Dockerized Survival Prediction Web App Using Decision Trees

Added Jun 2025 3 design docs

Shipping a model inside a working, hosted web application is a distinct skill from training it, and this project practices that skill end to end on an approachable problem: predicting Titanic passenger survival with a decision tree. The intern builds a Flask application backed by a scikit-learn decision tree classifier, with pandas and NumPy handling the data preparation. Users upload passenger data through the browser, trigger model training, and review the results: a visualization of the fitted tree alongside evaluation metrics such as accuracy and a confusion matrix that show how well the model separates survivors from non-survivors. The application is then containerized with Docker, giving it a reproducible runtime that can be hosted anywhere a container runs — the step that turns a local demo into a deployable service. The project's arc — data upload, model training, evaluation display, containerized deployment — mirrors the lifecycle of real internal machine learning tools. The intern finishes able to speak concretely about serving models with Flask and packaging Python applications with Docker, with a hosting-ready artifact to show for it.

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