Santander_Customer_Satisfaction

Description
From frontline support teams to C-suites, customer satisfaction is a key measure of success. Unhappy customers don't stick around. What's more, unhappy customers rarely voice their dissatisfaction before leaving.

Santander Bank is asking Kagglers to help them identify dissatisfied customers early in their relationship. Doing so would allow Santander to take proactive steps to improve a customer's happiness before it's too late.

In this competition, you'll work with hundreds of anonymized features to predict if a customer is satisfied or dissatisfied with their banking experience.

Dataset Description
You are provided with an anonymized dataset containing a large number of numeric variables. The "TARGET" column is the variable to predict. It equals one for unsatisfied customers and 0 for satisfied customers.

The task is to predict the probability that each customer in the test set is an unsatisfied customer.
We drop all nan values from target column the dataset to avoid problems when uploading the dataset to OpenML. Also, the ID column is dropped.