Fake News Detection | Natural Language Processing
- Developed and deployed robust Machine Learning models for news classification, distinguishing between fake and real news.
- Conducted in-depth analysis to identify the category with the highest prevalence of fake news, aiding in determining optimal regulation enforcement strategies.
- Expertly processed and purified extensive datasets, achieving an impressive 96% accuracy using Logistic Regression as the primary classifier.
- Explored various modeling approaches by employing additional classifiers: Random Forest, XG Boost, ADA Boost, and Naïve Bayes.
