Churn for Bank Customers

Introduction

In this tutorial, we will guide you through the process of building a powerful machine learning model using Catalyst QuickML to predict whether or not a client would leave.

In this tutorial, we’ll first do preprocess the datasets to make sure they’re tidy and prepared for training. A data pipeline will be built next to handle data transformation, and an ML pipeline will be built to train and test the model. Finally, we’ll provide an endpoint for the trained model that enables interaction with external apps and provides churn for bank customers.

The churn for bank customers ML model is built using the following Catalyst service:

Catalyst QuickML : Using this service, we will first pre-process the sample dataset by implementing node operations on them and constructing the data pipeline. This pre-processed data will be used to create an ML model by executing ML algorithms. Finally, this churn for bank customers ML model can be accessed by external applications using the endpoint URL generated in QuickML.

The final output, after creating all the required data and ML pipelines in the Catalyst console, will look like this: