1. Deleting the missing values: This is the simplest approach, but it can lead to loss of information, especially if a large portion of the data is missing. I usually consider this option when the percentage of missing values is low and the loss of information is minimal.
2. Imputing the missing values: This involves filling in the missing values with a reasonable estimate. For example, I worked on a project where I used the mean, median, or mode of the available data to fill in the missing values. This approach is useful when the missing values are missing at random and the dataset is large enough to maintain its integrity even after imputation.
3. Using predictive models: In some cases, I have used regression or machine learning models to predict the missing values based on the available data. This is useful when there is a strong relationship between the variable with missing values and other variables in the dataset.
It's important to remember that the choice of handling missing values should be based on the specific context of the data and the goals of the analysis. There's no one-size-fits-all solution.