Icp4d Customer Churn Classifier

Abstract

1. Introduction

In this code pattern, we will create and deploy a customer churn prediction model using IBM Cloud Pak for Data. The basis for our model is a data set that contains customer demographics and trading activity data. We will use a Jupyter notebook to visualize the data, build hypotheses for prediction, and then build, test, and save a prediction model. Finally, we will enable a web service and use the model from an app.

The use case describes a stock trader company that can use churn prediction to target offers for at-risk customers. Once deployed, the model can be used for inference from an application using the REST API. A simple app is provided to demonstrate using the model from a Python app.

2. Objective

Infuse AI into your application. Create and deploy a customer churn prediction model with IBM Cloud Private for Data, Db2 Warehouse, Spark MLlib, and Jupyter notebooks.

This project demonstrates how Jupyter Notebook, Machine Learning, Python can be applied to a real-world Data Science problem.

4. Technology Stack

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Python 3.8 or later with Jupyter Notebook / JupyterLab (or Google Colab)
• pip for dependencies
• Python 3.8 or later
• pip / virtualenv for dependencies
• VS Code, PyCharm or Jupyter Notebook
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/IBM/icp4d-customer-churn-classifier.git
cd icp4d-customer-churn-classifier

1. Navigate to the left-hand () hamburger menu and choose Deployments:
2. Click on New deployment space +:
3. Click on the top tile for 'Create an empty space':
4. Give your deployment space a unique name, an optional description, then click Create.
5. When you get a notification that the space is ready, click on View new space.
6. Run the entire notebook using the menu Cell ▷ Run All or run the cells individually with the play button as shown here.

#Insert the name of your deployment space here:
DEPLOYMENT_SPACE_NAME = 'INSERT-YOUR-DEPLOYMENT-SPACE-NAME-HERE'

# Now set the default space to the GUID for your deployment space. If this is successful, you will see a 'SUCCESS' message.
client.set.default_space('INSERT_SPACE_ID_HERE')

git clone https://github.com/IBM/icp4d-customer-churn-classifier
cd icp4d-customer-churn-classifier/stocktraderapp

# Create the virtual environment using Python.
# Note, it may be named python3 on your system.
python -m venv venv       # Python 3.X

# Source the virtual environment. Use one of the two commands depending on your OS.
source venv/bin/activate  # Mac or Linux
./venv/Scripts/activate   # Windows PowerShell

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Turn the analysis into an interactive dashboard
• Automate data refresh with a scheduled job
• Add a predictive model on top of the analysis

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What is the source of the dataset and how was missing data handled?
2. Which exploratory analysis steps revealed the most useful insight?
3. Why were these particular charts chosen to present the data?
4. Which statistical or ML technique supports the conclusions?
5. How could the analysis be automated or refreshed with new data?

9. Source Code & License

This project is developed by IBM and published on GitHub under the Apache License 2.0. Please follow the license terms and credit the original author when you use or modify this code.