This paper is published in Volume-5, Issue-1, 2019
Area
Data Analytics
Author
Bharathi A. N.
Co-authors
Dr. N. Yuvaraj, Dhivya B.
Org/Univ
KPR Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India
Pub. Date
11 February, 2019
Paper ID
V5I1-1282
Publisher
Keywords
House prices, Regression, Price prediction, Lasso regression

Citationsacebook

IEEE
Bharathi A. N., Dr. N. Yuvaraj, Dhivya B.. Predicting housing prices using advanced regression techniques, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Bharathi A. N., Dr. N. Yuvaraj, Dhivya B. (2019). Predicting housing prices using advanced regression techniques. International Journal of Advance Research, Ideas and Innovations in Technology, 5(1) www.IJARIIT.com.

MLA
Bharathi A. N., Dr. N. Yuvaraj, Dhivya B.. "Predicting housing prices using advanced regression techniques." International Journal of Advance Research, Ideas and Innovations in Technology 5.1 (2019). www.IJARIIT.com.

Abstract

The prices of House increases every year, so there is a need for the system to predict house prices in the future. House price prediction can help the developer to determine the selling price of a house. It also can help the customer to arrange the right time to purchase a house. There are some factors that influence the price of a house which depends on physical conditions, concept, location, and others. House prices vary for each place and in different communities. There are various techniques for predicting house prices. One of the efficient ways is by the use of a regression technique. Regression is a reliable method of identifying which variables have an impact on a topic of interest. Random forests are very accurate and robust to over-fitting. The process of performing a regression allows to confidently determine which factors matter the most, which factors can be ignored and how the factors influence each other. The main objective is to use an advanced methodology for prediction.