Customer Churn Prediction: Machine Learning Model Development

Introduction

This tutorial will guide you through building a powerful machine-learning model using Catalyst QuickML to predict customer churn.

The process will begin with preprocessing the dataset to ensure it’s clean and ready for training. Next, you’ll build a data pipeline to handle necessary transformations, followed by creating an ML pipeline to train and test the model. Finally, you’ll expose the trained model through an endpoint, allowing external applications to interact with it and retrieve churn predictions.

This churn prediction model will be constructed using the Catalyst QuickML service, starting with preprocessing the sample dataset using node operations to create the data pipeline. Once the data is ready, ML algorithms will be executed to train the model. Once trained, the model can be accessed via an endpoint URL generated in QuickML, making it available for use in external applications.

The final result will look like this after all of the necessary data and ML pipelines are created in the Catalyst console: