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@article{195123,
        author = {Mr.M Ramakrishna Raju and B. Hema Lakshmi and A. S Siri Chandana and G. Satya Sindhur and CH. B S Venkat},
        title = {HOUSE PRICE TREND ANALYSIS USING REAL ESTATE DATA},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {8171-8175},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=195123},
        abstract = {This project aims to create a smart way to track and predict house prices using real estate data. In fast-growing cities, knowing the right price is very important for buyers and investors. We found that old-school manual methods of guessing prices often miss how modern home features and market changes work together. To solve this, we used the XGBoost algorithm to make predictions much more accurate. This study utilizes house prices: Advanced Regression Techniques dataset from Kaggle, which contains structured real estate attributes and sale prices. The dataset underwent systematic preprocessing, including handling missing values, categorical encoding, and feature normalization. Exploratory Data Analysis (EDA) was performed to identify key patterns and influential variables affecting property prices. For predictive modeling, the XGBoost regression algorithm was implemented because of its high efficiency, regularization capability, and ability to handle complex nonlinear relationships. To validate the model performance, a comparative analysis was conducted using standard regression metrics. The proposed system achieved a significant Mean Absolute Error (MAE) of approximately $20,532, Mean Squared Error (MSE) of 7.45 X 103, and Rsquared(R2) value of 0.8924, demonstrating high predictive accuracy. Additionally, the system incorporates trend visualization techniques to analyze price variations, enabling a better understanding of market dynamics. This approach provides an accurate, scalable, and practical solution for real estate forecasting, supports informed decision making and reduces uncertainty in property investment.},
        keywords = {Data Science, Price analysis, Real, Estate, Trends},
        month = {March},
        }