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

The video tutorial discusses categorical features in data science, using an example from Jack's vendor Passbook Data Science Handbook in Python. It explains how to create a dataset with features like price, rooms, and neighborhood, and demonstrates the use of sklearn's dictionary vectorizer for data vectorization. The tutorial covers one-hot encoding, highlighting its impact on feature expansion and the challenges of high dimensionality. It concludes with a discussion on handling sparse matrices and a preview of the next video on text features.

What is the significance of the P value in the context of the example provided?

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What are the three features mentioned in the data example?

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What is the role of the dictionary vectorizer in the data processing?

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Describe the process of one-hot encoding as explained in the text.

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How does the dimensionality of the data change after one-hot encoding?

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Explain the impact of having a large number of unique values in a categorical feature.

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What are the advantages of using a sparse matrix in data processing?

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