Predicting future housing prices: a machine learning approach

Abstract

In our ecosystem, the real estate sector is the least transparent. Daily changes in housing prices as well as sometimes exaggerated prices rather than valuations are a part of life in the housing market. Our research project's major focus is on predicting future housing prices using actual machine learning. Here, we want to concentrate our judgments on each fundamental factor that goes into calculating the price. Machine learning has played a significant role in picture identification, spam restructuring, normal speech command, product suggestion, and medical diagnosis in recent years. The current machine learning method aids us in improving security warnings, maintaining public safety, and improving medicinal advancements. Machine learning technology also improves customer service and makes automobiles safer. The current research discusses the prediction of future housing prices provided by a machine learning system. The data was preprocessed after it was collected. In this procedure, we employ the Forest Neural Gradient Boosting Algorithm (FNGBA). We evaluate and compare several prediction techniques for the selection of prediction methods. Our findings demonstrate the necessity for a successful approach to the problem and the capability of our method to provide predictions that can be compared to existing models of housing price prediction. When compared to employing independent methods, the findings showed that this technique delivers the least mistake and the maximum accuracy.