A20

---
title: "A20 by Noah Perry & Andrew Hall"
output: html_notebook
---

Use Association Rules to do a market basket analysis for Superstore Sales. As a result of your analysis, what advice would you give to Superstore Sales?

```{r}
library(readxl)
library(arules)
library(arulesViz)

url <- "https://community.tableau.com/servlet/JiveServlet/downloadBody/1236-102-2-15278/Sample%20-%20Superstore.xls"
destfile <- "Sample_20_20Superstore.xls"
curl::curl_download(url, destfile)
ss <- read_excel(destfile)

#ss <- as (ss, "transactions") # convert to 'transactions' class
write.csv(ss,'superstore.csv')

#convert to transactions file
models <- read.transactions(file = "superstore.csv",
 sep=",", format = 'single', cols = c(1,19))
summary(models)
size(head(models))
LIST(head(models))

#Identify Frequent Item Sets
frequentItems <- eclat (models, parameter = list(supp = 0.08, maxlen = 5))
inspect(frequentItems)
itemFrequencyPlot(models, topN=10, type="absolute", main="Item Frequency")

#Identify Rules
rules <- apriori (models, parameter = list(supp = 0.06, conf = .7, maxlen = 3))
rules_conf <- sort (rules, by="confidence", decreasing=TRUE)
```