Published Paper Details:

The estimation of changes in housing prices is often done through house price prediction Because location, area, and population are closely connected with housing price, more information is needed in addition to house price prediction to estimate the cost of a specific home. Many studies have used conventional machine learning techniques to predict home prices effectively, but they seldom address the shortcomings of particular models and often overlook the more sophisticated but less well- liked models. Therefore, this study will use both conventional and sophisticated machine learning techniques to examine the differences between several sophisticated models in order to examine the diverse effects of features on prediction methods. Additionally, a thorough validation of several model implementation strategies on regression will be provided in this study, with positive outcomes. As the purpose of literature is to extract meaningful information from historical property market data. Historical property is assessed with machine learning techniques. exchanges in India to find practical models for home sellers and purchasers. The large disparity between the most expensive homes' pricing is made clear. Furthermore, studies show that the Multiple Linear Regression method, which relies on mean squared error assessment, is competitive.