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A Survey: A Predictive Analytics Approach Using Imbalanced Data
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| Author:
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USMAN ASHFAQ, BOOMA POOLAN MARIKANNAN
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| Abstract:
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The focal point of this writing survey is to utilize machine learning and information digging strategies for the
expectation of understudy dropout rate based on their execution. This audit similarly underlines on the significance
of dropout rate in the instructive condition. Variables that influence the dropout rate and understudy execution
has likewise been expounded. The criticalness of instructive information mining in anticipating understudy
execution and dropouts have likewise been inspected deliberately. Various investigations and work have been done
in the field of Educational Data Mining (EDM). EDM is exceptionally helpful for the information extraction from the
vast instructive datasets and examination between various machine learning calculations have been made on the
bases of their execution exactness’s. In the survey, there is an investigation on the utilization of information prehandling
strategies
and
highlight
choice
techniques.
It
is
to
improve
the
precision
for
the
information
examination
of
the
valuable
qualities.
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| Keyword:
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Data Mining, Educational Data Mining (EDM), Student Drop-out, Machine learning Techniques
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| EOI:
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| DOI:
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| Download:
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Request For Article
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