segunumoru1/Financial-Inclusion-in-Africa-Bank-Account-Prediction

The goal of the machine learning model is to predict which individuals are most likely to have or use a bank account. # Financial Inclusion in Africa ### Objective The objective of this project is to work with the 'Financial Inclusion in Africa' dataset provided by the Zindi platform. The dataset contains demographic information and details about financial services used by approximately 33,600 individuals across East Africa. The goal of the machine learning model is to predict which individuals are most likely to have or use a bank account. ### Dataset Description The dataset contains demographic information and usage of financial services by individuals across East Africa. The aim is to utilize this data to promote financial inclusion, ensuring that individuals and businesses have access to affordable financial products and services that meet their needs, including transactions, payments, savings, credit, and insurance, delivered in a responsible and sustainable manner. #### COLUMN EXPLANATION 1. country - Country the interviewee is in 2. year - Year Survey is done 3. uniqueid - unique identifier for each interviewee 4. location_type - Type of location (Urban/Rural) 5. cellphone_access - if the interviewee has access to a cell phone (Yes/No) 6. household_size - Number of people living in one house 7. age_of_respondent - The age of the interviewee 8. gender_of_respondent - Gender of the interviewee (Male or Female) 9. relationship_with_head - The interviewee’s relationship with the head of the house:Head of Household, Spouse, Child, Parent, Other relative, Other non-relatives, Don't know 10. marital status - The marital status of the interviewee: Married/Living together, Divorced or Separated, Widowed, Single/Never married, Don't know 11. education_level - The highest level of education: No formal education, Primary education, Secondary education, Vocational/Specialised training, tertiary education, Other/Dont know/RTA 12. job_type - The type of job the interviewee has: Farming and Fishing, Self-employed, Formally employed Government, Formally employed Pri …