APP IPL PREDICTION_code.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cb911009",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.metrics import accuracy_score\n",
    "import pickle\n",
    "from matplotlib import pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b10bb424",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>Season</th>\n",
       "      <th>city</th>\n",
       "      <th>date</th>\n",
       "      <th>team1</th>\n",
       "      <th>team2</th>\n",
       "      <th>toss_winner</th>\n",
       "      <th>toss_decision</th>\n",
       "      <th>result</th>\n",
       "      <th>dl_applied</th>\n",
       "      <th>winner</th>\n",
       "      <th>win_by_runs</th>\n",
       "      <th>win_by_wickets</th>\n",
       "      <th>player_of_match</th>\n",
       "      <th>venue</th>\n",
       "      <th>umpire1</th>\n",
       "      <th>umpire2</th>\n",
       "      <th>umpire3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>IPL-2017</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>05-04-2017</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>Yuvraj Singh</td>\n",
       "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
       "      <td>AY Dandekar</td>\n",
       "      <td>NJ Llong</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>IPL-2017</td>\n",
       "      <td>Pune</td>\n",
       "      <td>06-04-2017</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>Rising Pune Supergiant</td>\n",
       "      <td>Rising Pune Supergiant</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Rising Pune Supergiant</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>SPD Smith</td>\n",
       "      <td>Maharashtra Cricket Association Stadium</td>\n",
       "      <td>A Nand Kishore</td>\n",
       "      <td>S Ravi</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>IPL-2017</td>\n",
       "      <td>Rajkot</td>\n",
       "      <td>07-04-2017</td>\n",
       "      <td>Gujarat Lions</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>CA Lynn</td>\n",
       "      <td>Saurashtra Cricket Association Stadium</td>\n",
       "      <td>Nitin Menon</td>\n",
       "      <td>CK Nandan</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>IPL-2017</td>\n",
       "      <td>Indore</td>\n",
       "      <td>08-04-2017</td>\n",
       "      <td>Rising Pune Supergiant</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>GJ Maxwell</td>\n",
       "      <td>Holkar Cricket Stadium</td>\n",
       "      <td>AK Chaudhary</td>\n",
       "      <td>C Shamshuddin</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>IPL-2017</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>08-04-2017</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Delhi Daredevils</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>bat</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>KM Jadhav</td>\n",
       "      <td>M Chinnaswamy Stadium</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id    Season       city        date                        team1  \\\n",
       "0   1  IPL-2017  Hyderabad  05-04-2017          Sunrisers Hyderabad   \n",
       "1   2  IPL-2017       Pune  06-04-2017               Mumbai Indians   \n",
       "2   3  IPL-2017     Rajkot  07-04-2017                Gujarat Lions   \n",
       "3   4  IPL-2017     Indore  08-04-2017       Rising Pune Supergiant   \n",
       "4   5  IPL-2017  Bangalore  08-04-2017  Royal Challengers Bangalore   \n",
       "\n",
       "                         team2                  toss_winner toss_decision  \\\n",
       "0  Royal Challengers Bangalore  Royal Challengers Bangalore         field   \n",
       "1       Rising Pune Supergiant       Rising Pune Supergiant         field   \n",
       "2        Kolkata Knight Riders        Kolkata Knight Riders         field   \n",
       "3              Kings XI Punjab              Kings XI Punjab         field   \n",
       "4             Delhi Daredevils  Royal Challengers Bangalore           bat   \n",
       "\n",
       "   result  dl_applied                       winner  win_by_runs  \\\n",
       "0  normal           0          Sunrisers Hyderabad           35   \n",
       "1  normal           0       Rising Pune Supergiant            0   \n",
       "2  normal           0        Kolkata Knight Riders            0   \n",
       "3  normal           0              Kings XI Punjab            0   \n",
       "4  normal           0  Royal Challengers Bangalore           15   \n",
       "\n",
       "   win_by_wickets player_of_match                                      venue  \\\n",
       "0               0    Yuvraj Singh  Rajiv Gandhi International Stadium, Uppal   \n",
       "1               7       SPD Smith    Maharashtra Cricket Association Stadium   \n",
       "2              10         CA Lynn     Saurashtra Cricket Association Stadium   \n",
       "3               6      GJ Maxwell                     Holkar Cricket Stadium   \n",
       "4               0       KM Jadhav                      M Chinnaswamy Stadium   \n",
       "\n",
       "          umpire1        umpire2 umpire3  \n",
       "0     AY Dandekar       NJ Llong     NaN  \n",
       "1  A Nand Kishore         S Ravi     NaN  \n",
       "2     Nitin Menon      CK Nandan     NaN  \n",
       "3    AK Chaudhary  C Shamshuddin     NaN  \n",
       "4             NaN            NaN     NaN  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "match=pd.read_csv('matches.csv')\n",
    "match.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4b1a7d63",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>match_id</th>\n",
       "      <th>inning</th>\n",
       "      <th>batting_team</th>\n",
       "      <th>bowling_team</th>\n",
       "      <th>over</th>\n",
       "      <th>ball</th>\n",
       "      <th>batsman</th>\n",
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       "      <th>bowler</th>\n",
       "      <th>is_super_over</th>\n",
       "      <th>...</th>\n",
       "      <th>bye_runs</th>\n",
       "      <th>legbye_runs</th>\n",
       "      <th>noball_runs</th>\n",
       "      <th>penalty_runs</th>\n",
       "      <th>batsman_runs</th>\n",
       "      <th>extra_runs</th>\n",
       "      <th>total_runs</th>\n",
       "      <th>player_dismissed</th>\n",
       "      <th>dismissal_kind</th>\n",
       "      <th>fielder</th>\n",
       "    </tr>\n",
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       "    <tr>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>TS Mills</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>1</th>\n",
       "      <td>1</td>\n",
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       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
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       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>1</td>\n",
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       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>TS Mills</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>TS Mills</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>TS Mills</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   match_id  inning         batting_team                 bowling_team  over  \\\n",
       "0         1       1  Sunrisers Hyderabad  Royal Challengers Bangalore     1   \n",
       "1         1       1  Sunrisers Hyderabad  Royal Challengers Bangalore     1   \n",
       "2         1       1  Sunrisers Hyderabad  Royal Challengers Bangalore     1   \n",
       "3         1       1  Sunrisers Hyderabad  Royal Challengers Bangalore     1   \n",
       "4         1       1  Sunrisers Hyderabad  Royal Challengers Bangalore     1   \n",
       "\n",
       "   ball    batsman non_striker    bowler  is_super_over  ...  bye_runs  \\\n",
       "0     1  DA Warner    S Dhawan  TS Mills              0  ...         0   \n",
       "1     2  DA Warner    S Dhawan  TS Mills              0  ...         0   \n",
       "2     3  DA Warner    S Dhawan  TS Mills              0  ...         0   \n",
       "3     4  DA Warner    S Dhawan  TS Mills              0  ...         0   \n",
       "4     5  DA Warner    S Dhawan  TS Mills              0  ...         0   \n",
       "\n",
       "   legbye_runs  noball_runs  penalty_runs  batsman_runs  extra_runs  \\\n",
       "0            0            0             0             0           0   \n",
       "1            0            0             0             0           0   \n",
       "2            0            0             0             4           0   \n",
       "3            0            0             0             0           0   \n",
       "4            0            0             0             0           2   \n",
       "\n",
       "   total_runs  player_dismissed dismissal_kind fielder  \n",
       "0           0               NaN            NaN     NaN  \n",
       "1           0               NaN            NaN     NaN  \n",
       "2           4               NaN            NaN     NaN  \n",
       "3           0               NaN            NaN     NaN  \n",
       "4           2               NaN            NaN     NaN  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dele=pd.read_csv('deliveries.csv')\n",
    "dele.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "66e18e20",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>match_id</th>\n",
       "      <th>inning</th>\n",
       "      <th>total_runs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1523</th>\n",
       "      <td>11413</td>\n",
       "      <td>2</td>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1524</th>\n",
       "      <td>11414</td>\n",
       "      <td>1</td>\n",
       "      <td>155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1525</th>\n",
       "      <td>11414</td>\n",
       "      <td>2</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1526</th>\n",
       "      <td>11415</td>\n",
       "      <td>1</td>\n",
       "      <td>152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1527</th>\n",
       "      <td>11415</td>\n",
       "      <td>2</td>\n",
       "      <td>157</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1528 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      match_id  inning  total_runs\n",
       "0            1       1         207\n",
       "1            1       2         172\n",
       "2            2       1         184\n",
       "3            2       2         187\n",
       "4            3       1         183\n",
       "...        ...     ...         ...\n",
       "1523     11413       2         170\n",
       "1524     11414       1         155\n",
       "1525     11414       2         162\n",
       "1526     11415       1         152\n",
       "1527     11415       2         157\n",
       "\n",
       "[1528 rows x 3 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#find the total runs scored in all the innings of all match\n",
    "total_score_df=dele.groupby(['match_id','inning']).sum()['total_runs'].reset_index()\n",
    "total_score_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "129d8d6a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      match_id  inning  total_runs\n",
      "0            1       1         207\n",
      "2            2       1         184\n",
      "4            3       1         183\n",
      "6            4       1         163\n",
      "8            5       1         157\n",
      "...        ...     ...         ...\n",
      "1518     11347       1         143\n",
      "1520     11412       1         136\n",
      "1522     11413       1         171\n",
      "1524     11414       1         155\n",
      "1526     11415       1         152\n",
      "\n",
      "[756 rows x 3 columns]\n"
     ]
    }
   ],
   "source": [
    "total_score_df=total_score_df[total_score_df['inning']==1]\n",
    "print(total_score_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b2daf364",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>Season</th>\n",
       "      <th>city</th>\n",
       "      <th>date</th>\n",
       "      <th>team1</th>\n",
       "      <th>team2</th>\n",
       "      <th>toss_winner</th>\n",
       "      <th>toss_decision</th>\n",
       "      <th>result</th>\n",
       "      <th>dl_applied</th>\n",
       "      <th>winner</th>\n",
       "      <th>win_by_runs</th>\n",
       "      <th>win_by_wickets</th>\n",
       "      <th>player_of_match</th>\n",
       "      <th>venue</th>\n",
       "      <th>umpire1</th>\n",
       "      <th>umpire2</th>\n",
       "      <th>umpire3</th>\n",
       "      <th>match_id</th>\n",
       "      <th>total_runs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>IPL-2017</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>05-04-2017</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>Yuvraj Singh</td>\n",
       "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
       "      <td>AY Dandekar</td>\n",
       "      <td>NJ Llong</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>IPL-2017</td>\n",
       "      <td>Pune</td>\n",
       "      <td>06-04-2017</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>Rising Pune Supergiant</td>\n",
       "      <td>Rising Pune Supergiant</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Rising Pune Supergiant</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>SPD Smith</td>\n",
       "      <td>Maharashtra Cricket Association Stadium</td>\n",
       "      <td>A Nand Kishore</td>\n",
       "      <td>S Ravi</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>IPL-2017</td>\n",
       "      <td>Rajkot</td>\n",
       "      <td>07-04-2017</td>\n",
       "      <td>Gujarat Lions</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>CA Lynn</td>\n",
       "      <td>Saurashtra Cricket Association Stadium</td>\n",
       "      <td>Nitin Menon</td>\n",
       "      <td>CK Nandan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>IPL-2017</td>\n",
       "      <td>Indore</td>\n",
       "      <td>08-04-2017</td>\n",
       "      <td>Rising Pune Supergiant</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>GJ Maxwell</td>\n",
       "      <td>Holkar Cricket Stadium</td>\n",
       "      <td>AK Chaudhary</td>\n",
       "      <td>C Shamshuddin</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>IPL-2017</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>08-04-2017</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Delhi Daredevils</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>bat</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>KM Jadhav</td>\n",
       "      <td>M Chinnaswamy Stadium</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>157</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id    Season       city        date                        team1  \\\n",
       "0   1  IPL-2017  Hyderabad  05-04-2017          Sunrisers Hyderabad   \n",
       "1   2  IPL-2017       Pune  06-04-2017               Mumbai Indians   \n",
       "2   3  IPL-2017     Rajkot  07-04-2017                Gujarat Lions   \n",
       "3   4  IPL-2017     Indore  08-04-2017       Rising Pune Supergiant   \n",
       "4   5  IPL-2017  Bangalore  08-04-2017  Royal Challengers Bangalore   \n",
       "\n",
       "                         team2                  toss_winner toss_decision  \\\n",
       "0  Royal Challengers Bangalore  Royal Challengers Bangalore         field   \n",
       "1       Rising Pune Supergiant       Rising Pune Supergiant         field   \n",
       "2        Kolkata Knight Riders        Kolkata Knight Riders         field   \n",
       "3              Kings XI Punjab              Kings XI Punjab         field   \n",
       "4             Delhi Daredevils  Royal Challengers Bangalore           bat   \n",
       "\n",
       "   result  dl_applied                       winner  win_by_runs  \\\n",
       "0  normal           0          Sunrisers Hyderabad           35   \n",
       "1  normal           0       Rising Pune Supergiant            0   \n",
       "2  normal           0        Kolkata Knight Riders            0   \n",
       "3  normal           0              Kings XI Punjab            0   \n",
       "4  normal           0  Royal Challengers Bangalore           15   \n",
       "\n",
       "   win_by_wickets player_of_match                                      venue  \\\n",
       "0               0    Yuvraj Singh  Rajiv Gandhi International Stadium, Uppal   \n",
       "1               7       SPD Smith    Maharashtra Cricket Association Stadium   \n",
       "2              10         CA Lynn     Saurashtra Cricket Association Stadium   \n",
       "3               6      GJ Maxwell                     Holkar Cricket Stadium   \n",
       "4               0       KM Jadhav                      M Chinnaswamy Stadium   \n",
       "\n",
       "          umpire1        umpire2 umpire3  match_id  total_runs  \n",
       "0     AY Dandekar       NJ Llong     NaN         1         207  \n",
       "1  A Nand Kishore         S Ravi     NaN         2         184  \n",
       "2     Nitin Menon      CK Nandan     NaN         3         183  \n",
       "3    AK Chaudhary  C Shamshuddin     NaN         4         163  \n",
       "4             NaN            NaN     NaN         5         157  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#merge the total run and match id from this dataframe to the match dataframe\n",
    "\n",
    "match_df=match.merge(total_score_df[['match_id','total_runs']],left_on='id',right_on='match_id')\n",
    "match_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8a0536a",
   "metadata": {},
   "source": [
    "# data preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da2c0d4c",
   "metadata": {},
   "source": [
    "take only those teams which are playing today"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0f184001",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Sunrisers Hyderabad', 'Mumbai Indians', 'Gujarat Lions',\n",
       "       'Rising Pune Supergiant', 'Royal Challengers Bangalore',\n",
       "       'Kolkata Knight Riders', 'Delhi Daredevils', 'Kings XI Punjab',\n",
       "       'Chennai Super Kings', 'Rajasthan Royals', 'Deccan Chargers',\n",
       "       'Kochi Tuskers Kerala', 'Pune Warriors', 'Rising Pune Supergiants',\n",
       "       'Delhi Capitals'], dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "match_df['team1'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "54e2619c",
   "metadata": {},
   "outputs": [],
   "source": [
    "teams=['Sunrisers Hyderabad', 'Mumbai Indians',\n",
    "     'Royal Challengers Bangalore',\n",
    "       'Kolkata Knight Riders', 'Kings XI Punjab',\n",
    "       'Chennai Super Kings', 'Rajasthan Royals',\n",
    "       'Delhi Capitals']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ba58146c",
   "metadata": {},
   "outputs": [],
   "source": [
    "match_df=match_df[match_df['team1'].isin(teams)]\n",
    "match_df=match_df[match_df['team2'].isin(teams)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "826b8492",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(437, 20)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "match_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66650ea7",
   "metadata": {},
   "source": [
    "remove matches with duckward lewis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2342a59c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dl_applied\n",
       "0    427\n",
       "1     10\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dl_method=match_df['dl_applied']\n",
    "dl_method.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "70bf1d7f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dl_applied\n",
       "0    427\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "match_df=match_df[dl_method==0]\n",
    "match_df['dl_applied'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89e70e24",
   "metadata": {},
   "source": [
    "preparing the final dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8088af8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "match_df=match_df[['match_id','city','winner','total_runs']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d6d46f9b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 102143 entries, 0 to 102142\n",
      "Data columns (total 24 columns):\n",
      " #   Column            Non-Null Count   Dtype \n",
      "---  ------            --------------   ----- \n",
      " 0   match_id          102143 non-null  int64 \n",
      " 1   city              100937 non-null  object\n",
      " 2   winner            101964 non-null  object\n",
      " 3   total_runs_x      102143 non-null  int64 \n",
      " 4   inning            102143 non-null  int64 \n",
      " 5   batting_team      102143 non-null  object\n",
      " 6   bowling_team      102143 non-null  object\n",
      " 7   over              102143 non-null  int64 \n",
      " 8   ball              102143 non-null  int64 \n",
      " 9   batsman           102143 non-null  object\n",
      " 10  non_striker       102143 non-null  object\n",
      " 11  bowler            102143 non-null  object\n",
      " 12  is_super_over     102143 non-null  int64 \n",
      " 13  wide_runs         102143 non-null  int64 \n",
      " 14  bye_runs          102143 non-null  int64 \n",
      " 15  legbye_runs       102143 non-null  int64 \n",
      " 16  noball_runs       102143 non-null  int64 \n",
      " 17  penalty_runs      102143 non-null  int64 \n",
      " 18  batsman_runs      102143 non-null  int64 \n",
      " 19  extra_runs        102143 non-null  int64 \n",
      " 20  total_runs_y      102143 non-null  int64 \n",
      " 21  player_dismissed  5000 non-null    object\n",
      " 22  dismissal_kind    5000 non-null    object\n",
      " 23  fielder           3656 non-null    object\n",
      "dtypes: int64(14), object(10)\n",
      "memory usage: 18.7+ MB\n"
     ]
    }
   ],
   "source": [
    "new_df=match_df.merge(dele,on='match_id')\n",
    "new_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "70f2a9ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df=new_df[new_df['inning']==2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1d0c8140",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "int64\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Check the data type of 'total_runs_y' column\n",
    "print(new_df['total_runs_y'].dtype)\n",
    "\n",
    "# Replace NaN values with 0\n",
    "new_df['total_runs_y'] = new_df['total_runs_y'].fillna(0)\n",
    "\n",
    "# Convert the column to a numeric data type\n",
    "new_df['total_runs_y'] = pd.to_numeric(new_df['total_runs_y'], errors='coerce')\n",
    "\n",
    "# Calculate cumulative sum of 'total_runs_y' grouped by 'match_id'\n",
    "new_df['current_score'] = new_df.groupby('match_id')['total_runs_y'].cumsum()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1e8e4d5c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>match_id</th>\n",
       "      <th>city</th>\n",
       "      <th>winner</th>\n",
       "      <th>total_runs_x</th>\n",
       "      <th>inning</th>\n",
       "      <th>batting_team</th>\n",
       "      <th>bowling_team</th>\n",
       "      <th>over</th>\n",
       "      <th>ball</th>\n",
       "      <th>batsman</th>\n",
       "      <th>...</th>\n",
       "      <th>legbye_runs</th>\n",
       "      <th>noball_runs</th>\n",
       "      <th>penalty_runs</th>\n",
       "      <th>batsman_runs</th>\n",
       "      <th>extra_runs</th>\n",
       "      <th>total_runs_y</th>\n",
       "      <th>player_dismissed</th>\n",
       "      <th>dismissal_kind</th>\n",
       "      <th>fielder</th>\n",
       "      <th>current_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>1</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>207</td>\n",
       "      <td>2</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>CH Gayle</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>1</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>207</td>\n",
       "      <td>2</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Mandeep Singh</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>1</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>207</td>\n",
       "      <td>2</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Mandeep Singh</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>1</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>207</td>\n",
       "      <td>2</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Mandeep Singh</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>1</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>207</td>\n",
       "      <td>2</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>Mandeep Singh</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</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",
       "      <td>...</td>\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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102138</th>\n",
       "      <td>11415</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>152</td>\n",
       "      <td>2</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>RA Jadeja</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102139</th>\n",
       "      <td>11415</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>152</td>\n",
       "      <td>2</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>SR Watson</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102140</th>\n",
       "      <td>11415</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>152</td>\n",
       "      <td>2</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "      <td>SR Watson</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>SR Watson</td>\n",
       "      <td>run out</td>\n",
       "      <td>KH Pandya</td>\n",
       "      <td>155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102141</th>\n",
       "      <td>11415</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>152</td>\n",
       "      <td>2</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>20</td>\n",
       "      <td>5</td>\n",
       "      <td>SN Thakur</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102142</th>\n",
       "      <td>11415</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>152</td>\n",
       "      <td>2</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>SN Thakur</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SN Thakur</td>\n",
       "      <td>lbw</td>\n",
       "      <td>NaN</td>\n",
       "      <td>157</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>49546 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        match_id       city               winner  total_runs_x  inning  \\\n",
       "125            1  Hyderabad  Sunrisers Hyderabad           207       2   \n",
       "126            1  Hyderabad  Sunrisers Hyderabad           207       2   \n",
       "127            1  Hyderabad  Sunrisers Hyderabad           207       2   \n",
       "128            1  Hyderabad  Sunrisers Hyderabad           207       2   \n",
       "129            1  Hyderabad  Sunrisers Hyderabad           207       2   \n",
       "...          ...        ...                  ...           ...     ...   \n",
       "102138     11415  Hyderabad       Mumbai Indians           152       2   \n",
       "102139     11415  Hyderabad       Mumbai Indians           152       2   \n",
       "102140     11415  Hyderabad       Mumbai Indians           152       2   \n",
       "102141     11415  Hyderabad       Mumbai Indians           152       2   \n",
       "102142     11415  Hyderabad       Mumbai Indians           152       2   \n",
       "\n",
       "                       batting_team         bowling_team  over  ball  \\\n",
       "125     Royal Challengers Bangalore  Sunrisers Hyderabad     1     1   \n",
       "126     Royal Challengers Bangalore  Sunrisers Hyderabad     1     2   \n",
       "127     Royal Challengers Bangalore  Sunrisers Hyderabad     1     3   \n",
       "128     Royal Challengers Bangalore  Sunrisers Hyderabad     1     4   \n",
       "129     Royal Challengers Bangalore  Sunrisers Hyderabad     1     5   \n",
       "...                             ...                  ...   ...   ...   \n",
       "102138          Chennai Super Kings       Mumbai Indians    20     2   \n",
       "102139          Chennai Super Kings       Mumbai Indians    20     3   \n",
       "102140          Chennai Super Kings       Mumbai Indians    20     4   \n",
       "102141          Chennai Super Kings       Mumbai Indians    20     5   \n",
       "102142          Chennai Super Kings       Mumbai Indians    20     6   \n",
       "\n",
       "              batsman  ... legbye_runs noball_runs  penalty_runs  \\\n",
       "125          CH Gayle  ...           0           0             0   \n",
       "126     Mandeep Singh  ...           0           0             0   \n",
       "127     Mandeep Singh  ...           0           0             0   \n",
       "128     Mandeep Singh  ...           0           0             0   \n",
       "129     Mandeep Singh  ...           0           0             0   \n",
       "...               ...  ...         ...         ...           ...   \n",
       "102138      RA Jadeja  ...           0           0             0   \n",
       "102139      SR Watson  ...           0           0             0   \n",
       "102140      SR Watson  ...           0           0             0   \n",
       "102141      SN Thakur  ...           0           0             0   \n",
       "102142      SN Thakur  ...           0           0             0   \n",
       "\n",
       "        batsman_runs  extra_runs  total_runs_y  player_dismissed  \\\n",
       "125                1           0             1               NaN   \n",
       "126                0           0             0               NaN   \n",
       "127                0           0             0               NaN   \n",
       "128                2           0             2               NaN   \n",
       "129                4           0             4               NaN   \n",
       "...              ...         ...           ...               ...   \n",
       "102138             1           0             1               NaN   \n",
       "102139             2           0             2               NaN   \n",
       "102140             1           0             1         SR Watson   \n",
       "102141             2           0             2               NaN   \n",
       "102142             0           0             0         SN Thakur   \n",
       "\n",
       "        dismissal_kind    fielder  current_score  \n",
       "125                NaN        NaN              1  \n",
       "126                NaN        NaN              1  \n",
       "127                NaN        NaN              1  \n",
       "128                NaN        NaN              3  \n",
       "129                NaN        NaN              7  \n",
       "...                ...        ...            ...  \n",
       "102138             NaN        NaN            152  \n",
       "102139             NaN        NaN            154  \n",
       "102140         run out  KH Pandya            155  \n",
       "102141             NaN        NaN            157  \n",
       "102142             lbw        NaN            157  \n",
       "\n",
       "[49546 rows x 25 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#make a new column for the second inning current score ball by ball\n",
    "\n",
    "new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e8023db3",
   "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",
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       "        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>city</th>\n",
       "      <th>winner</th>\n",
       "      <th>total_runs_x</th>\n",
       "      <th>inning</th>\n",
       "      <th>batting_team</th>\n",
       "      <th>bowling_team</th>\n",
       "      <th>over</th>\n",
       "      <th>ball</th>\n",
       "      <th>batsman</th>\n",
       "      <th>non_striker</th>\n",
       "      <th>bowler</th>\n",
       "      <th>is_super_over</th>\n",
       "      <th>wide_runs</th>\n",
       "      <th>bye_runs</th>\n",
       "      <th>legbye_runs</th>\n",
       "      <th>noball_runs</th>\n",
       "      <th>penalty_runs</th>\n",
       "      <th>batsman_runs</th>\n",
       "      <th>extra_runs</th>\n",
       "      <th>total_runs_y</th>\n",
       "      <th>player_dismissed</th>\n",
       "      <th>dismissal_kind</th>\n",
       "      <th>fielder</th>\n",
       "      <th>current_score</th>\n",
       "      <th>runs_left</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>1</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>207</td>\n",
       "      <td>2</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>CH Gayle</td>\n",
       "      <td>Mandeep Singh</td>\n",
       "      <td>A Nehra</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>1</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>207</td>\n",
       "      <td>2</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Mandeep Singh</td>\n",
       "      <td>CH Gayle</td>\n",
       "      <td>A Nehra</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>1</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>207</td>\n",
       "      <td>2</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Mandeep Singh</td>\n",
       "      <td>CH Gayle</td>\n",
       "      <td>A Nehra</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>1</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>207</td>\n",
       "      <td>2</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Mandeep Singh</td>\n",
       "      <td>CH Gayle</td>\n",
       "      <td>A Nehra</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>1</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>207</td>\n",
       "      <td>2</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>Mandeep Singh</td>\n",
       "      <td>CH Gayle</td>\n",
       "      <td>A Nehra</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>201</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",
       "      <td>...</td>\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",
       "      <td>...</td>\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>102138</th>\n",
       "      <td>11415</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>152</td>\n",
       "      <td>2</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>RA Jadeja</td>\n",
       "      <td>SR Watson</td>\n",
       "      <td>SL Malinga</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>152</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102139</th>\n",
       "      <td>11415</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>152</td>\n",
       "      <td>2</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>SR Watson</td>\n",
       "      <td>RA Jadeja</td>\n",
       "      <td>SL Malinga</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>154</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102140</th>\n",
       "      <td>11415</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>152</td>\n",
       "      <td>2</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "      <td>SR Watson</td>\n",
       "      <td>RA Jadeja</td>\n",
       "      <td>SL Malinga</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>SR Watson</td>\n",
       "      <td>run out</td>\n",
       "      <td>KH Pandya</td>\n",
       "      <td>155</td>\n",
       "      <td>-2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102141</th>\n",
       "      <td>11415</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>152</td>\n",
       "      <td>2</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>20</td>\n",
       "      <td>5</td>\n",
       "      <td>SN Thakur</td>\n",
       "      <td>RA Jadeja</td>\n",
       "      <td>SL Malinga</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>157</td>\n",
       "      <td>-4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102142</th>\n",
       "      <td>11415</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>152</td>\n",
       "      <td>2</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>SN Thakur</td>\n",
       "      <td>RA Jadeja</td>\n",
       "      <td>SL Malinga</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SN Thakur</td>\n",
       "      <td>lbw</td>\n",
       "      <td>NaN</td>\n",
       "      <td>157</td>\n",
       "      <td>-4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>49546 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        match_id       city               winner  total_runs_x  inning  \\\n",
       "125            1  Hyderabad  Sunrisers Hyderabad           207       2   \n",
       "126            1  Hyderabad  Sunrisers Hyderabad           207       2   \n",
       "127            1  Hyderabad  Sunrisers Hyderabad           207       2   \n",
       "128            1  Hyderabad  Sunrisers Hyderabad           207       2   \n",
       "129            1  Hyderabad  Sunrisers Hyderabad           207       2   \n",
       "...          ...        ...                  ...           ...     ...   \n",
       "102138     11415  Hyderabad       Mumbai Indians           152       2   \n",
       "102139     11415  Hyderabad       Mumbai Indians           152       2   \n",
       "102140     11415  Hyderabad       Mumbai Indians           152       2   \n",
       "102141     11415  Hyderabad       Mumbai Indians           152       2   \n",
       "102142     11415  Hyderabad       Mumbai Indians           152       2   \n",
       "\n",
       "                       batting_team         bowling_team  over  ball  \\\n",
       "125     Royal Challengers Bangalore  Sunrisers Hyderabad     1     1   \n",
       "126     Royal Challengers Bangalore  Sunrisers Hyderabad     1     2   \n",
       "127     Royal Challengers Bangalore  Sunrisers Hyderabad     1     3   \n",
       "128     Royal Challengers Bangalore  Sunrisers Hyderabad     1     4   \n",
       "129     Royal Challengers Bangalore  Sunrisers Hyderabad     1     5   \n",
       "...                             ...                  ...   ...   ...   \n",
       "102138          Chennai Super Kings       Mumbai Indians    20     2   \n",
       "102139          Chennai Super Kings       Mumbai Indians    20     3   \n",
       "102140          Chennai Super Kings       Mumbai Indians    20     4   \n",
       "102141          Chennai Super Kings       Mumbai Indians    20     5   \n",
       "102142          Chennai Super Kings       Mumbai Indians    20     6   \n",
       "\n",
       "              batsman    non_striker      bowler  is_super_over  wide_runs  \\\n",
       "125          CH Gayle  Mandeep Singh     A Nehra              0          0   \n",
       "126     Mandeep Singh       CH Gayle     A Nehra              0          0   \n",
       "127     Mandeep Singh       CH Gayle     A Nehra              0          0   \n",
       "128     Mandeep Singh       CH Gayle     A Nehra              0          0   \n",
       "129     Mandeep Singh       CH Gayle     A Nehra              0          0   \n",
       "...               ...            ...         ...            ...        ...   \n",
       "102138      RA Jadeja      SR Watson  SL Malinga              0          0   \n",
       "102139      SR Watson      RA Jadeja  SL Malinga              0          0   \n",
       "102140      SR Watson      RA Jadeja  SL Malinga              0          0   \n",
       "102141      SN Thakur      RA Jadeja  SL Malinga              0          0   \n",
       "102142      SN Thakur      RA Jadeja  SL Malinga              0          0   \n",
       "\n",
       "        bye_runs  legbye_runs  noball_runs  penalty_runs  batsman_runs  \\\n",
       "125            0            0            0             0             1   \n",
       "126            0            0            0             0             0   \n",
       "127            0            0            0             0             0   \n",
       "128            0            0            0             0             2   \n",
       "129            0            0            0             0             4   \n",
       "...          ...          ...          ...           ...           ...   \n",
       "102138         0            0            0             0             1   \n",
       "102139         0            0            0             0             2   \n",
       "102140         0            0            0             0             1   \n",
       "102141         0            0            0             0             2   \n",
       "102142         0            0            0             0             0   \n",
       "\n",
       "        extra_runs  total_runs_y player_dismissed dismissal_kind    fielder  \\\n",
       "125              0             1              NaN            NaN        NaN   \n",
       "126              0             0              NaN            NaN        NaN   \n",
       "127              0             0              NaN            NaN        NaN   \n",
       "128              0             2              NaN            NaN        NaN   \n",
       "129              0             4              NaN            NaN        NaN   \n",
       "...            ...           ...              ...            ...        ...   \n",
       "102138           0             1              NaN            NaN        NaN   \n",
       "102139           0             2              NaN            NaN        NaN   \n",
       "102140           0             1        SR Watson        run out  KH Pandya   \n",
       "102141           0             2              NaN            NaN        NaN   \n",
       "102142           0             0        SN Thakur            lbw        NaN   \n",
       "\n",
       "        current_score  runs_left  \n",
       "125                 1        207  \n",
       "126                 1        207  \n",
       "127                 1        207  \n",
       "128                 3        205  \n",
       "129                 7        201  \n",
       "...               ...        ...  \n",
       "102138            152          1  \n",
       "102139            154         -1  \n",
       "102140            155         -2  \n",
       "102141            157         -4  \n",
       "102142            157         -4  \n",
       "\n",
       "[49546 rows x 26 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#make columns for runs left\n",
    "new_df['runs_left']=new_df['total_runs_x']-new_df['current_score']+1\n",
    "pd.set_option('display.max_columns', None)\n",
    "new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "3efe5d4a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>match_id</th>\n",
       "      <th>city</th>\n",
       "      <th>winner</th>\n",
       "      <th>total_runs_x</th>\n",
       "      <th>inning</th>\n",
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       "      <th>noball_runs</th>\n",
       "      <th>penalty_runs</th>\n",
       "      <th>batsman_runs</th>\n",
       "      <th>extra_runs</th>\n",
       "      <th>total_runs_y</th>\n",
       "      <th>player_dismissed</th>\n",
       "      <th>dismissal_kind</th>\n",
       "      <th>fielder</th>\n",
       "      <th>current_score</th>\n",
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       "      <th>balls_left</th>\n",
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       "  </thead>\n",
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       "      <td>Royal Challengers Bangalore</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>207</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
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       "      <td>Sunrisers Hyderabad</td>\n",
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       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Mandeep Singh</td>\n",
       "      <td>CH Gayle</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>207</td>\n",
       "      <td>118</td>\n",
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       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>1</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>207</td>\n",
       "      <td>2</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Mandeep Singh</td>\n",
       "      <td>CH Gayle</td>\n",
       "      <td>A Nehra</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>117</td>\n",
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       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>1</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>207</td>\n",
       "      <td>2</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Mandeep Singh</td>\n",
       "      <td>CH Gayle</td>\n",
       "      <td>A Nehra</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>205</td>\n",
       "      <td>116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>1</td>\n",
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       "      <td>Sunrisers Hyderabad</td>\n",
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       "      <td>Royal Challengers Bangalore</td>\n",
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       "      <td>Mandeep Singh</td>\n",
       "      <td>CH Gayle</td>\n",
       "      <td>A Nehra</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>4</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
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       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102138</th>\n",
       "      <td>11415</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>152</td>\n",
       "      <td>2</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>RA Jadeja</td>\n",
       "      <td>SR Watson</td>\n",
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       "      <td>152</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102139</th>\n",
       "      <td>11415</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>152</td>\n",
       "      <td>2</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>SR Watson</td>\n",
       "      <td>RA Jadeja</td>\n",
       "      <td>SL Malinga</td>\n",
       "      <td>0</td>\n",
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       "      <td>2</td>\n",
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       "      <td>154</td>\n",
       "      <td>-1</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>102140</th>\n",
       "      <td>11415</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>152</td>\n",
       "      <td>2</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "      <td>SR Watson</td>\n",
       "      <td>RA Jadeja</td>\n",
       "      <td>SL Malinga</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>SR Watson</td>\n",
       "      <td>run out</td>\n",
       "      <td>KH Pandya</td>\n",
       "      <td>155</td>\n",
       "      <td>-2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102141</th>\n",
       "      <td>11415</td>\n",
       "      <td>Hyderabad</td>\n",
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       "      <td>152</td>\n",
       "      <td>2</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>20</td>\n",
       "      <td>5</td>\n",
       "      <td>SN Thakur</td>\n",
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       "      <td>0</td>\n",
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       "      <td>2</td>\n",
       "      <td>0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>157</td>\n",
       "      <td>-4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102142</th>\n",
       "      <td>11415</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>152</td>\n",
       "      <td>2</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>SN Thakur</td>\n",
       "      <td>RA Jadeja</td>\n",
       "      <td>SL Malinga</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SN Thakur</td>\n",
       "      <td>lbw</td>\n",
       "      <td>NaN</td>\n",
       "      <td>157</td>\n",
       "      <td>-4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>49546 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        match_id       city               winner  total_runs_x  inning  \\\n",
       "125            1  Hyderabad  Sunrisers Hyderabad           207       2   \n",
       "126            1  Hyderabad  Sunrisers Hyderabad           207       2   \n",
       "127            1  Hyderabad  Sunrisers Hyderabad           207       2   \n",
       "128            1  Hyderabad  Sunrisers Hyderabad           207       2   \n",
       "129            1  Hyderabad  Sunrisers Hyderabad           207       2   \n",
       "...          ...        ...                  ...           ...     ...   \n",
       "102138     11415  Hyderabad       Mumbai Indians           152       2   \n",
       "102139     11415  Hyderabad       Mumbai Indians           152       2   \n",
       "102140     11415  Hyderabad       Mumbai Indians           152       2   \n",
       "102141     11415  Hyderabad       Mumbai Indians           152       2   \n",
       "102142     11415  Hyderabad       Mumbai Indians           152       2   \n",
       "\n",
       "                       batting_team         bowling_team  over  ball  \\\n",
       "125     Royal Challengers Bangalore  Sunrisers Hyderabad     1     1   \n",
       "126     Royal Challengers Bangalore  Sunrisers Hyderabad     1     2   \n",
       "127     Royal Challengers Bangalore  Sunrisers Hyderabad     1     3   \n",
       "128     Royal Challengers Bangalore  Sunrisers Hyderabad     1     4   \n",
       "129     Royal Challengers Bangalore  Sunrisers Hyderabad     1     5   \n",
       "...                             ...                  ...   ...   ...   \n",
       "102138          Chennai Super Kings       Mumbai Indians    20     2   \n",
       "102139          Chennai Super Kings       Mumbai Indians    20     3   \n",
       "102140          Chennai Super Kings       Mumbai Indians    20     4   \n",
       "102141          Chennai Super Kings       Mumbai Indians    20     5   \n",
       "102142          Chennai Super Kings       Mumbai Indians    20     6   \n",
       "\n",
       "              batsman    non_striker      bowler  is_super_over  wide_runs  \\\n",
       "125          CH Gayle  Mandeep Singh     A Nehra              0          0   \n",
       "126     Mandeep Singh       CH Gayle     A Nehra              0          0   \n",
       "127     Mandeep Singh       CH Gayle     A Nehra              0          0   \n",
       "128     Mandeep Singh       CH Gayle     A Nehra              0          0   \n",
       "129     Mandeep Singh       CH Gayle     A Nehra              0          0   \n",
       "...               ...            ...         ...            ...        ...   \n",
       "102138      RA Jadeja      SR Watson  SL Malinga              0          0   \n",
       "102139      SR Watson      RA Jadeja  SL Malinga              0          0   \n",
       "102140      SR Watson      RA Jadeja  SL Malinga              0          0   \n",
       "102141      SN Thakur      RA Jadeja  SL Malinga              0          0   \n",
       "102142      SN Thakur      RA Jadeja  SL Malinga              0          0   \n",
       "\n",
       "        bye_runs  legbye_runs  noball_runs  penalty_runs  batsman_runs  \\\n",
       "125            0            0            0             0             1   \n",
       "126            0            0            0             0             0   \n",
       "127            0            0            0             0             0   \n",
       "128            0            0            0             0             2   \n",
       "129            0            0            0             0             4   \n",
       "...          ...          ...          ...           ...           ...   \n",
       "102138         0            0            0             0             1   \n",
       "102139         0            0            0             0             2   \n",
       "102140         0            0            0             0             1   \n",
       "102141         0            0            0             0             2   \n",
       "102142         0            0            0             0             0   \n",
       "\n",
       "        extra_runs  total_runs_y player_dismissed dismissal_kind    fielder  \\\n",
       "125              0             1              NaN            NaN        NaN   \n",
       "126              0             0              NaN            NaN        NaN   \n",
       "127              0             0              NaN            NaN        NaN   \n",
       "128              0             2              NaN            NaN        NaN   \n",
       "129              0             4              NaN            NaN        NaN   \n",
       "...            ...           ...              ...            ...        ...   \n",
       "102138           0             1              NaN            NaN        NaN   \n",
       "102139           0             2              NaN            NaN        NaN   \n",
       "102140           0             1        SR Watson        run out  KH Pandya   \n",
       "102141           0             2              NaN            NaN        NaN   \n",
       "102142           0             0        SN Thakur            lbw        NaN   \n",
       "\n",
       "        current_score  runs_left  balls_left  \n",
       "125                 1        207         119  \n",
       "126                 1        207         118  \n",
       "127                 1        207         117  \n",
       "128                 3        205         116  \n",
       "129                 7        201         115  \n",
       "...               ...        ...         ...  \n",
       "102138            152          1           4  \n",
       "102139            154         -1           3  \n",
       "102140            155         -2           2  \n",
       "102141            157         -4           1  \n",
       "102142            157         -4           0  \n",
       "\n",
       "[49546 rows x 27 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#1 over extra added in the beginning\n",
    "new_df['balls_left']=126-(new_df['over']*6+new_df['ball'])\n",
    "new_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "898e5ba2",
   "metadata": {},
   "source": [
    "find the wickets left after each ball"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8ee8bc08",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "        match_id       city          winner  total_runs_x  inning  \\\n",
       "102138     11415  Hyderabad  Mumbai Indians           152       2   \n",
       "102139     11415  Hyderabad  Mumbai Indians           152       2   \n",
       "102140     11415  Hyderabad  Mumbai Indians           152       2   \n",
       "102141     11415  Hyderabad  Mumbai Indians           152       2   \n",
       "102142     11415  Hyderabad  Mumbai Indians           152       2   \n",
       "\n",
       "               batting_team    bowling_team  over  ball    batsman  \\\n",
       "102138  Chennai Super Kings  Mumbai Indians    20     2  RA Jadeja   \n",
       "102139  Chennai Super Kings  Mumbai Indians    20     3  SR Watson   \n",
       "102140  Chennai Super Kings  Mumbai Indians    20     4  SR Watson   \n",
       "102141  Chennai Super Kings  Mumbai Indians    20     5  SN Thakur   \n",
       "102142  Chennai Super Kings  Mumbai Indians    20     6  SN Thakur   \n",
       "\n",
       "       non_striker      bowler  is_super_over  wide_runs  bye_runs  \\\n",
       "102138   SR Watson  SL Malinga              0          0         0   \n",
       "102139   RA Jadeja  SL Malinga              0          0         0   \n",
       "102140   RA Jadeja  SL Malinga              0          0         0   \n",
       "102141   RA Jadeja  SL Malinga              0          0         0   \n",
       "102142   RA Jadeja  SL Malinga              0          0         0   \n",
       "\n",
       "        legbye_runs  noball_runs  penalty_runs  batsman_runs  extra_runs  \\\n",
       "102138            0            0             0             1           0   \n",
       "102139            0            0             0             2           0   \n",
       "102140            0            0             0             1           0   \n",
       "102141            0            0             0             2           0   \n",
       "102142            0            0             0             0           0   \n",
       "\n",
       "        total_runs_y  player_dismissed dismissal_kind    fielder  \\\n",
       "102138             1                 0            NaN        NaN   \n",
       "102139             2                 0            NaN        NaN   \n",
       "102140             1                 1        run out  KH Pandya   \n",
       "102141             2                 0            NaN        NaN   \n",
       "102142             0                 1            lbw        NaN   \n",
       "\n",
       "        current_score  runs_left  balls_left  wickets_left  \n",
       "102138            152          1           4             5  \n",
       "102139            154         -1           3             5  \n",
       "102140            155         -2           2             4  \n",
       "102141            157         -4           1             4  \n",
       "102142            157         -4           0             3  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#fill the nan with 0 and rest with 1\n",
    "new_df['player_dismissed'] = new_df['player_dismissed'].fillna(\"0\")\n",
    "new_df['player_dismissed'] = new_df['player_dismissed'].apply(lambda x:x if x ==\"0\" else \"1\")\n",
    "\n",
    "new_df['player_dismissed'] = new_df['player_dismissed'].astype(int)\n",
    "\n",
    "wickets=new_df.groupby('match_id')['player_dismissed'].cumsum()\n",
    "new_df['wickets_left']=10-wickets\n",
    "new_df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78ce0465",
   "metadata": {},
   "source": [
    "current run rate crr=runs/over"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "41dc4284",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>125</th>\n",
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       "      <td>6.0</td>\n",
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       "      <th>126</th>\n",
       "      <td>1</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
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       "      <td>Sunrisers Hyderabad</td>\n",
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       "      <td>Mandeep Singh</td>\n",
       "      <td>CH Gayle</td>\n",
       "      <td>A Nehra</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>207</td>\n",
       "      <td>118</td>\n",
       "      <td>10</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10.525424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>1</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>207</td>\n",
       "      <td>2</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Mandeep Singh</td>\n",
       "      <td>CH Gayle</td>\n",
       "      <td>A Nehra</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>207</td>\n",
       "      <td>117</td>\n",
       "      <td>10</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.615385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>1</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>207</td>\n",
       "      <td>2</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Mandeep Singh</td>\n",
       "      <td>CH Gayle</td>\n",
       "      <td>A Nehra</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>205</td>\n",
       "      <td>116</td>\n",
       "      <td>10</td>\n",
       "      <td>4.5</td>\n",
       "      <td>10.603448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>1</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>207</td>\n",
       "      <td>2</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>Mandeep Singh</td>\n",
       "      <td>CH Gayle</td>\n",
       "      <td>A Nehra</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>201</td>\n",
       "      <td>115</td>\n",
       "      <td>10</td>\n",
       "      <td>8.4</td>\n",
       "      <td>10.486957</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     match_id       city               winner  total_runs_x  inning  \\\n",
       "125         1  Hyderabad  Sunrisers Hyderabad           207       2   \n",
       "126         1  Hyderabad  Sunrisers Hyderabad           207       2   \n",
       "127         1  Hyderabad  Sunrisers Hyderabad           207       2   \n",
       "128         1  Hyderabad  Sunrisers Hyderabad           207       2   \n",
       "129         1  Hyderabad  Sunrisers Hyderabad           207       2   \n",
       "\n",
       "                    batting_team         bowling_team  over  ball  \\\n",
       "125  Royal Challengers Bangalore  Sunrisers Hyderabad     1     1   \n",
       "126  Royal Challengers Bangalore  Sunrisers Hyderabad     1     2   \n",
       "127  Royal Challengers Bangalore  Sunrisers Hyderabad     1     3   \n",
       "128  Royal Challengers Bangalore  Sunrisers Hyderabad     1     4   \n",
       "129  Royal Challengers Bangalore  Sunrisers Hyderabad     1     5   \n",
       "\n",
       "           batsman    non_striker   bowler  is_super_over  wide_runs  \\\n",
       "125       CH Gayle  Mandeep Singh  A Nehra              0          0   \n",
       "126  Mandeep Singh       CH Gayle  A Nehra              0          0   \n",
       "127  Mandeep Singh       CH Gayle  A Nehra              0          0   \n",
       "128  Mandeep Singh       CH Gayle  A Nehra              0          0   \n",
       "129  Mandeep Singh       CH Gayle  A Nehra              0          0   \n",
       "\n",
       "     bye_runs  legbye_runs  noball_runs  penalty_runs  batsman_runs  \\\n",
       "125         0            0            0             0             1   \n",
       "126         0            0            0             0             0   \n",
       "127         0            0            0             0             0   \n",
       "128         0            0            0             0             2   \n",
       "129         0            0            0             0             4   \n",
       "\n",
       "     extra_runs  total_runs_y  player_dismissed dismissal_kind fielder  \\\n",
       "125           0             1                 0            NaN     NaN   \n",
       "126           0             0                 0            NaN     NaN   \n",
       "127           0             0                 0            NaN     NaN   \n",
       "128           0             2                 0            NaN     NaN   \n",
       "129           0             4                 0            NaN     NaN   \n",
       "\n",
       "     current_score  runs_left  balls_left  wickets_left  crr     req_rr  \n",
       "125              1        207         119            10  6.0  10.436975  \n",
       "126              1        207         118            10  3.0  10.525424  \n",
       "127              1        207         117            10  2.0  10.615385  \n",
       "128              3        205         116            10  4.5  10.603448  \n",
       "129              7        201         115            10  8.4  10.486957  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df['crr']=new_df['current_score']/(120-new_df['balls_left'])*6\n",
    "new_df['req_rr']=new_df['runs_left']/ new_df['balls_left']*6   \n",
    "new_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "079e3908",
   "metadata": {},
   "source": [
    "check if batting team is winner then 1 else 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d981fd4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>match_id</th>\n",
       "      <th>city</th>\n",
       "      <th>winner</th>\n",
       "      <th>total_runs_x</th>\n",
       "      <th>inning</th>\n",
       "      <th>batting_team</th>\n",
       "      <th>bowling_team</th>\n",
       "      <th>over</th>\n",
       "      <th>ball</th>\n",
       "      <th>batsman</th>\n",
       "      <th>non_striker</th>\n",
       "      <th>bowler</th>\n",
       "      <th>is_super_over</th>\n",
       "      <th>wide_runs</th>\n",
       "      <th>bye_runs</th>\n",
       "      <th>legbye_runs</th>\n",
       "      <th>noball_runs</th>\n",
       "      <th>penalty_runs</th>\n",
       "      <th>batsman_runs</th>\n",
       "      <th>extra_runs</th>\n",
       "      <th>total_runs_y</th>\n",
       "      <th>player_dismissed</th>\n",
       "      <th>dismissal_kind</th>\n",
       "      <th>fielder</th>\n",
       "      <th>current_score</th>\n",
       "      <th>runs_left</th>\n",
       "      <th>balls_left</th>\n",
       "      <th>wickets_left</th>\n",
       "      <th>crr</th>\n",
       "      <th>req_rr</th>\n",
       "      <th>result</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>125</th>\n",
       "      <td>1</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>207</td>\n",
       "      <td>2</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>CH Gayle</td>\n",
       "      <td>Mandeep Singh</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>1</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
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       "      <td>1</td>\n",
       "      <td>207</td>\n",
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       "      <td>3.0</td>\n",
       "      <td>10.525424</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>1</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>207</td>\n",
       "      <td>2</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>1</td>\n",
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       "      <td>Mandeep Singh</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>10.615385</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>1</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>207</td>\n",
       "      <td>2</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
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       "      <td>4</td>\n",
       "      <td>Mandeep Singh</td>\n",
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       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
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       "      <td>10</td>\n",
       "      <td>4.5</td>\n",
       "      <td>10.603448</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>1</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>207</td>\n",
       "      <td>2</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>Mandeep Singh</td>\n",
       "      <td>CH Gayle</td>\n",
       "      <td>A Nehra</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>201</td>\n",
       "      <td>115</td>\n",
       "      <td>10</td>\n",
       "      <td>8.4</td>\n",
       "      <td>10.486957</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     match_id       city               winner  total_runs_x  inning  \\\n",
       "125         1  Hyderabad  Sunrisers Hyderabad           207       2   \n",
       "126         1  Hyderabad  Sunrisers Hyderabad           207       2   \n",
       "127         1  Hyderabad  Sunrisers Hyderabad           207       2   \n",
       "128         1  Hyderabad  Sunrisers Hyderabad           207       2   \n",
       "129         1  Hyderabad  Sunrisers Hyderabad           207       2   \n",
       "\n",
       "                    batting_team         bowling_team  over  ball  \\\n",
       "125  Royal Challengers Bangalore  Sunrisers Hyderabad     1     1   \n",
       "126  Royal Challengers Bangalore  Sunrisers Hyderabad     1     2   \n",
       "127  Royal Challengers Bangalore  Sunrisers Hyderabad     1     3   \n",
       "128  Royal Challengers Bangalore  Sunrisers Hyderabad     1     4   \n",
       "129  Royal Challengers Bangalore  Sunrisers Hyderabad     1     5   \n",
       "\n",
       "           batsman    non_striker   bowler  is_super_over  wide_runs  \\\n",
       "125       CH Gayle  Mandeep Singh  A Nehra              0          0   \n",
       "126  Mandeep Singh       CH Gayle  A Nehra              0          0   \n",
       "127  Mandeep Singh       CH Gayle  A Nehra              0          0   \n",
       "128  Mandeep Singh       CH Gayle  A Nehra              0          0   \n",
       "129  Mandeep Singh       CH Gayle  A Nehra              0          0   \n",
       "\n",
       "     bye_runs  legbye_runs  noball_runs  penalty_runs  batsman_runs  \\\n",
       "125         0            0            0             0             1   \n",
       "126         0            0            0             0             0   \n",
       "127         0            0            0             0             0   \n",
       "128         0            0            0             0             2   \n",
       "129         0            0            0             0             4   \n",
       "\n",
       "     extra_runs  total_runs_y  player_dismissed dismissal_kind fielder  \\\n",
       "125           0             1                 0            NaN     NaN   \n",
       "126           0             0                 0            NaN     NaN   \n",
       "127           0             0                 0            NaN     NaN   \n",
       "128           0             2                 0            NaN     NaN   \n",
       "129           0             4                 0            NaN     NaN   \n",
       "\n",
       "     current_score  runs_left  balls_left  wickets_left  crr     req_rr  \\\n",
       "125              1        207         119            10  6.0  10.436975   \n",
       "126              1        207         118            10  3.0  10.525424   \n",
       "127              1        207         117            10  2.0  10.615385   \n",
       "128              3        205         116            10  4.5  10.603448   \n",
       "129              7        201         115            10  8.4  10.486957   \n",
       "\n",
       "     result  \n",
       "125       0  \n",
       "126       0  \n",
       "127       0  \n",
       "128       0  \n",
       "129       0  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def result(row):\n",
    "    return 1 if row['batting_team']==row['winner'] else 0\n",
    "\n",
    "new_df['result']=new_df.apply(result,axis=1)\n",
    "new_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53b98ffb",
   "metadata": {},
   "source": [
    "all datapreprocessing done now take the required columns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1d023037",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>batting_team</th>\n",
       "      <th>bowling_team</th>\n",
       "      <th>city</th>\n",
       "      <th>runs_left</th>\n",
       "      <th>balls_left</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>58646</th>\n",
       "      <td>Rajasthan Royals</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>69</td>\n",
       "      <td>18</td>\n",
       "      <td>3</td>\n",
       "      <td>178</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>6.470588</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>24510</th>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>73</td>\n",
       "      <td>85</td>\n",
       "      <td>9</td>\n",
       "      <td>139</td>\n",
       "      <td>5.152941</td>\n",
       "      <td>11.485714</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10440</th>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>101</td>\n",
       "      <td>61</td>\n",
       "      <td>8</td>\n",
       "      <td>189</td>\n",
       "      <td>9.934426</td>\n",
       "      <td>9.050847</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22868</th>\n",
       "      <td>Rajasthan Royals</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>88</td>\n",
       "      <td>34</td>\n",
       "      <td>8</td>\n",
       "      <td>246</td>\n",
       "      <td>15.529412</td>\n",
       "      <td>11.093023</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14340</th>\n",
       "      <td>Rajasthan Royals</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Centurion</td>\n",
       "      <td>74</td>\n",
       "      <td>36</td>\n",
       "      <td>6</td>\n",
       "      <td>164</td>\n",
       "      <td>12.333333</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              batting_team           bowling_team       city  runs_left  \\\n",
       "58646     Rajasthan Royals         Mumbai Indians  Ahmedabad         69   \n",
       "24510  Chennai Super Kings  Kolkata Knight Riders    Chennai         73   \n",
       "10440       Mumbai Indians        Kings XI Punjab     Mumbai        101   \n",
       "22868     Rajasthan Royals    Chennai Super Kings    Chennai         88   \n",
       "14340     Rajasthan Royals    Chennai Super Kings  Centurion         74   \n",
       "\n",
       "       balls_left  wickets_left  total_runs_x     req_rr        crr  result  \n",
       "58646          18             3           178  23.000000   6.470588       0  \n",
       "24510          85             9           139   5.152941  11.485714       1  \n",
       "10440          61             8           189   9.934426   9.050847       0  \n",
       "22868          34             8           246  15.529412  11.093023       0  \n",
       "14340          36             6           164  12.333333   6.500000       0  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df=new_df[['batting_team','bowling_team','city','runs_left','balls_left','wickets_left','total_runs_x','req_rr','crr','result']]\n",
    "final_df.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "75358955",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "batting_team      0\n",
       "bowling_team      0\n",
       "city            589\n",
       "runs_left         0\n",
       "balls_left        0\n",
       "wickets_left      0\n",
       "total_runs_x      0\n",
       "req_rr            5\n",
       "crr               0\n",
       "result            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "72efac8d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\BISHNU KANTA\\AppData\\Local\\Temp\\ipykernel_25988\\2709626079.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  final_df.dropna(inplace=True)\n"
     ]
    }
   ],
   "source": [
    "final_df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "4e6c7152",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df=final_df[final_df['balls_left']!=0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ca8b108",
   "metadata": {},
   "source": [
    "DATAFRAME IS COMPLETELY READY "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9156b64f",
   "metadata": {},
   "source": [
    "# now use model selection and training  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "9c8d3a0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "x=final_df.drop(\"result\",axis='columns')\n",
    "y=final_df['result']\n",
    "from sklearn.model_selection import train_test_split\n",
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a98afbfa",
   "metadata": {},
   "source": [
    "one hot encoding for the string columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "11a7ae90",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "trf = ColumnTransformer(\n",
    "    transformers=[\n",
    "        ('batting_team', OneHotEncoder(sparse=False, drop='first'), ['batting_team']),\n",
    "        ('bowling_team', OneHotEncoder(sparse=False, drop='first'), ['bowling_team']),\n",
    "        ('city', OneHotEncoder(sparse=False, drop='first'), ['city'])\n",
    "    ],\n",
    "    remainder='passthrough'\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "7b5dd11b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.pipeline import Pipeline\n",
    "pipe=Pipeline(steps=[('steps1',trf),('step2',LogisticRegression(solver='liblinear'))])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "75632a04",
   "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>runs_left</th>\n",
       "      <th>balls_left</th>\n",
       "      <th>wickets_left</th>\n",
       "      <th>total_runs_x</th>\n",
       "      <th>req_rr</th>\n",
       "      <th>crr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>39037.000000</td>\n",
       "      <td>39037.000000</td>\n",
       "      <td>39037.000000</td>\n",
       "      <td>39037.000000</td>\n",
       "      <td>39037.000000</td>\n",
       "      <td>39037.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>94.891334</td>\n",
       "      <td>62.745677</td>\n",
       "      <td>7.574839</td>\n",
       "      <td>167.520045</td>\n",
       "      <td>10.803712</td>\n",
       "      <td>7.485691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>50.522212</td>\n",
       "      <td>33.318950</td>\n",
       "      <td>2.133738</td>\n",
       "      <td>29.601152</td>\n",
       "      <td>15.125840</td>\n",
       "      <td>2.309635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-15.000000</td>\n",
       "      <td>-2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>-516.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>55.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>148.000000</td>\n",
       "      <td>7.324675</td>\n",
       "      <td>6.276923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>94.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>9.107143</td>\n",
       "      <td>7.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>132.000000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>187.000000</td>\n",
       "      <td>11.217391</td>\n",
       "      <td>8.769231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>243.000000</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>250.000000</td>\n",
       "      <td>684.000000</td>\n",
       "      <td>42.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          runs_left    balls_left  wickets_left  total_runs_x        req_rr  \\\n",
       "count  39037.000000  39037.000000  39037.000000  39037.000000  39037.000000   \n",
       "mean      94.891334     62.745677      7.574839    167.520045     10.803712   \n",
       "std       50.522212     33.318950      2.133738     29.601152     15.125840   \n",
       "min      -15.000000     -2.000000      0.000000     65.000000   -516.000000   \n",
       "25%       55.000000     35.000000      6.000000    148.000000      7.324675   \n",
       "50%       94.000000     63.000000      8.000000    168.000000      9.107143   \n",
       "75%      132.000000     92.000000      9.000000    187.000000     11.217391   \n",
       "max      243.000000    119.000000     10.000000    250.000000    684.000000   \n",
       "\n",
       "                crr  \n",
       "count  39037.000000  \n",
       "mean       7.485691  \n",
       "std        2.309635  \n",
       "min        0.000000  \n",
       "25%        6.276923  \n",
       "50%        7.500000  \n",
       "75%        8.769231  \n",
       "max       42.000000  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "1ed9a4ca",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\BISHNU KANTA\\anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:828: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
      "  warnings.warn(\n",
      "C:\\Users\\BISHNU KANTA\\anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:828: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
      "  warnings.warn(\n",
      "C:\\Users\\BISHNU KANTA\\anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:828: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;steps1&#x27;,\n",
       "                 ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                                   transformers=[(&#x27;batting_team&#x27;,\n",
       "                                                  OneHotEncoder(drop=&#x27;first&#x27;,\n",
       "                                                                sparse=False),\n",
       "                                                  [&#x27;batting_team&#x27;]),\n",
       "                                                 (&#x27;bowling_team&#x27;,\n",
       "                                                  OneHotEncoder(drop=&#x27;first&#x27;,\n",
       "                                                                sparse=False),\n",
       "                                                  [&#x27;bowling_team&#x27;]),\n",
       "                                                 (&#x27;city&#x27;,\n",
       "                                                  OneHotEncoder(drop=&#x27;first&#x27;,\n",
       "                                                                sparse=False),\n",
       "                                                  [&#x27;city&#x27;])])),\n",
       "                (&#x27;step2&#x27;, LogisticRegression(solver=&#x27;liblinear&#x27;))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;steps1&#x27;,\n",
       "                 ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                                   transformers=[(&#x27;batting_team&#x27;,\n",
       "                                                  OneHotEncoder(drop=&#x27;first&#x27;,\n",
       "                                                                sparse=False),\n",
       "                                                  [&#x27;batting_team&#x27;]),\n",
       "                                                 (&#x27;bowling_team&#x27;,\n",
       "                                                  OneHotEncoder(drop=&#x27;first&#x27;,\n",
       "                                                                sparse=False),\n",
       "                                                  [&#x27;bowling_team&#x27;]),\n",
       "                                                 (&#x27;city&#x27;,\n",
       "                                                  OneHotEncoder(drop=&#x27;first&#x27;,\n",
       "                                                                sparse=False),\n",
       "                                                  [&#x27;city&#x27;])])),\n",
       "                (&#x27;step2&#x27;, LogisticRegression(solver=&#x27;liblinear&#x27;))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">steps1: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                  transformers=[(&#x27;batting_team&#x27;,\n",
       "                                 OneHotEncoder(drop=&#x27;first&#x27;, sparse=False),\n",
       "                                 [&#x27;batting_team&#x27;]),\n",
       "                                (&#x27;bowling_team&#x27;,\n",
       "                                 OneHotEncoder(drop=&#x27;first&#x27;, sparse=False),\n",
       "                                 [&#x27;bowling_team&#x27;]),\n",
       "                                (&#x27;city&#x27;,\n",
       "                                 OneHotEncoder(drop=&#x27;first&#x27;, sparse=False),\n",
       "                                 [&#x27;city&#x27;])])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">batting_team</label><div class=\"sk-toggleable__content\"><pre>[&#x27;batting_team&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(drop=&#x27;first&#x27;, sparse=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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">bowling_team</label><div class=\"sk-toggleable__content\"><pre>[&#x27;bowling_team&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(drop=&#x27;first&#x27;, sparse=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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">city</label><div class=\"sk-toggleable__content\"><pre>[&#x27;city&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(drop=&#x27;first&#x27;, sparse=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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">remainder</label><div class=\"sk-toggleable__content\"><pre>[&#x27;runs_left&#x27;, &#x27;balls_left&#x27;, &#x27;wickets_left&#x27;, &#x27;total_runs_x&#x27;, &#x27;req_rr&#x27;, &#x27;crr&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(solver=&#x27;liblinear&#x27;)</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('steps1',\n",
       "                 ColumnTransformer(remainder='passthrough',\n",
       "                                   transformers=[('batting_team',\n",
       "                                                  OneHotEncoder(drop='first',\n",
       "                                                                sparse=False),\n",
       "                                                  ['batting_team']),\n",
       "                                                 ('bowling_team',\n",
       "                                                  OneHotEncoder(drop='first',\n",
       "                                                                sparse=False),\n",
       "                                                  ['bowling_team']),\n",
       "                                                 ('city',\n",
       "                                                  OneHotEncoder(drop='first',\n",
       "                                                                sparse=False),\n",
       "                                                  ['city'])])),\n",
       "                ('step2', LogisticRegression(solver='liblinear'))])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "ca6051d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=pipe.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "19e218e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.798155737704918"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "accuracy_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "9259bc6b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.29581929, 0.70418071])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe.predict_proba(x_test)[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e340ea19",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "f915bd57",
   "metadata": {},
   "outputs": [],
   "source": [
    "def match_summary(row):\n",
    "    print(\"Batting Team-\" + row['batting_team'] + \" | Bowling Team-\" + row['bowling_team'] + \" | Target- \" + str(row['total_runs_x']))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7fb662e9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "898e919c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def match_progression(x_df, match_id, pipe):\n",
    "    match = x_df[x_df['match_id'] == match_id]\n",
    "    match = match[(match['ball'] == 6)]\n",
    "    temp_df = match[['batting_team', 'bowling_team', 'city', 'runs_left', 'balls_left', 'wickets_left', 'total_runs_x', 'crr', 'req_rr']].dropna()\n",
    "    temp_df = temp_df[temp_df['balls_left'] != 0]\n",
    "    result = pipe.predict_proba(temp_df)\n",
    "    temp_df['lose'] = np.round(result.T[0] * 100, 1)\n",
    "    temp_df['win'] = np.round(result.T[1] * 100, 1)\n",
    "    temp_df['end_of_over'] = range(1, temp_df.shape[0] + 1)\n",
    "\n",
    "    target = temp_df['total_runs_x'].values[0]\n",
    "    runs = list(temp_df['runs_left'].values)\n",
    "    new_runs = runs[:]\n",
    "    runs.insert(0, target)\n",
    "    temp_df['runs_after_over'] = np.array(runs)[:-1] - np.array(new_runs)\n",
    "    # Get the wickets_left column as a list\n",
    "\n",
    "    wickets = list(temp_df['wickets_left'].values)\n",
    "\n",
    "# Calculate the number of wickets lost in each over (starting from the 6th ball)\n",
    "    wickets_in_over = [wickets[i] - wickets[i - 1] for i in range(1, len(wickets))]\n",
    "    \n",
    "# The first ball will have 0 wickets lost\n",
    "    wickets_in_over.insert(0, 0)\n",
    "\n",
    "# Update the wickets_in_over column in temp_df\n",
    "    temp_df['wickets_in_over'] = wickets_in_over\n",
    "\n",
    "  \n",
    "\n",
    "    print(\"Target -\", target)\n",
    "    print(\"Length of wickets:\", len(wickets))\n",
    "    print(\"Length of new_wickets:\", len(wickets) + 1)\n",
    "\n",
    "    temp_df = temp_df[['end_of_over', 'runs_after_over', 'wickets_in_over', 'lose', 'win']]\n",
    "    return temp_df, target\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "8e3264c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Target - 182\n",
      "Length of wickets: 19\n",
      "Length of new_wickets: 20\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>end_of_over</th>\n",
       "      <th>runs_after_over</th>\n",
       "      <th>wickets_in_over</th>\n",
       "      <th>lose</th>\n",
       "      <th>win</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6070</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>70.1</td>\n",
       "      <td>29.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6076</th>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>39.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6083</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>-2</td>\n",
       "      <td>85.9</td>\n",
       "      <td>14.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6089</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>86.8</td>\n",
       "      <td>13.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6096</th>\n",
       "      <td>5</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>80.6</td>\n",
       "      <td>19.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6102</th>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>80.1</td>\n",
       "      <td>19.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6108</th>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>78.8</td>\n",
       "      <td>21.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6115</th>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>67.9</td>\n",
       "      <td>32.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6122</th>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>-1</td>\n",
       "      <td>80.1</td>\n",
       "      <td>19.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6128</th>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>90.1</td>\n",
       "      <td>9.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6134</th>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>91.8</td>\n",
       "      <td>8.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6140</th>\n",
       "      <td>12</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>81.1</td>\n",
       "      <td>18.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6146</th>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "      <td>-1</td>\n",
       "      <td>89.8</td>\n",
       "      <td>10.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6152</th>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>-2</td>\n",
       "      <td>97.3</td>\n",
       "      <td>2.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6158</th>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>97.6</td>\n",
       "      <td>2.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6164</th>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>-1</td>\n",
       "      <td>98.9</td>\n",
       "      <td>1.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6170</th>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>99.3</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6176</th>\n",
       "      <td>18</td>\n",
       "      <td>7</td>\n",
       "      <td>-1</td>\n",
       "      <td>99.7</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6183</th>\n",
       "      <td>19</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>99.9</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      end_of_over  runs_after_over  wickets_in_over  lose   win\n",
       "6070            1                1                0  70.1  29.9\n",
       "6076            2               12                0  61.0  39.0\n",
       "6083            3                4               -2  85.9  14.1\n",
       "6089            4                3                0  86.8  13.2\n",
       "6096            5               13                0  80.6  19.4\n",
       "6102            6                5                0  80.1  19.9\n",
       "6108            7                6                0  78.8  21.2\n",
       "6115            8               15                0  67.9  32.1\n",
       "6122            9                5               -1  80.1  19.9\n",
       "6128           10                2               -1  90.1   9.9\n",
       "6134           11                1                0  91.8   8.2\n",
       "6140           12               22                0  81.1  18.9\n",
       "6146           13                4               -1  89.8  10.2\n",
       "6152           14                4               -2  97.3   2.7\n",
       "6158           15                3                0  97.6   2.4\n",
       "6164           16                4               -1  98.9   1.1\n",
       "6170           17                1                0  99.3   0.7\n",
       "6176           18                7               -1  99.7   0.3\n",
       "6183           19                3                0  99.9   0.1"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_df,target = match_progression(new_df,69,pipe)\n",
    "temp_df####OVER BY OVER DETAILS"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87983422",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "682d30b7",
   "metadata": {},
   "source": [
    "# SAME MATCH PROGRESS INTO GRAPH"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "74a8c9fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as pl\n",
    "pl.figure(figsize=(20,10))\n",
    "pl.plot(temp_df['end_of_over'],abs(temp_df['wickets_in_over']),color='yellow',linewidth=5)\n",
    "pl.plot(temp_df['end_of_over'],temp_df['win'],color='green',linewidth=4)\n",
    "pl.plot(temp_df['end_of_over'],temp_df['lose'],color='red',linewidth=4)\n",
    "pl.bar(temp_df['end_of_over'],temp_df['runs_after_over'])\n",
    "pl.title('TARGET::'+str(target))    \n",
    "pl.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82997a2d",
   "metadata": {},
   "source": [
    "# almost done now presentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "53b2dd15",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Sunrisers Hyderabad',\n",
       " 'Mumbai Indians',\n",
       " 'Royal Challengers Bangalore',\n",
       " 'Kolkata Knight Riders',\n",
       " 'Kings XI Punjab',\n",
       " 'Chennai Super Kings',\n",
       " 'Rajasthan Royals',\n",
       " 'Delhi Capitals']"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "teams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "0858ec20",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Hyderabad', 'Mumbai', 'Indore', 'Kolkata', 'Bangalore',\n",
       "       'Chandigarh', 'Jaipur', 'Chennai', 'Cape Town', 'Port Elizabeth',\n",
       "       'Durban', 'Centurion', 'East London', 'Johannesburg', 'Kimberley',\n",
       "       'Ahmedabad', 'Dharamsala', 'Ranchi', 'Delhi', 'Abu Dhabi', nan,\n",
       "       'Sharjah', 'Cuttack', 'Pune', 'Visakhapatnam', 'Bengaluru',\n",
       "       'Mohali'], dtype=object)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df['city'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "def6957f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "2b4f1654",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model loaded successfully: Pipeline(steps=[('steps1',\n",
      "                 ColumnTransformer(remainder='passthrough',\n",
      "                                   transformers=[('batting_team',\n",
      "                                                  OneHotEncoder(drop='first',\n",
      "                                                                sparse=False),\n",
      "                                                  ['batting_team']),\n",
      "                                                 ('bowling_team',\n",
      "                                                  OneHotEncoder(drop='first',\n",
      "                                                                sparse=False),\n",
      "                                                  ['bowling_team']),\n",
      "                                                 ('city',\n",
      "                                                  OneHotEncoder(drop='first',\n",
      "                                                                sparse=False),\n",
      "                                                  ['city'])])),\n",
      "                ('step2', LogisticRegression(solver='liblinear'))])\n"
     ]
    }
   ],
   "source": [
    "import pickle\n",
    "\n",
    "# Load the pickled model\n",
    "with open('pipe.pkl', 'rb') as model_file:\n",
    "    loaded_pipe = pickle.load(model_file)\n",
    "\n",
    "print(\"Model loaded successfully:\", loaded_pipe)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4b56b48e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "9b02f3f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model loaded successfully: Pipeline(steps=[('steps1',\n",
      "                 ColumnTransformer(remainder='passthrough',\n",
      "                                   transformers=[('batting_team',\n",
      "                                                  OneHotEncoder(drop='first',\n",
      "                                                                sparse=False),\n",
      "                                                  ['batting_team']),\n",
      "                                                 ('bowling_team',\n",
      "                                                  OneHotEncoder(drop='first',\n",
      "                                                                sparse=False),\n",
      "                                                  ['bowling_team']),\n",
      "                                                 ('city',\n",
      "                                                  OneHotEncoder(drop='first',\n",
      "                                                                sparse=False),\n",
      "                                                  ['city'])])),\n",
      "                ('step2', LogisticRegression(solver='liblinear'))])\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import pickle\n",
    "\n",
    "# Get the current working directory\n",
    "current_dir = os.getcwd()\n",
    "\n",
    "# Set the path to the pickle file (assuming it's in the same directory)\n",
    "pickle_file_path = os.path.join(current_dir, 'pipe.pkl')\n",
    "\n",
    "# Load the pickled model\n",
    "with open(pickle_file_path, 'rb') as model_file:\n",
    "    loaded_pipe = pickle.load(model_file)\n",
    "\n",
    "print(\"Model loaded successfully:\", loaded_pipe)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "5b259530",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "# Pickle the model and pipeline\n",
    "with open('pipe.pkl', 'wb') as model_file:\n",
    "    pickle.dump(pipe, model_file)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a402699e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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