3 Use Cases
- Classification Use Case
A retail store has information about its customers' behavior and the purchases they make. Now with the available data, they want you to analyze and identify the type of customers most likely to be positive responders to an Affinity Card loyalty program. High Affinity Card responders are defined as those customers who, when given a loyalty or affinity card, hyper-respond, that is, increase purchases more than the Affinity Card program's offered discount. In our data set, a responder is designated with value 1, and a non-responder with value 0. In this use case, you will demonstrate how to identify such customers using the Support Vector Machine model. - Clustering Use Case
A retail store has information about its customers' behavior and the purchases they make. With that data, they would like you to analyze and identify if there are groups of customers with similar characteristics. Use Oracle Machine Learning to segment customers by finding clusters in the data set that can be then used to support targeted marketing campaigns to increase retail sales. In this use case, you will learn how to identify such segments using the k-Means algorithm.