Deletion of Rows or Columns: This is a straightforward approach. If the missing values are minimal and randomly distributed, you can consider removing rows or columns with missing data. However, be cautious as this can result in a loss of valuable information.
Imputation Techniques: Imputation involves filling in missing values with estimated or calculated values. You’ve listed several imputation methods, including mean, median, or mode imputation, linear regression imputation, interpolation, K-Nearest Neighbors (KNN), and Multiple Imputation by Chained Equations (MICE). Each of these techniques has its own strengths and weaknesses, and the choice depends on the nature of your data and the problem you’re trying to solve.
Categorical Data Handling: Creating a distinct category for missing values, like “Unknown” or “N/A,” is a valid approach for categorical data. It allows you to retain the missing data information without making assumptions about the nature of the missing values.
Unique Category for Missing Data: In some cases, it might be insightful to treat missing data as a unique category, especially when imputation could introduce bias or inaccuracies into your analysis.
Advanced Techniques: When dealing with intricate analyses or situations where standard imputation methods are insufficient, advanced statistical techniques like Expectation-Maximization (EM) algorithms or structural equation modeling can be very useful. These methods can model missing data more accurately and help draw more reliable conclusions.
Data Validation Rules: Proactive measures like setting up data validation rules in tools like Excel are essential for preventing missing or erroneous data in future entries. This helps maintain data quality and integrity from the source.