QuestionQuestion

In this assignment, you will analyze a dataset containing data on annual spending amounts (reported in monetary units) of a Portuguese wholesale distributor customers for diverse product categories. The data set is attached.

The attributes of this dataset are:
Variable Description
Channel Channel - Horeca (Hotel/Restaurant/Cafe) or Retail channel
Region Region - Lisnon, Oporto or Other
Fresh annual spending (m.u.) on fresh products
Milk annual spending (m.u.) on milk products
Grocery annual spending (m.u.)on grocery products
Frozen annual spending (m.u.)on frozen products
Detergents_Paper annual spending (m.u.) on detergents and paper products
Delicassen annual spending (m.u.)on and delicatessen products

Cluster the distributor's customers using hierarchical and K-means clustering algorithms and based on the amount of money they have spent on each product category (Do not use Channel and Region variables for clustering).

After you describe the data, answer the following questions in the format below:

1 - How many clusters did you choose? how?
2 - Describe each cluster.
3 - Do you observe any pattern in clusters? What type of customers do you see in each cluster? Use visualizations aid.
4 - Using "Channel" and "Region" variables summarize your data (you can use tapply() or aggregate() functions). For example, find the mean annual spending on each product category based on the region or channel. Does the customers in each region or channel behave differently? How does it compare to the behavior of customers in each cluster you built?
5 - Do you think it makes more sense to group the customers based on the channel and region or their spending habits?
6 - How do you think a cluster analysis can help this wholesale distributor?

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# Reading the data
SpendData<-read.csv("Path/l22uAOErcsOud7vt4YGp.csv")

# We'll look at the head of the data
head(SpendData)
##   Channel Region Fresh Milk Grocery Frozen Detergents_Paper Delicassen
## 1       2      3 12669 9656    7561    214             2674       1338
## 2       2      3 7057 9810    9568   1762             3293       1776
## 3       2      3 6353 8808    7684   2405             3516       7844
## 4       1      3 13265 1196    4221   6404             507       1788
## 5       2      3 22615 5410    7198   3915             1777       5185
## 6       2      3 9413 8259    5126    666             1795       1451
# We can see we have successfully read the data and data looks fine

# Now we will look at the structure of the data
str(SpendData)
## 'data.frame':    440 obs. of 8 variables:
## $ Channel         : int 2 2 2 1 2 2 2 2 1 2 ...
## $ Region          : int 3 3 3 3 3 3 3 3 3 3 ...
## $ Fresh          : int 12669 7057 6353 13265 22615 9413 12126 7579 5963 6006 ...
## $ Milk            : int 9656 9810 8808 1196 5410 8259 3199 4956 3648 11093 ...
## $ Grocery         : int 7561 9568 7684 4221 7198 5126 6975 9426 6192 18881 ...
## $ Frozen          : int 214 1762 2405 6404 3915 666 480 1669 425 1159 ...
## $ Detergents_Paper: int 2674 3293 3516 507 1777 1795 3140 3321 1716 7425 ...
## $ Delicassen      : int 1338 1776 7844 1788 5185 1451 545 2566 750 2098 ...
# From the above output we can see that all the variables are Integer and there are 440 Observations and 8 Variables...

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