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Logistic Regression analysis on an NBA player dataset in Python.

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NBA Logistic Regression

Analysis on an NBA player dataset to determine:

  • How to define a good rebounder, Good Rebounder, (> 8 total rebounds per game).
    • This index was categorical, so I converted it to a numerical column called RebounderNumeric in order to make a prediction on a binary value.
  • What variables in the dataset can be used to make a prediction of whether a player will be a good rebounder.
  • How accurate a prediction can be, and how to tune the model to avoid overfitting.

In order to test the model, I created 3 players with hypothetical statistics based upon the chosen predictor variables:

  • Pos - Position
  • MP - Minutes Played
  • PS/G - Points Scored Per Game
  • AST - Assists Per Game
  • STL - Total Steals Per Game

I stacked the parameters vertically using Numpy into a new test set and ran a prediction using sklearn on RebounderNumeric.

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