Preprocessing Data for Machine Learning - Deep Dive

Dive into a comprehensive 20-minute video tutorial on preprocessing data for machine learning, focusing on logistic regression. Explore the Snape artificial data generator and examine the effects of standardization, encoding, data imbalance, and correlation on your models. Learn about the Variance Inflation Factor and strategies for dealing with multicollinearity. Discover how to handle missing data effectively. Follow along with code examples available on GitHub to enhance your understanding of these crucial preprocessing techniques for logistic regression and improve your machine learning workflows.

Introduction
Snape – Artificial Data Generator
Effects of Standardization
Effects of Encoding
Effects of Data Imbalance
Effects of Correlation
Variance Inflation Factor Explained
Dealing with Multicollinearity
Effects of Missing Data
Summary