King County Houses Prices ML Predictions
A machine learning project that predicts house prices in King County, WA using regression models and feature engineering.
King County Houses Prices ML Predictions
This project uses machine learning to predict house prices in King County, Washington, which includes Seattle. Using a dataset of house sales from 2014-2015, the model helps homeowners, real estate agents, and investors accurately estimate property values.
Key Features
- Advanced Regression Models: Implemented and compared multiple models including Linear Regression, Random Forest, XGBoost, and Gradient Boosting
- Feature Engineering: Created meaningful features from the raw data to improve model performance
- Price Influencing Factors: Identified key factors that influence house prices in King County
- Interactive Visualizations: Developed visualizations to explore relationships between features and house prices
- Geospatial Analysis: Included location-based analysis to understand neighborhood influence on pricing
Tech Stack
- Python
- Scikit-learn
- Pandas
- NumPy
- Matplotlib/Seaborn
- XGBoost
- GeoPandas
Model Performance
The final model achieved an R² score of 0.92 on the test set, with a Mean Absolute Error (MAE) of $42,000, making it a reliable tool for price predictions. The Random Forest model performed best among all tested algorithms.
Insights
Analysis revealed that location, square footage, number of bedrooms/bathrooms, waterfront views, and property condition were the strongest predictors of house prices. Houses with waterfront properties commanded a premium of approximately 30% compared to similar inland properties.
Business Applications
- Homeowners: Accurately estimate property value for selling or refinancing
- Real Estate Agencies: Provide data-driven price recommendations to clients
- Investors: Identify undervalued properties with high potential returns
- Urban Planners: Understand housing market trends for development planning