Advancing Cricket Analytics: A Comparative Analysis of Decision Trees and Linear Regression for IPL Score Prediction

Abstract

This comprehensive research delves deeply into the intricate application of advanced machine learning (ML) algorithms to predict cricket scores, with a particular focus on the dynamic Indian Premier League (IPL). The paper initiates with a meticulous literature review [1][2] to establish a robust theoretical foundation, guiding the subsequent exploration of predictive modeling methodologies. The methodology section takes a thorough approach to data collection from esteemed sources like ESPN cric info and Kaggle [3]. This exhaustive data gathering ensures the acquisition of a diverse and comprehensive dataset, incorporating player statistics, team performance metrics, venue details, and match outcomes. The dataset is then subjected to a rigorous preprocessing phase, following the principles outlined in "Feature Engineering for ML" [4], encompassing data cleaning, handling missing values, and feature engineering.

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