Dataset
africa-mauritius-monthly-hotel-room-occupancy-rate-2019-to-2023-for-all-hot-446c0b79
A Parquet dataset of 60 records providing monthly hotel room occupancy rates across one African country for 2019 to 2023, adapted from MDPA by Electric Sheep Africa and distributed with metadata and source provenance.
Data preview
Dataset card
This dataset tracks monthly hotel room occupancy rates in Mauritius from 2019 to 2023, sourced from MDPA and republished by Electric Sheep Africa as an ML-ready Parquet file on Hugging Face. It is a small time series of 60 rows intended for tabular regression and forecasting work.
What this dataset contains
The dataset covers a single tourism indicator, the monthly hotel room occupancy rate, for Mauritius across a five-year window. The 60 rows are organised by year and month, allowing observation of both seasonal and pandemic-era demand patterns in the Mauritian hotel sector. Source attribution is captured through columns such as source_provider (MDPA), source_dataset, and source_url.
Structure and fields
Each row includes indicator_id, country_iso3 (MU), year, dimension_month, and value, the occupancy measurement stored as a float. Supporting provenance fields include source_period_start_year, source_period_end_year, source_period_label, source_resource_id, source_package_id, license_id, and retrieved_at. The dataset is delivered as a single 'train' split of 60 rows under a CC BY 4.0 license.
How analysts use it
Analysts can pivot on year and indicator_id to build a month-by-month occupancy matrix, or filter by country_iso3 to isolate the Mauritius series for benchmarking against regional peers. The year, dimension_month, and value fields make it suitable for time-series forecasting of tourism demand and recovery trajectories. Investment funds covering African hospitality, airlines, or Mauritius-exposed equities could use the trend to validate or stress-test proprietary tourism demand models.
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