This project focuses on detecting fake news using machine learning algorithms. It preprocesses news articles, extracts features using TF-IDF, and employs multiple classifiers to identify whether a news article is real or fake. The project also evaluates model performance using metrics like accuracy, precision, recall, F1 score, and support.
Fake news detection is a critical step in combating misinformation. This project uses machine learning algorithms such as:
- Logistic Regression
- Decision Tree Classifier
- Random Forest Classifier
- Gradient Boosting Classifier
The text data is preprocessed, transformed into numerical features using TF-IDF Vectorizer, and fed into these classifiers for prediction.
- Preprocessing text (removal of stopwords, punctuation, and irrelevant data).
- TF-IDF for feature extraction.
- Multiple classifiers for improved accuracy.
- Model evaluation using standard performance metrics.