scholarly journals Academic Performance Prediction of Undergraduate Students using Decision Tree Algorithm

2021 ◽  
Vol 13 (SUP 1) ◽  
pp. 97-100
Author(s):  
Rashmi V. Varade ◽  
Blessy Thankanchan
Keyword(s):  
Data Mining ◽  
Academic Performance ◽  
Decision Tree ◽  
Undergraduate Students ◽  
Performance Prediction ◽  
Decision Tree Algorithm ◽  
Health Care Fraud ◽  
Meaningful Information ◽  
Tree Data ◽  
Education Business

Data mining is a technique for extracting meaningful information or patterns from large amounts of data. These techniques are frequently utilised for analysis and prediction in practically all fields around the world. It's employed in a variety of fields, including education, business, health care, fraud detection, financial banking, and manufacturing engineering. This study explores the Decision Tree data mining methodology for predicting undergraduate students' academic performance.

An Analysis of Factors Influencing Foreign Language Self-Efficacy Based on C5.0 Decision Tree Algorithm in Data Mining

2014 ◽  
Vol 538 ◽  
pp. 460-464
Author(s):  
Xue Li
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Foreign Language ◽  
Language Learning ◽  
Learning Strategies ◽  
Self Efficacy ◽  
Foreign Language Learning ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
C5.0 Decision Tree

Based on inter-correlation and permeability among disciplines, the author makes an attempt to apply the information science to cognitive linguistics to provide a new perspective for the study of foreign languages. The correlation between self-efficacy and such four factors as anxiety, learning strategies, motivation and learners’ past achievement is analyzed by means of data mining and the extent to which the above factors affect self-efficacy in language learning is explored in this paper. The paper employs the decision tree algorithm in SPSS Clementine. C5.0 decision tree algorithm is adopted to analyze data in the study. The results are elicited from the researches carried out in this paper. The increased anxiety is bound to weaken learners’ motivation over time. It is obvious that learners have low self-efficacy. It is very important to employ strategies in foreign language learning. Ignorance of using learning strategies may result in unplanned learning with unsatisfactory achievements in spite of more efforts involved. Self-efficacy in foreign language learning may be weakened accordingly. Learners’ past achievement is a reference dimension in measuring self-efficacy with weaker influence.

Data-Driven Decision Tree Classification for Product Portfolio Design Optimization

2009 ◽  
Vol 9 (4) ◽  
Author(s):  
Conrad S. Tucker ◽  
Harrison M. Kim
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Engineering Design ◽  
Optimization Techniques ◽  
Product Portfolio ◽  
Performance Expectations ◽  
Data Set ◽  
Tree Data ◽  
Portfolio Design ◽  
Product Concepts

The formulation of a product portfolio requires extensive knowledge about the product market space and also the technical limitations of a company’s engineering design and manufacturing processes. A design methodology is presented that significantly enhances the product portfolio design process by eliminating the need for an exhaustive search of all possible product concepts. This is achieved through a decision tree data mining technique that generates a set of product concepts that are subsequently validated in the engineering design using multilevel optimization techniques. The final optimal product portfolio evaluates products based on the following three criteria: (1) it must satisfy customer price and performance expectations (based on the predictive model) defined here as the feasibility criterion; (2) the feasible set of products/variants validated at the engineering level must generate positive profit that we define as the optimality criterion; (3) the optimal set of products/variants should be a manageable size as defined by the enterprise decision makers and should therefore not exceed the product portfolio limit. The strength of our work is to reveal the tremendous savings in time and resources that exist when decision tree data mining techniques are incorporated into the product portfolio design and selection process. Using data mining tree generation techniques, a customer data set of 40,000 responses with 576 unique attribute combinations (entire set of possible product concepts) is narrowed down to 46 product concepts and then validated through the multilevel engineering design response of feasible products. A cell phone example is presented and an optimal product portfolio solution is achieved that maximizes company profit, without violating customer product performance expectations.

A novel Gini index decision tree data mining method with neural network classifiers for prediction of heart disease

2018 ◽  
Vol 22 (3) ◽  
pp. 225-242 ◽  
Author(s):  
K. Mathan ◽  
Priyan Malarvizhi Kumar ◽  
Parthasarathy Panchatcharam ◽  
Gunasekaran Manogaran ◽  
R. Varadharajan
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Heart Disease ◽  
Decision Tree ◽  
Gini Index ◽  
Mining Method ◽  
Data Mining Method ◽  
Tree Data ◽  
Neural Network Classifiers

Application of Data Mining Technology in CRM

2014 ◽  
Vol 513-517 ◽  
pp. 2103-2106
Author(s):  
Ya Fei Liu ◽  
Meng Ya Li
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Classification Methods ◽  
Decision Tree Algorithm ◽  
Core Competitiveness ◽  
Mining Technology ◽  
The Core ◽  
The Core Competitiveness ◽  
Mining Model ◽  
Potential Customers

Regarding to the technical difficulties of reachingvaluable knowledge and information from CRM mass of data and information, this article brings up the idea that we could apply the data mining technology in CRM. The data mining classification methods helps us to find out the potential customers with high value. The article describe how the methods and processes how could we apply the data mining technology to CRM and on the basis of decision tree algorithm to construct the CRM data mining model, and help companies better understand the customers behaviors and improve the core competitiveness of enterprises.

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