COMPREHENSIVE DATA ANALYSIS AND PREDICTION ON INDIAN PREMIER LEAGUE USING MACHINE LEARNING TECHNIQUES

Alt Text: Comprehensive Data Analysis and Prediction on Indian Premier League Using Machine Learning Techniques
Title: Comprehensive Data Analysis and Prediction on Indian Premier League Using Machine Learning Techniques
Caption: Using machine learning to predict IPL match outcomes based on player statistics and historical performance.
Description: This study applies machine learning algorithms—Support Vector Machines, Random Forest Classifier, Logistic Regression, and K-Nearest Neighbor—to predict Indian Premier League match outcomes. With an impressive 88.10% accuracy using the Random Forest algorithm, the study emphasizes the power of data-driven cricket analytics in decision-making for traders and sponsors.
Keywords: IPL Prediction, Machine Learning, Cricket Analytics, Random Forest, Player Statistics

International Journal of Engineering and Techniques – Volume 10 Issue 3, June 2024

Dr. V. Gayatri1, V. Bharathi2
1Associate Professor, Department of Computer Science & Engineering, Geethanjali Institute of Science and Technology, Gangavaram, Andhra Pradesh, India.
2Associate Professor, Department of Computer Science & Engineering, Geethanjali Institute of Science and Technology, Gangavaram, Andhra Pradesh, India.

Abstract

Cricket is India’s most popular sport, and the Indian Premier League (IPL) attracts millions of fans nationwide. Predicting match outcomes is valuable for traders and sponsors, requiring precise analytical models. This study explores machine learning approaches for IPL prediction using Support Vector Machines, Random Forest Classifier, Logistic Regression, and K-Nearest Neighbor. Our experiments revealed that the Random Forest algorithm achieved an exceptional 88.10% accuracy, proving its effectiveness in analyzing player statistics and historical match performance. This research highlights the impact of computational analytics in cricket, guiding stakeholders in decision-making.

Keywords

IPL Prediction, Machine Learning, Cricket Analytics, Random Forest, Player Statistics

How to Cite

Gayatri, V., Bharathi, V., “Comprehensive Data Analysis and Prediction on Indian Premier League Using Machine Learning Techniques,” International Journal of Engineering and Techniques, Volume 10, Issue 3, June 2024. ISSN 2395-1303

Submit your research article: editorijetjournal@gmail.com
Tags: ijet journal, IPL Prediction, Machine Learning in Sports, Cricket Analytics, AI in Cricket, High-Impact Factor Journal

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