Full-Stack Property Price Estimation Platform With XGBoost

Added Jun 2025 3 design docs

Pricing a property well is one of the highest-stakes decisions in real estate, and agencies increasingly expect data products, not spreadsheets, to support it. This project delivers a complete price-estimation product: a trained model, an API that other systems can call, and an interface that people can actually use. The intern trains an XGBoost regression model on housing data, using scikit-learn utilities for preprocessing and hyperparameter tuning to squeeze real accuracy out of the features, then exposes the model through a FastAPI REST service with validated request schemas. A Streamlit frontend lets users enter property attributes, request a price estimate, and explore feature importance visualizations that reveal which characteristics drive each prediction. Both services are containerized with Docker so the full stack runs with a single command in any environment, mirroring how modern teams package multi-component systems. The project walks the intern through the entire machine learning product lifecycle — training, tuning, serving, interface design, and containerized deployment — and demonstrates the ability to turn a regression model into a deployable application of the kind real estate technology companies actually ship.

Related projects

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.

real-estatedata-scienceflask+4

View project

Easy

Train-and-Download Regression Network Builder for CSV Data

The distance between understanding neural networks and having trained one on your own data is shorter than most beginners think. This project removes the setup friction entirely: bring a CSV file, train a real network in the browser, and leave with a working model file. The intern builds a Streamlit web application where users upload a CSV dataset, pick the target column, and train a regression neural network built with Keras on TensorFlow. Training progress is visualized live through loss curves, and once training completes the app plots predictions against actual values so users can judge the fit at a glance. A download button exports the trained model, completing the cycle from raw data to portable artifact. The intern handles the practical engineering underneath: parsing uploads, basic preprocessing, choosing sensible default architectures, and keeping training responsive inside a web application. The project gives the intern end-to-end experience with the deep learning workflow — data ingestion, network construction, training visualization, and model export — while producing an approachable tool that makes a first neural network achievable for the next learner who uses it.

educationreal-estatedata-sciencedeep-learning+4

View project

Medium

Symptom-Based Early UTI Screening Tool for Rural Healthcare

Urinary tract infections are among the most common infections affecting women worldwide, yet in rural and underserved communities they often go unnoticed until they escalate into painful, costly complications. Seeing a clinician for an early assessment is not always practical, so a lightweight screening tool that works from symptom descriptions and basic health data can make a real difference in prompting timely care. The intern builds a complete screening application around this need. A questionnaire-style interface built with streamlit collects symptoms such as burning sensation, urinary frequency, and fever alongside simple health indicators, and a machine-learning classifier written in python estimates the likelihood of a UTI from those inputs. A fastapi backend exposes the trained model as an inference service, validates incoming data, and keeps the interface cleanly separated from the prediction logic. The app also surfaces educational content on symptoms, prevention, and red flags that warrant an urgent doctor visit. Completing the project takes the intern through the full arc of applied machine learning in healthcare: preparing training data, evaluating a classifier honestly, serving it behind an API, and presenting predictions responsibly as guidance rather than diagnosis.

healthcarefastapimachine-learning+2

View project