# NNmodelFakeNews Neural Network Model Fake News based on TF-IDF and PassiveAggressiveClassifier "NNmodelFakeNews" is a machine learning model developed for detecting fake news using TF-IDF (Term Frequency-Inverse Document Frequency) vectorization methods and the PassiveAggressiveClassifier for classification. This model is trained on a corpus of news text data, where each article is represented as a TF-IDF vector, reflecting the importance of each word in the article relative to the entire news corpus. The PassiveAggressiveClassifier algorithm is then used to determine whether an article is fake or real. The model demonstrates high accuracy and effectively filters fake news from the information stream. "NNmodelFakeNews" can be valuable in combating mi- sinformation and maintaining information credibility in modern media.

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