WeatherAus.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('weatherAUS.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>...</th>\n",
       "      <th>Humidity3pm</th>\n",
       "      <th>Pressure9am</th>\n",
       "      <th>Pressure3pm</th>\n",
       "      <th>Cloud9am</th>\n",
       "      <th>Cloud3pm</th>\n",
       "      <th>Temp9am</th>\n",
       "      <th>Temp3pm</th>\n",
       "      <th>RainToday</th>\n",
       "      <th>RISK_MM</th>\n",
       "      <th>RainTomorrow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2008-12-01</td>\n",
       "      <td>Albury</td>\n",
       "      <td>13.4</td>\n",
       "      <td>22.9</td>\n",
       "      <td>0.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>W</td>\n",
       "      <td>44.0</td>\n",
       "      <td>W</td>\n",
       "      <td>...</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1007.7</td>\n",
       "      <td>1007.1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.9</td>\n",
       "      <td>21.8</td>\n",
       "      <td>No</td>\n",
       "      <td>0.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2008-12-02</td>\n",
       "      <td>Albury</td>\n",
       "      <td>7.4</td>\n",
       "      <td>25.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WNW</td>\n",
       "      <td>44.0</td>\n",
       "      <td>NNW</td>\n",
       "      <td>...</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1010.6</td>\n",
       "      <td>1007.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.2</td>\n",
       "      <td>24.3</td>\n",
       "      <td>No</td>\n",
       "      <td>0.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2008-12-03</td>\n",
       "      <td>Albury</td>\n",
       "      <td>12.9</td>\n",
       "      <td>25.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WSW</td>\n",
       "      <td>46.0</td>\n",
       "      <td>W</td>\n",
       "      <td>...</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1007.6</td>\n",
       "      <td>1008.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>23.2</td>\n",
       "      <td>No</td>\n",
       "      <td>0.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2008-12-04</td>\n",
       "      <td>Albury</td>\n",
       "      <td>9.2</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NE</td>\n",
       "      <td>24.0</td>\n",
       "      <td>SE</td>\n",
       "      <td>...</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1017.6</td>\n",
       "      <td>1012.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.1</td>\n",
       "      <td>26.5</td>\n",
       "      <td>No</td>\n",
       "      <td>1.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2008-12-05</td>\n",
       "      <td>Albury</td>\n",
       "      <td>17.5</td>\n",
       "      <td>32.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>W</td>\n",
       "      <td>41.0</td>\n",
       "      <td>ENE</td>\n",
       "      <td>...</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1010.8</td>\n",
       "      <td>1006.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>29.7</td>\n",
       "      <td>No</td>\n",
       "      <td>0.2</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date Location  MinTemp  MaxTemp  Rainfall  Evaporation  Sunshine  \\\n",
       "0  2008-12-01   Albury     13.4     22.9       0.6          NaN       NaN   \n",
       "1  2008-12-02   Albury      7.4     25.1       0.0          NaN       NaN   \n",
       "2  2008-12-03   Albury     12.9     25.7       0.0          NaN       NaN   \n",
       "3  2008-12-04   Albury      9.2     28.0       0.0          NaN       NaN   \n",
       "4  2008-12-05   Albury     17.5     32.3       1.0          NaN       NaN   \n",
       "\n",
       "  WindGustDir  WindGustSpeed WindDir9am  ... Humidity3pm  Pressure9am  \\\n",
       "0           W           44.0          W  ...        22.0       1007.7   \n",
       "1         WNW           44.0        NNW  ...        25.0       1010.6   \n",
       "2         WSW           46.0          W  ...        30.0       1007.6   \n",
       "3          NE           24.0         SE  ...        16.0       1017.6   \n",
       "4           W           41.0        ENE  ...        33.0       1010.8   \n",
       "\n",
       "   Pressure3pm  Cloud9am  Cloud3pm  Temp9am  Temp3pm  RainToday  RISK_MM  \\\n",
       "0       1007.1       8.0       NaN     16.9     21.8         No      0.0   \n",
       "1       1007.8       NaN       NaN     17.2     24.3         No      0.0   \n",
       "2       1008.7       NaN       2.0     21.0     23.2         No      0.0   \n",
       "3       1012.8       NaN       NaN     18.1     26.5         No      1.0   \n",
       "4       1006.0       7.0       8.0     17.8     29.7         No      0.2   \n",
       "\n",
       "   RainTomorrow  \n",
       "0            No  \n",
       "1            No  \n",
       "2            No  \n",
       "3            No  \n",
       "4            No  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Date', 'Location', 'MinTemp', 'MaxTemp', 'Rainfall', 'Evaporation',\n",
       "       'Sunshine', 'WindGustDir', 'WindGustSpeed', 'WindDir9am', 'WindDir3pm',\n",
       "       'WindSpeed9am', 'WindSpeed3pm', 'Humidity9am', 'Humidity3pm',\n",
       "       'Pressure9am', 'Pressure3pm', 'Cloud9am', 'Cloud3pm', 'Temp9am',\n",
       "       'Temp3pm', 'RainToday', 'RISK_MM', 'RainTomorrow'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop('RISK_MM',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Date']=pd.to_datetime(df['Date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Location</th>\n",
       "      <th>MinTemp</th>\n",
       "      <th>MaxTemp</th>\n",
       "      <th>Rainfall</th>\n",
       "      <th>Evaporation</th>\n",
       "      <th>Sunshine</th>\n",
       "      <th>WindGustDir</th>\n",
       "      <th>WindGustSpeed</th>\n",
       "      <th>WindDir9am</th>\n",
       "      <th>...</th>\n",
       "      <th>Humidity9am</th>\n",
       "      <th>Humidity3pm</th>\n",
       "      <th>Pressure9am</th>\n",
       "      <th>Pressure3pm</th>\n",
       "      <th>Cloud9am</th>\n",
       "      <th>Cloud3pm</th>\n",
       "      <th>Temp9am</th>\n",
       "      <th>Temp3pm</th>\n",
       "      <th>RainToday</th>\n",
       "      <th>RainTomorrow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>142188</td>\n",
       "      <td>2017-06-20</td>\n",
       "      <td>Uluru</td>\n",
       "      <td>3.5</td>\n",
       "      <td>21.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>E</td>\n",
       "      <td>31.0</td>\n",
       "      <td>ESE</td>\n",
       "      <td>...</td>\n",
       "      <td>59.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>1024.7</td>\n",
       "      <td>1021.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.4</td>\n",
       "      <td>20.9</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>142189</td>\n",
       "      <td>2017-06-21</td>\n",
       "      <td>Uluru</td>\n",
       "      <td>2.8</td>\n",
       "      <td>23.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>E</td>\n",
       "      <td>31.0</td>\n",
       "      <td>SE</td>\n",
       "      <td>...</td>\n",
       "      <td>51.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1024.6</td>\n",
       "      <td>1020.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.1</td>\n",
       "      <td>22.4</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>142190</td>\n",
       "      <td>2017-06-22</td>\n",
       "      <td>Uluru</td>\n",
       "      <td>3.6</td>\n",
       "      <td>25.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NNW</td>\n",
       "      <td>22.0</td>\n",
       "      <td>SE</td>\n",
       "      <td>...</td>\n",
       "      <td>56.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1023.5</td>\n",
       "      <td>1019.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.9</td>\n",
       "      <td>24.5</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>142191</td>\n",
       "      <td>2017-06-23</td>\n",
       "      <td>Uluru</td>\n",
       "      <td>5.4</td>\n",
       "      <td>26.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>37.0</td>\n",
       "      <td>SE</td>\n",
       "      <td>...</td>\n",
       "      <td>53.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1021.0</td>\n",
       "      <td>1016.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.5</td>\n",
       "      <td>26.1</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>142192</td>\n",
       "      <td>2017-06-24</td>\n",
       "      <td>Uluru</td>\n",
       "      <td>7.8</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SE</td>\n",
       "      <td>28.0</td>\n",
       "      <td>SSE</td>\n",
       "      <td>...</td>\n",
       "      <td>51.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1019.4</td>\n",
       "      <td>1016.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>15.1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Date Location  MinTemp  MaxTemp  Rainfall  Evaporation  Sunshine  \\\n",
       "142188 2017-06-20    Uluru      3.5     21.8       0.0          NaN       NaN   \n",
       "142189 2017-06-21    Uluru      2.8     23.4       0.0          NaN       NaN   \n",
       "142190 2017-06-22    Uluru      3.6     25.3       0.0          NaN       NaN   \n",
       "142191 2017-06-23    Uluru      5.4     26.9       0.0          NaN       NaN   \n",
       "142192 2017-06-24    Uluru      7.8     27.0       0.0          NaN       NaN   \n",
       "\n",
       "       WindGustDir  WindGustSpeed WindDir9am  ... Humidity9am  Humidity3pm  \\\n",
       "142188           E           31.0        ESE  ...        59.0         27.0   \n",
       "142189           E           31.0         SE  ...        51.0         24.0   \n",
       "142190         NNW           22.0         SE  ...        56.0         21.0   \n",
       "142191           N           37.0         SE  ...        53.0         24.0   \n",
       "142192          SE           28.0        SSE  ...        51.0         24.0   \n",
       "\n",
       "        Pressure9am  Pressure3pm  Cloud9am  Cloud3pm  Temp9am  Temp3pm  \\\n",
       "142188       1024.7       1021.2       NaN       NaN      9.4     20.9   \n",
       "142189       1024.6       1020.3       NaN       NaN     10.1     22.4   \n",
       "142190       1023.5       1019.1       NaN       NaN     10.9     24.5   \n",
       "142191       1021.0       1016.8       NaN       NaN     12.5     26.1   \n",
       "142192       1019.4       1016.5       3.0       2.0     15.1     26.0   \n",
       "\n",
       "        RainToday  RainTomorrow  \n",
       "142188         No            No  \n",
       "142189         No            No  \n",
       "142190         No            No  \n",
       "142191         No            No  \n",
       "142192         No            No  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Canberra            3418\n",
       "Sydney              3337\n",
       "Perth               3193\n",
       "Darwin              3192\n",
       "Hobart              3188\n",
       "Brisbane            3161\n",
       "Adelaide            3090\n",
       "Bendigo             3034\n",
       "Townsville          3033\n",
       "AliceSprings        3031\n",
       "MountGambier        3030\n",
       "Launceston          3028\n",
       "Ballarat            3028\n",
       "Albany              3016\n",
       "Albury              3011\n",
       "PerthAirport        3009\n",
       "MelbourneAirport    3009\n",
       "Mildura             3007\n",
       "SydneyAirport       3005\n",
       "Nuriootpa           3002\n",
       "Sale                3000\n",
       "Watsonia            2999\n",
       "Tuggeranong         2998\n",
       "Portland            2996\n",
       "Woomera             2990\n",
       "Cairns              2988\n",
       "Cobar               2988\n",
       "Wollongong          2983\n",
       "GoldCoast           2980\n",
       "WaggaWagga          2976\n",
       "NorfolkIsland       2964\n",
       "Penrith             2964\n",
       "Newcastle           2955\n",
       "SalmonGums          2955\n",
       "CoffsHarbour        2953\n",
       "Witchcliffe         2952\n",
       "Richmond            2951\n",
       "Dartmoor            2943\n",
       "NorahHead           2929\n",
       "BadgerysCreek       2928\n",
       "MountGinini         2907\n",
       "Moree               2854\n",
       "Walpole             2819\n",
       "PearceRAAF          2762\n",
       "Williamtown         2553\n",
       "Melbourne           2435\n",
       "Nhil                1569\n",
       "Katherine           1559\n",
       "Uluru               1521\n",
       "Name: Location, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Location.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date                 0\n",
       "Location             0\n",
       "MinTemp            637\n",
       "MaxTemp            322\n",
       "Rainfall          1406\n",
       "Evaporation      60843\n",
       "Sunshine         67816\n",
       "WindGustDir       9330\n",
       "WindGustSpeed     9270\n",
       "WindDir9am       10013\n",
       "WindDir3pm        3778\n",
       "WindSpeed9am      1348\n",
       "WindSpeed3pm      2630\n",
       "Humidity9am       1774\n",
       "Humidity3pm       3610\n",
       "Pressure9am      14014\n",
       "Pressure3pm      13981\n",
       "Cloud9am         53657\n",
       "Cloud3pm         57094\n",
       "Temp9am            904\n",
       "Temp3pm           2726\n",
       "RainToday         1406\n",
       "RainTomorrow         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(142193, 23)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['Evaporation','Sunshine'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date                 0\n",
       "Location             0\n",
       "MinTemp            637\n",
       "MaxTemp            322\n",
       "Rainfall          1406\n",
       "WindGustDir       9330\n",
       "WindGustSpeed     9270\n",
       "WindDir9am       10013\n",
       "WindDir3pm        3778\n",
       "WindSpeed9am      1348\n",
       "WindSpeed3pm      2630\n",
       "Humidity9am       1774\n",
       "Humidity3pm       3610\n",
       "Pressure9am      14014\n",
       "Pressure3pm      13981\n",
       "Cloud9am         53657\n",
       "Cloud3pm         57094\n",
       "Temp9am            904\n",
       "Temp3pm           2726\n",
       "RainToday         1406\n",
       "RainTomorrow         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.0    18052\n",
       "1.0    14827\n",
       "8.0    12407\n",
       "6.0     8869\n",
       "2.0     7153\n",
       "3.0     6836\n",
       "5.0     6743\n",
       "4.0     5254\n",
       "0.0     4957\n",
       "9.0        1\n",
       "Name: Cloud3pm, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Cloud3pm.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4015246882757942"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Cloud3pm.isna().sum()/142193"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3773533155640573"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Cloud9am.isna().sum()/142193"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.Cloud9am.fillna(method='ffill',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    7.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Cloud3pm.mode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.Cloud3pm.fillna(method='ffill',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date                 0\n",
       "Location             0\n",
       "MinTemp            637\n",
       "MaxTemp            322\n",
       "Rainfall          1406\n",
       "WindGustDir       9330\n",
       "WindGustSpeed     9270\n",
       "WindDir9am       10013\n",
       "WindDir3pm        3778\n",
       "WindSpeed9am      1348\n",
       "WindSpeed3pm      2630\n",
       "Humidity9am       1774\n",
       "Humidity3pm       3610\n",
       "Pressure9am      14014\n",
       "Pressure3pm      13981\n",
       "Cloud9am             0\n",
       "Cloud3pm             2\n",
       "Temp9am            904\n",
       "Temp3pm           2726\n",
       "RainToday         1406\n",
       "RainTomorrow         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.0    32016\n",
       "1.0    23774\n",
       "7.0    23287\n",
       "2.0    16196\n",
       "3.0    14415\n",
       "6.0    11059\n",
       "5.0     8174\n",
       "4.0     7801\n",
       "0.0     5468\n",
       "9.0        1\n",
       "Name: Cloud3pm, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Cloud3pm.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['RainTomorrow']=df['RainTomorrow'].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['RainTomorrow']=df['RainTomorrow'].cat.codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>WindGustDir</th>\n",
       "      <th>WindGustSpeed</th>\n",
       "      <th>WindDir9am</th>\n",
       "      <th>WindDir3pm</th>\n",
       "      <th>WindSpeed9am</th>\n",
       "      <th>...</th>\n",
       "      <th>Humidity9am</th>\n",
       "      <th>Humidity3pm</th>\n",
       "      <th>Pressure9am</th>\n",
       "      <th>Pressure3pm</th>\n",
       "      <th>Cloud9am</th>\n",
       "      <th>Cloud3pm</th>\n",
       "      <th>Temp9am</th>\n",
       "      <th>Temp3pm</th>\n",
       "      <th>RainToday</th>\n",
       "      <th>RainTomorrow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\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>W</td>\n",
       "      <td>44.0</td>\n",
       "      <td>W</td>\n",
       "      <td>WNW</td>\n",
       "      <td>20.0</td>\n",
       "      <td>...</td>\n",
       "      <td>71.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1007.7</td>\n",
       "      <td>1007.1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.9</td>\n",
       "      <td>21.8</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\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>WNW</td>\n",
       "      <td>44.0</td>\n",
       "      <td>NNW</td>\n",
       "      <td>WSW</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>44.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1010.6</td>\n",
       "      <td>1007.8</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.2</td>\n",
       "      <td>24.3</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\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>WSW</td>\n",
       "      <td>46.0</td>\n",
       "      <td>W</td>\n",
       "      <td>WSW</td>\n",
       "      <td>19.0</td>\n",
       "      <td>...</td>\n",
       "      <td>38.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1007.6</td>\n",
       "      <td>1008.7</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>23.2</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\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>NE</td>\n",
       "      <td>24.0</td>\n",
       "      <td>SE</td>\n",
       "      <td>E</td>\n",
       "      <td>11.0</td>\n",
       "      <td>...</td>\n",
       "      <td>45.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1017.6</td>\n",
       "      <td>1012.8</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>18.1</td>\n",
       "      <td>26.5</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\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>W</td>\n",
       "      <td>41.0</td>\n",
       "      <td>ENE</td>\n",
       "      <td>NW</td>\n",
       "      <td>7.0</td>\n",
       "      <td>...</td>\n",
       "      <td>82.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1010.8</td>\n",
       "      <td>1006.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>29.7</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date Location  MinTemp  MaxTemp  Rainfall WindGustDir  WindGustSpeed  \\\n",
       "0 2008-12-01   Albury     13.4     22.9       0.6           W           44.0   \n",
       "1 2008-12-02   Albury      7.4     25.1       0.0         WNW           44.0   \n",
       "2 2008-12-03   Albury     12.9     25.7       0.0         WSW           46.0   \n",
       "3 2008-12-04   Albury      9.2     28.0       0.0          NE           24.0   \n",
       "4 2008-12-05   Albury     17.5     32.3       1.0           W           41.0   \n",
       "\n",
       "  WindDir9am WindDir3pm  WindSpeed9am  ...  Humidity9am  Humidity3pm  \\\n",
       "0          W        WNW          20.0  ...         71.0         22.0   \n",
       "1        NNW        WSW           4.0  ...         44.0         25.0   \n",
       "2          W        WSW          19.0  ...         38.0         30.0   \n",
       "3         SE          E          11.0  ...         45.0         16.0   \n",
       "4        ENE         NW           7.0  ...         82.0         33.0   \n",
       "\n",
       "   Pressure9am  Pressure3pm  Cloud9am  Cloud3pm  Temp9am  Temp3pm  RainToday  \\\n",
       "0       1007.7       1007.1       8.0       NaN     16.9     21.8         No   \n",
       "1       1010.6       1007.8       8.0       NaN     17.2     24.3         No   \n",
       "2       1007.6       1008.7       8.0       2.0     21.0     23.2         No   \n",
       "3       1017.6       1012.8       8.0       2.0     18.1     26.5         No   \n",
       "4       1010.8       1006.0       7.0       8.0     17.8     29.7         No   \n",
       "\n",
       "  RainTomorrow  \n",
       "0            0  \n",
       "1            0  \n",
       "2            0  \n",
       "3            0  \n",
       "4            0  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['RainToday']=df['RainToday'].astype('category')\n",
    "df['RainToday']=df['RainToday'].cat.codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 142193 entries, 0 to 142192\n",
      "Data columns (total 21 columns):\n",
      "Date             142193 non-null datetime64[ns]\n",
      "Location         142193 non-null object\n",
      "MinTemp          141556 non-null float64\n",
      "MaxTemp          141871 non-null float64\n",
      "Rainfall         140787 non-null float64\n",
      "WindGustDir      132863 non-null object\n",
      "WindGustSpeed    132923 non-null float64\n",
      "WindDir9am       132180 non-null object\n",
      "WindDir3pm       138415 non-null object\n",
      "WindSpeed9am     140845 non-null float64\n",
      "WindSpeed3pm     139563 non-null float64\n",
      "Humidity9am      140419 non-null float64\n",
      "Humidity3pm      138583 non-null float64\n",
      "Pressure9am      128179 non-null float64\n",
      "Pressure3pm      128212 non-null float64\n",
      "Cloud9am         142193 non-null float64\n",
      "Cloud3pm         142191 non-null float64\n",
      "Temp9am          141289 non-null float64\n",
      "Temp3pm          139467 non-null float64\n",
      "RainToday        142193 non-null int8\n",
      "RainTomorrow     142193 non-null int8\n",
      "dtypes: datetime64[ns](1), float64(14), int8(2), object(4)\n",
      "memory usage: 20.9+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['WindGustDir']=df['WindGustDir'].astype('category')\n",
    "df['WindGustDir']=df['WindGustDir'].cat.codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['WindDir9am']=df['WindDir9am'].astype('category')\n",
    "df['WindDir9am']=df['WindDir9am'].cat.codes\n",
    "df['WindDir3pm']=df['WindDir3pm'].astype('category')\n",
    "df['WindDir3pm']=df['WindDir3pm'].cat.codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MinTemp          0.088017\n",
       "MaxTemp         -0.160257\n",
       "Rainfall         0.237831\n",
       "WindGustDir      0.049821\n",
       "WindGustSpeed    0.236004\n",
       "WindDir9am       0.038672\n",
       "WindDir3pm       0.021193\n",
       "WindSpeed9am     0.095098\n",
       "WindSpeed3pm     0.093836\n",
       "Humidity9am      0.261376\n",
       "Humidity3pm      0.452641\n",
       "Pressure9am     -0.246471\n",
       "Pressure3pm     -0.228169\n",
       "Cloud9am         0.207989\n",
       "Cloud3pm         0.244413\n",
       "Temp9am         -0.023738\n",
       "Temp3pm         -0.195059\n",
       "RainToday        0.313715\n",
       "RainTomorrow     1.000000\n",
       "Name: RainTomorrow, dtype: float64"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()['RainTomorrow']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Date', 'Location', 'MinTemp', 'MaxTemp', 'Rainfall', 'WindGustDir',\n",
       "       'WindGustSpeed', 'WindDir9am', 'WindDir3pm', 'WindSpeed9am',\n",
       "       'WindSpeed3pm', 'Humidity9am', 'Humidity3pm', 'Pressure9am',\n",
       "       'Pressure3pm', 'Cloud9am', 'Cloud3pm', 'Temp9am', 'Temp3pm',\n",
       "       'RainToday', 'RainTomorrow'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x1aad2e10278>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(df.loc[:, 'MinTemp'],df.loc[:,'Rainfall']\n",
    "          ,   kind='regg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x1aad3585668>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RmmghhBAFQ5KWEEKIgiFJ67SNuQ4gB+Q1F7/Z9nphdr7mWUPWtIQQQhQMGWkJIYQoGJK0hBBCFAxJWkIIIQqGJC0hhBAFoyiS1g033KBxWjnJm7zJm7wVy1tWivj6l1FRJK3OzmLo2CKEEBM3265/RZG0hBBCzA6StIQQQhQMSVpCCCEKhiQtIYQQBUOSlhBCiIIhSUsIIUTBkKQlhBCiYEjSEkIIUTAkaQkhhCgYkrSEEEIUDElaQgghCoYkLSGEKGAamE0n0EvSEkKIArbneB8t3eFchzFjJGkJIYQoGJK0hBBCFAxJWkIIIQqGJC0hhBAFQ5KWEEKIgiFJSwghCpxGSt6FEELkwGA0wUe+u2NC95lF27Rw5ToAIYSYCZv2t7NhczMtPSEaq/zcdW0T61fW5zqsYdp6w3zowe3sO9E/ofvNopyV+5GWUspUSr2glPpZ8vNqpdSvlVIHk++rch2jEKKwbdrfzj1P7qU9GKGyxE17MMI9T+5l0/72XIc2ZFdLL2/92h/Zd6If01ATuq90xJhZnwT2pX3+WeC3WutlwG+TnwshxKRt2NyM21T4PS6Uct67TcWGzc25Dg2An+5u410bttARjFLuc/HgBy+b0P1nT8rKcdJSSi0A3gw8kHbzW4EHkx8/CLxtpuMSQhSXlp4QJW5z2G0lbpPWnlCOInJorfnKrw/w8UdeIJqwWVzj58cfu4qrl9VO8HGmKcA8lOs1rX8H/g8QSLutQWt9AkBrfUIplV+TzkKIgtNY5ac9GMHvOX3JC8ctFlT5cxZTJG7xmcde5Ke72wBY11TD1997MZV+T85iKgQ5G2kppW4C2rXWEyuTOX3/O5VS25VS2zs6OqY4OiFEPtm0v53bN27l6vt/x+0bt054Lequa5uIW5pQLIHWzvu4pbnr2qZpinhs7f0R3rVx61DCuv2yRh760GVZJ6z0659zy+wZaqlcLeAppf4JeB+QAHxAOfA4cCmwPjnKmgts0lqvGOux1q5dq7dv3z7dIQshciBVROE2FSVuk3DcIm5p7r353AlV/6WqB1t7QizIYfXg3rY+Pvzgdk70RTAU/O2bV/OnVy1GqTOKL7KqxvDOXab37NrJsobA+N9cWDK+/pxND2qtPwd8DkAptR74a631e5VS/wLcAdyXfP9ErmIUQsysTGXp6UUUAH6Pi1AswYbNzUNJJ5ty9vUr63Ne4v7LvSf51Pd3EYpZlHpM/t+7L+K6lQ1n/bizZ5yV+zWtTO4DfqCU+hBwDHhHjuMRQqSZrv1O6SOq9LL0wWicuRUlw743vYhitPvdCzlPUilaa77xh2b++Zf70RrmV5bw7Q9cyoo5UzM6kkKMGaa13gRsSn7cBbwul/EIITKbSIKYaHIbbUQVtzThuDVqEUU2I7FciiYs/ubxPfxoZysAlyyqYsP7LqG2zDtlzyFtnIQQIoNs9ztNZjPvaGXpHpcxZhFFvpazA3QNRHnvA9uGEtYtF83nex++fEoTFsyukZYkLSFE1rJNEJPZzNtY5Scct4bdFo5bLKsPcO/N51If8NEXjlMf8A0rwhjtfguq/GdddXg2DpwK8tav/ZHnj/QA8Jk3ruDL77wA34if31SYTUkrL6YHhRD5YbwpvWz3O7X0hKgscQ+7bbzRz13XNnHPk3sJxRLDqgRTMYw21Tfa/dY1Vedsrev3r7Tz8YdfYCCawOc2+Mo7L+TG8+dO2/PNpulBSVpCCGD89apN+9vpDcU40hXCbSoaAl5cppFxv9NkNvOuX1nPvTDhsvTR7pdprasjGOETj75AeYl7Wprmaq357z8e4Us/fxlbQ0O5l2/dcSnnza+YsueY7SRpCVFkJlvdt2FzM7GERddAgphl4zENAj7X0JReKqEtqPRxqj9Ka2+YZXVlfOHNq894/LFGTWOZaFn6yNf6D289b+j+n39iz7DRXn84TtdgDFtrFlb7p3zkFbds/u7JvTy87RgAaxZU8M33r6Wh3HfWjz0emR4UQhSksyn/PnCqn/5IAgOFqRQJS9M1GGMw2ssnHn2BwVgCn8ukLuBlWUOAUCxBValTUHD7xq1nJMnJjJrGe23pCWpdUzWP7Tw+6msdOdrrHIgC4HOZQ+tsU1Vl2BeK85Hv7eDZQ10AvPn8ufzrOy6gxDP161eZ2LMoa+WsI8ZUko4YQjhu37j1jGm5UCxBfcDHI3deMeZ913zxl4TjFi7jdH1WzLKwNZhKYRoACq1hXqWPMq+Lk/2RoSKL9BHVrRfPZ0tz97AEk/55KoFlOyrM1BWjtSdMld9NXcAZyfSH45wKRtAaLl5YNSyplbhN9p8MooD5VSUEfM4ITGtNXzjO03dfN+mf+eHOQT70nedp7hwE4OPXLeVT1y/HmODxIhlk3RFjy7bnuHhh0Z3ilF8dMYQQU2vT/nZ2HuvB1hqPaVBb5qW8xJ11+bfHZRCOWdhao5Qz5WTZYCjwugwSlsYwFDaajmAU01DEEjYuY/iUosel+NqmQyyoKqGyxM3hzgGeO9JNfcCDxzR44VgPH3roeeYGvEQtTXmJe9xRYWp9yrI1hzsHiVk2cUtzKhilazCGoRRxy8ZlKGytaQ9GeGzn8aHk2doTwu8xKfWaQwkLJtY0N1OC9bgNPvLdnfSF43hcBv9y6xreeuH8Cf27TQXLLvzBR7YkaQlRoNIvomUek67BGArnz9OEpWnrCwPgMlVWF+Zl9QGOdA3QHz6dgOLKqfqrLfM6j2cDShNJOEnD1jZdg9awKcVQzELB0GgvGElgKOgZjGNrUMoZubX1RzGUotTrGne6rqUnhKngRF8UpU7/Ca41oCFm2WicaTKvyxx6rC3N3UMjzNRobaLrbOn3TZ+K/PQPdtMXjmFpqC3zsPH9a3M22olbdk6eNxdkn5YQBWjk5t0j3SF6QnEqStyASv3HqWAk6wvzXdc24TZN5lT4WNEQYE6FD3eyGKO8xM28ihJcpsKyodTj4t6bz8WynQQZs2xilo2tk8XXaRM7McvGUBBN2CgFhlIYSmFrZ3ruWHeI/Sf7ae4YIGHZZ4wKN+1vpz8c52h3mLhlE0vYxKzTI4tE2ijDsqEu4KyzjRxhrl9ZP7Tf62RfmI5gdChJjrd/a2QlYm8oTnfISVgr5wR44i+uzun0XMKaPSMtSVpCFKCRm3ctW2MoGIgmmFfpG5om05qsu6GnX9RTm3g/tv4cPC6TUCxBwOdiToWPeZUlfPW2iwAnESUHO9ga4hmmqTymQermVCPz1FK6pRlaM0tYmuO9EUrTihdSydmfvC31XOnSPzcMNTT9l2nqb/3Keu66tgm/101dwMucct+EunVYtuZoV4iuwRgAPrfBYx+5kvmVJaPedyYk7Nkz0pLpQSEK0MjNux7TcEYhlk3A5ybgcw8VYEykMm5kyfmm/e38Ys/JoSKDJTX+oRL32zduxWMaWFpj2XooERnKSUQvHe8b9tgKiCdsXIaBjcZtKuKWdgZlQ/N9DDuiI5WcK0p8nOyPnhGvwkmEC6v9tPaEcZkKrfWYU3+T6VXYWOXneG+IU/1RogknQVSWuFk5J0CZN/eXURlpCSHy2sjWRbVl3qERi9aazoEIrT1hDpzqn3T7otQoJ2bZLKsvo7rUzeGuEH/1w13cvnErB9uDlPtcJCw9tFYFzugp0yXUMJyvRZNFFPHkhdbrMrBsjctQzK/0MRBNDD3/zmM9HOsOceBUMGOMqRHeka4QCVtT5jEztnpKN5lehdetqKO1J+xMcQJ1ZR7KS9z8+WvOGffnOBMSUoghhMhnIzfvukxFpd9NXZmXk/0RekNORV1/JMELLT18/JGdLKjyE4wmst5wnD4iCUbidA3E0WgicZv2YIS+cBzb1sl1Ls1Y183UWpaVIZ2V+1w0JI8eSY0OUwlTAbatiY+zNSdVnNETTvD+dYv5xPXLR/3eiXbr+NGOVv75V69ga3AZiiq/m6X1gZwdIpnJbCrEkKQlRAHIVG59783nDtu8m5q2u+Erf6A3FEehMJQzdRSJ27zaHmTFnPKhNZxbW3sz7p1KPV96+XzCslFKoW0IxSyOdYeGRkouFKixk5bWDH3/SB0DMerLfcOm9E5PC7o5FTxzWjCdy1C4nU1kJGybr//h0KivC7Lv1mHbmn/51St8fdMhAJbWl/GtO9ayqKZ07H+sHEhNWc4GsrlYiDyVSlQH24MEIwmq/G5qy7zjHje/4vO/QGuNmdwkHE04G4SdMnSTaMJ21qAAn8ugofx0D8F7bz4XcFo2tfdHSNg2lj18uk/hTOlFkhdKr8s464um12XQVFvK3TesZP3Keq6+/3fJPV6DhGKZxmfDpab74pZFwoZz6kqHJaSRP6vUz3a0bh2hWIJPfX8Xv9x7CoBrltXyn+++OFmdOWOy3lz8Xz/8FR+6esl0xzPTZHOxELky0X6A6fuCQtEEtnZaKnldJuUl7qzbD6VP22mcaTCdtuYUSdgc7Q4PFU988MHn8RgKjbNOkmn0pHHK2FOmYmpqQVUJgzGLF1t72bC5mY5glM5gNGM1YiaWrTENpxw/fY/YaEUWY/U4PNEX5kPf2c7LJ/oBuGPdIr5w02pcZv6WAAQj8VyHMGMkaQkxzbLtB5ie2PrDcUq9JhUlPuLJC3LC0s4m2z6FxzToC8UyPl9TbSkHTgWJW2eOUEbLAUOJTUM0i0q09MeZihqAhKU52Rfmy785iM9tUO4z6Qklsu70ELNsSNal1Jd5CEbidASjxCwbt6HoC2d3Ud/d0suHH9o+1PHji29ZzfvWLZ7kq5oZCmcD92yRv386CFEk0gsaBqIJTvZFON4b4hOPvjBU1Tdys3AoZtEZjBGMxJNrSnpo5GMqRcyyCUatYfdPHXYYDMecjbu5fNETdLQ7NLRhOBq36RiIg9aYE2zfZxqKhK1p642QSCb7uK0JRhLjVlD+7MU23rlhCx3BKAGfi+988NK8T1jgFLjISEsIMWVSe6qCkThtvRGUcooHQjFraMQ1cu+Q12UQs2w6glHqAl6OdJ0ux45aNgZQW+7hvl/s4/6n9nOgfWDojKtToTiK00krNfVXKFKhJiYYs9tU1JR6ONUfxWUqDJzCEYAqv3vU6VStNV/97at85TcHAGfP17c/cClL68vO4lXMHKWgPzx7RlqStISYZqkS645gdKj027bB61K4TcV9v9jH4a7QsEa3qV5/kYTFyGIprZ0WgCf7opzoi+IxFaYCbUNbX8TZ82Q6Iw63YWAYiljCotj3n6Z+dqf6o3hMY6h/Ym2Zj4DPlXEfViRu8X8ee5End7cBcNmSaja89xKqSj0zHf6kGUoRjMpISwgxQaMVW6RKrCMJy2mvZIONprbMR8KyOdIVxm0YwxrdzqsooabUQyhmcbI/iqGcEZPCme5Kzz+xZFcJd3J0YaGdDb84ozKszPEWm7qAU1kZ8LmoC3jPOJ5l5D6s9mCEOx/awa6WXgDeuXYBX3rb+XhchbVqotTsWtOSpCXEFBiv2OJe4BOPvkAoZuF1KWrLfJSXuDnYHsRtGMyp8NHWGyG55YlTwQj1AR/3vX0Nn39iz1CH8/goPeZGVvRlYijnAles+1A7ghFqynx8+OolPLbzeMZ9WKk/LA51DNAbihOznCa+f3PjKj58zZJhLaQKhbOmJUlLCJGF1EVw57EeFDCnwpfxmI31K+v56m0XDTvIMBRLELc0CyqdQww1mlhqIcfSaK35/BN76A/H8XtM5lX6ONo1/rlYo7EzdZstIoMxmyrbZs2CStYsqHT2uJ3qZzBmkbA1f/bQdlBQ6jHpjySG9q594rpl/FkWXfDz1WwrxCiscbAQeSS94s/WGls7VWupC8hYR2P0heO4DYXXZdDaE+ZoV+h089iklp4wHf0RXIaiPRjlePKxUt8z0cq62aC1N8IHH3yeJ3a1Oh0u1OmmvHHb6XfYGz6dsKpL3Tx3uDvXYZ8VKcQQQmQlveIvVZaOIlky7R71aIzUMfOfeWw3CcseViAxciAUtTTRkJMEU/UYqe8p9sKKydIafrzrBM8f6WEgmsBUKtnMd/gPzG04vRkPnurPSZxTxUhugYjELXwjGgEXIxlpCTFJ6d3Ca8u82DhTetGENTT1N9rhi/c/tZ+eUHxCayip2T0ZYN2cTBgAACAASURBVGXneG8kuTlZZ1zvM5MdLmIFnv2T3bqGzvgqdjLSEmKS0ruFlyd70p0KRlBaUR/wndGANr2y8NX2gWRFoJrwPqrCvsTOHI0zdRbNsOHL+Zk7O7ALrVpwJDP5h0/XQDTnh1HOhML+1xIih+66tom4pQnFEmitiVlOX78y7/ApmpHdLtqDEeK2swYG4DLkf8PpEh9jh7LLUNQGPCyrD8xgRFPPSCatzoGxu+EXCxlpCTFJqVL2TJ3Y00veN2xuJpaw6BpIDG14NRUkbLDs8TuYi8nTOMlJpVqEKKgp9Qzrlj/aFG6hMIxk0grK9KAQYhypworbN24ddrBgesn7gVP99EcSGCjQMBg7vdt3ZMIylNPZITKLzkearAqvSdRy1qtS1YAjf56lHpMvvfU8brlkATD+kSSFyEwm5ZYxTl4uJpK0xKw10eNCxrpPqr9gulTJe+rwQ0vrcY9FtzWSsEbhMkAp5ZyAbNuU+z1DiQfga79/lV0tvUPHmZT7XNSUefjKbw9SVeoZ+gOj0JPUSAqo8nt4tX0g16HMCElaYlbK9riQbO/TWOXnSNcA/eHTU4Ael3O+U38W3QpSFYEyVTg6W0ON301fOI5lQ3swSqnHWT9cXFtKVyg2lLDqyrw0lHtRSmV99lghqw94JWkJUcxGdlUf7bDAkWdcGco5Nj6SsNFao7XTnul1K+t47khsqEfgYMxiIhXIUso+PltDx8DpH2o0YbP/1AAf+M7zw6YGF1T6qCr1Dn3fyE3eKZMZaeeruoCPLc2dJCw7rw+rnArF/eqEGEX6HquUkRe3kVV/A9EEveEE4Zg1dCKwBgYiCf53zynKfSae5LH1kyGjrMnTOH8suE2FNaIrfqZN3pkqOu95cu+4Z27lq/pyL3FL09w5mOtQpp0kLTErNVb5CceHtz8feXFLH42lbwI+Y8VJOce9R+M2tWXekV8VM2RZfYCaUg/dg/GhbQijbfIe+W/r97hwm4oNm5tzFP3ZWVjt/N7uONqT40imnyQtMSuN3GOVfnFLnQL83JFuTvZF6E8e1T7aSMjWTtIKxW1aekIFdeBiMUltO/CYDPV3rA/4uPfmc8+Y9stmpF1Iako9lHpdbGvuynUo007WtMSslL7HKr38GRgqtvCaTk+3tr4w4VgCPUYySn1JElbuaO0cz2IoNe76VHo3k5RM04iFQinFsvoy/nCgA8vWmEbxrpBK0hKzVqby59s3bh2aNqovd8640mg6Z0lft0LmFLMoqkvd41YLpg7mzHTmVqFaNbecXS297Djaw2VLqnMdzrSRpCVmpWz2WwV8buZVQnt/hLilMZTz17wMpvLXvEofZV7XuNN8o420C7V6EGB5fRl+j8njO1slaQlRTMbbb5U+bRTwuTENRUcwSqnX5GTf7OjvVogUcLw3jGkoFlePP81XbBuNvW6Tm9bM5cndbXz+ptWUeYvz8i6FGGLWGatybGSBRudAhNaeMLa26R6cPafDFiJDOYkrYWm6BmMFW75+NmpKvYRiFo8+dyzXoUwbSVpi1hmrciz9dOGT/RG6B+NU+d3UlHqxbVumBvOYrcFtGiyoKqG8xF2w5etno7Haz9K6Mr7xh0OEY9b4dyhAkrTErDPeHq31K+t55M4rWFYfYEFVCV6XSVtfBEMZcsR9HtM4XTI6glESll2w5etn63Wr6ukciLGxSJO2JC0x64y1RytdakTWORDFQGEYCrfLoIiriQueZTtd34/3Rop2TWc8i2pKecsF8/japldp7ii+foSStMSskz4FONYG1NSILGbZpBpiaO1MJS6qdk6IlfyVXzTOmhaAHmtjXZH7wk2r8LoM7v7RiySs4jo1YHb+KSJmvbEqxzbtb+f+p/bzavvAUNdwW2lcpoHWUBfwEolbGc9vEvlhfqVv2Llls81vXm7nhnPn8MMdrXzowe288dw5vPvyhbkOa0rkLGkppXzAZsCbjOMxrfXfKaWqge8Di4EjwDu11sXfUEtMq2w7em/a385nHttNT8jp6O4ykicMazBsm0q/m7beMDFLyygrzzgNcw1chsJlGtQHfLkOKacuWljFka4QfzjQwaIstgAUilxOD0aB67TWFwAXAjcopa4APgv8Vmu9DPht8nMhJm0iHb03bG4mGElgGgrTMHCbJl7TwGMqFIqOgTix1PTTTL8QMSZbg21ryktcBd/dYqrctGYu8yp8/HBHKy3dxVGYkrOkpR2pVUJ38k0DbwUeTN7+IPC2HIQnishEOnq39IRI2KfXsACUckZaMWksmPcq/G4W15RlXKOcjdymwe2XLUSj+djDO4kmCn/KNKeFGEopUym1C2gHfq213gY0aK1PACTfZ/zNU0rdqZTarpTa3tHRMXNBi4KT7dlZt2/cSkcwimWfXsy3bOfAR0sSVl5zm4raUjfbP/96HrnziqJPWOnXv2Bv95jfW1Pm5U8uXsCLrX3848/3zVCE0yenSUtrbWmtLwQWAJcppc6bwH03aq3Xaq3X1tXVTV+QoqBt2t9OfzjOvpP9NHcMEIw4XS3S92V99TcHuOu7O3juSBc6OcpK2JposnJQ5De3qagp9bCsoTzXocyY9OtfoHL8PoPnzqvgz65ZwkNbjvLk7rYZiHD65EX1oNa6Vym1CbgBOKWUmqu1PqGUmoszChMia6mii9T5SiVuA0M5x4wc7wlTG7Bwm+bQ2Vlf23QIW2sMIK6dsnZFhsMeRd5xGc4BiLKGNb6F1aUsqvbzmR/u5pJFVcyvLMl1SJOSs5GWUqpOKVWZ/LgEuB7YDzwJ3JH8tjuAJ3IToShE6UUXoWgCW2sGYxaVJW48poEGBqPW0JrHhs3NztSf1sRths7MksnAwuD3uEbdZyeGMw3FO9Y2ojV89kcvFuw+tlxOD84Ffq+UehF4HmdN62fAfcDrlVIHgdcnPxciK+lFF/HkYXgGilDMoqmujJVzAlSUuIcucC09Ibwug4QMqwpSpd9T8EeKzKTqUg83nDeHpw928uMXjuc6nEnJ2fSg1vpF4KIMt3cBr5v5iEQxaOkJYSpo7hggYWkStsalIGY55YAjT6dtrPKTsOxZvRG1UHldxlAVqCSt7F22pJrDnYN8+dcHuGnNPDyuwmqMVFjRCjGOMo/J8d4ICUvjMhRaQ9yGuKU52B6kLxwftvZx17VNeFzmGI8o8pEC5pT7zqgCFeMzlOKv37iC1p4w33++8I4wyYtCDCGmikptsFKcXqBK0ad7BaZ3yCjzmLgUJApzin/WcRkwv9JPeYmbUCwxbOQsstPaHWJ+ZQn/+ftXMZTiPVcsynVIWZOkJYpKMJpgfqWPzoEYoYSTpNymQgPLGgKEYgnuf2o/gzFr6OTi1p6QJKwC4DYVPpdBfXKENVp3fjE+pRSXLKriyd1ttPVFch3OhMj0oCgqjVV+XKZBU10ZLlPhdRkopfCYzq96idukuXNwqFgjGEnQF07kOGoxHp/LYGG1nwVV/nG784vsXLCgEpeh2Hm0sFq7ykhLFJW7rm3inif3Eool8JiGc6wIirqAF2Do8MdUh4zOgaiUtxeAORXO6KovHOcXf3ltrsMpCiUek1Vzy9nV0ks0YeEtkLVdGWmJopJ+VlZqU3FNmZsyr2toOmlJzemTi0eeYCzyU8DnPqPyU5y9SxZVEY5b/G5f4fRwkJGWKBojjx/513dcyIutvTzwzGHag0FKPSYfvnoJhzsH+MnuE2fUaYj8JetX02NpfRnlPheP7WjlxvPn5jqcrEjSEkUh1QkjVVzRHozwmcd2o3EObVzoNgnHLR54upmBmIWR7Nwu8p8C6gM+2UQ8DQyluLCxik0HOjjVH6GhPP/PIJPpQVEUNmxuJm5ZnOyL8MqpICf7IvSF4wxEE8OOJBmIWclzl3IdschWTZlnVnRuz5VLF1dha833thXGni1JWqIoHGwP0hmMkUi2bkrYmpilicRt9ic7vPeH49jSW7DgLKsP5DqEolZT5uW1K+p5eNuxgjhvS5KWKAqxhA3Kme5QqOHrVRpCMYujRXJy62ziMpB1rBlwx5WL6RyI8r8vnch1KOOSpCWKgtt0el3ELZvIiHOw4raWkVUBchnwieuWybTgDGjpDlFb5uXffnWAh/N8mlCSligKyxvKcSenBSVBFTZTQbnPxQPvv5RPXL881+HMCoZSrGuqprUnTEuez0hI0hJFYV1TNaG4M7pSI75mjLxB5C1DwfwqP1+97SIZYc2wixdW4XUZbGnuynUoY5KkJYrCluZuTHU6YaXnKVuGXnlP4Yywyrwuac2UI163ySWLqniptY+23nCuwxmVJC1RFFp6QvjcJh7TwOc2cZvyq11ImupKZYSVB65aWotGs3Fzc65DGZVsLhYFZWTXi3VN1Wxp7qYjGCVh2c6G4fyv2hUjyObh/FDl93DRwioeee4YH33tOdQH8m+zsSQtUTBGdr043DnAtsNduAxF3JICjEJV43fzyJ1X5DoMkfSa5XW8cKyHbz19mM+9aVWuwzmDzKGIvLdpfzu3b9zKXd/dQXvQOZVYKUVPyNksLAmrcHkM+Ld3XpjrMESa2jIvb7lgHv+z9ShdA9Fch3MGSVoir6VGV+3BCJZtY9uatr4wJ/vCRBNOtaAkrMKjgFVzAmx8/6UyJZiHPn7dUqIJm3//zcFch3IGmR4UeWvT/nY+8egLDMYS+FwmLsNAA7at6RyI5To8MUm1ZR62f/71uQ5DjGFpfYD3XL6Q7207xvvWLWJ5Q/600pKRlshLqRFWKGbhSm4aTtgaK/lec+Z+LJH/6so8/OutF+Q6DJGFv7x+OWVeF597/CWsPNo3IklL5KUNm5txmwqvywCtMJTCNBSu5E5hBbhMSVuFRCn4l1svkOnAAvDwtmM8teckr1/dwI6jPXzy0Rfypr2TTA+KGTWyZH20MueWnhCmgmjcIjFKGXtcDsQqGC5DsbSuVBJWgbmosZKXWvv45d6TLKktzXU4gIy0xBRJVfhdff/vuH3jVjbtP/P47vSiitRBjfc8uTfj9wa8Lo52h52ENYLHkOKLQqKASr+bz96Yf+XTYmxKKd5+8Xx8bpOHtx0jGInnOiRJWuLsZZuMUlN+6Ycyuk3Fhgy771u6B0d9vpgc4FhQVjSU8a8yLViwAj43t126kJ5QjLt/9CJa5/ZPRkla4qxlm4xaekKUuM1ht5W4TVp7hneV3rS/nQHJTEXh09cv46lPvUYSVoFbUlvKG1bP4X9fOsl///FITmORpCXOWrbJqLHKTzg+fHEqHLdYUOUfdlumkZcoLF5T8Z0PyNEixeSaZbWsmlvOl37+MjuO9uQsDkla4qxs2t9OfzjO/pPBoSPtIXMyuuvaJuKWJhRLoLXzPm7pYSfTbtrfzs5jufsfQpy9ujIPG963VkZXRUYpxa0XL6DS7+EvHt5J92Bu9kpK0hKTllrLKvWaKCBm2bT1hekIRs5IRgDrV9Zz783nUh/w0ReOUx/wDTuGIvV4CjkDq1DVlrqlrL2IlXhMbr9sIV2DMT756As52b8lJe9i0lJrWRUlPrwuk45glEjCIhSzuO/ta1i/sj5jiftozVE3bG4mbjnTh3m0l1FMwPYvvCHXIYhpNr+yhL+/+Vw+9/hL/OfvXuWT1y+b0eeXpCUmraUnRGWJG3AqjAI+N1pr+sLxoYR1z5N7iSUsgpEEJ3rDbG3uQgFul0FTbSl337AScBLWtsNd2NrZ0+MxpXN7ofn0DF+8RO7cdmkjzx/u5t9/e4CLF1VyzbK6GXtuSVpi0hqr/LQHI/g9p3+N0teyNmxuJpaw6BqMoTWk9gJrIGHZvHIyyJ8++DwAHtMgVUlr2VrmBwvMp69fJkUXs4hSii/dch572vr45KO7+PknrmZuRcmMPLesaYlJG6+woqUnRDCSwEBhj9jbYWuwk++1dhJZ6js0DPUXFPnNLVWCs9LD247xkxfaePP58xiIJnjXhq38z5ajM9LqSZKWmLTxCisaq/xEEzZKwcj9iHrEx3FL9mUVGq+p+KZUCc5qdQEvb79oPse6Q/z65ZMz8pwyPSjOyvqV9aNetNY1VbP1cBeJTL2YRpDCi8Jyy4Vz+cptF+c6DJEH1iyo5FDHIE8f7GTFnPJpfz4ZaYlpsWl/O4/tPE6FzyVHiBSZGr9LEpYY5k3nz6G61MMPd7RMe39CSVpiWqTK4RurS1lY7ZfEVSRKXIp/e+dFuQ5D5Bmvy+QdaxvpC8X54pMvT+tzyfSgmDLpe7JO9oUxDQNbawykK3sxKHEbfP09l8galshoYbWf9Svq+NHOVl6/up4bzps7Lc8jSUtMiU372/nrx3YzEE0QS9jYGhK2jUISVjFYt6SKR+66MtdhiDx33coG2oNR7nliL1cvq6PMO/UpRqYHxZS47xf76A3F0SOKACVhFb4j971ZEpbIimkovvS282gPRvl/vzs4Lc8hIy0xKSPbM73aMYChFIah0BlOGRaF6ZYLp2eKRxSvixZWceslC/j2M4d5/7rFzK+c2k3HMtISE5bp0MeEzVDzTDWi6sJjyq9ZIVq3pEqqBMWkfOr1zmbzr296dcofW0ZaYsLSD30E8HtcQ70Cba0x1fB9VzHZOFxwZB+WmKxUV4wLG6t45LkWGqv8BHxOj9J3X77wrB9f/gQWE5bp0Me5FT6UQgovCpzC6SMoCUucrauX1mLZmp3Heqf0cWWkJSYsU6Ncl2mwoiFApd9Da0+IvnAcl6HoDcel20WBkApBMZXqAl4W1/jZfqSba5fVokauG0xSViMtpdRqpdSblFI3p96m5NlFQRqtUe7dN6zkkTuv4Om7r6O8xM28yhJM6dZeENymkoQlptzaxdV0DcY43Dk4ZY857khLKfVNYC3wMk5jbnBmgJ48mydWSjUCDwFzko+7UWv9H0qpauD7wGLgCPBOrbWcvz7DMh3emNpUun5lPbe29vLAM4cZjFmUekw+fPWSYZtOU6MxUyniMmGY1xTw8dcuzXUYogidN6+Cn73YxvNHummqK5uSx8xmevBqYLXWI/t0n7UE8Fda651KqQCwQyn1a+ADwG+11vcppT4LfBa4e4qfW4whVR3oNtVQdeA9T+7lXpyE9dXfHOBrmw6RsG28pkGp1+SxnccB2NLcTUtPiIDXRUd/hGhCijDy3afkLCwxTTwug1svWcAPt7dy0wVTs30im6S1DVgOvDIlz5iktT4BnEh+HFRK7QPmA28F1ie/7UFgE5K0ZlSm6sBQLMGGzc0AfG3TIWytcZsGloZTfVFs4Mu/OYgC6so8xFwGg3FLxlh5zACa73tzrsMQRe7WSxr57tZj/PzFE9x+2cxUD34L2KaU2quU2qmUekEptfOsnzmNUmoxcBFOgmxIJrRUYsvY6EwpdadSartSantHR8dUhjNrbdrfzu0bt/LckW5O9kXoD5/u1lziNmntCbFhczOWrTGVQqFIWDYjx1LtAzG6BmJIpXv+KvMYkrAKWPr1L9jbnetwxnTBggrOqSvlRztap+Txskla3wb+FHgb8A7g1uT7KaGUKgN+BPyl1ro/2/tprTdqrddqrdfW1dVNVTizVvqGYa+piFk2bX3hocQVjlssqPLT0hPC6zKGDnW0RhlK9Yan93gCMXmfvn4Ze+69MddhiLOQfv0LVFbnOpwxKaW49ZJGth/tmZKCjGySVovW+nGt9UGt9aHU21k/M6CUcuMkrO9prR9P3nxKKTU3+fW5QPtUPJcYW/qUYMDnJmFp4pampSdE50CEuKW569qm5EZBFzYaO0Mtu0wH5rdPy/qVyIFbLpqPoeDxnWc/2somab2slHpIKfWOqSx5V07R/reAfVrrL6d96UngjuTHdwBPnO1zifGlNgwHI3F6QnFMQ6FwOluc7I/SPRhhw+Zm1jVVO+2atJZOFwXmlgvnSsISOTGnwsdVS2t5fOfxjH/sTkQ2hRgVyffpieqsS96Bq4D3AS8ppXYlb/sb4D7gB0qpDwHHmMKpSDG6VIl6RzCKUuA2DMDGsjUuQ6G1oj0Y4aGtRxmMxLG0jKoKifQRFLmSaus0t6KEpw928o//u49z6som3dJp3KSltX7fpB55/Md9BkY90PZ10/GcYnR3XdvEPU/uJZKwMICYnZaUtNM/0O9xcaxrkLgNLkNhT/kuCDEdZEpQ5INz55VT4jbZ2tzFOWexZyubzcVenL1T5wK+1O1a6zsn/awi76xfWc+9wEcf3kkoZjmd2rXzV0Xc1vhcBif7wsSTM4IJ6c1UEI5IhaDIE27T4NLFVTx9sJPeUGzSj5PNmtZDON0pbsIpST8HiEz6GUV+08Pfpz6NJWw6Bib/iyZm3oIKb65DEGKYK5pqANjaPPky/WyS1nKt9eeAAa31t4AbgPMm/YwiL6VK3qOWhds4c71KSi4Ky4IKL8987vpchyHEMJV+D6vnlfP8kW7CscmdFptNIUZqw02vUmoVcApYNKlnE3nr/qf2094fwbZBDh4ubDIlKPLZlefUsretnyd2Hee2SXTIyCZpfUspVQX8HfBLwA/cM+FnEnkn1RT3YHuQzoEYpgLTULJeVcAkYYl8t7jGz6q55Xzn2SO869LGCR9ZMu70oNZ6g9a6R2v9e631Qq11rdb6vyYdscgL6R0wQtEE4HS3kIRVmAwkYYnCoJTig1cuZv/JIM8dnvja1pgjLaXUIuAWoBGnK/tB4FGt9cBkghX5I70DRtzWeExFbLSeTCLvNd/35jGPkxEin7zlgnn8w89e5gfbW7k8WZyRrVFHWkqpjwH/DVTiHE9SDiwDnldKXXMW8Yo8kOqAAeAxDdkoXMCOJBNWauScfpzMpv3SBU3knxKPyU0XzOV/XzrBYHKmJ1tjTQ/+OfAGrfUXgeuAVVrru4Ebgf+YbLAiPzRW+QnHnZKLMq+LuIyyCs7KhtKhKcH0kbNSznu3qYaOkxEinzy87RhvPn8e4bjFc0cmNkU4VtJSaV93AwEArfURwDOJOEUeuevaJuKWJhRLEIzEs9r7IPLHkfvezFOfWj/0efrIOSV1nIwQ+eiSRVW4TcXW5q4J3W+sa9W3cc7R+i/gWeAbAEqpOqBvsoGK/LB+ZT333nwu9QEfUUvL9GAByVRwkT5yTkkdJyNEPirxmKyaW87e41mfSAWMkbSSndc/AGwG3qG1/mby9g6t9VVnEavIEy+29rK3rQ/LlqRVKD59/bKMt6ePnLV23qeOkxEiX82t8NEenFiDpTGrB7XWu4HdSqk1SqmP4jRK+KPW+sXJhymmwmiVYmNVkKV/Da1p64swwS0SIofGanyb6h25YXMzrT0hFkj1oCgAdQHvhMves2mY+zfAe4CfJG96WCn1Pa31P008RDEVUpViblMNqxS7tbWXx3YeP+P2e5P3S7/Pyyf6sTVyvkiByGYP1vqV9ZKkREHxuUyiiYk1icumI8b7gEu01iEApdQ/AjsASVo5kl4pBuD3uAjFEjzwzGHqAt4zbk9VkMUti66BBDHLRvYQFw7ZNCyK0cPbjnGwfYBI3OJ7W4/yniuy6w6YTdI6OuL7XIDU0eZQS0+IyhL3sNtK3CaDMYuFo1SQheMWfaE4hqEwDSUl7gXAa8Ir/ygJSxSvEreJrZnQ9SibSucQsFcp9YBS6pvASzjNc7+slPryJGMVZ2G0SrFSjzlqBVksYYMCQykUClPWsvKeJCxR7Pwe54/swVj2G4yzSVo/B74IbAG2AvcCvwP2Jt/EDButUuzDVy8ZtYLMncxStq3RWo96ZLTIDzIlKGaDUq8ziReKZn+2xLjTg8kztEQeGatSbM2Cyoy3L28o53DnAD2DMaIJKXHPVysbSodtGhaimE1mpJVN9eANwD/gnKHlwumUobXW1ZOKUkyJ0SrFRrt9XVM12492E7dklJWvavwuSVhiVnnXpY1s2NzMhY2VWd8nm0KM/wTeibOWJQfYFoDUfqwDp/qJW850YCRhDy12yigr/7gU7LjnjbkOQ4gZVV3qdATsHoxlfZ9sklYrsEtrLQkrz23a3879T+3nQPsApnLOxpJKwfznUvDqP8kalph9yn1OFXRvOJ71fbJJWv8H+KlSahMQTd2otf7qxMIT02noaIr+CGhNLPknhi0JK6/JGpaYzQxDYSinQCxb2SStvwfiOOdqyWgrT23Y3EzcsohMcHe5yB2pEBSzndapJYupTVr1WutLJhmTmCEH24P0hbIfYovckoQlBHz7mSPYGg53DPLwtmO8+/KF494nm31av1VKXXf24Ynpsml/Oz2hOHHpzVQQJGEJ4eiPOH9oB0Z0+BlLNknrz4DfKKUGlFLdSqkepdTE2vKKaZNay5rInLDIHUlYQpzWHnTKJGpKsz9XOJvpwdpJxiNmQKp5rlKgJW/lLQNoloQlxDBtvWEMBQ3lvqzvM+5IS2ttAe8A7k5+PBe4cNJRiimVOmZdycFYeevT1y+ThCVEBke7BplbUYLbzGbSz5FNR4z/BNzAtcD/xWmg+w3g0knGKaZQY5Wf9mAEn8tgMJZ9/y4xM77zgUvljCshRnj35Qvpj8T5whN7+PPXNGVVgJGSTXq7Umt9FxAB0Fp3A9lPQIpplWqeW16SzUyvmEnrllRJwhJiFL/f345la9avmNj/I9lc6eJKKYNk9x+lVA2yX2vGpFoytfSEaKzys66pmi3N3UOf33VtE/fefC4bNjdzsi8qLZryxLolVTxy15W5DkOIvPX4zuPMq/BxycKqCd1v1KSllHJprRPA14AfAXVKqb/H6UP492cTrMhOqjLQbSoqS9wc7hzguSPd1Ac81JR6aQ9GuOfJvdx68Xx6QzFJWHlCpgSFGNuJvjBPH+zgI+vPwTAmth4/1kjrOeBirfVDSqkdwPU4Hd7fobXeM/lwRbZSlYGWrTncOUgoZqGBnsE4tWU+/B4XHcEIX9t0aGhnucgtKWkXYnwPbTkKwG2XZr+WlTJW0hpKf1prOfAxB1p6QpgK+Kg2lgAAHtZJREFUTvRFnZL25O2RhM3LJ/rxuQwStiZh2UiLwdyThCXE+KIJi4e3HeMNq+fQWO2f8P3HSlp1SqlPj/ZFrfWXJ/xsYkIaq/y8cKwHpcBQyjnILPk1rTUJWxNN2CgY9jUxs2r8LjlWRIgsPXuoi75wnMW1pZO6/1jVgyZQBgRGeRPT7K5rm4jbNlo7Z2KlJyVbM5Sl0j4UOSAJS4jshKIJNh/oYNXcchZOYpQFY4+0Tmit751caGIqrF9Zz7K6Mg51DBAdMf+nANlPnFvS5UKIifnDgQ5iCZs3rG6Y9GOMNdKSS2IeeNP5c7FRGCP+pcq9Zm4CEoBzcKMkLCGy1xeOs6W5iwsbKyfUtmmksUZar5v0o4opsWl/Ow88cxjbPvO0mb6odL/IpbmVJbkOQYiC8su9J9Earl81+VEWjJG0kp0vRI6k9miFkq2ZUsNe03BK4GUNKzcU0FRXSn1g8n8pCjFbpNozPXuok10tvXz8uqV87LqlZ/WY2XcpFDMqtUfL6zJGFGBoPKaBwklgYub43QZNdaXELc1d1zblOhwhCkIkbvH5H+9hYbWfj7327BIWSNLKW6nu7bVl3qFydo1TNRiznDJ3Ay0LjzPE7zaoKfNSH/Bx783nSscLIbL0z0+9QnPnIP/09vPxuc9+LV66rOapVPf28hI3Aa9Jf9oa1lACkw6QM+KWC+fyldsuznUYQhScZw918u0/HuaOdYu4aunUHM0oSStP3XVtU3JNK4GlwVRI14sZ5lLw6j9JhaAQkxGMxPnko7toqivl7htXTtnjyvRgnlq/sp57bz6X+oCPSMLG6zLwuQxMmQ+cEWUeQxKWEJNka80PtrfQG4rxX++5GL9n6sZHkrTy2PqV9Txy5xUsry/D1k7PQRltzYw9996Y6xCEKFg/f+kEhzoGufmCeaycUz6lj53TpKWU+rZSql0ptSfttmql1K+VUgeT7yd22EqR2bS/nY6BKAlbii5mijS+FWLytjR3seVQF1edU8Mli6qn/PFzPdL6DnDDiNs+C/xWa70M+G3y81ll0/52bt+4lavv/x2fePQF3KZifmUJHpkbnFYrG0olYQlxFna39PKz3W2snBPgxvPnTstz5LQQQ2u9WSm1eMTNbwXWJz9+ENgE3D1jQeXYyIMfT/SG6Y8kpNfgNCvzGDz1qfW5DkOIgvXS8T5+uKOFxbWl3HbpQoxpumDleqSVSYPW+gRA8n3GDTFKqTuVUtuVUts7OjpmNMDplNpU7Pe4GIgmnG7unN6jJaZejd8la1iioKRf/4K9uW9etONoN99//hiN1X7ev24RHtf0pZaCLXnXWm8ENgKsXbu2aC7nLT0hKkvcAHQEo3JO1jRb2VAqIyxRcNKvf02r1uTsEnHbpY3c/8v9/Gjnca5eWsvX33sxAZ97Wp8zH5PWKaXUXK31CaXUXKA91wHNhE3729mwuZmOYJTOgSgNAR/huIXsH54+Cyq8krCEmKRYwubPv7uDX718ivdcvpAv3nwubnP6J+/yMWk9CdwB3Jd8/0Ruw5l+6etYc8q9HO+NcLw3LNOB00i6XAgxeX3hOP+z5Qgn/397dx4dZ33fe/z9nVWrbVmWvEkGC2zEYrPZgAk4LjgXCBRDCg1Om8BpCG5vUgL0tEB7ShrXtLRN0uSG5lxzEgrZIFzSpG4TSCCJ69skBMxug8GOWSSMLcmWtVjLbL/+MSMjG3mRPDPPzPN8Xuf4oJmRHn0fS8zHv713iM/97inccP7xWJEG3T0NLTN7iOyki2lm1g58jmxYPWJmnwTeBq71rsLiGBnHSqUdXf2J7DZN7r3ECoFaXHmkwBKZuPbuAb711FskUhm+cf1ifqfI+3B6PXtw5SFeCtRZXm3dA4QNdvQMEcKIhiy3Ma6jripCIuUYTKirMB+WzK1TYIlM0KbcDMHqeIRVS08oemBBaXYPBk5zXRXPt3UTwgjljhsxIBaGgUSGprpKegcTdPUnNSnjGDxww2Ltzi4yAc45/uv1Tn76yi7OnDOF+z6+iIbauCe1lOKU98BZtbSFRCrDcDrDYDLNUDJNOuOYObmC2ooIPQMJOhVYE1ZfFeHNey5XYIlMQCqd4dFn2/npK7u48vRZPPSp8zwLLFBLq2SEQ0YmnduqKTeemUhnaKiJs2VXH5BdXOyUXOOi1pXIxO0bTvHt37zFW7sHuPjkRr5y3RlFm3BxKAqtErB2w3bqq2Ps3pcghGEGaefYsy/J5Iro/qBSYI1P6/RqBZbIBHX2DfPgr9+kdzDJRxc3c3rTFM8DCxRaJaGte4B4JEQIGE6/N90iEnJs79rnXWFlbMncOh5adb7XZYiUpTe69vHtp94iZHDjhS3MmVrldUn7KbRKQG08wtaOfmB/z2Bu/MpI6CyScVOXoMj4fezcOQD88Pl3ePBXb9I0tZIHbjiHOfWlE1ig0CoJI2uy0hl3wGSLlFYXj9uSuXUKLJEJcM5x78+38cUnXufcuVNZ+/GzmVIV87qs91FoeWBky6a27gGa66ro2pdg9pQK3t4zCKD9BidIi4ZFJiadcfzFoy/x/55t5yNnzuae31tY0E1vj4VCq8gOPnqko2+IvqEU0bARj4QYTmUUWBOgLkGRiUllMnzvmTY27+jl5ovncevyeSUx4eJQFFpFNvroEYCqWIS6qihd/QnSaafAmgAFlsjEpNIZvvv022zZ2cdfX3EKn7xgrtclHVFptv98rK17gMpo+IDnptXEcc4Ri4YIle4/cEqOFg2LTNxHFzfz6+272bKzj7+96rSyCCxQaBVdc10Vg8n0Ac8NJtOEQyFObKihua6KihLtSy4lEYNn77rE6zJEypJzjs//x2Ye27STv77iFD5+3nFel3TU9O5YZKuWtpBMOwYSKZzL/jeZdrRMq6arf5gdPYM4B1E1uQ6paXKcbX9/uddliJSt/97WxTd//RarlraUTQtrhEKryJa1NnLNWbPp7Bvm1Z19dPYNc81Zs7n90lZ270uQTDuG0xmSmu4+ptuWz+O/71zudRkiZeu3nf08vmknH14wg9svbfW6nHHTRIwiW7+lg0efe4eG2jhzomEGk2kefe4dAFJaSHxYmnAhcmx6BpM89PTbNNTG+cdrTt9/qkQ5UWgV2egDH7d19zOUym7b9KUnt3pcWWlTYIkcm4xzfP/ZdpLpDH9w7nHUxMvz7b88qy5jIwc+vrN3SDteHCUFlsixG05l2NbZz99dvWD/lk3lSKFVZCMHPqYVWEfUNDmu8SuRPNizL8G9v9jKRa2NrDyn2etyjokmYhTZkpapDCW168WR1MRCCiyRPHDO8R8v7iBsxpqrTivp3S6OhkKriEYmYcTC5f1LU2j1VRE2rb7M6zJEfGHLzj5e29XHrR+az6wplV6Xc8wUWkU0Mglj5uRKwmU4a6cYWqdXa9GwSJ6kM47HN+1kWk2M688/3uty8kKhVUQjWzhNqowytSqKYutAS+bW8fity7wuQ8Q3nnu7m87+YdZctYBo2B9v9/64izIxsoVT72CSvYNJYj75JTpWZtljRXTSsEj+pDOO/3q9k4VNk7nk1Olel5M3etcsopEtnHb1DWVbWQYRdRPyr9cv1jlYInn28jt72bMvwWd+58Syn3wxmkKriJa1NrL6ylNxLrvQL+Nc4Ndq3bZ8ntZgiRTAr367m4aaOMtP9k8rCxRaRbestZGz5tQxqSJKMsDbNoVDxm3L53Hz8vlelyLiO+/sHaS9e5BzW6aW5VZNh6PFxUW2fksHewcS7BlIel2KZ+KREGv/8Gy1sEQKpH8oSTwS4u8+ssDrUvJOoVVE67d0cNe6zUQDvk7r08tOUGCJFEjGOX708k4uam1kUkXU63LyTqFVRP/w+BY6eodIu+B2C159xkx1CYoU0Fu7B+jqH+byhTO9LqUgFFpFsn5LB6939BM2Ark+qyoW5o+XtiiwRApsW0c/IYML5zV4XUpBKLSKZO2G7YRwJNMEat/B5rpK/nbFaeoOFCmS33b2c3rzFCZX+q9rEBRaRfP6rl5SmeAEVgi4X0eKiBTVUDJNe/cAK86Y5XUpBaPQKoL1WzrYvS9YswXPbalXYIkU2Rtd+8g4+MCJ07wupWC0TqvA1m/p4M8ffdHrMooqEsru/iEixfXW7n3EwiHOnDPF61IKRqFVYGs3bKdnMDitrMpoiK9/Qt2CIl5o6x7k5FmTiEfCXpdSMOoezKP1WzpYu2E7bd0DNNdVsWppC1s7+kgEZOeLN++53OsSRAIr4xzvdA+ydJ5/uwZBoZU3oxcOT6mM0tE3xF3rNjOQSHtdWlE0TY57XYJIoHX0DpNIZzjDx12DoNDKm7UbtpNMp9ndnyKRzhALh5hUGSGRShMOQTrjdYWFUxsPs+bqhV6XIRJo7+wdAGBhk0JLjsLWjj56BpKEQkY4ZAyn0uzsSft+invT5Dhrrl6oMSwRj+3sGaIiGuL4+mqvSykohVaeJFIZMAiZkUpnSPm4ZTViydw6HdwoUiJ29Q4zf3otYZ/t6n4wzR7Mk2jYSGccQ8k0yQCckaXAEiktO3uHmD+91usyCk4trQk6eKZgZSTEXsDvDayQwS0X6xwskVLSP5yifzhF6wyFloxhrJmCu/oTAJiBXzdxb6iJ8U/XnK7xK5ES070v+/7j9/EsUGhNyNoN24mGjapY9q+vKhYhnXY4/Lu3YMhQYImUqL6hFACNk/y/9ERjWhPQ1j1AZfS9Fee9g0ky+DewINslqMASKU19w9ldd6ZPqvC4ksJTaE1Ac10Vg8n3Fg3v7Bn0sJrCMnRwo0ip6x1MYQb11TGvSym4kg0tM7vUzF4zs21mdofX9Yy2amkLybRjIJHCObd/mya/TDQ1sudgLWmp519vWMw/X3eW1yWJyGH0DSWpr44TCZfsW3relOSYlpmFgX8BPgS0A8+Y2Trn3CveVpa1rLWR1WTHttq7s6vQQ/hn5uBJ02t4/NYPel2GiBylvqEU0wMwngUlGlrAOcA259x2ADN7GFgBlERoQTa4RsZ4LrjnZ7TvHfK4ovyIhOCOy072ugwRGYe+4SQnBWCNFpRu9+BsoG3U4/bccyWptiJK2Mq/e9CAmy/ShAuRctM3mKKx1v+TMKB0Q2us9/8DJueZ2U1mttHMNnZ2dhaprLH1DadoqqskHinVv86jc+tyLRoWKQej3/969+6hfzg43YOl+i7bDjSPetwE7Bj9Cc65+5xzi5xzixoaGopa3MGa66pIpDOky3j7ptbpNQoskTIx+v2velIdDmioVWh56RlgnpnNNbMYcB2wzqti1m/pYOV9T3HBP/yclfc9xfotHQe8vmppC139ibLdc3BaTUzjWCJlKpPbgicooVWSEzGccykz+wzwEyAM3O+c2+xFLQdv2fTm7n5WfftZauJh5k+fxKqlLQynMyTL9HRiHS0iUt7SGUcYhZbnnHM/Bn7sdR2jt2zq6B2io28YByTTGbZ39nHL916gZzDpdZnjVhsP8dWVZyusRMrcSAdPQ00wJmKUbGiVirbuAaZURukbStLRN7z/+YyDjv5EWW6OWxkN8fLnL/O6DBHJg5Gx9Gm1/t8NA0p3TKtkjGzZ1JlrYY2e11iOgQXwJx88wesSRCRPMs5RE4/s38Db7xRaRzCyZdNQKo3x/qAqt0NCb9O0dhFfSWcc02qC0coChdYRLWttZPWVp1Idi7xv8Vg4BNFwiKpYeMyvLRVG9iysB25YrMAS8ZmMc0yujHpdRtEotI7CB09q4HcXzjpgb8GQQSQU4tPLTuCPl7aUbItrUkWE81rqdRaWiE85R2C6BkETMY4omc7wN+s2852n3wagOhZmcmWU4+qrWbW05YAg+OefbS2pca6QwUt/c4nXZYhIATmgOl7avT35pNA6jJ6BJP/7u8/yy227Afjwghl88dozqByjO/Dm5fNZ2DSFP3/0RXoGk/uPK/HSitNnel2CiBSYcy5QLS11Dx7CG137uPprv9wfWH960Yncu/KsMQNrxLLWRv7pmtM5+7ipTKuJEQlB2KNuw2nVUZ2DJRIAGaeWVuD9alsXf/Kd5+gZTBKLhPjH31vIVWce3Sbzo48suezLG3ijax/pVPFO2goBU2tifOGa04v2PUXEO0FraQXnTo/Sd3/zNnf9+yZSGUd9dYz7PrGIs4+rG9c11m/pYO2G7bze0Y9RvG7CsMG8xhruuOxkTboQCQgHJT+DOZ8UWjnpjOPuH73K/b98A4DWGbV8/fpFNNVVjes6o/cqjISM4VRhQ8sMbr1Ya69EgiwWDs5Ij0IL6BtKcvNDz/OL17Lncl3c2shXVp5JTXz8fz0jexWm0o7hInQLKrBEJFbmZ/mNR+BDq23PADc+uJHXdvUBcOMFc7nzwycTnuDCq7buAYaTKbr6C7+JbtPkuAJLRIiqpRUMG9/cw6pvPcvufQkiIePuq0/jo4vnjPs6I2NYbd0D7O4fZjBZ+BaWAWuuXljw7yMipU8trQD4t+fauf37L5FMO6ZURvm/Hz+b81rqx32dg8/b6ugdKkC1BwqHYH5jrSZbiAigMS1fy2QcX/jpa3xt/W8BOKGhmvtvWMxx9dUTut7o87aAgs8VDIdg9pQqbr+0tcDfSUTKhVpaPjWQSHHr917gJ5t3AXDhvGnc+7GzjmmzyZHztkbEwiEymTSF2hBjfmMtt1/aqlaWiOynMS0ferdnkBsf3MjmHb0AfGLJcdx1xSlEjvGH3VxXRUff0P6WVnUszL5E+pjrHUvTlAoeu2VpQa4tIuUr6tXWOx4IRDy/1L6XFff+ks07egmHjNUrTmX1itOOObDgvfO2BhIpnHP0DqUIAfFI6H1HmUyEAbGwMa0mxpqrFuThiiLiN+oe9JEfvfQutz3yAsOpDDXxCF/7g7NYOr8hb9df1trIarJjW+3dAzigeWolkypjbO/sZyg5sa7C+qoo82dMor17gKa6qvftKC8iMkITMXzAOcdXf76NLz3xOgBzplZx/w2LOLGxNu/fa/R+gyvve4qOvuwMwkQ6QzQcIuQcyXEklwFf/P0zFFIiclSiammVt6Fkmr949CXWvbgDgHPmTmXtH55NXfWxHUk9ej1Wc10VS1qm8uvte2jrHqAmFsbM6OwfpnsgSTqTDakkblwHRGo6u4iMV8iCM6blu9Dq6Bvipm8+ywttewH4/UVNrLlqwTH3+R68HuvN3f08/eYeGmpipDMZ2rsHgezBi5mDGlUHPx6LAXVVUWoqoprOLiLjEqDM8ldovbKjlxsffIYdPUOYwZ2XtfKpC1uwPPxED16P1TuYImSwu3+Y0RtgHC6gjPfWcZlBNBRiSmWE3uEUzsFJMyZp7EpExi1AmeWf0HrilV189uHnGUikqYqF+cp1Z/KhU6bn7foHr8dKpDM45xjPnrjxSIjhVCYbXA5mTamgtiJKo3P0DCZ56Kbz8laviARHPv5hXi58EVqd/cPc9K2NOAezJlfw9esXc8qsSXn9Hgevx4qFQwyMcz1W2jmqYmES6QyxcIjaimwIDibT4z4CRURkRHAiyyfrtHb2DOEcnDlnCj/8zAfyHljw/vVYkyoj496yKRoyJlVGyDiorYjgXPZ6ybRj1dKWvNcsIsEQoIaWP0ILYMUZs3joU+fRWFtRkOsva21k9ZWn0lhbQc9gkuPra2iuqyQ2jpXo4ZBxfH0Nn73oROZOq6FnMEljbQWrrzxV41giMmEWoLaWOVe84+AL5bjWBe7NV18qer/uyIzCZDpNz0CSgcMcSVJXFaF1xmSNW4nI0TqqN7T4zHnu2Y0bOW325ELXU2xj3r8vWloNNXFPBiJHWl/H19dQXxNnUkWEGZPihCw79d3INtvjYWP2lCrauweKXqOIiJ/4YiKGl0bvhjHS8opFQmQyDjPDOZg5pUKTLUSkYII0pqXQmoCDd8YYWVs1sg/hPY+9ytbOfqJmzJgcJxwyTbYQkYIJ0piWQmucRu+METZ4vq2bT35zI/Mba/afc7WstXF/sLV3D9BYW6FFwyJSMGppySGN7IyRSjt29AwRIhteb3Tt4651m1nNe12GCikRkfzyxUSMYmrrHqAyGqarf5gQRiiU/ZN2jmjYWLthu9clikjABKmlpdAap+a6KgaTaRLpzP5fFOeyO2RURsOaISgiRRekMS2F1jiN7IwRDhkZ58g4h3PQUBvXDEER8YRaWnJI+9dmTa0i7RwGzNQMQRHxUIAySxMxJkIzBEWklASppaXQOgaaISgipSE4qaXuQRGRMheklpZCS0SkzAUosxRaIiLlLkgnFyu0RETKXHAiS6ElIlL2AtTQUmiJiJQ77YghIiJlYe60ahonxb0uo2g8CS0zu9bMNptZxswWHfTanWa2zcxeM7NLvKhPRKRc1MQjVETDXpdRNF4tLt4EfARYO/pJMzsFuA44FZgFPGlm851z6eKXKCIipcaTlpZz7lXn3GtjvLQCeNg5N+ycewPYBpxT3OpERKRUldqY1mygbdTj9txz72NmN5nZRjPb2NnZWZTiRERKQZDf/woWWmb2pJltGuPPisN92RjPubE+0Tl3n3NukXNuUUNDQ36KFhEpA0F+/yvYmJZzbvkEvqwdaB71uAnYkZ+KRESk3JVa9+A64Dozi5vZXGAe8LTHNYmISInwasr71WbWDiwBfmRmPwFwzm0GHgFeAR4HPq2ZgyIiMsKTKe/OuR8APzjEa3cDdxe3IhERKQel1j0oIiJySAotEREpGwotEREpGwotEREpG+bcmGt3y4qZdQJvHeNlpgFdeSinnOie/S9o9wv+uecu59ylR/okM3v8aD7PL3wRWvlgZhudc4uO/Jn+oXv2v6DdLwTznoNE3YMiIlI2FFoiIlI2FFrvuc/rAjyge/a/oN0vBPOeA0NjWiIiUjbU0hIRkbKh0BIRkbIR6NAys2vNbLOZZcxs0UGv3Wlm28zsNTO7xKsaC8HMLs3d1zYzu8PregrBzO43sw4z2zTqualm9oSZbc39t87LGvPNzJrN7Bdm9mru9/qzued9e99mVmFmT5vZi7l7/nzued/ec9AFOrSATcBHgA2jnzSzU4DrgFOBS4GvmVm4+OXlX+4+/gW4DDgFWJm7X795gOzPbrQ7gJ855+YBP8s99pMU8GfOuZOB84BP5362fr7vYeAi59zpwBnApWZ2Hv6+50ALdGg55151zr02xksrgIedc8POuTeAbcA5xa2uYM4BtjnntjvnEsDDZO/XV5xzG4A9Bz29Angw9/GDwFVFLarAnHPvOueey33cB7wKzMbH9+2y+nMPo7k/Dh/fc9AFOrQOYzbQNupxe+45P/DzvR3JdOfcu5B9gwcaPa6nYMzseOBM4Df4/L7NLGxmLwAdwBPOOd/fc5B5cghkMZnZk8CMMV76K+fcvx/qy8Z4zi9rA/x8bwKYWQ3wfeAW51yv2Vg/cv/InW5+hplNAX5gZqd5XZMUju9Dyzm3fAJf1g40j3rcBOzIT0We8/O9HckuM5vpnHvXzGaS/Ze5r5hZlGxgfcc592+5p31/3wDOub1mtp7sWGYg7jmI1D04tnXAdWYWN7O5wDzgaY9rypdngHlmNtfMYmQnnKzzuKZiWQdcn/v4euBQLe2yZNkm1TeAV51zXxr1km/v28waci0szKwSWA5swcf3HHSB3hHDzK4Gvgo0AHuBF5xzl+Re+yvgj8jOyLrFOfeYZ4XmmZl9GPgyEAbud87d7XFJeWdmDwHLyB5TsQv4HPBD4BFgDvA2cK1z7uDJGmXLzC4A/j/wMpDJPf2XZMe1fHnfZraQ7ESLMNl/hD/inFttZvX49J6DLtChJSIi5UXdgyIiUjYUWiIiUjYUWiIiUjYUWiIiUjYUWiIiUjYUWuJLZubM7FujHkfMrNPM/jP3+MrD7XBvZgvM7IXcnz1m9kbu4yeLUb+IjE1T3sWXzKwf2Aqc75wbNLPLgL8H2p1zV4zzWg8A/+mcezT/lYrIeKilJX72GHB57uOVwEMjL5jZDWZ2b+7jB8zs/5jZr8xsu5ldc6QLm9kduXOcXjKzu3LPnWhmm3JneW02s2+a2SW5674+cmabma0xswdzZ19tNbM/yvudi/iUQkv87GGy23FVAAvJ7gxxKDOBC4ArgHsOd9HcjiJzgHPJnuF0vpmdn3v5JOALwILc97zGOXc+cCcHnum0gOyZZh8AVpvZ9PHdmkgwKbTEt5xzLwHHk21l/fgIn/5D51zGOfcKcKQA+V9kA+d54DngRGB+7rVtzrlXnHMZ4BVgZAzs5Vwto7/fkHOug+whpIuP6qZEAs73u7xL4K0j2/JZBtQf5vOGR318pLM8DFjjnPvGAU+anXjQdTKjHmc48P+3gweTNbgschTU0hK/ux9Y7Zx7OY/X/AnwSTOrBjCzJjObNs5rXJU7RWAacCGwMY/1ifiWWlria865duAreb7mj82sFXgqd8BiH/CxcV7mGbITRZqBzznnduWzRhG/0pR3kSIzszVAl3Puy17XIlJu1D0oIiJlQy0tEREpG2ppiYhI2VBoiYhI2VBoiYhI2VBoiYhI2VBoiYhI2fgfvA5f8zCL2+QAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(df.loc[:, 'MinTemp'],df.loc[:,'Temp9am']\n",
    "          ,   kind='regg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x1aad3392ba8>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(df.loc[:, 'MaxTemp'],df.loc[:,'Temp9am']\n",
    "          ,   kind='regg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZgAAAEGCAYAAABYV4NmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAAUIklEQVR4nO3df5Be1X3f8ffHko3BFMIPgbFEIzxonABpilExtWOPXSVBSd1AUsgoKUVNNFWHoUncxk2hf4RMXGVM44SaNNAhASOIY9AoTpDboQ4RJSQTfngxpOKHCXJwYYOMZIsh2C3Ygm//eM7Wj5bd1YPYs4+Q3q+ZZ557v/eec89lNPvh3Hv3bqoKSZLm25vGPQBJ0sHJgJEkdWHASJK6MGAkSV0YMJKkLhaPewAHiuOPP76WL18+7mFI0hvKAw888LWqWjLTNgOmWb58ORMTE+MehiS9oST537Nt8xKZJKkLA0aS1IUBI0nqwoCRJHVhwEiSujBgJEldGDCSpC4MGElSFwaMJKkLf5N/Hp31724a9xB0AHrg1y8e9xCksXAGI0nqwoCRJHVhwEiSujBgJEldGDCSpC4MGElSFwaMJKkLA0aS1IUBI0nqwoCRJHVhwEiSujBgJEldGDCSpC4MGElSFwaMJKmLbgGT5IYkO5M8PFQ7NskdSZ5o38cMbbs8yfYkjyc5d6h+VpJtbdvVSdLqhyW5tdXvS7J8qM3adownkqztdY6SpNn1nMHcCKyeVrsM2FpVK4CtbZ0kpwFrgNNbm2uSLGptrgXWAyvaZ6rPdcBzVXUqcBVwZevrWOAK4D3A2cAVw0EmSVoY3QKmqu4Gdk8rnwdsbMsbgfOH6rdU1UtV9SSwHTg7yUnAUVV1T1UVcNO0NlN9bQZWtdnNucAdVbW7qp4D7uDVQSdJ6myh78GcWFU7ANr3Ca2+FHh6aL/JVlvalqfX92pTVXuA54Hj5ujrVZKsTzKRZGLXrl2v47QkSdMdKDf5M0Ot5qjvb5u9i1XXVdXKqlq5ZMmSkQYqSRrNQgfMs+2yF+17Z6tPAicP7bcMeKbVl81Q36tNksXA0Qwuyc3WlyRpAS10wGwBpp7qWgvcNlRf054MO4XBzfz722W0F5Kc0+6vXDytzVRfFwB3tvs0nwd+OMkx7eb+D7eaJGkBLe7VcZLPAB8Ejk8yyeDJro8Dm5KsA54CLgSoqkeSbAIeBfYAl1bVy62rSxg8kXY4cHv7AFwP3JxkO4OZy5rW1+4kHwO+0Pb71aqa/rCBJKmzbgFTVT81y6ZVs+y/AdgwQ30COGOG+ou0gJph2w3ADSMPVpI07w6Um/ySpIOMASNJ6sKAkSR1YcBIkrowYCRJXRgwkqQuDBhJUhcGjCSpCwNGktSFASNJ6sKAkSR1YcBIkrowYCRJXRgwkqQuDBhJUhcGjCSpCwNGktSFASNJ6sKAkSR1YcBIkrowYCRJXRgwkqQuDBhJUhcGjCSpCwNGktSFASNJ6sKAkSR1YcBIkroYS8Ak+TdJHknycJLPJHlrkmOT3JHkifZ9zND+lyfZnuTxJOcO1c9Ksq1tuzpJWv2wJLe2+n1Jli/8WUrSoW3BAybJUuDngZVVdQawCFgDXAZsraoVwNa2TpLT2vbTgdXANUkWte6uBdYDK9pndauvA56rqlOBq4ArF+DUJElDxnWJbDFweJLFwBHAM8B5wMa2fSNwfls+D7ilql6qqieB7cDZSU4Cjqqqe6qqgJumtZnqazOwamp2I0laGAseMFX1N8AngKeAHcDzVfXHwIlVtaPtswM4oTVZCjw91MVkqy1ty9Pre7Wpqj3A88BxPc5HkjSzcVwiO4bBDOMU4B3A25JcNFeTGWo1R32uNtPHsj7JRJKJXbt2zT1wSdJrMo5LZD8IPFlVu6rq28BngfcCz7bLXrTvnW3/SeDkofbLGFxSm2zL0+t7tWmX4Y4Gdk8fSFVdV1Urq2rlkiVL5un0JEkwnoB5CjgnyRHtvsgq4DFgC7C27bMWuK0tbwHWtCfDTmFwM//+dhnthSTntH4untZmqq8LgDvbfRpJ0gJZvNAHrKr7kmwGvgjsAR4ErgOOBDYlWccghC5s+z+SZBPwaNv/0qp6uXV3CXAjcDhwe/sAXA/cnGQ7g5nLmgU4NUnSkAUPGICqugK4Ylr5JQazmZn23wBsmKE+AZwxQ/1FWkBJksbD3+SXJHVhwEiSujBgJEldGDCSpC4MGElSFwaMJKkLA0aS1IUBI0nqwoCRJHVhwEiSujBgJEldGDCSpC4MGElSFwaMJKkLA0aS1IUBI0nqwoCRJHVhwEiSujBgJEldGDCSpC4MGElSFwaMJKkLA0aS1IUBI0nqwoCRJHUxUsAk2TpKTZKkKYvn2pjkrcARwPFJjgHSNh0FvKPz2CRJb2BzBgzwr4CPMAiTB/hOwPwt8NsdxyVJeoObM2Cq6pPAJ5P8XFX91gKNSZJ0EBjpHkxV/VaS9yb56SQXT33296BJvivJ5iRfSvJYkn+Y5NgkdyR5on0fM7T/5Um2J3k8yblD9bOSbGvbrk6SVj8sya2tfl+S5fs7VknS/hn1Jv/NwCeAHwD+QfusfB3H/STwP6rqe4DvBx4DLgO2VtUKYGtbJ8lpwBrgdGA1cE2SRa2fa4H1wIr2Wd3q64DnqupU4CrgytcxVknSftjXPZgpK4HTqqpe7wGTHAV8APgXAFX1LeBbSc4DPth22wjcBfx74Dzglqp6CXgyyXbg7CRfAY6qqntavzcB5wO3tza/0vraDPyXJJmP8UuSRjPq78E8DLx9no75TmAX8KkkDyb53SRvA06sqh0A7fuEtv9S4Omh9pOttrQtT6/v1aaq9gDPA8dNH0iS9Ukmkkzs2rVrnk5PkgSjB8zxwKNJPp9ky9RnP4+5GHg3cG1VnQl8k3Y5bBaZoVZz1Odqs3eh6rqqWllVK5csWTL3qCVJr8mol8h+ZR6POQlMVtV9bX0zg4B5NslJVbUjyUnAzqH9Tx5qvwx4ptWXzVAfbjOZZDFwNLB7Hs9BkrQPoz5F9qczffbngFX1VeDpJO9qpVXAo8AWYG2rrQVua8tbgDXtybBTGNzMv79dRnshyTnt6bGLp7WZ6usC4E7vv0jSwhppBpPkBb5ziektwJuBb1bVUft53J8DPp3kLcBfAz/DIOw2JVkHPAVcCFBVjyTZxCCE9gCXVtXLrZ9LgBuBwxnc3L+91a8Hbm4PBOxm8BSaJGkBjRQwVfV3hteTnA+cvb8HraqHmPkx51Wz7L8B2DBDfQI4Y4b6i7SAkiSNx369Tbmq/gj4R/M8FknSQWTUS2Q/MbT6JgazD+9pSJJmNepTZP9kaHkP8BUGv8woSdKMRr0H8zO9ByJJOriM+i6yZUn+MMnOJM8m+YMky/bdUpJ0qBr1Jv+nGPxuyTsYvIblc60mSdKMRg2YJVX1qara0z43Ar5bRZI0q1ED5mtJLkqyqH0uAr7ec2CSpDe2UQPmZ4GfBL4K7GDw+hVv/EuSZjXqY8ofA9ZW1XMASY5l8AfIfrbXwCRJb2yjzmD+3lS4AFTVbuDMPkOSJB0MRg2YNyU5ZmqlzWBGnf1Ikg5Bo4bEbwB/kWQzg1fE/CQzvHxSkqQpo/4m/01JJhi84DLAT1TVo11HJkl6Qxv5MlcLFENFkjSS/XpdvyRJ+2LASJK6MGAkSV0YMJKkLgwYSVIXBowkqQsDRpLUhQEjSerCgJEkdWHASJK6MGAkSV0YMJKkLgwYSVIXBowkqYuxBUySRUkeTPLf2vqxSe5I8kT7Hv4Lmpcn2Z7k8STnDtXPSrKtbbs6SVr9sCS3tvp9SZYv9PlJ0qFunDOYXwAeG1q/DNhaVSuArW2dJKcBa4DTgdXANUkWtTbXAuuBFe2zutXXAc9V1anAVcCVfU9FkjTdWAImyTLgHwO/O1Q+D9jYljcC5w/Vb6mql6rqSWA7cHaSk4CjquqeqirgpmltpvraDKyamt1IkhbGuGYw/xn4JeCVodqJVbUDoH2f0OpLgaeH9ptstaVteXp9rzZVtQd4Hjhu+iCSrE8ykWRi165dr/ecJElDFjxgknwY2FlVD4zaZIZazVGfq83eharrqmplVa1csmTJiMORJI1i8RiO+T7gx5L8KPBW4Kgkvwc8m+SkqtrRLn/tbPtPAicPtV8GPNPqy2aoD7eZTLIYOBrY3euEJEmvtuAzmKq6vKqWVdVyBjfv76yqi4AtwNq221rgtra8BVjTngw7hcHN/PvbZbQXkpzT7q9cPK3NVF8XtGO8agYjSepnHDOY2Xwc2JRkHfAUcCFAVT2SZBPwKLAHuLSqXm5tLgFuBA4Hbm8fgOuBm5NsZzBzWbNQJyFJGhhrwFTVXcBdbfnrwKpZ9tsAbJihPgGcMUP9RVpASZLGw9/klyR1YcBIkrowYCRJXRgwkqQuDBhJUhcGjCSpCwNGktSFASNJ6sKAkSR1cSC9KkZSR0/96veNewg6AP3dX97WrW9nMJKkLgwYSVIXBowkqQsDRpLUhQEjSerCgJEkdWHASJK6MGAkSV0YMJKkLgwYSVIXBowkqQsDRpLUhQEjSerCgJEkdWHASJK6MGAkSV0YMJKkLgwYSVIXCx4wSU5O8j+TPJbkkSS/0OrHJrkjyRPt+5ihNpcn2Z7k8STnDtXPSrKtbbs6SVr9sCS3tvp9SZYv9HlK0qFuHDOYPcAvVtX3AucAlyY5DbgM2FpVK4CtbZ22bQ1wOrAauCbJotbXtcB6YEX7rG71dcBzVXUqcBVw5UKcmCTpOxY8YKpqR1V9sS2/ADwGLAXOAza23TYC57fl84BbquqlqnoS2A6cneQk4KiquqeqCrhpWpupvjYDq6ZmN5KkhTHWezDt0tWZwH3AiVW1AwYhBJzQdlsKPD3UbLLVlrbl6fW92lTVHuB54LgZjr8+yUSSiV27ds3PSUmSgDEGTJIjgT8APlJVfzvXrjPUao76XG32LlRdV1Urq2rlkiVL9jVkSdJrMJaASfJmBuHy6ar6bCs/2y570b53tvokcPJQ82XAM62+bIb6Xm2SLAaOBnbP/5lIkmYzjqfIAlwPPFZVvzm0aQuwti2vBW4bqq9pT4adwuBm/v3tMtoLSc5pfV48rc1UXxcAd7b7NJKkBbJ4DMd8H/DPgW1JHmq1/wB8HNiUZB3wFHAhQFU9kmQT8CiDJ9AuraqXW7tLgBuBw4Hb2wcGAXZzku0MZi5rep+UJGlvCx4wVfXnzHyPBGDVLG02ABtmqE8AZ8xQf5EWUJKk8fA3+SVJXRgwkqQuDBhJUhcGjCSpCwNGktSFASNJ6sKAkSR1YcBIkrowYCRJXRgwkqQuDBhJUhcGjCSpCwNGktSFASNJ6sKAkSR1YcBIkrowYCRJXRgwkqQuDBhJUhcGjCSpCwNGktSFASNJ6sKAkSR1YcBIkrowYCRJXRgwkqQuDBhJUhcGjCSpCwNGktTFQR0wSVYneTzJ9iSXjXs8knQoOWgDJski4LeBHwFOA34qyWnjHZUkHToO2oABzga2V9VfV9W3gFuA88Y8Jkk6ZCwe9wA6Wgo8PbQ+CbxneIck64H1bfUbSR5foLEdCo4HvjbuQRwI8om14x6CXs1/n1OuyOvt4btn23AwB8xM/9Vqr5Wq64DrFmY4h5YkE1W1ctzjkGbiv8+FcTBfIpsETh5aXwY8M6axSNIh52AOmC8AK5KckuQtwBpgy5jHJEmHjIP2EllV7Unyr4HPA4uAG6rqkTEP61DipUcdyPz3uQBSVfveS5Kk1+hgvkQmSRojA0aS1IUBo3nnK3p0IEpyQ5KdSR4e91gOFQaM5pWv6NEB7EZg9bgHcSgxYDTffEWPDkhVdTewe9zjOJQYMJpvM72iZ+mYxiJpjAwYzbd9vqJH0qHBgNF88xU9kgADRvPPV/RIAgwYzbOq2gNMvaLnMWCTr+jRgSDJZ4B7gHclmUyybtxjOtj5qhhJUhfOYCRJXRgwkqQuDBhJUhcGjCSpCwNGktSFASPNIsnLSR5K8nCSzyX5rhHa/MU+tv9h63N7kufb8kNJ3jt/I5cODD6mLM0iyTeq6si2vBH4q6raME99fxD4aFV9eD76G/GYi9vvKU2th8HPgFcWagw6tDiDkUZzD+2lnUmOTLI1yReTbEvy/98WneQb7fuDSe5KsjnJl5J8uv1An1WSH2qzmW1Jfqe9CYH2S4Ebktyb5AtJ3p3kj5N8Ocm/bPu8KclvttnWtiQXtPoPJvmTJLcADyY5te3zX4EvAicluai1eTjJr7V2P53kP7XlX0zyV235XUnumtf/sjpoGTDSPrS/cbOK77zy5kXgx6vq3cCHgN+YJTzOBD7C4O/ivBN43xzHOAK4AfinVfV9wBHA+qFdvlJV5wD3AtcDPw68F/hY235hO873Az8EXJXkhLbtHOCXWr+0/a6vqjMZvJz0P7bzOBN4X5IPA3cD72/7vx94PsnbgR8A/my285CGGTDS7A5P8hDwdeBY4I5WD/BrSf4X8CcMZjYnztD+/qqabJegHgKWz3Gs7wWeqKovt/WbgA8MbZ8Kt23AvVX1zap6FnglyZEMfvD/flW9XFVfBf4cWNna3FNVTw319eWq+kJbfg9wZ1V9raq+Dfw+8IGqmgSOS/I24O3AJgZB834MGI3IgJFm93+r6u8D3w28Bbi01f8ZsAQ4q21/FnjrDO1fGlp+GVg8x7HmvHw21Ncr0/p9pfU7V/tvzrE+V7t7gXXAowxC5f0MZkNzPsggTTFgpH2oqueBnwc+muTNwNHAzqr6dpIPMQig1+tRBm+hfmdbvwj409fQ/m5gTZJFSU5kcDluYoR29wIfSnJcksUM3n49ddy7gY+27weAc4EXquobr2FcOoTN9X9UkpqqejDJXzL4Afxp4HNJJhhc+vrSPPT/f9rbfT/b7vncB/zOa+hiM4PZxV8y+ANv/7aqdu7juQKqajLJLwN3MZjNfK6q/nvb/GcM/rbP3S1M/4bB+Uoj8TFlSVIXXiKTJHVhwEiSujBgJEldGDCSpC4MGElSFwaMJKkLA0aS1MX/Aw07jd9cnL1LAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax=sns.countplot(df['RainTomorrow'],label='count of values')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.reset_index(drop=True,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols=np.array(df.columns)\n",
    "ratio = [df[c].isna().sum()/142193 for c in cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.0,\n",
       " 0.0,\n",
       " 0.004479826714395224,\n",
       " 0.002264527789694289,\n",
       " 0.009887969168665123,\n",
       " 0.0,\n",
       " 0.06519308264119894,\n",
       " 0.0,\n",
       " 0.0,\n",
       " 0.00948007285872019,\n",
       " 0.018495987847503042,\n",
       " 0.01247600092831574,\n",
       " 0.025388028946572615,\n",
       " 0.09855618771669492,\n",
       " 0.09832410878172625,\n",
       " 0.0,\n",
       " 1.4065389998101173e-05,\n",
       " 0.00635755627914173,\n",
       " 0.0191711265674119,\n",
       " 0.0,\n",
       " 0.0]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 119588 entries, 2 to 142192\n",
      "Data columns (total 21 columns):\n",
      "Date             119588 non-null datetime64[ns]\n",
      "Location         119588 non-null object\n",
      "MinTemp          119588 non-null float64\n",
      "MaxTemp          119588 non-null float64\n",
      "Rainfall         119588 non-null float64\n",
      "WindGustDir      119588 non-null int8\n",
      "WindGustSpeed    119588 non-null float64\n",
      "WindDir9am       119588 non-null int8\n",
      "WindDir3pm       119588 non-null int8\n",
      "WindSpeed9am     119588 non-null float64\n",
      "WindSpeed3pm     119588 non-null float64\n",
      "Humidity9am      119588 non-null float64\n",
      "Humidity3pm      119588 non-null float64\n",
      "Pressure9am      119588 non-null float64\n",
      "Pressure3pm      119588 non-null float64\n",
      "Cloud9am         119588 non-null float64\n",
      "Cloud3pm         119588 non-null float64\n",
      "Temp9am          119588 non-null float64\n",
      "Temp3pm          119588 non-null float64\n",
      "RainToday        119588 non-null int8\n",
      "RainTomorrow     119588 non-null int8\n",
      "dtypes: datetime64[ns](1), float64(14), int8(5), object(1)\n",
      "memory usage: 16.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['Temp9am', 'Temp3pm','Pressure9am',\n",
    "       'Pressure3pm','MaxTemp','Date'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Location', 'MinTemp', 'Rainfall', 'WindGustDir', 'WindGustSpeed',\n",
       "       'WindDir9am', 'WindDir3pm', 'WindSpeed9am', 'WindSpeed3pm',\n",
       "       'Humidity9am', 'Humidity3pm', 'Cloud9am', 'Cloud3pm', 'RainToday',\n",
       "       'RainTomorrow'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Location']=df['Location'].astype('category')\n",
    "df['Location']=df['Location'].cat.codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['Location','Rainfall','WindDir9am','WindDir3pm','Humidity9am', 'WindSpeed3pm'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['WindGustDir','Cloud9am'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1aadf0dd438>"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x1296 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f,ax=plt.subplots(figsize=(18,18))\n",
    "sns.heatmap(df.corr(),annot=True,linewidth=.5,ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "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>MinTemp</th>\n",
       "      <th>WindGustSpeed</th>\n",
       "      <th>WindSpeed9am</th>\n",
       "      <th>Humidity3pm</th>\n",
       "      <th>Cloud3pm</th>\n",
       "      <th>RainToday</th>\n",
       "      <th>RainTomorrow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>12.9</td>\n",
       "      <td>46.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>9.2</td>\n",
       "      <td>24.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>17.5</td>\n",
       "      <td>41.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>14.6</td>\n",
       "      <td>56.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>14.3</td>\n",
       "      <td>50.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   MinTemp  WindGustSpeed  WindSpeed9am  Humidity3pm  Cloud3pm  RainToday  \\\n",
       "2     12.9           46.0          19.0         30.0       2.0          0   \n",
       "3      9.2           24.0          11.0         16.0       2.0          0   \n",
       "4     17.5           41.0           7.0         33.0       8.0          0   \n",
       "5     14.6           56.0          19.0         23.0       8.0          0   \n",
       "6     14.3           50.0          20.0         19.0       8.0          0   \n",
       "\n",
       "   RainTomorrow  \n",
       "2             0  \n",
       "3             0  \n",
       "4             0  \n",
       "5             0  \n",
       "6             0  "
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "y=df['RainTomorrow']\n",
    "x=df.drop('RainTomorrow',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=42,stratify=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1aadf888860>"
      ]
     },
     "execution_count": 98,
     "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(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn import preprocessing\n",
    "from sklearn import metrics\n",
    "res_knn=[]\n",
    "Ks =15\n",
    "mean_acc = np.zeros((Ks-1))\n",
    "std_acc = np.zeros((Ks-1))\n",
    "for n in range(1,Ks): \n",
    "    #Train Model and Predict  \n",
    "    neigh = KNeighborsClassifier(n_neighbors = n).fit(X_train,y_train)\n",
    "    yhat=neigh.predict(X_test)\n",
    "    res_knn.append(neigh)\n",
    "    mean_acc[n-1] = metrics.accuracy_score(y_test, yhat)\n",
    "    std_acc[n-1]=np.std(yhat==y_test)/np.sqrt(yhat.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.78351033, 0.82431641, 0.81938289, 0.83334727, 0.83485241,\n",
       "       0.84087298, 0.84212727, 0.84463584, 0.84421774, 0.84580651,\n",
       "       0.84530479, 0.84680993, 0.84639184, 0.84697717])"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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4xz8yfvx4jjvuOC6++GIAjj/+eH71q19RVlbGBRdc0OJIEW+88QYXX3wxfr+fQYMGcdpppyXoqLuevcFwY7FCJo4XF6285lNe3ryAhZtf4I0tr7I3VEuPQA9OHno6s4/5AacPn8aheYMJRoKNVV01wRr2xpQ2u79r3IqwqGU1oRr2Bmsoq9vSpEpsX3hfi/EcXjCc8YUT+eJRFzO+cCLjDpnIoLwh1jVmOsUSVAq88847QOvXoObPn9/YvTdz5kyWLVvGoEGDeOGFF2JfqtFFF13ElVde2fj8448/xufzUV5ejuM4+Hw+LrnkEo499lief/55pk2bxl//+ldGjBjR7LUO9pNIxFF27wsRisR3n0k6OOqwonwZCzfPZ+Gm+azYvhSAwfmH8ZWxX+WM4TM44bCT3SqvKNn+bLL9/eib2y8hcYSdMHsbkliwhrrwPob1GWFdaiYpLEGlmaqyYsWKxlbV3/72t1a3Xb9+fWMr6Pnnn298HA6HmTlzJo888ggPPPAAv//977nhhhvYtGkTI0aM4Nprr2XTpk2sWLGCSZMmNY5uDnDyySfz5z//ma9//ets376dV199lUsuiZ22q/uqC0XYsy+Uka2l2mANb2xZxMLN83l50wK2792GT3yUHHosPzrxF5wxYgZH9R+X0i8YAV/ArvF0kAABvw+/Txpny/VmWnenYY9als4YG8ZabJwzy7d/evkmE0Z6v5PpoEtQiSoLT5QlS5YwefLkuE4yd999Ny+//DJZWVn07du3sXvvtttu46STTuKkk06iuLiYo48+ms9//vM8++yzPPTQQ2RlZTFw4EBuueUW+vXrx4knnsj48eOZMWMGd9xxB4sWLWLChAkcccQRnHLKKck+5IzgOEp1XZi6cCTdoTTx0e4PWbjpBV7ePJ//K3uDYCRI75wCTj38DM4YcRanDjuD/j0GpDtM0wa/Twj4hIDf5/72HsdLtfXk1fDYUW87vGUx+0DTCRzF15Bk9ieW6Oepmp24PWy6jTT75S9/yahRo7jooovSHUrcMv0zbUt9OMKefeG0V9yB22W2+JP/NnbdfbBzLQCj+h7B50bM4IzhZ3HMoOOtsCBDiUDA5yPgF7J8busoyy8Hfbd5e9l0Gxnq5ptvTncIBw1VpaY+zN5geltNu+p28tqHC1m4eT6LNr9EVf0uAr4Axw8+ia9OmMnnhs9gRN9RaY0xUwhua8TvE1S91gReC0FTO6663+cmoYBfvETkddeZpLEEZQ4K4YjD7n2hlI0E3jBSwsZdG9i0az0bq9azyXu8qWoDjjr071HItJFnc8aIGZwy9PSE3QvUVcW2RgJ+t2vsQK2Rhm4tR70ur6husOhkpk7TbjHnANd7RIh6//jiMMlxUCQoVbV/XAnSFbuE9wbD1NSFk/Jte3ddVWPy2bhrfWMC2rhrPXtDtY3b5fpzGd53FEf2H8t5R36Z04edSfHAEnwS/3WJ7sQnbldYwzWajrZGpOFiPR3/+25MWN4MxJl4LeZg1e0TVG5uLpWVlfTv39+SVCepKpWVleTmZlahSWscr3w82Mny8X3hfXxYtclNPrvWs7HKaxXt2tBkuB6f+Bjaexgj+o7muMEnMqLvaEb2Gc2IvqMYlD/koE1G0V1jDa2jTEoCIoK/Ydpgk1G6fYIaMmQIZWVlVFRUtL2xaVNubi5DhgxJdxhtqgtF2FMXalfJ7ifVZbxfucZLPl6rqGo9H+/ZSvRtu0W9BjKi72hmjDyHEX1HMbLvaEb0Hc3hBcObTBaXDgLkBPxkB3z4fDSp+oru9lIFmjzf3+3V0Ws7DWXUDV1iAZ/PCgZMpyQ1QYnIdOAuwA/8VVVvj1lfADwEDPVi+Z2q/i1qvR9YDHysqmd3JIasrCyGDx/ewSMwXY2qsqcuTF0o/kKIYCTI7W//jHuW/KExEeVn92Zk39EcO+gERowbzci+oxjhtYYybRRrnwg5WT5yAj6y/b6EJITokuXYazZNSp6hQ2XUxsQjaQnKSy5zgDOAMuA9EXlOVddEbXYVsEZVzxGRQmCdiDysqg0DiM0G1gIH99VjE5eQVwjRnrHy1u9cx3fmX8bK7aV8dfzlfHnsJYzoO5oBPQoz+pt/lt9NSDkBX1ISQ8O1He9Zwl/fmHgkswV1DLBBVTcBiMhjwHlAdIJSIF/cM0EesBMIe9sPAT4P/Ao48DDf5qBXWx+mtj7+QghV5cGV9/HT139Aj0BP5p37ONNHnpPUGDtDBHL8fnKy3FZSJl3DMSZZkpmgBgNbo56XAcfGbHM38BzwCZAPXKiqDVe0/wD8wFveKhG5ArgCYOjQoZ2P2nQpHRlHb8feCr638Du8uOnfnDL0dP7ftL9QlHdoEqPsGL9P3G67BHbdGdOVJDNBtfTXFPsFdxpQCpwGjAQWisibwMnAdlVdIiKfPdCbqOq9wL3gjiTR2aBN19GRcfRe++hlrl3wLarqd/LzU37DtyZfnVHVdcnuujOmK0lmgioDDot6PgS3pRRtJnC7ujfXbBCRzcBRwInAuSJyFpAL9BaRh1T1q0mM13QRHRlHry5cx21v/YR7l93NEf3G8OgF/2Rc4cQkRhkf67ozpnXJTFDvAaNFZDjwMXAREDtM9hbgdOBNESkCjgQ2qepNwE0AXgvqBktOBjo2jt77O9bwnfmXsWbHSi4vnsVPTrqt2bQUqdTQdddQDm6MaVnSEpSqhkXkauBF3DLz+1V1tYjM8tbPBW4F5onIStwuwR+q6o5kxWS6ro6Mo6eq3L98Lre+8SPycnrz4HlPc8aIGUmMcr+GYXsaxpELRP22a0nGxKfbj2Zuui5VbZxuvbY+3K5x9Cpqy7lu4Sxe2byA04ZN464z/0xhr6KExieAr0ny8TUmIeuqMyZ+Npq5yWgRRwlF3OnWwxEl7Dgdnnb95c0LuO6lb1Ndv5tfnfp7Lp80q1OtFp94ScjfPBkZY5LHEpRJKVUl5CWgcFQySkRDfl94H7e++SPuL53LmAHjeeKLLzBmwLi492+YQqEhCVmXnDHpZQnKJEV091xDyygU0aRNErimYiVXzr+MdZVr+PaUa7jpxF+QG4h/UNue2X7ycgKWjIzJIJagTKclsnuuvRx1+OuyOfzyrZvpk9OPR89/jlOHnRH3/j4RevcIkBPwJzFKY0xHWIIyHeY4SmVtMG1Tp5fXfMrsl67gtY9eZtqIs7nzjD8xoGdh3PvnBHz0zs2yggZjMpQlKNNhNcH23Y+USC9u/DfXL7ySfaFa7jj9j3xtwjfj7p4TIC83QM9s++dvTCazv1DTIcGww7523JOUKHtDe/nZGzfywIq/ML5wEn+aMY8j+h8V9/4Bn1DQI8uGETKmC7AEZTqkui6U8vdcub2U78y/zJ0iY+r1/PCEn5ITyIl7/x7ZfvKtEMKYLsMSlGm3vcH23TTbWY46zF1yF79++6f07zGAJ774AicNPTXu/UWgoEeWFUIY08VYgjLt4jjukEPJoqpsr93G2srVrKlYydodqyktX8z6nes4a9S5/O5zf6Jfj/5xv16230dBDyuEMKYrsgRl2qW6LpyQm2rBvZ60rnINa3esYu2OVazZsYq1O1azc9/+4RgH9jqUMYUTuLrkBr4y9lIrhDDmIGJ/vSZu9eFIu6a4aOCow0e7N7tJqGJVY0LaXLWRhrulegR6MmbAOGaMPIcxA8Z7P+Pa1Vpq4PcKIbKsEMKYLs0SlImLqjsHU1t27qtk7Y7VjUlo7Y5VvF+5hr2hWgAEYXifkYwZMJ4vjrmYMQPGMXbABIYWDEvIxIFWCGFM92EJysSlNhghElMYUReu4/n1z7CmoXuuYiXbaj9tXN+vxwDGDhjPpeMvY8yACYwZMI4j+4+lZ1bPhMcnAr1zs8jNskIIY7oLS1CmTRFH2dtCYcTsF7/FPz94kmx/Nkf0G8NJQ09zW0SFExg7YDyFPYtS0pLJ9vvo3SPLRhc3ppuxBGXaVF0Xajau3hsfLeKfHzzJ7GN+wA3H3UyWPyvlcQnQKydArxz7Z2xMd2R/2eaA6kIR6sNOk2X14XpuevU6hhWM4Ppjb0pLcrJCCGO6P0tQplWtFUbMXXoXG3et5+EvPNuuKS0SJTfLT+9cK4QwpruzBGVaVVPffDDYLbs/4g/v3M5Zo87l9OHTUhqPFUIYc3CxBGVaFI60PBjsLa/fAAi/OOW3KY0nyxsRwgohjDl4WIIyLdpTF25WGLFw03wWbPw3Pz7xFwzpPTRp7y2415gCfh9ZfnfqdRtHz5iDT1ITlIhMB+4C/MBfVfX2mPUFwEPAUC+W36nq30QkF3gDyPGWP6mqP01mrGa/ulCEUKRpYcS+8D5+/Op3Gd3vSL49dXbC3ssnQpbfTUYBn7g/VvhgjCGJCUpE/MAc4AygDHhPRJ5T1TVRm10FrFHVc0SkEFgnIg8D9cBpqlojIlnAWyIyX1X/m6x4jctxlD0tTKXxx3d/x5Y9H/LklxaQ7c9u9+sKuEnI7yUhn9s6skIHY0xrktmCOgbYoKqbAETkMeA8IDpBKZAv7lkqD9gJhFVVgRpvmyzvJz1Ttx5kaoLNB4PdXLWROYvv5Pwjv8JnDjulzdfw+4Qsnw+/l4yy/D67dmSMabdkJqjBwNao52XAsTHb3A08B3wC5AMXqqoDjS2wJcAoYI6qvpPEWA0QaqEwQlX50avfJcufzc9Ovr3ZPlleqyjLt791ZK0iY0wiJLOzv6WzVGwraBpQCgwCioG7RaQ3gKpGVLUYGAIcIyLjW3wTkStEZLGILK6oqEhc9AehPfuad+29sOGfvPrhS/zg+J9QlHdok3UBn9CvVza9c7Poke0ny++z5GSMSZhkJqgy4LCo50NwW0rRZgJPq2sDsBk4KnoDVa0CXgOmt/QmqnqvqpaoaklhYWGiYj/otDRLbm2ollte/z5jB0zg8uIrm+2Tn5v6ESSMMQePZCao94DRIjJcRLKBi3C786JtAU4HEJEi4Ehgk4gUikgfb3kP4HPA+0mM9aDW2iy5//vfX/NxdRm3n/YHAr6mvcG5WX6yA1ZtZ4xJnqRdg1LVsIhcDbyIW2Z+v6quFpFZ3vq5wK3APBFZidsl+ENV3SEiE4G/e9ehfMDjqvrvZMV6sGtpltwPKt9n7tK7uHDs1zhm8AlN1olAvg3QaoxJMtFEzd+dAUpKSnTx4sXpDqNLqQ9HqNrb9NqTqvKlp2awavty3rpsOYU9D2myPt+mUjfGJJCILFHVktjldpY5iLU2GOyz6x7n7a2vc/tpdzVLTgGfWHIyxqSEXURIsYijLY5xlw57W5glt7p+Dz9740YmFU3haxO+2WwfK4wwxqSKfRVOsVDEYU9diL3BMPm5WWkrNIg4Sm0LhRG//c+tbK8tZ965T+D3NR3/zgojjDGpZGebFGsY4y7sKLv2Btm9N9SsFZMKLc2Su6ZiJfeV3sPXJnyTyQObdgdbYYQxJtUsQaVYONI0LdSFI1TW1FNTHyZVBSstzZLrqMONi2ZTkNuXmz7z82b75OUE8NlwRcaYFLKvxCkWO0o4uMNr1NaH2ReMkJ8bSOqEfK0VRjy+5mHe/eQ//P6MufTN7ddknRVGGGPSwVpQKRSOOAcc8dZRZfe+ELtqgy0mskRoaZbcqrpd3Prmjzn60OO4aNzXmu1jhRHGmHSwr8UpFIrE14UXjDjsrA3SI9tPXnbiutZamyX312//jF11ldx++r/xSdPvLFYYYYxJFzvzpFDIaV+raF8wwo7aevYGm3fJdUR1C7PkLtu2mAdW/IVvFl/JuMKJTdZZYYQxJp0sQaVQKNz+bjtVN7HsqKmnPtzx+6fqQhGCMd2GESfCjYtmU9iziO8f/5Nm+1hhhDEmnezrcYqoaqfKySOOUrU3RE4gQl5OoF3Torc2S+5DK+9neflS/jTjb/TOKWiyzgojjDHpZi2oFAk7igI/fvW7XPH8V/moanOHXqc+7F6fqq4LxV2W3tIsuTv2VvDrt3/KiYedwvlHXthsHyuMMMakmyWoFAlFHEKREI+smsdzHzzFyQ9M5va3f0ZtqLbdr6W4wxRV1NS3OWxSS7PkAvzyrZupCVVz26n/22ySQSuMMMZkAjsLpUgooqyrXMO+8D5+evKvOWf0Bfzh3d/wmbP3RBUAACAASURBVHkTeeb9f3ToJl1V2FMXorKmnmAr17dauufp3Y//j8dWP8C3p1zLkf3HNFlnhRHGmExhCSpFQhGH0vIlAEwfeQ53z7iff124iMKeA7ly/mV84fHPsXJ7aYdeu7Vhk/YGw83upwo7YW5cdB2D8gbz3WNvavZaVhhhjMkUlqBSoKFAonTbEvrk9GVYwQgAjh50PPMvfoM7P/cnNuxaz5kPn8ANC69ix96KDr1P9LBJkVZmyf1b6VzW7FjJLz77W3pl5zVZZ4URxphMYgkqBRrKu0vLl1A8cGqTaz5+n59LJ8zk/y5bwRVTruaxNQ9wwrwJ/GXp3YQizSvv2tIwbFJlTX2zwojymk/5zX9+wamHn8HnR32h2b5WGGGMySSWoFIgHFH2hfexdscqioumtrhNQW4ffn7KHSz66ntMGXg0P3n9+5z+0LG8/tErHXrPlq5o/fzNmwhG6vnVqb+3wghjTMazM1IKhCPK6u3LiWiE4oEtJ6gGR/Q/ikfPf46/n/sE9ZE6Lnz6bGY+95UOl6U3eGvr6zz9/j+4quR7jOg7qsk6K4wwxmQiS1ApEIwqkGitBRVNRJg28mxe//pSfnTiL3h9yyJOfmAyv377p9QGazrw/kFuWnQdQ3sP49pjvt9svRVGGGMyUZsJSkSuFpG+qQimO3IcxVG3QGJgr0MZmDco7n1zA7lce8z3efuy5Zw9+nzuevcOPvP3STz9/mPtKku/d+kfWb/zfX556p30CPRoss4KI4wxmSqeFtRA4D0ReVxEpkvsxQtzQLEFEh1xaN5g5sz4W2NZ+nfmz+S8x09nRfmyNvf9uHord/73NqaNOJszR5zVbH3vHlYYYYzJTG0mKFW9GRgN3AdcBqwXkdtEZGRb+3oJbZ2IbBCRG1tYXyAi/xKR5SKyWkRmessPE5FXRWStt3x2u48sQ4QdZU/9bjbs+oDiohJEILsd4+hFiy5L37hrA9MeObHNsvRbXvsBoNz62d81W5eb5Serg7EYY0yyxXV2Urc/aZv3Ewb6Ak+KyB2t7SMifmAOMAMYC1wsImNjNrsKWKOqk4DPAneKSLb3Ht9T1THAccBVLezbJYTCTmNLp3jgVLL9Pvr2yqagRxa+DjRGWytLv3fpH5uVpb/64UKe3/As1x17I0MLDm+yzgojjDGZLp5rUNeKyBLgDuBtYIKqXglMBb54gF2PATao6iZVDQKPAefFbKNAvtdtmAfsBMKq+qmqLgVQ1WpgLTC4fYeWGUKOw7LyxQBMKprSOAp5bpafAXnZ9MoJ0JE+09iy9Fte/wGnPXRMY1l6XbiOH716PSP7jmbWlOYN0PycLCuMMMZktHi+Qg8ALlDVj6IXqqojImcfYL/BwNao52XAsTHb3A08B3wC5AMXqmqTsXlEZBgwGXinpTcRkSuAKwCGDh3axqGkVsRRVKF02xKGFYygb24/svz7k4KIkJcToEeWn+q6EPUdmC+qoSz9pU3Pc8vrP+DCp89m+sizGZQ/hM1VG3n8gufJCeQ02SfL76NHtr/Tx2eMMckUTxffC7gtGwBEJF9EjgVQ1bUH2K+lr+expWfTgFJgEFAM3C0ivaPeKw94CrhOVfe09Caqeq+qlqhqSWFhYRyHkzqhFgoksnzNP3K/T+jTM5s+PbPwd6BVE1uW/saWV7m/dC7nHvFFTj78tGbb5+da154xJvPFk6DuAaJvvqn1lrWlDDgs6vkQ3JZStJnA0+raAGwGjgIQkSzc5PSwqj4dx/tlnFDEoaK2nI+rt1JcNBWfyAG71XICfvr3yiavg91+0WXpN534c2479X+bbdMj2wojjDFdQzxnKtGom268Lrh4voK/B4wWkeFe4cNFuN150bYApwOISBFwJLDJuyZ1H7BWVX8fx3tlpFBEWRZ1g2481XsiQq+cAAPycsjN6lg33KF5g5l9zA8Y0LNpi1IE8uyeJ2NMFxFPgtrkFUpkeT+zgU1t7aSqYeBq4EXcIofHVXW1iMwSkVneZrcCJ4jISuAV4IequgM4EfgacJqIlHo/zW/iyXDhiEPptiX4xMeEoskE/PG3i3w+oaBHFn17ZhNIUDGDFUYYY7qSeL5OzwL+H3Az7jWkV/CKEtqiqi/gXsOKXjY36vEnwJkt7PcWLV/D6jLCEQfFvf50RL8x9Mrq1a4E1SA74KN/Xg77ghGq60PNRiiPlxVGGGO6mjYTlKpux+2eM+0QiiiqSmn5ksYRHFoqkIhXj2w/OQEfNcFwm9O8t8QKI4wxXU2bZy0RyQW+CYwDchuWq+rlSYyryws5Dlv3bGHnvh0UF03F7ztwgUQ8fD6hd24WPbP8VNeFG4dRaosVRhhjuqJ4zloP4o7HNw14HbcarzqZQXUHobBDqXeD7uSBJZ1qPcUKtGM0CiuMMMZ0VfGcNUep6k+AWlX9O/B5YEJyw+raoqd4z/ZnM2bAeLICib+kFs9oFFYYYYzpquJJUA0DvFWJyHigABiWtIi6gVBEGwskxhVOJNufTSCBLahoDaNR9M/LISdmRlwrjDDGdGXxnDXv9eaDuhn3PqY1wG+SGlUXF3YcHHVYsX1Z4wSFWR2o4GuPlkajsMIIY0xXdsAzmIj4gD2qugt4AxiRkqi6uFBY2bDzA2qC1RQXlRDwCamaRisn4Ccnz099OGKFEcaYLu2AZzBv1IirUxRLtxFyoqZ4Hzi1cQTzVMoJWNeeMaZri+fMuVBEbvAmEezX8JP0yLoox/EKJMoX0ysrj1F9j0h6954xxnRH8VykaLjf6aqoZYp197Uo5HgjmG9bwsSiyfh9dg+SMcZ0RDwjSQxPRSDdRTiiBCNBVles4PLiKxFI2Fh6xhhzMIlnJImvt7RcVR9IfDhdXyjisHbHKuoj9RQXudefUlUgYYwx3Uk8XXxHRz3OxZ0eYylgCaoFoYh7gy5A8cCSDg0Qa4wxJr4uvmuin4tIAe7wRyaG4yiON0Bsv9z+DO19eEKHODLGmINJR86ee4HRiQ6kOwhGTfE+aeBURMQq+IwxpoPiuQb1L9yqPXAT2ljg8WQG1VWFHaU2VMu6yjVMH3mOWyBhFXzGGNMh8VyD+l3U4zDwkaqWJSmeLi0Udli1vRRHHSYXpecGXWOM6S7iSVBbgE9VtQ5ARHqIyDBV/TCpkXVBsSNIWPeeMcZ0XDxf8Z8AomfGi3jLTJRwxEHVvUF3cP4QDuk10G7QNcaYTojnDBpQ1WDDE+9xdvJC6prCjnuZrrR8SeMI5naDrjHGdFw8CapCRM5teCIi5wE7khdS1xSKOFTV7WJz1UaKi6YiYgUSxhjTGfGcQWcBPxKRLSKyBfgh8O14XlxEpovIOhHZICI3trC+QET+JSLLRWS1iMyMWne/iGwXkVXxHkw6hSLK8vKlgHuDrt3/ZIwxndPmWVRVN6rqcbjl5eNU9QRV3dDWfiLiB+YAM7x9LxaRsTGbXQWsUdVJwGeBO0WkoftwHjA93gNJt3Bkf4HExEMm2wgSxhjTSW0mKBG5TUT6qGqNqlaLSF8R+WUcr30MsEFVN3nXrR4DzovZRoF8cQerywN24payo6pveM8zXijiuFO8b1vMyL6jKcjtYwUSxhjTSfGcRWeoalXDE2923bPi2G8wsDXqeZm3LNrdwBjgE2AlMNubJDFuInKFiCwWkcUVFRXt2TVhwpHmBRKWoIwxpnPiOYv6RSSn4YmI9AByDrB946YtLNOY59OAUmAQUAzcLSK943jt/S+oeq+qlqhqSWFhYXt2TZhgxGFbzSd8WvNJY4GE3yr4jDGmU+JJUA8Br4jIN0XkcmAh8Y1kXgYcFvV8CG5LKdpM4Gl1bQA2A0fF8doZJfr6U/HAqWRb68kYYzotntHM7xCRFcDncFtFt6rqi3G89nvAaBEZDnwMXARcErPNFtzpO94UkSLgSGBTO+JPO1Ul7LhTbPjFz7jCSVZebowxCRDXmVRVF6jqDar6PaBGRObEsU8YuBp4EVgLPK6qq0VklojM8ja7FThBRFYCrwA/VNUdACLyKPAf4EgRKRORb7b76FIgFHX96agB4+iZ1dNu0DXGmASIZyw+RKQYuBi4ELcb7ul49lPVF4AXYpbNjXr8CXBmK/teHM97pFvYcVB174E6a5RbpGhdfMYY03mtJigROQK3W+5ioBL4ByCqemqKYusSQmHlo92b2VW3k+KiqfhE8FkLyhhjOu1ALaj3gTeBcxpuzBWR61MSVRfijmC+GLACCWOMSaQDnU2/CGwDXhWRv4jI6bRcOn7Qchwl4ijLti0h15/LUf3H2QgSxhiTIK0mKFV9RlUvxC37fg24HigSkXtEpMXrRgebkOPeU7y8fAnjDplIlj/LEpQxxiRIPGPx1arqw6p6Nu69TKVAs4FfD0ahiBJxIqzYXsrkohIAGyTWGGMSpF1nU1Xdqap/VtXTkhVQVxKOOHyw8332hmopHliC32cFEsYYkyj2db8TQhHdP4JE0VRrPRljTALZGbWDIo7iqDuCRH52b0b0HUVWwFpPxhiTKJagOigUcQskSssXM6loCj7xEbAWlDHGJIydUTsoFHGoD9ezpmJl1BQb1oIyxphEsQTVQeGIsnrHCkJOiOKBUwn4BHfeRWOMMYlgCaqDQo5D6bb9BRI2grkxxiSWnVU7IBxxUHVHMB/Q8xAG5x9m3XvGGJNglqA6IOx4U2xsW+LNoCs2xbsxxiSYnVU7IBhxqAlWs37n+26CApsDyhhjEswSVAeEI8qK7aUoSvHAqfitQMIYYxLOElQ7qSrhiEPpNm+KjaKpZAXsYzTGmESzM2s7hR1FcQskhvQeyoCehTbEkTHGJIGdWdupcQQJr0AC7AZdY4xJBktQ7RSKKJX7drBlz4f7CySsgs8YYxLOzqztFI44LC9fCsDkgUdbcjLGmCRJ6tlVRKaLyDoR2SAizSY5FJECEfmXiCwXkdUiMjPefdNBVQk77gjmgjCpaLJ17xljTJIkLUGJiB+YA8wAxgIXi8jYmM2uAtao6iTgs8CdIpId574pF4p4N+iWL2FUvyPJy863G3SNMSZJknl2PQbYoKqbVDUIPAacF7ONAvni3kSUB+wEwnHum3KhiIOqsmzb4sYCCbtB1xhjkiOZCWowsDXqeZm3LNrdwBjgE2AlMFtVnTj3TblwRPmkpoyKveUUD5yKiBVIGGNMsiTz7NpS00Jjnk8DSoFBQDFwt4j0jnNf901ErhCRxSKyuKKiojPxtikY2T+C+WSb4t0YY5IqmWfYMuCwqOdDcFtK0WYCT6trA7AZOCrOfQFQ1XtVtURVSwoLCxMWfCynYYr38iUEfAHGFk4kYAUSxhiTNMlMUO8Bo0VkuIhkAxcBz8VsswU4HUBEioAjgU1x7ptSIWf/DbpjB0wgN5BrBRLGGJNESTvDqmoYuBp4EVgLPK6qq0VklojM8ja7FThBRFYCrwA/VNUdre2brFjjEYoojjos3740agQJS1DGGJMsgWS+uKq+ALwQs2xu1ONPgDPj3TedwhGHzVUb2VO/u7FAwm8VfMYYkzTWBIhTMGYE82xrPRljTFLZWTYOEUcbp3jvEejBEf3HWHm5McYkmZ1l4xA9gvmEQ4oJ+AJ2g64xxiSZJag4hCIOYSfMqorljQUS1sVnjDHJZWfZOIQjyrrKNewL76N44FR8IvisBWWMMUllCSoOoagRJIqLSmwEc2OMSQFLUG0IR5zGKd4LcvowvM9Iu//JGGNSwM60bYieYqO4aCoiYkMcGWNMCliCakPIcdgX3sfaHasoHuiNIGGDxBpjTNLZmbYN4Yiyevtywk6Y4qKp+H1WIGGMMalgCeoAVJVwxKG03CuQGGhTbBhjTKrY2fYAwo66BRLbllDUayCH5g0mK2CtJ2OMSQVLUAfQOIJE+RKKi0oACFgLyhhjUsLOtgcQiih76nezYdcH+wskrILPGGNSwhLUAYQiDivKlwHuCOYBnyBiCcoYY1LBElQrVJWIoywrd6fYmFQ0xUYwN8aYFLIzbisab9DdtoTDC4bTr0d/694zxpgUsgTViqYFEjbFuzHGpJqdcVsRjigVe7fzcfVWd4p3sDmgjDEmhSxBtSIYM4K53wokjDEmpSxBtcBxFEeV0vIl+MTHxEOKyQrYR2WMMalkZ90WBKOuP43udxS9svNsiCNjjEmxpJ51RWS6iKwTkQ0icmML678vIqXezyoRiYhIP2/dbG/ZahG5Lplxxgo7iqpSui26QMK694wxJpWSlqBExA/MAWYAY4GLRWRs9Daq+ltVLVbVYuAm4HVV3Ski44FvAccAk4CzRWR0smKNFY44bN2zhcp9FUweWOIWSFgFnzHGpFQyz7rHABtUdZOqBoHHgPMOsP3FwKPe4zHAf1V1r6qGgdeB85MYaxPBiEOpd4NucdFUS07GGJMGyTzzDga2Rj0v85Y1IyI9genAU96iVcDJItLfW3cWcFgr+14hIotFZHFFRUWng444iqp7g262P5uxhROse88YY9IgmQmqpbO6trLtOcDbqroTQFXXAr8BFgILgOVAuKUdVfVeVS1R1ZLCwsJOBx19g+7YARPI9mfbDbrGGJMGyTzzltG01TME+KSVbS9if/ceAKp6n6pOUdWTgZ3A+qREGSMUcXDUYcX2ZRQPbJhiw1pQxhiTaslMUO8Bo0VkuIhk4yah52I3EpEC4BTgnzHLD/F+DwUuICaBJUsoomzY+QE1wWqKi6YiYgUSxhiTDoFkvbCqhkXkauBFwA/cr6qrRWSWt36ut+n5wEuqWhvzEk+JSH8gBFylqruSFWu0JlO8F9kU78YYky5JS1AAqvoC8ELMsrkxz+cB81rY96RkxtaScMRxp3gvX0zPrF6M7nckASuQMMaYtLDmQZToKTYmHjIZv89vBRLGGJMmdvaNEnIcgpEgqytWRE3xbh+RMcakg519o0Qiyvs7VlMfqW8skPBbBZ8xxqSFJagYDQUSk4tKyLbWkzHGpI2dgWOUli+hX25/hhYMs/JyY4xJIzsDxyjdtoRJRVMQEbtB1xhj0sgSVJTaYC3rKtc0FkhYF58xxqSPnYGjrNxeSkQjFBdNxSeCz1pQxhiTNpagoizdtn+KDRvB3Bhj0ssSVJRl25YwKG8wRXmH2v1PxhiTZnYWjrJs2+LG6082xJExxqSXJSjPrn272FS1keIid4oNGyTWGGPSy87CnsWf7L/+5PdZgYQxxqSbJSjPe5+8B8CkoinWejLGmAyQ1Ok2upLpo6bjI5eC3D5kBaz1ZIwx6WYJyjPl0CkM7z2eYMQhYC0oY4xJOzsTt8DugTLGmPSzBBUj4BNELEEZY0y6WYKKYSOYG2NMZrCzcQzr3jPGmMxgCSqGDXFkjDGZIalnYxGZLiLrRGSDiNzYwvrvi0ip97NKRCIi0s9bd72IrPaWPyoiucmMtYHNAWWMMZkhaQlKRPzAHGAGMBa4WETGRm+jqr9V1WJVLQZuAl5X1Z0iMhi4FihR1fGAH7goWbE2CPitQMIYYzJFMltQxwAbVHWTqgaBx4DzDrD9xcCjUc8DQA8RCQA9gU+SFqnHuveMMSZzJPOMPBjYGvW8zFvWjIj0BKYDTwGo6sfA74AtwKfAblV9qZV9rxCRxSKyuKKiolMBW4IyxpjMkcwzckt9ZdrKtucAb6vqTgAR6Yvb2hoODAJ6ichXW9pRVe9V1RJVLSksLOxUwH67/mSMMRkjmQmqDDgs6vkQWu+mu4im3XufAzaraoWqhoCngROSEqUxxpiMlMwE9R4wWkSGi0g2bhJ6LnYjESkATgH+GbV4C3CciPQUt2rhdGBtEmM1xhiTYZI2WKyqhkXkauBF3Cq8+1V1tYjM8tbP9TY9H3hJVWuj9n1HRJ4ElgJhYBlwb7JiNcYYk3lEtbXLQl1PSUmJLl68ON1hGGOMaQcRWaKqJbHLrWzNGGNMRrIEZYwxJiNZgjLGGJORLEEZY4zJSJagjDHGZCRLUMYYYzJStyozF5EK4KN0xxGHAcCOdAeRIN3pWKB7HY8dS+bqTseTiGM5XFWbjVXXrRJUVyEii1uq+e+KutOxQPc6HjuWzNWdjieZx2JdfMYYYzKSJShjjDEZyRJUenSncQW707FA9zoeO5bM1Z2OJ2nHYtegjDHGZCRrQRljjMlIlqCMMcZkJEtQKSIih4nIqyKyVkRWi8jsdMeUCCLiF5FlIvLvdMfSGSLSR0SeFJH3vf9Gx6c7po4Skeu9f2OrRORREclNd0ztISL3i8h2EVkVtayfiCwUkfXe777pjDFerRzLb71/ZytE5BkR6ZPOGNujpeOJWneDiKiIDEjU+1mCSp0w8D1VHQMcB1wlImPTHFMizKZ7zHZ8F7BAVY8CJtFFj0lEBgPXAiWqOh53stCL0htVu80DpscsuxF4RVVHA694z7uCeTQ/loXAeFWdCHwA3JTqoDphHs2PBxE5DDgDdzb0hLEElSKq+qmqLvUeV+OeAAenN6rOEZEhwOeBv6Y7ls4Qkd7AycB9AKoaVNWq9EbVKQGgh4gEgJ7AJ2mOp11U9Q1gZ8zi84C/e4//DnwhpUF1UEvHoqovqWrYe/pfYEjKA+ugVv7bAPwv8AMgoVV3lqDSQESGAZOBd9IbSaf9AfcfpZPuQDppBFAB/M3rrvyriPRKd1AdoaofA7/D/Sb7KbBbVV9Kb1QJUaSqn4L7ZQ84JM3xJMrlwPx0B9EZInIu8LGqLk/0a1uCSjERyQOeAq5T1T3pjqejRORsYLuqLkl3LAkQAKYA96jqZKCWrtOF1IR3beY8YDgwCOglIl9Nb1SmJSLyY9yu/4fTHUtHiUhP4MfALcl4fUtQKSQiWbjJ6WFVfTrd8XTSicC5IvIh8Bhwmog8lN6QOqwMKFPVhhbtk7gJqyv6HLBZVStUNQQ8DZyQ5pgSoVxEDgXwfm9PczydIiLfAM4GLtWufTPqSNwvQ8u9c8EQYKmIDEzEi1uCShEREdxrHGtV9ffpjqezVPUmVR2iqsNwL8IvUtUu+U1dVbcBW0XkSG/R6cCaNIbUGVuA40Skp/dv7nS6aMFHjOeAb3iPvwH8M42xdIqITAd+CJyrqnvTHU9nqOpKVT1EVYd554IyYIr3N9VplqBS50Tga7gtjVLv56x0B2UaXQM8LCIrgGLgtjTH0yFeK/BJYCmwEvdvvEsNqyMijwL/AY4UkTIR+SZwO3CGiKzHrRa7PZ0xxquVY7kbyAcWeueBuWkNsh1aOZ7kvV/Xbl0aY4zprqwFZYwxJiNZgjLGGJORLEEZY4zJSJagjDHGZCRLUMYYYzKSJSjT5XgjJt8Z9fwGEflZgl57noh8KRGv1cb7fNkbNf3VmOXDvOO7JmrZ3SJyWRuvN0tEvt7GNpeJyN2trKtpR/gdIiKHNox6LyKfjR4BX0R+KSIvikiOiDwmIqOTHY/JfJagTFdUD1yQyGH9E0FE/O3Y/JvAd1T11BbWbQdmi0h2vC+mqnNV9YF2vH/CeIPSxuO7wF9a2P/HuPcJfkFV64F7cMd4NAc5S1CmKwrj3nx6feyK2BZQQ8vA+8b+uog8LiIfiMjtInKpiLwrIitFZGTUy3xORN70tjvb29/vzePznjePz7ejXvdVEXkE98bY2Hgu9l5/lYj8xlt2C/AZYK6I/LaF46vAnVLiG7ErRGSkiCwQkSVejEd5y38mIjd4j4/2YvyPF3P03D2DvP3Xi8gdMa99p4gsFZFXRKTQW1YsIv+V/XMX9fWWvyYit4nI67jJ9MveMS4XkTdaOCaALwILYt7ze8BZwDmqus9b/Kb33yDexGe6KUtQpquaA1wqIgXt2GcS7vxVE3BH9ThCVY/BnS7kmqjthgGn4E4lMlfcCf++iTsy+NHA0cC3RGS4t/0xwI9Vtcn8XiIyCPgNcBru6BRHi8gXVPUXwGLccdi+30qstwPfa6FVdi9wjapOBW4A/tTCvn8DZqnq8UAkZl0xcKH3GVwo7jw+AL2Apao6BXgd+Km3/AHgh97cRSujlgP0UdVTVPVO3MFCp6nqJODc2IC8z2qX10JqcCIwC5ihqo1djKrqABtw/3uZg5glKNMleSPBP4A7OV+83vPm5aoHNgIN01CsxE1KDR5XVUdV1wObgKOAM4Gvi0gp7jQp/YGG6yTvqurmFt7vaOA1b+DWhlGrT47z+DYD7wKXNCwTdyT8E4AnvDj+DBwavZ+4s7Pmq+r/eYseiXnpV1R1t6rW4Y43eLi33AH+4T1+CPiMl/z7qOrr3vK/x8T/j6jHbwPzRORbuJMkxjoUt2UYbQMguJ9trO24o7Gbg5g1oU1X9gfcMef+FrUsjPfFS0QEiL6OE/3t3Yl67tD0byF2/C/FPZFeo6ovRq8Qkc/iTs/REmnzCA7sNtxx9Rq6zHxAlaoWH2Cftt4z+jOI0Po5IJ4x0BqPW1VnicixuK3OUhEpVtXKqG33AbFTz5cDlwKviEilqkYXjOR6+5iDmLWgTJelqjuBx3G73xp8CEz1Hp8HZHXgpb8sIj7vutQIYB3wInCluFOmICJHSNuTGr4DnCIiA7yuuotxu8/ioqrv47Zyzvae7wE2i8iXvRhERCbF7LMLqBaR47xF8U737gMart1dArylqruBXSJykrf8a63FLyIjVfUdVb0F2AEcFrPJBzRtpTbE+wFwAfCQiEQn3iOA1XHGbropa0GZru5O4Oqo538B/iki7+IWGrTWujmQdbgn4iLcazl1IvJX3BPsUq9lVkEb046r6qcichPwKm7L5gVVbe80Eb8ClkU9vxS4R0Ruxk2+jwGxM5l+E/iLiNQCrwG743ifWmCciCzxtr/QW/4N3OtwPXG7O2e2sv9vvdJwwf3cm8SkqrUislFERqnqhph174nITOA5ETkVqAH2Ncygaw5eNpq5Md2MiOQ1FB2IyI3Aoao6O81hISLnA1NV9eY2trse2KOq96UmMpOprAVlTPfzea/lFgA+Ai5LbzguzdioegAAAEBJREFUVX1GRPrHsWkV8GCy4zGZz1pQxhhjMpIVSRhjjMlIlqCMMcZkJEtQxhhjMpIlKGOMMRnJEpQxxpiM9P8BzV2esuHQGnIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The best accuracy was 84.70% with k= 14\n"
     ]
    }
   ],
   "source": [
    "plt.plot(range(1,Ks),mean_acc,'g')\n",
    "plt.fill_between(range(1,Ks),mean_acc - 1 * std_acc,mean_acc + 1 * std_acc, alpha=0.10)\n",
    "plt.legend(('Accuracy ', '+/- 3xstd'))\n",
    "plt.ylabel('Accuracy ')\n",
    "plt.xlabel('Number of Neighbors (K)')\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "print( \"The best accuracy was {0:.2f}%\".format( mean_acc.max()*100), \"with k=\", mean_acc.argmax()+1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 4800x4800 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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qKIgQERExqHRh5YWXM37+Tw47/pMLQHGRpcJ+69at48iRI8yfPx9PT0+2bNnCM888w9KlS6t8Dau19rd9KIgQERExyGLw2Rk9+3rSs68nAHlnzHz8dvlP/oWFhaxYsYL58+fbdmN06NCBH374gS+++AKTyURycnKZazIzM22ZBS8vLxwdHcnIyKiwT1VjVEVrIkREROqhkpISSkpKcHQs+6vawcEBi8VCWFgY+/fvJz8/39a2c+dOOnfuDICrqyshISHs2rXL1p6amkpaWpptoWZVY1RFQYSIiIhBtbmwslmzZnTt2pUlS5YQHx/P0aNHeeutt0hLS6N379706dMHX19f5s6dy8GDB/n000/ZunUrw4YNs40xdOhQ1q1bx9dff01iYiLz5s2je/fuhIaGAtg1RmVUzhARETHIgmOt3mxq+vTpxMXF8eyzz5Kfn0/btm158cUXadu2LQCzZ88mJiaG8ePH4+Pjw8SJE8tszRw8eDAZGRnExsbabjY1efJkW7uLi0uVY1RGQYSIiEg95efnx3PPPVdhe1BQELGxsZWOERkZSWRkZLXGqIiCCBEREYPMVgfM1XyUd3Wvr0sKIkRERAwyG9ydUXaMhvsELi2sFBEREUOUiRARETHIYnXEUs1HgVsuwk2haouCCBEREYNUzhARERExQJkIERERgyxUf3dFxU/OqP8URIiIiBhUMzebarhFgYY7cxEREalTykSIiIgYVNmzLy5kjIZKQYSIiIhBFhywUN01EQ33jpUNN/wRERGROqVMhIiIiEEqZ4iIiIghNXOzqYYbRDTcmYuIiEidUiZCRETEIIvVAUt1bzalR4GLiIg0PpYaKGfoZlMiIiLS6CgTISIiYlDNPAq84X6eVxAhIiJikBkHzNW8WVR1r69LDTf8ERERkTqlTISIiIhBKmeIiIiIIWaqX44w18xU6kTDDX9ERESkTikTISIiYpDKGSIiImJIY38AV8OduYiIiNQpZSJEREQMsuKApZoLK60N+D4RCiJEREQMUjlDRERExABlIkRERAzSo8BFRETEEHMNPAq8utfXpYY7cxEREalTykSIiIgYpHKGiIiIGGLBEUs1k/qVXT9ixAiOHz9e7vz06dPp168fKSkpxMTEEB8fj8lk4r777mPw4MFl+q5Zs4b169eTk5NDREQEUVFR+Pj42NrtGaMiCiJERETqqbi4OCwWi+3rbdu28Y9//IM+ffpQUlJCdHQ0oaGhxMXFER8fT0xMDC1btiQiIgKATZs2sXr1aqKjowkICGDx4sXMmDGDhQsXAtg1RmUURIiIiBhktjpgrmY5orLrvb29y3z9/fffc91119GsWTO+/fZb0tPTWbp0Ke7u7gQHB7N79242bNhgCwA2bNjA8OHD6du3LwBTpkwhMjKSxMREQkND2b59e5VjVEYLK0VERAw6uyaiuoc90tPT2blzJ4MGDQJg3759hIWF4e7ubuvTq1cvEhISACgqKiIpKYmePXva2gMCAvD39yc+Pt6uMaqiIEJERKQB2LJlCy1atKBXr14AZGRklMtUeHt7k5mZCUB2djYWiwWTyVRhn6rGqIrKGSIiIgZZDT4K/PB3x0j+IRUAS5Glit6ltmzZwk033YSjo32vZ7VaL3heF0pBhIiIiEFmHDAbeIBWm2ta0+aa1gAU5RTz6we/V9r/t99+IyUlxVbKADCZTCQnJ5fpl5mZacsseHl54ejoSEZGRoV9qhqjKipniIiI1HP//ve/6dKlC4GBgbZzYWFh7N+/n/z8fNu5nTt30rlzZwBcXV0JCQlh165dtvbU1FTS0tIIDw+3a4yqKIgQERExyGKticWVlb9GUVERX375JX/961/LnO/Tpw++vr7MnTuXgwcP8umnn7J161aGDRtm6zN06FDWrVvH119/TWJiIvPmzaN79+6EhobaPUZlVM4QERExyGJwTcT/jlGZb775hqKiIvr371/mvIuLC7NnzyYmJobx48fj4+PDxIkTy2zNHDx4MBkZGcTGxtpuNjV58uQLGqMyCiIamZwsJ5bOCODHLzzJz3UkuHMBY585Rrercs/bPyWxCUtnBBD/czMcHaHfsAzGTT+Gi2vtLNg5ne7MoqcD+eWr5jTzNHPbmBPcMyHd1r7lfR/WL/UjLdkVB0fo0D2Psc8co9Pl+ZWMKlLe88sPcs2gbKb+rT07v25eaV93DzNxW/fTsk0xNwd2x2KundsUm/yKmTD3CL1uOENuthMfLfflvdda2tpvHnmKv444TduOBRQXO/DbDx78Y2Yr0pKb1Mp8pH7o379/uQDirKCgIGJjYyu9PjIyksjIyArb7RmjIipnNDJvvhDAgd3uPL/8IG98vp9OPfOYfl97zmQ6letbkOfIMyND8PQxE/t/v/PCyj/Y/Z0HS2cEGH791fP9mT8xqML2lx9ux5lMJxZ89DuPzT7C+6+15N/vnrs9q+myYh6ITuX1LftZ+PEBWrcvZNrIEHLP6K0s9vvr307j2tS+FfEAj846SsrvTav9uvdGpRG1ILnC9mlxh2nubebJ2zqwOLoNf3s8nb+OOGVr73Z1Dp+t9SFqWChT7w7BpYmFWe8cxMm59lfhy/lZcKiRo6G66D9516xZwyOPPFLmXF5eHjfffDO7du2ioKCA2NhYhg4dypAhQ4iOjiYtLc3W9+eff2bcuHEMHDiQ22+/nalTp17sb6FB27fTnb+OOE3niDwC2hVx/5RU8nKcOJJU/pPM3h+bcfqEM0+8kkJgaCFdeufxQHQqm9a0KPNL+8sPvRl3Qxi3tu/OQ/068fXHXobm9kd8U379wYMn56cQ0jWfa2/OYti4E3z4lp+tT+9+Z7hyQDatg4sI6lDIuOeOkZPpXCM/4KVxuKx1EaMmp7EgKrDqzsA1g7IIDC3kgzf8ztt+w+0ZLP1yHx8l7eHNrfu57hb79tf/r+DO+XS/OpcFkwP5Y68b3232YsM//Bg69qStzyuPt+XTt1twMMGNgwluxE4OJDCkkKAOBYZeU6rv7B0rq3s0VBc9iLjppps4cOBAmS0l//nPf/Dy8qJHjx7ExMRw9OhR5s6dy5IlS/D29mbatGmYzWbMZjMvvPACAwcOZNWqVcTExHDFFVdc7G+hQQuPyOP7f3uSdcoJsxn+/Z4PLfyLaBdW/odQcZEDTk5WnF3Ofcpp6mahuNCR3/eU3t1s1zceLHm2DaMmp7J02z5GPH6ceRPakrDDvdx4Vdm/0x3fVkW0CSm0net5/RkO7WtKYX75/2QlxbDpnRZ4mkoIDNUPUamag4OVyQuTWT3fn5OprlX29/Yt5pGZR5k/MfC8JYwe157h0VlHWT3fn4f6deK91y7jqUXJhPU6f3mwMp165nHimAtH/zgX0O/82oN2YQUVZk08fUoAzptJFLkYLvqaCD8/PyIiItiyZQsPPvggcO4GGsePH+fLL79k3bp1NG9eWqOMiori1ltvJT4+nqCgIHJzc+nbty+XXXYZACEhIRf7W2jQHp11hHlPtOXubt1wdLLi5VPCS+/8gVuz8j+kwnrl4eBQWoIYOfE4udlOrFlYWp/NSC9966xZ2JLISWn0vTULgFZti9jzvQeb17Sgc0TeBc0t85Qz3r4lZc55tSjBYnEg67Qzl7UuBuBgQlMm3tqBogJHvP1KePndJJp52p+alsbrjodOUJDryJb3faruDEycf4SNb/mSktgUk19OufaRT6TzTow/X39cuqc+LbkJ3a/OYdDI0+z7pdkFzc27RQmZJ8v+SM465YyTE3j5lHDi2P8GPVZGP53Gz9ua2xUQSe24GAsr67M6WVg5aNAgli5dytixYzlx4gS7d+/mySef5ODBg5SUlHD33XeX6V9YWMixY8fo1q0b/fv3Z8yYMfTp04fevXtz44034ubmVuFrPfPyKVz//P/11xvdGdjvwv5jX2o+XObH0T+aMPu9RDxNZj7/l4nnRwezZMt+PH3MZfp6+5YQ/cZhFj3dhvdea4mzi5URjx9n97fNcfjzPX8owY2EHc1Y/nIr23UlxQ506VP6SSz9iAvjbgwr02a1OpQpecx65w+6XZmL1c6UXpuQQpZ8tp8zmc5sXuPDSw+3Y9EnB/A0mau+WBqtwNACho8/wWM3d7Sr/1//dhovnxLWvXn+MgZAu875dI7IZcy0VNs5Zxcre38s/Tnj17qIf3y5v0ybg4OV64dk2c49GxnMbz964HCBGe2Hnj9Gu7ACJt0eemEXXqJOWdM4Rekjsy1cvA8VFux/9kVlYzRUdRJEXHfddSxYsIDdu3ezd+9ewsLCCAwM5Pfff6dJkyYsW7as3DVn75713HPPER8fz/bt23n//fdZtWoVb775Jl5e56/DvzStBZ7NG26UV5MK8x3453x/5ryfZNuNEdotnx+/8GTbBhO3/1ft9awrB2Tzzo54Mk4449bMQvpRF/45rxX+QUUA5Oc5Mu65Y0TcmF3muiZNS0sgLfyLWfLZuR+iG9/y42SaC2OfOWY75+tfmmEw+Raf95OYo2NpxuQsF1crrYOLgCLCeuYx5rowtq4zMfTB8vMXOSusVx6my0p4+6f4MudfWvMHX230Zu5jbcuc73ZVDp165vHJ4T2lJ/78Of/xwT28Ft2GTe+0wM3dwj9mBrDjy7K7OwoLSn/mnEpz4dGbzgUtt489ia9/MW+9dC7oPpnmAkDGyfNn4sxmyDpd9v/FA1NT6XtrFlHDQjid7nKBfxOXphYO/rTAH4ASazFHSKrjGTUOdRJEuLq60r9/f7Zs2UJ8fLztphahoaEUFBRQWFhI+/btK7w+PDyc8PBw7r33XoYNG8Yvv/xCv379Ltb0G6ySEgdKih3539uuOzhQ5c1OTH6lP9y+/tibFv5FdOhWWqpoH55ParLrn7/Uy3Nypkxbc28zeTlO5+3fqWceJ1NdOfqHK63bl7bv+qY57cIKaOJW8QQtFgetTpcqfbfZiwO7y2Ytl247wKKn2/DztvJbPFfObcW/4s5lITpdnk/UghQeG9SR9KOlv7j/iHejVVARxw6df4ulxexQpu1MphPuHubz9t+/0x2/gGICggs5drC0/fLrcji0rylFBef+00ZOSmPQyNNMviOE4yna2lnXrDWwu8KqTMSFGzhwIE888QQODg62ACAoKIjrr7+eF198kUcffZTAwEDS09P56quvuP/++8nLy+OTTz7hmmuuwcfHh19//ZX8/HzatGlTV99Gg9KsuYUuvXN4c0YAj848SnNTCZ+t9eF4iisRN5zhZKoLT98dUrowrGdpkLB5jQ/B4fm4e1j44TNP1sS25KlFyTj9+c4Z8fhxXnq4HX6tiunzl2wKCxz4bbsHXi1KuPH2C1ul3j68gG5X5RD7VBAPv3iE4ymubPiHHw/POGrr886ClnS7KofL2hSTm+XEx6tbkHXKmd5/OVNjf09yacrNdiI3u3zp83iKKydTXWnhX8zctUnMmxDE/l3unEpz4VTauU/5Xn+W+w7tb2pbZPnea5fxTNxhTqS68OMXnjRpaqHrlblknXLiq49M5V6rMgcT3NjzfTMmzkshbnprWgYWMWzcCeKeP7el+u7HjnP339OZObYdOVlOmPxKs3hnMp0oKVbGtS5cyKO8KxujoaqzICI8PJxWrVrRrl07PD09beefffZZli1bxrx588jKysLX15crrriCpk2bYjabbbflzMnJISAggClTptChQ4e6+jYanGlxh/nHiwE8PzqYgjxHgjoUMv2tgwR1KCQtxZUjSU0pzD/3w+jQ/qYsfzmAvBxHgjoUEP3GYa4bfK6ee/XAbKKXHGbNgpYsf7kV7h4WQrvnMSoq7Xwvb8f8DrFwSiBP3toR9+Zm7vp7OgPvOW1rz8lyYv7EIE4fd8HDy0yHHnnMXZuIf+D5MyEi9nJ2thIYWkgTN/vr6T9s8WL2o20Z+eRxxkxLJS/HkcQ97qx+tWXVF5/Hyw+35YlXjrDg/34n74wTH7x+GVvea2Frv2XUKZq6WXlpzcEy1z01PIQ933sYek2R6nDYtm1bneSBi4uLueuuu3jqqae49tpra3z83NxchgwZQsaB9loTIY3SwIDL63oKInWixFrMl2zk448/plmz2llMf/Z3zLDPHsClWfV2x6x98F8AACAASURBVBTnFrHhphW1Ot/aUieZiNOnT7Nx40aaNGnCVVddVRdTEBERqTaVM+rA8OHDadGiBVOmTMHJSTdJERERaYjqJIjYtm1bXbysiIhIjaqJZ1/oPhEiIiKNUGMvZ2jFoYiIiBiiTISIiIhBjT0ToSBCRETEoMYeRKicISIiIoYoEyEiImJQY89EKIgQERExyEr1t2g25McHqpwhIiIihigTISIiYpDKGSIiImJIYw8iVM4QERERQ5SJEBERMaixZyIURIiIiBjU2IMIlTNERETEEGUiREREDLJaHbBWM5NQ3evrkoIIERERgyw4VPtmU9W9vi6pnCEiIiKGKBMhIiJiUGNfWKkgQkRExKDGviZC5QwRERExRJkIERERg1TOEBEREUMuRjnjwIEDxMXFER8fj4uLCxEREbzwwgsApKSkEBMTQ3x8PCaTifvuu4/BgweXuX7NmjWsX7+enJwcIiIiiIqKwsfHx9ZuzxgVUTlDRESknjp8+DCTJk2iW7duvPHGGyxevJj+/fsDUFJSQnR0NF5eXsTFxTFq1ChiYmLYsWOH7fpNmzaxevVqJkyYwOLFi8nNzWXGjBm2dnvGqIwyESIiIgZZa6CcUVkm4q233uL666/ngQcesJ1r27YtANu3byc9PZ2lS5fi7u5OcHAwu3fvZsOGDURERACwYcMGhg8fTt++fQGYMmUKkZGRJCYmEhoaatcYlVEmQkRExCArYLVW86hgbLPZzE8//YS/vz8TJ07kjjvuYPLkySQlJQGwb98+wsLCcHd3t13Tq1cvEhISACgqKiIpKYmePXva2gMCAvD39yc+Pt6uMaqiIEJERKQeysrKoqCggPfff5/+/fszZ84c/Pz8iIqKIicnh4yMDLy9vctc4+3tTWZmJgDZ2dlYLBZMJlOFfaoaoyoKIkRERAw6e9vr6h7nHdtiAeCGG27gtttuo2PHjkRFReHg4MB3331X5dys1opyHDVHayJEREQMMro7I3tHEtm//FE6RrH5vH28vLxwdHQkMDDQds7Z2ZlWrVqRnp6OyWQiOTm5zDWZmZm2zMLZ6zMyMirsU9UYVVEmQkRE5CLzjAihzbibaDPuJgJG9ztvHxcXFzp06MDRo0dt58xmM2lpabRs2ZKwsDD2799Pfn6+rX3nzp107twZAFdXV0JCQti1a5etPTU1lbS0NMLDwwGqHKMqCiJEREQMOnuzqeoeFbnzzjv5/PPP+eyzz0hJSWHx4sUAXHPNNfTp0wdfX1/mzp3LwYMH+fTTT9m6dSvDhg2zXT906FDWrVvH119/TWJiIvPmzaN79+6EhoYC2DVGZVTOEBERMejsDovqjlGRAQMGkJmZybJlyzhz5gydOnXi1VdfpVmzZgDMnj2bmJgYxo8fj4+PDxMnTiyzNXPw4MFkZGQQGxtru9nU5MmTbe0uLi5VjlEZBREiIiL12J133smdd9553ragoCBiY2MrvT4yMpLIyMgK2+0ZoyIKIkRERAxq7E/xVBAhIiJiUGMPIrSwUkRERAxRJkJERMQgi9UBBz0KXERERC5Ube/OqO9UzhARERFDlIkQERExqDQTUd2FlTU0mTqgIEJERMQg7c4QERERMUCZCBEREYOsfx7VHaOhUhAhIiJikMoZIiIiIgYoEyEiImJUI69nKIgQERExqgbKGaicISIiIo2NMhEiIiIGNfbbXiuIEBERMUi7M0REREQMUCZCRETEKKtD9RdGNuBMhIIIERERgxr7mgiVM0RERMQQZSJERESM0s2mRERExAjtzhARERExoNJMxIABA3BwsC9C+uyzz2pkQiIiIg1KAy5HVFelQcTLL798seYhIiLS4DT2ckalQUSfPn0u1jxERESkgbmgNRGJiYksWrSI6OhoTp8+DcA333zDgQMHamVyIiIi9Zq1ho4Gyu4g4ttvv+Xxxx8nNzeXHTt2UFBQAMDx48dZuXJlbc1PRESkHnOooaNhsjuIWLFiBZMmTSI6Ohpn53NVkB49erBv375amZyIiIjUX3bfJ+LIkSN069at3Hk3Nzdyc3NrdFIiIiINQiO/2ZTdmYiWLVuSlJRU7vyPP/5IUFBQjU5KRESkQWjkayLszkSMHDmSBQsWkJWVhdVqZffu3WzevJkPPviAqVOn1uYcRUREpB6yO4gYOHAgXl5evP322zg5OTF//nzat2/P1KlTueGGG2pzjiIiIvWTHgVuv6uuuoqrrroKAKvVavfdLEVERC5Fjf1R4Bf8AK5ffvmFlJQUAIKCgujZs2eNT0pERERg5cqVrFq1qsy5a6+9llmzZgGQkpJCTEwM8fHxmEwm7rvvPgYPHlym/5o1a1i/fj05OTlEREQQFRWFj4+Prd2eMSpidxCRnJzM9OnTSU1NpVWrVgC2P8+YMYO2bdvaO5SIiMil4SLszggLC+Oll16yfe3q6gpASUkJ0dHRhIaGEhcXR3x8PDExMbRs2ZKIiAgANm3axOrVq4mOjiYgIIDFixczY8YMFi5caPcYlbE7iJgzZw5t2rRh0aJFeHp6ApCdnc28efN45ZVXeP311+0dSkRE5NJwEdZEODs7l8kcnLV9+3bS09NZunQp7u7uBAcHs3v3bjZs2GALADZs2MDw4cPp27cvAFOmTCEyMpLExERCQ0PtGqMydm/xTEpKYty4cbYAAsDT05MxY8aQmJho7zAiIiJyAZKSkrjjjjsYNWoUsbGxnDlzBoB9+/YRFhaGu7u7rW+vXr1ISEgAoKioiKSkpDLLDgICAvD39yc+Pt6uMapidxDRoUMHjhw5Uu780aNHCQkJsXcYERGRS4aDtWaOioSHhxMdHc38+fN55JFH2L17N88++yxWq5WMjAy8vb3L9Pf29iYzMxMorRZYLBZMJlOFfaoaoyqVljN+++0325+HDBnCokWLSEpKIiwsDCiNYD799FNGjx5t14uJiIhcUgyuicjfu5/8vaUPr7SWlFTY77+fpt2+fXvatm3Lvffea9eDL60XYdtHpUHEhAkTyp0738O25s2bx6BBg2psUiIiIpcyty6dcOvSCQBLfgE5X31v13WtW7fGw8OD1NRUTCYTycnJZdozMzNtmQUvLy8cHR3JyMiosE9VY1Sl0iBi8+bNdg0iIiLSKF3km00dP36cnJwc/P39cXFxYe3ateTn5+Pm5gbAzp076dy5M1C6iyMkJIRdu3bZFkmmpqaSlpZGeHg4ULrzo7IxqlJpEHF2G4mIiIicRy1v8YyLi+Paa6/Fz8+P1NRU4uLi6NKlCx07dsRsNuPr68vcuXO5//77SUhIYOvWrcyZM8d2/dChQ1m8eDEdO3akVatWLFmyhO7duxMaGgqUlkuqGqMyF3SzqZycHHbs2EF6ejol/1PDueeeey5kKBEREanC8ePHeeGFF8jOzqZFixb07t2bsWPH4ujoiKOjI7NnzyYmJobx48fj4+PDxIkTy2zNHDx4MBkZGcTGxtpuNjV58mRbu4uLS5VjVMbuIOLXX3/lmWeewcPDg/T0dAICAjhx4gROTk4EBgYqiBARkcanljMRzz//fKWXBgUFERsbW2mfyMhIIiMjqzVGReze4rlkyRJuv/121qxZg6urK3PmzOGDDz6gR48e3HzzzYZeXEREpEFr5I8CtzuIOHTokC1YcHZ2pqCgAA8PDx588EHefvvtWpugiIiI1E92BxEeHh4UFhYC4Ofnx8GDBwHIy8sjNze3dmYnIiJSn53dnVHdo4Gye01Ez549+eGHHwgODqZ///4sWrSI7777jj179pS5GYaIiEhjUdUdJ+0do6GyO4iYNGmSbUdGZGQk3t7eJCQkMGzYMO64445am6CIiIjUT3YHEU2bNi3z9S233MItt9xS4xMSERFpMC7Co8Drs0qDiM8//9zugQYMGFDtyYiIiEjDUWkQ8cYbb9g1iIODg4IIERGRRqbSIGLdunUXax4iIiINjgM1sLCyRmZSNy7ottcN0Z2Db8PZqUldT0PkonNuV1zXUxCpG5ZCOHyRXusiP4CrvrH7PhEiIiIi/+2Sz0SIiIjUGu3OEBEREUMaeRChcoaIiIgYckFBxHfffce0adMYM2YMJ06cAOCTTz5hx44dtTI5ERGR+uzsba+rezRUdgcRn3zyCXPnzqVDhw4cO3aM4uLSld8ODg688847tTZBERGRekuPArfP2rVrefrpp3nggQdwdDx3WefOnUlKSqqVyYmIiEj9ZffCyrS0NNq3b1/uvJOTE0VFRTU6KRERkQZBCyvt07ZtW3799ddy57dt20ZISEiNTkpERKQhaOxrIuzORIwdO5aZM2eSkpKCxWLh888/58iRI3z11VfMmTOnNucoIiIi9ZDdmYgrr7ySRYsWcezYMVq3bs3mzZvJz89nwYIF9OzZszbnKCIiUj+dve11dY8G6oJuNtW+fXueffbZ2pqLiIhIw9LI10TYHUScPHmy0nZfX99qT0ZEREQaDruDiLvvvhsHh4pTLl988UWNTEhERKShqImFkY1iYeXKlSvLfG02m0lKSuLdd9/lgQceqOl5iYiI1H8qZ9gnKCio3Lng4GB8fHxYtmwZ1113XY1OTEREROq3aj/Fs0WLFhw8eLAm5iIiItKw1MR9HhpDJuK3334r87XVauX06dN88MEHdOrUqcYnJiIiUu+pnGGfCRMmlDvn7u5Ojx49eOyxx2p0UiIiIlL/2R1EbN68uczXjo6OODtXuxoiIiLScDXyTIRdd6wsLi7miSee4Pjx47i6uuLq6qoAQkREGr3G/uwMu4IIFxcXTpw4gdXagL9TERERqVF2Pztj+PDhvPPOOxQXF9fmfERERKSBsLsmsWPHDuLj4/nhhx8ICgqiadOmZdrnzZtX45MTERGp1xr5mgi7g4hOnTppK6eIiEgdefbZZ/n222+ZP38+ERERAKSkpBATE0N8fDwmk4n77ruPwYMHl7luzZo1rF+/npycHCIiIoiKisLHx8fWbs8YFakyiNi9ezddu3Zl3LhxF/K9ioiIXPIu1rMzNm3aRGFhYZlzJSUlREdHExoaSlxcHPHx8cTExNCyZUtbkLFp0yZWr15NdHQ0AQEBLF68mBkzZrBw4UK7x6hMlWsiJk2aRHZ2dtXfoYiISGNkreZRhbS0NFauXMmUKVPKnN++fTvp6elMmTKF4OBgbrnlFvr378+GDRtsfTZs2MDw4cPp27cvoaGhTJkyhT179pCYmGj3GJWpMojQjgwREZG6YbFYmDNnDqNHj8bPz69M2759+wgLC8Pd3d12rlevXiQkJABQVFREUlISPXv2tLUHBATg7+9PfHy8XWNUxa41EZU9AlxERKTRquWFlf/6179wc3Pj5ptvLteWkZGBt7d3mXPe3t5kZmYCkJ2djcViwWQyVdinqjGqYlcQ8dxzz+Hi4lJpn5iYGLteUERE5FJhdE1ETtI+cpP2A2A1l5y3z+HDh1m7di1xcXGG5nYxKgl2BRFhYWG4ubnV9lxEREQaBY+QMDxCwgAwFxaQ+fO35fokJCRw+vRp/va3v5U5P2XKFPr160erVq1ITk4u05aZmWnLLHh5eeHo6EhGRkaFfUwmU6VjVMWuIGLkyJHl0iEiIiKNXi2WM6677rpyt1YYM2YMkyZNok+fPhw4cIC1a9eSn59v+6C/c+dOOnfuDICrqyshISHs2rXLttMiNTWVtLQ0wsPDgdIkQWVjVKXKIELrIURERM6vNrd4enh44OHhUe68v78/fn5+eHt74+vry9y5c7n//vtJSEhg69atzJkzx9Z36NChLF68mI4dO9KqVSuWLFlC9+7dCQ0NBaBPnz5VjlGZKoMI7c4QERGpf1xcXJg9ezYxMTGMHz8eHx8fJk6cWOb+DoMHDyYjI4PY2FjbzaYmT558QWNUpsogYuvWrQa+NRERkUbgIt/2etu2bWW+DgoKIjY2ttJrIiMjiYyMrLDdnjEqoud5i4iIGNXIn51h91M8RURERP6bMhEiIiIGXaxnZ9RXCiJERESMUjlDRERE5MIpEyEiImJUI89EKIgQERExqLGviVA5Q0RERAxRJkJERMQolTNERETECJUzRERERAxQJkJERMQolTNERETEkEYeRKicISIiIoYoEyEiImKQw59HdcdoqBREiIiIGKVyhoiIiMiFUyZCRETEoMZ+nwgFESIiIkapnCEiIiJy4ZSJEBERqY4GnEmoLgURIiIiBjX2NREqZ4iIiIghykSIiIgY1cgXViqIEBERMUjlDBEREREDlIkQERExSuUMERERMULlDBEREREDlIkQERExSuUMERERMaSRBxEqZ4iIiIghykSIiIgY1NgXViqIEBERMaqRlzMURIiIiNRDa9asYfPmzaSnp9OkSRO6du3Kww8/TGBgIAApKSnExMQQHx+PyWTivvvuY/DgweXGWL9+PTk5OURERBAVFYWPj4+t3Z4xKqM1ESIiIgY5WK01cpxPQEAATzzxBCtWrODVV1/F0dGR6OhoAEpKSoiOjsbLy4u4uDhGjRpFTEwMO3bssF2/adMmVq9ezYQJE1i8eDG5ubnMmDHD1m7PGFVRJkJERMSoWixn3HjjjWW+fuCBBxg7diynT58mISGB9PR0li5diru7O8HBwezevZsNGzYQEREBwIYNGxg+fDh9+/YFYMqUKURGRpKYmEhoaCjbt2+vcoyqKBMhIiJSzxUWFrJ582YCAwPx9vZm3759hIWF4e7ubuvTq1cvEhISACgqKiIpKYmePXva2gMCAvD39yc+Ph6gyjHsoUyEiIiIQbW9O+P777/nxRdfpLCwkDZt2jB37lwcHR3JyMjA29u7TF9vb28yMzMByM7OxmKxYDKZKuxT1Rj2UCZCRETEKGsNHRW4/PLLWbZsGQsXLqRt27bMnDmTkpKSqqdVwTqLmqZMhIiIyEWWdXQf2Uf3A2Axmyvs5+bmRuvWrWndujVhYWHcdtttbN++HZPJRHJycpm+mZmZtsyCl5eXLWNRUZ+qxrCHMhEiIiIGnS1nXOjhHRBGUO/bCep9O4G97N9SabVacXJyIiwsjP3795Ofn29r27lzJ507dwbA1dWVkJAQdu3aZWtPTU0lLS2N8PBwgCrHsIeCCBEREaNqsZzx5ptvsnfvXtLS0khISGDmzJl4eXnRtWtX+vTpg6+vL3PnzuXgwYN8+umnbN26lWHDhtmuHzp0KOvWrePrr78mMTGRefPm0b17d0JDQwHsGqMqKmeIiIjUQ+np6bzwwgtkZWXh5eVF9+7defXVV/Hw8ABg9uzZxMTEMH78eHx8fJg4cWKZrZmDBw8mIyOD2NhY282mJk+ebGt3cXGpcoyqKIgQERExqDZ3Zzz33HOVXhcUFERsbGylfSIjI4mMjKzWGJVRECEiImJUI392htZEiIiIiCHKRIiIiFRDQ36Ud3UpiBARETHKai09qjtGA6VyhoiIiBiiTISIiIhBtf3sjPpOQYSIiIhR2p0hIiIicuGUiRARETHIwVJ6VHeMhkpBhIiIiFGNvJxRb4KIiRMnsnv3bl555RV69+5tO//SSy/h5OTE1KlT63B2l45rrj/KkKF/0KFTBs08Shjyl6FYzFVXtdzci3lj+edc5p9v9zVGmHwKeGzSTnpekU5erjMfrQ9h7TthtvYBgw4z7K7fadkqD6sFEg+YWP5mF37f71Mr85FLyzU3pHLL8EN0CMukmUcJt15/S4XvZY/mRYx6aD+9rkynhV8Bp0825fNPAln7zw5YLA61Mj+TTwF/n7KHnn1OkJfjwv/9K5i1/+xgax8wOIWhI5JK3/9WBxL3e7FiSTi/J9j/6GaRmlRvgggofXTp8uXLywQRUrOaNDWze6cfu3ZcxuiH9tp93SMTdpOc3JzL/POr7lyJyNHxXOafx4I5V5y3fer0H3FwsDL5sRto6Z9L1LQdZJxuymeb2gGQcboJK//RhSMpzXF2snD7nUnMmvcto0cMIj/PpVpzk0tfk6Zm9uxowa6ffBn9yL5K+7bwK8DLu5A3F3TlSLIHQe3O8MS03Tg4wLsrOhp6/ZFj99PSP48FL/U8b/vTM3fg4ABPjb+Olq3ymPTcTjJONeGzT4IAyDjVhFVxnTly2ANnZwu33X2QmQt+4IE7/qL3fx3R7ox65KabbuKLL77gm2++4brrrivXfvr0aRYuXMiPP/6Is7Mz119/PY8//jhubm51MNuGadtnpT+Mul1+wu5rrr7uKIFBZ1i5rAtX9Ekv1963fwoj79tHy1a5pB7z4J2Vnfn2q9YXPLd27bPodvlJxt17E0ePNOePRG8+/CCL24Yn2YKIHT/6l7nmrTe6Mvi2gwS1PcP+BGUjpHLb/t0GgG49T1bZ9/Afnsx57lywm3a0GR++d4br+h8rE0T0HXCUex44QMuAPFKPNmPNWx35dlvABc+tXUg23XqeZtzf+nEsxYM/fvdi4/tZ3HbXQVsQsWP7ZWWuWf56OIOHHSaoXQ77400X/JpSA3SzqfrDZDIxfPhwli9fjsVSfqXJ7NmzSU9PJzY2lpdffpk9e/bw+uuv18FMGw9vUwHjH99DzJyI86Zwe/RM55EJu3l7RWceGX0Ta9/uRFT0z3QKP33Br9Wp82lOpLtx9Ehz27ldv/jRLjgLV1dzuf5OThYGDTlEVpYrKcnNy7WL1DRP7yJysl1tX3ePOMnDT/7GO8s68WjkjaxdFcqk53bSqUvGBY/dMTyDE8ebcizFw3Zu18++tA3JrvD9P/C2w2RlupBy2KNcu8jFUK8yEQAjRoxg48aNbN26lQEDBtjOJycn8/PPP7NixQratWsHwIQJE5g2bRoPP/yw7fnqUrMmTP6Fj9aHkJLsibdP+ezFiFH7eWdVZ775qvQTXlpqM7pdfoJBtxxkf/yFZQa8vAvJzGhS5lxWZhOcnMDTq5CTJ9wBaBecxatLvsTV1UxmRlOee+pa8nKVypXa5R+Qy8Bbk1n8SnfbuRGjD7BmeUe++TPzkHasGd17nWLgrcns33thmQFvUxFZFb3/vYs4mV6acW3bPptXl36DaxMzmaebMP3Jq/T+r0MqZ9QzHh4e/O1vf2PlypX069fPdj45ORl3d3dbAAEQHh6O2Wzm2LFjdOx4/hrlgRNf4ejoBIBvs2B8m7Wv1flfSm4adAhPryI2rO1QYZ927bMI63KKBx76zXbO2dlC/K++APhdlkfcqs/KtDk4wHU3HLWdmz7lWvb+6ouDnWvVjqQ057EH/0Lz5kUMvOUQU5//kYkP9yPnjGvVF4sY4GUq5IVXt/PVZ635z+fnSnXt2p8hrGsGox9JsJ1zdrEQv6c0gPZrmccb73xZps3BAa7tl2o793zUlezd3QIHO3+THE324PH7b6C5ZxEDb0vm6Zk7eHLs9Y3+/X8i7xAn8w8BYLWWz9zUGu3OqH+GDx/OunXr2Lx5c6X9HOz4rdPR7wacnZpU2U/K63r5STp1Ps1Hn3/455nSd/rGLRt5fcHlbP44mKZuJbz1Rjd2/NSyzLVFhaWB26lTTXnswb/Yzt92RxK+vvksX9rVdu7UidJPWJkZTfA2FZYZx8u7ELMZsrPO/RuWlDiSetSDVODAPh/+sXoL/W9K5qP1oTX1rYvYNPcs4qWF35O4z5sl87uVaWvqXsJbr4Xzy/+sVSgqLK0UnzrZlMfvv8F2/ra7D9LCN58VS8Jt506daAqULhr2quj9n3kuQCh9/zcj9WgzDiSYWPreVvoNOsL/fdC4PyD5ubfDz70dACWWQpLP7K7bCTUS9TKIcHNzY+TIkfzzn/+kc+fOODk5ERQURF5eHocOHbJlI/bu3YuTkxMBARe+iEmq9s9lXVj//rksRMdOGTw59RcmPNSPE8dLSwsHk7zwb5VL6tHzl5MsZscybTlnXHBvVnze/vsTfPC7LJ+A1jkc+7O9R88THDroRVGRU4XzdHC0YjbXzpY7adw8mhcxa+H3pB1zJ2bW5VitZd9nB3/3xL916YLK8yl9/59rO5Ptgrt78Xn7H4g34deygIA2ORw78uf7P+IUh5M8q3z/19aWa6laYy9n1Nt33u23347VauX7778HICgoiCuuuIJXXnmF/fv38+uvv/Laa68xaNAgrYe4AB7Ni2gfmklA6xwA2odk0T40k6ZuJbTwzefNf26hY1jposhTJ904fNDLdqSllf7gO3zIk5yc0k9G77/diSHD/mDonb/Tus0ZgkMyGTIsib79jlzw3A794cWvu3yZ8NQvBIdkctW1xxh2VyIfrQux9blnVAJde5zgMv9c2odm8tiknXh5FfLTdv9KRhYp5dG8iPYdsmjVJheA9qHZtO+QZXv/x727lY6dSxdFurkX8+KC7ZhLHHlzQVe8vIow+RTg6X0uW7D2nx0Ycschhv4tiYDAHIJDsxgy/CDX/+XoeV+/MoeSPPl1pw+PT91DcGgWV12fxtARf/DRB8G2PiNGH6Dr5Se5zD+P9h2y+PtTe/DyLuKn7y6rZGSpVWd3Z1T3aKDqZSYCSu8Zce+997JgwQLbuejoaGJjY5k4cSJOTk707duXv//973U4y4bnqmtTmTR1h+3rRUu3AfD0xOs5nuZOYFAOTZraX0/c/l0Ac1/szT337eOBh/aSl+fM7we8eWdleNUXn8ecF/vw2KSdvPr6V+TlOfPBux1t2zsBPJoXM2nqDlq0KOBMjguJ+01Mi7qe9LTzfxIU+W9XXX+cJ5/dZft64YqvAZj696s5nupOYNtc2/s/tFMWncIzAVi54QvbNcdT3RgzvHTR9/Zv/Hnl+V6MeOAAox/ZR16uM4n7vXjnrU6G5jf3uQgee3oP85d+Q36uC/96O8S2vRPAw7OYJ5/dRQvfQnLOuPD7Pi+mTbia9DR3Q68nUl0O27Zta7ghUCVyc3MZMmQIfwmdqDUR0ig5FBXX9RRE6kSJpZDPD7/Oxx9/TLNmtfMB4+zvmKtvfhFnl6bVGqukuIDv3UoWcAAAFSRJREFUN02v1fnWlnqbiRAREan3GvnujHq7JkJERETqN2UiREREDGrsuzMURIiIiBhlsZYe1R2jgVI5Q0RERAxRJkJERMSoRr6wUkGEiIiIQQ7UwJqIGplJ3VA5Q0RERAxRJkJERMSomrhttW57LSIi0vg09i2eKmeIiIiIIcpEiIiIGKXdGSIiImKEg9WKQzXXNFT3+rqkcoaIiIgYokyEiIiIUZY/j+qOcR5vv/02//nPf0hJScHd3Z0+ffowfvx4vL29bX1SUlKIiYkhPj4ek8nEfffdx+DBg8uMs2bNGtavX09OTg4RERFERUXh4+NzQWNURJkIERERg86WM6p7nM9vv/3GXXfdxZtvvsmsWbM4dOgQL774oq29pKSE6OhovLy8iIuLY9SoUcTExLBjxw5bn02bNrF69WomTJjA4sWLyc3NZcaMGRc0RmWUiRAREamH5syZU+brxx57jMcee4ycnBw8PDzYvn076enpLF26FHd3d4KDg9m9ezcbNmzg/9u7/5iqr/uP409kXAWq/BBaCpWhYKC4sipqlq21oZsCzqW2VLcW28wmDbVaJZFSqZ2ublMRh6yzVFvF6hbbpUW3rFEijbi2sdNJBbsCUiz2Mn6IlV8Fr/y4937/MN7vmPLrw49byuuR3ETPOff9OR8S9e37nPP5REdHA3D48GESEhKYN28eAKmpqSQmJlJRUUFYWFi/YvRGlQgRERGj7EP06Yfm5mZMJhPu7u4AlJWVERERgYeHh2PMrFmzKC0tBaCjo4MLFy4wc+ZMR39gYCABAQGUlJT0K0ZfVIkQERExaoSeWNnR0cGBAweIjY3F1dUVgMbGxm77IwC8vb1pamoCoKWlBZvNho+PT49j+orRFyURIiIiI+xKw+c0NHwOgM3e1etYq9XK5s2bAVixYkW/r2EfgaOjSiJEREQMMvrYaz+f6fj5TAegq+sa1TWnbjnOZrORnp6O2WwmKyvLsZQB4OPjg9ls7ja+qanJUVnw8vJi3LhxNDY29jimrxh90Z4IERERo24sZwz2c8vQdjIyMigpKWH79u1MmjSpW39ERATnz5/HYrE42s6ePcvdd98NgMlkIjQ0lKKiIkd/bW0tdXV1REZG9itGX5REiIiIfANlZmby8ccfs379egAaGhpoaGjAarUCMHfuXPz8/EhPT6eyspIjR45w/PhxHn74YUeMxYsXk5uby4cffkhFRQUZGRlERUURFhbW7xi90XKGiIiIQS6265/BxriV9957D4Bnn322W/tbb71FQEAAbm5ubNmyhczMTJKSkvD19SU5Obnb0cyFCxfS2NhIVlaW42FTKSkpjv7+xOiNkggRERGjhvF0RkFBQZ9fDQ4OJisrq9cxiYmJJCYmDipGT7ScISIiIoaoEiEiImKUXgUuIiIiRuhV4CIiIiIGqBIhIiJi1Ag99vqbSkmEiIiIUXZgkEc8R/OeCC1niIiIiCGqRIiIiBg01jdWKokQERExys4Q7IkYkpk4hZYzRERExBBVIkRERIzS6QwRERExxAa4DEGMUUrLGSIiImKIKhEiIiIG6XSGiIiIGDPG90RoOUNEREQMUSVCRETEqDFeiVASISIiYtQYTyK0nCEiIiKGqBIhIiJi1Bh/ToSSCBEREYPG+hFPLWeIiIiIIapEiIiIGDXGN1YqiRARETHKZgeXQSYBttGbRGg5Q0RERAxRJUJERMQoLWeIiIiIMUOQRDB6kwgtZ4iIiIghqkSIiIgYpeUMERERMcRmZ9DLETqdISIiImONKhEiIiJG2W3XP4ONMUopiRARETFqjO+J0HKGiIiIGKJKhIiIiFHDvLHygw8+4K9//Svl5eW0tbXx/vvv4+rq6uivqqoiMzOTkpISfHx8ePLJJ1m4cGG3GAcPHuTQoUO0trYSHR3N2rVr8fX1HVCMnqgSISIiYtSN5YzBfnrQ3t7OrFmzeOyxx27q6+rqIi0tDS8vL3bt2sUTTzxBZmYmhYWFjjFHjx7lT3/6E6tXr2bnzp20tbXx8ssvDyhGb1SJEBER+YaaP38+AEVFRTf1nTp1ivr6el5//XU8PDyYOnUqxcXFHD58mOjoaAAOHz5MQkIC8+bNAyA1NZXExEQqKioICwvrV4zeqBIhIiJilJ0hqEQYu3RZWRkRERF4eHg42mbNmkVpaSkAHR0dXLhwgZkzZzr6AwMDCQgIoKSkpF8x+qJKhIiIiFFOPJ3R2NiIt7d3tzZvb2+ampoAaGlpwWaz4ePj0+OYvmL0RUmEiIjICLt87Uu+ajcDYB+m50TYR+DoqJIIERERo2w2YOBJgL9pCv6mKQB02TowX/10wDF8fHwwm83d2pqamhyVBS8vL8aNG0djY2OPY/qK0RftiRARETFqmE9n9CYiIoLz589jsVgcbWfPnuXuu+8GwGQyERoa2m1TZm1tLXV1dURGRvYrRl+URIiIiHxDtbS0UFFRQXV1NQAVFRVUVFRgsViYO3cufn5+pKenU1lZyZEjRzh+/DgPP/yw4/uLFy8mNzeXDz/8kIqKCjIyMoiKiiIsLAygXzF6o+UMERERo4Z5Y+XJkydJT093/P6ZZ54BYMeOHdx7771s2bKFzMxMkpKS8PX1JTk5udvRzIULF9LY2EhWVpbjYVMpKSmOfjc3tz5j9EZJhIiIiFHD/MTKuLg44uLieuwPDg4mKyur1/CJiYkkJiYOKkZPtJwhIiIihqgSISIiYpDdbhv0Ec3hOuI5EpREiIiIGGW397oc0e8Yo5SWM0RERMQQVSJERESMsg/BxspRXIlQEiEiImKUzQYug9zTMIr3RGg5Q0RERAxRJUJERMQoLWeIiIiIEXabDfsglzNG8xFPLWeIiIiIIapEiIiIGKXlDBERETHEZgeXsZtEaDlDREREDFElQkRExCi7HRjscyJGbyVCSYSIiIhBdpsd+yCXM+yjOInQcoaIiIgYoiRChs1XbV84ewoiTnP56kVnT0FGgt02NJ9RSkmEDJuv2iqdPQURp/nKctHZU5ARYLfZh+QzWimJEBEREUO+tRsrb2xU6bK1O3kmY5fNZqXLqp+/s7jYOp09hTHNbrfq7x8n6bJ1ACOzYbHL3j7o5YguRu+f1W9tEmGxWAD4xxevOXkmY1tV81lnT0HEacxfFzt7CmOaxWLhtttuG5bYbm5u+Pr68lHDkSGJ5+vri5ub25DEGkkuBQUFo3cxphc2m40rV67g7u6Oi4uLs6cjIiIjxG63Y7FYmDx5MuPGDd+qfUdHB52dQ1NFcHNzw2QyDUmskfStrUSMGzcOf39/Z09DREScYLgqEP/NZDKNyn/4h5I2VoqIiIghSiJERETEECURIiIiYoiSCBERA5KTk4mJieFf//pXt/bf/e53bN261UmzEhlZSiJERAwymUzk5OQ4exoiTqMkQgbk4MGDrFixolvb1atXiY+Pp6ioiGvXrpGVlcXixYtZtGgRaWlp1NXVOcaeOXOGp59+mtjYWB566CHWrVs30rcgMmTmz5/PxYsX+eijj27Z39DQwMaNG4mPj+dnP/sZ27ZtczzDRuTbQEmEDMj8+fMpLy/HbDY72j744AO8vLz4/ve/T2ZmJtXV1aSnp5OdnY23tzcvvvgiVqsVq9XKr3/9a2JjY9m/fz+ZmZnMnj3biXcjMjg+Pj4kJCSQk5ODzXbzUwu3bNlCfX09WVlZbN68mXPnzvHqq686YaYiw0NJhAyIv78/0dHRHDt2zNF27Ngx5s+fz6VLlzhx4gQbNmwgPDyc4OBg1q5dS21tLSUlJbS2ttLW1sa8efMICAggNDSURx991Il3IzJ4v/jFL7h8+TLHjx/v1m42mzlz5gwvvPAC4eHh3HPPPaxevZq8vDxaW1udNFuRoaUkQgYsLi6O999/H7vdTn19PcXFxSxYsIDKykq6urpYunQp8fHxjhJue3s7NTU1eHl58eCDD/LUU0+xadMmjh49qtKujHq33XYbP//5z3nzzTexWq2OdrPZjIeHByEhIY62yMhIrFYrNTU1TpipyND71j6xUobPfffdx44dOyguLuazzz4jIiKCKVOm8PnnnzN+/Hj27Nlz03e8vb0B+NWvfkVJSQmnTp3iL3/5C/v372f37t14eXmN9G2IDJmEhARyc3PJy8vrdZwewS/fNqpEyICZTCYefPBBjh07Rn5+PgsWLAAgLCyMa9eu0d7eTlBQULePp6en4/uRkZEsX76cN954g9bWVj755BNn3YrIkHB3d+fxxx/nwIEDjncpBAcHc/XqVS5evOgY99lnn+Hq6kpgYKCTZioytJREiCGxsbHk5+dTU1NDTEwMcP0vzfvvv59NmzZx+vRpamtrKS4u5pVXXqG5uZna2lr27NlDSUkJdXV1nDhxAovFwl133eXkuxEZvIceegi73c7HH38MXP/zMHv2bLZt28b58+f59NNP+eMf/0hcXNyIvNdBZCRoOUMMiYyM5M477yQkJIRJkyY52l966SX27NlDRkYGzc3N+Pn5MXv2bCZMmIDVaqWyspIjR47Q2tpKYGAgqampTJ8+3Yl3IjI0TCYTy5YtY8eOHY62tLQ0srKySE5OxtXVlXnz5rFy5UonzlJkaH1rXwUuw6uzs5MlS5bw/PPP86Mf/cjZ0xERESdQJUIGrKGhgb/97W+MHz+eH/zgB86ejoiIOImSCBmwhIQEJk+eTGpqKq6urs6ejoiIOImSCBmwgoICZ09BRES+AXQ6Q0RERAxREiEiIiKGKIkQERERQ5REiIiIiCFKIkRERMQQJREiTlRdXU1MTAx1dXUA5OXlsWTJkhGdg9VqJSYmhqKioh7HxMTEUFhY2K94dXV1xMTEUF1dbXhOzvg5iMjA6YinyC0kJydTXFwMwIQJEwgJCWH58uXMnTt3WK8bExPT7wd4FRYWkpKSoiO3IuI0qkSI9ODRRx8lNzeXN954g+nTp/PSSy/1+L/rjo6OIbnm+PHjHa9NFxH5plMlQqQHEyZMwNfXF19fX9asWUN+fj6FhYUEBQWRnJxMeHg4bW1tFBQUsGDBAtasWUNNTQ07d+7k7NmzeHp6cv/995OUlMSECRMAqK+vZ+vWrfz73/8mODiYxMTEbtfMy8tj7969vPPOO4623NxcDh06xOXLl/Hz8yMxMZHo6GhSUlIAHG9RfeGFF4iLi6OpqYmdO3fyz3/+E1dXV+bMmcNzzz2Hl5cXAK2trWzbto1Tp05x++23D/iFUA0NDfzhD3/g008/5dq1a0yfPp3nnnuOsLCwbuPKy8vZuHEjZrOZ733ve6xbt47bb7+9233l5ubS0NBASEgIzzzzDPfee++A5iIizqVKhEg/uLq64urqSldXl6Pt73//O4GBgbz++ussXbqUzs5OUlNTCQoKYvfu3fz2t7+lrKyM1157zfGdLVu20NHRQXZ2NklJSezdu7fX67733nvk5OSwbNky9u3bx/PPP4+Hhwf+/v5s3LgR+P9/jG8kEzfaX3nlFbKysmhtbWXz5s2OmK+++ioXL14kMzOTdevW8eabbw7oZ9HR0UFUVBTbt29n9+7dfPe732X9+vU3VWP27t1LUlIS2dnZWK3WbnM4cuQIhw4dIjk5mZycHBYsWMC6desce0NEZHRQEiHSh66uLg4ePIjFYiEqKsrRHh4ezuOPP05QUBB33nknx48fx9PTk5UrVxIcHExERAQrV67k6NGjWK1WvvzyS4qKikhNTSUsLIw5c+awdOnSXq/95z//mSeffJL4+HiCgoKYOXMmMTExuLq6MnHiRABHtWT8+PEUFxdTVVVFWloa06ZNY+rUqaSkpHD69GkuX75MW1sb+fn5rFq1ihkzZjBjxgyeeuqpAf08AgICSEhIYNq0aUyZMoU1a9bQ0tJCaWlpt3FLly5lzpw5hIWFkZqaSnFxMZWVlY77WrVqFXPnziUwMJBHHnmEe+65h/z8/AHNRUScS8sZIj14++23effdd+ns7MTT05Pk5ORuJfv/Ld9XVlZy4cIF4uPju7V3dnby1VdfUVVVhYeHB8HBwY6+iIiIHq9/9epVLl26NKAS/xdffEFzczOLFi26qa+mpgZPT0+sVmu36/Y2h1vp6upi3759fPTRR1y5cgWr1Up7ezv19fXdxv133KCgICZOnIjZbCYgIIDa2lo2bdrUbXxnZyd+fn4DmouIOJeSCJEe/PSnP2XJkiW4u7vj6+t7U/+NfQ433KhUrF279qaxkydPpry8HBcXl35f3263D3jO165dIygoiC1bttzU5+fnh9lsBhjQPP7XW2+9xbFjx1i1ahXBwcGYTCZWrFiB1WrtNq6na1gsFgDWr1/P1KlTu/W5u7sbnpeIjDwlESI9mDhxIkFBQf0eHxoaysmTJ/H398dkMt3UP2XKFNra2jCbzY5qRFlZWY/xPD09ueOOOygqKiI8PPym/huvYbdarY5fh4aGcunSJTw8PPDx8bnpO4GBgbi6ulJaWuo4rtrbHG6lpKSEBx54gAceeAC4vln066+/vmlcaWkp06dPB64/D+Prr78mODgYHx8ffH19qa+v57777hvQtUXkm0V7IkSGyE9+8hO+853v8PLLL1NWVkZ1dTUnT550bKwMCQkhKiqK3//+91RUVFBYWMi7777ba8xly5Zx4MAB8vLyqKmp4dy5c5w4cQK4vjcB4NSpUzQ3N9PR0cHs2bOZNm0aGzZs4Ny5c9TU1HDmzBm2b98OXE9MfvzjH5OdnU1JSQklJSXs27dvQPcZGBjI6dOnKS8vp7y8nK1bt94yaXrnnXcoLCykoqKCjIwMoqKimDp1Ki4uLiQmJpKTk8PRo0eprq7m/PnzHDx4kE8++WRAcxER51IlQmSIeHh4sGPHDnbt2kVKSgpWq5XAwEBiY2MdY1588UW2bt3Ks88+y1133cUvf/lLfvOb3/QYc9GiRVgsFvbv38+VK1fw9/d3HAsNCAjgscceIz09nZaWFscRz/T0dHbt2sWGDRuwWCzccccd/PCHP3TEXLVqFdu2bSM5ORl/f39WrlzJ+vXr+32fTzzxBP/5z39YvXo1Pj4+PP3001RVVd00bvny5WRnZ1NVVUVkZCRpaWmOvkceeQQ3NzfefvttMjMzmTRpEjNmzFBlQmSUcSkoKBj4wquIiIiMeVrOEBEREUOURIiIiIghSiJERETEECURIiIiYoiSCBERETFESYSIiIgYoiRCREREDFESISIiIoYoiRARERFD/g9Ftc93kz0gPAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import plot_confusion_matrix,confusion_matrix\n",
    "def plot_cnf(model):\n",
    "    plt.style.use('classic')\n",
    "    plt.figure(figsize=(60,60))\n",
    "    np.set_printoptions(precision=2)\n",
    "    pred=model.predict(X_test)\n",
    "    cnf_matrix = confusion_matrix(y_test,pred)\n",
    "    cnf = plot_confusion_matrix(res_knn[-1],X_test,y_test,display_labels=['yes','No'])\n",
    "    cnf.ax_.set_title('confusion matrix')\n",
    "    plt.show()\n",
    "plot_cnf(res_knn[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}