ML Pipeline Automation — Scikit-Learn & Feature Engineering

I will build a complete scikit-learn ML pipeline for your project — covering automated preprocessing (imputation, encoding, scaling), feature engineering (interaction terms, polynomial features, date decomposition), feature selection, model training, and a pipeline object that applies all preprocessing consistently to new data without leakage. ML pipelines built without scikit-learn's Pipeline object apply preprocessing fitted on training data to test data incorrectly, producing leakage that inflates evaluation metrics and produces models that fail in production — a properly structured pipeline eliminates this risk by design.

The pipeline covers ColumnTransformer for mixed feature types, custom feature engineering transformers, RFECV or SelectFromModel feature selection, GridSearchCV or Optuna hyperparameter optimisation within the pipeline, and a saved pipeline object ready for inference on new data.