Kimaniroy0/kenya-financial-status-datasprint2026

DataSprint 2026 Predicting financial outcomes of Kenyan adults using the 2024 FinAccess Household Survey. SDC × iLab Africa. # 🇰🇪 Predicting Financial Outcomes in Kenya ### DataSprint 2026 — Strathmore Data Community × iLab Africa > *One Week. Real Data. Real Impact.* **Author:** Roy Kimani (@Kimaniroy0) **Competition:** SDC DataSprint 2026 **Dataset:** 2024 FinAccess Household Survey — 20,871 Kenyan adults across all 47 counties **Task:** Multiclass classification — predict whether a person's financial situation has *Improved*, *Stayed the same*, or *Worsened* --- ## 📌 Table of Contents 1. Background & Problem Statement 2. Repository Structure 3. Dataset 4. Tools & Libraries 5. Methodology 6. Key EDA Findings 7. Modelling 8. Results 9. Key Drivers of Financial Status 10. Recommendations 11. Challenges & How I Addressed Them 12. How to Run --- ## Background & Problem Statement Kenya has made significant progress in financial inclusion over the past decade — mobile money has transformed how millions save, borrow, and transact. But the 2024 FinAccess Household Survey tells a harder story: **9.9% of Kenyan adults remain fully excluded from financial services**, and more strikingly, **52.6% of those surveyed said their financial situation had worsened** compared to the previous year. This project uses machine learning to answer a simple but urgent question: > **Which factors most strongly predict financial deterioration among Kenyan adults — and what should policymakers, banks, and NGOs do about it?** The model predicts one of three outcomes for each individual: | Class | Meaning | Share of Dataset | |-------|---------|-----------------| | **Worsened** | Financial situation deteriorated vs. last year | 52.6% | | **Stayed the same** | No significant change | 26.9% | | **Improved** | Financial situation improved vs. last year | 20.5% | --- ## Repository Structure ``` kenya-financial-status-datasprint2026/ │ ├── notebooks/ │ └── DataSprint2026_Notebook.ipynb # Full pipeline: EDA → Modelling → Interpretation │ ├── visualisations/ │ ├── chart1_target_distribution.png # …