https://github.com/md-emon-hasan/ml-project-e-commerce-fraud-detection
🛒 Provide a robust model that assists in flagging suspicious transactions, ultimately helping businesses improve security and reduce financial losses.
https://github.com/md-emon-hasan/ml-project-e-commerce-fraud-detection
anomaly-detection classification data-science ecommerce financial-data fraud-detection fraudulent-transactions machine-learning online-shop risk-management
Last synced: 7 months ago
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🛒 Provide a robust model that assists in flagging suspicious transactions, ultimately helping businesses improve security and reduce financial losses.
- Host: GitHub
- URL: https://github.com/md-emon-hasan/ml-project-e-commerce-fraud-detection
- Owner: Md-Emon-Hasan
- License: mit
- Created: 2024-10-31T17:58:16.000Z (11 months ago)
- Default Branch: master
- Last Pushed: 2024-11-01T16:05:23.000Z (11 months ago)
- Last Synced: 2025-01-13T08:46:20.201Z (9 months ago)
- Topics: anomaly-detection, classification, data-science, ecommerce, financial-data, fraud-detection, fraudulent-transactions, machine-learning, online-shop, risk-management
- Language: Jupyter Notebook
- Homepage: https://ml-project-e-commerce-fraud-detection.onrender.com
- Size: 18.9 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 🛒 E-commerce Fraud Detection Project
Welcome to the **E-commerce Fraud Detection** repository! This project aims to address the growing concern of fraudulent activities in online marketplaces. By leveraging machine learning, this system helps identify potential fraudulent transactions, showcasing the impact of data science on enhancing security in e-commerce platforms.
## 📋 Contents
- [Introduction](#introduction)
- [Topics Covered](#topics-covered)
- [Getting Started](#getting-started)
- [Machine Learning Model](#machine-learning-model)
- [Data](#data)
- [Best Practices](#best-practices)
- [FAQ](#faq)
- [Troubleshooting](#troubleshooting)
- [Contributing](#contributing)
- [Additional Resources](#additional-resources)
- [Challenges Faced](#challenges-faced)
- [Lessons Learned](#lessons-learned)
- [Why I Created This Repository](#why-i-created-this-repository)
- [License](#license)
- [Contact](#contact)
---
## 📖 Introduction
This repository presents an E-commerce Fraud Detection system, employing machine learning to flag suspicious transactions. By predicting potential fraud, this project emphasizes the importance of secure e-commerce experiences and provides a foundational tool for combating online fraud.
---
## 🔍 Topics Covered
- **Fraud Detection Models:** Training machine learning models to detect anomalies in transactions.
- **Data Preprocessing:** Cleaning and preparing data for model input.
- **Model Evaluation:** Using metrics like accuracy, recall, and precision for performance assessment.
- **Deployment:** Techniques for deploying the model for practical use cases in real-time or batch processing.
---
## 🚀 Getting Started
To get started with this project, follow these steps:
1. **Clone the repository:**
```bash
git clone https://github.com/Md-Emon-Hasan/ML-Project-E-commerce-Fraud-Detection.git
```
2. **Navigate to the project directory:**
```bash
cd ML-Project-E-commerce-Fraud-Detection
```
3. **Create and activate a virtual environment:**
```bash
python -m venv venv
source venv/bin/activate # For Windows: `venv\Scripts\activate`
```
4. **Install the dependencies:**
```bash
pip install -r requirements.txt
```
5. **Run the application (if there is a UI component):**
```bash
python app.py
```
6. **Access the application at:**
```
http://127.0.0.1:5000/
```
---
## 🧠 Machine Learning Model
The project utilizes a machine learning model to predict fraudulent transactions, enhancing e-commerce security.
### Key Components:
- **Data Exploration:** Understanding features related to fraudulent activities.
- **Model Training:** Applying algorithms like Random Forest, Logistic Regression, and XGBoost.
- **Model Evaluation:** Assessing model performance with metrics such as ROC-AUC and F1-score to maximize fraud detection accuracy.
---
## 📊 Data
- The dataset used for this project can be found [here](https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud) (or provide the actual dataset link).
- The dataset includes features such as transaction amount, time, and other encoded attributes.
- Preprocessing steps include outlier removal, normalization, and data balancing to address class imbalance in fraud detection.
---
## 🌟 Best Practices
Suggestions for maintaining and improving this project:
- **Data Security:** Ensure data privacy when handling sensitive transaction data.
- **Model Updates:** Retrain periodically with new data to adapt to evolving fraud patterns.
- **Documentation:** Keep all documentation updated for ease of future modifications and improvements.
---
## ❓ FAQ
**Q: What is the purpose of this project?**
A: This project aims to predict potentially fraudulent transactions to secure online shopping experiences.
**Q: How can I contribute to this repository?**
A: See the [Contributing](#contributing) section for more details.
**Q: Is this deployable on cloud platforms?**
A: Yes, it can be deployed on platforms like AWS, Heroku, or Render.
---
## 🛠️ Troubleshooting
Common issues and solutions:
- **Issue: Low Model Performance**
*Solution:* Experiment with feature engineering or try different algorithms to enhance model accuracy.
- **Issue: Dependencies Not Installed**
*Solution:* Confirm the virtual environment is activated and use `pip install -r requirements.txt`.
---
## 🤝 Contributing
Contributions are welcome! Here’s how to get started:
1. **Fork the repository.**
2. **Create a branch:**
```bash
git checkout -b feature/new-feature
```
3. **Make changes.**
4. **Commit:**
```bash
git commit -m 'Add feature or fix issue'
```
5. **Push and create a pull request.**
---
## 📚 Additional Resources
For further reading on fraud detection in machine learning:
- **Fraud Detection Dataset:** [Kaggle](https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud)
- **Machine Learning Course:** [Coursera](https://www.coursera.org/learn/machine-learning)
- **Flask Documentation:** [flask.palletsprojects.com](https://flask.palletsprojects.com/)
---
## 💪 Challenges Faced
Key challenges during development:
- Handling imbalanced classes to avoid biased predictions.
- Ensuring the model performs well on unseen data.
---
## 📚 Lessons Learned
Valuable insights gained:
- Enhanced understanding of fraud detection in e-commerce.
- Learned the significance of data preprocessing, especially with imbalanced datasets.
---
## 🌟 Why I Created This Repository
This repository was developed to explore ways machine learning can help combat fraud in e-commerce, reinforcing the importance of data science in online security.
---
## 📝 License
This repository is licensed under the [MIT License](https://opensource.org/licenses/MIT). See the [LICENSE](LICENSE) file for details.
---
## 📬 Contact
- **Email:** [iconicemon01@gmail.com](mailto:iconicemon01@gmail.com)
- **WhatsApp:** [+8801834363533](https://wa.me/8801834363533)
- **GitHub:** [Md-Emon-Hasan](https://github.com/Md-Emon-Hasan)
- **LinkedIn:** [Md Emon Hasan](https://www.linkedin.com/in/md-emon-hasan)
- **Facebook:** [Md Emon Hasan](https://www.facebook.com/mdemon.hasan2001/)
---