In [21]:

import pandas as pd import numpy as np import seaborn as sns sns.set_style("whitegrid") import matplotlib.pyplot as plt import sklearn

In [22]:

data = pd.read_csv("matches.csv")

ANALYZING DATA ROWS

In [23]:

data.head()

Out[23]:

id Season city date team1 team2 toss_winner toss_decision result dl_applied winner win_by_runs win_by_wickets player_of_match venue umpire1 umpire2 umpire3 0 1 IPL-2017 Hyderabad 05-04-2017 Sunrisers Hyderabad Royal Challengers Bangalore Royal Challengers Bangalore field normal 0 Sunrisers Hyderabad 35 0 Yuvraj Singh Rajiv Gandhi International Stadium, Uppal AY Dandekar NJ Llong NaN 1 2 IPL-2017 Pune 06-04-2017 Mumbai Indians Rising Pune Supergiant Rising Pune Supergiant field normal 0 Rising Pune Supergiant 0 7 SPD Smith Maharashtra Cricket Association Stadium A Nand Kishore S Ravi NaN 2 3 IPL-2017 Rajkot 07-04-2017 Gujarat Lions Kolkata Knight Riders Kolkata Knight Riders field normal 0 Kolkata Knight Riders 0 10 CA Lynn Saurashtra Cricket Association Stadium Nitin Menon CK Nandan NaN 3 4 IPL-2017 Indore 08-04-2017 Rising Pune Supergiant Kings XI Punjab Kings XI Punjab field normal 0 Kings XI Punjab 0 6 GJ Maxwell Holkar Cricket Stadium AK Chaudhary C Shamshuddin NaN 4 5 IPL-2017 Bangalore 08-04-2017 Royal Challengers Bangalore Delhi Daredevils Royal Challengers Bangalore bat normal 0 Royal Challengers Bangalore 15 0 KM Jadhav M Chinnaswamy Stadium NaN NaN NaN

SUMMARY OF IPL DATA SET

In [24]:

data.describe()

Out[24]:

id dl_applied win_by_runs win_by_wickets count 756.000000 756.000000 756.000000 756.000000 mean 1792.178571 0.025132 13.283069 3.350529 std 3464.478148 0.156630 23.471144 3.387963 min 1.000000 0.000000 0.000000 0.000000 25% 189.750000 0.000000 0.000000 0.000000 50% 378.500000 0.000000 0.000000 4.000000 75% 567.250000 0.000000 19.000000 6.000000 max 11415.000000 1.000000 146.000000 10.000000

In [25]:

data.isnull().sum()

Out[25]:

id 0 Season 0 city 7 date 0 team1 0 team2 0 toss_winner 0 toss_decision 0 result 0 dl_applied 0 winner 4 win_by_runs 0 win_by_wickets 0 player_of_match 4 venue 0 umpire1 2 umpire2 2 umpire3 637 dtype: int64

REMOVING NULL VALUES

In [26]:

data = data.iloc[:,:-1] data.dropna(inplace=True)

In [27]:

data["team1"].unique()

Out[27]:

array(['Sunrisers Hyderabad', 'Mumbai Indians', 'Gujarat Lions', 'Rising Pune Supergiant', 'Kolkata Knight Riders', 'Royal Challengers Bangalore', 'Delhi Daredevils', 'Kings XI Punjab', 'Chennai Super Kings', 'Rajasthan Royals', 'Deccan Chargers', 'Kochi Tuskers Kerala', 'Pune Warriors', 'Rising Pune Supergiants', 'Delhi Capitals'], dtype=object)

In [28]:

plt.figure(figsize = (10,6)) sns.countplot(y = 'winner',data = data,order= data['winner'].value_counts().index) plt.xlabel('Wins') plt.ylabel('Team') plt.title('Number of IPL matches won by each team')#

Out[28]:

Text(0.5, 1.0, 'Number of IPL matches won by each team')

In [29]:

plt.figure(figsize = (10,6)) sns.countplot(x = "toss_decision", data=data) plt.xlabel('Toss Decision',fontsize=12) plt.ylabel('Count',fontsize=12) plt.title('Toss Decision')

Out[29]:

Text(0.5, 1.0, 'Toss Decision')

In [30]:

x= ["city", "toss_decision", "result", "dl_applied"] for i in x: print(data[i].unique()) print(data[i].value_counts()) ['Hyderabad' 'Pune' 'Rajkot' 'Indore' 'Mumbai' 'Kolkata' 'Bangalore' 'Delhi' 'Chandigarh' 'Kanpur' 'Jaipur' 'Chennai' 'Cape Town' 'Port Elizabeth' 'Durban' 'Centurion' 'East London' 'Johannesburg' 'Kimberley' 'Bloemfontein' 'Ahmedabad' 'Cuttack' 'Nagpur' 'Dharamsala' 'Kochi' 'Visakhapatnam' 'Raipur' 'Ranchi' 'Abu Dhabi' 'Sharjah' 'Mohali' 'Bengaluru'] Mumbai 101 Kolkata 77 Delhi 73 Hyderabad 64 Bangalore 63 Chennai 57 Jaipur 47 Chandigarh 46 Pune 38 Durban 15 Bengaluru 13 Centurion 12 Ahmedabad 12 Visakhapatnam 12 Rajkot 10 Mohali 10 Indore 9 Dharamsala 9 Johannesburg 8 Cuttack 7 Ranchi 7 Port Elizabeth 7 Cape Town 7 Abu Dhabi 7 Sharjah 6 Raipur 6 Kochi 5 Kanpur 4 Nagpur 3 Kimberley 3 East London 3 Bloemfontein 2 Name: city, dtype: int64 ['field' 'bat'] field 455 bat 288 Name: toss_decision, dtype: int64 ['normal' 'tie'] normal 734 tie 9 Name: result, dtype: int64 [0 1] 0 724 1 19 Name: dl_applied, dtype: int64

In [31]:

x= data.drop(["winner"], axis=1) y = data["winner"]

In [32]:

data.drop(["id", "Season","city","date", "player_of_match", 'umpire1', "venue", "umpire2"], axis=1, inplace=True)

In [33]:

x = data.drop(["winner"], axis=1) y = data["winner"]

In [34]:

x= pd.get_dummies(x, ["team1","team2", "toss_winner", "toss_decision", "result"], drop_first = True)

In [35]:

from sklearn.preprocessing import LabelEncoder le = LabelEncoder() y = le.fit_transform(y)

In [36]:

from sklearn.model_selection import train_test_split x_train, x_test, y_train, y_test = train_test_split(x, y, train_size = 0.8)

In [37]:

from sklearn.ensemble import RandomForestClassifier model = RandomForestClassifier(n_estimators=200,min_samples_split=3,max_features = "auto")

In [38]:

model.fit(x_train, y_train)

Out[38]:

RandomForestClassifier(min_samples_split=3, n_estimators=200)

In [39]:

y_pred = model.predict(x_test)

In [40]:

import csv from sklearn.metrics import accuracy_score ac = accuracy_score(y_pred, y_test) print(ac) final=pd.DataFrame(y_pred) final.to_csv('http://localhost:8888/tree/ed%20project/final.csv')

Out[42]:

<bound method DataFrame.info of team1 team2 \ 0 Sunrisers Hyderabad Royal Challengers Bangalore 1 Mumbai Indians Rising Pune Supergiant 2 Gujarat Lions Kolkata Knight Riders 3 Rising Pune Supergiant Kings XI Punjab 5 Gujarat Lions Sunrisers Hyderabad .. ... ... 750 Chennai Super Kings Kings XI Punjab 751 Kolkata Knight Riders Mumbai Indians 752 Chennai Super Kings Mumbai Indians 754 Delhi Capitals Chennai Super Kings 755 Mumbai Indians Chennai Super Kings toss_winner toss_decision result dl_applied \ 0 Royal Challengers Bangalore field normal 0 1 Rising Pune Supergiant field normal 0 2 Kolkata Knight Riders field normal 0 3 Kings XI Punjab field normal 0 5 Sunrisers Hyderabad field normal 0 .. ... ... ... ... 750 Kings XI Punjab field normal 0 751 Mumbai Indians field normal 0 752 Chennai Super Kings bat normal 0 754 Chennai Super Kings field normal 0 755 Mumbai Indians bat normal 0 winner win_by_runs win_by_wickets 0 Sunrisers Hyderabad 35 0 1 Rising Pune Supergiant 0 7 2 Kolkata Knight Riders 0 10 3 Kings XI Punjab 0 6 5 Sunrisers Hyderabad 0 9 .. ... ... ... 750 Kings XI Punjab 0 6 751 Mumbai Indians 0 9 752 Mumbai Indians 0 6 754 Chennai Super Kings 0 6 755 Mumbai Indians 1 0 [743 rows x 9 columns]>