Housing Price Predictor
Business Problem
Accurately pricing residential properties is critical for buyers, sellers, and real estate professionals. Manual pricing methods are often subjective and fail to capture complex interactions between location, size, and quality.
The goal of this project is to support data-driven pricing decisions by building a predictive model that estimates housing prices and highlights the key factors influencing value.
The Process
- Data Cleaning: Standardized raw housing datasets and handled missing values.
- Feature Engineering: Created location-based bins and price tiers to improve model accuracy.
- Model Selection: Evaluated multiple algorithms, selecting Random Forest for its balance of performance and interpretability.
- Validation: Compared predictions against actual market prices to ensure reliability.
Insights & Business Value
The model demonstrates strong predictive performance with an R² Score of 0.82 and an 18% reduction in RMSE compared to baseline estimates. Beyond raw predictions, the analysis identified that location and living area remain the primary price drivers.