PREDICTIVE REGRESSION ANALYSIS OF HOUSING PRICE IN IOWA

This thesis presents an analysis of predictive modeling techniques and outlier detection methods in the context of real estate sale price prediction. The study aims to find an optimal regression model by integrating variable selection procedures, influential observation removal, and regression outlier detection, and understand the impacts of different variable selection methods. The investigation, conducted on a real-world housing dataset, evaluates the models using metrics such as Mean Square Error (MSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). This thesis presents an analysis of predictive modeling techniques and outlier detection methods in the context of real estate sale price prediction. The study aims to find an optimal regression model by integrating variable selection procedures, influential observation removal, and regression outlier detection, and understand the impacts of different variable selection methods. The investigation, conducted on a real-world housing dataset, evaluates the models using metrics such as Mean Square Error (MSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE).