Student Performance Evaluation in Education Sector Using Prediction and Clustering Alg

IJSRD - International Journal for Scientific Research & Development| Vol. 3, Issue 10, 2015 | ISSN (online): 2321-0613

Student Performance Evaluation in Education Sector using Prediction and Clustering Algorithms Solankar Punam Anil1 Jagatap Trupti Baban2 Rupnawar Sachin Hanumant3 Shitole Vibhavari Jayvant4 Prof. Kumbhar S. L.5 1,2,3,4 Student 5Assistant Professor 1,2,3,4,5 Department of Computer Engineering 1,2,3,4,5 SBPCOE Indapur Abstract— Data mining is the crucial steps to find out previously unknown information from large relational database. various technique and algorithm are their used in data mining such as association rules, clustering and classification and prediction techniques. Ease of the techniques contains particular characteristics and behaviour.

In this paper the prime focus on clustering technique and prediction technique. Now a days large amount of data stored in educational database increasing rapidly. The database for particular set of student was collected. The clustering and prediction is made on some detailed manner and the results were produce. The K-means clustering algorithm is used here. To find nearest possible a cluster a similar group the turning point India is the performance in higher education for all students. This academic performance is influenced by various factor, therefore to identify the difference between high learners and slow learner students it is important for student performance to develop predictive data mining model.

Key words: Data Mining, Clustering, Classification, Predictive Model I. INTRODUCTION The attainment to predict a student’s performance is most important in educational sector. Student performance is based upon various factors like personal, social and other variables. A most promising tool to get this objective is the use of Data Mining. Data mining is the simply the process of extracting useful information from large amount of data.

To mine the unknown data, different techniques were used such as the Supervised and unsupervised learning technique, pattern mining, clustering, classification technique, prediction, Association rule etc. The K-Means Algorithm concentrated in this paper is the clustering technique, which is the partition, and supervised learning type data mining algorithm. Clustering is the process of grouping a given set of paradigm into disjoint clusters. This is done such that patterns in the same cluster are likewise and patterns belonging to two different clusters are different.

In this paper another one of the data mining techniques were used such as classification. Classification is a predictive data mining technique; create prediction about values of data using known results found from various data.

Classification maps data into predefined sets of classes.

Before examining the data it is often referred to as supervised learning because the classes are determined.

Prediction models that contain all personal, social, and other variables are needs for the performance of the students for effective prediction. The prediction of student performance with high accuracy is beneficial for to recognize the students with low academic achievements initially. It is required that

the students identified can be cared more by the teacher so that their performance is improved in future.

The remaining paper is organized as follows. The Related work is included in second section related to the proposed work. Section 3 contains the proposed work with the K- Means clustering algorithm, classification, and the new procedure for the implementation. Section 4 consists of the conclusion.

II. RELATED WORK International conference on computer supported Brijesh Kumar Bhardwaj and Saurbh Pal, Predicting student’s future learning behavior-with the use of student modeling, By making the students model the destination can be accomplished.

The destination can be achieved by making student models that assemble the learner’s characteristics, contains all information such as their knowledge; behaviour and motivation to learn .The learning are also measured with user experience of the learner and their overall satisfaction.

Suchita Borkar and K. Rajeswari studied the use of education data mining for discovering different patterns to improve the performance of student’s data and identified the attributes which effect student’s academic performance. In this paper to operate large amount of data to their performance is improved by using the clustering algorithm.

In this paper the High prediction accuracy, processing speed is increased.

In S. Ganga and Dr. T. Meyyappan paper performance of student educational data mining describes a research region concerned with the desire data mining, machine learning and statistics to information generated from educational setting. At high levels, the field search for grow up the methods for exploring this data.

The categorization of clustering algorithm into unsupervised and semi supervised strategies based on whether we have certain prior knowledge about the clusters and clustering process to utilize to improve the clustering performance of student data. In our work, focus on the KMeans algorithm as centroid based semi-supervised model.

It can be achieved by various algorithms that to present a systematic commentary on various clustering techniques applied for educational data mining to predict academic performance of student and its implications find them.

Pooja Thakar, Anil Mehta and Manisha conducted study on Performance Analysis and Prediction in Educational Data Mining.

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Student Performance Evaluation in Education Sector using Prediction and Clustering Algorithms (IJSRD/Vol. 3/Issue 10/2015/054)

III. PROPOSED WORK A. Data Set: We are collecting the data of engineering college. This shows the student academic performance and the personal information like economical problem, family problem etc.

The data also contain the student family background, student details, subjects marks, semester wise percentage have been recorded.

Fig. 1: Analysis of student performance IV. DATA PROCESSING METHODS A. Clustering: Clustering analysis or clustering is the task of grouping set of object in such a way that object in the same group are more allied to each other than to those in other groups (cluster) it is main task of exploratory data mining, and for statistical analysis in general purpose it is also useful in machine learning pattern recognition, image analyze bioinformatics and also information get back.

B. K-Means Clustering Algorithm: K-Means is one of the unsupervised learning algorithms that used to solve the problem of clustering. The k-means algorithm is easier and simple to understand the problem.

The main intellection is to define K centers, one for each cluster. These centers should be placed in designing way because of various location reason result vary. So, the better choice is to place them as much as possible far away from each other. Another step is to take each point belonging to a given data set and attach it to the closest center. After the first point is complete there is no waiting state and an early group age is done. For recalculating that point of the K new centroid as barycenter of the clustering resulting from the previous step. After we have these K new centroid, A new obligatory has to be done between the similar data set points and the closest new center. A loop has been generated. As result of this loop we may notice that the K center change their step by step location varies is does not change. After that this algorithm target at minimizing an objective function known as soured error function.

C. Naviebayes: In machine learning, naive Bayes classifiers are a family of intelligible probabilistic classifiers depend on applying Bayes theorem with strong (naive) independence assumptions between the objectives. Naive Bayes classifiers are highly scalable, requiring a number of parameters linear in the number of variables (features/predictors) in a learning

problem. Maximum-likelihood training can be done by evaluating a closed-form expression, which takes linear time, rather than by expensive iterative approximation used for many other types of classifiers. The Navie Bayesian classifier is based on Bayes theorem with independence assumptions between predictors. A naĂŻve Bayesian model is easy to build, with no complicated iterative parameter estimation which makes it particularly useful for very large datasets. Despite its simplicity, the Naive Bayesian classifier often does surprisingly well is widely used because it often outperforms more sophisticated classification methods.

D. Implementation of Mining Model In implementation of mining model various algorithms and techniques like, classification, clustering, regression, neural network, artificial intelligence association rules and genetic algorithm, decision tree are used for knowledge discovery from various database. From these algorithm and k means classification is one of the most frequently used problems by data mining and machine learning (ML) researches .It consist of predicting the value of category class based on values of other class which are predicting class (attributes ).

There are different classification methods in our present study. We are going to use the Bayesian classification algorithm.

Bayes classification proposed that is based on conditional probability of Bayes rule. Bayes rule is one of the techniques to the similarity of property given the set of data as evidence or input Bayes, Bayes theorem is as follows: The approach is called “Navie�, because it considers the independence between the various classes or attributes values. Navie byes classification generally viewed as both descriptive and a predictive types of algorithm. The probabilities are predictive type and the used to predict the class membership. For a target tuple.

The naive Bayes approaches has several advantages: It is easy to use unlike other classification approaches the Navie Bayes approach required only one scan of the training data; easily handle mining value by simply omitting that probability one more advantage of the Navie Bayes classifier is that it requires a small amount of training data to estimate the various parameters such as means and variance which are necessary for classification because independent variables are assumed, there is no need to determine entire covariance matrix, only the variances of the variables for each class need to be determined. In spite of their Navie design and apparently over simplified assumptions, Navie Bayes classifiers have worked quite well in many complex real world situations.

V. CONCLUSION By referred these Navie Bayesian classification and kMeans clustering technique on student database to predict the student academic performance on the basis of student database. This predicted data from student database is usefull for student performance improvement. This study shows student performance and easy to identify those student which having less mark and poor performance.

According to the present study the performance off the

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Student Performance Evaluation in Education Sector using Prediction and Clustering Algorithms (IJSRD/Vol. 3/Issue 10/2015/054)

students are not always depend on their own efforts. Other factors have influenced the students’ performance according to our investigation. This proposal will improve the insights over existing methods.

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