Techniques of Predictive Analysis | Report
Added on 2022-08-24
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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.
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.
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