Predictive real estate modeling Our project digs into the core of Boston’s real estate industry and uncovers the complex web of real estate predictive modeling. It is important to understand the variables that affect TOTAL_VALUE, LAND_VALUE and GROSS_TAX in the ever-changing real estate market. While Random Forest Regression and Gradient Boosting Regression identify subtle patterns and help us predict property value trends, Linear Regression only shows linear relationships. We pay attention to categorical predictions, building types, owners and property use are identified by logistic regression, decision trees, random forests and SVM. In addition to simple prediction, we analyze the interpretability and flexibility of each algorithm to provide a comprehensive guide to navigate the many details that affect the Boston market.