Predictive regression analysis of housing price in Iowa

Zhida Guo

Diplomová práce

Anotace:

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).

Abstract:

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).

Klíčová slova

regression analysis, prediction, cross validation, empirical analysis

Keywords

cross validation, prediction, empirical analysis, Regression analysis

Jazyk práce: angličtina

Datum vytvoření / odevzdání či podání práce: 4. 12. 2023

Obhajoba závěrečné práce

• Obhajoba proběhla 29. 1. 2024
• Vedoucí: Karel Helman
• Oponent: Adam Čabla

Vysoká škola ekonomická v Praze

Magisterský studijní program / obor:
Economic Data Analysis / Data Analysis and Modeling