[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122889-en":3,"doc-seo-122889-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122889,549758252649,"Ivy","https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819",8,"Research & Report","House Prices Prediction Using Statistics with Machine Learning","After the 2009 housing crisis and the subsequent bubble burst, research increasingly emphasized methods for estimating house prices. This study uses the Ames housing dataset (2006–2010) and models the influence of many predictors, including area, utilities, house style, location, age, grade living area, bedrooms, and garage. Statistical approaches (multiple and stepwise linear regression) are combined with machine learning models LASSO and XGBoost. Prediction quality is assessed via RMSE, and the best performance is reported for XGBoost with 25 features, R² of 0.973, and RMSE of 0.027, with LASSO supporting feature selection.","Journal of Advanced Zoology  \nISSN: 0253-7214  \nVolume 44 IssueS-6 Year 2023 Page 685:691  \nHouse Prices Prediction Using Statistics with Machine Learning  \nLoai Nagib Alqubati1*, Kiran Kumari Patil Loai Nagib2  \n1,2School of CSE, REVA University, India  \n*Corresponding author’s E-mail: [loai.nagib21@gmail.com](loai.nagib21@gmail.com)  \n\n| Article History\u003Cbr>Received: 06 June 2023\u003Cbr>Revised: 05 Sept 2023\u003Cbr>Accepted: 29 Nov 2023\u003Cbr>CC License\u003Cbr>CC-BY-NC-SA 4.0 | Abstract\u003Cbr>After the housing crisis in 2009 that affected the global economy and the bubble that burst, researchers began to focus on how to estimate house prices. In the United States, for instance, they adopted the hedonic price index (HPI) method in estimating house prices. After Ames house pricing dataset was released, which contains houses data from 2006 to 2010, and detailed features that help in studying the estimation of house prices. In this paper, we suggest that House prices are determined by many features such as area, utilities, house style, location, age, grade living area, number of bedrooms, garage, and so on. Statistical methods were applied with two models which are multiple and stepwise linear regression, also, two machine learning algorithms which are LASSO and XGBoost regression. The accuracy of prediction was evaluated by the root mean square error (RMSE). XGBoost with 25 features, 0.973 R2, and 0.027 RMSE is the Best model. LASSO has helped in feature selection for XGBoost.\u003Cbr>Keywords: House price prediction, SPSS, Feature Engineering, Machine Learning, XGBoost, and Lasso |\n| --- | --- |\n\n1. Introduction  \nOne of the big financial decisions [14] that people make in their lives is to buy a house [4] . The development of civilization is the main reason for the increase in housing demand, resulting in prices increasing too [6] . This, in turn, results in people trying to keep an eye on house pricing and trying to figure out a more accurate way to predict the house pricing trends that take place at different times [5]. But how to use machine learning algorithms to predict housing prices? Not to mention, it is also a challenge to get accurate results.  \nThe housing market is growing rapidly, and prices change a lot too depending on various factors, so house pricing is a real trend problem, therefore an important motivating factor for predicting house prices.  \nIf you were looking for a home before the economic crisis in 2009, you could choose from the group of loans provided by banks to maintain your payments [4] . The traditional home price forecast is based on cost and sales price comparison, therefore there is a need to build a model to efficiently predict house prices, this model uses different features to describe each house, and every feature contributes to the price.  \nThe house is determined by location, size, area, rooms, and other features [7] . In this paper, we are going to explain the methodologies that have been used. We have divided the work in this paper into 4 phases, which are Data description, exploratory data analysis (EDA) which is the approach of analyzing or initial investigation on datasets to test the hypothesis by SPSS (Statistical Package for the Social Sciences), machine learning algorithms such as LASSO (Least Absolute Shrinkage and Selection Operator), XGBoost (eXtreme Gradient Boosting) regression and feature engineering.“IBM SPSS Statistics is a powerful statistical software platform. It delivers a robust set of features that helps your organization extract actionable insights from its data.” [1] . We used Statistical methods in SPSS for preprocessing, preparing datasets, handling missing values, Outliers, and feature selection. We have built 2 models in SPSS which are Multiple linear regression and stepwise multiple regression. Stepwise based on the probability of F (P-value), SPSS starts entering the independent variables with the smallest P-value 0.05 then in the next step again the variable from the list not in ","cbCaitQwbqQwCqaL","https://ap.wps.com/l/cbCaitQwbqQwCqaL","pdf",426285,1,7,"English","en",105,"# Introduction\n# Related Work\n# Methodology\n# Results\n# Conclusion","[{\"question\":\"Which dataset and time range are used for house price modeling?\",\"answer\":\"The study uses the Ames house pricing dataset containing house data from 2006 to 2010.\"},{\"question\":\"What features are considered when estimating house prices?\",\"answer\":\"Key features include area, utilities, house style, location, age, grade living area, number of bedrooms, garage, and related attributes.\"},{\"question\":\"Which models are used and which achieves the best predictive performance?\",\"answer\":\"Multiple and stepwise linear regression are used alongside LASSO and XGBoost. XGBoost with 25 features reports the best results (R² = 0.973, RMSE = 0.027).\"}]","House Prices Prediction Using Statistics with Machine Learning | PDF",1785813518,18,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"house-prices-prediction-using-statistics-with-machine-learning","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/house-prices-prediction-using-statistics-with-machine-learning/122889/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Which dataset and time range are used for house price modeling?","Question",{"text":75,"@type":76},"The study uses the Ames house pricing dataset containing house data from 2006 to 2010.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What features are considered when estimating house prices?",{"text":80,"@type":76},"Key features include area, utilities, house style, location, age, grade living area, number of bedrooms, garage, and related attributes.",{"name":82,"@type":73,"acceptedAnswer":83},"Which models are used and which achieves the best predictive performance?",{"text":84,"@type":76},"Multiple and stepwise linear regression are used alongside LASSO and XGBoost. 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