How can I ensure data quality in my machine learning projects?

How can I ensure data quality in my machine learning projects?

Ensuring data quality involves several steps, including data cleaning, validation, and preprocessing. What specific issues are you facing?

I'm worried about missing values and outliers in my dataset.

You can handle missing values by either removing them or imputing them with statistical methods. For outliers, consider using techniques like Z-score or IQR to identify and manage them.

What tools can I use to automate data quality checks?

You can use libraries like Pandas for data manipulation, and tools like Great Expectations or Apache Griffin for automated data quality checks.

Are there best practices for maintaining data quality over time?

Yes, regularly auditing your data, implementing data governance policies, and using version control for datasets can help maintain quality.