For Machine Learning and Web Analytics blogpost http://markedmondson.me/intro-to-machine-learning-with-web-analytics-random-forests-and-k-means

ml_trans_data.R

## want: 30049 x 187 
## userId, product1_view, product2_view, ...., productN_view, productBought
pv <- reshape2::recast(product_views, 
                       dimension1 ~ productSku + variable, 
                       fun.aggregate=sum)

library(dplyr)
## if a user buys more than once, the row will be duplicated
pt <-  product_trans %>% select(productSku, dimension1)

model_data <- left_join(pv, pt)

## NAs are no sale
model_data$boughtSku[is.na(model_data$boughtSku)] <- "NoSale"

## splitting into training and test:

## 75% of the sample size
smp_size <- floor(0.75 * nrow(model_data))

## set the seed to make your partition reproductible
set.seed(123)
train_ind <- sample(seq_len(nrow(model_data)), size = smp_size)

## split the data
train <- model_data[train_ind, ]
test <- model_data[-train_ind, ]

## what to use in the model
predictors <- train[,which(!names(train) %in% c("dimension1","boughtSku"))]
response <- as.factor(train[,"boughtSku"])