Perform Feature Engineering, Data Preprocessing, and Model Selection for Optimal Performance

Learning Objectives

At the end of this theory note, trainees must be able to:

1. Clean and preprocess raw data to address missing values, outliers, and inconsistencies.
2. Apply feature engineering techniques including transformation, scaling, and encoding.
3. Apply feature selection and dimensionality reduction to optimise the input feature set.
4. Compare candidate models using defined performance and suitability criteria.
5. Construct and document an end-to-end preprocessing and model selection pipeline.