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Data Mining Application for Determining Students’ Academic Performance

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Description

Download Data Mining Application for Determining Students’ Academic Performance. Computer Science students who are writing their projects can get this material to aid their research work.

Abstract

The project mainly focused on developing an application for information from pool of data (i.e. a large database) to form basis for decision making. Information extracted from the database in the course of data mining process can be presented in graphical format in form of graphs patterns, histogram, etc. and also in text format.

The reason for suggesting the project is the need for employing computer software medium for sanitizing academic standard through computer based decision making.

Data mining package can present clear reasons and factor that affects students’ performance and hence allow administrators to derive strategic means of tackling such issues.

The package will be developed in a .net (dot net) integrated development environment (.net IDE).

The package IDE is chosen following the fact that extracted information needs to be presented in an enhanced pictorial/graphical format and easy communication with the database for program flexibility in windows platform.

Introduction

Data mining is a branch of computer science which deals with the process of extracting patterns from large data sets by combining methods from statistics and artificial intelligence with database management.

Data mining is seen as an increasingly important tool by modern business to transform data into business intelligence giving an informational advantage. It is currently used in a wide range of profiling practices, such as marketing, surveillance, fraud detection, and scientific discovery. [Clifton, 2010]

The related terms data dredging, data fishing and data snooping refer to the use of data mining techniques to sample portions of the larger population data set that are (or may be) too small for reliable statistical inferences to be made about the validity of any patterns discovered.

These techniques can, however, be used in the creation of new hypotheses to test against the larger data populations. [Clifton, 2010]

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