🕸️ Scrape & Classify – Web Data Extraction Pipeline

A modular Python pipeline for extracting, cleaning, and classifying web data using open-source tools. Designed for smart web prospecting, offline research, and scalable automation.

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📌 Features

• 🔍 Web Scraping: Collects structured data from target websites using requests and BeautifulSoup.
• 🧹 Data Cleaning: Standardizes and prepares text for analysis using pandas, re, and unicodedata.
• 🧠 ML Classification: Predicts categories or relevance using a trained scikit-learn classifier.
• 🗃️ Export Options: Saves results to .csv, .json, and .txt formats for integration and sharing.
• 🔐 Offline-First: Fully operable without internet after initial setup. No external API dependencies.
• 📁 Modular Design: Easy to adapt, reuse, or extend in parts or as a full pipeline.

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🚀 Use Cases

• Local business discovery and qualification
• Lead generation and client research
• Academic data collection and preprocessing
• Custom classifiers for specific domains or keywords

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🧰 Stack

Function	Tools Used
Scraping	requests, BeautifulSoup
Cleaning	pandas, re, unicodedata
ML Classification	scikit-learn, joblib
Exporting	csv, json, plain text handling
Logging & CLI	argparse, logging, .env

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🛠️ How It Works

# 1. Install dependencies
pip install -r requirements.txt

# 2. Run the scraper (customize inside config or via CLI)
python scrape.py --target "https://example.com" --output data/raw.csv

# 3. Clean and preprocess the scraped data
python clean.py --input data/raw.csv --output data/clean.csv

# 4. Classify the cleaned data using the trained model
python classify.py --input data/clean.csv --model models/classifier.pkl --output results/predictions.csv

Each step is independent and can be used modularly.

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📊 Example Output

name,website,description,category,score
"ACME Bakery","acme.com","...","food",0.92
"FutureTech Solutions","futuretech.ai","...","tech",0.88
...

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📂 Folder Structure

scrape_classify_pipeline/
│
├── data/                 # Raw and processed data
├── models/               # Trained models (.pkl)
├── results/              # Classification outputs
├── scrape.py             # Web scraping logic
├── clean.py              # Data cleaning script
├── classify.py           # ML classification pipeline
├── utils/                # Reusable utility functions
├── config.env            # Environment variables
└── README.md             # Project overview

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📦 Dependencies

• Python 3.8+
• pandas
• beautifulsoup4
• scikit-learn
• requests
• joblib

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🔒 Privacy-First Design

This pipeline does not rely on cloud APIs or send data externally. Everything runs locally, making it ideal for private research or restricted environments.

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📌 License

Licensed under the MIT License – feel free to modify and use for personal or commercial projects.

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🙌 Author

Jose Daniel Soto 📧 Email | 🌐 GitHub | 🔗 LinkedIn