Consider the data set below low 2 Yes Medium Order ID Customer ID Customer Name Segment Product ID Sales Quantity Discount given? Order Priority MX-2014-143658 SC-20575 Sonia Cooley Consumer OFF-LA-10002782 13.08 3 yes Medium MX-2012-155047 KW-16570 Kelly Williams Home office FUR-FU-10004015 252.16 8 no MX-2013-134096 DP-13000 Darren Powers Consumer OFF-EN-10001375 56.12 MX-2014-100727 DM-13525 Don Miller Home office OFF-SU-10004480 300.72 MX-2014-116337 MG-18205 Mitch Gastineau Home office FUR-BO-10000624 875.22 MX-2011-137897M-15580 Jill Matthias Consumer TEC-PH-10000018 2124.5 High Medium High 14 no 3 no 5 yes Category Office Supplies Furniture Office Supplies Office Supplies Technology Technology The idea is to make a data-driven decision to decide which two orders are the most similar. Your task as a data analyst is to help the organiza dentify the similarity between the orders based on your findings from the above data set. Clearly specify the attributes used to determine imilarity. Why did you select/reject them? (Do not use any filter or wrapper method. Use your logic or common knowledge.) Find the two imilar orders. Show all calculations. The most effective method will fetch maximum credits. Ensure that bias in the data is not considered. larks]
Consider the data set below low 2 Yes Medium Order ID Customer ID Customer Name Segment Product ID Sales Quantity Discount given? Order Priority MX-2014-143658 SC-20575 Sonia Cooley Consumer OFF-LA-10002782 13.08 3 yes Medium MX-2012-155047 KW-16570 Kelly Williams Home office FUR-FU-10004015 252.16 8 no MX-2013-134096 DP-13000 Darren Powers Consumer OFF-EN-10001375 56.12 MX-2014-100727 DM-13525 Don Miller Home office OFF-SU-10004480 300.72 MX-2014-116337 MG-18205 Mitch Gastineau Home office FUR-BO-10000624 875.22 MX-2011-137897M-15580 Jill Matthias Consumer TEC-PH-10000018 2124.5 High Medium High 14 no 3 no 5 yes Category Office Supplies Furniture Office Supplies Office Supplies Technology Technology The idea is to make a data-driven decision to decide which two orders are the most similar. Your task as a data analyst is to help the organiza dentify the similarity between the orders based on your findings from the above data set. Clearly specify the attributes used to determine imilarity. Why did you select/reject them? (Do not use any filter or wrapper method. Use your logic or common knowledge.) Find the two imilar orders. Show all calculations. The most effective method will fetch maximum credits. Ensure that bias in the data is not considered. larks]
Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Chapter1: Introduction
Section: Chapter Questions
Problem 1PE
Related questions
Question
![36801
60329
Consider the data set below
Order ID
Customer ID Customer Name Segment Product ID Sales Quantity Discount given? Order Priority Category
MX-2014-143658 SC-20575 Sonia Cooley
Consumer OFF-LA-10002782 13.08
3 yes
Medium Office Supplies
MX-2012-155047 KW-16570 Kelly Williams Home office FUR-FU-10004015 252.16
8 no
low
Furniture
MX-2013-134096 DP-13000 Darren Powers Consumer OFF-EN-10001375 56.12 2 Yes
Medium
MX-2014-100727 DM-13525 Don Miller Home office OFF-SU-10004480 300.72
14 no
High
MX-2014-116337 MG-18205 Mitch Gastineau Home office FUR-BO-10000624 875.22
Medium
MX-2011-137897 UM-15580 Jill Matthias Consumer TEC-PH-10000018 2124.5
Office Supplies
Office Supplies
3 no
5 yes
High
Technology
Technology
The idea is to make a data-driven decision to decide which two orders are the most similar. Your task as a data analyst is to help the organizati
identify the similarity between the orders based on your findings from the above data set. Clearly specify the attributes used to determine t
similarity. Why did you select/reject them? (Do not use any filter or wrapper method. Use your logic or common knowledge.) Find the two m
similar orders. Show all calculations. The most effective method will fetch maximum credits. Ensure that bias in the data is not considered.
Marks]
Options](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Fb97a9e49-f5c1-4ea5-8736-c78f0d8c614a%2F69bf5cf2-f900-45fa-8834-1bd064be9533%2Fizrz9ef_processed.jpeg&w=3840&q=75)
Transcribed Image Text:36801
60329
Consider the data set below
Order ID
Customer ID Customer Name Segment Product ID Sales Quantity Discount given? Order Priority Category
MX-2014-143658 SC-20575 Sonia Cooley
Consumer OFF-LA-10002782 13.08
3 yes
Medium Office Supplies
MX-2012-155047 KW-16570 Kelly Williams Home office FUR-FU-10004015 252.16
8 no
low
Furniture
MX-2013-134096 DP-13000 Darren Powers Consumer OFF-EN-10001375 56.12 2 Yes
Medium
MX-2014-100727 DM-13525 Don Miller Home office OFF-SU-10004480 300.72
14 no
High
MX-2014-116337 MG-18205 Mitch Gastineau Home office FUR-BO-10000624 875.22
Medium
MX-2011-137897 UM-15580 Jill Matthias Consumer TEC-PH-10000018 2124.5
Office Supplies
Office Supplies
3 no
5 yes
High
Technology
Technology
The idea is to make a data-driven decision to decide which two orders are the most similar. Your task as a data analyst is to help the organizati
identify the similarity between the orders based on your findings from the above data set. Clearly specify the attributes used to determine t
similarity. Why did you select/reject them? (Do not use any filter or wrapper method. Use your logic or common knowledge.) Find the two m
similar orders. Show all calculations. The most effective method will fetch maximum credits. Ensure that bias in the data is not considered.
Marks]
Options
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