Difference between revisions of "ANLY482 AY2016-17 T2 Group10 Analysis & Findings"

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[[ANLY482_AY2016-17_T2_Group10_Documentation|<font color="#3c3c3c"><strong>DOCUMENTATION</strong></font>]]
 
[[ANLY482_AY2016-17_T2_Group10_Documentation|<font color="#3c3c3c"><strong>DOCUMENTATION</strong></font>]]
  
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[[ANLY482_AY2016-17_T2_Group10_Analysis_&_Findings|<font color="#3c3c3c"><strong>EDA</strong></font>]]
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[[ANLY482_AY2016-17_T2_Group10_Analysis_&_Findings:_Analysis|<font color="#3c3c3c"><strong>Analysis</strong></font>]]
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[[ANLY482_AY2016-17_T2_Group10_Analysis_&_Findings:_Implications|<font color="#3c3c3c"><strong>Implications</strong></font>]]
 
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[[ANLY482_AY2016-17_Term_2|<font color="#636363"><< ANLY482 AY2016-17 T2 Projects</font>]]
 
[[ANLY482_AY2016-17_Term_2|<font color="#636363"><< ANLY482 AY2016-17 T2 Projects</font>]]
 
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Exploratory Data Analysis is the approach to understand main characteristics of datasets through use of graphical techniques such as bar chart, histogram, and scatterplot. Our main responsibility for EDA lies in determining relationships among explanatory and response variables, and in doing so, generate insights on ways to tackle aforementioned business problems.
 
Exploratory Data Analysis is the approach to understand main characteristics of datasets through use of graphical techniques such as bar chart, histogram, and scatterplot. Our main responsibility for EDA lies in determining relationships among explanatory and response variables, and in doing so, generate insights on ways to tackle aforementioned business problems.
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===<span style="line-height: 0.1em;text-indent: 10px;background-color:#1b96fe;padding:5px;border-radius:5px;font-size:15px"><font color="white">Relationship between Sales, Price for different Channels</font></span>===
 
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We want to identify any purchasing patterns on sales and price by different channels (GP, Hospital, Pharmacy). We will follow the assumption that sales per transaction is an estimator of demand.
 
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We would like to determine purchasing pattern on frequencies of transactions based on different channels and products.
 
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===<span style="line-height: 0.1em;text-indent: 10px;background-color:#1b96fe;padding:5px;border-radius:5px;font-size:15px"><font color="white">Relationship between No. of Interaction and Sales</font></span>===
 
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We want to identify any purchasing patterns on sales and price by different channels (GP, Hospital, Pharmacy). We will follow the assumption that sales per transaction is an estimator of demand.
 
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===<span style="line-height: 0.1em;text-indent: 10px;background-color:#1b96fe;padding:5px;border-radius:5px;font-size:15px"><font color="white">Relationship between No. of Transactions and Channels by Products</font></span>===
 
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We intend to find out any relationship between number of interactions and sales aggregated by sales rep, customer, product, and quarter. This is governed by the understanding that the company segments each customer and product pair by past sales performance, and the better the grade would lead to more interactions. Hence, it is important to analyze whether more interactions would lead to more sales, and whether more interactions due to more sales are necessary. Also, we hope to find out an optimum minimal number of interactions for each product that will result in good sales performance.
 
 
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Revision as of 13:09, 15 April 2017

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Data Preparation

Exploratory Data Analysis is the approach to understand main characteristics of datasets through use of graphical techniques such as bar chart, histogram, and scatterplot. Our main responsibility for EDA lies in determining relationships among explanatory and response variables, and in doing so, generate insights on ways to tackle aforementioned business problems.

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