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Customer Churn Analysis.R
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Customer Churn Analysis.R
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# -----------------------------------------------------------------
# Customer Churn Analysis
#
# Jovan Trajceski
# -----------------------------------------------------------------
# Clear up data in global environment
rm(list=ls())
# Data Loading and Pre-processing
library(tidyverse)
library(patchwork)
library(caret)
library(vcd)
library(gridExtra)
library(knitr)
library(corrplot)
library(scales)
library(lme4)
library(DMwR2)
library(DMwR)
library(InformationValue)
library(ROCR)
library(rpart)
library(randomForest)
library(xgboost)
library(xgboostExplainer)
library(MASS)
library(ggmosaic)
library(e1071)
library(ranger)
library(penalized)
library(rpart.plot)
library(ggcorrplot)
library(caTools)
library(snow)
library(doParallel)
doFuture::registerDoFuture()
future::plan("multisession")
library(naniar)
suppressPackageStartupMessages(c(library(caret),library(corrplot),library(smotefamily)))
library(ROSE)
library(kableExtra)
library(knitr)
setwd("C:/Users/Jovan Trajceski/Downloads/Churn")
bankChurn <- read_csv('Churn_Modelling.csv')
glimpse(bankChurn)
# Data Cleaning
bankChurn <- bankChurn %>%
dplyr::select(-RowNumber, -CustomerId, -Surname) %>% #remove unwanted column
mutate(Geography = as.factor(Geography),
Gender = as.factor(Gender),
HasCrCard = as.factor(HasCrCard),
IsActiveMember = as.factor(IsActiveMember),
Exited = as.factor(Exited),
Tenure = as.factor(Tenure),
NumOfProducts = as.factor(NumOfProducts))
# Check NAs
sapply(bankChurn, function(x) sum(is.na(x)))
vis_miss(bankChurn)
# Data Overview
summary(bankChurn)
# Target
ggplot(bankChurn, aes(Exited, fill = Exited)) +
geom_bar() +
theme(legend.position = 'none')
table(bankChurn$Exited)
round(prop.table(table(bankChurn$Exited)),3)
# Continuous Variable Distribution
bankChurn %>%
keep(is.numeric) %>%
gather() %>%
ggplot() +
geom_histogram(mapping = aes(x=value,fill=key), color="black") +
facet_wrap(~ key, scales = "free") +
theme_minimal() +
theme(legend.position = 'none')
# Correlation Matrix
numericVarName <- names(which(sapply(bankChurn, is.numeric)))
corr <- cor(bankChurn[,numericVarName], use = 'pairwise.complete.obs')
ggcorrplot(corr, lab = TRUE)
# Categorical Variable Distribution
bankChurn %>%
dplyr::select(-Exited) %>%
keep(is.factor) %>%
gather() %>%
group_by(key, value) %>%
summarize(n = n()) %>%
ggplot() +
geom_bar(mapping=aes(x = value, y = n, fill=key), color="black", stat='identity') +
coord_flip() +
facet_wrap(~ key, scales = "free") +
theme_minimal() +
theme(legend.position = 'none')
# Age
age_hist <- ggplot(bankChurn, aes(x = Age, fill = Exited)) +
geom_histogram(binwidth = 5) +
theme_minimal() +
scale_x_continuous(breaks = seq(0,100,by=10), labels = comma)
age_boxplot <- ggplot(bankChurn, aes(x = Exited, y = Age, fill = Exited)) +
geom_boxplot() +
theme_minimal() +
theme(legend.position = 'none')
age_hist | age_boxplot
# Balance
balance_hist <- ggplot(bankChurn, aes(x = Balance, fill = Exited)) +
geom_histogram() +
theme_minimal() +
scale_x_continuous(breaks = seq(0,255000,by=30000), labels = comma) +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5))
balance_box <- ggplot(bankChurn, aes(x = Exited, y = Balance, fill = Exited)) +
geom_boxplot() +
theme_minimal() +
theme(legend.position = 'none')
balance_hist | balance_box
# Credit Score
credit_hist <- ggplot(bankChurn, aes(x = CreditScore, fill = Exited)) +
geom_histogram() +
theme_minimal() +
#scale_x_continuous(breaks = seq(0,255000,by=30000), labels = comma) +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5))
credit_box <- ggplot(bankChurn, aes(x = Exited, y = CreditScore, fill = Exited)) +
geom_boxplot() +
theme_minimal() +
theme(legend.position = 'none')
credit_hist | credit_box
# Estimated Salary
estimated_hist <- ggplot(bankChurn, aes(x = EstimatedSalary, fill = Exited)) +
geom_histogram() +
theme_minimal() +
#scale_x_continuous(breaks = seq(0,255000,by=30000), labels = comma) +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5))
estimated_box <- ggplot(bankChurn, aes(x = Exited, y = EstimatedSalary, fill = Exited)) +
geom_boxplot() +
theme_minimal() +
theme(legend.position = 'none')
estimated_hist | estimated_box
# Categorical Variables Exploration
gender_graph <- bankChurn %>%
dplyr::select(Gender, Exited) %>%
table(.) %>%
as.data.frame() %>%
ggplot(.) +
ggmosaic::geom_mosaic(aes(weight = Freq, x = product(Gender), fill = Exited)) +
ggthemes::theme_tufte() +
scale_fill_brewer(type = "qual") +
labs(x = 'Gender')
geography_graph <- bankChurn %>%
dplyr::select(Geography, Exited) %>%
table(.) %>%
as.data.frame() %>%
ggplot(.) +
ggmosaic::geom_mosaic(aes(weight = Freq, x = product(Geography), fill = Exited)) +
ggthemes::theme_tufte() +
scale_fill_brewer(type = "qual") +
labs(x = 'Geography')
tenure_graph <- bankChurn %>%
dplyr::select(Tenure, Exited) %>%
table(.) %>%
as.data.frame() %>%
ggplot(.) +
ggmosaic::geom_mosaic(aes(weight = Freq, x = product(Tenure), fill = Exited)) +
ggthemes::theme_tufte() +
scale_fill_brewer(type = "qual") +
labs(x = 'Tenure')
HasCrCard_graph <- bankChurn %>%
dplyr::select(HasCrCard, Exited) %>%
table(.) %>%
as.data.frame() %>%
ggplot(.) +
ggmosaic::geom_mosaic(aes(weight = Freq, x = product(HasCrCard), fill = Exited)) +
ggthemes::theme_tufte() +
scale_fill_brewer(type = "qual") +
labs(x = 'HasCrCard')
IsActiveMember_graph <- bankChurn %>%
dplyr::select(IsActiveMember, Exited) %>%
table(.) %>%
as.data.frame() %>%
ggplot(.) +
ggmosaic::geom_mosaic(aes(weight = Freq, x = product(IsActiveMember), fill = Exited)) +
ggthemes::theme_tufte() +
scale_fill_brewer(type = "qual") +
labs(x = 'IsActiveMember')
NumOfProducts_graph <- bankChurn %>%
dplyr::select(NumOfProducts, Exited) %>%
table(.) %>%
as.data.frame() %>%
ggplot(.) +
ggmosaic::geom_mosaic(aes(weight = Freq, x = product(NumOfProducts), fill = Exited)) +
ggthemes::theme_tufte() +
scale_fill_brewer(type = "qual") +
labs(x = 'NumOfProducts')
(gender_graph | geography_graph) / (IsActiveMember_graph | HasCrCard_graph ) / (tenure_graph | NumOfProducts_graph)
# Feature selection with chi-square test
chi.square <- vector()
p.value <- vector()
cateVar <- bankChurn %>%
dplyr::select(-Exited) %>%
keep(is.factor)
for (i in 1:length(cateVar)) {
p.value[i] <- chisq.test(bankChurn$Exited, unname(unlist(cateVar[i])), correct = FALSE)[3]$p.value
chi.square[i] <- unname(chisq.test(bankChurn$Exited, unname(unlist(cateVar[i])), correct = FALSE)[1]$statistic)
}
chi_sqaure_test <- tibble(variable = names(cateVar)) %>%
add_column(chi.square = chi.square) %>%
add_column(p.value = p.value)
knitr::kable(chi_sqaure_test)
# Formatted table
chi_sqaure_test %>%
kbl(caption = "Feature selection with chi-square test") %>%
kable_classic(full_width = T, html_font = "Cambria") %>%
row_spec(3:3, bold = T, color = "white", background = "red") %>%
row_spec(5:5, bold = T, color = "white", background = "red") %>%
footnote(general = "The chi-square for Tenure and HasCrCard are pretty small, at the same time, their p-values are greater than 0.05, so it confirms our hypothesis that these two features will not provide useful information on the reponse (target) variable. Thus I decided to drop these two variables.",
)
# Drop 2 variables with pval > 0.05
bankChurn <- bankChurn %>%
dplyr::select(-Tenure, -HasCrCard)
# Build Predictive Models
# Data Partition: split the data using a stratified sampling approach.
set.seed(1234)
sample_set <- bankChurn %>%
pull(.) %>%
sample.split(SplitRatio = .7)
bankTrain <- subset(bankChurn, sample_set == TRUE)
bankTest <- subset(bankChurn, sample_set == FALSE)
# Class Balancing: Let’s look at the class distribution again.
round(prop.table(table(bankChurn$Exited)),3)
round(prop.table(table(bankTrain$Exited)),3)
round(prop.table(table(bankTest$Exited)),3)
# Balance train set
# check table
table(bankTrain$Exited)
#check classes distribution
prop.table(table(bankTrain$Exited))
#over sampling
data_balanced_over <- ovun.sample(Exited ~ ., data = bankTrain, method = "over",N = 11148)$data
table(data_balanced_over$Exited)
# method over instructs the algorithm to perform over sampling.
# N refers to number of observations in the resulting balanced set.
# Undersampling
data_balanced_under <- ovun.sample(Exited ~ ., data = bankTrain, method = "under", N = 2852, seed = 1)$data
table(data_balanced_under$Exited)
# Now the data set is balanced. But, you see that we’ve lost significant information from the sample.
# Let’s do both undersampling and oversampling on this imbalanced data.
# This can be achieved using method = “both“.
# In this case, the minority class is oversampled with replacement and majority class is undersampled without replacement.
data_balanced_both <- ovun.sample(Exited ~ ., data = bankTrain, method = "both", p=0.5, N=7000, seed = 1)$data
table(data_balanced_both$Exited)
# Logistic Regression
## Train the model
logit.mod <- glm(Exited ~., family = binomial(link = 'logit'), data = data_balanced_both)
## Look at the result
summary(logit.mod)
## Predict the outcomes against our test data
logit.pred.prob <- predict(logit.mod, bankTest, type = 'response')
logit.pred <- as.factor(ifelse(logit.pred.prob > 0.5, 1, 0))
head(bankTest,10)
head(logit.pred.prob,10)
#View the confusion matrix of logistic regression.
caret::confusionMatrix(logit.pred, bankTest$Exited, positive = "1")
# Decision Tree
ctrl <-
trainControl(method = "cv", #cross-validation
number = 10, #10-fold
selectionFunction = "best")
grid <-
expand.grid(
.cp = seq(from=0.0001, to=0.005, by=0.0001)
)
set.seed(1234)
tree.mod <-
train(
Exited ~.,
data = data_balanced_both,
method = "rpart",
metric = "Kappa",
trControl = ctrl,
tuneGrid = grid
)
tree.mod
# Make predictions based on our candidate model
tree.pred.prob <- predict(tree.mod, bankTest, type = "prob")
tree.pred <- predict(tree.mod, bankTest, type = "raw")
# View the confusion Matrix of decision tree.
caret::confusionMatrix(tree.pred, bankTest$Exited, positive = "1")
# Random Forest
## Create a control object.
ctrl <- trainControl(method = "cv",
number = 10,
selectionFunction = "best")
## Create a grid search based on the available parameters.
grid <- expand.grid(.mtry = c(1:8))
## Build the random forest model
rf.mod <-
train(Exited ~.,
data = data_balanced_both,
method = 'rf',
metric = 'Kappa',
trControl = ctrl,
tuneGrid = grid)
rf.mod
## Make the predictions
rf.pred <- predict(rf.mod, bankTest, type = "raw")
rf.pred.prob <- predict(rf.mod, bankTest, type = "prob")
# View the confusion matrix of random forest.
caret::confusionMatrix(rf.pred, bankTest$Exited, positive = "1")
# eXtreme Gradient Boosting (XgBoost)
## Create a control object
ctrl <-
trainControl(method = "cv",
number = 10,
selectionFunction = "best")
modelLookup("xgbTree")
## Grid Search
grid <- expand.grid(
nrounds = 40,
max_depth = c(4,5,6,7,8),
eta = c(0.1,0.2,0.3,0.4,0.5),
gamma = 0.01,
colsample_bytree = 1,
min_child_weight = 1,
subsample = c(0.5, 1)
)
## Build XGBoost
set.seed(1234)
xgb.mod <-
train(
Exited ~ .,
data = data_balanced_both,
method = "xgbTree",
metric = "Kappa",
trControl = ctrl,
tuneGrid = grid
)
xgb.mod
## Make the prediction
xgb.pred <- predict(xgb.mod, bankTest, type = "raw")
xgb.pred.prob <- predict(xgb.mod, bankTest, type = "prob")
#View the confusion matrix of XGBoost.
caret::confusionMatrix(xgb.pred, bankTest$Exited, positive = "1")
# Compare Models’ Performance
## Logistic Regression
test <- bankTest$Exited
pred <- logit.pred
prob <- logit.pred.prob
# Logistic Regression ROC curve
roc.pred <- prediction(predictions = prob, labels = test)
roc.perf <- performance(roc.pred, measure = "tpr", x.measure = "fpr")
plot(roc.perf, main = "ROC Curve for Customer Churn Prediction Approaches", col = 2, lwd = 2)
abline(a = 0, b = 1, lwd = 3, lty = 2, col = 1)
## Logistic Regression Performance Metrics
accuracy <- mean(test == pred)
precision <- posPredValue(pred, test, positive = "1")
recall <- caret::sensitivity(pred, test, positive = "1")
fmeasure <- (2 * precision * recall)/(precision + recall)
confmat <- caret::confusionMatrix(pred, test, positive = "1")
kappa <- as.numeric(confmat$overall["Kappa"])
auc <- as.numeric(performance(roc.pred, measure = "auc")@y.values)
comparisons <- tibble(approach="Logistic Regression", accuracy = accuracy, fmeasure = fmeasure,kappa = kappa, auc = auc)
## Classification Tree
test <- bankTest$Exited
pred <- tree.pred
prob <- tree.pred.prob[,2]
## Classification Tree ROC Curve
roc.pred <- prediction(predictions = prob, labels = test)
roc.perf <- performance(roc.pred, measure = "tpr", x.measure = "fpr")
plot(roc.perf, col=3, lwd = 2, add=TRUE)
## Classification Tree Performance Metrics
accuracy <- mean(test == pred)
precision <- posPredValue(pred, test, positive = "1")
recall <- caret::sensitivity(pred, test, positive = "1")
fmeasure <- (2 * precision * recall)/(precision + recall)
confmat <- caret::confusionMatrix(pred, test, positive = "1")
kappa <- as.numeric(confmat$overall["Kappa"])
auc <- as.numeric(performance(roc.pred, measure = "auc")@y.values)
comparisons <- comparisons %>%
add_row(approach="Classification Tree", accuracy = accuracy, fmeasure = fmeasure, kappa = kappa, auc = auc)
## Random Forest
test <- bankTest$Exited
pred <- rf.pred
prob <- rf.pred.prob[,2]
## Random Forest ROC Curve
roc.pred <- prediction(predictions = prob, labels = test)
roc.perf <- performance(roc.pred, measure = "tpr", x.measure = "fpr")
plot(roc.perf, col=4, lwd = 2, add=TRUE)
## Random Forest Performance Metrics
accuracy <- mean(test == pred)
precision <- posPredValue(pred, test, positive = "1")
recall <- caret::sensitivity(pred, test, positive = "1")
fmeasure <- (2 * precision * recall)/(precision + recall)
confmat <- caret::confusionMatrix(pred, test, positive = "1")
kappa <- as.numeric(confmat$overall["Kappa"])
auc <- as.numeric(performance(roc.pred, measure = "auc")@y.values)
comparisons <- comparisons %>%
add_row(approach="Random Forest", accuracy = accuracy, fmeasure = fmeasure, kappa = kappa, auc = auc)
## XGBoost
test <- bankTest$Exited
pred <- xgb.pred
prob <- xgb.pred.prob[,2]
# Plot ROC Curve.
roc.pred <- prediction(predictions = prob, labels = test)
roc.perf <- performance(roc.pred, measure = "tpr", x.measure = "fpr")
plot(roc.perf, col=5, lwd = 2, add=TRUE)
# Get performance metrics.
accuracy <- mean(test == pred)
precision <- posPredValue(pred, test, positive = "1")
recall <- caret::sensitivity(pred, test, positive = "1")
fmeasure <- (2 * precision * recall)/(precision + recall)
confmat <- caret::confusionMatrix(pred, test, positive = "1")
kappa <- as.numeric(confmat$overall["Kappa"])
auc <- as.numeric(performance(roc.pred, measure = "auc")@y.values)
comparisons <- comparisons %>%
add_row(approach="eXtreme Gradient Boosting", accuracy = accuracy, fmeasure = fmeasure, kappa = kappa, auc = auc)
# Draw ROC legend.
legend(0.6, 0.6, c('Logistic Regression', 'Classification Tree', 'Random Forest', 'eXtreme Gradient Boosting'), 2:5)
knitr::kable(comparisons)
# Formatted table
comparisons %>%
kbl(caption = "Comparison Table") %>%
kable_classic(full_width = T, html_font = "Cambria") %>%
row_spec(3:3, bold = T, color = "white", background = "green") %>%
footnote(general = "We already know that the response variable is quite imbalanced, so I will not use prediction accuracy as our only metric, instead, I will use several metrics here to select the best model. From the ROC curve and the comparison table, Random Forest achieves a better performance. I’ll go with Random Forest as our final model. ",
)
# Feature Importance
# First is the feature importance plot of Random Forest model.
vip::vip(rf.mod)
# Second is the feature importance plot of XGBoost model.
vip::vip(xgb.mod)
# Combined
c1<-vip::vip(rf.mod, aesthetics = list(colour="darkgrey", fill="grey"))+ ggtitle("Random Forest")
c2<-vip::vip(xgb.mod, aesthetics = list(colour="black", fill="black"))+ ggtitle("XGBoost")
grid.arrange(c1, c2, ncol = 2)
# This code is free to use for academic purposes only, provided that a proper reference is cited.
# This code comes without technical support of any kind.
# Under no circumstances will the author be held responsible for any use of this code in any way.