The aim of the project is to build a machine learning model to predict the sale price of homes based on different explanatory variables describing aspects of residential houses.

Predicting house prices is useful to identify fruitful investments, or to determine whether the price advertised for a house is over or under-estimated.

We aim to minimise the difference between the real price and the price estimated by our model. We will evaluate model performance using the mean squared error (mse) and the root squared of the mean squared error (rmse).

We will use the house price dataset available on Kaggle.com