Explainable Machine Learning for SDG 7 Scenario Analysis: Ethiopia Electricity Access Panel Dataset and Python Scripts

This repository contains the processed panel dataset and Python scripts supporting the paper "Can Ethiopia Achieve Universal Electricity Access by 2030? An Explainable Machine Learning and Scenario Analysis Framework" submitted to IEEE Access. The dataset covers 45 Sub-Saharan African countries over 2001–2023 (1,035 observations), assembled from the World Bank World Development Indicators. Scripts include data collection, preprocessing, model training and validation (Linear Regression, Random Forest, XGBoost, LightGBM), SHAP explainability analysis, and SDG 7 scenario forecasting.