Predict interruptions in education

Connect enrolment histories with household, demographic and financial context to explore education interruptions.

The record concepts include enrolment status, exit timing, progression history, enrolment history, qualification / completion, education transitions and household composition. Inspect progression and recorded breaks in study. Connect household circumstances before the assessment date. Separate a recorded break from unknown follow-up.

This dataset is prepared to order. Choose the city, date range and required scope; the accepted order defines the population, final fields, formats and delivery.

Onboarded city options

Nairobi (Kenya) · Lagos (Nigeria)

Custom city requests

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

Data type

Synthetic

Connected domains

Education, Households, Demographics, Finance

Catalogue structure

Longitudinal

Key features

Record concepts for your order-specific extract.

Who it is for

• Education researchers
• universities
• ML teams
• policy analysts

Entities represented

Resident-level

This identifies the entities described by the catalogue. The analytical row grain, eligibility, keys and dates are agreed for your extract.

Explore recorded education interruptions using earlier enrolment and household histories.

Record concepts for your extract

• enrolment status
• exit timing
• progression history
• enrolment history
• qualification / completion
• education transitions
• household composition

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

Publish the interruption rule and academic calendar; evaluate recall and probability calibration at a fixed prediction date, excluding later records.

Proposed questions for your custom order

1. Which students are likely to experience a recorded interruption in education during the next academic period, based on their earlier enrolment, progression and household histories?
2. Who returns to education after a period outside it?
3. At which recorded education transitions can earlier learner histories predict a documented interruption or departure within an agreed period?
4. Do household changes coincide with interruptions in schooling?
5. Which active learners experience a documented interruption in the next academic period, separately identifying transfers, completions and unknown outcomes?
6. Which earlier progression and household patterns signal a higher chance of interruption?
7. Can temporary breaks be distinguished from transfers and longer departures?
8. How well do predictions transfer to learners entering in a later cohort?

Proposed research and analytical applications

• Prediction prototyping
• Feature exploration
• Temporal validation
• Cohort comparison

Study and modeling guidance

Information available at prediction or evaluation time

Use only evidence valid and available at the agreed anchor; do not use later outcomes as features.

Comparison and evaluation

Specify comparable cohorts or held-out evaluation before reporting results.

Incomplete observation

Agree follow-up and retain incomplete observation as censored or unknown.

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

• Verify exit reasons, term definitions, transfer records and follow-up. Do not call every enrolment gap dropout or assume a ready-made dropout label.
• 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/
│   ├── education_records.parquet
│   ├── household_history.parquet
│   ├── citizens.parquet
│   └── finance_records.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.