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

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Rinku Dulloo, Snehal Godse,Vaishali Suryawanshi, Sonali Ingole,Pratik Yewale, Nitin Dhande


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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