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 Interior 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 -- Gender X27 -- Number of Children at Home X28 -- Age X29 -- Income X30 -- Recommend to others
Answered Same DayOct 15, 2021

Answer To: MKTG 720, Customer Analytics  Students are to submit response in WORD file electronically via...

Naveen answered on Oct 16 2021
140 Votes
---
title: "Assignment 2"
output: pdf_document
---
```{r}
# Installing required packages
# install.packages("factoextra")
# Loading required package
library(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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