DR4

  The best possible approach to handling missing data in categorical features is to label them as missing. You may be adding some new classes for this feature, which tell the algorithms that some values are missing. This may also get arou equirements for the missing values. In case of missing some numerical data, you should always flag the values. Flagging the observations with a specific indicator as a variable of missingness is ideal.  an outlier, this will help your model’s performance. Outliers are usually innocent until proven guilty. You must not remove an outlier just because it is a bigger number.Big numbers may be very informative sometimes in some webapex.net specific data models. We cannot stress it out without enough good reasons for removing an outlier like a suspicious measurement, which is unlikely to be real data. Handling missing data Handling missing data can be a tricky affair when it comes to machine learning. In order to be clear e first point itself, you need