Techniques of Predictive Analysis | Study

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.