How do I clean data for machine learning?

How do I clean data for machine learning?

Cleaning data for machine learning involves several steps, including handling missing values, removing duplicates, and normalizing data.

What are some common techniques for handling missing values?

Common techniques include removing rows with missing values, imputing values using mean/median, or using algorithms that support missing data.

How do I identify and remove duplicates in my dataset?

You can identify duplicates by checking for identical rows or using specific features. In Python, you can use the drop_duplicates() function in pandas.

What is data normalization and why is it important?

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