Fake News Classifier

This project is a machine learning model that classifies news articles as fake or real. It includes data loading, preprocessing, model training, evaluation, and an API for making predictions.

Table of Contents

• Project Overview
• Setup Instructions
• Usage
• Model Training
• API Usage
• Contributing
• License

Project Overview

The Fake News Classifier project aims to build a model that can accurately classify news articles as fake or real based on their title and text content. The project includes data preprocessing, feature engineering, model training, evaluation, and a REST API for serving predictions. The base model was trained on the WELFake dataset published in IEEE Transactions on Computational Social Systems (Volume: 8, Issue: 4, August 2021).

Key Features

• Data Loading and Preprocessing: Load and clean the dataset to prepare it for model training.
• Feature Engineering: Extract meaningful features from the text data using techniques like TF-IDF.
• Model Training: Train a machine learning model to classify news articles as fake or real.
• Model Evaluation: Evaluate the model's performance using metrics such as precision, recall, and F1-score.
• API for Predictions: Serve the model using a REST API to classify new news articles.

Setup Instructions

Prerequisites

• Python 3.7 or higher
• pip (Python package installer)
• git (for cloning the repository)

Installation

1. Clone repository

git clone https://github.com/IAMSebyi/Fake-News-Classifier.git
cd Fake-News-Classifier

2. Create a virtual environment

python -m venv venv
source venv/bin/activate    # On Windows use `venv\Scripts\activate`

3. Install dependencies

pip install -r requirements.txt

Usage

Running the Main Script

To preprocess data, train the model, and evaluate it:

python main.py

Model Training

If you want to specifically train the model:

python -m src.models.train

API Usage

To start the API server for making predictions:

python -m src.app.api

Once the server is running, you can make POST requests to http://127.0.0.1:5000/predict with a JSON payload containing the text to classify.

Example request:

{
  "text": "Sample news article text to classify."
}

Contributing

Contributions are welcome! Please fork the repository and submit a pull request for any improvements or bug fixes.

Steps to Contribute

1. Fork the repository
2. Create a new branch (git checkout -b feature-branch)
3. Commit your changes (git commit -m 'Add some feature')
4. Push to the branch (git push origin feature-branch)
5. Open a pull request

License

This project is licensed under the MIT License - see the LICENSE file for details.