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I am working on a dataset which contains more than 80 features, and thousands of instances. Among those features there are some nominal ones, such as IP SOURCE, IP DESTINATION, Flow ID, which don't have any signification for my machine learning model. My question is, should I remove those features manually, or do I have to replace their values with numerical ones?

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  • $\begingroup$ I guess you have to replace the IPs with nominal equivalents as a simplification $\endgroup$ Commented Dec 22, 2017 at 18:09

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You don't need to remove your features from your data set you may just drop the features which you don't want considered as features or lables. Dropping a feature or label works by excluding the columns while fitting the data into the training set.

i used it in my project shown above

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