dgrubis/nba-draft-model-2018

Jupyter notebook that outlines the process of creating a machine learning predictive model. Predicts the peak "Wins Shared" by the current draft prospects based on numerous features such as college stats, projected draft pick, physical profile and age. I try out multiple models and pick the best performing one for the data from my judgement.

NBA-Draft-Model-2018

Jupyter notebook that outlines the process of creating a machine learning predictive model. Predicts the peak "Wins Shared" by the current draft prospects based on numerous features such as college stats, projected draft pick, physical profile and age. I try out multiple models and pick the best performing one for the data from my judgement.

Uses linear regression, a neural network multi-layer perceptron regressor, ridge regression, lasso regression and a linear support vector regression.

All models were created with scikit-learn.

Database was built from scratch from RealGm, Basketball Reference and NBA.com.

*Since database was built from scratch I don't have as much data points as I'd like so model results experience pretty significant variance.

*Wins shared also is a metric that has flaws so not a perfect measure for predicting NBA success.

"draft_model_cleaning" is a python file where I merged and exported csv files to create the database.