issing data can be a tricky affair when it comes

 Filter out any unwanted outliers

Outliers may usually create some problems with certain types of data model tincona.com s. For example, the linear regression models may be less robust than outliers. Most commonly, if you have a legitimate reason for removing an outlier, this will help your model’s performance. Outliers are usually innocent until proven guilty. You must not remove an outlier just because timevinger.org it is a bigger number.Big numbers may be very informative sometimes in some 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 about it at the first point itself, you need to understand that one cannot simply ignore timesofamerica.info the missing values in the given datasets. You should handle them in some ways, as most of the algorithms may not accept any missing values. Two of the most commonly recommended ways to deal with missing 

1.    Dropping the observation, which has some missing values.

2.    Imputing the missing values based on the observations.

Dropping values is a suboptimal option as when you drop some observations, you are actually dropping some valuable information. The fact that some values are missing may be informative by itself. Also, in the real world, you may often need to make some predictions on the new data even if some of the features are not available.

Imputing a missing value is also not an optimal option because the values were originally missing. But you may have filled it, which always leads to the loss of some valuable information no matter how sophisticated the imputationmethod is. Missing data is informative by itself, as we discussed, and you must tell your algorithms if a value is missing.