lindelani-3/nba-statistics

Dimensionality Reduction and Analysis of NBA Player Statistics

This repository contains the Jupyter Notebook and associated data for my project on analyzing NBA player statistics.

Project Overview

This project aims to explore complex NBA player performance data using advanced dimensionality reduction techniques including t-SNE, MDS (Multidimensional Scaling), and UMAP (Uniform Manifold Approximation and Projection). The goal is to extract and visualize meaningful insights from the data while comparing the effectiveness of these techniques.

Key Objectives

• Data Preprocessing: Implementing robust data cleaning and preparation methods.
• Advanced Dimensionality Reduction: Utilizing t-SNE, MDS, and UMAP for in-depth data analysis.
• Comparative Analysis: Evaluating the results of each technique to understand their specific strengths and applications in sports data.
• Visualization: Creating clear and informative visualizations to represent the findings.

Technologies Used

• Python
• Pandas and Numpy for data manipulation
• SKLearn and UMAP for dimensionality reduction
• Matplotlib and Seaborn for visualization

Datasets

The datasets include nba_2022-23_all_stats_with_salary.csv, nba_salaries_clean.csv, and nba_stats_clean.csv, providing extensive details on player performances and salaries.

Author

Lindelani Delisa Dlamini

License

This project is licensed under the MIT License - see the LICENSE file for details.

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