxSN++JEmSmkdmXp2ZW2Tm2pk5MjM/VbHv5Mx8W2a+MzOvr2ecA6qtgubo0VbQlKRe6I+evI9m5vaZ2TaB63TgpswcDdxUbgPsQTHUZDQwFbgAiqQQ+CYwjqI09DfbEkNJkjSErFwJxx1XVNCcONEKmpLUS7UYrjkZmFmuzwSmVLRfmoU7gI0iYjPgU8C8zHwmM58F5lFM+ipJkoaK5cvh05+G732vqKB59dWw/vr1jkqSmlJfk7wEfhERd0fE1LJtZGY+Ua7/BRhZrnc230/V8wBJkqRB6PHH4cMfhjlzigqaZ54Jw4bVOypJalp9nUJh18xcFhFvBuZFxB8qd2ZmRkT28R6vKRPJqQAjR46kpaWlT9dbvnx5n6/RX6aNae1y/8jh3R/TCJolTjDWWmiWOKE2sdbq/5Oe/l/1wLLnaxJHNUYOh3Muu+a17TGbv7FusahJ3HdfMUXCc88VFTQtsCJJfdanJC8zl5WfT0XE1RTv1D0ZEZtl5hPlcMynysM7m+9nGTC+XXtLJ/ebAcwAGDt2bI4fP76jw6rW0tJCX6/RXw6dfl2X+6eNaeWMBxp/WsNmiROMtRaaJU6oTaxLDh7fr9dr09P/q7r7/6SW2j/XWj0TDRJz58IBB8Ab31hU0LTAiiT1i17/hBMR6wFrZOaL5fongf+gmNfnEODU8rPtV7qzgaMiYhZFkZXny0TwRuCUimIrnwRO6m1cklQvo2qUXE0b01rXxE2qifPPh6OPLhK7a6+FzX1TQ5L6S19+jT0SuDoi2q7zk8y8ISLuAi6PiMOAR4D9y+PnAnsCi4GXgM8DZOYzEfEt4K7yuP/IzGf6EJckSWpUK1fC8ccXBVb22gt+8hMLrEhSP+t1kpeZDwOvG1eRmX8FPtZBewJHdnKti4GLextLbz2w7Hl/Oy5J0kBZvhwOPhhmzy4qaJ5+ugVWJKkGmuPlGUmS1Nwef7zoubv33qKC5pEd/t5XktQPTPIkSVJtWUFTkgZULSZDlyRJKsydC7vuCplFBU0TPEmqOZM8SZJUG+efXwzRHD0a5s93igRJGiAmeZIkqX+tXAnHHVe8dzdxItx6q1MkSNIA8p08SZLUf/72N/jnfy4qaB5zDJxxhhU0JWmAmeRJkqT+YQVNSWoIJnmSJKnvrKApSQ3Dd/IkSVLfXH+9FTQlqYGY5EmSpN47//yiB88KmpLUMEzyJElSz61cCV/7mhU0JakB+U6eJEnqGStoSlJDM8mTJEnVq6ygec45cNRR9Y5IktSOSZ4kSarO/fcXQzOfe67oxZs4sd4RSZI64Dt5kiSpe9dfD7vsUlTQ/PWvTfAkqYGZ5EmSpK61r6C5/fb1jkiS1AWTPEmS1DEraEpSU/KdPEmS9HpW0JSkpmWSJ0mSVmcFTUlqaiZ5kiRplYULYcIEK2hKUhMzyZMkSau88Y3w1rfCtddaYEWSmpRJniRJWmWLLeCOOyCi3pFIknrJ6pqSJGl1JniS1NRM8iRJqqOI+E5E/CEi7o+IqyNio4p9J0XE4oj4Y0R8qo5hSpKaiEmeJEn1NQ/YLjPfC/wJOAkgIrYFDgTeA0wAzo8I5zCQJHXLJE+SpDrKzF9kZmu5eQewRbk+GZiVmS9n5p+BxcBO9YhRktRcTPIkSWocXwCuL9c3Bx6r2Le0bHudiJgaEQsiYsHTTz9d4xAlSY3O6pqSJNVYRPwSeEsHu76RmdeUx3wDaAUu6+n1M3MGMANg7Nix2YdQJUmDgEmeJEk1lpkf72p/RBwKTAI+lpltSdoyYMuKw7Yo2yRJ6pLDNSVJqqOImACcAOydmS9V7JoNHBgRa0fENsBo4M56xChJai725EmSVF/nAmsD86KYn+6OzPxSZi6MiMuBRRTDOI/MzJV1jFOS1CQaJskrf5N5FjAMuDAzT61zSJIk1Vxmvr2LfScDJw9gOJKkQaAhhmuW8/6cB+wBbAscVM4PJEmSJEnqgYZI8ijm/VmcmQ9n5ivALIr5gSRJkiRJPdAoSV7VcwFJkiRJkjoXqyo11zGIiH2BCZn5xXL7s8C4zDyq3XFTganl5juBP/bx1psC/9fHawyUZom1WeIEY62FZokTjLVWahHr1pk5op+vOWhFxNPAI/WOg+b6e9tIfG6943PrPZ9d7zTKc+vwe2SjFF6pai6gysle+0NELMjMsf11vVpqllibJU4w1lpoljjBWGulmWIdrBolIfbvQu/43HrH59Z7PrveafTn1ijDNe8CRkfENhHxBuBAivmBJEmSJEk90BA9eZnZGhFHATdSTKFwcWYurHNYkiRJktR0GiLJA8jMucDcAb5tvw39HADNEmuzxAnGWgvNEicYa600U6yqLf8u9I7PrXd8br3ns+udhn5uDVF4RZIkSZLUPxrlnTxJkiRJUj8YkkleREyIiD9GxOKImF7veLoSEUsi4oGIuDciFtQ7nkoRcXFEPBURD1a0bRIR8yLiofJz43rG2KaTWP8tIpaVz/beiNiznjGWMW0ZEbdExKKIWBgRx5TtDfdcu4i1EZ/rOhFxZ0TcV8b672X7NhExv/y/4Gdl4adGjPOSiPhzxTPdvp5xVoqIYRHxu4iYU2431DNVY4iIaRGREbFpvWNpBhHxnYj4Q0TcHxFXR8RG9Y6pkTXTz3WNorPv4apO++99jWjIJXkRMQw4D9gD2BY4KCK2rW9U3fpoZm7fgGVaLwEmtGubDtyUmaOBm8rtRnAJr48V4Mzy2W5fvhdab63AtMzcFtgZOLL8+9mIz7WzWKHxnuvLwO6Z+T5ge2BCROwMfJsi1rcDzwKH1S9EoPM4Ab5e8UzvrVeAHTgG+H3FdqM9U9VZRGwJfBJ4tN6xNJF5wHaZ+V7gT8BJdY6nYTXpz3WNoKvv4epe++99DWfIJXnATsDizHw4M18BZgGT6xxTU8rMW4Fn2jVPBmaW6zOBKQMZU2c6ibXhZOYTmXlPuf4ixX8gm9OAz7WLWBtOFpaXm2uVSwK7A1eU7XV/rl3E2ZAiYgtgInBhuR002DNVQzgTOIEG/rvcaDLzF5nZWm7eQTF/sDrmz3W90EzfwxtN++99jWooJnmbA49VbC+lsf9SJ/CLiLg7IqbWO5gqjMzMJ8r1vwAj6xlMFY4qh8Nc3AhDICtFxCjg/cB8Gvy5tosVGvC5lkMr7gWeovgt+f8Cz1X8INUQ/xe0jzMz257pyeUzPTMi1q5fhKv5HsUP76+W22+iAZ+p6iciJgPLMvO+esfSxL4AXF/vIBpYs/1c13A6+B6urn2P1b/3NaShmOQ1m10zcweKYQhHRsSH6x1QtbIo3drIv7m9AHgbxbC4J4Az6hpNhYhYH7gSODYzX6jc12jPtYNYG/K5ZubKzNye4jfiOwHvqm9EHWsfZ0RsRzFU613AB4BNgBPrF2EhIiYBT2Xm3fWORfUVEb+MiAc7WCYD/wr8v3rH2Ii6eW5tx3yDYljdZfWLVINZVz9v6PWa6Xtfw8yTN4CWAVtWbG9RtjWkzFxWfj4VEVdT/HB6a32j6tKTEbFZZj4REZtR9EY0pMx8sm09In4INMTLsxGxFsV/uJdl5lVlc0M+145ibdTn2iYzn4uIW4APAhtFxJplz1ND/V9QEeeEzDy9bH45In4EHF/H0NrsAuxdFtZZB9gQOIsGfqaqjcz8eEftETEG2Aa4rxjJyxbAPRGxU2b+ZQBDbEidPbc2EXEoMAn4WDrfVVea6ue6RtLJzxvq2uu+90XEf2fmv9Q5rtcZij15dwGjywpwbwAOBGbXOaYORcR6EbFB2zrFi+sPdn1W3c0GDinXDwGuqWMsXSqTpTb70ADPtnyn6SLg95n53YpdDfdcO4u1QZ/riLbqdBExHPgExfsHtwD7lofV/bl2Eucf2p5p+cyn0ADPNDNPyswtMnMUxf+jN2fmwTTYM1X9ZOYDmfnmzBxV/j1ZCuxggte9iJhAMRxs78x8qd7xNLim+bmukXTx84a60Mn3voZL8GAI9uRlZmtEHAXcCAwDLs7MhXUOqzMjgavL34CuCfwkM2+ob0irRMRPgfHAphGxFPgmcCpweUQcBjwC7F+/CFfpJNbxUZSiT2AJcES94quwC/BZ4IHyvSwohjs14nPtLNaDGvC5bgbMLKuwrQFcnplzImIRMCsi/hP4HcU3vHrqLM6bI2IEEMC9wJfqGGN3TqSxnqnUjM4F1gbmlT8D3JGZjfzvvm6a7Oe6RtLh9/AGqYitfhCOAJAkSZKkwWMoDteUJEmSpEHLJE+SJEmSBhGTPEmSJEkaREzyJEmSJGkQMcmTJEmSpEHEJE+SJGmIiYg3RcS95fKXiFhWrj9XTi8zkLFMiYhtK7b/IyK6nCy+k+uMioi6zSMaEf/abvv28rOucWloMsmTJEkaYjLzr5m5fWZuD3wfOLNc3x54tb/vFxFdzc08BXgtycvM/5eZv+zvGAbAakleZn6oXoFIJnmSJEmqNCwifhgRCyPiFxExHCAi3hYRN0TE3RHx64h4V9k+KiJujoj7I+KmiNiqbL8kIr4fEfOB0zo6PyI+BOwNfKfsSXxbed6+5TU+EBG3R8R9EXFnRGxQ3u/XEXFPuXSZTEXh3Ij4Y0T8MiLmVlx/SURsWq6PjYiWcn2niPhtRPyuvP87y/ZDI+Kq8ut4KCJOK9tPBYaXX8NlZdvyDmIZFhHfiYi7yud1RNm+WUTcWp7/YETs1sc/Qw1xXf1WRZIkSUPPaOCgzDw8Ii4HPgP8NzAD+FJmPhQR44Dzgd2Bc4CZmTkzIr4AnE3ROwewBfChzFwZETe1Pz8zd4+I2cCczLwCICIoP98A/Aw4IDPviogNgb8DTwGfyMx/RMRo4KfA2C6+nn2Ad1L0Fo4EFgEXd/MM/gDslpmt5dDRU8rnAEVv5/uBl4E/RsQ5mTk9Io4qe0O7chjwfGZ+ICLWBm6LiF8AnwZuzMyTI2IYsG4315G6ZJInSZKkSn/OzHvL9buBURGxPvAh4H/akjBg7fLzgxRJCsCPgdMqrvU/ZYLX1fmdeSfwRGbeBZCZLwBExHrAuRGxPbASeEc31/kw8NPMXAk8HhE3d3M8wBuBmWUSmcBaFftuyszny1gWAVsDj1VxTYBPAu9t60ks7zMauAu4OCLWAn5e8fylXjHJkyRJUqWXK9ZXAsMpXvF5roqeqvb+Vn729vyOHAc8CbyvvO4/+nCtVla9vrRORfu3gFsyc5+IGAW0VOxr/3x68vN0AEdn5o2v2xHxYWAicElEfDczL+3BdaXV+E6eJEmSulT2ov05IvaD195ze1+5+3bgwHL9YODXPTz/RWCDDm77R2CziPhAec4GZQGXN1L08L0KfBYY1k34twIHlO/DbQZ8tGLfEmDHcv0zFe1vBJaV64d2c/02K8qeuK7cCHy57biIeEdErBcRWwNPZuYPgQuBHaq8p9QhkzxJkiRV42DgsIi4D1gITC7bjwY+HxH3UyRdx/Tw/FnA18siJ29rOzgzXwEOAM4pz5lH0dt2PnBI2fYuVvUWduZq4CGKd/EuBX5bse/fgbMiYgFFr1yb04D/iojfUX1P3Qzg/rbCK524sIzjniimVfhBef3xwH3l/Q4AzqrynlKHIjPrHYMkSZI0ICLiEioKvUiDkT15kiRJkjSI2JMnSZIkSYOIPXmSJEmSNIiY5EmSJEnSIGKSJ0mSJEmDiEmeJEmSJA0iJnmSJEmSNIiY5EmSJEnSIPL/BxtVhNZIoA39AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Humidity9am\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Humidity3pm\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pressure9am\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pressure3pm\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Temp9am\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Temp3pm\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for feature in continuous_feature:\n",
    "    print(feature)\n",
    "    plt.figure(figsize=(15,6))\n",
    "    plt.subplot(1, 2, 1)\n",
    "    df[feature].hist()\n",
    "    plt.subplot(1, 2, 2)\n",
    "    stat.probplot(df[feature], dist=\"norm\", plot=plt)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "smaller-burner",
   "metadata": {},
   "source": [
    "We don't require feature transformation as everything looks ok."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "stupid-custody",
   "metadata": {},
   "source": [
    "### Saving the preprocessed data into a csv file. Now we can use this csv file for model training and further process."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "alike-buffer",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"preprocessed.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "fossil-powell",
   "metadata": {},
   "outputs": [],
   "source": [
    "df2=pd.read_csv(\"preprocessed.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "introductory-pizza",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(145460, 25)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "inner-wagner",
   "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>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>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>RainToday</th>\n",
       "      <th>RainTomorrow</th>\n",
       "      <th>Date_month</th>\n",
       "      <th>Date_day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2008-12-01</td>\n",
       "      <td>30</td>\n",
       "      <td>13.4</td>\n",
       "      <td>22.9</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>8.3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</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>7.0</td>\n",
       "      <td>16.9</td>\n",
       "      <td>21.8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2008-12-02</td>\n",
       "      <td>30</td>\n",
       "      <td>7.4</td>\n",
       "      <td>25.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</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>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>17.2</td>\n",
       "      <td>24.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2008-12-03</td>\n",
       "      <td>30</td>\n",
       "      <td>12.9</td>\n",
       "      <td>25.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.6</td>\n",
       "      <td>4.4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</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>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>23.2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2008-12-04</td>\n",
       "      <td>30</td>\n",
       "      <td>9.2</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.6</td>\n",
       "      <td>8.9</td>\n",
       "      <td>11.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>14.0</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>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>18.1</td>\n",
       "      <td>26.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2008-12-05</td>\n",
       "      <td>30</td>\n",
       "      <td>17.5</td>\n",
       "      <td>32.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</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</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date  Location  MinTemp  MaxTemp  Rainfall  Evaporation  Sunshine  \\\n",
       "0  2008-12-01        30     13.4     22.9       0.6          2.4       8.3   \n",
       "1  2008-12-02        30      7.4     25.1       0.0          3.6      10.0   \n",
       "2  2008-12-03        30     12.9     25.7       0.0          2.6       4.4   \n",
       "3  2008-12-04        30      9.2     28.0       0.0         14.6       8.9   \n",
       "4  2008-12-05        30     17.5     32.3       1.0          5.4       3.0   \n",
       "\n",
       "   WindGustDir  WindGustSpeed  WindDir9am  WindDir3pm  WindSpeed9am  \\\n",
       "0          4.0           44.0         5.0         3.0          20.0   \n",
       "1          2.0           44.0         0.0         6.0           4.0   \n",
       "2          5.0           46.0         5.0         6.0          19.0   \n",
       "3         11.0           24.0        13.0        14.0          11.0   \n",
       "4          4.0           41.0        12.0         0.0           7.0   \n",
       "\n",
       "   WindSpeed3pm  Humidity9am  Humidity3pm  Pressure9am  Pressure3pm  Cloud9am  \\\n",
       "0          24.0         71.0         22.0       1007.7       1007.1       8.0   \n",
       "1          22.0         44.0         25.0       1010.6       1007.8       7.0   \n",
       "2          26.0         38.0         30.0       1007.6       1008.7       7.0   \n",
       "3           9.0         45.0         16.0       1017.6       1012.8       7.0   \n",
       "4          20.0         82.0         33.0       1010.8       1006.0       7.0   \n",
       "\n",
       "   Cloud3pm  Temp9am  Temp3pm  RainToday  RainTomorrow  Date_month  Date_day  \n",
       "0       7.0     16.9     21.8          0             0          12         1  \n",
       "1       7.0     17.2     24.3          0             0          12         2  \n",
       "2       2.0     21.0     23.2          0             0          12         3  \n",
       "3       7.0     18.1     26.5          0             0          12         4  \n",
       "4       8.0     17.8     29.7          0             0          12         5  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  }
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