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In the present era, classification of data is one of the most important issues in various sciences in order to
detect and predict events. In statistics, the traditional view of these classifications will be based on classic
methods and statistical models such as logistic regression. In the present era, known as the era of explosion
of information, in most cases, we are faced with data that cannot find the exact distribution. Therefore, the
use of data mining and machine learning methods that do not require predetermined models can be useful.
In many countries, the exact identification of the type of groundwater resources is one of the important
issues in the field of water science. In this paper, the results of the classification of a data set for groundwater resources were compared using regression, neural network, and support vector machine.
The results of these classifications showed that machine learning methods were effective in determining the exact type of springs.

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Nonrespose is a source of error in the survey results and National statistical organizations are always looking for ways to
control and reduce it. Predicting nonrespons sampling units in the survey before conducting the survey is one of the solutions
that can help a lot in reducing and treating the survey nonresponse. Recent advances in technology and the facilitation of
complex calculations have made it possible to apply machine learning methods, such as regression and classification trees
or support vector machines, to many issues, including predicting the nonresponse of sampling units in statistics. . In this
article, while reviewing the above methods, we will predict the nonresponse sampling units in a establishment survey using
them and we will show that the combination of the above methods is more accurate in predicting the correct nonresponse
than any of the methods.