Feature Engineering For Numerical Data
An animated walkthrough of preparing numerical features for machine learning, covering how to represent data as feature vectors, visually and statistically explore a dataset, and spot outliers. It compares four normalization techniques side by side, shows how binning turns continuous values into categories, and outlines what makes a numerical feature useful. Designed for students and practitioners starting out in data science or machine learning who need intuition before applying these steps in code.
Narrated · 16:9 · Preview before teaching · automatic layout checks do not establish subject accuracy
The prompt that made it
Understand feature vectors. Explore your dataset's potential features visually and mathematically. Identify outliers. Understand four different techniques to normalize numerical data. Understand binning and develop strategies for binning numerical data. Understand the characteristics of good continuous numerical features.
Make the next one in this series
- Compare min-max scaling, z-score, and robust scaling on a dataset with extreme outliers
- Show how one-hot encoding turns categorical features into vectors alongside numerical ones
- Visualize how correlated features can distort distance-based models like k-nearest neighbors
