Python for Deep Learning - Build Neural Networks in Python - One-hot encoding using scikit-learn

The video tutorial explains how to use the column transformer and one hot encoder from sklearn to transform categorical data into numerical data. It covers the importation of necessary libraries, the creation of a column transformer object, and the specification of parameters such as transformers and remainder. The tutorial provides a step-by-step guide on implementing the column transformer with code examples, including fitting and transforming a dataset. It concludes with an interpretation of the output, explaining how categorical data is converted into numerical data using one hot encoding.

What is the primary purpose of using a Column Transformer in data preprocessing?

• To perform data augmentation

• To split data into training and test sets

• To visualize data

• To transform and encode data

Which parameter of the Column Transformer specifies the columns to be transformed?

• Transformers

• Remainder

• Columns

• Estimator

What does the 'remainder' parameter in the Column Transformer do?

• Specifies the columns to be transformed

• Defines the type of encoding to use

• Determines what happens to non-transformed columns

• Sets the name of the transformer

In the implementation of the Column Transformer, what is the purpose of using 'fit_transform'?

• To split the dataset

• To visualize the dataset

• To apply transformations to the dataset

• To save the dataset

Why is the first index ignored in the transformed dataset?

• To reduce computation time

• To avoid redundancy as the information is captured in other indices

• To increase accuracy

• To simplify the dataset

What does the encoded value '01' represent in the transformed dataset?

• Italy

• Germany

• Spain

• France

What is the next step after transforming categorical data into numerical data?

• Visualizing the data

• Splitting the dataset into training and test sets

• Performing data augmentation

• Normalizing the data