How can I improve data quality for machine learning?

How can I improve data quality for machine learning?

Improving data quality involves several steps, such as data cleaning, validation, and enrichment. What specific issues are you facing?

I'm dealing with a lot of missing values in my dataset.

You can handle missing values by using techniques like imputation, where you fill in missing data with estimates, or by removing rows with missing values. Which approach do you prefer?

Imputation sounds good. What methods can I use for that?

Common imputation methods include mean, median, mode, or using algorithms like K-Nearest Neighbors. Do you have a specific method in mind?

I think K-Nearest Neighbors would work. How do I implement that?

You can implement K-Nearest Neighbors imputation using libraries like Scikit-learn in Python. Would you like a code example?