EDA & Feature Engineering in Machine Learning | A Complete Guide in 15 Mins

What is EDA in machine learning, and how does feature engineering improve model accuracy? This guide walks through the full data preprocessing workflow — from exploratory data analysis and data transformation to handling outliers and feature selection — in under 15 minutes. Ideal for data science students, ML practitioners, and anyone preparing data for a Python-based machine learning project. Chapter: - 0:00 Introduction to EDA - 2:20 Univariate Analysis - 3:00 Multivariate Analysis - 4:15 Why Feature Selection Matters - 6:00 Filter Methods & SelectKBest - 7:30 Visualizing Feature Importance - 9:30 Data Transformation Overview - 11:00 Log Transformation for Skewed Data - 11:45 Outlier Clipping - 12:30 Scaling: MinMax vs Standard vs Robust Scaler 🗒️ Articles mentioned in the video: - Data Transformation: https://datavisualdesign.substack.com/p/data-science-101-data-transformation - Feature Selection: https://datavisualdesign.substack.com/p/feature-selection-in-machine-learning - EDA: https://datavisualdesign.substack.com/p/data-science-101-semi-automated-exploratory 🎬 If you want to build video like this using Hera AI, get 15% off using this link: https://hera.cello.so/gpjkXUu6nk2 ☕ Stay Connected: - Support our channel: https://buymeacoffee.com/visualdesign - Substack: https://substack.com/@datavisualdesign - X: https://x.com/GongDestin - Towards Data Science: https://towardsdatascience.com/author/destingong/ - Medium: https://medium.com/@destingong #eda #machinelearning #ml #ai #datascience #featureengineering