This code pattern walks you through the full cycle of a data science project. You begin by understanding the business perspective of the problem – here we used customer churn. Then, you use the available data set to gain insights and build a predictive model for use with future data. You’ll deploy the model into production and use it to score data collected from a user interface.
Customer churn, when a customer ends their relationship with a business, is one of the most basic factors in determining the revenue of a business. You need to know which of your customers are loyal and which are at risk of churning, and you need to know the factors that affect these decisions from a customer perspective. This code pattern explains how to build a machine learning model and use it to predict whether a customer is at risk of churning. This is a full data science project, and you can use your model findings for prescriptive analysis later or for targeted marketing.
When you have completed this code pattern, you’ll understand how to:
- Use Jupyter Notebooks to load, visualize, and analyze data
- Run Notebooks in IBM Watson Studio
- Load data from IBM Cloud Object Storage
- Build, test, and compare different machine learning models using scikit-learn
- Deploy a selected machine learning model to production using Watson Studio
- Create a front-end application to interface with the client and start consuming your deployed model
- Understand the business problem.
- Load the provided Notebook into the Watson Studio platform.
- The Telco customer churn data set is loaded into the Jupyter Notebook.
- Describe, analyze, and visualize data in the notebook.
- Pre-process the data, build machine learning models, and test them.
- Deploy a selected machine learning model to production.
- Interact and consume your model using a front-end application.
Find the detailed instructions in the readme file. These instructions show you how to:
- Sign up for Watson Studio.
- Create a new project.
- Upload the data set.
- Import the Notebook to Watson Studio.
- Import the data set into the Notebook.
- Follow the steps in the Notebook.
- Create a Watson Machine Learning Service instance.
- Either deploy to IBM Cloud or deploy locally.