Year: 2024
Type: article
Abstract: : House price prediction is a critical task in the real estate industry, involving the use of statistical and machine learning techniques to estimate property values based on various features. This study aims to develop a predictive model that utilizes historical data, including attributes such as location, size, number of rooms, property age, and market conditions. By leveraging advanced algorithms like linear regression, decision trees, and neural networks, the model strives to provide accurate and reliable price forecasts. The results of this prediction can benefit homeowners, buyers, real estate agents, and policymakers by enabling data-driven decisions. This paper discusses the dataset, preprocessing techniques, feature selection, model implementation, evaluation metrics, and challenges, offering insights into how predictive modeling can enhance transparency and efficiency in the housing market.
Authors: Prof. Anup Bhange, Rajvaibhav Patil, MD Masoom Khan, Prajwal Pazare, Harsh Patil
Language: English
Topic: Housing Market and Economics
Subfield: Economics and Econometrics
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W4405917224