Python for Machine Learning - The Complete Beginners Course - Implementation in Python: Encoding Categorical Data - Mult

The video tutorial explains how to handle categorical data in a dataset by converting it into numerical values using the sklearn library. It introduces the column transformer and one hot encoder, detailing their usage and parameters. The tutorial provides a step-by-step guide on encoding a specific column in the dataset, transforming it into numerical data. The process involves fitting and transforming the dataset, resulting in a fully numerical dataset ready for further analysis.

Why is it necessary to convert categorical data into numerical data?

• To ensure data privacy

• To reduce the size of the dataset

• To enable mathematical computations and model predictions

• To make the data more visually appealing

What is the primary role of the 'Transformers' array in a column transformer?

• To define the transformations to be applied

• To specify the output format

• To list the columns to be dropped

• To store the original dataset

What does the 'remainder' parameter in a column transformer specify?

• The order of column transformations

• The action to take on non-transformed columns

• The default transformation for all columns

• The type of encoding to use

Which column is transformed using one hot encoding in the example provided?

• The fourth column

• The third column

• The second column

• The first column

What do the encoded values 001, 010, and 100 represent in the transformed dataset?

• Different numerical ranges

• Different encoding methods

• Different states: New York, Florida, California

• Different data types