Int. J. Adv. Sci. Eng. Vol.3 No.2 325-329 (2016) 325 ISSN 2349 5359 Vivek Anand et al International Journal of Advanced Science and Engineering www.mahendrapublications.com *Corresponding Author: neela.madheswari@gmail.com Received: 20.09.2016 Accepted: 20.11.2016 Published on: 30.11.2016 ABSTRACT: Machine learning is a subfield of computer science that evolved from the study of pattern recognition and computational learning theory in artificial intelligence. The focus of this paper is based on the student’s result, which is a data science work and applying the concept of machine learning. Nowadays at the end of every year or semester in the schools or colleges, the teachers have to find out all the students in their classes who are eligible for writing exams. Every time they have to analyze a lot of data and find who is eligible and who is not. To overcome this difficulty, this work proposed on students result prediction model in the form of web service and applying machine learning concept, find the possibility of students about their overall performance so that they can predict the eligible students for writing the exams. KEYWORDS: machine learning, web service, result prediction, accuracy, precision, recall © 2015 mahendrapublications.com, All rights reserved Students Results Prediction using Machine Learning Techniques Vivek Anand, Saurav Kumar, A. Neela Madheswari * Department of Computer Science and Engineering, Mahendra Engineering College, Namakkal-637503, India I. INTRODUCTION Machine learning is one of the fastest growing areas of computer science with far-reaching applications. The term machine learning refers to the automated detection of meaningful patterns in data. It is also widely used in scientific applications such as Bioinformatics, medicine and astronomy [1]. The machine learning algorithms used for Human Activity Recognition (HAR) are: 1) Decision tree, 2) Boosted Decision Tree (AdaBoost), 3) Random Forest, and 4) Support Vector machine. The comparison of these four algorithms are analysed in terms of classification accuracy percentage. It is one of the applications of machine learning techniques [2]. Machine learning technology has been successfully applied in many information retrieval systems, both experimental and fielded. The instant availability of enormous amounts of textual information on the internet and in digital libraries has provoked a new interest in software agents that acts on behalf of users, sifting through what is there to identify documents that may be relevant to the user’s individual needs. The application of machine learning techniques to information retrieval is very common [3]. Machine learning is a branch of artificial intelligence that employs a variety of statistical, probabilistic and optimization techniques that allows computers to learn from past examples and to detect hard-to-discern patterns from large, noisy or complex datasets. This capability is well- suited for medical applications, especially those that depend on complex proteomic and genomic measurements. As a result, machine learning is frequently used in cancer diagnosis and detection. Machine learning is applied to cancer prognosis and prediction. A broad survey of the different types of machine learning methods are used, the types of data are integrated and the performance of those methods in cancer prediction and prognosis is conducted. A number of trends are noted, including a growing dependence on protein biomarkers and microarray data, a strong bias towards applications in prostate and breast cancer, and a heavy reliance on older technologies such as artificial neural networks (ANN) instead of more recent developed or more easily interpretable machine learning methods [4]. The application of machine learning is applied to an important environmental problem such as detection of oil spills from radar images of the sea surface. Only about 10% of oil spills originates from natural sources such as leakage from sea beds. Much more prevalent is pollution caused intentionally by ships that want to dispose cheaply of oil residues in their tanks. Radar images from satellites such as RADARSAT and ERS1 provide an opportunity for monitoring coastal waters day and night, regardless of weather conditions. Oil slicks are less reflective of radar than the average ocean surface, so they appear dark in an image. An oil slick’s shape and size vary in time depending on weather and sea conditions. A spill usually starts out as one or two slicks that later break up into several small slicks. Oil spill detection requires a highly trained human operator to assess each region in each image. It was therefore essential that the system be readily customizable to each user’s particular needs and circumstances. This requirement motivates the use of machine learning. The system will be customized by training on examples of spills and nonspills provided by the user, and by allowing the user to control the tradeoff between false positives and false negatives [5]. Though machine learning techniques are applied for various fields, this paper demonstrates the application of machine learning technique for students’ performance prediction. II. RELATED WORK There are different approaches for students result prediction. Data mining is a computational method of processing data which is successfully applied in many areas that aim to obtain useful knowledge from the data. Data mining technique is classified with two types of algorithms namely: 1) unsupervised algorithms and 2) supervised algorithms. Classification models in supervised algorithms are: 1) Naive Bayes (NB) algorithm, 2) Multilayer Perceptron (MLP), and 3) J48. Tests were conducted using the following metrics: name of the attribute, Merit, Merit deviation, Rank, Rank and deviation. Comparison of the three algorithms are