
This project explores how machine learning models can forecast tennis match outcomes using historical ATP player statistics. I trained and compared multiple models, from logistic regression to neural networks, to identify which approaches produce the most reliable predictions. The final system achieved around 70% accuracy, offering insights into both player performance trends and model selection.

