birhanu-ma/ethiopia-fi-forecast

# ethiopia-fi-forecast Ethiopia Financial Inclusion Forecasting (Selam Analytics) 📌 Project Overview This project provides a data-driven framework to analyze and forecast financial inclusion trajectories in Ethiopia. Using a unified dataset of World Bank Findex surveys, National Bank of Ethiopia (NBE) reports, and private sector milestones (Telebirr, Safaricom/M-Pesa), this tool identifies why digital payment usage is exploding while account ownership remains plateaued. The "Inclusion Gap" Challenge: Despite reaching over 139 million mobile money accounts in 2025, account ownership rates have only grown moderately since 2021. This repository models the impact of key policy enablers—like the Fayda National ID and FX Liberalization—to predict if Ethiopia will meet its 2027 inclusion targets. 🏗 Project Structure The repository is organized following data science best practices: Plaintext ├── data/ │ ├── raw/ # Immutable original datasets (Findex, NBE, IMF) │ ├── processed/ # Unified schema after task-1 cleaning │ └── enrichment_log.md # Documentation of new proxy indicators added ├── notebooks/ │ ├── 01_data_cleaning.ipynb # Task 1: Unification & Enrichment │ └── 02_eda_analysis.ipynb # Task 2: Trend & Gap Analysis ├── src/ │ ├── eda_engine.py # Custom Python class for automated EDA │ └── forecasting.py # Time-series models (SARIMA/Prophet) ├── reports/ │ └── insights_summary.pdf# Final analysis of the "Access Stagnation" ├── reports/ | ├── figures/ ├── requirements.txt # Python dependencies └── README.md # You are here 🚀 Getting Started 1. Installation Clone the repository and install dependencies: Bash git clone github.com cd ethiopia-fi-forecasting pip install -r requirements.txt 2. Running the Analysis To view the Exploratory Data Analysis (Task 2): Navigate to notebooks/02_eda_analysis.ipynb. Use the EdaAnalysis class from src/ to …