altechemist/NDTA631

Mobile Money in Motion: Sending vs Withdrawing Patterns in South Africa and Beyond # Mobile Money in Motion: Sending vs Withdrawing Patterns in South Africa and Beyond ## Assessment Information - **Assessment:** Group Assignment (NDTA 631) - **Programme:** Diploma in ICT - **Module Code:** NDTA 631 - **Module Description:** Data Analysis and Visualization - **Examiner(s):** Melvin Kisten - **Group Size:** 6–7 students ## Project Overview This project explores financial inclusion through the lens of mobile money. Using World Bank Findex datasets, we compare sending money and withdrawing money from mobile money accounts, focusing on South Africa and placing findings in a global context. **Central Research Question:** > Do South Africans and global populations use mobile money primarily to send funds, withdraw funds, or both—and how does this vary by gender, region, and over time? ## Datasets Used - `WB_FINDEX_FIN13BM.csv`: Sent money from a mobile money account, monthly - `WB_FINDEX_FIN13CM.csv`: Took out money from a mobile money account, monthly - Source: World Bank Open Data ## Folder Structure ``` NDTA631/ ├── README.md # Project documentation ├── requirements.txt # Python dependencies ├── data/ │ ├── raw/ # Raw World Bank Findex CSVs │ │ ├── WB_FINDEX_FIN13BM.csv │ │ └── WB_FINDEX_FIN13CM.csv │ └── proccessed/ # Cleaned/processed data ├── notebooks/ │ ├── 01_data_preparation.ipynb │ ├── 02_numerical_analysis.ipynb │ ├── 03_visualization.ipynb │ ├── 04_database_integration.ipynb │ └── 05_data_analysis.ipynb ├── reports/ # Final report and outputs ``` ## Project Workflow ### Data Loading & Cleaning - Import datasets using Pandas - Merge on country, year, and demographic fields - Handle missing values, renaming, filtering for South Africa ### Data Preparation - Create combined features (send vs withdraw ratios) - Segment data by gender and region ### Exploratory Data Analysis (EDA) - Summary statistics - Correlations between send/withdraw ### Databa …