nifemi-beep/financial-inclusion

End-to-end data analysis of financial inclusion across Kenya, Rwanda, Tanzania, and Uganda using the Zindi FinAccess dataset. Covers data cleaning in Power Query, DAX measures, and a 3-page interactive Power BI dashboard exploring the demographic and socioeconomic factors that influence bank account ownership across East Africa (2016–2018). # Financial Inclusion in East Africa — Data Analysis Project An end-to-end data analysis project exploring the demographic, geographic, and socioeconomic factors that influence financial inclusion across four East African countries using the Zindi FinAccess dataset. --- ## Project Overview Despite growing mobile penetration and economic development efforts, large portions of East Africa's population remain unbanked. This project analyses survey data from **23,524 individuals** across **Kenya, Rwanda, Tanzania, and Uganda** collected between **2016 and 2018** to answer the question: > **What factors influence whether an individual in East Africa has access to a bank account?** --- ## Key Findings - Only **14.1%** of respondents have a bank account — roughly 6 in every 7 people are financially excluded - **Kenya** leads with the highest inclusion rate; **Uganda** lags behind - **Urban residents** are significantly more likely to be banked than rural ones - **Cellphone access** is one of the strongest predictors of financial inclusion - **Education level** and **formal employment** dramatically increase the likelihood of having a bank account - A **gender gap** exists but is smaller than the urban-rural and education gaps - Financial inclusion **improved gradually** from 2016 to 2018 --- ## Tools & Skills Used | Tool | Purpose | |------|---------| | Power BI (Power Query) | Data cleaning and transformation | | Power BI (DAX) | KPI measures and calculations | | Power BI (Dashboard) | 3-page interactive dashboard | | ReportLab (Python) | PDF report generation | --- ## Dashboard Structure The Power BI dashboard is organised across three pages: **Page 1 — Overview** KPI cards (total surveyed, inclusion rate, unbanked %, total banked), inclusion rate by country, trend over time, and slicers for country, year, gender, and location. **Page 2 — Demographics** Inclusion by gender, age group, urban vs rural location, and household size category. **Page 3 — Socio …