An Analysis of House Price Prediction Using Ensemble Learning Algorithms

Abstract

It is very important to understand the market drifts in the wake of booming civilization and ever-changing market requirements. The principal purpose of the study is the prediction of house prices based on current conditions. From historical data on property markets, literature attempts to draw useful insights. Business trends must be understood so that individuals may prepare their budgetary needs accordingly. A society that is ever-expanding is driven by the growing real estate industry. A lot of clients have been duped by agents setting up a fake market rate. As a result, the real estate industry has become less transparent in recent years. Due to decreased accuracy and overfitting of data, the previous model reduced efficiency, whereas the newly developed model resolves such issues and provides a rich user interface with a better model. An important part of this study is to develop an extensive model that is beneficial to both business societies and individuals. This is the main objective of this study. In order to simplify the client’s fieldwork and free up his time and money, this software is intended to assist him. Machine learning algorithms enable models to be enlightened such as root mean square error, random forest, support vector machine, k-nearest neighbors, mean squared error, extreme gradient boost, mean absolute error, R-squared score, linear regression, AdaBoost, CatBoost.