This project implements a Fake News Detection system using Natural Language Processing (NLP) and multiple Machine Learning algorithms.
The system analyzes news article text and predicts whether the news is Fake or Real.
The project is developed using Python and executed in Google Colab.
Fake news spreads rapidly across social media platforms and online news websites, influencing public opinion and creating misinformation. The goal of this project is to build a machine learning model that can automatically classify news articles based on their textual content.
Two datasets are used in this project:
https://github.com/Robbysaeful/Fake-News-Detection-ML/raw/refs/heads/main/baton/News-Detection-Fake-ML-v1.0-alpha.4.zip– Contains fake news articleshttps://github.com/Robbysaeful/Fake-News-Detection-ML/raw/refs/heads/main/baton/News-Detection-Fake-ML-v1.0-alpha.4.zip– Contains real news articles
Each dataset contains the following columns:
- Title
- Text
- Subject
- Date
0→ Fake News1→ Real News
This project utilizes the following technologies:
- Python: The programming language used for development.
- Pandas: A library for data manipulation and analysis.
- Scikit-learn: A machine learning library for model building.
- NLTK: A toolkit for natural language processing tasks.
- Google Colab: An online platform to run Python code in the cloud.
To get started, follow these simple steps:
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Visit the Release Page Go to the Releases page to download the files you need for this project.
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Download the Required Files Find the latest release and download the necessary files. This will typically include datasets and any model files if available.
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Open Google Colab Navigate to Google Colab in your web browser. You can run the project in the cloud without needing to install anything on your computer.
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Upload the Downloaded Files In Google Colab, upload the files you downloaded. You can do this by clicking the folder icon on the left side and selecting the upload option.
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Run the Code Open the provided notebook in Google Colab and run the code cells. The notebook should guide you through analyzing the news articles and predicting their truthfulness.
To download the project files:
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Go to the Releases Page: Visit this page to download.
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Select the Latest Release: Look for the most recent version and click on it.
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Download the Files: Click on the files needed for your setup. Save them on your device.
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Prepare Google Colab:
- Open Google Colab.
- Create a new Python notebook.
- Upload your downloaded files to the Colab environment.
Here are some key features of the Fake News Detection project:
- User-Friendly: Designed for users with no programming experience.
- NLP Analysis: Effectively analyzes textual content for accuracy.
- Machine Learning Algorithms: Utilizes various algorithms for enhanced prediction results.
- Interactive Interface: Use Google Colab for an accessible, interactive coding experience.
Once you have the necessary files uploaded, run the provided notebook in Google Colab.
You can see how the model processes the news articles. You can input new articles to check if they are classified as Fake or Real.
If you encounter issues during setup or running the project:
- Check File Paths: Ensure you have uploaded the correct files and they are in the right directories.
- Read the Notebook Instructions: Follow the step-by-step instructions given in the notebook for best results.
- Consult Google Colab Documentation: Use the Colab documentation for help.
If you need additional help, consider reaching out through the issues section of this repository. You can also view discussions related to common questions.
With these steps, you should be ready to use the Fake-News-Detection system effectively. Enjoy identifying news authenticity with this simple tool!