Bank Churn Prediction

Abstract

1. Introduction

The app is deployed to Render, and can be found at this URL

By reading the dataset into a dataframe using pandas, we removed unnecessary data fields including individual customer IDs and names. This left us with a list of columns for Credit Score, Geography, Gender, Age, Length of time as a Bank customer, Balance, Number Of Bank Products Used, Has a Credit Card, Is an Active Member, Estimated Salary and Exited.

Utilizing Matplotlib, Seaborn and Pandas, we next analyzed the data. We can see that our dataset was imbalanced. The majority class, "Stays" (0), has around 80% data points and the minority class, "Exits" (1), has around 20% datapoints. To address this, we utilized SMOTE in our machine learning algorithms (Synthetic Minority Over-sampling Technique). More on that later on.

2. Objective

Bank customers churn dashboard with predictions from several machine learning models.

This project demonstrates how Jupyter Notebook, Seaborn, Pandas 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/zunicd/Bank-Churn-Prediction.git
cd Bank-Churn-Prediction

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 zunicd 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.