IPL Team Win Prediction Project Using Machine Learning
This article was published as a part of the Data Science Blogathon.
Machine Learning and Data Science are one of the fastest-growing technological fields. This field results in amazing changes in the medical field, production, robotics etc. The main reason for the advancement in this field is the increase in the computational power and availability of large amounts of data. In Data Science, this data is analyzed and made suitable for creating machine learning models and products.
In today’s article, we are going to discuss the IPL team win prediction. Based on some match stats, we’re predicting who is the winner of an IPL match. Through this project, you will get familiar with the exploratory data analysis and feature engineering techniques that need to be applied to process data.
So let’s go to the dataset.
The data set we are using here is the IPL dataset, which contains the details regarding the winner and match stats. It contains the details like teams played, winner, venue of the match, won by how many wickets and runs, toss decision, whether DLS applied or not, names of the umpires etc. Performing exploratory data analysis and data engineering on this data is very important.
So let’s start the implementation of the IPL team win predictor model.
As usual, the first step is importing all the required libraries.
import pandas as pd import numpy as np import seaborn as sns sns.set_style("whitegrid") import matplotlib.pyplot as plt import sklearn
So let’s import the dataset. We’ve already discussed the dataset.
data = pd.read_csv("matches.csv")
Analyzing the first 5 rows of the dataset
Let’s get a brief summary of the IPL dataset.
Checking whether there are any null values present in the dataset.
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
As you can see, the values of umpire3 are null in almost all rows, so we are dropping the column umpire3. And also dropped some rows containing the null values after removing the umpire3 column.
data = data.iloc[:,:-1] data.dropna(inplace=True)
Now let’s look into the total teams listed in this dataset.
array(['Sunrisers Hyderabad', 'Mumbai Indians', 'Gujarat Lions', 'Rising Pune Supergiant', 'Royal Challengers Bangalore', 'Kolkata Knight Riders', 'Delhi Capitals', 'Kings XI Punjab', 'Chennai Super Kings', 'Rajasthan Royals', 'Kochi Tuskers Kerala', 'Pune Warriors', 'Rising Pune Supergiants'], dtype=object)
Here you can see the name Delhi Daredevils and Delhi Capitals; Delhi Daredevils is the old name of the Delhi Capitals. Similarly, Decan Chargers is the old name of Sunrisers Hyderabad. So we are changing the old name to the newer one.
#for Delhi Capitals data['team1']=data['team1'].str.replace('Delhi Daredevils','Delhi Capitals') data['team2']=data['team2'].str.replace('Delhi Daredevils','Delhi Capitals') data['winner']=data['winner'].str.replace('Delhi Daredevils','Delhi Capitals') #for sunrisers Hyderabad data['team1']=data['team1'].str.replace('Deccan Chargers','Sunrisers Hyderabad') data['team2']=data['team2'].str.replace('Deccan Chargers','Sunrisers Hyderabad') data['winner']=data['winner'].str.replace('Deccan Chargers','Sunrisers Hyderabad')
Number of IPL matches won by each team.
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')
Total number of matches played in a different stadium
plt.figure(figsize = (10,6)) sns.countplot(y = 'venue',data = data,order = data['venue'].value_counts().iloc[:10].index) plt.xlabel('No of matches',fontsize=12) plt.ylabel('Venue',fontsize=12) plt.title('Total Number of matches played in different stadium')
The decision was taken by the toss winning team.
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')
Now let’s check the unique values presented in each feature.
x = ["city", "toss_decision", "result", "dl_applied"] for i in x: print("------------") 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
We don’t need all the features or columns in order to create the model. It will reduce model accuracy, so we are dropping some of the features that don’t affect our result.
data.drop(["id", "Season","city","date", "player_of_match", 'umpire1', "venue", "umpire2"], axis=1, inplace=True)
Our data looks like this.
We can convert our data into dependent and independent.
X = data.drop(["winner"], axis=1) y = data["winner"]
Several categorical values are present in the input data, so we are converting them into numerical values using the pandas, get_dummies method.
X = pd.get_dummies(X, ["team1","team2", "toss_winner", "toss_decision", "result"], drop_first = True)
The output data is also a categorical value, so we are converting it into numerical using LabelEncoder of sklearn.
from sklearn.preprocessing import LabelEncoder le = LabelEncoder() y = le.fit_transform(y)
Now let’s convert our data into a training set in order to create the model and test set for evaluating the created model.
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)
Model Creation and Evaluation
The next and most important step in the model creation step. So we are using Random Forest Classification, Logistic Regression and Decision Tree Classification for this.
from sklearn.ensemble import RandomForestClassifier
model = RandomForestClassifier(n_estimators=200,min_samples_split=3, max_features = "auto")
Training the random forest classifier model
Predicting the model with x_test values and saving it as y_pred
y_pred = model.predict(x_test)
Using the accuracy score of sklearn, we are evaluating the predicted result and accuracy of the model
from sklearn.metrics import accuracy_score ac = accuracy_score(y_pred, y_test)
#output - 0.92
The accuracy of the test set is about 92% which is good. It’s all about the prediction and Evaluation
This article shows the implementation of the IPL Win prediction model. You have got an insight into how to analyze a given raw data and convert that into useful features by removing unwanted features, that is, performing exploratory data analysis. So let’s identify the main key points from the article.
- IPL Dataset analysis: This analysis includes checking for null values and replacing them, describing the dataset’s feature columns, and analyzing each feature.
- Visualization of data: Done a graphical representation of the dataset in order to get an understanding of the teams, matches and the toss decision of the IPL Matches
- Performed preprocessing techniques on the IPL Data: Performed several feature engineering techniques in order to make the dataset suitable for making the model. Encoding for converting categorical features to numerical features and techniques to avoid null values are used.
- Model creation and Evaluation: This was the main part of the project, and we used the RandomForestClassifier model. Later we tested this model with the test set for evaluation.
I am hoping that you got an idea regarding the above-mentioned steps. Please make sure that you’re practising and trying to grasp each step. I hope you like my article.
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