Deployment of ID3 decision tree algorithm for placement prediction
K. Madan. International Journal of Trend in Scientific Research and Development, 2 (3):
740-744(April 2018)
Abstract
This paper details the ID3 classification algorithm. Very simply, ID3 builds a decision tree from a fixed set of examples. The resulting tree is used to classify future samples. The decision node is an attribute test with each branch (to another decision tree) being a possible value of the attribute. ID3 uses information gain to help it decide which attribute goes into a decision node. The main aim of this paper is to identify relevant attributes based on quantitative and qualitative aspects of a student's profile such as CGPA, academic performance, technical and communication skills and design a model which can predict the placement of a student. For this purpose ID3 classification technique based on decision tree has been used. Kirandeep | Prof. Neena Madan"Deployment of ID3 decision tree algorithm for placement prediction" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-3 , April 2018, URL: http://www.ijtsrd.com/papers/ijtsrd11073.pdf http://www.ijtsrd.com/engineering/computer-engineering/11073/deployment-of-id3-decision-tree-algorithm-for-placement-prediction/kirandeep
%0 Journal Article
%1 noauthororeditor
%A Madan, Kirandeep Prof. Neena
%D 2018
%J International Journal of Trend in Scientific Research and Development
%K Computer Decisions Engineering algorithm techniquesID3 treeClassification
%N 3
%P 740-744
%T Deployment of ID3 decision tree algorithm for placement prediction
%U http://www.ijtsrd.com/engineering/computer-engineering/11073/deployment-of-id3-decision-tree-algorithm-for-placement-prediction/kirandeep
%V 2
%X This paper details the ID3 classification algorithm. Very simply, ID3 builds a decision tree from a fixed set of examples. The resulting tree is used to classify future samples. The decision node is an attribute test with each branch (to another decision tree) being a possible value of the attribute. ID3 uses information gain to help it decide which attribute goes into a decision node. The main aim of this paper is to identify relevant attributes based on quantitative and qualitative aspects of a student's profile such as CGPA, academic performance, technical and communication skills and design a model which can predict the placement of a student. For this purpose ID3 classification technique based on decision tree has been used. Kirandeep | Prof. Neena Madan"Deployment of ID3 decision tree algorithm for placement prediction" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-3 , April 2018, URL: http://www.ijtsrd.com/papers/ijtsrd11073.pdf http://www.ijtsrd.com/engineering/computer-engineering/11073/deployment-of-id3-decision-tree-algorithm-for-placement-prediction/kirandeep
@article{noauthororeditor,
abstract = {This paper details the ID3 classification algorithm. Very simply, ID3 builds a decision tree from a fixed set of examples. The resulting tree is used to classify future samples. The decision node is an attribute test with each branch (to another decision tree) being a possible value of the attribute. ID3 uses information gain to help it decide which attribute goes into a decision node. The main aim of this paper is to identify relevant attributes based on quantitative and qualitative aspects of a student's profile such as CGPA, academic performance, technical and communication skills and design a model which can predict the placement of a student. For this purpose ID3 classification technique based on decision tree has been used. Kirandeep | Prof. Neena Madan"Deployment of ID3 decision tree algorithm for placement prediction" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-3 , April 2018, URL: http://www.ijtsrd.com/papers/ijtsrd11073.pdf http://www.ijtsrd.com/engineering/computer-engineering/11073/deployment-of-id3-decision-tree-algorithm-for-placement-prediction/kirandeep
},
added-at = {2018-09-04T08:23:39.000+0200},
author = {Madan, Kirandeep Prof. Neena},
biburl = {https://www.bibsonomy.org/bibtex/2a68f5dd673275ca5ec888b119c94f8ef/ijtsrd},
interhash = {44e0d1cc62af9283e68d380de1530c5e},
intrahash = {a68f5dd673275ca5ec888b119c94f8ef},
issn = {2456-6470},
journal = {International Journal of Trend in Scientific Research and Development},
keywords = {Computer Decisions Engineering algorithm techniquesID3 treeClassification},
language = {English},
month = {April},
number = 3,
pages = {740-744},
timestamp = {2018-10-02T11:01:27.000+0200},
title = {Deployment of ID3 decision tree algorithm for placement prediction
},
url = {http://www.ijtsrd.com/engineering/computer-engineering/11073/deployment-of-id3-decision-tree-algorithm-for-placement-prediction/kirandeep},
volume = 2,
year = 2018
}