[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120275-en":3,"doc-seo-120275-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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},120275,13056703019404,"Miles","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Research on House Price Prediction based on Machine Learning","Accurate house price prediction is critical for home buyers and investment groups, influencing purchase strategy while also affecting economic stability and broader societal development. This study evaluates and compares multiple machine learning models for the prediction task, leveraging historical data to learn complex nonlinear relationships. Random Forests are generally stronger than Linear Regression and a single Decision Tree due to improved pattern capture and reduced overfitting risk. The work also contrasts interpretability and limitations of linear models with outlier and nonlinearity handling.","Research on House Price Prediction based on Machine Learning  \nXiangjun Yang  \nDepartment of Computer Science, Gonzaga University, 99258, United States  \nAbstract. Accurately predicting house prices is of vital importance to individual home buyers and investment groups, which not only profoundly affects the formulation of home-buying strategies, but also is closely related to the smooth operation of the economy and the overall development of the society. In recent years, machine learning techniques have shown remarkable potential in house price prediction, as these models can mine the complex nonlinear correlations in large amounts of historical data to produce more detailed and accurate predictions. This study aims to evaluate and compare the performance of various machine learning models on the task of house price prediction. For the house price prediction task, Random Forests generally perform better than Linear Regression and Single Decision Tree because they can better capture complex patterns in the data and reduce the risk of overfitting. Linear regression models are simple and easy to interpret, but may not be accurate enough when dealing with nonlinear relationships and outliers. The advantages of random forests are reflected in higher predictive accuracy, robustness to outliers, and the ability to handle interactions between variables automatically.  \n1.Introduction  \nHousing prices are an important indicator of the well-being of urban residents, which is directly related to people's financial behavior and quality of life. Timely and accurate prediction of real estate prices can help reduce people's economic losses, enhance people's sense of well-being, and strengthen social stability. Machine learning has become an important tool for real estate price prediction because it can handle massive amounts of data, portray nonlinear relationships, and constantly revise the model. This project proposes to predict housing prices using several machine learning methods and compare and analyze their performance.  \nHousing price forecasting is a key link in real estate market analysis, and is of great guiding significance to investors, government policymakers, and ordinary homebuyers. When discussing the importance of housing price forecasting and its methodology, we first need to recognize the complexity and variability of the real estate market [1] . Real estate prices are affected by factors such as economy, policy and society, and have obvious cyclical and geographical characteristics. In addition, the problems of information asymmetry and data availability increase the difficulty of house price forecasting. However, with the  \nCorresponding author: [xyang@zagmail.gonzaga.edu](xyang@zagmail.gonzaga.edu)  \n© The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 ([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)).  \ndevelopment of big data and artificial intelligence technology, researchers have begun to try to utilize advanced methods such as machine learning and deep learning to improve the accuracy and efficiency of house price prediction.  \nIn recent years, research on real estate price prediction has grown significantly. People have been looking for more efficient prediction models from traditional statistics to machine learning. Machine learning methods such as multiple linear regression, decision tree, and random forest have been widely used in real estate price prediction because they effectively deal with complex nonlinear relationships and massive feature information. Meanwhile, deep learning algorithms represented by convolutional neural networks and recurrent neural networks also show good application prospects in the field of real estate price prediction. This project intends to combine the above theories and methods to establish a housing price prediction model that can fully tak","cbCaijVUnToGQy79","https://ap.wps.com/l/cbCaijVUnToGQy79","pdf",493224,1,9,"English","en",105,"# Introduction\n## Motivation and challenges\n# Data and Methods\n## Data collection\n## Data pre-processing\n## Model evaluation","[{\"question\":\"Why is accurate house price prediction important?\",\"answer\":\"It supports home-buying and investment decisions, reduces economic losses, and contributes to social stability. It also guides investors and policymakers through market analysis.\"},{\"question\":\"Which machine learning model is reported to perform best and why?\",\"answer\":\"Random Forests typically outperform Linear Regression and a single Decision Tree. They better capture complex data patterns and lower the risk of overfitting.\"},{\"question\":\"What data source and key variables are used in the study?\",\"answer\":\"The study uses a dataset from Kaggle. Variables include LotFrontage, LotArea, OverallQual, living area measures, and garage-related capacity features that relate to property value.\"}]","Research on House Price Prediction based on Machine Learning | PDF",1785729195,23,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"research-on-house-price-prediction-based-on-machine-learning","",{"@graph":36,"@context":86},[37,54,69],{"@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/research-on-house-price-prediction-based-on-machine-learning/120275/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04","2026-08-03",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why is accurate house price prediction important?","Question",{"text":76,"@type":77},"It supports home-buying and investment decisions, reduces economic losses, and contributes to social stability. It also guides investors and policymakers through market analysis.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which machine learning model is reported to perform best and why?",{"text":81,"@type":77},"Random Forests typically outperform Linear Regression and a single Decision Tree. They better capture complex data patterns and lower the risk of overfitting.",{"name":83,"@type":74,"acceptedAnswer":84},"What data source and key variables are used in the study?",{"text":85,"@type":77},"The study uses a dataset from Kaggle. Variables include LotFrontage, LotArea, OverallQual, living area measures, and garage-related capacity features that relate to property value.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":107,"slug":138},19,"General","general"]