What is data preparation in supervised learning?

What is data preparation in supervised learning?

Data preparation in supervised learning involves cleaning, transforming, and organizing data to improve model performance.

Why is data cleaning important?

Data cleaning is crucial because it removes inaccuracies and inconsistencies, leading to more reliable model predictions.

What techniques are used for data transformation?

Common techniques include normalization, encoding categorical variables, and feature scaling to enhance model training.

How do I handle missing data?

You can handle missing data by imputation, removing records, or using algorithms that support missing values.