BIGTUNZ/Predicting-Financial-Inclusion-in-East-Africa

This Streamlit app predicts the likelihood of an individual in East Africa having a bank account. Users input features like location, age, household size, job type, income, and education. The app uses a pre-trained model to classify bank account ownership probability, providing insights into financial inclusion based on demographic factors. # Predicting-Financial-Inclusion-in-East-Africa This project is a **Streamlit-based web application** that predicts the likelihood of an individual having a bank account in East Africa. The app leverages a pre-trained machine learning model to analyze user-provided demographic and socio-economic factors to make predictions. # Bank Account Prediction in East Africa This project is a **Streamlit-based web application** that predicts the likelihood of an individual having a bank account in East Africa. The app leverages a pre-trained machine learning model to analyze user-provided demographic and socio-economic factors to make predictions. ... ## Features - **User Inputs**: - Location type (Urban/Rural) - Cellphone access (Yes/No) - Household size (1-10) - Age of respondent (18-100) - Marital status (Married/Single) - Job type (Various categories) - Income status (Yes/No) - Education level (Primary, Secondary, Tertiary, etc.) - **Prediction Output**: - Displays whether the individual is likely to have a bank account. - Provides the probability of the prediction. ## Installation 1. Clone the repository: ```bash git clone github.com