sevvaliclal/BinaryClassificationwithaBankChurnDataset

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)

🔁 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:

streamlit run app.py