In today's data-driven world, news companies amass terabytes of data, seeking valuable insights. News article classification is pivotal for efficient information access. This repository implements machine learning techniques, including clustering and rule-based algorithms, to categorize news articles. Algorithms such as SVM, Neural Networks, and Random Forest are employed for text classification. The system aims to accurately classify news articles into categories like Finance, Sports, etc., enhancing user experience. Join us in revolutionizing news analysis!
Approach:
- Utilize clustering and rule-based algorithms for grouping similar text.
- Employ ML algorithms to learn mapping functions between text and tags.
- Implement SVM, Neural Networks, Random Forest for text classification.Results:
- Efficiently classify news articles into various categories.
- Enhance user experience by enabling quick access to relevant information.Let's collaborate to build a powerful solution for automated news classification!