What are some effective data preparation techniques?

What are some effective data preparation techniques?

Effective data preparation techniques include data cleaning, normalization, transformation, and feature selection.

Can you explain data cleaning in more detail?

Data cleaning involves removing inaccuracies, duplicates, and irrelevant data to improve data quality.

What is normalization and why is it important?

Normalization scales data to a standard range, which helps improve the performance of machine learning algorithms.

How do I choose the right features for my model?

Choosing the right features involves techniques like correlation analysis, feature importance, and domain knowledge.