This project aims to develop a machine-learning model capable of detecting fake news articles using multiple classification techniques. By analyzing a labeled dataset of news articles, the system classifies news as genuine or fabricated. The project employs Logistic Regression, Decision Tree, Gradient Boost, and Random Forest classifiers to ensure accuracy and reliability. The model provides evaluation metrics, and it can be deployed to real-world applications to automatically detect fake news.
Multiple classifiers implemented: Logistic Regression, Decision Tree, Gradient Boost, Random Forest