Real Estate Predictor

Vision

The goal of this project was to build a full-stack application that predicts real estate trends using machine learning, starting from raw web data. The system enables users to explore housing market insights based on zip code input, while also providing a secure, user-friendly interface for data access and interaction.

Approach

Part I: Data Pipeline & Machine Learning

• Data Collection: Employed Selenium and BeautifulSoup to scrape real estate listings from Zillow, using zip code inputs to filter location-specific data.

• Data Processing: Cleaned and preprocessed the collected data using Pandas and NumPy for consistency and accuracy.

• Modeling: Implemented multiple predictive models — Linear Regression, Support Vector Machines (SVM), and an Ensemble Gradient Boosting model — using scikit-learn to evaluate price trends and patterns.

• Visualization: Leveraged Matplotlib and Seaborn to generate clear, interpretable visualizations. All datasets were stored in structured .csv files for reuse and versioning.

Part II: Full Stack Integration

• Backend: Developed a Flask server with SQLAlchemy for ORM-based database management.

• Authentication: Integrated user registration and login with password encryption using Bcrypt.

• Frontend: Designed a minimal, responsive interface using Bootstrap, HTML, and CSS to ensure a smooth user experience across devices.

Challenges

• Navigating dynamic web elements on Zillow during scraping while maintaining data accuracy.

• Balancing model complexity with interpretability in a relatively small and noisy dataset.

• Integrating all components — from data scraping to ML predictions and UI — into a cohesive full-stack architecture.

Conclusion

This project provided an end-to-end experience in building a data-driven application, from real-time web scraping and machine learning to full-stack deployment. It reinforced practical skills in automation, data science, and secure web development, while also emphasizing the importance of clean design and user accessibility.