  This work brings together some of the most common machine learning (ML)
algorithms, and the objective is to make a comparison at the level of obtained
results from a set of unbalanced data. This dataset is composed of almost 17
thousand observations made to astronomical objects to identify pulsars (HTRU2).
The methodological proposal based on evaluating the accuracy of these different
models on the same database treated with two different strategies for
unbalanced data. The results show that in spite of the noise and unbalance of
classes present in this type of data, it is possible to apply them on standard
ML algorithms and obtain promising accuracy ratios.
