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# 🏦 Banking Customer Churn Prediction Challenge

This competition uses a **structured banking customer dataset** designed to simulate a realistic churn prediction scenario in the financial services domain.

Participants are expected to develop a production-grade machine learning solution capable of estimating the **probability that a customer will leave the bank**.

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## 📊 Dataset Overview

The dataset reflects real-world banking attributes and includes:

### 👤 Demographic Attributes

* Customer profile characteristics
* Personal segmentation indicators

### 💰 Financial Metrics

* Account balance–related variables
* Credit and monetary behavior indicators

### 🔁 Account Activity Indicators

* Usage patterns
* Engagement signals
* Tenure-related features

The dataset includes both:

* **Numerical features**
* **Categorical features**

This requires robust preprocessing and structured modeling practices.

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## 🎯 Core Objective

Build a supervised learning model that:

* Learns from historical customer data
* Predicts churn probability (continuous values between 0 and 1)
* Produces reliable ranking of churn risk

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## 🏗 Engineering Expectations

This challenge emphasizes more than leaderboard performance. Participants must demonstrate:

* Proper categorical encoding
* Structured preprocessing pipelines
* Rigorous validation methodology
* Clean and modular implementation
* Full reproducibility

The goal is to simulate **real-world ML deployment standards**, where engineering discipline is as important as predictive accuracy.