Housing Price Prediction

Overview

Developed a regression model to predict housing prices based on various features like location, size, amenities, and market trends.

Methodology

Compared multiple regression algorithms including Linear Regression, Random Forest, and Gradient Boosting. Performed extensive feature engineering and hyperparameter tuning.

Key Findings

• Gradient Boosting Regressor achieved the lowest RMSE of $23,500
• Location and square footage were the most influential features
• Seasonal market trends showed significant impact on pricing patterns

Data Visualization