House Price Prediction

Designed and implemented a robust house price prediction system using advanced regression algorithms and feature engineering techniques. The solution analyzes numerous property attributes including square footage, number of rooms, neighborhood statistics, and proximity to amenities to deliver highly accurate price estimates.

Feature selection and engineering from diverse property datasets required extensive domain knowledge and data preprocessing. Balancing model complexity and performance while ensuring explainability of predictions was crucial for user trust and adoption.

The prediction model achieved a mean absolute percentage error of less than 5% on test data, providing valuable insights for real estate agents, buyers, and sellers. The interactive web interface allows users to explore property valuations across different neighborhoods and understand key factors influencing prices.