House Price Prediction Using Gradient Boosting and Linear Regression

Everyone dreams of buying and living in their own house, which is suitable for their lifestyle and suits their personality. The main objective for which people look at a house is the square footage of the house, the number of bedrooms and bathrooms, the location of the house, and the year it was built. This qualitative research helps to get an accurate price for the house that is suitable for their budget and will not affect them financially. It also helps a person buy a house according to his or her requirements. There are many machine learning algorithms out there, like Gradient boosting regression, linear regression, polynomial regression, and random forest regression. After researching on every algorithm, the best accuracy algorithm will be chosen to predict the house price.