Predicting Real Estate Prices Using Deep Learning and Regression Techniques

The real estate market is highly dynamic, influenced by a variety of factors such as location, property features, economic conditions, and market trends. Predicting real estate prices accurately is a challenging yet crucial task for buyers, sellers, and investors. In this study, we explore the use of deep learning and regression techniques to predict real estate prices. Deep learning models, such as artificial neural networks (ANNs) and convolutional neural networks (CNNs), are leveraged to capture complex patterns and relationships within large datasets. In addition, traditional regression techniques, including linear regression and decision trees, are used for comparison to understand their effectiveness in predicting property values. The study demonstrates that combining deep learning methods with regression approaches can enhance prediction accuracy, offering valuable insights for stakeholders in the real estate market.