Project Status — Ongoing Development This project is currently under active development. Core functionality is implemented, while model optimization, monitoring, and deployment features are being finalized.
This project presents a full-stack machine learning and MLOps platform designed for automated property price estimation. It integrates a gradient-boosted regression model (XGBoost) with a Django-based web application to provide real-time predictions through an interactive interface. The system supports dataset ingestion, model training, artifact generation, and persistent prediction history, forming a complete workflow from data to deployment.
The goal of the platform is not only to produce accurate predictions but to demonstrate a reproducible machine learning lifecycle: structured training scripts, versioned model artifacts, and a web-served inference service operating within a single cohesive architecture. This implementation serves as a practical example of how machine learning models transition from experimentation to usable software systems.
In addition to prediction capability, the platform emphasizes maintainability and repeatability. Retraining automatically updates the active model artifact, allowing the application to continuously incorporate improved performance without modifying the web layer. This separation between training and inference components reflects real production environments where models evolve independently from the serving application.
The project also functions as an educational and architectural reference for applied MLOps practices. It demonstrates how data preprocessing, model training, evaluation outputs, and deployment endpoints can be organized into a structured repository suitable for collaboration, extension, and future integration of monitoring or automated retraining pipelines.