Bulk Phone Data Cleanup

I have an Excel file of roughly 10,000 phone records. The numbers are randomly spread across several columns and appear in mixed formats—some already include the country code, many do not. I need a clean, accurate dataset in one column that I can rely on for outreach and analytics.

Scope of work
• Extract every phone number from all existing columns and place it in a single, dedicated column.
• Filter out everything except valid mobile numbers; landlines or malformed entries must be discarded.
• Where a country code is missing, append the single code I will supply so that every entry follows the same international format.
• Verify each number’s WhatsApp availability and mark the result (Yes/No).
• Query Truecaller (or a comparable lookup) to capture the display name shown for each number and add that name into a separate column.

Deliverables
1. The updated Excel workbook with:
– Column A: Cleaned mobile numbers (uniform format, one per row)
– Column B: WhatsApp status
– Column C: Truecaller-retrieved name
2. A short log describing any rules or scripts used so I can reproduce the process later if needed.

Acceptance criteria
• All ~10,000 numbers processed; error rate below 1 %.
• No duplicates and no landlines in the final list.
• Every entry carries the correct country code.
• WhatsApp and Truecaller fields populated or clearly marked when data is unavailable.

Payment will be calculated per number successfully processed, so accuracy directly affects your payout. If you prefer to use Excel VBA, Python (pandas, openpyxl), or another reliable toolchain, that’s fine—just ensure the final sheet meets the criteria above.