ChairoSolutions/Kenya-financial-inclusion-risk

Machine learning project using Kenya FinAccess data to predict, explain and map financial exclusion risk. # Kenya Financial Inclusion Risk Prediction Machine learning project using Kenya FinAccess 2021 survey data to predict, explain, and assess financial exclusion risk among Kenyan adults. ## Project Overview Despite Kenya’s global recognition in mobile money innovation, a significant portion of the population still lacks access to formal financial services such as: - Banking services - Insurance products - SACCO services - Pension schemes - Regulated credit facilities This project uses the **FinAccess 2021 Household Survey Microdata** to: - identify financially excluded individuals, - understand drivers of exclusion, - analyze vulnerable populations, - and build predictive machine learning models for financial exclusion risk. The project follows a complete end-to-end data science workflow: - data understanding, - preprocessing, - exploratory analysis, - machine learning, - explainability, - and Flask and streamlit deployment. ## Live Applications ### Streamlit Application kenya-financial-inclusion-r… The Streamlit application allows users to interact with the trained machine learning model through a web interface and generate financial exclusion risk predictions in real time. Tableau Dashboard public.tableau.com The Tableau dashboard provides interactive visual exploration of financial exclusion trends across demographic and geographic groups. ## Interactive Dashboard The dashboard provides interactive insights into: - Financial exclusion by county - Financial exclusion by marital status - Financial exclusion by education level - Comparison of rural vs urban financial exclusion ## Data Access & Reproducibility The original FinAccess 2021 microdata workbook (finaccess_2021_microdata.xlsx) is approximately 193 MB and is not included in this repository due to GitHub file s …