summary in this we started by improving our rating predictions from the previous we saw couple of different ways in which to do so and then combined them all in single prediction by learning how to use set of weights these techniquesensemble or stacked learningare general techniques that can be used in many situations and not just for regression they allow you to combine different ideas even if their internal mechanics are completely differentyou can combine their final outputs in the second half of the we switched gears and looked at another method of recommendationshopping basket analysis or association rule mining in this modewe try to discover (probabilisticassociation rules of the customers who bought are likely to be interested in form this takes advantage of the data that is generated from sales alone without requiring users to numerically rate items this is not available in scikit-learn (yet)so we wrote our own code (for changeassociation rule mining needs to be careful to not simply recommend bestsellers to every user (otherwisewhat is the point of personalization?in order to do thiswe learned about measuring the value of rules in relation to the baseline as the lift of rule in the next we will build music genre classifier