gild-Jg/Fiscal-Policy-and-SDG-Analysis-in-Africa-gill-
Domain:
socioeconomicRecord type:
project
Creator:
gil
Host:
End-to-end data science project analyzing fiscal policy, macroeconomic indicators, Sustainable Development Goals (SDGs), and GDP growth across selected African countries using Python and Machine Learning.
# 📊 Fiscal Policy and SDG Analysis in Africa
## Leveraging Data Science and Machine Learning to Evaluate Fiscal Policy and Sustainable Development Across Africa
## 📖 Project Overview:
This project investigates the relationship between fiscal policy and economic growth across selected African countries using macroeconomic and government finance indicators.The analysis combines Exploratory Data Analysis (EDA), feature engineering, statistical analysis, and machine learning to identify the fiscal indicators that are most strongly associated with GDP Growth Rate.The study aligns fiscal indicators with selected Sustainable Development Goals (SDGs) to demonstrate how data-driven approaches can support evidence-based policymaking.
## 🎯 Objectives:
- Analyze fiscal and macroeconomic indicators across selected African countries.
- Investigate relationships between fiscal variables and GDP Growth Rate.
- Map fiscal indicators to selected Sustainable Development Goals (SDGs).
- Build predictive machine learning models for GDP Growth Rate.
- Compare model performance and identify the most suitable algorithm.
- Generate policy insights from the analytical findings.
## 📂 Dataset
The project combines data from multiple international sources including:
- International Monetary Fund (IMF)
- World Bank Open Data
- World Bank Exchange Rate API
## Countries included:
- Egypt
- Kenya
- Nigeria
- South Africa
The final analytical dataset contains fiscal, macroeconomic, and trade indicators spanning multiple years.
## 🛠 Technologies:
- Python
- Pandas
- NumPy
- Matplotlib
- Seaborn
- Scikit-learn
- XGBoost
- Kaggle Notebooks
- GitHub
- Power BI
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## 🔬 Methodology
This project followed the **CRISP-DM (Cross-Industry Standard Process for Data Mining)** framework, providing a structured approach from data collection to model evaluation.
### 1. Data Collection
- Retrieved fiscal and macroeconomic data from the International Monetary Fund (IMF).
- Collected exchange rate data …
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