What Is Data Imputation: Purpose, Techniques, & Methods
Missing data can be a major obstacle for organizations seeking to extract maximum value from their data assets. Incomplete datasets with missing values severely limit the ability to derive meaningful insights, leading to incorrect analysis, false predictions, and poor decision-making. Such drawbacks can result in missed revenue opportunities and inefficient resource allocation, severely impacting businesses. Data imputation helps resolve these issues by substituting these missing values with estimated ones using various techniques. This enhances the integrity and completeness of datasets, allowing your organization to derive reliable insights, build accurate models, and make better decisions. Let’s look into an in-depth overview of data imputation and its underlying principles, popular imputation techniques, use cases, and more. What is Data Imputation? Data imputation is the process of replacing missing or unavailable entries in a dataset with substituted values. This proce...
Comments
Post a Comment