Dataset

africa-mauritius-quarterly-hotel-room-occupancy-rate-2019-to-2023-for-all-h-04a9bd9a

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Compiled by Electric Sheep Africa, this MDPA-derived dataset offers 20 records of quarterly hotel room occupancy rates for Mauritius across the 2019-2023 period, distributed in ML-ready Parquet format with standardized Hugging Face metadata and source traceability.

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Dataset card

This is a small ML-ready Parquet dataset of 20 rows containing quarterly hotel room occupancy rates for Mauritius from 2019 to 2023, originally published by MDPA and re-engineered by Electric Sheep Africa. It is distributed on Hugging Face under a CC BY 4.0 license and is tagged for tabular regression and time-series forecasting tasks.

What this dataset contains

The dataset reports a single time-series indicator of hotel room occupancy in Mauritius, broken out by quarter across the years 2019 through 2023. Each row is tagged to the country via the country_iso3 ("MU") and country_name fields, with source attribution captured in source_provider ("MDPA") and source_dataset. Retrieval provenance is recorded in retrieved_at and source_url, and the indicator is identified by indicator_id and indicator_name.

Structure and fields

The schema is a single flat table with 20 rows in the train split and 21 columns. Quantitative fields include year, value, source_period_start_year, and source_period_end_year (all int64 or float64, with value holding the occupancy rate). Categorical metadata includes indicator_id, indicator_name, country_iso3, country_name, source_sheet, unit, dimension_year (e.g., "Quarter 1"), source_period_label, source_provider, source_dataset, source_resource, source_package_id, source_resource_id, source_url, and license_id.

How analysts use it

Analysts can pivot the data into a year-by-indicator matrix using year, indicator_id, and value, or sort by year to view a five-year quarterly trend. The quarter-level granularity in dimension_year supports seasonal cycle analysis, which can feed into hospitality, tourism, and macro models covering Mauritius. Given the very small row count, the file is best treated as a reference time series for benchmarking forecasts rather than as a training corpus for production models.

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