Description
Overcop is an e-commerce platform specialising in the sale of luxury trainers.
As a Data Project Manager in the Overcop team, my role is to optimise the sales process and adjust prices in line with market fluctuations.
This includes setting up a standalone environment using Docker, API integration, MySQL databases, log streaming and alert systems.
All this automation has made it possible to set up a web scraping script (httpx, BeautifulSoup, Selenium, etc.) and word processing using techniques such as regex and Levenshtein distance.
The project aims to streamline the sales process and improve pricing strategies, in order to maximise our website's revenues.
Scraping-Application
To update prices, we've implemented a scraper. We copy shoe prices to another database while adding a coefficient of 0.93.
We circumvented anti-bot techniques by replicating web requests using Burp-Suite, specifying headers and cookies, retrieving various JSON data, and utilizing proxy rotations.
Web-Application
To carry out our project, we set up a web application hosted on the Streamlit Community Cloud.
It allows you to see live which pairs have been updated by our scraper, create exceptions for the database, carry out precise searches on the database, or view our MySQL database directly.