Year: 2025
Type: article
Abstract: The real estate industry plays a vital role in the economy, and accurate house price prediction is essential for buyers, sellers, investors, and financial institutions.This project focuses on building a data-driven model to predict house prices based on various property features such as location, area, number of bedrooms and bathrooms, year of construction, and nearby amenities.Using machine learning algorithms, the system analyzes historical housing data to learn patterns and correlations between property features and their market values.Algorithms such as Linear Regression, Decision Tree, and Random Forest are evaluated for performance, and the best-performing model is selected based on accuracy and error metrics.The objective of this project is to create a reliable, efficient, and scalable solution that can predict house prices with high precision.This can help users make informed decisions and reduce the uncertainty in real estate investments.The outcome demonstrates how machine learning can effectively support real-world applications in the real estate domain.
Language: English
Topic: Housing Market and Economics
Subfield: Economics and Econometrics
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W4414800194
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