A Data-Driven Approach to Predict Scores in T20 Cricket Match Using Machine Learning Classifier

Accurate score prediction is essential for teams to develop winning strategies because of the growing popularity of T20 cricket and the significance of setting a challenging target in the first innings. The suggested method entails gathering historical information on T20 matches and applying feature engineering approaches to extract pertinent features. To forecast the first innings score, various regression methods, like XGBoost regression, Lasso regression, and Ridge regression are trained on the dataset. Metrics such as mean absolute error, root mean squared error, and R-squared values are used to assess the performance of the models. The findings demonstrate the potential of machine learning techniques for predicting the first innings score in T20 cricket matches, offering useful information for team strategy. The developed models, implemented codes, and user interface designs are deployed in this link: https://github.com/AST-TheCoder/T20 .