Evaluating The Performance Of An Employee Using Decision Tree Algorithm

The main objective is to evaluate the performance of employee using Decision Tree algorithm. The data mining classification methods like decision tree, rule mining, clustering etc. can be applied for predicting the performance of an employee working in an organization. The employee data are evaluated for giving promotion, yearly increment and career advancement. In order to provide yearly increment for an employee, it should be evaluated by using past historical data of employees. The historical data stored in the table are subjected to learning by using the decision tree algorithm and the performance are found by testing the attributes of an employee against the rules generated by the decision tree classifier. This paper concentrates on collecting data about employees, generating a decision tree from the historical data, testing the decision tree with attributes of an employee and generating the output as whether to give the promotion or not. The information about an employee are collected by using the user interface. This information is compared with the trained data stored in the decision tree. The final goal node is to determine whether the employee will get yearly increment, promotion or not.