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The input is a dataset where none of the samples combinations of events that it is supposed to handle
is assigned to a specific group. The clustering and manage. It is not a one-time exercise, as it has
method firstly identifies a set of groups and then to be updated with the changing environment and
associates each sample to a specific group. requirements. This sector of analytics is mainly
driven by the user’s imaginations and capability to
Regression – Is the task of determining the numeric
response of numeric or categorical variables. comprehend the situations both present and future
An example would be; given the number of past along with their correct solutions. Computer shall
purchases what’s the probability of a purchase of process what is been coded in the software/ tool.
a specific product. Linear Regression algorithm There is always a risk of wrong outcome, if the situation
could be an effective tool/ formula for Predictive is not correctly analysed and programmed. The risk is
Analytics. further increased, as the user’s increased reliability
on the tool’s managing capabilities and with lower
Classification – Is the task of deciding which level of compensating controls and monitoring of the
category a new object belongs is based on a model correctness of the results.
constructed from relationships between collections Summary
of existing objects that are already labelled.
Predictive analytics is the use of statistics Analytics is driven by your imagination, it is
and modelling techniques to determine future important to keep updating the analytics that have
performance based on current and historical data. been implemented successfully. There is a something
Larger the historical data and with maximum possible known as Analytics Life Cycle (ALC), which means that
variables of parameters with the user, better shall if the user finds certain analytical results are under
be predictions. There are three pillars to predictive control or within permissible risk limits, it’s time to
analytics, they are the needs of the entity that is move-on and explore other areas with analytics. At
using the models, the data and the technology used the same time, it is also advised that users should
to study it, and the actions and insights that come as revisit the previously analysed reports from time
a result. There are three types of predictive analytics to time, to make sure all is well. Lastly, to have an
techniques: predictive models, descriptive models, effective and more importantly efficient system of
and decision models. analytics, users must think out of box and should
not limit their imagination with the solution available
Artificial Intelligence (AI) as a standard known to solve the requirements. Solutions are created
definition is the ability of a computer or a robot based on the need, and this process shall be the
controlled by a computer to do tasks that are centre point of future development too. Hence, it is
usually done by humans because they require human important to keep the thinking process active, hungry
intelligence and discernment. There has been an age old for more and progressive to achieve higher heights/
statement about computers “Garbage In Garbage Out”, improvements.
to achieve effective and efficient artificial intelligence
from computers, it is important to program the tools/
software with maximum possible permutations and

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