Beginner-Friendly Voting and Stacking Classifier Builder

Ensemble learning is usually taught as a formula, but the underlying idea is simple: several weak opinions, combined well, beat one confident guess. This project turns that idea into an application a beginner can experiment with directly, making one of machine learning's most powerful techniques concrete.

The intern builds a Streamlit application where users train several simple scikit-learn classifiers on a chosen dataset and combine their predictions using hard and soft voting, stacking, and blending strategies. The interface shows each base model's individual accuracy next to the combined result, so the effect of ensembling is visible immediately rather than taken on faith. Users can swap base models in and out and watch how diversity among the learners changes the ensemble's performance, with Python handling training and evaluation behind a clean interactive front end.

The project cements the intern's understanding of why and when ensembles work, gives them working knowledge of scikit-learn's ensemble APIs, and provides practice in building approachable educational tooling — demonstrating they can both apply a core machine learning technique and explain it to someone newer than themselves.