Fake News Detector using Python

Project Overview

This project aims to create a simple yet effective Fake News Detector using Python. The detector utilizes natural language processing techniques to analyze and classify news articles as either real or fake. The core functionality is built upon machine learning algorithms, enabling the model to learn from labeled datasets and make predictions on new articles.

Installation

To run the Fake News Detector, follow these steps:

1. Clone the repository to your local machine:

   git clone https://github.com/your-username/fake-news-detector.git

2. Navigate to the project directory:

   cd fake-news-detector

3. Install the required dependencies:

   pip install -r requirements.txt

Usage

1. Ensure that you have a labeled dataset for training the model. The dataset should contain two columns: one for the news text and another for the labels (real or fake).

2. Place the dataset in the data directory.

3. Run the training script to train the Fake News Detector model:

   python train_model.py

   This script will preprocess the data, train the model, and save the trained model to the models directory.

4. Once the model is trained, you can use the detector to classify new articles. Modify the test_article.txt file in the data directory with the text of the article you want to classify.

5. Run the detector script:

   python detect_fake_news.py

   The script will output the classification result (real or fake) based on the trained model.

Project Structure

• data: Contains the labeled dataset for training and the test article.
• models: Stores the trained model after running the training script.
• src: Contains the source code for the Fake News Detector.
  • preprocess.py: Handles data preprocessing.
  • train_model.py: Script for training the machine learning model.
  • detect_fake_news.py: Script for classifying new articles using the trained model.
  • model.py: Defines the architecture of the machine learning model.

Contributing

Contributions are welcome! If you find any issues or have suggestions for improvement, please open an issue or create a pull request.

License

This project is unlicensed.

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