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MKTG 720, Customer Analytics  Students are to submit response in WORD file electronically via Blackboard.  Submission should include Students Name and Course Number.  Submissions must follow the...

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MKTG 720, Customer Analytics
 Students are to submit response in WORD file electronically via Blackboard.
 Submission should include Students Name and Course Number.
 Submissions must follow the rules of basic writing fundamental and typed in a 12-point
font, in 1.5 line spacing.
 Include all references (if you use any) in a separate Reference Page.
 Include all the out puts from R, if any.

Identifying customers – Cluster Analysis.

The data used for this assignment consists of self-reported information. Please refer to the
Assignment2_DataDescription file.

Using Assignment2_Data, conduct cluster analysis and select the best clustering solution.

Q1. Conduct clustering analysis using data on Agreement scale (Variables X1-X20).

Q2. Conduct clustering analysis using data on Important scale (Variables X21-X24).

Q3. Conduct clustering analysis using data on above two sets of variables X1-X24).

Q4. Conduct clustering analysis using data on Demographics (Variables X26-X29).

Q5. Conduct clustering analysis using data on above three sets of variables.

Q6. Which one is best solution and why?
- Prepare a slide with your response to Q6 for presenting in the class on 10/8.

Assignment #2

Data Description
Scales
X1-X20 Agreement Scale (1=Strongly Disagree --- 7=Strong Agree) Customer agreement with statement.
X21-X24 Important SAcale (1=Least Importanty --- 4=Most Important) Important to customers in purchase decision making.
X25 - Distance between home and restaurant
X26-29 Demographics
X30 - Recommend to others (1=yes; 0=no) Customers are willing to recommend the restaurant to others or not.
Variables
ID
X1 -- Try New and Different Things
X2 -- Party Person
X3 -- People Come to Me
X4 -- Avoid Fried Foods
X5 -- Likes to Go Out Socially
X6 -- Self-Confident
X7 -- Eat Balanced, Nutritious Meals
X8 -- Buy New Products
X9 -- Careful about What I Eat
X10 -- Try New Brands
X11 -- Friendly Employees
X12 -- Fun Place to Eat
X13 -- Large Size Portions
X14 -- Fresh Food
X15 -- Reasonable Prices
X16 -- Attractive Interio
X17 -- Excellent Food Taste
X18 -- Knowledgeable Employees
X19 -- Proper Food Temperature
X20 -- Speed of Service
X21 -- Price
X22 -- Food Quality
X23 -- Atmosphere
X24 -- Service
x25 -- Distance Driven to Restaurant
X26 -- Gende
X27 -- Number of Children at Home
X28 -- Age
X29 -- Income
X30 -- Recommend to others
Answered Same Day Oct 15, 2021

Solution

Naveen answered on Oct 16 2021
132 Votes
---
title: "Assignment 2"
output: pdf_document
---
```{r}
# Installing required packages
# install.packages("factoextra")
# Loading required package
li
ary(factoextra)
# Reading data
df <- read.csv('assignment2data.csv', header = TRUE,sep = ',')
# Print first SIX records
head(df)
# Print structure of the data
str(df)
```
## Q1
```{r}
# Extracting the Agreement scale data
Agreement_scale <- df[,2:21]
# Print first six records
head(Agreement_scale)
# Split the plotting ratio
par(mfrow = c(3,1))
# Elbow method
set.seed(1234)
fviz_nbclust(Agreement_scale,kmeans, method = "wss")
# Silhouette method
set.seed(1234)
fviz_nbclust(Agreement_scale,kmeans, method = "silhouette")
# Gap Statistic method
set.seed(1234)
fviz_nbclust(Agreement_scale,kmeans, method = "gap_stat")
# Extracting the results
set.seed(1234)
model1 <- kmeans(Agreement_scale, centers = 2)
# Print the model
print(model1)
# Split the plotting ratio
par(mfrow = c(1,1))
# Visualize the results
fviz_cluster(model1, data = Agreement_scale)
```
## Q2
```{r}
# Extracting the Important scale data
Important_scale <- df[,22:25]
# Print first six records
head(Important_scale)
# Split the plotting ratio
par(mfrow = c(3,1))
# Elbow method
set.seed(1234)
fviz_nbclust(Important_scale,kmeans, method = "wss")
# Silhouette method
set.seed(1234)
fviz_nbclust(Important_scale,kmeans, method = "silhouette")
# Gap Statistic method
set.seed(1234)
fviz_nbclust(Important_scale,kmeans, method = "gap_stat")
# Extracting the results
set.seed(1234)
model2 <-...
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