Understanding how to deal with missing data is really important in feature engineering, especially when we're using supervised learning. Here are some easy tips to help you out:

1. Know Why Data is Missing

• Types of Missing Data:
  • MCAR (Missing Completely At Random): This means the missing data has nothing to do with the other data.
  • MAR (Missing At Random): Here, the missing data relates to the data we have but not to the data that's missing.
  • MNAR (Missing Not At Random): This type means the missing data is connected to data we can’t see.

2. Ways to Fill in Missing Data

• Mean/Median/Mode Imputation: This is a simple method. But be careful; it can mess up the connections in your data. For example, if your data is uneven, use the median instead of the mean.
• K-Nearest Neighbors (KNN): This method fills in blank spots by looking at similar items nearby.
• Multiple Imputation: This means you create a few filled-in versions of your data and then combine them to get better results.

3. Keep Track of Missing Data

• By adding a simple indicator that shows whether data is missing, you can gather useful information for your analysis.

4. Use Models That Can Handle Missing Data

• Some methods, like Decision Trees, can work with missing data without needing to fill in the gaps.

5. Use Regularization Techniques

• Techniques like L1 or L2 regularization can help prevent your model from getting too complicated because of missing data.

Example

Imagine you have a dataset with customer info. If some people's incomes are missing, instead of just removing those entries, you could use median imputation or KNN to keep important data.

By following these tips, you can make sure your model works well even when some data is missing.