Exploratory Data Analysis for Machine Learning

Abstract:

Exploratory Data Analysis (EDA) is a foundational step in any machine learning workflow. This section focuses on the practical aspects of retrieving, cleaning, transforming, and exploring data to prepare it for modeling. You will learn how to access data from various sources (CSV, JSON, SQL databases, APIs, and cloud storage), inspect its structure, manage missing values and outliers, encode categorical features, and apply key statistical transformations. The goal is to develop a robust data preprocessing pipeline that ensures data quality and model readiness.