The **Data Science Club (DSC)** is launching a **Bi-Weekly Kaggle Challenge Series** to encourage students and data enthusiasts to develop practical machine learning skills through hands-on competitions. Each challenge in this series is designed to simulate a real-world data science problem — from working with messy datasets to generating meaningful predictions.

In Part 2 of the series, participants are challenged with a customer churn prediction problem in the banking domain. Customer retention is a critical concern for financial institutions, and accurately identifying customers who are likely to close their accounts can help organizations take proactive measures to reduce losses.

The dataset provided contains banking customer information, including demographic details, account activity, and financial indicators. Like real-world data, the dataset may include inconsistencies, categorical attributes, and class imbalance, requiring careful preprocessing and thoughtful model design.