main.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **IPL Match Winner Prediction System**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Importing required libraries**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing required libraries\n",
    "import numpy as np  # Library for numerical operations\n",
    "import pandas as pd  # Library for data manipulation and analysis\n",
    "import matplotlib.pyplot as plt  # Library for creating visualizations\n",
    "import seaborn as sns  # Library for advanced data visualization\n",
    "from sklearn.model_selection import train_test_split  # Function to split data into training and testing sets\n",
    "from sklearn.ensemble import RandomForestClassifier  # Random Forest classifier model\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC     # Import the SVC class for support vector machine classification\n",
    "from sklearn.metrics import accuracy_score  # Function to calculate accuracy score\n",
    "# Importing LightGBM library\n",
    "import lightgbm as lgb  # Light Gradient Boosting Machine library"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Importing our dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>city</th>\n",
       "      <th>date</th>\n",
       "      <th>player_of_match</th>\n",
       "      <th>venue</th>\n",
       "      <th>neutral_venue</th>\n",
       "      <th>team1</th>\n",
       "      <th>team2</th>\n",
       "      <th>toss_winner</th>\n",
       "      <th>toss_decision</th>\n",
       "      <th>winner</th>\n",
       "      <th>result</th>\n",
       "      <th>result_margin</th>\n",
       "      <th>eliminator</th>\n",
       "      <th>method</th>\n",
       "      <th>umpire1</th>\n",
       "      <th>umpire2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>335982</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>4/18/2008</td>\n",
       "      <td>BB McCullum</td>\n",
       "      <td>M Chinnaswamy Stadium</td>\n",
       "      <td>0</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>field</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>runs</td>\n",
       "      <td>140.0</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Asad Rauf</td>\n",
       "      <td>RE Koertzen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>335983</td>\n",
       "      <td>Chandigarh</td>\n",
       "      <td>4/19/2008</td>\n",
       "      <td>MEK Hussey</td>\n",
       "      <td>Punjab Cricket Association Stadium, Mohali</td>\n",
       "      <td>0</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>bat</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>runs</td>\n",
       "      <td>33.0</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MR Benson</td>\n",
       "      <td>SL Shastri</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>335984</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>4/19/2008</td>\n",
       "      <td>MF Maharoof</td>\n",
       "      <td>Feroz Shah Kotla</td>\n",
       "      <td>0</td>\n",
       "      <td>Delhi Daredevils</td>\n",
       "      <td>Rajasthan Royals</td>\n",
       "      <td>Rajasthan Royals</td>\n",
       "      <td>bat</td>\n",
       "      <td>Delhi Daredevils</td>\n",
       "      <td>wickets</td>\n",
       "      <td>9.0</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Aleem Dar</td>\n",
       "      <td>GA Pratapkumar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>335985</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>4/20/2008</td>\n",
       "      <td>MV Boucher</td>\n",
       "      <td>Wankhede Stadium</td>\n",
       "      <td>0</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>bat</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>wickets</td>\n",
       "      <td>5.0</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SJ Davis</td>\n",
       "      <td>DJ Harper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>335986</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>4/20/2008</td>\n",
       "      <td>DJ Hussey</td>\n",
       "      <td>Eden Gardens</td>\n",
       "      <td>0</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>Deccan Chargers</td>\n",
       "      <td>Deccan Chargers</td>\n",
       "      <td>bat</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>wickets</td>\n",
       "      <td>5.0</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>BF Bowden</td>\n",
       "      <td>K Hariharan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>335987</td>\n",
       "      <td>Jaipur</td>\n",
       "      <td>4/21/2008</td>\n",
       "      <td>SR Watson</td>\n",
       "      <td>Sawai Mansingh Stadium</td>\n",
       "      <td>0</td>\n",
       "      <td>Rajasthan Royals</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>bat</td>\n",
       "      <td>Rajasthan Royals</td>\n",
       "      <td>wickets</td>\n",
       "      <td>6.0</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Aleem Dar</td>\n",
       "      <td>RB Tiffin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>335988</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>4/22/2008</td>\n",
       "      <td>V Sehwag</td>\n",
       "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
       "      <td>0</td>\n",
       "      <td>Deccan Chargers</td>\n",
       "      <td>Delhi Daredevils</td>\n",
       "      <td>Deccan Chargers</td>\n",
       "      <td>bat</td>\n",
       "      <td>Delhi Daredevils</td>\n",
       "      <td>wickets</td>\n",
       "      <td>9.0</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IL Howell</td>\n",
       "      <td>AM Saheba</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>335989</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>4/23/2008</td>\n",
       "      <td>ML Hayden</td>\n",
       "      <td>MA Chidambaram Stadium, Chepauk</td>\n",
       "      <td>0</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>field</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>runs</td>\n",
       "      <td>6.0</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DJ Harper</td>\n",
       "      <td>GA Pratapkumar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>335990</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>4/24/2008</td>\n",
       "      <td>YK Pathan</td>\n",
       "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
       "      <td>0</td>\n",
       "      <td>Deccan Chargers</td>\n",
       "      <td>Rajasthan Royals</td>\n",
       "      <td>Rajasthan Royals</td>\n",
       "      <td>field</td>\n",
       "      <td>Rajasthan Royals</td>\n",
       "      <td>wickets</td>\n",
       "      <td>3.0</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Asad Rauf</td>\n",
       "      <td>MR Benson</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>335991</td>\n",
       "      <td>Chandigarh</td>\n",
       "      <td>4/25/2008</td>\n",
       "      <td>KC Sangakkara</td>\n",
       "      <td>Punjab Cricket Association Stadium, Mohali</td>\n",
       "      <td>0</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>field</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>runs</td>\n",
       "      <td>66.0</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Aleem Dar</td>\n",
       "      <td>AM Saheba</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "       id        city       date player_of_match  \\\n",
       "0  335982   Bangalore  4/18/2008     BB McCullum   \n",
       "1  335983  Chandigarh  4/19/2008      MEK Hussey   \n",
       "2  335984       Delhi  4/19/2008     MF Maharoof   \n",
       "3  335985      Mumbai  4/20/2008      MV Boucher   \n",
       "4  335986     Kolkata  4/20/2008       DJ Hussey   \n",
       "5  335987      Jaipur  4/21/2008       SR Watson   \n",
       "6  335988   Hyderabad  4/22/2008        V Sehwag   \n",
       "7  335989     Chennai  4/23/2008       ML Hayden   \n",
       "8  335990   Hyderabad  4/24/2008       YK Pathan   \n",
       "9  335991  Chandigarh  4/25/2008   KC Sangakkara   \n",
       "\n",
       "                                        venue  neutral_venue  \\\n",
       "0                       M Chinnaswamy Stadium              0   \n",
       "1  Punjab Cricket Association Stadium, Mohali              0   \n",
       "2                            Feroz Shah Kotla              0   \n",
       "3                            Wankhede Stadium              0   \n",
       "4                                Eden Gardens              0   \n",
       "5                      Sawai Mansingh Stadium              0   \n",
       "6   Rajiv Gandhi International Stadium, Uppal              0   \n",
       "7             MA Chidambaram Stadium, Chepauk              0   \n",
       "8   Rajiv Gandhi International Stadium, Uppal              0   \n",
       "9  Punjab Cricket Association Stadium, Mohali              0   \n",
       "\n",
       "                         team1                        team2  \\\n",
       "0  Royal Challengers Bangalore        Kolkata Knight Riders   \n",
       "1              Kings XI Punjab          Chennai Super Kings   \n",
       "2             Delhi Daredevils             Rajasthan Royals   \n",
       "3               Mumbai Indians  Royal Challengers Bangalore   \n",
       "4        Kolkata Knight Riders              Deccan Chargers   \n",
       "5             Rajasthan Royals              Kings XI Punjab   \n",
       "6              Deccan Chargers             Delhi Daredevils   \n",
       "7          Chennai Super Kings               Mumbai Indians   \n",
       "8              Deccan Chargers             Rajasthan Royals   \n",
       "9              Kings XI Punjab               Mumbai Indians   \n",
       "\n",
       "                   toss_winner toss_decision                       winner  \\\n",
       "0  Royal Challengers Bangalore         field        Kolkata Knight Riders   \n",
       "1          Chennai Super Kings           bat          Chennai Super Kings   \n",
       "2             Rajasthan Royals           bat             Delhi Daredevils   \n",
       "3               Mumbai Indians           bat  Royal Challengers Bangalore   \n",
       "4              Deccan Chargers           bat        Kolkata Knight Riders   \n",
       "5              Kings XI Punjab           bat             Rajasthan Royals   \n",
       "6              Deccan Chargers           bat             Delhi Daredevils   \n",
       "7               Mumbai Indians         field          Chennai Super Kings   \n",
       "8             Rajasthan Royals         field             Rajasthan Royals   \n",
       "9               Mumbai Indians         field              Kings XI Punjab   \n",
       "\n",
       "    result  result_margin eliminator method    umpire1         umpire2  \n",
       "0     runs          140.0          N    NaN  Asad Rauf     RE Koertzen  \n",
       "1     runs           33.0          N    NaN  MR Benson      SL Shastri  \n",
       "2  wickets            9.0          N    NaN  Aleem Dar  GA Pratapkumar  \n",
       "3  wickets            5.0          N    NaN   SJ Davis       DJ Harper  \n",
       "4  wickets            5.0          N    NaN  BF Bowden     K Hariharan  \n",
       "5  wickets            6.0          N    NaN  Aleem Dar       RB Tiffin  \n",
       "6  wickets            9.0          N    NaN  IL Howell       AM Saheba  \n",
       "7     runs            6.0          N    NaN  DJ Harper  GA Pratapkumar  \n",
       "8  wickets            3.0          N    NaN  Asad Rauf       MR Benson  \n",
       "9     runs           66.0          N    NaN  Aleem Dar       AM Saheba  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reading the CSV file 'ipl.csv' and storing the data in a DataFrame called 'data'\n",
    "data = pd.read_csv('ipl.csv')\n",
    "\n",
    "# Displaying the first 5 rows of the dataset\n",
    "data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>city</th>\n",
       "      <th>date</th>\n",
       "      <th>player_of_match</th>\n",
       "      <th>venue</th>\n",
       "      <th>neutral_venue</th>\n",
       "      <th>team1</th>\n",
       "      <th>team2</th>\n",
       "      <th>toss_winner</th>\n",
       "      <th>toss_decision</th>\n",
       "      <th>winner</th>\n",
       "      <th>result</th>\n",
       "      <th>result_margin</th>\n",
       "      <th>eliminator</th>\n",
       "      <th>method</th>\n",
       "      <th>umpire1</th>\n",
       "      <th>umpire2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>811</th>\n",
       "      <td>1216547</td>\n",
       "      <td>Dubai</td>\n",
       "      <td>9/28/2020</td>\n",
       "      <td>AB de Villiers</td>\n",
       "      <td>Dubai International Cricket Stadium</td>\n",
       "      <td>0</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>field</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>tie</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Y</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Nitin Menon</td>\n",
       "      <td>PR Reiffel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>812</th>\n",
       "      <td>1237177</td>\n",
       "      <td>Dubai</td>\n",
       "      <td>11/5/2020</td>\n",
       "      <td>JJ Bumrah</td>\n",
       "      <td>Dubai International Cricket Stadium</td>\n",
       "      <td>0</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>field</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>runs</td>\n",
       "      <td>57.0</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CB Gaffaney</td>\n",
       "      <td>Nitin Menon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>813</th>\n",
       "      <td>1237178</td>\n",
       "      <td>Abu Dhabi</td>\n",
       "      <td>11/6/2020</td>\n",
       "      <td>KS Williamson</td>\n",
       "      <td>Sheikh Zayed Stadium</td>\n",
       "      <td>0</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>field</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>wickets</td>\n",
       "      <td>6.0</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PR Reiffel</td>\n",
       "      <td>S Ravi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>814</th>\n",
       "      <td>1237180</td>\n",
       "      <td>Abu Dhabi</td>\n",
       "      <td>11/8/2020</td>\n",
       "      <td>MP Stoinis</td>\n",
       "      <td>Sheikh Zayed Stadium</td>\n",
       "      <td>0</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>bat</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>runs</td>\n",
       "      <td>17.0</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PR Reiffel</td>\n",
       "      <td>S Ravi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>815</th>\n",
       "      <td>1237181</td>\n",
       "      <td>Dubai</td>\n",
       "      <td>11/10/2020</td>\n",
       "      <td>TA Boult</td>\n",
       "      <td>Dubai International Cricket Stadium</td>\n",
       "      <td>0</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>bat</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>wickets</td>\n",
       "      <td>5.0</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CB Gaffaney</td>\n",
       "      <td>Nitin Menon</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          id       city        date player_of_match  \\\n",
       "811  1216547      Dubai   9/28/2020  AB de Villiers   \n",
       "812  1237177      Dubai   11/5/2020       JJ Bumrah   \n",
       "813  1237178  Abu Dhabi   11/6/2020   KS Williamson   \n",
       "814  1237180  Abu Dhabi   11/8/2020      MP Stoinis   \n",
       "815  1237181      Dubai  11/10/2020        TA Boult   \n",
       "\n",
       "                                   venue  neutral_venue  \\\n",
       "811  Dubai International Cricket Stadium              0   \n",
       "812  Dubai International Cricket Stadium              0   \n",
       "813                 Sheikh Zayed Stadium              0   \n",
       "814                 Sheikh Zayed Stadium              0   \n",
       "815  Dubai International Cricket Stadium              0   \n",
       "\n",
       "                           team1                team2          toss_winner  \\\n",
       "811  Royal Challengers Bangalore       Mumbai Indians       Mumbai Indians   \n",
       "812               Mumbai Indians       Delhi Capitals       Delhi Capitals   \n",
       "813  Royal Challengers Bangalore  Sunrisers Hyderabad  Sunrisers Hyderabad   \n",
       "814               Delhi Capitals  Sunrisers Hyderabad       Delhi Capitals   \n",
       "815               Delhi Capitals       Mumbai Indians       Delhi Capitals   \n",
       "\n",
       "    toss_decision                       winner   result  result_margin  \\\n",
       "811         field  Royal Challengers Bangalore      tie            NaN   \n",
       "812         field               Mumbai Indians     runs           57.0   \n",
       "813         field          Sunrisers Hyderabad  wickets            6.0   \n",
       "814           bat               Delhi Capitals     runs           17.0   \n",
       "815           bat               Mumbai Indians  wickets            5.0   \n",
       "\n",
       "    eliminator method      umpire1      umpire2  \n",
       "811          Y    NaN  Nitin Menon   PR Reiffel  \n",
       "812          N    NaN  CB Gaffaney  Nitin Menon  \n",
       "813          N    NaN   PR Reiffel       S Ravi  \n",
       "814          N    NaN   PR Reiffel       S Ravi  \n",
       "815          N    NaN  CB Gaffaney  Nitin Menon  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Displaying the last 5 rows of the dataset\n",
    "data.tail(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Understanding our data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rows and Columns of the dataset :-  (816, 17)\n"
     ]
    }
   ],
   "source": [
    "# Shape of our data\n",
    "print(\"Rows and Columns of the dataset :- \",data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 816 entries, 0 to 815\n",
      "Data columns (total 17 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   id               816 non-null    int64  \n",
      " 1   city             803 non-null    object \n",
      " 2   date             816 non-null    object \n",
      " 3   player_of_match  812 non-null    object \n",
      " 4   venue            816 non-null    object \n",
      " 5   neutral_venue    816 non-null    int64  \n",
      " 6   team1            816 non-null    object \n",
      " 7   team2            816 non-null    object \n",
      " 8   toss_winner      816 non-null    object \n",
      " 9   toss_decision    816 non-null    object \n",
      " 10  winner           812 non-null    object \n",
      " 11  result           812 non-null    object \n",
      " 12  result_margin    799 non-null    float64\n",
      " 13  eliminator       812 non-null    object \n",
      " 14  method           19 non-null     object \n",
      " 15  umpire1          816 non-null    object \n",
      " 16  umpire2          816 non-null    object \n",
      "dtypes: float64(1), int64(2), object(14)\n",
      "memory usage: 108.5+ KB\n"
     ]
    }
   ],
   "source": [
    "# Identifying information about composition and potential data quality\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'city', 'date', 'player_of_match', 'venue', 'neutral_venue',\n",
       "       'team1', 'team2', 'toss_winner', 'toss_decision', 'winner', 'result',\n",
       "       'result_margin', 'eliminator', 'method', 'umpire1', 'umpire2'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Displaying the columns in our dataset\n",
    "data.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Column Description**\n",
    "\n",
    "- 'id': Unique identifier for each match.\n",
    "\n",
    "- 'city': City where the match was played.\n",
    "\n",
    "- 'date': Date of the match.\n",
    "\n",
    "- 'player_of_match': Name of the player who was awarded \"Player of the Match.\"\n",
    "\n",
    "- 'venue': Stadium or venue where the match was played.\n",
    "\n",
    "- 'neutral_venue': Binary indicator (0 or 1) indicating if the match was played on a neutral venue (1) or not (0).\n",
    "\n",
    "- 'team1': Name of the first team participating in the match.\n",
    "\n",
    "- 'team2': Name of the second team participating in the match.\n",
    "\n",
    "- 'toss_winner': Name of the team that won the toss.\n",
    "\n",
    "- 'toss_decision': Decision taken by the toss-winning team (either 'field' or 'bat').\n",
    "\n",
    "- 'winner': Name of the team that won the match.\n",
    "\n",
    "- 'result': The result of the match (e.g., 'runs', 'wickets', 'tie', etc.).\n",
    "\n",
    "- 'result_margin': The margin by which the winning team won the match (e.g., runs or wickets).\n",
    "\n",
    "- 'eliminator': Binary indicator (0 or 1) indicating if the match was decided by an eliminator (1) or not (0).\n",
    "\n",
    "- 'method': The method used to decide the match (e.g., Duckworth-Lewis, etc.).\n",
    "\n",
    "- 'umpire1': Name of the first on-field umpire.\n",
    "\n",
    "- 'umpire2': Name of the second on-field umpire."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Royal Challengers Bangalore', 'Kings XI Punjab',\n",
       "       'Delhi Daredevils', 'Mumbai Indians', 'Kolkata Knight Riders',\n",
       "       'Rajasthan Royals', 'Deccan Chargers', 'Chennai Super Kings',\n",
       "       'Kochi Tuskers Kerala', 'Pune Warriors', 'Sunrisers Hyderabad',\n",
       "       'Gujarat Lions', 'Rising Pune Supergiants',\n",
       "       'Rising Pune Supergiant', 'Delhi Capitals'], dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get unique values in the 'team1' column\n",
    "data['team1'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Kolkata Knight Riders', 'Chennai Super Kings', 'Rajasthan Royals',\n",
       "       'Royal Challengers Bangalore', 'Deccan Chargers',\n",
       "       'Kings XI Punjab', 'Delhi Daredevils', 'Mumbai Indians',\n",
       "       'Kochi Tuskers Kerala', 'Pune Warriors', 'Sunrisers Hyderabad',\n",
       "       'Rising Pune Supergiants', 'Gujarat Lions',\n",
       "       'Rising Pune Supergiant', 'Delhi Capitals'], dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get unique values in the 'team2' column\n",
    "data['team2'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Kolkata Knight Riders', 'Chennai Super Kings', 'Delhi Daredevils',\n",
       "       'Royal Challengers Bangalore', 'Rajasthan Royals',\n",
       "       'Kings XI Punjab', 'Deccan Chargers', 'Mumbai Indians',\n",
       "       'Pune Warriors', 'Kochi Tuskers Kerala', nan,\n",
       "       'Sunrisers Hyderabad', 'Rising Pune Supergiants', 'Gujarat Lions',\n",
       "       'Rising Pune Supergiant', 'Delhi Capitals'], dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get unique values in the 'winner' column\n",
    "data['winner'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Royal Challengers Bangalore', 'Chennai Super Kings',\n",
       "       'Rajasthan Royals', 'Mumbai Indians', 'Deccan Chargers',\n",
       "       'Kings XI Punjab', 'Kolkata Knight Riders', 'Delhi Daredevils',\n",
       "       'Kochi Tuskers Kerala', 'Pune Warriors', 'Sunrisers Hyderabad',\n",
       "       'Gujarat Lions', 'Rising Pune Supergiants',\n",
       "       'Rising Pune Supergiant', 'Delhi Capitals'], dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get unique values in the 'toss_winner' column\n",
    "data['toss_winner'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replacing 'Rising Pune Supergiants' with 'Rising Pune Supergiant' in the 'team1', 'team2', 'winner', and 'toss_winner' columns.\n",
    "data.team1.replace({'Rising Pune Supergiants': 'Rising Pune Supergiant'}, regex=True, inplace=True)\n",
    "data.team2.replace({'Rising Pune Supergiants': 'Rising Pune Supergiant'}, regex=True, inplace=True)\n",
    "data.winner.replace({'Rising Pune Supergiants': 'Rising Pune Supergiant'}, regex=True, inplace=True)\n",
    "data.toss_winner.replace({'Rising Pune Supergiants': 'Rising Pune Supergiant'}, regex=True, inplace=True)\n",
    "\n",
    "# Replacing 'Deccan Chargers' with 'Sunrisers Hyderabad' in the 'team1', 'team2', 'winner', and 'toss_winner' columns.\n",
    "data.team1.replace({'Deccan Chargers': 'Sunrisers Hyderabad'}, regex=True, inplace=True)\n",
    "data.team2.replace({'Deccan Chargers': 'Sunrisers Hyderabad'}, regex=True, inplace=True)\n",
    "data.winner.replace({'Deccan Chargers': 'Sunrisers Hyderabad'}, regex=True, inplace=True)\n",
    "data.toss_winner.replace({'Deccan Chargers': 'Sunrisers Hyderabad'}, regex=True, inplace=True)\n",
    "\n",
    "# Replacing 'Delhi Daredevils' with 'Delhi Capitals' in the 'team1', 'team2', 'winner', and 'toss_winner' columns.\n",
    "data.team1.replace({'Delhi Daredevils': 'Delhi Capitals'}, regex=True, inplace=True)\n",
    "data.team2.replace({'Delhi Daredevils': 'Delhi Capitals'}, regex=True, inplace=True)\n",
    "data.winner.replace({'Delhi Daredevils': 'Delhi Capitals'}, regex=True, inplace=True)\n",
    "data.toss_winner.replace({'Delhi Daredevils': 'Delhi Capitals'}, regex=True, inplace=True)\n",
    "\n",
    "# Replacing 'Pune Warriors' with 'Rising Pune Supergiant' in the 'team1', 'team2', 'winner', and 'toss_winner' columns.\n",
    "data.team1.replace({'Pune Warriors': 'Rising Pune Supergiant'}, regex=True, inplace=True)\n",
    "data.team2.replace({'Pune Warriors': 'Rising Pune Supergiant'}, regex=True, inplace=True)\n",
    "data.winner.replace({'Pune Warriors': 'Rising Pune Supergiant'}, regex=True, inplace=True)\n",
    "data.toss_winner.replace({'Pune Warriors': 'Rising Pune Supergiant'}, regex=True, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The code provided is used to standardize and clean up the team names in the dataset. There are inconsistencies in the team names, and some teams have multiple variations in their names, such as \"Rising Pune Supergiant\" and \"Rising Pune Supergiants,\" \"Deccan Chargers\" and \"Sunrisers Hyderabad,\" and \"Delhi Daredevils\" and \"Delhi Capitals.\"\n",
    "\n",
    "By performing these replacements, we ensure that all team names are consistent and represented with the correct name throughout the dataset. This is important because when building models or performing data analysis, having consistent and clean data is crucial for accurate and meaningful results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>id</th>\n",
       "      <th>city</th>\n",
       "      <th>date</th>\n",
       "      <th>player_of_match</th>\n",
       "      <th>venue</th>\n",
       "      <th>neutral_venue</th>\n",
       "      <th>team1</th>\n",
       "      <th>team2</th>\n",
       "      <th>toss_winner</th>\n",
       "      <th>toss_decision</th>\n",
       "      <th>winner</th>\n",
       "      <th>result</th>\n",
       "      <th>result_margin</th>\n",
       "      <th>eliminator</th>\n",
       "      <th>method</th>\n",
       "      <th>umpire1</th>\n",
       "      <th>umpire2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8.160000e+02</td>\n",
       "      <td>803</td>\n",
       "      <td>816</td>\n",
       "      <td>812</td>\n",
       "      <td>816</td>\n",
       "      <td>816.000000</td>\n",
       "      <td>816</td>\n",
       "      <td>816</td>\n",
       "      <td>816</td>\n",
       "      <td>816</td>\n",
       "      <td>812</td>\n",
       "      <td>812</td>\n",
       "      <td>799.000000</td>\n",
       "      <td>812</td>\n",
       "      <td>19</td>\n",
       "      <td>816</td>\n",
       "      <td>816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>32</td>\n",
       "      <td>596</td>\n",
       "      <td>233</td>\n",
       "      <td>36</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>48</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>4/19/2009</td>\n",
       "      <td>AB de Villiers</td>\n",
       "      <td>Eden Gardens</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>field</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>wickets</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>D/L</td>\n",
       "      <td>HDPK Dharmasena</td>\n",
       "      <td>S Ravi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>101</td>\n",
       "      <td>2</td>\n",
       "      <td>23</td>\n",
       "      <td>77</td>\n",
       "      <td>NaN</td>\n",
       "      <td>108</td>\n",
       "      <td>106</td>\n",
       "      <td>106</td>\n",
       "      <td>496</td>\n",
       "      <td>120</td>\n",
       "      <td>435</td>\n",
       "      <td>NaN</td>\n",
       "      <td>799</td>\n",
       "      <td>19</td>\n",
       "      <td>78</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.563496e+05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.094363</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.321652</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.058943e+05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.292512</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.068427</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.359820e+05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.012278e+05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.292980e+05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.082626e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.237181e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>146.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  id    city       date player_of_match         venue  \\\n",
       "count   8.160000e+02     803        816             812           816   \n",
       "unique           NaN      32        596             233            36   \n",
       "top              NaN  Mumbai  4/19/2009  AB de Villiers  Eden Gardens   \n",
       "freq             NaN     101          2              23            77   \n",
       "mean    7.563496e+05     NaN        NaN             NaN           NaN   \n",
       "std     3.058943e+05     NaN        NaN             NaN           NaN   \n",
       "min     3.359820e+05     NaN        NaN             NaN           NaN   \n",
       "25%     5.012278e+05     NaN        NaN             NaN           NaN   \n",
       "50%     7.292980e+05     NaN        NaN             NaN           NaN   \n",
       "75%     1.082626e+06     NaN        NaN             NaN           NaN   \n",
       "max     1.237181e+06     NaN        NaN             NaN           NaN   \n",
       "\n",
       "        neutral_venue                        team1           team2  \\\n",
       "count      816.000000                          816             816   \n",
       "unique            NaN                           11              11   \n",
       "top               NaN  Royal Challengers Bangalore  Mumbai Indians   \n",
       "freq              NaN                          108             106   \n",
       "mean         0.094363                          NaN             NaN   \n",
       "std          0.292512                          NaN             NaN   \n",
       "min          0.000000                          NaN             NaN   \n",
       "25%          0.000000                          NaN             NaN   \n",
       "50%          0.000000                          NaN             NaN   \n",
       "75%          0.000000                          NaN             NaN   \n",
       "max          1.000000                          NaN             NaN   \n",
       "\n",
       "           toss_winner toss_decision          winner   result  result_margin  \\\n",
       "count              816           816             812      812     799.000000   \n",
       "unique              11             2              11        3            NaN   \n",
       "top     Mumbai Indians         field  Mumbai Indians  wickets            NaN   \n",
       "freq               106           496             120      435            NaN   \n",
       "mean               NaN           NaN             NaN      NaN      17.321652   \n",
       "std                NaN           NaN             NaN      NaN      22.068427   \n",
       "min                NaN           NaN             NaN      NaN       1.000000   \n",
       "25%                NaN           NaN             NaN      NaN       6.000000   \n",
       "50%                NaN           NaN             NaN      NaN       8.000000   \n",
       "75%                NaN           NaN             NaN      NaN      19.500000   \n",
       "max                NaN           NaN             NaN      NaN     146.000000   \n",
       "\n",
       "       eliminator method          umpire1 umpire2  \n",
       "count         812     19              816     816  \n",
       "unique          2      1               48      47  \n",
       "top             N    D/L  HDPK Dharmasena  S Ravi  \n",
       "freq          799     19               78      84  \n",
       "mean          NaN    NaN              NaN     NaN  \n",
       "std           NaN    NaN              NaN     NaN  \n",
       "min           NaN    NaN              NaN     NaN  \n",
       "25%           NaN    NaN              NaN     NaN  \n",
       "50%           NaN    NaN              NaN     NaN  \n",
       "75%           NaN    NaN              NaN     NaN  \n",
       "max           NaN    NaN              NaN     NaN  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To show statistical summary of the columns of our data\n",
    "data.describe(include ='all')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Checking NULL Values**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                   0\n",
       "city                13\n",
       "date                 0\n",
       "player_of_match      4\n",
       "venue                0\n",
       "neutral_venue        0\n",
       "team1                0\n",
       "team2                0\n",
       "toss_winner          0\n",
       "toss_decision        0\n",
       "winner               4\n",
       "result               4\n",
       "result_margin       17\n",
       "eliminator           4\n",
       "method             797\n",
       "umpire1              0\n",
       "umpire2              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To count the null values\n",
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fill missing values in 'city' column with 'Unknown'\n",
    "data['city'].fillna('Unknown', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fill missing values in 'player_of_match', 'result', and 'eliminator' columns with 'Not Available'\n",
    "cols_to_fill = ['player_of_match', 'result', 'eliminator']\n",
    "data[cols_to_fill] = data[cols_to_fill].fillna('Not Available')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate the mean of the 'result_margin' column\n",
    "mean_result_margin = data['result_margin'].mean()\n",
    "\n",
    "# Fill missing values in 'result_margin' column with the mean\n",
    "data['result_margin'].fillna(mean_result_margin, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# To drop the unwanted columns\n",
    "data.drop(['id','method'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop rows with missing values in the 'winner' column\n",
    "data.dropna(subset=['winner'], inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city               0\n",
       "date               0\n",
       "player_of_match    0\n",
       "venue              0\n",
       "neutral_venue      0\n",
       "team1              0\n",
       "team2              0\n",
       "toss_winner        0\n",
       "toss_decision      0\n",
       "winner             0\n",
       "result             0\n",
       "result_margin      0\n",
       "eliminator         0\n",
       "umpire1            0\n",
       "umpire2            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To count the null values\n",
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(812, 15)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the shape of our data after handling null values\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert the 'date' column to datetime format\n",
    "data['date'] = pd.to_datetime(data['date'])\n",
    "\n",
    "# Extract the year from the 'date' column and create a new 'season' column\n",
    "data['season'] = pd.DatetimeIndex(data['date']).year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>city</th>\n",
       "      <th>date</th>\n",
       "      <th>player_of_match</th>\n",
       "      <th>venue</th>\n",
       "      <th>neutral_venue</th>\n",
       "      <th>team1</th>\n",
       "      <th>team2</th>\n",
       "      <th>toss_winner</th>\n",
       "      <th>toss_decision</th>\n",
       "      <th>winner</th>\n",
       "      <th>result</th>\n",
       "      <th>result_margin</th>\n",
       "      <th>eliminator</th>\n",
       "      <th>umpire1</th>\n",
       "      <th>umpire2</th>\n",
       "      <th>season</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bangalore</td>\n",
       "      <td>2008-04-18</td>\n",
       "      <td>BB McCullum</td>\n",
       "      <td>M Chinnaswamy Stadium</td>\n",
       "      <td>0</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>field</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>runs</td>\n",
       "      <td>140.0</td>\n",
       "      <td>N</td>\n",
       "      <td>Asad Rauf</td>\n",
       "      <td>RE Koertzen</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Chandigarh</td>\n",
       "      <td>2008-04-19</td>\n",
       "      <td>MEK Hussey</td>\n",
       "      <td>Punjab Cricket Association Stadium, Mohali</td>\n",
       "      <td>0</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>bat</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>runs</td>\n",
       "      <td>33.0</td>\n",
       "      <td>N</td>\n",
       "      <td>MR Benson</td>\n",
       "      <td>SL Shastri</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Delhi</td>\n",
       "      <td>2008-04-19</td>\n",
       "      <td>MF Maharoof</td>\n",
       "      <td>Feroz Shah Kotla</td>\n",
       "      <td>0</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Rajasthan Royals</td>\n",
       "      <td>Rajasthan Royals</td>\n",
       "      <td>bat</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>wickets</td>\n",
       "      <td>9.0</td>\n",
       "      <td>N</td>\n",
       "      <td>Aleem Dar</td>\n",
       "      <td>GA Pratapkumar</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mumbai</td>\n",
       "      <td>2008-04-20</td>\n",
       "      <td>MV Boucher</td>\n",
       "      <td>Wankhede Stadium</td>\n",
       "      <td>0</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>bat</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>wickets</td>\n",
       "      <td>5.0</td>\n",
       "      <td>N</td>\n",
       "      <td>SJ Davis</td>\n",
       "      <td>DJ Harper</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kolkata</td>\n",
       "      <td>2008-04-20</td>\n",
       "      <td>DJ Hussey</td>\n",
       "      <td>Eden Gardens</td>\n",
       "      <td>0</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>bat</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>wickets</td>\n",
       "      <td>5.0</td>\n",
       "      <td>N</td>\n",
       "      <td>BF Bowden</td>\n",
       "      <td>K Hariharan</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         city       date player_of_match  \\\n",
       "0   Bangalore 2008-04-18     BB McCullum   \n",
       "1  Chandigarh 2008-04-19      MEK Hussey   \n",
       "2       Delhi 2008-04-19     MF Maharoof   \n",
       "3      Mumbai 2008-04-20      MV Boucher   \n",
       "4     Kolkata 2008-04-20       DJ Hussey   \n",
       "\n",
       "                                        venue  neutral_venue  \\\n",
       "0                       M Chinnaswamy Stadium              0   \n",
       "1  Punjab Cricket Association Stadium, Mohali              0   \n",
       "2                            Feroz Shah Kotla              0   \n",
       "3                            Wankhede Stadium              0   \n",
       "4                                Eden Gardens              0   \n",
       "\n",
       "                         team1                        team2  \\\n",
       "0  Royal Challengers Bangalore        Kolkata Knight Riders   \n",
       "1              Kings XI Punjab          Chennai Super Kings   \n",
       "2               Delhi Capitals             Rajasthan Royals   \n",
       "3               Mumbai Indians  Royal Challengers Bangalore   \n",
       "4        Kolkata Knight Riders          Sunrisers Hyderabad   \n",
       "\n",
       "                   toss_winner toss_decision                       winner  \\\n",
       "0  Royal Challengers Bangalore         field        Kolkata Knight Riders   \n",
       "1          Chennai Super Kings           bat          Chennai Super Kings   \n",
       "2             Rajasthan Royals           bat               Delhi Capitals   \n",
       "3               Mumbai Indians           bat  Royal Challengers Bangalore   \n",
       "4          Sunrisers Hyderabad           bat        Kolkata Knight Riders   \n",
       "\n",
       "    result  result_margin eliminator    umpire1         umpire2  season  \n",
       "0     runs          140.0          N  Asad Rauf     RE Koertzen    2008  \n",
       "1     runs           33.0          N  MR Benson      SL Shastri    2008  \n",
       "2  wickets            9.0          N  Aleem Dar  GA Pratapkumar    2008  \n",
       "3  wickets            5.0          N   SJ Davis       DJ Harper    2008  \n",
       "4  wickets            5.0          N  BF Bowden     K Hariharan    2008  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# displaying our data\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **EDA**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['M Chinnaswamy Stadium',\n",
       "       'Punjab Cricket Association Stadium, Mohali', 'Feroz Shah Kotla',\n",
       "       'Wankhede Stadium', 'Eden Gardens', 'Sawai Mansingh Stadium',\n",
       "       'Rajiv Gandhi International Stadium, Uppal',\n",
       "       'MA Chidambaram Stadium, Chepauk', 'Dr DY Patil Sports Academy',\n",
       "       'Newlands', \"St George's Park\", 'Kingsmead', 'SuperSport Park',\n",
       "       'Buffalo Park', 'New Wanderers Stadium', 'De Beers Diamond Oval',\n",
       "       'OUTsurance Oval', 'Brabourne Stadium',\n",
       "       'Sardar Patel Stadium, Motera', 'Barabati Stadium',\n",
       "       'Vidarbha Cricket Association Stadium, Jamtha',\n",
       "       'Himachal Pradesh Cricket Association Stadium', 'Nehru Stadium',\n",
       "       'Holkar Cricket Stadium',\n",
       "       'Dr. Y.S. Rajasekhara Reddy ACA-VDCA Cricket Stadium',\n",
       "       'Subrata Roy Sahara Stadium',\n",
       "       'Shaheed Veer Narayan Singh International Stadium',\n",
       "       'JSCA International Stadium Complex', 'Sheikh Zayed Stadium',\n",
       "       'Sharjah Cricket Stadium', 'Dubai International Cricket Stadium',\n",
       "       'Maharashtra Cricket Association Stadium',\n",
       "       'Punjab Cricket Association IS Bindra Stadium, Mohali',\n",
       "       'Saurashtra Cricket Association Stadium', 'Green Park',\n",
       "       'M.Chinnaswamy Stadium'], dtype=object)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the unique venues present in the 'venue' column\n",
    "data['venue'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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EQRAEQUOE4hUEQRAEQdAQoXgFQRAEQRA0RCheQRAEQRAEDRGKVxAEQRAEQUOE4hUEQRAEQdAQoXgFQRAEQRA0RCheQRAEQRAEDRGKVxAEQRAEQUOE4hUEQRAEQdAQoXgFQRAEQRA0RCheQRAEQRAEDRGKVxAEQRAEQUOE4hUEQRAEQdAQoXgFQRAEQRA0RCheQRAEQRAEDRGKVxAEQRAEQUOE4hUEQRAEQdAQoXgFQRAEQRA0RCheQRAEQRAEDTHjIDcu6W/As8CrwCtmtpak+YHfA0sDfwO2N7OnBtmPIAiCIAiCaYEmLF4bmdlqZrZW+v5F4DIzWx64LH0PgiAIgiCY7hmNqcYtgRPT5xOBrUahD0EQBEEQBI0zaMXLgIslTZS0d1q2sJk9kj4/CizcSVDS3pImSJrwxBNPDLibQRAEQRAEg2egPl7Ahmb2sKSFgEsk3VP80cxMknUSNLPjgOMA1lprrY7rBEEQBEEQjCUGavEys4fT++PAWcA6wGOSFgVI748Psg9BEARBEATTCgNTvCTNIWmu1mfgvcAdwLnAbmm13YBzBtWHIAiCIAiCaYlBTjUuDJwlqdXOb83sQkk3A6dK2gN4ENh+gH0IgiAIgiCYZhiY4mVm9wOrdlj+JLDJoNoNgiAIgiCYVonM9UEQBEEQBA0RilcQBEEQBEFDhOIVBEEQBEHQEKF4BUEQBEEQNEQoXkEQBEEQBA0RilcQBEEQBEFDDLpkUDDKXHvc5qXW32Dv8wfUkyAIgiAIwuIVBEEQBEHQEKF4BUEQBEEQNEQoXkEQBEEQBA0RilcQBEEQBEFDhOIVBEEQBEHQEKF4BUEQBEEQNEQoXkEQBEEQBA0RilcQBEEQBEFDhOIVBEEQBEHQEKF4BUEQBEEQNEQoXkEQBEEQBA0RilcQBEEQBEFDhOIVBEEQBEHQEFmKl6QNcpYFQRAEQRAEI5Nr8To6c1kQBEEQBEEwAjN2+1HSesD6wHhJny38NDcwbpAdC4IgCIIgmN7oqngBMwNzpvXmKix/BthuUJ0KgiAIgiCYHumqeJnZlcCVkn5lZg821KcgCIIgCILpkl4WrxazSDoOWLooY2YbD6JTQRAEQRAE0yO5itdpwLHAL4BXB9edIAiCIAiC6ZdcxesVM/vpQHsSBEEQBEEwnZObTuI8SftIWlTS/K3XQHsWBEEQBEEwnZFr8dotvR9YWGbAsv3tThAEQRAEwfRLluJlZssMuiNBEARBEATTO1mKl6RdOy03s19nyI4DJgAPm9nmkpYBfgcsAEwEdjGzl/O7HARBEARBMDbJ9fFau/B6O3Aw8MFM2f2BuwvfDwd+aGZvBJ4C9sjcThAEQRAEwZgmS/Eys30Lr72ANfCM9l2RtDiwGZ6GAkkCNgZOT6ucCGxVod9BEARBEARjjlyLVzvPATl+X0cCnwdeS98XAJ42s1fS938Ai3USlLS3pAmSJjzxxBMVuxkEQRAEQTDtkOvjdR4exQheHPstwKk9ZDYHHjeziZLeVbZjZnYccBzAWmutZT1WD4IgCIIgmObJTSfxvcLnV4AHzewfPWQ2AD4o6QPArMDcwFHAvJJmTFavxYGHS/Y5CIIgCIJgTJLr43UlcA8wFzAf0DMK0cy+ZGaLm9nSwIeBP5nZTsDlwHZptd2Acyr0OwiCIAiCYMyRpXhJ2h64CfgQsD1wo6TtukuNyBeAz0q6D/f5Or7idoIgCIIgCMYUuVONXwHWNrPHASSNBy5lKDqxK2Z2BXBF+nw/sE7ZjgZBEARBEIx1cqMaZ2gpXYknS8gGQRAEQRAE5Fu8LpR0EXBK+r4DcMFguhQEQRAEQTB90lXxkvRGYGEzO1DSNsCG6afrgZMH3bkgCIIgCILpiV4WryOBLwGY2ZnAmQCSVk6/bTHAvgVBEARBEExX9PLTWtjMJrcvTMuWHkiPgiAIgiAIplN6KV7zdvlttj72IwiCIAiCYLqnl+I1QdJe7Qsl7QlMHEyXgiAIgiAIpk96+XgdAJwlaSeGFK21gJmBrQfYryAIgiAIgumOroqXmT0GrC9pI2CltPgPZvangfcsCIIgCIJgOiMrj5eZXY7XWAyCIAiCIAgqEtnngyAIgiAIGiIUryAIgiAIgoYIxSsIgiAIgqAhQvEKgiAIgiBoiNwi2WOeJ479San1x39inwH1JAiCIAiC1yth8QqCIAiCIGiIULyCIAiCIAgaIhSvIAiCIAiChgjFKwiCIAiCoCFC8QqCIAiCIGiIULyCIAiCIAgaIhSvIAiCIAiChnjd5PEKgiAIgtcrfzvy0dIySx+wyAB6EoTFKwiCIAiCoCFC8QqCIAiCIGiIULyCIAiCIAgaIhSvIAiCIAiChgjFKwiCIAiCoCFC8QqCIAiCIGiIULyCIAiCIAgaIhSvIAiCIAiChhiY4iVpVkk3SbpN0p2SvpGWLyPpRkn3Sfq9pJkH1YcgCIIgCIJpiUFavF4CNjazVYHVgPdLWhc4HPihmb0ReArYY4B9CIIgCIIgmGYYmOJlzn/T15nSy4CNgdPT8hOBrQbVhyAIgiAIgmmJgfp4SRonaRLwOHAJ8FfgaTN7Ja3yD2CxEWT3ljRB0oQnnnhikN0MgiAIgiBohIEqXmb2qpmtBiwOrAO8uYTscWa2lpmtNX78+EF1MQiCIAiCoDEaiWo0s6eBy4H1gHklzZh+Whx4uIk+BEEQBEEQjDaDjGocL2ne9Hk24D3A3bgCtl1abTfgnEH1IQiCIAiCYFpixt6rVGZR4ERJ43AF71QzO1/SXcDvJH0TuBU4foB9CIIgCIJpgmt/Xd5feYNdw9VmemNgipeZ3Q6s3mH5/bi/VxAEQRAEweuKyFwfBEEQBEHQEKF4BUEQBEEQNEQoXkEQBEEQBA0RilcQBEEQBEFDhOIVBEEQBEHQEKF4BUEQBEEQNEQoXkEQBEEQBA0RilcQBEEQBEFDhOIVBEEQBEHQEKF4BUEQBEEQNEQoXkEQBEEQBA0RilcQBEEQBEFDhOIVBEEQBEHQEKF4BUEQBEEQNEQoXkEQBEEQBA0RilcQBEEQBEFDhOIVBEEQBEHQEKF4BUEQBEEQNEQoXkEQBEEQBA0RilcQBEEQBEFDhOIVBEEQBEHQEKF4BUEQBEEQNEQoXkEQBEEQBA0RilcQBEEQBEFDhOIVBEEQBEHQEKF4BUEQBEEQNEQoXkEQBEEQBA0RilcQBEEQBEFDhOIVBEEQBEHQEKF4BUEQBEEQNEQoXkEQBEEQBA0x42h3IAiCIAiCaZtHv/tAqfUXOXCZKZ8f++Htpdtb+DOrlJYZKwzM4iVpCUmXS7pL0p2S9k/L55d0iaS/pPf5BtWHIAiCIAiCaYlBTjW+Avyfma0IrAt8StKKwBeBy8xseeCy9D0IgiAIgmC6Z2CKl5k9Yma3pM/PAncDiwFbAiem1U4EthpUH4IgCIIgCKYlGnGul7Q0sDpwI7CwmT2SfnoUWHgEmb0lTZA04Yknnmiim0EQBEEQBANl4IqXpDmBM4ADzOyZ4m9mZoB1kjOz48xsLTNba/z48YPuZhAEQRAEwcAZqOIlaSZc6TrZzM5Mix+TtGj6fVHg8UH2IQiCIAiCYFphkFGNAo4H7jazHxR+OhfYLX3eDThnUH0IgiAIgiCYlhhkHq8NgF2AyZImpWVfBr4DnCppD+BBYPsB9iEIgiAIgmCaYWCKl5ldA2iEnzcZVLtBEARBEATTKlEyKAiCIAiCoCFC8QqCIAiCIGiIqNUYBEEQBGOA248rlwRglb0XGlBPgjqExSsIgiAIgqAhQvEKgiAIgiBoiFC8giAIgiAIGiJ8vIJpkpN+9b7SMrt89KIB9CQIgmmNI896tNT6B2y9yIB6EgTlCYtXEARBEARBQ4TiFQRBEARB0BCheAVBEARBEDREKF5BEARBEAQNEYpXEARBEARBQ4TiFQRBEARB0BCheAVBEARBEDRE5PEKgj7z1dPeX2r9b37owgH1JAiCIJjWCItXEARBEARBQ4TiFQRBEARB0BCheAVBEARBEDRE+HgFQRAEQRB04PEfn1Nq/YU+tWXPdcLiFQRBEARB0BCheAVBEARBEDREKF5BEARBEAQNET5eQRAEQaPsd9ZDpWV+tPUSA+hJEDRPWLyCIAiCIAgaIhSvIAiCIAiChgjFKwiCIAiCoCHCxysIpiF2P6tcnUeAE7aOWo912Pz0k0vLnL/dTgPoSRAErwfC4hUEQRAEQdAQoXgFQRAEQRA0RCheQRAEQRAEDRE+XkEQjHk2P+OXpdY/f9uPDagnQRAE3RmYxUvSLyU9LumOwrL5JV0i6S/pfb5BtR8EQRAEQTCtMcipxl8B7SFaXwQuM7PlgcvS9yAIgiAIgtcFA1O8zOwq4N9ti7cETkyfTwS2GlT7QRAEQRAE0xpN+3gtbGaPpM+PAguPtKKkvYG9AZZccskGuhYEQRAEwbTGY0ddX2r9hfdfb0A96Q+jFtVoZgZYl9+PM7O1zGyt8ePHN9izIAiCIAiCwdC04vWYpEUB0vvjDbcfBEEQBEEwajSteJ0L7JY+7wac03D7QRAEQRAEo8bAfLwknQK8C1hQ0j+Ag4DvAKdK2gN4ENh+UO0HQRBM72x1+mWl1j97u0361vaHzriz1PqnbfvWvrUdBGOZgSleZrbjCD/178oPgiAIgiAYQ0TJoCAIgiAIgoYIxSsIgiAIgqAholZjEAQAfODsr5WWuWCrQwfQkyAIgumXsHgFQRAEQRA0RCheQRAEQRAEDRGKVxAEQRAEQUOEj1cwXfKzk95Xav2P73LRlM/fP6WcLMD/7XhR75WC6ZLNTz+91Prnb7fdgHoSNMEff/+vUutvusOCA+pJMFYJi1cQBEEQBEFDhOIVBEEQBEHQEKF4BUEQBEEQNET4eAVBEASvG04544nSMjtuO34APQler4TFKwiCIAiCoCFC8QqCIAiCIGiIULyCIAiCIAgaIny8giAIXodsc8YNpWXO3HbdAfQkCF5fhMUrCIIgCIKgIULxCoIgCIIgaIhQvIIgCIIgCBoifLyCIAiCIJguefzoS0vLLLTvuwfQkyHC4hUEQRAEQdAQoXgFQRAEQRA0RCheQRAEQRAEDRE+XkEwHbHpOXuVWv+PW/68b21vdtZ3S63/h60P7FvbY5UPnn5+aZlzt9t8AD0JgqApwuIVBEEQBEHQEKF4BUEQBEEQNEQoXkEQBEEQBA0RPl5BEIw6m535k9Iyf9hmnwH0JAiCYLCExSsIgiAIgqAhQvEKgiAIgiBoiFC8giAIgiAIGiIUryAIgiAIgoYYFcVL0vsl3SvpPklfHI0+BEEQBEEQNE3jipekccCPgU2BFYEdJa3YdD+CIAiCIAiaZjQsXusA95nZ/Wb2MvA7YMtR6EcQBEEQBEGjyMyabVDaDni/me2Zvu8CvM3MPt223t7A3unrCsC9XTa7IPCvil2qIxttR9vRdrQdbUfb0Xa03c5SZja+4y9m1ugL2A74ReH7LsAxNbc5YTRko+1oO9qOtqPtaDvajrbLvEZjqvFhYInC98XTsiAIgiAIguma0VC8bgaWl7SMpJmBDwPnjkI/giAIgiAIGqXxWo1m9oqkTwMXAeOAX5rZnTU3e9woyUbb0Xa0HW1H29F2tB1tZ9O4c30QBEEQBMHrlchcHwRBEARB0BCheAVBEARBEDREKF5BEARBEAQN0bhz/esdSasAS1PY92Z25qh1qAeS1uj2u5ndkrGNTc3sj23LPmFmx2b24fv0JwijNJJmMbOX2pbNb2b/bqBtWZsTZqf+jCB7iJl9vfB9HPBrM9upRPuNnqv9ONcK25qb4f0e+PFK7c6Hp8sptp3d79Fse6zdm0YbSXuY2fGF7+OAr5rZN0puZ1TO1bptj2a/q1L3WdQvxqTiJWleYFemvkns10XmSDM7QNJ5wFQRBWb2wcy2xwNfwOtMzlqQ3zhD9pfAKsCdwGstUSDr5ibpQ8CFZvaspK8CawDfzFR+1gK+AiyF7zN5t22VHqLf7/KbAT3/N/A1SS+Z2Z9SXz4PbATknux3A8dJmhE4ATjFzP6TKTuM1sPJzG7PFDlT0lZm9r8kvyhwPrBmlfZLcjzwsdYXSXMC5wCbZMguIelLZnaYpFmAU4Fbcxuucq5KOpoO11aLbtdnova5JunjwDeAFwt9MWDZXrKFbWwGvJXh1/chGXKHAh8F/trWds41gqTlgcOY+t7Ss+99aLvyvUnS5sChTH1vmTuz7XkpeT/vh2wf+r6JpG2BPYD5gV8BV+a0m9qufK72YZ/XabuSrKRTzWx7SZMZfp/IfRbVPt7UeBZJWgbYt0PbWbrDsG2NxahGSdcBNwCTGbpJYGYndpFZ08wmSnpnp9/NLOuCkXQx8Hvgc8AngN2AJ8zsCxmyd5lZ5YLgkm43s1UkbQh8E/gu8HUze1uG7L3AgUy9zx6s2p9cJC2IKysHAu8H3gzsaF6rs8x2VgB2B3YErgV+bmaXZ8hdAXwQv1gmAo8D15rZZzNk9wI+gFdcWALPOfc5M7u4h1xHBb9FzsUq6RBgQTPbJymMf8D/8wkZsgJOxo/3RsAFZnZkL7mCfOlzVdJu3X7vdn32C0l/AdYzs0plQCQdC8yO77Nf4Mf9JjPbI0P2XmDlsud1Qf4a4CDgh8AW+Lk+Q9FyOcC2K9+bJN0HbANMbrfQZsqXvp/3QzbJ1+37DsCPgeeAj5jZtSVkK5+rfeh3nbYryUpa1MwekbRUp99znkV9ON6Vn0WSbsMHw+1tZyvbU6ia8n40X8Ato9j2xPR+e2HZzZmyxwMr1mj71vR+GH6RT1mWIXtNH/77SsD2+IhjV2DXErILAbfjFitVaHscXkz9bFx5+gJwHvC7EvttT+Ab7ccvQ/5Tqa3JwPqZMu9Mr6NwRX2L9Pot8MMSbR+Bj8ZuBrbNWH+NwuttwCT8wbAGsEaJdmudq6N1rgEXArPXaPf2tvc5gaszZc8AFqrRduveMrl9WQNtVz7ewOW4gli17cr387rPgjp9B5YHrgN+BlyVrtPsc6/OudqHfV6n7VrXWM3jVfvZX/VZBNzYr/8xVi1enwH+i2uuU/xdrMsccwfz5jAsw8yZtnODma0r6SLgR8A/gdPNbLkM2XfiFpNHU7+zTaxJ/ny8vNJ78AfpC/hofNUM2U1wS9FlDN9nudOcBwHvwqdBLgA2xZW57brIPIvvc6X3mYFX0mezfLN4ywJwGXC8md1U+O1eM1uhh/xk4L3AicBXzOzmlvWwi0zRGib84X87abrOzH6Q2fcJZrZWr2Vtv2/T1vbXgJvwG17XYyapmwXQLGNKPG2n8rlaZzo+yZc+1wqyq+M31BsZfp7nTj3dZGbrSLoBtyg8CdxpZm/MkF0Lnwq+o63tXDeG64ANgdOBP+HX+nd6nd99arvO8V4bn/a6sq3t3Guk9P28H7J1+y7pHuDTZnZpsjB/FviYmb01s+3K52of9nmdtivJFp4HU/1E5vOg6vHu0HbpZ5Gkj+DK9sVtbZf24RyTPl7Ay/g021fIn2PevE9tf1PSPMD/AUcDcwOfyZQ9Hi8KPsxUWYLtcfPo98zs6eRvdGCm7O64WXUmKviX4VMuq+LWo90lLQz8ppuAmc2Vue1e3I47rT7X4bd1MuQPwSslXJOUrmWBv/SQae/7mSMs78UckpY1s/thip/AHD1ktmj7fit+3LagxzEzs40kzQB8yMx+X7KvReqcqyfjVr7NKEzHl5Avfa4V+BmutFS9xs5LfiTfBW7B9/fPM2VPBA6v0fb++DTnfvhDdWN83zXRdp3j/S38YTgr/kArS5X7eT9koV7f1zGzZ8Cf3MD3k4tBLnXO1br7vE7blWT79DyodLzNbK6kHC9hZn+v2PbK+DWyMcOfoVkDyvYOjbkXcD/u+zLqfSnZ7+v7sI1xwBuAJVuvTLl7a7Z7U3qfiCubAu7JlH1Hp1fJ9lfB/bS2ab0aOF7jcCW3zjbeD/wduAIfnf4NeF9m25+p0e6Emv2ufK5SYzq+D+farTX6PQOFqWRgFmCeEvLZ/7Hfr7pt1zzed9Rsu/L9vO6zoE7fgYVxhfXC9H1FYI8S8reO4j6v03Zl2bbtLFThOVb3eE+uIXsfMHM//vtYtXjdBzxfRVDSuril6i34SGEc8Jz1MDX2IWIL4FZJv8X9hapM9+2LO98+xnCNO2eq8jpJK5rZXTltdWBCsgT8HH8g/he4PlO2aJWbFbdSTWTAEVd1j5mZvSppg5w+dtnGhSla7c1p0T2WkQ4itb0j7mhdhUslfQ63PE2xFFp+yHedc/V/6f0ReYTgP/Gor1zqnGt/lLQ3U/e75/82s9ck/RhYPX1/qbiNDK6WdBg+ZVd6KkLSm/BrZSmGR03lXCe12qbe8b5A0nutR8BJFyrfz2vKQr2+/wqfcvtK+v5n/Ho7fiSBNiqfq9Tf53XariOLpA/iEcxvwAOdlsIj13OmaOse71skrW1mN1eQvQOYF+9zLcaqj9dZ+EG6nPLz0xOADwOnAWvhvjtvMrMv9ZDravK3vAicEzqL2sc6LO8kfx/wNjN7Mmf9Ntm7geWAB6jgX9a2raWBuS0/JUO7/BLAkWa2beb6lSKuCsdsA3w02pp6+xBwl5l9ImMbPwUWw8+XogKTO0WLpJWY2t/p1xlyP8SnGNuVp5z0IQ90WGyWkZogyVc+V+Wh7lfjUaCt6fhvmNm5OW23bWtpSpxrffjf38OVvDOt5M1xBP86y1ScWlFTx+LK5quFDUxsoO06x/tZfPr8JVzpLpvaoM79vLJs3b5LutnM1pZ0q5mtnpZNMrPVMtuufK72YZ/XabvuNXYbPui+1MxWl7QRsLPlRQ7XPd73AG8EHsTvqWV8Ga/ADQA3U8GPcti2xqji1VEJylR+JpjZWkXn6uKFkyH/ITM7rdeyQZBuru8xs1cqyC7VabmVSCeRnL43xK1I15jZWWX7kbYj3GE5S5mSdDzw/arWuuQovWFrv0maCY9UWzdDtq6yXMdRvNbDdLSQNN7Myvh0tcufC/wOOMc6+/UNjMID7VU8eKXUA61m2xPNrIn8cNMUNe/nlWXrkh7E2wKXmNkaaTblcDN756DbHssUnsG3AasnS/NtlhckVut413kOqmYqqmHbGouKVx0kXQW8G8/R8yjwCPDRnIOe5G8xszV6LRtB9gQ6TH2VeIgfD6yA53MqG4GzZKflluloKOkn+EjhlLRoB+CvZvapDNnilN8MwGrA38xs58y2K0dcJfl78bwz/07f5wNusIxosbrIIypbjuKrthzFzew9DbRdydKWZCufq5L+jPuy/R63HD2V2eWW/Dvx82szfHT5O+B8M3sxQ3bXTstz/3cdJHXMt2U9kq9Kak3D7odPY5xF+ei+Sm0X5Osc73eM0PZVOW2PJnX6Lq+0cDSe+uQOYDywXQnrbOVzte4+r9l2rWtM0qXAVnhapAXxc35tM1s/R74fSFqI4ffFqg73lRiTPl7J1NnpJpFj6twFf/h/Go9GXAIftfRqc1M8keZikn5U+GluPCw1h/MLn2cFtsb9X3L5e3rNTPlIlj8wlNphVmAZ4F7y5tXBTcNvaU2/SDoR97nKYULh8yt45vnsRIPUjwb9Du7Dcjn+/98BHJwjKGlWPDN1eybzLGUZeCGN6F6Rl9h4HD/nslD1LOodLW1ArgJS+Vw1szdJWgef0v+KpLvwfGtZkYlpBHmlvATLxsBewC/xa60Xa7f1exM8OjH3oSBgJ2AZMzs0TYsvaoUUJl0oWudmxSOp786Qm8jQtQnDfSJzI/Sqtt2izr2prg9n5ft5zWcB1Oi7md2SBgkr4MfuXksVLjKpc67W2uc12651jeH5GF/En787AfPgkec9qXu86/iXaXhKiplxN5Ce/uEdsT546Df9AhYovBYDDgAOGXCbq+Kh3Q+m99ZrG2C+itucAbiuglzt5HV4HrBflFj/fGCpwvelgPMaOt79iAZdBL/gtwQWKSF3Gh7a/9d0vC8Gjioh/xPcIfMTeAqLW4ETMmWPxW9mD+FBFZPxPGY5spPT+XVb+r4wPiVSdf9VPVcXTP/h1ZJys+HpU87A/RKPrtjveUlRZ5nr/xRPOHt3+j4fFSMG8ajIK0qsP2vOskG03a/jnWSXAM4osX7l+3m/nwU5facQWd3pVaPtUudqnX3e57Yry1Zoq9bxBm5Lsrem7xvl3lPbtiPcavedKv9juplqzPWPkEepHczUkUO5GvNM+E5/U1pUdpRT3NYKwB8sIzljWn893Pozp5ktKWlV4ONmtk/F9ieb2cqZ616Jj3RaI/+1cUvWf6C7g6Fq1KBL8i3lpVI0aNrGYkx9zHOmE241dwBtlWvK9g/rsK2lKeco3mqz9T4n8Ecze3uGbCsR6ET85vIsrky8uYfoSNvLPleTZW9r3OK1HD51dqplOIkn+VPxrPt/xKcrrzSzKpbO1vV6h2VOK7fcBtocprP8Tzpsq6W05V7fld0Y6rbdQb7UvalNtpQP5wjbqOzvVlO2Z99H8PtsYZZvDW/fbqlztU221j6v2XbZa2wbPOfcQviztJYfZZnjXce/bITtTblPlGGsTjUWb0Qz4NGJuf/leNzEOSxyqATr4yP4v+EnzBKSdst8iLdncn8Uz/Cdy5HA+3B/J8zstpHm+ju0XczEPgNu8SozzdnRhySTExiqQbcRqQZdCfnZcIXrvYVlRn5x8cNxn6H2dBQ5/hAtpfrp5DP1KH7DyKIwdbWsmR0iaUlJ61je1FXLp+l5SW/As6gvmtl0nZQMdc/V2/DSToeYWXabBY7H66eVvj41vEbmDLiyf2qJTfwvTXG2ptTHkzm9reHVMcbhPj8508KL4KP32eRZwVtTjnPjCVUH1nZBvvLxHsGHMzubd537ec1nQaW+m9nuudvv0Xblc7UP+7xO23WvsSOALcyszFR4q+1axxu/j8+J3/tPlvQ4w6fpu7W9TYe2e/qddtzWWLR4aXi01yu4EvQ9M7s3Q/ZGyygq3UV+Il4n8d70/U24z9LAo5Fafa8yGk8+Py1a++wMy3BYLmxjEdyXwPDR9KOZchPNbM2iha3OqLQscuf6VSwjf1YH2T3x6a5VcAVyTuBrZvazTPmf4g/ujc3sLckScbGZrd1DFElfw513N8Gnvwwvkt1VCU7KwlLAfeYVDpamRvqPsiRlcw4AM/tvSdmFcP/L1sj9TuAnZvZYpnwx8ugV4EEz+0eJ9nfClfQ18Gzw2+HHu+eDRcMjpl4BHrOMCGR5pNZH8Rt50R/yWeBXOZbdqm33Aw2PNHsFD5wpUyy6zv28smySr9x3SQvgA8opkd74YCMr3U+dc7UP+7xO23WvsWvNrFJ+xD4c79lxZWkGhvzLTs45Zm2WzlbbPzez0nm9xqTiVYWCprw9PiI8k2pJDqeq8ddpWdvvbzaze9q09SmUaPt04AfAMfhUzP7AWmb24Rz5OiQF5Ot4qQjhBaAPMbNfZshWrkGX5N+E+94sbGYrSVoF+KCZfTNT/o94CZ2ySsBWeCTnZDO7qIxsYRuVpq4krZbavtPM7pY0C+7v858ecnsC38Z90pYB9rYS+bPqnquS9sEtJXPi58mzeIj9TzLa3gAvIv4r3EoHsCbuW7dTmQdLHSS9GVd2BVzWa2QuaW4ze0ZD0YnDsPzEktua2Rkl+1qr7X7dm16PSLoEt5y0gkZ2At5lZu8evV5NuxQsRu/EfW7PpqLrSIW23wYch7s+TMYrDFRNJl6/P2NJ8ZK0s5n9pm3abArWJa2C+lc8+Je4BaN1se2MV4kfcV5f0s/NbK8R+lCm7QWBo/B0GMIdvffvpq1LOtLMDmgzDxcbz0r+lqxG67faSqO963KUJ3lB17txP61D8VHGEWZ2Q2bbV+JRPD8rKC93mNlKmfJn4MER7QXCR0y6J/creytwHf4QPs/MDs1pr207N+LT0zcnBWw8bvEa0S9AnhpgZ1z5eBtwmJll1QuUdAewkZk9Ia9JebKZrVeiv5XPVUlfxf/rp22oNuWy+Dl7Yy9FWZ5v7ZNmdmvb8tXwYz+ipVrSNWa2oaYuhls2seRJZrZLr2Vtv59vZptrKOJKhZ/N8n0Z58UTOi/NcF/EbudprbZrHu9TzWz7tmlOIC/dS837eWXZfvQ9bWOqe5Ay/GbrnKt92Od12q51jamGb1wfjvcE4Eu4ovxBYE8ze183mYLs583sCI1QCaXb9TkSY83Hq1VcuHSxTTPbqE7DkmYyd6L/JPApPOcOeIbuC3u0vVc/+mBm/8JHVWU4Kb1/r07buH/Rs4Xvz6ZlPbFUnkFevHk/M3u2h0g7s5vZTT6DNYUy0yjnplcZ3gGsal66Z3b8OJdWvIAf4c7lC0n6Fj519dUeMjsAq5nZ80nBvZD8Qs0vW0peamb3J0tZNjXP1V3wfTZl+jr1YXvc76uXhXLudqUrbWOSpK7XvJltmN7rFuIdFlYu9/fqOiVuZpun92Vqtn0BcAMl0qbUbbvm8d4/vW9epW1q3M9rykL9vgNcLOnDDPk3bQf0tIzXPFdr9btO23WvMavnG1f3eM9gZpekz6dJ6lqppo2WxXtC17VKMKYsXv1A0jz4vHzLKf1KfMqs1xTOBcBWZvZy2/JV8QzbS3eR3Wak36C3iXUkTbsgX1rjLoukX+PV2c9JfdkSuD29eo1O18L9o1oXzX+Aj1l+lNsfcb+f05LVaDvcVLxpxb+T0+awaLL27yW3VXbqqr3tMlE7j+MJR1t8uPi917lS51yVdI+NEDXZ7bfCOnfjVtWn2pbPj1tXR5Qfaaqt0O9eU25fAr6MB3I8z5Dl6GXgOOtSUmykabpC27muBKXPsbpt1703vZ7RUJWDlpI8A0OO2iNagOqeq3Wo03a/+i1pcdx3teXndTU+c5PtJ1YWSfcDnyss+l7xe9Pn+ZhSvDQ8celU5CggadrpDtxxFoZG6V1vQJK+CayHR2M8n5a9E59y/FhBm+4k2zKxLoRPxfwpfd8If6B0Hb2oRs3BDibpYeSY1NN2Dur2u5l9o4vs7cCnzOzq9H1D3GE6t+1l8fn59YGn8LxOO5vZ3zLlS6ezkPQ8XpAV/CG8XPpeusal3KF+CYZPH434QJT0NEMRlwLeXvjeK3XHbiP9lmS7ltaoc65Kugz4tpld1rZ8Y9xBvatVRV54dy/8htjaP2vioee/tC4BDW1TbUvi54nw6e2/51qEJB3WTckaQaY1TTcr7iB/W2p7FWCCZU71SvoMHn16PpmZ6+u2XfN4t085DSNj6qny/bzus6Bu3+tQ51ztwz6v03a/rrFLcF/O1mzMzrgP54jVPPpwvOtMc3Z00ykIl67VONamGlsWko4KSOY2lrPhxZm/IWlSLyEz+2ryYblInsX+vXh6h63NrKsJsmVilXQxsKKZPZK+L4o7Evdq+8S0/icZXnPwWHy00I3WjbNV2qd4smdr3d0UqwxebSldaVvXSMqeKjT3F3q3pDlwk3HZqcoq6SzeUrKNjkg6FI9Y+ytD+9vonmF6y7bv2dPEvRSrDPk65+p+wDmSrmHoWl0Lv17b/1Onto+T9E98Svet+H66C/immZ3XQ3aZ1M+fA2eZ2QXp+6Z4osMszOxL8uzWLYv4FWZ2fg+ZjVJbZwJrmNnk9H0lMiskJF4Gvgt8heHnyogDhLpt1znerSmndI4/gt9bWulTctKe1Lmf13oW9KHvJPlVmNonr6v1pM65WrffNdvuyzUGjDezoiL0K0kH9JCpe7zrTHO27r/b4EEBLf/uHYGsaOt2xpTFq4XqFT2+HjjQzK5J3zfAw1FzR6WfBT6On+wfMLP7eogUZe82s7cUvs+AR61lPeRVo+agOiR6y5na6KDtG/Av4HLLLAEj6Uh8CueUJL8DHtL7GxjZ+qMRHCmndCSjRmXazqils0jHbGVrm6Ke1ql6rspLLH2EIV+pu3AH/0r5bsqiDs7NnZZ1kT8MT5lyclq0Ix4Y8eUM2TvNrN1HbKplXeTvB9Yx9+UsRR/arnxvUoco3U7LusjXuZ9Xlq3bd3mg1Sq05QfsZj1pk698rvZhn9dpu+41dhk+GG7V/d0R2N3MNsmQrXW866CUfLXXshzGmsWrxXx4csGWCX7OtCyHTwInyn29lLbx0V5CBQVEeHLC+4AfKDl8Z5obL5N0EcMLTV+a2W+oUXMQkKQNLIXkS1qfvCSmnawt8wM7S1rJzL6YsY3WzaB9unJ1ult/Wj5hK+CZ8lsO8lswlEE/h5fSg+Qvkj6Np7OYs4R8He7ATfGlc72MMpXO1aRg9UwxMkD+mSzTxRD/MomCN8MDG14DkNckvRX3/+rF7ZJ+0dZ2mdxp9+H+ZVWo23ade9Nz8vxnv8Ov5x3JTEqZqHM/ryML9fq+rtXIzk+9c7XuPq/Tdt1r7GO4j9cP8b5fh89C5FD3eNdhDknL2lDE9jIMOf2XYqxavHbHFY5hCkiZaRZ5WRPM7JnM9d/Z7Xfzwr4529kG99kBuMrMzsqUmwFYF7gfTzEAHqKfm8R0TfyB2FI4n8J90yrl6ZFHe000s9WqyJds6ypgs9YUozzC7Q9mlpu1v1Y6izrIAwvOwRWwot9Oab+Apql6ro4mcgfgYvDMVcA3LN/x93Y8F1PLqjw/Pt2Yk15gVnxgV2z7p7nWPkln4ZbCy8lMe9KvttM2qt6blsZThmyAP0ivBQ6wfB/Myvfzus+COn2XdDzwfauYD6rOudqHfV6n7VrXWB368eyv0fb7cV/j+1PbS+El+0rneByTiheAPIt6tgLSr2mr0aTTdGGFbcwDYD2iODO3NSlH8ZK0MJ7U8w1mtqmkFfEp0+Mz2xmWeV6eIuH2nCnWfpKmdpewEhngJd0J/Iy2FAE5irqkD5nZab2WjSDbihxqZdUeeOTQ9ICkHXHLcvHG/kUz+31Xwf603TEwoomHymhT9n7eL9k6pMH4uXh5pZegfODN6wn1ITgubafW8U6zPUsz3C/v15myswCt6Op7rEI1FBjbitd8wPIMj1K7qsv6rwGT8MK7rYtkClbPeTwLSeviD8O3ADPjGfSfs/zkjt/D6+2daRUOnKTN8BF1cZ91reemziHE8+GJHt9oZj3zisnTQZwAfMXMVpU0I14dPtcn4Ct4xYHWCHwrvOjyt3vI1Y5GkXQFnnBvRtzB83HgWjPrqsgX5G+2jPJAI8hWLpqsCpFDbfK1ztU61FHU5QlqP8/U53lWkuK0jUXxqW2Am0pYlWsVg69D3bbrHO9kbduDqfd5lq9T2kap+3kfZSv3XdJ9wGeZelD1YGbblc/Vuvu8ZtuVZCW9jFv+T8WnJtufwblWyjrH+yQ8Qn0SQ7WarYTStxJTX2NZSluRMenjJS+Lsj+wOL4D18UVkm4HfnV8Hnwz/AF6Cp5TqUnN8xg8r9JpeLTXrsCbSsh/HL/QX5H0IkMjrJyb47F4wd2NgF/gyf5y/KQmMuTbBkPO9VfgUxs5LGhmpyolrTOzVyRlF0A2s29JuhC33oA7Yk6VaLMDdZPGAsxjXpJlT+DXZnZQmo7K5Wq5w/a5ZJaokkcJfQBYrG2UODf5iWOrRA4VKX2uqk+pS/BouhPw6D6AP+NRTDkW0pPTupsDn8DLDT3RS0hT58NqWQbfIOkNmVPyVaJni32oozzVapt696aTgHuA9+GFuXdiKOlkTyrez2vL9qHvT1iJclwdqHSuJmrt85ptV5VdFI9C3AG/j/0eON3Mns7tdB+O91p49G4Vw8VBwLvw6/MCYFO8PmdpxQszG3MvfIQxKzApfX8zbgXKlV8fH93djdf8a6rfE9L77YVltzbU9u1t73Pi0SBNtH0FsABwS/q+LnBlhe0shOePWRJYsoTcJsBsFfs+Gb9hXAys3X78MuQv7/D6Uw+ZVfGb2YPpvfXaBpgvs93LcCvXuPTaGR9oDOxcxX0elgKOSK+V0+s7eG3O3LZvbm+vda1nyE7s0O+bM+Rew53R/5Re2cerQ9uT25dlyl+TztXb0348GE/u3ETble9NrfUK95aZ8GjrMtdYpft5Hdm6fQd+gluVd0zX5jbANiXarnSu9mmf12m7smxh/cXxfH3/BHZp4lxJ658GLFqmr21tzwDclr4vDFxSZVtj0uIFvGhmL0pC0izmRV5zCy6Px61fK+Oj2lLRZvKCzQfiN8biHHGOxv28pJmBSZKOwPOwlBmV1jGzvlDowxvwcj/Z+Wpq8lnc4rOcpGvxqNDtcoXleZW+D7wBP15L4qO9rFB5fPT+U0n/xn2drgKusbYM6SNwCF4G5Bozu1mezPUvuX23CqVYzOw2ec3F91l1/546kUNQ4Vy1NMUi6T023Bfxi5JuAXIiYMEjthZI/W5Ng+X6JP4vvT+Sptb/iUfh9uKz+Dn5Ah4pdpaVLKpO/ejZ2czsMklK+/JgSRPx4vSDbrvOvam1z59OUzGP4oOkXCrfz2vK1u37bLgV+72FZQbkZkGveq4WZavu8360XUW2ZV3eEXgP7vqTVcEkUfd4LwjcJekmygc7vWBmr0l6RR6c9zieGLs8VbS10X7hvj7z4iPCq/CosQt6yHwMr3l3BV5+ZqGKbd+GT7Gtg2fVXhNYM1N2KVxhmhufFvgBntA1t+09ca37KXwk/gL5o/GvpX22LX6RPgIc2uAxmxFXlFYCZqqwzxdgaJS3EXB8hT68AU/y+XfglYb+9/7peAuf4r0FeG+m7NXAzE0do7a2K5+r+BTABoXv65NpsUrrr4FHaf0nvf8ZD67Ikd0cj1pdKV0jE/FqE7ltL4unjrgR90VZrYTs2riyszg+9XcG8LYS8tfhys6Z6R61NXBvQ23XOd574n6f78Ajvh7Ho71y2y59P++HbD/6XudV51ztwz6v03YlWXwQOxFPQ7E5MGOFfVb3eL+z0ytT9iep7U/gg+9bgROqHPsx61zfIkWWzAP80byI9UjrvYY79rUcH4f9ccsM71eNxJuS9jezo3ot6yI/Gb/B3mBmq8lrAH7bepQ7SrKz2PCowFnx0UOlqIyy1IwkmWBma0m6DVjdfNRRJlngzniY/Mq4f9o1+DTr9V1kDsLPkf9ajYjXVj8lvQ+/YL8KnGR5DvK/xp2dz6WQo6dbf9Snup51zlX1IXVJCsBYIcnf2+3abpObkquu27Ie23gr7u+0C/B5Mzu1h0hLrnIUalq3ctqTPrRd53gvY2YP9FqW2Y+s+3m/ZOv0XTVrDtY5V+vu85ptV5JNz+AHGMpV17pPVYoGrXquSFoKWN7MLpU0OzDOSlZDkafzmNtKRLgPkx+Lipekk8xsl17L2n5/Z7dtWo/wfg1F9+2Hjy7OIrOeWmEbnaLUbrXMFBFKEXLyEkdvM7OXlJmdeoS26xR+XhT4d47ipvqRJJfikYyH4abix3F/q/Uz5f+Fl+w5Fs+4/7cMmd3SxxdyH7wjbOd2M1tF0lF4Pqizco+5RqiPad3rYu420m9JNjdyqNa5mtafJ7VZKnWJpE/h2e6fTt/nA3Y0s59U7HdOhYZlcWVrS+AhfLrxD2b2Qje5frTdD+q2Xed4jyBbprB76ft5P2Tr9l31I4frRC3X3ef9bjvnGluq2++WEQ3ah+O9F7A3ML+ZLScPaDnW8rLmX9a+XqdlOYxVH6/20hjj8Cm/EemlWGXQHt13YHHzdKmnJs8N9BFgGUnFKJhiBt4c/iFpXuBs4BJJTzFkwRup7UWAxYDZJK1e6P/ceJRjVU7CfbbOMLPP9Vi3ciRJYkt8WvUzePTOPLjZOgszWzBZMd4BfCtdbPd2u1jbFRRJs1sqjl6SifI6eMsAX5Inf32th0yrD99Ibc+Zvvf0OcpVrEaiH+dqsqhuS7Jwaqi6Q+4x28vMftz6YmZPpRvmiIqXpPXwKc3xGp6zb248uKAX9+FO7ecAz+B+hJ8s9L2blbFWFKq6pz15CR80/NjMHhpA25WPt9zi/lZgHnny1aLsrJ2lOlL6fl5Xtk99rxQ5XOdcrdvvmm3XusZyFKsM6pwr4DWL18FdCTCzv0jq6hsnT90xO7BgGgQWn6GLlWh7CmNK8ZKnI/gyrkQ801qMF5c9bpBtW2bl9RG4DvepWhB3Em/xLCXKepjZ1unjwfKyQfPgfmvdeB9eEmnx1HbrpHmWvDIoI/Xl3fKnUk7JjDvw4qKPVGxuIeAR8yzcJ0qaDY8oeTJHWO4IuSTux7I0vt+ylJ90szke959ZUtKquC/FPpl93wNYDbjfzJ6XO41nObnLnWZPIjmuJsvdrmZ2Z4bseOALTJ2aoFcQSD/O1XNw/6yJFKzCJRgnSS1FPd1cZ+4hMzN+jGZkqNQUuBKVE8hxCEPKT9lyUv8EJuD53oqOws/ig4VedEt70vKNPBXoVE+2btt1jvcKuK/OvHgZr6LsXr0arnM/78OzoFbfE0/K3RiKNQdz7kl1ztW6/a7Tdt1rrDJ9fPa/ZGYvtwZUcpeGXgaBjwMH4D7CExl6hj6Dp2EpjzXgRNjvF3DYKLe/Ep7Qc9fWq6T8ArjjbJZTfgf5xRhKq5DloAhs24f/PR9eFHaN1qvH+ufh/kmX434+F6Xv5wLnlmh3AgUnc/wGkB2+jD9AfoKP7Bcv+Z9vxCNXbi0su6OE/BodXsvlHDf8obhR4fu7gOsy270YV/ruxh1IfwkcXuGYlz5Xy+yfEeS/iysam6TXqXhplhzZpdrOV9XpS8l+z9R6xyOnKwXwjLDtXzTRdsXjvV7N/1b5fl73WVCn7/hA7lw8h9Xj+ExEmTQ3SxU+lzpX+7DP67RdWbbuqw/H+whcgbsHj6o8C/hWpuy+ffsfTe2wPu30pfCElq3vG+H1qj5DQ9FfeMTP5cBjePTQo3gSuG4y5wMrpc+L4iPM84C78Ppavdr8EvD1wve/48rEPcCXeshu0XahfB2PEjwXWKbE/z4U9325gvx8VB0jSCgRSZK2M6nDstsqHLvZK8jcmN5vrdI2cAM+KpvAkAXoFnz6qGt0Y6d2ctumej6rWudqkjsOWLnsvi7Iz4BHDp+eXh/HHWC7yXwdeHP6PAuei+vf+APx3VX7ktnfY4G3ps/zpH01GU/psOO03Had441bWJZPn4Ur9/9J96aug7IkU/l+Xke2H32vecwqn6t92Od12h7Na6wvz/50b9kLz+d1evrcVXHEA9oWKXzfFbfq/wj3FSv/fwa5swaw82/Ey4iAT9/8C/g/4ER6jAi7bPPb+JTMApnrl06iBtxZ+PxlPAM6uLm2ZzJO/EE9R+H7rel9HJ5fqpvs7SSFAzdR/xmfE98TuKjEfrq3zAmeZN5IIa1AYfmGlEujcQmFRLe4z1eZZKDr4Q+Sv6fvqwI/yZQ9HfdruAW3JnwO+F2Jts8kPRjT9xXTNpelR4oFfDT2NXx6dGk8IvKszHZvSO8X4dUaVgf+miFX61xN696FK5v3pvNvcq5s1RdwZ+sGijvPXpGuj7fgZX8G2nbh8wHA2enzIgw4QXLdtuscb9yFoGVp+wg+sFgAeDcZyZmpcT+vI1u377hFdqrUDfgAoWei4Drnah/2eZ22B3KNpWP2U9IAYBDHu21bswErlFj/FpKChfsJ/xP3YT2UHkaXkV6lkndOA8xmZv9Mn3cGfmlm38d9ZtapuM2bcCfUH2au/4KZvYaX7clNolYMdd0ELzeAeQhrrqP1c4WvR6Vlr+InUQ/RKU7h2+D5ryaa2S/wRKa53IH7FZThSHwevJ3/pN9y+QTwZUl/l/QQrijvXbIf7yP5X5jZbfgFlNv2p/Dp3Yfxi/5TJdp+kxV8sszsLnzUeH+G7MfwY3Rmeo1Py3L4Zooq/D9cWfwFeT4/tc9VvJTG8nhiyS1whX+LrhIFJG0g6RJJf5Z0v6QHJPXaXy9bujPix/oUM3vVzO5m8L6sLxc+vwefcsKaKdZct+06x/sVGwrj3xxX2p40s0uBOTLarnM/r/ssqNP3jensV/TztK1e1DlX6+7zOm0P6ho7BrgUT98yEn159suTcU8i+UZLWq0tqKQT42woa8EOwHFmdoaZfQ03LpRmTDnXw7CimhvjU3CY53WqtEEzO7ukyIQUWfhzfLTxX7xWVDcekrQvnil/DYYO+my4FaUXc0qaqXXBmdmvkvwseGRFN5Si4p7Hb6zFyLAykUeHAbfKM6rnZvxd2Mwmty80s8nyPChZmNlfgXWL0X3ynEd/LbGNh9rOkaxakWb2LzySsip3Svopnp4A/MK9Kx27rrlnzDPr75cUqNcsI9eMpMPN7Av4jeo/uJJbJnt+3XMVM3swBSG8PS26Oim7uRyPK4kTyTxOeOb2lXAXgI1wZbNF5ehdSVsCj5rZjV1We1rS5rhivgHuW9dy3O01MOrW9rfx4/cLMxvJabtu23WO92vytDJP4feWbxV+y2m7zv287rOgTt9nKSggU0ht5zRe51ytu8/rtD2Qa8zMbgZuxpP+jkS/nv0H4YraFUl+kqRegXPjJM1oZq/g+7w46K+kQ401xetPkk7F/RDmw+eYWzmlXu4m2CLt5H2ZOplnVgJVG4pmO1ZeuDknidoeeNTUu4EdbKgo6Lq4n1gvTgd+JunTLeuVpDnwkcLpPWSPxDX8Z4C7zWxCkl+dclGGJwKH49NGuZaPebv8VuWBtCSwo6QP4w+ktTLlHpIncDVJM+HZ5LMKyraF6Lf4D17b7pyMTXwU2AefBgLPxP45XOnqqhAl5fKXpAgiSf/BE5F2K7HxAUlfxG9MWckz26h7riJpf9x3olU65TeSjjOzozP78B8z+2N+lwE/pqfjVsEfWkokKekDeIbpqrwNWDndeDcdYZ2P4/4ei+B+US1r0ybAH2q0fRMeiPFD3K9kEG3XOd5fx30Xx+HBMncCrZyJORbdOvfzus+COn1/QdLyZjasdJg8TU1O3rc652rdfV6n7b5cY5LWAr7C1GX3uiVQrf3sT/zPzP7TpqxNpUS3cQpwZYoqfwFPlIukN5JfymwYYyqBahpN7IA7gZ5qZg+n5a0onosytnEbPqIepkBYiTxfkhZj6pPmqlz5sqRw+m/hflmtXChL4v/jq0kT7ya/GJ6S4bY0Tdo6YWcys79n9uFmM1u7ZL9PwR3wf962fE/gPWa2Q8Y2lsbDtHfElZWlgLUsIwlqYRsL4tOz78ZHThfjGaZ7hn5LOg4vxNpSYrbFsy8vgKeIOCC3H2WRdDvwKTNrXegb4r5pI96gJH0XV3paVs4pP+HTzr0spLVJ/V6vNT2eBgnX97ixFuW/gz9YzmS4dTU7833QDMmyNpcV6p6m4y3rkXeuzv28T8+CSn2X5047GvgmQyk81sIHOweY2QW92q5DnX0+LSDpXjwPZvszeMQ8X/043mn944HL8Lqx2+IJ0Wcys0/0kFs3tX1x4b72JmDOKvelMaV49QNJN5rZ22rIH06aLmJ4FvYsi1kdkvm/Nad8n5XIqt2Htn+APwTPJfNhKGlh3EH8ZYbfoGYGtrYefiiSrsenUn+HO7T/RdIDVi+nWikk3YAHCLyavs+Ij3g2BCabWdc8ZpI2wOuKtSvqIybcLcjeam2Zw9UjQ7RSaShJ55jZlr3aGARKpa3M8661EhDebGYrZ8pf3mGxWV4h+lqkQc5mTG0Rr1w2qkTbtazxQXOkKbcD8dRC4D6w3+vkWhEMR9I1ZrbhKLU9O25taxU2vwj4Zute1RRjbaqxHxwlL8VyMdVG01vhERGN1DgskhSt0bqwWwrAuoVlhs+3d8TMHgPWl7QRQzeoP5jZnzLbfAx3al8YN2//hd5m4SlI+nqXn83MDs3YzHy49ahlUp4Dj3B5VVLOOVDFX6nFlZJ+hpu6DVf4r5C0RvoDnc7Z63FfnU5BDU1xAnCjpLPS963w/ZCFmZXxSes35wEvUm5KvV+cje+n80ah7aAEZnYHsNto92OMcpCkX+CWp+Iz+MyRReqTBlV/SPeXrwyyrV68HhWvlfHoiY0Zurl1VSDauB93Om1c8RpN6jwMzexyPO9XWbmt5I7l2+DZ+pcH5pW0jpndlLGJ5zosmwP3a1kADwfuxRHAJElX4NN17wC+nUz7l2bIV/FXarFqej+obfnqjHzOzizpI7jCO1Xx9EHf3FIbP0j7qzWq3d3MSvlZSdoMz9hezLqfXSaqBovnTokOgBfNrJNPYRD0lZZlvNeyAbE77r4xE8OfwQO9N6XB8muS5rGS9WP7zZicapS0Ba65lh4VSroPrxtYxiEPSUfjJ8di+AOxXVvvWfA5zQn/FI/2W0nSKnh+qm+W6UtVksa/MMOnMXJ9vDpajxp6GLb6sBBeMWBHPEN0rzQeRdm5cOfQPRjKhP54D5kZcAvfAwyFLN9sQ2HNOe026q+U/MB2wvdTe5i0mVlWOgpJR+A+LC/gkW6rAJ8xs99kys+Hp1kpnmtZ/1nSsXiU1EZ4Gozt8DxBe2TIzo6n0FjSzPZKyvoKZnZ+ZtuH4zniLs5ZP8nsbGa/0fD6dVPInaZMCvPyVLDGq60+ZkE26/qUdAIdrMk554tqFg+WtL+ZHdVrWT9pWY1HYpD+hJ0GRG1t91RA+rDP6xZVX5+pz7VfZ8rea2Yr5KzbbySdgw9cL6EwMM98fu8L/KboW1eVsWrx2gE4UtIZeD6Pe0rItvJRdX3odmBCep/I1A+0XH6O+wX8DMDMbpf0W/wBl0VVx/500hyET98VRxm5o/ui9WhWPIdMVmRgv0iK0jHAMepR6b6FpPmBz+LKyIl4duesC8c8VPnHyc8qJ4KxEy1/wmIEZlcLaxpY3N5yNk1K77Z4YMX+lqKJRujzNcA1kiaYWfb0Xgfea2afl7Q18Dfc6ngV0FPxknQoHs35V4Ye5mWsyuub2SqSbjezb0j6PpBrNTwBv0ZbdQ0fxgMjshQvvNLAWUnp/h95QQmt/ElzdfitzMi2jjX+HOrVxyzun1nxskFdBxjqX/Hg3Ui5CQt8tMOyTn1YHk91016TtJcP5fe7/FbmXK1CK6fdQnhy5pbrxUZ4mbARFa+6+1zSImm92ZJjelE+KyWEpJPwaNtJFPycgSzFC7hO0ormOQ1L0cFntnV99vSZTbRyIlZhYeBmSbfg0eYXWVXLlVXMNjvaL/xE+Th+o7wez60xV4bcFXiZg0p1AwvbKV0TjVSyheHlZyaVkD8cfwhegPuBnJfbd+A+MrPzZ25vFuCK0T4PevTxu/jD/wt49EmVbXwPV3qarEdWu9oAhTqiVKgpSspojluc3p8+55YrKl3loE3+pvR+A16YdhY8mCRHdkJ6v7WwrEyJpwfwwUjp403nKg1TLesif1/V/UbN+pgdtjcDPeqC4hbkB3BF74HC6zbg0xlt7JjuYU8V78W4W0JWZQrgGjx1xu34w/hg4JB+7osubS8D/AB/kFepQXsxsGjh+6K9ru8+7PPd0v59lqHSb5fjivs2mf2+u879MMlXqmyBl8nbFFdaF2i9mjjeqX3hyWN/l67Xb1OiCkvrNVYtXpjZM5JOx/NBHYCP0A6U9CPrni+o3V8mizT9cbSZ3Zn8jq7Htf35JX3OzE7pvgUA/iVpOdIoWNJ2lMultRXVHfsfomLOkRGYHVi8iqCkS3Frwo8tcwqoIv+H36C+CnxFQ7lbyqRW+DhuMXtVUivypadszeknsw7VBoCJkvbpIlekmPpjVvzhdAv5o9JzJd2DTzV+UtJ43Ok8h6pW5RbnyZMUfxfvs+HW4hxeTtG/rWtsOcpZgB7ClZgqI9mj8cCGXstGos5+u07Syta/qLrl8YfbiJhPBR4lad8e99yRuA6//y3IcAvUs/gDOYfZzOwySTK3EB8saSKe7yqLFKHYbjHLuU7Opl4wxBJmVrz/P4anCRqRuvvczE4ETpS0rZl1S1jajTvwvHFlnl1F3l9RDir6zMoTIS9uZj9O329kqHLL582sVz5MwG/Mkh7FazS/ggdfnS7pEjP7fG5/xqTiJU/7vzueWuHXwDpm9njy77gLv9l1xErk62rj7TaU62N34M/mzt+L4NMgOYrXp/BSE2+W9DA+Utm5RB9KO/YXHvz34xFxf2C4/0iu/8lkhqZNxuEnbVX/rl3x0d26vVasg5nVLollZp2mj3KoM/0k1aw2YGb7tm1wXoay5/dqfAb8YfJd/Eb3qqTn8RqZOVSpclBs+zLzRJ5nSDofmNXynWEPwn3SlpB0Mp7N/aOZsjB0nfyRzOtE0nr4lNH4NkV7bvxayWVe4B5JN1Nyv+GBDB+V1LKGtAYXubnTnmX4efkobinO4ZeSvor71e2d61eXFKUHgfWS68DyZnZpUpxnwxWwXryUzpm/SPo0PrU8Z2a/kUe4vwtXvC7ArSnXkDdAqRsMcZmkixh6duxAXsAOVNznBa6V57R6g5ltKmlFPPdejnvCgnj1jZsof55Cuen3di6X5yos6zP7eeDDhe+z4IPTOXD3hJ6Klzwx9K54nchfAAea2f9a519qI4sxqXjhUz8/tDbfJjN7XlJXB9y2G8zMuCLzXIb1o70m2mmpzUeVWbLAvD7fu+URcTNYRgmYNp7HI+zKOPa3Hvx/T6+Z06ssxRpkrwCPWY/ErSNh7pz+T4Zye3WlrqN3XZKi36rteEXmza1V8+4bHbbXq57bkfSn2kCR5/CpkZ7YcN+21rLn6Bwl2okqVQ6GtU1KX5Ksu9kDDTO7JPlgrIsrH/ubl33KpTV1U+Y6mRl/2M/IcEX7GTwwIJdK1vjESFn1eyK/gb3VMgNtOvBL/FpeP30v5VcnaS/cVWR+3HdoceBYfNDRi/1x6/t+eJTyRpRL87AdHix1q5ntLs89mHtfOUo1UhOZ2aeTD2Xr3nKcmZ3VTaZArX2OKxsnMJRW4c/A78lL+3JwZhsj8Qf8GSx8ILkMPu341gzZ0j6ziZnN7KHC92vME2g/mZ7HOcyPT8cOS/Sa7lk5NTqnMCajGlvIi1QXncz/3WX1TvLCR/HrmtkXe6x7OW4OfxifE39zUrpmxKcm3txFtuN0U6HfuVanjjeUZD4eCJLmTtO684/Qds99nk7KQ5naITIri7qkSWa2WrpJbY5P/V1lZqv2EK2NPCpxbeDktGhH3I/oSz3k7sF9o/7Wtnx3vNrAcj3kl8BvSNdYhWoDks5jaIAxAz6iP7XXeV6Q/x4+nX5m2Wk3Vahy0K+2k3yjlSUK7S7VflNuEtWojylpsmUmuO0gO8HM1lIh4a+k23KvT0mT8KjhGwvyPfsjj9I+3Mw+1229Htu4yczWSdOTG+FWtru73c8LsofhwRB/pRAMYSUS/bZZ+mbHCzLn1GStu89vNrO12+QnmdlqmfILM+TOcJP1iBDvsa01gH3MbM+q28ho4z4z61jQWtJfe92PC+tWjtYuMiYtXpL2xqe5XmR41FRuZIML+E397DRq6fVAqlMTrTUKXgE/WVtRkVvg9dhy+3tiMsMvaWb35srBVA/iFv/BozV/ZiNn7v0truxMZGiUMqVL5O3zI3FfpclVHqQMFevdDDjNpq61NUg+AKxWUH5OxOuSdVW8cOXwYkmbWarpJulLwEeAd/Zq1Lyo9wXFh48N9wfpxfcKn18BHjSzf5SQL/q2vUA5Zfnq9FDKrnIwQtuvyP3qstvWUGWJOxkeGZileKUB1lTnaObD9Pk0DdKefyzrQVzDGt+aBqlTH/MWSWubFywuS12/upfM7OXWNZ0Gsz3vE+ZT4HUzoE9I0/A/x+9x/8WV/hw+BCxrJVMTtehg6VuMfEtf3X3+nKQFCvLrkukDLGl73A3hCvzaPFrSgZbpJ9WOmd0iKauajNy/+iCGrIRX4sEUvfp+o6S9bOrydR8n8xks6RDczeh+quUAHdrWWLR4SfoLPh9dZgqhJVvMoTIDbrJ8p5mtN4JI35B0FbBZa0Qjzy31BzN7R3fJKfJb4A/Umc1sGUmr4Sddju/MUbhfVtGf4Bn8xJnbzHYp+39ySQ+zTaxC3rUk/x08sOAFfGQ8L3C+1Sj9VKLt24F3tSx7yfJ3hWX4zkjaBE8dshUekbgOfvyfymz7ROCYig/D4nYWBJ6sqPRWae/yDotLWQJqtH0vsIpVTAQpac3C11lxt4ZXLMNxVtLF+HTN54BP4FNeT5hZrq9UcVvZ1vi0ft36mPfgPrMP4lPK2T5ikt6DB7CsiE+7bQB81MyuyGz7COBp3H9mX7yo/F1m1jO7uKSf4grLaQzPy1Q6ZYC8LuzcZpbl2C/pbGDvqtaeqpa+tF7dfb4G7gu9Eu4sPx7YLue/y+sdv6f1v+WBN5eWsLYVZ4BmwINPFjCz92XInpH625rl2QVY1cy65kaT54A8G1dOWwPANXFfr63Mq6z0avteYOWqivawbY1RxetCfK71+Z4rTy17QuHrK3h6hp/XMZWWaHvYQ0Ge9PB2y0wml8zhG+MP/taFeoeZrdRdsvP0T8HcfKeZdZxfVx8SDUpaG59qvJJqjv2z4E6QLUfvOfD0ED0vlrpI2hH4Dj693Mpc/0Uz+32m/NvxepXXAdt3sSx2ki39MEwj1+/gKVMOBU7CnWFnwNNJXJjZtvDcZ8uY2aHyqc9FLa9iQC0kdRyI5EwXyp3iP2R9LBbcmo7KWG+ima0pzz+2SlpWd9p1ylRQj/Xq1sdcqtNyy5g6TYMRMeRXdwOe2ueBzLZnwBMbvzfJX9Rumegie0KHxWaZiYLTNqrmRrwC9zetEgyBUt3g1jFOlr5bMpXduvt8GTyCd4Ukfy9u2e85yGtXDtPxu63EuVb0ZWw9g8/IuTd2mg7ttKyL/MYM+ZLdafnl61pK3yf7oSuMyalGfJrnOnlIaKns8Wa2+yA71oNfAzdpeA27Mv5Z/+swzZZrRZpT0pKW/IMkLclQ9E83Db4V5j0rbh28Db9QV8GnKXMshd/CTfizUs2x/3orZFQ2s+ckXU1+mH5lzOyUdINtPTy/YD2Ke8OwaSPho6pNgMeTQpM7ZddzBNiBY4AvA/PgiRk3NbMbJL0Zt3ZmKV54JOVruKJ/KH78fszwNBUjonolfw4sfJ4Vtwq0Bh29qBKAMgUN92WcAR8Vz5Mji6dIAXgk/f9/4tNIWYxgjc9V1E+gXn3MB9WhskUm5+Hn2R8AJL0Ft0D1HBAmDjazr5NShkgaJ+lkM9spo9+17ucampq+i+HJQHOmpusEQ4DXYv0ynsz0Pbil77xM2br7/HS8asqdSf4d+PWdozxdqKmjMS/IbLdjwFEJXpC0oXmiaOQJVV8o0fafGEpYW5bK0drtjFXF62f4zsuOmpL0eTM7QkOlf4aRe2Oug5l9K43IWw6wZWvY3SkvKzJOHj68H25JyeH/8Izmf8WVgWWAfZL1aETlz1KNRkln4lnfJ6fvK5Ef3fKGHKtcO+pDluW6JEVpE9yX4xBJSyqjVqRVT0NR3MaDch+W5c3shGTS7xUqP6OlcjeSDjGzG9K27lE5v7i3mdkakm5N8k9JylKaNULJn9yGzWyL4vdkbTsyU7yVyLIqRV/GV/AIx56lihLflPug/B8+jTM3XiA9l+L/blkCslJ4WM36mKpX2eLbeO61D+A1+H6NW0tzWULSl8zssHSOnYpH9eb0e1b8+LQr+bkWr62omBvRqqcmavFFvO+Tcb/GC/DrJYe6+/wTuH/zFvgA9jDcn7UnZnagpG3x6U3IjMaUdKSZHaDO/sa5Cswn8Txk8+DX6L8ply6mDpWjtdsZq1ONWeb3NpktzOw81YwMlPQq7lj4JUs7T+VqXE31IC1hHp4dD/+dYpIHDs2dvkpTdq1onXtLTntNNR3ZbYqybb0jcB+A7Pp3SW43/KJaCzfntzSHZ4ATrYGCz3IfkteAjc3sLfKolovrTB+VaPsg/L+vYGZvkvQGPLhggy4yU87F9vOy5Hl6Ix6qfnNSwMbj/ztn2ut2Gyr5s4o8J9kfzeztvWRH2J7waYEVe6w3Dj/PNqrSzlhEfYg6Ttu5D1e2n6zYj63wPEZzAdua2Z9LyAqPGp6MK+t/NLMfZsqehmcz/wgecLUTHpW4f6Z85alp1QiGSPKVaw4n+a2ouM+T/Hq4EeNF3Pf0iSr9KNHemmY2UVLH4KIyiqw8owFm9ky/+pfRZi23gWHbGqOK17fx0eB5DDf5lUonUbHt2/HpmtWBHczs37mKYJUHaT9RvcKmp+B+Rq0cNzvhSuOOGbLP4j5aL5Ff/64lOwOwo5md3GvdQdBSVlQxbLtm25Pw8+yWQttT/IdGkHmVIX+w2fCpN9L3Wc1sppFk27azEz6FsAY+0tsOT4NxWoZsy3flBjya9UlcceoYzt1BvmiVngFYDfibmfVMNpymGLex/ISrLbmNzexPGqGAcY6SL2lZvL7geriyfj2eb+7+HnKVrfGSzjezzeWJU4uyrWssK9JbHhDxHiuRm69DfzfBUyv8rVe/k3xxEDATrgRcS5oitTz/0VvN/aNaSv5MeCqNrOTMcr+dVYFKU9OF7ZQKhkgyv8HPleyaw33Y5+3WphXx3IBPJfkRrU6SrjGzDTV1st1S6YGqoD4Voq/Zhx/g50jVaO0pjNWpxtbDvhjS3zW1wUjmzSnC+fO0r5gXD94BD5vftdt229ia9CBNbf5THtnYlX70XfULm+6Om3lbI8mrgJ/mCNaZdjNPTvcZhvJoNc3/kiWlZd0cT00zcwleNjOT1Gq7Z6I/MyuTKb0jSdl9AB9Nb4LfWLcys9yi6Odr6pI/uVMoMFSQHnzK7RQzuzZT9r/AZEmXMDzKrdeD9J24+8IWHX4z8grr/hb3k9k6ff8w7gfTK/q2tV8ndF2rU8fMNk/vWclx21G9yhbt/c1KiFygvVD1U7gi8H3yw/RbfnVPy90fHqVHqaM26k5NA6VTE7Vkdk6Wmx2BX6Xr/AT8fB8pl1fdff693qt0xsw2TO+V7ucaXv2k0/a7TWv3qxB9HVrGlaJS//pJJ1GFgnlzGzwXV8tysyOehT3LF6PN8rESfrNd0szmzZBtJetrWVGyQr770XdJdwMr2igccNWIUkvy38HLNPye4Q/TJiyclS0/fWj7c3jNvPfgPhgfA35r1erilW07y4qbsZ1ZKFfyp11+PrymXW6If+NJhgttT2WNbNA6epmZbdJrWQe5rg7iluEIne5jL5rZq+n7OGAWqxB1XhZJe+IWo1VwpWVO4OtmdmyJbcwMvCl9vdfM/tdt/YJcX1ITyfNp7YLXHL4bj2TuWnO47j6XRzU+YkNRsLMBC1tbwucRZE+ytvRDnZZ1kGtFzn4qvZ+U3nfGddectCkbtA/COi2b1hmzipcqFjZVyvjba1kX+TXNCxa3vs8DbJnZdq0HaZ2+J1+I/axcEs6i/PKpz+37vOdURrLYtZgSpWb5iSU7+cBlT6PURR4R2LL8XFbC8tOPtt+D+/SB+1hd0lC7pbPH92O6Lm3nCuCDuEV+Il40+rrcwVEVRprCaNHN8qMh/6ov4Fab3+Ej4R2A+ax3lYPKFm25c/nseLqTd8GwAJQLLSMDe9v2Zi+rMKUp5Xdb8pOS+/RdbGbrd5ecIv9t4Ajz+pwtZfv/zOyrZfpRBUnvwgdUf8P33RLAbjmDQtVMTaSpaw6faIWaw2a2dBfZuvt8ArC+pZxUSfm81jJ8mDS17+iMeFqkrj6YhfWnGtS1bzO37TKy/UD1orWnMCanGlWvsOkckpa15HeRNP/cWk0At0vaj+GZc7NGV2b2vfQgfQbPn/L1kg/SOn2vW9j0BDzq6YfARvgNY4YcQasXpVZ5GqVfmEcEPksqeCxpRqtYp7ICk3FfLUufm6JK5vp+TNcBzGPuML4n8GszO0juW9kTTe3r5I33VtLrVJdor+rw8WLT9K5y0Jr+6WjR7iH7cdxS8obUj2IAyjE9ZKcgd7Q+HrcYLSkvP/RxM9snQ3xWKzinm9l/k/KQy6Zm9uWC/FPyaL2eileyqG7L1L6ruQ/D7wPvtVQJRNKb8OnhNbtK0ZfURJVrDlN/n89ohUSg5pUDukYtyytvtNJftJzahacjOq5E2ypaqeS+x12fJepfIfrKqGa0dpExqXhRr7DpZ3Bfhvvxk2Ypht8oe/FT3BH0J+n7LmlZVp2ppGhVtVrU6fvBFdtsMZuZXSZJ5kkVD5YndP16hW39A3hL7spyh9lPUihUjZc5ypoSqEK6ycxUuIFfh5fUmBkfIR82qLYLfdgT379/gimlOQ4xs18Oum2r4MdhZgel90oPJA3lmZtRXpdye4aK+OZStP7Oipd16ZlLqzWlJq8usYYNVZc4mB4lweoODCxFc0n6fpv1+rxkmegmexRerHnfmlPQR+J5485N271tJBeBDjwnaQ1LTsby7P/ZuZXw9Diz2FBi6dnw3Hc5nINflxMpVzKnxUxWKL9mZn9O95sRUZ9SE5lZx2nx9NtlPcTr7vMnJH3QzM5N8lvi7hzd+nsYcJikw3pZcXuwB/BLDaWEeAqf/elGvwrR12F9G4rW/oak7wN/rLKhsap4vWDudP2K3DnxcdxE3BMzuzBNm7VM8PdYuRwua9twn40/yUso9CRNwRyOO3+KktEgdfpu9XPOvCR3uv6LpE/jxcJ75ZQCaI/EaUWplYkEqaXsVuRDDOVbA/i3efTUONzKOXDFC08kurqlEH+5L8h1wMAVL6la5npJK+D151rn6N14np+cUPezcV+6Q/BUKdeY2c3yaMG/5PTbpk6HcGTJAcLCDE8o/HJaloUqukAkKlu0zezomm1jXh+0uOjVkdZt4wDgNEn/xO9pi+DTrLmcDFxWmLrbnfzE0oub2ftLtNXOBEm/YHi0dq8gh8rBEEXkVSaOxgehM+OWm9x0FAdQb59/AjhZ0jFJ/iG8ZFNPzOxLaTp4eYafa1k+u+auOqsmxQvL8P9Mz68rJf3KRq8QfUuxfV6ekeBJYNEqGxqriledwqbgJ8wK+EmzqqQyN6hXJS1nZn8FSA+F3BvUEcAWVtJHqDXCSl8/aAXHbknfLprpO8j2KwR4f9zMuh+eyXwjMi9U6kWpQQ1ltw6W6t4ljkrLXk0j8iZ4EihGNz2bljVB6cz1aTrgTHza4Tj8HFsdt9JuYymZaxcEkM7vKed4UkS2zem0hqcoaDk8l7nPVa4uUdMFAmpYtPvQ9kNpyseSxWd/hhSMriTl+M34PRVKOKgn+cPTVHIrEOBQM7soU/w6SStbSuxcgU/izt4tK9XVDA3wOmJm56X3ugEbx+CRr6fh5+muDDn5d6UP+/yvwLpy3zCsRB6zZInfH1gcj5JfF3/+Zkf3qeAr1VL2M6eHaxWir0mnaO2s0lbtjFnn+hYqX9i04w3KzLLMlfJaT7/Cw69bN8fdzezyDNlrrULOLvUpKWYdJH3I2iL5Oi0bUNu34EkOi8ru6YP835L+DLy1/WaWfEruMLPlB9V2oa1f4yU8zsEv8i2B29NroLlrVCF/mTwZ5eHWVqhXHpX7RTPbtEebj+OO6R3JmcLR8ALdLYfn7xWnkzK2sSZDGeCvsswM8PJw+ZYLxKotFwgze0+JtotJjrMt2nXblhdSPwp4N35fuxjYv4MFsSjTl2CKKmgoNcGM+ED6fnyqsTWgzMm4X7XtvqQmUgqM0vDanlOutxFkau1z9SEfVtr3awM3mNlqSQH8tvUoVF2Q7+grZWY9K0Soj4Xo66Ca0dpj0uIlz0l1FZ4or2fSuTYq+4elaaZVGbKYgY80ut4cCxfJBEm/Z6hKOpB1g9IInzt9H6kPywH/MLOX5JE8q+COy0/nyOMOwu1KVqdlxTZPNbPtNXX+lrI3xwOBy9ssAXUdW3txOvAzSZ+2FOUlD+E+Jv3WBH9NrxbnpPfa5YgyqJK/bLl2pQt8mkBSjvPtC5TPS9TeVu2s9ebZtR8ijahVqHHag8ouEAWqWuNrtW1m/6JcyRmoGUxR0xq/ecm+trc90r0J6JlTqk4wRJHn5Q7tk+TVPR6hd8BS3QCWbvmwcnnRzF6UhNw3757kYpBLHV+pBczseEn7F6Yfexb27geSrsHdTK7GI0ArKV0wRhUv3Mfl7biz8XLArfjI9KgM2Tr+Ya9K2tG8nEWWhS1RvEieZyg9AORdLDbC507fR+IMYC1Jb8Sngc7Bc5B1rc8ladO0zmKSflT4aW7cotCNVrLVWjdJc6f+UspuH/gaXtz775JaPgVL4pFfXxtw20BeDqUB8iPgLGAhSd8i5S/rITNS0kco5F/rwpN1p2+S38hBDI86PiT3JikP8f8+HiX4OH7M78GnNnpRywWi5nRhpbYldfN9MzM7tMuPB8n9Pv9oZqdm9LFdvnJCTmvz85G0EIWppwwq35usRjBEG7vgitan8WnmJegxpd6Hff6zNKB6xjLLMnXgH+lcOxu4RNJTQBm/qzq+UrUK0ddkF1zv2Bb4rqSXcONP6TQ3Y3aqMZ08a+Pmyk/gClXPnDWSfoKHxH4YL2b7X2CSZUZiSfoh7ujdnswzp7zF/NaW9FPSMtajVqP6UAamMHV0ID5iObqXWTvJrYo7wx/CcAflZ4HLzeypXm3XRZ6raB98+sfwEcexVqLWZI22Z8Pz7ADcZ2ZlIofqtn05nUfjTfgzoJL5y7pMFQrY3sy6OqlLusEyS7102cYZwB0M+WXtAqxaYhrkNtxX5VLzYIqNgJ17TYNIEu7o/VD6vjQlXCCSTO2pyrJtS/q/DovnwCPPFjCzngE0KpEHsYPsOLycVKl8YwX5dkV5KbxWY46ijKTD26epOi0bQfZuvMZhMRjiAjPrGrEtaYWRpr6VmQy0zj5P8jeZ2TpV5QvbeScwD54z7uVe6yeZr+FBBZvgfqMG/MLMeg5oJW2O3/+XYKgQ/TcsRWcOGnm09TtxBWwj4O9WIbhjTCpe8npsc+AjuqtxH62spHVt21ma8jfHTr5clvMwlHQtnrPmmfT9LXitxpVy26+KvOjxkXh4/hZm9oCkO3LbljSTmf1P7ni7EvBwr33eYQphGD2mEorbORVX9Fom/Y8A85rZh3LkxypyX6MWs+IjrVfM7PMDbLPr6LF94NAmO2J4fJLtas1K/7fduvuvljKTg6RJZrZar2Vd5Ft+N7fhEaWvKTP7vKTJZrZybl87yLcqW0zEb+rP4krEiEqJpDenqZ6O/o45A8LCtubCLUF7AKcC38+5r6pmZQlJ5wD7Zk7ntstWUpQL8p0Scnath1pY7/347MGwYAjrERgg6TU8a/unrM2pvVN/RthG3X1e2oCg/hVkL6YOmQW/t73YwCxGLST9Fd/nv8X1jklWscD5WJ1qvB1PcLcSnsPlaUnX51gjVCijYak8gjJKa7Swej4k38bN0Zvh02a/prxfRVV2xy2D30pK1zIMlWwYEbkj5NFmdmeaxrkej+KcX9LnzOyUkWRbUwiSDsX9F07Cb1A7US4MdyUbnhX5ckl3lZAfk1ihQkLiWnkC3EFSTAa6JJ5jR8C8wN+BEXNW1Z0mpHMdufnlfjA7mtmkjG28IGlDM7sG3IJAufxGT8sjva7Cw+0fJ2+aFOAWSWubWVWfkyrThZ/F03e01z0E8urIpQfpZ/Hr8kQ8j1kZS3YrjcGnCsuMLrVz25gPuDOd20UlIMdJ/X9m9qSkGSTNYGaXSzqyl5CkT+JW9GU1PDnvXHih7p5Y9fQ+d+K5DG+RtKsNj/bN8tml/j5fLb0XIwl7nS+/xadmi/eI4ntu29fjaWNI++sleQDViAqn+pQ7rSY/wmdddsSjta+UdJWloK8yjEmLV4s0QvsoHuGwiJmNmHRPfSqtkcz/3wbeYGabSloRWM/Mjs+U3wovPjwXsK3l5TcaNSTd2TLbSzoAeJeZbSVpEdzPoOtUZZKbymKQa0VI6/4GOKZ1g5L0Nny0mJvOYkzSNrKcAR9s/MjMyjiyVm3758BZZnZB+r4pXii7TLLhfvVlLeAHZtYzoaek1XDloZWc8d94CZjcqOc5cEVtBlwRmQef7us5mpd0Dz4t/SBDrgGWYz3psK2lKWmNr4I8NH8b3HLz43YLTBNoqBbtMCwj96CkS/GUH4fh1Tkex9PPdC2dkwaR8yW5Yo3AZ3MtN2k7pXOnacjt4x14RZATgW8m62pj5W+aJj0zFsNnLj7C8GfwsT0su1uY2XkjWdX7MOjLJg3Mdsf1jsXNrHzmfDMbcy/cGfH3wH3Apbgz7cY9ZPYHHsCjCe9Pnx8AbgM+XaLtP+IZtW9L32cEJveQORrXlluvO/EM0T/CH6RN7LMN8Iz5fy78//sz5G4tfP4D8NFOv/XYxnX4Q2wcQw+060r0/W48ou5v6fVaWjYZrxE2yP12Wc6yAbX9QOFY/QUP8d+wobanOqd7necD7s8tJdefG1dcyrZzeM6yEWSX6vQq0Xblcw23fMxb+D4fsE+G3Gu4ovksngW89XoWd8DuJrszsEuH5bsAH8lo+43ABh2Wb4hHyOb87znSPWVGPLXAfrhvWtnjvhBu4V0SWDJT5iB8IP8YrkA9iqe5yT6XcUvyKbiVbele53kf9vlngT06LN8DOCDzf2+Nl/Uq/oetMuR2S/vrWTwy8/L0OhfYpuwxa/qFW5VvxJ/fP0//Z9lK2xrtP1NxB3wOeBteb6qs7L412745vd9aWDaph8xu3V4N7bN78CiphYAFWq8Muctx8/LqwNO4ZZF0o7sns+2l8SjKfwFP4NEwS5foe8cHGiUfbCX316x4tMxt+ENs/vRaOvd/j+UXnjn+q+n/Lo37Bl40Sn1ZGC+qnrPuAviA5hZ8SuSonPO8ID/Vg4+Syj0lH+L9ONc63YPIHBjVOC43AnN2WD5HzvECzgdW7rB8ZeC8kn1ZAFcI1iwptwU+qHkOH+C8hjv758hOxpW+1iB8YeCSDLmpjkt6FvwDj+wd5D6fiJdJal8+c+55Xvdcw2d6qp5zlzD1AKOR+xIe2b1wP7Y1Vn28zsJzUr2izJxUktYGHrJUz0zSrriz8oPAwZZvXn5OXrrF0nbWxf3MRsQaNIN24T9mVqWu1MfxB9ki+Ijo0bR8E3rUsGth7ku3ZdmG5UVf/2cpdFyeK+YDwIM2wOSMib4UH65C4Vx9NH2veq7WYUd8RH9W+n5VWtYTeU6ib+KWlAvx6/MzZtY1X94I/hvz48Vx98/s9+9SX1th+Tvh1vF392i7k8+P8LJYWT4/GiHCjt6pKPpxro2TJEtPCHm0YNeix31gJuswNWlmz6lHvcPEwtYh47yZTU5TrSMi6Xw8Ke8dKdLsFrxCxrKSfm5mR2b9Az9P16XNOT9TtmpqoqmynZvZiZKuxt1QulF3n89oHTLcmxfJzvUv65RrrKcuIWkLXLk7I33/OkP3tf2tR3R/YnzxOW9eUH2hrF7X5xHc7xJJO+M+aUdZlRJGTWiKA9A8J+EH+o341Nl38TDebjK3APOnz+/A839si5dD6WkeLmxnDfxG/J/0/mdglUzZStN9fdpn30n7ab30H9bAnWibaHtWfCrkJ3gOtl8Cv8yQuwpYPn1+I+6vczRwGXBYQ32vZSGt2GZfztXRepFGxLgF4njcT+q2DLl2a/CuwGbAQiXavqPDsp5TpKmPS+PTPkWL6vwl2r4Nt7zcmr5vBBzfxLmWru1T8QHRJgxFJQ7yON8NzNFh+VxkWOqAv3T57b4esncWPn8ZH3i32s62UAITCsduhtbnTNmf4NNsn8CtZrcCJ0zj+3wyHaw2uLUuy5Ug3b9/ACyXXj8AfpUhdzswe/q8Of4cXBOvuZtltcIHJksWvi9FSTeEGvv+dnxQtGo61p8Crqy0rSY6PIAdcEt6P7B1s6KHqbN4MeG5Qw4ufJ+U0WbxYM+Ij2JXooPZtss2Kk339WmfXd7h9aeG2j4NVxr+ij9QL8ZHCr3kJhc+H4o7/4KP5Afqb4TniFuk8H1XfLr0R5R4GFdsu9a52qc+vAl3uL4Y98f4U+75Qnoo4uVA3t/+nzLkZ03X1kp4nroy/f4BnqNvhvTaHi8Z1Etu9uK1jEcdf4YSvidUfIj341xL//WTeFWF03Er2rgBnyOfw31elyosWxq3hB+YIX8KsFeH5XsCv+8hO6nw+TLgw51+y+jDpbhV8+jUn6Mo4X/a9r+zBuCjvM93xS2D78SVtbnwYLObyXR7wac1v5O2MwEPUJhKGewgV7yv/RL4QuF7lvIEvB+Prj4Jd9J/EHjfoPd7sY94Pss9yvR7qm010eEB7IAb8WmPO4Bl0rKpRrptMneQfMJwBegdxd9yd3r6fEbVfo/2vhul43Vrer89vc+E1/nqJXd74fO1FBw4KfEgr9jnUbM61T1X+9SH2/AH+Tr4qHRNMv1n0o34HnxUOBMwPufcxwc0R+C+gBPTMXgiLes6wGHIOfxZ3E/nlfR6jR5O4km+m3X1O5n/u9JDvF/nGj4gWZmSA8Ka58kn0sPvyfR6EPhkpuzCeODNFfgU7ffxSgPXU1BER5A9D9gXt6o+RfL7wRNMZ/lopfXnwIN+SjvnM0qBN3X2eZLfNO3nJ9O1diWeX7JsP+aig79Zl/VvT9fHDKnPaxV+u6vEdhbELWabAwsOen8X2r0SL5P3Z9z1ZgYqGgDGqo9XlZxUp+B5N/6F+55cDSAvoZNTTqQ4/52br6Sdy1P49pkMr9WYneSwDipUhC+0nVMRvi4tn4KnU/j1o7jVrxe3S/oe8DD+MLwYIOU6GjTjbMiXagfgOHPfhDMkTRpw23XP1X7wipn9tKyQvJzJefjU13/My2w9T56P33fxm/kyZvZs2t7ceH6v79HFz8sqlJ1pYz4z+0v6vBtwipntm3KITWR4yoGR2BI/Xp9hKBVFzvVV+1xLvq4n4lG/ApaQtJuZXZUjXxUzOxY4NqX2oXXcMmUfA9ZPflWtRM5/MLM/ZYjvge/bdwM72JDfz7p4hGFuH4o52rJ8cQupiRaUNB/D0yIsltt2Vers87T+H8mvjTgVklbGc1DOn77/C7eW3dFD9EjcTegZPDHwhCS/Ou4/lcv6DJUEAw/SaIId8DQYe5jZo5KWxO9ZpRnTebzKkhzhFwUubl1wkt6Ea+1dlZ9ifpWquVZUI+t9XVSxIrxGqGLfwvKq2e+J14pcGfgVPur5mpn9rIfcbPjDdlHcJ+y2tHx9PNy8ZwLYqki6A1jNPIDjHmDv1kNMJTL+12i/8rnap/YPxp2Fz2L4ICEnn9WtlpHfrYPcX4A3WdtNKTmK32Nmy2duZz682HRxgNFVAVEhW7m8wsR3zezs9D0751xhewviEWo9b7D9ONfk2e4/YqkUTTpXTjGzNbtLvr6RtA1wOD4QFPQu0C1pf4aCIR5meDDEz80sKyBCntz3YNxPacZC21UH9o0g6TrgK2Z2efr+LuDb1iN3Wlp3MXxf32Yp63sKjpjJMioXyDP2rw2cnBbtiGca+HKFvzJqjEnFS54t+DCmTlw3sBNW3esldr1QpwVaD5bC+5x4AtS395A7qNvvllHIWR3qUXZaNi0h6St4BOW/8LQAa5iZJavTiWa2wah2cMBI6nRssh4KyUp5PXBmjuJRkPuzmb2p7G9t6+2JK+uL46PrdYHrew1u5El6H8UfpF/ErW7PJ+vqld0Ur6QkfwefnjwUt74viE9F7GpmF/Zou/a5pg5lbjotC4Yj6T68hFrXOqQjyO5rKUq+Ytv34NbRiXg1EADM7Mmq22yCTgORKoOTim3fjg9SWkrbONyVZeDnebrOjwbegk/rjwP+a2bzlN3WWJ1qPAEPdf8hbsHZnc4hrn3DqmSn7cAoTvdVqgifo1hlcAZTl4M4HfcbmiYxs2/Ja4K2rE4tBWIG3LdkusbMRiwNlMHH8USNr0p6gfzByV3yEirDMn+n0O17MtveHx8R32BmG8kLfX87Q26vJLs08F4zaw2sVqRzKaMix+CRdfPgQQibmtkNqe1T8JQaI9Knc22CpF8wVM90Z9zxeaCkqeV1zey6Qbc1IB4rq3Spf6mJqqb4GW3ulxe6bs047IxH6TfFvPggB/yaa4pj8MCd04C18ECFnoPBToxVi9dEM1tThaK0rWWj3bduVJ3u61PblSvCJ/lZcb+KdqXxY11k3pzWPwKPQG0xNx6B0yu/UTCKqEI5lJrtLYb7P76AWwHAb3CzAVub2cMZ27jZzNZOvlFvM7OXVCh7NaB+T7JUhFvS3Wb2lsJvlaZdK/RhFjy8fcO06GrgJ9ZA4eF+/0dJG+K1OT+Vse4CdSxEko7CHaXPZviU+oh5AuV1Bd9tZv+Wl/35Ha4grwa8xcy269FmaxC6PW41qeTzm1wulqZgQOl1ffbJdWQ+4BsMnWtXAd+wcvU9KyFpR9y6fDk+oHsHns/t9w20PcHM1mpzS6h07o9Vi9dLaaT1F0mfxqcH5hzlPuWwfmG67xuSvk8NJ8cymNmh6eMZ8uSDs5pZGUftk3Crw/twp9ad8Jwy3VgBjzyZF88Q3eJZ3MIQTKOkKeZ34YrXBXgk1DW4U20v2VYh9GXM7FBJSwCLmlnXAt9JsXqbpI0ZSjp6gZldVqLr/0jTg2cDl0h6CrdEDJLXCp/bC3I3MrJNCtYPgB/Ia3wu3oTSlbhM0raUnFouInew/gjwITy/YW6C5BuSkn0C7jpRtv25cbeR9xaWWY/26wZDtBc0X6ut7ZzC5ifhObQmMTRNafS+PusGoZAUrP1SP8bhqSSeKbsdeeLT4qCup4+XmZ0i6Qrcqg2ekuLRLiL95Hl5sM0keZLoR6g40zZWLV5r4w/9eXGfinmAI2x4lfeRZEs7U/YLSTea2dsk3YAXpn0SD31+4wDb3Kbb791Gdm3budU8s3PLR2wm4GozWzdDdj0zuz6zy0W58+jy4DKzD5bdZpCHpMmkRIFmtqq8OPxvzOw9GbI/xZWRjc3sLWmEfLGZrd1DtK/Iiy/PA1xoZi8PsJ1u/p+zmllORvG6fbgC+CA+mJ6IB0ZcZ2afaaDtZ/G0DK8AL5J5T00BADum17/wCgOfM7OlSrQtPLLxY/jD+FQ8meefM2TH4XU4P5fbXpLrS+CNpFnN7MW2ZVkWPEl3AytWVXTrIOm3eFaBV/H8X3PjeRmzIvw0QoWHHKu0PCBhknmm/nrZ40siaSm8LufMuG/ePLhV+b6y2xqTFi8zuzl9/C/u31WGI6joTNkHzk+j8e/iuXuMDuUj+szp+KhoUvpeTIvRa2RXpHRKCEl7AVeY2fXpBnk8Q74QH80wqffyrQkGR9VyKOBTfGtIuhWmlPUYdPma1oP0TjN7c2r3ykG3mdrpi/9nTeYxs2fkwQW/NrODNFT6aKBY9VQe9+BTopu3Hl6SSimKSfG4BLduboT7uO0j6TZ8CmrEAZ95qpMqQTL9SvdyhqQtzeyVJL8Ingg1x2XmDnyKtEwahilUcR0psGI613bCZ2y+iCv7uakVDqV6maafAqtKWhX3Iz0et/K9M1O+MgXl7kV8qrUyY1LxSiOlAxkKwwXA8tIylHamrIukA/BEgYeli6zqdF8VtsEdAlfBs2GfUkVDB45Llouv4dXk58Qz+HZjfzx9BPiodlU8B9rqeHLJrhGVTT04g45MSIOEn+M31f/ikYo5/C8pQQYgaTzDp+MGQnqQ3itpyZxpiyLTgnW1pjV+RnlY/vZ4QfNGUYUUHgzdmy6XdCHuK5VbL7DV7gL4Q3sX3BqxL35/Wg13gu4VJDJJ0rlp3Sk5vbrNBFj/Am/OBk6TtB0+qDkXz0w/IoXzdC48GOUmhvuH5Z6nVVxHWsyUZjy2Ao4xs/9JKmN5+5+ZPSlpBkkzmNnlko7MlH3FzEzSlnglk+MlDdxHGjqm/wCqZVMYk4oXfpEciz8UXu2xbjsTJP2eEs6UfWBxPHncm9MUzrW4IjbwSCDzXERnS5oDT/D4/XSz+koZxcbMfpE+Xkl+AtlXbKgg6+b4SPxJ4NI0R94VSaea2fZpn011YVuEyvcdSVvhU1T7pEXHpofi3GaWa0H5EZ7/a2FJ38KDSL7a9852Zj7gzvRAKj5Iez2QpgXrah1r/CHARcA1ZnazpGXx+oEDRyOk8KCHr1KHe9MBwEJpqvosM7s4o/nrcSViKzP7R2H5BHkwUy9mxV0+in3tORPQya0lZ3qzbf2fJ0vw2biT/Metd3Rov87TN5rZh5LF7cQ0fXh1puzP8ES9twFXpSm4Mj5eT8vTGV0FnCzpcQrXag+elfQlXNl+h9zXe+BT+Ynj6ZD+owpj1cercgSjpE5ZjS3TxFqLdJGthWfeXS+9njazFRtoexxe5+rDeCLTL5jZRSXkZ8GnCZdmuLY/YioMefTPZnhJjwdxn58702/Dor9GkF/UzB5JF/ZUNDGv/3pD0un4efk8PjC4FlfEemWlbt/Om/EIWuFlVBqxMie/rqkoM8iQJ+5d0lIy0qaQdK2NwfxwaWDUSuGxWjr23zazrv6lI2xrPlxR/7CZbZKxvpIFZE4AM/tv2TabRsMjC4WnJbgdL7GVFVnYhz7cZGbrSLoK2Ad3HbmpivUmbW/G1pRpxrpz4FO0MzBU4eHkTN+2RfAgjJvN7Gp59vh32QCjrQtt32hmb+vHtsaqxes8SftQIau2mZX1Cesns+GOiPOk1z/xavEDQx4h9mG85t6luCNilfw+5+D+CxMp7PMefB3PJTQOOLegdL2TjLwvZtbyX9jHzL5Q/E3S4cAXppYK6mApFF5ehms9fJDw8XSDu9nMPpC5qQWB583sBEnj1VDC3KKCpRLZ4wsyW+BWhZmBZSStBhzSxFQjNazxyYL8TfyBdiHuWvAZM/tNV8H+8KKZvSgJSbOY2T2SVuglJA+SWtAKuaySP+DDwOcz236rPMJvft+kniCvfE2rD2/C/YYWNrOVJK0CfNDMvpnZfhXafeLOHGH5iKhzMs/nMqeloZrrSKvt/fEo0mfxtEir435ePS2UyQBwvplthLsfZJVpKvAs/gx7NR27Vq68Juhbyb+xavGqk1V7cfyEbY0srwb2bzNT9xVJx+FOjM/iBb5vwEeHTeQ9eQ0fTV2Dm9CHHXAz2y9zO5XK5EiaEZir+F/TiEe5o1N1KNGkyMo9cJLlYgNc+VoXeDzdMHvJHYRbdlcwszfJE/aeNkhrjmpmjy9sZyI+7XSFpfw8KuQLHCR1rPFKucQkbY1P638WuMqaySZ+Fh7kdAC+757CS8B0VdIl/QnYvd1ynSzcJ1iGz65qlK9J61+J+wv/rHC8B14SrC6SJtAhmaeZfamBtm8zj3R+H54s+WvASe336C7ylwHbWAX/5nR9vh13KbgWj6p82cx2KrutCm33reTfmLR4Wb2s2icAv8XzxYDPFZ8A9AyTr8GSwCy4z8XDwD+ApwfYXpF+Wfiuk7SymZWy0CXz81Nty7Lm8yV9EjeDL6vhEVpz4Rdd0GckfRm3dI0H7sUHCcfgIfO5fg1b46PgWwDM7J9KBX0HSK3s8QX+Z2b/kYb5eDeVi6vOtdq6l2+GK7nt/2FgmNnW6ePB6eE0D3n5Cefq5C5gZg8ma2UOc7SUriR7RRrY5TK7md3Utq+6TpnJ02d0Oidy02gcaWYHaISAjlzrqpndJ2lcui5PkEcRZyleVVxHiuLp/QO4wnWnyp1s/wUmS7qE4X6YOUYAmZfy2gNP5XCEPIJ14OQMOnMZk4oXgCpk7U2MN7PiyPJX8qjDgWFm708n5ltx68H/AStJ+jdeR65rPcSabZc15Y7EhsBHk7XxJYZuMoO0Ov0Wv4EfhpuyWzybM60cVGJX/GZ4Hu7jdWOFkenLye+mFdVY5kFYlRktOWNLOsSS83Oa9iqznTslfQQYJ68Jux8NBMFAbWv8+fKcUi8An5RHkr7YQ6YvpP39dfCp3jSd9Gvcf6cb83X5bfbM5uuWr/mXpOUYisDdjh4pGqx6+owWrb7WcZSvm8yziutIi4mSLsYjRr+UBlVlopbPJD+NUTuStB5+brWiGQdaLrDQcB1ldfi2xuhUY8esvTkaczJznsDQvPCOuLm7pyNnP0g319b0zebAAmY2bxNt10HTgIO7KmQ6Dsojz3y+PkNTjHPiEUzXtQ1aRpL/HJ5a4D240vwx4LdWo6BwRptTpqPbp6Y7TVV32c7seDqGVibzi4BDrZnSO5fgg42iErGTZSStTfLz4/X/Xk3/YwEze2gwvR3W7gnAn83ssPRwOhVPvHtwD7lj8YjCr7b88NIA9RvAIma2d0bbncrXHGxmT2f2fVngOPxcfwrPmr9TmftanfuSKgZypPvx43hEX+lknnWmU+WRhKsB95vZ0+m8W6zsbEjFtt+Bp9y41swOT8fvgFyXmZptX8iQslosat5eiaD3tsao4lU5a286YY/Gp1MMH83uN8iHuKT9GHqQ/Y+hVBLXAZMtVVqf1pEnrWvl3rrazLJNvMlpdWmGjxRys+ZvgZdDKZ3pOKhO8s9bE6+H9nG8BFBWslBJ78GVFwEXmdklA+uot9eX7PGSPmRmp/VaNghUqPnYbVmPbQj3s/oInph04b52cuQ2T8YDhTbCS/f8MENuDtw5ex2GEjyvhvvt7JnrA9q2zRXw7PdZJcmUgj5SX2Yws2eVGQiiGhnYk/yUQA4zW0YNBnLI/Y6PrqssJWvhR/Ao1K7/WyOkBWpRZfZEngh2i4auz775/o1Vxes0XFmqlLW3aST9gKGw/DHR53bkkSx7MWQi3hqvUdbTiiHpl3iU1Z0MmaTNMlN4pDn8jWnLdGwNFBd/vZEeJuvjVtm34sfsWjxf0nVm9kTJ7ZWOLBxNOlnHyljMarZd2RovDy74CJ7Ucn68YPa5NsAAHg0Vewa3vPwMP1eOh1LFnpdlqDbnnWbWc6owDeS+hys9ZwE/wf383gZ8P0fxS9vpdLyz0hXVvS+pQiCHuuc2NDy45EgzO6dH23cBb8QtfKVcR+TBMjvg59vKuFX7zF5KXGHWpFX8vGjZNTP74tRSHbczDk/8uiM+uLvaehQm7wf9UlZh7Cpel+Mjo+ysvZI+nxzxjqazQ+PATZWjQb/+t9y5fT1LjvFphHh95oV6l9XIVaahqvC3Aaubl7K5zRqI2Hq9IelMhhL8TrQSNQ7Vp8jC0UDSpriz8PZ4zcAWc+PW9XUa6ENpa7ykb+OBQn/HFbazgAlWLwApt7+dorxamOVFJc6IF2B/c1p0N15bs5eD+414Gojrk/yX8NQEX7e2+ocjyL8ZV/aOwKMaW8wNHJhjtap7X5J0g5mtq1QHNy3rGq2tHrkN8evtZEtls7psp7TriKS9cWVnMXw6+VTgnLLnWvH/Fpb1HNzI0xB9BL9Ob8IHh8ua2fPd5PpFHWW1nbHqXH9wBZlWAscqOazGMv3632J4tt5XIbu8x/WSVjSzuyq2/bSqZzoOSmAVkl4W6Fdk4WjwT/wa+SDuw9HiWdyHZuCkh17ZaaY9gT/jSsh5ZvaSypVvqYzVjPKStBh+njyCJw8V7vf6fUkbmdk/u4jPYma/Sp/vlbSfmeXm/gJYIbU1L7BFYfmzuGU/h7r3pdKBHEnpGocXAu+0/x+U11DsinnkaFnXkWNwRfcjlnJBVjzXJGkDM7s2fVmfHg7ykv6BDy5+ik8lPyvpgaaUrsSm/drQmLR41WE0fTjGMvJsy7vhI2rwKY1fmdmRGbLvxJP0PUqFkYLcUfhFKmQ6Dpqj6I+ktsoEnUa50yKSZrKhMldNtVnZKp0ewu/BLRGbAJcD7waW6GU1qouGZ2CfCuuRgV3Sr4BJ7fcQuU/smma2WxfZe/D/3Br8nYxbQ5Tazp3mXM+6FNLuITsHfl8SFe5LGh7IIYYCOXIsdpVzYSX50q4j8lJzH8L3+yK4xeujZrZEybbXBH6J7y/hQQ0f63bM5LUct8KLg/8Wj8qcbBUz7Zfs79zmRcHn7/S7VYiwH1OKl6RrzGxDTZ1HJbuY7Gj6cIwm8vDyLwArMjwCJzv5W/LpaEUPXW1mt2bK3YcndJxMIey4m1k7yb0NjzhaLsnuUcNqFgwY9SmycDRJlofDmPo6GdgNXtIWZnaepI6KhmWmhJFHFG6OPxjfjpdq+kj/ejpVe13T4JjZN3rI3zPSlJike81sxOz3/ZjmTNsZjysgSzM88GfgJeTqIOkcPFdelVxYtVxH0vqL435eOwJz4LU1v1zyP8yT+pylPEoS8K7U5gdwxW0P4AIbYKkoSeeb2ebyVErG8Jkeq3JvGFNTjWa2YXovnUel4MOxmKQfFX6amx4J86YTTsZ9VzYDPoFbr3o6Srdp+39Lr9Zv82dq+0+Y2bkV+vxjPHT4KnwK5oe4U2UwYCRtgjvTv1BCbFVJz+A3ptnSZ9L3WUcWm6Y4ATgIP9c2whMQDzRPkJmdlz4+38kaX2I7LwFnAGdImhu3EAyMXopVBt3Ora5TSHWnOQucg+dLu5SShY8lbQMcDiyEn+PZBoAk/yb8/rY0w5W+HKWxTi4sqOc6gnluue/j08JvwrPo5zXclg9LKc+e9ciHZW4luhwv3TMTQw72P8F92waCmW2e3vvmNzmmLF5Fkol9YYafsN2cUFfFHfIPYXhNqmeBy62B8j2jiVKkTtF5U9LNZrZ2D7l2bX/KT+SXafoJ7ktxHiVq0I1Vq8n0gKQTcSfvf+MPpquAa15H18mU6DJlRrn1oe0xaY1XxcSvku7HFY+pfgKOMLPl+trRzn0ola6jTfY+PJVBpQLwySn/WKbOCzVxRKE+Ucd1pA9t9y0fVtrebCUHiJVJfolLMVzvuKrsdsaUxauFpH3xUeljFNIT4CkLOpIcB2+T9NumfTimEVr/+RFJm+HOxB3nrIv0SdufDVe43ltYZvQesc2bRpUdv/dS3ILqtPxr5KHj2+HWxzcwRu8ZJXhJniDyL5I+jZf4mnOQDU4H1viqZdiuZLhje5HSD7OKnC/pA2Z2QQXZx6oqXYlXzOynVQTrTomb2Q8kXcGQ68juua4jfWBxM3t/vzbWoNJ1OD69eheFxO1UOFfHpMUrjTTeVsW5ejR8OKYFJG2Oj0SXwEencwPfyJ0ClHSZteUT6rSsn6hz0eAWNq37YYxlJO2M+wmtDPwLL7J+dVVH5LGCpLXxSOB58ZQYcwPftVSCaEBt1rbGS5rF2rLrd1o2CDpZjXIsSZIWMbNHB9m3XiR/4TnwgeH/KOcvfBTuZH425Sz5rQHvfnji1bPa5Hu6b0i6hqEp8S1IU+KWSjd1keu7o3hZ1Md8WE0i6V5glX5cU2NV8boceI9ViNqpesKOdSTNahnRMp3k8Lppl+OOjS0/gLnxfDtd88UUtrEHnjenqOyG4jSNIulfwF/xqZDLzexvo9ujwSIv+/JlPE/PZOAwM3umu1Tf+1A5onI0pylVMfGrpEfxKLVTgDMss8xPh+3Mh5eoKt5bBm4xG2Fg2HNAOIKTdlE+x32j0pR4n1xHag3CVS9562hWlvgj8KF+OPKPKcVLQ+HLb8XzsPyB4SOFruHLaRuj5sMxmiQr4WO41etq3F+nZzSJPOz4AHya6WGGbhbPAD83s2MytnEacA8e7n0IHnp9t5ntX/6fBE0h6a14uaAN8QfbvWa2S6bsUsDyZnapvB7djGb27OB6W4/kdzIRnzbYHJjLzD7acB9KW+MlLYIntPwNhXQK+MDo2JyBUV1UsQxb8tN9N+6Y/QHgBlwJOyd3+kjSnsD+wOJ42aF18ei8rg7qGp51fyosMx3FaCHpOvy6PB3PhfYw8B3rEgnahzZrD8LTdirX/R2NAYaG0rwsBqwKXMZwvaN08vWx5q/Rimb8e3rNnF5laNyHY1rAzN4oaUl8+mgz4MeSnu41HWBmRwFHSdrXqhc5fqOZfUjSlmZ2oqTf4spfMI2SouKWxB1Jl8ZDt7NqikraC9gb9yFcDn8oHovnmZpWWdTMvpI+XyRpNB68VSIq3wd8FN/HxYHnM7gFb+BYtcSvmNmreO6qiyTNjCeo/DBwZLKg9EwEiitdawM3mNlG8mS9386Q6+bIbXgpn67UCCpYG3ioNc0qaVc8yu9BvMB3znTf/rgStB8+Jb4x7iyfRUWr1ccZGoQXr49n8OSqvdqcO1mRSw/ARtkPspV8fCKej7I2Y83iNRs+En28bflCwDM5U2kdfDjmwSNoBubDMS2QbhJvB96Ja+3/xq1eh5XYxkpMPRr/dYbcTWa2jqSrgH3wRKo39TJrFy7UoGHkeX6uSa+rej1M2mQn4YWPb7TMGnSjTYowexdDo/hho/qGfF8qW+MlbWtmZwy6j21tdkz42qKsJSBZ/HbEnfP/m2PFUIrMTufc28wz999pmYWq6yDpEjyooFhzcCcz6xpUkJT6d5vZvyW9A/gdsC/u5/cWG2DdwT65jlQahHeY5szOh9UPP8iqyHO9jbe2HJJpRuBxK1m/FsaexesovOxIu/PiBnjE3Cd7bcDMbk4f/4uPKF8v/B24Gfi2mX2irLA8WeK7cMXrAnx0eg3QU/ECjkt+GF/FRwxzAl/LkLtV0lfM7Hdl+xvUw4ZSjsxu5ctyvGRmLyvl55HX45vWR3jz4CPa4sOgNao3oIngmzrW+GslHQ+8wcw2lbQiniDz+EF1luFlyL6BW+tKIWkJ3MrVSsR5CvBBM7sncxP/kDQv7uB+iaSncMtRE4w3s6Kf168kHZAhN66gyO+AZ4xv5V+blNOwPHfWgUyd2qCXpa5otSqe71lWq8QvJX0VWNLM9k4K8wpmdn43IasRIW+FrAT4/13SzO4tu52KHI3nCmtnfrzyQPkkxWY2Zl540d6Rfruzh+yR6f08/OE/7DXa/62BfbcqXhX+93i9rV/jmeBz5Sfj0x63pe8LA5dkyM0AbF+xz0vhET+X4NOVo74fXy8v3F/nLuDvhfPnJ5myR+DTXPfgKQXOAr412v9pWn/hU2Zz4tOGJ+ADzHUzZf+IF/huXZ8z4iVVmur7rRVkrsOVpO/iJYLq9uGd+JTnTA3958twK9e49NoZrxbQS+4O3OeRdI28o/hbZtu34YaGdYA1W68Sfd+3xv/+PfD5Vl9xC9qkTNkZGZppWwJPVbNaiba3AO4FHkjfVxv08xsvOj/isay0zUF2eAA74O4qv6Xf10zv7+z0Gu3/1tD+mxN4P/CtdMN7sITsTel9Im6WFnBPpuyIJ26m/KZ4YMD5vI6U5VE+V25MN8ZbC8tyHwoz4GVYTsOdf/dq3WzjNbDjdXN6Lx6vSQ22f0sFmXe0nxe4j1OZbZyUs2xA/3mpdC96Ak8LcTZuiekl9xXgWjxr/q0FReSNwLWZbY9ohCjR/5VwZX3X1itTbkKHc+22DLm9cBeXv6fPf8anWe8FvpD7v3HrdLHtgQ4w8KCi0r91e421qcbHJa1jZjcVFya/ra7zrDaUDXgC8IKZvZZkxwGzDKKz0xKSJuD/8zrcCfQdlhFFUmBCMun/HD/5/4tbznK4VNLn8JFSsa5YTr6aFfDs1lfjSTyzHLyD+pjZQ63pwkRWSRUze03S2cDZVsH/4fWGpCPN7ABJ59G5SHaO4/pz8iLGlra5Lp4dfJrFOqd8+CBwcInNDPPlSvfzUhHqqpiN3KoHFXxLnoJjUeBiS09wfMCyb4++tvJvnSdpHyrkAEvbqeM68nLyt26da8sV+9CFA/Bgm7lwP+ulzOxf8mLhN+Pll3rxPzP7T9t9adBuDPepQ5Ld5PB/f5UNjjXF60DgVHlV+5YitRaurefWiroMD2Fu5eKYDbgYWL9/3Zwm2bTOQ9DM9kkfj02h93Ob2e2Z4juk908VN0kPvxlJ3wG2BD5rZn8s09+gNg9JWh8weV20/fGb5YjI74YHAZ8mReNJehVPlti1DtvrnJZz9vdqbOOzuPVlOUnXAuPxaZyBIU8+2nroza7htTnNMmsWtm82s+0v4dPZ7TVBXwaOy26sQjZySZ83syNGCi6wjKAC6xDMZWZ/zujyRIY7ph9Y3AT5vojb4e4Dt5rZ7pIWxlOS5HAQ7mu9hKSTcR/rj2bIvWzuBP+UpPvM7F8AZva8pJcz275T0keAccm3bD/cmDBIDgD+IGl7husd6+GpZ0ozphQvM7tJ0jr4A/yjafGdeDTL4yMKDmdWKyRAM7P/Jo17usbMnpCXCmpPYpr9QGwfGUp6R+bIsGq5oVfw+f+XUmQJYUFpjE/gwSyL4U7eFzNcce7EZ/Cb8Npm9gCApGWBn0r6jJn9cID9rU2yltxpDeS+KtIna/xTuNvECvhD+V7c/2VgmNlcvdcqzZoth3sz+26Xtg8DDpN0mJl9qUZ7W+GO4WWykbcGIBO6rjUAatxL23khWaZfkaeOeRx3LchhIrANnjNN+KAs51yYTdLq+KBs5vS5VVx81q6SQ+yLT9W+hEeUXoxHOg4MM/uLpJVxJ/qV0uIrgY9bhaTkMMbSSfSDNBrc11KCPElrAseY2Xqj27PBIulY3AlyI+AX+IjnJjPbI1O+48gwcxqkUiqKZEH5On6xjUuLXyEsKNMkkm7FK0r8q235eHxKZfXR6Vk+ks7B7w9dk38OqO0b8DQD/03f58T3W09rvKSJeDTgw+n7O4Af2zScwqNIOkc+hEc3vgE4y8w6FdDuJFs5c71qZCPXKGRRV39ygCHpJ7jF8MPA/+EzQJPMrGekf3qGbmop1Y+ktwCnmdlKPeQu7/a7mW2U0fYe1hapK+k7ZvbFXrLTEq9HxWtt3KHvn7imvQiwgzVQEX40kXS7ma1SeJ8T+KOZvT1TvnKdqpH8CaxHvhpJn8GT5u3dbkHBc85M0xaUsYikbqWzzMwO7SJ7x0g3326/TUvIc82tDtzEcH/E0r48FdqeZBVqHqb11sZD3rcA1sAz4G9uZg8NoKt9QdJcuOXkI8Cb8CjOHcxs8RLbqJq5vnY2co1OFvW+5wCTtDQlXEfSzMnn8Xvzm3G/sJ3MbFLZtssi6QLgZDM7OX0/Bpgt14AwgP4cZ2Z7l5UbU1ON/cDMbpZnN26VVrjXKtZHG2O0SnA8L+kNwJO4c2cu9wMzkedE2U5Vf4JdabOgmNn98gLOF+MZvoP+8lyHZXPgtTYXwJMOj0Q3P41cH47RJie/3KB4TtIabdb4rNI56b62H35dvIg/nKf1afnHcQX3q/hAzCRtXXIb+1Mtc33lbOQa3SzqtXOAtajhOvKH5Pd5CT7FuHWmf1o/2BY4V9JreIT+06OldCV+VkXodad4pRPmk3goM8AVkn72OlC+zpdHJX4XTwxpeIRiLs8Dk1I0Ttk6VVX9CWZqn7ZKbT6RjmPQZ8xsSimVZJHYH080/Du6l1kBWLXg6FykjA/HqGJmV45i8wcAp0kaZo3vJqCpIyFnx6MZj5fUiKWuBl/Cp7p+Apwi6fcVtvGimb0oCUmzmNk98kjorpjZiRXaavFPXHH7IEPO1uBZ1D9TY7s5jJM0o5m9gpfgKlpbsp/nFYMK2oMJ5gH+Cnw6nWulaxbmoqFoToA98dQd1wLfkDR/7hRrv6k6UzamFC9JXUcmmTeZn+KWm/9v77zDJKuq9f1+MxKGKChgIEclMwQRUCRdRRQVSSMYEAy/q4KSvBgBUURQQERACRcQRocriCAgURCQMEMaEBEkKIoKSM7DfL8/9q7p6pruqlPVVXWqutb7PP1Un1O9z17TU121zgrfqijRfjSf22ts1vU2VSmiX0q6kNRk0Ey7eUU/qxValaIYDxGUviO/ye1LGmZ+OjDZBUZy2J7Y6Gd6HSUZhuOAt5LmwE4EnnNrHXpN0WI0fiydkKVi+xjSXMYVSQ7Yr4A3SfoyqcarSBRlTMr1kmYyd2fiUyTH6jDbj49g9xwV9RJu2KcCV0t6jBQN/T2ApJVpTj7kgzTfVFDbTNDN8pzqbs7K43b5q5luzqZpk98x/Jr9VOMl6VHgb6QX343UtB4XuVuVdLvtdRqdGy+0oxgzd1ddXqT4scC1lqdgPYGSFMFIqS+RHMeIerUZSUeS6m5+QirObrrouJ9R0rvblST+WpGqWXWMnXNF954rGg8UisZLWgF4pNJlpaSztJTtBztjbWfITThTSLVeKze5dnNSFOYS24VuzCR9jxTxOTuf2pUUNfwnsJnt99dZuwqplq62aaij46XyzUFFA+y5fG5VYKFKmrrANcbSVLAgKdL4aj6eCMznBqPFJC1HSg0+lY+3IDmAD5Ea3HryZrodfsdc1+wzx2siaQTJFGBt4DfAVNt3NXGNW0gvuL/k4xWB/+tkQWSZtKsYM6cYd2gySla9fgdgM9LdybW2z2vlOkFnybUTL5FqVarfHMaizdQ3SJpue4NKE0o+d6u70JEp6WRSNL6SBvso8KrthtH47DBuUvnwkjQvSQV9w07ZO1ZylGYp29fVnN8M+Kft+xqsH7P8R70CeTUY7C7pWpKm1dGkpoY9gAm26zWo9ASSfknrTQUtdd9KupFUD/YPSesCl5Mc17VJwqhFXuefIxXXP5mPFwOm2B5plmJbaIffUUtfpRqzh30JcImk+Ui/iN9JOsR20QGfBwBXSbqf9GGyHON7WHa7ijGfBWZKuozh3V5F/lB/TBqHMTWf+oykrW030oUKuoztCWXbUDLPZ6flthwNeYQsBtsFNqyJvF8p6faCa19THTFwGlI+b3vNazvHkOq8anmKIWdmVGy/KukeScu6dfmPiaqahpIzBNXSNfWYZPsKSXJSsT9YSdaj5x0vxlY60qoW5iTb/8jf7w6cavv7SoPhbyu496dsH1+19xOSPsXIQ6zbQpv8jmH0leMFkP/h25H+8csDPySNTShE/kNZheF1FK106vULbSnGJLV6n9uiDVuSomuVEROnk4Rvg6DX+Cjpg/fzpELpZUip+W7wqqSVaqLxhcY0AY9K2t72r/PaDwBzNab0GEvZnll70vbMXJJQhMVIauatyn/sBZyaozYCngb2yum0wxusfSk7DfdK+jxJaHihgvuWRo7gfGIMpSOtdt9Wp+i2JDvdufGq6N4Ts6Nb+SyZSKrF7Chj9Ttq6SvHS9IZJOXYi4BDbN/ZwjV2ItUA3CHpa8BkSYcVzY33IW0pxrR9eq4bWdb2PU3acB+wLENFr8vkc0HQU3hofukLwCFd3n4s0fjPAmcp6RqJVJPysY5Y2T5eW+e5SQWvMSb5D9s3A2tJWjQfV78nTmuwfB9SPdjeJJmVLYGPj8WebpAjhbMlLdpi6cgXabL7NnOlpGmkKPJiwJUAkt5I8WapS4BfSKrIOHwmn+sY7fA75rpmn9V4zWborqal+hMNCYhuRvpjOQr4hu23td3gHqFNxZjvJ/2u5rW9Qs7RH1rvzlJDre6LkrR2bsrHbyOp5r+rwL5tK+wPgtHQyN1tc6jUe3XBjvkYQzQ+R27oh6YISVOBK23/tOb8XiT9viIf5kh6A7AR6f/v5kojUcG185EimsszfEj2uJ6MoTShYT2SFldTpSN5/Tw0qYWpFNbahfRZNM1DUxbWA5a0/dsC15hAcra2yqcuA06uFPp3gnb4HXNds58cr3ZQKZSVdDgw0/bZ3Sqe7Wdy7cKWwO8qvys1UCNX6jIalaLdIGMt7A+CRuSOKxiaR1kZXL076c214yNJqqLxz1Si8SRJg1FvjiTtbvtnkvYd6XnbP+iQuWNGSUj5PFK0o3r48Lykv/dHClxjL1JN1ZWkD8LNSTeEpxa04RJS5H8GVWldV+nZ1Vm7KilKOUeENK+tq5rfC0gaMTLnOvpmkra0faVSo9RIa1stRRk4+irV2Cb+nsOU2wBH5DueQS8oLsIrtp+qycXPrreg1rFSEk9t5TXXcmF/EBShkmKUtE3NTdiXlTqDuzEL7uu2z8nR+K1IEeYTSBHi0VgwP3ZiYHVHsf0vYBMlWYHKDdxvbF/ZxGUOANZz1tuS9DrgeqCQ4wUsbfs9TexXzTnAiSR9wo5FXDpBi6Ujm5Mc3JGaHkyDGmBJzzByVLlh5EjSNNs7jxaZ7lZEul0MouO1M2nUwFG2n8z55QNKtqkfuEvSR0jFjauQ6hquL7JQ0qdJE+RfJDlrFRG8ono3YynsD4JmkKRNnSUOJG1C927MKh/e2wE/dRrNcli9BbZPyun4p92ns0ttXwVcBSBpJUlfB3a1vUaB5Y+TFOMrPJPPFeV6SWuNVORfgFm2T2hhXelUl44AhUpHbH8zp/outt2o/m2k9WO5OdgnP75vDNfoGQYx1bgS8LDtlyS9i6TLcYazLkgwMrld+KvAf+VTvwW+VaQGRdK9wNs9wvifJvZfAtK4oFavEQSNkDQZOI1UlwjwJPDJbjTfKE2U+DspGj+ZVOB/kwuIO0u6yfZGHTaxIyjNjt2FNCx7LVI34bn1nKGq1Oq6ec35pJu5DwB32P5Ewb3/SJK6eYCkZ1WJvjSMoEg6mDT+7DyGa2GVMr6mGVopHalaO932Bp22cYR9x0297yA6XreR6giWJ3UpnA+sYfu9JZrV80jayfY5jc6NsvYSUs1GXWXjEdaJJFD4eVLUQSRtnePGe/Fr0H3yG/veto8epcut0/svQIrGz7R9b47Gr2X70gJrjyaJr/6C4en4nu3WzpHwKcCbSR2E04Dzba9QYO036z1vu1BHalVtX+36hmOHJD0w8tLOKte3A0k32N64ur5ZVaLBDdZ+lyRVUvta67jDOV7qfQfR8aqoEh9IGt58XBTXN0Z1FJ4LrF2PFEW4kSZUkvNd7bbAp20/kM+tSKp7uaRfUytB71Jm5Ggs0XhJV41w2r1c6C3pZdLM1v1sT8/n7u+G4yJpEdtPa/jw5Tk0ciJyym0n260M9i4dSaeQVOv/h9TVuTcwj+3PFlhbmsM51m7MXmEQHa8bSYrJXwXeb/uBoiHWQUTStsB7SbVx1W8yiwCrF/mQUhI3vBaYSVVBfr0OmrzuVlJb+WM155cgSWOEsxy0lTIjR4MWjc+F8DuRol5vIEW8PmF7mSaucRUjF1vXdTglXWj7fdmJqAxdrlre2IkoK+XWDsZSOlImrXRj9iKD6HitThIb/IPtqUrDZXe2fUTJpvUkktYh1VEcyvBRGM8AV9l+osA1Wooo1nOIw1kOOkGZkaNWovE5KvyU7VNqzu8JLGz7mM5a3R4kLU2q85pC6tQ8z/ZXCqxbv+pwflL0ZpbtAzti6PC9S0u5jZVWSkck7U7yGc6sOV+ZKXr2yCvnus4OwBHAkiSHt0hX4xLAErb/WHN+DeDf/Vb7O3COVy2SliF10BxZti29jKR5XEAkb5S13wEeBC6giSLUeqnMomnOIOgXWonG5yLpjWv/NpXmNE4vUrPTayjpY+3aah1nkXRxru16slIrlCUtPkh6nzreVXMv61yjn2u8mi4dya/PrVwjzqs0Xuka2+uPvHKu69xHen3f3YS9Pwd+bPuamvPvAP6f7Y8UvVYvMIhyEhXvuRLifhNjmLk0QGyUu3gqYoGVu5QibzJT8mP1QNwichLrSHp6hPMi3d0GQduRtB2wBlWvsS41c+xBisZ/OztdKzAk5DoarxnphshpSHbhAXi9hO0/kyLsDamp0ZoArM9QR2o9pgEfAp7KUgrnkLop1yUNXN6rgJ0NmwB6jarSkTdL+mHVU4vQeCj4PLVOF4Dt55SU7Ivyr2acrszKtU5X3vv3kvpO0mNgHC9JCwM7kFqWVyXpQq1ge+lSDesfTiENDR6m8FyEVt+gbE9sZV0QtIqkE0nz97YATgZ2JI266jg5jVJdJDyLBiLFwARJSzmJkc5BSRV+EJjBUI3WLJIsxJ4F1k2y/Y/8/e7Aqba/n4vmbyuyca6T2pckQvppJX3D1Wxf2OS/oZv8A5gObM/QtABIpSNfarB2kqQFncfOVcifrc0Mqp4u6RfArxieAamn1VhPA6wZp68nGBjHi6S3chPwNeBa25b0oZJt6ieesn1xq4slrQmszvAowhntMCwI2sgmTrNc77B9iKTvAy2/7pulhWj8kcBvJO0HVBoA1s/nj+qUnb3CGKJO1dHALcnReNuzmwgUnkZyXjbJx38nRc561vGyfTtwu6SzWygdOQX4P0mf9dCkh+WB4/NzRVkEeJ6hwn5orHx/n6T32r6o+mSO4N3fxN49wSA5XgcBu5LCyFOzxx0U5ypJR5L+OKrvUhp2e2XNnXeRHK+LSBIR1wLheAW9xgv58XklYc/HSUN9O8ZYovG2z5D0KCk1tybpA+wu4BtjuVHqJpLOJX1wX2y7UYSvsmZD4G/OA7ElfYxUWP8QcHCBAvcrJU0DHgEWI43CQUk7rWF9V2Yl27tImgJg+/k+Su82XTpi+yhJzwLXKA9jJ41z+66bUPC3vUcL9n6RdIOxM8Pner6dPlSzH7ji+qwDtSvpjnIVkkDnebmuIBiFsXR7Kc3XWge41fY6OQ3yM9vbtNvOIBgLSuNqjiPNSjye5Mj81PY36i4c254vMHc0vit6Vr2ApK1J9W0bkyJGp7nB/ECl+Zlb2/6PpHcCPwe+QKrReqvtHRusF6mL8o3ANNt/z+fXA5a0/dsCdl9Pep1cl7tRVwKmNirs7wUk/YkRSkecZ14WWL9w/vlnGv3sCGuXJv2NbZpP/R7Yx/bDDdbNR7o5qTSb3AWcbfvFZm0om4FzvKrJ6a8pwC62Vy7bnvFKpcsod2BtQaonuNv2W0o2LQhGJb/Rz+8Oq2RL+iLpZnBBYCpJnuCyQXG8KihNC5hC6ur8G2n49M9GSolJut15lJKk44FHbR+cj2+zvW4X7P2vbOvqwKUkR2IPp9mTPY2kG23XG77eyb0vA85mqHFkd2C3QboRH2jHK2iMhmaijYjtHxS4xo+Br5A+XPYjhadvazHkHARtpw2pq3bYMLDReCUx1d2Bj5IKwM8CNiONTHrXCD9/J7Cu7Vk5evPpStdbIwmODti9MSlVd4PHMI+2m2QNsom0UDrShr3ncoy75Sz3CuF4BXVRm2aiVV1veWAR23eMxa4gaCdjTV11wJ6BicZLOg9YjRQB+V/bj1Q9N6I6vKSvkmQRHgOWBSbnFO3KwOm2N61d0wG7r7C9VaNzvchYSkfasPcVpMaEqfnUFFKksOd/b+0iHK+gY0iqK3DajburIChCL6SuxkJOi36YNG5oTtNUl/THxoSkLVpJz0namFSjdWlF4kBJfHWhTr63SJqfJDlyFalpqFJQvwhphuy4L6EYS5e6knjtcaTCeAPXk4bT/7UDpvYkg9TVCICkfWwf2+hc0Ba+nx/nJ3Wg3E56k1qbpCXz9pLsCoJaJkp6je1ZpILpT1c91w/vk+cDT5GKpXt63l4FpdExc31foYGuE7ZvGOFcU2lZSZsCB9OcMPRnSF12byL9viuO19PAj5rZv9u0qXRkTF3qWYpi+yI/O8Leq5CEbmudvr6qh+yHN5R283Gg1sn6xAjngjFiewuY0y4+2fbMfLwm6c0uCHqFqcDVkh4jSUr8HiCnrjpaXN8mlrb9nrKNaJL358clSVpYV+bjLUhRkLqOV5toWhg636QfK+kLto/rpHEdoJ4QaVF2ZKhLfY9Kl3qjRZIOtP09Sccx8mDzvUdYVstppNrHo0mvkz1IEwv6ioFxvLLWykeAFST9uuqphYGeH2ra56xWcboAbN8p6a1lGhQE1dj+dq49qaSuKh8ME0i1Xh1njNH46yWtVf131utUmmtyl9vqldqurKX1v10yo2VhaKch5pswd3q3Z/UJm63JHYUXnIRmZ0lahCROvkyBdZUxQdPHsPck21dIUo6cHZy75Tsm99IJBsbxIt1BPQK8nqEUGCRpgyj0HgVJu9v+2Wgh6iKhaeAOSSczdFe0G/E7D3qMdqSuxkjT0fiskWfSe/keku4npRorKbN+GJK9dHVBPfAvUsF8NxiLMPSZwEqkEUOVaJkZ/8LQ0yW9liT3MYPUpf6HRotsX5C/fd72OdXPSdqp4N4vKY11ulfS50nTAhZqsKbnGMji+lzct4rtyyVNIg2abVoIbhCQ9BnbJ43W3VjkDioXo/4/4J351DXACf0ofBcE7aYqGr8ZOcWZWRiYXa/bK7+XjUqOCvQ0kn5Eks+odLntAtxnu+ORxjEKQ99NitQNzIdoFp5d2vbf8vHyNNmlLukW25MbnRtl7YakyNlrgW+RGhqOHOmmqZcZOMdL0qdIhbOL214pF+udOEitrEEQ9A7ZeVqBVDT8P1VPPQPckQv+G11jJeBh2y9JehepgeUM20+23eAOkIvr35EPr7HdaEZl6Ug6h9SN90jDHx5HSJppe60W1m1LkgDZmSQSXGERkgNbV/Ff0kTgCNv7N7t3rzGIjtdtwEbAjbbXy+daeiENEkrDez/F3PUMn6yzZprtnavSIcPokzRIEHSNVqPx+X1tA9Lf50WkLsc1bL+3g+aOCyRtB6zB8C65hjIcOVq2LmncU3WasqWOvW7QjtIRSacDP7J9c5N7r0P6fR3K8JqsZ4CrbD9R4Bo32N64mX17kUGq8arwku2XlWeZSnoNIzgFwVycT0qDXE7B7h9gn/zYd0NMg6DbVEfjSbVDSwMnkuQtGjHbScV9B+C4XPh9a+esHTuSnqHOe6/tRbpgw4kkTa4tgJNJHXs3FVx+cIfM6iQL5sexdDe+DdhN0kPAcxSsJ7R9O3C7pLM9whiogtyam+POyXtXrt2NDti2MYiO19WSvgJMkrQN8N/ABQ3WBLCA7S83s6ASgu+HOpMg6AE+R47GA9i+V9KSBde+kmvFPsaQTMM87TexfdheGEDSt0iNT2eSPsR3I3WXdoNNbK8t6Q7bh0j6PlCoy9H21R22re3YPik/jqW78d1jNGN5Sa1qcc0PPA5U1+CZ7kiPtI1BdLz+B9gTmEkSwrvI9k/LNakvuFDSe21f1OzCfBd+BEmvRwzdIXX8jjYI+oixROP3AD4LfNv2A5JWYGgIca+zvfPUgMwJkm6nOxIBL+TH5yW9ifShXtfpqxOp65v3tdzwtCdzp1hHLR2p4jDbH6253pmkOZtFGIsW18m2r6vZu+PjodpN3wmPtYGDbf/U9k5O89dOlXRW2Ub1KpKekfQ0KW14oaQXJD1ddb4I3yO9uS5qexHbC/fDm1MQdJnaaPw5FIzG2/4j8GXglnz8gO0jOmZpe3lO0m6SJkqaIGk3qtJIHebCLI1wJOl39yBwdr0FlfevEb766X3tTOANpOjV1aS0dtHO/jWqD3LR+/pN7D3J9hWkGvOHnEZzbVdw7UiCtf0mYjuQEa9lJB1k+3BJ8wLTSDoswQhU0gFj5F+27278Y0Ew0LQcjZf0fuAoYF6SSPS6wKG9XOhdxUdIWmXHkiJJ1+VzHcf2t/K3v5R0ITC/7X6YVDBWVra9k6QP2D5d0tkMlzKZC0kHAZUbg8pNt4CXgZ80sXfTWlyS3k6abrBETWPAIsDEJvbuCQaxq1HAWaQ3ty2Ai20fXa5VvYvGMOhaQ/PXNifdXf2K4d0/fZWXD4JOIulQ29+oOp5IkoTYrcDaGaS6l99VdWvfaXvNjhk8Dsi/t1OBs4t01Y0XJN1keyNJ15DqnP8J3FSkzkrS4bYPGsPetVpciwLfq6fFJWlz0nzIz5IaTio8A1xg+95W7SmDgXG8ahyIeYCTSHdWp0AxpeJBpEpgcMRB17ZHHXQt6bQ6l3bBeoIgGAjy38ufa6PxORXTaO0NtjeWdGuV43VHP0i2SFoVOAFYyvaaktYmlSYc1oW9VybVGO1CGmVzGsNHRo1LJO0F/JL0Pn4aKeL09UrxfYH1b2ZosDgAtq/pgKm1+y43Hpq1BsnxGkmhuIJdQKl4kFEadP1N1wy6znVyQRCMkbFE4yWdAlxBSld+GNgbmMf2ZztkbtuQdDVwAHBSWdG6nPp6H8kBfJXkjBxru/AcX0mbAVNsf64zVvYGkr4L7Ar8kapRSY3S2pKOsf1FSRcwsq5jw7R4dtL3Z249yb76/B6YGi/bW5RtQ5/T9KBrpRlo99XeRUn6DLCC7f8ZeWUQDA410fhjGYrGXy1pcsFo/BeAr5JS+VOB35LSOP3AArZvqnRzZhqq9beLHGHbg6Sq/kuS87sZcCVJ8LPe2vVI9Wg7AQ/QJ7IGkl5H0iHblOQE/R74lu3HCyz/EOnz4KWGPzmcSpftUU2uq+YcUqrxZIrrSfYcAxPxqiDpO6R88pP5eDFgP9tfK9WwHkfSVFKnUfWg64VsT6mzZgawQW3YPt9d3hH1J0EQ0XhJFwOfB86xPVnSjsCetrftwt4zgCdJJSe/rHYmJJ1re4cR1qwKTMlfj5HG3+xvu+7czF5C0mWkmbnV7+fvsr11gbUXAzvZfrbFvRcEXrA9Ox9PBOaz/XyBtTNsN9NB2ZMMouM1pwai6lyhAZ2DjFoYdF0vXSDpLttrjPRcEATFGC1tU6EfuholrUjqitsEeIIUOdqtG7U8kla0fX+Ta2aTIkR72r4vn7u/SGF6rzDSe7MajM6TdBzptfZmYB1Saru6WWrvgnvfAGxdcdwkLUSqq9ukzprF87d7A/8GzqvZu3BKuBcYmFRjFRMlzVe5s1GahzZfyTb1PNnBOjp/FeUFSavUdpwoDSZ/YZQ1QTCQtBiNr6RtdiB1DlciGFOAf3XI1LaSHZ+tcyRkAvA8qYao446X7fvV/KzGHUj2XSXpEuDnpIajfuJSSbuSGjggjUr6bYM10/PjDODXY9h7/upome1nJS3QYM0MktNX+T0fUPWcgb5xemEwI15fJo3UqHTc7QH82vb3yrOq98nqwAczdyfLqC94pWn0xwGHkf5wIHVGHgR80S2o4AfBeGUs0XhJ021v0OhcLyFpEdKYpDeTZsFeno/3I5UifKALNow4q9H2ngXWLgh8gOTkbgmcAZxn+9LOWTw2NKS6L9Lcxtn5qQnAs25SADbfHCxj+44m1lwHfKFSuyhpfdLQ7VE75McbA+d4wRyHoDJ49jLbjTz9gUfSn4AvkRyoOUWNjYoxc/fjAUAlrH0ncFR1oX4QBEn+AdiwJho/vUhKXtLdwHaVtJnSyKCLbNdtgCkTSeeTUot/IL0fV0aK7WP7ti7ZcIeHZjWundNeF9t+R5PXWYzktO1qu8hQ875F0u+A7Uk34DNIqb/rbO9bb13V+g1JUcJ/kP6/3wDsYntG3YVp7U7AJbafkfQ1YDKpKaCnB8LXMoipRmxfTMFBqMEcnsq/t6awfSfw8Q7YEwTjjbOAK6r07/YATi+49kvA7yTdT/owW46kft/LrFipKZJ0MmlQ9rL16kY7QGWvZmY1bgi8vvr90PYTkv4OHNgxS9uIpHeOdL6gFteitp/OWmBn2P5mvmkohO2bJb0FWC2fusf2KwWXf932OVm6Y2vSqKcTgbcV3b8XGBjHS9K1tjfT3ANO+2awaclcleUhzmV4UWMIzwZBG7B9RP4Aq0RMvlU0Gm/7klw7+ZZ86k8ttPt3mzkftrZflfRwl50ugAs0fFajgUZjmo4gOcW13EUqYemHLtTqGqn5gY1I0asitr9G0huBnUkSJk0haR6GN2r9TtJJBZ2vSrZlO+Antn8jqeNCu+1mIFONQfOM0vI+7lvdg6BfyGn91RleJH5GeRbVR9KrDA3DFjCJVFjflZvhLGuzse3r8/F8FJjVKOlm2xuO8lxfTAuoRdIywDG2P1zgZ3cCvg5ca/u/c1fqkUXW5vUnk6bHVKK5HwVetb1XgbUXkmY7bkNKM75Aqslbp8jevcJAOV5ZL+Qu229p+MNBR5G0oe2by7YjCMqmHdF4Sd8kzbJbHbgI2Jb0wRiTJeowUkNDgTX32V652ed6GSX12rtsr96FvW6vdZRGOjfK2gWA9wAzbd+bI29r9XJDw0gMTKoR5oSz75G0rO2/lm1Pv9FC23Xt+tUZEh58ktThGAQDje3N8uPCY7jMjiRtpVtt7yFpKYakJYLRuULSh4FzXTwKcbmkbwNfq6zJjsshJLX7nqdKkwtSR+O6pFRrkbXzA3sy92dB0dm7r0payfZf8vVWpKAKvZPI6rmSlpS0bD79p4L79gwD5XhlFgPuknQTQ2HuvhAaLJPR2q4LrFueIWfrFVLR7wa2H+yUrUHQb7QhGv+C7dmSZmWZhn8Dy7TPwnHLZ4B9gVmSXqRYlHE/0nvgfZJuy+fWBW4GGqbLeoTpVd/PAqbavq7g2jNJzs67gUNJqvd3N7H3AaSa4epGkJFq5uZC0vbA94E3kV7jy2Zb+kqMe6BSjQCSNh/pvO2ru21LP9FK27WkPwCLkFqHf55Dww/YXqFbdgdBv5DlFb7QSjRe0o+Br5CEPfcDngVus13oAy1onhypqXzg3+UmFfDLRtISALYfbXLdrbbXq/osmAf4ve2Nm7jGfAzvaizUCCLpdlIDwOXZhi2A3YvorvUSAxPxkrQysFStg5XbUh8px6q+oqI0X7jtmqSc/WZgKWAJ4F7qjDcJggGnpWh8TnMd7qR4f6KSmvoizYhaDjJZg2sVhqfNRpVVkLQc8GR2tO7PH/77SHqIJAT6cqdtbpX8WvkmaTbmhHxqFnBcE2Ujle7DJ3NDxz9JGmxFbahocd1R0eKSdFjBDvlXbD8uaYKkCbavknRM0b17hQllG9BFjgGeHuH8U/m5oD4X1rRdPwicXW+B7Q8Ca5HalA+W9ACwmKSNOmppEPQnXwfeR0rffL/qqy65zuiiquMH+8npkrSxpJslPSvpZUmvShrpvboTe+9Fmjv7W1KN1m9JEzrqMY2k+o6kdYFzgL+Saux+3CFT28WXgE1JQr2L216MpIG1qaQvFbzGT7Kz+jXS6KA/kiQ2ivL1LIC6GUk65RTghIJrn8zZlmuAsyQdS9VNSr8wMKnGBi3AdYeDBsMp2nY9wrqlSNovu5KEEqMGJRh4qqLx19Wc3wx4pFKE3OAap5OiLX3XKSxpOuk94RxSw83HgFVtH9SFvWcCGwI32F5XSdjzO7Z3qLNmjmSEpKOA2bYPzPIUt/WynISkW4FtbD9Wc34J0qDquh2e+d+4o+1p9X6ukQ05TXg4qTvx7KLdpUpjml4gBY12AxYFznKDCSq9xiBFvF5b57lJ3TKi35C0iJIwY+V4J2AX4APZkSqM7X/ZPs72psBmbTY1CPqVYxh7NP5twA2S/iLpDkkz1YSaeNnYvg+YaPtV26eRJAO6wYsV0VZJ89n+E0O1R6NRPRB7S+AKANuzR/7xnmKeWqcL5tR5zdNocf43jlWd/++STiJ9jlyUb+Qb+iK5AeVC27Ntz7J9uu0f9pvTBQNU4wVMl/Qp28NUiXOoueGMqAHmKOB6Un0WwOGkcUuTgE2Az462UFKjCfbRSRoEKdo11+xS2zNzV3AR3t1ek7rK85LmBW6T9D1SzW23ggIP5xKKXwGXSXoCeKjBmqskTSPVNi1GlpDImlI9W9+VqWdfUdsvl7Q/8AuG1yL+p+D6nUmO9VG2n8y/twMarKnIQc2WtGiz2ZZeY5BSjUsB55FeXBVHawNgXuBDtv9Zlm29TA5NT67Sq5kTElYWfqyz9lHgb8BU4EaG3ylGJ2kQAJLutb3KKM/VFeSUtCSpm3FlYCapyL4r9VHtIher/4v0XvwlUvroxzkK1k07Ns97X9KoQF7SrqThztNs/yOfWxdYspfFPDV8WsCwp0jlIw2jXrlWtxbbXrGgDSsBD9t+SdK7gLVJMx+fLLD2fGA94DKGO317F9m7VxgYx6tC7kBZMx/eZbsvBO/Korb+TdKaToOvkXSn7TXrrJ1IGu0whfTH9RuSXsxdHTY7CPoGSVOBK0eJxm9je5c6ay8h3UheQyrMX9j2JzpoblvJ7xFn2N6tbFuKoqEJA2LuSQMvAfcBX7V9RQnm9TxK2mcbAMuTmkLOB9aw/d4Caz8+0nnbRYfJ9wQD53gFzZF1U95dGxGU9GaSjlehQtKcx59C6oo8xPaP2m5sEPQhY4nGq2bUiqRbbE/upL3tRtK1wJbdlGGocZ4qmFR+M6/tlspwsiO5Jqnge9Sb0n5HY5gLWnmNSjqQJPx7XNHi+vHCINV4Ba1xJHCBpP2AW/O5yaTaryMbLc4O13Ykp2t54IekD5kgCEhNJ8AmNdH43xSNxufW/ooDMbH6uIm6mzK5H7gu14RWp49+0KkNXTOeKUsUfI6kZN/y+5PtV4HblUbyjEs0ylxQoOhA9lckTSF1r74/n2uY4sx7P8AIWpBF05y9QkS8goZIeg+pjmQN0ov+LuC7ti9usO4M0gfJRSTl+js7bWsQDBKSHgRmU1M/mSlcd1Mm+YN8Lmwf0oW9Xwt8keQEnA0c3Y9dct0kS3BU5oKukyO2P7O9TcH1q5Oasv5ge6qkFYCdbTfUApP0uqrD+YGdgMVtf6Ppf0iJDKTjJekNwEYkJ+LmKKzvDJJmM3QHW1sLYdefhxYEQdARJL2eNFppF+BUknJ7X3fKdQtJN9neSNIM0uzeZ4C73eKcUUnLALvabphBGWX9DNvrt7K2LAYu1ZgLVr9BagEWcJykQ22fWq5l4w/bg6QTFwRBC2TxzgNJEfXqmqEtO7jtQ8CjwGnA88Ce0lDQsJNpznHA9Bwp/CmpJvFZ4A/NXCD/n+9EKkF5EwXTu5Kq6xcnkGoh+86PGbiIl6R7gE0q4eQcurzediPRvCAIgqDNSLqUpAm1PykF9XHgUdtf7uCeB1Nnbmw30pzjgawzV2guqKSFgR2AjwCrAucCu9heuon9rqo6nEUaXXeU7XuaMLt0BtHxuh54V6WDJgv3/c72JuVa1ttImpgLR4Mg6ACSNgaOA95K6micCDw33lPylVRRzSieUUe8BeWTu9qXoyra5DqDxfOaF4CbSDMer7VtSff3Qx1iu+m7EF2rSNo3f3sfcGMWYjPwAaBvRmuUyANZM+gXJM2hwfLYg6Dz/IgRZhbWWyBp8XrP90lX4yv58RFJ2wH/AOr+u4LykHQEqTbuj0DlZtwkLbl6HER6ff8YmCrpFy3uvx1zp6UPbeVaZTEwEa/ROmcqRGi5PpIWIAk07kqSk7iQ1Kl4bamGBcE4QdJ02xvURH7q6htVtdf3c1fj+4DfA8uQIn6LkLT+Go0cC0ogl+usbfulFtevSPocmQKsAnwTOM/2nwusPRFYgFTUfzKwI3CT7T1bsaUsBsbxCtpH1gk6FtjN9sSy7QmC8YCka4CtSR8o/yTNLPxEtUBq0D4kTQB2tD2tbFv6CUkXAzvZfrYN11qT5IDtUm80VtXP32F77arHhUhC3u8Yqy3dZOAcr5I6aMYFeZbZLqQBp9OBX9j+ZblWBcH4YKwzC/MN0SoMf19rlP4pnfye/CmSwHJ1zdAnu7D3dNsbdHqf8UAWhTXwZpKO1xWkEUlAd+YlSrrR9tsk3UAq1H+cNPqvodPWSwxMjVcVZ5HqlN5HVQdNqRb1AVmo8VZgGnCA7ZEGrQZB0CK2H8rfvgg0VfqQZXL2AZYGbgM2JrX498MN5fmkVOPlDNUMdYvLJe1P+kyoVs3vh9q4bjM9P84AykoDX5ilLI4EbiE5gj+tu6IHGcSIV3TQtICkRWw/XbYdQTBekbQpcDBzd4s1rNPKauIbAjfYXlfSW4Dv2N6hQ+a2DUm32V63pL0fGOF0X9TGDTp5HN38/Sh8O4gRr+igaQJJB9r+HnBYtcBghW6El4NgQDiFlGKcQfORnxdtvygJSfPZ/pOkftEmvFDSe21f1O2Nba/Q7T37HUmrAIcz95DsQs6qpH1sH9voXM3zGwJ/q0yZkfQx4MPAQ5IO7rcI5SA6XodJWpQ0LqLSQfOlck3qae7OjzNKtSIIxj9PNZp/WoeHcwrmV8Blkp4gqbP3LJKeYagj8yuSXiLdGHdtpFju1t4XWNb2p7NTsZrtCzu9dx9zGqkT8WhSd+EeJBX5onyc1JxVzSdGOFfNSaTGEyS9E/gu8AVgXeAnpO7GvmHgUo1BEAS9RNUYlJ1JoqnnMrxo+ZYmr7c5qTD/kopQdL8hSd3QCsxaUjOAj9leMzti15eV+uwHqsp1Ztpeq/pcg3VTSKr1m5Fq+iosDMy2vVWdtbdXunslHU+abHBwPi4tVd0qAxPxqqTMqjozqjHwH9KE9b9037reRdIxtr8o6QJG/72dZPuG7lsXBOOC79ccV3fZmYIF8pImAksBlbqlNwB/HbN1HSbPyv1G1fEE4Exgty5sv5LtXbJTgO3nNVJNRVDNS/n/6F5Jnwf+DixUYN31JImU1zP8Nf8MjUXMJ0p6je1ZwFbAp6ue6zs/pu8MHgOVlNn0UZ5/HelOMzRzhnNmfjxqlOdfD5xKyvcHQdAktrcY6zUkfYGU/vkXMLtyaWDtsV67Cywj6SDbh+eC6WmkDupu8LKkSeSbSkkrURVtDEZkH5KI6d7At0g3Bh9vtCh37T4EvD1Lp6xi+/L8+59EcsBGYypwtaTHgBfIETNJKwN9V1wfqcYqJH3G9kll29FvSHq/7QvKtiMI+hlJryM5T5uRHIFrgUNtP15g7X3A24r8bK+RI0xnATNJNUMX2T6mS3tvQ5oduDpwKbApSbT2d93YfxCR9ClSxGpx2yvluroT66Ua87qNgTcCl1bkjCStCizUbDq+bAbO8cr/Ufszt1hfP+jdlMZYO1mCIKiPpMtI8+5+lk/tBrzL9tYF1l4FbJNTMX1BVW0bwDykAurrSN2dTde2jcGO15F0z0SS43isG/v2G5LqanfZ3r7gdW4DNgJurIzDqq4XGwQG0fG6HTiRmpZt29G1VwdJ1zLUyfJ+cidLdW1GEAStI+lO22vWnKv7gSRp3/ztGsBqwG8YXpj/g07Y2g6yszga7sbNcI627QasaPtQScsCb7B9U6f37jckPQr8jZT2u5Ga+aC2ry54nYr6/K2215P0GuCWiq7mIDBINV4VZtk+oWwj+pBJtq/I3UYPAQdLmgGE4xUE7eFSSbuSapwgtcj/tsGahfPjX/PXvPmr52lHbVsb+DGpJm5L4FBSndEvSWK0wXDeAGxDmq34EZKTP9X2XU1e52pJXwEm5VTvfwMDVaoyiBGvg4F/A+cx/M6wrwTYuo2k60m1J/8HXEnqZPmu7X4RaQyCnqRGz2pBhiLxE4Fnu6FnVSaSlgK+A7zJ9raSVgfebvuULux9i+3JlehLPjdHuiAYmdwEMYU0uucQ2z9qYu0EYE/gv0iv+d/a7ruxP2NhEB2vGBHRAlk5+G7gtaROlkWBI2zfWKZdQTAeyCmvZWy3JP+Q68N2sv1kPl4M+Lntd7fPys4g6WKSKOdXba+TU0+3dqPmR9KNwCbAzdkBW4JUvL1ep/fuR7LDtR3J6VqeNLPxVNt/b+IatfIhE4EzbHdDPqQnGLhUY4yIaA3bN+dvnwX2yH8su5Jy/UEQjAHblvQboFVnY4mK05Wv94SkJdtiXOd5ve1pkg4CsD1LUreGZf+QlP1YUtK3Sendr3Vp775C0hnAmsBFpCjXnS1eqlo+ZF5Sav22NpnZFwyc45VnPM2F7TO6bUs/IGkR4HPAm0l3N5fl4/1IondnlWddEIwrbpG0YdVNTjO8KmnZSsQs6yT1SzrjudxZWNHS2pgOazNJOoBUn3RWrlXdipT2+qDtu+uvHlh2B54j6XjtXaUz2+yIp08CZ2VHewvgYttHt9vYXmYQU43HVR3OT/qDu8V2X8166haSzgeeAP5A+l0tSfpD28f2bSWaFgTjCkl/AlYmiUw+x9AHWsNuL0nvIc2suzqvewfwaduNivNLJ8tKHEeKptwJLAHsaLuRmvlY9jyaFN16kNSld47tRzu1X9A78iG9wMA5XrUoDZb9ue33lG1LL1Izj2siaeTDsrZfLNeyIBhf5CjVXOQu4iLrX0/So4I+06PKdV2rkZzGe2y/0oU9BbyTVDLxQeB2khN2ru16KupBC/SCfEivEI6XNA9wZ3TnjUyl62e04yAI2kuuzaoWKS5UcJ8L6lepWXtN2w1sM5J2Ig30fkbS14DJwGHdjIDkm8qtge8Cq9leoFt7B4PHINZ4VQ97nkBSYp82+oqBZx1JT+fvRdJeeZrm8/pBENRB0vak4cFvIkneLEfqJF6jwNq9SLU3S5MKlTcmlQf0QxTh67bPkbQZqZzhKOAE4G3d2FzSWqSo1y7AY8BB3dh3UJH0HeB7NR24+9kemKaGgYt4Sdq86nAW8JDth8uyJwiCAOZM1dgSuDwrem8B7G57zwJrZ5JEP2+wva6ktwDfsb1DZ60eO1UK5ocDM22fXa2r1aE9VyE5W7uSdNN+Tio5ub9TewaJkf5vBy2TMjARL0nzA58lFa/OBE7pp7lmQRCMe16x/bikCZIm2L5K0jEF175o+0VJSJrP9p8k9Uv5xN8lnURSRT8ia0VN6PCel5DquXYZgyxC0BoT82v0JQBJk4D5SrapqwyM4wWcDrwC/B7YlpRi3KdUi4IgCIZ4UtJCpEHZZ0n6N6m7sQgP50ahXwGXSXqC1B3ZD+wMvAc4yvaTkt4IHNDJDW2v1MnrB3U5C7hC0mn5eA/S5/PAMDCpxpruvNcANw1SaDMIgt5G0oLAi6T6yd1I0yHOsv14k9fZPK+9xPbLbTe0Q7TaVBD0H5K2JdXzAVzWD7In7WSQHK/ozguCYNySi5SXoSqT0Q/aSCM0FSwL/Ml2w6aCIOhHBsnxepWhsL2AScDzRHdeEAQlUjUke0SKvDdJ+hbwCeB+YPbQ0t7XRhpLU0HQP0i61vZmI7zeB+4zeGBqvGxPLNuGIAiCWmwvDHOcp0eAMxlKN76x4GV2Blbqp9RiFWNpKmiJ3AVaz9ltOC0gaA7bm+XHhcu2pWwGxvEKgiDocba3vU7V8Qk5GvSNAmvvBF5LStX1G2NpKmiV9+XHz+XHM/Pjbh3ed6DJQrV32X5L2baUycCkGoMgCHoZSdcDx5M0pQxMAT5ne5MCazcAzic5YC9VztvevjPWto92NRW0uPfAa0p1mzz/9wuD3DwREa8gCILe4CPAsfnLpAHCHym49nTgCJJG4ewGP9tT2K6ObnVbVkCSNrV9XT7YhM5riA06iwF3SbqJqshmP9wktIuIeAVBEPQ5km62vWHZdjRDnWLrCo8DR9r+cQdtWB84lRRlA3gS+GQ/dIP2KzXTY+Zg++pu21IW4XgFQRCUiKQDbX9P0nGM4IDY3rvANX5ASjH+muGpxr51ICS9DrjedscV+CUtCmD7qU7vNahIWhlYqhJdrDq/GfCI7b+UY1n3iVRjEARBufwxP04fwzUqdUobV50z/TEkGxhZQFXSuzq851LAd4A32d5W0urA222f0sl9B5RjGHkA+VP5ufd305gyiYhXEARBiUg6Hji7NhIwKIwgoLoccHc3BFQlXQycBnzV9jp5qsmtlSknQfuolw6vniwzCETEKwiCoFz+DByVZxROA6bavrXIQkm72/6ZpH1Het72D9poZ6f4FilSN0xAtUt7v972NEkHAdielcW2g/bz2jrPTeqWEb1AdG8EQRCUiO1jbb8d2JxUUH6qpD9J+qakVRssXzA/LjzKVz/wSpaOmCOgCmzQpb2fy7VkBpC0MSn1FbSf6ZI+VXtS0l7AjBLsKY1INQZBEPQYktYjddutPd6nbki6HPggcDjwelK6ccMi+mVt2HsycBywJkkDbQlgR9t3dHrvQSPX050HvMyQo7UBMC/wIdv/LMu2bhOOVxAEQQ+Q64u2BXYFtgJ+R0o7nl9nzQ/rXbNIR2TZjCKg+jPb/+nS/q8BVsv732P7lW7sO6jkVPKa+fAu21eWaU8ZhOMVBEFQIpK2IanUvxe4iaRcf36NsOhoaz9edXgI8M3q5213W5B0zEhaDdjf9lxpqQ7stQCwL7Cc7U9JWgVYzfaFnd47GFzC8QqCICgRSVcCZwO/tP3EGK4z1/ibXkbS2sBRpG7GX5HGJf0IeBvwfdtHd8GGX5DSXh+zvWZ2xK63vW6n9w4Gl+hqDIIgKBHb7dLa6re76J8CJwB/AN4D3EYaGbSb7Re7ZMNKtneRNAXA9vOS1KW9gwElHK8gCIKgDOaz/b/5+3sk7WP7wC7b8LKkSQx1Na5ElfJ/0BkkvQHYiPR7v3mQCushHK8gCIK+pWbO4QKSnq48Bdj2IuVYVoj5c/dmJcL0UvVxl8YdfRO4BFhG0lnApsAnurDvwJLlI74BXEn6vz5O0qG2Ty3Xsu4RNV5BEARB15F0VZ2n3cYUbCM7XkcScBVwg+3HurHvoCLpHmCTrN3W1ZmcvUJEvIIgCIKuY3uLsm3IzA88Qfo8XF0Stq8p2abxzOPAM1XHz+RzA0M4XkEQBMFAIukIYBfgLmB2Pm0gHK82UzXW6j7gRknnk37XHwAGSrA2HK8gCIJgUPkgSbcrCuo7T2WE1V/yV4VRBYLHK+F4BUEQBIPK/cA8RCdjx7F9SNk29ArheAVBEAQDhaTjSGmu54HbJF1BlfPVD6OW+hVJSwAHAmuQ6uuAturZ9TzheAVBEAQ9haRbbE/u4BbT8+MM4Ncd3CeYm7OAXwDvAz4LfBx4tFSLukzISQRBEARB0BUkzbC9vqQ7bK+dz91se8OybesWEfEKgiAISifrOb0T+KvtGV3acxXgcGB1hqe9VuzG/gPKK/nxEUnbAf8AFi/Rnq4zoWwDgiAIgsFD0oWS1szfvxG4E/gkcKakL3bJjNNI8yJnAVsAZwA/69Leg8phkhYF9gP2B04GvlSuSd0lUo1BEARB15F0l+018vdfAd5i+2OSFgauq6ShOmxDJe010/Za1ec6vXcwuESqMQiCICiDV6q+3wr4KYDtZyTNHnlJ23lJ0gTgXkmfB/4OLNSlvQcKSQfa/l5VR2k1Bv4D/Mz2X+ZePb4IxysIgiAog79J+gLwMDCZNKwaSZNI2lrdYB9gAWBv4FvAlqQuu6D93J0fp4/y/OuAc4F1umNOeUSqMQiCIOg6kpYEDgXeCBxv+9J8fgtgfdtHlWlf0H0kfcb2SWXb0WnC8QqCIAgGEkmrAgcAy1GVARokMc9uk3/n+wPLM6C/83C8giAIgq4jaXnbD47y3Kdt/6QLNtwOnEgSUn21cr5bchaDSPzOo8YrCIIgKIfLJZ0MHGV7Vs1z6pINs2yf0KW9gsTA/85DxysIgiAog/WApYAZkt5R/USn63wkLS5pceACSf8t6Y2Vc/l80DkG/nceqcYgCIKgNCStD1xB6m6cTYp2uZM6XpIeIEkYjBRZcyjXd478u69loH7n4XgFQRAEpSBpS+BY4LfA8STHCwDbD5VlVxB0kqjxCoIgCLqOpJ8DSwMfsT2zy3tvCPzN9j/z8ceADwMPAQfb/k837Rkk8u96Lmyf0W1byiJqvIIgCIIyuNz2Zt12ujInAS8DSHon8F3SnMangI53Uw44G1Z9vQM4GNi+TIO6TaQagyAIgoFC0u2218nfHw88avvgfHyb7XVLNG+gkPRa4Oe231O2Ld0iIl5BEATBoDFRUqXUZivgyqrnogSnuzwHrFC2Ed0kXmBBEATBoDEVuFrSY8ALwO8BJK1MSjcGHULSBQwNyZ4ArA5MK8+i7hOpxiAIgqCnkPSGSuF7B/fYmDQn8lLbz+VzqwIL2b6lk3sPMpI2rzqcBTxk++Gy7CmDcLyCIAiCnkLSb2xvV7YdQfuQND/wWWBlYCZwyggTCwaCcLyCIAiCUpA0kdTduEXZtgSdRdIvgFdIad1tSZGufcq1qhyixisIgiAoBduvSpotaVHbUVs1vlnd9loAkk4BbirZntIIxysIgiAok2eBmZIuI3W4AWB77/JMCjrAK5VvbM+SujUHvfeIVGMQBEFQGpI+PtJ526d325agc0h6lSHHWsAk4HmGZnMuUpZt3SYcryAIgqBUJC0BYPvRsm0Jgk4TAqpBEARB11Hi4KyldQ/wZ0mPSvpG2bYFQScJxysIgiAogy8BmwIb2l7c9mLA24BNJX2pXNOCoHNEqjEIgiDoOpJuBbax/VjN+SVIoqbrlWNZEHSWiHgFQRAEZTBPrdMFc+q85inBniDoCuF4BUEQBGXwcovPBUFfE6nGIAiCoOvUyAsMewqY33ZEvYJxSTheQRAEQRAEXSJSjUEQBEEQBF0iHK8gCIIgCIIuEY5XEARBEARBlwjHKwiCvkXSVZLeXXPui5JOKMumIAiCeoTjFQRBPzMV2LXm3K75fBAEQc8RjlcQBP3M/wHbSZoXQNLywJuASZL+IOkWSedIWig//6CkQ/L5mZLeks8fLGn/ykUl3ZmvhaTdJd0k6TZJJ0ma2OV/YxAE44hwvIIg6Fts/we4Cdg2n9oVuBT4KrC17cnAdGDfqmWP5fMnAPtTB0lvBXYBNrW9LvAqsFs7/w1BEAwWrynbgCAIgjFSSTeenx/PAz4IXCcJYF7gD1U/f25+nAHs0ODaWwHrAzfna00C/t0mu4MgGEDC8QqCoN85Hzha0mRgAeAW4DLbU0b5+Zfy46sMvQfOYngGYP78KOB02we11+QgCAaVSDUGQdDX2H4WuAo4lRT9ugHYVNLKAJIWlLRqg8s8CEzOPz8ZWCGfvwLYUdKS+bnFJS3X9n9EEAQDQzheQRCMB6YC6wBTbT8KfAKYKukOUprxLQ3W/xJYXNJdwOeBPwPY/iPwNeDSfK3LgDd25F8QBMFAELMagyAIgiAIukREvIIgCIIgCLpEOF5BEARBEARdIhyvIAiCIAiCLhGOVxAEQRAEQZcIxysIgiAIgqBLhOMVBEEQBEHQJcLxCoIgCIIg6BL/H46XArwj1K5QAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a figure of size 10x6 inches for the plot\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "# Use Seaborn's countplot to plot the number of matches played at each venue\n",
    "# The 'x' parameter is set to 'venue', and 'data' specifies the DataFrame to use for plotting\n",
    "sns.countplot(x='venue', data=data)\n",
    "\n",
    "# Rotate the x-axis labels by 90 degrees for better visibility\n",
    "plt.xticks(rotation=90)\n",
    "\n",
    "# Add labels for x and y axes\n",
    "plt.xlabel('Venue')\n",
    "plt.ylabel('Count')\n",
    "\n",
    "# Add a title to the plot\n",
    "plt.title('Matches Count by Venue')\n",
    "\n",
    "# Display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a figure of size 10x6 inches for the plot\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "# Use Seaborn's countplot to plot the number of matches played in each city\n",
    "# The 'x' parameter is set to 'city', and 'data' specifies the DataFrame to use for plotting\n",
    "sns.countplot(x='city', data=data)\n",
    "\n",
    "# Rotate the x-axis labels by 90 degrees for better visibility\n",
    "plt.xticks(rotation=90)\n",
    "\n",
    "# Add labels for x and y axes\n",
    "plt.xlabel('City')\n",
    "plt.ylabel('Count')\n",
    "\n",
    "# Add a title to the plot\n",
    "plt.title('Matches Count by City')\n",
    "\n",
    "# Display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get the top 10 venues with the highest number of matches played\n",
    "venue_counts = data['venue'].value_counts().head(10)\n",
    "\n",
    "# Create a figure of size 10x6 inches for the plot\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "# Use Seaborn's barplot to plot the count of matches for each of the top 10 venues\n",
    "# The 'x' parameter is set to the venue names (index of venue_counts), and 'y' is set to the corresponding match counts (values of venue_counts)\n",
    "sns.barplot(x=venue_counts.index, y=venue_counts.values)\n",
    "\n",
    "# Add labels for x and y axes\n",
    "plt.xlabel('Venue')\n",
    "plt.ylabel('Count')\n",
    "\n",
    "# Add a title to the plot\n",
    "plt.title('Top 10 Venues')\n",
    "\n",
    "# Rotate the x-axis labels by 90 degrees for better visibility\n",
    "plt.xticks(rotation=90)\n",
    "\n",
    "# Display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a figure of size 10x6 inches for the plot\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "# Get the top 10 players with the highest number of \"Player of the Match\" awards\n",
    "top_players = data['player_of_match'].value_counts().head(10)\n",
    "\n",
    "# Use Seaborn's barplot to plot the count of \"Player of the Match\" awards for each of the top 10 players\n",
    "# The 'x' parameter is set to the player names (index of top_players), and 'y' is set to the corresponding award counts (values of top_players)\n",
    "sns.barplot(x=top_players.index, y=top_players.values)\n",
    "\n",
    "# Rotate the x-axis labels by 90 degrees for better visibility\n",
    "plt.xticks(rotation=90)\n",
    "\n",
    "# Add labels for x and y axes\n",
    "plt.xlabel('Player')\n",
    "plt.ylabel('Count')\n",
    "\n",
    "# Add a title to the plot\n",
    "plt.title('Top 10 Players of the Match')\n",
    "\n",
    "# Display the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extracting day, month, and year from the 'date' column\n",
    "data['day'] = data['date'].dt.day\n",
    "data['month'] = data['date'].dt.month\n",
    "data['year'] = data['date'].dt.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Number of matches played over the years\n",
    "plt.figure(figsize=(10, 6))\n",
    "data['year'].value_counts().sort_index().plot(kind='bar')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Number of Matches')\n",
    "plt.title('Number of Matches Played Over the Years')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Number of matches played over the months\n",
    "plt.figure(figsize=(10, 6))\n",
    "data['month'].value_counts().sort_index().plot(kind='bar')\n",
    "plt.xlabel('Month')\n",
    "plt.ylabel('Number of Matches')\n",
    "plt.title('Number of Matches Played Over the Months')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Matches and Results by Season\n",
    "season_matches = data['season'].value_counts()\n",
    "season_results = data['winner'].groupby(data['season']).value_counts().unstack(fill_value=0)\n",
    "\n",
    "# Plotting Matches and Results by Season\n",
    "plt.figure(figsize=(10, 6))\n",
    "bar_width = 0.4\n",
    "\n",
    "plt.bar(season_matches.index, season_matches.values, width=bar_width, color='black', label='Matches')\n",
    "plt.bar(season_results.index + bar_width, season_results.values.sum(axis=1), width=bar_width, color='blue', label='Results')\n",
    "plt.xlabel('Season')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Matches and Results by Season')\n",
    "plt.legend()\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate win percentage for each team\n",
    "team_wins = data['winner'].value_counts()\n",
    "team_matches = data['team1'].value_counts() + data['team2'].value_counts()\n",
    "win_percentage = (team_wins / team_matches).sort_values(ascending=False)\n",
    "\n",
    "# Plot win percentage for each team\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.barplot(x=win_percentage.index, y=win_percentage.values)\n",
    "plt.xticks(rotation=90)\n",
    "plt.xlabel('Team')\n",
    "plt.ylabel('Win Percentage')\n",
    "plt.title('Win Percentage of Each Team')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Toss Decision Frequency\n",
    "toss_decision_counts = data['toss_decision'].value_counts()\n",
    "\n",
    "# Plot the frequency of toss decisions\n",
    "plt.figure(figsize=(6, 4))\n",
    "sns.countplot(x='toss_decision', data=data)\n",
    "plt.xlabel('Toss Decision')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Frequency of Toss Decisions')\n",
    "plt.show()\n",
    "\n",
    "# Toss Decision vs. Match Result\n",
    "plt.figure(figsize=(6, 4))\n",
    "sns.countplot(x='toss_decision', hue='result', data=data)\n",
    "plt.xlabel('Toss Decision')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Toss Decision vs. Match Result')\n",
    "plt.legend(title='Match Result', loc='upper right')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Distribution of Result Margin\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.histplot(data['result_margin'], bins=20, kde=True)\n",
    "plt.xlabel('Result Margin')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Distribution of Result Margin')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Umpire-wise Analysis\n",
    "umpire1_counts = data['umpire1'].value_counts().reset_index()\n",
    "umpire1_counts.columns = ['umpire', 'matches_umpired']\n",
    "\n",
    "umpire2_counts = data['umpire2'].value_counts().reset_index()\n",
    "umpire2_counts.columns = ['umpire', 'matches_umpired']\n",
    "\n",
    "# Combine counts from umpire1 and umpire2\n",
    "umpire_counts = umpire1_counts.merge(umpire2_counts, on='umpire', how='outer')\n",
    "umpire_counts['matches_umpired'] = umpire_counts['matches_umpired_x'].fillna(0) + umpire_counts['matches_umpired_y'].fillna(0)\n",
    "umpire_counts = umpire_counts[['umpire', 'matches_umpired']]\n",
    "umpire_counts = umpire_counts.sort_values(by='matches_umpired', ascending=False).head(10)\n",
    "\n",
    "# Plotting Umpire-wise Analysis\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.barplot(x='matches_umpired', y='umpire', data=umpire_counts, palette='viridis')\n",
    "plt.xlabel('Matches Umpired')\n",
    "plt.ylabel('Umpire')\n",
    "plt.title('Top 10 Umpires with Most Matches Umpired')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Team 1\n",
    "team1_counts = data['team1'].value_counts()\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.countplot(x='team1', data=data, order=team1_counts.index)\n",
    "plt.xlabel('Team 1')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Matches Count by Team 1')\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Team 2\n",
    "team2_counts = data['team2'].value_counts()\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.countplot(x='team2', data=data, order=team2_counts.index)\n",
    "plt.xlabel('Team 2')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Matches Count by Team 2')\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Toss Winner\n",
    "toss_winner_counts = data['toss_winner'].value_counts()\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.countplot(x='toss_winner', data=data, order=toss_winner_counts.index)\n",
    "plt.xlabel('Toss Winner')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Matches Count by Toss Winner')\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Result\n",
    "result_counts = data['result'].value_counts()\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.countplot(x='result', data=data, order=result_counts.index)\n",
    "plt.xlabel('Result')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Matches Result')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6i7zh4zDJepJVqxlyepS6T94kncyo92h32A+403URXZVx4QRuAQ5wXUR3SDfUk95at+3n+qXvkTdkNMmNq7YtU/fp24QHfv4S1LwR40nX15CqsxNM1y+bT97g3bc9blKNVM99lv6HfBXTuJVt31tNGlKNPfei/OuqSDR+uusiuiKjNmsj0fhh2Aums+IMoGTVGtb/9af2l3SavnsfTfHhZ7P+b7eR3LgSJECo/xAGnnQVoX6D2br6U2rmvcig6dcAsGXpe2z612/BGPKGj2PQyd9CgnZkz+o5zxIo6EvRpGkYY6jcdnhlCgOOycrzxDtjIzC5vKx0hetCOiNjwhmJxguBecB4x6WozPbv8rLSY1wX0RmZtFl7GxpMteuOjkTj57kuojMyouf0xoyZT4afNKF8YxXwhfKy0lrXhbQnU3rOO9Fgqu4zCoi6LqIjvu85I9H4NOAfrutQWace2NvP84P6uuf0Rmb/hes6VFYqwOfHPn0dTuAS7AFkpXrCVyLR+LGui2iLbzdrI9F4EfApMNx1LSqrLQAOKC8rTbkupCU/95xRNJiq500CfHnlii97Tm+yoSVAH9e1qJywGHtoxVe9p197zivRYKreMxY403URLfkunJFoPB/4L9d1qJzzfdcFtOS7cGKHldCBulRvmxyJxk9yXURzfgznNa4LUDnLV+89X+0QikTjR2IvCVPKBQNMKC8r/cx1IeC/ntNXn1wq5wjw+WEOHfFNzxmJxncDlqInuCu3qoGRfrhixU895xVoMJV7/YFTXRcB/grn110XoJTnq64LAJ9s1kai8QOBd1zXoZSnFhjqeioHv/ScX3JdgFLN9AWcD+7rl3Ce7roApVo4w3UBzjdrI9H4GOxJ7kr5yWZgSHlZ6VZXBfih59RNWuVH/YATO1yqB/khnKe7LkCpNjjda+t0s9ab+HYtWTKCu8o6m4DB5WWlaReNu+45Z6DBVP41ANjLVeOuw3mU4/aV6sjBrhp2Hc5DHbevVEcOcdWws3B6o+vt46p9pTop98IJTHXcvlKdMSkSjTsZz8plOA5y2LZSnRUCDnTRsMtw7u+wbaW6wsmmrctw6jQLKlPkTjgj0XgYmOiibaV2Qk5t1k4Awo7aVqqrdnPRqKtw7u6oXaV2RoF3qmmvchXOYY7aVWpnjertBjWcSnVOzoRTp/ZTmSZnwqk9p8o0ORNO7TlVpsmZcGrPqTKNhlMpnxrR2w32ejgj0XgQ6PVjRkrtovzebtBFzxnEzuakVCbp9Xl8XISz0UGbSu2q7A+nN5KZ+wlalBOV/3cPK371NSp+e2Wby9Qvn0/F/15NxcNXsuaJKACpugRrHv8eFb+9krpFM7ctu+4vP6Fx84YerxsH54K7mnIv5bDtrDJWVi2bHpi9clrw3fResnx4fSCdtzwUrloWzqtZGg5vXRYOpVeFQoHKUDC/OiBF9QEGpDF9cfXVwtSEgvmDzMqHVxUV7RVNtHw4VZuS1b9b3D9yQ2Rz3uC+6cZEUkLFUVP5UmXBoJOD6eKpxQ3LfnF3/6GnPVtd/U51uO9eW0IlU+/Y0vOFy2Yo7flmmnEVkEaHbWesPJJbDw18uGhG4O0NRwYXFo5kw5iAmNHA6KZlCgyUJFOj90vWt7meOpHa1aFg5YpQuGp5OFS7LBxKrgiFWBMKhTcFA31rA4HiJAxGpKi7X0O/vYtoWN+ABECEgS0fT7xdRfHUYvKH5JUAhEtshxXIF0gZSBkkJJA2Azf+cwOjrx2NCL0wjIjp1GS6IlICnGeMeUBERgL3GmN2at4Vl+FUHRhM1foTg3OXnByYUz85sHhQP+rGizBpV9dbaEzfscnGvmOTjaNpp8+pEdm8OhSqXB4OJZaFQ3XLwuHkylCItaFguCoQKKoNBEoaYQgi3RaOhjUNmJRhye1LSNenGXTiIAYcMYCSQ0tY8eAKNr62keFnDWfjqxspObyEQH6vfTNLdnK5EuBK4AFjTAW7MCGSy81a1YyQTk+SpYunB2evPjYwLzhWKnYLS2o0MMRVTUXG9BufTPYbn2z/fZkISHVFKLR+ZSiUWBYOb1kWDjWuDIVkbSiYVxUI9t0SkKYQF3TUpkkbtpRvYcz3x5BuSLPkp0soHFtI/vB8It+JAJCqTbE+vp49rt6DVY+sIlWXYvDJgykcV9gtr7sNnZ2GvgwYKyLzgE+BicaYfUUk6D12DPawzP3GmN+0tyLtOR3py5aaowLzF80Izt58SOCjoqFUjRNhPDDedW1dVZw2/Ysbkv0nNiShna64KhCoWhUKVc6sov4H9Y17nrq5ZvaqcCiwLhjMqwoGi7aIDAiXhIYHJxUFA/kBAvkBCicUUr+8nvzh2w8zrntuHUNOGUJiVoLCCYUUTy1m+a+WE7k+0pMvs7PhjAL7GmMmi0gEeMG7/1IgYYyZKiL5wJsi8ndjzNK2VqTh7CV7yNpVJwXmLjsxOLdxHykf2oet40XcDH/hSkk6XVLS0FDStyHNPQHDbZUbj2m5zIfDUuab8frkfQelly6WYO0VH27+wtEH9/nQ1G+tWx8MFqxe0zAgua5h96K9+oYqV9SHgmE7m0e6ocenM/nczqsuOhHYT0SaNnOLsR/EvgtnJVl8Cl+QVOMU+WTRjODblV8MLAjvIesiQUmPwsH5mX5z7l/qeK08RWWdYbe7NnPLMfkkvVxdPiWPvYcE5fTxofDX7t00ISBw05Qw1xbVTmG17bjOerqOvxyXz9jyFemPRrHhzD/UFm786xq+NK1/+aia2nWrQqHA+lAwvzoQ6FcvMjBtd2x1x3w8uxpOAa42xrzc6Se4mGUsEo3HsZMYZYUSNm86PvDeZ9ODs+sOCiwqLqFmggg9+gVIdU4KUpXBYOXKUGjT8nBo87JwqH55OJyuCAUDlcFgQXUg0G+ryKA0DEKkvb1L9y24cMHVHbUnIoOAd40xo5s2a73vnJdh3/NnGmOSIjIBWGVM23uBXfWcyxy12y2+IMuXTg/OXnV84F0zQVaOyqNxjAhTXdelPi8IwWGp1LBhqdSwg7a2PUl1IzSuCwbXrQyHNi4PhTYvC4cbVoRD6YpQKFgZDBSETdubn80ZYzaIyJsishD4qNlDDwMR4F0REWA9HcxN6yqcyx2122X5NNQfHvjgkxmBt6sOD37QZwQbxwXEjAHGuK5NdZ8QhEamUsNHplLDD6bVEN/f2XUZY85r5b40cKN362xNTvi25xzOxrUnBucuPSkwZ+t+gSWDi9gyQURHp1e936HkdM8ppNOTZfFn04Oz1xwTmBccI2tGhyW1G1m8s0rttBW93WBO9ZxF1FUfE3j/s+nB2dUHBz7uP5jEeBEmYAe5VqotaXIonBXY06F69Ez/MbJ6xUmBOctPDM5NT5TlwwpoGJdrxxZVt/iUWKLtk5V7iJNwlpeVpiPR+Eq6cadKmMaGgwMffzo98HblUYEF+bvJ+jFBMbujo8urXfeei0ZdXhnyIbsQzoEkNkwLvrt4emD2lgMCnw0opnaCiM6UrXpEzoVzNp2+QM6YvWXZkhnBtyuODcyT8bJqVJ40jkHHIlK9IyfD2ao+bK07MrBg0Yzg24nDAh8WDmXT+IAwFhjbi/Up1STnwjmn6YdRrF99UnBu+YnBuQ37ytKhfakfL8Jkh7Up1WQlsUSli4adnFvb5P2bD/jbPlJ+cEjSI50VoVT7nieWOM1Fw06HCtk/sGQToMFUfjbLVcOuRnxv8rrj9pXqyIuuGnYdzn87bl+p9qwB5rlq3G04Y4mlwGdOa1CqbS8RSzjbKeO65wT4q+sClGqDs01a8Ec4/+K6AKVakQL+4bIAP4RzDg7O+FeqA7OIJTa5LMB9OO02vW7aKr+Juy7AfTgt3bRVfpIGHnddhF/C+Saw1nURSnleIZZw/lXLH+GMJdLAn12XoZTnf10XAH4Jp/Vr1wUoBWwC/ua6CPBTOGOJD4B/uS5D5bwniCXaHuC2F/knnNZ9rgtQOe8R1wU08Vs4n8Unw2aqnDSPWOJd10U08Vc4Y4kU8KDrMlTO+pnrAprzVzith4BeH4ZQ5bxPgKdcF9Gc/8Jph4T4g+syVM653Tuk5xv+C6f1E6DBdREqZyzFhx2CP8MZSyxDj3uq3lNGLOG72db9GU7rVmCz6yJU1lsBPOq6iNb4N5yxxHrgLtdlqKx3O7GEL79C+Tec1i+wMwAr1RPeB/7HdRFt8Xc4Y4nNwG2uy1BZyQBXecfWfcnf4bQewB6DUqo7PUYs8abrItrj/3Da7wOXYj/plOoOCeAG10V0xP/hBLxPuPtdl6Gyxs3EEutcF9GRzAin9QMcTVevssr7ZMgHfeaEM5aoAf7LdRmupNKGA35TwylP1O1w/zUv1lN0W3Wrz0mmDBc+s4VJv65h4v013P6GvUxxfW2aIx+pZd8Hanjm4+S25b/0pzoqNvvqDLbu1gBc4uedQM1lTjgBYomXgd+5LsOFX77dwMTBO/655lak2FTf9lfxpz9sZGujYcEVRbxzWV9+804D5VVp/rgwyeVTwsz+Zl/umWUP8T3/SZIDhgcY2S+z3hJddKOfLgnrSCb+Ja4DKlwX0ZtWVqeJf9rINw7M23ZfKm244R/1/HxafpvPE6A2CY1pw5akIS8o9M8XwgGhLglbGyEYsI/f83YD3zui7XVlgZfIsJNaMi+cdqDfs4BkR4tmi2tfqufn0woIyPb77pvdwGkTQoxop6c7Y+8QfcMw4hc17HFPDdcfnsfAPsJ5k8I8+0kjJzxWy41H5vPAnAbO3y9MYVjaXFeGWwtc6HLek52ReeGEpr2317suoze8sCjJ0L7CQSOD2+6r2Jzm6Q8bufqQvHaeCbNXpQgGoOI7RSz9dhG/mNnAkk1piguE+HmFzL2siANHBHl+USNn7B3mm89t4Yyn6pi5wnfngO8KA1yQCXtnW3I6s/UuixX/ATjPdRk96Qev1PPY/CShANQ3QvVWQ34I8oNCgTf18fKEYc8BwmfX9NvhuVfFt3DobkHO39+G+JJnt3DyuBBn7RPetsx3Xq7ntC+E+HRDmrwgnLF3mK88VcfLX+/ba6+xh91JLOH7Y5qtcTqzdTf4JjDJu2Wl26cVcPu0AgBeK2/kzrcaeOG8wh2WKbqt+nPBBNijOMCr5SnO3x9qGwyzVqa49tDtve2nG1KsrE5zTCTE+2u2UhASRGBL9nxheB240XUROyszN2ubxBJ1wFewZ3wo4LlPktz8LzvKy1UH51HTYNjngRqmPlTLxZPD7Dds++bxTa9u5dbj7E6gcyeF+fXcJFMfquXbHWwuZ4iPgdOJJTL2oyazN2ubxIpPBZ4h0z9sVHdZBxzqTc6csbLjzRxLPE8On6CgdlAHnJLpwYRsCSdALPEwGXAys+pRaeA8Yok5rgvpDtkTToBY4k70+s9cdh2xxLOui+gu2RVOgFjiJjLkxGbVrWLEEve6LqI7ZV84ravx4VCHqsf8iFjiFtdFdLfsDKc9TesiNKC54EZiiZ+6LqInZGc4AW8c0vOBO12XonqEAa4hlrjddSE9JTuOc3YkVnwddiS/rD2zO8ekgIuJJR5zXUhPyo1wAsSKz8FeC5oVp7/ksM3A17xj21ktd8IJECs+DjuleH/Xpaid8in2lLwPXRfSG7L3O2drYolXgaOwE9eozPIicHCuBBNyLZwAscR84CDgBdelqE77GfaUvCrXhfSm3NqsbS5WLNgR/X4MBDtYWrlRh93x46tJbXtL7oazSaz4COzx0NGuS1E7eBc7gsEHrgtxJfc2a1uyQ55MBp52XImyGoAfAYfkcjBBe84dxYrPBO4BRjquJFe9hx2Ia4HrQvxAe87mYomngb2Ae7EHulXvSAI3Y/fGajA92nO2JVZ8EPAgMMV1KVnuP8C3iCXed12I32g42xMrDgBXAreiJy50t0+A72fT9ZfdTcPZGbHiIdhRFq4CCjtYWrVvLRADHvYuTlBt0HB2Rax4KPA94Ao0pF1Vi7344A5vUirVAQ3nzogVD2N7SPs4rsbv1mG/uz9ALLHWdTGZRMO5K2xIrwMuBoY6rsZv5gO/BP5ALLHVdTGZSMPZHWLFYeB07Aj008jd60YNEAfu9i4yULtAw9ndYsV7Apdie9MRjqvpLQuBp4AniCUWuy4mW2g4e0qsOAScDJwGnEL2BfVjbCCfzKXLuHqThrM32CtgDgROxQb1QDJv0zeFPb3u79hAzndcT9bTcLoQKx4JzAAOB6YCE/HfZWsNwFzg39jZut4kltjstqTcouH0g1hxX+AA7KmCU71/x9N7vesm7Bk7TbdZwCxiiS291L5qhYbTr2LF+cDu3m0P77Z7s3/7AfneLc/7t+WFDA1ADVANbAAqvVsFzcMYS6zv4VejdoKGM5vYnVD52EmR6zJ5bkql4VTKt/R6TqV8SsOplE9pOJXyKQ1njhJL//4+pn+cHCIiERH5RER+jz0fNtXssTNE5FHv50dF5F4ReUtElojIGd79I0TkdRGZJyILReQoJy8kR2g4c8944AFjzD7YC6DbMgI4Enu6YZl333nAy8aYycD+wLyeK1OFXBeget0yY8ysTiz3jDEmDXwoIsO8++YAj4hI2Ht8Xk8VqbTnzEXNe8vmB7kLWizX/AJpATDGvA58EVgFPCoiF/RIhQrQcOa6tSIy0dsx9OWOFhaR0cBaY8xDwMPYq2tUD9HN2twWxc62th57BUpRB8sfA9wgIknsObvac/YgPX1PKZ/SzVqlfErDqZRPaTiV8ikNp1I+peFUyqc0nEr5lIZTKZ/ScCrlUxpOpXxKw6mUT2k4lfIpDadSPqXhVMqnNJxK+ZSGUymf0nAq5VP/D+hNCde1rji6AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pie Chart\n",
    "plt.pie(data['result'].value_counts(), labels=data['result'].value_counts().index, autopct='%1.1f%%')\n",
    "plt.title('Match Results Distribution')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Box Plot\n",
    "plt.boxplot(data['result_margin'])\n",
    "plt.ylabel('Result Margin')\n",
    "plt.title('Box Plot of Result Margin')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "winner                       Chennai Super Kings  Delhi Capitals  \\\n",
      "team1                                                              \n",
      "Chennai Super Kings                           65               4   \n",
      "Delhi Capitals                                 6              50   \n",
      "Gujarat Lions                                  0               2   \n",
      "Kings XI Punjab                                6               3   \n",
      "Kochi Tuskers Kerala                           0               1   \n",
      "Kolkata Knight Riders                          6               3   \n",
      "Mumbai Indians                                 4               3   \n",
      "Rajasthan Royals                               4               3   \n",
      "Rising Pune Supergiant                         2               3   \n",
      "Royal Challengers Bangalore                    7               6   \n",
      "Sunrisers Hyderabad                            6               8   \n",
      "\n",
      "winner                       Gujarat Lions  Kings XI Punjab  \\\n",
      "team1                                                         \n",
      "Chennai Super Kings                      0                4   \n",
      "Delhi Capitals                           1                7   \n",
      "Gujarat Lions                            5                2   \n",
      "Kings XI Punjab                          2               46   \n",
      "Kochi Tuskers Kerala                     0                1   \n",
      "Kolkata Knight Riders                    2                5   \n",
      "Mumbai Indians                           1                6   \n",
      "Rajasthan Royals                         0                3   \n",
      "Rising Pune Supergiant                   1                1   \n",
      "Royal Challengers Bangalore              1                7   \n",
      "Sunrisers Hyderabad                      0                6   \n",
      "\n",
      "winner                       Kochi Tuskers Kerala  Kolkata Knight Riders  \\\n",
      "team1                                                                      \n",
      "Chennai Super Kings                             0                      2   \n",
      "Delhi Capitals                                  1                      6   \n",
      "Gujarat Lions                                   0                      1   \n",
      "Kings XI Punjab                                 0                      9   \n",
      "Kochi Tuskers Kerala                            3                      0   \n",
      "Kolkata Knight Riders                           1                     55   \n",
      "Mumbai Indians                                  1                      2   \n",
      "Rajasthan Royals                                0                      3   \n",
      "Rising Pune Supergiant                          0                      5   \n",
      "Royal Challengers Bangalore                     0                      7   \n",
      "Sunrisers Hyderabad                             0                      9   \n",
      "\n",
      "winner                       Mumbai Indians  Rajasthan Royals  \\\n",
      "team1                                                           \n",
      "Chennai Super Kings                       9                 3   \n",
      "Delhi Capitals                            7                 5   \n",
      "Gujarat Lions                             1                 0   \n",
      "Kings XI Punjab                           7                 7   \n",
      "Kochi Tuskers Kerala                      0                 0   \n",
      "Kolkata Knight Riders                    10                 4   \n",
      "Mumbai Indians                           61                 5   \n",
      "Rajasthan Royals                          3                44   \n",
      "Rising Pune Supergiant                    4                 1   \n",
      "Royal Challengers Bangalore              11                 5   \n",
      "Sunrisers Hyderabad                       7                 7   \n",
      "\n",
      "winner                       Rising Pune Supergiant  \\\n",
      "team1                                                 \n",
      "Chennai Super Kings                               1   \n",
      "Delhi Capitals                                    2   \n",
      "Gujarat Lions                                     0   \n",
      "Kings XI Punjab                                   1   \n",
      "Kochi Tuskers Kerala                              0   \n",
      "Kolkata Knight Riders                             2   \n",
      "Mumbai Indians                                    4   \n",
      "Rajasthan Royals                                  0   \n",
      "Rising Pune Supergiant                           13   \n",
      "Royal Challengers Bangalore                       1   \n",
      "Sunrisers Hyderabad                               3   \n",
      "\n",
      "winner                       Royal Challengers Bangalore  Sunrisers Hyderabad  \n",
      "team1                                                                          \n",
      "Chennai Super Kings                                    2                    4  \n",
      "Delhi Capitals                                         8                    8  \n",
      "Gujarat Lions                                          2                    3  \n",
      "Kings XI Punjab                                        5                    6  \n",
      "Kochi Tuskers Kerala                                   1                    1  \n",
      "Kolkata Knight Riders                                  5                    2  \n",
      "Mumbai Indians                                         4                    6  \n",
      "Rajasthan Royals                                       6                    4  \n",
      "Rising Pune Supergiant                                 3                    4  \n",
      "Royal Challengers Bangalore                           53                    7  \n",
      "Sunrisers Hyderabad                                    2                   50  \n"
     ]
    }
   ],
   "source": [
    "# Cross-tabulation\n",
    "cross_tab = pd.crosstab(data['team1'], data['winner'])\n",
    "print(cross_tab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Cross-tabulation\n",
    "cross_tab = pd.crosstab(data['team1'], data['winner'])\n",
    "\n",
    "# Plotting Heatmap for Cross-tabulation\n",
    "plt.figure(figsize=(12, 8))\n",
    "sns.heatmap(cross_tab, annot=True, cmap='YlGnBu', fmt='d')\n",
    "plt.xlabel('Winner')\n",
    "plt.ylabel('Team 1')\n",
    "plt.title('Team 1 vs. Winner Cross-tabulation')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compute correlation matrix\n",
    "corr_matrix = data.corr()\n",
    "\n",
    "# Plot heatmap of correlation matrix\n",
    "plt.figure(figsize=(10, 8))\n",
    "sns.heatmap(corr_matrix, annot=True, cmap='YlGnBu')\n",
    "plt.title('Correlation Matrix Heatmap')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Data Preparation**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['city', 'date', 'player_of_match', 'venue', 'neutral_venue', 'team1',\n",
       "       'team2', 'toss_winner', 'toss_decision', 'winner', 'result',\n",
       "       'result_margin', 'eliminator', 'umpire1', 'umpire2', 'season', 'day',\n",
       "       'month', 'year'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To display the columns of the data\n",
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Kolkata Knight Riders', 'Chennai Super Kings', 'Delhi Capitals',\n",
       "       'Royal Challengers Bangalore', 'Rajasthan Royals',\n",
       "       'Kings XI Punjab', 'Sunrisers Hyderabad', 'Mumbai Indians',\n",
       "       'Rising Pune Supergiant', 'Kochi Tuskers Kerala', 'Gujarat Lions'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Get the unique venues present in the 'winner' column \n",
    "data['winner'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Mapping Categorical data into numerical form**\n",
    "The purpose of this conversion is to transform the categorical data (team names) into numerical form, which is required for many machine learning algorithms that can only handle numerical data. By mapping team names to unique numbers, we can represent them as numerical features in the dataset, enabling us to use them in the model training process."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a dictionary to map team names to unique numbers\n",
    "team_mapping = {\n",
    "    'Kolkata Knight Riders': 1,\n",
    "    'Chennai Super Kings': 2,\n",
    "    'Delhi Capitals': 3,\n",
    "    'Royal Challengers Bangalore': 4,\n",
    "    'Rajasthan Royals': 5,\n",
    "    'Kings XI Punjab': 6,\n",
    "    'Sunrisers Hyderabad': 7,\n",
    "    'Mumbai Indians': 8,\n",
    "    'Rising Pune Supergiant': 9,\n",
    "    'Kochi Tuskers Kerala': 10,\n",
    "    'Gujarat Lions': 11\n",
    "}\n",
    "\n",
    "# Replace team names in 'team1' and 'team2' columns with unique numbers\n",
    "data['team1'] = data['team1'].map(team_mapping)\n",
    "data['team2'] = data['team2'].map(team_mapping)\n",
    "\n",
    "# Replace winner names in 'winner' column with unique numbers\n",
    "data['winner'] = data['winner'].map(team_mapping)\n",
    "data['toss_winner'] = data['toss_winner'].map(team_mapping)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "We create a dictionary team_mapping where each team name is mapped to a corresponding unique number. Then, we use the map function in Pandas to replace the team names in the 'team1', 'team2', 'winner', and 'toss_winner' columns with their respective unique numerical values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['M Chinnaswamy Stadium',\n",
       "       'Punjab Cricket Association Stadium, Mohali', 'Feroz Shah Kotla',\n",
       "       'Wankhede Stadium', 'Eden Gardens', 'Sawai Mansingh Stadium',\n",
       "       'Rajiv Gandhi International Stadium, Uppal',\n",
       "       'MA Chidambaram Stadium, Chepauk', 'Dr DY Patil Sports Academy',\n",
       "       'Newlands', \"St George's Park\", 'Kingsmead', 'SuperSport Park',\n",
       "       'Buffalo Park', 'New Wanderers Stadium', 'De Beers Diamond Oval',\n",
       "       'OUTsurance Oval', 'Brabourne Stadium',\n",
       "       'Sardar Patel Stadium, Motera', 'Barabati Stadium',\n",
       "       'Vidarbha Cricket Association Stadium, Jamtha',\n",
       "       'Himachal Pradesh Cricket Association Stadium', 'Nehru Stadium',\n",
       "       'Holkar Cricket Stadium',\n",
       "       'Dr. Y.S. Rajasekhara Reddy ACA-VDCA Cricket Stadium',\n",
       "       'Subrata Roy Sahara Stadium',\n",
       "       'Shaheed Veer Narayan Singh International Stadium',\n",
       "       'JSCA International Stadium Complex', 'Sheikh Zayed Stadium',\n",
       "       'Sharjah Cricket Stadium', 'Dubai International Cricket Stadium',\n",
       "       'Maharashtra Cricket Association Stadium',\n",
       "       'Punjab Cricket Association IS Bindra Stadium, Mohali',\n",
       "       'Saurashtra Cricket Association Stadium', 'Green Park',\n",
       "       'M.Chinnaswamy Stadium'], dtype=object)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the unique venues present in the 'venue' column\n",
    "data['venue'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a dictionary to map each unique venue name to a unique number\n",
    "venue_mapping = {venue: i for i, venue in enumerate(data['venue'].unique())}\n",
    "\n",
    "# Replace the venue names in the 'venue' column with the corresponding unique numbers\n",
    "data['venue'] = data['venue'].map(venue_mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['field', 'bat'], dtype=object)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the unique venues present in the 'toss_decsion' column\n",
    "data['toss_decision'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a dictionary to map 'toss_decision' values to numerical values\n",
    "temp = {'field': 0, 'bat': 1}\n",
    "\n",
    "# Use the map() function to replace 'toss_decision' values with numerical values\n",
    "data['toss_decision'] = data['toss_decision'].map(temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a set of unique umpires\n",
    "umpires_set = set(data['umpire1'].unique()).union(set(data['umpire2'].unique()))\n",
    "\n",
    "# Create a dictionary to map umpire names to unique numbers\n",
    "umpire_dict = {umpire: i for i, umpire in enumerate(umpires_set, 1)}\n",
    "\n",
    "# Apply the dictionary to create new encoded columns for 'umpire1' and 'umpire2'\n",
    "data['umpire1'] = data['umpire1'].map(umpire_dict)\n",
    "data['umpire2'] = data['umpire2'].map(umpire_dict)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a dictionary to map each unique venue name to a unique number\n",
    "player_of_match_mapping = {venue: i for i, venue in enumerate(data['player_of_match'].unique())}\n",
    "\n",
    "# Replace the venue names in the 'venue' column with the corresponding unique numbers\n",
    "data['player_of_match'] = data['player_of_match'].map(player_of_match_mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a dictionary to map each unique venue name to a unique number\n",
    "city_mapping = {venue: i for i, venue in enumerate(data['city'].unique())}\n",
    "\n",
    "# Replace the venue names in the 'venue' column with the corresponding unique numbers\n",
    "data['city'] = data['city'].map(city_mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>city</th>\n",
       "      <th>date</th>\n",
       "      <th>player_of_match</th>\n",
       "      <th>venue</th>\n",
       "      <th>neutral_venue</th>\n",
       "      <th>team1</th>\n",
       "      <th>team2</th>\n",
       "      <th>toss_winner</th>\n",
       "      <th>toss_decision</th>\n",
       "      <th>winner</th>\n",
       "      <th>result</th>\n",
       "      <th>result_margin</th>\n",
       "      <th>eliminator</th>\n",
       "      <th>umpire1</th>\n",
       "      <th>umpire2</th>\n",
       "      <th>season</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2008-04-18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>runs</td>\n",
       "      <td>140.0</td>\n",
       "      <td>N</td>\n",
       "      <td>16</td>\n",
       "      <td>34</td>\n",
       "      <td>2008</td>\n",
       "      <td>18</td>\n",
       "      <td>4</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2008-04-19</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>runs</td>\n",
       "      <td>33.0</td>\n",
       "      <td>N</td>\n",
       "      <td>8</td>\n",
       "      <td>23</td>\n",
       "      <td>2008</td>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2008-04-19</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>wickets</td>\n",
       "      <td>9.0</td>\n",
       "      <td>N</td>\n",
       "      <td>24</td>\n",
       "      <td>38</td>\n",
       "      <td>2008</td>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2008-04-20</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>wickets</td>\n",
       "      <td>5.0</td>\n",
       "      <td>N</td>\n",
       "      <td>30</td>\n",
       "      <td>43</td>\n",
       "      <td>2008</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2008-04-20</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>wickets</td>\n",
       "      <td>5.0</td>\n",
       "      <td>N</td>\n",
       "      <td>14</td>\n",
       "      <td>9</td>\n",
       "      <td>2008</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   city       date  player_of_match  venue  neutral_venue  team1  team2  \\\n",
       "0     0 2008-04-18                0      0              0      4      1   \n",
       "1     1 2008-04-19                1      1              0      6      2   \n",
       "2     2 2008-04-19                2      2              0      3      5   \n",
       "3     3 2008-04-20                3      3              0      8      4   \n",
       "4     4 2008-04-20                4      4              0      1      7   \n",
       "\n",
       "   toss_winner  toss_decision  winner   result  result_margin eliminator  \\\n",
       "0            4              0       1     runs          140.0          N   \n",
       "1            2              1       2     runs           33.0          N   \n",
       "2            5              1       3  wickets            9.0          N   \n",
       "3            8              1       4  wickets            5.0          N   \n",
       "4            7              1       1  wickets            5.0          N   \n",
       "\n",
       "   umpire1  umpire2  season  day  month  year  \n",
       "0       16       34    2008   18      4  2008  \n",
       "1        8       23    2008   19      4  2008  \n",
       "2       24       38    2008   19      4  2008  \n",
       "3       30       43    2008   20      4  2008  \n",
       "4       14        9    2008   20      4  2008  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# to display our data\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# List of unwanted columns\n",
    "unwanted_columns = ['date','result','eliminator','season','day','month','year']\n",
    "\n",
    "# Drop the unwanted columns from the DataFrame\n",
    "data.drop(columns=unwanted_columns, inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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>city</th>\n",
       "      <th>player_of_match</th>\n",
       "      <th>venue</th>\n",
       "      <th>neutral_venue</th>\n",
       "      <th>team1</th>\n",
       "      <th>team2</th>\n",
       "      <th>toss_winner</th>\n",
       "      <th>toss_decision</th>\n",
       "      <th>winner</th>\n",
       "      <th>result_margin</th>\n",
       "      <th>umpire1</th>\n",
       "      <th>umpire2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>140.0</td>\n",
       "      <td>16</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>33.0</td>\n",
       "      <td>8</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>9.0</td>\n",
       "      <td>24</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>30</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>14</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   city  player_of_match  venue  neutral_venue  team1  team2  toss_winner  \\\n",
       "0     0                0      0              0      4      1            4   \n",
       "1     1                1      1              0      6      2            2   \n",
       "2     2                2      2              0      3      5            5   \n",
       "3     3                3      3              0      8      4            8   \n",
       "4     4                4      4              0      1      7            7   \n",
       "\n",
       "   toss_decision  winner  result_margin  umpire1  umpire2  \n",
       "0              0       1          140.0       16       34  \n",
       "1              1       2           33.0        8       23  \n",
       "2              1       3            9.0       24       38  \n",
       "3              1       4            5.0       30       43  \n",
       "4              1       1            5.0       14        9  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# checking for the null values in updated dataframe\n",
    "sns.heatmap(data.isnull(),cmap='rainbow',yticklabels=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Splitting our data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the data into features (X) and the target variable (y)\n",
    "X = data.drop(['winner'], axis=1)\n",
    "y = data['winner']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train shape: (649, 11)\n",
      "y_train shape: (649,)\n",
      "X_test shape: (163, 11)\n",
      "y_test shape: (163,)\n"
     ]
    }
   ],
   "source": [
    "# Split the data into training and testing sets (80% training, 20% testing)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Display the shapes of the training and testing sets\n",
    "print(\"X_train shape:\", X_train.shape)\n",
    "print(\"y_train shape:\", y_train.shape)\n",
    "print(\"X_test shape:\", X_test.shape)\n",
    "print(\"y_test shape:\", y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Identifying Important features**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(n_estimators=500, random_state=1)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create an instance of the RandomForestClassifier with hyperparameters\n",
    "forest = RandomForestClassifier(n_estimators=500, random_state=1)\n",
    "\n",
    "# Train the RandomForestClassifier on the training data\n",
    "forest.fit(X_train, y_train.values.ravel())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 1) city                           0.078633\n",
      " 2) player_of_match                0.139000\n",
      " 3) venue                          0.072343\n",
      " 4) neutral_venue                  0.009793\n",
      " 5) team1                          0.138632\n",
      " 6) team2                          0.132579\n",
      " 7) toss_winner                    0.119872\n",
      " 8) toss_decision                  0.027776\n",
      " 9) winner                         0.096575\n",
      "10) result_margin                  0.092051\n",
      "11) umpire1                        0.092748\n"
     ]
    }
   ],
   "source": [
    "# Get the feature importances from the trained RandomForestClassifier\n",
    "importances = forest.feature_importances_\n",
    "\n",
    "# Loop over each feature and its importance\n",
    "for i in range(X_train.shape[1]):\n",
    "    # Print the feature number, name, and importance score\n",
    "    print(\"%2d) %-*s %f\" % (i + 1, 30, data.columns[i], importances[i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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t/vWTHJPkwiQXJfnHEdsjSZIkrZFVFcj/DZyZ5LM007p9DSDJg2mGWaxSu+LekcCewEJg/yQLp532MuCaqnow8G7g7e3+fYENq+oRwC7Ay6eKZ0mSJGmcVlogV9Vbgb8DPgw8vqqq85i/GuG5dwOWVtUlVXUr8HFg72nn7A0c094/AXhqkgAF3Kvtwd6YZiz0dSO1SJIkSVoDqxwqUVXfmmHf/4743FsDl3W2Lwces7JzqmpZkmuB+9EUy3sDP6dZte9vO+Oh75TkIOAggO22227EWJIkSdLKjbKSXh92A24Hfh/YHvi7JA+cflJVHVVVi6pq0VZbbTXXGSVJkjRA4yyQrwC27Wxv0+6b8Zx2OMXmwFXA84EvVNVtVfUr4BvAojFmlSRJkoDRZ6N4ALBDVX0pycbAelV1/WoedjawQ5LtaQrh/WgK366TgAOAbwL7AGdUVSX5KfAU4Ngk9wIeC/zbiG2aUwsOObnvCHfLpUfs1XcESZKkeW21PchJ/oJmTPD7213bAJ9Z3eOqahlwMHAqcBFwfFUtSXJ4kme1p30QuF+SpcBrgKmp4I4E7p1kCU2h/aGqumDkVkmSJEn30Cg9yK+iGRP8bYCq+mGS3xnlyavqFOCUafsO7dy/mWZKt+mPu2Gm/ZIkSdK4jTIG+ZZ2mjbgzrHCtYrzJUmSpIk1SoF8ZpLXAxsneRrwSeB/xhtLkiRJ6scoBfIhwJXAhcDLaYZMvHGcoSRJkqS+jDIGeWPg6Kr6ANy5hPTGwG/HGUySJEnqwyg9yKfTFMRTNga+NJ44kiRJUr9GKZA3ameVAO6cYWKT8UWSJEmS+jNKgXxjkkdPbSTZBbhpfJEkSZKk/owyBvlvgE8m+RkQ4P7A88YZSpIkSerLagvkqjo7yUOBHdtdF1fVbeONJUmSJPVjlB5kgF2BBe35j05CVX1kbKmkObDgkJP7jnC3XHrEXn1HkCRprbDaAjnJscCDgPOA29vdBVggS5IkaXBG6UFeBCysKpeXliRJ0uCNMovF92guzJMkSZIGb5Qe5C2B7yf5DnDL1M6qetbYUkmSJEk9GaVAPmzcISRJkqT5YpRp3s6ciyCSJEnSfLDaMchJHpvk7CQ3JLk1ye1JrpuLcJIkSdJcG+UivfcB+wM/BDYG/hw4cpyhJEmSpL6MUiBTVUuBdavq9qr6ELDHeGNJkiRJ/RjlIr3fJtkAOC/JO4CfM2JhLUmSJE2aUQrdF7XnHQzcCGwL/Nk4Q0mSJEl9GaVAfnZV3VxV11XVP1fVa4A/GXcwSZIkqQ+jFMgHzLDvwFnOIUmSJM0LKx2DnGR/4PnAA5Oc1Dm0KXD1uINJkiRJfVjVRXpn0VyQtyXwzs7+64ELxhlKkiRJ6stKC+Sq+kmSy4GbXU1PkiRJa4tVjkGuqtuBO5JsPkd5JEmSpF6NMg/yDcCFSU6jmeYNgKp69dhSSZIkST0ZpUA+sb1JkiRJg7faArmqjmlX0ntIu+viqrptvLEkSZKkfqy2QE6yO3AMcCkQYNskB1TVV8eaTJIkSerBKAuFvBN4elU9qaqeCDwDePcoT55kjyQXJ1ma5JAZjm+Y5BPt8W8nWdA5tlOSbyZZkuTCJBuN2CZJkiTpHhulQF6/qi6e2qiq/wXWX92DkqwLHAnsCSwE9k+ycNppLwOuqaoH0xTdb28fux5wHPCXVfUwYHfAYR2SJEkau1EK5MVJ/ivJ7u3tA8DiER63G7C0qi6pqluBjwN7Tztnb5rhGwAnAE9NEuDpwAVVdT5AVV3VTjknSZIkjdUoBfIrgO8Dr25v32/3rc7WwGWd7cvbfTOeU1XLgGuB+9FcEFhJTk1ybpLXzfQCSQ5KsjjJ4iuvvHKESJIkSdKqjTKLxS1J3gecDtxBM4vFrXOQ6/HArsBvgdOTnFNVp0/LdhRwFMCiRYtqzJkkSZK0FlhtD3KSvYAfAe8B3gcsTbLnCM99BbBtZ3ubdt+M57TjjjcHrqLpbf5qVf26qn4LnAI8eoTXlCRJktbIqLNYPLmqdq+qJwFPZrRZLM4GdkiyfTuP8n7ASdPOOQk4oL2/D3BGVRVwKvCIJJu0hfOTaIZ2SJIkSWM1ykp611fV0s72JcD1q3tQVS1LcjBNsbsucHRVLUlyOLC4qk4CPggcm2QpcDVNEU1VXZPkXTRFdgGnVNXJd6dhkiRJ0j0xSoG8OMkpwPE0xeq+wNlJ/gygqla6DHVVnUIzPKK779DO/Zvb55vpscfRTPUmSZIkzZlRCuSNgF/SDHMAuBLYGHgmTcG80gJZkiRJmjSjzGLxkrkIIkmSJM0Hqy2Qk2wP/BWwoHt+VT1rfLEkSZKkfowyxOIzNBfT/Q/NPMiSJEnSYI1SIN9cVf8+9iSSJEnSPDBKgfyeJG8CvgjcMrWzqs4dWypJkiSpJ6MUyI8AXgQ8hbuGWFS7LUmSJA3KKAXyvsADq+rWcYeRJEmS+jbKUtPfA7YYcw5JkiRpXhilB3kL4AdJzmb5MchO8yZJkqTBGaVAftPYU0iSJEnzxCgr6Z05F0EkSZKk+WClBXKS62lmq1jhEFBVtdnYUkmSJEk9WWmBXFWbzmUQSZIkaT4YZRYLSZIkaa1hgSxJkiR1WCBLkiRJHRbIkiRJUocFsiRJktRhgSxJkiR1WCBLkiRJHRbIkiRJUocFsiRJktRhgSxJkiR1WCBLkiRJHRbIkiRJUocFsiRJktRhgSxJkiR1WCBLkiRJHRbIkiRJUsdYC+QkeyS5OMnSJIfMcHzDJJ9oj387yYJpx7dLckOS144zpyRJkjRlbAVyknWBI4E9gYXA/kkWTjvtZcA1VfVg4N3A26cdfxfw+XFllCRJkqYbZw/ybsDSqrqkqm4FPg7sPe2cvYFj2vsnAE9NEoAkzwZ+DCwZY0ZJkiRpOeMskLcGLutsX97um/GcqloGXAvcL8m9gX8A/nmM+SRJkqQVzNeL9A4D3l1VN6zqpCQHJVmcZPGVV145N8kkSZI0aOuN8bmvALbtbG/T7pvpnMuTrAdsDlwFPAbYJ8k7gC2AO5LcXFXv6z64qo4CjgJYtGhRjaMRkiRJWruMs0A+G9ghyfY0hfB+wPOnnXMScADwTWAf4IyqKuAJUyckOQy4YXpxLEmSJI3D2ArkqlqW5GDgVGBd4OiqWpLkcGBxVZ0EfBA4NslS4GqaIlqSJEnqzTh7kKmqU4BTpu07tHP/ZmDf1TzHYWMJJ0mSJM1gvl6kJ0mSJPXCAlmSJEnqsECWJEmSOiyQJUmSpA4LZEmSJKnDAlmSJEnqsECWJEmSOiyQJUmSpA4LZEmSJKnDAlmSJEnqsECWJEmSOiyQJUmSpA4LZEmSJKnDAlmSJEnqsECWJEmSOiyQJUmSpA4LZEmSJKnDAlmSJEnqsECWJEmSOtbrO4AkSZNswSEn9x3hbrn0iL36jiDNe/YgS5IkSR0WyJIkSVKHBbIkSZLUYYEsSZIkdVggS5IkSR0WyJIkSVKHBbIkSZLUYYEsSZIkdVggS5IkSR0WyJIkSVKHBbIkSZLUMdYCOckeSS5OsjTJITMc3zDJJ9rj306yoN3/tCTnJLmw/fMp48wpSZIkTRlbgZxkXeBIYE9gIbB/koXTTnsZcE1VPRh4N/D2dv+vgWdW1SOAA4Bjx5VTkiRJ6hpnD/JuwNKquqSqbgU+Duw97Zy9gWPa+ycAT02SqvpuVf2s3b8E2DjJhmPMKkmSJAHjLZC3Bi7rbF/e7pvxnKpaBlwL3G/aOc8Bzq2qW6a/QJKDkixOsvjKK6+cteCSJElae83ri/SSPIxm2MXLZzpeVUdV1aKqWrTVVlvNbThJkiQN0jgL5CuAbTvb27T7ZjwnyXrA5sBV7fY2wKeBF1fVj8aYU5IkSbrTOAvks4EdkmyfZANgP+CkaeecRHMRHsA+wBlVVUm2AE4GDqmqb4wxoyRJkrScsRXI7Zjig4FTgYuA46tqSZLDkzyrPe2DwP2SLAVeA0xNBXcw8GDg0CTntbffGVdWSZIkacp643zyqjoFOGXavkM7928G9p3hcW8B3jLObJIkSdJM5vVFepIkSdJcs0CWJEmSOiyQJUmSpA4LZEmSJKnDAlmSJEnqsECWJEmSOiyQJUmSpA4LZEmSJKnDAlmSJEnqsECWJEmSOiyQJUmSpA4LZEmSJKnDAlmSJEnqsECWJEmSOiyQJUmSpI71+g4gafYtOOTkviPcLZcesVffESRJupM9yJIkSVKHBbIkSZLUYYEsSZIkdVggS5IkSR0WyJIkSVKHBbIkSZLUYYEsSZIkdVggS5IkSR0WyJIkSVKHBbIkSZLUYYEsSZIkdVggS5IkSR0WyJIkSVKHBbIkSZLUYYEsSZIkdYy1QE6yR5KLkyxNcsgMxzdM8on2+LeTLOgc+8d2/8VJnjHOnJIkSdKUsRXISdYFjgT2BBYC+ydZOO20lwHXVNWDgXcDb28fuxDYD3gYsAfwH+3zSZIkSWM1zh7k3YClVXVJVd0KfBzYe9o5ewPHtPdPAJ6aJO3+j1fVLVX1Y2Bp+3ySJEnSWKWqxvPEyT7AHlX15+32i4DHVNXBnXO+155zebv9I+AxwGHAt6rquHb/B4HPV9UJ017jIOCgdnNH4OKxNKYfWwK/7jvEmAy5bTDs9tm2yWTbJpNtm1xDbt/Q2vaAqtpq+s71+kgyW6rqKOCovnOMQ5LFVbWo7xzjMOS2wbDbZ9smk22bTLZtcg25fUNuW9c4h1hcAWzb2d6m3TfjOUnWAzYHrhrxsZIkSdKsG2eBfDawQ5Ltk2xAc9HdSdPOOQk4oL2/D3BGNWM+TgL2a2e52B7YAfjOGLNKkiRJwBiHWFTVsiQHA6cC6wJHV9WSJIcDi6vqJOCDwLFJlgJX0xTRtOcdD3wfWAa8qqpuH1fWeWqQQ0daQ24bDLt9tm0y2bbJZNsm15DbN+S23WlsF+lJkiRJk8iV9CRJkqQOC2RJkiSpwwJZkiRJ6rBAliRNjCTrJHlu3zkkDZsF8jyS5MQkeyXx5zJhkjwgyR+19zdOsmnfmWZT26Yd+84xbkke2neG2ZBk/Rn2bdlHltlWVXcAr+s7h6RhsxCbX/4DeD7wwyRHDK0gSbJVktcnOSrJ0VO3vnOtqSR/AZwAvL/dtQ3wmd4CzbIkzwTOA77Qbj8yyfQ5zYfii30HWBNJnpzkcuDnSb6YZEHn8ES3bZovJXltkm2T3Hfq1neo2ZDkD5OcluR/k1yS5MdJLuk712wZevu6khzad4bZkOQZSV427f2EJC/tKdKccJq3eSjJ5sD+wBuAy4APAMdV1W29BltDSc4CvgacA9w5r3VVfaq3ULMgyXnAbsC3q+pR7b4Lq+oRvQabJUnOAZ4CfGUI7Uvy7ys7BBxQVZvNZZ7ZlORs4MB2Lvl9gLcBL6qqbyX57tTPb9Il+fEMu6uqHjjnYWZZkh8Af8uK75NX9RZqFg29fV1JflpV2/WdY00k+Rfg8cC5wDOBf6uq97bHzq2qR/eZb5zGtlCI7pkk9wNeCLwI+C7wUZp/nAcAu/eXbFZsUlX/0HeIMbilqm5NAty5bPqQPnneVlXXTrWvNcntewnwd8AtMxzbf46zzLYNqmoJQFWdkOQi4MQk/8Bk/8yWU1Xb951hjK6tqs/3HWKMBtW+JNet7BCw8VxmGZNnAo9qF387DPhYkgdW1d/StHGwLJDnkSSfBnYEjgWeWVU/bw99Isni/pLNms8l+eOqOqXvILPszCSvBzZO8jTglcD/9JxpNi1J8nxg3SQ7AK8Gzuo505o4G/heVa3QhvYXwCS7Lcn9q+oXcOeqpE8FPgc8qN9osyfJJsBrgO2q6qD23+WOVfW5nqPNhi8n+VfgRDof4qrq3P4izaqhte83wK5V9cvpB5JcNvdxZt16VbUMoKp+0w65OyrJJ4EN+o02Xg6xmEdmKh6TbFhVM/V0TYwk19P0XgW4F82b4m3tdk3yV9rQXFUPvAx4Ok2bTgX+qwbyn6stRt7A8u17c1Xd3Guwe6gdq3pzVf227yyzrb1Q9MqqOn/a/i2AV1XVW3sJNsuSfILmK/oXV9XD23+jZ1XVI/tNtuaSfHmG3VVVT5nzMGMwtPYleQtwUlV9Z4Zjb5/0b02TfA7416o6c9r+twCvr6rBXstmgTyPzDSeZ+hjfCTp7kqyuKoWdcdVJzm/qnbuO5s0JEk2Bqiqm2Y4tnVVXTH3qeaGQyzmgST3B7am+Yr+Udw1rmczYJPegs2yJH8KnFFV17bbWwC7V9Vn+sy1ptoLhlb4pDmEC4bgzh6fmdo3kT0+U5L8CfBm4AE074WD+EYDht221q3tL+4CSPIgZh5TPnHai7TfBDyx3XUmcPjU++akSvLCqjouyWtmOl5V75rrTLMpzUUaLwAeWFWHJ9kOuP9MPcuTZKowXln7AAtkjdUzgANppgfrvklcD7y+j0Bj8qaq+vTURjue6U1M/pRoizr3NwL2BQYx5VTrtZ37GwHPAZb1lGU2/RvwZ8CFQxkO0/FvDLdt0BSQXwC2TfJR4A9p3kOH4Gjge8DUYigvAj5E8/OcZPdq/xzUHPEd/wHcQTPjz+E0v78/BezaZ6hZNPT2rcAhFvNIkudM+pRnq5Lkgqraadq+iZ0ubFWSnFNVu/SdY1ySfKeqdus7x5poe8af2i48MShDbtuUdsafx9L0jn+rqn7dc6RZkeS86WOpZ9qn+WVqOORQh/0MvX0zsQd5Hpj66glYMNPXT5P+1VPH4iTvAo5st19Fc6HNREvSHSO+Dk2P8mD+b01bgGEdYBdg857izKbXAackOZPlr6Yfwv+3IbdtykbANTT/1xYmoaq+2nOm2XBTksdX1dehWVgDWGH856RayTzk1wKLq+qzc51nFt2WZF3uGvazFU2P61AMvX0rGMwv8Qk39dXTvWc4NqQu/r8C/gn4BE27TqOZEm3SvbNzfxlwKXd9PToE53DXLCTLgB/TzNox6d4K3EBTaA1tuqIht40kbweeByzhrl/SBQyhQH4FcEw7FjnA1Qxn+Ag0/yYfCnyy3X4OzXvKzkmeXFV/01ewNfTvwKeB30nyVmAf4I39RppVQ2/fChxiMY8kOQb466r6Tbt9H+CdVTWI5RyT7FtVn1zdPmkuJPleVT287xzjMOS2ASS5GNhp0qfAXJUkmwFU1coWophISb4F/GFV3d5ur0ezwurjacbML+wz3z3RTvX5WJoPM0+l+WBzelVd1GuwWTL09q2MPcjzy05TxTFAVV3TzmoxFP/IXb0Gq9o3UZJsSNMLsoDO/6mqOryvTLMtyR+wYvs+0lug2XFKkqdX1Rf7DjIGQ24bwCXA+gxk5gpY+SwPUytYDmh4zH1ovi2dmpXjXsB9q+r2JBP586yqO5Ic2Y7N/UHfeWbb0Nu3MhbI88s6Se5TVdfAnWM/J/5nlGRP4I+BraeNP9uMYcyG8FmaN/tzGNAv7ClJjqVZhe084PZ2dwGTXiC/Anht+0t5MAvXtIbcNoDfAuclOZ3lx1i/ur9Ia2zoszxMeQfNz+4rNP8unwj8S5J7AV/qM9gaOj3Jc4ATBzpzzNDbtwKHWMwjSV5MM63bVI/qvsBbq+rY/lKtuSQ7A4+kmRrm0M6h64EvT30gmFRrwdfZFwEL15Y3Rc1/SQ6YaX9VHTPXWTS6zlf1PwamZsE5u6p+1l+q2dGuGHsvmk6fmxnYh9Kht28mFsjzTJKFNPMMQrOoxvf7zDObkqxfVbf1nWO2JTkKeG9VXdh3lnFI8kng1VX1876zzLZ2nP8ONBcOAQxlJoRBt23IkrwDeAvNzBVfAHYC/rad6WjidacJk+YzC2TNmSQ7AG8DFrL8L+2JXnEuyfeBB9P0itzCXZ+sd1rlAydEO6fuI4HvsPzX2c/qK9NsSPLnwF/TLNBzHk3P1jcnfYVAGHbb4M6pzw5jxZUCJ/q9BO6a87hdefRPgNcAXx3KfLNJ/i/wTQbyVX2Sh1bVD6ZN93mnqjp3rjPNpqG3b1UmfnyrJsqHaFbAejfwZOAlNPPqTro9+w4wZof1HWBM/ppmFahvVdWTkzwU+JeeM82WIbcN4IPA39KM+799NedOmqnfy3sBn6yqa6cu1BuIl9MU/cuSDOGr+tcAB7H8dJ9Tiru+EZ5UQ2/fSlkgay5tXFWnJ0lV/QQ4LMk5LD8ueeJU1U+SPB7Yoao+1E6gPtOc1hOpqs5M8gCa9n0pySbAun3nmgU3V9XNSUiyYdtLsmPfoWbJkNsGcG1Vfb7vEGPyuSQ/oBli8Yr2/eTmnjPNmqoa1EWIVXVQ++eT+84yDkNv36pYIGsu3dJepPHDJAcDVzCAQjLJm2hWz9uRppd8feA44A/7zDVbkvwFTQ/CfWlms9ga+E+a+TAn2eVJtgA+A5yW5BrgJ70mmj1DbhvAl5P8K3Aiyw/7mfive6vqkHYc8rXt1Gc3Anv3nWs2DXF8fJKNaBa+ejxNz+rXgP+sqkF8uBl6+2biGGTNmSS7AhcBWwBvplmu+B1V9a0+c62pJOcBjwLO7axRf8GAxiCfR3PF+bc77buwqh7Ra7BZlORJNP8ev1BVt/adZzYNsW3tuPjpapLHWCd5SlWdkeTPZjpeVSfOdaZxGOr4+CTH08zMNHUx5fOBLapq3/5SzZ6ht28m9iBrzlTV2e3dG2jGHw/FrVVVSabWqL/X6h4wYW6pqlunxkG2K18N4pP1DENjtqa52HLiDbltA/2690nAGcAzZzhWNL3lQzDU8fEPn7YK4JfbC7iHYujtW4EFsuZMkkXAG7jrynMABtDTenyS9wNbtMMRXgp8oOdMs+nMJK8HNk7yNJqv2f6n50xrbMhDY4batpWtNjdlklebq6o3tX8OqfNgJkMdH39uksdOfSOa5DHA4p4zzaaht28FFsiaSx8F/h64ELij5yyzqYCvA9cBDwEOrarT+o00qw4BXkbzc3s5cArwX70mmh1/Sjs0BqCqfpZkKBcQDbVtg19tLsm/0Aw9+027fR/g76rqjb0Gmz1DHR+/C3BWkp+229sBFye5kGFM+zn09q3AAllz6cqqOqnvEGNwb5pe46uBTwAX9Btn1j0b+EhVDalXHIY9NGaQbauq97d33z7gi4P2rKrXT21U1TVJ/hgYRIFcVX/a3j2sHUu+Oc2CKJNuj74DjNnQ27cCL9LTnEnyVGB/4HSWv/J8EGPrkuwEPA94DnB5Vf1Rz5FmRZIP0cx1+VWaDwBfqKpl/aZac0leS3Ml/dNoFrB5KfCxqnpvr8FmwZDbBpBkKfBLmivpvwZ8vaqu7TfV7EhyAbBrVd3Sbm8MLK6qh/WbbPa0veLbsvxQu4mfgWSo7Zoy9PZNZw+y5tJLgIfSjIecGmIxpItPfgX8ArgK+J2es8yaqnpJkvVpFkTZHzgyyWlV9ec9R1tTWwEn0AyN2ZFmPu5BfKhh2G2jqh6cZDvgCTQLahyZ5DdV9ch+k82KjwKntx9MoXnfPKbHPLMqyZuBA4FLWP73wKTPYjHVrh9x10XME9+uKUNv30zsQdacSXJxVQ3hYozlJHkl8FyaouSTwPFVNbire9sieQ+aX9hPrKote460RpKcW1WPnrZvENPzDbltAEm2oSmOnwTsTDO86etV9bZeg82SJHtw1wea06rq1D7zzKYkFwOPGMqUg1OG2q4pQ2/fTOxB1lw6K8nCARaP2wJ/U1Xn9R1kHJLsSTN0ZHfgKzQX6D23x0hrJMkraGbieGD7dfaUTYFv9JNqdgy5bdP8FDgb+Jeq+su+w4zBRcCyqZUrk2xaVdf3HWqWfI9mLvxf9Zxjtg21XVOG3r4V2IOsOZPkIpqV2H5MMwY5DPTq1yFJ8t80Y48/PzUucpIl2Ry4D83Y3EM6h66vqqv7STU7hty2riQ706zo9USaq+l/CJxZVR/sNdgs6K5cWVUPSrIDzYplk75yJXDndJ+fpSm4uteiPKu3ULNgqO2aMvT2zcQCWXMmyQNm2l9VP2mP36eqrpnbVFpTSb5ZVY/rO4fWLknuTVMkPwF4IUBVzfgeM0mGvnJlkiXA+5k23WdVndlbqFkw1HZNGXr7ZuIQC82ZqUJ4FU4HHr2aczT/bNR3AK1dkiwGNgTOopnF4okjvL9MisGuXNn6bVX9e98hxmCo7Zoy9PatwAJZ80n6DqB7ZEi/vDUZ9qyqK1d2MMkBVTWpMz8McuXKjq8leRtwEst/VT/p04UNtV1Tht6+FTjEQvPGTFfea/7z56b5ZpL/TSZZh2blyqfTdBqcCvxXDeSXdbs4yHRVVRM9XVinXVM/p6lrbCa6XVOG3r6Z2IMsaU3Z86/5ZmL/TVbVHcAH2tvgVNWTV3V8gnv/vzLDvkF8qGl9ZYZ9Q2rfCiyQNXZJtq+qH49y6tjDaBxe1HcAaZqJ+8Wd5EJWkXstmu3nr5nMhVFu6NzfCPgTmun6hmLo7VuBQyw0dknOqapdkpy+qqmKktx3SFNRTbok1zPzL+ypr9Y2m+NI0kiSfHdqBohJ0Znl51Xtn8e2f76Q5v/bISs+angm8Wc3kyQbAqdW1e59ZxmHobcP7EHW3FinvejkIUleM/1gVb2r/dPieB6pqk37ziDdQxO3KEpnusunTSsQ/yHJuSw/r/WQDaXXbhNgm75DjNHQ22eBrDmxH/Bsmn9vFl0TKsnv0JnSrap+2mMcrcWS/DXwIeB6mpUdHwUcUlVfBKiqg3uMt6aS5A+r6hvtxh8A6/ScaS5N5FC7aUNk1gW2Ag7vL9HsGnr7ZuIQC82ZJHtW1ef7zqG7J8mzgHcCv0+zzOgDgIuq6mG9BtNaK8n5VbVzkmcALwf+CTh2Umeu6EqyC3A0sHm76zfAS4cyndZM16R09yV53yR+wJm2ENYy4JdVtayvPLNt6O2biQWy5ky7DO6baJaHBTgTOLyqru0vlVYnyfnAU4AvVdWjkjwZeGFVvaznaFpLJbmgqnZK8h7gK1X16aGMXZ3Svl8y/f1xgmd5AGaegm/qOpW+MkkzcYiF5tLRNOu4P7fdfhHN16R/1lsijeK2qroqyTpJ1qmqLyf5t75Daa12TpIvAtsD/5hkUzrL3w7BKjoOJnKWhyQPBR4GbJ6k+56/Ga7GqXnIAllz6UFV9ZzO9j8nOa+vMBrZb5LcG/gq8NEkvwJu7DmT1m4vAx4JXFJVv01yX+Al/UaaMxM5RhfYkWZqsC2AZ3b2Xw/8RR+BpFWxQNZcuinJ46vq6wBJ/hC4qedMWr29aX5Ofwu8gGZs5KAvztC89zjgvKq6MckLgUcD7+k501yZyHGRVfVZ4LNJHldV3+w7j7Q6jkHWnEmyM/AR7rr45BrggKq6oL9UWpUk69KMPV7l6lfSXEpyAbAzsBPwYZqZLJ5bVU/qM9dcmNSx1kney6oXQnn1HMaRVsseZM2Zqjof2DnJZu32dd3jk37xyRBV1e1J7kiyuRdTah5ZVlWVZG/gfVX1wSRry0WjEzfHc2tx3wGku8MeZM0bM13drP4l+SzNPLOn0Rl7bI+P+pLkTOALwEuBJ9BMP3h+VT2i12CzYHVzPEuaG/Ygaz6Z1ItPhu7E9tblJ2v16XnA82nmB/5Fku2Af+0502x5aVW9p53j+T40s/0cCwyiQE7yZWZ4/6iqp/QQR1opC2TNJxZd89MWVbXcBVBtL5fUi7Yo/iiwa5I/Ab5TVR/pO9csmeoo+GOaxU+WJBlS58FrO/c3Ap5Ds/CENK84xELzxqRefDJ0K5nY35+VepPkuTQ9xl+hKSifAPx9VZ3QZ67ZkORDwNY0czzvTLOs71eGvJBGku9U1W5955C67EHWnEiyDrBPVR2/itMm9eKTQUqyP83X2NsnOalzaFPg6n5SSQC8Adi1qn4FkGQr4EvAxBfIDHyO57Y9U9YBduGumY2kecMCWXOiqu5I8jpgpQVyVR08h5G0emcBPwe2BN7Z2X894NR86tM6U8Vx6yqaYmsIhj7H8zk0w+lCM7TixzQfCqR5xSEWmjNJjgB+DXyC5WdDsDdS0siSvINm+MF/t7ueB1xQVf/QX6rZsTbP8SzNJ0P5xK3J8DzgVTRLFp/T3pwbc55Lcn2S69rbzUluT3Ld6h8pjU0B76cpIncCjuo3zqxaVk3P1dQcz0fSDGsahCT7Jtm0vf/GJCcmcXpPzTv2IEsaWXs1/d7AY6vqkL7zaO20kgtHL6iqnfrKNFuGPMcz3PVzSvJ44C00F1seWlWP6TmatBx7kDVnkmzS9hgc1W7v0E7RpAlRjc8Az+g7i9Y+SV6R5EJgxyQXdG4/Zjjj4p8H3EI7xzOwDcOZ4xng9vbPvYCjqupkYIMe80gzsgdZcybJJ2iGVby4qh6eZBPgrKp6ZL/JtCpJ/qyzuQ6wCHhSVT2up0haSyXZnGbxjLcB3W8wrh/StQxJfhfYtd38zrQLEidaks8BVwBPo7kA8SaaNu7cazBpGgtkzZkki6tqUXcO3STn+8Y4v7Xzsk5ZBlwKfGBIv7Sl+WLIczxD800isAdwYVX9MMnvAY9wKW3NN07zprl0a5KNaVfMS/Igmq8SNY9V1WDmYJUmwJDneKad2/lXwOOBH9J86P5hv6mkFTkGWXPpMJqLT7Ztl4k9HXhdr4m0WkkekuT0JN9rt3dK8sa+c0kDNeQ5nknyJuAfgH9sd60PHNdfImlmDrHQnEpyP+CxNF8dfquqft1zJK1Ge1X93wPv7wyN+V5VPbzfZNLwDHmOZ4Ak5wGPAs7tvJ8MYgYSDYtDLDRnknwK+CDw+aq6o+88GtkmVfWdZoa3Oy3rK4w0cFNzPD++3T6KplNhKG6tqkoyNdTuXn0HkmYymK9tNBH+H/AC4IdJjkiyY9+BNJJft+PFp36h7UOzBLWk2fe0qjqxql7T3j4N7Nl3qNnQzqP+uSTvB7ZI8hc046s/0G8yaUUOsdCca6dq2p/mYpTLaN4cj6uq23oNphkleSBNL9YfANcAPwZeUFU/6TWYNCBJXgG8Engg8KPOoU2Bb1TVC3sJNsvaeaxfAzydZqjdqVV1Wr+ppBVZIGtOtWOQXwi8CPgZ8FGarxIfUVW79xhNK5FkQ2AfYAFwX+A6mjVDDu8zlzQka9Ecz8fQLKF9dt9ZpFWxQNacSfJpYEfgWODDVfXzzrHFVbWot3BaqSRfAH4DnMtdq2BRVe/sK5OkyZTkB8CDgZ8AN07t9yI9zTcWyJozSZ5cVV/uO4fuHmeskDRbkjxgpv0O2dJ84ywWmjNV9eUkDwcWAht19n+kv1QawVlJHlFVF/YdRNJksxDWpLAHWXOmnSB+d5oC+RSaK7O/XlX79JlLq5bk+zRfif6YZuXD0IxB9itRSdIgWSBrzrRXL+8MfLeqdk7yuzSzVzyt52haBb8SlSStbRxiobl0U1XdkWRZks2AXwHb9h1Kq2YhLEla21ggay4tTrIFzbzH5wA3AN/sNZEkSdI0DrFQL5IsADarqgv6ziJJktRlgayxS/LoVR2vqnPnKoskSdLqWCBr7JJ05z7u/oObmg3hKXMcSZIkaaUskDVnkmwMvJJmaekCvgb8v6q6uddgkiRJHRbImjNJjgeuAz7a7no+sHlVPbe/VJIkScuzQNacSfL9qlq4un2SJEl9WqfvAFqrnJvksVMbSR4DLO4xjyRJ0grsQdacSXIRsCPw03bXdsDFwDJculiSJM0TFsiaMytbsniKK7ZJkqT5wAJZkiRJ6nAMsiRJktRhgSxJkiR1WCBL0jyT5PYk53VuC+7Bczw7iVMoStI9sF7fASRJK7ipqh65hs/xbOBzwPdHfUCS9apq2Rq+riRNPHuQJWkCJNklyZlJzklyapLfa/f/RZKzk5yf5FNJNknyB8CzgH9te6AflOQrSRa1j9kyyaXt/QOTnJTkDOD0JPdKcnSS7yT5bpK92/Me1u47L8kFSXbo529CksbPAlmS5p+NO8MrPp1kfeC9wD5VtQtwNPDW9twTq2rXqtoZuAh4WVWdBZwE/H1VPbKqfrSa13t0+9xPAt4AnFFVuwFPpimy7wX8JfCetmd7EXD57DZZkuYPh1hI0vyz3BCLJA8HHg6clgRgXeDn7eGHJ3kLsAVwb+DUe/B6p1XV1e39pwPPSvLadnsjmkV9vgm8Ick2NEX5D+/B60jSRLBAlqT5L8CSqnrcDMc+DDy7qs5PciCw+0qeYxl3fWu40bRjN057redU1cXTzrkoybeBvYBTkry8qs4YvQmSNDkcYiFJ89/FwFZJHgeQZP0kD2uPbQr8vB2G8YLOY65vj025FNilvb/PKl7rVOCv0nZVJ3lU++cDgUuq6t+BzwIuDS9psCyQJWmeq6pbaYratyc5HzgP+IP28D8B3wa+Afyg87CPA3/fXmj3IOD/Aq9I8l1gy1W83JuB9YELkixptwGeC3wvyXk0wz0+MgtNk6R5yaWmJUmSpA57kCVJkqQOC2RJkiSpwwJZkiRJ6rBAliRJkjoskCVJkqQOC2RJkiSpwwJZkiRJ6vj/8079mZ6Nha0AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the feature importances as a bar chart\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.bar(range(X_train.shape[1]), importances, align='center')\n",
    "plt.title('Feature Importance')\n",
    "plt.xticks(range(X_train.shape[1]), X_train.columns, rotation=90)\n",
    "plt.xlabel('Features')\n",
    "plt.ylabel('Importance Score')\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Why we did this feature slection using Random Forest?**\n",
    "\n",
    "Feature selection using random forest is a popular technique to identify and prioritize important features in a dataset when building predictive models. Random forests are an ensemble learning method that combines multiple decision trees to improve predictive performance and reduce overfitting.\n",
    "\n",
    "The process of feature selection using random forest involves the following steps:\n",
    "\n",
    "1. Train a Random Forest model: We first train a Random Forest classifier on the training dataset, which consists of input features (X_train) and the target variable (y_train). Random Forests are capable of measuring the importance of each feature in predicting the target variable.\n",
    "\n",
    "2. Feature Importance Scores: After training the Random Forest model, we can access the feature importance scores using the feature_importances_ attribute of the model. Feature importance scores indicate how much each feature contributes to the prediction task. Higher importance scores mean that a feature has more influence on the model's predictions.\n",
    "\n",
    "3. Sorting Feature Importance: We sort the feature importance scores in descending order to identify the most important features at the top of the list and the least important ones at the bottom.\n",
    "\n",
    "4. Visualize the Feature Importance: To better understand the results, we often visualize the feature importance scores using a bar chart. The bar chart helps us identify the most significant features by their relative heights on the chart.\n",
    "\n",
    "5. Feature Selection: Based on the feature importance scores, we can decide to keep only the most important features in the dataset for model training. This step is essential in reducing the dimensionality of the dataset, which can lead to faster training times and potentially better model performance.\n",
    "\n",
    "By using Random Forest for feature selection, we can identify the most relevant features, which can be crucial for model interpretability, reducing complexity, and improving the overall performance of the predictive model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Training our Model**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **1. Logistic Regression**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Vikas Jadaun\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Initialize the logistic regression model\n",
    "model = LogisticRegression()  \n",
    "\n",
    "# Train the model on the training data\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.26993865030674846\n"
     ]
    }
   ],
   "source": [
    "# Make predictions on the testing data\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "# Evaluate the model's performance using accuracy as an example\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(\"Accuracy:\", accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **2. SVM**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC()"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Initialize the logistic regression model\n",
    "model = SVC()\n",
    "\n",
    "# Train the model on the training data\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.17177914110429449\n"
     ]
    }
   ],
   "source": [
    "# Make predictions on the testing data\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "# Evaluate the model's performance using accuracy as an example\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(\"Accuracy:\", accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **3. Random Forest** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(n_estimators=13)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Initialize the logistic regression model\n",
    "model = RandomForestClassifier(n_estimators=13)\n",
    "\n",
    "# Train the model on the training data\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.6196319018404908\n"
     ]
    }
   ],
   "source": [
    "# Make predictions on the testing data\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "# Evaluate the model's performance using accuracy as an example\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(\"Accuracy:\", accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **4. LGBMClassifier**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 368\n",
      "[LightGBM] [Info] Number of data points in the train set: 649, number of used features: 11\n",
      "[LightGBM] [Info] Start training from score -2.044616\n",
      "[LightGBM] [Info] Start training from score -2.093406\n",
      "[LightGBM] [Info] Start training from score -2.270740\n",
      "[LightGBM] [Info] Start training from score -2.093406\n",
      "[LightGBM] [Info] Start training from score -2.285778\n",
      "[LightGBM] [Info] Start training from score -2.226937\n",
      "[LightGBM] [Info] Start training from score -2.068713\n",
      "[LightGBM] [Info] Start training from score -2.068713\n",
      "[LightGBM] [Info] Start training from score -3.339939\n",
      "[LightGBM] [Info] Start training from score -4.865995\n",
      "[LightGBM] [Info] Start training from score -4.172848\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
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      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LGBMClassifier()"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create an instance of the LGBMClassifier model\n",
    "model = lgb.LGBMClassifier(\n",
    "    boosting_type='gbdt',       # The type of boosting algorithm to use ('gbdt': Gradient Boosting Decision Tree)\n",
    "    num_leaves=31,              # Number of leaves in one tree (default: 31)\n",
    "    max_depth=-1,               # Maximum tree depth for base learners (-1 means no limit, default: -1)\n",
    "    learning_rate=0.1,          # Learning rate or shrinkage rate to prevent overfitting (default: 0.1)\n",
    "    n_estimators=100            # Number of boosting iterations (default: 100)\n",
    ")\n",
    "\n",
    "# Fit the model on the training data\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.6871165644171779\n"
     ]
    }
   ],
   "source": [
    "# Make predictions on the testing data\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "# Evaluate the model's performance using accuracy as an example\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(\"Accuracy:\", accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Buidling a predictive system**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to get user input for prediction\n",
    "def get_user_input():\n",
    "    City = input(\"Enter the city: \")\n",
    "    Player_of_match = input(\"Enter the player of the match: \")\n",
    "    Venue = input(\"Enter the venue: \")\n",
    "    Neutral_venue = int(input(\"Is it a neutral venue? (0 for No, 1 for Yes): \"))\n",
    "    Team1 = input(\"Enter the first team: \")\n",
    "    Team2 = input(\"Enter the second team: \")\n",
    "    Toss_winner = input(\"Enter the toss winner: \")\n",
    "    Toss_decision = input(\"Enter the toss decision (bat or field): \")\n",
    "    Result_margin = float(input(\"Enter the result margin: \"))\n",
    "    Umpire1 = input(\"Enter the name of the first umpire: \")\n",
    "    Umpire2 = input(\"Enter the name of the second umpire: \")\n",
    "\n",
    "\n",
    "    # Display the user input and win probability for Team 1\n",
    "    print(\"\\n\\nUser Input:\")\n",
    "    print(f\"City: {City}\")\n",
    "    print(f\"Player of the Match: {Player_of_match}\")\n",
    "    print(f\"Venue: {Venue}\")\n",
    "    print(f\"Neutral Venue: {'Yes' if Neutral_venue == 1 else 'No'}\")\n",
    "    print(f\"Team 1: {Team1}\")\n",
    "    print(f\"Team 2: {Team2}\")\n",
    "    print(f\"Toss Winner: {Toss_winner}\")\n",
    "    print(f\"Toss Decision: {Toss_decision}\")\n",
    "    print(f\"Umpire1: {Umpire1}\")\n",
    "    print(f\"Umpire2: {Umpire2}\")\n",
    "    print(\"\\n\")\n",
    "\n",
    "    # Map user input to numerical forms based on the mappings\n",
    "    city_numeric = city_mapping.get(City, -1)\n",
    "    player_of_match_numeric = player_of_match_mapping.get(Player_of_match, -1)\n",
    "    venue_numeric = venue_mapping.get(Venue, -1)\n",
    "    team1_numeric = team_mapping.get(Team1,-1)\n",
    "    team2_numeric = team_mapping.get(Team2,-1)\n",
    "    toss_winner_numeric = team_mapping.get(Toss_winner,-1)\n",
    "    toss_decision_numeric = temp.get(Toss_decision,-1)\n",
    "    umpire1_numeric = umpire_dict.get(Umpire1,-1)\n",
    "    umpire2_numeric = umpire_dict.get(Umpire2,-1)\n",
    "\n",
    "    user_data = pd.DataFrame({\n",
    "        'city': [city_numeric],\n",
    "        'player_of_match': [player_of_match_numeric],\n",
    "        'venue': [venue_numeric],\n",
    "        'neutral_venue': [Neutral_venue],\n",
    "        'team1': [team1_numeric],\n",
    "        'team2': [team2_numeric],\n",
    "        'toss_winner': [toss_winner_numeric],\n",
    "        'toss_decision': [toss_decision_numeric],\n",
    "        'result_margin': [Result_margin],\n",
    "        'umpire1': [umpire1_numeric],\n",
    "        'umpire2': [umpire2_numeric]\n",
    "    })\n",
    "\n",
    "    return user_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to make predictions on user input\n",
    "def make_predictions(user_data):\n",
    "    # Make predictions on the user input data\n",
    "    predictions = model.predict(user_data)\n",
    "\n",
    "    # Get the probability of winning for the 1st team (team1)\n",
    "    win_probability_team1 = predictions[0]\n",
    "\n",
    "    # Convert probability to percentage\n",
    "    win_probability_percentage_team1 = win_probability_team1 * 10\n",
    "\n",
    "    return win_probability_percentage_team1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Welcome to the IPL Match Winner Predictor!\n",
      "\n",
      "\n",
      "User Input:\n",
      "City: Jaipur\n",
      "Player of the Match: SR Watson\n",
      "Venue: Sawai Mansingh Stadium\n",
      "Neutral Venue: No\n",
      "Team 1: Rajasthan Royals\n",
      "Team 2: Delhi Daredevils\n",
      "Toss Winner: Delhi Daredevils\n",
      "Toss Decision: bat\n",
      "Umpire1: Aleem Dar\n",
      "Umpire2: RB Tiffin\n",
      "\n",
      "\n",
      "Win Probability for Team 1: 50.00%\n",
      "\n",
      "\n",
      "Thank you for using the IPL Match Winner Predictor!\n"
     ]
    }
   ],
   "source": [
    "# Main function to run the predictive system\n",
    "def main():\n",
    "    print(\"Welcome to the IPL Match Winner Predictor!\")\n",
    "\n",
    "    while True:\n",
    "        user_data = get_user_input()\n",
    "        win_probability_team1 = make_predictions(user_data)\n",
    "\n",
    "        # Display the win probability for the 1st team (team1)\n",
    "        print(f\"Win Probability for Team 1: {win_probability_team1:.2f}%\")\n",
    "\n",
    "        # Ask if the user wants to make another prediction\n",
    "        another_prediction = input(\"Do you want to make another prediction? (yes or no): \")\n",
    "        if another_prediction.lower() != 'yes':\n",
    "            break\n",
    "\n",
    "    print(\"\\n\\nThank you for using the IPL Match Winner Predictor!\")\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    main()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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