Dataset
africa-mauritius-monthly-hotel-room-occupancy-rate-2019-to-2023-for-all-hot-657f1ac6
Assembled by Electric Sheep Africa from MDPA, this dataset provides 60 rows of monthly hotel room occupancy rates for Mauritius spanning 2019-2023, formatted as ML-ready Parquet with consistent Hugging Face metadata and source attribution.
Data preview
Dataset card
This Hugging Face dataset, published by Electric Sheep Africa, reports monthly hotel room occupancy rates for Mauritius from 2019 to 2023, sourced from MDPA and standardized into an ML-ready Parquet file. It is tagged for tabular regression and time-series forecasting workflows.
What this dataset contains
The dataset covers monthly hotel room occupancy in Mauritius over a five-year window, with the core metric recorded in the value column alongside year and dimension_month fields. Each row is tied to indicator_id and country_iso3, with country_name and country_iso3 both set to MU for Mauritius. Provenance is captured through source_provider, source_dataset, source_url, and a retrieved_at timestamp, and the file is distributed under a CC BY 4.0 license.
Structure and fields
The single train split holds 60 rows in tabular format. Time dimensions are split across year (int64) and dimension_month (string), while the measured occupancy rate sits in value (float64) with unit recorded as source_units_unspecified. Identifier fields include indicator_id, country_iso3, and country_name, and source bookkeeping columns include source_sheet, source_period_start_year, source_period_end_year, source_period_label, source_resource, source_package_id, source_resource_id, license_id, and retrieved_at.
How analysts use it
Analysts can pivot value by year and indicator_id to build a monthly occupancy time series for trend and seasonality analysis, or merge with country_iso3 for cross-country comparisons where additional African series are available. The dataset can serve as a tourism-demand proxy, feeding into hospitality, travel, and macro consumption models for Mauritius. Its small size and consistent schema make it suitable for benchmarking forecasting pipelines on a single-country series.
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