Churn Prediction Using Apache Spark ML Pipelines with Zeppelin Notebooks

Video created by maprcommunity Employee on Jun 16, 2017

    Churn prediction is big business. It minimizes customer defection by predicting which customers are likely to cancel a service. Though originally used within the telecommunications industry, it has become common practice for banks, ISPs, insurance firms, and other verticals.


    The prediction process is data-driven and often uses advanced machine learning techniques. In this webinar, we'll look at customer data, do some preliminary analysis, and generate churn prediction models – all with Spark machine learning (ML) and a Zeppelin notebook.


    Spark’s ML library goal is to make machine learning scalable and easy. Zeppelin with Spark provides a web-based notebook that enables interactive machine learning and visualization.


    In this tutorial, we'll do the following:


    Review classification and decision trees
    Use Spark DataFrames with Spark ML pipelines
    Predict customer churn with Apache Spark ML decision trees
    Use Zeppelin to run Spark commands and visualize the results


    Have a question? ask it here: Churn Prediction Webinar Chat Questions