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### "Market Analytics"
# Importing the dataset
data = read.csv("shutterfly_data.csv", header = TRUE)
# Changing date format
data$order_date = as.Date(data$order_date, format='%d-%b-%y')
# Organizing the data with a new 'Year' variable
data$Year = as.numeric(format(data$order_date, '%Y'))
# Creating a cohort of orders in the year 2014 only
cohorts_2014 = data[data$Year==2014, ]
## Exploratory Data Analysis on Cohorts_2014
# 1. Order Frequency
order_frequency = as.data.frame(table(cohorts_2014$customer_id))
colnames(order_frequency)[1] = "unique_cust_id"
# 2. Merging Order Frequency to Cohorts 2014
cohorts_2014 = merge(cohorts_2014, order_frequency,
by.x = "customer_id",
by.y = "unique_cust_id",
all.x = TRUE)
# 3. Creating a cohort of customers based on order date months
cohorts_2014$cohort = as.numeric(format(cohorts_2014$order_date, "%m"))
# 4. Creating a cohort of customers based on monthly activity
cohorts_2014$order_date = format(cohorts_2014$order_date, "%Y-%m")
# 5. Renaming cohort column according to month label
groups = c("Jan Cohorts","Feb Cohorts","Mar Cohorts","Apr Cohorts","May Cohorts","Jun Cohorts",
"Jul Cohorts","Aug Cohorts","Sep Cohorts","Oct Cohorts","Nov Cohorts","Dec Cohorts")
for(i in 1:12){
cohorts_2014[cohorts_2014$cohort==i,"cohort"] = groups[i]
}
rm(i, groups)
cohorts_2014$cohort = factor(cohorts_2014$cohort,
ordered= T, levels =c("Jan Cohorts","Feb Cohorts","Mar Cohorts",
"Apr Cohorts","May Cohorts","Jun Cohorts",
"Jul Cohorts","Aug Cohorts","Sep Cohorts",
"Oct Cohorts","Nov Cohorts","Dec Cohorts"))
# 6. Creating a matrix of cohorts retention
cohorts.ret = reshape2::dcast(cohorts_2014, cohort~Freq, value.var = "customer_id",
fun.aggregate = length)
# 7. Creating a visualization plot of cohorts retention based on order frequency in 2014
breaks <- quantile(cohorts.ret[,2:42], probs = seq(.05, .95, .05), na.rm = TRUE)
colors <- sapply(round(seq(155, 80, length.out = length(breaks) + 1), 0),
function(x){ rgb(x,x,155, maxColorValue = 155) } )
library(DT)## Warning: package 'DT' was built under R version 3.3.3DT::datatable(cohorts.ret,
class = 'cell-border stripe',
rownames = FALSE,
options = list(
ordering=F,
dom = 't',
pageLength = 12)) %>%
formatStyle("cohort",
backgroundColor = 'lightgrey',
fontWeight = 'bold') %>%
formatStyle(names(cohorts.ret[c(-1)]),fontWeight = 'bold',color = 'white', backgroundColor = styleInterval(breaks,colors))## Predictive Model for Cohort Analysis
# Removing non-critical variables
cohorts_2014$category_name <- NULL
cohorts_2014$product_name <- NULL
cohorts_2014$order_id <- NULL
cohorts_2014$Year <- NULL
cohorts_2014$cohort <- NULL
cohorts_2014$order_date <- NULL
# *Logistic Regression*
# Factoring order_sequence column and re-assigning values as NR(not retained) if order=1 and
# R(retained) if order>=2
cohorts_2014$category[cohorts_2014$order_sequence == 1] = "NR"
cohorts_2014$category[cohorts_2014$order_sequence >= 2] = "R"
# Feature Encoding the category column
cohorts_2014$category = factor(cohorts_2014$category, levels = c("NR", "R"), labels = c(0, 1))
cohorts_2014## customer_id order_sequence revenue units Freq category
## 1 8656 1 0.00000000 0 8 0
## 2 8656 2 0.00000000 0 8 1
## 3 8656 4 0.00000000 0 8 1
## 4 8656 3 0.84708387 4 8 1
## 5 8656 3 3.69113027 9 8 1
## 6 8656 3 5.63310774 1 8 1
## 7 8656 3 13.29867812 1 8 1
## 8 8656 1 18.99000000 1 8 0
## 9 10826 1 4.99000000 0 2 0
## 10 10826 1 50.23000000 1 2 0
## 11 19172 1 86.96000000 3 1 0
## 12 20022 1 30.59999996 204 1 0
## 13 32261 2 12.00000000 1 2 1
## 14 32261 1 28.14000000 1 2 0
## 15 32956 1 11.23000000 1 1 0
## 16 33363 1 79.38000000 60 1 0
## 17 38174 1 20.98000000 1 3 0
## 18 38174 2 108.25000000 2 3 1
## 19 38174 2 9.98000000 0 3 1
## 20 41154 1 26.08000000 1 1 0
## 21 41218 1 2.49000000 20 1 0
## 22 41251 1 5.99000000 1 1 0
## 23 41419 1 20.49000000 125 1 0
## 24 56403 1 2.13381750 1 2 0
## 25 56403 1 7.12618250 1 2 0
## 26 57759 1 6.08000000 0 2 0
## 27 57759 1 61.43000000 40 2 0
## 28 60399 1 7.99000000 1 2 0
## 29 60399 1 3.74000000 0 2 0
## 30 61848 3 21.98000000 1 4 1
## 31 61848 2 7.91420600 2 4 1
## 32 61848 2 1.83579399 1 4 1
## 33 61848 1 14.18000000 1 4 0
## 34 67367 1 9.98000000 1 21 0
## 35 67367 10 10.98000000 1 21 1
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## 39 67367 11 19.83000000 1 21 1
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## 43 67367 7 1.34584337 1 21 1
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## [ reached getOption("max.print") -- omitted 45764 rows ]# Data splitting into train and test with 80:20
library(caTools)## Warning: package 'caTools' was built under R version 3.3.3library(tidyverse)## Warning: package 'tidyverse' was built under R version 3.3.3## -- Attaching packages ------------------------------------------------------------- tidyverse 1.2.1 --## v ggplot2 2.2.1 v purrr 0.2.4
## v tibble 1.4.1 v dplyr 0.7.4
## v tidyr 0.7.2 v stringr 1.2.0
## v readr 1.1.1 v forcats 0.2.0## Warning: package 'ggplot2' was built under R version 3.3.3## Warning: package 'tibble' was built under R version 3.3.3## Warning: package 'tidyr' was built under R version 3.3.3## Warning: package 'readr' was built under R version 3.3.3## Warning: package 'purrr' was built under R version 3.3.3## Warning: package 'dplyr' was built under R version 3.3.3## Warning: package 'stringr' was built under R version 3.3.3## Warning: package 'forcats' was built under R version 3.3.3## -- Conflicts ---------------------------------------------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()cohorts_2014$order_sequence <- NULL
split = sample.split(cohorts_2014$category, SplitRatio = 0.8)
training_set = subset(cohorts_2014, split == TRUE)
test_set = subset(cohorts_2014, split == FALSE)
# Model: GLM
logistic_model = glm(formula = category~., family= binomial("logit"),data= training_set, maxit=100)
# Simple GLM model don't seem to work with the dataset because the target variable(category) are
# perfectly separated [Warning: "glm.fit: fitted probabilities numerically 0 or 1 occurred"]
# Now we will try penalized likelihood model (Bias reduction)
library(logistf)## Warning: package 'logistf' was built under R version 3.3.3#logistf_model = logistf(formula = category~., data = training_set, method="Penalized ML")
# This model is unable to converge as well
# Bayesian Model (Naive Bayes)
library(e1071)## Warning: package 'e1071' was built under R version 3.3.3bayes_model = naiveBayes(x=training_set[-5], y=training_set$category)
# Test set Prediction
results = predict(bayes_model, newdata = test_set[-5])
# Model Evaluation
confusion_matrix = table(test_set[, 5], results)
confusion_matrix## results
## 0 1
## 0 5867 501
## 1 3362 2756# 8623/12486 = 70%
# From the results of confusion matrix the Naive Bayes model did poor job in predicting
# customer retention
# *Model Validation*
# K-Fold Cross Validation
library(caret)## Warning: package 'caret' was built under R version 3.3.3## Loading required package: lattice##
## Attaching package: 'caret'## The following object is masked from 'package:purrr':
##
## liftfolds = createFolds(training_set$category, k = 20)
cv = lapply(folds, function(x) {
training_fold = training_set[-x, ]
test_fold = training_set[x, ]
bayes_model = naiveBayes(formula = category ~ .,
data = training_fold,
type = 'C-classification',
kernel = 'radial')
results = predict(bayes_model, newdata = test_fold[-5])
cm = table(test_fold[, 5], results)
accuracy = (cm[1,1] + cm[2,2]) / (cm[1,1] + cm[2,2] + cm[1,2] + cm[2,1])
return(accuracy)
})
# After performing k-fold cross validation there is no improvement in prediction accuracy
# of naive bayes model
# Shutterfly may want to look into the data features again and possibly include more parameters
# in order to come up with a improved prediction accuracy.Note that the echo = FALSE parameter was added to the code chunk to prevent printing of the R code that generated the plot.