Scraper for generating B2B leads (Corporate databases)

Objective: Develop an automated web scraper in Python to collect structured contact and financial data of potential B2B clients from public business directories.

My solution and technical implementation:

Parsing HTML tables: The script efficiently navigates through directory pages and extracts the necessary information from the complex tabular structure of the websites using the BeautifulSoup library.

Operational stability: To prevent blocking by target servers, custom HTTP headers were configured to mimic requests from a real browser. This ensured uninterrupted data collection during long sessions.

Deep data cleaning: The collected "raw" information often contained extraneous characters and formatting artifacts. Using the Pandas library, I implemented logic for automatic cleaning of key metrics. For example, the fields "Company Revenue" and "Number of Employees" were programmatically cleaned of text and converted into strict numerical values.

Preparation for CRM: The final dataset is automatically exported in a valid CSV format with the correct column structure.

Technologies used:
Python, BeautifulSoup, Pandas, HTTP Headers Configuration.

Result:
The client received a fully automated lead generation tool. The output is a perfectly clean CSV file that can be instantly imported into any CRM system without the need for additional manual processing or formatting error corrections.