Web Scraping for Machine Learning: Building Training Datasets

Finding quality training data is ML's biggest challenge. Web scraping is essential for building custom datasets for text classification, image recognition, and sentiment analysis. Planning Your Dataset Before scraping, define your target variable, features needed, required volume, and class balance strategy. Scraping Text for NLP import requests from bs4 import BeautifulSoup import re class ReviewScraper : def __init__ ( self ): self . session = requests . Session () self . session . headers . update ({ ' User-Agent ' : ' MLDataBot/1.0 ' }) def scrape_reviews ( self , url , selectors ): resp = self . session . get ( url , timeout = 15 ) soup = BeautifulSoup ( resp . text , ' html.parser ' ) reviews = [] for el in soup . select ( selectors [ ' container ' ]): text = el . select_one ( selectors [ ' text ' ]) rating = el . select_one ( selectors [ ' rating ' ]) if text and rating : reviews . append ({ ' text ' : text . get_text ( strip = True ), ' rating ' : self . _parse_rating ( rating