### Data Description

The dataset provided for this competition contains information about customers of a financial institution, designed to help you build a model to predict customer churn. The data includes various attributes related to the customers' demographics, account information, and transaction history. Here's a brief overview of the key columns in the dataset:

- **CustomerId**: A unique identifier for each customer.
- **Surname**: The last name of the customer (irrelevant for modeling but useful for identification).
- **CreditScore**: A numerical score representing the customer's creditworthiness.
- **Geography**: The country of residence of the customer (e.g., France, Germany, Spain).
- **Gender**: The gender of the customer (Male or Female).
- **Age**: The age of the customer.
- **Tenure**: The number of years the customer has been with the bank.
- **Balance**: The current balance in the customer's account.
- **NumOfProducts**: The number of products the customer has with the bank.
- **HasCrCard**: Whether the customer has a credit card (1 = Yes, 0 = No).
- **IsActiveMember**: Whether the customer is an active member (1 = Yes, 0 = No).
- **EstimatedSalary**: The estimated annual salary of the customer.
- **Exited**: The target variable, indicating whether the customer has churned (1 = Yes, 0 = No).

### Purpose of the Data

The data is structured to help you identify patterns that might indicate why a customer would decide to leave the bank. By analyzing these attributes, your task is to build a model that predicts the likelihood of customer churn based on the features provided.

### Training and Test Sets

- **train.csv**: Contains the labeled data, including the `Exited` column, which indicates whether the customer churned. This is the data you will use to train your model.
- **test.csv**: Contains the same features as the training set but without the `Exited` column. Your task is to predict the probability of churn for each customer in this dataset.

This dataset provides a rich set of features that, when analyzed and modeled effectively, can yield valuable insights into customer behavior and churn prediction.


## Files

*   **train.csv** - the training dataset; `Exited` is the binary target
*   **test.csv** - the test dataset; your objective is to predict the probability of `Exited`
*   **sample_submission.csv** - a sample submission file in the correct format