Difference between revisions of "ANLY482 AY2016-17 T2 Group19 Methodology"

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! style="border-style: none; border-width: 0 1px 1px 0; width: 12%"| [[ANLY482_AY2016-17_T2_Group19|<font color="#000000"><b>OVERVIEW</b></font>]]
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[[File:Protege_overview.svg|40px|link= ANLY482_AY2016-17_T2_Group19 ]] &nbsp;
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[[ANLY482_AY2016-17_T2_Group19|<font color="#000000"><b>OVERVIEW</b></font>]]
  
! style="border-style: none; border-width: 0 1px 1px 0; width: 12%;  border-left:3px solid #a4a4a6; background-color:#ffffff"| [[ANLY482_AY2016-17_T2_Group19_Data| <font color="#000000"><b>DATA</b></font>]]
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[[ANLY482_AY2016-17_T2_Group19_Data| <font color="#000000"><b>DATA</b></font>]]
  
! style="border-style: none; border-width: 0 1px 1px 0; width: 12%; border-left:3px solid #a4a4a6; background-color:#ffffff"| [[ANLY482_AY2016-17_T2_Group19_Methodology|<font color="#a1212e"><b>METHODOLOGY</b></font>]]
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[[ANLY482_AY2016-17_T2_Group19_Methodology|<font color="#a1212e"><b>METHODOLOGY</b></font>]]
  
! style="border-style: none; border-width: 0 1px 1px 0; width: 12%; border-left:3px solid #a4a4a6; background-color:#ffffff"| [[ANLY482_AY2016-17_T2_Group19_Analysis| <font color="#000000"><b>ANALYSIS</b></font>]]
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[[ANLY482_AY2016-17_T2_Group19_Analysis| <font color="#000000"><b>ANALYSIS</b></font>]]
  
! style="border-style: none; border-width: 0 1px 1px 0; width: 12%; border-left:3px solid #a4a4a6; background-color:#ffffff"| [[ANLY482_AY2016-17_T2_Group19_Findings| <font color="#000000"><b>FINDINGS</b></font>]]
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[[ANLY482_AY2016-17_T2_Group19_Findings| <font color="#000000"><b>FINDINGS</b></font>]]
  
! style="border-style: none; border-width: 0 1px 1px 0; width: 12%; border-left:3px solid #a4a4a6; background-color:#ffffff"| [[ANLY482_AY2016-17_T2_Group19_Poster| <font color="#000000"><b>POSTER</b></font>]]
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[[ANLY482_AY2016-17_T2_Group19_Poster| <font color="#000000"><b>POSTER</b></font>]]
  
 
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==<div style="background: #4d4d4d; padding: 20px;  line-height: 0.1em;  text-indent: 10px; font-size:20px; font-family: Rockwell;  border-radius: 7px; border-bottom:3px solid #ba3749"><font color= #ffffff>Data Set Acknowledgement</font></div>==
 
==<div style="background: #4d4d4d; padding: 20px;  line-height: 0.1em;  text-indent: 10px; font-size:20px; font-family: Rockwell;  border-radius: 7px; border-bottom:3px solid #ba3749"><font color= #ffffff>Data Set Acknowledgement</font></div>==

Revision as of 20:15, 10 January 2017



Protegelogo-01.svg

Protege overview.svg   OVERVIEW

Protege data.svg   DATA

Protege Methods.svg   METHODOLOGY

Protege Analysis.svg   ANALYSIS

Protegemaster-03.svg   FINDINGS

Protege poster.svg   POSTER

Data Set Acknowledgement

-Insert size of data, what time period is it from? (yyyy-yyyy)

-Insert Name of Model

Explanation of the use of this model, why use this, any special treatment done in order to use this model?

Since logistic regression is sensitive to data with skewed distributions, it becomes important to standardize our continuous data to fit a normal distribution as closely as possible. This resulted in balance, duration, campaign, previous and pdays being transformed to provide a better fit for the model.

Steps and approach elaboration