kemkemindata/Financial-inclusion-in-East-Africa

Research project on Financial inclusion in Kenya, Uganda, Tanzania, Rwanda ## Financial Inclusion Prediction in East Africa ## INTRODUCTION This project uses a machine learning model to predict whether individuals in East Africa are likely to have or use a bank account based on their demographic information. The project is built using Streamlit for the user interface and Scikit-learn for the machine learning components. This application allows users to input personal and demographic information to predict the likelihood of financial inclusion (i.e., having or using a bank account). The model is trained on data from several East African countries: Kenya, Rwanda, Tanzania, and Uganda. The prediction is based on multiple factors including age, gender, education, and location. Table of Contents -INTRODUCTION -FEATURES -INSTALLATION -RUN -DATASET -MODEL DESCRIPTION -FILE STRUCTURE ## FEATURES -User Input: A sidebar allows users to input data such as age, country, education, and marital status. -Prediction: The application uses a trained machine learning model to predict whether the user is likely to have or use a bank account. -Visualization: Displays relevant images and text output based on the prediction. ## INSTALLATION Install required dependencies: pip install -r requirements.txt **Clone the repository:** git clone github.com cd financial-inclusion **Run the Streamlit application:** streamlit run app.py ## RUN Once the application is running, you will be presented with a form in the sidebar where you can input your demographic data, such as: -Country -Location type (Urban/Rural) -Cellphone access -Age -Gender -Marital status -Education level After filling out the form, click the "Predict" button to get the prediction on whether the individual is likely to have or use a bank account. ## DATASET The dataset used for this project includes demographic information and financial service usage data from individuals across Kenya, Rwanda, Tanzania, and Uganda. It has been …