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AI-Assisted Invoice Processing & Data Cleaning
Description: Demonstrated end-to-end invoice processing workflow — taking 12 raw invoices with formatting errors, duplicates, missing categories, and currency mismatches, and producing a clean, audit-ready dataset. Identified and removed 2 duplicate invoices, standardized all date and category formats, flagged USD currency for conversion, and delivered a reconciled summary of KES 909,200. Reflects AI-assisted data cleaning workflow.
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Messy Data In. Clearer Decisions Out. 📊
Over the past year, I've noticed most businesses don't struggle because they lack data — they struggle because that data never turns into a decision.
That's the gap I work in:
→ Cleaning and analyze data with Python & R
→ Turning findings into investor-ready pitch decks
→ Polishing the final write-up so nothing gets lost in translation
If your team has data sitting in spreadsheets with no story attached, or a pitch deck that needs to actually land — that's exactly where I can help.
Open to projects. Let's talk 👇
BEFORE: Client had messy Excel inventory with 500 rows, duplicates (A12 / a12), blanks, wrong prices like "$ 2.5" and "N/A", and different warehouse names (KER / ker / Kericho).
AFTER: I did:
Removed 12 duplicate SKUs
Standardized item names to Title Case
Fixed QTY and Prices to 2 decimals
Unified Warehouse to Kericho
Made sheet ready for pivot dashboard
Result: Clean, ready-to-use inventory in 2 days using Excel.
Tools: Excel, Remove Duplicates, Text to Columns, TRIM, PROPER
Behind every clean chart is a messy spreadsheet 🧹
This is what a real data cleaning job looks like before it becomes an elegant bar chart — duplicate rows, missing values, inconsistent formatting, all sorted out with Python and Pandas.
#collage attempt: a look inside the data analyst's actual desk, not just the pretty output.
