Overview The Fake News Detection project aims to leverage machine learning techniques to identify and classify news articles as either real or fake. This system is designed to help combat the spread of misinformation by providing a tool to evaluate the credibility of news sources and articles.
Key Features Text Classification: Utilizes various machine learning algorithms to classify news articles based on their content. Techniques include Natural Language Processing (NLP) and deep learning models. Feature Extraction: Implements methods for extracting meaningful features from text, such as TF-IDF vectors, word embeddings, and syntactic patterns. Model Training and Evaluation: Trains models on large datasets of labeled news articles and evaluates their performance using metrics such as accuracy, precision, recall, and F1-score. Real-Time Prediction: Provides a mechanism for real-time prediction of news articles, allowing users to assess the credibility of new content as it becomes available. Data Visualization: Offers visualizations of model performance and feature importance to help understand and interpret the results.