[krishnatejanagarajupalli1976-debug]

W2D1: Feature Engineering & Encoding

Implemented feature engineering and preprocessing techniques:

• LabelEncoder
• OneHotEncoder
• OrdinalEncoder
• StandardScaler
• MinMaxScaler
• RobustScaler
• Before/after scaling distribution plots
• SelectKBest for top 5 feature selection
• Feature leakage considerations

Branch: feat/aiml-W2-krishnateja

[fabclmnt]

Thanks for taking the time to open this.

This doesn't fit the scope of the project. fg-data-profiling is an EDA
library: it describes a dataset and surfaces its problems. Feature
engineering and preprocessing like encoders, scalers, SelectKBest, are
downstream modelling steps that belong in a scikit-learn pipeline, not here.

Scope aside, the notebook is standalone: it sits at the repo root, never
imports the package, and runs on a generated toy dataset, so it doesn't
add to the library, the docs, or the test suite.

The branch name suggests this was meant for a course repository rather
than this one. Closing for now. If you do want to contribute, the open
issues and CONTRIBUTING.md are the place to start.