Depends on nature of missing data: 1. If only small amount is missing, then remove missing data. 2. If missing completely at random, then use simple imputation techniques such as mean value fill, backfill, frontfill, etc. 3. Use KNN to impute values with more accuracy. If missing data is following some pattern, then it may be useful to keep it, as it signals an underlying cause.