Project Description

This project will design and train a neural network to predict sports betting outcomes. By analyzing historical sports data, team/player performance statistics, etc., the neural network will provide probabilistic predictions for match outcomes, such as win/loss/draw or over/under scoring.

The project is exciting because it demonstrates how artificial intelligence can enhance decision-making in sports betting, a domain with high stakes and widespread popularity. It’s both educational and fun, as it combines machine learning concepts with real-world applications in sports analytics and gambling. Participants will gain hands-on experience in data preprocessing, neural network architecture design, and evaluation.

Success will be defined by the model achieving at least 60% accuracy in predicting sports outcomes based on historical test data. Additionally, the project will create a user-friendly interface where users can input data (e.g., current odds, team stats) and view the model’s predictions.

Goals

1. Collect and preprocess historical sports data (e.g., NBA, NFL, or soccer matches).
2. Design and implement a neural network for predicting match outcomes.
3. Train the model on historical data and evaluate its performance using accuracy, precision, and recall.
4. Explore the impact of key features (e.g., home-field advantage, player injuries) on predictions.
5. Create a web-based interface for users to input new data and view the model's predictions.