How can I improve data quality for better anomaly detection?

How can I improve data quality for better anomaly detection?

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 and outliers in my dataset.

For missing values, you can use imputation techniques or remove those records. For outliers, consider using statistical methods to identify and handle them. Would you like more details on these methods?

Yes, please explain the imputation techniques.

Common imputation techniques include mean/mode imputation, k-nearest neighbors, and regression imputation. Each has its pros and cons depending on your data's nature.

What about detecting outliers?

Outlier detection can be done using methods like Z-scores, IQR, or machine learning algorithms like Isolation Forest. Would you like to explore one of these methods further?