HOUSE PRICE PREDICTION

Abstract

The aim of this project is to predict residential house price of Kathmandu valley with the utmost accuracy using machine learning. This project involves collecting, cleaning, and analysing extensive data sets of residential housing prices available in the web, implementing regression models like Random Forest, Gradient Boosting, Extreme Gradient Boosting, Hybrid Regression and Stacked Generalisation Regression for comparative analysis and choosing the best model to predict price. The model is trained on recent residential housing price data and optimised by hyper parameter tuning to enhance prediction accuracy. The resulting best model serves as a valuable tool for home investors, developers, and home buyers in Nepal, enabling them to make informed decisions based on reliable price predictions. This project contributes to the advancement of machine learning and data science research in Nepal, and provide opportunities for further research and development in this field.