Using Enhanced Machine Learning Techniques, House Price Prediction

Authors

  • T. Swaroopa  Department of Computer Applications, Madanapalle Institute of Technology and Science, Angallu, Andhra Pradesh, India
  • K. Roopa  Department of Computer Applications, Madanapalle Institute of Technology and Science, Angallu, Andhra Pradesh, India

Keywords:

Machine learning, House price, Prediction.

Abstract

The goal of the paper is to help prospective homeowners make well-informed selections that take into account their finances and market trends. It attempts to forecast house prices for those who do not own a home by examining their financial plans and goals. Various regression techniques, such as Linear Regression and Decision Tree Regression, are employed to estimate speculated prices. The analysis involves assessing the accuracy and R2 score values of the predictions using relevant data. The study's objective is to determine the most effective regression technique for house price prediction. The ultimate objective is to assist sellers in accurately calculating the price at which to sell a home and to assist buyers in determining when it is best to make a real estate acquisition.

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2023-08-30

Issue

Volume 9, Issue 4, July-August-2023

Section

Research Articles

License

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
T. Swaroopa, K. Roopa, " Using Enhanced Machine Learning Techniques, House Price Prediction " International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 9, Issue 4, pp.285-291, July-August-2023.