• Saransh Rathore
• Deepesh Manhar
• Amit Kumar
• Anuyoksha Singh

The Olympic Games produce extensive data covering athlete performance, historical trends, country-wise participation, and medal distribution. Leveraging machine learning techniques, this study aims to uncover patterns, predict outcomes, and generate insights beneficial to athletes, coaches, and sports analysts. Various machine learning methods, including classification, regression, and clustering, are applied to analyze Olympic datasets. The key objectives include forecasting medal winners based on past performances, identifying participation trends across nations, and evaluating the influence of socio-economic factors on Olympic success. The study involves data preprocessing, feature engineering, and the evaluation of models such as decision trees, support vector machines, and deep learning networks. The findings highlight the potential of predictive analytics in sports, demonstrating how machine learning can enhance strategic decision-making and optimize athletic performance.

Olympic Games, Machine Learning, Sports Analytics, Predictive Modeling, Athlete Performance, Medal Prediction, Data Mining, Classification, Regression, Clustering, Sports Strategy, Feature Engineering, Decision Trees, Support Vector Machines, Deep Learning, Data-Driven Insights.

"Olympic data analysis : A Machine learning approach", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 3, page no.g90-g93, March-2025, Available :http://www.jetir.org/papers/JETIR2503611.pdf

"Olympic data analysis : A Machine learning approach", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 3, page no. ppg90-g93, March-2025, Available at : http://www.jetir.org/papers/JETIR2503611.pdf