Unveiling Fake News with Machine Learning and Natural Language Processing Techniques

The detection of fake news using machine learning and natural language processing techniques. With the proliferation of misinformation on social media and digital platforms, identifying fake news has become crucial. The project preprocesses textual data, applies various machine learning models, including logistic regression, Naïve Bayes, decision trees, random forest, neural networks, support vector machines, and AdaBoost Classifier, and employs dimensionality reduction techniques like PCA and t-SNE. The dataset is categorized into cases with and without speaker and political party information, and label encoding is used for numeric conversion. The results highlight the challenges and complexities of fake news detection.