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GDB - CS614 Dated:Jan 10, 20
Dear Students,



GDB of CS614 is announced on VULMS. GDB would be opened for submissions from Wednesday 15-Jan-2020 to Thursday 16-Jan-2020. GDB is as under along with important note for submission;



Scenario



Pulse Globe Energy Limited (PGEL) is involved in the undertaking of gas exploration, development and production activities in Australia and Asia. PGEL has investments in upstream gas activities and electricity generation that complement wholesale energy contracts to support the retail customer base.

For one of the projects PGEL has already acquired the Seismic data which provides a “time picture” of subsurface structure to aid in gas exploration.

You are hired by the company as Data Analyst to suggest / identify the most desirable areas for gas exploration by using your analytical stills, as company would have to invest a huge budget of 1 million dollar for this task.



After reading the above scenario you are required to answer the following question:



Suggest the most suitable data mining technique for the given scenario and also support your answer with one valid reason (Not lengthy more than 2 – 3 lines).



Format of your answer would be as given below:



Technique Name: ________________________

1 Strong Reason to Choose: _______________________________________________________

______________________________________________________________________________



Important Notes:

NO GDB is accepted via e-mail in any case
Lengthy replies of GDB will cause in deduction of marks. So you write your answer precisely within 3 to 5 lines in 2 points along with choice.
If you would not mention your choice at start of your answer, your GDB points would not be marked.

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Replies to This Discussion

Please share ideas.

CS614 GDB idea Solution Fall 2019

Prediction is a very powerful aspect of data mining that represents one of four branches of analytics. Predictive analytics use patterns found in current or historical data to extend them into future. It gives organization insight into what trends will happen next in their data.

Prediction tells that in which class the specific item would lie in future or to which class this specific event can be assigned in any time in future. So prediction is the suitable data mining technique to suggest / identify the most desirable areas for gas exploration.


Note: - We are not responsible for any mistake, this is for your idea or help, need to verify yourself.

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SOLUTION IDEA PLZ

Solution:

Prediction is a very powerful aspect of data mining that represents one of four branches of analytics. Predictive analytics use patterns found in current or historical data to extend them into future. It gives organization insight into what trends will happen next in their data.

Prediction tells that in which class the specific item would lie in future or to which class this specific event can be assigned in any time in future. So prediction is the suitable data mining technique to suggest / identify the most desirable areas for gas exploration.

Prediction here is not like a palmists approach that if this line then this. Prediction means
that what’s the probability of an item/event/customer to go in a specific class. This means
that prediction tells that in which class this specific item would lie in future or to which
class this specific event can be assigned in any time in future, say after six years. How
prediction actually works? First of all a model is built using exiting data. The existing
data set is divided into two subsets, one is called the training set and the other is called
test set. The training set is used to form model and the associated rules. Once model built
and rules defined, the test set is used for grouping. It must be noted the test set groupings
are already known but they are put in the model to test its accuracy. Accuracy, we will
discuss in detail in following slides but is dependent on many factors like the model,
training data and test data selection and sizes and many more things. So, the accuracy
gives the confidence level, that the rules are accurate to that much level. Prediction can be
well understood by considering a simple example. Suppose a business wants to know
about their customers their propensity to buy/spend/purchase. In other words, how much
the customer will spend in next 6 months? Similarly a mobile phone company can install
a new tower based on the knowledge spending habits of its customers in the surroundings.
It is not the case that companies install facilities or invest money because of their gut
feelings. If you think like this you are absolutely wrong. Why companies should bother
about their customers? Because if they know their customers, their interests, their like and
dislikes, their buying patterns then it is possible to run targeted marketing campaigns and
thus increasing profit. ...... #according to handout its not suitable for this scenorio

and what about ...seismic data. Prediction is not 

Sorry to interrupt but prediction is not correct DM technique for seismic data. Just recognizing and understanding historical trends and giving conclusion is never enough to chart an accurate prediction of future.

You are  Right because its use for to  extract  models to describing important data etc

tou right konsa h koi help krdo bta ka aj last date

mujy b bta do

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