EMPLOYMENT CHANCE PREDICTION BASED ON DECISION TREES AND NAIVE BAYES CLASSIFIER USING DATA MINING TECHNIQUES
Journal Title: Elysium Journal of Engineering Research and Management - Year 2015, Vol 2, Issue 4
Abstract
Data Mining is the non-trivial extract information from a data set and transforms it into an understandable structure for further use. Data mining is the search for the relationship and global pattern that exist in large databases but are hidden among vast amount of data. Major strength of Decision trees are construction of decision tree classifiers doesn’t require any account knowledge for exploratory knowledge discovery database, handle high dimensional(Lookup) data simple and fast, used for many applications such as medicine, manufacturing, financial analysis, astronomy etc and basis of several commercial rule induction systems. An another important model used in data mining for Naïve Bayesian classifiers assume that the effect of an column values on given class is self-determining of the values of the other columns. This is class conditional independence called. It’s made to simplify the computations involved and, in this sense, is considered called “Naive”. This paper is cover to help prospective student community to make wise career decisions using these data mining tools. A student enters his/her Entrance Rank, Gender (Male/Female), Area (rural/urban) and Reservation (GEN /MBC/SC/SCA/ST) category. Based on the entered information the model will return which Course of study is Outstanding, Excellent, Distinction, very good, good, average, satisfactory and Re-appear for him/her based on history data analysis using data mining techniques. Also in this paper we compare the performance of decision trees and Naive Bayes classifier on the same training and test data for this problem.
Authors and Affiliations
Jagadhesan B. , Sarvanan P. , Pooranachandran Dr. C.
Keywords
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