Telco Customer Churn In R And H2o

Abstract

1. Introduction

ZhouFang928 in a blog post Telco Customer Churn with R in SQL Server 2016 presented a great analysis of telco customer churn prediction. I found it missed one of my favorite machine-learning library H2O in the comparison. This showcase presents how easy it is to use H2O library to build very good quality predictive models.

Showcase for using H2O and R for churn prediction (inspired by ZhouFang928 examples).

Instalation of the packages requires Rtools compatible with your R version.

2. Objective

Showcase for using H2O and R for churn prediction (inspired by ZhouFang928 examples)

This project demonstrates how R, Machine Learning 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.

• See the project README for exact requirements
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/WLOGSolutions/telco-customer-churn-in-r-and-h2o.git
cd telco-customer-churn-in-r-and-h2o

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 WLOGSolutions 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.