Data transformation pipeline
Project phase: Data Engineering and Preparation
You build a reproducible pipeline that turns raw data into the feature set your model trains on: cleaning and type conversion, imputation, encoding, and scaling, plus any derived features, in a fixed rerunnable order. The pipeline is fitted on training data only and applied consistently to validation and test data so no information leaks between splits, with each step justified by why that technique fits the variable in question.
Starting Points
Key Points
- Plan: You can provide a clear design/plan explaining the steps of your pipeline and why you chose specific transformation methods.
- Standards: Your code follows professional data engineering standards (e.g., using Scikit-learn Pipelines or similar) and avoids hard-coded values.
- Validation: You have tested the pipeline with a single command to prove that running raw data through it consistently produces the exact same training set.
- No Leakage: You have demonstrated that your scaling and imputation parameters are derived strictly from the training split.
- Documentation: Every transformation step includes a short justification of the chosen technique.
- Reproducibility: The pipeline is parameterized, meaning it can be reused on different files without rewriting the logic.