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@article{175192,
        author = {Ambar Kumar and Aniket Kumar Singh and Abhijeet and Archit Gupta and Mrs. Rekha BN},
        title = {Smart Residential Property Valuation Using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {2048-2050},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=175192},
        abstract = {Accurate property valuation is critical for stakeholders in dynamic real estate markets, yet traditional methods often suffer from subjectivity and inefficiency. This paper presents a Smart Residential Property Valuation System that leverages machine learning to predict housing prices in Bangalore. Using a Random Forest Regressor, the system processes features such as location, square footage, number of bathrooms, and BHK configurations. Advanced data preprocessing techniques—including one-hot encoding, median imputation, and standard scaling—ensure robust input quality. The model is optimized via GridSearchCV for hyperparameter tuning, achieving an R² score of 0.85. A Tkinter-based graphical user interface (GUI) enables non-technical users to input property details and receive instant predictions. Exploratory data analysis (EDA) reveals key trends through visualizations like price distribution histograms and correlation heatmaps. The system addresses gaps in accessibility, explainability, and real-world deployment, offering a scalable solution for transparent and efficient property transactions.},
        keywords = {Property Valuation; Machine Learning; Random Forest Regressor; Real Estate Analytics; Explainable AI},
        month = {April},
        }