In Project 3, we decided to implement a random forest algorithm, and our latest blog post, covering our fascinating research on the Boston real estate appraisal, dives deeper into specific predictions and analysis, shedding light on important aspects of the real estate market. Examine YEAR_BUILT, APP_BLDG_COND and related properties to see if you can identify buildings in need of renovation. Our goal is to examine the data set and understand how tax rates, which are closely related to the objects ‘APP_TOTAL_VALUE’, ‘CITY’ and ‘ZIP_CODE’, vary by property and geographic region. As we come to the end of this research, we realize that although our models provide insightful information, they are only tools available to decision makers. Real estate is a dynamic industry that requires constant adaptation and improvement. Examining the Boston Real Estate Appraisal dataset shows how data science can be used to decipher the complexities of real-world problems. The insights gained from this study can be used by anyone interested in data, including politicians, investors, and data enthusiasts, as a compass to help us better understand Boston’s dynamic real estate market.