Techniques of Predictive Analysis

Techniques of predictive analysis
Introduction
Predictive analysis is the subsection of progressive analytics employed in the prediction of future
phenomena referring to the historical data available [6]. Various techniques are required and
employed in predictive analysis such as data mining, data modelling, machine learning, statistics
and artificial intelligence in the analysis of already available data to foretell future occurrences or
the possibility of their happenings [7]. For the success of predictive analysis, data mining that
chooses the most suitable data to suit circumstances that are being predicted. This requires the
incorporation of the business management, information technologists and data scientists to carry
out the modelling process towards meeting the future target. Current and historical data are
leveraged where patterns identified from them are then used to determine existing risks and also
the available opportunities for the time to come. In the process of predictive analysis, the
constructed model is used to determine relationships existing among several aspects in the
evaluation of risks associated with a given condition and therefore awarding scores or
weightages. Predictive analysis is widely applied by businesses and healthcare industry among
others to help them plan for the future development in advance and to give them enough time to
adjust to the available and expected changes [4].
Data mining techniques, textual analysis in conjunction with statistics are used by businesses to
construct predictive intelligence that uncover patterns and any available relationships from either
structured or unstructured data [1]. All types of data collected by organizations are used in
building models that will help in predicting the future of phenomena in healthcare industry.
Organizations that apply predictive analysis are proactive and stay forward looking and
stabilized in terms of managing pressure coming from their competitors. The predictive analysts

are capable of achieving and obtaining the intended results through the assistance of business
intelligence tools (software) such as Qlikview, RapidMiner Studio, SAS Predictive analytics,
Microsoft R and TIBCO among others. In the analysis process, the analysts propose some of the
actions that have to be performed and met by the organizations in order to benefit from
predictions and also give alternatives decisions for optimum benefits [6].
Qlikview
Qlikview as one of the business intelligence tools, it was preferred in this report over other BI
software since it is one of the leading BI platforms. Qlikview is accompanied with couples of
advantages when applied in the predictive analytics. Such advantages include allowing creation
of easily understandable visual relationships in the inter-complex sources of data [8]. This
platforms is as well mobile, collaborative with the others and social and it can be run in machines
without licensing attached to it. Referring to the above diagram, Qlikview allows consolidation
of important data from several sources into a unit application and allowing for easy data
exploration resulting to informed decision making. Historical data are leveraged and engaged in
visualization through the help of QLik and generating solutions that suits the situation. Qlik is
depended on by other customers to extract meaning and gain insightful information hidden in
various sources of data [5]. This analytic tool is reliable and very important in the visualization
of data in the predictive analysis. The process is undertaken as in the below diagram:

1) Diagram for predictive analytics process
Process of predictive analysis
First and foremost, the analysts need to define the project. In the definition of the project,
expected results, objectives to be met, sources of data sets to be used in the project all need to be
brought forth and put into consideration [10]. Consequently, follows the data collection process
which include mining of data that best suit the predictive analysis from all the earlier identified
sources for analysis. In the analysis process, data is undertaken into series of inspections,
transformation, cleaning and modelling with the aim of determining useful information leading
to drawing conclusion. Standard statistical models are applied in the test of assumptions,
hypothesis and thus validation. Since the aim is to create to carryout predictive analysis,
predictive models are developed with the ability to accurately predict the future with minimum
or no errors involved if all factors kept constant. Qlikview is engaged in the process of predictive
data analysis to ensure that different sources are successfully accessed and relevant data drawn.
Data modelling also allows choices of coming up with many models for the best solution.