What are some effective AI data cleaning techniques?

What are some effective AI data cleaning techniques?

Some effective AI data cleaning techniques include outlier detection, missing value imputation, and data normalization.

Can you explain how outlier detection works?

Outlier detection identifies data points that differ significantly from the rest of the dataset, often using statistical methods.

What methods can I use for missing value imputation?

Common methods for missing value imputation include mean/mode imputation, k-nearest neighbors, and regression imputation.

How does data normalization improve data quality?

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