winner.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "import pydotplus\n",
    "from six import StringIO\n",
    "from IPython.display import Image  \n",
    "from sklearn.tree import export_graphviz\n",
    "from sklearn.naive_bayes import BernoulliNB , GaussianNB ,MultinomialNB\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.linear_model import RidgeClassifier\n",
    "from sklearn.linear_model import LassoCV\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('IPL Matches 2008-2020.csv')\n",
    "df = df[['team1' , 'team2' , 'venue' , 'toss_winner' ,'toss_decision' , 'winner' , 'neutral_venue']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>team1</th>\n",
       "      <th>team2</th>\n",
       "      <th>venue</th>\n",
       "      <th>toss_winner</th>\n",
       "      <th>toss_decision</th>\n",
       "      <th>winner</th>\n",
       "      <th>neutral_venue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>M Chinnaswamy Stadium</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>field</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Punjab Cricket Association Stadium, Mohali</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>bat</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Delhi Daredevils</td>\n",
       "      <td>Rajasthan Royals</td>\n",
       "      <td>Feroz Shah Kotla</td>\n",
       "      <td>Rajasthan Royals</td>\n",
       "      <td>bat</td>\n",
       "      <td>Delhi Daredevils</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Wankhede Stadium</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>bat</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>Deccan Chargers</td>\n",
       "      <td>Eden Gardens</td>\n",
       "      <td>Deccan Chargers</td>\n",
       "      <td>bat</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         team1                        team2  \\\n",
       "0  Royal Challengers Bangalore        Kolkata Knight Riders   \n",
       "1              Kings XI Punjab          Chennai Super Kings   \n",
       "2             Delhi Daredevils             Rajasthan Royals   \n",
       "3               Mumbai Indians  Royal Challengers Bangalore   \n",
       "4        Kolkata Knight Riders              Deccan Chargers   \n",
       "\n",
       "                                        venue                  toss_winner  \\\n",
       "0                       M Chinnaswamy Stadium  Royal Challengers Bangalore   \n",
       "1  Punjab Cricket Association Stadium, Mohali          Chennai Super Kings   \n",
       "2                            Feroz Shah Kotla             Rajasthan Royals   \n",
       "3                            Wankhede Stadium               Mumbai Indians   \n",
       "4                                Eden Gardens              Deccan Chargers   \n",
       "\n",
       "  toss_decision                       winner  neutral_venue  \n",
       "0         field        Kolkata Knight Riders              0  \n",
       "1           bat          Chennai Super Kings              0  \n",
       "2           bat             Delhi Daredevils              0  \n",
       "3           bat  Royal Challengers Bangalore              0  \n",
       "4           bat        Kolkata Knight Riders              0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Royal Challengers Bangalore', 'Kings XI Punjab',\n",
       "       'Delhi Daredevils', 'Mumbai Indians', 'Kolkata Knight Riders',\n",
       "       'Rajasthan Royals', 'Deccan Chargers', 'Chennai Super Kings',\n",
       "       'Kochi Tuskers Kerala', 'Pune Warriors', 'Sunrisers Hyderabad',\n",
       "       'Gujarat Lions', 'Rising Pune Supergiants',\n",
       "       'Rising Pune Supergiant', 'Delhi Capitals'], dtype=object)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.team1.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['M Chinnaswamy Stadium',\n",
       "       'Punjab Cricket Association Stadium, Mohali', 'Feroz Shah Kotla',\n",
       "       'Wankhede Stadium', 'Eden Gardens', 'Sawai Mansingh Stadium',\n",
       "       'Rajiv Gandhi International Stadium, Uppal',\n",
       "       'MA Chidambaram Stadium, Chepauk', 'Dr DY Patil Sports Academy',\n",
       "       'Newlands', \"St George's Park\", 'Kingsmead', 'SuperSport Park',\n",
       "       'Buffalo Park', 'New Wanderers Stadium', 'De Beers Diamond Oval',\n",
       "       'OUTsurance Oval', 'Brabourne Stadium',\n",
       "       'Sardar Patel Stadium, Motera', 'Barabati Stadium',\n",
       "       'Vidarbha Cricket Association Stadium, Jamtha',\n",
       "       'Himachal Pradesh Cricket Association Stadium', 'Nehru Stadium',\n",
       "       'Holkar Cricket Stadium',\n",
       "       'Dr. Y.S. Rajasekhara Reddy ACA-VDCA Cricket Stadium',\n",
       "       'Subrata Roy Sahara Stadium',\n",
       "       'Shaheed Veer Narayan Singh International Stadium',\n",
       "       'JSCA International Stadium Complex', 'Sheikh Zayed Stadium',\n",
       "       'Sharjah Cricket Stadium', 'Dubai International Cricket Stadium',\n",
       "       'Maharashtra Cricket Association Stadium',\n",
       "       'Punjab Cricket Association IS Bindra Stadium, Mohali',\n",
       "       'Saurashtra Cricket Association Stadium', 'Green Park',\n",
       "       'M.Chinnaswamy Stadium'], dtype=object)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.venue.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Keeping only consistent teams\n",
    "consistent_teams = ['Kolkata Knight Riders', 'Chennai Super Kings', 'Rajasthan Royals',\n",
    "                    'Mumbai Indians', 'Kings XI Punjab', 'Royal Challengers Bangalore',\n",
    "                    'Delhi Daredevils', 'Sunrisers Hyderabad' , 'Delhi Capitals']\n",
    "\n",
    "consistent_venues = ['M Chinnaswamy Stadium',\n",
    "       'Punjab Cricket Association Stadium, Mohali', 'Feroz Shah Kotla', 'Wankhede Stadium', 'Sawai Mansingh Stadium',\n",
    "       'MA Chidambaram Stadium, Chepauk', 'Eden Gardens', 'Dr DY Patil Sports Academy', 'Brabourne Stadium',\n",
    "       'Sardar Patel Stadium, Motera', 'Himachal Pradesh Cricket Association Stadium', 'Subrata Roy Sahara Stadium',\n",
    "       'Rajiv Gandhi International Stadium, Uppal', 'Shaheed Veer Narayan Singh International Stadium',\n",
    "       'JSCA International Stadium Complex', 'Barabati Stadium', 'Maharashtra Cricket Association Stadium',\n",
    "       'Dr. Y.S. Rajasekhara Reddy ACA-VDCA Cricket Stadium', 'Punjab Cricket Association IS Bindra Stadium, Mohali',\n",
    "       'M.Chinnaswamy Stadium', 'Holkar Cricket Stadium', 'Vidarbha Cricket Association Stadium, Jamtha', 'Nehru Stadium',\n",
    "       'Saurashtra Cricket Association Stadium']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[(df['team1'].isin(consistent_teams)) & (df['team2'].isin(consistent_teams)) & (df['neutral_venue']==0) & (df['venue'].isin(consistent_venues))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop('neutral_venue', inplace=True , axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Royal Challengers Bangalore', 'Kings XI Punjab',\n",
       "       'Delhi Daredevils', 'Mumbai Indians', 'Rajasthan Royals',\n",
       "       'Chennai Super Kings', 'Kolkata Knight Riders',\n",
       "       'Sunrisers Hyderabad', 'Delhi Capitals'], dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# delhi capitals and delhi daredevils are same team\n",
    "df.team1.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['team1'] = np.where(df.team1 == 'Delhi Daredevils' , 'Delhi Capitals' , df.team1)\n",
    "df['team2'] = np.where(df.team2 == 'Delhi Daredevils' , 'Delhi Capitals' , df.team2)\n",
    "df['toss_winner'] = np.where(df.toss_winner == 'Delhi Daredevils' , 'Delhi Capitals' , df.toss_winner)\n",
    "df['winner'] = np.where(df.winner == 'Delhi Daredevils' , 'Delhi Capitals' , df.winner)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['M Chinnaswamy Stadium',\n",
       "       'Punjab Cricket Association Stadium, Mohali', 'Feroz Shah Kotla',\n",
       "       'Wankhede Stadium', 'Sawai Mansingh Stadium',\n",
       "       'MA Chidambaram Stadium, Chepauk', 'Eden Gardens',\n",
       "       'Dr DY Patil Sports Academy', 'Brabourne Stadium',\n",
       "       'Sardar Patel Stadium, Motera',\n",
       "       'Himachal Pradesh Cricket Association Stadium',\n",
       "       'Subrata Roy Sahara Stadium',\n",
       "       'Rajiv Gandhi International Stadium, Uppal',\n",
       "       'Shaheed Veer Narayan Singh International Stadium',\n",
       "       'JSCA International Stadium Complex', 'Barabati Stadium',\n",
       "       'Maharashtra Cricket Association Stadium',\n",
       "       'Dr. Y.S. Rajasekhara Reddy ACA-VDCA Cricket Stadium',\n",
       "       'Punjab Cricket Association IS Bindra Stadium, Mohali',\n",
       "       'M.Chinnaswamy Stadium', 'Holkar Cricket Stadium'], dtype=object)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# M Chinnaswamy Stadium and M.Chinnaswamy Stadium are same team\n",
    "# Punjab Cricket Association IS Bindra Stadium, Mohali and Punjab Cricket Association Stadium, Mohali are same\n",
    "df.venue.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['venue'] = np.where(df.venue == 'M.Chinnaswamy Stadium' , 'M Chinnaswamy Stadium' , df.venue)\n",
    "df['venue'] = np.where(df.venue == 'Punjab Cricket Association IS Bindra Stadium, Mohali' , 'Punjab Cricket Association Stadium, Mohali' , df.venue)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['M Chinnaswamy Stadium',\n",
       "       'Punjab Cricket Association Stadium, Mohali', 'Feroz Shah Kotla',\n",
       "       'Wankhede Stadium', 'Sawai Mansingh Stadium',\n",
       "       'MA Chidambaram Stadium, Chepauk', 'Eden Gardens',\n",
       "       'Dr DY Patil Sports Academy', 'Brabourne Stadium',\n",
       "       'Sardar Patel Stadium, Motera',\n",
       "       'Himachal Pradesh Cricket Association Stadium',\n",
       "       'Subrata Roy Sahara Stadium',\n",
       "       'Rajiv Gandhi International Stadium, Uppal',\n",
       "       'Shaheed Veer Narayan Singh International Stadium',\n",
       "       'JSCA International Stadium Complex', 'Barabati Stadium',\n",
       "       'Maharashtra Cricket Association Stadium',\n",
       "       'Dr. Y.S. Rajasekhara Reddy ACA-VDCA Cricket Stadium',\n",
       "       'Holkar Cricket Stadium'], dtype=object)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.venue.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>team1</th>\n",
       "      <th>team2</th>\n",
       "      <th>venue</th>\n",
       "      <th>toss_winner</th>\n",
       "      <th>toss_decision</th>\n",
       "      <th>winner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>M Chinnaswamy Stadium</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>field</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Punjab Cricket Association Stadium, Mohali</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>bat</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Rajasthan Royals</td>\n",
       "      <td>Feroz Shah Kotla</td>\n",
       "      <td>Rajasthan Royals</td>\n",
       "      <td>bat</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Wankhede Stadium</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>bat</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Rajasthan Royals</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Sawai Mansingh Stadium</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>bat</td>\n",
       "      <td>Rajasthan Royals</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         team1                        team2  \\\n",
       "0  Royal Challengers Bangalore        Kolkata Knight Riders   \n",
       "1              Kings XI Punjab          Chennai Super Kings   \n",
       "2               Delhi Capitals             Rajasthan Royals   \n",
       "3               Mumbai Indians  Royal Challengers Bangalore   \n",
       "5             Rajasthan Royals              Kings XI Punjab   \n",
       "\n",
       "                                        venue                  toss_winner  \\\n",
       "0                       M Chinnaswamy Stadium  Royal Challengers Bangalore   \n",
       "1  Punjab Cricket Association Stadium, Mohali          Chennai Super Kings   \n",
       "2                            Feroz Shah Kotla             Rajasthan Royals   \n",
       "3                            Wankhede Stadium               Mumbai Indians   \n",
       "5                      Sawai Mansingh Stadium              Kings XI Punjab   \n",
       "\n",
       "  toss_decision                       winner  \n",
       "0         field        Kolkata Knight Riders  \n",
       "1           bat          Chennai Super Kings  \n",
       "2           bat               Delhi Capitals  \n",
       "3           bat  Royal Challengers Bangalore  \n",
       "5           bat             Rajasthan Royals  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getNumber_team(x):\n",
    "    if x=='Royal Challengers Bangalore':\n",
    "        return 0\n",
    "    elif x=='Kings XI Punjab':\n",
    "        return 1\n",
    "    elif x=='Delhi Capitals':\n",
    "        return 2\n",
    "    elif x=='Mumbai Indians':\n",
    "        return 3     \n",
    "    elif x=='Rajasthan Royals':\n",
    "        return 4\n",
    "    elif x=='Chennai Super Kings':\n",
    "        return 5\n",
    "    elif x=='Kolkata Knight Riders':\n",
    "        return 6\n",
    "    else:\n",
    "        return 7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['team1'] = df['team1'].apply(getNumber_team)\n",
    "df['team2'] = df['team2'].apply(getNumber_team)\n",
    "df['toss_winner'] = df['toss_winner'].apply(getNumber_team)\n",
    "df['winner'] = df['winner'].apply(getNumber_team)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['M Chinnaswamy Stadium',\n",
       "       'Punjab Cricket Association Stadium, Mohali', 'Feroz Shah Kotla',\n",
       "       'Wankhede Stadium', 'Sawai Mansingh Stadium',\n",
       "       'MA Chidambaram Stadium, Chepauk', 'Eden Gardens',\n",
       "       'Dr DY Patil Sports Academy', 'Brabourne Stadium',\n",
       "       'Sardar Patel Stadium, Motera',\n",
       "       'Himachal Pradesh Cricket Association Stadium',\n",
       "       'Subrata Roy Sahara Stadium',\n",
       "       'Rajiv Gandhi International Stadium, Uppal',\n",
       "       'Shaheed Veer Narayan Singh International Stadium',\n",
       "       'JSCA International Stadium Complex', 'Barabati Stadium',\n",
       "       'Maharashtra Cricket Association Stadium',\n",
       "       'Dr. Y.S. Rajasekhara Reddy ACA-VDCA Cricket Stadium',\n",
       "       'Holkar Cricket Stadium'], dtype=object)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.venue.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getNumber_venue(x):\n",
    "    if x=='M Chinnaswamy Stadium':\n",
    "        return 1\n",
    "    elif x=='Punjab Cricket Association Stadium, Mohali':\n",
    "        return 2\n",
    "    elif x=='Feroz Shah Kotla':\n",
    "        return 3\n",
    "    elif x=='Wankhede Stadium':\n",
    "        return 4\n",
    "    elif x=='Sawai Mansingh Stadium':\n",
    "        return 5\n",
    "    elif x=='MA Chidambaram Stadium, Chepauk':\n",
    "        return 6\n",
    "    elif x=='Eden Gardens':\n",
    "        return 7\n",
    "    elif x=='Dr DY Patil Sports Academy':\n",
    "        return 8\n",
    "    elif x=='Brabourne Stadium':\n",
    "        return 9\n",
    "    elif x=='Sardar Patel Stadium, Motera':\n",
    "        return 10 \n",
    "    elif x=='Himachal Pradesh Cricket Association Stadium':\n",
    "        return 11\n",
    "    elif x=='Subrata Roy Sahara Stadium':\n",
    "        return 12\n",
    "    elif x=='Rajiv Gandhi International Stadium, Uppal':\n",
    "        return 13\n",
    "    elif x=='Shaheed Veer Narayan Singh International Stadium':\n",
    "        return 14\n",
    "    elif x=='JSCA International Stadium Complex':\n",
    "        return 15\n",
    "    elif x=='Maharashtra Cricket Association Stadium':\n",
    "        return 16\n",
    "    elif x=='Dr. Y.S. Rajasekhara Reddy ACA-VDCA Cricket Stadium':\n",
    "        return 17\n",
    "    elif x=='Barabati Stadium':\n",
    "        return 18\n",
    "    else:\n",
    "        return 19"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['venue'] = df['venue'].apply(getNumber_venue)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getNumber_tossDecision(x):\n",
    "    if x=='field':\n",
    "        return 0\n",
    "    else:\n",
    "        return 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['toss_decision'] = df['toss_decision'].apply(getNumber_tossDecision)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "team1            int64\n",
       "team2            int64\n",
       "venue            int64\n",
       "toss_winner      int64\n",
       "toss_decision    int64\n",
       "winner           int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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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>team1</th>\n",
       "      <th>team2</th>\n",
       "      <th>venue</th>\n",
       "      <th>toss_winner</th>\n",
       "      <th>toss_decision</th>\n",
       "      <th>winner</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>6</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>751</th>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>752</th>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>753</th>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>754</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>17</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>755</th>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>510 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     team1  team2  venue  toss_winner  toss_decision  winner\n",
       "0        0      6      1            0              0       6\n",
       "1        1      5      2            5              1       5\n",
       "2        2      4      3            4              1       2\n",
       "3        3      0      4            3              1       0\n",
       "5        4      1      5            1              1       4\n",
       "..     ...    ...    ...          ...            ...     ...\n",
       "751      3      6      4            3              0       3\n",
       "752      3      5      6            5              1       3\n",
       "753      2      7     17            2              0       2\n",
       "754      5      2     17            5              0       5\n",
       "755      3      5     13            3              1       3\n",
       "\n",
       "[510 rows x 6 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>team1</th>\n",
       "      <th>team2</th>\n",
       "      <th>venue</th>\n",
       "      <th>toss_winner</th>\n",
       "      <th>toss_decision</th>\n",
       "      <th>winner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>team1</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.130983</td>\n",
       "      <td>0.543821</td>\n",
       "      <td>0.395610</td>\n",
       "      <td>0.171171</td>\n",
       "      <td>0.501920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>team2</th>\n",
       "      <td>-0.130983</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.064434</td>\n",
       "      <td>0.422630</td>\n",
       "      <td>-0.016893</td>\n",
       "      <td>0.324945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>venue</th>\n",
       "      <td>0.543821</td>\n",
       "      <td>-0.064434</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.207111</td>\n",
       "      <td>0.092240</td>\n",
       "      <td>0.294169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>toss_winner</th>\n",
       "      <td>0.395610</td>\n",
       "      <td>0.422630</td>\n",
       "      <td>0.207111</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.085666</td>\n",
       "      <td>0.426797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>toss_decision</th>\n",
       "      <td>0.171171</td>\n",
       "      <td>-0.016893</td>\n",
       "      <td>0.092240</td>\n",
       "      <td>0.085666</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.110825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>winner</th>\n",
       "      <td>0.501920</td>\n",
       "      <td>0.324945</td>\n",
       "      <td>0.294169</td>\n",
       "      <td>0.426797</td>\n",
       "      <td>0.110825</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  team1     team2     venue  toss_winner  toss_decision  \\\n",
       "team1          1.000000 -0.130983  0.543821     0.395610       0.171171   \n",
       "team2         -0.130983  1.000000 -0.064434     0.422630      -0.016893   \n",
       "venue          0.543821 -0.064434  1.000000     0.207111       0.092240   \n",
       "toss_winner    0.395610  0.422630  0.207111     1.000000       0.085666   \n",
       "toss_decision  0.171171 -0.016893  0.092240     0.085666       1.000000   \n",
       "winner         0.501920  0.324945  0.294169     0.426797       0.110825   \n",
       "\n",
       "                 winner  \n",
       "team1          0.501920  \n",
       "team2          0.324945  \n",
       "venue          0.294169  \n",
       "toss_winner    0.426797  \n",
       "toss_decision  0.110825  \n",
       "winner         1.000000  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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3fZukJ0t6CrAbcAWwW/nL/jbbD44xxcDatudJ2hc4CnhFeX428CJgJXCjpM8BfwA+BLzC9gOS/gn4B+Bj5Wcesr3rOLH9caqEHzz9dU1v8oyIKaTOZq5+VNl5vytwWtlk9L+SLgR2pniz82RJ6wBn2l4qaTnw7PKX+/eAc7uU+yNgF2B34JPA3hTz21w0zv1nlF+vBGZ2nL/A9j0Akm4AtqZoynsecEmZoNYFLu34zDd7f9sREWtW00eFVTm78Zjfqe3FFEnhN8DXJB1k+y6Kms0i4DDgpC7lXkRRW9ka+E75uV2BxePcv7L8OsRjE+fKjv2RawLOsz273J5n+20d9z3QJa6IiFo0vSlsoonlPmCjcn8xcEDZf7I5RTK5XNLWFM1WXwK+DOwkaTNgLdvfBj4M7NTlGYuBNwI/tz0M3AnsC1wywdgBLgN2kfQcgLLPZrsKyo2ImDTD6m+ry4SawmzfIekSSdcB3weWAddQJMv32f6dpDcDR0h6BLgfOIhivv9TJI0ktvd3ecYtZTPVSA3lYmCLstYzIbZ/L+ktwGmSnlSe/hDws4mWHRExWaZ8H4vtA0edOmLU9a8AXxnjo91qKaOfsVXH/icp+lpGjo/u2N+jY/92yj4W26cCp3Zce1XH/g8o+oJGP3Nmv/FFRKxJTR8tlDfvIyJaZlWz++6bk1gknUAx+qvT8bZPqSOeiIimmvJNYVWxfVjdMUREtIFTY4mIiCqlxhIREZVKYomIiEplVFhERFQqo8IiIqJSaQqLiIhKpSksIiIqVec8YP1IYomIaJk0hT0B/Nd6dUcwmAse+FXdIQxsu7WfXncIA9lvp8PrDmFg37nq83WHMLAD5ryn7hAGdkbvW3pKU1hERFRqVcNTSxJLRETLNDutJLFERLRO+lgiIqJSGRUWERGVGm54Y9hE17yPiIg1bKjPrR+S9pZ0o6SbJB05xvW/kbSs3H4kacdeZabGEhHRMlXVWCRNA04AXgmsAJZIOsv2DR233Qz8qe27JO0DLABe3K3c1FgiIlrGfW59mAfcZHu57YeB04H9HvMs+0e27yoPLwO26FVoEktERMsM97lJmi/pio5t/qiiZgC/7jheUZ4bz9uA7/eKL01hEREt029TmO0FFE1X4xlrfNmYhUt6GUVi2bXXc5NYIiJapsIxYSuALTuOtwBuHX2TpBcCJwH72L6jV6FJLBERLTNUXWpZAsyStA3wG+D1wIGdN0jaimKKszfZ/lk/hSaxRES0TFVv3tteJelwYCEwDTjZ9vWSDimvnwh8BNgU+IIkgFW253YrN4klIqJlqnxB0vbZwNmjzp3Ysf+3wN8OUmYSS0REyzT7vfsew40lTZd06JoKRtJMSdet5mc/JukVXa4fIumg1Y8uIqIZhnFfW1161VimA4cCX5j8UCbG9kd6XD+x2/WIiLaosPN+UvR6QfIYYFtJSyUdV27XSbpW0gEAkp4paXF5z3WSdpM0TdKpHff+/XgPkDRH0jWSLgUO6zg/rXzeknKOmrd3XHtfWe41ko4pz50q6bXl/jGSbig/95ny3NGS3lvuz5Z0WXn9vyU9tTy/SNKxki6X9DNJu63WTzUiYhL1+4JkXXrVWI4EdrA9W9L+wCHAjsBmFHPKLKYYmrbQ9ifKeWfWB2YDM2zvAEWTWpdnnAK80/aFko7rOP824B7bO0t6EnCJpHOB7YFXAy+2/aCkTToLK49fA2xv2+M8+6sdz/wYcBTwnpGfie15kvYtz4/ZvFa+wTofYPdN5vC8jZ7d5VuMiKiOW15j6bQrcJrtIdv/C1wI7EwxDvqtko4GXmD7PmA58GxJn5O0N3DvWAVK2hiYbvvC8tTXOi7vBRwkaSnwY4rhbrMoftGfYvtBANt3jir2XuAh4CRJfwU82OOZXwF277hlZEnqK4GZ4/0wbC+wPdf23CSViFiTml5jGSSxjLm0jO3FFL+YfwN8TdJB5YRlOwKLKJq3TupS5nipVxS1itnlto3tc3t8BturKCZW+zZFzeacHt/XaCvLr0Nk1FxENNCw3ddWl16J5T5go3J/MXBA2fexOUUyuVzS1sBttr8EfBnYSdJmwFq2vw18GNhprMJt3w3cI2lk7pm/6bi8EHiHpHUAJG0naQPgXOBgSeuX50c3hW0IbFyOzX4PRbNc5zPvAe7q6D95E0XtKyKiFSqc3XhSdP2L3PYdki4phwB/H1gGXEMR8/ts/07Sm4EjJD0C3A8cRDE75imSRhLX+7s85q3AyZIepEgmI06iaIq6SsXrnr8HXm37HEmzgSskPUzxYs8HOj63EfAdSU+mqN2MNXDgzcCJZXJaXsYQEdEKQw1f9V6usbo0Vbxj5uta9UO84IHldYcwsO3We3rdIQyk6UvHjuU7V32+7hAGdsCc99QdwsDO+OVZE16x/oCtX93X/2Df/OWZE37W6kgfQkREyzT9D5c1llgknQDsMur08bZPWVMxRERMBU0fbrzGEovtw3rfFRERvTS7hyVNYRERrdP0vvEkloiIllmVprCIiKhS+lgiIqJSGRUWERGVSh9LRERUKqPCIiKiUk2f0iWJJSKiZdIU9gTwvo3vqjuEgezy8PZ1hzCw+5v97+hxXjT0YO+bGqaN825988rP1h1CLdJ5HxERlcpw44iIqFSdi3j1I4klIqJlmp1WklgiIlpnVUaFRURElTIqLCIiKpVRYRERUammjwpbq+4AIiJiMLb72vohaW9JN0q6SdKRY1yXpH8rry+TtFOvMpNYIiJaZhj3tfUiaRpwArAP8DzgDZKeN+q2fYBZ5TYf+Pde5SaxRES0zJCH+9r6MA+4yfZy2w8DpwP7jbpnP+CrLlwGTJf0zG6FJrFERLSM+/yvDzOAX3ccryjPDXrPYySxRES0zLDd1yZpvqQrOrb5o4rSGMWPzkj93PMYGRUWEdEy/Y4Ks70AWNDllhXAlh3HWwC3rsY9j5EaS0REy/RbY+nDEmCWpG0krQu8Hjhr1D1nAQeVo8NeAtxj+7fdCk2NJSKiZfrsmO/J9ipJhwMLgWnAybavl3RIef1E4GxgX+Am4EHgrb3KbX1ikXQ2cKDtu+uOJSJiTajyBUnbZ1Mkj85zJ3bsGzhskDJbn1hs7zuZ5UuaZntoMp8RETGIpk+b3/g+Fknvk/Sucv//S/pBub+npK9LukXSZpJmSvqJpC9Jul7SuZLWK+9dJOlYSZdL+pmk3crz0yQdJ2lJ+Ubp28vze0j6oaRvANfW9K1HRIypwuHGk6LxiQVYDOxW7s8FNpS0DrArcNGoe2cBJ9h+PnA3sH/HtbVtzwPeAxxVnnsbRUfUzsDOwN9J2qa8Ng/4oO3Rb6ECPGYY32l3rJjI9xcRMRB7uK+tLm1ILFcCcyRtBKwELqVIMLvx+MRys+2lHZ+b2XHtjDHO70Ux2mEp8GNgU4rkBHC57ZvHC8r2Attzbc99w6ZbDP5dRUSspqqmdJksje9jsf2IpFsoRiL8CFgGvAzYFvjJqNtXduwPAeuNcW2IR79vAe+0vbCzEEl7AA9MPPqIiOpVNSpssrShxgJFc9h7y68XAYcASz3x1W4WAu8om9aQtJ2kDSZYZkTEpKpyduPJ0PgaS+ki4IPApbYfkPQQj28GWx0nUTSLXSVJwO+BV1dQbkTEpGn6qLBWJBbbFwDrdBxv17E/s9y9Hdih4/xnOvb36Ni/nbKPxUXv1gfKrdOicouIaJymL/TVisQSERGPypr3ERFRqax5HxERlRoabvaosCSWiIiWSVNYRERUKk1hERFRqdRYIiKiUnmPJSIiKtX0KV2SWCIiWiZNYRERUam8eR8REZVKjSUiIirV9MSipgf4RCZpvu0FdccxiMQ8+doWL7Qv5rbF2zRtWY/liWp+3QGshsQ8+doWL7Qv5rbF2yhJLBERUakkloiIqFQSS7O1sY03MU++tsUL7Yu5bfE2SjrvIyKiUqmxREREpZJYIiKiUkksERFRqSSWiIaTtJak19UdR0S/klgaTtL2dcfQjaR1xji3WR2xDELS1pJeUe6vJ2mjumMaj+1h4PC64xiUpF0knSfpZ5KWS7pZ0vK64xpLmbyvqzuOqSKJpfnOrTuAsUh6maQVwK2SzpU0s+NyI2MeIenvgG8BXyxPbQGcWVtA/TlP0nslbSlpk5Gt7qB6+DLwr8CuwM7A3PJr45TJ+xpJW9Udy1SQSSgbQNK/jXcJmL4GQxnEp4E/s329pNdS/OJ7k+3LKOJussOAecCPAWz/XNLT6g2pp4PLr4d1nDPw7Bpi6dc9tr9fdxADeCZwvaTLgQdGTtr+y/pCaqcklmZ4K/CPwMoxrr1hDcfSr3VtXw9g+1uSfgKcIelIaPhiEbDS9sNSkf8krU3DY7a9Td0xrIYfSjoOOIOO/7dtX1VfSF19tO4ApooklmZYAlxn+0ejL0g6es2H05dHJD3D9u8AyprLnsB3gW3rDa2nCyV9AFhP0iuBQ4H/qTmmriStD/wDsJXt+ZJmAc+1/d2aQ+vmxeXXuR3nDLy8hlh6sn2hpK2BWbbPL3/m0+qOq43y5n0DlG3lD9l+sO5Y+lV2fP/e9jWjzk8HDrP9iVoC64OktYC3AXtRNNstBE5yg/8xSPomcCVwkO0dJK0HXGp7dr2RTR1l39t8YBPb25bJ+0Tbe9YcWusksUS0gKQrbM+VdLXtF5XnrrG9Y92xjUfSxsBRwO7lqQuBj9m+p76oxidpKWXfW8fP+FrbL6g1sBbKqLAGkfQqSVdLulPSvZLuk3Rv3XF109KYby6Hvz5mqzuuHh4uaykGkLQtY/fJNcnJwH3A68rtXuCUWiPqbqXth0cO2tD31lTpY2mWzwJ/BVzb5GaZUT5L+2LubPN/MvDXQNOH7h4FnANsKek/gF2At9QaUW/b2t6/4/ijZa2gqVrX99ZUaQprEEk/BPYsx9S3QhtjHouki23vWncc3UjaFHgJRb/QZbZvrzmkriRdChxh++LyeBfgM7ZfWm9kY2tj31tTJbE0iKSdgX+maIvuHJ75r7UF1UNLY96p43AtihrMO5rcXwEgaQawNR0tDbYX1xdRd5JmA18BNqb4RX0n8JbRAz5i6klTWLN8Arifonlm3Zpj6VcbY/6Xjv1VwC0UfQCNJelY4ADgemCkdmigsYnF9lJgR0lPKY+b3ve2C3A0jyZvAbbd5JdQGyk1lgYZGflTdxyDaGPMbSTpRuCFtpveYY+kN9r+uqR/GOt6U2uzkn4K/D3FsO6hkfO276gtqJZKjaVZzpe0l+1Gz7U1SutilvQkYH9gJo9tVvpYXTH1YTmwDs0fCQawQfm1sRN7jqNtU9A0VmosDSLpPop/lCuBR3i0Kv6UWgProqUxnwPcw+P/Mv2XcT9UM0nfBnYELuCxfVnvqi2oKUbSMRRv2rdlCprGSmKJJxxJ19neoe44BiHpzWOdt/2VNR1LvyR9Gvg48AeKodI7Au+x/fVaAxtHOcJxNNtu5BQ0TZbE0jCSngrMougMB5o98gfaF7OkBcDnbF9bdyxTmaSltmdLeg3waor+ix82ffRdTFz6WBpE0t8C76ZYH2QpxTsLl9LQSfugnTFTrA/yFkk3UzR5jDTfvbDesMbX0hFLI4vA7QucZvvOkRmlm6ilfW+NlMTSLO+mWAjpMtsvU7F6ZNOn8m5jzPvUHcBq+DJjjFhquP8pR1r9AThU0ubAQzXH1M13eLTvrQ2DJBoriaVZHrL9kCQkPcn2TyU9t+6gemhdzLZ/KWlXiunRTyl/4W1Yd1w9tG7Eku0jy/dv7rU9JOkBYL+64+piC9t71x3EVJDE0iwrymnnz6RYkfEu4NZaI+qtdTFLOoribfvnUkyKuA7wdYr5t5qqNYtmSXq57R9I+quOc523nLHmo+rLjyS9IH1vE5fO+4aS9KcUU2Gc0znjapO1JeZyIsQXAVd1TI++rOF9LK0ZsSTpo7aPkjTWTMa2ffAY52sn6QbgOUBr+t6aKomlYcZqorF9c91xddO2mCVdbnuepKts7yRpA4pFs/IL5AlMxeqRj2P7l2s6lrZLU1iDtLGJpo0xA/8p6YvA9HLVwIOBL9Uc05jaOj0KgKRPAp+2fXd5/FTgH21/qNbARpH0lHIes/vqjmWqSGJpltdQNtEA2L5VUtOnxWhjzMPARRQLT20HfMT2efWGNK62To8CsI/tD4wc2L5L0r5AoxIL8A3gVRSjwUzRBDbCQJOHdDdSEkuzPGzbkkZWCdyg1wcaoI0xb0Sx7sadwOnAsnrDGZ/tL5a7x9pu8lDdsUwrRwquBChXwHxSzTE9ju1XlbsXU8wWfZHtn9YYUutlaeJmGd1Ecz4NbaLp0LqYbX/U9vOBw4BnUawceH7NYfVynaRLJB0jaV8V68k33deBCyS9TdLBwHkU67M01SnAM4HPSfqFpG9JenfdQbVRaizNsjnwLYommucCHwFeUWtEvbUx5hG3Ab8D7gCeVnMsXdl+jqStgN0omm2+IOlu27PrjWx8tj8taRnF/w8C/tn2wprDGlc5RPpCihd+XwYcAuwAHF9rYC2UUWENMjJKadS5pg+DbWPM76BYNGskKX7T9g31RtWdpC0oksqfUkzmeCdwse1P1RpYD+VIq1m2z5e0PjDNdiM7ySVdQNGndSlFH9zFtm+rN6p2So2lAcpfdIcCzy7/whuxEXBJPVF118aYO2xNMcvu0roDGcCvgCXAJ20fUncw/SibRucDmwDbAjOAE4E964yri2XAHIpayj3A3ZIutf2HesNqn9RYGqBsL38q8CngyI5L99m+s56oumtjzG0maUeKyTN3B7YCfg5caPvLtQbWRfki6jzgxx0vol5r+wW1BtaDpA2BtwLvBZ5hu3EDDpouiSWiJcpfeLtSNIm9keKt8Jm1BtWFpB/bfrGkq22/SNLaFLMdNLKZVNLhFD/bOcAveXSE2A9qDayF0hQW0QKSrqAYqvsjimGxu7fgjfALJX0AWE/SKymaTv+n5pi6WQ/4V+BK26vqDqbNUmOJaAFJm9v+fZfrb27aapKS1qJ4X2gvilFhC4GTnF86U14SS8QUMNbovIi6pCksYmpozNKMkq6lmAplTE3tY4nqJLFETA1NanoYmSLlsPLr18qvfwM8uObDiTUtTWERU8DIyKu64+gk6RLbu/Q6F1NP5gqLmBqa+FLqBuVaPQBI+n88OltzTGGpsUS0QDkZ4ikUa4acRLFUwZG2z601sC4kzQFOplhV1BRvsx/cxOWUo1pJLBEtIOka2ztK+jOKvosPA6e0YSSYpKdQ/K65Z9T5xg2RjmqkKSyiHUZGfe1LkVCuoUEjwbqxfe/opFLKlPRTVBJLRDtcKelcisSysFylc7jmmCaqFYkxBpemsIgWKN9inw0st323pE2ALWw3dvXLXvJS59SVGktEO7wUuLFMKm+kWDd+rOalNkmNZYpKYoloh38HHiynz38fxey7X603pAlr4hDpqEASS0Q7rConb9wPON728RSLqjWWpHdLeooKX5Z0laS9Rq7bPrzO+GLyJLFEtMN9kt4PvAn4nqRpwDo1x9TLwbbvpZjdeHOKxbOOqTekWBOSWCLa4QBgJcUv699RLPN7XL0h9dTaIdIxMRkVFtESkp4O7FweXm77tjrj6UXSKRQJcBtgR2AasMj2nFoDi0mXxBLRApJeR1FDWUTxV/9uwBG2v1VnXN1MxSHS0Z8klogWkHQN8MqRWoqkzYHzbe9Yb2Tjk7QLsNT2A+UQ6Z0oBh40fUnlmKD0sUS0w1qjmr7uoPn/fqfiEOnoQxb6imiH70taCJxWHh8AnF1jPP1YZduSRoZIf1nSm+sOKiZf0//iiYiCgS8CL6ToCF9Qbzh9aeMQ6ahA+lgiWmCsebUkLWvy+vGSngEcCCyxfZGkrYA9bKc5bIpLYoloMEnvAA4Fng38ouPSRsAltt9YS2B9atsQ6ahGEktEg0naGHgq8CngyI5L99m+s56o+tPGIdJRjSSWiJgUbRwiHdVI531ETJY2DpGOCmS4cURMljYOkY4K5K+HiJgsbRwiHRVIH0tETIo2DpGOaqQpLCIq1TlEWlLnhJMbkVUjnxBSY4mISrV5iHRUI4klIiIqlc77iIioVBJLRERUKoklIiIqlcQSERGVSmKJiIhK/R8TNZsN7+7GWgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.heatmap(df.corr())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(labels='winner', axis=1)\n",
    "y = df['winner']\n",
    "\n",
    "X = np.array(X)\n",
    "y = np.array(y)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "for i in range(len(y)):\n",
    "    if y[i]==X[i][0]:\n",
    "        y[i]=0\n",
    "    else:\n",
    "        y[i]=1\n",
    "        \n",
    "# imbalanced dataset so it is going to \n",
    "print(np.unique(y, return_counts=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "zeros = 0\n",
    "for i in range(len(X)):\n",
    "    if y[i] == X[i][0]:\n",
    "        if zeros <= 250:\n",
    "            y[i] = 0\n",
    "            zeros = zeros + 1\n",
    "        else:\n",
    "            y[i] = 1\n",
    "            t = X[i][0]\n",
    "            X[i][0] = X[i][1] \n",
    "            X[i][1] = t\n",
    "    else:\n",
    "        y[i] = 1\n",
    "        \n",
    "\n",
    "for i in range(len(X)):\n",
    "    if X[i][3]==X[i][0]:\n",
    "        X[i][3]=0\n",
    "    else:\n",
    "        X[i][3]=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(array([0, 1]), array([251, 259], dtype=int64))\n"
     ]
    }
   ],
   "source": [
    "X = np.array(X , dtype='int32')\n",
    "y = np.array(y , dtype='int32')\n",
    "\n",
    "y = y.ravel()\n",
    "\n",
    "print(np.unique(y, return_counts=True))\n",
    "# now balanced dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "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 , random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy :  0.5490196078431373\n",
      "time :  0.011977195739746094\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.57      0.48      0.52        52\n",
      "           1       0.53      0.62      0.57        50\n",
      "\n",
      "    accuracy                           0.55       102\n",
      "   macro avg       0.55      0.55      0.55       102\n",
      "weighted avg       0.55      0.55      0.55       102\n",
      "\n",
      "[[25 27]\n",
      " [19 31]]\n"
     ]
    }
   ],
   "source": [
    "alg1 = LogisticRegression(solver='liblinear')\n",
    "\n",
    "start = time.time()\n",
    "alg1.fit(X_train , y_train)\n",
    "end = time.time()\n",
    "total_time1 = end - start\n",
    "\n",
    "y_pred1 = alg1.predict(X_test)\n",
    "\n",
    "print('accuracy : ', alg1.score(X_test , y_test))\n",
    "print('time : ' , total_time1)\n",
    "print(classification_report(y_test , y_pred1))\n",
    "print(confusion_matrix(y_test , y_pred1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy :  0.5\n",
      "time :  0.10471940040588379\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.51      0.42      0.46        52\n",
      "           1       0.49      0.58      0.53        50\n",
      "\n",
      "    accuracy                           0.50       102\n",
      "   macro avg       0.50      0.50      0.50       102\n",
      "weighted avg       0.50      0.50      0.50       102\n",
      "\n",
      "[[22 30]\n",
      " [21 29]]\n"
     ]
    }
   ],
   "source": [
    "alg2 = RandomForestClassifier(n_estimators=60)\n",
    "\n",
    "start = time.time()\n",
    "alg2.fit(X_train , y_train)\n",
    "end = time.time()\n",
    "total_time2 = end - start\n",
    "\n",
    "y_pred2 = alg2.predict(X_test)\n",
    "\n",
    "print('accuracy : ', alg2.score(X_test , y_test))\n",
    "print('time : ' , total_time2)\n",
    "print(classification_report(y_test , y_pred2))\n",
    "print(confusion_matrix(y_test , y_pred2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy :  0.6470588235294118\n",
      "time :  0.001994609832763672\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.64      0.69      0.67        52\n",
      "           1       0.65      0.60      0.63        50\n",
      "\n",
      "    accuracy                           0.65       102\n",
      "   macro avg       0.65      0.65      0.65       102\n",
      "weighted avg       0.65      0.65      0.65       102\n",
      "\n",
      "[[36 16]\n",
      " [20 30]]\n"
     ]
    }
   ],
   "source": [
    "alg3 = DecisionTreeClassifier(max_depth=1 , criterion='gini')\n",
    "\n",
    "start = time.time()\n",
    "alg3.fit(X_train , y_train)\n",
    "end = time.time()\n",
    "total_time3 = end - start\n",
    "\n",
    "y_pred3 = alg3.predict(X_test)\n",
    "\n",
    "print('accuracy : ', alg3.score(X_test , y_test))\n",
    "print('time : ' , total_time3)\n",
    "print(classification_report(y_test , y_pred3))\n",
    "print(confusion_matrix(y_test , y_pred3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Printing tree alongwith class names\n",
    "dot_data = StringIO()\n",
    "export_graphviz(alg3, out_file=dot_data, filled=True, rounded=True, special_characters=True, feature_names = ['team1', 'team2', 'venue','toss_winner', 'toss_decision'])\n",
    "graph = pydotplus.graph_from_dot_data(dot_data.getvalue()) \n",
    "Image(graph.create_png())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dot_data = export_graphviz(alg3, out_file=None,\n",
    "                          feature_names=['team1', 'team2', 'venue', 'toss_winner', 'toss_decision'])\n",
    "graph = pydotplus.graph_from_dot_data(dot_data)\n",
    "graph.write_pdf(\"ipl_winner_decision_tree.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy :  0.5588235294117647\n",
      "time :  0.0030100345611572266\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.58      0.50      0.54        52\n",
      "           1       0.54      0.62      0.58        50\n",
      "\n",
      "    accuracy                           0.56       102\n",
      "   macro avg       0.56      0.56      0.56       102\n",
      "weighted avg       0.56      0.56      0.56       102\n",
      "\n",
      "[[26 26]\n",
      " [19 31]]\n"
     ]
    }
   ],
   "source": [
    "alg4 = BernoulliNB()\n",
    "\n",
    "start = time.time()\n",
    "alg4.fit(X_train,y_train)\n",
    "end = time.time()\n",
    "total_time4 = end - start\n",
    "\n",
    "y_pred4 = alg4.predict(X_test)\n",
    "\n",
    "print('accuracy : ', alg4.score(X_test , y_test))\n",
    "print('time : ' , total_time4)\n",
    "print(classification_report(y_test , y_pred4))\n",
    "print(confusion_matrix(y_test , y_pred4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy :  0.5784313725490197\n",
      "time :  0.002992868423461914\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.60      0.52      0.56        52\n",
      "           1       0.56      0.64      0.60        50\n",
      "\n",
      "    accuracy                           0.58       102\n",
      "   macro avg       0.58      0.58      0.58       102\n",
      "weighted avg       0.58      0.58      0.58       102\n",
      "\n",
      "[[27 25]\n",
      " [18 32]]\n"
     ]
    }
   ],
   "source": [
    "alg5 = GaussianNB()\n",
    "\n",
    "start = time.time()\n",
    "alg5.fit(X_train,y_train)\n",
    "end = time.time()\n",
    "total_time5 = end - start\n",
    "\n",
    "y_pred5 = alg5.predict(X_test)\n",
    "\n",
    "print('accuracy : ', alg5.score(X_test , y_test))\n",
    "print('time : ' , total_time5)\n",
    "print(classification_report(y_test , y_pred5))\n",
    "print(confusion_matrix(y_test , y_pred5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy :  0.5098039215686274\n",
      "time :  0.0019898414611816406\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.52      0.46      0.49        52\n",
      "           1       0.50      0.56      0.53        50\n",
      "\n",
      "    accuracy                           0.51       102\n",
      "   macro avg       0.51      0.51      0.51       102\n",
      "weighted avg       0.51      0.51      0.51       102\n",
      "\n",
      "[[24 28]\n",
      " [22 28]]\n"
     ]
    }
   ],
   "source": [
    "alg6 = MultinomialNB()\n",
    "\n",
    "start = time.time()\n",
    "alg6.fit(X_train,y_train)\n",
    "end = time.time()\n",
    "total_time6 = end - start\n",
    "\n",
    "y_pred6 = alg6.predict(X_test)\n",
    "\n",
    "print('accuracy : ', alg6.score(X_test , y_test))\n",
    "print('time : ' , total_time6)\n",
    "print(classification_report(y_test , y_pred6))\n",
    "print(confusion_matrix(y_test , y_pred6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_axis = []\n",
    "y_axis = []\n",
    "for k in range(1, 26, 2):\n",
    "    clf = KNeighborsClassifier(n_neighbors = k)\n",
    "    score = cross_val_score(clf, X_train, y_train, cv = KFold(n_splits=5, shuffle=True, random_state=0))\n",
    "    x_axis.append(k)\n",
    "    y_axis.append(score.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "import matplotlib.pyplot as plt\n",
    "plt.plot(x_axis, y_axis)\n",
    "plt.xlabel(\"k\")\n",
    "plt.ylabel(\"cross_val_score\")\n",
    "plt.title(\"variation of score on different values of k\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy :  0.5686274509803921\n",
      "time :  0.0019533634185791016\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.57      0.62      0.59        52\n",
      "           1       0.57      0.52      0.54        50\n",
      "\n",
      "    accuracy                           0.57       102\n",
      "   macro avg       0.57      0.57      0.57       102\n",
      "weighted avg       0.57      0.57      0.57       102\n",
      "\n",
      "[[32 20]\n",
      " [24 26]]\n"
     ]
    }
   ],
   "source": [
    "alg7 = KNeighborsClassifier(n_neighbors=19, weights='distance', algorithm='auto', p=2, metric='minkowski')\n",
    "\n",
    "start = time.time()\n",
    "alg7.fit(X_train, y_train)\n",
    "end = time.time()\n",
    "total_time7 = end - start\n",
    "\n",
    "y_pred7 = alg7.predict(X_test)\n",
    "\n",
    "print('accuracy : ', alg7.score(X_test , y_test))\n",
    "print('time : ' , total_time7)\n",
    "print(classification_report(y_test , y_pred7))\n",
    "print(confusion_matrix(y_test , y_pred7))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVC(C=100000.0, gamma=0.005)\n",
      "accuracy :  0.5980392156862745\n",
      "time :  31.382102966308594\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.63      0.50      0.56        52\n",
      "           1       0.57      0.70      0.63        50\n",
      "\n",
      "    accuracy                           0.60       102\n",
      "   macro avg       0.60      0.60      0.59       102\n",
      "weighted avg       0.60      0.60      0.59       102\n",
      "\n",
      "[[26 26]\n",
      " [15 35]]\n"
     ]
    }
   ],
   "source": [
    "clf = SVC(kernel='rbf')\n",
    "grid = {'C': [1e2,1e3, 5e3, 1e4, 5e4, 1e5],\n",
    "       'gamma': [1e-3, 5e-4, 1e-4, 5e-3]}\n",
    "\n",
    "alg8 = GridSearchCV(clf, grid)\n",
    "\n",
    "start = time.time()\n",
    "alg8.fit(X_train, y_train)\n",
    "end = time.time()\n",
    "total_time8 = end - start\n",
    "\n",
    "y_pred8 = alg8.predict(X_test)\n",
    "\n",
    "print(alg8.best_estimator_)\n",
    "\n",
    "print('accuracy : ', alg8.score(X_test , y_test))\n",
    "print('time : ' , total_time8)\n",
    "print(classification_report(y_test , y_pred8))\n",
    "print(confusion_matrix(y_test , y_pred8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy :  0.5490196078431373\n",
      "time :  0.0309755802154541\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.57      0.44      0.50        52\n",
      "           1       0.53      0.66      0.59        50\n",
      "\n",
      "    accuracy                           0.55       102\n",
      "   macro avg       0.55      0.55      0.54       102\n",
      "weighted avg       0.55      0.55      0.54       102\n",
      "\n",
      "[[23 29]\n",
      " [17 33]]\n"
     ]
    }
   ],
   "source": [
    "alg9 = LinearSVC(multi_class='crammer_singer')\n",
    "\n",
    "start = time.time()\n",
    "alg9.fit(X_train, y_train)\n",
    "end = time.time()\n",
    "total_time9 = end - start\n",
    "\n",
    "y_pred9 = alg9.predict(X_test)\n",
    "\n",
    "print('accuracy : ', alg9.score(X_test , y_test))\n",
    "print('time : ' , total_time9)\n",
    "print(classification_report(y_test , y_pred9))\n",
    "print(confusion_matrix(y_test , y_pred9))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy :  0.5490196078431373\n",
      "time :  0.17553138732910156\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.57      0.44      0.50        52\n",
      "           1       0.53      0.66      0.59        50\n",
      "\n",
      "    accuracy                           0.55       102\n",
      "   macro avg       0.55      0.55      0.54       102\n",
      "weighted avg       0.55      0.55      0.54       102\n",
      "\n",
      "[[23 29]\n",
      " [17 33]]\n"
     ]
    }
   ],
   "source": [
    "ridge = RidgeClassifier()\n",
    "\n",
    "parameters={'alpha':[1e-15,1e-10,1e-8,1e-3,1e-2,1,5,10,20,30,35,40]}\n",
    "alg10=GridSearchCV(ridge,parameters)\n",
    "\n",
    "start = time.time()\n",
    "alg10.fit(X_train, y_train)\n",
    "end = time.time()\n",
    "total_time10 = end - start\n",
    "\n",
    "y_pred10 = alg10.predict(X_test)\n",
    "\n",
    "print('accuracy : ', alg10.score(X_test , y_test))\n",
    "print('time : ' , total_time10)\n",
    "print(classification_report(y_test , y_pred10))\n",
    "print(confusion_matrix(y_test , y_pred10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "alg1 :  [1]\n",
      "alg2 :  [1]\n",
      "alg3 :  [1]\n",
      "alg4 :  [0]\n",
      "alg5 :  [1]\n",
      "alg6 :  [1]\n",
      "alg7 :  [1]\n",
      "alg8 :  [0]\n",
      "alg9 :  [0]\n",
      "alg10 : [1]\n"
     ]
    }
   ],
   "source": [
    "test = np.array([2, 4, 1, 1, 1]).reshape(1,-1)\n",
    "\n",
    "print('alg1 : ' , alg1.predict(test))\n",
    "print('alg2 : ' , alg2.predict(test))\n",
    "print('alg3 : ' , alg3.predict(test))\n",
    "print('alg4 : ' , alg4.predict(test))\n",
    "print('alg5 : ' , alg5.predict(test))\n",
    "print('alg6 : ' , alg6.predict(test))\n",
    "print('alg7 : ' , alg7.predict(test))\n",
    "print('alg8 : ' , alg8.predict(test))\n",
    "print('alg9 : ' , alg9.predict(test))\n",
    "print('alg10 :' , alg10.predict(test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "alg1 :  [0]\n",
      "alg2 :  [1]\n",
      "alg3 :  [0]\n",
      "alg4 :  [0]\n",
      "alg5 :  [0]\n",
      "alg6 :  [0]\n",
      "alg7 :  [1]\n",
      "alg8 :  [0]\n",
      "alg9 :  [0]\n",
      "alg10 : [0]\n"
     ]
    }
   ],
   "source": [
    "test = np.array([4, 2, 1, 0, 1]).reshape(1,-1)\n",
    "\n",
    "print('alg1 : ' , alg1.predict(test))\n",
    "print('alg2 : ' , alg2.predict(test))\n",
    "print('alg3 : ' , alg3.predict(test))\n",
    "print('alg4 : ' , alg4.predict(test))\n",
    "print('alg5 : ' , alg5.predict(test))\n",
    "print('alg6 : ' , alg6.predict(test))\n",
    "print('alg7 : ' , alg7.predict(test))\n",
    "print('alg8 : ' , alg8.predict(test))\n",
    "print('alg9 : ' , alg9.predict(test))\n",
    "print('alg10 :' , alg10.predict(test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_model=pd.DataFrame({\n",
    "'Model_Applied':['Logistic_Regression', 'Random_Forest', 'Decision_tree', 'BernoulliNB', 'GausianNB', 'MultinomialNB', 'KNN', 'SVC', 'Linear_SVC', 'Ridge_Classifier'],\n",
    "'Accuracy':[alg1.score(X_test,y_test), alg2.score(X_test,y_test), alg3.score(X_test,y_test), alg4.score(X_test,y_test),\n",
    "            alg5.score(X_test,y_test), alg6.score(X_test,y_test), alg7.score(X_test,y_test), alg8.score(X_test,y_test),\n",
    "            alg9.score(X_test,y_test), alg10.score(X_test,y_test)],\n",
    "'Training_Time':[total_time1, total_time2, total_time3, total_time4, total_time5, total_time6, total_time7, total_time8, \n",
    "                 total_time9, total_time10]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>Model_Applied</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Training_Time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic_Regression</td>\n",
       "      <td>0.549020</td>\n",
       "      <td>0.011977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Random_Forest</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.104719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Decision_tree</td>\n",
       "      <td>0.647059</td>\n",
       "      <td>0.001995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BernoulliNB</td>\n",
       "      <td>0.558824</td>\n",
       "      <td>0.003010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GausianNB</td>\n",
       "      <td>0.578431</td>\n",
       "      <td>0.002993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>MultinomialNB</td>\n",
       "      <td>0.509804</td>\n",
       "      <td>0.001990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>KNN</td>\n",
       "      <td>0.568627</td>\n",
       "      <td>0.001953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>SVC</td>\n",
       "      <td>0.598039</td>\n",
       "      <td>31.382103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Linear_SVC</td>\n",
       "      <td>0.549020</td>\n",
       "      <td>0.030976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Ridge_Classifier</td>\n",
       "      <td>0.549020</td>\n",
       "      <td>0.175531</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Model_Applied  Accuracy  Training_Time\n",
       "0  Logistic_Regression  0.549020       0.011977\n",
       "1        Random_Forest  0.500000       0.104719\n",
       "2        Decision_tree  0.647059       0.001995\n",
       "3          BernoulliNB  0.558824       0.003010\n",
       "4            GausianNB  0.578431       0.002993\n",
       "5        MultinomialNB  0.509804       0.001990\n",
       "6                  KNN  0.568627       0.001953\n",
       "7                  SVC  0.598039      31.382103\n",
       "8           Linear_SVC  0.549020       0.030976\n",
       "9     Ridge_Classifier  0.549020       0.175531"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Accurcy comparison of different Models'}, xlabel='Model_Applied', ylabel='Accuracy'>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_model.plot(kind='bar',x='Model_Applied', ylim=[0,1] , y='Accuracy', figsize=(10,10) , ylabel='Accuracy', title='Accurcy comparison of different Models')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Training time comparison of different Models'}, xlabel='Model_Applied', ylabel='Training Time'>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_model.plot(kind='bar',x='Model_Applied', ylim=[0,0.14] , y='Training_Time', figsize=(10,10), ylabel='Training Time', title='Training time comparison of different Models')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pickle as pkl\n",
    "\n",
    "with open('winner.pkl', 'wb') as f:\n",
    "    pkl.dump(alg3, f)\n",
    "\n",
    "with open('winner.pkl', 'rb') as f:\n",
    "    model = pkl.load(f)\n",
    "\n",
    "model.predict(test)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}