Visual Builder for Scikit-Learn Preprocessing and Modeling Pipelines

Real machine learning code is not a pile of one-off preprocessing calls — it is a pipeline, where scaling, encoding, and modeling steps are chained so the same transformations apply identically at training time and prediction time. Learning to think in pipelines early prevents the data leakage mistakes that plague ad-hoc workflows.

The intern builds a Streamlit application where users assemble a scikit-learn Pipeline interactively: choosing preprocessing steps such as scaling or encoding, selecting an estimator, and arranging everything into an ordered workflow. The app visualizes the assembled pipeline as a diagram, runs it against a dataset, and reports the results, so users see how a single object encapsulates the whole path from raw data to prediction. Python drives the construction and execution behind the interface, and users can rearrange steps to discover how ordering changes outcomes.

By building the tool the intern internalizes one of scikit-learn's most important abstractions, learns why pipelines are the unit of deployment in serious machine learning systems, and practices translating an API concept into a visual, interactive learning experience that makes good engineering habits easier to adopt.