IRJET - Employee Performance Prediction System using Data Mining

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 07 Issue: 02 | Feb 2020 www.irjet.net p-ISSN: 2395-0072
© 2020, IRJET | Impact Factor value: 7.34 | ISO 9001:2008 Certified Journal | Page 1552
Employee Performance Prediction System using Data Mining
Tejas Raut1, Priya Kale2, Rashmi Sonkusare3, A. K. Gaikwad4
1,2,3Student, Dept. of Computer Science & Engineering, DES’S COET, Dhamangaon Rly
4Professor, Dept. of Computer Science & Engineering, DES’S COET, Dhamangaon Rly
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Abstract - Human capital is of a high concern for
companies’ management where their most interest is in
hiring the highly qualified personnel which are expected to
perform highly as well. Human Resources Management
(HRM) has become one of theessential interestsofmanagers
and decision makers in almost all types of businesses to
adopt plans for correctly discovering highly qualified
employees. Accordingly, managements become interested
about the performance of these employees. Results show
that professional skill development programs are needed in
order to prepare employees to perform their tasks more
efficiently. The knowledge flow model of the Open source
tool is also used to frame the elements. To get a highly
accurate model, several experiments were executed based
on the previous techniques that are implemented tool for
enabling decision makers and human resources
professionals to predict and enhance the performance of
their employees.
Key Words: HRM, Employee Performance Prediction,
Data Mining, KNN, Dataset.
1. INTRODUCTION
HRM has a leading role in deciding the competitiveness
and effectiveness for better continuation. Organizations
consider HRM as “people practices”. Therefore, it becomes
the responsibility of the HRM to allocate the best employees
to the appropriate job at the right time, train and qualify
them, and build evaluation systems to monitor their
performance and an attempt to preserve the potential
talents of employees. A young yet promising of this kind is
data mining. It standouts due to its wide-array of techniques
from the different domains such as statistics, artificial
intelligence, machine learning,algorithms,databasesystems
and visualization. These influences served as groundwork
for its applications to business for which the academic
institutionisunexceptionallyclassified.Generally,regardless
of discipline, data mining has gained popularity due to its
tools with potentials to identify trends within data and turn
them out into knowledge mostly with predictive attributes
that could significantly lead to better and strong bases for
decision making with a wide range of Open source tools
availability.
Data mining is a young and promising field of information
and knowledge discovery. It started to be an interest target
for information industry, because of the existence of huge
data containing large amounts of hidden knowledge. With
data mining techniques, such knowledge can be extracted
and accessed transforming the databases tasks from storing
and retrieval to learning and extractingknowledge.Decision
tree is one of the most used techniques, since it creates the
decision tree from the data given using simple equations
depending mainly on calculation of the gain ratio, which
gives automatically some sort of weights to attributes used,
and the researcher can implicitly recognize the most
effective attributes on the predicted target. Asa resultofthis
technique, a decision tree would be built with classification
rules generated from it.
Previous studies specified several attributes affecting the
employee performance. Some of these attributes are
personal characteristics, others are educational and finally
professional attributeswerealsoconsidered.CheinandChen
(2006) used several attributes to predict the employee
performance. They specified age, gender, marital status,
experience, education, major subjects and school tires as
potential factors that might affect the performance. Then
they excluded age, gender and marital status, so that no
discrimination would exist in the process of personal
selection. As a result for their study, they found that
employee performance is highly affected by education
degree, the school tire, and thejob experience.Most recently,
the prevalence of intelligent machine learning algorithms in
the field of computer science has led to the development of
robust quantitative methodstoderiveinsightsfromindustry
data.
Supervised machine learning methods—wherein
computers learn from analyses of large-scale, historical,
labelled datasets—have been shown to garner insights in
various fields, like biology and medical sciences,
transportation, political science as well asmanyotherfields.
Owing to the advancements in information technology,
researchers have also studied numerous machine learning
approaches to improve the outcomes of human resource
(HR) management. A detailed listing of recent studies in
using supervised machine learning on employee turnover is
described in Table 1, and lists the data included and related
machine learning algorithms that were used therein,
including decision tree (DT) methods, random forest (RF)
methods, gradient boosting trees (GBT) methods, extreme
gradient boosting (XGB), logistic regression (LR), support
vector machines (SVM), neural networks (NN), linear
discriminant analysis (LDA), Naïve Bayes (NB) methods, K -
nearest neighbor (KNN), Bayesian networks (BN) and
induction rule methods (IND)

2. RELATED WORK
In general, Classification contains some steps to
complete its process. The first step is called thelearningstep
where in the model; predefinedclassesarebuiltbyanalyzing
a set of training dataset variables. Each variable is assumed
that has a relation and regards to a predefined class. The
second step is responsible for estimating the accuracy of
model or classifier (validatingthemodel)throughtesting the
model using a different dataset. If the classifier’s accuracy
was considered acceptable, the model or classifier can be
used to apply to new unseen data to give prediction about
specific unknown label class and this is considered the third
step. Therefore, the model acts as a classifier in the process
of decision-making. There are various classification
techniques have been used in the prediction process suchas
DT, Naïve Bayes, SVM, etc.
Human capital is of a high concern for companies’
management where their most interestisinhiringthe highly
qualified personnel which are expected to perform highlyas
well. Foremost, the Human Resource (HR). Management
plays the role of ensuring this by closely adhering to the
standards set by the higher management or by some
heuristic needs of applicants with distinctive qualifications
and potentials. However, oft-quoted factors that may affect
employee performance are attributed to educational
backgrounds, working experiences, as well as personal
qualities. These when converged provide a picture of how
well an employee performs his tasks. Assessment of human
resource performance is a sensitive task. To avoid partiality,
an efficient tool to deal with various data and assist
managers to make decisions and plans is of great help. In
data mining, historical data such as those attributes that
influence performance could be exploited as learning
experiences. These can be used to predict future
circumstances and rich resource of knowledge and decision
supports.
In general, data classification is a two-step process. In
the first step, which is called the learning step, a model that
describes a predetermined set of classes or concepts is built
by analyzing a set of training database instances. Each
instance is assumed to belong to a predefined class. In the
second step, the model is tested using a different data set
that is used to estimate the classification accuracy of the
model. If the accuracy of the model is considered acceptable,
the model can be used to classify future data instances for
which the class label is not known. At the end,themodel acts
as a classifier in the decision making process. There are
several techniques that can be used for classificationsuchas
decision tree, Bayesian methods, rule based algorithms, and
Neural Networks.
3. IMPLEMENTATION
Feature selection is a one of the main concepts of DM
and Machine Learning. Where, it is a process of selecting
necessary useful variables in a datasettoimprovetheresults
of machine learning and make it more accurate. At which,
Using too many numbers of variables in a dataset reduce
predictive performance. The data set may contain too many
features; some of them do not promote the prediction
accuracy, and thus make the predictive model excessively
complicated.
Therefore, unnecessary useless variables must be
avoided to make the model efficientlyworks.Deciding which
unnecessary variable to avoid can be done by a manual
manner using domain knowledge or it can be done
automatically. In the section of Business understanding with
proper endorsement and approval of some academic
administrators, questions as to how the (DM) data mining
functionalities are best applied in any Technological Institute
has been identified. Recent studies had However, oft-quoted
factors that may affectemployeesperformanceareattributedto
educational backgrounds, working experiences, as well as
personal qualities. These when converged provide a picture of
how well an employee performshistasks.Assessmentofhuman
resource performance is a sensitive task. To avoid partiality, an
efficient tool to deal with various data and assist managers to
make decisions and plans is of great help. In data mining,
historical data such as those attributes that influence
performance could be exploited as learning experiences.

4. CONCLUSION
This paper has concentrated on the possibility of building a
classification model for predicting the employees’
performance. Applying the DM techniques in the different
problem domains in the HRM field is considered as an
important and urgent issue. Especially,atthepublicsectorin
Egypt. In addition, increasing the horizons of academic and
practice research on DM in HR for reaching a government
sector with a high performance. This study further outlooks
for applications of results to analyzeenhancementprograms
for senior employees of any organization and to identify
patterns affecting both teacher and student performance
using other data miningtechniquessuchasassociationrules.
REFERENCES
[1] Islam, M. J., Wu, Q. M. J., Ahmadi, M., and Sid-Ahmed,
M. A., (2010), "Investigating the Performance of
Naive- Bayes Classifiers and K- Nearest Neighbor
Classifiers" Journal of Convergence Information
Technology Volume 5, Number 2, April 2010.
[2] Al-Radaideh, Q.A., Al-Nagi, E., (2012). “Using Data
Mining Techniques to Build a Classification Model
for Predicting Employees Performance”,
International Journal of Advanced Computer
Science and Applications, 3(2), pp 144 – 151.
[3] S.Yasodha and P. S.Prakash, (2012), "Data Mining
Classification Technique for Talent Management
using SVM," the International Conference on
Computing, Electronics and Electrical Technologies,
2012.
[4] Hua Hu, Jing Ye, and Chunlai Chai, (2009), “A Talent
Classification Method Based on SVM”, in
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Computing and Education, Chengdu, China, 2009,
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[5] Kirimi JM, Motur CA (2016), “Application of Data
Mining Classification in Employee Performance
Prediction”. International Journal of Computer
Applications, Volume 146 – No.7, July 2016.
[6] Desouki M. S., Al-Daher J (2015),“UsingData Mining
Tools to Improve the Performance Appraisal
Procedure, HIAST Case”. International Journal of
Advanced Information in Arts, Science &
Management Vol.2, No.1, February 2015.
[7] ZHAO Xin (2008). A Study of Performance
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International Seminar on Future Information
Technology and Management Engineering
[8] Yan Huang (2009). Study of College Human
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Information Processing.
[9] ChenXiaofan,and WangFengbin(2010).Application
of Data Mining on Enterprise Human Resource
Performance Management. 3rdInternational
Conference on Information Management,
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[10] Honglei Zhang (2009). Fuzzy Evaluation on the
Performance of Human Resources Management of
Commercial Banks Based on Improved Algorithm.
2009 2nd International Conference on Power
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