Fake News Detection

Abstract

1. Introduction

Fake news has become a significant issue in today's digital age, where information spreads rapidly through various online platforms. This project leverages machine learning algorithms to automatically determine the authenticity of news articles, providing a valuable tool to combat misinformation.

The project aims to develop a machine-learning model capable of identifying and classifying any news article as fake or not. The distribution of fake news can potentially have highly adverse effects on people and culture. This project involves building and training a model to classify news as fake news or not using a diverse dataset of news articles. We have used four techniques to determine the results of the model.

2. Objective

This project detects whether a news is fake or not using machine learning.

This project demonstrates how Jupyter Notebook, Machine Learning, Python can be applied to a real-world AI & Machine Learning problem.

4. Technology Stack

• Python 3
• Scikit-learn
• Matplotlib
• Regular Expression

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Python 3.8 or later with Jupyter Notebook / JupyterLab (or Google Colab)
• pip for dependencies
• Python 3.8 or later
• pip / virtualenv for dependencies
• VS Code, PyCharm or Jupyter Notebook
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/kapilsinghnegi/Fake-News-Detection.git
cd Fake-News-Detection

1. Clone this repository to your local machine:
2. Navigate to the project directory:
3. Execute the Jupyter Notebook or Python scripts associated with each classifier to train and test the models. For example:
4. The code will produce evaluation metrics and provide a prediction for whether the given news is true or false based on the trained model.

git clone https://github.com/kapilsinghnegi/Fake-News-Detection.git

cd fake-news-detection

python random_forest_classifier.py

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Deploy the model as a web app with Streamlit, Flask or FastAPI
• Compare against an additional model and report the metric difference
• Add explainability (SHAP / Grad-CAM)

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What dataset does the project use and how was it pre-processed?
2. Which algorithm / model architecture is used and why was it chosen over alternatives?
3. How are training and testing data split, and how is overfitting avoided?
4. Which evaluation metrics (accuracy, precision, recall, F1) are reported and what do they mean here?
5. How would you deploy this model for real users?

9. Source Code & License

This project is developed by kapilsinghnegi and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.