← All projects

PROJECT 006 / 102 · Commerce

Sales data cleaning sensitivity.

How do plausible cleaning choices alter gross invoiced sales?

Focused analytical studyRetailExecuted notebook

THE RESULT

What the data shows

The largest computed metric is 1.067e+07 for Retain source repeats; the smallest is 8.911e+06 for Known customers only. Metric: gross_gbp (GBP).

Unknown customers and repeated lines are different quality issues; neither is automatically an invalid sale.

Sales data cleaning sensitivity — chart from the computed study output
Computed study output. Full values and units are available in the results download.
Computed results · 3 of 3 rows. Values rounded for display; source units and raw column names are retained in the download.
policyrowsgross gbpdifference from retained gbp
Retain source repeats530,10410,666,684.540
Remove exact repeats524,87810,642,110.80-24,573.74
Known customers only397,8848,911,407.90-1,755,276.64
Download the complete result table ↓

THE METHOD

From source to answer

Compare retained source rows, exact-row deduplication and exclusion of unidentified purchasers.

The Python source exposes this study’s transformations. The complete project download includes shared preparation and evaluation routines.

THE NEXT DECISION

What follows from the finding

Choose cleaning rules from source-system evidence and document their financial effect.

Where the conclusion stops

Historical invoice lines; credits are not reliably matched to original sales. Gross purchases are not profit. Unidentified customers cannot support customer-level conclusions. Exact repeated lines remain unless the study explicitly compares removal.

Related studies may reuse observations or holdouts. These are historical analyses; associations and backtests do not demonstrate commercial impact. Further model tuning needs new, untouched evaluation data.