Open AccessReal Estate Cost Estimation Through Data Mining Techniques
Author(s) -
Sandali Khare,
Mahendra Kumar Gourisaria,
Harshvardhan GM,
Subhankar Joardar,
Vijander Singh
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1099/1/012053
Subject(s) - real estate , decision tree , random forest , regression , estimation , polynomial regression , regression analysis , computer science , linear regression , tree (set theory) , estate , econometrics , artificial intelligence , machine learning , economics , finance , statistics , mathematics , management , mathematical analysis
Real estate is one of the most fast-paced and emerging industries today. Nowadays everyone wants to be the owner of their house rather than live on rent. Therefore, people are very cautious in searching for the most suitable house. Different people have a different budgets and so varies their desire. This paper draws attention to the house rate predictions based on different objectives like financial status and expectations of non-house holders. It consists of two prediction sets, one with all the available features required for buying the house and the other with a few selected features. It involves varying machine learning regression techniques like linear regression, polynomial regression, decision tree, and random forest. Here, all the above techniques are compared, and it is found that polynomial regression with all the features gives the best results.