An end-to-end Data Science project predicting Human Development (HDI) using R. Features automated ETL (World Bank API), advanced EDA (PCA, Preston Curve), and a comparative analysis of Linear Regression vs. Random Forest models to uncover non-linear economic drivers.

Machine learning analysis predicting Sri Lanka's adolescent fertility rates using 64 years of World Bank data (1960-2023). Achieves 78% accuracy with linear regression model.

A Python project that analyzes World Bank indicator datasets (1990–2020) for selected countries. It performs statistical analysis, generates line plots, bar charts, and correlation heatmaps, and visualizes trends in agriculture land use, forest cover, CO₂ emissions, urban population, renewable energy consumption, and mortality rates.

Germany trade balance analysis using World Bank macroeconomic data with time-series visualization and regression modeling.

🌍 Analyze the Human Development Index across 180+ countries using R, combining data science techniques with economic theory for informed insights.