| --- |
| title: Binary Classification with a Bank Churn Dataset |
| emoji: 🚀 |
| colorFrom: indigo |
| colorTo: indigo |
| sdk: streamlit |
| app_file: src/app.py |
| sdk_version: 1.54.0 |
| pinned: false |
| license: mit |
| --- |
| |
| # Customer Churn Prediction |
|
|
| An end-to-end machine learning project for predicting **customer churn** using structured banking data. |
| The project covers the full pipeline from data analysis to model deployment readiness. |
|
|
| --- |
|
|
| ## 📌 Project Overview |
|
|
| The goal of this project is to predict whether a customer will leave the bank (`Exited = 1`) based on |
| demographic, financial, and behavioral features. |
|
|
| The main focus is: |
| - Robust feature engineering |
| - Model comparison using multiple algorithms |
| - ROC-AUC–based evaluation for imbalanced data |
| - Cross-validation and hyperparameter tuning |
| - Saving the final model for inference and deployment |
|
|
| --- |
|
|
| ## 📊 Dataset |
|
|
| - Rows: **165,034** |
| - Target: `Exited` (binary classification) |
| - No missing values |
| - Categorical features encoded |
| - Multiple engineered behavioral features added |
|
|
| --- |
|
|
| ## ⚙️ Feature Engineering |
|
|
| Key engineered features include: |
| - Balance usage indicator (`HasBalance`) |
| - Balance per product ratio |
| - Age and credit score segmentation |
| - Activity × product interaction score |
| - Tenure and salary-based ratios |
|
|
| Identifier columns (`id`, `CustomerId`, `Surname`) were removed to avoid noise. |
|
|
| --- |
|
|
| ## 🤖 Models Trained |
|
|
| The following models were evaluated: |
|
|
| - Logistic Regression |
| - Decision Tree |
| - Random Forest |
| - Gradient Boosting |
| - AdaBoost |
| - LightGBM |
| - XGBoost |
| - CatBoost |
|
|
| All models were evaluated using **ROC-AUC** as the primary metric. |
|
|
| --- |
|
|
| ## 🏆 Model Performance (ROC-AUC) |
|
|
| | Model | ROC-AUC | |
| |------|--------| |
| | CatBoost | 0.8898 | |
| | LightGBM | 0.8896 | |
| | XGBoost | 0.8893 | |
| | Gradient Boosting | 0.8881 | |
| | AdaBoost | 0.8767 | |
| | Random Forest | 0.8750 | |
| | Logistic Regression | 0.8180 | |
|
|
| --- |
|
|
| ## 🔁 Validation & Tuning |
|
|
| - **Stratified K-Fold Cross Validation** |
| - **RandomizedSearchCV** for hyperparameter tuning |
| - Class imbalance handled using class weights |
| - Final model trained with optimal hyperparameters |
|
|
| --- |
|
|
| ## 🚀 Deployment |
|
|
| The trained model is saved and can be used for inference via: |
| - Streamlit |
| - Hugging Face Spaces |
| - Any Python-based backend |
|
|
| To run the Streamlit app locally: |
| ```bash |
| streamlit run app.py |
