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DTSTART:20170312T030000
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DTSTART:20161106T010000
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DTSTAMP:20161122T212412Z
UID:104FF69D-AE1F-11E6-A7C6-0050568D7F66
DTSTART;TZID=PST8PDT:20161207T160000
DTEND;TZID=PST8PDT:20161207T170000
DESCRIPTION:: Now more than ever\, planning and decision-making is data\, r
 ather than gut\, driven. While there are many existing models for regressi
 on\, prediction and learning\, choosing an appropriate model involves unde
 rstanding the associated data deeply. Often\, choosing a standard model is
  still insufficient if the analyst is not customizing their model to handl
 e the intricacies of their data.\n\nWe describe our development process of
  a Markov chain and Dynamic Markov Chain-based model for predicting colleg
 e enrolments. The model is compared and contrasted against previously-atte
 mpted models. We include a demo of using our interface into the model\, wh
 ich allows for roughly 600 configurations of parameters\, and many additio
 nal override value options.\n\nThe ideas presented here are applicable to 
 many dynamical systems involving population migration in a discrete-time p
 rocess.\n\nCo-sponsored by: Okanagan College\n\nSpeaker(s): McCall Milliga
 n\, Jim Nastos \, McCall Milligan\, Jim Nastos \n\nRoom: 103\, Bldg: E\, 1
 000 KLO Rd.\, Kelowna\, British Columbia\, Canada\, V1Y 4X8
LOCATION:Room: 103\, Bldg: E\, 1000 KLO Rd.\, Kelowna\, British Columbia\, 
 Canada\, V1Y 4X8
ORGANIZER:youry@ieee.org
SEQUENCE:3
SUMMARY:Data Analytics in Educational Institutions: Building a Predictive M
 odel for Student Enrolment
URL;VALUE=URI:https://events.vtools.ieee.org/m/42254
X-ALT-DESC:Description: &lt;br /&gt;&lt;p&gt;&lt;strong&gt;:&lt;/strong&gt; Now more than ever\, pl
 anning and decision-making is data\, rather than gut\, driven. While there
  are many existing models for regression\, prediction and learning\, choos
 ing an appropriate model involves understanding the associated data deeply
 . Often\, choosing a standard model is still insufficient if the analyst i
 s not customizing their model to handle the intricacies of their data.&lt;/p&gt;
 \n&lt;p&gt;&amp;nbsp\;&lt;/p&gt;\n&lt;p&gt;We describe our development process of a Markov chain
  and Dynamic Markov Chain-based model for predicting college enrolments. T
 he model is compared and contrasted against previously-attempted models. W
 e include a demo of using our interface into the model\, which allows for 
 roughly 600 configurations of parameters\, and many additional override va
 lue options.&lt;/p&gt;\n&lt;p&gt;&amp;nbsp\;The ideas presented here are applicable to man
 y dynamical systems involving population migration in a discrete-time proc
 ess.&lt;/p&gt;
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