Original title: Comparative Analysis of Linear Regression, Gaussian Elimination, and LU Decomposition for CT Real Estate Purchase Decisions
Authors: Xilin Cheng
The article explores how computational algorithms impact real estate decisions in Connecticut. It evaluates three methods—Linear Regression (from Scikit-learn), Gaussian Elimination with partial pivoting, and LU Decomposition—using financial and market data. These methods analyze town specifics, yearly data, interest rates, and median sale ratios to predict house purchase advisability. Results highlight stark differences in predictive accuracy: Linear Regression and LU Decomposition offer reliable guidance, while Gaussian Elimination struggles with stability and performance. The study underscores the significance of picking the right algorithm and sheds light on their practical use in real estate investments. By measuring accuracy through R-squared scores and Mean Squared Error, the research provides detailed insights into each method’s pros and cons, enriching real estate analysis and predictive modeling knowledge.
Original article: https://arxiv.org/abs/2311.13471