• Patna Adilakshmi
• Dr. B. Sreenivasa Rao
• Dr. Pogadadanda Veeraiah

As IPL attracted more supporters, the quest for precise match predictions emerged for its significance to team planning and spectators’ interest. This project applies machine learning algorithms on IPL datasets of past seasons encompassing team statistics, location, and score results. The datasets are cleaned, visualized, and relevant features are extracted that are expected to be good predictors. Logistic Regression, Random forest, and Gradient Boosting were the chosen models to predict the outcomes of the matches. With a success rate of 89.15%, Random Forest turned out to be the best overall model. The system not only forecasts results but makes recommendations regarding players and teams, thus allowing better choices in the next seasons of the IPL.

IPL, Logistic Regression, Gradient Boosting

"OPTIMIZING IPL MATCH PREDICTIONS WITH MACHINE LEARNING TECHNIQUES", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 1, page no.b186-b192, January-2025, Available :http://www.jetir.org/papers/JETIR2501145.pdf

"OPTIMIZING IPL MATCH PREDICTIONS WITH MACHINE LEARNING TECHNIQUES", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 1, page no. ppb186-b192, January-2025, Available at : http://www.jetir.org/papers/JETIR2501145.pdf