HOUSE PRICE PREDICTION USING MACHINE LEARNING

HOUSE PRICE PREDICTION USING MACHINE LEARNING

1 Dr. SHANKARAGOWDA B B , 2 KEERTHI B S

1,Associate Professor and HOD,, Department of Master of Computer Application, BIET, Davangere

2Student, Department of MCA, BIET, Davangere

Abstract— This study shows how machine learning methods may be applied to estimate real estate or property prices using the Bangalore house price data set. This project aims to provide precise predictions about house price trends. When choosing a home, buyers take into account a number of criteria, such as location, size of the property, and proximity to places of employment, schools, parks, restaurants, and hospitals. The neighbourhood, the number of rooms a buyer wants, and the number of bathrooms he or she will require can all be used to anticipate a home's pricing in this project. The regression methods was used to complete this project because it consistently outperforms alternative models in the execution of housing cost prediction, aids in optimal model selection, and uses a flexible and probabilistic methodology. Learning Python and building expertise in data analytics, machine learning, and artificial intelligence are the main goal.

Keywords:Machine learning, house price prediction, MAPE, MAE, RMSE, Regression analysis.