Which graduate groups have the largest differences between predicted and observed outcomes?

Group-specific prediction error, calibration gap and discrimination for the agreed employment target.

The study calls for Group definitions available at completion, Frozen dated predictions, Verified outcome labels and Group sizes and observed follow-up. The measure to examine is group-specific prediction error, calibration gap and discrimination for the agreed employment target.

Research datasets are prepared after an order for the selected city, date range and study requirements. The final schema and dated relationships are agreed for that extract.

Onboarded city options

Nairobi (Kenya) · Lagos (Nigeria)

Custom city requests

African Major Cities · Additional cities require availability, scope and quote confirmation.

Data type

Synthetic

Priced domains

Demographics, Education, Employment, Households

Catalogue structure

Question-specific research data

Key features

Record concepts for your order-specific extract.

Who it is for

• ML engineers
• data scientists
• research teams
• analysts

Proposed study unit

Dated forecast within a graduate group

Planned records and relationships for this study.

Record concepts for your extract

• Group definitions available at completion
• Frozen dated predictions
• Verified outcome labels
• Group sizes and observed follow-up

The agreed specification defines the final columns, keys, relationships and observation period for your order.

Compare subgroup metrics under the same target and horizon; require adequate group sizes and report uncertainty.

Proposed questions for your custom order

1. Which graduate groups have the largest differences between predicted and observed outcomes?

Proposed research and analytical applications

• Prediction prototyping
• feature exploration
• temporal validation

Study and modeling guidance

Information available at prediction or evaluation time

Define groups and predictors using information available at completion; do not use later work outcomes to select groups.

Comparison and evaluation

Compare subgroup metrics under the same target and horizon; require adequate group sizes and report uncertainty.

Incomplete observation

Use consistent outcome follow-up across groups or censoring-aware evaluation; retain unknown and unavailable group values.

Data generation

FCTE synthetic city engine

Data classification

Synthetic

FCTE generation methods and published city/domain checks provide source context. The order defines the final extract and the release-specific checks, documentation and limitations supplied with it.

Known limitations

• Frozen predictions, reviewed group definitions and reference outcomes are additional requirements, not confirmed deliverables.
• No horizon or error threshold is established by this question.
• Synthetic subgroup differences do not establish real-world discrimination or fairness.
• Define first employment, target horizon and follow-up before training. Verify job-start dates and outcome completeness; evaluate later cohorts using only information available at prediction time.
• Synthetic records describe generated scenarios; they do not establish observed population prevalence or real-world causal effects.

Catalogue base price

$395 USD

Price basis

Base price per selected city

Availability

Prepared after order

The displayed catalogue amount is a base price per selected city. Your requested dates, population, fields, formats and usage rights determine the agreed scope and final quote. Applicable published discounts and taxes are confirmed with that quote.

Your prepared package

Your order confirms the data files, documentation, evidence, examples and release controls included in the prepared package.

• Agreed data extract
• Schema and field documentation
• Release-specific evidence and limitations
• Agreed loading or analysis examples
• Licence, manifest, checksums and delivery note

SD_RESEARCH_DATASET/
├── data/
│   ├── citizens.parquet
│   ├── education_records.parquet
│   ├── employment_records.parquet
│   └── household_history.parquet
├── tasks/
│   ├── task_definition.yaml
│   ├── train.parquet
│   ├── validation.parquet
│   └── test.parquet
├── evaluation/
│   ├── baseline_model.py
│   ├── metrics.json
│   └── example_notebook.ipynb
├── documentation/
│   ├── README.md
│   ├── DATA_DICTIONARY.csv
│   ├── SCHEMA.md
│   └── METHODOLOGY.md
├── evidence/
│   ├── CERTIFICATION.json
│   ├── VALIDATION_REPORT.pdf
│   └── KNOWN_LIMITATIONS.md
├── MANIFEST.json
├── CHECKSUMS.sha256
└── LICENSE.pdf

Delivery process

Confirm the cities, date range, records, formats, rights and price in writing. The order-specific extract is then prepared and supplied under the agreed payment and delivery conditions.

One secure download package for the agreed release and licensed entity.

Delivery timing, access expiry, formats and prerequisites are confirmed in the accepted order.

Support, refreshes, future releases, hosting and APIs are included only when expressly ordered.

Data Licensing Agreement · 2026-10

A non-exclusive, non-transferable licence for the named customer within the accepted scope and duration.

Permitted use

Internal research, development, testing and analytics within the accepted order.

Model training

Model training, external applications, commercial distribution and access by other legal entities must be expressly addressed in the order.

Redistribution

Raw or substantially reconstructable data cannot be resold, published or redistributed without separate written authorization.

Attribution

Preserve release provenance and applicable third-party notices. Published outputs must identify the data as synthetic.