Paper Key : IRJ************612
Author: Amrita T E,Gurudharshini P ,Maganti Gowthami,Sasirekha K
Date Published: 09 Nov 2024
Abstract
House pricing is quite complex within the real estate industry and mostly depends on many variables determining a house price. There exist various traditional formulas for computing house prices. However, they mostly prove inadequate since they mostly depend on personal judgment and not much consideration of the available data. This research work creates an all-encompassing method for the computation of house prices using advanced XGBoost and LightGBM machine-learning algorithms. We include systematization of data preprocessing techniques, feature selection, hyperparameter tuning, and rigorous model evaluation to improve prediction accuracy. The findings have big insights into the relationships of property characteristics with market value, thus contributing to enhanced decision-making for stakeholders in the real estate sector.