# 🏦 Bank Customer Churn Prediction

## 📌 Overview
Customer churn is a critical problem in the banking industry. Retaining existing customers is often more cost-effective than acquiring new ones.

In this competition, your goal is to build a machine learning model that predicts whether a bank customer will **churn (leave the bank)** based on historical customer data.

This is a **binary classification** problem designed for **beginners and intermediate** data science learners.

---

## 🎯 Objective
Given a set of customer features, predict the target variable:

- **Churn = 1** → Customer will leave the bank  
- **Churn = 0** → Customer will stay with the bank  

---

## 📊 Dataset Description
The dataset contains anonymized bank customer information, including:

- Demographic features (e.g., age, gender)
- Account information (e.g., balance, tenure)
- Product usage and activity indicators

### Files Provided
- **train.csv** – Training data with target variable
- **test.csv** – Test data without target
- **sample_submission.csv** – Example submission format

---

## 🧪 Evaluation Metric
Submissions are evaluated using **ROC AUC (Receiver Operating Characteristic – Area Under Curve)**.

- Higher score = better model performance
- Probabilistic predictions are encouraged

---

## 📤 Submission Format
Your submission file must be a CSV with the following structure: