Newsact is a tiny tool that identifies fake from real, genuine from phoney in the media made with Scikit
In the age of social media, distinguishing between real and fake news has become increasingly important. This project demonstrates how to build a machine learning model to detect fake news using Python. By leveraging the power of Python and machine learning libraries, you can build a model to classify news as either REAL or FAKE.
- Detect Fake News: Build a machine learning model that can accurately classify news articles as real or fake.
- Understand Key Concepts: Learn about essential terms such as TF-IDF Vectorization and Passive-Aggressive Classification.
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Fake News: Fake news refers to misleading or false information spread through media, often to achieve specific political or social goals. Understanding fake news is crucial for ensuring the reliability of information.
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TfidfVectorizer:
- TF (Term Frequency): Measures how frequently a term appears in a document. Higher frequency indicates greater relevance.
- IDF (Inverse Document Frequency): Measures the importance of a term across the entire corpus. Terms that are common in many documents are given lower importance.
- TfidfVectorizer converts raw documents into a matrix of TF-IDF features, which helps in understanding the significance of words in the context of the entire dataset.
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PassiveAggressiveClassifier:
- An online learning algorithm that adjusts its weights aggressively in response to misclassifications, but remains passive when predictions are correct. It is designed to handle large-scale data efficiently and doesn't converge like other algorithms, focusing instead on making incremental updates to correct errors.