Performance Evaluation Model using Unsupervised K-Means Clustering
Bolisetty Kanaka Durga Prasad, Bhaskar Choudhary, B. Ankayarkanni · 2020
Performance Appraisal is an evaluation process done by every company to analyze whether the goals and outlines of that company or organization have been met by the employees or not. It helps to evaluate each and every recruit in terms of their skills, knowledge, identify their strengths and weaknesses and gives them a report for future improvement. The organizations overall performance includes the individual contribution of the each employee which becomes the resultant or actual output which is measured with the intended output which the organization's higher officials set in order to achieve it. The appraisal system is generally irregular and is often influenced by personal views which make the organization to use a method which overcomes all such irregularities. This paper consists an implementation of an intelligent performance appraisal model which surmounts such factors and irregularities by using machine learning algorithms. The present scenario of performance appraisal involves more manual work in evaluation which is not accurate. It involves various aspects where the appraisal will be favorable only to a certain group of employees based on certain factors but by using machine learning algorithms we can improve and increase the accuracy to make sure that the employees would get the appraisal without any prejudice and will ensure a fair evaluation which is by considering all the required factors. We use powerful “scikitlearn” machine learning systems in python, for building a model for the classification of performance of the employees. We use clustering techniques to implement a self-learning model i.e. unsupervised learning models. We also use an ensemble of these techniques for increasing our accuracy.