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@article{177412,
        author = {Bharathi.Dodla and Mr. Suresh Tiruvalluru},
        title = {House Price Prediction Simplified: Deep Learning in Real Estate Analytics},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {515-519},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=177412},
        abstract = {The real estate sector is witnessing a paradigm shift through the integration of artificial intelligence and machine learning. Predicting house prices accurately is a complex task influenced by numerous variables such as location, property features, and market dynamics. This paper presents a simplified yet effective approach to house price prediction using deep learning techniques. We explore how neural networks can outperform traditional statistical models by capturing non-linear relationships and complex interactions among features. The proposed model is built using structured datasets and employs a multi-layered feedforward neural network to predict property prices with enhanced accuracy. Experimental results demonstrate the superiority of the deep learning approach in terms of performance metrics like Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE), making it a promising solution for modern real estate analytics..},
        keywords = {},
        month = {May},
        }