How can machine learning help with missing data in datasets?

How can machine learning help with missing data in datasets?

Machine learning can help by predicting missing values based on existing data patterns, improving data quality.

What techniques are commonly used for this?

Common techniques include regression imputation, k-nearest neighbors, and using algorithms like Random Forest.

Are there any specific libraries or tools you recommend?

Yes, libraries like Scikit-learn, TensorFlow, and Keras are great for implementing these techniques.

What are the limitations of using machine learning for missing data?

Limitations include potential bias in predictions, the need for a substantial amount of data, and complexity in model selection.