How does data science complement machine learning?

Can you explain how data science complements machine learning?

Sure! Data science provides the foundational techniques and tools for data analysis, which is crucial for training machine learning models.

What specific data science techniques are important for machine learning?

Techniques like data cleaning, exploratory data analysis, and feature engineering are vital for preparing data for machine learning.

How does feature engineering impact machine learning performance?

Feature engineering helps in selecting and transforming variables to improve model accuracy, making it a key step in the process.

Can you give an example of feature engineering?

Certainly! For instance, creating a 'total spending' feature from separate 'food' and 'entertainment' spending categories can provide more insight for a model.