  The insurance industry, with its large datasets, is a natural place to use
big data solutions. However it must be stressed, that significant number of
applications for machine learning in insurance industry, like fraud detection
or claim prediction, deals with the problem of machine learning on an
imbalanced data set. This is due to the fact that frauds or claims are rare
events when compared with the entire population of drivers. The problem of
imbalanced learning is often hard to overcome. Therefore, the main goal of this
work is to present and apply various methods of dealing with an imbalanced
dataset in the context of claim occurrence prediction in car insurance. In
addition, the above techniques are used to compare the results of machine
learning algorithms in the context of claim occurrence prediction in car
insurance. Our study covers the following techniques: logistic-regression,
decision tree, random forest, xgBoost, feed-forward network. The problem is the
classification one.
