IP_WK14_Association_Rules
Geoffrey Chege
2022-04-04
1. Introduction
Defining the question
I am a Data analyst at Carrefour Kenya and are currently undertaking a project that will inform the marketing department on the most relevant marketing strategies that will result in the highest no. of sales (total price including tax).
I am expected to find out the associations between products.
Metric for success
Be able to effectively identify associations between the different products
Understanding the context
Carrefour operates different store formats, as well as multiple online offerings to meet the growing needs of its diversified customer base. In line with the brand’s commitment to provide the widest range of quality products and value for money, Carrefour offers an unrivalled choice of more than 500,000 food and non-food products, and a locally inspired exemplary customer experience to create great moments for everyone every day.
Recording the experimental design
- Problem Definition
- Association Analysis
- Provide insights based on my analysis
- Provide recommendations
Data Relevance
Link to the dataset: http://bit.ly/SupermarketDatasetII
2. Installing packages and loading libraries
# Installing the necessary packages
install.packages(c("arules", "tidyverse"))# Loading the libraries
library(arules)## Loading required package: Matrix##
## Attaching package: 'arules'## The following objects are masked from 'package:base':
##
## abbreviate, writelibrary(tidyverse)## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --## v ggplot2 3.3.5 v purrr 0.3.4
## v tibble 3.1.6 v dplyr 1.0.8
## v tidyr 1.2.0 v stringr 1.4.0
## v readr 2.1.2 v forcats 0.5.1## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x tidyr::expand() masks Matrix::expand()
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
## x tidyr::pack() masks Matrix::pack()
## x dplyr::recode() masks arules::recode()
## x tidyr::unpack() masks Matrix::unpack()3. Loading the dataset
# Reading the dataset
assos <- read.transactions("C:/Users/user/Downloads/Supermarket_Sales_Dataset II Par 3.csv", sep = ",", rm.duplicates = TRUE)## distribution of transactions with duplicates:
## 1
## 5assos## transactions in sparse format with
## 7501 transactions (rows) and
## 119 items (columns)# Verifying the object's class
class(assos)## [1] "transactions"
## attr(,"package")
## [1] "arules"# Previewing first 5 transactions
inspect(assos[1:5])## items
## [1] {almonds,
## antioxydant juice,
## avocado,
## cottage cheese,
## energy drink,
## frozen smoothie,
## green grapes,
## green tea,
## honey,
## low fat yogurt,
## mineral water,
## olive oil,
## salad,
## salmon,
## shrimp,
## spinach,
## tomato juice,
## vegetables mix,
## whole weat flour,
## yams}
## [2] {burgers,
## eggs,
## meatballs}
## [3] {chutney}
## [4] {avocado,
## turkey}
## [5] {energy bar,
## green tea,
## milk,
## mineral water,
## whole wheat rice}# Getting a summary of the transactions
summary(assos)## transactions as itemMatrix in sparse format with
## 7501 rows (elements/itemsets/transactions) and
## 119 columns (items) and a density of 0.03288973
##
## most frequent items:
## mineral water eggs spaghetti french fries chocolate
## 1788 1348 1306 1282 1229
## (Other)
## 22405
##
## element (itemset/transaction) length distribution:
## sizes
## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
## 1754 1358 1044 816 667 493 391 324 259 139 102 67 40 22 17 4
## 18 19 20
## 1 2 1
##
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 2.000 3.000 3.914 5.000 20.000
##
## includes extended item information - examples:
## labels
## 1 almonds
## 2 antioxydant juice
## 3 asparagus# Plotting the most frequent items both with and without setting the support lower limit
options(repr.plot.width = 15, repr.plot.height = 10)
par(mfrow = c(1, 2))
itemFrequencyPlot(assos, topN = 10,col="lightblue", main = "Frequency plot (default)", cex = 1.5, cex.main= 1.75, cex.lab=1.5, cex.axis=1.2)
itemFrequencyPlot(assos, support = 0.1,col="orange", main = "Frequency plot(supp=0.1)", cex = 1.5, cex.main= 1.75, cex.lab=1.5, cex.axis=1.2)
# Building a model based on association rules using the apriori function
# supp = 0.001, conf = 0.8
rules <- apriori (assos, parameter = list(supp = 0.001, conf = 0.8))## Apriori
##
## Parameter specification:
## confidence minval smax arem aval originalSupport maxtime support minlen
## 0.8 0.1 1 none FALSE TRUE 5 0.001 1
## maxlen target ext
## 10 rules TRUE
##
## Algorithmic control:
## filter tree heap memopt load sort verbose
## 0.1 TRUE TRUE FALSE TRUE 2 TRUE
##
## Absolute minimum support count: 7
##
## set item appearances ...[0 item(s)] done [0.00s].
## set transactions ...[119 item(s), 7501 transaction(s)] done [0.00s].
## sorting and recoding items ... [116 item(s)] done [0.00s].
## creating transaction tree ... done [0.00s].
## checking subsets of size 1 2 3 4 5 6 done [0.01s].
## writing ... [74 rule(s)] done [0.00s].
## creating S4 object ... done [0.00s].rules## set of 74 rules# Building a model based on association rules using the apriori function
# supp = 0.002, conf = 0.8
rules1 <- apriori (assos, parameter = list(supp = 0.002, conf = 0.8))## Apriori
##
## Parameter specification:
## confidence minval smax arem aval originalSupport maxtime support minlen
## 0.8 0.1 1 none FALSE TRUE 5 0.002 1
## maxlen target ext
## 10 rules TRUE
##
## Algorithmic control:
## filter tree heap memopt load sort verbose
## 0.1 TRUE TRUE FALSE TRUE 2 TRUE
##
## Absolute minimum support count: 15
##
## set item appearances ...[0 item(s)] done [0.00s].
## set transactions ...[119 item(s), 7501 transaction(s)] done [0.00s].
## sorting and recoding items ... [115 item(s)] done [0.01s].
## creating transaction tree ... done [0.00s].
## checking subsets of size 1 2 3 4 5 done [0.00s].
## writing ... [2 rule(s)] done [0.00s].
## creating S4 object ... done [0.00s].rules1## set of 2 rules# Building a model based on association rules using the apriori function
# supp = 0.001, conf = 0.6
rules2 <- apriori (assos, parameter = list(supp = 0.001, conf = 0.6))## Apriori
##
## Parameter specification:
## confidence minval smax arem aval originalSupport maxtime support minlen
## 0.6 0.1 1 none FALSE TRUE 5 0.001 1
## maxlen target ext
## 10 rules TRUE
##
## Algorithmic control:
## filter tree heap memopt load sort verbose
## 0.1 TRUE TRUE FALSE TRUE 2 TRUE
##
## Absolute minimum support count: 7
##
## set item appearances ...[0 item(s)] done [0.00s].
## set transactions ...[119 item(s), 7501 transaction(s)] done [0.00s].
## sorting and recoding items ... [116 item(s)] done [0.00s].
## creating transaction tree ... done [0.00s].
## checking subsets of size 1 2 3 4 5 6 done [0.01s].
## writing ... [545 rule(s)] done [0.00s].
## creating S4 object ... done [0.00s].rules2## set of 545 rulesI will use model wih 74 rules
# Observing rules built in our model i.e. first 10 model rules
inspect(rules[1:10])## lhs rhs support confidence
## [1] {frozen smoothie, spinach} => {mineral water} 0.001066524 0.8888889
## [2] {bacon, pancakes} => {spaghetti} 0.001733102 0.8125000
## [3] {nonfat milk, turkey} => {mineral water} 0.001199840 0.8181818
## [4] {ground beef, nonfat milk} => {mineral water} 0.001599787 0.8571429
## [5] {mushroom cream sauce, pasta} => {escalope} 0.002532996 0.9500000
## [6] {milk, pasta} => {shrimp} 0.001599787 0.8571429
## [7] {cooking oil, fromage blanc} => {mineral water} 0.001199840 0.8181818
## [8] {black tea, salmon} => {mineral water} 0.001066524 0.8000000
## [9] {black tea, frozen smoothie} => {milk} 0.001199840 0.8181818
## [10] {red wine, tomato sauce} => {chocolate} 0.001066524 0.8000000
## coverage lift count
## [1] 0.001199840 3.729058 8
## [2] 0.002133049 4.666587 13
## [3] 0.001466471 3.432428 9
## [4] 0.001866418 3.595877 12
## [5] 0.002666311 11.976387 19
## [6] 0.001866418 11.995203 12
## [7] 0.001466471 3.432428 9
## [8] 0.001333156 3.356152 8
## [9] 0.001466471 6.313973 9
## [10] 0.001333156 4.882669 8# Inspecting the first 5 rules with the highest lift
inspect(head(rules, n = 5, by = "lift"))## lhs rhs support confidence coverage lift count
## [1] {eggs,
## mineral water,
## pasta} => {shrimp} 0.001333156 0.9090909 0.001466471 12.722185 10
## [2] {french fries,
## mushroom cream sauce,
## pasta} => {escalope} 0.001066524 1.0000000 0.001066524 12.606723 8
## [3] {milk,
## pasta} => {shrimp} 0.001599787 0.8571429 0.001866418 11.995203 12
## [4] {mushroom cream sauce,
## pasta} => {escalope} 0.002532996 0.9500000 0.002666311 11.976387 19
## [5] {chocolate,
## ground beef,
## milk,
## mineral water,
## spaghetti} => {frozen vegetables} 0.001066524 0.8888889 0.001199840 9.325253 8# Inspecting the first 5 rules with the highest confidence
inspect(head(rules, n = 5, by = "confidence"))## lhs rhs support confidence coverage lift count
## [1] {french fries,
## mushroom cream sauce,
## pasta} => {escalope} 0.001066524 1.00 0.001066524 12.606723 8
## [2] {ground beef,
## light cream,
## olive oil} => {mineral water} 0.001199840 1.00 0.001199840 4.195190 9
## [3] {cake,
## meatballs,
## mineral water} => {milk} 0.001066524 1.00 0.001066524 7.717078 8
## [4] {cake,
## olive oil,
## shrimp} => {mineral water} 0.001199840 1.00 0.001199840 4.195190 9
## [5] {mushroom cream sauce,
## pasta} => {escalope} 0.002532996 0.95 0.002666311 11.976387 19# Looking at the least popular transactions
itm <- itemFrequency(assos, type = "relative")
head(sort(itm), n = 10)## water spray napkins cream bramble tea
## 0.0003999467 0.0006665778 0.0009332089 0.0018664178 0.0038661512
## chutney mashed potato chocolate bread dessert wine ketchup
## 0.0041327823 0.0041327823 0.0042660979 0.0043994134 0.0043994134# We may want to make a promotion to increase the sale of Tea
# Let us look at what people buy after buying tea
tea = subset(rules, subset = lhs %pin% "tea")
# Then order by confidence
tea = sort(tea, by="confidence", decreasing=TRUE)
inspect(tea[1:5])## lhs rhs support
## [1] {black tea, spaghetti, turkey} => {eggs} 0.001066524
## [2] {green tea, ground beef, tomato sauce} => {spaghetti} 0.001333156
## [3] {black tea, frozen smoothie} => {milk} 0.001199840
## [4] {black tea, salmon} => {mineral water} 0.001066524
## [5] {cookies, green tea, milk} => {french fries} 0.001066524
## confidence coverage lift count
## [1] 0.8888889 0.001199840 4.946258 8
## [2] 0.8333333 0.001599787 4.786243 10
## [3] 0.8181818 0.001466471 6.313973 9
## [4] 0.8000000 0.001333156 3.356152 8
## [5] 0.8000000 0.001333156 4.680811 8# We may want to make a promotion to increase the sale of ground beef
# Let us look at what people buy after buying ground beef
beef = subset(rules, subset = lhs %pin% "ground beef")
beef## set of 12 rules# Then order by confidence
beef = sort(beef, by="confidence", decreasing=TRUE)
inspect(beef[1:5])## lhs rhs support confidence coverage lift count
## [1] {ground beef,
## light cream,
## olive oil} => {mineral water} 0.001199840 1.0000000 0.001199840 4.195190 9
## [2] {ground beef,
## pancakes,
## whole wheat rice} => {mineral water} 0.001333156 0.9090909 0.001466471 3.813809 10
## [3] {brownies,
## eggs,
## ground beef} => {mineral water} 0.001066524 0.8888889 0.001199840 3.729058 8
## [4] {ground beef,
## salmon,
## shrimp} => {spaghetti} 0.001066524 0.8888889 0.001199840 5.105326 8
## [5] {chocolate,
## ground beef,
## milk,
## mineral water,
## spaghetti} => {frozen vegetables} 0.001066524 0.8888889 0.001199840 9.325253 8Insights
The insights that can be made from the analysis are as follows:
The three most frequently bought items are mineral water, eggs and spaghetti.
The 3 least frequently bought items are water spray, napkins and cream. Tea is also among the least frequently purchased items.
Ground beef, frozen vegetabls and pancakes fell off the most frequently bought items list after support was set to 0.1.
Recommendations
In light of the above insights, the following recommendations can be made:
To increase the sale of tea, there could be a promotion where tea is sold with milk, eggs or cookies.
To increase the sale of ground beef, an offer can be given where ground beef us sold with say, a free bottle of mineral water.