Fake-News-Detector

Project Overview

This project focuses on building a machine learning model to detect fake news articles. The model is trained on a dataset of news articles labeled as real or fake.

Key Features

• Data Collection and Cleaning: The project includes code for loading the dataset, cleaning the text data (removing punctuation, stop words, etc.), and preprocessing the data for model training.
• Feature Engineering: Text features are extracted from the news articles, such as TF-IDF (Term Frequency-Inverse Document Frequency) to represent the importance of words in the context of the dataset.
• Model Training: Various machine learning models are explored and trained on the preprocessed data, such as Naive Bayes, Support Vector Machines (SVM), and Random Forest.
• Model Evaluation: The trained models are evaluated using metrics like accuracy, precision, recall, and F1-score to determine their performance in detecting fake news.
• User Interface (Optional): A simple user interface can be created to allow users to input a news article and receive a prediction from the trained model.

Technologies Used

• Python: The primary programming language for implementing the project.
• Libraries:
  • Pandas: For data manipulation and analysis.
  • NLTK (Natural Language Toolkit): For text processing tasks like tokenization, stemming, and stop word removal.
  • Scikit-learn: For machine learning algorithms, data preprocessing, and model evaluation.
  • TensorFlow/PyTorch (Optional): For deep learning models if desired.

How to Run

1. Install dependencies:

   pip install -r requirements.txt

2. Run the notebook:

   jupyter notebook notebooks/fake_news_detection.ipynb

Future Enhancements

• Explore deep learning models: Implement models like Recurrent Neural Networks (RNNs) or Transformers for potentially better performance.
• Improve data cleaning: Incorporate more sophisticated text cleaning techniques, such as named entity recognition and sentiment analysis.
• Build a web application: Create a user-friendly web interface for easy interaction with the model.
• Deploy the model: Deploy the trained model to a cloud platform for real-time fake news detection.

Note:

This is a basic outline. The specific implementation details will vary depending on the chosen approach and the complexity of the project.

Disclaimer:

This project is for educational and research purposes only. It is crucial to use this model responsibly and ethically.