mlscraper is a Python library designed to automatically extract structured data from HTML pages without requiring developers to manually write CSS selectors or XPath rules. Instead of defining extraction logic by hand, users provide a few examples of the data they want to retrieve from a webpage. It analyzes those examples within the HTML document and determines patterns or rules that can be used to extract the same type of information from similar pages. Once trained, the generated scraper can process new pages and return the extracted data in structured formats such as dictionaries or lists. This approach simplifies web scraping tasks by shifting the focus from rule-writing to example-based training. Internally, the project processes HTML documents, identifies relevant elements in the DOM, and builds extraction logic based on statistical or heuristic analysis of the training samples. The result is a developer-oriented tool that aims to automate common scraping workflows.
Features
- Learns how to extract data from HTML pages using example outputs
- Automatically identifies relevant nodes within the HTML DOM
- Generates reusable scraping rules after a training phase
- Extracts structured data such as dictionaries, lists, or values
- Works with common HTML parsing libraries for document processing
- Designed for integration into Python-based data collection workflows