IPL.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import joblib\n",
    "import pickle\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from datetime import datetime\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.preprocessing import StandardScaler,MinMaxScaler\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from sklearn.metrics import r2_score,mean_absolute_error,mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>mid</th>\n",
       "      <th>date</th>\n",
       "      <th>venue</th>\n",
       "      <th>bat_team</th>\n",
       "      <th>bowl_team</th>\n",
       "      <th>batsman</th>\n",
       "      <th>bowler</th>\n",
       "      <th>runs</th>\n",
       "      <th>wickets</th>\n",
       "      <th>overs</th>\n",
       "      <th>runs_last_5</th>\n",
       "      <th>wickets_last_5</th>\n",
       "      <th>striker</th>\n",
       "      <th>non-striker</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2008-04-18</td>\n",
       "      <td>M Chinnaswamy Stadium</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>SC Ganguly</td>\n",
       "      <td>P Kumar</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2008-04-18</td>\n",
       "      <td>M Chinnaswamy Stadium</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>BB McCullum</td>\n",
       "      <td>P Kumar</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>222</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   mid        date                  venue               bat_team  \\\n",
       "0    1  2008-04-18  M Chinnaswamy Stadium  Kolkata Knight Riders   \n",
       "1    1  2008-04-18  M Chinnaswamy Stadium  Kolkata Knight Riders   \n",
       "\n",
       "                     bowl_team      batsman   bowler  runs  wickets  overs  \\\n",
       "0  Royal Challengers Bangalore   SC Ganguly  P Kumar     1        0    0.1   \n",
       "1  Royal Challengers Bangalore  BB McCullum  P Kumar     1        0    0.2   \n",
       "\n",
       "   runs_last_5  wickets_last_5  striker  non-striker  total  \n",
       "0            1               0        0            0    222  \n",
       "1            1               0        0            0    222  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('ipl.csv')\n",
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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",
       "        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>date</th>\n",
       "      <th>venue_ACA-VDCA Stadium, Visakhapatnam</th>\n",
       "      <th>venue_Barabati Stadium, Cuttack</th>\n",
       "      <th>venue_Dr DY Patil Sports Academy, Mumbai</th>\n",
       "      <th>venue_Dubai International Cricket Stadium, Dubai</th>\n",
       "      <th>venue_Eden Gardens, Kolkata</th>\n",
       "      <th>venue_Feroz Shah Kotla, Delhi</th>\n",
       "      <th>venue_Himachal Pradesh Cricket Association Stadium, Dharamshala</th>\n",
       "      <th>venue_Holkar Cricket Stadium, Indore</th>\n",
       "      <th>venue_JSCA International Stadium Complex, Ranchi</th>\n",
       "      <th>...</th>\n",
       "      <th>bowl_team_Mumbai Indians</th>\n",
       "      <th>bowl_team_Rajasthan Royals</th>\n",
       "      <th>bowl_team_Royal Challengers Bangalore</th>\n",
       "      <th>bowl_team_Sunrisers Hyderabad</th>\n",
       "      <th>runs</th>\n",
       "      <th>wickets</th>\n",
       "      <th>overs</th>\n",
       "      <th>runs_last_5</th>\n",
       "      <th>wickets_last_5</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2008-04-18</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>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>59</td>\n",
       "      <td>0</td>\n",
       "      <td>222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2008-04-18</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>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>61</td>\n",
       "      <td>1</td>\n",
       "      <td>5.2</td>\n",
       "      <td>59</td>\n",
       "      <td>1</td>\n",
       "      <td>222</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 43 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        date  venue_ACA-VDCA Stadium, Visakhapatnam  \\\n",
       "0 2008-04-18                                      0   \n",
       "1 2008-04-18                                      0   \n",
       "\n",
       "   venue_Barabati Stadium, Cuttack  venue_Dr DY Patil Sports Academy, Mumbai  \\\n",
       "0                                0                                         0   \n",
       "1                                0                                         0   \n",
       "\n",
       "   venue_Dubai International Cricket Stadium, Dubai  \\\n",
       "0                                                 0   \n",
       "1                                                 0   \n",
       "\n",
       "   venue_Eden Gardens, Kolkata  venue_Feroz Shah Kotla, Delhi  \\\n",
       "0                            0                              0   \n",
       "1                            0                              0   \n",
       "\n",
       "   venue_Himachal Pradesh Cricket Association Stadium, Dharamshala  \\\n",
       "0                                                  0                 \n",
       "1                                                  0                 \n",
       "\n",
       "   venue_Holkar Cricket Stadium, Indore  \\\n",
       "0                                     0   \n",
       "1                                     0   \n",
       "\n",
       "   venue_JSCA International Stadium Complex, Ranchi  ...  \\\n",
       "0                                                 0  ...   \n",
       "1                                                 0  ...   \n",
       "\n",
       "   bowl_team_Mumbai Indians  bowl_team_Rajasthan Royals  \\\n",
       "0                         0                           0   \n",
       "1                         0                           0   \n",
       "\n",
       "   bowl_team_Royal Challengers Bangalore  bowl_team_Sunrisers Hyderabad  runs  \\\n",
       "0                                      1                              0    61   \n",
       "1                                      1                              0    61   \n",
       "\n",
       "   wickets  overs  runs_last_5  wickets_last_5  total  \n",
       "0        0    5.1           59               0    222  \n",
       "1        1    5.2           59               1    222  \n",
       "\n",
       "[2 rows x 43 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols_to_drop = ['mid','batsman','bowler','striker','non-striker']\n",
    "df.drop(cols_to_drop,axis=1,inplace=True)\n",
    "\n",
    "\n",
    "# convert the date column to datetime column\n",
    "df['date'] = df['date'].apply(lambda x: datetime.strptime(x,'%Y-%m-%d'))\n",
    "\n",
    "\n",
    "# we have to remove temporary teams or the teams which are not available now\n",
    "consistent_teams = ['Chennai Super Kings', 'Delhi Daredevils', \n",
    "                    'Kings XI Punjab', 'Kolkata Knight Riders', \n",
    "                    'Mumbai Indians', 'Rajasthan Royals', \n",
    "                    'Royal Challengers Bangalore', 'Sunrisers Hyderabad']\n",
    "\n",
    "df = df[(df['bat_team'].isin(consistent_teams)) & (df['bowl_team'].isin(consistent_teams))]\n",
    "\n",
    "\n",
    "# we don't want first five overs data\n",
    "df = df[df['overs']>=5.0]\n",
    "\n",
    "\n",
    "\n",
    "def f(x):\n",
    "    if x=='M Chinnaswamy Stadium':\n",
    "        return 'M Chinnaswamy Stadium, Bangalore'\n",
    "    elif x=='Feroz Shah Kotla':\n",
    "        return 'Feroz Shah Kotla, Delhi'\n",
    "    elif x=='Wankhede Stadium':\n",
    "        return 'Wankhede Stadium, Mumbai'\n",
    "    elif x=='Sawai Mansingh Stadium':\n",
    "        return 'Sawai Mansingh Stadium, Jaipur'\n",
    "    elif x=='Eden Gardens':\n",
    "        return 'Eden Gardens, Kolkata'\n",
    "    elif x=='Dr DY Patil Sports Academy':\n",
    "        return 'Dr DY Patil Sports Academy, Mumbai'\n",
    "    elif x=='Himachal Pradesh Cricket Association Stadium':\n",
    "        return 'Himachal Pradesh Cricket Association Stadium, Dharamshala'\n",
    "    elif x=='Subrata Roy Sahara Stadium':\n",
    "        return 'Maharashtra Cricket Association Stadium, Pune'\n",
    "    elif x=='Shaheed Veer Narayan Singh International Stadium':\n",
    "        return 'Raipur International Cricket Stadium, Raipur'\n",
    "    elif x=='JSCA International Stadium Complex':\n",
    "        return 'JSCA International Stadium Complex, Ranchi'\n",
    "    elif x=='Maharashtra Cricket Association Stadium':\n",
    "        return 'Maharashtra Cricket Association Stadium, Pune'\n",
    "    elif x=='Dr. Y.S. Rajasekhara Reddy ACA-VDCA Cricket Stadium':\n",
    "        return 'ACA-VDCA Stadium, Visakhapatnam'\n",
    "    elif x=='Punjab Cricket Association IS Bindra Stadium, Mohali':\n",
    "        return 'Punjab Cricket Association Stadium, Mohali'\n",
    "    elif x=='Holkar Cricket Stadium':\n",
    "        return 'Holkar Cricket Stadium, Indore'\n",
    "    elif x=='Sheikh Zayed Stadium':\n",
    "        return 'Sheikh Zayed Stadium, Abu-Dhabi'\n",
    "    elif x=='Sharjah Cricket Stadium':\n",
    "        return 'Sharjah Cricket Stadium, Sharjah'\n",
    "    elif x=='Dubai International Cricket Stadium':\n",
    "        return 'Dubai International Cricket Stadium, Dubai'\n",
    "    elif x=='Barabati Stadium':\n",
    "        return 'Barabati Stadium, Cuttack'\n",
    "    else:\n",
    "        return x\n",
    "\n",
    "ignored_stadiums = ['Newlands', \"St George's Park\",\n",
    "                    'Kingsmead', 'SuperSport Park', 'Buffalo Park',\n",
    "                    'New Wanderers Stadium', 'De Beers Diamond Oval',\n",
    "                    'OUTsurance Oval', 'Brabourne Stadium']\n",
    "\n",
    "df = df[True^(df['venue'].isin(ignored_stadiums))]\n",
    "df['venue'] = df['venue'].apply(f)\n",
    "\n",
    "df_new = pd.get_dummies(data=df,columns=['venue','bat_team','bowl_team'])\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "df_new = df_new[['date','venue_ACA-VDCA Stadium, Visakhapatnam',\n",
    "       'venue_Barabati Stadium, Cuttack', 'venue_Dr DY Patil Sports Academy, Mumbai',\n",
    "       'venue_Dubai International Cricket Stadium, Dubai',\n",
    "       'venue_Eden Gardens, Kolkata', 'venue_Feroz Shah Kotla, Delhi',\n",
    "       'venue_Himachal Pradesh Cricket Association Stadium, Dharamshala',\n",
    "       'venue_Holkar Cricket Stadium, Indore',\n",
    "       'venue_JSCA International Stadium Complex, Ranchi',\n",
    "       'venue_M Chinnaswamy Stadium, Bangalore',\n",
    "       'venue_MA Chidambaram Stadium, Chepauk',\n",
    "       'venue_Maharashtra Cricket Association Stadium, Pune',\n",
    "       'venue_Punjab Cricket Association Stadium, Mohali',\n",
    "       'venue_Raipur International Cricket Stadium, Raipur',\n",
    "       'venue_Rajiv Gandhi International Stadium, Uppal',\n",
    "       'venue_Sardar Patel Stadium, Motera',\n",
    "       'venue_Sawai Mansingh Stadium, Jaipur',\n",
    "       'venue_Sharjah Cricket Stadium, Sharjah',\n",
    "       'venue_Sheikh Zayed Stadium, Abu-Dhabi',\n",
    "       'venue_Wankhede Stadium, Mumbai','bat_team_Chennai Super Kings',\n",
    "       'bat_team_Delhi Daredevils', 'bat_team_Kings XI Punjab',\n",
    "       'bat_team_Kolkata Knight Riders', 'bat_team_Mumbai Indians',\n",
    "       'bat_team_Rajasthan Royals', 'bat_team_Royal Challengers Bangalore',\n",
    "       'bat_team_Sunrisers Hyderabad','bowl_team_Chennai Super Kings',\n",
    "       'bowl_team_Delhi Daredevils', 'bowl_team_Kings XI Punjab',\n",
    "       'bowl_team_Kolkata Knight Riders', 'bowl_team_Mumbai Indians',\n",
    "       'bowl_team_Rajasthan Royals', 'bowl_team_Royal Challengers Bangalore',\n",
    "       'bowl_team_Sunrisers Hyderabad','runs', 'wickets', 'overs', 'runs_last_5', 'wickets_last_5',\n",
    "       'total']]\n",
    "\n",
    "df_new.reset_index(inplace=True)\n",
    "df_new.drop('index',inplace=True,axis=1)\n",
    "\n",
    "df_new.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "scaled_cols = scaler.fit_transform(df_new[['runs', 'wickets', 'overs', 'runs_last_5', 'wickets_last_5']])\n",
    "pickle.dump(scaler, open('scaler.pkl','wb'))\n",
    "\n",
    "scaled_cols = pd.DataFrame(scaled_cols,columns=['runs', 'wickets', 'overs', 'runs_last_5', 'wickets_last_5'])\n",
    "df_new.drop(['runs', 'wickets', 'overs', 'runs_last_5', 'wickets_last_5'],axis=1,inplace=True)\n",
    "df_new = pd.concat([df_new,scaled_cols],axis=1)\n",
    "\n",
    "\n",
    "X_train = df_new.drop('total',axis=1)[df_new['date'].dt.year<=2016]\n",
    "X_test = df_new.drop('total',axis=1)[df_new['date'].dt.year>=2017]\n",
    "\n",
    "X_train.drop('date',inplace=True,axis=1)\n",
    "X_test.drop('date',inplace=True,axis=1)\n",
    "\n",
    "\n",
    "y_train = df_new[df_new['date'].dt.year<=2016]['total'].values\n",
    "y_test = df_new[df_new['date'].dt.year>=2017]['total'].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ridge Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/lumiq/anaconda3/envs/ipl/lib/python3.9/site-packages/sklearn/model_selection/_search.py:292: UserWarning: The total space of parameters 6 is smaller than n_iter=10. Running 6 iterations. For exhaustive searches, use GridSearchCV.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=10, estimator=Ridge(),\n",
       "                   param_distributions={'alpha': [0.001, 0.01, 1, 5, 10, 20]},\n",
       "                   scoring='neg_mean_squared_error')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ridge = Ridge()\n",
    "parameters={'alpha':[1e-3,1e-2,1,5,10,20]}\n",
    "ridge_regressor = RandomizedSearchCV(ridge,parameters,cv=10,scoring='neg_mean_squared_error')\n",
    "ridge_regressor.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'alpha': 20}\n",
      "-348.1536046297452\n",
      "MAE: 12.368640632597904\n",
      "MSE: 266.5091758230592\n",
      "RMSE: 16.325108753789642\n",
      "r2 score of ridge : 0.7369641251644409\n"
     ]
    }
   ],
   "source": [
    "print(ridge_regressor.best_params_)\n",
    "print(ridge_regressor.best_score_)\n",
    "\n",
    "prediction_r = ridge_regressor.predict(X_test)\n",
    "print('MAE:', mean_absolute_error(y_test, prediction_r))\n",
    "print('MSE:', mean_squared_error(y_test, prediction_r))\n",
    "print('RMSE:', np.sqrt(mean_squared_error(y_test, prediction_r)))\n",
    "print(f'r2 score of ridge : {r2_score(y_test,prediction_r)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/lumiq/anaconda3/envs/ipl/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Density'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(y_test-prediction_r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['iplmodel_ridge.sav']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(ridge_regressor,'iplmodel_ridge.sav')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}