OnzyBoy/Financial-Inclusion-in-East-Africa

# 🌍 Financial Inclusion Prediction in East Africa ## 📌 Project Overview This project predicts the likelihood of an individual having a **bank account** in East Africa (**Kenya, Rwanda, Tanzania, and Uganda**) using demographic and socioeconomic data. It was completed as part of the **GOMYCODE Data Science Capstone Project**. --- ## 📊 Dataset The dataset is provided by Zindi Africa. To respect the competition rules, the data files are **not hosted in this repository**. 🔗 **Data Source:** Financial Inclusion in Afri… --- ## 🌐 Live Demo (Streamlit App) An interactive web application was built using Streamlit to allow users to make real-time predictions. 🚀 **Try the App:** Live Demo 💡 *Input user details and instantly see the predicted likelihood of having a bank account.* --- ## 📈 Interactive Dashboard An interactive dashboard was created using Tableau to explore key insights visually. 🔗 **View Dashboard:** public.tableau.com 💡 *For the best experience, open the dashboard in **fullscreen mode**.* --- ## 🔄 Methodology (CRISP-DM) - 🧠 **Business Understanding:** Identifying key factors that drive financial inclusion and support outreach strategies. - 🧹 **Data Preparation:** Data cleaning, handling missing values, One-Hot Encoding, and feature scaling. - 🤖 **Modeling:** Compared multiple machine learning models: - Logistic Regression - Decision Tree - Random Forest - XGBoost - Support Vector Machine (SVM) - Naive Bayes - 🏆 **Champion Model:** **Random Forest Classifier** - Accuracy: **82%** - Recall: **75%** - F1-Score: **0.53** - ROC AUC: **0.87** --- ## 🔍 Key Insights - 🎓 **Education Level** and 📱 **Cellphone Access** were the strongest predictors of financial inclusion. - 🎯 The model achieved a **Recall of ~74%**, meaning it successfully identified **3 out of 4 individuals** who have ban …