introduction to machine learning and deep learning users to show the products they might be interested in buying the website has historical data on users and would like to implement this as feature to increase sales let' now see how this real-world problem maps on to the abstract problem of binary classification we described earlier the first thing that one might notice is that given particular user and particular productone would want to predict whether the user will buy the product since this is the value to be predictedit maps on to + where we will let the value of denote the prediction that the user will buy the product and the value of denote the prediction that the user will not buy the product note that there is no particular reason for picking these valueswe could have swapped this (let denote the does not buy case and denote the buy case)and there would be no difference we just use + to denote the two classes of interest to categorize data nextlet' assume that we can represent the attributes of the product and the users buying and browsing history as rn this step is referred to as feature engineering in machine learning and we will cover it later in the for nowit suffices to say that we are able to generate such mapping thuswe have historical data of what the users browsed and boughtattributes of productand whether the user bought the product or not mapped on to {( )( )(xnyn)nowbased on this datawe would like to generate function or model ywhich we can use to determine which products particular user will buyand use this to populate the landing page for users we can measure how well the model is doing on unseen data by populating the landing page for usersseeing whether they buy the products or notand evaluating the error efduregression this section introduces another taskregression herewe have data of the form {( )( )(xnyn)}where rn and rand our task is to generate computational procedure that implements the function