[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119863-en":3,"doc-seo-119863-105":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},119863,16904993612988,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",6,"Technology","House Price Prediction using Machine Learning Algorithms - Random Forest for Better Accuracy","House price prediction supports buyers, sellers, and real estate agents by estimating home values from housing-market data and attributes. The study builds a predictive model using four machine learning algorithms: linear regression, polynomial regression, decision tree, and random forest. Each method is selected for specific strengths such as interpretability, flexible curve fitting, and graphical decision structure. Results show random forest achieves higher accuracy, reaching about 89% on the provided dataset, enabling more reliable price projections.","House Price Prediction using Machine Learning  \nAlgorithms  \nAngulakshmi M 1*, Deepa M2, Mala Serene I3, Thilagavathi M4, Aarthi P 5  \n1*School of Computer Science Engineering and Information Systems,  \nVellore Institute of Technology, Vellore, India  \n[angulakshmi.m@vit.ac.in](angulakshmi.m@vit.ac.in)  \n2School of Computer Science Engineering and Information Systems,  \nVellore Institute of Technology, Vellore, India  \n[mdeepa@vit.ac.in](mdeepa@vit.ac.in)  \n3School of Computer Science Engineering and Information Systems,  \nVellore Institute of Technology, Vellore, India  \n[imalaserene@vit.ac.in](imalaserene@vit.ac.in)  \n4School of Computer Science Engineering and Information Systems,  \nVellore Institute of Technology, Vellore, India  \n[mthilagavathi@vit.ac.in](mthilagavathi@vit.ac.in)  \n5School of Computer Science Engineering and Information Systems,  \nVellore Institute of Technology, Vellore, India  \n[aarthi.p2021@vitstudent.ac.in](aarthi.p2021@vitstudent.ac.in)  \nAbstract—House prices are a major financial decision for everyone involved in the housing market, including potential home buyers. A major part of the real estate industry is housing. An accurate housing price prediction is a valuable tool for buyer and seller as well as real estate agents. The study is done for the purpose of knowledge among the people to understand and estimate the pricing of their houses. The prediction will be made using four machine learning algorithms such as linear regression, polynomial regression, random forest, decision tree. Linear Regression has good interpretability. Decision tree is a graphical representation of all possible solutions. Polynomial regression can be easily fitted to a wide variety of curves. Regression and classification issues are resolved with random forests .Among the given algorithm, Random forest provides better accuracy of about 89% for given dataset.  \nKeywords-Machine learning, House price prediction, Decision Tree, Random Forest, Polynomial regression  \nI. INTRODUCTION  \nMachine learning is focused on creating self-learning algorithms for projects that will base future activity on historical data. The prediction of house prices is based on a similar phenomenon. The market demand for housing is always rising each year as a result of population growth and people moving to other cities for financial reasons. The purchase of a house is one of the biggest and most significant decisions a family can make, since it consumes all the invested funds and covers them with loans [1] . Machine learning has become an important prediction approach in recent years, owing to the growing trend towards big data, because it can predict house prices more accurately based on their attributes, regardless of previous year's data. Predicting house prices can assist in determining the selling price of a house in a specific region and can  \nassist people in purchasing the house at the right time [2] .  \nThe field of machine learning is used in a wide range of computing applications. Machine learning is important because it performs some tasks such as providing a view of trends in customer behaviour and business operational patterns and assisting in the development of new products. The cost of a home is determined by a number of interconnected factors. In this project, an attempt was made to build a predictive model for evaluating price based on price-influencing factors. Area, rooms, location, city, and other factors all have an impact on house prices [3] . For example, if we are going to sell a house, we need to know what price tag to put on it. The most important source of analysis and predictions for a real estate business is data. A business manager should always be aware of predictions of future variations of an entity so they can act accordingly to  \navoid losses in the future. The purchase of a home is a lifelong dream for most people, but many people do it incorrectly and cost them a lot of money [4] . As a result, the most accurat","cbCaiqujMo7PQ7lp","https://ap.wps.com/l/cbCaiqujMo7PQ7lp","pdf",449061,1,7,"English","en",105,"# Introduction\n## Literature Survey\n## Machine Learning Algorithms\n## Result and Accuracy","[{\"question\":\"Which machine learning algorithms are used for house price prediction?\",\"answer\":\"The model compares linear regression, polynomial regression, decision tree, and random forest for predicting house prices from dataset attributes.\"},{\"question\":\"Why is random forest expected to perform better in this study?\",\"answer\":\"Random forests combine ensemble learning and can reduce errors through aggregating decision trees, and the study reports about 89% accuracy on the given dataset.\"},{\"question\":\"How do the algorithms differ in their roles for prediction?\",\"answer\":\"Linear regression offers good interpretability, polynomial regression fits various curves, decision trees provide a graphical representation of solutions, and random forests help resolve regression/classification issues with improved accuracy.\"}]","House Price Prediction using Machine Learning Algorithms - 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