Update README.md

README.md

@@ -1,195 +1,202 @@
-# 💳 Credit Card & Transaction Fraud Detection System
-
-## Overview
-A sophisticated real-time fraud detection system using ensemble machine learning models and advanced analytics to protect financial transactions.
-
-### 🌟 Key Features
-- **Real-time Fraud Detection**: Instant transaction monitoring and analysis
-- **Multi-Model Ensemble**: XGBoost, LightGBM, Random Forest, and Gradient Boosting
-- **Advanced Analytics Dashboard**: Interactive visualizations and insights
-- **Role-Based Access Control**: User, Analyst, Manager, and Admin roles
-- **Email Alert System**: Automated notifications for suspicious activities
-- **Batch Processing**: Handle multiple transactions simultaneously
-
-## 🚀 Technical Stack
-- **Frontend**: Streamlit
-- **Backend**: Python
-- **ML Models**: 
-  - XGBoost
-  - LightGBM
-  - Random Forest
-  - Gradient Boosting
-- **Data Processing**: Pandas, NumPy
-- **Visualization**: Plotly, Matplotlib
-- **Security**: SHA-256 encryption
-
-## 📊 Features In Detail
-
-### 1. Fraud Detection
-- Real-time transaction scoring
-- Risk level assessment (LOW, MEDIUM, HIGH, CRITICAL)
-- Behavioral analysis
-- Location-based risk assessment
-- Velocity checks
-
-### 2. User Interface
-- Interactive dashboards
-- Real-time monitoring
-- Advanced visualization
-- Risk score breakdown
-- Transaction patterns analysis
-
-### 3. Security Features
-- Role-based access control
-- Password encryption
-- Session management
-- Activity logging
-- Audit trails
-
-### 4. Alert System
-- Email notifications
-- Risk-based alerting
-- Customizable thresholds
-- Batch alert processing
-
-## 🚀 Dataset
-The dataset used for this project is the [Credit Card Fraud Detection](https://www.kaggle.com/datasets/priyamchoksi/credit-card-transactions-dataset?resource=download&select=credit_card_transactions.csv)
-dataset from Kaggle.
-
-## 📁 Project Structure
-```
-Credit-Card-Fraud-Detection/
-├── 📁 data/                      # Data directory
-│   ├── users.csv                 # User credentials and roles
-│   ├── transactions.csv          # Dataset of credit card transactions
-│   └── models/                   # ML model implementations
-│       ├── xgb_model.json        # XGBoost model file
-│       └── features.pkl         # Saved feature configurations
-│
-├── 📁 .streamlit/               # Streamlit configurations
-│   ├── config.toml             # Streamlit settings
-│   └── secrets.toml            # Secure credentials
-│
-├── 📄 app.py                    # Main Streamlit application
-├── 📄 requirements.txt          # Project dependencies
-├── 📄 .env                      # Environment variables
-├── 📄 .gitignore               # Git ignore patterns
-├── 📄 LICENSE                   # License information
-└── 📄 README.md                # Project documentation
-```
-
-## 🛠️ Installation
-
-1. Clone the repository:
-```bash
-git clone https://github.com/D3V-S4NJ4Y/Credit-Card-Fraud-Detection-System
-```
-
-2. Install required packages:
-```bash
-pip install -r requirements.txt
-```
-4. Run the application:
-```bash
-streamlit run app.py
-```
-
-## 👥 User Roles
-
-   **Default--> Usersname: Password**
-   - admin: admin
-   - manager: 123456
-   - Register New User with Correct Email id for Email Alerts
-
-1. **User**
-   - View basic transaction details
-   - Submit transactions
-   - Receive alerts
-
-3. **Manager**
-   - Advanced analysis
-   - Team management
-   - Performance monitoring
-
-4. **Admin**
-   - Full system access
-   - User management
-   - System configuration
-
-## 📈 Analytics Features
-
-### Transaction Analysis
-- Risk scoring
-- Behavioral patterns
-- Anomaly detection
-- Historical analysis
-
-### Visualization
-- 3D risk analysis
-- Decision flow diagrams
-- Risk component breakdown
-- Performance metrics
-
-### Reporting
-- Real-time dashboards
-- Batch analysis reports
-- Custom filters
-- Export capabilities
-
-## ⚙️ Configuration
-
-### Email Setup
-```python
-EMAIL_CONFIG = {
-    "smtp_user": "your-email@gmail.com",
-    "smtp_password": "your-app-password",
-    "smtp_server": "smtp.gmail.com",
-    "smtp_port": 587
-}
-```
-
-### Risk Thresholds
-```python
-RISK_THRESHOLDS = {
-    'CRITICAL': 0.9,
-    'HIGH': 0.7,
-    'MEDIUM': 0.5,
-    'LOW': 0.2
-}
-```
-
-## 🔒 Security Best Practices
-
-1. Use environment variables for sensitive data
-2. Regular password updates
-3. Session timeout implementation
-4. Activity logging
-5. Data encryption
-
-## 📝 Logs and Monitoring
-
-- System health monitoring
-- User activity logs
-- Model performance tracking
-- Error logging
-
-## 📄 License
-This project is licensed under the MIT License - see the [LICENSE.md](LICENSE.md) file for details.
-
-## 👨‍💻 Author
-**Sanjay Kumar**
-- Email: sanjay.dev925@gmail.com
-- GitHub: [Your GitHub Profile](https://github.com/D3V-S4NJ4Y)
-
-## 🙏 Acknowledgments
-- Machine Learning libraries contributors
-- Streamlit community
-- Security framework developers
-
-## 📞 Support
-For support and queries:
-- Create an issue in the repository
-- Contact: sanjay.dev925@gmail.com
-
----
-⭐ Star this repository if you find it helpful!
+---
+title: Fraud-Detection-System
+sdk: streamlit
+emoji: 🚀
+colorFrom: indigo
+colorTo: purple
+---
+# 💳 Credit Card & Transaction Fraud Detection System
+
+## Overview
+A sophisticated real-time fraud detection system using ensemble machine learning models and advanced analytics to protect financial transactions.
+
+### 🌟 Key Features
+- **Real-time Fraud Detection**: Instant transaction monitoring and analysis
+- **Multi-Model Ensemble**: XGBoost, LightGBM, Random Forest, and Gradient Boosting
+- **Advanced Analytics Dashboard**: Interactive visualizations and insights
+- **Role-Based Access Control**: User, Analyst, Manager, and Admin roles
+- **Email Alert System**: Automated notifications for suspicious activities
+- **Batch Processing**: Handle multiple transactions simultaneously
+
+## 🚀 Technical Stack
+- **Frontend**: Streamlit
+- **Backend**: Python
+- **ML Models**: 
+  - XGBoost
+  - LightGBM
+  - Random Forest
+  - Gradient Boosting
+- **Data Processing**: Pandas, NumPy
+- **Visualization**: Plotly, Matplotlib
+- **Security**: SHA-256 encryption
+
+## 📊 Features In Detail
+
+### 1. Fraud Detection
+- Real-time transaction scoring
+- Risk level assessment (LOW, MEDIUM, HIGH, CRITICAL)
+- Behavioral analysis
+- Location-based risk assessment
+- Velocity checks
+
+### 2. User Interface
+- Interactive dashboards
+- Real-time monitoring
+- Advanced visualization
+- Risk score breakdown
+- Transaction patterns analysis
+
+### 3. Security Features
+- Role-based access control
+- Password encryption
+- Session management
+- Activity logging
+- Audit trails
+
+### 4. Alert System
+- Email notifications
+- Risk-based alerting
+- Customizable thresholds
+- Batch alert processing
+
+## 🚀 Dataset
+The dataset used for this project is the [Credit Card Fraud Detection](https://www.kaggle.com/datasets/priyamchoksi/credit-card-transactions-dataset?resource=download&select=credit_card_transactions.csv)
+dataset from Kaggle.
+
+## 📁 Project Structure
+```
+Credit-Card-Fraud-Detection/
+├── 📁 data/                      # Data directory
+│   ├── users.csv                 # User credentials and roles
+│   ├── transactions.csv          # Dataset of credit card transactions
+│   └── models/                   # ML model implementations
+│       ├── xgb_model.json        # XGBoost model file
+│       └── features.pkl         # Saved feature configurations
+│
+├── 📁 .streamlit/               # Streamlit configurations
+│   ├── config.toml             # Streamlit settings
+│   └── secrets.toml            # Secure credentials
+│
+├── 📄 app.py                    # Main Streamlit application
+├── 📄 requirements.txt          # Project dependencies
+├── 📄 .env                      # Environment variables
+├── 📄 .gitignore               # Git ignore patterns
+├── 📄 LICENSE                   # License information
+└── 📄 README.md                # Project documentation
+```
+
+## 🛠️ Installation
+
+1. Clone the repository:
+```bash
+git clone https://github.com/D3V-S4NJ4Y/Credit-Card-Fraud-Detection-System
+```
+
+2. Install required packages:
+```bash
+pip install -r requirements.txt
+```
+4. Run the application:
+```bash
+streamlit run app.py
+```
+
+## 👥 User Roles
+
+   **Default--> Usersname: Password**
+   - admin: admin
+   - manager: 123456
+   - Register New User with Correct Email id for Email Alerts
+
+1. **User**
+   - View basic transaction details
+   - Submit transactions
+   - Receive alerts
+
+3. **Manager**
+   - Advanced analysis
+   - Team management
+   - Performance monitoring
+
+4. **Admin**
+   - Full system access
+   - User management
+   - System configuration
+
+## 📈 Analytics Features
+
+### Transaction Analysis
+- Risk scoring
+- Behavioral patterns
+- Anomaly detection
+- Historical analysis
+
+### Visualization
+- 3D risk analysis
+- Decision flow diagrams
+- Risk component breakdown
+- Performance metrics
+
+### Reporting
+- Real-time dashboards
+- Batch analysis reports
+- Custom filters
+- Export capabilities
+
+## ⚙️ Configuration
+
+### Email Setup
+```python
+EMAIL_CONFIG = {
+    "smtp_user": "your-email@gmail.com",
+    "smtp_password": "your-app-password",
+    "smtp_server": "smtp.gmail.com",
+    "smtp_port": 587
+}
+```
+
+### Risk Thresholds
+```python
+RISK_THRESHOLDS = {
+    'CRITICAL': 0.9,
+    'HIGH': 0.7,
+    'MEDIUM': 0.5,
+    'LOW': 0.2
+}
+```
+
+## 🔒 Security Best Practices
+
+1. Use environment variables for sensitive data
+2. Regular password updates
+3. Session timeout implementation
+4. Activity logging
+5. Data encryption
+
+## 📝 Logs and Monitoring
+
+- System health monitoring
+- User activity logs
+- Model performance tracking
+- Error logging
+
+## 📄 License
+This project is licensed under the MIT License - see the [LICENSE.md](LICENSE.md) file for details.
+
+## 👨‍💻 Author
+**Sanjay Kumar**
+- Email: sanjay.dev925@gmail.com
+- GitHub: [Your GitHub Profile](https://github.com/D3V-S4NJ4Y)
+
+## 🙏 Acknowledgments
+- Machine Learning libraries contributors
+- Streamlit community
+- Security framework developers
+
+## 📞 Support
+For support and queries:
+- Create an issue in the repository
+- Contact: sanjay.dev925@gmail.com
+
+---
+⭐ Star this repository if you find it helpful!