https://towardsdatascience.com/modeling-price-with-regularized-linear-model-xgboost-55e59eae4482

We would like to model the price of a house, we know that the price depends on the location of the house, square footage of a house, year built, year renovated, number of bedrooms, number of garages, etc. So those factors contribute to the pattern — premium location would typically lead to a higher price. However, all houses within the same area and have same square footage do not have the exact same price. The variation in price is the noise. Our goal in price modeling is to model the pattern and ignore the noise. The same concepts apply to modeling hotel room prices too.

Therefore, to start, we are going to implement regularization techniques for linear regression of house pricing data.