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PROJECT 082 / 102 · Commerce

Atypical wholesale purchasing profiles.

Which customer spending profiles are unusual relative to this sample?

Focused analytical studyWholesale customersExecuted notebook

THE RESULT

What the data shows

The largest computed metric is 0.7205 for 154.0; the smallest is 0.3702 for 147.0. Metric: review_score (Relative anomaly score).

There are no anomaly labels. The review fraction is a workflow assumption, not a measured error rate.

Atypical wholesale purchasing profiles — chart from the computed study output
Computed study output. Full values and units are available in the results download.
Computed results · 12 of 440 rows. Values rounded for display; source units and raw column names are retained in the download.
row idfreshmilkgroceryfrozendetergents paperdelicassenreview score
1546225513775780.7205
33833337,02115,601155500.6651
658520,95945,8283624,2311,4230.6293
7520,3981,13734,40739750.6276
18336,84743,95020,17036,53423947,9430.6166
16112,4345402831,09232,2330.613
14237,0367,1528,2532,9952030.6106
4744,46654,25955,5717,78224,1716,4650.6087
35622,6861342183,15795480.6051
1281408,8473,8231421,06230.599
8516,11746,19792,7801,02640,8272,9440.5988
28942,7862864711,38832220.5986
Download the complete result table ↓

THE METHOD

From source to answer

Isolation Forest on log-transformed standardized category spending, with a declared 5% review fraction.

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

Review atypical profiles for data quality or legitimate specialized purchasing; do not label them fraudulent.

Where the conclusion stops

Annual customer spending uses source monetary units, not an assumed currency. There are no margins, transactions or dates. Customer segments are descriptive and do not establish promotion response.

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.