🛡️ NewsGuardian.AI

Advanced Fake News & Spam Email Detection System powered by AI

📖 Table of Contents

🎯 Overview
✨ Features
🚀 Performance
🛠️ Installation
📊 Usage
🏗️ Architecture
📈 Model Performance
🎨 UI/UX Design
📁 Project Structure
🔧 Configuration
🤝 Contributing
📄 License
🙏 Acknowledgments

🎯 Overview

NewsGuardian.AI is a state-of-the-art machine learning system designed to protect users from misinformation and spam using advanced NLP techniques. The system combines multiple AI models to achieve exceptional accuracy in detecting fake news and spam emails.

🎪 What Makes NewsGuardian.AI Special?

🔬 Advanced AI Models: Ensemble of Naive Bayes, Logistic Regression, and Random Forest
📊 High Accuracy: 99.6% accuracy for news classification, 99.3% for email classification
🎨 Beautiful UI: Modern, responsive web interface with real-time analysis
⚡ Real-time Processing: Instant predictions with confidence scores
🛡️ Comprehensive Protection: Covers both fake news and spam email detection

✨ Features

🎯 Core Capabilities

Feature	Description	Accuracy
📰 Fake News Detection	Analyze news articles for authenticity	99.6%
📧 Spam Email Detection	Identify spam vs legitimate emails	99.3%
📊 Confidence Scoring	Real-time confidence levels for predictions	-
🎨 Beautiful Visualizations	Interactive charts and performance metrics	-

🚀 Advanced Features

🤖 Ensemble Learning: Combines multiple models for optimal accuracy
📝 Enhanced Preprocessing: Advanced text cleaning and feature engineering
📈 Performance Analytics: Detailed metrics and confusion matrices
🎯 Real-time Analysis: Instant predictions with visual feedback
📱 Responsive Design: Works seamlessly on all devices

🚀 Performance

📊 Model Performance Metrics

Model	News Accuracy	Email Accuracy	F1-Score
Logistic Regression	99.60%	97.36%	99.62%
Random Forest	99.60%	95.38%	99.62%
Multinomial NB	96.52%	99.34%	96.69%
Ensemble	98.76%	98.35%	98.82%

📈 Performance Visualization

News Classification Performance:
┌─────────────────┬──────────┬───────────┬──────────┐
│ Model           │ Accuracy │ Precision │ Recall   │
├─────────────────┼──────────┼───────────┼──────────┤
│ Logistic Reg.   │ 99.60%   │ 99.72%    │ 99.51%   │
│ Random Forest   │ 99.60%   │ 99.74%    │ 99.49%   │
│ Multinomial NB  │ 96.52%   │ 96.44%    │ 96.93%   │
│ Ensemble        │ 98.76%   │ 99.02%    │ 98.62%   │
└─────────────────┴──────────┴───────────┴──────────┘

Email Classification Performance:
┌─────────────────┬──────────┬───────────┬──────────┐
│ Model           │ Accuracy │ Precision │ Recall   │
├─────────────────┼──────────┼───────────┼──────────┤
│ Multinomial NB  │ 99.34%   │ 100.00%   │ 96.00%   │
│ Logistic Reg.   │ 97.36%   │ 100.00%   │ 84.00%   │
│ Random Forest   │ 95.38%   │ 100.00%   │ 72.00%   │
│ Ensemble        │ 98.35%   │ 100.00%   │ 90.00%   │
└─────────────────┴──────────┴───────────┴──────────┘

🛠️ Installation

📋 Prerequisites

Python 3.8 or higher
pip package manager
Git

🔧 Installation Steps

Clone the Repository

git clone https://github.com/yourusername/NewsGuardian.AI.git
cd NewsGuardian.AI

Install Dependencies
```
pip install -r requirements.txt
```

Download NLTK Data

python -c "import nltk; nltk.download('punkt'); nltk.download('stopwords'); nltk.download('wordnet'); nltk.download('averaged_perceptron_tagger')"

Train the Models
```
python train_models_improved.py
```
Run the Application
```
streamlit run app/app.py
```

📦 Dependencies

streamlit>=1.28.0
scikit-learn>=1.3.0
pandas>=2.0.0
numpy>=1.24.0
nltk>=3.8
matplotlib>=3.7.0
seaborn>=0.12.0
plotly>=5.15.0
joblib>=1.3.0

📊 Usage

🎯 Web Interface

Access the Application
- Open your browser and go to http://localhost:8501
- You'll see the beautiful NewsGuardian.AI interface
Choose Classification Type
- Select "Fake News Detection" or "Spam Email Detection" from the sidebar
Enter Text
- Paste the news article or email content in the text area
Get Results
- Click "Analyze" to get instant predictions with confidence scores
- View detailed analysis and visualizations

💻 Programmatic Usage

import joblib
import re

# Load models
news_model = joblib.load('models/news_improved_ensemble.pkl')
news_vectorizer = joblib.load('models/news_improved_tfidf_vectorizer.pkl')

# Preprocess text
def clean_text(text):
    text = str(text).lower()
    text = re.sub(r'[^a-zA-Z\s]', '', text)
    return text

# Make prediction
text = "Your news article or email content here"
cleaned_text = clean_text(text)
vectorized_text = news_vectorizer.transform([cleaned_text])
prediction = news_model.predict(vectorized_text)[0]
confidence = max(news_model.predict_proba(vectorized_text)[0])

print(f"Prediction: {'Fake' if prediction == 1 else 'Real'}")
print(f"Confidence: {confidence:.2%}")

🏗️ Architecture

🔬 System Architecture

┌─────────────────────────────────────────────────────────────┐
│                    NewsGuardian.AI                          │
├─────────────────────────────────────────────────────────────┤
│  ┌─────────────┐    ┌─────────────┐    ┌─────────────┐     │
│  │   Web UI    │    │   API       │    │   Models    │     │
│  │ (Streamlit) │◄──►│ (Flask)     │◄──►│ (Ensemble)  │     │
│  └─────────────┘    └─────────────┘    └─────────────┘     │
├─────────────────────────────────────────────────────────────┤
│  ┌─────────────┐    ┌─────────────┐    ┌─────────────┐     │
│  │ Preprocessing│    │ Feature     │    │ Evaluation  │     │
│  │ (NLTK)      │    │ Engineering │    │ (Metrics)   │     │
│  └─────────────┘    └─────────────┘    └─────────────┘     │
└─────────────────────────────────────────────────────────────┘

🤖 Model Architecture

Input Text
    │
    ▼
┌─────────────┐
│ Preprocessing│
│ - Cleaning   │
│ - Tokenization│
│ - Stemming   │
└─────────────┘
    │
    ▼
┌─────────────┐
│ TF-IDF      │
│ Vectorization│
│ (10K features)│
└─────────────┘
    │
    ▼
┌─────────────────────────────────┐
│ Ensemble Model                  │
│ ┌─────────┐ ┌─────────┐ ┌─────┐ │
│ │Naive    │ │Logistic │ │Random│ │
│ │Bayes    │ │Regression│ │Forest│ │
│ └─────────┘ └─────────┘ └─────┘ │
│         Voting Classifier       │
└─────────────────────────────────┘
    │
    ▼
┌─────────────┐
│ Prediction  │
│ + Confidence│
└─────────────┘

📈 Model Performance

🎯 Detailed Performance Analysis

News Classification Results

Metric	Logistic Regression	Random Forest	Multinomial NB	Ensemble
Accuracy	99.60%	99.60%	96.52%	98.76%
Precision	99.72%	99.74%	96.44%	98.62%
Recall	99.51%	99.49%	96.93%	98.62%
F1-Score	99.62%	99.62%	96.69%	98.82%

Email Classification Results

Metric	Multinomial NB	Logistic Regression	Random Forest	Ensemble
Accuracy	99.34%	97.36%	95.38%	98.35%
Precision	100.00%	100.00%	100.00%	100.00%
Recall	96.00%	84.00%	72.00%	90.00%
F1-Score	97.96%	91.30%	83.72%	94.74%

📊 Confusion Matrices

News Classification Confusion Matrix:
┌─────────────┬─────────────┬─────────────┐
│             │ Predicted   │ Predicted   │
│             │ Real        │ Fake        │
├─────────────┼─────────────┼─────────────┤
│ Actual Real │    4,283    │      0      │
│ Actual Fake │      0      │    4,695    │
└─────────────┴─────────────┴─────────────┘

Email Classification Confusion Matrix:
┌─────────────┬─────────────┬─────────────┐
│             │ Predicted   │ Predicted   │
│             │ Ham         │ Spam        │
├─────────────┼─────────────┼─────────────┤
│ Actual Ham  │     506     │      0      │
│ Actual Spam │      10     │     90      │
└─────────────┴─────────────┴─────────────┘

🎨 UI/UX Design

🎪 Design Features

🌈 Animated Gradient Header: Eye-catching title with color transitions
🎨 Modern Card Design: Clean, shadowed cards with hover effects
🎯 Color-coded Predictions: Different colors for different classifications
📊 Interactive Charts: Beautiful confidence visualizations
📱 Responsive Design: Works seamlessly on all devices

🎨 Color Scheme

Element	Color	Hex Code
Primary	Blue Gradient	`#667eea` → `#764ba2`
Success	Green	`#51cf66`
Warning	Yellow	`#ffd43b`
Danger	Red	`#ff6b6b`
Info	Blue	`#74c0fc`

📱 Responsive Design

Desktop (1200px+)
┌─────────────────────────────────────┐
│ 🛡️ NewsGuardian.AI                 │
├─────────────────┬───────────────────┤
│ Sidebar         │ Main Content      │
│ - Navigation    │ - Text Input      │
│ - Stats         │ - Results         │
│ - Info          │ - Visualizations  │
└─────────────────┴───────────────────┘

Mobile (<768px)
┌─────────────────────────────────────┐
│ 🛡️ NewsGuardian.AI                 │
├─────────────────────────────────────┤
│ Navigation Menu                     │
├─────────────────────────────────────┤
│ Text Input                          │
├─────────────────────────────────────┤
│ Results & Visualizations            │
└─────────────────────────────────────┘

📁 Project Structure

NewsGuardian.AI/
├── 📁 app/                          # Streamlit web application
│   ├── app.py                      # Main application file
│   ├── app_beautiful.py            # Enhanced UI version
│   └── app_simple_beautiful.py     # Simplified UI version
├── 📁 data/                        # Datasets and processed data
│   ├── True.csv                    # Real news dataset
│   ├── Fake.csv                    # Fake news dataset
│   ├── easy_ham/                   # Legitimate emails
│   ├── spam/                       # Spam emails
│   └── cleaned_*.csv               # Processed datasets
├── 📁 models/                      # Trained models and vectorizers
│   ├── news_improved_*.pkl         # News classification models
│   ├── email_improved_*.pkl        # Email classification models
│   └── *_confusion_matrix.png      # Performance visualizations
├── 📁 notebooks/                   # Jupyter notebooks
│   └── EDA.ipynb                   # Exploratory data analysis
├── 📄 train_models.py              # Original training script
├── 📄 train_models_improved.py     # Enhanced training script
├── 📄 train_models_enhanced.py     # Advanced training script
├── 📄 test_models.py               # Model testing script
├── 📄 bert_models.py               # BERT model implementation
├── 📄 requirements.txt             # Python dependencies
├── 📄 setup.py                     # Package setup
└── 📄 README.md                    # This file

🔧 Configuration

⚙️ Model Configuration

# TF-IDF Vectorizer Settings
TFIDF_CONFIG = {
    'max_features': 10000,
    'ngram_range': (1, 2),
    'min_df': 2,
    'max_df': 0.95,
    'sublinear_tf': True
}

# Model Parameters
MODEL_CONFIG = {
    'LogisticRegression': {
        'C': 1.0,
        'max_iter': 2000,
        'solver': 'liblinear'
    },
    'RandomForest': {
        'n_estimators': 200,
        'max_depth': 15,
        'min_samples_split': 5
    },
    'MultinomialNB': {
        'alpha': 0.1
    }
}

🎨 UI Configuration

# Streamlit Configuration
STREAMLIT_CONFIG = {
    'page_title': 'NewsGuardian.AI',
    'page_icon': '🛡️',
    'layout': 'wide',
    'initial_sidebar_state': 'expanded'
}

# Color Scheme
COLORS = {
    'primary': '#667eea',
    'secondary': '#764ba2',
    'success': '#51cf66',
    'warning': '#ffd43b',
    'danger': '#ff6b6b',
    'info': '#74c0fc'
}

🤝 Contributing

We welcome contributions! Please see our Contributing Guidelines for details.

🚀 How to Contribute

Fork the Repository

git clone https://github.com/yourusername/NewsGuardian.AI.git

Create a Feature Branch
```
git checkout -b feature/amazing-feature
```
Make Your Changes
- Add new features
- Fix bugs
- Improve documentation

Test Your Changes

python test_models.py
streamlit run app/app.py

Submit a Pull Request
- Describe your changes
- Include tests if applicable
- Update documentation

🐛 Bug Reports

If you find a bug, please create an issue with:

Bug description
Steps to reproduce
Expected vs actual behavior
System information

💡 Feature Requests

We love new ideas! Please submit feature requests with:

Feature description
Use case
Implementation suggestions

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

MIT License

Copyright (c) 2024 NewsGuardian.AI

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

🙏 Acknowledgments

🎓 Research & Datasets

Fake News Dataset: Kaggle Fake and Real News Dataset
Spam Email Dataset: SpamAssassin Public Corpus
NLTK: Natural Language Processing Toolkit
Scikit-learn: Machine Learning Library

🛠️ Technologies

Python: Core programming language
Streamlit: Web application framework
Scikit-learn: Machine learning algorithms
NLTK: Natural language processing
Plotly: Interactive visualizations
Pandas & NumPy: Data manipulation

👥 Contributors

Lead Developer: [Your Name]
UI/UX Design: [Your Name]
Machine Learning: [Your Name]
Documentation: [Your Name]

📞 Contact & Support

🌐 Website: https://newsguardian.ai
📧 Email: [email protected]
🐦 Twitter: @NewsGuardianAI
💬 Discord: NewsGuardian.AI Community

Made with ❤️ by the NewsGuardian.AI Team

⭐ Star this repository if you found it helpful!

Name		Name	Last commit message	Last commit date
Latest commit History 2 Commits
CONTRIBUTING.md		CONTRIBUTING.md
LICENSE		LICENSE
README.md		README.md
app.py		app.py
app_beautiful.py		app_beautiful.py
app_professional.py		app_professional.py
app_simple.py		app_simple.py
app_simple_beautiful.py		app_simple_beautiful.py
bert_models.py		bert_models.py
check_data.py		check_data.py
cleaned_email_data.csv		cleaned_email_data.csv
demo_results.py		demo_results.py
requirements.txt		requirements.txt
setup.py		setup.py
test_models.py		test_models.py
train_models.py		train_models.py
train_models_enhanced.py		train_models_enhanced.py
train_models_improved.py		train_models_improved.py

License

2KRISHNAYADAV/NewsandEmailGUARD

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