Real estate value prediction using multivariate regression models

14th International Conference on Science, Engineering and Technology

自然科学;工业技术

Manjula, R.^1 ; Jain, Shubham^1 ; Srivastava, Sharad^1 ; Rajiv Kher, Pranav^1

School of Computing Science and Engineering, VIT University, Vellore

Tamil Nadu

632014, India^1

关键词: Feature engineerings;    Important features;    Multiple regressions;    Multivariate regression models;    Polynomial regression;    Real estate market;    Residual sum of squares;    Value prediction;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/263/4/042098/pdf
DOI  :  10.1088/1757-899X/263/4/042098

来源: IOP

【 摘 要 】

The real estate market is one of the most competitive in terms of pricing and the same tends to vary significantly based on a lot of factors, hence it becomes one of the prime fields to apply the concepts of machine learning to optimize and predict the prices with high accuracy. Therefore in this paper, we present various important features to use while predicting housing prices with good accuracy. We have described regression models, using various features to have lower Residual Sum of Squares error. While using features in a regression model some feature engineering is required for better prediction. Often a set of features (multiple regressions) or polynomial regression (applying a various set of powers in the features) is used for making better model fit. For these models are expected to be susceptible towards over fitting ridge regression is used to reduce it. This paper thus directs to the best application of regression models in addition to other techniques to optimize the result.