https://github.com/jyablonski/nba_elt_mlflow

ML Pipeline for NBA ELT Project
https://github.com/jyablonski/nba_elt_mlflow

python scikit-learn

Last synced: 5 months ago
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ML Pipeline for NBA ELT Project

• Host: GitHub
• URL: https://github.com/jyablonski/nba_elt_mlflow
• Owner: jyablonski
• Created: 2022-03-03T23:09:10.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2026-01-02T17:59:37.000Z (6 months ago)
• Last Synced: 2026-01-09T04:48:16.322Z (6 months ago)
• Topics: python, scikit-learn
• Language: Python
• Homepage:
• Size: 929 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# ML Pipeline for NBA ELT Project

![Workflows](https://github.com/jyablonski/nba_elt_mlflow/actions/workflows/ci_cd.yml/badge.svg)

![Coverage Status](https://coveralls.io/repos/github/jyablonski/nba_elt_mlflow/badge.svg)

## ML Pipeline

The ML Pipeline pulls input data built by dbt for today's NBA Games and uses a Logistic Regression Model to generate Win Prediction %s for every team.

These Win Predictions are served on the Dash Server as well as the REST API.

## Tests

To run tests locally, run `make test`.

The same Test Suite is ran after every commit on a PR via GitHub Actions.

## Project

![nba_pipeline_diagram](https://github.com/jyablonski/nba_elt_mlflow/assets/16946556/b66284b0-147a-449c-98e4-5ac269cf5a55)

1. Links to other Repos providing infrastructure for this Project

   - [Dash Server](https://github.com/jyablonski/nba_elt_dashboard)

   - [Ingestion Script](https://github.com/jyablonski/nba_elt_ingestion)

   - [dbt](https://github.com/jyablonski/nba_elt_dbt)

   - [Terraform](https://github.com/jyablonski/aws_terraform)

   - [REST API](https://github.com/jyablonski/nba_elt_rest_api)

   - [Internal Documentation](https://github.com/jyablonski/doqs)