Open AccessMachine Learning House Price Prediction
Author(s) -
Manu Shahi,
Abhay Pratap Singh,
Amita Goel Vasudha Bahl,
Nidhi Sengar
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/ijmtst061236
Subject(s) - real estate , margin (machine learning) , computer science , valuation (finance) , lasso (programming language) , random forest , machine learning , house price , econometrics , artificial intelligence , point (geometry) , economics , finance , mathematics , geometry , world wide web
This document present the implementation of Machine Learning algorithms for the prediction of the houseand the real estate prices. As the house and real estate prices are subject to change with the marketconditions, so it become very difficult to predict the real estate prices with the conventional methods as it maysometimes gives some exaggerated result that may incur losses. To predict the prices more accurately andprecisely we predict the prices based on the statics of that particular area which has all the trends andfactors on which the price is dependent. To analyse these data , several algorithms are used namely randomforest, linear regression , lasso regression etc. Use of these algorithms decreases the margin of error andmore precise result are achieved. So,we at this point recommend the real estate agents and house vendors aswell as the people to look into the model for better valuation of the house. This model can also be integratedwith the real estates websites to give better recommendation based on the prices using Machine LearningAlgorithms.