Objective: Develop a machine learning model capable of distinguishing between real and fake news articles using ivy. This project addresses the critical issue of misinformation in the digital age, employing natural language processing (NLP) techniques to analyze and classify news content. It's an opportunity to contribute to the fight against fake news, leveraging technology to promote the dissemination of accurate information.
Task Details:
Dataset: Utilize the dataset available at Fake News Dataset for this task. This dataset contains a collection of news articles along with labels indicating their authenticity, providing a solid foundation for training and evaluating your model.
Expected Output: Your contribution should include a Jupyter notebook detailing your model's development process, encompassing data preprocessing, feature engineering, model training, and evaluation stages. Additionally, include the trained model files in the corresponding directory.
Submission Directory: Please submit your Jupyter notebook and model files in the Contributor_demos/Fake News Detection subdirectory within the unifyai/demos repository.
How to Contribute:
Fork the unifyai/demos repository to your GitHub account.
Clone the forked repository to your local system.
Create a new branch dedicated to your work on this specific use case.
Develop your model, ensuring to document the process comprehensively in the Jupyter notebook.
Place your notebook and model files in the Contributor_demos/Fake News Detection directory.
Upon completion, push your contributions to your forked repository.
Open a Pull Request (PR) to the unifyai/demos repository with a clear and descriptive title, such as "Fake News Detection Demo Submission".
Contribution Guidelines:
Make sure your code is thoroughly documented to facilitate understanding and replication by others.
Summarize your methodology, significant discoveries, and any challenges you encountered in your PR description, providing valuable insights into your project.
Objective: Develop a machine learning model capable of distinguishing between real and fake news articles using ivy. This project addresses the critical issue of misinformation in the digital age, employing natural language processing (NLP) techniques to analyze and classify news content. It's an opportunity to contribute to the fight against fake news, leveraging technology to promote the dissemination of accurate information.
Task Details:
Dataset: Utilize the dataset available at Fake News Dataset for this task. This dataset contains a collection of news articles along with labels indicating their authenticity, providing a solid foundation for training and evaluating your model.
Expected Output: Your contribution should include a Jupyter notebook detailing your model's development process, encompassing data preprocessing, feature engineering, model training, and evaluation stages. Additionally, include the trained model files in the corresponding directory.
Submission Directory: Please submit your Jupyter notebook and model files in the
Contributor_demos/Fake News Detectionsubdirectory within theunifyai/demosrepository.How to Contribute:
unifyai/demosrepository to your GitHub account.Contributor_demos/Fake News Detectiondirectory.unifyai/demosrepository with a clear and descriptive title, such as "Fake News Detection Demo Submission".Contribution Guidelines: