The complex and dynamic nature of residential and commercial real estate markets underscores the importance of precise price projections. The project intends to enable end users to evaluate real estate based on particular demands and areas by utilizing machine-learning techniques. The main prediction technique used is a linear regression model, which accurately forecasts asset prices after prolonged training. Ensuring the precision of the model is contingent upon this training phase. The study uses visual data analysis, which offers a user interface for quick access to predicted model findings and a visual summary of price changes, to improve user knowledge of real estate price swings. This interface, which is made to be easily accessible in a variety of settings, serves users who want to make well-informed real estate investment decisions. The study contains an overview of using machine-learning algorithms, namely support vector machine (SVM) and linear regression models (LR), for real estate price forecasting, starting with the Kaggle dataset. Notably, the data are carefully cleaned, anomalies are eliminated, and SVM and linear regression models are tested. A crucial research component compares the metric values of the two algorithms, concentrating on each model's performance to ascertain which produces more precise price predictions. This work combines machine-learning algorithms and data science approaches to provide a complete approach to real estate price forecasting. The comparison of the linear regression and SVM models highlights the commitment to providing accurate and useful forecasts for clients navigating the complexities of the real estate market.


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    Title :

    Predicting Real Estate Price: A Comparative Analysis of Linear Regression and Support Vector Machine


    Additional title:

    Smart Innovation, Systems and Technologies



    Conference:

    Congress on Control, Robotics, and Mechatronics ; 2024 ; Warangal, India February 03, 2024 - February 04, 2024



    Publication date :

    2024-10-31


    Size :

    12 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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