This project builds a predictive model to forecast NBA game outcomes based on past performance statistics. Using Python and machine learning techniques, the model analyzes a team's recent games and applies a ridge regression model to predict the likelihood of winning or losing against a specific opponent. The model achieves a 64% accuracy rate, making it a valuable tool for analyzing NBA matchups.
This NBA Game Predictor project leverages statistical data from teams’ past performances to forecast game results. The model is trained using key performance metrics such as field goals, minutes played, three-point attempts, and shot attempts from each team’s last 10 games against specific opponents. By using pandas, scikit-learn, and numpy in Jupyter Notebook, the data is prepared, analyzed, and fed into a ridge regression model.
- Python: Core programming language for data processing and model training.
- pandas: For data manipulation and preparation.
- scikit-learn: Machine learning library used to implement the ridge regression model.
- numpy: For numerical operations and handling data arrays.
- Jupyter Notebook: Environment for developing and visualizing the model.
- Predictive Accuracy: Achieves 64% accuracy in predicting NBA game outcomes based on historical data.
- Data-Driven Insights: Uses a team’s last 10 games against specific opponents to build meaningful predictions.