Year: 2025
Type: article
Abstract: : The real estate market is growing fast, and knowing the right value of a property has become very important. Traditional ways of estimating house prices often take time and depend on human judgment, which can sometimes lead to errors or inconsistencies. This project aims to solve that problem by using machine learning to predict house prices more accurately and efficiently. The system focuses on key factors such as location, total area, number of rooms, and other important features that influence property value. In this project, a linear regression model is applied to analyze past housing data and find patterns between different features and their corresponding prices. The model learns from historical data and can then predict the price of a new house based on its characteristics. Various evaluation methods are used to test how accurate and reliable the predictions are, ensuring better performance. This approach helps homeowners, buyers, and real estate professionals make smarter financial decisions by providing quick and data-based price estimates. Overall, the project shows how artificial intelligence and data science can make the process of estimating house prices faster, more consistent, and easier to understand.
Authors: Anuja Pawar, Kirti Pujari, Prachi Pawar, A. D. Harale
Language: English
Topic: Housing Market and Economics
Subfield: Economics and Econometrics
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W4416208562