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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.

Acquiring customers.
The data set for the assignment contains information about customers and marketing campaigns
for several telemarketing campaigns for a bank (Moro et al. 2014).
Demographic: age, job type, marital status, education level, has credit in default, has a personal
loan.
Marketing campaign: communication type, month of last contact, day of week of last contact,
duration of last contact, number of people contacted in this campaign, number of days that
passed after last contact, number of times contacted before this campaign, outcome of previous
campaign.
Outcome: purchase a term deposit or not.
The goal here is to predict if a customer will subscribe to a term deposit (purchase a product)
after receiving a telemarketing campaign. For the assignment, develop three different models,
compare on all (AIC, training Accuracy, test Accuracy), select a best model per your judgment,
eport the results, and describe using odds ratio.
 Model 1: Demographic variable: All
 Model 2: Marketing campaign: contact, month, day_of_week, duration, previous,
poutcome
 Model 3: Demographic and Marketing campaign
Data Source: Moro, S., Cortez, P., & Rita, P XXXXXXXXXXA data-driven approach to predict the
success of bank telemarketing. Decision Support Systems, 62, 22-31.
Link: https:
www.sciencedirect.com/science/article/abs/pii/S016792361400061X
Assignment #3
Answered Same Day Oct 22, 2021

Solution

Naveen answered on Oct 23 2021
148 Votes
# Removing all objects
m(list = ls())
# Installing required package
# install.packages(caret)
# Loading Required package
li
ary(caret)
# Reading dataset
df <- read.csv('assignment3.csv')
# printing first six records
head(df)
# Checking structure of the dataset
str(df)
# Converting variables character data type to factor datatype
df$job <- as.factor(df$job)
df$marital <- as.factor(df$marital)
df$education <- as.factor(df$education)
df$default <- as.factor(df$default)
df$loan <- as.factor(df$loan)
df$contact = as.factor(df$contact)
df$month = as.factor(df$month)
df$day_of_week = as.factor(df$day_of_week)
df$poutcome = as.factor(df$poutcome)
# Splitting data into train and test datasets
set.seed(1234)
Samples <- sample(1:nrow(df), round(nrow(df)*.7))
Train_df <- df[Samples,]
Test_df <- df[-Samples,]
# Building logistic regression model with Demographic variables
model1 <- glm(purchase ~ age + job + marital + education + default + loan,
data =...
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