📰 Fake News Detection System

A machine learning pipeline for detecting fake news and misinformation using NLP techniques.

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📋 Overview

This project builds a text authenticity classifier that:

• Detects fake news articles with high accuracy
• Uses TF-IDF vectorization for text feature extraction
• Implements ensemble classification methods

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🚀 Quick Start

# Clone the repository
git clone https://github.com/dlifeofjay/text_authenticity.git
cd text_authenticity

# Install dependencies
pip install -r requirements.txt

# Run inference
python text_aut.py

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📁 Project Structure

text_authenticity/
├── Fake News Detector.ipynb  # Training & analysis
├── text_aut.py               # Inference script
├── text_aut.joblib           # Trained model
├── text_cv.joblib            # Count vectorizer
├── text_OrdEnc.joblib        # Ordinal encoder
└── requirements.txt          # Dependencies

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🔬 Methodology

1. Text Preprocessing: Cleaning, tokenization, stopword removal
2. Feature Extraction: TF-IDF / Count Vectorization
3. Classification: Ensemble methods for robust detection
4. Evaluation: Accuracy, precision, recall, F1-score

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📊 Results

The model achieves strong performance in distinguishing authentic news from fabricated content.

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👨‍💻 Author

Jubril Ifekoya - Data Scientist & ML Engineer