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@article{178313,
        author = {Shrisaiprasad Jagannath Hema and Prof. Anamika Shukla},
        title = {A Dual-Platform Approach for Cricket Match Prediction using Machine Learning and Visual Analytics},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {8603-8606},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=178313},
        abstract = {Cricket analytics has gained significant 
momentum in recent years due to the surge in available 
sports data and advancements in machine learning. This 
research presents a novel dual-platform framework that 
integrates Python-based predictive modeling with 
Power BI-powered visual analytics for cricket match 
prediction. Historical data comprising team statistics, 
individual player metrics, toss decisions, and venue 
characteristics were extracted and preprocessed to train 
machine learning models, including Random Forest and 
XGBoost classifiers.  
The prediction system achieved an accuracy of over 
85% on test datasets. In parallel, dynamic dashboards 
were developed in Power BI to provide an interactive 
interface for analyzing team performances, player 
statistics, and match forecasts. This combination of 
predictive intelligence and visual storytelling bridges 
the gap between raw data interpretation and strategic 
cricket decision-making. The system is intended to assist 
analysts, coaches, fantasy league players, and 
enthusiasts in making data-driven decisions. This paper 
discusses 
the 
methodology, 
implementation, 
performance evaluation, and future enhancement 
possibilities, emphasizing the synergy between artificial 
intelligence and business intelligence in sports.},
        keywords = {Cricket analytics, match prediction,  machine learning, Power BI, data visualization,  ensemble models, Python, sports intelligence, Random  Forest, XGBoost.},
        month = {May},
        }