  As known, attribute selection is a method that is used before the
classification of data mining. In this study, a new data set has been created
by using attributes expressing overall satisfaction in Turkey Statistical
Institute (TSI) Life Satisfaction Survey dataset. Attributes are sorted by
Ranking search method using attribute selection algorithms in a data mining
application. These selected attributes were subjected to a classification test
with Naive Bayes and Random Forest from machine learning algorithms. The
feature selection algorithms are compared according to the number of attributes
selected and the classification accuracy rates achievable with them. In this
study, which is aimed at reducing the dataset volume, the best classification
result comes up with 3 attributes selected by the Chi2 algorithm. The best
classification rate was 73% with the Random Forest classification algorithm.
