Authors: Mrs. R. BHUVANESHWARI, Ms. T. MISHA
Abstract: Predicting house prices is both vital and complex due to the ever-changing nature of the real estate market. Conventional statistical approaches often fall short in identifying intricate data trends, making machine learning a more suitable solution. This project adopts the Support Vector Machine (SVM) algorithm to forecast housing prices by analyzing historical data and key market influences. Known for its ability to manage high-dimensional datasets and model nonlinear relationships, SVM proves to be a dependable method for accurate price prediction. The system evaluates multiple factors including geographic location, property dimensions, prevailing market trends, and economic conditions to improve prediction precision. Through SVM’s capabilities in both classification and regression, the model delivers strong, data-informed insights that assist homebuyers, sellers, and investors in navigating the dynamic real estate environment effectively
DOI: http://doi.org/
