Nkosinathiz/Financial-Barriers-to-Mobile-Money

This project explores barriers to mobile money adoption in South Africa and selected Sub-Saharan African countries using World Bank Open Data. It analyzes 2024 datasets on financial access challenges, focusing on affordability and agent availability. The work includes data cleaning, statistical analysis, visualizations, and database integration. # Data Analysis and Visualization Group Assignment ## Overview This project analyzes barriers to mobile money adoption in South Africa and selected Sub-Saharan African countries using data from the World Bank Open Data platform. The study focuses on two datasets, WB_FINDEX_FIN14A (reason: mobile money agents are too far away) and WB_FINDEX_FIN14D (reason: don't have enough money to use a mobile money account), collected in 2024. The analysis includes data preparation, numerical analysis, visualizations, and database integration, with the goal of identifying trends and proposing interventions to enhance financial inclusion. ## Project Structure - *README.md*: This file, providing an overview and instructions. - *report.docx*: A 7-page project report. - *WB_FINDEX_FIN14A/*: Directory for raw data files (if extracted from the script). - *WB_FINDEX_FIN14D/*: Directory for raw data files (if extracted from the script). - *FinancialBarriers/*: Directory for the generated visualizations and database files (e.g., findex.db). ## Requirements ### Software - Python 3.8+ - LaTeX distribution (e.g., TeX Live or MikTeX) for report compilation - SQLite for database operations ### Python Libraries - pandas - numpy - matplotlib - seaborn - sqlite3 - streamlit Install dependencies using: bash pip install pandas numpy matplotlib seaborn ## Installation 1. Clone the repository or download the files: bash git clone cd data-analysis-visualization-ndta631 2. Ensure all required Python libraries are installed as listed above. 3. Install a LaTeX editor (e.g., Overleaf or TeXShop) if not already present. 4. Place any raw data CSV files in the data/ directory if separated from the script. ## Usage ### Running the Analysis 1. Open the Python script (e.g., analysis.py if created) containing the data processing and visualization code provided earlier. 2. Run the script to generate visualizations and the SQLite database: bash python analysis.py - Visualizations will display interactivel …