Data Science and Machine Learning (Theory and Projects) A to Z - Feature Engineering: Categorical Features

The video tutorial focuses on feature engineering, particularly preparing datasets for machine learning algorithms. It discusses the challenges of handling data inconsistencies and converting categorical features into numeric form. The tutorial introduces one-hot encoding as a method to improve model performance by expanding categorical features into binary vectors. An example from the Python Data Science Handbook is used to illustrate these concepts.

What is the role of dimensionality reduction in the context of feature engineering?

How can inconsistencies in data affect the preparation of a data matrix?

Discuss the importance of converting categorical features to numeric form in data preparation.