Upload Cameroon/datacard_Water.md with huggingface_hub

Cameroon/datacard_Water.md

@@ -0,0 +1,31 @@
+# Datacard for Cameroon Water Indicators (1960-2024)
+
+This dataset contains a time-series of key water indicators for Cameroon, spanning from 1960 to 2024. The data has been aggregated from multiple sources, cleaned, and processed into a single, analysis-ready CSV file.
+
+The raw data was sourced from **The World Bank** data portal. The original files were provided in Excel (.xls) format.
+
+- **Temporal Coverage**: 1960-2024
+- **Geographic Coverage**: Cameroon
+- **Format**: Comma-Separated Values (CSV)
+
+---
+
+## Data Points (Features)
+
+The dataset includes the following water indicators, with 'Year' serving as the primary date column:
+
+1. `annual_freshwater_withdrawals_total_of_internal_resources_`: Annual freshwater withdrawals, total (% of internal resources)
+2. `marine_protected_areas_of_territorial_waters_`: Marine protected areas (% of territorial waters)
+3. `people_using_at_least_basic_drinking_water_services_of_population_`: People using at least basic drinking water services (% of population)
+4. `people_with_basic_handwashing_facilities_including_soap_and_water_of_population_`: People with basic handwashing facilities including soap and water (% of population)
+
+---
+
+## Data Preparation & Missing Data Handling
+
+The raw data was processed using a Python script to transform it into a clean, structured format. The key steps were:
+
+1.  **Filtering**: The data was filtered to include only records for 'Cameroon'.
+2.  **Reshaping**: The original wide-format data (years as columns) was melted into a long format.
+3.  **Merging**: Data from all indicator files was merged into a single DataFrame on 'Year'.
+4.  **Handling Missing Data**: Missing values (`NaN`) were filled using a two-step strategy: linear interpolation followed by a back-fill to handle any remaining gaps at the start of the series.