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WebCat

Neural network-based website categorization system using machine learning for automatic content classification.

GitHub

Technologies:

Python TensorFlow Neural Networks NLP Web Scraping

WebCat is a machine learning system designed to automatically categorize websites based on their content using neural networks and natural language processing techniques.

Overview

The project addresses the challenge of automatically classifying websites into predefined categories based on their textual content, visual elements, and structural features.

Key Features

• Content Analysis - Extracts and processes textual content from web pages
• Multi-label Classification - Supports websites belonging to multiple categories
• Neural Network Architecture - Uses deep learning for accurate predictions
• Scalable Design - Handles large volumes of websites efficiently

Technical Architecture

┌─────────────┐     ┌──────────────┐     ┌─────────────┐
│  Web Scraper │────▶│  Preprocessor │────▶│  Feature    │
│             │     │              │     │  Extractor  │
└─────────────┘     └──────────────┘     └──────┬──────┘
                                                │
                                                ▼
┌─────────────┐     ┌──────────────┐     ┌─────────────┐
│  Category   │◀────│   Neural     │◀────│  Vector     │
│  Output     │     │   Network    │     │  Embedding  │
└─────────────┘     └──────────────┘     └─────────────┘

Results

The system achieves high accuracy in classifying websites across various categories including news, e-commerce, education, entertainment, and more.

Future Work

• Integration with browser extensions
• Real-time classification API
• Support for additional languages