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README.md

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----
-license: mit
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+---
+license: mit
+task_categories:
+  - tabular-classification
+  - tabular-regression
+language:
+  - en
+tags:
+  - economics
+  - development
+  - world-bank
+  - global
+  - climate
+  - healthcare
+  - dataverse-2026
+pretty_name: DataVerse — Global Development Indicators Dataset
+size_categories:
+  - 1K<n<10K
+---
+
+# 🌐 DataVerse — Global Development Indicators Dataset
+
+This dataset was compiled for **DataVerse-2026**, a data analytics competition organized by the Departmental Statistics Association (DSA), Department of Statistics, Faculty of Science, **The Maharaja Sayajirao University of Baroda**. The competition challenges participants to solve real-world problems through visualization, machine learning, and analytical storytelling.
+
+> 📅 Competition Dates: Feb 26–28, 2026 · 📍 Venue: Dept. of Statistics, MSU Baroda · 👥 Team Size: 1–4 Members
+
+---
+
+## 📋 Dataset Overview
+
+A longitudinal panel dataset covering **global development indicators** across world regions and country groups from **2000 to 2020**. Each row represents a region-year observation and spans economic, environmental, health, digital, and governance dimensions.
+
+The dataset is structured around aggregated World Bank-style regional groupings (e.g., Africa Eastern and Southern, Arab World, Central Europe and the Baltics, etc.) rather than individual countries, making it well-suited for comparative regional analysis.
+
+---
+
+## 📊 Features
+
+The dataset contains **47 columns** organized into the following thematic groups:
+
+### 🆔 Identifiers
+| Column | Description |
+|---|---|
+| `year` | Year of observation (2000–2020) |
+| `country_code` | World Bank region/group code (e.g., `AFE`, `ARB`, `CEB`) |
+| `country_name` | Full name of the region or country group |
+| `region` | Sub-region classification *(often null for aggregated groups)* |
+| `income_group` | World Bank income classification *(often null)* |
+| `currency_unit` | Primary currency *(often null for regional aggregates)* |
+
+### 💰 Economic Indicators
+| Column | Description |
+|---|---|
+| `gdp_usd` | GDP in current USD |
+| `population` | Total population |
+| `gdp_per_capita` | GDP per capita (USD) |
+| `inflation_rate` | Annual inflation rate (%) |
+| `unemployment_rate` | Unemployment rate (%) |
+| `fdi_pct_gdp` | Foreign direct investment as % of GDP |
+
+### 🌱 Environmental Indicators
+| Column | Description |
+|---|---|
+| `co2_emissions_kt` | CO₂ emissions in kilotons |
+| `energy_use_per_capita` | Energy use per capita (kg oil equivalent) |
+| `renewable_energy_pct` | Renewable energy as % of total |
+| `forest_area_pct` | Forest area as % of land area |
+
+### ❤️ Health & Social Indicators
+| Column | Description |
+|---|---|
+| `life_expectancy` | Average life expectancy at birth |
+| `child_mortality` | Under-5 mortality rate (per 1,000 live births) |
+| `health_expenditure_pct_gdp` | Health expenditure as % of GDP |
+| `hospital_beds_per_1000` | Hospital beds per 1,000 people |
+| `physicians_per_1000` | Physicians per 1,000 people |
+| `electricity_access_pct` | Population with access to electricity (%) |
+| `school_enrollment_secondary` | Secondary school enrollment rate |
+
+### 📡 Digital & Connectivity Indicators
+| Column | Description |
+|---|---|
+| `internet_usage_pct` | Internet users as % of population |
+| `mobile_subscriptions_per_100` | Mobile subscriptions per 100 people |
+
+### 📐 Derived / Composite Indices
+| Column | Description |
+|---|---|
+| `calculated_gdp_per_capita` | Recalculated GDP per capita |
+| `real_economic_growth_indicator` | Derived economic growth metric |
+| `econ_opportunity_index` | Composite economic opportunity score |
+| `co2_emissions_per_capita_tons` | Per capita CO₂ emissions (metric tons) |
+| `co2_intensity_per_million_gdp` | CO₂ intensity per million USD of GDP |
+| `green_transition_score` | Score reflecting transition to green energy |
+| `ecological_preservation_index` | Index measuring ecological health |
+| `renewable_energy_efficiency` | Efficiency ratio of renewable energy use |
+| `human_development_composite` | Composite HDI-style score |
+| `healthcare_capacity_index` | Derived healthcare system capacity score |
+| `digital_connectivity_index` | Composite digital access/readiness score |
+| `health_development_ratio` | Ratio of health indicators to development level |
+| `education_health_ratio` | Ratio of education to health indicators |
+| `human_development_index` | HDI estimate |
+| `climate_vulnerability_index` | Climate risk and vulnerability score |
+| `digital_readiness_score` | Digital readiness composite |
+| `governance_quality_index` | Governance quality composite |
+| `global_resilience_score` | Overall resilience composite |
+| `global_development_resilience_index` | Master composite development index |
+
+### 🗓️ Temporal Features
+| Column | Description |
+|---|---|
+| `years_since_2000` | Years elapsed since 2000 |
+| `years_since_century` | Same as above (century baseline) |
+| `is_pandemic_period` | Binary flag — `1` for year 2020 (COVID-19) |
+
+---
+
+## 🔍 Potential Use Cases
+
+This dataset is particularly well-suited for:
+
+- **Exploratory Data Analysis (EDA)** — regional trends, missing data patterns, distributions
+- **Time-series forecasting** — predict GDP, HDI, or emissions over time
+- **Clustering / Unsupervised learning** — group regions by development profile
+- **Regression modeling** — predict composite indices from base indicators
+- **Dashboard / Visualization** — build interactive development dashboards
+- **Analytical storytelling** — narrate global development trends
+
+---
+
+## ⚠️ Data Notes
+
+- Many columns contain `null` values, especially for later years (2016–2020) where source data was unavailable.
+- The dataset uses **regional aggregates** (World Bank group codes), not individual country data.
+- The `is_pandemic_period` flag marks 2020 as a notable anomaly year — many indicators show sharp discontinuities.
+- Some derived indices may have been computed by the dataset author and are not direct World Bank outputs.
+
+---
+
+## 🏆 About DataVerse-2026
+
+DataVerse-2026 is a data analytics hackathon organized by the **Departmental Statistics Association (DSA)**, Department of Statistics, Faculty of Science, MSU Baroda (Est. 1949). The competition runs over 2 rounds and spans 3 domains, bringing together students to demonstrate data-driven thinking, creativity, and collaboration.
+
+- 🌐 Website: [threed2y.github.io/DataVerse-2026](https://threed2y.github.io/DataVerse-2026)
+- 📧 Contact: [dataversestats@gmail.com](mailto:dataversestats@gmail.com)
+- 🏛️ Organizer:Departmental Statistics Association, Dept. of Statistics, Faculty of Science, The Maharaja Sayajirao University of Baroda
+
+---
+
+## 📄 License
+
+This dataset is released under the **MIT License**.