Humach Retention: Customer Risk
Frame a retention problem, define legal features and reason about validation, calibration and actionable interventions.
Download mini_customers.csvUse Python, SQL, Excel or your preferred workflow unless a task specifies otherwise.
Project dataset
Download CSVInspect the CSV before you answer the tasks
Scroll horizontally or vertically to examine the exact miniature data used by the validator.
| customer_id | segment | age | spend | visits | region |
|---|---|---|---|---|---|
| A01 | New | 24 | 120 | 3 | East |
| A02 | New | 31 | 85 | 2 | West |
| A03 | Loyal | 42 | 210 | 7 | East |
| A04 | Loyal | 38 | 195 | 6 | North |
| A05 | New | 27 | 92 | 2 | West |
| A06 | At risk | 51 | 61 | 1 | North |
| A07 | Loyal | 44 | 240 | 8 | East |
| A08 | At risk | 36 | 55 | 1 | West |
| A09 | New | 29 | 132 | 4 | East |
| A10 | Loyal | 47 | 205 | 7 | North |
| A11 | At risk | 40 | 70 | 2 | West |
| A12 | New | 33 | 118 | 3 | East |
Project workspace
Analyse → validate → submit.
Numeric and decision tasks are auto-checked. Written reasoning is checked for completeness and key ideas, then included in your local submission report.
0/5 validated
How many customers are labelled “At risk”?
What is mean spend across customers?
If multiple records per customer existed, which validation design would protect independence?
Describe one ethical limitation of using a retention-risk score to target customers.
Submit your project locally
HumachLearn stores no server-side data. Submission creates a portable JSON report containing your answers, validation state and timestamp.