Implementing ID3 algorithm for gender identification of Bangladeshi people

Data mining is the procedure of breaking down data from unlike perspectives and resuming it into useful information. It is very important in the field of classification of the objects. It has been fruitfully applied in expert systems to get knowledge. We can determine appropriate classification of unknown objects according to decision tree rules by applying inductive methods to the given values of attributes of those objects. In this paper a decision tree learning algorithm ID3 is applied to build a decision tree in achieving our goal to gender identification of unknown objects. Our experiments have used records of 50 individuals among which 26 were male and 24 were female subjects having age groups of 19 to 25 years. The classification related to the training sets is done by proper calculation. The output of the work is the classified decision tree and the decision rules. It has been observed that the proposed decision tree can recognize 45 subjects gender from 50 individuals. It is a faster process in recognition of individuals' gender and having accuracy level 85% to 90%.