https://github.com/nfordumass/nbafinalsmvp_ml

A python machine learning analysis of who should've won NBA Finals MVP each year.
https://github.com/nfordumass/nbafinalsmvp_ml

jupyter-notebook machine-learning python scikit-learn

Last synced: 1 day ago
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A python machine learning analysis of who should've won NBA Finals MVP each year.

• Host: GitHub
• URL: https://github.com/nfordumass/nbafinalsmvp_ml
• Owner: NFordUMass
• Created: 2024-07-17T22:51:32.000Z (7 months ago)
• Default Branch: main
• Last Pushed: 2024-07-30T01:41:03.000Z (6 months ago)
• Last Synced: 2025-02-02T06:17:15.535Z (1 day ago)
• Topics: jupyter-notebook, machine-learning, python, scikit-learn
• Language: HTML
• Homepage:
• Size: 2.33 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
## Who should've won NBA Finals MVP?

### A Python Machine Learning Analysis

### Directory

#### What matters

[finalsmvp.ipynb](finalsmvp.ipynb) has a walk-through of the data collection, processing, and machine learning itself.

[output](output) contains several csvs created at different key points of the notebook.

[tableau](tableau) shows some of the output I was able to visualize.

 


#### What does not

[csvs](csvs) contains, well, csvs, of the cumulative series box scores for each finals winning team.

[series](series) contains the html pulled from basketball reference.

[helper_funcs.ipynb](helper_funcs.ipynb) contains much of the backend helper functions used to pull the data. This file can mostly be ignored for the non-technical viewer.

[README.md](README.md) well I suppose if you read this far, then this is inaptly categorized.

### Development

we use python

### Next Steps

...