Demo Bank Customer Churn

Abstract

1. Introduction

This dataset contains 10,000 records, each of it corresponds to a different bank's user. The target is ExitedTask, a binary variable that describes whether the user decided to leave the bank. There are row and customer identifiers, four columns describing personal information about the user (surname, location, gender and age), and some other columns containing information related to the loan (such as credit score, current balance in the user's account and whether they are an active member among others).

2. Objective

Demo DVC project training a classification model on tabular data

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/iterative/demo-bank-customer-churn.git
cd demo-bank-customer-churn

$ git clone https://github.com/iterative/demo-bank-customer-churn
$ cd demo-bank-customer-churn

$ virtualenv -p python3 .venv
$ source .venv/bin/activate
$ pip install -r requirements.txt

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 iterative and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.