Linear Regression-Based Prediction Model For Real Estate Properties in India

The real estate market is one of the most competitive in terms of price, and it is also one of the few that is subject to change on a consistent basis. It is one of the most significant areas where the principles of machine learning are applied to improve and increase the accuracy of cost prediction...

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Bibliographic Details
Published inICIIP ... proceedings (Online) pp. 158 - 163
Main Authors Verma, Harsh, Singh, Amrit, Avasthi, Sandhya
Format Conference Proceeding
LanguageEnglish
Published IEEE 22.11.2023
Subjects
accuracy
Decision Tree
feature engineering
Linear regression
Machine learning
Machine learning algorithms
Prediction algorithms
Predictive models
price prediction
Real estate price
Software algorithms
Online AccessGet full text
ISSN2640-074X
DOI10.1109/ICIIP61524.2023.10537685

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Summary:The real estate market is one of the most competitive in terms of price, and it is also one of the few that is subject to change on a consistent basis. It is one of the most significant areas where the principles of machine learning are applied to improve and increase the accuracy of cost predictions. The price of a home depends on three factors: its physical condition, its design, and its location. The current method of estimating property prices does not account for fluctuations in market prices or the rate of inflation. The objective of this paper is to provide Indian customers with a forecast of residential prices that takes their needs and budgets into account. To enhance the overall performance of the prediction models, the mean target encoding is also incorporated into the methodology. According to the findings of this study, the estimation error that is produced by linear regression is the lowest and accuracy of the implemented system is 98.6%.
ISSN:2640-074X
DOI:10.1109/ICIIP61524.2023.10537685