Predicting House Prices Using Regression Model

This paper shows a study on the development of a predictive model for house prices. The model uses machine learning techniques to analyze a large dataset of of housing market data, including demographic information, real estate trends, and economic indicators. The goal of the model is to accurately predict the sale prices of a house given its features and traits. To achieve this target, the study employs feature selection and engineering methods to determine the most significant predictors of house prices [1]. The results of the model are evaluated using standard metrics, sucha as root mean squared error [RMSE] and mean absolute error [MAE]. The results show that the proposed model outperforms benchmark model and provides a reliable prediction of house prices. The model can be used by real estate professionals, policymakers, and homebuyers to gain insights into the housing market and make the right choice [3].