Feature Engineering and Preprocessing

Perform a train-test split and explain its role in preventing data leakage.

Apply feature scaling using normalization (min-max) and standardization (Z-score), and identify when each is appropriate.

Encode categorical variables using one-hot, label, and ordinal encoding, and identify when each is appropriate.

Apply variance thresholding to filter out features with little to no variation.

Use correlation analysis to identify and remove redundant, highly-correlated features.

Apply a complete preprocessing pipeline to a raw real-world dataset, including EDA, cleaning, encoding, scaling, and splitting.