40. Data Preprocessing for Machine Learning (Missing Values, Scaling, Encoding)
Chala mitrano, model aadhi kitchen — missing values, scaling, encoding. Train var fit, test la transform. Leakage nako.
What you will learn in this chapter
- Why preprocessing before models
- Impute missing values from train
- StandardScaler the right way
- Simple category encoding
- Practice before neural nets
Fictional data
Blinkit / Amazon Now / Mauli Dairy figures in this chapter are fictional sample data for learning only. They do not show real company numbers. People names are for practice; e-mails use example.com.
Concepts in this chapter
- 40.1Why Preprocessing Before Models
- 40.2Missing Values — Impute Simply
- 40.3StandardScaler
- 40.4Simple Encoding (get_dummies)
- 40.5Practice, Tip and Close
The chapter recap is at the end of the last concept page.