Predicting-Baseball-Statistics

Classification and Regression Applications in Python Using scikit-learn

This repository contains the prediction of baseball statistics using MLB Statcast Metrics.

Goals

• Using MLB Statcast Metrics, summarize and examine baseball statistics.

Classification

• Build and train models to predict home runs and extra-base hits implementing the following approaches:

  • Logistic Regression
  • k-Nearest Neighbors
  • Decision-Classification Tree
  • Random Forest Classification
  • Support Vector Machine Classification
  • XGBoost Classification

• Implement over-sampling for imbalanced data to improve the quality of predictive modeling (i.e., generalizability).

• Apply regularization and cross-validation techniques for model evaluation, selection, and optimization.

Regression

• Build and train models to predict hit distance implementing the following approaches:

  • Linear Regression
  • Decision-Regression Tree
  • Random Forest Regression

• Apply regularization (Ridge, Lasso, Elastic Net) and cross-validation (k-fold) techniques for model evaluation, selection, and optimization.