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Project Overview
Transformed a heavily compressed, poorly formatted Excel dataset containing over 1,700 rows of Kenyan livestock market records into a clean, analysis-ready database.
The Problem (Messy Data)
Crucial text strings in Market, County, and Breed columns were truncated and cut off.
Text and numerical formatting were inconsistent across rows.
Column widths were restricted, making the sheet unreadable.
The Solution (Clean Data)
Expanded column layouts and adjusted text wrapping for full data visibility.
Standardized market names (e.g., correcting "Kirwara Marke" to "Kirwara Market") and breed classifications.
Structured grading systems and wholesale pricing formatting for seamless filtering and future dashboard reporting.
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• Figma
• Auto Layout
• Components & Variants
• Design System
• Prototyping
Services
• UI/UX Design
• Product Design
• Dashboard Design
• Web Design
• Mobile App Design
• SaaS Design
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Before:
Raw transaction data — inconsistent client names, random formatting, no clear structure. Took forever to make sense of, let alone present.
After:
Clean, structured, color-coded sheet — proper headers, sorted by region & department, payment methods standardized, profit margins clearly visible.
What changed it:
Sub CleanAndFormatData()
Call StandardizeClientNames
Call SortByRegionDepartment
Call FormatHeaders
Call ApplyColorCoding
Call CalculateMargins
End Sub
⏱️ A task that used to take 1-2 hours of manual cleanup now runs in seconds.
Data cleaning is one of the most underrated VBA use cases — most people only think automation = formulas, but cleaning + structuring raw data is where it saves the most time.
Drop a 👍 if you deal with messy spreadsheets regularly — happy to share how I approach this.
E-Commerce Customer Data Cleaning & ETL Pipeline
Description:
Extracted raw transaction records from an e-commerce SQL database, executing initial structural normalization and data filtering within MySQL. Following extraction, migration, and structural staging, conducted advanced data cleansing, schema standardization, and formatting within Microsoft Excel to produce a pristine, production-ready dataset for retail business intelligence.
Workflow & Tools Used:
Data Extraction & Querying: MySQL (Filtering, joining, and database staging)
Data Cleansing & Normalization: Microsoft Excel (Handling missing values, removing duplicate customer records, formatting text data, and verifying data integrity)
Domain Focus: E-Commerce & Retail Metrics
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