https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2Ff48666dbf6dc3882a23c91000928c455%2FDesign%20sem%20nome.png?generation=1706043006289244&alt=media" alt="">In the synthetic dataset for the Playground Series S4 E1 Binary Classification with a Bank Churn Dataset, various features have been engineered to capture relevant information about customers. The dataset includes label-encoded surnames and features derived from them using the TFIDF vectorizer. The credit score serves as a numerical representation of a customer's creditworthiness, while the geography feature indicates the country of residence, with one-hot encoding for France, Spain, and Germany.

Gender is represented with one-hot encoding for male and female categories. Age, tenure, balance, and the number of products used by the customer offer insights into their banking behavior. The presence of a credit card, active membership status, and estimated salary are also included as binary features.

Notable engineered features provide additional insights. Mem_no_Products is the product of the number of products and active membership status, offering a combined metric. Cred_Bal_Sal represents the ratio of the product of credit score and balance to estimated salary, providing a relative measure of financial health. The balance-to-salary ratio (Bal_sal) and the tenure-to-age ratio (Tenure_Age) offer further dimensions for analysis. Finally, Age_Tenure_product is a feature capturing the interaction between age and tenure.

The target variable, 'Exited,' indicates whether a customer has churned, with a value of 1 for churned customers and 0 for those who have not. This dataset, with its diverse set of features and engineered metrics, provides a comprehensive foundation for binary classification tasks, enabling the exploration of factors influencing customer churn in the banking domain. Analysts and data scientists can leverage these features to build predictive models and gain insights into the dynamics of customer retention.