Abstract
Bank Customer Churn is an open-source Data Science project. This project focuses on predicting customer churn for a bank using machine learning. By analyzing features like credit score, age, balance, and geography, the model identifies at-risk customers, uncovers key churn drivers, and provides insights to help the bank implement targeted retention strategies. The goal of this project is to predict if a bank customer will churn (exit) using various machine learning techniques. The dataset includes customer information such as credit score, geography, gender, age, balance, and other relevant features. It is built using Jupyter Notebook. The complete source code is publicly available on GitHub under the MIT License, making it a useful reference for students building a Data Science mini project or final-year project.
1. Introduction
The goal of this project is to predict if a bank customer will churn (exit) using various machine learning techniques. The dataset includes customer information such as credit score, geography, gender, age, balance, and other relevant features.
The bank is experiencing a significant churn rate among its customers, which is impacting its revenue and overall business performance. Identifying the key factors that contribute to customer churn and developing a predictive model to forecast which customers are likely to exit can help the bank proactively address these issues. The goal of this project is to analyze customer data and build a machine learning model that can accurately predict customer churn, enabling the bank to implement targeted retention strategies and improve customer satisfaction.
2. Objective
This project focuses on predicting customer churn for a bank using machine learning. By analyzing features like credit score, age, balance, and geography, the model identifies at-risk customers, uncovers key churn drivers, and provides insights to help the bank implement targeted retention strategies.
This project demonstrates how Jupyter Notebook 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
- Git (to clone the repository)
6. Installation & Setup
git clone https://github.com/DavieObi/Bank-Customer-Churn.git
cd Bank-Customer-Churn- model.pkl (Trained Machine Learning Model)
- scaler.pkl (Feature Scaler)
- label_encoder.pkl (Label Encoder)
- requirements.txt (python dependencies)
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.
- What is the source of the dataset and how was missing data handled?
- Which exploratory analysis steps revealed the most useful insight?
- Why were these particular charts chosen to present the data?
- Which statistical or ML technique supports the conclusions?
- How could the analysis be automated or refreshed with new data?
9. Source Code & License
This project is developed by DavieObi 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.
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