Deep Learning - Deep Neural Network for Beginners Using Python - One-Hot Encoding (NN Implementation)

The video tutorial covers the process of one hot encoding using pandas, specifically focusing on encoding a 'rank' column. It explains how to use the 'get_dummies' function and the importance of dropping the original column after encoding. The tutorial also addresses error handling, demonstrating how to correct a typo in the code. Finally, it introduces the concept of scaling GRE and GPA scores to ensure uniformity in data analysis.

What function in pandas is used to perform one hot encoding?

• pivot

• merge

• concat

• get_dummies

What is the purpose of setting a prefix when using get_dummies?

• To sort the data

• To specify the data type

• To rename the original column

• To differentiate the new columns

Which axis value should be used to drop a column in pandas?

• 3

• 0

• 1

• 2

What was the error encountered in the code related to the column name?

• A missing function

• Incorrect data type

• A typo in the column name

• A missing column

Why is it important to scale GRE and GPA scores?

• To make them more readable

• To increase their values

• To ensure uniformity in data scale

• To decrease their values