House Price Estimator With a Streamlit Front End and Flask API

Home buyers, sellers, and agents all anchor on price estimates, and even a modest predictive model becomes far more useful once it is reachable both by people and by other software. This project builds a price predictor with exactly those two doors into it. The intern trains a gradient boosting regression model with scikit-learn on housing data, then delivers it two ways: a Streamlit web app where users enter property details through form controls and receive an estimated price with supporting visuals, and a lightweight Flask REST API that returns predictions as JSON for programmatic callers. Building both surfaces from one model artifact teaches the intern to separate modeling from serving, and to keep a single source of truth for preprocessing so the app and the API can never disagree about a prediction. The intern finishes with practical experience in boosted tree models, model persistence, basic API design with Flask, and interface building with Streamlit — a compact tour of the skills that turn a regression notebook into something a small real estate business could genuinely put in front of customers.