PayalSorathiya/Zindi-Financial-Inclusion-in-Africa

# Zindi Financial Inclusion in Africa A machine learning project to predict which individuals are most likely to have or use a bank account in East African countries. This project was developed as part of the Zindi Africa machine learning competition focused on financial inclusion. ## Problem Statement Financial inclusion remains one of the main obstacles to economic and human development in Africa. For example, across Kenya, Rwanda, Tanzania, and Uganda, only 9.1 million adults (or 14% of adults) have access to or use a commercial bank account. The objective is to create a machine learning model to predict which individuals are most likely to have or use a bank account. The models and solutions developed can provide insights into: - The state of financial inclusion in Kenya, Rwanda, Tanzania, and Uganda - Key demographic factors that might drive individuals' financial outcomes - Strategies for improving financial inclusion across these regions ## Dataset The dataset contains demographic information and financial services usage data from individuals across four East African countries: - **Kenya** - **Rwanda** - **Tanzania** - **Uganda** ### Features The dataset includes various demographic and socio-economic variables such as: - Age, gender, education level - Employment status and type - Location (country, urban/rural) - Household information - Mobile phone usage - Financial services usage patterns ### Target Variable - **bank_account**: Binary variable indicating whether an individual has a bank account (1) or not (0) ## Project Structure ``` Zindi-Financial-Inclusion-in-Africa/ │ ├── data/ │ ├── Train.csv # Training dataset │ ├── Test.csv # Test dataset │ └── SampleSubmission.csv # Sample submission format │ ├── notebooks/ │ ├── EDA.ipynb # Exploratory Data Analysis │ ├── preprocessing.ipynb # Data preprocessing and feature engineering │ ├── modeling.ipynb # Model training and evaluation │ …