Fake News Detection Using NLP and Machine Learning: Developed a machine learning model to detect fake news using natural language processing (NLP) techniques. The system employed TF-IDF vectorization for feature extraction and the Passive-Aggressive Classifier for real-time detection. Integrated the LIAR dataset for training, resulting in improved accuracy and performance. The model was optimized for text data preprocessing, feature extraction, and classification, achieving high accuracy in distinguishing between real and fake news.
This project demonstrates expertise in NLP, model training, and evaluation, along with practical applications in misinformation detection
