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Copy pathCustomer Sales Revenue Prediction.R
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Customer Sales Revenue Prediction.R
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library(tidyverse)
library(caret)
library(lubridate)
# Retaining only first 5 factors and collapsing the rest in factor variables
collapse.factor <- function(x) {
x <- fct_lump_n(x, n=5)
x <- fct_explicit_na(x, na_level = "Other")
return(x)
}
# Function to plot missing value percentages in each column in a dataframe
get.missing.perc <- function(df) {
na.perc <- colMeans(is.na(df)) * 100
data.frame(perc=na.perc[na.perc > 0], name=names(na.perc[na.perc > 0])) %>%
arrange(perc) %>%
mutate(name=factor(name, levels = name)) %>%
ggplot(aes(y=name, x=perc, fill=perc<50)) +
geom_bar(stat = "identity") +
labs(title="Percentage of missing values", y="Variable", x="Percentage")
}
# Importing the test and train Datasets
train.data <- read.csv("Train.csv", na.strings = c("", "NA"))
test.data <- read.csv("Test.csv", na.strings = c("", "NA"))
# Getting the Unique customer IDs from both Train and Test data
custIds.train <- unique(train.data$custId)
custIds.test <- unique(test.data$custId)
# Checking if there are any duplicate customers in Test data and Train data
which(custIds.test %in% custIds.train) # Shows 0 common customer IDs
# Getting the Max and Min data seen in the Train dataset
maxdate = max(ymd(train.data$date))
mindate = min(ymd(train.data$date))
# Combining both Train and Test to do some Pre-Processing on some Important columns
# Then grouping the data using custId
full.data <- train.data %>% dplyr::select(-revenue) %>%
rbind(test.data) %>%
mutate(date=ymd(date),
across(.cols=where(is.character), .fns=collapse.factor)) %>%
group_by(custId) %>%
summarise(channelGrouping = max(ifelse(is.na(channelGrouping) == TRUE, -9999, channelGrouping)),
first_ses_from_the_period_start = min(date) - mindate,
last_ses_from_the_period_end = maxdate - max(date),
interval_dates = max(date) - min(date),
unique_date_num = length(unique(date)),
maxVisitNum = max(visitNumber, na.rm = TRUE),
browser = as.factor(first(ifelse(is.na(browser), -9999, browser))),
operatingSystem = as.factor(first(ifelse(is.na(operatingSystem) == TRUE, -9999, operatingSystem))),
deviceCategory = as.factor(first(ifelse(is.na(deviceCategory) == TRUE, -9999, deviceCategory))),
subContinent = as.factor(first(ifelse(is.na(subContinent) == TRUE, -9999, subContinent))),
country = as.factor(first(ifelse(is.na(country) == TRUE, -9999, country))),
region = as.factor(first(ifelse(is.na(region) == TRUE, -9999, region))),
networkDomain = as.factor(first(ifelse(is.na(networkDomain) == TRUE, -9999, networkDomain))),
source = as.factor(first(ifelse(is.na(source) == TRUE, -9999, source))),
medium = as.factor(first(ifelse(is.na(medium) == TRUE, -9999, medium))),
isVideoAd_mean = mean(ifelse(is.na(adwordsClickInfo.isVideoAd) == TRUE, 0, 1)),
isMobile = mean(ifelse(isMobile == TRUE, 1 , 0)),
isTrueDirect = mean(ifelse(is.na(isTrueDirect) == TRUE, 0, 1)),
bounce_sessions = sum(ifelse(is.na(bounces) == TRUE, 0, 1)),
pageviews_sum = sum(pageviews, na.rm = TRUE),
pageviews_mean = mean(ifelse(is.na(pageviews), 0, pageviews)),
pageviews_min = min(ifelse(is.na(pageviews), 0, pageviews)),
pageviews_max = max(ifelse(is.na(pageviews), 0, pageviews)),
pageviews_median = median(ifelse(is.na(pageviews), 0, pageviews)),
session_cnt = NROW(visitStartTime)) %>%
mutate(pctBounce = bounce_sessions/session_cnt,
allBounce = as.numeric(bounce_sessions >= session_cnt),
bounceViewRatio = bounce_sessions/(pageviews_sum+1))
# Splitting the full.data into train and test data again
train.data <- train.data %>%
group_by(custId) %>%
summarise(revenue=sum(revenue)) %>%
mutate(revenue=log(revenue+1)) %>%
inner_join(full.data %>% filter(custId %in% custIds.train), by="custId")
test.data <- full.data %>% filter(custId %in% custIds.test)
rm(full.data)
# Modelling
# Classifying if a customer will spend money or not using LDA classifier
fitControl <- trainControl(method = "cv")
fit.lda <- train(as.factor(revenue>0) ~ channelGrouping +
allBounce + region +
browser + operatingSystem + country +
bounce_sessions + pageviews_sum,
data=train.data, trControl=fitControl,
method="lda")
fit.lda
# Linear Discriminant Analysis
#
# 47249 samples
# 8 predictor
# 2 classes: 'FALSE', 'TRUE'
#
# No pre-processing
# Resampling: Cross-Validated (10 fold)
# Summary of sample sizes: 42525, 42524, 42523, 42523, 42524, 42525, ...
# Resampling results:
#
# Accuracy Kappa
# 0.9452899 0.6641091
table(train.data$revenue>0, predict(fit.lda, train.data))
# FALSE TRUE
# FALSE 41757 408
# TRUE 2178 2906
# Adding revenue>0 probability to training data
train.data$posterior <- predict(fit.lda, train.data, type="prob")[, "TRUE"]
# Fitting MARS model to predict the amount of LOG revenue a customer will generate
fitControl <- trainControl(method = "cv")
tuneGrid <- expand.grid(nprune=seq(20, 30, by=1), degree= 1:3)
fit.mars <- train(revenue ~ bounceViewRatio + channelGrouping + posterior +
allBounce + as.numeric(first_ses_from_the_period_start) +
as.numeric(last_ses_from_the_period_end) + pctBounce + region +
source + browser + operatingSystem + country + networkDomain +
log(bounce_sessions + 1) + bounce_sessions + pageviews_sum +
log(pageviews_sum + 1) + log(pageviews_mean + 1) + pageviews_max +
pageviews_median + log(session_cnt),
data=train.data, trControl=fitControl, tuneGrid=tuneGrid,
method="earth")
fit.mars
# Multivariate Adaptive Regression Spline
#
# 47249 samples
# 19 predictor
#
# No pre-processing
# Resampling: Cross-Validated (10 fold)
# Summary of sample sizes: 42524, 42524, 42524, 42524, 42524, 42525, ...
# Resampling results across tuning parameters:
#
# degree nprune RMSE Rsquared MAE
# 1 20 0.7236465 0.6944663 0.2931321
# 1 21 0.7236465 0.6944663 0.2931321
# 1 22 0.7236465 0.6944663 0.2931321
# 1 23 0.7236465 0.6944663 0.2931321
# 1 24 0.7236465 0.6944663 0.2931321
# 1 25 0.7236465 0.6944663 0.2931321
# 1 26 0.7236465 0.6944663 0.2931321
# 1 27 0.7236465 0.6944663 0.2931321
# 1 28 0.7236465 0.6944663 0.2931321
# 1 29 0.7236465 0.6944663 0.2931321
# 1 30 0.7236465 0.6944663 0.2931321
# 2 20 0.6945988 0.7185354 0.2398561
# 2 21 0.6947592 0.7184175 0.2398997
# 2 22 0.6945226 0.7185992 0.2401436
# 2 23 0.6943829 0.7187026 0.2399386
# 2 24 0.6943724 0.7187125 0.2400128
# 2 25 0.6942832 0.7187846 0.2400029
# 2 26 0.6942832 0.7187846 0.2400029
# 2 27 0.6942832 0.7187846 0.2400029
# 2 28 0.6942832 0.7187846 0.2400029
# 2 29 0.6942832 0.7187846 0.2400029
# 2 30 0.6942832 0.7187846 0.2400029
# 3 20 0.6896715 0.7225496 0.2325857
# 3 21 0.6896411 0.7225770 0.2324190
# 3 22 0.6896647 0.7225595 0.2323578
# 3 23 0.6896298 0.7225877 0.2323725
# 3 24 0.6896298 0.7225877 0.2323725
# 3 25 0.6896298 0.7225877 0.2323725
# 3 26 0.6896298 0.7225877 0.2323725
# 3 27 0.6896298 0.7225877 0.2323725
# 3 28 0.6896298 0.7225877 0.2323725
# 3 29 0.6896298 0.7225877 0.2323725
# 3 30 0.6896298 0.7225877 0.2323725
#
# RMSE was used to select the optimal model using the smallest value.
# The final values used for the model were nprune = 23 and degree = 3.
plot(fit.mars)
# Finally, preparing the test data
# Getting the LDA probabilities
test.data$posterior <- predict(fit.lda, test.data, type="prob")[, "TRUE"]
# Predicting test data using MARS
preds.mars <- predict(fit.mars, newdata=test.data)
preds.mars[preds.mars < 0] <- 0
tibble(custId = test.data$custId, predRevenue = preds.mars[,"y"]) %>%
write.csv("Submission.csv", row.names = F)