Australia Weather.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b638d2af",
   "metadata": {},
   "source": [
    "<h1>Importing the datasets</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9c8085a3",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>row ID</th>\n",
       "      <th>Location</th>\n",
       "      <th>MinTemp</th>\n",
       "      <th>MaxTemp</th>\n",
       "      <th>Rainfall</th>\n",
       "      <th>Evaporation</th>\n",
       "      <th>Sunshine</th>\n",
       "      <th>WindGustDir</th>\n",
       "      <th>WindGustSpeed</th>\n",
       "      <th>WindDir9am</th>\n",
       "      <th>...</th>\n",
       "      <th>Humidity9am</th>\n",
       "      <th>Humidity3pm</th>\n",
       "      <th>Pressure9am</th>\n",
       "      <th>Pressure3pm</th>\n",
       "      <th>Cloud9am</th>\n",
       "      <th>Cloud3pm</th>\n",
       "      <th>Temp9am</th>\n",
       "      <th>Temp3pm</th>\n",
       "      <th>RainToday</th>\n",
       "      <th>RainTomorrow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Row0</td>\n",
       "      <td>Albury</td>\n",
       "      <td>13.4</td>\n",
       "      <td>22.9</td>\n",
       "      <td>0.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>W</td>\n",
       "      <td>44.0</td>\n",
       "      <td>W</td>\n",
       "      <td>...</td>\n",
       "      <td>71.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1007.7</td>\n",
       "      <td>1007.1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.9</td>\n",
       "      <td>21.8</td>\n",
       "      <td>No</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Row1</td>\n",
       "      <td>Albury</td>\n",
       "      <td>7.4</td>\n",
       "      <td>25.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WNW</td>\n",
       "      <td>44.0</td>\n",
       "      <td>NNW</td>\n",
       "      <td>...</td>\n",
       "      <td>44.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1010.6</td>\n",
       "      <td>1007.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.2</td>\n",
       "      <td>24.3</td>\n",
       "      <td>No</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Row2</td>\n",
       "      <td>Albury</td>\n",
       "      <td>17.5</td>\n",
       "      <td>32.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>W</td>\n",
       "      <td>41.0</td>\n",
       "      <td>ENE</td>\n",
       "      <td>...</td>\n",
       "      <td>82.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1010.8</td>\n",
       "      <td>1006.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>29.7</td>\n",
       "      <td>No</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Row3</td>\n",
       "      <td>Albury</td>\n",
       "      <td>14.6</td>\n",
       "      <td>29.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WNW</td>\n",
       "      <td>56.0</td>\n",
       "      <td>W</td>\n",
       "      <td>...</td>\n",
       "      <td>55.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1009.2</td>\n",
       "      <td>1005.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.6</td>\n",
       "      <td>28.9</td>\n",
       "      <td>No</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Row4</td>\n",
       "      <td>Albury</td>\n",
       "      <td>7.7</td>\n",
       "      <td>26.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>W</td>\n",
       "      <td>35.0</td>\n",
       "      <td>SSE</td>\n",
       "      <td>...</td>\n",
       "      <td>48.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1013.4</td>\n",
       "      <td>1010.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.3</td>\n",
       "      <td>25.5</td>\n",
       "      <td>No</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  row ID Location  MinTemp  MaxTemp  Rainfall  Evaporation  Sunshine  \\\n",
       "0   Row0   Albury     13.4     22.9       0.6          NaN       NaN   \n",
       "1   Row1   Albury      7.4     25.1       0.0          NaN       NaN   \n",
       "2   Row2   Albury     17.5     32.3       1.0          NaN       NaN   \n",
       "3   Row3   Albury     14.6     29.7       0.2          NaN       NaN   \n",
       "4   Row4   Albury      7.7     26.7       0.0          NaN       NaN   \n",
       "\n",
       "  WindGustDir  WindGustSpeed WindDir9am  ... Humidity9am  Humidity3pm  \\\n",
       "0           W           44.0          W  ...        71.0         22.0   \n",
       "1         WNW           44.0        NNW  ...        44.0         25.0   \n",
       "2           W           41.0        ENE  ...        82.0         33.0   \n",
       "3         WNW           56.0          W  ...        55.0         23.0   \n",
       "4           W           35.0        SSE  ...        48.0         19.0   \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       NaN       NaN     17.2     24.3         No   \n",
       "2       1010.8       1006.0       7.0       8.0     17.8     29.7         No   \n",
       "3       1009.2       1005.4       NaN       NaN     20.6     28.9         No   \n",
       "4       1013.4       1010.1       NaN       NaN     16.3     25.5         No   \n",
       "\n",
       "   RainTomorrow  \n",
       "0           0.0  \n",
       "1           0.0  \n",
       "2           0.0  \n",
       "3           0.0  \n",
       "4           0.0  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "df_train = pd.read_csv(r'C:\\Users\\Legion\\Desktop\\Python\\Datasets\\Australia Forecast\\Weather Training Data.csv')\n",
    "df_test = pd.read_csv(r'C:\\Users\\Legion\\Desktop\\Python\\Datasets\\Australia Forecast\\Weather Test Data.csv')\n",
    "df = pd.concat([df_train,df_test])\n",
    "\n",
    "df.reset_index(inplace = True)\n",
    "df.drop('index',axis = 'columns',inplace = True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d7e7776",
   "metadata": {},
   "source": [
    "<h1>Cleaning the dataset</h1>\n",
    "    \n",
    "<p1>At first, we drop the 'row ID' and 'RainToday' column because obviously this values won't help us by training our model and also it would damage the model if we want to fill the missing values in the RainToday column</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9668f285",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['row ID','RainToday'],axis = 'columns', inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fdaed21",
   "metadata": {},
   "source": [
    "<p>So now we want to take a look at NaN values and see how many missing data we have in our dataset</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "08c26612",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sunshine         67816\n",
       "Evaporation      60843\n",
       "Cloud3pm         57094\n",
       "Cloud9am         53657\n",
       "RainTomorrow     42677\n",
       "Pressure9am      14014\n",
       "Pressure3pm      13981\n",
       "WindDir9am       10013\n",
       "WindGustDir       9330\n",
       "WindGustSpeed     9270\n",
       "WindDir3pm        3778\n",
       "Humidity3pm       3610\n",
       "Temp3pm           2726\n",
       "WindSpeed3pm      2630\n",
       "Humidity9am       1774\n",
       "Rainfall          1406\n",
       "WindSpeed9am      1348\n",
       "Temp9am            904\n",
       "MinTemp            637\n",
       "MaxTemp            322\n",
       "Location             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum().sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b687d51",
   "metadata": {},
   "source": [
    "<h3>Handling the missing data</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea04cb01",
   "metadata": {},
   "source": [
    "<p>At first insight, we can observe, in the first few columns, we have a large portion of the data labeled as missing<p>\n",
    "<p> But let's take a closer view to the dataset and calculate the percentage of the missing data on the whole dataset</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b10258f6",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Total</th>\n",
       "      <th>Percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Sunshine</th>\n",
       "      <td>67816</td>\n",
       "      <td>47.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Evaporation</th>\n",
       "      <td>60843</td>\n",
       "      <td>42.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cloud3pm</th>\n",
       "      <td>57094</td>\n",
       "      <td>40.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cloud9am</th>\n",
       "      <td>53657</td>\n",
       "      <td>37.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RainTomorrow</th>\n",
       "      <td>42677</td>\n",
       "      <td>30.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pressure9am</th>\n",
       "      <td>14014</td>\n",
       "      <td>9.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pressure3pm</th>\n",
       "      <td>13981</td>\n",
       "      <td>9.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WindDir9am</th>\n",
       "      <td>10013</td>\n",
       "      <td>7.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WindGustDir</th>\n",
       "      <td>9330</td>\n",
       "      <td>6.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WindGustSpeed</th>\n",
       "      <td>9270</td>\n",
       "      <td>6.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WindDir3pm</th>\n",
       "      <td>3778</td>\n",
       "      <td>2.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Humidity3pm</th>\n",
       "      <td>3610</td>\n",
       "      <td>2.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Temp3pm</th>\n",
       "      <td>2726</td>\n",
       "      <td>1.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WindSpeed3pm</th>\n",
       "      <td>2630</td>\n",
       "      <td>1.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Humidity9am</th>\n",
       "      <td>1774</td>\n",
       "      <td>1.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rainfall</th>\n",
       "      <td>1406</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WindSpeed9am</th>\n",
       "      <td>1348</td>\n",
       "      <td>0.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Temp9am</th>\n",
       "      <td>904</td>\n",
       "      <td>0.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MinTemp</th>\n",
       "      <td>637</td>\n",
       "      <td>0.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MaxTemp</th>\n",
       "      <td>322</td>\n",
       "      <td>0.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Location</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Total  Percentage\n",
       "Sunshine       67816       47.69\n",
       "Evaporation    60843       42.79\n",
       "Cloud3pm       57094       40.15\n",
       "Cloud9am       53657       37.74\n",
       "RainTomorrow   42677       30.01\n",
       "Pressure9am    14014        9.86\n",
       "Pressure3pm    13981        9.83\n",
       "WindDir9am     10013        7.04\n",
       "WindGustDir     9330        6.56\n",
       "WindGustSpeed   9270        6.52\n",
       "WindDir3pm      3778        2.66\n",
       "Humidity3pm     3610        2.54\n",
       "Temp3pm         2726        1.92\n",
       "WindSpeed3pm    2630        1.85\n",
       "Humidity9am     1774        1.25\n",
       "Rainfall        1406        0.99\n",
       "WindSpeed9am    1348        0.95\n",
       "Temp9am          904        0.64\n",
       "MinTemp          637        0.45\n",
       "MaxTemp          322        0.23\n",
       "Location           0        0.00"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "percent = (df.isna().sum()/df.isna().count())*100\n",
    "percent = percent.sort_values(ascending = False)\n",
    "total = df.isna().sum().sort_values(ascending = False)\n",
    "\n",
    "df_insight = pd.concat([total,percent],keys = ['Total','Percentage'],axis = 1)\n",
    "df_insight['Percentage'] = df_insight['Percentage'].apply(lambda x: round(x,2) )\n",
    "df_insight"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e651fb4",
   "metadata": {},
   "source": [
    "<p>So it means that in the first two columns, we have more than 40% of the data missing, even by filling them with any strategy it could harm the model we would like to train later, so it's much more like than a tradeoff and we're going to drop them</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8be2e040",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['Sunshine','Evaporation'],axis = 'columns', inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e25c0735",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>WindSpeed3pm</th>\n",
       "      <th>Humidity9am</th>\n",
       "      <th>Humidity3pm</th>\n",
       "      <th>Pressure9am</th>\n",
       "      <th>Pressure3pm</th>\n",
       "      <th>Cloud9am</th>\n",
       "      <th>Cloud3pm</th>\n",
       "      <th>Temp9am</th>\n",
       "      <th>Temp3pm</th>\n",
       "      <th>RainTomorrow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Albury</td>\n",
       "      <td>13.4</td>\n",
       "      <td>22.9</td>\n",
       "      <td>0.6</td>\n",
       "      <td>W</td>\n",
       "      <td>44.0</td>\n",
       "      <td>W</td>\n",
       "      <td>WNW</td>\n",
       "      <td>20.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1007.7</td>\n",
       "      <td>1007.1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.9</td>\n",
       "      <td>21.8</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albury</td>\n",
       "      <td>7.4</td>\n",
       "      <td>25.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>WNW</td>\n",
       "      <td>44.0</td>\n",
       "      <td>NNW</td>\n",
       "      <td>WSW</td>\n",
       "      <td>4.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1010.6</td>\n",
       "      <td>1007.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.2</td>\n",
       "      <td>24.3</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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>20.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1010.8</td>\n",
       "      <td>1006.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>29.7</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Albury</td>\n",
       "      <td>14.6</td>\n",
       "      <td>29.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>WNW</td>\n",
       "      <td>56.0</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "      <td>19.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1009.2</td>\n",
       "      <td>1005.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.6</td>\n",
       "      <td>28.9</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Albury</td>\n",
       "      <td>7.7</td>\n",
       "      <td>26.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>W</td>\n",
       "      <td>35.0</td>\n",
       "      <td>SSE</td>\n",
       "      <td>W</td>\n",
       "      <td>6.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1013.4</td>\n",
       "      <td>1010.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.3</td>\n",
       "      <td>25.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Location  MinTemp  MaxTemp  Rainfall WindGustDir  WindGustSpeed WindDir9am  \\\n",
       "0   Albury     13.4     22.9       0.6           W           44.0          W   \n",
       "1   Albury      7.4     25.1       0.0         WNW           44.0        NNW   \n",
       "2   Albury     17.5     32.3       1.0           W           41.0        ENE   \n",
       "3   Albury     14.6     29.7       0.2         WNW           56.0          W   \n",
       "4   Albury      7.7     26.7       0.0           W           35.0        SSE   \n",
       "\n",
       "  WindDir3pm  WindSpeed9am  WindSpeed3pm  Humidity9am  Humidity3pm  \\\n",
       "0        WNW          20.0          24.0         71.0         22.0   \n",
       "1        WSW           4.0          22.0         44.0         25.0   \n",
       "2         NW           7.0          20.0         82.0         33.0   \n",
       "3          W          19.0          24.0         55.0         23.0   \n",
       "4          W           6.0          17.0         48.0         19.0   \n",
       "\n",
       "   Pressure9am  Pressure3pm  Cloud9am  Cloud3pm  Temp9am  Temp3pm  \\\n",
       "0       1007.7       1007.1       8.0       NaN     16.9     21.8   \n",
       "1       1010.6       1007.8       NaN       NaN     17.2     24.3   \n",
       "2       1010.8       1006.0       7.0       8.0     17.8     29.7   \n",
       "3       1009.2       1005.4       NaN       NaN     20.6     28.9   \n",
       "4       1013.4       1010.1       NaN       NaN     16.3     25.5   \n",
       "\n",
       "   RainTomorrow  \n",
       "0           0.0  \n",
       "1           0.0  \n",
       "2           0.0  \n",
       "3           0.0  \n",
       "4           0.0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e66e0b74",
   "metadata": {},
   "source": [
    "<h3>Transforming the categorical values to numericals and filling the NaN or missing values</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92633553",
   "metadata": {},
   "source": [
    "<p>Now we would like to transform the categorical columns to numerical one. We are going to do so with make_column_transformer and SimpleImputer </p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "618d4173",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Categorical Columns: Location | WindGustDir | WindDir9am | WindDir3pm\n",
      "_________________________________\n",
      "Numerical Columns: MinTemp | MaxTemp | Rainfall | WindGustSpeed | WindSpeed9am | WindSpeed3pm | Humidity9am | Humidity3pm | Pressure9am | Pressure3pm | Cloud9am | Cloud3pm | Temp9am | Temp3pm | RainTomorrow\n"
     ]
    }
   ],
   "source": [
    "#printing out the categorical columns\n",
    "categorical = df.select_dtypes(include = 'object').columns\n",
    "print('Categorical Columns:',' | '.join(categorical))\n",
    "\n",
    "print('_________________________________')\n",
    "\n",
    "numerical = df.select_dtypes(include = 'float64').columns\n",
    "print('Numerical Columns:',' | '.join(numerical))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4e850f4",
   "metadata": {},
   "source": [
    "<p>Here we fill the NaN and missing values, we first choose the strategy depending on the numerical or categorical values of data and let them be transformed</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c4820b18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                  transformers=[(&#x27;simpleimputer-1&#x27;,\n",
       "                                 SimpleImputer(strategy=&#x27;most_frequent&#x27;),\n",
       "                                 Index([&#x27;Location&#x27;, &#x27;WindGustDir&#x27;, &#x27;WindDir9am&#x27;, &#x27;WindDir3pm&#x27;], dtype=&#x27;object&#x27;)),\n",
       "                                (&#x27;simpleimputer-2&#x27;,\n",
       "                                 SimpleImputer(strategy=&#x27;median&#x27;),\n",
       "                                 Index([&#x27;MinTemp&#x27;, &#x27;MaxTemp&#x27;, &#x27;Rainfall&#x27;, &#x27;WindGustSpeed&#x27;, &#x27;WindSpeed9am&#x27;,\n",
       "       &#x27;WindSpeed3pm&#x27;, &#x27;Humidity9am&#x27;, &#x27;Humidity3pm&#x27;, &#x27;Pressure9am&#x27;,\n",
       "       &#x27;Pressure3pm&#x27;, &#x27;Cloud9am&#x27;, &#x27;Cloud3pm&#x27;, &#x27;Temp9am&#x27;, &#x27;Temp3pm&#x27;,\n",
       "       &#x27;RainTomorrow&#x27;],\n",
       "      dtype=&#x27;object&#x27;))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                  transformers=[(&#x27;simpleimputer-1&#x27;,\n",
       "                                 SimpleImputer(strategy=&#x27;most_frequent&#x27;),\n",
       "                                 Index([&#x27;Location&#x27;, &#x27;WindGustDir&#x27;, &#x27;WindDir9am&#x27;, &#x27;WindDir3pm&#x27;], dtype=&#x27;object&#x27;)),\n",
       "                                (&#x27;simpleimputer-2&#x27;,\n",
       "                                 SimpleImputer(strategy=&#x27;median&#x27;),\n",
       "                                 Index([&#x27;MinTemp&#x27;, &#x27;MaxTemp&#x27;, &#x27;Rainfall&#x27;, &#x27;WindGustSpeed&#x27;, &#x27;WindSpeed9am&#x27;,\n",
       "       &#x27;WindSpeed3pm&#x27;, &#x27;Humidity9am&#x27;, &#x27;Humidity3pm&#x27;, &#x27;Pressure9am&#x27;,\n",
       "       &#x27;Pressure3pm&#x27;, &#x27;Cloud9am&#x27;, &#x27;Cloud3pm&#x27;, &#x27;Temp9am&#x27;, &#x27;Temp3pm&#x27;,\n",
       "       &#x27;RainTomorrow&#x27;],\n",
       "      dtype=&#x27;object&#x27;))])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">simpleimputer-1</label><div class=\"sk-toggleable__content\"><pre>Index([&#x27;Location&#x27;, &#x27;WindGustDir&#x27;, &#x27;WindDir9am&#x27;, &#x27;WindDir3pm&#x27;], dtype=&#x27;object&#x27;)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">simpleimputer-2</label><div class=\"sk-toggleable__content\"><pre>Index([&#x27;MinTemp&#x27;, &#x27;MaxTemp&#x27;, &#x27;Rainfall&#x27;, &#x27;WindGustSpeed&#x27;, &#x27;WindSpeed9am&#x27;,\n",
       "       &#x27;WindSpeed3pm&#x27;, &#x27;Humidity9am&#x27;, &#x27;Humidity3pm&#x27;, &#x27;Pressure9am&#x27;,\n",
       "       &#x27;Pressure3pm&#x27;, &#x27;Cloud9am&#x27;, &#x27;Cloud3pm&#x27;, &#x27;Temp9am&#x27;, &#x27;Temp3pm&#x27;,\n",
       "       &#x27;RainTomorrow&#x27;],\n",
       "      dtype=&#x27;object&#x27;)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">remainder</label><div class=\"sk-toggleable__content\"><pre></pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "ColumnTransformer(remainder='passthrough',\n",
       "                  transformers=[('simpleimputer-1',\n",
       "                                 SimpleImputer(strategy='most_frequent'),\n",
       "                                 Index(['Location', 'WindGustDir', 'WindDir9am', 'WindDir3pm'], dtype='object')),\n",
       "                                ('simpleimputer-2',\n",
       "                                 SimpleImputer(strategy='median'),\n",
       "                                 Index(['MinTemp', 'MaxTemp', 'Rainfall', 'WindGustSpeed', 'WindSpeed9am',\n",
       "       'WindSpeed3pm', 'Humidity9am', 'Humidity3pm', 'Pressure9am',\n",
       "       'Pressure3pm', 'Cloud9am', 'Cloud3pm', 'Temp9am', 'Temp3pm',\n",
       "       'RainTomorrow'],\n",
       "      dtype='object'))])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.compose import make_column_transformer, ColumnTransformer\n",
    "from sklearn.impute import SimpleImputer\n",
    "\n",
    "imp = SimpleImputer(strategy = 'most_frequent')\n",
    "ct = make_column_transformer((imp,categorical),\n",
    "                             (SimpleImputer(strategy = 'median'),numerical),\n",
    "                            remainder = 'passthrough')\n",
    "ct"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80ca359e",
   "metadata": {},
   "source": [
    "<p>Here we fit and transform the data and update the dataframe </p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "729bdced",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Location</th>\n",
       "      <th>WindGustDir</th>\n",
       "      <th>WindDir9am</th>\n",
       "      <th>WindDir3pm</th>\n",
       "      <th>MinTemp</th>\n",
       "      <th>MaxTemp</th>\n",
       "      <th>Rainfall</th>\n",
       "      <th>WindGustSpeed</th>\n",
       "      <th>WindSpeed9am</th>\n",
       "      <th>WindSpeed3pm</th>\n",
       "      <th>Humidity9am</th>\n",
       "      <th>Humidity3pm</th>\n",
       "      <th>Pressure9am</th>\n",
       "      <th>Pressure3pm</th>\n",
       "      <th>Cloud9am</th>\n",
       "      <th>Cloud3pm</th>\n",
       "      <th>Temp9am</th>\n",
       "      <th>Temp3pm</th>\n",
       "      <th>RainTomorrow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Albury</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "      <td>WNW</td>\n",
       "      <td>13.4</td>\n",
       "      <td>22.9</td>\n",
       "      <td>0.6</td>\n",
       "      <td>44.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1007.7</td>\n",
       "      <td>1007.1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>16.9</td>\n",
       "      <td>21.8</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albury</td>\n",
       "      <td>WNW</td>\n",
       "      <td>NNW</td>\n",
       "      <td>WSW</td>\n",
       "      <td>7.4</td>\n",
       "      <td>25.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1010.6</td>\n",
       "      <td>1007.8</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>17.2</td>\n",
       "      <td>24.3</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Albury</td>\n",
       "      <td>W</td>\n",
       "      <td>ENE</td>\n",
       "      <td>NW</td>\n",
       "      <td>17.5</td>\n",
       "      <td>32.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1010.8</td>\n",
       "      <td>1006.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>29.7</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Albury</td>\n",
       "      <td>WNW</td>\n",
       "      <td>W</td>\n",
       "      <td>W</td>\n",
       "      <td>14.6</td>\n",
       "      <td>29.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>56.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1009.2</td>\n",
       "      <td>1005.4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>20.6</td>\n",
       "      <td>28.9</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Albury</td>\n",
       "      <td>W</td>\n",
       "      <td>SSE</td>\n",
       "      <td>W</td>\n",
       "      <td>7.7</td>\n",
       "      <td>26.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1013.4</td>\n",
       "      <td>1010.1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>16.3</td>\n",
       "      <td>25.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Location WindGustDir WindDir9am WindDir3pm MinTemp MaxTemp Rainfall  \\\n",
       "0   Albury           W          W        WNW    13.4    22.9      0.6   \n",
       "1   Albury         WNW        NNW        WSW     7.4    25.1      0.0   \n",
       "2   Albury           W        ENE         NW    17.5    32.3      1.0   \n",
       "3   Albury         WNW          W          W    14.6    29.7      0.2   \n",
       "4   Albury           W        SSE          W     7.7    26.7      0.0   \n",
       "\n",
       "  WindGustSpeed WindSpeed9am WindSpeed3pm Humidity9am Humidity3pm Pressure9am  \\\n",
       "0          44.0         20.0         24.0        71.0        22.0      1007.7   \n",
       "1          44.0          4.0         22.0        44.0        25.0      1010.6   \n",
       "2          41.0          7.0         20.0        82.0        33.0      1010.8   \n",
       "3          56.0         19.0         24.0        55.0        23.0      1009.2   \n",
       "4          35.0          6.0         17.0        48.0        19.0      1013.4   \n",
       "\n",
       "  Pressure3pm Cloud9am Cloud3pm Temp9am Temp3pm RainTomorrow  \n",
       "0      1007.1      8.0      5.0    16.9    21.8          0.0  \n",
       "1      1007.8      5.0      5.0    17.2    24.3          0.0  \n",
       "2      1006.0      7.0      8.0    17.8    29.7          0.0  \n",
       "3      1005.4      5.0      5.0    20.6    28.9          0.0  \n",
       "4      1010.1      5.0      5.0    16.3    25.5          0.0  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c,n = np.asarray(categorical),np.asarray(numerical)\n",
    "transformed = ct.fit_transform(df)\n",
    "df = pd.DataFrame(transformed,columns = np.concatenate((c,n)))\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b019f24",
   "metadata": {},
   "source": [
    "<h3>Checking if there is still some missing data</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30ab5677",
   "metadata": {},
   "source": [
    "<p>We would like to make sure that there is no missing data</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3c2f65be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"There isn't any missing data in the dataset\""
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def check_nan():\n",
    "    for count in df.isna().sum():\n",
    "        if count>0:\n",
    "            return ('There is still some missing values')\n",
    "        \n",
    "        return (\"There isn't any missing data in the dataset\")\n",
    "    \n",
    "check_nan()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c9cbd566",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Location</th>\n",
       "      <th>WindGustDir</th>\n",
       "      <th>WindDir9am</th>\n",
       "      <th>WindDir3pm</th>\n",
       "      <th>MinTemp</th>\n",
       "      <th>MaxTemp</th>\n",
       "      <th>Rainfall</th>\n",
       "      <th>WindGustSpeed</th>\n",
       "      <th>WindSpeed9am</th>\n",
       "      <th>WindSpeed3pm</th>\n",
       "      <th>Humidity9am</th>\n",
       "      <th>Humidity3pm</th>\n",
       "      <th>Pressure9am</th>\n",
       "      <th>Pressure3pm</th>\n",
       "      <th>Cloud9am</th>\n",
       "      <th>Cloud3pm</th>\n",
       "      <th>Temp9am</th>\n",
       "      <th>Temp3pm</th>\n",
       "      <th>RainTomorrow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>13.4</td>\n",
       "      <td>22.9</td>\n",
       "      <td>0.6</td>\n",
       "      <td>44.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1007.7</td>\n",
       "      <td>1007.1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>16.9</td>\n",
       "      <td>21.8</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>6</td>\n",
       "      <td>15</td>\n",
       "      <td>7.4</td>\n",
       "      <td>25.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1010.6</td>\n",
       "      <td>1007.8</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>17.2</td>\n",
       "      <td>24.3</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>17.5</td>\n",
       "      <td>32.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1010.8</td>\n",
       "      <td>1006.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>29.7</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>14.6</td>\n",
       "      <td>29.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>56.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1009.2</td>\n",
       "      <td>1005.4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>20.6</td>\n",
       "      <td>28.9</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>7.7</td>\n",
       "      <td>26.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1013.4</td>\n",
       "      <td>1010.1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>16.3</td>\n",
       "      <td>25.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Location  WindGustDir  WindDir9am  WindDir3pm MinTemp MaxTemp Rainfall  \\\n",
       "0         2           13          13          14    13.4    22.9      0.6   \n",
       "1         2           14           6          15     7.4    25.1      0.0   \n",
       "2         2           13           1           7    17.5    32.3      1.0   \n",
       "3         2           14          13          13    14.6    29.7      0.2   \n",
       "4         2           13          10          13     7.7    26.7      0.0   \n",
       "\n",
       "  WindGustSpeed WindSpeed9am WindSpeed3pm Humidity9am Humidity3pm Pressure9am  \\\n",
       "0          44.0         20.0         24.0        71.0        22.0      1007.7   \n",
       "1          44.0          4.0         22.0        44.0        25.0      1010.6   \n",
       "2          41.0          7.0         20.0        82.0        33.0      1010.8   \n",
       "3          56.0         19.0         24.0        55.0        23.0      1009.2   \n",
       "4          35.0          6.0         17.0        48.0        19.0      1013.4   \n",
       "\n",
       "  Pressure3pm Cloud9am Cloud3pm Temp9am Temp3pm RainTomorrow  \n",
       "0      1007.1      8.0      5.0    16.9    21.8          0.0  \n",
       "1      1007.8      5.0      5.0    17.2    24.3          0.0  \n",
       "2      1006.0      7.0      8.0    17.8    29.7          0.0  \n",
       "3      1005.4      5.0      5.0    20.6    28.9          0.0  \n",
       "4      1010.1      5.0      5.0    16.3    25.5          0.0  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "for column in categorical:\n",
    "    df[column] = LabelEncoder().fit_transform(df[column])\n",
    "#     print(df[column])\n",
    "    \n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ddd6e2a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "obj_col = df.select_dtypes(include = 'object')\n",
    "for column in obj_col:\n",
    "    df[column] = df[column].astype('float64')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6734d3c1",
   "metadata": {},
   "source": [
    "<h2>Correlation Matrix</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e6d47b5",
   "metadata": {},
   "source": [
    "Now we plot the correlation matrix which is useful to know what features are the most relevant to the target set which is 'RainTomorrow'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2c956ef5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1600x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "plt.figure(figsize = (16,8))\n",
    "sns.heatmap(df.corr(),cmap = 'RdPu',annot = True,linewidth = 0.5,linecolor = 'black')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e866f32",
   "metadata": {},
   "source": [
    "<h3>Selecting the appropriate features for our model</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0580747f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.figure(figsize=(20, 8))\n",
    "\n",
    "info = dict(df.corr()['RainTomorrow'].sort_values(ascending = False))\n",
    "info = {key:info[key] for key in info if info[key]>0}\n",
    "# print(info)\n",
    "\n",
    "plt.title('Correlation between RainTomorrow and other columns')\n",
    "plt.stem(info.keys(),info.values())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a3dd700",
   "metadata": {},
   "source": [
    "<h2>Spliting the dataset into train and test sets</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d87722b",
   "metadata": {},
   "source": [
    "So now we define our train set and we would like to use the most relevant features to target feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "bed22a1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X,y = df[[key for key in info.keys() if key!='RainTomorrow']],df['RainTomorrow']\n",
    "xtrain,xtest, ytrain,ytest = train_test_split(X,y,shuffle = True, test_size = 0.25,random_state = 101)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b5d0050",
   "metadata": {},
   "source": [
    "<h3>Scaling our train and test feature with MinMaxScaler</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6ac1e3e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler = MinMaxScaler()\n",
    "xtrain = scaler.fit_transform(xtrain)\n",
    "xtest = scaler.transform(xtest)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f2b121b",
   "metadata": {},
   "source": [
    "<h2>Deploying ML Models</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f402552",
   "metadata": {},
   "source": [
    "because of the property of our target set, we use classification algorithms such as RandomForestClassifier and GradientBoostingClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c26b6885",
   "metadata": {},
   "source": [
    "<h3>RandomForestClassifier</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "daebcce6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(max_depth=17)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" checked><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(max_depth=17)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(max_depth=17)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rfc = RandomForestClassifier(n_estimators = 100, max_depth = 17,criterion = 'gini')\n",
    "rfc.fit(xtrain,ytrain)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74c8ab71",
   "metadata": {},
   "source": [
    "<h2>Accuracy</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0aa540ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set accuracy is: 0.8550732791358406\n",
      "Train set accuracy is: 0.9524680244551967\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "ypred_rfc = rfc.predict(xtest)\n",
    "ypred_t_rfc = rfc.predict(xtrain)\n",
    "\n",
    "print('Test set accuracy is:',accuracy_score(ytest,ypred_rfc))\n",
    "print('Train set accuracy is:',accuracy_score(ytrain,ypred_t_rfc))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd422c55",
   "metadata": {},
   "source": [
    "<h2>Confusion Matrix</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e0332f34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "cm = confusion_matrix(ytest,ypred_rfc)\n",
    "cm = pd.DataFrame(cm)\n",
    "sns.heatmap(cm,annot = True,linewidth = 1,linecolor = 'black',cmap = 'RdPu',fmt = 'g',annot_kws = {'size':25})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19f47d00",
   "metadata": {},
   "source": [
    "<h3>GradientBoostingClassifier</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c0cceba0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GradientBoostingClassifier(learning_rate=0.07, max_depth=10)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" checked><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GradientBoostingClassifier</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingClassifier(learning_rate=0.07, max_depth=10)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "GradientBoostingClassifier(learning_rate=0.07, max_depth=10)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "gbc = GradientBoostingClassifier(loss = 'log_loss',learning_rate = 0.07,max_depth = 10)\n",
    "\n",
    "gbc.fit(xtrain,ytrain)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65b96c54",
   "metadata": {},
   "source": [
    "<h2>Accuracy</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7b9100f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set accuracy is: 0.8527103434695772\n",
      "Train set accuracy is: 0.9251059600165035\n"
     ]
    }
   ],
   "source": [
    "ypred_gbc = gbc.predict(xtest)\n",
    "ypred_t_gbc = gbc.predict(xtrain)\n",
    "\n",
    "print('Test set accuracy is:',accuracy_score(ytest,ypred_gbc))\n",
    "print('Train set accuracy is:',accuracy_score(ytrain,ypred_t_gbc))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "713632c7",
   "metadata": {},
   "source": [
    "<h2>Confusion Matrix</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "86bfd2ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(ytest,ypred_gbc)\n",
    "cm = pd.DataFrame(cm)\n",
    "sns.heatmap(cm,annot = True,linewidth = 1,linecolor = 'black',cmap = 'RdPu',fmt = 'g',annot_kws = {'size':25})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63a88c63",
   "metadata": {},
   "source": [
    "<h3>Neural Network</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "53893a0e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/40\n",
      "53/53 [==============================] - 1s 1ms/step - loss: 0.1330\n",
      "Epoch 2/40\n",
      "53/53 [==============================] - 0s 1ms/step - loss: 0.1178\n",
      "Epoch 3/40\n",
      "53/53 [==============================] - 0s 1000us/step - loss: 0.1106\n",
      "Epoch 4/40\n",
      "53/53 [==============================] - 0s 1ms/step - loss: 0.1080\n",
      "Epoch 5/40\n",
      "53/53 [==============================] - 0s 1ms/step - loss: 0.1072\n",
      "Epoch 6/40\n",
      "53/53 [==============================] - 0s 1ms/step - loss: 0.1066\n",
      "Epoch 7/40\n",
      "53/53 [==============================] - 0s 1ms/step - loss: 0.1062\n",
      "Epoch 8/40\n",
      "53/53 [==============================] - 0s 1ms/step - loss: 0.1061\n",
      "Epoch 9/40\n",
      "53/53 [==============================] - 0s 1ms/step - loss: 0.1057\n",
      "Epoch 10/40\n",
      "53/53 [==============================] - 0s 1ms/step - loss: 0.1055\n",
      "Epoch 11/40\n",
      "53/53 [==============================] - 0s 1ms/step - loss: 0.1054\n",
      "Epoch 12/40\n",
      "53/53 [==============================] - 0s 981us/step - loss: 0.1053\n",
      "Epoch 13/40\n",
      "53/53 [==============================] - 0s 1ms/step - loss: 0.1052\n",
      "Epoch 14/40\n",
      "53/53 [==============================] - 0s 1ms/step - loss: 0.1050\n",
      "Epoch 15/40\n",
      "53/53 [==============================] - 0s 981us/step - loss: 0.1050\n",
      "Epoch 16/40\n",
      "53/53 [==============================] - 0s 981us/step - loss: 0.1049\n",
      "Epoch 17/40\n",
      "53/53 [==============================] - 0s 1ms/step - loss: 0.1050\n",
      "Epoch 18/40\n",
      "53/53 [==============================] - 0s 984us/step - loss: 0.1049\n",
      "Epoch 19/40\n",
      "53/53 [==============================] - 0s 981us/step - loss: 0.1048\n",
      "Epoch 20/40\n",
      "53/53 [==============================] - 0s 942us/step - loss: 0.1046\n",
      "Epoch 21/40\n",
      "53/53 [==============================] - 0s 1ms/step - loss: 0.1046\n",
      "Epoch 22/40\n",
      "53/53 [==============================] - 0s 981us/step - loss: 0.1045\n",
      "Epoch 23/40\n",
      "53/53 [==============================] - 0s 923us/step - loss: 0.1045\n",
      "Epoch 24/40\n",
      "53/53 [==============================] - 0s 942us/step - loss: 0.1044\n",
      "Epoch 25/40\n",
      "53/53 [==============================] - 0s 923us/step - loss: 0.1044\n",
      "Epoch 26/40\n",
      "53/53 [==============================] - 0s 942us/step - loss: 0.1044\n",
      "Epoch 27/40\n",
      "53/53 [==============================] - 0s 923us/step - loss: 0.1043\n",
      "Epoch 28/40\n",
      "53/53 [==============================] - 0s 923us/step - loss: 0.1043\n",
      "Epoch 29/40\n",
      "53/53 [==============================] - 0s 1ms/step - loss: 0.1043\n",
      "Epoch 30/40\n",
      "53/53 [==============================] - 0s 1ms/step - loss: 0.1042\n",
      "Epoch 31/40\n",
      "53/53 [==============================] - 0s 1ms/step - loss: 0.1041\n",
      "Epoch 32/40\n",
      "53/53 [==============================] - 0s 1ms/step - loss: 0.1040\n",
      "Epoch 33/40\n",
      "53/53 [==============================] - 0s 942us/step - loss: 0.1041\n",
      "Epoch 34/40\n",
      "53/53 [==============================] - 0s 962us/step - loss: 0.1041\n",
      "Epoch 35/40\n",
      "53/53 [==============================] - 0s 1000us/step - loss: 0.1040\n",
      "Epoch 36/40\n",
      "53/53 [==============================] - 0s 1000us/step - loss: 0.1040\n",
      "Epoch 37/40\n",
      "53/53 [==============================] - 0s 962us/step - loss: 0.1039\n",
      "Epoch 38/40\n",
      "53/53 [==============================] - 0s 981us/step - loss: 0.1039\n",
      "Epoch 39/40\n",
      "53/53 [==============================] - 0s 1ms/step - loss: 0.1039\n",
      "Epoch 40/40\n",
      "53/53 [==============================] - 0s 1ms/step - loss: 0.1039\n"
     ]
    }
   ],
   "source": [
    "import tensorflow.keras as kr\n",
    "\n",
    "num = len(info.keys())-1\n",
    "model = kr.Sequential()\n",
    "\n",
    "#input layer\n",
    "model.add(kr.layers.Dense(num,activation = 'ReLU'))\n",
    "\n",
    "#hidden layers\n",
    "model.add(kr.layers.Dense(num,activation = 'ReLU'))\n",
    "model.add(kr.layers.Dense(num,activation = 'ReLU'))\n",
    "model.add(kr.layers.Dense(num,activation = 'ReLU'))\n",
    "\n",
    "#output layer\n",
    "model.add(kr.layers.Dense(1,activation = 'ReLU'))\n",
    "\n",
    "model.compile(optimizer = 'adam',loss = 'mse')\n",
    "\n",
    "hist = model.fit(xtrain,ytrain,\n",
    "                 batch_size = 2048,\n",
    "                 epochs = 40)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8e1141f",
   "metadata": {},
   "source": [
    "<h2>Confusion Matrix</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "6416a4a7",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1111/1111 [==============================] - 1s 484us/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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ubLMO9uj1cmfh/MdFe5GaqHm8P+5yPHauKpk78uq8gWV+4RYbOLEkUTh7IAYndlwotw+JcSn49ZMDwvmYhd7lxq6d/hv2bfinTLKiyaHvTuDyidvC+fMjulb6Gnq2ZWRk4O/DJX+GhwzygqGhocbYs+ejhMd9nn8e7du1LbddQwMDTJ5YMlci+eFDxN0tf97Sx4FByMlRAgAGew3EtMlva/sWqIboQSbZUZ8xyXjKTu25pHberL1dmZjO/dV/uYX/Fllpu0d/PSs89vDtDD19zf9pe5f6hzfrcQ6O/HKmwnYPbjomPLZ2sEQHD+cyMS06OqChXckY91Gt+lsS49CqMVo916zS12jjdKnPt3GzhpCbaP4HheqHA3/9LfzjDgDDh2keKgGARyklXxTatG5VadvimJRHmr9oxN75F/8cPwEA0NPTwzz/9yttm2qenoRHfVbf3/9T9zglS+3cxMKoTIydk7XwODMtG4lxKZW2G3uxZKa6hY0ZOvXR/Euyh09JiffysVvIyVRqjCumiH2Iu1dLJnz28Omkob825falPI8S0tUqKL1e6VxBtPa0+Xyp/thZaqikpbMTunR2LTfWxMRYeJyXV/my7bxc9ZjyJpP+vmOX8Lhndzc4OjhU2jbRs4JJxlNm18Ja7VzTUIWpZckvO03zOzTJSlePa9m1qcY4J9cmwuMrEbFatX21VJxzlyZlnjcp1V8AyH5cceJSrHSfy+tvVdmW+nxVKhXSkzMkaZfqnrv37iGy1BBIRVUMAHDtVJJAnzl3vtL2T58tqcY1bGgFZ6cWGuNOnDwpPO7Vs0el7VLtwDkZ0uDqkqfs+RFd1M6vnrxTJqZ0YmFsrt03cfE39uYuZb8t2Ta3hrFZyTK7+FvJWrVdOq5ZB/syz4sTIWPzypfyAep9bu5Stt0n4TG85PO9GXlXq+W89GzauWefsJeCnp4eXhnqU2H8mFdHYPufO6BSqRAdcxk7du/B8Jc178fxID4em77/QTifOG4s9PTKfmdTKpW4fvOmcN6uTdEcrLv37mPr738gLPwY7sfHAwAa2VjjuS5dMNjLE/379a3amyXJ8Ru4NPg5PkUmFkZ45f3+wvntC/cQdzm+TFzyvVThsamlMRo3a1hp286u6ntJ2DnblImxFVVRkrQYhgGgNlwjbgNQ7y8AOLlWvnGWla05rGzNhXPxkMuT6DuqG5qXSoLCtlQ834SeXYWFhdi5p2SS9fPuPWFnV/GqK5cO7TF31nvC5Ob/LfkYS5Z9hovRMcjKykZeXh7u3ruPn7b8hlffeAtJyUWTsYcOGVTuhlk3b99Gfn7JpOXGjRvht+1/YOjI1/DND5tx49YtZGVlISsrC3F372HX3n2Y9t5sjBn/NhISEqv7MRDVOFYynqIpX46AtYOlcP7joj0a42KO3VI77zvaDX+s+LvCtvuOcVM7N9FQARFXOzLTtNvePLvUsIa+vh7kJoZqO4vGXriHrPRsYWOwvqPdcPin0xW22U/U3wYG+jA0MqjyFubFLBqZYcrKkcJ58r1H2LdB8x4k9Ow7e+682o6a5e2NIfb2+HGwt7fDilVrcP9BPH7b/gd+2/6HxlhHB3uMH/sGJowbW257qanqu4buP/QXvt/8k3Du1Lw57OxskZ7+GNdv3kRBQVFCcj7qAl57czy2/7y50uSIdIPfwKXBz/EpGTjRA96TewvnR387i9N7ozXG3jgbh3vXEoTzUQFesG1etoJQrPvgjmUmTmoasjAyVV9pkZejeWMtMWW2+j/84nZUqkKEbz0nnPf06VRmF9TSGjW1wpgPB5e5ru0wi5iengwBW98W9ulQqVRYM2VLuRuH0bNvx+6SCZ9mZqYY+FL/CqLVDfH2woY1K/Fcl/InI5uammLUyBF4eeiQCtt6nKE+J6g4wejYoT12/PYLDu3ZgR+//Ro7t21BeMgBDBtS8vciITERc/63SOt+k7RkEv6vPqtykpGcnIygoCAMHz4cHh4e8PDwwPDhw7F8+XIkJSXpoo91XscXWsFv/WjhPP52Mr4qtXGUWGFhodp+GubWpgj6Zza6ealv3KPfQA9D3nkBC/+YXGY8WP+/bbYrulaQX/neE0Vx6vMaGhiWLYBt+/wvtZUqAdsmYahfP2G772JdB7TD8n/8Nd6LRRyrrWlrXkOXl9oJ57tWHSmzcRnVH9nZ2TgYUlL5G+w1EEZG2s1tSk1Lg//8/+GV117H+QtFe8+Ym5vhuS6d4d6jO1o6O0EmkyEzMxOr1q7HgCEv44+du8ttT6ksuz24s1ML/Pjt13Dp0F7teiMbG6wIXKY2d+T02Uhh+StRXVSl4ZIzZ87A29sbJiYm8PT0RNu2Rfs5JCQkYM2aNfj8889x6NAhdO/evcJ2lEollErtViDUdc6dm2DJnmnCDpuPEtKxeNC6MqtBxMK3RsK1Xxthl03b5tZYdmgGHj5Ixf3riWhgoI8WnRyFlShxVxRI/DcF3Qe5AACyNAyFiG+eZmBU+Q3dAMDQSP2PiVLDslfF7WSsnvwL5vw8Qdja+921ozDhs2G4Ex2PPGU+mrRpLGyrnp2hRNiWMxg89QWhjcw07VbSlPbG4iEY6leyg+Ox38/ju7k7qtwOPTtCDh9BZmamcO5byaqSYmnp6Rj39hRcv1k0XGlna4tF8+fC86UX1ZJ4RUICVq/dgD9370FWVhb+t+QjFBTkY9TIEWXaNDEum9wEzP0AZmbl3/Bw4fw5+OvvUGTnFP19+HPXHvTp/bxW74GkwzK/NKqUZMycOROvvfYaNm7cWGbnx8LCQkybNg0zZ85EREREhe0EBgbio48+qnpv65gmbW2x7K8Zwl1OH6dkYpHXWty/od2ErnXv/oaUB6kYs2iQcIdXG0crte27AeDyidv4Ysz3atUSTbeTz85QTw7kxtolGeINrcTtFDv6WySyM5SY/f2bsGxcNKnTxMIYLs+3VItLvvcIQWOD0bZHyZK//LyCSvfsEPOd1R9vflTyre/sgRgEvfFDpfdBoWfbzlJDJS2aN0P3bs9p9bpPv1ghJBjWDRvitx+/17inhb2dHQI/WQorK0t8/+PPRa8NWoE+vZ+Hg736KikT0W3aGza0Qt9KEgZLCwv06/sCDv5VVI05c+5chfGkG/V7kEM6VUrWLly4gNmzZ2vcWlomk2H27NmIioqqtJ2AgACkpaWpHc8aOycbfPb3e8JOmFmPc7B48HqtNqoq7ddlBzG5zUf4bdkBXD0Zi7Skx8jLzUfy/VScPXgZy98Mxrw+/4eku49gUWoIIimu7O6D6cmZauelJ6FWpKF9ySZDWenZFS4LPb03GhOdl2Dje9sReegyku+nIk+Zh7TkDFw7dQffzd2Bd10/RXT4TfX+arEte2mDpvTG1JUl93W4GHYdy0Z8o9X24/TsSkhIRMTpklVFlS1bLRavUGDP/pKt7t+Z/Halm2a97zcdNtZFc6VycpTY+vufZWIaWlmpnXdo167crflL69i+ZCglKSm53lR+6dlTpUqGvb09Tp8+jfbt22t8/vTp07CzK7tNtphcLtfqtsh1lU0TK3wW+p6w9DQnKxdLh27AtdN3nqi9pLuP8OOHe/Hjh3srjCu9h8X1M/+Wef7+dfUKSuPmlS+NBaC2hPbu1YQKIovkZCqx+6sw7P4qrMK40ntj3NDQ3/L0H9sDfhvGCOdXT8bio2Ebn3hlCj07du3dJ9yoTCaTaT1UcurMWbUbnA14sfJ9KoyMjNDboxd279sPoGhFi5izUwvIZDJhvw4rS+0Seysr9bi09HTYNm6s1WtJGnp6rGVIoUpJxpw5czB16lRERkZiwIABQkKRkJCA0NBQfPPNN1ixYkUlrTzbrGzN8dnfM+HQshEAIDcnD5/4fo3o8JuVvLJ6mrS1Vdsp9NqpO2VicjKVSIxLEVaqaLvLZqvnSuLuXlFUEFk1bbo3Fx5r6q8mvUc+B//gcdD/794sN8/F4cNB68odwqH6pfQdV3t2d0MTR+228E5IVE/A7bX4sgQADvYlccX7ZpRmbGyMpk0ccfdeUQWz+C6vlRFPGH2Wv5TVVjImGZKoUpLh5+eHRo0aYeXKlVi/fr2wpltfXx9ubm4IDg7GqFGjdNLRusCsoQk+DZmJZu2LvqHn5eYjcNR3OB9yVec/+4VXS8ad464ocOv8XY1x0eE38dKbPQEUrXqpjH4DPbRzd1J7vRRcnm8pzC0pyC9A+NbKb6rWY0hHzNsyAfoNilah3Il+gEVea7Xe74OebRcvReN27B3hfLiWe2MAgKGB+rwjpVIJA4PK5ywVT84EACMjzYlADzc3Icm49+CBVv25XyrOwMAAFubmFUSTLuhpMaxFlavyBNrRo0fj5MmTyMrKwv3793H//n1kZWXh5MmT9TrBMDY3wrJDM+DcuejeHgX5BVg+NrjMXVd1QW5iCJ93S8q7h74tf8nbyf9uCQ8UbT1eWTWj18udhU22CgpUkr2fkfNKbkd/9sBlPHxQ8bycLv3b4n+/T4bBf8tn711LwELPr5D+MLPC11H9UbqKYWJsDC/PAVq/VjwUcSlGuyXQMZevCI/tbDUPZ3gNKNmj48bNW0jUYqn/8ZOnhMddXDtpNY+DqDZ64lU6BgYGcHBwgIODg1YZ/7NMbmyAj/ZNF1ZLFBSosHLizzj2e+U3WZLC5BUj0KiJFQAg4c5D7N9Y/k6XZ/bHqN2U7fVFg8qN1dOT4bUFXsJ55IEYpCVV/4ZjfUe7weO/zcPy8wrwUyVzTTp4OGPx7mmQGxd924y/nYyAAWvwKCG92n2hZ0NuXh72H/xLOPceOACmopUdFRGvQPlxy2+VvuZidIzaDdh6uLlpjOv9vIcwrFJQUIDvgn/SGFcsLPwfXL9RUjEc8GK/CqJJV2R60h31WT1/+9XXwLABFu96B536tAZQtNvkV1O34PDPFW+rrY2X3uypceOrYnITQ7y7dpSwlwYArJv+W5n9MEpTZuViW+Ah4bz3yOfU7qdS2qQVI4TESaVSVTrxtNfLndVWjGjiPfl5fPBjyX0edvxfKG5fuFdufMuuTfHR/neFG7sl/puCgP6r8fB+aoU/h+qXI0fDkVpqlZq2Ez6L2dnZoncvd+H8cNhRrF63QZiwKXYrNhaz5s4XzuVyOYYN0ZywGxoYYOb0acL5j1t+FSaLil2/cRMLl34inFtZWWL0ayM1xpJu6clkkh31maywvL9FT1lxOXAw3q3hnlTNq3M98XbQcOH8cUomrp3WfqXE+ZCr2PF/oRqf25m9CsrsPJzZF43Lx28j/lYS8nLyYdnYDO09nNHv9e5CBQMAvvH/AztWHq70ZzYw0Efg4ffV5mSc2nMJR345g0eKdNg52cBrkoeQOAHA78v/xvfzKt7k6uMDfujyUltcCL2GC4evI+5yPDLTcmDW0BhOnRzRZ7QbWpUanjn+x3kEjv4eqoLyl8RujFmkdkfZO5fuI/m+9kuev5+3A3cuaTcOXtscwHrhcWH24woiadp7s3HkaDgAoImjA0L376nyEMPV6zcwZtwEtXkWHdq1g+8wH7Rp3QpyuRyJSUk4cfIUdu87oLasdNaMdzF9yqRy21apVHh31gdCHwFgQP9+GOw1EA729khLT8eJiFPYvmOn0K5MJsPGNSvxYt8+5TVbb8mMzctNAKVywOSTyoO0NDjrQ8naqmt4g7RqEm9UZW5tKuy6qY1HiopL/uYNTfDSmz2FyZqa5GTl4vu5O7B3fXi5MaXl5xXgk+GbEBj6njCHxH2Ya7n3Gznyyxn8MH+nVm0bGDZA98Ed0X1wxwrjDn13Amun/VphggGU/XydXJvAybWJVn0BgO2f/1V5ENVpDx+m4J/jx4XzV4b6PNEchvZt22DN/y2H//wAPH5cNCx45do1XLl2rcLXTXhzbIUJBlB0q/mVX3yGd2bOwqkzZwEAoUeOIvTIUY3xBgYGWLZkEROMGsTVJdLgcEktdmrPpQqXZuZkKhH261m822mZ1glGsfTkDMzqGYTfg0KQkZqlMUYRm4yVb/+M5W8Ga/Wt4VLYdaRUkDQVFKhwMew6Al5ajdWTf6lwUy8ibe05cFDtdupVHSoprW/v57Hn960YNXI4TIyNK4zt2d0N329ch4C5/lq1bWxsjOBNGxAwxx92tprvrCqTydCvT2/8/suP1XofVH16ejLJjvqMwyW1nH4DPTh3bgL7lo1gZWcBE3M50pIykHT3EWL+uVnmDqlPwkDeAJ1fbAs7J2uYNTTBo4THuHtFgasnY5+ovSZtbdGsvT2s7MxhYWOKzLRspMSn48qJ22qTTqliHC6pWUqlEjFXruLW7dtITUtHQUEBzM3M4Ohgj86dOsHGpvw7I1dGpVLh/IWLiL3zLx6mPISRkRHs7ezQo1s3WFtrt0leffY0hkv+svhUsra80hdK1lZdwySDqJZikkGk2dNIMkIspUsyBqbV3ySDczKIiIhE6vswh1Q4J4OIiIh0gpUMIiIiEe6yKg0mGURERCIcLpEGkwwiIiIR7pMhDc7JICIiIp1gJYOIiEiEhQxpMMkgIiIS4XCJNDhcQkRERDrBJIOIiEhEJpNJdlRFYGAgevToAXNzc9ja2sLX1xfXRDfpy8nJgZ+fH2xsbGBmZoaRI0ciISFBLSYuLg4+Pj4wMTGBra0t5s6di/z8fLWYsLAwdOvWDXK5HK1bt0ZwcHCZ/qxbtw5OTk4wMjKCu7s7Tp8+XaX3wySDiIhIpKZukHb06FH4+fnh5MmTCAkJQV5eHry8vJCZmSnEzJ49G3v27MH27dtx9OhRPHjwACNGjBCeLygogI+PD3Jzc3HixAls3rwZwcHBWLx4sRATGxsLHx8f9O/fH1FRUZg1axYmT56MQ4cOCTFbt26Fv78/lixZgnPnzqFLly7w9vZGYmKi1u+H9y4hqqV47xIizZ7GvUsimq6QrC2Pe3Oe+LVJSUmwtbXF0aNH0bdvX6SlpaFx48bYsmULXn31VQDA1atX0aFDB0RERKBXr144cOAAhg4digcPHsDOzg4AsHHjRsyfPx9JSUkwNDTE/PnzsW/fPkRHRws/a8yYMUhNTcXBgwcBAO7u7ujRowfWrl0LoOjGfs2aNcPMmTOxYMECrfrPSgYREZGITE8m2aFUKpGenq52KJVKrfqRlpYGALC2Lrrrb2RkJPLy8uDp6SnEtG/fHs2bN0dERAQAICIiAq6urkKCAQDe3t5IT09HTEyMEFO6jeKY4jZyc3MRGRmpFqOnpwdPT08hRhtMMoiIiET0ZNIdgYGBsLS0VDsCAwMr7YNKpcKsWbPQu3dvdOrUCQCgUChgaGgIKysrtVg7OzsoFAohpnSCUfx88XMVxaSnpyM7OxvJyckoKCjQGFPchja4hJWIiEiHAgIC4O/vr3ZNLpdX+jo/Pz9ER0fj2LFjuuqazjHJICIiEpFynwy5XK5VUlHajBkzsHfvXoSHh6Np06bCdXt7e+Tm5iI1NVWtmpGQkAB7e3shRrwKpHj1SekY8YqUhIQEWFhYwNjYGPr6+tDX19cYU9yGNjhcQkREJKInk0l2VEVhYSFmzJiBHTt24PDhw3B2dlZ73s3NDQYGBggNDRWuXbt2DXFxcfDw8AAAeHh44NKlS2qrQEJCQmBhYQEXFxchpnQbxTHFbRgaGsLNzU0tRqVSITQ0VIjRBisZREREIjW146efnx+2bNmCXbt2wdzcXJj/YGlpCWNjY1haWmLSpEnw9/eHtbU1LCwsMHPmTHh4eKBXr14AAC8vL7i4uGDcuHEICgqCQqHAokWL4OfnJ1RUpk2bhrVr12LevHl4++23cfjwYWzbtg379u0T+uLv74/x48eje/fu6NmzJ1atWoXMzExMnDhR6/fDJIOIiKiW2LBhAwDgxRdfVLv+ww8/YMKECQCAlStXQk9PDyNHjoRSqYS3tzfWry9Z8q6vr4+9e/di+vTp8PDwgKmpKcaPH4+PP/5YiHF2dsa+ffswe/ZsrF69Gk2bNsW3334Lb29vIWb06NFISkrC4sWLoVAo0LVrVxw8eLDMZNCKcJ8MolqK+2QQafY09smIartKsra6Xp8lWVt1DSsZREREIlXdDpw048RPIiIi0glWMoiIiESqes8R0oxJBhERkQiHS6TB4RIiIiLSCVYyiIiIRDhcIg0mGURERCIy1vklwY+RiIiIdIKVDCIiIpGq3nOENGOSQUREJFJT9y551jDJICIiEmElQxqck0FEREQ6wUoGERGRCIdLpMEkg4iISIRLWKXBj5GIiIh0gpUMIiIiEc77lAaTDCIiIhHOyZAGh0uIiIhIJ1jJICIiEuHET2kwySAiIhLhnAxpMMkgIiIS45wMSbAgRERERDrBSgYREZEIh0ukwSSDiIhIhBM/pcGPkYiIiHSClQwiIiIRbsYlDSYZREREIpyTIQ0OlxAREZFOsJJBREQkxq/gkmCSQUREJMI5GdJgrkZEREQ6wUoGERGRCCd+SoNJBhERkQg345IGkwwiIiIRGUsZkmCuRkRERDrBSgYREZEIh0ukwSSDiIhIjEmGJPgxEhERkU6wkkFERCTCeZ/SYJJBREQkwh0/pcHhEiIiItIJVjKIiIhEuLpEGkwyiIiIRDgnQxrM1YiIiEgnWMkgIiIS48RPSTDJICIiEuGcDGkwySAiIhLhnAxpMFcjIiIinWAlg4iISISbcUmDSQYREZEIh0ukweESIiIi0glWMoiIiES4ukQaTDKIiIjEOCdDEszViIiISCdYySAiIhLhxE9psJJBREQkItOT7qiK8PBwDBs2DI6OjpDJZNi5c6fa8xMmTIBMJlM7Bg0apBaTkpKCsWPHwsLCAlZWVpg0aRIyMjLUYi5evIg+ffrAyMgIzZo1Q1BQUJm+bN++He3bt4eRkRFcXV2xf//+qr0ZMMkgIiKqNTIzM9GlSxesW7eu3JhBgwYhPj5eOH799Ve158eOHYuYmBiEhIRg7969CA8Px9SpU4Xn09PT4eXlhRYtWiAyMhLLly/H0qVLsWnTJiHmxIkTeP311zFp0iScP38evr6+8PX1RXR0dJXej6ywsLCwSq/QEdl/tanBeLeGe0JUOxzAeuFxYfbjGuwJUe0iMzaHrv/pyn7nR8naMv76rSd6nUwmw44dO+Dr6ytcmzBhAlJTU8tUOIpduXIFLi4uOHPmDLp37w4AOHjwIIYMGYJ79+7B0dERGzZswMKFC6FQKGBoaAgAWLBgAXbu3ImrV68CAEaPHo3MzEzs3btXaLtXr17o2rUrNm7cqPV7qHVzMkr/YiWiIjJj85ruAlG9IuWcDKVSCaVSqXZNLpdDLpc/UXthYWGwtbVFw4YN8dJLL2HZsmWwsbEBAERERMDKykpIMADA09MTenp6OHXqFIYPH46IiAj07dtXSDAAwNvbG1988QUePXqEhg0bIiIiAv7+/mo/19vbu9zkpjwcLiEiIhLTk+4IDAyEpaWl2hEYGPhE3Ro0aBB+/PFHhIaG4osvvsDRo0cxePBgFBQUAAAUCgVsbW3VXtOgQQNYW1tDoVAIMXZ2dmoxxeeVxRQ/r61aV8kgIiJ6lgQEBJSpCjxpFWPMmDHCY1dXV3Tu3BmtWrVCWFgYBgwYUK1+6kKtSzIKYm/WdBeIagV959bC48LMtBrsCVHtIjO11P0PkXAzruoMjVSmZcuWaNSoEW7evIkBAwbA3t4eiYmJajH5+flISUmBvb09AMDe3h4JCQlqMcXnlcUUP68tDpcQERGJyWTSHTp07949PHz4EA4ODgAADw8PpKamIjIyUog5fPgwVCoV3N3dhZjw8HDk5eUJMSEhIWjXrh0aNmwoxISGhqr9rJCQEHh4eFSpf0wyiIiIaomMjAxERUUhKioKABAbG4uoqCjExcUhIyMDc+fOxcmTJ3Hnzh2EhobilVdeQevWreHt7Q0A6NChAwYNGoQpU6bg9OnTOH78OGbMmIExY8bA0dERAPDGG2/A0NAQkyZNQkxMDLZu3YrVq1erDem8//77OHjwIL788ktcvXoVS5cuxdmzZzFjxowqvZ9at4SVwyVERThcQqSZzNRS50tYc97/WbK2jFa/qXVsWFgY+vfvX+b6+PHjsWHDBvj6+uL8+fNITU2Fo6MjvLy88Mknn6hN0kxJScGMGTOwZ88e6OnpYeTIkVizZg3MzMyEmIsXL8LPzw9nzpxBo0aNMHPmTMyfP1/tZ27fvh2LFi3CnTt30KZNGwQFBWHIkCFVeu9MMohqKSYZRJo9lSRj9i+StWW0cqxkbdU1HC4hIiIinah1q0uIiIhqGm+QJg0mGURERGISLmGtzzhcQkRERDrBSgYREZEYKxmSYJJBREQkxjq/JJhkEBERiXHmpySYqxEREZFOsJJBREQkxjkZkmCSQUREJMY6vyT4MRIREZFOsJJBREQkxuESSTDJICIiEmOOIQkOlxAREZFOsJJBREQkxuESSTDJICIiEmOSIQkOlxAREZFOsJJBREQkwl3FpcEkg4iISIzDJZJgkkFERCTGJEMSnJNBREREOsFKBhERkRi/gkuCSQYREZEYZ35KgrkaERER6QQrGURERGL8Ci4JJhlERERiXF0iCeZqREREpBOsZBAREYmxkiEJJhlERERizDEkweESIiIi0glWMoiIiMQ4XCIJJhlERERiTDIkwSSDiIhIhBt+SoNzMoiIiEgnWMkgIiIS43CJJJhkEBERiTHJkASHS4iIiEgnWMkgIiIS41dwSTDJICIiEuPyEkkwVyMiIiKdYCWDiIhIjF/BJcEkg4iISIzDJZJgrkZEREQ6wUoGERGRGAsZkmCSQUREJMYkQxJMMoiIiMS446ckOCeDiIiIdIKVDCIiIjEWMiTBJIOIiEiMSYYkOFxCREREOsFKBhERkRg345IEkwwiIiIR5hjS4HAJERER6QQrGURERGKsZEiClQwiIiIxPZl0RxWEh4dj2LBhcHR0hEwmw86dO9WeLywsxOLFi+Hg4ABjY2N4enrixo0bajEpKSkYO3YsLCwsYGVlhUmTJiEjI0Mt5uLFi+jTpw+MjIzQrFkzBAUFlenL9u3b0b59exgZGcHV1RX79++v0nsBmGQQERHVGpmZmejSpQvWrVun8fmgoCCsWbMGGzduxKlTp2Bqagpvb2/k5OQIMWPHjkVMTAxCQkKwd+9ehIeHY+rUqcLz6enp8PLyQosWLRAZGYnly5dj6dKl2LRpkxBz4sQJvP7665g0aRLOnz8PX19f+Pr6Ijo6ukrvR1ZYWFhYxc9AJ2T/zbIpiL1Zwz0hqh30nVsLjwsz02qwJ0S1i8zUErr+pyv/z78ka6vBCK8nep1MJsOOHTvg6+sLoKiK4ejoiA8++ABz5swBAKSlpcHOzg7BwcEYM2YMrly5AhcXF5w5cwbdu3cHABw8eBBDhgzBvXv34OjoiA0bNmDhwoVQKBQwNDQEACxYsAA7d+7E1atXAQCjR49GZmYm9u7dK/SnV69e6Nq1KzZu3Kj1e2Alg4iISEwm3aFUKpGenq52KJXKKncpNjYWCoUCnp6ewjVLS0u4u7sjIiICABAREQErKyshwQAAT09P6Onp4dSpU0JM3759hQQDALy9vXHt2jU8evRIiCn9c4pjin+OtphkEBERiclkkh2BgYGwtLRUOwIDA6vcJYVCAQCws7NTu25nZyc8p1AoYGtrq/Z8gwYNYG1trRajqY3SP6O8mOLntcXVJURERDoUEBAAf39/tWtyubyGevN0MckgIiISk3AJq1wulySpsLe3BwAkJCTAwcFBuJ6QkICuXbsKMYmJiWqvy8/PR0pKivB6e3t7JCQkqMUUn1cWU/y8tjhcQkREJKYn4SERZ2dn2NvbIzQ0VLiWnp6OU6dOwcPDAwDg4eGB1NRUREZGCjGHDx+GSqWCu7u7EBMeHo68vDwhJiQkBO3atUPDhg2FmNI/pzim+Odoi0kGERFRLZGRkYGoqChERUUBKJrsGRUVhbi4OMhkMsyaNQvLli3D7t27cenSJbz11ltwdHQUVqB06NABgwYNwpQpU3D69GkcP34cM2bMwJgxY+Do6AgAeOONN2BoaIhJkyYhJiYGW7duxerVq9WGdN5//30cPHgQX375Ja5evYqlS5fi7NmzmDFjRpXeD5ewEtVSXMJKpNlTWcK692/J2mow1LPyoP+EhYWhf//+Za6PHz8ewcHBKCwsxJIlS7Bp0yakpqbihRdewPr169G2bVshNiUlBTNmzMCePXugp6eHkSNHYs2aNTAzMxNiLl68CD8/P5w5cwaNGjXCzJkzMX/+fLWfuX37dixatAh37txBmzZtEBQUhCFDhlTpvTPJIKqlmGQQafZUkox9EiYZPtonGc8aDpcQERGRTnB1CRERkRhvkCYJJhlERERiMmYZUuBwCREREekEKxlERERi/AouCSYZRFSrpKQ8wqXLl3Ep5jKiL19B9OXLSEp+KDwf+NFijHh5mNbt5ebl4fyFi4g4dRrRl6/g1u1YPEpNRYFKBUsLczg1bw6357pixMvD0KJ5M63bValUuBgdg4jTZ3AxOgY3bt3Cw5RHyMvNhYWFBZo2ccRznTvDd9gQdGjXrkqfQX5+Pm7euo1Ll68In8P1GzeQl58PAGji4IDD+3dXqU2qIo6WSIJJRi1zX5GAl9+ZjqycHOGa35tvYMa4N5+4zceZmfCZ/A6SUlKEa74DPRE4x7+CVxXp4F21NdEAsGSmH8YM9dE6/m58PHb89TeOnj6D+KREZGXnwNbGGm2dnTHspf7wfN4D+vr6Ve4H1S1JyckY/dbbuB8fL0l7SqUSSwO/wN+Hw5D++HE5P/MhkpIf4sy58/j6+2C86vsyFnwwG2amphW2HbhiJfYdOqSW/JT2MCUFD1NScOFSNIJ/2YIBL/bDx4sC0MjGptJ+r16/Ed//9DNycqp+l06SEOdkSIJJRi2zZM1XagmGFFZ8+71aglGbbP5zJ/7v+x+QW2p7WwC4G6/A3XgFQk9EoGuHDli+YC6aVnHPfKpblLm5kiUYAJCZlY0/d+0pc93R3h62jRvDUG6I+w/icf/BAwBAYWEhtu/YhctXr2Hz1xtgbm5W5rXFtv25A1nZ2WrXGjeygYO9PYyNjZGQmIg7/8YJz4WGHcXVa9fxy/eb4FDJn+N4hYIJBj0zmGTUIrtDD+N45DlJ2zx7KRrbDxyUpK3urp1gZFj5TX4cRLcZLs/6X7bgqx9/Fs719PTQqnkzWJqb49/7D4TEKOrKFYz7YB62fbUKja2tn6zzVKdYN2yIjh3ao5NLB7h2dMG7s+dUq70urp3wqu/L6PdCb9iJ/nxevnoNn3yxHOeiLgAAYq5cxYfLPsWqLyq/FXebVi3x6vBX0L9PnzJDLf/G3cUXK1cjNOwoAOB+fDzenxeArZu/FzYfrEiDBvpo5dwSnVw6oJNLB1yMjsaOPfu0fctUXSxkSIJJRi3xKC0Nn3+9CQDQqnkzPM7MQuJDzaVYbeXm5mHxqjUoLCyEtaUlGltb41ps7BO39/mcD9DE3q5afSp27Gwk1v70i3DetUMHfDZnNpybNgVQNN59IPwfLF61BlnZ2VAkJ2PWss/wy/+tkOTnU+1jZWGB1UGfw7WjC5o4OlT+gkrIZIBHzx54/91peK5L53LjXNq3w4+bNmKS30ycOnMWAHDgr78xdeIEuLTXPJeiU0cXTB4/Dv1e6F1uuy2aN8P6lSswd9Fi7N53AABw4VI0/j4ShoEvld02utjIV17GqBHD4dK+HYyMjITrD2tpNfJZxdESaXD+bC0R+PUmPEpLBwAseW8GGjSo/hyEDb/+ith79wAA86ZOhkUF5d+nqbCwEF9+94OwLbBz06b4/vNPhQQDKKpq+LzYD18tXiRcOxdzGSHHTzz1/tLTYWZmhkEDB0iSYABAQysrBH+9vsIEo5iBQQN8tHCB2rW/Qg+XG//TNxsrTDBKWzRvDkyMjUvaPXykwvgebt3QrWsXtQSDqK5iklELHI88hz2hRb94RngNRA9X12q3ef3OHXy37XcAQK+uXfCK54BqtymVf86exdXbt4Xz/01/B8bl/EJ9vttzGNyvr3D+7dZtOu8f1U/OLVrAqUVz4fz2nX8ladfSwgLdunYpaTf2jiTtko7pyaQ76jEmGTUsOycHS9esBQA0tLTAnCmTqt2mSqXC4lVrkJefD0MDAyye6VftNqX017HjwuOm9vbo7datwvjRQwYLjy9dvwFFUrLO+kb1m5WFpfA4MzNTsnYtLSyExxkStks6JJPwqMeYZNSwNZt/wj2FAgAwb8pkNCz1y+hJbdmzFxeuXAUATB0zSm0YojY4evqM8Li3W7dKJ8G5uXaCyX+VjsLCQoSfOVNhPNGTKr26xdq6oWTtPijVrg0nL1M9wiSjBsXcuIGfdu4CAPTs3Bm+A6t/O+D4xCSsCt4MAHBq2gRTRo2qdptSepiaiuSUR8J51w4dKn1NA319dGrbVjgvPdRCJJWoi5eQlFxSJevq2kmSdhMSE3EhOkY47yJRu6RjMpl0Rz3G1SU1JL+gAB+uXI0ClQoGBg2wRKIhjY/XrkNmVtH6/SUz/WBoaCBJuwCw/NvvcPPfOCiSk5CfXwArC3O0cGyCHp07wXegp1b7WNyKu6t23txRu70vmjna4/TFiwCA26I2iKSw9utvhMdyuRwDX3pJknbXf/M9VCqVcD50kLck7ZKO1e/cQDJMMmrID7//iSu3ir6RT37tNbSswnbG5dkfdhRhp04DAF4e8BJ6de1a7TZLO/TPMbXzhOSHSEh+iNMXL+LrX7dipLcXFkybCiN5+XtpPEhIVDt3aKzdnhql4+6L2iCqrl379uOfExHC+RuvjYRt40bVbvdM5Dls+3OHcO71Un907NC+2u0S1RVMMmpA3IN4rP9lCwCgRRNHvPP66Gq3mfb4MQI3Fu2zYWlujvlTp1S7TTFrS0s0dbCHiZExMrIycfvuPWHXw/yCAmzdfwAXr1/H5qDPYV7OtsyZWVlq5+XFiZmZmJS0kZ1VQSRR1Vy7cRNLP/1cOG/RrBlmTn+n2u0mJCZh9oL/CVUMKytLLJpfvU3F6CliJUMSTDJqwJLVXyFHWbRt8OIZfpAbGla7zaBN3yL5UdFchzmT34a1lWUlr9BOq+bNMWrIIPTv5Y5mDur7F+QXFODY2Uis+mGzsMnXlZu38MFnn2PTp59obE+8Zbq2wzlG8pLPKJtbLpNEkh8+hJ//HCFZNjQ0xIrAT2BaKql9ElnZ2fDznyPc20Qmk+HTxR+W2W2UarF6vvRUKpz4+ZT9eegvnIyKAgAMe6k/nu/2XLXbPBkVhT//CgEAuHXsiJHeXtVus9jebzbireG+ZRIMoGhC5ovuPbF1zUr069lDuP7P2UgcOXlKY3v5Bfll2tBG6Ruk5Ynuc0L0JB4/zsBkv/dw9959AEV/xlZ8+gk6d+xYrXZz8/Iw84N5uBRzWbgWMGc2PPv3q1a79JRxCaskJE8y7t69i7fffrvCGKVSifT0dLWjPniYmoqgb74DAFiamWH+O9Uf0lDm5mLJ6qJ9NgwaNMCS92ZodV8EKckNDfFlwHw0aliy5O/nXZpvQ20smq+hzM3V6meUjitv4y4ibWVlZ2PKzPdx5dp1AMWVhkXw9qzeZM+CggJ8sGAhjkWcFK7NnDYV4994vVrtEtVVkicZKSkp2Lx5c4UxgYGBsLS0VDvqg0/Xb0Taf7ec9p80ETZWVtVuc+1PvyDuv7tIThw5Am2cWlS7zSdhamKCMUNLbgsfGR2jMYEovb0yAORomWQUDy9paoOoKpRKJd6d9QHOX7goXPtwwVwMf3lotdpVqVQIWPKx2rbhk8aPwwwJvkxQDeASVklUeU7G7t2av6EWu63FHgYBAQHw9/dXu/asJxrnL1/BgaPhAIr2hnht8KBqtxmfmITgP/4EULRz5vSxNfttqUfnzgCKbnqmzM1FfFISnJo0UYuxEm02lpSSotUGZKX31rCyMK9+Z6leysvLx3tzFyCi1IZwc2e9h7GjXqt220s/+xy79u0Xzt8Y9SrmzXqv2u1STanfyYFUqpxk+Pr6QiaTCTe30qSycr1cLoe8gmWOz6KHqSX/SEZduQKXQT5av3bdz1uw7uctwvnfm39AE3s7pD5OR35BAQDgnkKB514ernWbO0P+xs6Qv4XzzUGfo6cWN5KqSGPRDomP0tLLJBni3UfjE5PQ1smp0rYVSUnC45a1bAdTqhsKCgrwQcBChJVaij1z2lRMHj+u2m1/uvxLbP2jZKnqiFeGYfGCedVul6iuq/JwiYODA/7880+oVCqNx7lz53TRT6oDSg9pAGXnXwBFS3ZLT/a8eku73Tsvl4pr2bx5BZFEZalUKixY8hEOlbqz6uQJb0kylLFy7Xr8uOU34XzoIG98unjRU58bRRLjcIkkqlzJcHNzQ2RkJF555RWNz1dW5aivDA0MygwVVCQ9I0NYX28kl6ttcKWnV5Qb6uvrV6nNjMxMofJhaGCgNrdBilvL3/o3Tu3cWsOcE0MDA3Ru3w7n/pt5HxkTUyZGLCklRZh3AgDduS0zVdHSzz7H7n0HhPNxr4/G3PdnVrvdjd/9gI3f/SCce73UH198slT4O0p1WP3ODSRT5SRj7ty5Fd6dsHXr1jhy5Ei5z9dXfXv0QMT23yoP/M+AtyYIu2NOem0kZox7s0xMWyenKrX51tz5OHPxEgBgyIv9EDjHv5JXVM3+/+acAEATOzvY2mi+EdRLHr2EJOPk+Sg8TE2tcBLs3sNhwmMLMzP06OwqSX+pfghcsVJtKGP0yOFYOPeDare7ecuvWLl2vXD+Yp8X8OXnn6JBA24/RFSsyul2nz59MGhQ+ZMWTU1N0a8f14PXN0dOnlLbG2PA8x7lxvq8+CIMDYo24crLz8e3W7eXG5uZnY2fdu0Szof2f1HrvTWIVq/fiOBfSuYzjXh5KD5aGFDtoYztO3YhcMVK4fwFj174asUXwp9regZwuEQSTLlJo8eZmVj45Uq88/podGzTpsLYfUfCsHjVGuHcWC7H5FGvlhtv37gRRvsMxk87i1Yq/bRzF55z6QCvPi+oxeXl52PhlysRn1g06dNILsc7r4950rdEdcCij5dhV6lhDU0+/ORTLCm1DXixS6eOq51fjI7B+v/2pQGKhhkTEpMweYZ2Kz4aWdvgi0+WlrmemJSMxcs+UxsWViqVmD5b++rId+u+Kve5+w/iMWh42b8/Bf8NdQJFt6R3de9dJmb65Lfx7pRJWveDKlDPkwOpMMkgjQoLCxFy/ARCjp9Ay2bN0NutGzq0aonG1tYwNjJCZnYWrsfewV//HMel69eF18lkMnz6wWw0ttY8VFLM782xCD9zFv/ef4AClQqzP/scPv37wdPDA5bm5oi9dw+/7d0vbFcOFG2XXt4QDD0b8vPzkVvJ3in5+QUACiqMAYBs0Rb2KpUKx8vZiVaTJhp2uQWA3LxctbuqAsCZc+e1brcyhSis9DMAoDGmdCJCVBswyaBK3b57F7fvVn57dVMTY3z03kwM7te30lhLc3Ns+HgpJi1YiPikJKhUKuwJPYI9oZrn80we9RrGvjysql0nInoyrGRIgkkGaWRkKMcIr4E4eeFCmduzi5mbmsJ34ABMGDkCjlW4AZRz06bYuXEdgr75FvuOHC2zBBYoukHbB5Mmon8v9yq/B6p7Pv94KT7/eKkkbbl3d8O182cqD6yipo6OOmn3abVPWmKSIQlZYS1Zb1o8Easg9mYN94TEHqam4trtWMQnJeFRWhpy8/JgYmwMK3NztHV2RjtnJ7UbmD2JzKwsnIy6AEVSMrJyctDY2hptnZ3g0rqVRO+i7tF3bi08LsxMq8GeENUuMlNLnW+VUBCp/dBaZfTd6u+XJFYyqFI2VlaS3C22IqYmJhWuSCEiorqHSQYREZEYh0skwSSDiIhIjEmGJLj3LREREekEKxlERERirGRIgkkGERGRGJMMSXC4hIiIiHSClQwiIiIxVjIkwUoGERER6QSTDCIiItIJDpcQERGJcbhEEkwyiIiIRGRMMiTBJIOIiEiMSYYkOCeDiIiIdIKVDCIiIjFWMiTBJIOIiEiMSYYkOFxCREREOsFKBhERkRgrGZJgkkFERFQGkwwpcLiEiIiIdIKVDCIiIjEOl0iClQwiIiIxmUy6owqWLl0KmUymdrRv3154PicnB35+frCxsYGZmRlGjhyJhIQEtTbi4uLg4+MDExMT2NraYu7cucjPz1eLCQsLQ7du3SCXy9G6dWsEBwc/8UdVESYZREREtUjHjh0RHx8vHMeOHROemz17Nvbs2YPt27fj6NGjePDgAUaMGCE8X1BQAB8fH+Tm5uLEiRPYvHkzgoODsXjxYiEmNjYWPj4+6N+/P6KiojBr1ixMnjwZhw4dkvy9yAoLCwslb/UJFO8TXxB7s4Z7QlQ76Du3Fh4XZqbVYE+IaheZqSV0/U+X6sZVydrSa9O+8qD/LF26FDt37kRUVFSZ59LS0tC4cWNs2bIFr776KgDg6tWr6NChAyIiItCrVy8cOHAAQ4cOxYMHD2BnZwcA2LhxI+bPn4+kpCQYGhpi/vz52LdvH6Kjo4W2x4wZg9TUVBw8eLB6b1aElQwiIiIxmXSHUqlEenq62qFUKsv90Tdu3ICjoyNatmyJsWPHIi4uDgAQGRmJvLw8eHp6CrHt27dH8+bNERERAQCIiIiAq6urkGAAgLe3N9LT0xETEyPElG6jOKa4DSkxySAiIhKTcE5GYGAgLC0t1Y7AwECNP9bd3R3BwcE4ePAgNmzYgNjYWPTp0wePHz+GQqGAoaEhrKys1F5jZ2cHhUIBAFAoFGoJRvHzxc9VFJOeno7s7GwpPj0BV5cQERHpUEBAAPz9/dWuyeVyjbGDBw8WHnfu3Bnu7u5o0aIFtm3bBmNjY532UxdYySAiIhKTsJIhl8thYWGhdpSXZIhZWVmhbdu2uHnzJuzt7ZGbm4vU1FS1mISEBNjb2wMA7O3ty6w2KT6vLMbCwkLyRIZJBhERURkSTsqohoyMDNy6dQsODg5wc3ODgYEBQkNDheevXbuGuLg4eHh4AAA8PDxw6dIlJCYmCjEhISGwsLCAi4uLEFO6jeKY4jakxCSDiIiolpgzZw6OHj2KO3fu4MSJExg+fDj09fXx+uuvw9LSEpMmTYK/vz+OHDmCyMhITJw4ER4eHujVqxcAwMvLCy4uLhg3bhwuXLiAQ4cOYdGiRfDz8xOqJ9OmTcPt27cxb948XL16FevXr8e2bdswe/Zsyd8P52QQERGJ1dCOn/fu3cPrr7+Ohw8fonHjxnjhhRdw8uRJNG7cGACwcuVK6OnpYeTIkVAqlfD29sb69euF1+vr62Pv3r2YPn06PDw8YGpqivHjx+Pjjz8WYpydnbFv3z7Mnj0bq1evRtOmTfHtt9/C29tb8vfDfTKIainuk0Gk2VPZJ+POLcna0nNqJVlbdQ2HS4iIiEgnOFxCREQkxhukSYJJBhERkRiTDElwuISIiIh0gkkGERER6QSHS4iIiMQ4XCIJJhlERERiTDIkweESIiIi0glWMoiIiMRYyJAEkwwiIqIymGVIgcMlREREpBOsZBAREYlx4qckmGQQERGJMceQBIdLiIiISCdYySAiIhKRsZQhCSYZREREYpyTIQkOlxAREZFOsJJBREQkxkqGJJhkEBERiTHHkASTDCIiojKYZUiBczKIiIhIJ1jJICIiEuOcDEkwySAiIhJjjiEJDpcQERGRTrCSQUREVAZLGVJgkkFERCTGORmS4HAJERER6QQrGURERGIsZEiCSQYREZEYh0skweESIiIi0glWMoiIiMpgJUMKTDKIiIjEmGNIgkkGERGRGOdkSIJzMoiIiEgnWMkgIiISYyVDEqxkEBERkU4wySAiIiKd4HAJERGRiIzDJZJgkkFERCTGJEMSHC4hIiIinWAlg4iIqAxWMqTAJIOIiEiMOYYkOFxCREREOsFKBhERkRgnfkqCSQYREZEYkwxJcLiEiIiIdIJJBhEREemErLCwsLCmOwFwdzUiItKezv/pysmQri0jM+naqmNqTZJBtYNSqURgYCACAgIgl8trujtEtQL/XhA9GSYZpCY9PR2WlpZIS0uDhYVFTXeHqFbg3wuiJ8M5GURERKQTTDKIiIhIJ5hkEBERkU4wySA1crkcS5Ys4eQ2olL494LoyXDiJxEREekEKxlERESkE0wyiIiISCeYZBAREZFOMMkgIiIinWCSQYJ169bByckJRkZGcHd3x+nTp2u6S0Q1Kjw8HMOGDYOjoyNkMhl27txZ010iqlOYZBAAYOvWrfD398eSJUtw7tw5dOnSBd7e3khMTKzprhHVmMzMTHTp0gXr1q2r6a4Q1UlcwkoAAHd3d/To0QNr164FAKhUKjRr1gwzZ87EggULarh3RDVPJpNhx44d8PX1remuENUZrGQQcnNzERkZCU9PT+Ganp4ePD09ERERUYM9IyKiuoxJBiE5ORkFBQWws7NTu25nZweFQlFDvSIiorqOSQYRERHpBJMMQqNGjaCvr4+EhAS16wkJCbC3t6+hXhERUV3HJINgaGgINzc3hIaGCtdUKhVCQ0Ph4eFRgz0jIqK6rEFNd4BqB39/f4wfPx7du3dHz549sWrVKmRmZmLixIk13TWiGpORkYGbN28K57GxsYiKioK1tTWaN29egz0jqhu4hJUEa9euxfLly6FQKNC1a1esWbMG7u7uNd0tohoTFhaG/v37l7k+fvx4BAcHP/0OEdUxTDKIiIhIJzgng4iIiHSCSQYRERHpBJMMIiIi0gkmGURERKQTTDKIiIhIJ5hkEBERkU4wySAiIiKdYJJBREREOsEkg4iIiHSCSQYRERHpBJMMIiIi0gkmGURERKQT/w8xNV2vz2iPngAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(ytest,model.predict(xtest).round())\n",
    "cm = pd.DataFrame(cm)\n",
    "sns.heatmap(cm,annot = True,linewidth = 1,linecolor = 'black',cmap = 'RdPu',fmt = 'g',annot_kws = {'size':25})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08039ff3",
   "metadata": {},
   "source": [
    "<h2>Accuracy</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "69bc415f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3333/3333 [==============================] - 2s 456us/step\n",
      "Model accuracy on train set is: 0.8558756235700087\n",
      "1111/1111 [==============================] - 1s 471us/step\n",
      "Model accuracy on test set is: 0.8527103434695772\n"
     ]
    }
   ],
   "source": [
    "print('Model accuracy on train set is:',accuracy_score(ytrain,model.predict(xtrain).round()))\n",
    "print('Model accuracy on test set is:',accuracy_score(ytest,model.predict(xtest).round()))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f1d75d5",
   "metadata": {},
   "source": [
    "<h2>Losses vs Epoches</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8fc10646",
   "metadata": {},
   "source": [
    "as it is seen below, in our neural network model the more Epoches, the less the Loss value would be"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "7146ce6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (15,5))\n",
    "\n",
    "sns.lineplot(data = hist.history,color = 'red')\n",
    "# sns.despine()\n",
    "\n",
    "plt.xlabel('Epoches')\n",
    "plt.ylabel('Loss')\n",
    "plt.title('Losses vs Epoches')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ca7496f",
   "metadata": {},
   "source": [
    "<h1>Models and the accuracy score obtained by them</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "dc69af4b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1111/1111 [==============================] - 1s 468us/step\n",
      "3333/3333 [==============================] - 2s 476us/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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vY8eO8c4776yx/e233y7ZX1ZZvmaxWteuXeOJJ56IPn36rDUQ2xg1PPDAA9GlS5eYOHFiqeePGDGizMcqq3XV27Vr13j99ddjv/32+8E51alTJ/bbb7/Yb7/94rrrrouRI0fGJZdcElOnTo1+/fpVyLoAQFXQ8/1/er4Nq6Gsx919991j9913jyuvvDLuueeeOO6442L8+PFx0kknlXme66spIqJ169bRr1+/HzxO165d49xzz41zzz03Zs2aFTvttFNce+21cffdd6/3dWBT4euiUI2sWLEiJk6cGAcffHAceeSRazzOPPPMWLJkSTz88MMR8Z9rcL3++uvx0EMPrXGs1WcZHXHEEfH555+v9dPA1WM6duwYdevWjWeeeabU/ptuummDa1/9qd53z25KKcUf/vCHUuNatWoVe++9d9x2220xb968tdazWsuWLePAAw+Mu+++O8aNGxcHHHBAtGzZ8gdrOeigg2LatGnx/PPPl2xbtmxZ/PnPf45OnTpFjx49Nnheq62+m9b3m9KyOProo6OoqCh+85vfrLFv1apVP3jsRo0albmGta3Liy++WOq92VgaNWq01q+kHn300fHxxx/HLbfcssa+FStWxLJlyyIi4ssvv1xj/+q7oa7+ukN53hMAqGp6Pj3f+qyrv9nQ43711VdrvMff76HKOs911dS/f/8oKCiIkSNHrvX6b5999llERCxfvjy+/vrrUvu6du0am222WamvsTZq1EhfxybNmWxQjTz88MOxZMmSOPTQQ9e6f/fdd49WrVrFuHHj4phjjonzzjsvHnjggTjqqKNi6NCh0atXr/jyyy/j4Ycfjptvvjl23HHHOOGEE+LOO++MX/ziFzFt2rTYa6+9YtmyZfHEE0/EGWecEYcddlg0adIkjjrqqLjhhhsiJycnunbtGo8++miZrhfRvXv36Nq1a/zyl7+Mjz/+OAoKCuLBBx9c63W7rr/++ujbt2/svPPOccopp0Tnzp3j/fffj8ceeyymT59eauwJJ5wQRx55ZETEWhuKtbnwwgvj3nvvjQMPPDCGDx8ezZs3jzvuuCPmzp0bDz744Bpfe9gQDRo0iB49esR9990XW2+9dTRv3jx+9KMf/eB1Rr5rn332iVNPPTVGjRoV06dPj5/85CdRr169mDVrVkyYMCH+8Ic/lMx1bXr16hUREcOHD4/+/ftH3bp149hjj13vax588MExceLEGDhwYAwYMCDmzp0bN998c/To0SOWLl26wbWXR69eveK+++6LX/ziF7HrrrtG48aN45BDDonjjz8+7r///jjttNNi6tSp0adPnygqKoq333477r///nj88cdjl112iSuuuCKeeeaZGDBgQHTs2DEWLlwYN910U2y55ZYlFzju2rVrNG3aNG6++ebYbLPNolGjRtG7d+91Xp8OAKoDPZ+erzw934Ye94477oibbropBg4cGF27do0lS5bELbfcEgUFBXHQQQeVa5477bRT1K1bN66++uooLCyM/Pz82HfffaN169YxZsyYOP7442PnnXeOY489Nlq1ahXz5s2Lxx57LPr06RM33nhjvPvuu7HffvvF0UcfHT169Ijc3Nx46KGH4tNPPy3Vz/bq1SvGjBkT//M//xPdunWL1q1bl9wcCzYJVXBHU2AdDjnkkFS/fv20bNmydY4ZMmRIqlevXvr8889TSil98cUX6cwzz0xbbLFFysvLS1tuuWUaPHhwyf6U/nOb9UsuuSR17tw51atXL7Vt2zYdeeSRac6cOSVjPvvss3TEEUekhg0bpmbNmqVTTz01zZgxY623c2/UqNFaa5s5c2bq169faty4cWrZsmU6+eST0+uvv77GMVJKacaMGWngwIGpadOmqX79+mmbbbZJl1566RrHXLlyZWrWrFlq0qRJWrFixYa8jSmllObMmZOOPPLIkuPvtttu6dFHH11jXGzg7dxTSulf//pX6tWrV8rLyyt1y/N1vScjRoxY6+3X//znP6devXqlBg0apM022yxtv/326fzzz0+ffPLJel9/1apV6ayzzkqtWrVKOTk5JceeO3fuOm+JXlxcnEaOHJk6duyY8vPzU8+ePdOjjz6aBg8enDp27LjGe/Hd27ivrv+zzz4rNW7s2LEpItLcuXPXW+/SpUvTf/3Xf6WmTZumiCj1et988026+uqr03bbbZfy8/NTs2bNUq9evdLll1+eCgsLU0opPfnkk+mwww5L7dq1S3l5ealdu3Zp0KBB6d133y31On/9619Tjx49Um5u7lp/1wCgutHz6fnWZ10934Ye99VXX02DBg1KHTp0SPn5+al169bp4IMPTi+//PIGzXNdbrnlltSlS5dUt27dFBFp6tSpJfumTp2a+vfvn5o0aZLq16+funbtmoYMGVLymp9//nkaNmxY6t69e2rUqFFq0qRJ6t27d7r//vtLvcaCBQvSgAED0mabbZYiIu2zzz7rrQmqm5yUynnlaoBKsGrVqmjXrl0ccsghceutt1Z1OQAAbAR6PqAmcE02oFqbNGlSfPbZZ6UurAsAQM2i5wNqAmeyAdXSiy++GG+88Ub85je/iZYtW8arr75a1SUBAFDB9HxATeJMNqBaGjNmTJx++unRunXruPPOO6u6HAAANgI9H1CTOJMNAAAAADJyJhsAAAAAZCRkAwAAAICMcqu6gOqmuLg4Pvnkk9hss80iJyenqssBADYRKaVYsmRJtGvXLurU8TlmdaTPAwDKY0P7PCHb93zyySfRvn37qi4DANhEffjhh7HllltWdRmshT4PAMjih/o8Idv3bLbZZhHxnzeuoKCgiqsBADYVixcvjvbt25f0ElQ/+jwAoDw2tM8Tsn3P6q8OFBQUaL4AgDLzNcTqS58HAGTxQ32eC4YAAAAAQEZCNgAAAADISMgGAAAAABkJ2QAAAAAgIyEbAAAAAGQkZAMAAACAjIRsAAAAAJCRkA0AAAAAMhKyAQAAAEBGQjYAAAAAyEjIBgAAAAAZCdkAAAAAICMhGwAAAABkJGQDAAAAgIxyq7qA6qpJk6quAAD4vpSqugJqAn0eANQM1a03dCYbAAAAAGQkZAMAAACAjIRsAAAAAJCRkA0AAAAAMhKyAQAAAEBGQjYAAAAAyEjIBgAAAAAZCdkAAAAAICMhGwAAAABkJGQDAAAAgIyEbAAAAACQkZANAAAAADISsgEAAABARkI2AAAAAMhIyAYAAAAAGQnZAAAAACAjIRsAAAAAZCRkAwAAAICMhGwAAAAAkJGQDQAAAAAyErIBAAAAQEZCNgAAAADISMgGAAAAABkJ2QAAAAAgIyEbAAAAAGQkZAMAAACAjIRsAAAAAJCRkA0AAAAAMhKyAQAAAEBGQjYAAAAAyEjIBgAAAAAZCdkAAAAAICMhGwAAAABkJGQDAAAAgIyEbAAAAACQkZANAAAAADISsgEAAABARkI2AAAAAMhIyAYAAAAAGQnZAAAAACAjIRsAAAAAZCRkAwAAAICMhGwAAAAAkJGQDQAAAAAyErIBAAAAQEZVHrItWLAgzj777OjWrVvUr18/2rRpE3369IkxY8bE8uXLIyKiU6dOkZOTEzk5OVG3bt1o165dnHjiifHVV1+VOtbixYvjkksuie7du0f9+vWjbdu20a9fv5g4cWKklKpiegAAtZY+DwCoTXKr8sXfe++96NOnTzRt2jRGjhwZ22+/feTn58ebb74Zf/7zn2OLLbaIQw89NCIirrjiijj55JOjqKgo3n333TjllFNi+PDhcdddd0VExKJFi6Jv375RWFgY//M//xO77rpr5ObmxtNPPx3nn39+7LvvvtG0adMqnC0AQO2hzwMAapsqDdnOOOOMyM3NjZdffjkaNWpUsr1Lly5x2GGHlfpUcrPNNou2bdtGRMQWW2wRgwcPjnvvvbdk/8UXXxzvv/9+vPvuu9GuXbuS7VtvvXUMGjQo6tevXwkzAgAgQp8HANQ+VRayffHFF/GPf/wjRo4cWarx+q6cnJy1bv/444/jkUceid69e0dERHFxcYwfPz6OO+64Uo3Xao0bN15nHStXroyVK1eW/Lx48eKyTAMAgO/R5wEAtVGVXZNt9uzZkVKKbbbZptT2li1bRuPGjaNx48ZxwQUXlGy/4IILonHjxtGgQYPYcsstIycnJ6677rqIiPj888/jq6++iu7du5e5jlGjRkWTJk1KHu3bt882MQCAWk6fBwDURlV+44PvmzZtWkyfPj222267Up88nnfeeTF9+vR444034sknn4yIiAEDBkRRUVGmi91edNFFUVhYWPL48MMPM88BAIA16fMAgJqsyr4u2q1bt8jJyYl33nmn1PYuXbpERESDBg1KbW/ZsmV069YtIiK22mqr+P3vfx977LFHTJ06teRit2+//XaZ68jPz4/8/PxyzgIAgO/T5wEAtVGVncnWokWL2H///ePGG2+MZcuWlfn5devWjYiIFStWRJ06deLYY4+NcePGxSeffLLG2KVLl8aqVasy1wwAwA/T5wEAtVGVfl30pptuilWrVsUuu+wS9913X7z11lvxzjvvxN133x1vv/12SYMVEbFkyZJYsGBBzJ8/P6ZNmxbnnXdetGrVKvbcc8+IiLjyyiujffv20bt377jzzjtj5syZMWvWrLjtttuiZ8+esXTp0qqaJgBAraPPAwBqm5yU5UIXFWD+/PkxcuTIeOyxx+Kjjz6K/Pz86NGjRxx11FFxxhlnRMOGDaNTp07xwQcflDynVatWseuuu8aVV14ZO+20U8n2wsLCuOqqq+LBBx+MDz74IJo1axbbb799DBs2LA477LB13sXquxYvXhxNmjSJiMKIKKj4CQMA5Va1Xcv6re4hCgsLo6BADxGhzwMANq7K6g03tM+r8pCtutF8AUD1VZ27FiFb9afPA4CapbqFbNXu7qIAAAAAsKkRsgEAAABARkI2AAAAAMhIyAYAAAAAGQnZAAAAACAjIRsAAAAAZCRkAwAAAICMhGwAAAAAkJGQDQAAAAAyErIBAAAAQEZCNgAAAADISMgGAAAAABkJ2QAAAAAgIyEbAAAAAGQkZAMAAACAjIRsAAAAAJCRkA0AAAAAMhKyAQAAAEBGQjYAAAAAyEjIBgAAAAAZCdkAAAAAICMhGwAAAABkJGQDAAAAgIyEbAAAAACQkZANAAAAADISsgEAAABARkI2AAAAAMhIyAYAAAAAGQnZAAAAACAjIRsAAAAAZCRkAwAAAICMhGwAAAAAkJGQDQAAAAAyErIBAAAAQEZCNgAAAADISMgGAAAAABkJ2QAAAAAgIyEbAAAAAGQkZAMAAACAjIRsAAAAAJCRkA0AAAAAMhKyAQAAAEBGQjYAAAAAyCi3qguorgoLIwoKqroKAAAqmj4PANgYnMkGAAAAABkJ2QAAAAAgIyEbAAAAAGQkZAMAAACAjIRsAAAAAJCRkA0AAAAAMhKyAQAAAEBGQjYAAAAAyEjIBgAAAAAZCdkAAAAAICMhGwAAAABkJGQDAAAAgIyEbAAAAACQkZANAAAAADISsgEAAABARkI2AAAAAMhIyAYAAAAAGQnZAAAAACAjIRsAAAAAZCRkAwAAAICMhGwAAAAAkJGQDQAAAAAyErIBAAAAQEZCNgAAAADIKLeqC6iumoxqElG/qqsAACpCGpGqugSqEX0eANQc1anPcyYbAAAAAGQkZAMAAACAjIRsAAAAAJCRkA0AAAAAMhKyAQAAAEBGQjYAAAAAyEjIBgAAAAAZCdkAAAAAICMhGwAAAABkJGQDAAAAgIyEbAAAAACQkZANAAAAADISsgEAAABARkI2AAAAAMhIyAYAAAAAGQnZAAAAACAjIRsAAAAAZCRkAwAAAICMhGwAAAAAkJGQDQAAAAAyErIBAAAAQEZlDtk6deoUV1xxRcybN29j1AMAQBXR5wEAlF+ZQ7ZzzjknJk6cGF26dIn9998/xo8fHytXrtwYtQEAUIn0eQAA5VeukG369Okxbdq02HbbbeOss86KzTffPM4888x49dVXN0aNAABUAn0eAED5lfuabDvvvHNcf/318cknn8SIESPiL3/5S+y6666x0047xW233RYppYqsEwCASqLPAwAou9zyPvHbb7+Nhx56KMaOHRuTJ0+O3XffPU488cT46KOP4uKLL44nnngi7rnnnoqsFQCASqDPAwAouzKHbK+++mqMHTs27r333qhTp06ccMIJMXr06OjevXvJmIEDB8auu+5aoYUCALBx6fMAAMqvzCHbrrvuGvvvv3+MGTMmDj/88KhXr94aYzp37hzHHntshRQIAEDl0OcBAJRfmUO29957Lzp27LjeMY0aNYqxY8eWuygAACqfPg8AoPzKfOODhQsXxosvvrjG9hdffDFefvnlCikKAIDKp88DACi/Modsw4YNiw8//HCN7R9//HEMGzasQooCAKDy6fMAAMqvzCHbzJkzY+edd15je8+ePWPmzJkVUhQAAJVPnwcAUH5lDtny8/Pj008/XWP7/PnzIze3zJd4AwCgmtDnAQCUX5lDtp/85Cdx0UUXRWFhYcm2RYsWxcUXXxz7779/hRYHAEDl0ecBAJRfmT+S/N3vfhd77713dOzYMXr27BkREdOnT482bdrEXXfdVeEFAgBQOfR5AADlV+aQbYsttog33ngjxo0bF6+//no0aNAgfv7zn8egQYOiXr16G6NGAAAqgT4PAKD8ynVxjUaNGsUpp5xS0bUAAFDF9HkAAOVT7ivYzpw5M+bNmxfffPNNqe2HHnpo5qIAAKg6+jwAgLIrc8j23nvvxcCBA+PNN9+MnJycSClFREROTk5ERBQVFVVshQAAVAp9HgBA+ZX57qJnn312dO7cORYuXBgNGzaMf//73/HMM8/ELrvsEk899dRGKBEAgMqgzwMAKL8yn8n2/PPPx5QpU6Jly5ZRp06dqFOnTvTt2zdGjRoVw4cPj9dee21j1AkAwEamzwMAKL8yn8lWVFQUm222WUREtGzZMj755JOIiOjYsWO88847FVsdAACVRp8HAFB+ZT6T7Uc/+lG8/vrr0blz5+jdu3dcc801kZeXF3/+85+jS5cuG6NGAAAqgT4PAKD8yhyy/epXv4ply5ZFRMQVV1wRBx98cOy1117RokWLuO+++yq8QAAAKoc+DwCg/MocsvXv37/k3926dYu33347vvzyy2jWrFnJnacAANj06PMAAMqvTNdk+/bbbyM3NzdmzJhRanvz5s0zNV5DhgyJnJycyMnJiXr16kXnzp3j/PPPj6+//rpkzOr933307du31HGmTp0aBx10ULRo0SIaNmwYPXr0iHPPPTc+/vjjctcGAFAbbKw+L0KvBwDUDmUK2erVqxcdOnSIoqKiCi/kgAMOiPnz58d7770Xo0ePjj/96U8xYsSIUmPGjh0b8+fPL3k8/PDDJfv+9Kc/Rb9+/aJt27bx4IMPxsyZM+Pmm2+OwsLCuPbaayu8XgCAmmRj9nkRej0AoOYr89dFL7nkkrj44ovjrrvuiubNm1dYIfn5+dG2bduIiGjfvn3069cvJk+eHFdffXXJmKZNm5aM+a6PPvoohg8fHsOHD4/Ro0eXbO/UqVPsvffesWjRogqrEwCgptpYfV6EXg8AqPnKHLLdeOONMXv27GjXrl107NgxGjVqVGr/q6++mrmoGTNmxL/+9a/o2LHjBo2fMGFCfPPNN3H++eevdX/Tpk3X+dyVK1fGypUrS35evHhxmWoFAKgpKqPPi6i8Xk+fBwBUpjKHbIcffvhGKCPi0UcfjcaNG8eqVati5cqVUadOnbjxxhtLjRk0aFDUrVu35Oe77747Dj/88Jg1a1YUFBTE5ptvXubXHTVqVFx++eWZ6wcA2NRtrD4vomp6PX0eAFCZyhyyff/aGRXlxz/+cYwZMyaWLVsWo0ePjtzc3DjiiCNKjRk9enT069ev5OfVjVZKqdwX5L3oooviF7/4RcnPixcvjvbt25frWAAAm7KN1edFVE2vp88DACpTmUO2jaVRo0bRrVu3iIi47bbbYscdd4xbb701TjzxxJIxbdu2LRnzXVtvvXUUFhbG/Pnzy/wJZ35+fuTn52crHgCA9aqKXk+fBwBUpjLdXTQiok6dOlG3bt11PiqkqDp14uKLL45f/epXsWLFih8cf+SRR0ZeXl5cc801a93vYrgAAD+sMvq81a+j1wMAapoyn8n20EMPlfr522+/jddeey3uuOOOCr3mxVFHHRXnnXde/PGPf4xf/vKX6x3bvn37GD16dJx55pmxePHiOOGEE6JTp07x0UcfxZ133hmNGzd2a3cAgB9QWX1ehF4PAKh5yhyyHXbYYWtsO/LII2O77baL++67r9Qp/5kKy82NM888M6655po4/fTTf3D8GWecEVtvvXX87ne/i4EDB8aKFSuiU6dOcfDBB5e6FgcAAGtXWX1ehF4PAKh5clJKqSIO9N5778UOO+wQS5curYjDVZnFixdHkyZNIi6MiPpVXQ0AUBHSiAppd9ZrdQ9RWFgYBQUFG/31KpM+DwCorqpTn1fma7KtzYoVK+L666+PLbbYoiIOBwBANaHPAwDYMGX+umizZs1K3UI9pRRLliyJhg0bxt13312hxQEAUHn0eQAA5VfmkG306NGlmq86depEq1atonfv3tGsWbMKLQ4AgMqjzwMAKL8yh2xDhgzZCGUAAFDV9HkAAOVX5muyjR07NiZMmLDG9gkTJsQdd9xRIUUBAFD59HkAAOVX5pBt1KhR0bJlyzW2t27dOkaOHFkhRQEAUPn0eQAA5VfmkG3evHnRuXPnNbZ37Ngx5s2bVyFFAQBQ+fR5AADlV+aQrXXr1vHGG2+ssf3111+PFi1aVEhRAABUPn0eAED5lTlkGzRoUAwfPjymTp0aRUVFUVRUFFOmTImzzz47jj322I1RIwAAlUCfBwBQfmW+u+hvfvObeP/992O//faL3Nz/PL24uDhOOOEE1+oAANiE6fMAAMovJ6WUyvPEWbNmxfTp06NBgwax/fbbR8eOHSu6tiqxePHiaNKkScSFEVG/qqsBACpCGlGudqdMVvcQhYWFUVBQsNFfb2PS5wEAm4rq1OeV+Uy21bbaaqvYaqutyvt0AACqKX0eAEDZlfmabEcccURcffXVa2y/5ppr4qijjqqQogAAqHz6PACA8itzyPbMM8/EQQcdtMb2Aw88MJ555pkKKQoAgMqnzwMAKL8yh2xLly6NvLy8NbbXq1cvFi9eXCFFAQBQ+fR5AADlV+aQbfvtt4/77rtvje3jx4+PHj16VEhRAABUPn0eAED5lfnGB5deemn89Kc/jTlz5sS+++4bERFPPvlk3HPPPfHAAw9UeIEAAFQOfR4AQPmVOWQ75JBDYtKkSTFy5Mh44IEHokGDBrHjjjvGlClTonnz5hujRgAAKoE+DwCg/MocskVEDBgwIAYMGBAREYsXL4577703fvnLX8Yrr7wSRUVFFVogAACVR58HAFA+Zb4m22rPPPNMDB48ONq1axfXXntt7LvvvvHCCy9UZG0AAFQBfR4AQNmV6Uy2BQsWxO233x633nprLF68OI4++uhYuXJlTJo0ycVwAQA2Yfo8AIBsNvhMtkMOOSS22WabeOONN+L3v/99fPLJJ3HDDTdszNoAAKgE+jwAgOw2+Ey2v//97zF8+PA4/fTTY6utttqYNQEAUIn0eQAA2W3wmWzPPvtsLFmyJHr16hW9e/eOG2+8MT7//PONWRsAAJVAnwcAkN0Gh2y777573HLLLTF//vw49dRTY/z48dGuXbsoLi6OyZMnx5IlSzZmnQAAbCT6PACA7Mp8d9FGjRrF0KFD49lnn40333wzzj333LjqqquidevWceihh26MGgEAqAT6PACA8itzyPZd22yzTVxzzTXx0Ucfxb333ltRNQEAUMX0eQAAZZMpZFutbt26cfjhh8fDDz9cEYcDAKCa0OcBAGyYCgnZAAAAAKA2E7IBAAAAQEZCNgAAAADISMgGAAAAABkJ2QAAAAAgIyEbAAAAAGQkZAMAAACAjIRsAAAAAJCRkA0AAAAAMhKyAQAAAEBGQjYAAAAAyEjIBgAAAAAZCdkAAAAAICMhGwAAAABkJGQDAAAAgIxyq7qA6qrwosIoKCio6jIAAKhg+jwAYGNwJhsAAAAAZCRkAwAAAICMhGwAAAAAkJGQDQAAAAAyErIBAAAAQEZCNgAAAADISMgGAAAAABkJ2QAAAAAgIyEbAAAAAGQkZAMAAACAjIRsAAAAAJCRkA0AAAAAMhKyAQAAAEBGQjYAAAAAyEjIBgAAAAAZCdkAAAAAICMhGwAAAABkJGQDAAAAgIyEbAAAAACQkZANAAAAADISsgEAAABARkI2AAAAAMhIyAYAAAAAGQnZAAAAACCj3KouoNq6v0lEw6ouAgCq0H+lqq4ANg59HgDUDNWsX3UmGwAAAABkJGQDAAAAgIyEbAAAAACQkZANAAAAADISsgEAAABARkI2AAAAAMhIyAYAAAAAGQnZAAAAACAjIRsAAAAAZCRkAwAAAICMhGwAAAAAkJGQDQAAAAAyErIBAAAAQEZCNgAAAADISMgGAAAAABkJ2QAAAAAgIyEbAAAAAGQkZAMAAACAjIRsAAAAAJCRkA0AAAAAMhKyAQAAAEBGQjYAAAAAyEjIBgAAAAAZCdkAAAAAICMhGwAAAABkJGQDAAAAgIyEbAAAAACQkZANAAAAADISsgEAAABARkI2AAAAAMhIyAYAAAAAGQnZAAAAACAjIRsAAAAAZCRkAwAAAICMhGwAAAAAkJGQDQAAAAAyErIBAAAAQEZCNgAAAADISMgGAAAAABkJ2QAAAAAgIyEbAAAAAGQkZAMAAACAjIRsAAAAAJCRkA0AAAAAMhKyAQAAAEBG1T5kGzJkSOTk5MRVV11VavukSZMiJycnIiKeeuqpyMnJie222y6KiopKjWvatGncfvvtlVUuAAAbSJ8HANQk1T5ki4ioX79+XH311fHVV1+td9x7770Xd955ZyVVBQBAVvo8AKCm2CRCtn79+kXbtm1j1KhR6x131llnxYgRI2LlypWVVBkAAFno8wCAmmKTCNnq1q0bI0eOjBtuuCE++uijdY4755xzYtWqVXHDDTds8LFXrlwZixcvLvUAAKBy6PMAgJpikwjZIiIGDhwYO+20U4wYMWKdYxo2bBgjRoyIUaNGRWFh4QYdd9SoUdGkSZOSR/v27SuqZAAANoA+DwCoCTaZkC0i4uqrr4477rgj3nrrrXWOOfHEE6NFixZx9dVXb9AxL7rooigsLCx5fPjhhxVVLgAAG0ifBwBs6japkG3vvfeO/v37x0UXXbTOMbm5uXHllVfGH/7wh/jkk09+8Jj5+flRUFBQ6gEAQOXS5wEAm7pNKmSLiLjqqqvikUceieeff36dY4466qjYbrvt4vLLL6/EygAAyEKfBwBsynKruoCy2n777eO4446L66+/fr3jrrrqqujfv38lVQUAQFb6PABgU7bJnckWEXHFFVdEcXHxesfsu+++se+++8aqVasqqSoAALLS5wEAm6qclFKq6iKqk8WLF0eTJk2i8JaIgoZVXQ0AVKH/0iKURUkPUVjo2l/VlD4PAGqYSupXN7TP2yTPZAMAAACA6kTIBgAAAAAZCdkAAAAAICMhGwAAAABkJGQDAAAAgIyEbAAAAACQkZANAAAAADISsgEAAABARkI2AAAAAMhIyAYAAAAAGQnZAAAAACAjIRsAAAAAZCRkAwAAAICMhGwAAAAAkJGQDQAAAAAyErIBAAAAQEZCNgAAAADISMgGAAAAABkJ2QAAAAAgIyEbAAAAAGQkZAMAAACAjIRsAAAAAJCRkA0AAAAAMhKyAQAAAEBGQjYAAAAAyEjIBgAAAAAZCdkAAAAAICMhGwAAAABkJGQDAAAAgIyEbAAAAACQkZANAAAAADISsgEAAABARkI2AAAAAMhIyAYAAAAAGQnZAAAAACAjIRsAAAAAZCRkAwAAAICMhGwAAAAAkJGQDQAAAAAyErIBAAAAQEZCNgAAAADISMgGAAAAABkJ2QAAAAAgo9yqLqDaOrowoqCgqqsAAKCi6fMAgI3AmWwAAAAAkJGQDQAAAAAyErIBAAAAQEZCNgAAAADISMgGAAAAABkJ2QAAAAAgIyEbAAAAAGQkZAMAAACAjIRsAAAAAJCRkA0AAAAAMhKyAQAAAEBGQjYAAAAAyEjIBgAAAAAZCdkAAAAAICMhGwAAAABkJGQDAAAAgIxyq7qA6ialFBERixcvruJKAIBNyereYXUvQfWjzwMAymND+zwh2/d88cUXERHRvn37Kq4EANgUffHFF9GkSZOqLoO10OcBAFksWbJkvX2ekO17mjdvHhER8+bN0yBXY4sXL4727dvHhx9+GAUFBVVdDmthjTYN1mnTYJ02DYWFhdGhQ4eSXoLqpzb1ebXp74a51kzmWvPUlnlGmGtNlFKKJUuWRLt27dY7Tsj2PXXq/OcydU2aNKnRvyA1RUFBgXWq5qzRpsE6bRqs06ZhdS9B9VMb+7za9HfDXGsmc615ass8I8y1ptmQD+h0gQAAAACQkZANAAAAADISsn1Pfn5+jBgxIvLz86u6FNbDOlV/1mjTYJ02DdZp02Cdqr/atEbmWjOZa81UW+ZaW+YZYa61WU5yn3kAAAAAyMSZbAAAAACQkZANAAAAADISsgEAAABARkI2AAAAAMhIyPY9f/zjH6NTp05Rv3796N27d0ybNq2qS6q1Ro0aFbvuumtsttlm0bp16zj88MPjnXfeKTXm66+/jmHDhkWLFi2icePGccQRR8Snn35aRRVz1VVXRU5OTpxzzjkl26xR9fDxxx/Hz372s2jRokU0aNAgtt9++3j55ZdL9qeU4te//nVsvvnm0aBBg+jXr1/MmjWrCiuufYqKiuLSSy+Nzp07R4MGDaJr167xm9/8Jr57fyLrVPmeeeaZOOSQQ6Jdu3aRk5MTkyZNKrV/Q9bkyy+/jOOOOy4KCgqiadOmceKJJ8bSpUsrcRasVhP7vIr4Hd0U1Ka+cMyYMbHDDjtEQUFBFBQUxB577BF///vfS/bXlHmuTU3uJS+77LLIyckp9ejevXvJ/poyz9VqS+/ZqVOnNdY1Jycnhg0bFhE1Z131qWWQKDF+/PiUl5eXbrvttvTvf/87nXzyyalp06bp008/rerSaqX+/funsWPHphkzZqTp06engw46KHXo0CEtXbq0ZMxpp52W2rdvn5588sn08ssvp9133z3tueeeVVh17TVt2rTUqVOntMMOO6Szzz67ZLs1qnpffvll6tixYxoyZEh68cUX03vvvZcef/zxNHv27JIxV111VWrSpEmaNGlSev3119Ohhx6aOnfunFasWFGFldcuV155ZWrRokV69NFH09y5c9OECRNS48aN0x/+8IeSMdap8v3tb39Ll1xySZo4cWKKiPTQQw+V2r8ha3LAAQekHXfcMb3wwgvpn//8Z+rWrVsaNGhQJc+EmtrnVcTv6KagNvWFDz/8cHrsscfSu+++m95555108cUXp3r16qUZM2aklGrOPL+vpveSI0aMSNttt12aP39+yeOzzz4r2V9T5plS7eo9Fy5cWGpNJ0+enCIiTZ06NaVUc9ZVn7rhhGzfsdtuu6Vhw4aV/FxUVJTatWuXRo0aVYVVsdrChQtTRKSnn346pZTSokWLUr169dKECRNKxrz11lspItLzzz9fVWXWSkuWLElbbbVVmjx5ctpnn31KGiNrVD1ccMEFqW/fvuvcX1xcnNq2bZt++9vflmxbtGhRys/PT/fee29llEhKacCAAWno0KGltv30pz9Nxx13XErJOlUH3w8wNmRNZs6cmSIivfTSSyVj/v73v6ecnJz08ccfV1rt1I4+rzy/o5uq2tYXNmvWLP3lL3+psfOsDb3kiBEj0o477rjWfTVpninV7t7z7LPPTl27dk3FxcU1al31qRvO10X/n2+++SZeeeWV6NevX8m2OnXqRL9+/eL555+vwspYrbCwMCIimjdvHhERr7zySnz77bel1qx79+7RoUMHa1bJhg0bFgMGDCi1FhHWqLp4+OGHY5dddomjjjoqWrduHT179oxbbrmlZP/cuXNjwYIFpdapSZMm0bt3b+tUifbcc8948skn4913342IiNdffz2effbZOPDAAyPCOlVHG7Imzz//fDRt2jR22WWXkjH9+vWLOnXqxIsvvljpNddWtbXPq8l/N2pLX1hUVBTjx4+PZcuWxR577FFj51lbeslZs2ZFu3btokuXLnHcccfFvHnzIqLmzbO29p7ffPNN3H333TF06NDIycmpUeuqT91wuVVdQHXx+eefR1FRUbRp06bU9jZt2sTbb79dRVWxWnFxcZxzzjnRp0+f+NGPfhQREQsWLIi8vLxo2rRpqbFt2rSJBQsWVEGVtdP48ePj1VdfjZdeemmNfdaoenjvvfdizJgx8Ytf/CIuvvjieOmll2L48OGRl5cXgwcPLlmLtf39s06V58ILL4zFixdH9+7do27dulFUVBRXXnllHHfccRER1qka2pA1WbBgQbRu3brU/tzc3GjevLl1q0S1tc+rqX83akNf+Oabb8Yee+wRX3/9dTRu3Dgeeuih6NGjR0yfPr1GzTOi9vSSvXv3jttvvz222WabmD9/flx++eWx1157xYwZM2rUPCNqb+85adKkWLRoUQwZMiQiatbvrz51wwnZ2CQMGzYsZsyYEc8++2xVl8J3fPjhh3H22WfH5MmTo379+lVdDutQXFwcu+yyS4wcOTIiInr27BkzZsyIm2++OQYPHlzF1bHa/fffH+PGjYt77rkntttuu5g+fXqcc8450a5dO+sE8B21oS/cZpttYvr06VFYWBgPPPBADB48OJ5++umqLqvC1aZecvUZPxERO+ywQ/Tu3Ts6duwY999/fzRo0KAKK6t4tbX3vPXWW+PAAw+Mdu3aVXUpFU6fuuF8XfT/admyZdStW3eNO318+umn0bZt2yqqioiIM888Mx599NGYOnVqbLnlliXb27ZtG998800sWrSo1HhrVnleeeWVWLhwYey8886Rm5sbubm58fTTT8f1118fubm50aZNG2tUDWy++ebRo0ePUtu23Xbbkq8orF4Lf/+q1nnnnRcXXnhhHHvssbH99tvH8ccfH//93/8do0aNigjrVB1tyJq0bds2Fi5cWGr/qlWr4ssvv7Rulai29nk18e9GbekL8/Lyolu3btGrV68YNWpU7LjjjvGHP/yhxs2zNveSTZs2ja233jpmz55d49a1NvaeH3zwQTzxxBNx0kknlWyrSeuqT91wQrb/Jy8vL3r16hVPPvlkybbi4uJ48sknY4899qjCymqvlFKceeaZ8dBDD8WUKVOic+fOpfb36tUr6tWrV2rN3nnnnZg3b541qyT77bdfvPnmmzF9+vSSxy677BLHHXdcyb+tUdXr06dPvPPOO6W2vfvuu9GxY8eIiOjcuXO0bdu21DotXrw4XnzxRetUiZYvXx516pT+33LdunWjuLg4IqxTdbQha7LHHnvEokWL4pVXXikZM2XKlCguLo7evXtXes21VW3t82rS343a3hcWFxfHypUra9w8a3MvuXTp0pgzZ05svvnmNW5da2PvOXbs2GjdunUMGDCgZFtNWld9ahlU9Z0XqpPx48en/Pz8dPvtt6eZM2emU045JTVt2jQtWLCgqkurlU4//fTUpEmT9NRTT5W6LfLy5ctLxpx22mmpQ4cOacqUKenll19Oe+yxR9pjjz2qsGq+e0eolKxRdTBt2rSUm5ubrrzyyjRr1qw0bty41LBhw3T33XeXjLnqqqtS06ZN01//+tf0xhtvpMMOO6xW3nK7Kg0ePDhtscUWJbdGnzhxYmrZsmU6//zzS8ZYp8q3ZMmS9Nprr6XXXnstRUS67rrr0muvvZY++OCDlNKGrckBBxyQevbsmV588cX07LPPpq222ioNGjSoqqZUa9XUPq8ifkc3BbWpL7zwwgvT008/nebOnZveeOONdOGFF6acnJz0j3/8I6VUc+a5LjW1lzz33HPTU089lebOnZuee+651K9fv9SyZcu0cOHClFLNmWdKta/3LCoqSh06dEgXXHDBGvtqyrrqUzeckO17brjhhtShQ4eUl5eXdtttt/TCCy9UdUm1VkSs9TF27NiSMStWrEhnnHFGatasWWrYsGEaOHBgmj9/ftUVzRqNkTWqHh555JH0ox/9KOXn56fu3bunP//5z6X2FxcXp0svvTS1adMm5efnp/322y+98847VVRt7bR48eJ09tlnpw4dOqT69eunLl26pEsuuSStXLmyZIx1qnxTp05d6/+LBg8enFLasDX54osv0qBBg1Ljxo1TQUFB+vnPf56WLFlSBbOhJvZ5FfE7uimoTX3h0KFDU8eOHVNeXl5q1apV2m+//UoCtpRqzjzXpab2ksccc0zafPPNU15eXtpiiy3SMccck2bPnl2yv6bMc7Xa1Hs+/vjjKSLWWn9NWVd96obLSSmlSjttDgAAAABqINdkAwAAAICMhGwAAAAAkJGQDQAAAAAyErIBAAAAQEZCNgAAAADISMgGAAAAABkJ2QAAAAAgIyEbAAAAAGQkZAMoo6eeeipycnJi0aJFG/ycTp06xe9///uNVhMAANnp84AshGxAjTNkyJDIycmJ0047bY19w4YNi5ycnBgyZEjlFwYAQCb6PKA6E7IBNVL79u1j/PjxsWLFipJtX3/9ddxzzz3RoUOHKqwMAIAs9HlAdSVkA2qknXfeOdq3bx8TJ04s2TZx4sTo0KFD9OzZs2TbypUrY/jw4dG6deuoX79+9O3bN1566aVSx/rb3/4WW2+9dTRo0CB+/OMfx/vvv7/G6z377LOx1157RYMGDaJ9+/YxfPjwWLZs2VprSynFZZddFh06dIj8/Pxo165dDB8+vGImDgBQw+nzgOpKyAbUWEOHDo2xY8eW/HzbbbfFz3/+81Jjzj///HjwwQfjjjvuiFdffTW6desW/fv3jy+//DIiIj788MP46U9/GoccckhMnz49TjrppLjwwgtLHWPOnDlxwAEHxBFHHBFvvPFG3HffffHss8/GmWeeuda6HnzwwRg9enT86U9/ilmzZsWkSZNi++23r+DZAwDUXPo8oDoSsgE11s9+9rN49tln44MPPogPPvggnnvuufjZz35Wsn/ZsmUxZsyY+O1vfxsHHnhg9OjRI2655ZZo0KBB3HrrrRERMWbMmOjatWtce+21sc0228Rxxx23xnU+Ro0aFccdd1ycc845sdVWW8Wee+4Z119/fdx5553x9ddfr1HXvHnzom3bttGvX7/o0KFD7LbbbnHyySdv1PcCAKAm0ecB1ZGQDaixWrVqFQMGDIjbb789xo4dGwMGDIiWLVuW7J8zZ058++230adPn5Jt9erVi9122y3eeuutiIh46623onfv3qWOu8cee5T6+fXXX4/bb789GjduXPLo379/FBcXx9y5c9eo66ijjooVK1ZEly5d4uSTT46HHnooVq1aVZFTBwCo0fR5QHWUW9UFAGxMQ4cOLTmd/49//ONGeY2lS5fGqaeeutbrbazt4rvt27ePd955J5544omYPHlynHHGGfHb3/42nn766ahXr95GqREAoKbR5wHVjTPZgBrtgAMOiG+++Sa+/fbb6N+/f6l9Xbt2jby8vHjuuedKtn377bfx0ksvRY8ePSIiYtttt41p06aVet4LL7xQ6uedd945Zs6cGd26dVvjkZeXt9a6GjRoEIccckhcf/318dRTT8Xzzz8fb775ZkVMGQCgVtDnAdWNM9mAGq1u3bolXwmoW7duqX2NGjWK008/Pc4777xo3rx5dOjQIa655ppYvnx5nHjiiRERcdppp8W1114b5513Xpx00knxyiuvxO23317qOBdccEHsvvvuceaZZ8ZJJ50UjRo1ipkzZ8bkyZPjxhtvXKOm22+/PYqKiqJ3797RsGHDuPvuu6NBgwbRsWPHjfMmAADUQPo8oLpxJhtQ4xUUFERBQcFa91111VVxxBFHxPHHHx8777xzzJ49Ox5//PFo1qxZRPznawAPPvhgTJo0KXbccce4+eabY+TIkaWOscMOO8TTTz8d7777buy1117Rs2fP+PWvfx3t2rVb62s2bdo0brnllujTp0/ssMMO8cQTT8QjjzwSLVq0qNiJAwDUcPo8oDrJSSmlqi4CAAAAADZlzmQDAAAAgIyEbAAAAACQkZANAAAAADISsgEAAABARkI2AAAAAMhIyAYAAAAAGQnZAAAAACAjIRsAAAAAZCRkAwAAAICMhGwAAAAAkJGQDQAAAAAy+r81kaeHDJrX8gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "f,axes = plt.subplots(1,2,figsize = (15,5))\n",
    "\n",
    "models = {'NN':model,\"RFC\":rfc,'GBC':gbc}\n",
    "axes[1].barh(list(models.keys()),[accuracy_score(ytest,model_.predict(xtest).round())*100 for model_ in models.values()],\n",
    "        color = ['orange','green','blue'])\n",
    "\n",
    "axes[1].set(xlabel = 'Models', ylabel = 'Accuracy',title = 'Accuracy of the test test')\n",
    "\n",
    "axes[0].barh(list(models.keys()),[accuracy_score(ytrain,model_.predict(xtrain).round())*100 for model_ in models.values()],\n",
    "        color = ['orange','green','blue'])\n",
    "\n",
    "axes[0].set(xlabel = 'Models', ylabel = 'Accuracy',title = 'Accuracy of the train test')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a11a96c4",
   "metadata": {},
   "source": [
    "<h1>Conclusion</h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8356979e",
   "metadata": {},
   "source": [
    "we deployed 3 diffent ML models and as it can be looked, all 3 models have about 85% accuracy <i>on the test set</i>. But overall, we can conclude than the RandomForetClassifier has been the best,both on the <i>train set</i> and on the <i>test set</i>"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}