NBAPositionify: Leveraging Data Mining Techniques to Classify Professional Basketball Players into Positions

[Paper] [Slides]

This repository contains all the material for the CS 2756 (Principles of Data Mining) term project. The course was taught by Dr. Xiaowei Jia at the Univeristy of Pittsburgh during the spring 2024 semester. This project was developed by Shinwoo Kim, Robbie Fishel, and Birju Patel.

The objective is to project is to use various data mining techniques to construct an effective classifier that can predict the position of a professional basketball player based on their statistics. The dataset used for this project is the "NBA Players Stats Since 1950" dataset, which was obtained from Kaggle. The dataset contains various statistics for 4500 professional basketball players.

Requirements

The following Python packages are required to run the code in this repository:

• pandas
• numpy
• matplotlib
• scikit-learn
• pydotplus
• seaborn
• xgboost
• kaggle

These packages can be installed using the following commands:

pip install <package name>