Imputation methods aim to estimate the missing values based on the available information in the dataset. So you should know the following to estimate the best method:
Understand the missingness pattern: Analyze the pattern of missing values in the column.
- Are the missing values randomly distributed
- Do they have a specific pattern?
Understanding this pattern can help you make more informed decisions about handling the missing values.
Consider the implications: Think about the implications of replacing missing values with a specific value.
For example, replacing missing values with 50 assumes that the missing values are roughly in the middle of the range. Similarly, using the average of all the values in the column.
Explore imputation methods: Instead of replacing missing values with a single value like 50 or the column average, consider exploring more sophisticated imputation techniques.
For instance, you could use regression imputation, where you predict the missing values based on other variables. you can use techniques like multiple imputation, which generate multiple imputations based on the existing data distribution.
There are different ways to deal with NaN values depending on the context and the goal of the analysis. Some common methods are:
1- Removing the rows or columns that contain nulls. This is a simple and fast way to clean the data, but it can also result in losing important information or reducing the sample size.
2- Replacing the NaN with a constant value, such as zero, mean, median, or mode. This is a way to preserve the shape and size of the data, but it can also introduce bias or distortion in the statistics or models.
3- Imputing the NaN values using a technique such as linear regression, KNN. This is a way to estimate the most likely values for the missing data.
The best way to deal with NaN values depends on the nature and amount of the missing data, the type and purpose of the analysis, and the available resources and tools. There is no one-size-fits-all solution, but rather a trade-off between simplicity, accuracy, and efficiency.