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🌍 Global Development Analytics

An End-to-End Data Science Project in R

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🚀 Overview

This project delivers an end-to-end data science analysis of the key drivers behind the Human Development Index (HDI) across 180+ countries from 2000 to 2022.

Going beyond descriptive statistics, the study integrates economic theory, robust data engineering, and predictive modeling, comparing traditional econometric approaches (Linear Regression) with machine learning methods (Random Forest) to uncover non-linear dynamics in global development.

The result is a reproducible, research-grade workflow suitable for policy analysis, academic research, and applied data science portfolios.

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📊 Key Visualizations

1️⃣ Data Quality & Correlation Structure

Data Quality & Imputation	Correlation Analysis
Group-wise imputation of missing time-series data.	Strong correlation (0.91) between Life Expectancy and HDI.

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2️⃣ Economic & Structural Insights

The Preston Curve	Structural Clustering (PCA)
Diminishing returns of GDP on Life Expectancy.	Distinct regional and income-based development clusters.

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🧠 Modeling & Results

📈 Model Performance

• Linear Regression (Baseline): RMSE = 0.056
• Random Forest (ML): RMSE = 0.026
• Performance Gain: 54% improvement in predictive accuracy

➡️ This confirms that human development follows non-linear patterns poorly captured by linear models.

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🔍 Feature Importance (Random Forest)

Key Drivers of HDI:

1. GDP per Capita (Primary Driver)
2. Life Expectancy
3. Health Expenditure
4. Unemployment Rate (Critical bottleneck effect)

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📊 Key Findings

• Machine Learning Superiority: Random Forest significantly outperforms traditional regression, highlighting complex interactions in development indicators.
• Preston Curve Validated: Economic growth yields diminishing returns on health and human development after a threshold.
• Policy-Relevant Bottlenecks: Health spending and labor market conditions meaningfully constrain development outcomes beyond income alone.

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View the Report

📄 View Full Analysis Report - Download report.html and open in your browser for the complete interactive report with all visualizations and code.

🛠️ Tech Stack

🔧 Language

• R (4.x)

📦 Data Engineering

• tidyverse, janitor
• WDI (World Bank API)
• naniar (Missing data diagnostics & imputation)

📊 Visualization & EDA

• ggplot2, GGally
• corrplot
• factoextra (PCA & clustering)

🤖 Modeling

• tidymodels
• randomForest
• vip (Model interpretability)

🌐 Data Sources

• World Bank Open Data API
• UNDP Human Development Reports

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💻 How to Run the Project

1. Clone the repository

   git clone https://github.com/your-username/global-development-analytics.git

2. Open human_development_index_R

3. Run data collection:

   scripts/01_data_collection.R

4. Generate the full report:

   rmarkdown::render("report.Rmd")

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📂 Project Structure

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├── data/            # Raw and processed datasets
├── scripts/         # Modular R scripts (ETL, EDA, Modeling)
├── results/         # Figures and model outputs
├── report.Rmd       # Reproducible analysis report
├── mega-hdi-analysis.Rproj
└── README.md        # Project documentation

⭐ If you find this project useful, feel free to star the repository or reach out for collaboration.