Home|Journals|Articles by Year|Audio Abstracts

Research Article

EEO. 2021; 20(3): 3706-3716

House Price Prediction Using Regression Analysis

Aditya Joshi, Bhawnesh Kumar, Vandana Rawat, Mansi Srivastava, Prof. (Dr) C. S. Yadav.


A house is one of the basic necessities of a family and one of the most crucial long-term purchases made. House as a property is also one of the best investments. With the high scope and demand, real estate is a highly profitable business. While purchasing a house a person looks through look for their preferred price range, and has a good idea about the kind of features they will look for in their desired house such as the number of rooms, furnishing, locality, accessibility to market, hospital, and other essential services, etc. The person then decides if the house they are considering is worth the mentioned price or not. For this, a trustable price prediction system is needed. Similarly, if a person needs to sell a house, they need a prediction system to decide the price of the house with the specifications that the house has. The prediction system can give a good idea to a seller and help them set a good price by adding all the favorable specifications of their house. A price prediction system can help both the buyer and seller by predicting the price of the house according to the features it holds. It is known that we can create such predictions through Regression in Machine Learning.

Key words: House Price Prediction, Machine Learning, Regression

Full-text options

Share this Article

Online Article Submission
• ejmanager.com

ejPort - eJManager.com
Refer & Earn
About BiblioMed
License Information
Terms & Conditions
Privacy Policy
Contact Us

The articles in Bibliomed are open access articles licensed under Creative Commons Attribution 4.0 International License (CC BY), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.