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Multi-Layer Perceptron Generation from NBA Box 2019 Data

Application of First-Classification-Then-Regression Analysis Coupled with Stochastic Gradient Boosting

Steps to success:

  • Create an estimate MLP classifier using MLP_E-Classifier.py
  • Create a rank MLP classifier using MLP_R-Classifier.py
  • Create an overall MLP regressor using MLP_Creator.py
  • Apply the overall MLP regressor to holdout data using Final_Applier.py

Miscellanies:

  • MLP_Tester.py: Reads in the pickled MLP and testing sets from MLP_Creator, determines MAPE, and plots the results
  • Feature_Selector.py: Determine which features are best for use when creating MLP
  • Data_Viewer.py: View a 2D line graph comparing one feature to the engagements
  • Good_Words.py: Find the most commonly-used words in captions of pictures in the top 25% of engagements

Data:

  • training_set.csv: Training data with engagements filled in (7766 lines)
  • holdout_set.csv: Testing data with engagements empty (1000 lines)