Literature Review: Techniques of Predictive Analysis in Business

This report provides a comprehensive literature review of predictive analysis techniques in business analytics. It begins with an introduction to predictive analysis, highlighting its importance in mitigating future risks through data analysis. The report then delves into a literature review, discussing predictive analysis as a form of advanced analytics, its reliance on data, and the processes involved. The core of the report focuses on two main categories of predictive analysis techniques: regression techniques (including linear, logistic, and polynomial regression) and machine learning techniques (such as neural networks and multilayer perceptron). The report also explores the impacts of predictive analysis techniques on organizations, emphasizing their role in improving decision-making, reducing costs, and enhancing operational efficiency across various industries. The report concludes by summarizing the key findings and reiterating the significance of predictive analysis in the current business landscape, and the use of these tools to predict future outcomes.

Techniques of Predictive Analysis
Abstract— The main purpose of this report is to review the
techniques of predictive analysis in business analytics. In this
report several journals are analyzed for literature review. The
main objective of this report is to provide a brief information of
the predictive analysis and its techniques.
Keywords— Predictive analysis, Regression, Data analysis,
Business analytics, Linear regression, Analysis
I. INTRODUCTION
In Business analytics, predictive analysis plays a very
important role. Predictive analysis provides an opportunity to
the organizations to avoid future risks by analyzing previous
data. There are many different techniques are present in
predictive analysis. Predictive analysis uses highly diverse
arsenal of techniques which helps the organizations. Basically
the word predictive analysis comprises with two words predict
and analysis. According to the order of words predictive
analysis works reversely, that is analyzed the data first, then
based on the analysis made the predictions. Basically predictive
analysis deals with previously perceive data to predict future
events by applying some methods like machine learning. Data
is collected from different types of sources and after collecting
the data, some techniques are applied to transform the data into
a well-structured format. Filtering, data correlating and many
other techniques are used to transform the data.
II. LITERATURE REVIEW
A. Predictive Analysis
According to [1], [2], Predictive analysis is a type of
Business analytics also known as BA. Predictive analytics is a
form of advanced analytics, which uses both new and previous
data to predict activity and trends of the organizations. The
predictive analytics is mainly depends on data and the data is
the main aspect of this analytics because without any data set it
is not possible to predict anything. Data can be collected from
the various sources and filtering and other techniques are used
to transform the data into a structured form because some time
in the data set some values are missing which is reducing the
accuracy of prediction. After that, data is stored in the data
warehouse.
According to [1], Predictive analytics is a field of statistics
and different statistical techniques are used like data mining,
machine learning, data modelling, deep learning algorithms and
so on. This can be applied on the event which are unknown,
whether it is present, past or future event. For example,
identifying an accused after a crime or credit card fraud as it
happens. Predictive analytics core is mainly depends on the
explanatory variables and predicted variables from the previous
occurrence which is used for prediction.
Fig-1: Value chain of predictive analytics
Data mining tools and techniques are used for building the
predictive analytics model. The first step involves obtaining
data from the database. Then with the help of advanced
algorithms the data is processed to detect predictive
information and hidden patterns. Although one is a clear
relationship between data mining and statistics, the
methodologies used in data mining and data originated in areas
other than statistics.
B. Process of Predictive analytics
Predictive analytics comprise in seven different processes
and the process are Project definition, data collection, data
analysis, statistics, modelling, deployment and model
monitoring. Every process of the predictive analytics is an
important process. Process of predictive analytics is described
below.
i. Project Definition: This is the first process of
predictive analytics and in this process the objective
of the business, project outcomes and the data sets
are identified which is used for further processes.
ii. Data Collection: In this process data are collected
from the various sources and data mining
techniques are used in the data sets for the analysis.
This process provides an entire customer
interaction view.[6]
iii. Data analysis: In this process the data are processed
to check the data is correct or not its means that the
data cleaning and modelling is done for finding
useful information.
iv. Statistics: Statistic analysis allows to prove the
presumptions, theories and examine those using
standard statistical models.

v. Modelling: In this process of predictive analytics
provides the ability to create predictive models
automatically.
vi. Deployment: This process of predictive analytics
provides possibilities to deploy analytical results
into day to day decisions.
vii. Model Monitoring: This is the last process of the
predictive analytics, which monitors all the models
of predictive analytics to check the models are
providing expected results or not.
Fig-2: Predictive Analytics Process Flow
III. TECHNIQUES OF PREDICTIVE ANALYSIS
The techniques of predictive analytics can be categorized
into two techniques. The techniques of predictive analytics are
regression techniques/analysis and machine learning
techniques.
A. Regression techniques
According to [3], Regression analysis is a form of predictive
modelling technique which helps to identify the relationship
between a dependent variable which is also known as target and
independent variable which is also known as a predictor. It is a
very important tool for analyzing and modelling the data. The
regression techniques are used for predicting, modelling time
series and finding the relation between variables. Suppose the
relation between rash driving and the numbers of accidents by
drivers is the pre-eminent studied through regression.
There are many different types of regression techniques
which is used for making predictions they are linear regression,
logistic regression and polynomial regression.
The linear regression technique is one of the most widely
known technique for modelling. In this technique of regression
the relationship of dependent variable and predator variables
are analysed. [7]
According to [8], Logistic regression is also used to describe
the data and also explain the relationship between one
dependent variable which is a binary variable and one or more
independent variables.
Polynomial regression is used to fit data as an estimate to a
non-linear model.
B. Machine learning Techniques
According to [4], Machine learning is a part of artificial
intelligence. Machine learning was initially developed for the
computers to develop the ability to learn like a human. Today,
as it includes many advance statistical methods for
classification and regression. Now, it finds applications in
diverse field, including speech recognition, credit card fraud,
stock market analysis and medical diagnostics. It is enough to
predict the dependent variables without concentrating on the
underlying relationships between variables in some application.
But in some other cases the relationship may be very complex
and mathematically unknown of dependencies. Some of the
methods used in predictive analysis are neural network, MLA
that is multilayer perceptron, radial basis function (RBF).
The neural network is a nonlinear modelling technique
capable of modelling complex functions. [5]
According to [9], Multilayer perceptron contains input and
output layers of sigmoid nodes with some hidden layers.
The Radial basis functions are used for rectifying data. This
function is applied to the neural network areas and this function
is also used to replace sigmoidal transfer functions.
The machine learning technique of predictive analysis used
to deal with big data because now days the numbers of data is
increased due to more data is stored in the data warehouse than
ever before, and the data analysis became more complex. So
this technique of predictive analysis provides a better view of
previous data which helps to analysis, historical data and helps
to predict future events. [4], [10]
IV. IMPACTS OF PREDICTIVE ANALYSIS TECHNIQUE’S
According to [1], [11], Predictive analysis techniques are
playing very vital role in the organizations to get the desired
goal by analyzing historical and new data. Predictive analysis
techniques give an overview of future events and helps the
organizations to deal with future risks of the business.
Organizations are using such techniques to increase
productivity by analyzing old data which are very easily used
for analysis purpose. It is also allowing the organization to be
more active and flourish in the future predictions based on past
data and not traditional estimates.
Predictive analysis also helps the organization to look into
the cost which they are spending on the business for the
betterment of the organization. Sometime organizations are
spending a high amount in a particular field which comes with
an additional cost. Predictive analysis techniques help to reduce
the additional cost of analyzing previous data.
Predictive analysis helps to identify misallocated resources
or additional resource allocation. It also helps to achieve
desired results with the accuracy and saves on any incorrect
resource allocations which further assists to save both time and
cost.
It helps to get the fastest result depends on the predictions by
analyzing old data. This technique for prediction analysis takes
the chance to gain advantage of the future trends of the business.
The main impact of this technique of predictive analysis in
business analytics is that, it helps to reduce risk of a future event
before it going to happen by analyzing previous data. Predictive
analysis techniques help to improve operations of the
organization like quality and functionality.
Such techniques are used by several of industries and
organizations like airlines, hospitality and banks to deal with
future events and risk in their decision making process. The
bank uses this technique to deal with cyber threats like credit
card and internet banking fraud with the help of predictive
analysis in the real-time context. [12]

Fig-3: Business value of analytics with respect to time
V. CONCLUSIONS
The main aim of this report is to understand the techniques
of predictive analysis. Predictive analysis helps the
organizations to deal with future risks which are going to
happen in the future by analyzing data. This report is used to
give a brief literature review of the techniques of predictive
analysis. In this report the importance of predictive analysis and
why this technique is used in the current time and some other
aspects are discussed. In this literature review report the process
of predictive analytics and the techniques are described. In
these reports, different techniques of predictive analysis are
used in this literature review, and also discussed why such
techniques are important in predictive analysis. In the above
report, the main focus is on the impacts of predictive analysis
techniques in the businesses and why it's necessary.
REFERENCES
[1] J.G. Nies, J. Watson, O. Stern, M. McCreesh, Verint Americas Inc,
Predictive Modeling from Customer Interaction Analysis, U.S. Patent
Application 14/083,772, 2014.
[2] C. Holsappl, A. Lee-Post, R. Pakath, A unified foundation for business
analytics. Decision Support Systems, 64, pp.130-141, 2014.
[3] F. Kaytez, M.C. Taplamacioglu, E. Cam, F. Hardalac, Forecasting
electricity consumption: A comparison of regression analysis, neural
networks and least squares support vector machines. International
Journal of Electrical Power & Energy Systems, 67, pp.431-438, 2015.
[4] M.I. Jordan,T.M. Mitchell,Machine learning: Trends, perspectives, and
prospects. Science, 349(6245), pp.255-260, 2015.
[5] S. Han, J. Pool, J. Tran, W. Dally, Learning both weights and
connections for efficient neural network. In Advances in neural
information processing systems (pp. 1135-1143), 2015.
[6] S.M.M. Rubiano, J.A.D. Garcia, Analysis of data mining techniques for
constructing a predictive model for academic performance. IEEE Latin
America Transactions, 14(6), pp.2783-2788, 2016.
[7] L.Yang, S. Liu, S. Tsoka, L.G. Papageorgiou, Mathematical
programming for piecewise linear regression analysis. Expert systems
with applications, 44, pp.156-167, 2016.
[8] P.C. Austin, J. Merlo, Intermediate and advanced topics in multilevel
logistic regression analysis. Statistics in medicine, 36(20), pp.3257-3277,
2017.
[9] H. Ramchoun, M.A.J. Idrissi, Y. Ghanou, M. Ettaouil, Multilayer
Perceptron: Architecture Optimization and Training. IJIMAI, 4(1),
pp.26-30, 2016.
[10] A.G. Shoro, T.R. Soomro, Big data analysis: Apache spark perspective,
Global Journal of Computer Science and Technology, 2015.
[11] D. Appelbaum, A. Kogan, M. Vasarhelyi, Z. Yan, Impact of business
analytics and enterprise systems on managerial accounting. International
Journal of Accounting Information Systems, 25, pp.29-44, 2017.
[12] Z. Sun, L. Sun, K. Strang, Big data analytics services for enhancing
business intelligence, Journal of Computer Information Systems, 58(2),
pp.162-169, 2018.