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 outli...
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| description | 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). |
| format | Dissertation |
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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).</description><language>eng</language><publisher>Vysoká škola ekonomická v Praze</publisher><subject>cross validation ; empirical analysis ; prediction ; regression analysis</subject><creationdate>2024</creationdate><rights>Vysokoškolské kvalifikační práce obhájené na VŠE jsou veřejně dostupné online. https://knihovna.vse.cz/navody/vskp Theses and disertations defended at University of Economics, Prague are freely available online. https://knihovna.vse.cz/navody/vskp</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>311,315,780,27859,27914</link.rule.ids><linktorsrc>$$Uhttps://vskp.vse.cz/eid/90915$$EView_record_in_University_of_Economics_in_Prague$$FView_record_in_$$GUniversity_of_Economics_in_Prague$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>Guo, Zhida</creatorcontrib><title>PREDICTIVE REGRESSION ANALYSIS OF HOUSING PRICE IN IOWA</title><description>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).</description><subject>cross validation</subject><subject>empirical analysis</subject><subject>prediction</subject><subject>regression analysis</subject><fulltext>true</fulltext><rsrctype>dissertation</rsrctype><creationdate>2024</creationdate><recordtype>dissertation</recordtype><sourceid>RY1</sourceid><recordid>eNrjZDAPCHJ18XQO8QxzVQhydQ9yDQ729PdTcPRz9IkM9gxW8HdT8PAPDfb0c1cICPJ0dlXw9FPw9A935GFgTUvMKU7lhdLcDOpuriHOHrplxanxJRmpxanF8fmJmfEgbnIVkMouiLc0sDQ0NSZeJQDmCDAa</recordid><startdate>20240821</startdate><enddate>20240821</enddate><creator>Guo, Zhida</creator><general>Vysoká škola ekonomická v Praze</general><scope>RY1</scope></search><sort><creationdate>20240821</creationdate><title>PREDICTIVE REGRESSION ANALYSIS OF HOUSING PRICE IN IOWA</title><author>Guo, Zhida</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-vse_theses_oai_vse_cz_vskp_909153</frbrgroupid><rsrctype>dissertations</rsrctype><prefilter>dissertations</prefilter><language>eng</language><creationdate>2024</creationdate><topic>cross validation</topic><topic>empirical analysis</topic><topic>prediction</topic><topic>regression analysis</topic><toplevel>online_resources</toplevel><creatorcontrib>Guo, Zhida</creatorcontrib><collection>Databáze VŠKP</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Guo, Zhida</au><format>dissertation</format><genre>dissertation</genre><ristype>THES</ristype><Advisor>Čabla, Adam</Advisor><Advisor>Helman, Karel</Advisor><btitle>PREDICTIVE REGRESSION ANALYSIS OF HOUSING PRICE IN IOWA</btitle><date>2024-08-21</date><risdate>2024</risdate><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).
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><pub>Vysoká škola ekonomická v Praze</pub><oa>free_for_read</oa></addata></record> |
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| source | Databáze VŠKP |
| subjects | cross validation empirical analysis prediction regression analysis |
| title | PREDICTIVE REGRESSION ANALYSIS OF HOUSING PRICE IN IOWA |
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