Cricket Meets Machine Learning: A Classification Approach to IPL Match Prediction

Abstract

The Indian Premier League is one of the most well-known Twenty20 cricket tournaments, which produces a large amount of organized match data. The goal of this research paper is to create a classification system based on machine learning that uses past data from the Indian Premier League between 2008 and 2025 to forecast match outcomes. The dataset features characteristics such as team names, the winner of the toss, the decision made by the toss, and the venue. Various data cleaning and transformation operations have been performed to preprocess the dataset, which include dealing with missing values and encoding categorical variables. Several machine learning classifiers like Logistic Regression, Decision Tree, Random Forest, and Support Vector Machine have been utilized to create prediction models. To assess the models' performance, the dataset was split into training and test sets. Several evaluation metrics like accuracy score, confusion matrix, and classification report were utilized to compare various models' effectiveness. According to the experiment results, ensemble techniques like Random Forest provide more accurate predictions than other models.