This project aims to tackle the challenge of digital misinformation by building a robust classifier that distinguishes between real and fake news articles. By comparing traditional statistical methods with modern word embeddings, this project demonstrates how machine learning can enhance media literacy and information security.
Build a high-accuracy classifier for fake news detection.
Implementation of Classical NLP (TF-IDF + Logistic Regression).
Implementation of Deep Learning context using Word2Vec embeddings.
Rigorous performance comparison using metrics like Accuracy, Precision, Recall, and F1-Score.
- Develop a classical NLP model- Logestic.
- Develop a Word2Vec-based classifier.
- Compare between models performance.
- show any other results