shyakx/Rwandan-Bank-Churn-Prediction

# Rwanda Banking Churn Prediction System A comprehensive machine learning application for predicting customer churn in the Rwandan banking sector. This system combines advanced data visualization with ensemble machine learning models to help banks identify at-risk customers and implement targeted retention strategies. ## 🏦 Overview This application provides a complete solution for customer churn prediction in the Rwandan banking context, featuring: - **Real-time Dashboard** with key performance metrics - **Customer Lookup** with detailed risk assessment - **Retention Management** with bulk actions and filtering - **Advanced Analytics** with model evaluation and feature analysis - **Configurable Settings** for model parameters and thresholds ## 🎥 Video Demo Watch the application in action: Rwanda Banking Churn Prediction Demo ## 📊 Model Performance Results ### Initial Model Metrics - **Accuracy**: 35.83% - **Precision**: 29.56% - **Recall**: 84.51% - **F1 Score**: 43.80% ### Adjusted Threshold Metrics (Optimized for Maximum Recall) - **Accuracy**: 29.58% - **Precision**: 29.58% - **Recall**: 100.0% - **F1 Score**: 45.66% ### Confusion Matrix (Initial Model) ``` Predicted Actual Negative Positive Negative 26 143 Positive 11 60 ``` ### Confusion Matrix (Adjusted Threshold) ``` Predicted Actual Negative Positive Negative 0 169 Positive 0 71 ``` ## 🎯 Model Architecture ### Ensemble Model - **Primary Model**: XGBoost Classifier - **Secondary Model**: Logistic Regression - **Ensemble Method**: Weighted combination for improved performance ### Best Hyperparameters ```python { 'colsample_bytree': 0.7, 'learning_rate': 0.01, 'max_depth': 3, 'n_estimators': 300, 'scale_pos_weight': 3, 'subsample': 0.7 } ``` ### Model Optimization - **Threshold**: Adjusted to 0.3 for maximum recall - **Class Imbalance**: Handled using SMOTE oversampling a …