From Raw Data to Insights: A Practical Guide to EDA (Part 1)

Part 1 of a 4-part series: From Data to Decisions Series Context This is a four-part series built the way real machine learning work happens in banks, not the way it is shown in s or notebooks. Part 1 focuses on data understanding and exploratory analysis. This is where most projects silently succeed or fail. Part 2 will turn those insights into features and models, and show why feature engineering still matters than algorithms. Part 3 will move beyond accuracy and into decision logic, thresholds, and explainability, where most AI systems break in production.