Clustering on NBA League From 2003 to 2018

Project Overview

Using unsupervised machine learning to analyze player decline in the NBA based on the player archetype. This analysis will provide assistance to NBA teams for roster building and player adjustments.

The analysis will aim to provide insight on the following questions:

1. Determining the new player archytpe in the modern day position list.
2. Will adjusting the athletes playstyle help improve performance.

Datasets

• Kaggle Player Data
  • A data sample has been drafted.

Environment

cd db && python3 -m venv env

source env/bin/activate

pip install -r requirements.txt

cd db && export PYTHONPATH=$PWD

Additional packages

cd db

pip freeze > requirements.txt

FastAPI - Backend

cd db && source env/bin/activate

cd db && export PYTHONPATH=$PWD

cd db && python app/main.py

http://localhost:8000/docs

Run /transformLoad

Run /ml/pca

Run /ml/timeseries

Make sure Mongo is running

Frontend - Plotly Dash

cd db && source env/bin/activate

cd client && python app.py

Upload the file "TO_UPLOAD_FULL_SeasonsDataRaw" for a full test.

MongoDB

mongo

show dbs

use players

db.Cleaned_Dataset.find()

Team Project - UofT Bootcamp

Communication Protocols

• Slack: Team discussions, questions, suggestisons & resource sharing.
• Google Meet: Team meetings, discussions
• Trello: Work organization, scheduling

Source: https://github.com/joshb738/NBA_Player_Analysis