Predicting-Baseball-Statistics

Classification and Regression Applications in Python Using scikit-learn and TensorFlow-Keras

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 Classification
  • Decision Trees Classification
  • Random Forests Classification
  • Support Vector Machines Classification
  • XGBoost Classification
  • Neural Networks 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 Trees Regression
  • Random Forests Regression

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