The client’s billing team manually reviewed and calculated bills from large Excel files, a process that took hours per cycle and often introduced human errors and inconsistencies in calculations and reporting..

For a solution I built a Python script leveraging Pandas and OpenPyXL to parse, validate, and format billing data automatically. The system: Extracted data from Excel sheets, computed totals, and applied tax rules. Generated formatted bills in Excel and PDF formats for distribution. Implemented data validation and error reporting for missing or inconsistent entries. Reduced billing generation time from over 8 hours to under 10 minutes. Eliminated manual calculation errors, improving billing accuracy to over 99%. Freed up administrative staff time for higher-value financial analysis and reporting.. My main role(s) included Script Development: Created the Python automation workflow using Pandas and OpenPyXL. Data Validation: Added error-checking logic to identify and flag problematic rows before processing. Reporting & Output: Automated the generation of monthly billing summaries and formatted invoices. Optimization: Tuned performance to handle large datasets efficiently with minimal resource use. and i gained skills in Python Problem Solving

View the code at this Github link.