Abstract

Machine learning has a significant and quicker growth in this decade. Every day, new machine learning applications and algorithms are being developed. It plays a significant role in spam detection, recommendations, medical treatments as well. The present machine learning approach helps us improve public safety, security alerts, and medical interventions.

However, not much is being worked in domain of real estate sector, house prices continue to rise year after year and the most difficult issue is managing or forecasting real estate values. As a result, stakeholder, purchasers, and builders want to know which factors influence housing prices. We address the prediction of house prices generated by machine learning models in this research paper. We would also investigate the elements that influence housing market such as location, territory, stock market, and ageing and ultimately, develop a system to assist clients in acquiring a property that meets their needs.

This research intends to analyse, evaluate, and construct an intelligent system deploying artificial intelligence on the real estate sector using various Machine Learning models such as K-Means Clustering, Random Forest Regressor, XGBoost, Neural Networks with an addition of areas of interest that people might look for most visited venues. Moreover, this research will explore the differences between many advanced models using both classic and advanced machine learning methodologies. In addition, it will extensively examine a variety of ways in model implementation and deliver an optimistic outcome for housing price prediction.

A Smart House Price Prediction System using Machine Learning Models