Machine Learning Model

In this paper Compare the performance of two classification algorithm. It is useful to differentiate algorithms based on computational performance rather than classification accuracy alone. As although classification accuracy between the algorithms is similar, computational performance can differ significantly and it can affect to the final results. So the objective of this paper is to perform a comparative analysis of two machine learning algorithms namely, K Nearest neighbor, classification and Logistic Regression. In this paper it was considered a large dataset of 7981 data points and 112 features. Then the performance of the above mentioned machine learning algorithms are examined. In this paper the processing time and accuracy of the different machine learning techniques are being estimated by considering the collected data set, over a 60% for train and remaining 40% for testing. The paper is organized as follows. In Section I, introduction and background analysis of the research is included and in section II, problem statement. In Section III, our application and data analyze Process, the testing environment, and the Methodology of our analysis are being described briefly. Section IV comprises the results of two algorithms. Finally, the paper concludes with a discussion of future directions for research by eliminating the problems existing with the current research methodology.