HOUSE PRICE ESTIMATION BY USING DEEP LEARNING: A CASE STUDY

Abstract

Every year, the cost of housing increases, which generates the needs of a system that predict future house prices? A developer can estimate the promotion cost of a residence with use of a house price forecast. It also assists clients in deciding on the apparent timing of a property purchase. The cost of a home is influenced by a number of factors, and it mainly depends on physical surroundings, type of home, dimensions, area or locality, etc. House prices vary by location and community. Real estate prices can be predicted in a number of ways. One of the efficient methods is to use regression techniques. Regression is considered a reliable method for determining variables that exert influence on topics of significance. Random forests are extremely reliable and accurate against over-fitting. The process of running regression ensures that you can determine the most important factors, negligible factors, and how much each of these things influences the other. The primary intention is to implement the most cutting-edge forecasting method.