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

The video tutorial demonstrates how to use the pandas library in Python to read a CSV file containing a dataset for churn modeling. It explains the structure of the dataset, highlighting the presence of 13 independent variables and one dependent variable. The tutorial identifies three independent variables—row number, customer ID, and surname—as irrelevant for prediction purposes and suggests eliminating them. Finally, it outlines the process of splitting the dataset into independent variables (X) and the dependent variable (Y) for further analysis.

Which library is used to load the dataset in the tutorial?

• scikit-learn

• pandas

• matplotlib

• NumPy

How many independent variables are present in the dataset?

• 13

• 15

• 10

• 12

Which of the following variables is considered irrelevant for predicting customer behavior?

• Balance

• Credit Score

• Customer ID

• Age

What is the purpose of splitting the dataset into X and Y?

• To prepare data for visualization

• To distinguish between independent and dependent variables

• To divide the dataset into numerical and categorical data

• To separate training and testing data

What is the next step after identifying irrelevant variables in the dataset?

• Visualizing the data

• Combining the variables

• Normalizing the data

• Eliminating the irrelevant variables