COMPARISON OF LINEAR REGRESSION AND ISOTONIC REGRESSION ANALYSIS IMPLEMENTED FOR PROJECT MANAGEMENT IN SOFTWARE DEVELOPMENT LIFE CYCLE

In this research work, a decision making database of supervised learning is proposed, that is a set of possible estimator are working together to estimate the project completion task in it. Further, predicting the success of software projects based upon information related to estimated task and actual task is deemed to be one of the vital activities in software engineering research. In this paper to focuses an algorithm of isotonic regression classifier give better result when compare with Linear regression.