Open AccessA Comparative Study on House Price Prediction
Author(s) -
Akash Dagar and Shreya Kapoor
Publication year - 2020
Publication title -
international journal of modern trends in science and technology
Language(s) - English
Resource type - Journals
ISSN - 2455-3778
DOI - 10.46501/ijmtst061220
Subject(s) - random forest , decision tree , computer science , multivariable calculus , purchasing , machine learning , regression , product (mathematics) , linear regression , regression analysis , artificial intelligence , renting , econometrics , statistics , engineering , operations management , economics , mathematics , geometry , civil engineering , control engineering
Machine learning plays a major role from past years in image detection, spam reorganization, normal speechcommand, product recommendation and medical diagnosis. Present machine learning algorithm helps us inenhancing security alerts, ensuring public safety and improve medical enhancements. Due to increase inurbanization, there is an increase in demand for renting houses and purchasing houses. Therefore, todetermine a more effective way to calculate house price accurately is the need of the hour. So, an effort hasbeen made to determine the most accurate way of predicting house price by using machine learningalgorithms: Multivariable Linear Regression, Decision Tree Regression and Random Forest Regression and itis determined that Multivariable Linear Regression has showed most accuracy and less error.