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A sample Python project showing my data cleaning workflow — removing duplicates, handling missing values, standardizing text fields, and summarizing sales by city using Pandas. This reflects the kind of cleanup and analysis I do for client datasets.
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Excel PivotTable — Sales Summary
Client had raw sales data (14 rows across UK/USA, split by quarter) and needed it turned into something actually usable. I cleaned it up and built a PivotTable that breaks total sales down by rep and country, with grand totals for both.
What I did:
Structured the raw data into a proper table (consistent headers, currency formatting)
Built a PivotTable summarizing Sum of Sales by Last Name and Country
Used SUMIFS so the totals update automatically if the source data changes — nothing hardcoded
Checked every formula for errors before delivery
Delivered as a working .xlsx file, ready to plug into a bigger report or dashboard.
If you've got messy sales/ops data sitting in a spreadsheet and need it turned into something you can actually read at a glance, this is exactly the kind of thing I can help with.
Cleaned and validated a synthetic operational dataset containing duplicate records, missing values, invalid entries, and inconsistent formatting. Documented each issue, standardized the data, removed duplicates, and created a data-quality summary to demonstrate a practical end-to-end cleaning and validation workflow.
Built an automated weather monitoring system that logs conditions to Google Sheets based on temperature thresholds.
Instead of manually checking weather data and logging it, I built a Make.com scenario that:
Pulls current weather data on a schedule (Weather module)
Routes the data based on temperature conditions
Logs the results into different Google Sheets tabs/rows depending on which condition is met
Result: Weather data is now tracked automatically and organized by condition — no manual checking or data entry required, and the log stays consistently up to date.
