How do I collect training data for machine learning from websites?

Collecting training data from websites usually means one of these approaches:

1. Use a public API
   Best option if available. It’s cleaner, more stable, and often legally safer than scraping.

2. Web scraping

   • Use tools like requests, BeautifulSoup, Scrapy, or browser automation like Playwright/Selenium.
   • Parse page HTML, extract the text/images/tables you need, and store them in a structured format like CSV, JSON, or Parquet.

3. Download existing datasets

   • Sometimes the same content is already available via Kaggle, Hugging Face Datasets, Common Crawl, or academic datasets.
   • This is often easier than collecting it yourself.

Basic workflow

1. Define your task

   • What are you predicting?
   • What labels do you need?
   • What kind of examples count as positive/negative?

2. Find sources

   • Websites with relevant content
   • APIs
   • Public archives

3. Check permissions

   • Read the site’s Terms of Service
   • Check robots.txt
   • Look for copyright/licensing terms
   • Make sure your use is allowed, especially for commercial use or redistribution

4. Collect the data

   • Crawl or scrape pages
   • Capture the relevant content and metadata
   • Rate-limit requests and avoid overloading servers

5. Clean and label

   • Remove duplicates, boilerplate, ads, and navigation text
   • Normalize formats
   • Create labels manually or semi-automatically

6. Store it properly

   • Save raw data separately from cleaned data
   • Keep source URLs, timestamps, and license info for traceability

Important legal and ethical points

• Respect site policies and copyright.
• Don’t bypass access controls, paywalls, logins, CAPTCHAs, or anti-bot systems.
• Be polite to servers: use delays, caching, and small request volumes.
• Be careful with personal data: privacy laws may apply depending on your jurisdiction and the data type.

Example tools

• Static pages: requests + BeautifulSoup
• Large-scale crawling: Scrapy
• JavaScript-heavy sites: Playwright
• Table extraction: pandas.read_html
• APIs: requests, official SDKs

Tiny Python example

import requests
from bs4 import BeautifulSoup

url = "https://example.com"
html = requests.get(url, timeout=10).text
soup = BeautifulSoup(html, "html.parser")

texts = [p.get_text(strip=True) for p in soup.find_all("p")]
print(texts)

If you want, I can also help you with:

• building a scraper for a specific website,
• designing a dataset schema,
• or setting up a pipeline for cleaning and labeling scraped data.