Fake News Detection Using Machine Learning

This project focuses on building a machine learning model to detect fake news using natural language processing (NLP) techniques. The model is trained and evaluated using a dataset of labeled news articles.

Features

• Data Preprocessing: Cleaning and vectorizing textual data.
• Model Training: Applying machine learning algorithms such as Logistic Regression, Naive Bayes, and more.
• Evaluation Metrics: Accuracy, precision, recall, and F1-score for performance assessment.

Installation

1. Clone the repository:

   git clone https://github.com/your-repo/fake-news-detection.git
   cd fake-news-detection

2. Install required dependencies:

   pip install -r requirements.txt

Dataset

The dataset contains labeled news articles categorized as FAKE or REAL. Ensure you have the dataset saved in the data directory with the following structure:

data/
├── True_News.csv
├── Fake_News.csv

Usage

1. Run the Jupyter Notebook to preprocess data, train the model, and evaluate its performance:

   jupyter notebook Fake_News_Detection_Using_ML_Task_1.ipynb

2. Explore the notebook to:

   • Load and preprocess the dataset.
   • Train various machine learning models.
   • Evaluate and compare model performances.

Project Structure

.
├── data/               # Dataset files
├── Fake_News_Detection_Using_ML_Task_1.ipynb  # Jupyter Notebook
├── requirements.txt    # Python dependencies
├── README.md           # Project documentation

Requirements

• Python 3.7 or above
• Jupyter Notebook
• Libraries: numpy, pandas, scikit-learn, etc. (See requirements.txt)

Contributing

Contributions are welcome! Please submit a pull request or open an issue for any feature requests or bug reports.

License

This project is licensed under the MIT License. See LICENSE for more information.

Acknowledgments

• Dataset sourced from Kaggle.
• Inspiration and guidance from online tutorials and courses.

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Feel free to explore and modify the project to suit your needs!