model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 804,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 805,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 806,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('data/t20i_info.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 807,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>match_id</th>\n",
       "      <th>batting_team</th>\n",
       "      <th>bowling_team</th>\n",
       "      <th>ball</th>\n",
       "      <th>runs</th>\n",
       "      <th>player_dismissed</th>\n",
       "      <th>city</th>\n",
       "      <th>venue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Melbourne Cricket Ground</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Melbourne Cricket Ground</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Melbourne Cricket Ground</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>0.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Melbourne Cricket Ground</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Melbourne Cricket Ground</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  match_id batting_team bowling_team  ball  runs  \\\n",
       "0           0         2    Australia    Sri Lanka   0.1     0   \n",
       "1           1         2    Australia    Sri Lanka   0.2     0   \n",
       "2           2         2    Australia    Sri Lanka   0.3     1   \n",
       "3           3         2    Australia    Sri Lanka   0.4     2   \n",
       "4           4         2    Australia    Sri Lanka   0.5     0   \n",
       "\n",
       "  player_dismissed city                     venue  \n",
       "0                0  NaN  Melbourne Cricket Ground  \n",
       "1                0  NaN  Melbourne Cricket Ground  \n",
       "2                0  NaN  Melbourne Cricket Ground  \n",
       "3                0  NaN  Melbourne Cricket Ground  \n",
       "4                0  NaN  Melbourne Cricket Ground  "
      ]
     },
     "execution_count": 807,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 808,
   "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>Unnamed: 0</th>\n",
       "      <th>match_id</th>\n",
       "      <th>batting_team</th>\n",
       "      <th>bowling_team</th>\n",
       "      <th>ball</th>\n",
       "      <th>runs</th>\n",
       "      <th>player_dismissed</th>\n",
       "      <th>city</th>\n",
       "      <th>venue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Melbourne Cricket Ground</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Melbourne Cricket Ground</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  match_id batting_team bowling_team  ball  runs  \\\n",
       "0           0         2    Australia    Sri Lanka   0.1     0   \n",
       "1           1         2    Australia    Sri Lanka   0.2     0   \n",
       "\n",
       "  player_dismissed city                     venue  \n",
       "0                0  NaN  Melbourne Cricket Ground  \n",
       "1                0  NaN  Melbourne Cricket Ground  "
      ]
     },
     "execution_count": 808,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 809,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0             0\n",
       "match_id               0\n",
       "batting_team           0\n",
       "bowling_team           0\n",
       "ball                   0\n",
       "runs                   0\n",
       "player_dismissed       0\n",
       "city                8548\n",
       "venue                  0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 809,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 810,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(63888, 9)"
      ]
     },
     "execution_count": 810,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Batting team\n",
    "* Bowling team\n",
    "* City\n",
    "* Current score\n",
    "* Balls left\n",
    "* Wicket left\n",
    "* Current run rate\n",
    "* Last five/six->powerplay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 811,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df[df['city'].isnull()]['venue'][0].split(' ')[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 812,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df['city'] = df['city'].fillna(df['venue'].apply(lambda x:x.split(' ')[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 813,
   "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>Unnamed: 0</th>\n",
       "      <th>match_id</th>\n",
       "      <th>batting_team</th>\n",
       "      <th>bowling_team</th>\n",
       "      <th>ball</th>\n",
       "      <th>runs</th>\n",
       "      <th>player_dismissed</th>\n",
       "      <th>city</th>\n",
       "      <th>venue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>63883</th>\n",
       "      <td>121</td>\n",
       "      <td>964</td>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>Australia</td>\n",
       "      <td>19.3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Colombo</td>\n",
       "      <td>R Premadasa Stadium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63884</th>\n",
       "      <td>122</td>\n",
       "      <td>964</td>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>Australia</td>\n",
       "      <td>19.4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Colombo</td>\n",
       "      <td>R Premadasa Stadium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63885</th>\n",
       "      <td>123</td>\n",
       "      <td>964</td>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>Australia</td>\n",
       "      <td>19.5</td>\n",
       "      <td>0</td>\n",
       "      <td>DM de Silva</td>\n",
       "      <td>Colombo</td>\n",
       "      <td>R Premadasa Stadium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63886</th>\n",
       "      <td>124</td>\n",
       "      <td>964</td>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>Australia</td>\n",
       "      <td>19.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Colombo</td>\n",
       "      <td>R Premadasa Stadium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63887</th>\n",
       "      <td>125</td>\n",
       "      <td>964</td>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>Australia</td>\n",
       "      <td>19.7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Colombo</td>\n",
       "      <td>R Premadasa Stadium</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0  match_id batting_team bowling_team  ball  runs  \\\n",
       "63883         121       964    Sri Lanka    Australia  19.3     1   \n",
       "63884         122       964    Sri Lanka    Australia  19.4     0   \n",
       "63885         123       964    Sri Lanka    Australia  19.5     0   \n",
       "63886         124       964    Sri Lanka    Australia  19.6     2   \n",
       "63887         125       964    Sri Lanka    Australia  19.7     1   \n",
       "\n",
       "      player_dismissed     city                venue  \n",
       "63883                0  Colombo  R Premadasa Stadium  \n",
       "63884                0  Colombo  R Premadasa Stadium  \n",
       "63885      DM de Silva  Colombo  R Premadasa Stadium  \n",
       "63886                0  Colombo  R Premadasa Stadium  \n",
       "63887                0  Colombo  R Premadasa Stadium  "
      ]
     },
     "execution_count": 813,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 885,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Australia', 'New Zealand', 'South Africa', 'England', 'India',\n",
       "       'West Indies', 'Pakistan', 'Bangladesh', 'Afghanistan',\n",
       "       'Sri Lanka'], dtype=object)"
      ]
     },
     "execution_count": 885,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['batting_team'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 814,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Melbourne Cricket Ground', 'Simonds Stadium, South Geelong',\n",
       "       'Adelaide Oval', 'McLean Park', 'Bay Oval', 'Eden Park',\n",
       "       'The Rose Bowl', 'County Ground', 'Sophia Gardens',\n",
       "       'Riverside Ground', 'Green Park',\n",
       "       'Vidarbha Cricket Association Stadium, Jamtha',\n",
       "       'M Chinnaswamy Stadium',\n",
       "       'Central Broward Regional Park Stadium Turf Ground',\n",
       "       'Dubai International Cricket Stadium', 'Sheikh Zayed Stadium',\n",
       "       'Sydney Cricket Ground', 'Bellerive Oval', 'Westpac Stadium',\n",
       "       'Seddon Park', 'Mangaung Oval', 'Senwes Park',\n",
       "       'Kensington Oval, Bridgetown', \"Queen's Park Oval, Port of Spain\",\n",
       "       'R Premadasa Stadium', 'Warner Park, Basseterre',\n",
       "       'Sabina Park, Kingston', 'R.Premadasa Stadium, Khettarama',\n",
       "       'Saxton Oval', 'JSCA International Stadium Complex', 'Edgbaston',\n",
       "       'Old Trafford', 'Arun Jaitley Stadium',\n",
       "       'Saurashtra Cricket Association Stadium',\n",
       "       'Greenfield International Stadium', 'Gaddafi Stadium',\n",
       "       'The Wanderers Stadium', 'SuperSport Park', 'Newlands',\n",
       "       'Barabati Stadium', 'Holkar Cricket Stadium', 'Wankhede Stadium',\n",
       "       'Shere Bangla National Stadium, Mirpur',\n",
       "       'Sylhet International Cricket Stadium', 'National Stadium',\n",
       "       'Harare Sports Club', 'Carrara Oval',\n",
       "       'Brisbane Cricket Ground, Woolloongabba',\n",
       "       'Rajiv Gandhi International Cricket Stadium', 'Eden Gardens',\n",
       "       'Bharat Ratna Shri Atal Bihari Vajpayee Ekana Cricket Stadium',\n",
       "       'MA Chidambaram Stadium, Chepauk',\n",
       "       'Darren Sammy National Cricket Stadium, St Lucia',\n",
       "       'Warner Park, St Kitts',\n",
       "       'Dr. Y.S. Rajasekhara Reddy ACA-VDCA Cricket Stadium',\n",
       "       'M.Chinnaswamy Stadium', 'Manuka Oval', 'Perth Stadium',\n",
       "       'Buffalo Park', 'Kingsmead', \"St George's Park\",\n",
       "       'Punjab Cricket Association IS Bindra Stadium, Mohali',\n",
       "       'Rajiv Gandhi International Stadium, Uppal', 'Hagley Oval',\n",
       "       'Providence Stadium, Guyana',\n",
       "       'Pallekele International Cricket Stadium',\n",
       "       'Zahur Ahmed Chowdhury Stadium',\n",
       "       'Maharashtra Cricket Association Stadium', 'Boland Park',\n",
       "       'New Wanderers Stadium', 'Kennington Oval',\n",
       "       'Western Australia Cricket Association Ground',\n",
       "       'Brabourne Stadium', 'Jade Stadium', 'Gymkhana Club Ground',\n",
       "       'Trent Bridge', \"Lord's\", 'Maple Leaf North-West Ground',\n",
       "       'AMI Stadium', 'Providence Stadium',\n",
       "       'Beausejour Stadium, Gros Islet',\n",
       "       'Punjab Cricket Association Stadium, Mohali',\n",
       "       'Sir Vivian Richards Stadium, North Sound',\n",
       "       'Moses Mabhida Stadium', 'Stadium Australia',\n",
       "       'Shere Bangla National Stadium',\n",
       "       'Mahinda Rajapaksa International Cricket Stadium, Sooriyawewa',\n",
       "       'Subrata Roy Sahara Stadium', 'Sardar Patel Stadium, Motera',\n",
       "       'Arnos Vale Ground, Kingstown', 'Sharjah Cricket Stadium',\n",
       "       'Windsor Park, Roseau',\n",
       "       'Himachal Pradesh Cricket Association Stadium', 'Feroz Shah Kotla'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 814,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['venue'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 815,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "94"
      ]
     },
     "execution_count": 815,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['venue'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 816,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0             0\n",
       "match_id               0\n",
       "batting_team           0\n",
       "bowling_team           0\n",
       "ball                   0\n",
       "runs                   0\n",
       "player_dismissed       0\n",
       "city                8548\n",
       "venue                  0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 816,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 817,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "venue\n",
       "Shere Bangla National Stadium          3420\n",
       "R Premadasa Stadium                    2983\n",
       "Dubai International Cricket Stadium    2969\n",
       "New Wanderers Stadium                  2819\n",
       "Eden Park                              2532\n",
       "                                       ... \n",
       "Senwes Park                             122\n",
       "Hagley Oval                             121\n",
       "Subrata Roy Sahara Stadium              121\n",
       "Sardar Patel Stadium, Motera            121\n",
       "Carrara Oval                             64\n",
       "Name: count, Length: 94, dtype: int64"
      ]
     },
     "execution_count": 817,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['venue'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 818,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "81"
      ]
     },
     "execution_count": 818,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['city'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 819,
   "metadata": {},
   "outputs": [],
   "source": [
    "# eligible_cites = df['city'].value_counts()[df['city'].value_counts() > 500].index.tolist()\n",
    "eligible_venue = df['venue'].value_counts()[df['venue'].value_counts() > 480].index.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 820,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Shere Bangla National Stadium',\n",
       " 'R Premadasa Stadium',\n",
       " 'Dubai International Cricket Stadium',\n",
       " 'New Wanderers Stadium',\n",
       " 'Eden Park',\n",
       " 'Newlands',\n",
       " 'Pallekele International Cricket Stadium',\n",
       " 'Kensington Oval, Bridgetown',\n",
       " 'Melbourne Cricket Ground',\n",
       " 'Beausejour Stadium, Gros Islet',\n",
       " 'Kennington Oval',\n",
       " 'Kingsmead',\n",
       " 'Westpac Stadium',\n",
       " 'Central Broward Regional Park Stadium Turf Ground',\n",
       " 'Seddon Park',\n",
       " 'SuperSport Park',\n",
       " 'Old Trafford',\n",
       " 'Zahur Ahmed Chowdhury Stadium',\n",
       " 'R.Premadasa Stadium, Khettarama',\n",
       " 'Sheikh Zayed Stadium',\n",
       " 'Sydney Cricket Ground',\n",
       " 'Trent Bridge',\n",
       " 'The Rose Bowl',\n",
       " 'Bay Oval',\n",
       " 'Wankhede Stadium',\n",
       " 'Eden Gardens',\n",
       " 'Gaddafi Stadium',\n",
       " 'Vidarbha Cricket Association Stadium, Jamtha',\n",
       " \"Lord's\",\n",
       " 'Adelaide Oval',\n",
       " 'M Chinnaswamy Stadium',\n",
       " 'Warner Park, Basseterre',\n",
       " 'Sophia Gardens',\n",
       " \"Queen's Park Oval, Port of Spain\",\n",
       " 'Brisbane Cricket Ground, Woolloongabba',\n",
       " 'Edgbaston',\n",
       " 'Mahinda Rajapaksa International Cricket Stadium, Sooriyawewa',\n",
       " 'Shere Bangla National Stadium, Mirpur',\n",
       " 'The Wanderers Stadium',\n",
       " 'Bharat Ratna Shri Atal Bihari Vajpayee Ekana Cricket Stadium',\n",
       " 'Feroz Shah Kotla',\n",
       " 'Punjab Cricket Association IS Bindra Stadium, Mohali',\n",
       " 'County Ground',\n",
       " 'National Stadium',\n",
       " 'Stadium Australia']"
      ]
     },
     "execution_count": 820,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# eligible_cites\n",
    "eligible_venue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 821,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Shere Bangla National Stadium', 'R Premadasa Stadium', 'Dubai International Cricket Stadium', 'New Wanderers Stadium', 'Eden Park', 'Newlands', 'Pallekele International Cricket Stadium', 'Kensington Oval, Bridgetown', 'Melbourne Cricket Ground', 'Beausejour Stadium, Gros Islet', 'Kennington Oval', 'Kingsmead', 'Westpac Stadium', 'Central Broward Regional Park Stadium Turf Ground', 'Seddon Park', 'SuperSport Park', 'Old Trafford', 'Zahur Ahmed Chowdhury Stadium', 'R.Premadasa Stadium, Khettarama', 'Sheikh Zayed Stadium', 'Sydney Cricket Ground', 'Trent Bridge', 'The Rose Bowl', 'Bay Oval', 'Wankhede Stadium', 'Eden Gardens', 'Gaddafi Stadium', 'Vidarbha Cricket Association Stadium, Jamtha', \"Lord's\", 'Adelaide Oval', 'M Chinnaswamy Stadium', 'Warner Park, Basseterre', 'Sophia Gardens', \"Queen's Park Oval, Port of Spain\", 'Brisbane Cricket Ground, Woolloongabba', 'Edgbaston', 'Mahinda Rajapaksa International Cricket Stadium, Sooriyawewa', 'Shere Bangla National Stadium, Mirpur', 'The Wanderers Stadium', 'Bharat Ratna Shri Atal Bihari Vajpayee Ekana Cricket Stadium', 'Feroz Shah Kotla', 'Punjab Cricket Association IS Bindra Stadium, Mohali', 'County Ground', 'National Stadium', 'Stadium Australia']\n"
     ]
    }
   ],
   "source": [
    "# print(eligible_cites)\n",
    "print(eligible_venue)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 822,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "45\n"
     ]
    }
   ],
   "source": [
    "print(len(eligible_venue))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 823,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df = df[df['city'].isin(eligible_cites)]\n",
    "df = df[df['venue'].isin(eligible_venue)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 824,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(53469, 9)"
      ]
     },
     "execution_count": 824,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 825,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "45"
      ]
     },
     "execution_count": 825,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['venue'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 826,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>Unnamed: 0</th>\n",
       "      <th>match_id</th>\n",
       "      <th>batting_team</th>\n",
       "      <th>bowling_team</th>\n",
       "      <th>ball</th>\n",
       "      <th>runs</th>\n",
       "      <th>player_dismissed</th>\n",
       "      <th>city</th>\n",
       "      <th>venue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>2</td>\n",
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       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Melbourne Cricket Ground</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
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       "      <th>2</th>\n",
       "      <td>2</td>\n",
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       "      <td>0.3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Melbourne Cricket Ground</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>0.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Melbourne Cricket Ground</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Melbourne Cricket Ground</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  match_id batting_team bowling_team  ball  runs  \\\n",
       "0           0         2    Australia    Sri Lanka   0.1     0   \n",
       "1           1         2    Australia    Sri Lanka   0.2     0   \n",
       "2           2         2    Australia    Sri Lanka   0.3     1   \n",
       "3           3         2    Australia    Sri Lanka   0.4     2   \n",
       "4           4         2    Australia    Sri Lanka   0.5     0   \n",
       "\n",
       "  player_dismissed city                     venue  \n",
       "0                0  NaN  Melbourne Cricket Ground  \n",
       "1                0  NaN  Melbourne Cricket Ground  \n",
       "2                0  NaN  Melbourne Cricket Ground  \n",
       "3                0  NaN  Melbourne Cricket Ground  \n",
       "4                0  NaN  Melbourne Cricket Ground  "
      ]
     },
     "execution_count": 826,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 827,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['current_score'] = df.groupby('match_id')['runs'].cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 828,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>runs</th>\n",
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       "      <th>2</th>\n",
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       "      <td>0.3</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>0.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Melbourne Cricket Ground</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Melbourne Cricket Ground</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  match_id batting_team bowling_team  ball  runs  \\\n",
       "0           0         2    Australia    Sri Lanka   0.1     0   \n",
       "1           1         2    Australia    Sri Lanka   0.2     0   \n",
       "2           2         2    Australia    Sri Lanka   0.3     1   \n",
       "3           3         2    Australia    Sri Lanka   0.4     2   \n",
       "4           4         2    Australia    Sri Lanka   0.5     0   \n",
       "\n",
       "  player_dismissed city                     venue  current_score  \n",
       "0                0  NaN  Melbourne Cricket Ground              0  \n",
       "1                0  NaN  Melbourne Cricket Ground              0  \n",
       "2                0  NaN  Melbourne Cricket Ground              1  \n",
       "3                0  NaN  Melbourne Cricket Ground              3  \n",
       "4                0  NaN  Melbourne Cricket Ground              3  "
      ]
     },
     "execution_count": 828,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 829,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr>\n",
       "      <th>63884</th>\n",
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       "      <th>63885</th>\n",
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       "      <td>964</td>\n",
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       "      <td>Australia</td>\n",
       "      <td>19.5</td>\n",
       "      <td>0</td>\n",
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       "      <td>R Premadasa Stadium</td>\n",
       "      <td>125</td>\n",
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       "      <td>124</td>\n",
       "      <td>964</td>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>Australia</td>\n",
       "      <td>19.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Colombo</td>\n",
       "      <td>R Premadasa Stadium</td>\n",
       "      <td>127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63887</th>\n",
       "      <td>125</td>\n",
       "      <td>964</td>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>Australia</td>\n",
       "      <td>19.7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Colombo</td>\n",
       "      <td>R Premadasa Stadium</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0  match_id batting_team bowling_team  ball  runs  \\\n",
       "63883         121       964    Sri Lanka    Australia  19.3     1   \n",
       "63884         122       964    Sri Lanka    Australia  19.4     0   \n",
       "63885         123       964    Sri Lanka    Australia  19.5     0   \n",
       "63886         124       964    Sri Lanka    Australia  19.6     2   \n",
       "63887         125       964    Sri Lanka    Australia  19.7     1   \n",
       "\n",
       "      player_dismissed     city                venue  current_score  \n",
       "63883                0  Colombo  R Premadasa Stadium            125  \n",
       "63884                0  Colombo  R Premadasa Stadium            125  \n",
       "63885      DM de Silva  Colombo  R Premadasa Stadium            125  \n",
       "63886                0  Colombo  R Premadasa Stadium            127  \n",
       "63887                0  Colombo  R Premadasa Stadium            128  "
      ]
     },
     "execution_count": 829,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 830,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['ball_no'] = df['ball'].apply(lambda x:str(x).split('.')[1])\n",
    "df['over'] = df['ball'].apply(lambda x:str(x).split('.')[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 831,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>0</td>\n",
       "      <td>2</td>\n",
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       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
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       "      <td>Sri Lanka</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Melbourne Cricket Ground</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>0.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Melbourne Cricket Ground</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>3</td>\n",
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       "      <td>0</td>\n",
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       "      <td>Melbourne Cricket Ground</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  match_id batting_team bowling_team  ball  runs  \\\n",
       "0           0         2    Australia    Sri Lanka   0.1     0   \n",
       "1           1         2    Australia    Sri Lanka   0.2     0   \n",
       "2           2         2    Australia    Sri Lanka   0.3     1   \n",
       "3           3         2    Australia    Sri Lanka   0.4     2   \n",
       "4           4         2    Australia    Sri Lanka   0.5     0   \n",
       "5           5         2    Australia    Sri Lanka   0.6     3   \n",
       "6           6         2    Australia    Sri Lanka   1.1     0   \n",
       "\n",
       "  player_dismissed city                     venue  current_score ball_no over  \n",
       "0                0  NaN  Melbourne Cricket Ground              0       1    0  \n",
       "1                0  NaN  Melbourne Cricket Ground              0       2    0  \n",
       "2                0  NaN  Melbourne Cricket Ground              1       3    0  \n",
       "3                0  NaN  Melbourne Cricket Ground              3       4    0  \n",
       "4                0  NaN  Melbourne Cricket Ground              3       5    0  \n",
       "5                0  NaN  Melbourne Cricket Ground              6       6    0  \n",
       "6                0  NaN  Melbourne Cricket Ground              6       1    1  "
      ]
     },
     "execution_count": 831,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 832,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['ball_bowled'] = (df['over'].astype(int)*6 + df['ball_no'].astype(int))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 833,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "   Unnamed: 0  match_id batting_team bowling_team  ball  runs  \\\n",
       "0           0         2    Australia    Sri Lanka   0.1     0   \n",
       "1           1         2    Australia    Sri Lanka   0.2     0   \n",
       "2           2         2    Australia    Sri Lanka   0.3     1   \n",
       "3           3         2    Australia    Sri Lanka   0.4     2   \n",
       "4           4         2    Australia    Sri Lanka   0.5     0   \n",
       "5           5         2    Australia    Sri Lanka   0.6     3   \n",
       "6           6         2    Australia    Sri Lanka   1.1     0   \n",
       "\n",
       "  player_dismissed city                     venue  current_score ball_no over  \\\n",
       "0                0  NaN  Melbourne Cricket Ground              0       1    0   \n",
       "1                0  NaN  Melbourne Cricket Ground              0       2    0   \n",
       "2                0  NaN  Melbourne Cricket Ground              1       3    0   \n",
       "3                0  NaN  Melbourne Cricket Ground              3       4    0   \n",
       "4                0  NaN  Melbourne Cricket Ground              3       5    0   \n",
       "5                0  NaN  Melbourne Cricket Ground              6       6    0   \n",
       "6                0  NaN  Melbourne Cricket Ground              6       1    1   \n",
       "\n",
       "   ball_bowled  \n",
       "0            1  \n",
       "1            2  \n",
       "2            3  \n",
       "3            4  \n",
       "4            5  \n",
       "5            6  \n",
       "6            7  "
      ]
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     "execution_count": 833,
     "metadata": {},
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    "df.head(7)"
   ]
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  {
   "cell_type": "code",
   "execution_count": 834,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['ball_left'] = 120 - df['ball_bowled']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 835,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "       Unnamed: 0  match_id batting_team bowling_team  ball  runs  \\\n",
       "63883         121       964    Sri Lanka    Australia  19.3     1   \n",
       "63884         122       964    Sri Lanka    Australia  19.4     0   \n",
       "63885         123       964    Sri Lanka    Australia  19.5     0   \n",
       "63886         124       964    Sri Lanka    Australia  19.6     2   \n",
       "63887         125       964    Sri Lanka    Australia  19.7     1   \n",
       "\n",
       "      player_dismissed     city                venue  current_score ball_no  \\\n",
       "63883                0  Colombo  R Premadasa Stadium            125       3   \n",
       "63884                0  Colombo  R Premadasa Stadium            125       4   \n",
       "63885      DM de Silva  Colombo  R Premadasa Stadium            125       5   \n",
       "63886                0  Colombo  R Premadasa Stadium            127       6   \n",
       "63887                0  Colombo  R Premadasa Stadium            128       7   \n",
       "\n",
       "      over  ball_bowled  ball_left  \n",
       "63883   19          117          3  \n",
       "63884   19          118          2  \n",
       "63885   19          119          1  \n",
       "63886   19          120          0  \n",
       "63887   19          121         -1  "
      ]
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     "execution_count": 835,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 836,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['ball_left'] = df['ball_left'].apply(lambda x:0 if x<0 else x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 837,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "       Unnamed: 0  match_id batting_team bowling_team  ball  runs  \\\n",
       "63883         121       964    Sri Lanka    Australia  19.3     1   \n",
       "63884         122       964    Sri Lanka    Australia  19.4     0   \n",
       "63885         123       964    Sri Lanka    Australia  19.5     0   \n",
       "63886         124       964    Sri Lanka    Australia  19.6     2   \n",
       "63887         125       964    Sri Lanka    Australia  19.7     1   \n",
       "\n",
       "      player_dismissed     city                venue  current_score ball_no  \\\n",
       "63883                0  Colombo  R Premadasa Stadium            125       3   \n",
       "63884                0  Colombo  R Premadasa Stadium            125       4   \n",
       "63885      DM de Silva  Colombo  R Premadasa Stadium            125       5   \n",
       "63886                0  Colombo  R Premadasa Stadium            127       6   \n",
       "63887                0  Colombo  R Premadasa Stadium            128       7   \n",
       "\n",
       "      over  ball_bowled  ball_left  \n",
       "63883   19          117          3  \n",
       "63884   19          118          2  \n",
       "63885   19          119          1  \n",
       "63886   19          120          0  \n",
       "63887   19          121          0  "
      ]
     },
     "execution_count": 837,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 838,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 53469 entries, 0 to 63887\n",
      "Data columns (total 14 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   Unnamed: 0        53469 non-null  int64  \n",
      " 1   match_id          53469 non-null  int64  \n",
      " 2   batting_team      53469 non-null  object \n",
      " 3   bowling_team      53469 non-null  object \n",
      " 4   ball              53469 non-null  float64\n",
      " 5   runs              53469 non-null  int64  \n",
      " 6   player_dismissed  53469 non-null  object \n",
      " 7   city              45734 non-null  object \n",
      " 8   venue             53469 non-null  object \n",
      " 9   current_score     53469 non-null  int64  \n",
      " 10  ball_no           53469 non-null  object \n",
      " 11  over              53469 non-null  object \n",
      " 12  ball_bowled       53469 non-null  int32  \n",
      " 13  ball_left         53469 non-null  int64  \n",
      "dtypes: float64(1), int32(1), int64(5), object(7)\n",
      "memory usage: 5.9+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 839,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['player_dismissed'] = df['player_dismissed'].apply(lambda x:1 if x!='0' else '0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 840,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>match_id</th>\n",
       "      <th>batting_team</th>\n",
       "      <th>bowling_team</th>\n",
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       "      <th>runs</th>\n",
       "      <th>player_dismissed</th>\n",
       "      <th>city</th>\n",
       "      <th>venue</th>\n",
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       "      <th>ball_no</th>\n",
       "      <th>over</th>\n",
       "      <th>ball_bowled</th>\n",
       "      <th>ball_left</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>63881</th>\n",
       "      <td>119</td>\n",
       "      <td>964</td>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>Australia</td>\n",
       "      <td>19.1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Colombo</td>\n",
       "      <td>R Premadasa Stadium</td>\n",
       "      <td>124</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>115</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63882</th>\n",
       "      <td>120</td>\n",
       "      <td>964</td>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>Australia</td>\n",
       "      <td>19.2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Colombo</td>\n",
       "      <td>R Premadasa Stadium</td>\n",
       "      <td>124</td>\n",
       "      <td>2</td>\n",
       "      <td>19</td>\n",
       "      <td>116</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63883</th>\n",
       "      <td>121</td>\n",
       "      <td>964</td>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>Australia</td>\n",
       "      <td>19.3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Colombo</td>\n",
       "      <td>R Premadasa Stadium</td>\n",
       "      <td>125</td>\n",
       "      <td>3</td>\n",
       "      <td>19</td>\n",
       "      <td>117</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63884</th>\n",
       "      <td>122</td>\n",
       "      <td>964</td>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>Australia</td>\n",
       "      <td>19.4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Colombo</td>\n",
       "      <td>R Premadasa Stadium</td>\n",
       "      <td>125</td>\n",
       "      <td>4</td>\n",
       "      <td>19</td>\n",
       "      <td>118</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63885</th>\n",
       "      <td>123</td>\n",
       "      <td>964</td>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>Australia</td>\n",
       "      <td>19.5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Colombo</td>\n",
       "      <td>R Premadasa Stadium</td>\n",
       "      <td>125</td>\n",
       "      <td>5</td>\n",
       "      <td>19</td>\n",
       "      <td>119</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63886</th>\n",
       "      <td>124</td>\n",
       "      <td>964</td>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>Australia</td>\n",
       "      <td>19.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Colombo</td>\n",
       "      <td>R Premadasa Stadium</td>\n",
       "      <td>127</td>\n",
       "      <td>6</td>\n",
       "      <td>19</td>\n",
       "      <td>120</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63887</th>\n",
       "      <td>125</td>\n",
       "      <td>964</td>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>Australia</td>\n",
       "      <td>19.7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Colombo</td>\n",
       "      <td>R Premadasa Stadium</td>\n",
       "      <td>128</td>\n",
       "      <td>7</td>\n",
       "      <td>19</td>\n",
       "      <td>121</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0  match_id batting_team bowling_team  ball  runs  \\\n",
       "63881         119       964    Sri Lanka    Australia  19.1     1   \n",
       "63882         120       964    Sri Lanka    Australia  19.2     0   \n",
       "63883         121       964    Sri Lanka    Australia  19.3     1   \n",
       "63884         122       964    Sri Lanka    Australia  19.4     0   \n",
       "63885         123       964    Sri Lanka    Australia  19.5     0   \n",
       "63886         124       964    Sri Lanka    Australia  19.6     2   \n",
       "63887         125       964    Sri Lanka    Australia  19.7     1   \n",
       "\n",
       "      player_dismissed     city                venue  current_score ball_no  \\\n",
       "63881                0  Colombo  R Premadasa Stadium            124       1   \n",
       "63882                0  Colombo  R Premadasa Stadium            124       2   \n",
       "63883                0  Colombo  R Premadasa Stadium            125       3   \n",
       "63884                0  Colombo  R Premadasa Stadium            125       4   \n",
       "63885                1  Colombo  R Premadasa Stadium            125       5   \n",
       "63886                0  Colombo  R Premadasa Stadium            127       6   \n",
       "63887                0  Colombo  R Premadasa Stadium            128       7   \n",
       "\n",
       "      over  ball_bowled  ball_left  \n",
       "63881   19          115          5  \n",
       "63882   19          116          4  \n",
       "63883   19          117          3  \n",
       "63884   19          118          2  \n",
       "63885   19          119          1  \n",
       "63886   19          120          0  \n",
       "63887   19          121          0  "
      ]
     },
     "execution_count": 840,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 841,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(53469, 14)"
      ]
     },
     "execution_count": 841,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 842,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 53469 entries, 0 to 63887\n",
      "Data columns (total 14 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   Unnamed: 0        53469 non-null  int64  \n",
      " 1   match_id          53469 non-null  int64  \n",
      " 2   batting_team      53469 non-null  object \n",
      " 3   bowling_team      53469 non-null  object \n",
      " 4   ball              53469 non-null  float64\n",
      " 5   runs              53469 non-null  int64  \n",
      " 6   player_dismissed  53469 non-null  object \n",
      " 7   city              45734 non-null  object \n",
      " 8   venue             53469 non-null  object \n",
      " 9   current_score     53469 non-null  int64  \n",
      " 10  ball_no           53469 non-null  object \n",
      " 11  over              53469 non-null  object \n",
      " 12  ball_bowled       53469 non-null  int32  \n",
      " 13  ball_left         53469 non-null  int64  \n",
      "dtypes: float64(1), int32(1), int64(5), object(7)\n",
      "memory usage: 5.9+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 843,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['player_dismissed'] = df['player_dismissed'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 844,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['cumulative_dismissed'] = df.groupby('match_id')['player_dismissed'].cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 845,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['wicket_left'] = 10 - df['player_dismissed']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 846,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>63884</th>\n",
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       "      <td>964</td>\n",
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       "      <td>0</td>\n",
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       "      <td>R Premadasa Stadium</td>\n",
       "      <td>125</td>\n",
       "      <td>4</td>\n",
       "      <td>19</td>\n",
       "      <td>118</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
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       "      <th>63885</th>\n",
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       "      <td>964</td>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>Australia</td>\n",
       "      <td>19.5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Colombo</td>\n",
       "      <td>R Premadasa Stadium</td>\n",
       "      <td>125</td>\n",
       "      <td>5</td>\n",
       "      <td>19</td>\n",
       "      <td>119</td>\n",
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       "      <td>9</td>\n",
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       "      <td>124</td>\n",
       "      <td>964</td>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>Australia</td>\n",
       "      <td>19.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Colombo</td>\n",
       "      <td>R Premadasa Stadium</td>\n",
       "      <td>127</td>\n",
       "      <td>6</td>\n",
       "      <td>19</td>\n",
       "      <td>120</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63887</th>\n",
       "      <td>125</td>\n",
       "      <td>964</td>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>Australia</td>\n",
       "      <td>19.7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Colombo</td>\n",
       "      <td>R Premadasa Stadium</td>\n",
       "      <td>128</td>\n",
       "      <td>7</td>\n",
       "      <td>19</td>\n",
       "      <td>121</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0  match_id batting_team bowling_team  ball  runs  \\\n",
       "63883         121       964    Sri Lanka    Australia  19.3     1   \n",
       "63884         122       964    Sri Lanka    Australia  19.4     0   \n",
       "63885         123       964    Sri Lanka    Australia  19.5     0   \n",
       "63886         124       964    Sri Lanka    Australia  19.6     2   \n",
       "63887         125       964    Sri Lanka    Australia  19.7     1   \n",
       "\n",
       "       player_dismissed     city                venue  current_score ball_no  \\\n",
       "63883                 0  Colombo  R Premadasa Stadium            125       3   \n",
       "63884                 0  Colombo  R Premadasa Stadium            125       4   \n",
       "63885                 1  Colombo  R Premadasa Stadium            125       5   \n",
       "63886                 0  Colombo  R Premadasa Stadium            127       6   \n",
       "63887                 0  Colombo  R Premadasa Stadium            128       7   \n",
       "\n",
       "      over  ball_bowled  ball_left  cumulative_dismissed  wicket_left  \n",
       "63883   19          117          3                     8           10  \n",
       "63884   19          118          2                     8           10  \n",
       "63885   19          119          1                     9            9  \n",
       "63886   19          120          0                     9           10  \n",
       "63887   19          121          0                     9           10  "
      ]
     },
     "execution_count": 846,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 847,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['current_run_rate'] = (df['current_score']*6)/df['ball_bowled']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 848,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>63884</th>\n",
       "      <td>122</td>\n",
       "      <td>964</td>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>Australia</td>\n",
       "      <td>19.4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Colombo</td>\n",
       "      <td>R Premadasa Stadium</td>\n",
       "      <td>125</td>\n",
       "      <td>4</td>\n",
       "      <td>19</td>\n",
       "      <td>118</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>6.355932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63885</th>\n",
       "      <td>123</td>\n",
       "      <td>964</td>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>Australia</td>\n",
       "      <td>19.5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Colombo</td>\n",
       "      <td>R Premadasa Stadium</td>\n",
       "      <td>125</td>\n",
       "      <td>5</td>\n",
       "      <td>19</td>\n",
       "      <td>119</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>6.302521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63886</th>\n",
       "      <td>124</td>\n",
       "      <td>964</td>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>Australia</td>\n",
       "      <td>19.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Colombo</td>\n",
       "      <td>R Premadasa Stadium</td>\n",
       "      <td>127</td>\n",
       "      <td>6</td>\n",
       "      <td>19</td>\n",
       "      <td>120</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>6.350000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63887</th>\n",
       "      <td>125</td>\n",
       "      <td>964</td>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>Australia</td>\n",
       "      <td>19.7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Colombo</td>\n",
       "      <td>R Premadasa Stadium</td>\n",
       "      <td>128</td>\n",
       "      <td>7</td>\n",
       "      <td>19</td>\n",
       "      <td>121</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>6.347107</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0  match_id batting_team bowling_team  ball  runs  \\\n",
       "63883         121       964    Sri Lanka    Australia  19.3     1   \n",
       "63884         122       964    Sri Lanka    Australia  19.4     0   \n",
       "63885         123       964    Sri Lanka    Australia  19.5     0   \n",
       "63886         124       964    Sri Lanka    Australia  19.6     2   \n",
       "63887         125       964    Sri Lanka    Australia  19.7     1   \n",
       "\n",
       "       player_dismissed     city                venue  current_score ball_no  \\\n",
       "63883                 0  Colombo  R Premadasa Stadium            125       3   \n",
       "63884                 0  Colombo  R Premadasa Stadium            125       4   \n",
       "63885                 1  Colombo  R Premadasa Stadium            125       5   \n",
       "63886                 0  Colombo  R Premadasa Stadium            127       6   \n",
       "63887                 0  Colombo  R Premadasa Stadium            128       7   \n",
       "\n",
       "      over  ball_bowled  ball_left  cumulative_dismissed  wicket_left  \\\n",
       "63883   19          117          3                     8           10   \n",
       "63884   19          118          2                     8           10   \n",
       "63885   19          119          1                     9            9   \n",
       "63886   19          120          0                     9           10   \n",
       "63887   19          121          0                     9           10   \n",
       "\n",
       "       current_run_rate  \n",
       "63883          6.410256  \n",
       "63884          6.355932  \n",
       "63885          6.302521  \n",
       "63886          6.350000  \n",
       "63887          6.347107  "
      ]
     },
     "execution_count": 848,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 849,
   "metadata": {},
   "outputs": [],
   "source": [
    "# group by 'match id'\n",
    "groups = df.groupby('match_id')\n",
    "\n",
    "# 5 over = 30 balls\n",
    "match_id = df['match_id'].unique()\n",
    "last_five = []\n",
    "\n",
    "for id in match_id:\n",
    "    # Apply rolling sum on the 'runs' column\n",
    "    rolling_sums = groups.get_group(id)['runs'].rolling(window=30).sum()\n",
    "    last_five.extend(rolling_sums.values.tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 850,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['last_five'] = last_five"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 851,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         NaN\n",
       "1         NaN\n",
       "2         NaN\n",
       "3         NaN\n",
       "4         NaN\n",
       "         ... \n",
       "63883    32.0\n",
       "63884    32.0\n",
       "63885    32.0\n",
       "63886    33.0\n",
       "63887    32.0\n",
       "Name: last_five, Length: 53469, dtype: float64"
      ]
     },
     "execution_count": 851,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['last_five']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 852,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>match_id</th>\n",
       "      <th>batting_team</th>\n",
       "      <th>bowling_team</th>\n",
       "      <th>ball</th>\n",
       "      <th>runs</th>\n",
       "      <th>player_dismissed</th>\n",
       "      <th>city</th>\n",
       "      <th>venue</th>\n",
       "      <th>current_score</th>\n",
       "      <th>ball_no</th>\n",
       "      <th>over</th>\n",
       "      <th>ball_bowled</th>\n",
       "      <th>ball_left</th>\n",
       "      <th>cumulative_dismissed</th>\n",
       "      <th>wicket_left</th>\n",
       "      <th>current_run_rate</th>\n",
       "      <th>last_five</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Australia</td>\n",
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       "      <td>0.1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>119</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Melbourne Cricket Ground</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>118</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Melbourne Cricket Ground</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>117</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>0.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Melbourne Cricket Ground</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>116</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>4.5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Melbourne Cricket Ground</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>115</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>3.6</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  match_id batting_team bowling_team  ball  runs  \\\n",
       "0           0         2    Australia    Sri Lanka   0.1     0   \n",
       "1           1         2    Australia    Sri Lanka   0.2     0   \n",
       "2           2         2    Australia    Sri Lanka   0.3     1   \n",
       "3           3         2    Australia    Sri Lanka   0.4     2   \n",
       "4           4         2    Australia    Sri Lanka   0.5     0   \n",
       "\n",
       "   player_dismissed city                     venue  current_score ball_no  \\\n",
       "0                 0  NaN  Melbourne Cricket Ground              0       1   \n",
       "1                 0  NaN  Melbourne Cricket Ground              0       2   \n",
       "2                 0  NaN  Melbourne Cricket Ground              1       3   \n",
       "3                 0  NaN  Melbourne Cricket Ground              3       4   \n",
       "4                 0  NaN  Melbourne Cricket Ground              3       5   \n",
       "\n",
       "  over  ball_bowled  ball_left  cumulative_dismissed  wicket_left  \\\n",
       "0    0            1        119                     0           10   \n",
       "1    0            2        118                     0           10   \n",
       "2    0            3        117                     0           10   \n",
       "3    0            4        116                     0           10   \n",
       "4    0            5        115                     0           10   \n",
       "\n",
       "   current_run_rate  last_five  \n",
       "0               0.0        NaN  \n",
       "1               0.0        NaN  \n",
       "2               2.0        NaN  \n",
       "3               4.5        NaN  \n",
       "4               3.6        NaN  "
      ]
     },
     "execution_count": 852,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 853,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>last_five</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>63883</th>\n",
       "      <td>121</td>\n",
       "      <td>964</td>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>Australia</td>\n",
       "      <td>19.3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Colombo</td>\n",
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       "      <td>125</td>\n",
       "      <td>3</td>\n",
       "      <td>19</td>\n",
       "      <td>117</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>6.410256</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63884</th>\n",
       "      <td>122</td>\n",
       "      <td>964</td>\n",
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       "      <td>Australia</td>\n",
       "      <td>19.4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Colombo</td>\n",
       "      <td>R Premadasa Stadium</td>\n",
       "      <td>125</td>\n",
       "      <td>4</td>\n",
       "      <td>19</td>\n",
       "      <td>118</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>6.355932</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63885</th>\n",
       "      <td>123</td>\n",
       "      <td>964</td>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>Australia</td>\n",
       "      <td>19.5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Colombo</td>\n",
       "      <td>R Premadasa Stadium</td>\n",
       "      <td>125</td>\n",
       "      <td>5</td>\n",
       "      <td>19</td>\n",
       "      <td>119</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>6.302521</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63886</th>\n",
       "      <td>124</td>\n",
       "      <td>964</td>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>Australia</td>\n",
       "      <td>19.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Colombo</td>\n",
       "      <td>R Premadasa Stadium</td>\n",
       "      <td>127</td>\n",
       "      <td>6</td>\n",
       "      <td>19</td>\n",
       "      <td>120</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>6.350000</td>\n",
       "      <td>33.0</td>\n",
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       "    <tr>\n",
       "      <th>63887</th>\n",
       "      <td>125</td>\n",
       "      <td>964</td>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>Australia</td>\n",
       "      <td>19.7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Colombo</td>\n",
       "      <td>R Premadasa Stadium</td>\n",
       "      <td>128</td>\n",
       "      <td>7</td>\n",
       "      <td>19</td>\n",
       "      <td>121</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>6.347107</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0  match_id batting_team bowling_team  ball  runs  \\\n",
       "63883         121       964    Sri Lanka    Australia  19.3     1   \n",
       "63884         122       964    Sri Lanka    Australia  19.4     0   \n",
       "63885         123       964    Sri Lanka    Australia  19.5     0   \n",
       "63886         124       964    Sri Lanka    Australia  19.6     2   \n",
       "63887         125       964    Sri Lanka    Australia  19.7     1   \n",
       "\n",
       "       player_dismissed     city                venue  current_score ball_no  \\\n",
       "63883                 0  Colombo  R Premadasa Stadium            125       3   \n",
       "63884                 0  Colombo  R Premadasa Stadium            125       4   \n",
       "63885                 1  Colombo  R Premadasa Stadium            125       5   \n",
       "63886                 0  Colombo  R Premadasa Stadium            127       6   \n",
       "63887                 0  Colombo  R Premadasa Stadium            128       7   \n",
       "\n",
       "      over  ball_bowled  ball_left  cumulative_dismissed  wicket_left  \\\n",
       "63883   19          117          3                     8           10   \n",
       "63884   19          118          2                     8           10   \n",
       "63885   19          119          1                     9            9   \n",
       "63886   19          120          0                     9           10   \n",
       "63887   19          121          0                     9           10   \n",
       "\n",
       "       current_run_rate  last_five  \n",
       "63883          6.410256       32.0  \n",
       "63884          6.355932       32.0  \n",
       "63885          6.302521       32.0  \n",
       "63886          6.350000       33.0  \n",
       "63887          6.347107       32.0  "
      ]
     },
     "execution_count": 853,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 854,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df = df.groupby('match_id').sum()['runs'].reset_index().merge(df,on='match_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 855,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>match_id</th>\n",
       "      <th>runs_x</th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>batting_team</th>\n",
       "      <th>bowling_team</th>\n",
       "      <th>ball</th>\n",
       "      <th>runs_y</th>\n",
       "      <th>player_dismissed</th>\n",
       "      <th>city</th>\n",
       "      <th>venue</th>\n",
       "      <th>current_score</th>\n",
       "      <th>ball_no</th>\n",
       "      <th>over</th>\n",
       "      <th>ball_bowled</th>\n",
       "      <th>ball_left</th>\n",
       "      <th>cumulative_dismissed</th>\n",
       "      <th>wicket_left</th>\n",
       "      <th>current_run_rate</th>\n",
       "      <th>last_five</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>168</td>\n",
       "      <td>0</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Melbourne Cricket Ground</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>119</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>168</td>\n",
       "      <td>1</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Melbourne Cricket Ground</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>118</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>168</td>\n",
       "      <td>2</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Melbourne Cricket Ground</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>117</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>168</td>\n",
       "      <td>3</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>0.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Melbourne Cricket Ground</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>116</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>4.5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>168</td>\n",
       "      <td>4</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Melbourne Cricket Ground</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>115</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>3.6</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   match_id  runs_x  Unnamed: 0 batting_team bowling_team  ball  runs_y  \\\n",
       "0         2     168           0    Australia    Sri Lanka   0.1       0   \n",
       "1         2     168           1    Australia    Sri Lanka   0.2       0   \n",
       "2         2     168           2    Australia    Sri Lanka   0.3       1   \n",
       "3         2     168           3    Australia    Sri Lanka   0.4       2   \n",
       "4         2     168           4    Australia    Sri Lanka   0.5       0   \n",
       "\n",
       "   player_dismissed city                     venue  current_score ball_no  \\\n",
       "0                 0  NaN  Melbourne Cricket Ground              0       1   \n",
       "1                 0  NaN  Melbourne Cricket Ground              0       2   \n",
       "2                 0  NaN  Melbourne Cricket Ground              1       3   \n",
       "3                 0  NaN  Melbourne Cricket Ground              3       4   \n",
       "4                 0  NaN  Melbourne Cricket Ground              3       5   \n",
       "\n",
       "  over  ball_bowled  ball_left  cumulative_dismissed  wicket_left  \\\n",
       "0    0            1        119                     0           10   \n",
       "1    0            2        118                     0           10   \n",
       "2    0            3        117                     0           10   \n",
       "3    0            4        116                     0           10   \n",
       "4    0            5        115                     0           10   \n",
       "\n",
       "   current_run_rate  last_five  \n",
       "0               0.0        NaN  \n",
       "1               0.0        NaN  \n",
       "2               2.0        NaN  \n",
       "3               4.5        NaN  \n",
       "4               3.6        NaN  "
      ]
     },
     "execution_count": 855,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 856,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 856,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['batting_team'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 857,
   "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>match_id</th>\n",
       "      <th>runs_x</th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>batting_team</th>\n",
       "      <th>bowling_team</th>\n",
       "      <th>ball</th>\n",
       "      <th>runs_y</th>\n",
       "      <th>player_dismissed</th>\n",
       "      <th>city</th>\n",
       "      <th>venue</th>\n",
       "      <th>current_score</th>\n",
       "      <th>ball_no</th>\n",
       "      <th>over</th>\n",
       "      <th>ball_bowled</th>\n",
       "      <th>ball_left</th>\n",
       "      <th>cumulative_dismissed</th>\n",
       "      <th>wicket_left</th>\n",
       "      <th>current_run_rate</th>\n",
       "      <th>last_five</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>168</td>\n",
       "      <td>0</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Melbourne Cricket Ground</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>119</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>168</td>\n",
       "      <td>1</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Melbourne Cricket Ground</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>118</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>168</td>\n",
       "      <td>2</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Melbourne Cricket Ground</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>117</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   match_id  runs_x  Unnamed: 0 batting_team bowling_team  ball  runs_y  \\\n",
       "0         2     168           0    Australia    Sri Lanka   0.1       0   \n",
       "1         2     168           1    Australia    Sri Lanka   0.2       0   \n",
       "2         2     168           2    Australia    Sri Lanka   0.3       1   \n",
       "\n",
       "   player_dismissed city                     venue  current_score ball_no  \\\n",
       "0                 0  NaN  Melbourne Cricket Ground              0       1   \n",
       "1                 0  NaN  Melbourne Cricket Ground              0       2   \n",
       "2                 0  NaN  Melbourne Cricket Ground              1       3   \n",
       "\n",
       "  over  ball_bowled  ball_left  cumulative_dismissed  wicket_left  \\\n",
       "0    0            1        119                     0           10   \n",
       "1    0            2        118                     0           10   \n",
       "2    0            3        117                     0           10   \n",
       "\n",
       "   current_run_rate  last_five  \n",
       "0               0.0        NaN  \n",
       "1               0.0        NaN  \n",
       "2               2.0        NaN  "
      ]
     },
     "execution_count": 857,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 858,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['match_id', 'runs_x', 'Unnamed: 0', 'batting_team', 'bowling_team',\n",
       "       'ball', 'runs_y', 'player_dismissed', 'city', 'venue', 'current_score',\n",
       "       'ball_no', 'over', 'ball_bowled', 'ball_left', 'cumulative_dismissed',\n",
       "       'wicket_left', 'current_run_rate', 'last_five'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 858,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 859,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df = final_df[['batting_team', 'bowling_team', 'venue', 'current_score', 'ball_left', 'wicket_left', 'current_run_rate', 'last_five', 'runs_x']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 860,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "batting_team            0\n",
       "bowling_team            0\n",
       "venue                   0\n",
       "current_score           0\n",
       "ball_left               0\n",
       "wicket_left             0\n",
       "current_run_rate        0\n",
       "last_five           12749\n",
       "runs_x                  0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 860,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 861,
   "metadata": {},
   "outputs": [],
   "source": [
    "# replacing null value with for last_five column by mean\n",
    "# final_df['last_five'] = final_df['last_five'].fillna(final_df['last_five'].mean())\n",
    "final_df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 862,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "batting_team        0\n",
       "bowling_team        0\n",
       "venue               0\n",
       "current_score       0\n",
       "ball_left           0\n",
       "wicket_left         0\n",
       "current_run_rate    0\n",
       "last_five           0\n",
       "runs_x              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 862,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 863,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(40720, 9)"
      ]
     },
     "execution_count": 863,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 864,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df = final_df.sample(final_df.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 865,
   "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>batting_team</th>\n",
       "      <th>bowling_team</th>\n",
       "      <th>venue</th>\n",
       "      <th>current_score</th>\n",
       "      <th>ball_left</th>\n",
       "      <th>wicket_left</th>\n",
       "      <th>current_run_rate</th>\n",
       "      <th>last_five</th>\n",
       "      <th>runs_x</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18837</th>\n",
       "      <td>Pakistan</td>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>New Wanderers Stadium</td>\n",
       "      <td>86</td>\n",
       "      <td>51</td>\n",
       "      <td>10</td>\n",
       "      <td>7.478261</td>\n",
       "      <td>51.0</td>\n",
       "      <td>189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35480</th>\n",
       "      <td>Pakistan</td>\n",
       "      <td>Australia</td>\n",
       "      <td>R Premadasa Stadium</td>\n",
       "      <td>126</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>7.132075</td>\n",
       "      <td>38.0</td>\n",
       "      <td>149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5547</th>\n",
       "      <td>India</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>Newlands</td>\n",
       "      <td>105</td>\n",
       "      <td>45</td>\n",
       "      <td>10</td>\n",
       "      <td>8.400000</td>\n",
       "      <td>42.0</td>\n",
       "      <td>172</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      batting_team  bowling_team                  venue  current_score  \\\n",
       "18837     Pakistan     Sri Lanka  New Wanderers Stadium             86   \n",
       "35480     Pakistan     Australia    R Premadasa Stadium            126   \n",
       "5547         India  South Africa               Newlands            105   \n",
       "\n",
       "       ball_left  wicket_left  current_run_rate  last_five  runs_x  \n",
       "18837         51           10          7.478261       51.0     189  \n",
       "35480         14           10          7.132075       38.0     149  \n",
       "5547          45           10          8.400000       42.0     172  "
      ]
     },
     "execution_count": 865,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 867,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = final_df.drop(columns=['runs_x'])\n",
    "y = final_df['runs_x']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 868,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 869,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.25,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 870,
   "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>batting_team</th>\n",
       "      <th>bowling_team</th>\n",
       "      <th>venue</th>\n",
       "      <th>current_score</th>\n",
       "      <th>ball_left</th>\n",
       "      <th>wicket_left</th>\n",
       "      <th>current_run_rate</th>\n",
       "      <th>last_five</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1603</th>\n",
       "      <td>West Indies</td>\n",
       "      <td>Pakistan</td>\n",
       "      <td>Dubai International Cricket Stadium</td>\n",
       "      <td>103</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>5.567568</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8588</th>\n",
       "      <td>Bangladesh</td>\n",
       "      <td>West Indies</td>\n",
       "      <td>Central Broward Regional Park Stadium Turf Ground</td>\n",
       "      <td>66</td>\n",
       "      <td>88</td>\n",
       "      <td>9</td>\n",
       "      <td>12.375000</td>\n",
       "      <td>62.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47877</th>\n",
       "      <td>India</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Melbourne Cricket Ground</td>\n",
       "      <td>138</td>\n",
       "      <td>29</td>\n",
       "      <td>10</td>\n",
       "      <td>9.098901</td>\n",
       "      <td>46.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20359</th>\n",
       "      <td>India</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Melbourne Cricket Ground</td>\n",
       "      <td>63</td>\n",
       "      <td>25</td>\n",
       "      <td>10</td>\n",
       "      <td>3.978947</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50886</th>\n",
       "      <td>New Zealand</td>\n",
       "      <td>Pakistan</td>\n",
       "      <td>Punjab Cricket Association IS Bindra Stadium, ...</td>\n",
       "      <td>116</td>\n",
       "      <td>40</td>\n",
       "      <td>10</td>\n",
       "      <td>8.700000</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38644</th>\n",
       "      <td>Australia</td>\n",
       "      <td>Pakistan</td>\n",
       "      <td>Dubai International Cricket Stadium</td>\n",
       "      <td>104</td>\n",
       "      <td>60</td>\n",
       "      <td>10</td>\n",
       "      <td>10.400000</td>\n",
       "      <td>62.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42345</th>\n",
       "      <td>Pakistan</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Shere Bangla National Stadium</td>\n",
       "      <td>93</td>\n",
       "      <td>57</td>\n",
       "      <td>10</td>\n",
       "      <td>8.857143</td>\n",
       "      <td>58.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33877</th>\n",
       "      <td>Pakistan</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>Pallekele International Cricket Stadium</td>\n",
       "      <td>159</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>8.517857</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25202</th>\n",
       "      <td>Bangladesh</td>\n",
       "      <td>West Indies</td>\n",
       "      <td>Warner Park, Basseterre</td>\n",
       "      <td>96</td>\n",
       "      <td>21</td>\n",
       "      <td>10</td>\n",
       "      <td>5.818182</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22376</th>\n",
       "      <td>England</td>\n",
       "      <td>Pakistan</td>\n",
       "      <td>Kennington Oval</td>\n",
       "      <td>177</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>9.315789</td>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30540 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      batting_team bowling_team  \\\n",
       "1603   West Indies     Pakistan   \n",
       "8588    Bangladesh  West Indies   \n",
       "47877        India    Australia   \n",
       "20359        India    Australia   \n",
       "50886  New Zealand     Pakistan   \n",
       "...            ...          ...   \n",
       "38644    Australia     Pakistan   \n",
       "42345     Pakistan    Australia   \n",
       "33877     Pakistan  New Zealand   \n",
       "25202   Bangladesh  West Indies   \n",
       "22376      England     Pakistan   \n",
       "\n",
       "                                                   venue  current_score  \\\n",
       "1603                 Dubai International Cricket Stadium            103   \n",
       "8588   Central Broward Regional Park Stadium Turf Ground             66   \n",
       "47877                           Melbourne Cricket Ground            138   \n",
       "20359                           Melbourne Cricket Ground             63   \n",
       "50886  Punjab Cricket Association IS Bindra Stadium, ...            116   \n",
       "...                                                  ...            ...   \n",
       "38644                Dubai International Cricket Stadium            104   \n",
       "42345                      Shere Bangla National Stadium             93   \n",
       "33877            Pallekele International Cricket Stadium            159   \n",
       "25202                            Warner Park, Basseterre             96   \n",
       "22376                                    Kennington Oval            177   \n",
       "\n",
       "       ball_left  wicket_left  current_run_rate  last_five  \n",
       "1603           9           10          5.567568       45.0  \n",
       "8588          88            9         12.375000       62.0  \n",
       "47877         29           10          9.098901       46.0  \n",
       "20359         25           10          3.978947       15.0  \n",
       "50886         40           10          8.700000       43.0  \n",
       "...          ...          ...               ...        ...  \n",
       "38644         60           10         10.400000       62.0  \n",
       "42345         57           10          8.857143       58.0  \n",
       "33877          8            9          8.517857       35.0  \n",
       "25202         21           10          5.818182       25.0  \n",
       "22376          6           10          9.315789       41.0  \n",
       "\n",
       "[30540 rows x 8 columns]"
      ]
     },
     "execution_count": 870,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 871,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import machine learnign algorithm\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from xgboost import XGBRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 872,
   "metadata": {},
   "outputs": [],
   "source": [
    "# initial classifier\n",
    "lr = LinearRegression()\n",
    "dt = DecisionTreeClassifier(max_depth=5)\n",
    "rf = RandomForestClassifier(n_estimators=50,random_state=2,max_depth=5)\n",
    "xr = XGBRegressor(n_estimators=1000, learning_rate=0.2, max_depth=12, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 873,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import precision_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 877,
   "metadata": {},
   "outputs": [],
   "source": [
    "transformer = ColumnTransformer([\n",
    "    ('transformer', OneHotEncoder(drop='first', sparse_output=False), ['batting_team', 'bowling_team', 'venue'])\n",
    "], remainder='passthrough')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 878,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe = Pipeline(steps=[\n",
    "    ('step1', transformer),\n",
    "    ('step2', StandardScaler(with_mean=False)),\n",
    "    ('step3', XGBRegressor(n_estimators=1000, learning_rate=0.2, max_depth=12, random_state=1))\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 879,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Program Files\\Python311\\Lib\\site-packages\\sklearn\\compose\\_column_transformer.py:1624: FutureWarning: \n",
      "The format of the columns of the 'remainder' transformer in ColumnTransformer.transformers_ will change in version 1.7 to match the format of the other transformers.\n",
      "At the moment the remainder columns are stored as indices (of type int). With the same ColumnTransformer configuration, in the future they will be stored as column names (of type str).\n",
      "To use the new behavior now and suppress this warning, use ColumnTransformer(force_int_remainder_cols=False).\n",
      "\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-9 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-9 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-9 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-9 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-9 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-9 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-9 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-9 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-9 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-9 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-9 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-9 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-9 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-9 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-9\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;step1&#x27;,\n",
       "                 ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                                   transformers=[(&#x27;transformer&#x27;,\n",
       "                                                  OneHotEncoder(drop=&#x27;first&#x27;,\n",
       "                                                                sparse_output=False),\n",
       "                                                  [&#x27;batting_team&#x27;,\n",
       "                                                   &#x27;bowling_team&#x27;,\n",
       "                                                   &#x27;venue&#x27;])])),\n",
       "                (&#x27;step2&#x27;, StandardScaler(with_mean=False)),\n",
       "                (&#x27;step3&#x27;,\n",
       "                 XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
       "                              colsample_bylevel=None, colsample_bynode=None,\n",
       "                              colsample...\n",
       "                              feature_types=None, gamma=None, gpu_id=None,\n",
       "                              grow_policy=None, importance_type=None,\n",
       "                              interaction_constraints=None, learning_rate=0.2,\n",
       "                              max_bin=None, max_cat_threshold=None,\n",
       "                              max_cat_to_onehot=None, max_delta_step=None,\n",
       "                              max_depth=12, max_leaves=None,\n",
       "                              min_child_weight=None, missing=nan,\n",
       "                              monotone_constraints=None, n_estimators=1000,\n",
       "                              n_jobs=None, num_parallel_tree=None,\n",
       "                              predictor=None, random_state=1, ...))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-65\" type=\"checkbox\" ><label for=\"sk-estimator-id-65\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;Pipeline<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;step1&#x27;,\n",
       "                 ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                                   transformers=[(&#x27;transformer&#x27;,\n",
       "                                                  OneHotEncoder(drop=&#x27;first&#x27;,\n",
       "                                                                sparse_output=False),\n",
       "                                                  [&#x27;batting_team&#x27;,\n",
       "                                                   &#x27;bowling_team&#x27;,\n",
       "                                                   &#x27;venue&#x27;])])),\n",
       "                (&#x27;step2&#x27;, StandardScaler(with_mean=False)),\n",
       "                (&#x27;step3&#x27;,\n",
       "                 XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
       "                              colsample_bylevel=None, colsample_bynode=None,\n",
       "                              colsample...\n",
       "                              feature_types=None, gamma=None, gpu_id=None,\n",
       "                              grow_policy=None, importance_type=None,\n",
       "                              interaction_constraints=None, learning_rate=0.2,\n",
       "                              max_bin=None, max_cat_threshold=None,\n",
       "                              max_cat_to_onehot=None, max_delta_step=None,\n",
       "                              max_depth=12, max_leaves=None,\n",
       "                              min_child_weight=None, missing=nan,\n",
       "                              monotone_constraints=None, n_estimators=1000,\n",
       "                              n_jobs=None, num_parallel_tree=None,\n",
       "                              predictor=None, random_state=1, ...))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-66\" type=\"checkbox\" ><label for=\"sk-estimator-id-66\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;step1: ColumnTransformer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.compose.ColumnTransformer.html\">?<span>Documentation for step1: ColumnTransformer</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                  transformers=[(&#x27;transformer&#x27;,\n",
       "                                 OneHotEncoder(drop=&#x27;first&#x27;,\n",
       "                                               sparse_output=False),\n",
       "                                 [&#x27;batting_team&#x27;, &#x27;bowling_team&#x27;, &#x27;venue&#x27;])])</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-67\" type=\"checkbox\" ><label for=\"sk-estimator-id-67\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">transformer</label><div class=\"sk-toggleable__content fitted\"><pre>[&#x27;batting_team&#x27;, &#x27;bowling_team&#x27;, &#x27;venue&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-68\" type=\"checkbox\" ><label for=\"sk-estimator-id-68\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;OneHotEncoder<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.OneHotEncoder.html\">?<span>Documentation for OneHotEncoder</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>OneHotEncoder(drop=&#x27;first&#x27;, sparse_output=False)</pre></div> </div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-69\" type=\"checkbox\" ><label for=\"sk-estimator-id-69\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">remainder</label><div class=\"sk-toggleable__content fitted\"><pre>[&#x27;current_score&#x27;, &#x27;ball_left&#x27;, &#x27;wicket_left&#x27;, &#x27;current_run_rate&#x27;, &#x27;last_five&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-70\" type=\"checkbox\" ><label for=\"sk-estimator-id-70\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">passthrough</label><div class=\"sk-toggleable__content fitted\"><pre>passthrough</pre></div> </div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-71\" type=\"checkbox\" ><label for=\"sk-estimator-id-71\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;StandardScaler<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>StandardScaler(with_mean=False)</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-72\" type=\"checkbox\" ><label for=\"sk-estimator-id-72\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">XGBRegressor</label><div class=\"sk-toggleable__content fitted\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=None, early_stopping_rounds=None,\n",
       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "             gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
       "             interaction_constraints=None, learning_rate=0.2, max_bin=None,\n",
       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "             max_delta_step=None, max_depth=12, max_leaves=None,\n",
       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "             n_estimators=1000, n_jobs=None, num_parallel_tree=None,\n",
       "             predictor=None, random_state=1, ...)</pre></div> </div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('step1',\n",
       "                 ColumnTransformer(remainder='passthrough',\n",
       "                                   transformers=[('transformer',\n",
       "                                                  OneHotEncoder(drop='first',\n",
       "                                                                sparse_output=False),\n",
       "                                                  ['batting_team',\n",
       "                                                   'bowling_team',\n",
       "                                                   'venue'])])),\n",
       "                ('step2', StandardScaler(with_mean=False)),\n",
       "                ('step3',\n",
       "                 XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
       "                              colsample_bylevel=None, colsample_bynode=None,\n",
       "                              colsample...\n",
       "                              feature_types=None, gamma=None, gpu_id=None,\n",
       "                              grow_policy=None, importance_type=None,\n",
       "                              interaction_constraints=None, learning_rate=0.2,\n",
       "                              max_bin=None, max_cat_threshold=None,\n",
       "                              max_cat_to_onehot=None, max_delta_step=None,\n",
       "                              max_depth=12, max_leaves=None,\n",
       "                              min_child_weight=None, missing=nan,\n",
       "                              monotone_constraints=None, n_estimators=1000,\n",
       "                              n_jobs=None, num_parallel_tree=None,\n",
       "                              predictor=None, random_state=1, ...))])"
      ]
     },
     "execution_count": 879,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 880,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = pipe.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 881,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9803237915039062"
      ]
     },
     "execution_count": 881,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 882,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.0166356579493914"
      ]
     },
     "execution_count": 882,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_absolute_error(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 883,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 884,
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(pipe,open('model.pkl','wb'))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
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