40. Data Preprocessing for Machine Learning (Missing Values, Scaling, Encoding)
40.5 Practice, Tip and Close
Why. Close when you can impute, scale, and encode without leaking test information.
How.
Chapter practice
- Build a 12-row Mauli Dairy fictional table with NaNs in litres, a city column, and distance_km.
- Split, impute litres with train median, scale numeric columns, get_dummies for city.
- Fit LogisticRegression on the processed train matrix; predict on processed test.
- Write one leakage mistake you refused (e.g. scaling before split).
Thodkyaat sangaycha tar (quick recap)
- Impute / scale / encode before many models
- Fit transformers on train only
- StandardScaler for numeric ranges
- get_dummies (or similar) for a few categories
Samajla ka? Next: Chapter 41 — What Is a Neural Network (deep learning starts).