🇪🇹 Ethiopia Financial Inclusion Forecasting System - Time series analysis and forecasting for Access & Usage indicators (2025-2027). Built with Python, Streamlit, and WeasyPrint for PDF reports.
# Ethiopia Financial Inclusion Forecasting System
🇪🇹 A forecasting system that tracks Ethiopia's digital financial transformation using time series methods, developed for the 10Academy Week 10 Challenge.
## 🎯 Project Objectives
Build a forecasting system that predicts Ethiopia's progress on two core dimensions of financial inclusion:
1. **Access** — Account Ownership Rate (% of adults with financial account or mobile money)
2. **Usage** — Digital Payment Adoption Rate (% of adults making/receiving digital payments)
### Key Questions
- What drives financial inclusion in Ethiopia?
- How do events like product launches, policy changes, and infrastructure investments affect inclusion outcomes?
- How will financial inclusion rates evolve in 2025-2027?
## 📊 Ethiopia's Financial Inclusion Context
| Year | Account Ownership | Change |
|------|------------------|--------|
| 2011 | 14% | — |
| 2014 | 22% | +8pp |
| 2017 | 35% | +13pp |
| 2021 | 46% | +11pp |
| 2024 | 49% | +3pp |
### Key 2024 Indicators
- Mobile money account ownership: 9.45%
- Digital payment adoption: ~35%
- Account for wage receipt: ~15%
## 🏗️ Project Structure
```
ethiopia-fi-forecast/
├── .github/workflows/
│ └── unittests.yml # CI/CD pipeline
├── data/
│ ├── raw/ # Starter dataset
│ │ ├── ethiopia_fi_unified_data.csv
│ │ └── reference_codes.csv
│ └── processed/ # Analysis-ready data
│ ├── ethiopia_fi_enriched.csv
│ ├── event_indicator_matrix.csv
│ └── forecast_results.csv
├── notebooks/ # Jupyter notebooks for analysis
│ ├── 01_data_exploration.ipynb
│ ├── 02_eda.ipynb
│ ├── 03_impact_modeling.ipynb
│ └── 04_forecasting.ipynb
├── src/ # Source code modules
│ ├── data_loader.py
│ ├── visualization.py
│ ├── forecasting.py
│ └── generate_report.py # PDF report generator
├── scripts/
│ └── run_analysis.py # Full pipeline runner
├── …