Python for Deep Learning - Build Neural Networks in Python - Exploring the Dataset

The video tutorial provides an overview of a dataset used for churn modeling. It begins by explaining how to access the dataset and then details the various attributes it contains, such as Customer ID, Surname, Credit Score, Geography, Gender, Age, Tenure, Balance, Number of Products, Credit Card status, Active Member status, and Estimated Salary. These are identified as independent variables. The tutorial concludes by discussing the dependent variable, which indicates whether a customer has exited the bank or not.

What is the first step to access the data set?

• Use a command line interface

• Download from the internet

• Double-click to open

• Right-click and select 'Open'

Which attribute represents the customer's location?

• Balance

• Geography

• Surname

• Credit Score

How is the 'Has Credit Card' attribute represented?

• A for yes, B for no

• 1 for yes, 0 for no

• Yes or No

• 0 for yes, 1 for no

What does the 'Active Member' attribute indicate?

• If the customer has a high balance

• If the customer is using the bank

• If the customer is a new member

• If the customer has a loan

What is the dependent variable in the churn modeling data set?

• Customer ID

• Estimated Salary

• Customer exited or not

• Number of Products