Update README.md

README.md

@@ -1,168 +1,177 @@
-# Fraud Detection System
-
-An interactive web application for real-time fraud detection using machine learning.
-
-## Features
-
-- **Real-time Analysis**: Instant fraud detection with probability scores
-- **Interactive Interface**: User-friendly web interface with Bootstrap styling
-- **Machine Learning**: Random Forest classifier trained on transaction data
-- **Risk Assessment**: 5-level risk classification (Very Low to Very High)
-- **Visual Results**: Progress bars and color-coded risk indicators
-
-## Installation
-
-1. **Install Python dependencies:**
-   ```bash
-   pip install -r requirements.txt
-   ```
-
-2. **Run the application:**
-   ```bash
-   python app.py
-   ```
-
-3. **Or use the batch file (Windows):**
-   ```bash
-   start_app.bat
-   ```
-
-## Usage
-
-1. Open your browser and go to `http://localhost:5000`
-2. Fill in the transaction details:
-   - Personal Information (Gender, Age, Location)
-   - Account Information (Bank Branch, Account Type, Balance)
-   - Transaction Information (Amount, Type, Device, etc.)
-3. Click "Analyze Transaction"
-4. View the fraud probability and risk assessment
-
-## Input Fields
-
-### Personal Information
-- **Gender**: Female or Male
-- **Age**: Age of the account holder (18-100)
-- **State**: Indian states (Andhra Pradesh to Ladakh)
-- **City**: Major Indian cities (Mumbai, Delhi, Bangalore, etc.)
-
-### Account Information
-- **Bank Branch**: Indian bank branches (SBI, HDFC, ICICI, etc.)
-- **Account Type**: Savings, Current, Credit Card, Fixed Deposit, Salary, Joint
-- **Account Balance**: Current account balance in Indian Rupees (₹)
-
-### Transaction Information
-- **Transaction Amount**: Amount being transacted in Indian Rupees (₹)
-- **Transaction Type**: ATM Withdrawal, Bank Deposit, Fund Transfer, UPI Payment, etc.
-- **Transaction Device**: ATM, POS, Mobile Apps, UPI platforms, Internet Banking, etc.
-- **Transaction Date**: Day of month (1-31)
-- **Transaction Time**: Hourly time slots (24-hour format)
-- **Currency**: INR (default), USD, EUR, GBP, AED, SGD, CAD, AUD
-
-## API Endpoints
-
-### GET /
-Returns the main web interface
-
-### POST /predict
-**Request Body:**
-```json
-{
-    "gender": "1",
-    "age": "35",
-    "state": "15",
-    "city": "127",
-    "bank_branch": "127",
-    "account_type": "2",
-    "transaction_date": "22",
-    "transaction_time": "52151",
-    "transaction_amount": "32415.45",
-    "transaction_type": "3",
-    "account_balance": "74557.27",
-    "transaction_device": "17",
-    "transaction_currency": "0"
-}
-```
-
-**Response:**
-```json
-{
-    "prediction": 0,
-    "fraud_probability": 15.75,
-    "risk_level": "Very Low",
-    "risk_color": "#28a745",
-    "message": "Legitimate Transaction"
-}
-```
-
-## Risk Levels
-
-- **Very Low** (0-20%): Green (#28a745)
-- **Low** (20-40%): Teal (#20c997)
-- **Medium** (40-60%): Yellow (#ffc107)
-- **High** (60-80%): Orange (#fd7e14)
-- **Very High** (80-100%): Red (#dc3545)
-
-## Testing
-
-Run the API test script:
-```bash
-python test_api.py
-```
-
-## Files Structure
-
-```
-fraud_flask_app/
-├── app.py                 # Main Flask application
-├── model.pkl             # Trained Random Forest model
-├── encoders.pkl          # Label encoders for categorical features
-├── requirements.txt      # Python dependencies
-├── start_app.bat        # Windows startup script
-├── test_api.py          # API testing script
-├── templates/
-│   └── index.html       # Web interface template
-├── static/
-│   └── style.css        # Custom styling
-└── README.md           # This file
-```
-
-## Model Information
-
-- **Algorithm**: Random Forest Classifier
-- **Features**: 13 input features
-- **Training Data**: Bank transaction fraud dataset
-- **Performance**: Optimized for fraud detection accuracy
-
-## Technologies Used
-
-- **Backend**: Flask (Python)
-- **Frontend**: HTML5, CSS3, JavaScript, Bootstrap 5
-- **Machine Learning**: scikit-learn, pandas, numpy
-- **Icons**: Font Awesome
-
-## Browser Support
-
-- Chrome/Chromium (recommended)
-- Firefox
-- Safari
-- Edge
-
-## Troubleshooting
-
-### Model Loading Issues
-- Ensure `model.pkl` and `encoders.pkl` are in the same directory as `app.py`
-- Check Python package versions match requirements.txt
-
-### Connection Issues
-- Verify Flask is running on port 5000
-- Check firewall settings
-- Ensure no other application is using port 5000
-
-### Prediction Errors
-- Validate all input fields are filled correctly
-- Check input value ranges match the specified limits
-- Ensure numeric fields contain valid numbers
-
-## License
-
-This project is for educational and demonstration purposes.
+---
+license: mit
+title: Fraud_Detection_using_ML
+sdk: docker
+emoji: 🏢
+colorFrom: green
+colorTo: red
+pinned: true
+---
+# Fraud Detection System
+
+An interactive web application for real-time fraud detection using machine learning.
+
+## Features
+
+- **Real-time Analysis**: Instant fraud detection with probability scores
+- **Interactive Interface**: User-friendly web interface with Bootstrap styling
+- **Machine Learning**: Random Forest classifier trained on transaction data
+- **Risk Assessment**: 5-level risk classification (Very Low to Very High)
+- **Visual Results**: Progress bars and color-coded risk indicators
+
+## Installation
+
+1. **Install Python dependencies:**
+   ```bash
+   pip install -r requirements.txt
+   ```
+
+2. **Run the application:**
+   ```bash
+   python app.py
+   ```
+
+3. **Or use the batch file (Windows):**
+   ```bash
+   start_app.bat
+   ```
+
+## Usage
+
+1. Open your browser and go to `http://localhost:5000`
+2. Fill in the transaction details:
+   - Personal Information (Gender, Age, Location)
+   - Account Information (Bank Branch, Account Type, Balance)
+   - Transaction Information (Amount, Type, Device, etc.)
+3. Click "Analyze Transaction"
+4. View the fraud probability and risk assessment
+
+## Input Fields
+
+### Personal Information
+- **Gender**: Female or Male
+- **Age**: Age of the account holder (18-100)
+- **State**: Indian states (Andhra Pradesh to Ladakh)
+- **City**: Major Indian cities (Mumbai, Delhi, Bangalore, etc.)
+
+### Account Information
+- **Bank Branch**: Indian bank branches (SBI, HDFC, ICICI, etc.)
+- **Account Type**: Savings, Current, Credit Card, Fixed Deposit, Salary, Joint
+- **Account Balance**: Current account balance in Indian Rupees (₹)
+
+### Transaction Information
+- **Transaction Amount**: Amount being transacted in Indian Rupees (₹)
+- **Transaction Type**: ATM Withdrawal, Bank Deposit, Fund Transfer, UPI Payment, etc.
+- **Transaction Device**: ATM, POS, Mobile Apps, UPI platforms, Internet Banking, etc.
+- **Transaction Date**: Day of month (1-31)
+- **Transaction Time**: Hourly time slots (24-hour format)
+- **Currency**: INR (default), USD, EUR, GBP, AED, SGD, CAD, AUD
+
+## API Endpoints
+
+### GET /
+Returns the main web interface
+
+### POST /predict
+**Request Body:**
+```json
+{
+    "gender": "1",
+    "age": "35",
+    "state": "15",
+    "city": "127",
+    "bank_branch": "127",
+    "account_type": "2",
+    "transaction_date": "22",
+    "transaction_time": "52151",
+    "transaction_amount": "32415.45",
+    "transaction_type": "3",
+    "account_balance": "74557.27",
+    "transaction_device": "17",
+    "transaction_currency": "0"
+}
+```
+
+**Response:**
+```json
+{
+    "prediction": 0,
+    "fraud_probability": 15.75,
+    "risk_level": "Very Low",
+    "risk_color": "#28a745",
+    "message": "Legitimate Transaction"
+}
+```
+
+## Risk Levels
+
+- **Very Low** (0-20%): Green (#28a745)
+- **Low** (20-40%): Teal (#20c997)
+- **Medium** (40-60%): Yellow (#ffc107)
+- **High** (60-80%): Orange (#fd7e14)
+- **Very High** (80-100%): Red (#dc3545)
+
+## Testing
+
+Run the API test script:
+```bash
+python test_api.py
+```
+
+## Files Structure
+
+```
+fraud_flask_app/
+├── app.py                 # Main Flask application
+├── model.pkl             # Trained Random Forest model
+├── encoders.pkl          # Label encoders for categorical features
+├── requirements.txt      # Python dependencies
+├── start_app.bat        # Windows startup script
+├── test_api.py          # API testing script
+├── templates/
+│   └── index.html       # Web interface template
+├── static/
+│   └── style.css        # Custom styling
+└── README.md           # This file
+```
+
+## Model Information
+
+- **Algorithm**: Random Forest Classifier
+- **Features**: 13 input features
+- **Training Data**: Bank transaction fraud dataset
+- **Performance**: Optimized for fraud detection accuracy
+
+## Technologies Used
+
+- **Backend**: Flask (Python)
+- **Frontend**: HTML5, CSS3, JavaScript, Bootstrap 5
+- **Machine Learning**: scikit-learn, pandas, numpy
+- **Icons**: Font Awesome
+
+## Browser Support
+
+- Chrome/Chromium (recommended)
+- Firefox
+- Safari
+- Edge
+
+## Troubleshooting
+
+### Model Loading Issues
+- Ensure `model.pkl` and `encoders.pkl` are in the same directory as `app.py`
+- Check Python package versions match requirements.txt
+
+### Connection Issues
+- Verify Flask is running on port 5000
+- Check firewall settings
+- Ensure no other application is using port 5000
+
+### Prediction Errors
+- Validate all input fields are filled correctly
+- Check input value ranges match the specified limits
+- Ensure numeric fields contain valid numbers
+
+## License
+
+This project is for educational and demonstration purposes.