Day 03 — Part 1: Feature Engineering¶
Feature engineering is the single highest-leverage skill in applied machine learning. A mediocre algorithm on great features beats a state-of-the-art algorithm on raw columns almost every time. Today you will learn how to transform raw data into signals your model can actually use.
Estimated time: 3.5–4 hours reading + exercises Difficulty: Intermediate Prerequisites: Pandas, EDA, basic sklearn
Session Overview
Difficulty: Intermediate Reading time: ~2.5 hours | Exercises: ~2 hours Prerequisites: Pandas (merge, groupby, apply) · Classification and regression basics · Missing value handling · EDA workflow (Week 01 Day 05)
Session Map¶
| # | Topic | What You Will Learn | File |
|---|---|---|---|
| 1 | Feature Engineering Overview | The workflow, why features beat algorithms, leakage intuition | 01-feature-engineering-overview.md |
| 2 | Numeric Features | log transforms, binning, scaling, polynomial and interaction terms | 02-numeric-features.md |
| 3 | Categorical Features | OHE, target encoding, frequency encoding, handling rare categories | 03-categorical-features.md |
| 4 | Datetime and Text Features | Extracting time signals, cyclical encoding, TF-IDF | 04-datetime-and-text-features.md |
| 5 | Pipelines and Leakage | ColumnTransformer, Pipeline, what leakage is and how to prevent it | 05-pipelines-and-leakage.md |
| 6 | Exercises | Three-tier practice: warm-up → main → stretch | 06-exercises.md |
Key Questions This Session Answers¶
- Why does feature engineering matter more than model selection for tabular data?
- When should you log-transform a numeric column, and when does it hurt?
- What is the dummy variable trap, and how do you avoid it?
- Why is target encoding dangerous, and how do you do it safely?
- What exactly is data leakage, and how does a sklearn Pipeline prevent it?
Start Here
Begin with 01-feature-engineering-overview to understand the mental model before diving into specific techniques. Each section builds on the previous one.