Open AccessHouse Price Prediction using Machine Learning Techniques
Author(s) -
G. Gayathri Priya
Publication year - 2021
Publication title -
international journal for research in applied science and engineering technology
Language(s) - English
Resource type - Journals
ISSN - 2321-9653
DOI - 10.22214/ijraset.2021.35831
Subject(s) - regression , random forest , lasso (programming language) , computer science , support vector machine , artificial neural network , elastic net regularization , econometrics , real estate , regression analysis , hedonic regression , volatility (finance) , machine learning , proxy (statistics) , linear regression , artificial intelligence , polynomial regression , economics , statistics , mathematics , finance , feature selection , world wide web
The real estate market is one of the most price-driven, but it is still affected by volatility. This is one of the main uses of machine learning ideas to improve and predict costs with high precision. As housing prices are fluctuating, People are cautious when trying to buy a new house based on their budget and marketing strategy. The purpose of the paper is to forecast consistent home prices for non-owners based on their financial dispositions and aspirations. The paper involves predictions using various Regression techniques like linear regression, random forest regression, polynomial regression, robust regression, lasso regression, elastic net regression, stochastic gradient descent, svm regression, artificial neural network. On a data set, house price prediction has been done by combining all of the above-mentioned strategies to determine which is the most effective. The purpose of the paper is to assist the seller in accurately estimating the selling price of a house. Physical circumstances, and location, among other things, were all taken into account while determining the cost.