Fake News Detection App is a machine learning-powered web application that determines whether a news article is real or fake. Built using Python Flask for the backend and a UI layer, it leverages both PassiveAggressiveClassifier and Naive Bayes algorithms for classification. The model is trained using a Kaggle-sourced dataset and integrated into a minimal, responsive interface for ease of use and rapid testing.
- ML Models: Passive Aggressive Classifier, Naive Bayes
- Backend: Python, Flask
- ML Tools: Scikit-learn, Pandas, NumPy
- Dataset: Kaggle Fake News Dataset
- Frontend/UI: HTML, CSS (basic Flask templates)
- Deployment: Render
- ML-based fake vs real news classification
- Real-time prediction on custom input
- Trained on large dataset from Kaggle
- Model with highest accuracy gets connected (Naive Bayes / Passive Aggressive)
- Flask-powered backend with Python model integration
- Minimal UI for testing predictions
To run this project locally:
- Clone the repository:
git clone https://github.com/R-o-n-a-k/FND.git
cd FND
- Install dependencies:
pip install -r requirements.txt
- Run the flask app:
python app.py
Built to fight misinformation with machine learning 🧠