Fake News Detection Using NLP & Machine Learning

This study offers a comprehensive and repeatable method for identifying false information through the use of machine learning (ML) and natural language processing (NLP). Data collection, text preprocessing, TF-IDF feature extraction, model training, and real-time deployment via a Streamlit interface comprise the methodical pipeline that the suggested system uses. Using a labeled dataset of real and fraudulent news items, three traditional machine learning algorithms Random Forest, Multinomial Naive Bayes, and Logistic Regression were put into practice and assessed. With a test accuracy of 99.69%, the Random Forest classifier outperformed the others, followed by Logistic Regression (98.71%) and Naive Bayes (92.74%). The findings confirm that classical machine learning models work very well for text classification tasks like identifying false news when paired with robust feature engineering. This research offers a straightforward way to detect fake news using machine learning and natural language processing techniques. The process begins with gathering reliable data and cleaning it for consistency. Next, it converts the text into numerical form using TF-IDF. After that, different models are trained and evaluated. Finally, the system is launched through a Streamlit web interface, which enables real-time testing and user interaction. This step-by-step setup makes it easy to repeat or improve the process later.

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