Predicting the Outcome of Sports Events

In this mini project, we will develop an AI/ML model to predict the outcomes of sports events, focusing on a specific sport such as soccer or basketball. The project will begin by collecting historical data, including match statistics, player performance metrics, and team rankings. We will preprocess this data to handle missing values and normalize features. Using machine learning algorithms like logistic regression, decision trees, or neural networks, we will train our model to identify patterns that influence game outcomes. The model’s performance will be evaluated using metrics such as accuracy, precision, and recall on a separate test dataset. Additionally, we will implement cross-validation techniques to ensure robustness. Finally, we will visualize the predictions and insights through dashboards, enabling users to explore the model’s predictions and the factors influencing them. This project aims not only to enhance our understanding of predictive modeling in sports but also to provide valuable insights for fans and analysts alike.