Data Mining Techniques for Prediction of Employees' Performance

Running head: DATA MINING 1
Data mining
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DATA MINING 2
Table of Contents
1.0 Executive summary 2
2.0 Introduction 3
3.0 Decision Tree approach in prediction of the employees’ performance 4
4.0 The CRISP-DM model in prediction of the employees’ performance 5
5.0 Data mining techniques 6
5.1 Data pre-processing and preparation by clustering of the dataset 7
6.0 Classification technique 8
7.0 Association technique 9
8.0 The process of data mining 10
8.1 Modeling and experiments in CIS4015-N: Data Mining and CIS4035-N:
Machine Learning 11
9.0 conclusion 12
10.0 references 13
1.0 Executive summary

DATA MINING 3
The prediction of the employee’s performance is an essential requirement in any
organization. The performance of the employees is determined by various factors such as social,
personal, dependability and environmental factors among others. Data mining is one of the tools
used in the determination of the performance of the employees in an organization. Data mining
techniques are used in discovering the hidden information and dataset patterns. Besides, it is used
in the determination of the relationship between the large volumes of data during the decision-
making process in an organization. The report indicates that a single data contains a lot of the
datasets and information required in determination of employees’ performance. The type of
information used in evaluation of employees’ performance in an organization is determined by
the datasets used. It is significant in deciding which data processing method will be used in the
report.
2.0 Introduction
The performance of the employees is determined by the monitoring of the organizational
outcomes and evaluation of employees’ datasets. The data mining technique is a combination of
the machine learning, visualization techniques and statistics in discovering new knowledge
regarding the datasets. The retention of the employees is an indication of organizational
enrollment and performance. By use of the data mining techniques, the problems in an
organization will be identified in advance to avoid major damages (Valle, Varas and Ruz 2012
pp.9939). The raw data will be pre-processed in this report by filling up the values which are
missing, the transformation of the benefits which are given in the form and attribution of the
relevant variables. In this case, one of the used methods is Decision Tree technique (Huang, Tsou
and Lee 2016 pp.396). The report involves classification of the datasets into groups of classes
pre-defined. In other words, this is defined as supervised machine learning because the

DATA MINING 4
performance of the employees is determined through an examination of the data available in an
organization regarding their performance. In the process of improving the way employees
perform in an organization, the managers will have to monitor the daily performance of those
employees (Shazmeen, Baig and Pawar 2013 pp.1). In this process, data mining process will be
used in the prediction of the employees’ performance. Some of the questions that will be
answered based on the provided data in the report include the following.
1. What is the generation of the predictive variable data sources?
2. What are the different factors which affect the performance of the employees in an
organization?
3. How the Decision Tree model is constructed by use of the classified data mining techniques
in regard to the identified variables predicted with their values?
4. How is the dataset of the predictive valuables gathered?
5. What is the relationship between the factors affecting the efficiency of the model used?
The chosen analytics approaches suitable for this analysis is Decision Tree approach on the
performance of the employees and the CRISP-DM method (Sung, Chang and Lee 2019 pp.63).
The approaches used in this report are used to answer the proposed questions with valid
adaptation and clear justifications.
3.0 Decision Tree approach in prediction of the employees’ performance
The Decision Tree approach is a tree-like model or graph used for making the decisions
and the possible consequences in an organization. It includes the chances of the outcomes, utility
and resources costs. It is one of the ways of applying the algorithm in data mining technique