With Amazon Machine Learning (Amazon ML), you can build and train predictive applications and host your applications in a scalable cloud solution. In this tutorial, we show you how to use Amazon ML to create a datasource, build a machine learning (ML) model, and use the model to generate batch predictions.
Our sample exercise in the tutorial shows how to identify potential customers for targeted marketing campaigns, but you can apply the same principles to create and use a variety of machine learning models. To complete the sample exercise, you use the publicly available banking and marketing dataset from the University of California at Irvine (UCI) repository. This dataset contains information about customers as well as descriptions of their behavior in response to previous marketing contacts. You use this data to identify which customers are most likely to subscribe to your new product. In the sample dataset, the product is a bank term deposit. A bank term deposit is a deposit made into a bank with a fixed interest rate that cannot be withdrawn for a certain period of time, also known as a certificate of deposit (CD).
To complete the tutorial, you download sample data and upload the data to Amazon S3 to create a datasource—an Amazon ML object that contains information about your data. Next, you create an ML model from the datasource. You evaluate and adjust the ML model's performance, and then use it to generate predictions.
You need an AWS account for this tutorial. If you don't have an AWS account, see Setting Up Amazon Machine Learning.
Complete the following steps to get started using Amazon ML: