scholarly journals Application of Data Mining Classification in Employee Performance Prediction

2016 ◽  
Vol 146 (7) ◽  
pp. 28-35 ◽  
Author(s):  
John M. ◽  
Christopher A.
Keyword(s):  
Data Mining ◽  
Performance Prediction ◽  
Employee Performance

scholarly journals Employee Performance Prediction Using EPP Framework

2020 ◽  
pp. 297-302
Author(s):  
M. Karthika ◽  
T. Meyyappan
Keyword(s):  
Data Mining ◽  
Performance Prediction ◽  
Employee Performance ◽  
Research Work ◽  
Prediction Methods ◽  
Internal Factors ◽  
Using Data ◽  
Proper Solutions

In the today's industrial world, every company’s growth is depends on their employees. The company achievements are completely based on the employees in the organization. The employees’ performances are measured by the targets and achievements. But some external and internal factors affect the employees’ goals and achievements. Hence, the company has to find the performance of every employee and make proper solutions to improve the performance. This research work proposes a fully automated framework which can perform deep analysis of employees’ performance and job fitness using data mining and prediction methods.

scholarly journals Student Academic Performance Prediction using Supervised Learning Techniques

2019 ◽  
Vol 14 (14) ◽  
pp. 92 ◽  
Author(s):  
Muhammad Imran ◽  
Shahzad Latif ◽  
Danish Mehmood ◽  
Muhammad Saqlain Shah
Keyword(s):  
Data Mining ◽  
Supervised Learning ◽  
Student Performance ◽  
Performance Prediction ◽  
Class Imbalance ◽  
Ensemble Methods ◽  
Fine Tuning ◽  
Classification Error ◽  
Decision Tree Classifier ◽  
Tree Classifier

Automatic Student performance prediction is a crucial job due to the large volume of data in educational databases. This job is being addressed by educational data mining (EDM). EDM develop methods for discovering data that is derived from educational environment. These methods are used for understanding student and their learning environment. The educational institutions are often curious that how many students will be pass/fail for necessary arrangements. In previous studies, it has been observed that many researchers have intension on the selection of appropriate algorithm for just classification and ignores the solutions of the problems which comes during data mining phases such as data high dimensionality ,class imbalance and classification error etc. Such types of problems reduced the accuracy of the model. Several well-known classification algorithms are applied in this domain but this paper proposed a student performance prediction model based on supervised learning decision tree classifier. In addition, an ensemble method is applied to improve the performance of the classifier. Ensemble methods approach is designed to solve classification, predictions problems. This study proves the importance of data preprocessing and algorithms fine-tuning tasks to resolve the data quality issues. The experimental dataset used in this work belongs to Alentejo region of Portugal which is obtained from UCI Machine Learning Repository. Three supervised learning algorithms (J48, NNge and MLP) are employed in this study for experimental purposes. The results showed that J48 achieved highest accuracy 95.78% among others.

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