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AyushiAyushi3017/
Fake_News_Detector

Name: Fake_News_Detector
Availability: InStock

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📰 Fake News Detector — Streamlit App

A lightweight NLP-powered web app that classifies news articles as Real or Fake using an LSTM-based deep learning model. The dataset is hosted on Hugging Face, and the app is fully deployable on Streamlit Cloud.

🚀 Features

End-to-end text preprocessing (cleaning, tokenization, lemmatization)
LSTM neural network trained on a custom fake news dataset
Hugging Face dataset loading via direct raw URLs
Interactive Streamlit UI for predictions
Real-time confidence scoring and visualization

📂 Project Structure

.
├── app.py
├── requirements.txt
├── README.md

🛠 Tech Stack

Frontend: Streamlit
NLP: NLTK
Data Processing: Pandas, NumPy
Modeling: TensorFlow, Keras, Scikit-learn
Visualization: Matplotlib, Seaborn
Dataset Hosting: Hugging Face Datasets

📦 Dataset

Dataset used for training: https://huggingface.co/datasets/AyushiAyushi3017/fake-news-detector-dataset

Contains two CSV files: Fake.csv and True.csv.

🧠 Model Overview

Tokenizer + padded sequences
Embedding layer (100 dimensions)
LSTM layer (150 units)
Dropout regularization
Global Max Pooling
Dense layers with ReLU and Softmax
Trained for 15 epochs with 80/20 train-test split

▶️ Running Locally

1. Clone the repository

git clone https://github.com/<your-username>/<repo>.git
cd <repo>

2. Install dependencies

pip install -r requirements.txt

3. Run the Streamlit app

streamlit run app.py

🌐 Deployment

Fully compatible with Streamlit Cloud. Push your repo → connect Streamlit → deploy.

📌 Future Enhancements

Switch to transformer-based models (BERT/DistilBERT)
Add URL-based scraping and prediction
Add a REST API endpoint
Improve UI design and styling

🤝 Acknowledgements

Built using open-source tools and a custom Hugging Face dataset.

AyushiAyushi3017/Fake_News_Detector