Reference no: EM132191495
Ques #1 Lazy boy is using data analytics RFM technique to identify its best and worst customers. This will help them customize their marketing strategy.
Lazyboy sales files is available on the course website, called Lazyboy sales
- Perform RFM analysis (show all calculations in EXCEL, submit this excel file)
Submit following in WORD FILE
- provide the RFM score table in the word file also
- Identify three best and one worst customers
- Discus marketing strategies that would be appropriate for them
Ques#2.
Given the following ownership data for Tractors based on income level and lot size
(similar to example that we did in class but NOT the same; source shemuli etc)
Revised tractor data file is available on class website as tractor data
Develop a two level split decision tree using
First level split at lot size = 19
Second level split follow following rule
If lot size > 19 AND income <= 75.5 etc..
If lot size<=19 AND income <= 65.8 etc..
Do not worry about other splits
- Based on first and second level draw the partial tree
Gini index
- Before the split
- After the first split (lot size =19)
- After the second level splits
Q3 Given the following
Given the following Table with training Records with their actual class and the probability of them being class 1 members estimated by a classifier. Where 1 denotes a customer will buy washer/dryer combination and 0 implies they will not.
(source Shmueli etc)
- Draw a "lift" chart for a person buying a washer and dryer (1) using the above model. Draw it in EXCEL and submit excel file. ( 3 points)
- and provide the lift value for 10 cases of 1(success)
Q 4. Using Tableau
Hint: You can change the name of worksheet by double clicking on it
Using tractor data (ques #2)
- Worksheet 1: put your name on worksheet in title
Create 2 clusters and discuss
Between and within sum of square
Change the name of worksheet to your name_cluster
- Worksheet 2: Create the following worksheet in tableau, call it part 2
Save it in Tableau public server and submit the url
Given the clusters below (note they maybe different from part a) develop a confusion matrix for cluster 1 (note cluster1 implies ownership)
Attachment:- Final Take.zip