JoelKy-coder/predicting-financial-inclusion-kenya

An end-to-end machine learning classification project using the FinAccess 2024 Household Survey to identify demographic, socioeconomic, and technological barriers to financial inclusion among rural youth in Kenya. # Predicting Financial Exclusion Among Rural Youth in Kenya ## Project Description This project develops an end-to-end machine learning workflow to predict whether rural youth in Kenya are likely to be financially excluded. It uses survey data from the FinAccess 2024 Household Survey and combines data preparation, modeling, evaluation, and deployment into a single reproducible project. The work was built to support better decision-making for policymakers, financial institutions, and development organizations that need to identify barriers to financial inclusion. The system is designed to provide a practical starting point for understanding which factors are most associated with exclusion and how those insights can be translated into targeted interventions. The project includes both a training pipeline and a simple web application so that predictions can be made through Python or a browser-based interface. This makes the solution useful for technical users as well as non-technical stakeholders who want to explore input profiles and review results. ## Problem Statement Financial exclusion remains a significant issue for many rural youth in Kenya, even in a market where digital financial services have expanded rapidly. Exclusion can limit access to savings, credit, insurance, and other services that support livelihoods and economic resilience. Understanding who is most at risk of exclusion is important for designing effective programs and policies. A predictive model can help identify patterns in the data and support more targeted outreach, product design, and public policy decisions. ## Project Objectives - Build a reproducible classification workflow for predicting financial exclusion - Identify the main drivers associated with exclusion among rural youth - Provide a user-friendly way to make predictions from a trained model - Create a project structure that is easy to understand, extend, and reproduce ## Business Understanding The solution is intended …