KI-Prestige/FINANCIAL-INCLUSION-IN-AFRICA

Analyzing factors that affect and contribute to financial inclusion in Kenya, Rwanda, Tanzania and Uganda, and also predict individuals who are likely to have a bank account # FINANCIAL-INCLUSION-IN-AFRICA Analyzing factors that affect and contribute to financial inclusion in Kenya, Rwanda, Tanzania and Uganda, and also predict individuals who are likely to have a bank account The objective of this project is to create a machine learning model to predict which individuals are most likely to have or use a bank account. The models and solutions developed can provide an indication of the state of financial inclusion in Kenya, Rwanda, Tanzania and Uganda, while providing insights into some of the key factors driving individuals’ financial security. A survey was conducted and various information were collected from over 20,000 individuaks from four (4) countries across Africa including Uganda, Rwanda, Kenya and Tanzania. The information relates to features that indicates whether an individual is likely to own a bank account or not. Secondly, Conduct analysis and derive meaningful insights that can help policymakers, NGOs, and businesses develop targeted strategies to improve financial access and economic growth. To achieve this goal, will be performing the following analysis - Analyzing the distribution of respondents by country, age, gender and location through charts. - ⁠calculating the percentage of individuals with and without bank accounts in each country - ⁠identifying correlations between bank account ownership and other features such as education level, job type, and household size. - ⁠Factors influencing access, such as education, income levels, or infrastructure. 3. Model Selection and Evaluation: Train and evaluate different machine learning models to predict bank account ownership and use scoring metrics to evaluate the performance of your models.