This project utilizes machine learning models to predict NBA player performance using player statistics from the 2023 season. By analyzing key metrics such as points, rebounds, assists, and playing time, the project aims to provide insights into player performance. The data used for this analysis was sourced from Kaggle and includes over 500 player records.
- Data Source: NBA player statistics from the 2023 season.
- Data Cleaning: Involves handling missing data, removing inconsistencies, and selecting important features (e.g., points, assists, rebounds).
- Machine Learning Models:
- Linear Regression
- Decision Tree Regressor
- Random Forest Regressor
- K-Nearest Neighbors Regressor
- Evaluation Metrics: R-squared, F1 Score, Cross-Validation.
- Data Visualization: Use of Matplotlib, Seaborn, and Plotly for visual exploration and analysis.
The following Python libraries are required to run the code:
- pandas
- numpy
- matplotlib
- seaborn
- plotly
- scikit-learn