EricNjiraini/paygo-cookstove-market-entry

PayGo clean cookstove market entry analysis — Kenya, Uganda, Tanzania, Ethiopia # PayGo Clean Cookstove — Market Entry Analysis **Kenya · Uganda · Tanzania · Ethiopia** > A end-to-end portfolio project replicating the analytical workflow of a BI Manager at a PayGo energy company (e.g. M-KOPA, BURN, Sun King). Three interconnected modules — market sizing, credit risk scoring, and churn prediction — converging on a single market-entry recommendation. --- ## Project Narrative You are the data analyst for a hypothetical PayGo cookstove company evaluating: 1. **Which country and sub-national zone** to enter first 2. **Who to extend credit to** at point of onboarding 3. **Who is likely to churn** and when — so field agents can intervene early This mirrors real internal analytics at companies operating across East Africa's ~$1B PayGo clean energy market. --- ## Repository Structure ``` paygo-cookstove-market-entry/ ├── notebooks/ │ ├── 01_data_collection.ipynb # API pulls, DHS download, data audit │ ├── 02_country_comparison.ipynb # Module 1: macro market dimensions │ ├── 03_market_entry_scorecard.ipynb # Module 1: country ranking + heat map │ ├── 04_credit_risk_model.ipynb # Module 2: default probability model │ ├── 05_churn_prediction.ipynb # Module 3: survival + 30-day classifier │ └── 06_integrated_dashboard.ipynb # Ties all three modules together ├── data/ │ ├── raw/ # Downloaded source files (gitignored if large) │ ├── processed/ # Cleaned, merged datasets │ └── simulated/ # Synthetic customer ledger (see disclaimer) ├── outputs/ │ ├── figures/ # All charts and maps │ ├── models/ # Serialised .pkl model files │ └── tables/ # Scorecard CSVs, ranking outputs ├── docs/ │ ├── data_sources.md # Full provenance for every dataset │ ├── methodology.md # Analytical decisions + assumptions │ └── …