Predicting housing prices using regression models with Full stack web development

Abstract

Predicting housing prices accurately is a 
vital problem in the real estate industry, 
assisting buyers, sellers, investors, and 
policymakers 
in 
making 
informed 
decisions. This project presents a web
based predictive system that applies 
regression-based machine learning models 
to 
estimate housing prices using key 
parameters such as location, size, number 
of 
rooms, neighborhood facilities, and 
market conditions. A full-stack architecture 
integrates a trained ML model with an 
interactive user interface and efficient 
backend processing. The system allows 
users to input property attributes and 
returns real-time price predictions. It 
emphasizes usability, scalability, and 
reliable data-driven insights. Performance 
evaluation metrics such as RMSE, MAE, 
and R² are used to validate model efficiency. 
The project demonstrates the feasibility of 
combining artificial intelligence with web 
estate decision support application.