An Algorithm to Predict Real Estate Price using Machine Learning

Abstract

Real Estate Pricing has always been a varying from time to time keeping a buyer confuse on how to get exact price of the property at certain point of time. A machine learning algorithm is used to predict the exact price of the property. Predicting the accurate amount of the real estate is very much a matter of concern as people are investing too much now a days in property where a property dealer could easily charge more amount if the buyer is not knowing the market price of the property. The major focus of this research paper is to predict the accurate price of the property without a hassle. Apart from that it also focuses on increasing the accuracy of the already existing system. There is various machine learning algorithm available for prediction such as Naive Bayes, Logistic Regression, Classification, Random Forest KNN, Support Vector Machine, Lasso, Linear Regression etc. The aim of this research is to predict the market value of real estate properties based on geological location. By analyzing previous market patterns, value ranges, and upcoming developments, we can determine a starting price for a property based on geological variables. Our study is applicable to any location, and we have utilized three machine learning algorithms to make predictions. Our findings indicate that linear regression provides the most accurate predictions, with an accuracy rate of 85%. This system eliminates the need for clients to rely on brokers and provides them with the confidence to invest in real estate. The accuracy of our system surpasses that of previous methods used in the industry.