nobert-mumo/MTIBA-My-Health-Funds-savings-and-utilization-analysis

This project gives insights by analyzing data from the MTIBA platform on claims and savings in Kenya. # A deep dive into utilization and saving behavior of MTIBA's My Health Funds wallet This project gives insights by analyzing data from the MTIBA platform on claims and savings in Kenya. It looks into claims utilization patterns for My Health Funds program (a health savings wallet) such as typical claim amount, total claim amount dispersed over time as well as distribution of claims over different counties. It also looks into savings patterns such as savings across different days of the week, savings across gender and savings by family size. ## Table of Contents 1. Project Motivation 2. Installations 3. File Overview 4. Key Results 5. Acknowledgements ## Project Motivation The motivation of this project was to use Python to get insights into Utilizations and Savings of members with the My Health Funds wallet. The end result was to have data insights that will communicated back to buisness stakeholders in MTIBA to use for data driven decision making as well as understand the program better. In particular I wanted to practise using the CRISP-DM process in getting the insights by : - Identifying and getting the relevant datasets needed from the MTIBA platform - Gathering necessary business understanding of the My Health Funds program - Understanding the data on claims and savings - Preprocessing the data by doing cleaning and data enrichment - Performing relevant analysis - Communicating the insights with much simplicity as possible The business questions that the project seeks to answer are : - Savings Questions 1. What is the typical saving amount for a My Health Fund member ? 2. What is the typical remittance amount to a My Health Funds member 3. How have savings been doing throughout the years and months ? 4. How does saving frequency and amount compare across different days of the week ? 5. How are savings influenced by the size of the family ? 6. How are savings distributed by gender? 7. Which age bracket has the higest savings into their wallet ? - Clai …