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Comoros/datacard_Climate-Change.md

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+# Datacard for Comoros Climate Change Indicators (1960-2024)
+
+This dataset contains a time-series of key climate change indicators for Comoros, 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**: Comoros
+- **Format**: Comma-Separated Values (CSV)
+
+---
+
+## Data Points (Features)
+
+The dataset includes the following climate change indicators, with 'Year' serving as the primary date column:
+
+1. `land_area_where_elevation_is_below_5_meters_of_total_land_area_`: Land area where elevation is below 5 meters (% of total land area)
+2. `renewable_energy_consumption_of_total_final_energy_consumption_`: Renewable energy consumption (% of total final energy consumption)
+3. `total_greenhouse_gas_emissions_excluding_lulucf_per_capita_t_co2e_capita_`: Total greenhouse gas emissions excluding LULUCF per capita (t CO2e/capita)
+
+---
+
+## 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 'Comoros'.
+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.