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Yaroslav Matyash

Offer Yaroslav work on your next project.

Ukraine Zaporizhzhia, Ukraine
5 months 27 days back
Available for hire available for hire
on the service 6 years

Rating

Successful projects
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Rating
152
Web Programming
3604 place out of 6392
Python
1612 place out of 4449

Skills and abilities

Design & art

Photo, Audio & Video

Portfolio


  • Project of the parser Lawyers.com

    Python
    This is an advanced web scraping application created in Python using Django and Selenium (specifically undetected_chromedriver). The project is designed to collect comprehensive information about law firms and lawyers from the website lawyers.com for major cities in the USA and to save structured data in a PostgreSQL database.

    Capabilities
    Parsing law firms: Collecting firm names, addresses, phone numbers, ratings, number of reviews, and links to websites.
    Parsing lawyers: Extracting detailed profiles, including biography, licensing information, education, awards, social media links, and areas of practice.
    Database integration: Using Django ORM for reliable data management in PostgreSQL.
    Bypassing bot protection: Using undetected_chromedriver to circumvent bot detection mechanisms.
    Multithreading: Having multithreaded scripts (concurrent_parse.py, concurrent_lawyer.py) to run multiple browser instances simultaneously, speeding up data collection.
    Resume capability: Tracking parsing status (New, Done, Done1), allowing the process to be paused and resumed without data loss.
    Technology stack
    Python 3.8+
    Django (ORM and project structure)
    Selenium and Undetected Chromedriver (Browser automation)
    PostgreSQL (Database)
  • LoopNet Parser Project

    Python
    This is a high-performance web scraping application built on Python and Django. The project is designed to extract commercial real estate listings and detailed information about brokers from the website LoopNet.com. Unlike traditional Selenium-based parsers, this project uses curl_cffi to simulate TLS fingerprints of a real browser, making it faster and less detectable by bot protection.

    Capabilities
    Listing parsing: Collecting commercial real estate listings, including titles, URLs, and agency names.
    Broker parsing: Extracting detailed broker profiles, including names, phone numbers, emails, biographies, awards, specializations, and transaction history.
    Smart detection: Capable of parsing broker data directly from the listing page or navigating links to specialized profile pages.
    Database integration: Using Django ORM with PostgreSQL to store structured data (Listing and Broker models).
    High performance: Utilizes curl_cffi for fast HTTP requests with browser simulation (chrome110), avoiding the overhead of launching a full browser.
    Process management: Tracks the status of listings (New, Done, Broker Info Done, Error) for efficient management of the data collection pipeline.
    Technology stack
    Python 3.8+
    Django (ORM and admin panel)
    curl_cffi (HTTP client with TLS impersonation support)
    BeautifulSoup4 (HTML parsing)
    PostgreSQL (Database)
  • Lawyer.be Scraper

    Python
    This project is a web scraper based on Django, designed to collect data about lawyers from the Belgian directory advocaat.be.

    The data collection process is divided into two stages:

    Link collection: Crawling through search pages to gather basic information (names, profile links) and saving them in a PostgreSQL database.
    Data enrichment: Visiting each collected profile to extract detailed information (email, phone numbers, specializations, languages, membership in bar associations, etc.).

Activity

  Latest proposals 10
Video editing
25 USD
Tik Tok account
11 USD
Video editing for website
27 USD
Some parts need to be blurred in the video Personal project
11 USD
Video editor for TikTok
11 USD
Assembly of reels with animations
12 USD
Video editing for a news YouTube channel
12 USD
Video editor for e-commerce
33 USD
Assembly Minecraft video
11 USD
Create a business card Personal project
12 USD