michellehoog/Mobile-money

Zindi Mobile Money and Financial Inclusion in Tanzania challenge # Zindi Mobile Money and Financial Inclusion in Tanzania challenge The train dataset contains demographic information and what financial services are used by approximately 10,000 individuals across Tanzania. This data was extracted from the FSDT Finscope 2017 survey and prepared specifically for this challenge. More about the Finscope survey here. Each individual is classified into four mutually exclusive categories: - No_financial_services: Individuals who do not use mobile money, do not save, do not have credit, and do not have insurance - Other_only: Individuals who do not use mobile money, but do use at least one of the other financial services (savings, credit, insurance) - Mm_only: Individuals who use mobile money only - Mm_plus: Individuals who use mobile money and also use at least one of the other financial services (savings, credit, insurance) Financial Access Map This dataset is the geospatial mapping of all cash outlets in Tanzania in 2012. Cash outlets in this case included commercial banks, community banks, ATMs, microfinance institutions, mobile money agents, bus stations and post offices. This data was collected by FSDT. ## Instructions: 1. Examine the dataset. Are there any missing observations or columns where the data do not seem valid? 2. Get basic descriptive statistics for the dataset. 3. Create appropriate graphs to visually represent the relationship between financial services accessed (non, momible, both) and age, gender, marital status, land ownership and type of income. 4. Create appropriate graphs to visually represent the relationship between how often mobile services are used and age, gender, marital status, land ownership and type of income. 5. What can you conclude about use of financial services in Tanzania? Which demographic factors are associated with mobile money use?