Update README.md

README.md

@@ -1,165 +1,177 @@
-# Fake News Detection Project
-
-A machine learning project that classifies news articles as real or fake using both traditional NLP techniques and advanced transformer models.
-
-## 🎯 Project Overview
-
-This project implements multiple approaches to detect fake news:
-- **Traditional ML**: TF-IDF vectorization with Logistic Regression
-- **Deep Learning**: Fine-tuned BERT model for sequence classification
-
-## 📊 Performance Results
-
-### TF-IDF + Logistic Regression Model
-- **Accuracy**: 98.62%
-- **F1 Score**: 98.67%
-
-#### Detailed Classification Report:
-```
-               precision    recall  f1-score   support
-
-           0       0.98      0.99      0.99      4284  (Real News)
-           1       0.99      0.98      0.99      4696  (Fake News)
-
-    accuracy                           0.99      8980
-   macro avg       0.99      0.99      0.99      8980
-weighted avg       0.99      0.99      0.99      8980
-```
-
-## 📁 Project Structure
-
-```
-FakeNewsDetector/
-├── README.md
-├── requirements.txt
-├── notebooks/
-│   └── FakeNewsClassifier_HuggingFace.ipynb
-├── scripts/
-│   └── train.py
-├── models/
-│   └── bert-fake-news/  (generated after training)
-├── data/
-├── app/
-└── venv/
-```
-
-## 🚀 Quick Start
-
-### 1. Clone and Setup
-```bash
-git clone <repository-url>
-cd FakeNewsDetector
-```
-
-### 2. Create Virtual Environment
-```bash
-python -m venv venv
-
-# Windows PowerShell
-.\venv\Scripts\Activate.ps1
-
-# Windows CMD
-.\venv\Scripts\activate.bat
-
-# Git Bash
-source venv/Scripts/activate
-```
-
-### 3. Install Dependencies
-```bash
-pip install -r requirements.txt
-```
-
-### 4. Launch Jupyter Notebook
-```bash
-jupyter notebook
-```
-
-## 📚 Dataset
-
-The project uses the `mrm8488/fake-news` dataset from Hugging Face, which contains:
-- **Total articles**: ~45,000
-- **Training split**: 80% (~36,000 articles)
-- **Test split**: 20% (~9,000 articles)
-- **Classes**: 
-  - 0: Real News
-  - 1: Fake News
-
-## 🔧 Models Implemented
-
-### 1. TF-IDF + Logistic Regression
-- **Vectorizer**: TF-IDF with 5,000 max features, n-grams (1,2)
-- **Classifier**: Logistic Regression with balanced class weights
-- **Performance**: 98.62% accuracy
-
-### 2. BERT Fine-tuning
-- **Base Model**: `bert-base-uncased`
-- **Training**: 3 epochs with evaluation per epoch
-- **Optimizer**: AdamW with learning rate 2e-5
-- **Batch Size**: 8 per device
-
-## 🛠️ Usage
-
-### Running the Notebook
-1. Ensure your virtual environment is activated
-2. Start Jupyter: `jupyter notebook`
-3. Open `notebooks/FakeNewsClassifier_HuggingFace.ipynb`
-4. Make sure the kernel is set to "venv" or "FakeNewsDetector (venv)"
-5. Run all cells
-
-### Training BERT Model
-```bash
-python scripts/train.py
-```
-
-The trained model will be saved to `models/bert-fake-news/`
-
-## 📋 Requirements
-
-- Python 3.8+
-- pandas
-- scikit-learn
-- datasets (Hugging Face)
-- transformers
-- torch
-- matplotlib
-- seaborn
-- jupyter
-- ipywidgets
-
-## 🎯 Key Features
-
-- **High Accuracy**: Achieves 98.6% accuracy on test set
-- **Multiple Approaches**: Compares traditional ML vs. transformer models
-- **Easy Setup**: Simple virtual environment setup
-- **Comprehensive Analysis**: Includes confusion matrix and detailed metrics
-- **Production Ready**: Trained models can be saved and deployed
-
-## 🔍 Model Analysis
-
-The TF-IDF + Logistic Regression model shows excellent performance:
-- **Balanced Performance**: High precision and recall for both classes
-- **Low False Positives**: 98% precision for fake news detection
-- **Low False Negatives**: 99% recall for real news detection
-- **Robust**: Handles class imbalance well with balanced weights
-
-## 🚀 Future Improvements
-
-- [ ] Implement ensemble methods combining multiple models
-- [ ] Add cross-validation for more robust evaluation
-- [ ] Experiment with other transformer models (RoBERTa, DistilBERT)
-- [ ] Deploy model as a web API
-- [ ] Add real-time news article classification
-- [ ] Implement explainability features (LIME, SHAP)
-
-## 🤝 Contributing
-
-Contributions, issues, and feature requests are welcome! Feel free to check the [issues page](../../issues).
-
-## 📧 Contact
-
-For questions or suggestions, please open an issue or contact the project maintainer.
-
----
-
-**Note**: This project is for educational and research purposes. Always verify news from multiple reliable sources.
+---
+title: Fake News Detector
+colorFrom: gray
+colorTo: red
+sdk: streamlit
+sdk_version: "1.33.0"
+app_file: app.py
+pinned: false
+---
+
+Detect fake news using a fine-tuned BERT model. Enter any headline or statement and get an instant prediction on whether it's likely real or fake. Built with Streamlit and Hugging Face Transformers.
+
+# Fake News Detection Project
+
+A machine learning project that classifies news articles as real or fake using both traditional NLP techniques and advanced transformer models.
+
+## 🎯 Project Overview
+
+This project implements multiple approaches to detect fake news:
+- **Traditional ML**: TF-IDF vectorization with Logistic Regression
+- **Deep Learning**: Fine-tuned BERT model for sequence classification
+
+## 📊 Performance Results
+
+### TF-IDF + Logistic Regression Model
+- **Accuracy**: 98.62%
+- **F1 Score**: 98.67%
+
+#### Detailed Classification Report:
+```
+               precision    recall  f1-score   support
+
+           0       0.98      0.99      0.99      4284  (Real News)
+           1       0.99      0.98      0.99      4696  (Fake News)
+
+    accuracy                           0.99      8980
+   macro avg       0.99      0.99      0.99      8980
+weighted avg       0.99      0.99      0.99      8980
+```
+
+## 📁 Project Structure
+
+```
+FakeNewsDetector/
+├── README.md
+├── requirements.txt
+├── notebooks/
+│   └── FakeNewsClassifier_HuggingFace.ipynb
+├── scripts/
+│   └── train.py
+├── models/
+│   └── bert-fake-news/  (generated after training)
+├── data/
+├── app/
+└── venv/
+```
+
+## 🚀 Quick Start
+
+### 1. Clone and Setup
+```bash
+git clone <repository-url>
+cd FakeNewsDetector
+```
+
+### 2. Create Virtual Environment
+```bash
+python -m venv venv
+
+# Windows PowerShell
+.\venv\Scripts\Activate.ps1
+
+# Windows CMD
+.\venv\Scripts\activate.bat
+
+# Git Bash
+source venv/Scripts/activate
+```
+
+### 3. Install Dependencies
+```bash
+pip install -r requirements.txt
+```
+
+### 4. Launch Jupyter Notebook
+```bash
+jupyter notebook
+```
+
+## 📚 Dataset
+
+The project uses the `mrm8488/fake-news` dataset from Hugging Face, which contains:
+- **Total articles**: ~45,000
+- **Training split**: 80% (~36,000 articles)
+- **Test split**: 20% (~9,000 articles)
+- **Classes**: 
+  - 0: Real News
+  - 1: Fake News
+
+## 🔧 Models Implemented
+
+### 1. TF-IDF + Logistic Regression
+- **Vectorizer**: TF-IDF with 5,000 max features, n-grams (1,2)
+- **Classifier**: Logistic Regression with balanced class weights
+- **Performance**: 98.62% accuracy
+
+### 2. BERT Fine-tuning
+- **Base Model**: `bert-base-uncased`
+- **Training**: 3 epochs with evaluation per epoch
+- **Optimizer**: AdamW with learning rate 2e-5
+- **Batch Size**: 8 per device
+
+## 🛠️ Usage
+
+### Running the Notebook
+1. Ensure your virtual environment is activated
+2. Start Jupyter: `jupyter notebook`
+3. Open `notebooks/FakeNewsClassifier_HuggingFace.ipynb`
+4. Make sure the kernel is set to "venv" or "FakeNewsDetector (venv)"
+5. Run all cells
+
+### Training BERT Model
+```bash
+python scripts/train.py
+```
+
+The trained model will be saved to `models/bert-fake-news/`
+
+## 📋 Requirements
+
+- Python 3.8+
+- pandas
+- scikit-learn
+- datasets (Hugging Face)
+- transformers
+- torch
+- matplotlib
+- seaborn
+- jupyter
+- ipywidgets
+
+## 🎯 Key Features
+
+- **High Accuracy**: Achieves 98.6% accuracy on test set
+- **Multiple Approaches**: Compares traditional ML vs. transformer models
+- **Easy Setup**: Simple virtual environment setup
+- **Comprehensive Analysis**: Includes confusion matrix and detailed metrics
+- **Production Ready**: Trained models can be saved and deployed
+
+## 🔍 Model Analysis
+
+The TF-IDF + Logistic Regression model shows excellent performance:
+- **Balanced Performance**: High precision and recall for both classes
+- **Low False Positives**: 98% precision for fake news detection
+- **Low False Negatives**: 99% recall for real news detection
+- **Robust**: Handles class imbalance well with balanced weights
+
+## 🚀 Future Improvements
+
+- [ ] Implement ensemble methods combining multiple models
+- [ ] Add cross-validation for more robust evaluation
+- [ ] Experiment with other transformer models (RoBERTa, DistilBERT)
+- [ ] Deploy model as a web API
+- [ ] Add real-time news article classification
+- [ ] Implement explainability features (LIME, SHAP)
+
+## 🤝 Contributing
+
+Contributions, issues, and feature requests are welcome! Feel free to check the [issues page](../../issues).
+
+## 📧 Contact
+
+For questions or suggestions, please open an issue or contact the project maintainer.
+
+---
+
+**Note**: This project is for educational and research purposes. Always verify news from multiple reliable sources.