README.md

metadata

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.

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📌 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

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📊 Dataset

• Rows: 165,034
• Target: Exited (binary classification)
• No missing values
• Categorical features encoded
• Multiple engineered behavioral features added

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⚙️ 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.

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🤖 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.

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🏆 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

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🔁 Validation & Tuning

• Stratified K-Fold Cross Validation
• RandomizedSearchCV for hyperparameter tuning
• Class imbalance handled using class weights
• Final model trained with optimal hyperparameters

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🚀 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:

streamlit run app.py