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Kmeans + NLP for Wine Preference Groups.R
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Kmeans + NLP for Wine Preference Groups.R
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library(tm)
library(wordcloud)
#Data Tech for Wine - Sample Data Analysis
customer_data <- data.frame(seq(1:1000))
names(customer_data) <- 'ID'
customer_data$registration_date <- sort(sample(seq(as.Date('2019/01/01'),
as.Date('2020/01/01'), by="day"),
size = 1000, replace = TRUE),
decreasing = FALSE)
customer_data$name <- sample(x = NA, size = 1000, replace = TRUE)
customer_data$registration_methods <- sample(x = c('Facebook', 'Google',
'Manual', 'Unregistered'),
size = 1000, replace = TRUE)
customer_data$cookies <- sample(x = c('yes', 'no'), size = 1000, replace = TRUE)
table(customer_data$cookies)
customer_data$time_spent[customer_data$cookies == 'yes'] <- sample(x = c(1:60),
size = 489,
replace = TRUE)
customer_data$gender <- sample(x = c('F', 'M', NA), size = 1000, replace = TRUE)
customer_data$age <- sample(x = c(18:85), size = 1000, replace = TRUE)
customer_data$wine_knowledge <- sample(x = c('newbie', 'somewhat', 'expert'),
size = 1000, replace = TRUE)
customer_data$country <- sample(x = 'Australia',
size = 1000, replace = TRUE)
customer_data$region <- sample(x = c('South Australia', 'Victoria', 'New South Wales',
'Queensland', 'Tasmania', 'Western Australia',
'Australia Capital Territory', 'North Territories'),
size = 1000, replace = TRUE)
customer_data$wine <- sample(x = c('fruity', 'light', 'medium', 'full body',
'organic', 'vegan', 'no sugar', 'soft',
'sweet', 'other'),
size = 1000, replace = TRUE)
customer_data$food <- sample(x = c('Meat', 'Fish', 'Pasta',
'Sushi', 'Vegetarian', 'Vegan',
'Other'),
size = 1000, replace = TRUE)
customer_data$music <- sample(x = c('Rock', 'Rap', 'Pop',
'Classical', 'Country',
'Reggae', 'Jazz', 'Techno',
'Other'),
size = 1000, replace = TRUE)
customer_data$cities <- sample(x = c('Buenos Aires', 'Sydney', 'Rio',
'London', 'New York', 'Cape Town',
'Tokyo', 'Shanghai', 'Wellington',
'Paris', 'Tel Aviv', 'Moscow',
'Other'),
size = 1000, replace = TRUE)
customer_data$movie <- sample(x = c('Action', 'Comedy', 'Adventure',
'Scary', 'Drama', 'Reality', 'Suspence',
'Mystery', 'Other'),
size = 1000, replace = TRUE)
winery_data <- data.frame(seq(1:50))
names(winery_data) <- 'ID'
winery_data$registration_date <- sort(sample(seq(as.Date('2019/01/01'),
as.Date('2020/01/01'), by="day"),
size = 50, replace = TRUE),
decreasing = FALSE)
winery_data$name <- sample(x = NA, size = 50, replace = TRUE)
winery_data$country <- sample(x = 'Australia', size = 50, replace = TRUE)
winery_data$region <- sample(x = c('South Australia', 'Victoria', 'New South Wales',
'Queensland', 'Tasmania', 'Western Australia',
'Australia Capital Territory', 'North Territories'),
size = 50, replace = TRUE)
winery_data$wine_maker <- sample(x = NA, size = 50, replace = TRUE)
winery_data$n_products <- sample(x = 5:100, size = 50, replace = TRUE)
winery_data$n_sales <- sample(x = 1:500, size = 50, replace = TRUE)
winery_data$earnings <- sample(x = 20:2000, size = 50, replace = TRUE)
winery_data$customer_rating_avg <- sample(x = c(1:5, NA), size = 50, replace = TRUE)
winery_data$products_rating_avg <- sample(x = c(1:5, NA), size = 50, replace = TRUE)
winery_data$total_consumers <- sample(x = c(1:100, NA), size = 50, replace = TRUE)
winery_data$direct_consumers <- sample(x = c(1:100, NA), size = 50, replace = TRUE)
winery_data$guided_consumers <- sample(x = c(1:100, NA), size = 50, replace = TRUE)
winery_data$page_viewers <- sample(x = c(1:1000, NA), size = 50, replace = TRUE)
winery_data$product_viewers <- sample(x = c(1:1000, NA), size = 50, replace = TRUE)
winery_data$wine <- sample(x = c('fruity', 'light', 'medium', 'full body',
'organic', 'vegan', 'no sugar', 'soft',
'sweet', 'other'),
size = 50, replace = TRUE)
winery_data$food <- sample(x = c('Meat', 'Fish', 'Pasta',
'Sushi', 'Vegetarian', 'Vegan',
'Other'),
size = 50, replace = TRUE)
winery_data$music <- sample(x = c('Rock', 'Rap', 'Pop',
'Classical', 'Country',
'Reggae', 'Jazz', 'Techno',
'Other'),
size = 50, replace = TRUE)
winery_data$cities <- sample(x = c('Buenos Aires', 'Sydney', 'Rio',
'London', 'New York', 'Cape Town',
'Tokyo', 'Shanghai', 'Wellington',
'Paris', 'Tel Aviv', 'Moscow',
'Other'),
size = 50, replace = TRUE)
winery_data$movie <- sample(x = c('Action', 'Comedy', 'Adventure',
'Scary', 'Drama', 'Reality', 'Suspence',
'Mystery', 'Other'),
size = 50, replace = TRUE)
customer_interests <- customer_data[,12:16]
interests_corpus <- Corpus(VectorSource(customer_interests))
interests_corpus
inspect((interests_corpus[1:5]))
corpus_clean <- tm_map(interests_corpus, removeNumbers)
corpus_clean <- tm_map(corpus_clean, removeWords, stopwords())
corpus_clean <- tm_map(corpus_clean, removePunctuation)
corpus_clean <- tm_map(corpus_clean, stripWhitespace)
corpus_clean <- tm_map(corpus_clean, tolower)
inspect(corpus_clean[1:5])
interests_dtm <- DocumentTermMatrix(corpus_clean)
kmeans4 <- kmeans(interests_dtm, 4)
interests_with_cluster <- as.data.frame(cbind(customer_interests, kmeans4$cluster))
cluster1 <- subset(interests_with_cluster,
subset = interests_with_cluster$`kmeans4$cluster` == 1)
sort(table(cluster1$wine))
sort(table(cluster1$food))
sort(table(cluster1$music))
sort(table(cluster1$cities))
sort(table(cluster1$movie))
cluster3 <- subset(interests_with_cluster,
subset = interests_with_cluster$`kmeans4$cluster` == 3)
sort(table(cluster3$wine))
sort(table(cluster3$food))
sort(table(cluster3$music))
sort(table(cluster3$cities))
sort(table(cluster3$movie))