Dataset

africa-mauritius-hotel-room-occupancy-rate-2019-to-2023-for-large-hotels-by-bcab993e

Estimates, Events & Fund Flows Download 1 source

A small Parquet-format collection from Electric Sheep Africa reporting room occupancy rates for large hotels in Mauritius from 2019 to 2023, containing ten records drawn from MDPA and packaged with Hugging Face metadata for machine learning workflows.

Open on Hugging Face

Data preview

Subset

Loading preview...

Dataset card

This Hugging Face dataset, engineered by Electric Sheep Africa, compiles ten rows of large-hotel room occupancy rates for Mauritius from 2019 to 2023, sourced from MDPA and released in Parquet format under CC BY 4.0. It is a small, ML-ready time-series slice intended for tabular regression and forecasting experiments.

What this dataset contains

The dataset records large-hotel room occupancy rates for Mauritius at semester-level granularity across a five-year window. Each row is keyed by `indicator_id` and `year`, with the `value` column carrying the occupancy figure and `unit` recorded as source_units_unspecified. Coverage is limited to a single country (ISO code MU), one indicator, and ten observations, as confirmed by the train split.

Structure and fields

The schema combines indicator fields (`indicator_id`, `indicator_name`, `country_iso3`, `country_name`), temporal fields (`year`, `dimension_year`, `source_period_start_year`, `source_period_end_year`, `source_period_label`), and provenance metadata (`source_provider`, `source_dataset`, `source_resource`, `source_package_id`, `source_resource_id`, `source_url`, `retrieved_at`, `license_id`). The numeric measure sits in `value` (float64), while the `source_sheet` field links each row back to the original spreadsheet tab. The wide mix of string identifiers and two int64 year fields makes the file easy to join with other MDPA indicators.

How analysts use it

Analysts can pivot on `indicator_id` versus `year` using `value` to build a short occupancy trend, or filter `dimension_year` to compare Semester 1 against Semester 2 within each calendar year. Because the data is CC BY 4.0 and standardised, it can be combined with other African hospitality or tourism series from MDPA for travel-sector thematic work, such as tracking post-pandemic recovery in Mauritius hospitality. Its ten-row size makes it better suited as a tutorial example or a feature in a larger tourism model than as a standalone forecasting universe.

Documentation & references 1