"House Prices - Advanced Regression Techniques"

Design and implement a robust linear regression model for predicting housing prices, leveraging features such as square footage, number of bedrooms, and number of bathrooms. The objective is to develop an accurate and reliable model that can effectively estimate the market value of houses, providing valuable insights for real estate stakeholders. The model should be trained on a comprehensive dataset, considering diverse housing characteristics, to ensure its generalizability and effectiveness across various real estate scenarios. Additionally, the project aims to optimize the model's performance by exploring feature engineering, handling outliers, and evaluating different regression techniques, ultimately delivering a solution that enhances the precision and interpretability of house price predictions.