📰 Fake News Detector using Deep Learning

🚀 Overview

This project is a Fake News Detection System built using Deep Learning (LSTM/BERT) and NLP techniques. It classifies news articles as Real or Fake based on textual content. The model is trained on the ISOT Fake News Dataset and deployed using Streamlit & Hugging Face Spaces.

🔗 Live Demo: Click here to try it out!

✨ Features

✅ Detects Fake News with high accuracy using Deep Learning
✅ Uses LSTM/BERT for powerful text classification
✅ Preprocessing with Tokenization & Embeddings
✅ Deployed using Streamlit & Hugging Face
✅ Open-source and customizable

📂 Dataset

• Source: ISOT Fake News Dataset
• Classes: Real, Fake
• Columns: title, text, label

🏗️ Tech Stack

• Python 🐍
• TensorFlow/Keras for Deep Learning 🔥
• NLTK & Tokenizer for Text Processing ✂️
• Streamlit for Web Deployment 🌐
• Hugging Face Spaces for Hosting ☁️

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🛠️ Installation & Setup

1️⃣ Clone the Repository

git clone https://github.com/SatyamInCode/Fake_News_Detector.git
cd Fake-News-Detector

2️⃣ Install Dependencies

pip install -r requirements.txt

3️⃣ Run the Streamlit App Locally

streamlit run app.py

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📜 Training the Model

The model was trained using LSTM on tokenized sequences from news articles.

Training Code (Jupyter Notebook)

Check the Fake_News_Detector.ipynb file for detailed training steps.

Steps Involved:

1. Data Cleaning: Removing stopwords, punctuation, and special characters
2. Tokenization & Embeddings: Using Tokenizer and pad_sequences
3. Model Training: LSTM model training with Adam optimizer
4. Evaluation: Precision, Recall, and F1-score calculation

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🚀 Deployment on Hugging Face

1️⃣ Push Code to GitHub

git add .
git commit -m "Initial Commit"
git push origin main

2️⃣ Create a Space on Hugging Face

1. Go to Hugging Face Spaces
2. Create a new Space → Select Streamlit
3. Connect to your GitHub repository
4. Deploy and enjoy! 🎉

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📊 Model Performance

Metric	Score
Accuracy	99.94%
Precision	1.00
Recall	1.00
F1-score	1.00

🔍 Training Graphs & Logs: Available in Training Results

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📌 Future Improvements

• ✅ Integrate BERT for better accuracy
• ✅ Enable API Support for wider usability
• ✅ Enhance UI/UX for a smoother experience

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🤝 Contribution

1. Fork the repo
2. Create a new branch (feature-branch)
3. Commit your changes & push
4. Submit a Pull Request

🙌 Star this repo ⭐ if you found it useful!

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📧 Contact

For queries, reach out on 💼 GitHub → SatyamInCode