ML Data Preprocessing Pipeline — Feature Engineering & Selection

I will build a complete scikit-learn preprocessing pipeline for your machine learning project — covering missing value imputation, categorical encoding (one-hot, ordinal, target encoding), numerical scaling (standard, robust, or min-max), feature engineering (polynomial, interaction, date decomposition), feature selection (mutual information, correlation filtering, LASSO-based), and train/validation/test splitting with stratification. A scikit-learn Pipeline object ensures preprocessing is fitted on training data only and applied consistently to validation and test sets — the most common source of data leakage in ML projects that are not using a pipeline architecture.

The deliverable includes a Python notebook with the full preprocessing pipeline, a feature importance visualisation, a correlation heatmap, a preprocessing report covering encoding decisions and feature selection rationale, and a clean API for fitting the pipeline and transforming new data for model serving.

This service suits data scientists and ML engineers whose models are underperforming and suspect preprocessing as the cause, and teams starting a new ML project who want the data layer built correctly before model training begins.