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Synthetic dataset

Business Survival & Exit Prediction

Develop time-aware models of synthetic business continuity and exit, linking enterprise histories to employment and financial context.

Photorealistic portrait of an entrepreneur

ML-04 · Made-to-order synthetic dataset

Entities represented
Business-level
Catalogue structure
Longitudinal
Custom city requests
African Major Cities
Dates and final scope
Agreed with your quote

Overview

ML-04Machine LearningMade to order

Connect business registration, status, workforce and financial context to study firm survival.

The record concepts include registration / closure dates, survival status, workforce change, business lifecycle, ownership / founder context, workforce history and employment spells. Distinguish recorded exits from incomplete business observation. Examine workforce and financial context before assessment. Explore event timing across defined business groups.

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
Business, Employment, Finance, Demographics
Catalogue structure
Longitudinal

Key features

Record concepts for your order-specific extract.

Business

Business histories and relationships

Employment

Recorded jobs and dated work history

Finance

Income and financial context

Demographics

Age, identity and resident context

Who it is for

  • ML teams
  • economists
  • business researchers
  • development agencies

Data & Schema

Entities represented
Business-level

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

Explore business exits using earlier workforce and financial histories.

Record concepts for your extract

  • registration / closure dates
  • survival status
  • workforce change
  • business lifecycle
  • ownership / founder context
  • workforce history
  • employment spells

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

Illustrative format preview

Example columns and rows illustrate the proposed format; they do not verify paid-release records, fields or relationships.

Proposed preview columns

Selected fields · 10 of 11 columns

  • outcome_or_task
  • registration_closure_dates
  • survival_status
  • city
  • synthetic_preview
  • workforce_change
  • business_lifecycle
  • ownership_founder_context
  • workforce_history
  • employment_spells
All proposed preview columns 11 columns

These names describe the illustrative preview. Final fields and definitions are agreed for your order.

Study, context and observation fields
  • city
  • synthetic_preview
  • registration_closure_dates
  • survival_status
  • workforce_change
  • business_lifecycle
  • ownership_founder_context
  • workforce_history
  • employment_spells
  • outcome_or_task
Technical identifiers and evidence references
  • sample_record_id

Use Cases

Define exit dates, eligible active businesses and observation coverage; evaluate survival probabilities and preserve firms still active at the cutoff.

Proposed questions for your custom order

  1. Can a business's prior workforce and financial history predict whether it records an exit within 12 months and how long it remains active?
  2. Which businesses remain active over an agreed prediction horizon?
  3. Which recorded changes precede a business exit?
  4. How does survival prediction differ by sector, firm age and size?

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.

Methodology & Quality

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.

Research protocols to agree

Known limitations

  • Synthetic records describe generated scenarios; they do not establish observed population prevalence or real-world causal effects.

Delivery & License

Catalogue base price
$595 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

Package layout

Illustrative filenames. Your agreed order lists the final files and formats. Certification, benchmark splits and model examples are included only when specified.

SD_RESEARCH_DATASET/
├── data/
│   ├── business_records.parquet
│   ├── employment_records.parquet
│   ├── finance_records.parquet
│   └── citizens.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.

FAQ

Is this real personal data?

No. The records are synthetic and do not represent actual identifiable people or reproduce an individual's historical experience.

What does the catalogue price cover?

The displayed amount is a base price per selected city. The final scope and quote are agreed for your requested dates, population, fields, formats and rights. Adding a dataset to the cart starts the existing checkout inquiry; it does not purchase an unrestricted custom extract.

What can I inspect before ordering?

Inspect the illustrative CSV format preview and planned record concepts. Example rows do not establish the full paid schema or release coverage.

Is the dataset prepared for my requested dates?

Yes. Regular and Research datasets are prepared after an order, with the city, date range, population and other required scope agreed before preparation.

Can records be joined across domains?

The profile explains the intended relationships. The order-specific schema documents the supported keys, effective dates and joins for your agreed extract.

Can I use the dataset for AI or machine learning?

Review the study-specific inputs, outcomes and information cutoffs. The commercial order must expressly address model training and external applications. Public samples allow non-commercial AI and ML experimentation under the current free-sample policy.

Can I use the data commercially?

Commercial use follows the accepted dataset-specific scope and licence. A sample download, cart selection, inquiry or consent checkbox does not activate a commercial licence or complete payment.

Can I request another city, period or format?

Yes. Use Data Request to describe the required city, population, dates, record concepts and formats. Onboarded city options for this product: Nairobi and Lagos. Other African major cities can be requested; availability, scope and the quote must be confirmed.

What documentation and evidence are included?

The accepted order confirms the schema, methodology, evidence, examples and release controls supplied with your prepared extract.

How do I receive the dataset?

After scope, terms and payment conditions are agreed, Fidelra Africa prepares the order-specific extract and supplies it through a secure download package. Timing and download-access expiry are defined in the order.

Need different data?

Tell us the entities, fields, cities and time span you need.

Request custom data