---
license: other
license_name: ibm-sample-data-license-unverified
pretty_name: RetainIQ Training Data (IBM HR Attrition, synthetic)
language:
  - en
task_categories:
  - tabular-classification
tags:
  - hr-analytics
  - fairness
  - tabular
  - ai-governance
  - datasheet
---

# Datasheet — RetainIQ Training Data (IBM HR Attrition)

*This is the **dataset card** for RetainIQ's training data. Created for **The AI
Governance Lab** course following the **Datasheets for Datasets** template (Gebru et al.,
2018), because no official datasheet exists for this dataset. Authoring one ourselves is
the point: most real datasets arrive undocumented, and writing the datasheet is how a
governance-minded team takes responsibility for what it's about to build on. Answers
marked "not documented" reflect genuine gaps in the original source — and those gaps are
themselves findings.*

> ℹ️ **On the data file & licensing.** The IBM HR Analytics Employee Attrition sample is
> freely public and universally used for teaching, but its exact license terms are loosely
> specified. To stay clean, **we do not re-host the raw file here — we link to the original
> source** (see *Distribution & License* below). This card documents the data; download it
> from the canonical page.

## At a glance

| | |
|---|---|
| Dataset name | IBM HR Analytics Employee Attrition & Performance |
| Role in this course | Training data for **RetainIQ** (employee-attrition prediction) |
| Size | 1,470 rows × 35 columns — one row per (fictional) employee |
| Prediction target | `Attrition` (Yes = 237 / No = 1,233; **16.1%** positive — imbalanced) |
| Nature | **Synthetic** — fictional data created by IBM data scientists |
| Missing values | None |
| Protected attributes present | Age, Gender, MaritalStatus |
| License | Open / educational; exact terms loosely specified (see note above) |
| Source | **Linked to the original (not re-hosted)** — see *Distribution & License* |

## 1. Motivation
- **Why was it created?** As fictional sample data to demonstrate IBM Watson Analytics' HR-attrition capabilities — *not* collected from real HR operations and not created for our use.
- **Who created / funded it?** IBM data scientists. Details beyond that are not documented.
- **Key provenance fact:** it showcases analytics on *invented* people, so its patterns are designed, not observed.

## 2. Composition
- **Each row** = one fictional employee. **35 columns**, grouped:
  - **Target:** `Attrition` (Yes/No).
  - **Identifier (not a feature):** `EmployeeNumber` (unique per row; non-contiguous).
  - **Constant / no information:** `EmployeeCount` (=1), `StandardHours` (=80), `Over18` (='Y').
  - **Demographics / protected:** `Age` (18–60), `Gender` (Female/Male), `MaritalStatus` (Single/Married/Divorced).
  - **Education:** `Education` (1–5, ordinal), `EducationField` (6 categories).
  - **Role & organization:** `Department` (3), `JobRole` (9), `JobLevel` (1–5), `BusinessTravel` (3), `OverTime` (Yes/No).
  - **Compensation:** `MonthlyIncome` (1,009–19,999), `MonthlyRate`, `DailyRate`, `HourlyRate`, `PercentSalaryHike` (11–25), `StockOptionLevel` (0–3).
  - **Tenure & history:** `TotalWorkingYears` (0–40), `YearsAtCompany`, `YearsInCurrentRole`, `YearsSinceLastPromotion`, `YearsWithCurrManager`, `NumCompaniesWorked` (0–9), `TrainingTimesLastYear` (0–6).
  - **Engagement / satisfaction (1–4 ordinal scales):** `JobSatisfaction`, `EnvironmentSatisfaction`, `JobInvolvement`, `RelationshipSatisfaction`, `WorkLifeBalance`; plus `PerformanceRating` (only 3 or 4 ever appear).
  - **Other:** `DistanceFromHome` (1–29).
- **Important nuance:** many "numeric" columns are **encoded ordinal categories** (the 1–4 / 1–5 scales), not true quantities — they must be understood as categories.
- **Data-quality notes:** no missing values; `PerformanceRating` has no low ratings (only 3–4); the suspicious tidiness is consistent with synthetic origin.
- **Sensitive data:** contains protected attributes. Because the data is synthetic there is no real PII — but in a real RetainIQ deployment these would be real employees' personal data, with all the consent and privacy obligations that implies.
- **Labels:** the `Attrition` label ships with the data; how it was assigned is not documented.

## 3. Collection process
- **How was it collected?** Not applicable in the usual sense — the data was **generated** by IBM, not collected from real employees. The generation method is not documented.
- **Timeframe / sampling / ethical review:** not documented (and largely moot for synthetic data).
- **Governance note:** the emptiness of this section is the lesson. For a real RetainIQ, this is where consent, data sources, collection timeframe, and review would be recorded — and a reviewer would expect them.

## 4. Preprocessing / cleaning / labeling
- **As distributed:** already clean — no missing values, a mix of numeric and categorical fields.
- **Done for RetainIQ:** see the companion **`DATA_REMEDIATION_LOG.md`** in this repo for the complete, auditable record of every preparation decision (drops, encoding, scaling, class weights, bias treatments, and the demonstrated cleaning/privacy techniques).
- **Raw data retained?** Yes — this canonical copy is kept unmodified.

## 5. Uses
- **Intended (this course):** teaching end-to-end ML and AI governance through RetainIQ attrition prediction (binary classification), including fairness analysis (the protected attributes make this possible).
- **Cautions / not suitable for:** it is **synthetic** — patterns are designed, not real — so nothing learned here should be taken as a real-world claim about what drives attrition. The clean, tidy nature can breed false confidence. Treat the encoded scales and the `PerformanceRating` quirk with care.

## 6. Distribution & License
- **Availability:** public on Kaggle and many mirrors.
- **Our choice:** because the exact license terms are loosely specified, **we link to the canonical source rather than re-hosting the file** in this repo. Download the data here: https://www.kaggle.com/datasets/pavansubhasht/ibm-hr-analytics-attrition-dataset
- **For reproducibility:** the course keeps a verified local copy; learners pull the same file from the link above.

## 7. Maintenance
- **Maintained by:** no active maintainer; it is a static dataset.
- **This copy:** versioned for the course; could be checksummed for integrity.

## Connection to RetainIQ
We use this dataset as a stand-in for RetainIQ's training data — one employer's workforce.
**Teaching simplification (recorded honestly):** a real RetainIQ would train on consented,
real, possibly multi-customer workforce data; we flag the gap rather than pretend the
dataset is something it isn't.

## References
- Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., Daumé III, H., & Crawford, K. (2018). *Datasheets for Datasets.* https://arxiv.org/abs/1803.09010
- Dataset page (de facto documentation): https://www.kaggle.com/datasets/pavansubhasht/ibm-hr-analytics-attrition-dataset