How To Handle Missing Values In Machine Learning Data With Weka

Last Updated on December 13, 2019

Data is rarely clean and often you can have corrupt or missing values.

It is important to identify, mark and handle missing data when developing machine learning models in order to get the very best performance.

In this post you will discover how to handle missing values in your machine learning data using Weka.

After reading this post you will know:

• How to mark missing values in your dataset.
• How to remove data with missing values from your dataset.
• How to impute missing values.

Kick-start your project with my new book Machine Learning Mastery With Weka, including step-by-step tutorials and clear screenshots for all examples.

Let’s get started.

Predict the Onset of Diabetes

The problem used for this example is the Pima Indians onset of diabetes dataset.

It is a classification problem where each instance represents medical details for one patient and the
To finish reading, please visit source site