Paper Contents
Abstract
The main goal of the Fake News Detection project is to address the spread of false information online. This will be done by creating an automated system that can accurately distinguish between real and fake news articles. The project will include a detailed text classification process, covering data collection, preprocessing, feature extraction, model training, and evaluation. To improve accuracy in identifying fake news, various machine learning algorithms like Logistic Regression, Decision Tree, Random Forest, Multinomial Naive Bayes, and Support Vector Machine (SVM) will be used. Additionally, the project will feature a user-friendly interface for uploading data and interacting with the model, as well as visualization tools for presenting results. Future plans for the project include real-time data processing, support for multiple languages, and ongoing learning through user feedback to ensure the system remains effective against changing misinformation tactics. In summary, this project lays a strong foundation for combating fake news, encouraging informed decision-making, and maintaining public trust in the media.Keywords: Fakenews detection, logistic regression, feature extraction, visualization, user interface, decision tree.
Copyright
Copyright © 2024 Sinchana A J. This is an open access article distributed under the Creative Commons Attribution License.
