KhaalidADAN/East-Africa-Financial-Inclusion-EDA

# 🌐 Fintech Analysis on Financial Inclusion in East Africa ## 🔍 Project Overview This project provides a comprehensive analysis of **financial inclusion in East Africa**, focusing on **bank account ownership** across demographics in **Kenya, Uganda, Tanzania, and Rwanda**. Through an extensive **Exploratory Data Analysis (EDA)** approach, we uncover barriers and opportunities for expanding financial access, highlighting how demographic factors like **location, income, education, age, gender, and employment status** influence financial inclusion. ## 🎯 Exploratory Data Analysis (EDA) Each EDA stage unveils insights into the factors shaping financial inclusion across East Africa: - **Missing Data Analysis 🛠️**: Managed missing values, particularly in numeric fields such as age and household size, using median imputation to maintain data integrity for accurate analysis. - **Distribution of Key Variables 📊**: Explored the spread of age, income, and education levels to reveal initial demographic trends. - **Univariate Analysis 📈**: Examined each variable individually, identifying specific characteristics in financial access, such as the concentration of young respondents and location-based differences. - **Bivariate Analysis 🔗**: Assessed relationships between pairs of variables, highlighting how factors like income and education or gender and location influence bank account ownership. - **Multivariate Analysis 🎯**: Analyzed the combined impact of demographics, revealing complex interactions—such as urban, educated men having the highest account ownership rates, while rural women face significant barriers. - **Correlation Analysis 🔄**: Explored correlations among variables, confirming that higher education and income levels improve financial access. ## 📊 Key Findings These analyses led to several valuable insights into financial inclusion challenges in East Africa: 1. **Socioeconomic and Geographic Barriers**: Urban residents, educated individuals, and higher-income …