Untitled.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 330,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\gautam jAISWAL\\anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3063: DtypeWarning: Columns (1) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    }
   ],
   "source": [
    "data = pd.read_csv(\"all_matches.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>match_id</th>\n",
       "      <th>season</th>\n",
       "      <th>start_date</th>\n",
       "      <th>venue</th>\n",
       "      <th>innings</th>\n",
       "      <th>ball</th>\n",
       "      <th>batting_team</th>\n",
       "      <th>bowling_team</th>\n",
       "      <th>striker</th>\n",
       "      <th>non_striker</th>\n",
       "      <th>...</th>\n",
       "      <th>extras</th>\n",
       "      <th>wides</th>\n",
       "      <th>noballs</th>\n",
       "      <th>byes</th>\n",
       "      <th>legbyes</th>\n",
       "      <th>penalty</th>\n",
       "      <th>wicket_type</th>\n",
       "      <th>player_dismissed</th>\n",
       "      <th>other_wicket_type</th>\n",
       "      <th>other_player_dismissed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>335982</td>\n",
       "      <td>2007/08</td>\n",
       "      <td>2008-04-18</td>\n",
       "      <td>M Chinnaswamy Stadium</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>SC Ganguly</td>\n",
       "      <td>BB McCullum</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>335982</td>\n",
       "      <td>2007/08</td>\n",
       "      <td>2008-04-18</td>\n",
       "      <td>M Chinnaswamy Stadium</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>BB McCullum</td>\n",
       "      <td>SC Ganguly</td>\n",
       "      <td>...</td>\n",
       "      <td>0</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>335982</td>\n",
       "      <td>2007/08</td>\n",
       "      <td>2008-04-18</td>\n",
       "      <td>M Chinnaswamy Stadium</td>\n",
       "      <td>1</td>\n",
       "      <td>0.3</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>BB McCullum</td>\n",
       "      <td>SC Ganguly</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>335982</td>\n",
       "      <td>2007/08</td>\n",
       "      <td>2008-04-18</td>\n",
       "      <td>M Chinnaswamy Stadium</td>\n",
       "      <td>1</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>BB McCullum</td>\n",
       "      <td>SC Ganguly</td>\n",
       "      <td>...</td>\n",
       "      <td>0</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>335982</td>\n",
       "      <td>2007/08</td>\n",
       "      <td>2008-04-18</td>\n",
       "      <td>M Chinnaswamy Stadium</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>BB McCullum</td>\n",
       "      <td>SC Ganguly</td>\n",
       "      <td>...</td>\n",
       "      <td>0</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   match_id   season  start_date                  venue  innings  ball  \\\n",
       "0    335982  2007/08  2008-04-18  M Chinnaswamy Stadium        1   0.1   \n",
       "1    335982  2007/08  2008-04-18  M Chinnaswamy Stadium        1   0.2   \n",
       "2    335982  2007/08  2008-04-18  M Chinnaswamy Stadium        1   0.3   \n",
       "3    335982  2007/08  2008-04-18  M Chinnaswamy Stadium        1   0.4   \n",
       "4    335982  2007/08  2008-04-18  M Chinnaswamy Stadium        1   0.5   \n",
       "\n",
       "            batting_team                 bowling_team      striker  \\\n",
       "0  Kolkata Knight Riders  Royal Challengers Bangalore   SC Ganguly   \n",
       "1  Kolkata Knight Riders  Royal Challengers Bangalore  BB McCullum   \n",
       "2  Kolkata Knight Riders  Royal Challengers Bangalore  BB McCullum   \n",
       "3  Kolkata Knight Riders  Royal Challengers Bangalore  BB McCullum   \n",
       "4  Kolkata Knight Riders  Royal Challengers Bangalore  BB McCullum   \n",
       "\n",
       "   non_striker  ... extras  wides  noballs  byes  legbyes  penalty  \\\n",
       "0  BB McCullum  ...      1    NaN      NaN   NaN      1.0      NaN   \n",
       "1   SC Ganguly  ...      0    NaN      NaN   NaN      NaN      NaN   \n",
       "2   SC Ganguly  ...      1    1.0      NaN   NaN      NaN      NaN   \n",
       "3   SC Ganguly  ...      0    NaN      NaN   NaN      NaN      NaN   \n",
       "4   SC Ganguly  ...      0    NaN      NaN   NaN      NaN      NaN   \n",
       "\n",
       "   wicket_type  player_dismissed other_wicket_type other_player_dismissed  \n",
       "0          NaN               NaN               NaN                    NaN  \n",
       "1          NaN               NaN               NaN                    NaN  \n",
       "2          NaN               NaN               NaN                    NaN  \n",
       "3          NaN               NaN               NaN                    NaN  \n",
       "4          NaN               NaN               NaN                    NaN  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 332,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>match_id</th>\n",
       "      <th>innings</th>\n",
       "      <th>ball</th>\n",
       "      <th>runs_off_bat</th>\n",
       "      <th>extras</th>\n",
       "      <th>wides</th>\n",
       "      <th>noballs</th>\n",
       "      <th>byes</th>\n",
       "      <th>legbyes</th>\n",
       "      <th>penalty</th>\n",
       "      <th>other_wicket_type</th>\n",
       "      <th>other_player_dismissed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.943540e+05</td>\n",
       "      <td>194354.000000</td>\n",
       "      <td>194354.000000</td>\n",
       "      <td>194354.000000</td>\n",
       "      <td>194354.000000</td>\n",
       "      <td>5884.000000</td>\n",
       "      <td>774.000000</td>\n",
       "      <td>511.000000</td>\n",
       "      <td>3118.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.587790e+05</td>\n",
       "      <td>1.483839</td>\n",
       "      <td>9.532730</td>\n",
       "      <td>1.241122</td>\n",
       "      <td>0.066420</td>\n",
       "      <td>1.210401</td>\n",
       "      <td>1.045220</td>\n",
       "      <td>1.831703</td>\n",
       "      <td>1.293137</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.070766e+05</td>\n",
       "      <td>0.503402</td>\n",
       "      <td>5.682251</td>\n",
       "      <td>1.611592</td>\n",
       "      <td>0.340048</td>\n",
       "      <td>0.796209</td>\n",
       "      <td>0.372776</td>\n",
       "      <td>1.292153</td>\n",
       "      <td>0.829763</td>\n",
       "      <td>0.0</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>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.012280e+05</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.292990e+05</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9.400000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.082630e+06</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>14.400000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.254060e+06</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>19.900000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           match_id        innings           ball   runs_off_bat  \\\n",
       "count  1.943540e+05  194354.000000  194354.000000  194354.000000   \n",
       "mean   7.587790e+05       1.483839       9.532730       1.241122   \n",
       "std    3.070766e+05       0.503402       5.682251       1.611592   \n",
       "min    3.359820e+05       1.000000       0.100000       0.000000   \n",
       "25%    5.012280e+05       1.000000       4.500000       0.000000   \n",
       "50%    7.292990e+05       1.000000       9.400000       1.000000   \n",
       "75%    1.082630e+06       2.000000      14.400000       1.000000   \n",
       "max    1.254060e+06       6.000000      19.900000       6.000000   \n",
       "\n",
       "              extras        wides     noballs        byes      legbyes  \\\n",
       "count  194354.000000  5884.000000  774.000000  511.000000  3118.000000   \n",
       "mean        0.066420     1.210401    1.045220    1.831703     1.293137   \n",
       "std         0.340048     0.796209    0.372776    1.292153     0.829763   \n",
       "min         0.000000     1.000000    1.000000    1.000000     1.000000   \n",
       "25%         0.000000     1.000000    1.000000    1.000000     1.000000   \n",
       "50%         0.000000     1.000000    1.000000    1.000000     1.000000   \n",
       "75%         0.000000     1.000000    1.000000    4.000000     1.000000   \n",
       "max         7.000000     5.000000    5.000000    4.000000     5.000000   \n",
       "\n",
       "       penalty  other_wicket_type  other_player_dismissed  \n",
       "count      2.0                0.0                     0.0  \n",
       "mean       5.0                NaN                     NaN  \n",
       "std        0.0                NaN                     NaN  \n",
       "min        5.0                NaN                     NaN  \n",
       "25%        5.0                NaN                     NaN  \n",
       "50%        5.0                NaN                     NaN  \n",
       "75%        5.0                NaN                     NaN  \n",
       "max        5.0                NaN                     NaN  "
      ]
     },
     "execution_count": 333,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "match_id                  819\n",
       "season                     17\n",
       "start_date                599\n",
       "venue                      42\n",
       "innings                     6\n",
       "ball                      180\n",
       "batting_team               15\n",
       "bowling_team               15\n",
       "striker                   540\n",
       "non_striker               533\n",
       "bowler                    422\n",
       "runs_off_bat                7\n",
       "extras                      7\n",
       "wides                       5\n",
       "noballs                     4\n",
       "byes                        4\n",
       "legbyes                     5\n",
       "penalty                     1\n",
       "wicket_type                 9\n",
       "player_dismissed          510\n",
       "other_wicket_type           0\n",
       "other_player_dismissed      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 334,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['match_id', 'season', 'start_date', 'venue', 'innings', 'ball',\n",
       "       'batting_team', 'bowling_team', 'striker', 'non_striker', 'bowler',\n",
       "       'runs_off_bat', 'extras', 'wides', 'noballs', 'byes', 'legbyes',\n",
       "       'penalty', 'wicket_type', 'player_dismissed', 'other_wicket_type',\n",
       "       'other_player_dismissed'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 335,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 194354 entries, 0 to 194353\n",
      "Data columns (total 22 columns):\n",
      " #   Column                  Non-Null Count   Dtype  \n",
      "---  ------                  --------------   -----  \n",
      " 0   match_id                194354 non-null  int64  \n",
      " 1   season                  194354 non-null  object \n",
      " 2   start_date              194354 non-null  object \n",
      " 3   venue                   194354 non-null  object \n",
      " 4   innings                 194354 non-null  int64  \n",
      " 5   ball                    194354 non-null  float64\n",
      " 6   batting_team            194354 non-null  object \n",
      " 7   bowling_team            194354 non-null  object \n",
      " 8   striker                 194354 non-null  object \n",
      " 9   non_striker             194354 non-null  object \n",
      " 10  bowler                  194354 non-null  object \n",
      " 11  runs_off_bat            194354 non-null  int64  \n",
      " 12  extras                  194354 non-null  int64  \n",
      " 13  wides                   5884 non-null    float64\n",
      " 14  noballs                 774 non-null     float64\n",
      " 15  byes                    511 non-null     float64\n",
      " 16  legbyes                 3118 non-null    float64\n",
      " 17  penalty                 2 non-null       float64\n",
      " 18  wicket_type             9560 non-null    object \n",
      " 19  player_dismissed        9560 non-null    object \n",
      " 20  other_wicket_type       0 non-null       float64\n",
      " 21  other_player_dismissed  0 non-null       float64\n",
      "dtypes: float64(8), int64(4), object(10)\n",
      "memory usage: 32.6+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1fda3d01888>"
      ]
     },
     "execution_count": 337,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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XF0fPnkWbiKNfv34AREREcO7cOdLS0jh//jzR0dEcPHgQEeHy5ctFqvtGpqfFlFJuc/PNNzu2s6/tcu0aLlWrVnVsZy17HBERQVxcHHXr1mXAgAHMnTvXaRvuXibZ2Ro0r7zyCpGRkSQnJ/PFF1/oGjRFoCMXpcqh0nDr8J133sm+ffto1KgRy5cvp2bNmvmWP3LkCHXr1uWJJ57gwoULbN++nYEDB+YqFxoaynPPPcfp06fx9vZm4cKFPPvss0Xu5+LFi4mMjGTjxo14eXnh5eVFeno6devaZ5PKPntzzZo1OX/+fJHbupHoyEUp5RETJ06kW7dudOjQgTp16hRYPjY2FpvNRrNmzVi2bBnPPfec03J16tThn//8J5GRkQQFBdG8eXN69OhR5H56e3sTFhbGU089xUcffQTAiy++yJgxYwgPD+fKlSuOspGRkezdu1cv6LtA3D3ELKtatGhhEhISirSvu7+w5qrS8NepKh327dtHkyYl83uobhzOfs9EJNEYk+uLQjpyUUop5XZ6zUUpVWqFhoby22+/5Yh9/PHHBAQEFKqeESNGsGnTphyx5557jsGDB193H5VzmlyUUqXW1q1bCy7kgqlTp7qlHuU6PS2mlFLK7TS5KKWUcjtNLkoppdxOr7koVQ5NfWqdW+sbMb2D2+pKS0tjwYIFDB8+3G11qtJHRy5KqWKVlpbGtGnTnL6W/QuLqmzT5KKUcot58+bRsmVLbDYbTz75JEeOHMHX15fTp09z9epV2rZtyzfffENMTAyHDx/GZrPxwgsvEBsbS2RkJI8++qjjFuOePXsSHBxM06ZNmTFjBmBPPIMGDcLf35+AgAAmT55ckoerCqCnxZRS123fvn0sXryYTZs2UblyZYYPH863337L6NGjeeqppwgNDcXPz4+oqCjuvfdekpOTHVPhx8bGEh8fT3JyMg0aNABg5syZ3HLLLfz666+EhITQq1cvUlJS+Pnnn0lOTgbsIyBVemlyUUpdt7Vr15KYmEhISAgAv/76K3fccQfjxo1jyZIlTJ8+Pd91VVq2bOlILADvvPMOy5cvB+Do0aMcPHiQRo0a8cMPP/Dss89y//33ExUV5dmDUtfFo6fFRCRFRHaLSJKIJFixW0RktYgctH56W3ERkXdE5JCI7BKR5tnqibbKHxSR6GzxYKv+Q9a+kl8bSinPMMYQHR1NUlISSUlJHDhwgHHjxnHx4kVSU1MByMjIyHP/7FP1x8bGsmbNGrZs2cLOnTtp1qwZly5dwtvbm507d9K+fXumTp3K0KFDPX5cquiK45pLpDHGlm1isxhgrTHGF1hrPQfoAvhaj2HAe2BPFMBrQCjQEngtW7J4zyqbtV/nAtpQSnlAx44dWbp0KSdPngTgzJkzHDlyhNGjR9O/f3/Gjx/PE088ARQ8bX16ejre3t5Ur16d/fv389133wE4rt306tWL119/ne3bt3v+wFSRlcRpsR5Ae2t7DhALjLbic419mubvRKS2iNSxyq42xpwBEJHVQGcRiQVqGWO2WPG5QE/g63zaUOqG4M5bh13h5+fHhAkTiIqK4urVq1SuXJm33nqLbdu2sWnTJipWrMiyZcuYNWsWgwcPJjw8HH9/f7p06cL999+fo67OnTszffp0AgMDadSokWNly59//pnBgwdz9epVAP75z38W6zGqwvF0cjHANyJigPeNMTOAO40xxwGMMcdF5A6rbF3gaLZ9U61YfvFUJ3HyaSMHERmGfeTDn/70pyIfpFIK+vbtS9++fXPEskYdAJ9++qlje8GCBTnKtW/f3rFdtWpVvv76a6dt6Gil7PB0cgk3xhyz/nNfLSL78ykrTmKmCHGXWcluBtjXcynMvkoppfLm0Wsuxphj1s+TwHLs10xOWKe7sH6etIqnAvWz7V4POFZAvJ6TOPm0oZRSqhh4LLmIyM0iUjNrG4gCkoEVQNYdX9HA59b2CmCgdddYKyDdOrW1CogSEW/rQn4UsMp67byItLLuEht4TV3O2lBKKVUMPHla7E5guXV3cCVggTHm/4nINuATEXkc+AnoY5VfCXQFDgEXgcEAxpgzIvI6sM0qNz7r4j7wNDAbuAn7hfysE7UT82hDKaVUMXApuYiIvzEmuTAVG2N+AIKcxH8BOjqJG2BEHnXNBGY6iScA/q62oZRSqni4elpsuojEi8hwEant0R4ppZQq81wauRhj2oiILzAESBCReGCWMWa1R3unlCqSf/ft5tb6/nfxl9e1f9euXVmwYAG1a+f823TcuHHUqFGDv/3tb9dVvyp9XL7mYow5KCIvAwnAO0Az60L6S8aYT/PfWyl1I1u5cmVJd0EVM5dOi4lIoIhMBvYBHYAHjDFNrG2d91qpG9z//d//8c477wDw/PPP06GDfYaAtWvX8thjj+Hj48Pp06cBeOONN2jUqBF/+ctfOHDggKOOw4cP07lzZ4KDg2nbti3799u/FrdkyRL8/f0JCgoiIiKimI9MFZWr11zeBbYDQcaYEcaY7eD4HsvLnuqcUqpsiIiIYMOGDQAkJCSQkZHB5cuX2bhxI23btnWUS0xMZNGiRezYsYNPP/2Ubdu2OV4bNmwY//nPf0hMTGTSpEmOlSrHjx/PqlWr2LlzJytWrCjeA1NF5mpy6Yr9VuJfAUSkgohUBzDGfOypzimlyobg4GASExM5f/48VatWpXXr1iQkJLBhw4YcyWXDhg08+OCDVK9enVq1atG9e3fAPmPy5s2b6dOnj2OxsePHjwMQHh7OoEGD+OCDD3SlyjLE1Wsua4C/AFlzZlcHvgHCPNEppVTZUrlyZXx8fJg1axZhYWEEBgayfv16Dh8+TJMmTXKUtb77lsPVq1epXbu20zVfpk+fztatW/nqq6+w2WwkJSVx6623euxYlHu4OnKpZoxxLMZgbVf3TJeUUmVRREQEkyZNIiIigrZt2zJ9+nRsNluOZBIREcHy5cv59ddfOX/+PF988QUAtWrVokGDBixZsgSwrw+zc+dOwH4tJjQ0lPHjx3Pbbbdx9OjR3I2rUsfVkcsFEWmeda1FRIKBXz3XLaXU9bjeW4eLom3btrzxxhu0bt2am2++mWrVquU4JQbQvHlz+vbti81m45577snx+vz583n66aeZMGECly9f5pFHHiEoKIgXXniBgwcPYoyhY8eOBAXl+m62KoXE/sX4AgqJhACL+GNiyDpAX2NMogf7VqxatGhhEhISirTvvsZNCi7kAU327yuRdlXps2/fvlynn5RyN2e/ZyKSmG0xSAdXv0S5TUQaA42wT3W/3xhz2R2dVUopVf4UZuLKEMDH2qeZiGCMmeuRXimllCrTXJ248mPgf4AkIOteQANoclFKKZWLqyOXFoCfceUCjVJKqRueq7ciJwN3ebIjSimlyg9XRy63AXut2ZB/ywoaY7p7pFdKKaXKNFeTyzhPdkIp5V6pMRvcWl+9iW0LLlRI7du3Z9KkSbRokesuVqdiY2OZNGkSX375JbNnzyYhIYF3333X7f1S7uHqrcjfisg9gK8xZo01r1hFz3ZNKaVUWeXqlPtPAEuB961QXeAzF/etKCI7RORL63kDEdkqIgdFZLGIVLHiVa3nh6zXfbLVMcaKHxCRTtnina3YIRGJyRZ32oZSynNSUlJo0qQJTzzxBE2bNiUqKopff/2VpKQkWrVqRWBgIA8++CBnz5517DNv3jzCwsLw9/cnPj4egPj4eMLCwmjWrBlhYWE5puV3RqfkL51cvaA/AggHzoF94TDgDhf3fQ77OjBZ/gVMNsb4AmeBx63448BZY8yfsa8R8y8AEfEDHgGaAp2BaVbCqghMBboAfkA/q2x+bSilPOjgwYOMGDGCPXv2ULt2bZYtW8bAgQP517/+xa5duwgICODvf/+7o/yFCxfYvHkz06ZNY8iQIQA0btyYuLg4duzYwfjx43nppZfybVOn5C+dXE0uvxljfs96IiKVsH/PJV8iUg+4H/jQei7YFxhbahWZA/S0tntYz7Fe72iV7wEsMsb8Zoz5ETgEtLQeh4wxP1h9WwT0KKANpZQHNWjQAJvNBtin4T98+DBpaWm0a9cOgOjoaOLi4hzl+/XrB9gntDx37hxpaWmkp6fTp08f/P39ef7559mzZ0++beqU/KWTq8nlWxF5CbhJRO4DlgBfuLDfFOBF4Kr1/FYgzRiTaT1PxX6KDevnUQDr9XSrvCN+zT55xfNrIwcRGSYiCSKScOrUKRcORymVn6pVqzq2K1asSFpaWr7lr51+X0R45ZVXiIyMJDk5mS+++IJLly7lW8f06dOZMGECR48exWaz8csvvxT9AJTbuJpcYoBTwG7gSWAlBaxAKSLdgJPXTG6ZeyGHP0ZAeb3mrnjuoDEzjDEtjDEtbr/9dmdFlFLXwcvLC29vb8cqlR9//LFjFAOwePFiADZu3IiXlxdeXl6kp6dTt67978HZs2cX2IZOyV86uXq32FXgA+vhqnCgu4h0BaoBtbCPZGqLSCVrZFGPP2ZaTgXqA6nWaTcv4Ey2eJbs+ziLn86nDaVuCJ64dbio5syZw1NPPcXFixdp2LAhs2bNcrzm7e1NWFgY586dY+bMmQC8+OKLREdH89Zbb9GhQ4cC69cp+UsnV6fc/xEnf/0bYxq61IhIe+BvxphuIrIEWGaMWSQi04FdxphpIjICCDDGPCUijwAPGWMeFpGmwALs11juBtYCvthHKN8DHYGfgW3Ao8aYPXm1kV8fdcp9VZbplPuqOLh9yn3sc4tlqQb0AW4pYv9GA4tEZAKwA/jIin8EfCwih7CPWB4BsJLFJ8BeIBMYYYy5AiAizwCrsH/nZqYxZk8BbXjEw2MKM7m0++wukVaVUqpgrp4Wu/YK2RQR2Qi86uL+sUCstf0D9lHItWUuYU9azvZ/A3jDSXwl9us/18adtqGUUqp4uDrlfvNsTytgH8nU9EiPlFJKlXmuns/5d7btTCAFeNjtvVFKKVUuuHpaLNLTHVFKKVV+uHpa7K/5vW6Mecs93VFKKVUeFOZusRAga+KeB4A4cn5DXilVSowbN67Y60tJSaFbt24kJye7tW1VNhVmsbDmxpjzACIyDlhijBnqqY4ppZQqu1yd/uVPwO/Znv8O+Li9N0qpMi0zM5Po6GgCAwPp3bs3X331FQ8++KDj9dWrV/PQQw8B8M0339C6dWuaN29Onz59yMjIACAmJgY/Pz8CAwP529/+ViLHoa6fq8nlYyBeRMaJyGvAVmCu57qllCqLDhw4wLBhw9i1axe1atVi79697Nu3j6yJYWfNmsXgwYM5ffo0EyZMYM2aNWzfvp0WLVrw1ltvcebMGZYvX86ePXvYtWsXL7+c7xSGqhRzKblYX2IcjH1tlDRgsDHmH57smFKq7Klfvz7h4eEAPPbYY2zatIkBAwYwb9480tLS2LJlC126dOG7775j7969hIeHY7PZmDNnDkeOHKFWrVpUq1aNoUOH8umnn1K9evUSPiJVVIWZt6Q6cM4YM0tEbheRBtb6KkopBTifQn/w4ME88MADVKtWjT59+lCpUiWMMdx3330sXLgwVx3x8fGsXbuWRYsW8e6777Ju3bri6r5yI1eXOX4N+3xdY6xQZWCepzqllCqbfvrpJ7Zs2QLAwoULadOmDXfffTd33303EyZMYNCgQQC0atWKTZs2cejQIQAuXrzI999/T0ZGBunp6XTt2pUpU6aQlJRUUoeirpOrI5cHgWbAdgBjzDER0elflCql3H0rsquaNGnCnDlzePLJJ/H19eXpp58GoH///pw6dQo/P/tK5LfffjuzZ8+mX79+/PbbbwBMmDCBmjVr0qNHDy5duoQxhsmTJ5fIcajr52py+d0YY0TEAIjIzR7sk1KqDPLx8WHv3r1OX9u4cSNPPPFEjliHDh3Ytm1brrLx8fEe6Z8qXq4ml09E5H3si3A9AQyhcAuHKaVuUMHBwdx88838+9//LriwKjdcnVtskojcB5wDGgGvGmNWe7RnSqlyITExseBCqtwpMLmISEVglTHmL4AmFKWUUgUq8G4xa9XHiyLiVQz9UUopVQ64es3lErBbRFYDF7KCxpiRHumVUkqpMs3V5PKV9VBKKaUKlG9yEZE/GWN+MsBNnKUAAB15SURBVMbMKWzFIlIN+7T8Va12lhpjXhORBsAi4Bbs35sZYIz5XUSqYp+vLBj4BehrjEmx6hoDPA5cAUYaY1ZZ8c7A20BF4ENjzEQr7rSNwh6DUmXV2nX/49b6OnY4XGCZGjVqOCafLKz27dszadIkWrRoUaT9VelT0DWXz7I2RGRZIev+DehgjAkCbEBnEWkF/AuYbIzxxT5X2eNW+ceBs8aYPwOTrXKIiB/wCNAU6AxME5GK1o0GU4EugB/QzypLPm0opZQqBgUll+wTBTUsTMXGLuvPmMrWwwAdgKVWfA7Q09ruYT3Her2j2Ccq6gEsMsb8Zs1ldghoaT0OGWN+sEYli4Ae1j55taGUKgZvvvkmISEhBAYG8tprrznir7/+Oo0bN+a+++6jX79+TJo0yfHavHnzCAsLw9/fn/j4eK5evYqvr69jRuWrV6/y5z//mdOnT3Pq1Cl69epFSEgIISEhbNq0CYBvv/0Wm82GzWajWbNmnD9/vngPXDkUdM3F5LHtEmt0kQj8Gfso4zCQZozJtIqkAnWt7bpYK1saYzJFJB241Yp/l63a7PscvSYeau2TVxvX9m8YMAzgT3/6U2EPTynlxDfffMPBgweJj4/HGEP37t2Ji4ujevXqLFu2jB07dpCZmUnz5s0JDg527HfhwgU2b95MXFwcQ4YMITk5mccee4z58+czatQo1qxZQ1BQELfddhuPPvoozz//PG3atOGnn36iU6dO7Nu3j0mTJjF16lTCw8PJyMigWrVqJfhO3NgKSi5BInIO+wjmJmsb67kxxtTKb2frNmabiNQGlgNNnBXLVqez1/KKOxt15VfeWf9mADMAWrRoUejkqZTK7ZtvvuGbb76hWbNmAGRkZHDw4EHOnz9Pjx49uOmmmwB44IEHcuzXr18/ACIiIjh37hxpaWkMGTKEHj16MGrUKGbOnMngwYMBWLNmTY6pZs6dO8f58+cJDw/nr3/9K/379+ehhx6iXr16xXHIyol8k4sxpqI7GjHGpIlILNAK+xQylayRRT3gmFUsFagPpIpIJcALOJMtniX7Ps7ip/NpQynlYcYYxowZw5NPPpkjXtAklM6m669fvz533nkn69atY+vWrcyfPx+wnyLbsmWLI1FliYmJ4f7772flypW0atWKNWvW0LhxYzcclSosV1eiLDRrzZfa1vZNwF+AfcB6oLdVLBr43NpeYT3Hen2dMcZY8UdEpKp1F5gvEA9sA3xFpIGIVMF+0X+FtU9ebSilPKxTp07MnDnTcefYzz//zMmTJ2nTpg1ffPEFly5dIiMjg6++yvnthsWLFwP2SS69vLzw8rJ/b3vo0KE89thjPPzww1SsaP97Nyoqinfffdexb9bU/IcPHyYgIIDRo0fTokUL9u/f7/HjVc4VZrGwwqoDzLGuu1QAPjHGfCkie4FFIjIB2AF8ZJX/CPhYRA5hH7E8AmCM2SMinwB7gUxghHW6DRF5BliF/VbkmcaYPVZdo/NoQ6kbgiu3DntKVFQU+/bto3Xr1oD9FuV58+YREhJC9+7dCQoK4p577qFFixaOBALg7e1NWFgY586dY+bMmY549+7dGTx4sOOUGMA777zDiBEjCAwMJDMzk4iICKZPn86UKVNYv349FStWxM/Pjy5duhTfgascxP6HvmrRooVJSEgo0r4BcwLc3BvX7I7eXSLtqtJn3759NGni7JJm6ZKRkUGNGjW4ePEiERERzJgxg+bNm+e7T0JCAs8//zwbNmwopl6qvDj7PRORRGNMri8oeXLkopRSOQwbNoy9e/dy6dIloqOjC0wsEydO5L333nNca1FlhyYXpVSxWbBgQaHKx8TEEBMT46HeKE/y2AV9pZRSNy5NLkoppdxOk4tSSim30+SilFLK7fSCvlLl0F3rk9xa338jbW6tryhSUlLo1q0bycnJJCUlcezYMbp27VrS3VJ50JGLUqrMSUpKYuXKlSXdDZUPTS5KKbdISUmhcePGREdHExgYSO/evbl48SKJiYm0a9eO4OBgOnXqxPHjxwH7AmGjR4+mZcuW3HvvvY4vSaakpNC2bVuaN29O8+bN2bx5c452fv/9d1599VUWL16MzWZj8eLFeU7Nr0qOJhellNscOHCAYcOGsWvXLmrVqsXUqVN59tlnWbp0KYmJiQwZMoSxY8c6ymdmZhIfH8+UKVP4+9//DsAdd9zB6tWr2b59O4sXL2bkyJE52qhSpQrjx4+nb9++JCUl0bdvX8fU/ECOqflVydFrLkopt6lfvz7h4eEAPPbYY/zjH/8gOTmZ++67D4ArV65Qp04dR/mHHnoIgODgYFJSUgC4fPkyzzzzDElJSVSsWJHvv/++wHbzmppflRxNLkopt7l22vyaNWvStGlTtmzZ4rR81apVAahYsSKZmfb1/SZPnsydd97Jzp07uXr1qksLfuU1Nb8qOXpaTCnlNj/99JMjkSxcuJBWrVpx6tQpR+zy5cvs2bMnvypIT0+nTp06VKhQgY8//pgrV67kKlOzZs1cSxg7m5pflRwduShVDpXUrcNNmjRhzpw5PPnkk/j6+vLss8/SqVMnRo4cSXp6OpmZmYwaNYqmTZvmWcfw4cPp1asXS5YsITIykptvvjlXmcjISCZOnIjNZmPMmDH07dvX6dT8quTolPsWnXJflWWlYcr97N9DKQk6Nb/n6ZT7Sqkbik7NX/roNRellFv4+PiU2KglJiaGI0eO0KZNmxJpX+WmyUUppZTbeSy5iEh9EVkvIvtEZI+IPGfFbxGR1SJy0PrpbcVFRN4RkUMisktEmmerK9oqf1BEorPFg0Vkt7XPO2LdB5lXG0oppYqHJ0cumcD/GmOaAK2AESLiB8QAa40xvsBa6zlAF8DXegwD3gN7ogBeA0KBlsBr2ZLFe1bZrP06W/G82lBKKVUMPJZcjDHHjTHbre3zwD6gLtADmGMVmwP0tLZ7AHON3XdAbRGpA3QCVhtjzhhjzgKrgc7Wa7WMMVuM/Za3udfU5awNpZRSxaBY7hYTER+gGbAVuNMYcxzsCUhE7rCK1QWOZtst1YrlF091EiefNpS6IfjEfOXW+lIm3u/W+lT55/EL+iJSA1gGjDLGnMuvqJOYKUK8MH0bJiIJIpKQNaOqUso90tLSmDZtmuN5bGws3bp1K/Z+rFixgokTJ+b5ekpKCv7+/i7Xl5KSwoIFC/Ito0sCeHjkIiKVsSeW+caYT63wCRGpY40o6gAnrXgqUD/b7vWAY1a8/TXxWCtez0n5/NrIwRgzA5gB9i9RFukggd0//lTUXZUqt7KSy/Dhw91SX2ZmJpUqFf6/rO7du9O9e3e39AH+SC6PPvponmWSkpJISEi4oRcz8+TdYgJ8BOwzxryV7aUVQNYdX9HA59niA627xloB6daprVVAlIh4Wxfyo4BV1mvnRaSV1dbAa+py1oZSykPeeust/P398ff3Z8qUKcTExHD48GFsNhsvvPACABkZGfTu3ZvGjRvTv39/smYIyW/Nl5deeol27drx9ttv52rzypUrNGzYEGMMaWlpVKhQgbi4OADatm3LoUOHmD17Ns888wwAJ06c4MEHHyQoKIigoKBca8X88MMPNGvWjG3btnHlyhVeeOEFQkJCCAwM5P333wfs36nZsGEDNpuNyZMn5+pTYdabGTRoEE899RRt27bl3nvv5csvv3Qcl7O2yxJPjlzCgQHAbhHJWnP1JWAi8ImIPA78BPSxXlsJdAUOAReBwQDGmDMi8jqwzSo33hhzxtp+GpgN3AR8bT3Ipw2llAckJiYya9Ystm7dijGG0NBQ5s2b51iSGOynxXbs2MGePXu4++67CQ8PZ9OmTYSGhvLss8/y+eefc/vtt7N48WLGjh3LzJkzAfsI6Ntvv3XabsWKFbn33nvZu3cvP/74I8HBwWzYsIHQ0FBSU1P585//zMaNGx3lR44cSbt27Vi+fDlXrlwhIyODs2fPAva1aB555BFmzZqFzWZjxowZeHl5sW3bNn777TfCw8OJiopi4sSJTJo0yZEIrpW13kxCQgLvvvsuAPv372f+/PmMGjUq13ozKSkpfPvttxw+fJjIyEgOHTrE3LlznbbdoEED93xgxcBjycUYsxHn10UAOjopb4ARedQ1E5jpJJ4A5DpZaoz5xVkbSinP2LhxIw8++KBjksmHHnrI6RxfLVu2pF49+9lsm81GSkoKtWvXznfNl759++bbdtu2bYmLi+PHH39kzJgxfPDBB7Rr146QkJBcZdetW8fcuXMBe2Ly8vLi7NmznDp1ih49erBs2TLHpJrffPMNu3btYunSpYB9tuaDBw9SpUqVwr49+a438/DDD1OhQgV8fX1p2LAh+/fvz7NtTS5KqRuKqxPgZq3fAn+s4WKMyXfNF2ezImfXtm1bpk+fzrFjxxg/fjxvvvkmsbGxREREuNx/Ly8v6tevz6ZNmxzJxRjDf/7zHzp16pSjbGxsrMv1ZslvvZlr18ARkTzbLks0uShVDhX3rcMREREMGjSImJgYjDEsX76cOXPm8O9//7vAfRs1auRY86V169ZcvnyZ77//Pt9p+bMLDQ1l4MCBNGzYkGrVqmGz2Xj//fednrbq2LEj7733HqNGjeLKlStcuHABsJ/K+uyzz+jUqRM1atTg0UcfpVOnTrz33nt06NCBypUr8/3331O3bl2na8lcK7/1ZgYMGJBjvZklS5YQHR3Njz/+yA8//ECjRo3ybLugRFua6NxiSqnr1rx5cwYNGkTLli0JDQ1l6NChBAcHEx4ejr+/v+OCvjNVqlRh6dKljB49mqCgIGw2W64L7fmpWrUq9evXp1WrVoB9JHP+/HkCAnIvhfH222+zfv16AgICCA4OzrFw2c0338yXX37J5MmT+fzzzxk6dCh+fn40b94cf39/nnzySTIzMwkMDKRSpUoEBQU5vaAP9vVm9u7d67igD/a71jIyMnKtN9OoUSPatWtHly5dmD59OtWqVcuz7bJE13OxXM96Lozzcm9nXG43vWTaVaVOaVjPReXP2XozgwYNolu3bvTu3bsEe+Y6Xc9FKaVKkRtxvRlNLkqpMuGNN95gyZIlOWJ9+vRh7NixJdQjWLVqFaNHj84Ra9CgAcuXL88Ri4mJISYm9/y5s2fP9mT3SpQmF6VUmTB27NgSTSTOdOrUqUzf0eVJekFfKaWU22lyUUop5XaaXJRSSrmdXnNRqjxy9+3xetu7KiQduSilPKKk13MpqfVjrjVo0CDHHGFDhw5l7969111nWFjYddeRlxo1arilHk0uSimPuDa5XK/S9g31ovTnww8/xM/P77rbLswMBiVFk4tSyi1KYj0XIM81UbKLj48nLCyMZs2aERYWxoEDBwD7VDFZSwIAhIeHs2vXLi5cuMCQIUMICQmhWbNmfP65fUmo2bNn06dPHx544AGioqKc9scYwzPPPIOfnx/3338/J0/+sVZh+/btSUhI4MqVKwwaNAh/f38CAgIc08i0b9+e559/noiICJo0acK2bdt46KGH8PX15eWXX3bUkzW6OH78OBEREdhsNvz9/dmwYUOedR8+fJjOnTsTHBxM27Zt2b9/PwA//vgjrVu3JiQkhFdeeSXfz7gw9JqLUuq6ldR6LlmcrYmSXePGjYmLi6NSpUqsWbOGl156iWXLljF06FBmz57NlClT+P777/ntt98IDAzkpZdeokOHDsycOZO0tDRatmzJX/7yFwC2bNnCrl27uOWWW5z2Zfny5Rw4cIDdu3dz4sQJ/Pz8GDJkSI4ySUlJ/PzzzyQnJzuOMUuVKlWIi4vj7bffpkePHiQmJnLLLbfwP//zPzz//PPceuutjrILFiygU6dOjB07litXrnDx4sU86x42bBjTp0/H19eXrVu3Mnz4cNatW8dzzz3H008/zcCBA5k6dWr+H3QhaHJRSl23klzPBZyviZJdeno60dHRHDx4EBHh8uXLgP0b/q+//jpvvvkmM2fOZNCgQYB9LZcVK1YwadIkAC5dusRPP9mXM7/vvvvyTCwAcXFx9OvXj4oVK3L33XfToUOHXGUaNmzIDz/8wLPPPsv999+fYxSUtSRzQEAATZs2dbwXDRs25OjRozmSS0hICEOGDOHy5cv07NkTm83mtO6MjAw2b95Mnz5/rJv422+/AbBp0yaWLVsGwIABA3LNOFBUmlyUUtetJNdzAedromT3yiuvEBkZyfLly0lJSaF9+/YAVK9enfvuu4/PP/+cTz75hKzJa40xLFu2jEaNGuWoZ+vWrUXqz7W8vb3ZuXMnq1atYurUqXzyySeOkVrWe1ShQoUc71eFChVyXeeJiIggLi6Or776igEDBvDCCy8wcODAXHVPmTKF2rVr5zgFWJj+FoVec1GqPBqX7t5HASIiIvjss8+4ePEiFy5cYPny5YSHhxe47gnkXM8F4PLlyzmmwnfFkiVLuHr1KocPH3asiZJdeno6devWBXLP5zV06FBGjhxJSEiIY0TSqVMn/vOf/ziS5o4dO1zuS0REBIsWLeLKlSscP36c9evX5ypz+vRprl69Sq9evXj99dfZvn17YQ7X4ciRI9xxxx088cQTPP7442zfvt1p3bVq1aJBgwaOudmMMezcuROwX2datGgRgFsn1tSRi1LqumVfzwXItZ5Lly5duP9+5wuYZa3nMnLkSNLT08nMzGTUqFEuLxYGf6yJcuLECceaKNm9+OKLREdH89Zbb+U6TRUcHEytWrVyrLPyyiuvMGrUKAIDAzHG4OPj4/RGAWcefPBB1q1bR0BAAPfeey/t2rXLVebnn39m8ODBXL16FYB//vOfLh9rdrGxsbz55ptUrlyZGjVqMHfu3Dzrnj9/Pk8//TQTJkzg8uXLPPLIIwQFBfH222/z6KOP8vbbb9OrV68i9cMZj63nIiIzgW7ASWOMvxW7BVgM+AApwMPGmLNiH5O9DXQFLgKDjDHbrX2igazbJCYYY+ZY8WBgNnATsBJ4zhhj8mqjoP7qei6qLLuR13O53jVRjh07Rvv27dm/fz8VKujJnPwUZj0XT76Ts4HO18RigLXGGF9grfUcoAvgaz2GAe+BIxm9BoQCLYHXRMTb2uc9q2zWfp0LaEMppXKYO3cuoaGhvPHGG5pY3Mxjp8WMMXEi4nNNuAfQ3tqeA8QCo634XGMfRn0nIrVFpI5VdrUx5gyAiKwGOotILFDLGLPFis8FegJf59OGUqoMy2s9l+tZE2XgwIEMHDiwSPvu3r2bAQMG5IhVrVqVrVu3Frk/5UlxX3O50xhzHMAYc1xE7rDidYGj2cqlWrH84qlO4vm1kYuIDMM++uFPf/pTUY9JqVLBGOORu35Ki9K2nktAQECed1+VR4W9hFJaxoHO/kWYIsQLxRgzwxjTwhjT4vbbby/s7kqVGtWqVeOXX34p9H8ASrnCGMMvv/yS60aJ/BT3yOWEiNSxRhR1gKx5EVKB+tnK1QOOWfH218RjrXg9J+Xza0OpcqtevXqkpqZy6tSpku6KKqeqVavm+AKsK4o7uawAooGJ1s/Ps8WfEZFF2C/ep1vJYRXwj2wX8aOAMcaYMyJyXkRaAVuBgcB/CmhDqXKrcuXKNGjQoKS7oZSDx5KLiCzEPuq4TURSsd/1NRH4REQeB34CsuYiWIn9NuRD2G9FHgxgJZHXgW1WufFZF/eBp/njVuSvrQf5tKGUUqqYePJusX55vNTRSVkDjMijnpnATCfxBMDfSfwXZ20opZQqPqXlgr5SSqlyRJOLUkopt9PkopRSyu00uSillHI7TS5KKaXcTpOLUkopt9PkopRSyu00uSillHI7TS5KKaXcTpc5VmWKT8xXJdZ2ykTny/QqpXLT5KKUUqVASf3h5Kk/mvS0mFJKKbfTkYtSLipvf1kq5Uk6clFKKeV2mlyUUkq5nSYXpZRSbqfJRSmllNtpclFKKeV2ereYKpKS/DLjjUbfa1UWlduRi4h0FpEDInJIRGJKuj9KKXUjKZfJRUQqAlOBLoAf0E9E/Eq2V0opdeMol8kFaAkcMsb8YIz5HVgE9CjhPiml1A2jvF5zqQsczfY8FQi9tpCIDAOGWU8zRORAEdu7DThdxH2L7u/iztpK5hjcp6z3H8r+MZT1/kPZP4ZC91/+dd1t3uMsWF6Ti7P/dU2ugDEzgBnX3ZhIgjGmxfXWU5LK+jGU9f5D2T+Gst5/KPvHUJr6X15Pi6UC9bM9rwccK6G+KKXUDae8JpdtgK+INBCRKsAjwIoS7pNSSt0wyuVpMWNMpog8A6wCKgIzjTF7PNjkdZ9aKwXK+jGU9f5D2T+Gst5/KPvHUGr6L8bkuhShlFJKXZfyelpMKaVUCdLkopRSyu00uVynkp5mRkTqi8h6EdknIntE5DkrfouIrBaRg9ZPbysuIvKO1d9dItI8W13RVvmDIhKdLR4sIrutfd4REcmvjSIeR0UR2SEiX1rPG4jIVqvuxdaNGYhIVev5Iet1n2x1jLHiB0SkU7a4088orzaK2P/aIrJURPZbn0XrsvQZiMjz1u9PsogsFJFqpf0zEJGZInJSRJKzxUrsPc+vjUL0/03rd2iXiCwXkdrufm+L8vkViTFGH0V8YL9Z4DDQEKgC7AT8irkPdYDm1nZN4HvsU978HxBjxWOAf1nbXYGvsX8XqBWw1YrfAvxg/fS2tr2t1+KB1tY+XwNdrLjTNop4HH8FFgBfWs8/AR6xtqcDT1vbw4Hp1vYjwGJr2896/6sCDazPpWJ+n1FebRSx/3OAodZ2FaB2WfkMsH/p+Efgpmzvy6DS/hkAEUBzIDlbrMTe87zaKGT/o4BK1va/stXttve2sJ9fkf9NFHVHfRisX7xV2Z6PAcaUcJ8+B+4DDgB1rFgd4IC1/T7QL1v5A9br/YD3s8Xft2J1gP3Z4o5yebVRhD7XA9YCHYAvrX+cp7P9I3O8z9jvAGxtbVeyysm1731Wubw+o/zaKEL/a2H/z1muiZeJz4A/ZrS4xXpPvwQ6lYXPAPAh53/OJfae59VGYfp/zWsPAvOzv2fueG8L+/kV5d+EMUZPi10nZ9PM1C2hvmANb5sBW4E7jTHHAayfd1jF8upzfvFUJ3HyaaOwpgAvAlet57cCacaYTCdtOvppvZ5ulS/sceXXRmE1BE4Bs8R+au9DEbmZMvIZGGN+BiYBPwHHsb+niZStzyBLSb7n7v7/YAj2kVBR+u/Of0NFosnl+rg0zUxxEJEawDJglDHmXH5FncRMEeJuISLdgJPGmMTs4XzadFf/3XlclbCf3njPGNMMuID9dEleSttn4I19YtcGwN3AzdhnFM+rzdL4GRSkOPrmtuMRkbFAJjC/gLqL0v9i+Tw0uVyfUjHNjIhUxp5Y5htjPrXCJ0SkjvV6HeCkFc+rz/nF6zmJ59dGYYQD3UUkBfvs1R2wj2Rqi0jWl3yzt+nop/W6F3CmCMd1Op82CisVSDXGbLWeL8WebMrKZ/AX4EdjzCljzGXgUyCMsvUZZCnJ99wt/x9YNxV0A/ob6/xUEfqf33tb2M+vSDS5XJ8Sn2bGuoPlI2CfMeatbC+tAKKt7Wjs12Ky4gOtO1taAenW0H4VECUi3tZfslHYz9EeB86LSCurrYHX1OWsDZcZY8YYY+oZY3ywv3/rjDH9gfVA7zz6n9Vmb6u8seKPWHfCNAB8sV+QdfoZWfvk1UZhj+G/wFERaWSFOgJ7KSOfAfbTYa1EpLpVf1b/y8xnkE1Jvud5teEyEekMjAa6G2MuXnNc7npvC/v5FU1RL9bow3HRqyv2O7QOA2NLoP022Ieuu4Ak69EV+znUtcBB6+ctVnnBvpDaYWA30CJbXUOAQ9ZjcLZ4CyDZ2udd/pjZwWkb13Es7fnjbrGG1i/2IWAJUNWKV7OeH7Jeb5ht/7FWHw9g3dmT32eUVxtF7LsNSLA+h8+w33lUZj4D4O/AfquNj7HfMVSqPwNgIfZrRJex/9X9eEm+5/m1UYj+H8J+3SPr3/J0d7+3Rfn8ivLQ6V+UUkq5nZ4WU0op5XaaXJRSSrmdJhellFJup8lFKaWU22lyUUop5XaaXJRSSrmdJhellFJu9/8B39IyfcSmaqcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot(kind=\"hist\", stacked=True, bins=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before Removing Irrelevant Columns : (194354, 22)\n",
      "After Removing Irrelevant Columns : (194354, 6)\n"
     ]
    },
    {
     "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>innings</th>\n",
       "      <th>ball</th>\n",
       "      <th>batting_team</th>\n",
       "      <th>bowling_team</th>\n",
       "      <th>runs_off_bat</th>\n",
       "      <th>extras</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0.3</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   innings  ball           batting_team                 bowling_team  \\\n",
       "0        1   0.1  Kolkata Knight Riders  Royal Challengers Bangalore   \n",
       "1        1   0.2  Kolkata Knight Riders  Royal Challengers Bangalore   \n",
       "2        1   0.3  Kolkata Knight Riders  Royal Challengers Bangalore   \n",
       "3        1   0.4  Kolkata Knight Riders  Royal Challengers Bangalore   \n",
       "4        1   0.5  Kolkata Knight Riders  Royal Challengers Bangalore   \n",
       "\n",
       "   runs_off_bat  extras  \n",
       "0             0       1  \n",
       "1             0       0  \n",
       "2             0       1  \n",
       "3             0       0  \n",
       "4             0       0  "
      ]
     },
     "execution_count": 338,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "irrelevant = ['match_id', 'season', 'start_date', 'venue', 'striker', 'non_striker', 'bowler', 'wides', 'noballs', 'byes', 'legbyes',\n",
    "       'penalty', 'wicket_type', 'player_dismissed', 'other_wicket_type',\n",
    "       'other_player_dismissed']\n",
    "print(f'Before Removing Irrelevant Columns : {data.shape}')\n",
    "data = data.drop(irrelevant, axis=1) # Drop Irrelevant Columns\n",
    "print(f'After Removing Irrelevant Columns : {data.shape}')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define Consistent Teams\n",
    "const_teams = ['Kolkata Knight Riders', 'Chennai Super Kings', 'Rajasthan Royals',\n",
    "              'Mumbai Indians', 'Kings XI Punjab', 'Royal Challengers Bangalore',\n",
    "              'Delhi Daredevils', 'Sunrisers Hyderabad']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before Removing Inconsistent Teams : (194354, 6)\n",
      "After Removing Irrelevant Columns : (142184, 6)\n",
      "Consistent Teams : \n",
      "['Kolkata Knight Riders' 'Royal Challengers Bangalore'\n",
      " 'Chennai Super Kings' 'Kings XI Punjab' 'Rajasthan Royals'\n",
      " 'Delhi Daredevils' 'Mumbai Indians' 'Sunrisers Hyderabad']\n"
     ]
    },
    {
     "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>innings</th>\n",
       "      <th>ball</th>\n",
       "      <th>batting_team</th>\n",
       "      <th>bowling_team</th>\n",
       "      <th>runs_off_bat</th>\n",
       "      <th>extras</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0.3</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   innings  ball           batting_team                 bowling_team  \\\n",
       "0        1   0.1  Kolkata Knight Riders  Royal Challengers Bangalore   \n",
       "1        1   0.2  Kolkata Knight Riders  Royal Challengers Bangalore   \n",
       "2        1   0.3  Kolkata Knight Riders  Royal Challengers Bangalore   \n",
       "3        1   0.4  Kolkata Knight Riders  Royal Challengers Bangalore   \n",
       "4        1   0.5  Kolkata Knight Riders  Royal Challengers Bangalore   \n",
       "\n",
       "   runs_off_bat  extras  \n",
       "0             0       1  \n",
       "1             0       0  \n",
       "2             0       1  \n",
       "3             0       0  \n",
       "4             0       0  "
      ]
     },
     "execution_count": 340,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(f'Before Removing Inconsistent Teams : {data.shape}')\n",
    "data = data[(data['batting_team'].isin(const_teams)) & (data['bowling_team'].isin(const_teams))]\n",
    "print(f'After Removing Irrelevant Columns : {data.shape}')\n",
    "print(f\"Consistent Teams : \\n{data['batting_team'].unique()}\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1fd833ec708>"
      ]
     },
     "execution_count": 341,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from seaborn import heatmap\n",
    "heatmap(data=data.corr(), annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "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>innings</th>\n",
       "      <th>ball</th>\n",
       "      <th>batting_team</th>\n",
       "      <th>bowling_team</th>\n",
       "      <th>runs_off_bat</th>\n",
       "      <th>extras</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0.3</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0.4</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   innings  ball  batting_team  bowling_team  runs_off_bat  extras\n",
       "0        1   0.1             3             6             0       1\n",
       "1        1   0.2             3             6             0       0\n",
       "2        1   0.3             3             6             0       1\n",
       "3        1   0.4             3             6             0       0\n",
       "4        1   0.5             3             6             0       0"
      ]
     },
     "execution_count": 342,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder, OneHotEncoder\n",
    "le = LabelEncoder()\n",
    "for col in ['batting_team', 'bowling_team']:\n",
    "  data[col] = le.fit_transform(data[col])\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 355,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'numpy.ndarray' object has no attribute 'iloc'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-355-b2bbe543ed34>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mfeatures\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m:\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mlabels\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'numpy.ndarray' object has no attribute 'iloc'"
     ]
    }
   ],
   "source": [
    "features = data.iloc[:, :-1].values\n",
    "labels = data.iloc[:, 1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Set : (155483, 11)\n",
      "Testing Set : (38871, 11)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "train_features, test_features, train_labels, test_labels = train_test_split(features, labels, test_size=0.20, shuffle=True)\n",
    "print(f\"Training Set : {train_features.shape}\\nTesting Set : {test_features.shape}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Keeping track of model perfomances\n",
    "models = dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Set : (155483, 11)\n",
      "Testing Set : (38871, 11)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "train_features, test_features, train_labels, test_labels = train_test_split(features, labels, test_size=0.20, shuffle=True)\n",
    "print(f\"Training Set : {train_features.shape}\\nTesting Set : {test_features.shape}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 351,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Input contains NaN, infinity or a value too large for dtype('float32').",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-351-bcda076802ea>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mforest\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mRandomForestRegressor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;31m# Train Model\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0mforest\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrain_features\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrain_labels\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_forest.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[0;32m    293\u001b[0m         \"\"\"\n\u001b[0;32m    294\u001b[0m         \u001b[1;31m# Validate or convert input data\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 295\u001b[1;33m         \u001b[0mX\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maccept_sparse\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"csc\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mDTYPE\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    296\u001b[0m         \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maccept_sparse\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'csc'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    297\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0msample_weight\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[1;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[0;32m    576\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mforce_all_finite\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    577\u001b[0m             _assert_all_finite(array,\n\u001b[1;32m--> 578\u001b[1;33m                                allow_nan=force_all_finite == 'allow-nan')\n\u001b[0m\u001b[0;32m    579\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    580\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mensure_min_samples\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36m_assert_all_finite\u001b[1;34m(X, allow_nan, msg_dtype)\u001b[0m\n\u001b[0;32m     58\u001b[0m                     \u001b[0mmsg_err\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     59\u001b[0m                     (type_err,\n\u001b[1;32m---> 60\u001b[1;33m                      msg_dtype if msg_dtype is not None else X.dtype)\n\u001b[0m\u001b[0;32m     61\u001b[0m             )\n\u001b[0;32m     62\u001b[0m     \u001b[1;31m# for object dtype data, we only check for NaNs (GH-13254)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Input contains NaN, infinity or a value too large for dtype('float32')."
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "forest = RandomForestRegressor()\n",
    "# Train Model\n",
    "forest.fit(train_features, train_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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