Choosing the best way to fill in missing values is really important for cleaning and preparing data. Here are some ways to think about it, each good for different situations:
1. Understanding Why Data is Missing
- MCAR (Missing Completely At Random): The missing values have nothing to do with the data itself.
- MAR (Missing At Random): The missing values are related to some other data that we can see.
- MNAR (Missing Not At Random): The missing values are tied to data we can’t see.
2. Ways to Fill Missing Values
- Mean/Median/Mode Imputation:
- This method works well for numbers. For example, the average (mean) is often used when less than 5% of the data is missing.
- Regression Imputation:
- This is good for MAR data. It predicts the missing values using other available information.
- K-Nearest Neighbors (KNN):
- This method can work for both categories and numbers. It looks at nearby data points to fill in the missing ones.
- Multiple Imputation:
- This creates several different datasets with filled in values and combines the results. It’s very strong against bias but takes more time and effort to calculate.
3. Checking the Changes
- After filling in the missing values, it’s important to see how the data has changed. You can do this by:
- Comparing histograms (which are graphs that show data distribution),
- Using statistical tests like t-tests to compare averages.
In the end, the method you pick should depend on why the data is missing, how important the missing parts are, and how it affects the overall quality of the data.