International Research Journal of Education and Technology

cricket is the most popular game. The Indian Premier League (IPL) is one of the several series that are contested in the nation. A model with two techniques has been proposed. The first is a scoring prediction, and the second is a prediction of the team winning. Linear regression, logistic regression, decision trees, random forests, gradient boosting regressors, extra tree regressors, and XGB regressors are employed in these for score prediction. This study gathers and analyses IPL data spanning multiple years, including player, match, team, and ball-to-ball information, to generate several conclusions that help improve a player's performance. To forecast the winner, the model employed a supervised machine learning technique. For high accuracy, Extra tree regressor used for good accuracy with 90 % .

Keywords: Linear Regression, logistic regression, decision tree, random forest, gradient boosting regressor, extra tree regressor, XGB regressor IPL Winning Prediction, IPL Score Prediction, ball-to-ball Statistics.