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https://github.com/sayedgamal99/predicting-bank-customer-churn-using-machine-learning
This repository contains a machine learning project aimed at predicting whether a bank customer will churn (close their account) based on various features such as credit score, age, tenure, account balance, and product usage.
https://github.com/sayedgamal99/predicting-bank-customer-churn-using-machine-learning
docker flask machine-learning sklearn xgboost-classifier
Last synced: about 2 months ago
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This repository contains a machine learning project aimed at predicting whether a bank customer will churn (close their account) based on various features such as credit score, age, tenure, account balance, and product usage.
- Host: GitHub
- URL: https://github.com/sayedgamal99/predicting-bank-customer-churn-using-machine-learning
- Owner: sayedgamal99
- Created: 2024-11-17T09:50:41.000Z (about 2 months ago)
- Default Branch: main
- Last Pushed: 2024-11-25T21:42:33.000Z (about 2 months ago)
- Last Synced: 2024-11-25T22:28:54.492Z (about 2 months ago)
- Topics: docker, flask, machine-learning, sklearn, xgboost-classifier
- Language: Jupyter Notebook
- Homepage:
- Size: 19.5 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Bank Customer Churn Prediction App
## Table of Contents
1. [Overview](#overview)
2. [Project Highlights](#project-highlights)
3. [Data Analysis and Insights](#data-analysis-and-insights)
4. [Features](#features)
5. [Project Structure](#project-structure)
6. [Getting Started](#getting-started)
- [Prerequisites](#prerequisites)
- [Installation](#installation)
- [Docker Deployment](#docker-deployment)
7. [Usage](#usage)
8. [Technical Components](#technical-components)
9. [Model Performance](#model-performance)
10. [Example Scenario](#example-scenario)
11. [Contributing](#contributing)
12. [License](#license)
## Overview
The **Bank Customer Churn Prediction App** is a comprehensive data science project designed to predict customer account closures (churn) using advanced machine learning techniques. This end-to-end solution transforms raw customer data into actionable insights, helping financial institutions proactively manage customer retention.
## Project Highlights
- 🤖 **Advanced ML Ensemble**: XGBoost, LightGBM, and CatBoost
- 📊 **High Prediction Accuracy**: AUC Score of 0.891
- 🌐 **Web Interface**: Flask-based predictive application
- 🐳 **Containerized Deployment**: Docker support
- 📈 **Comprehensive Data Analysis**: In-depth exploratory analysis
### Technologies Used
- **Language**: Python 3.11.5
- **Core Libraries**:
- Pandas, NumPy
- Scikit-learn
- Matplotlib, Seaborn
#### Web & Deployment
- **Framework**: Flask
- **Containerization**: Docker
## Demonstration
## Data Analysis and Insights
### Key Findings
- **Churn Rate**: 21% of customers in the dataset
- **Geographic Trends**:
- Highest churn rate in Germany
- **Demographic Observations**:
- Females showed higher churn propensity
- **Customer Activity**:
- Non-active members more likely to churn
### Feature Engineering Highlights
- Created `Age-Tenure Ratio`
- Developed `Active Credit Card Status`
- Implemented targeted encoding strategies
### Visualization Insights
1. Zero-balance accounts have higher churn probability
2. Credit card ownership minimally impacts churn
3. Customers with 3-4 products exhibit increased churn risk
## Features
### Machine Learning
- **Ensemble Classifiers**:
- XGBoost
- LightGBM
- CatBoost
- Advanced preprocessing techniques
- Hyperparameter optimization
### Web Application
- Intuitive user interface
- Real-time churn prediction
- Sample data auto-fill functionality
### Deployment
- Docker containerization
- Easy-to-use deployment
## Project Structure
```plaintext
.
├── README.md # Project documentation
├── app/ # Flask application
│ ├── app.py # Main application entry point
│ ├── static/ # Static web resources
│ │ └── styles.css # Application styling
│ ├── templates/ # HTML templates
│ │ ├── base.html
│ │ ├── form.html
│ │ └── result.html
│ └── utils.py # Utility functions
├── models/ # Machine learning models
│ ├── train.py # Model training script
│ ├── test.py # Model testing script
│ └── model.joblib # Trained model
├── requirements.txt # Python dependencies
└── dockerfile # Docker configuration
```
## Getting Started
### Prerequisites
- Python 3.10+
- Docker (optional)
- pip package manager
### Installation
1. Clone the repository:
```bash
git clone https://github.com/your-username/bank-churn-prediction.git
cd bank-churn-prediction
```
2. Create virtual environment:
```bash
python -m venv .venv
source .venv/bin/activate # Unix/macOS
# or
.venv\Scripts\activate # Windows
```
3. Install dependencies:
```bash
pip install -r requirements.txt
```
### Docker Deployment
```bash
# Build Docker image
docker build -t churn-prediction-app .
# Run container
docker run -p 5000:5000 churn-prediction-app
# OR pull pre-built image
docker pull sayedgamal/churn-prediction-app:latest
docker run -p 5000:5000 sayedgamal/churn-prediction-app:latest
```
## Usage
### Web Application
1. Navigate to `http://localhost:5000`
2. Input customer details
3. Click "Predict" for churn probability
### Jupyter Notebook
Explore detailed analysis in `bank-customer-churn-prediction-0-891-auc-score.ipynb`
## Technical Components
- **Preprocessing**:
- Target encoding
- Feature scaling
- Custom engineering pipelines
- **Model Training**:
- Ensemble voting mechanism
- Cross-validation
- **Deployment**:
- Flask web framework
- Docker containerization
## Model Performance
- **AUC Score**: 0.891
- **Algorithms**: XGBoost, LightGBM, CatBoost
## Example Scenario
| Feature | Value |
| ------------- | ------- |
| Age | 45 |
| Credit Score | 750 |
| Geography | Germany |
| Gender | Female |
| Active Member | Yes |
etc..
**Predicted Churn Probability**: 76%
## Contributing
Contributions are welcome!
Steps to contribute:
1. Fork the repository
2. Create your feature branch
3. Commit changes
4. Push to the branch
5. Create a pull request
---
**Developed with ❤️ by Sayed Gamal**