Passive Aggressive Classifier to Identify Fake News

CAN A MACHINE LEARNING PROGRAM IDENTIFY WHEN AN ARTICLE MIGHT BE FAKE?

There is a substantial amount of information at our fingertips that allows us to be quickly informed. However, with that information comes false information that requires validation. The goal of this project is to develop a machine learning algorithm to detect that fake information, so that misinformation does not continue to be spread and we are accurately informed.
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Table of Contents

1. About The Project
   • Built With
2. Getting Started
   • Prerequisites
   • Installation
3. Description
4. Contributing
5. License
6. Contact

About The Project

Built With

• Powerpoint
• Word
• jupyter notebook

Getting Started

To get a local copy up and running follow these simple steps.

Prerequisites

• Microsoft Office Suite
• PDF Reader

Installation

1. Clone the repo

   git clone https://github.com/AMeyer89/PassiveAggressiveClassifierIdentifyFakeNews.git
   

Description

There is a substantial amount of information at our fingertips that allows us to be quickly informed. However, with that information comes false information that requires validation. The goal of this project is to develop a machine learning algorithm to detect that fake information, so that misinformation does not continue to be spread and we are accurately informed. The model that was used was a Passive Aggressive Classifier. They are commonly used for large-scale learning (3). It is considered an ‘online-learning algorithm ‘, because occurs in a consecutive order and the model is updated step-by-step. The data that was used to train this model was obtained from Kaggle. Accuracy scores and confusion matrix were used to gauge the model. A confusion matrix was created to show if there is an imbalance in classes causing the accuracy score to be high. The confusion matrix was used to calculated precision and recall. The PassiveAggressiveClassifier performed well in predicting if an article was fake or real. It had an accuracy of 96.44% with a precision 96.17% and recall of 96.81

Contributing

Contributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make are greatly appreciated.

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

License

Distributed under the MIT License. See LICENSE for more information.

Contact

April Meyer - swim53185@gmail.com

Project Link: https://github.com/AMeyer89/PassiveAggressiveClassifierIdentifyFakeNews