Market Basket Analysis
Piotr Radziszewski
Load the necessary libraries
Intro
Dataset for the project was taken from kaggle.com website: https://www.kaggle.com/datasets/irfanasrullah/groceries
Data Analysis
groceries <- read.csv('C:/Users/1032745/OneDrive - Blue Yonder/Desktop/PRIVATE/STUDIA/UL/PROJECTS/groceries.csv', header = TRUE)
groceries_copy <- groceriesData represents market basket. Each row is a separate basket. Each column represents iteams purchased.
dim(groceries)## [1] 9835 32Let us see which 5 products appear most and least frequently in the dataset:
vector_of_characters <- as.vector(t(groceries))
frequency_table <- sort(table(vector_of_characters), decreasing = TRUE)
frequency_df <- data.frame(value=names(frequency_table), count=frequency_table)
frequency_df <- frequency_df[-1,]
frequency_df <- frequency_df[order(-frequency_df$count.Freq, decreasing=FALSE),]
ggplot(frequency_df[1:10,], aes(x=reorder(value, count.Freq), y=count.Freq)) +
geom_col(fill="#007FFF") +
theme_classic() +
labs(x="Value", y="Count", title="Frequency of Values") +
scale_y_continuous(limits=c(0, max(frequency_df$count.Freq)), expand=c(0,0)) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
vector_of_characters2 <- as.vector(t(groceries))
frequency_table2 <- sort(table(vector_of_characters2), decreasing = FALSE)
head(frequency_table2)## vector_of_characters2
## baby food sound storage medium preservation products
## 1 1 2
## bags kitchen utensil baby cosmetics
## 4 4 6Whole milk is the most frequently bought product. “Sound storage medium” and “baby food” are the least frequently bought.
Let us have a look at count of items in the basket. What is the most common number of items?
groceries_copy$num_items <- rowSums(groceries_copy != "")
groceries_copy %>%
count(num_items) %>%
ggplot(aes(x=num_items, y=n)) +
geom_bar(fill = "blue", stat="identity") +
scale_fill_gradient(low="lightblue", high="blue")
The most popular basket contains only one product. Then it regulary decreases what can be seen on the chart.
ALGORITHMS
Now having completed data analysis let us perform algorithms on the data.
ECLAT
trans1<-read.transactions("C:/Users/1032745/OneDrive - Blue Yonder/Desktop/PRIVATE/STUDIA/UL/PROJECTS/groceries.csv", format="basket", sep=",", skip=1, header=TRUE)
round(itemFrequency(trans1),3)## abrasive cleaner artif. sweetener baby cosmetics
## 0.004 0.003 0.001
## baby food bags baking powder
## 0.000 0.000 0.018
## bathroom cleaner beef berries
## 0.003 0.052 0.033
## beverages bottled beer bottled water
## 0.026 0.081 0.111
## brandy brown bread butter
## 0.004 0.065 0.055
## butter milk cake bar candles
## 0.028 0.013 0.009
## candy canned beer canned fish
## 0.030 0.078 0.015
## canned fruit canned vegetables cat food
## 0.003 0.011 0.023
## cereals chewing gum chicken
## 0.006 0.021 0.043
## chocolate chocolate marshmallow citrus fruit
## 0.050 0.009 0.083
## cleaner cling film/bags cocoa drinks
## 0.005 0.011 0.002
## coffee condensed milk cooking chocolate
## 0.058 0.010 0.003
## cookware cream cream cheese
## 0.003 0.001 0.040
## curd curd cheese decalcifier
## 0.053 0.005 0.002
## dental care dessert detergent
## 0.006 0.037 0.019
## dish cleaner dishes dog food
## 0.010 0.018 0.009
## domestic eggs female sanitary products finished products
## 0.063 0.006 0.007
## fish flour flower (seeds)
## 0.003 0.017 0.010
## flower soil/fertilizer frankfurter frozen chicken
## 0.002 0.059 0.001
## frozen dessert frozen fish frozen fruits
## 0.011 0.012 0.001
## frozen meals frozen potato products frozen vegetables
## 0.028 0.008 0.048
## fruit/vegetable juice grapes hair spray
## 0.072 0.022 0.001
## ham hamburger meat hard cheese
## 0.026 0.033 0.025
## herbs honey house keeping products
## 0.016 0.002 0.008
## hygiene articles ice cream instant coffee
## 0.033 0.025 0.007
## Instant food products jam ketchup
## 0.008 0.005 0.004
## kitchen towels kitchen utensil light bulbs
## 0.006 0.000 0.004
## liqueur liquor liquor (appetizer)
## 0.001 0.011 0.008
## liver loaf long life bakery product make up remover
## 0.005 0.037 0.001
## male cosmetics margarine mayonnaise
## 0.005 0.058 0.009
## meat meat spreads misc. beverages
## 0.026 0.004 0.028
## mustard napkins newspapers
## 0.012 0.052 0.080
## nut snack nuts/prunes oil
## 0.003 0.003 0.028
## onions organic products organic sausage
## 0.031 0.002 0.002
## other vegetables packaged fruit/vegetables pasta
## 0.194 0.013 0.015
## pastry pet care photo/film
## 0.089 0.009 0.009
## pickled vegetables pip fruit popcorn
## 0.018 0.076 0.007
## pork potato products potted plants
## 0.058 0.003 0.017
## preservation products processed cheese prosecco
## 0.000 0.017 0.002
## pudding powder ready soups red/blush wine
## 0.002 0.002 0.019
## rice roll products rolls/buns
## 0.008 0.010 0.184
## root vegetables rubbing alcohol rum
## 0.109 0.001 0.004
## salad dressing salt salty snack
## 0.001 0.011 0.038
## sauces sausage seasonal products
## 0.005 0.094 0.014
## semi-finished bread shopping bags skin care
## 0.018 0.099 0.004
## sliced cheese snack products soap
## 0.025 0.003 0.003
## soda soft cheese softener
## 0.174 0.017 0.005
## sound storage medium soups sparkling wine
## 0.000 0.007 0.006
## specialty bar specialty cheese specialty chocolate
## 0.027 0.009 0.030
## specialty fat specialty vegetables spices
## 0.004 0.002 0.005
## spread cheese sugar sweet spreads
## 0.011 0.034 0.009
## syrup tea tidbits
## 0.003 0.004 0.002
## toilet cleaner tropical fruit turkey
## 0.001 0.105 0.008
## UHT-milk vinegar waffles
## 0.033 0.007 0.038
## whipped/sour cream whisky white bread
## 0.072 0.001 0.042
## white wine whole milk yogurt
## 0.019 0.256 0.140
## zwieback
## 0.007inspect(head(trans1))## items
## [1] {coffee,
## tropical fruit,
## yogurt}
## [2] {whole milk}
## [3] {cream cheese,
## meat spreads,
## pip fruit,
## yogurt}
## [4] {condensed milk,
## long life bakery product,
## other vegetables,
## whole milk}
## [5] {abrasive cleaner,
## butter,
## rice,
## whole milk,
## yogurt}
## [6] {rolls/buns}freq.items<-eclat(trans1, parameter=list(supp=0.025, maxlen=15))## Eclat
##
## parameter specification:
## tidLists support minlen maxlen target ext
## FALSE 0.025 1 15 frequent itemsets TRUE
##
## algorithmic control:
## sparse sort verbose
## 7 -2 TRUE
##
## Absolute minimum support count: 245
##
## create itemset ...
## set transactions ...[169 item(s), 9834 transaction(s)] done [0.01s].
## sorting and recoding items ... [54 item(s)] done [0.00s].
## creating sparse bit matrix ... [54 row(s), 9834 column(s)] done [0.00s].
## writing ... [88 set(s)] done [0.02s].
## Creating S4 object ... done [0.00s].inspect(freq.items) ## items support count
## [1] {curd, whole milk} 0.02613382 257
## [2] {brown bread, whole milk} 0.02521863 248
## [3] {butter, whole milk} 0.02755745 271
## [4] {newspapers, whole milk} 0.02735408 269
## [5] {domestic eggs, whole milk} 0.02999797 295
## [6] {fruit/vegetable juice, whole milk} 0.02664226 262
## [7] {whipped/sour cream, whole milk} 0.03223510 317
## [8] {other vegetables, whipped/sour cream} 0.02887940 284
## [9] {pip fruit, whole milk} 0.03009965 296
## [10] {other vegetables, pip fruit} 0.02613382 257
## [11] {pastry, whole milk} 0.03325198 327
## [12] {citrus fruit, whole milk} 0.03050641 300
## [13] {citrus fruit, other vegetables} 0.02887940 284
## [14] {sausage, whole milk} 0.02989628 294
## [15] {other vegetables, sausage} 0.02694733 265
## [16] {rolls/buns, sausage} 0.03060809 301
## [17] {bottled water, whole milk} 0.03437055 338
## [18] {bottled water, soda} 0.02898109 285
## [19] {tropical fruit, whole milk} 0.04230222 416
## [20] {other vegetables, tropical fruit} 0.03589587 353
## [21] {tropical fruit, yogurt} 0.02928615 288
## [22] {root vegetables, whole milk} 0.04891194 481
## [23] {other vegetables, root vegetables} 0.04738662 466
## [24] {root vegetables, yogurt} 0.02582876 254
## [25] {soda, whole milk} 0.04006508 394
## [26] {other vegetables, soda} 0.03274354 322
## [27] {rolls/buns, soda} 0.03833638 377
## [28] {soda, yogurt} 0.02735408 269
## [29] {whole milk, yogurt} 0.05603010 551
## [30] {other vegetables, yogurt} 0.04342079 427
## [31] {rolls/buns, yogurt} 0.03437055 338
## [32] {rolls/buns, whole milk} 0.05664023 557
## [33] {other vegetables, rolls/buns} 0.04260728 419
## [34] {other vegetables, whole milk} 0.07484238 736
## [35] {whole milk} 0.25554200 2513
## [36] {other vegetables} 0.19351230 1903
## [37] {rolls/buns} 0.18395363 1809
## [38] {yogurt} 0.13951597 1372
## [39] {soda} 0.17439496 1715
## [40] {root vegetables} 0.10900956 1072
## [41] {tropical fruit} 0.10494204 1032
## [42] {bottled water} 0.11053488 1087
## [43] {sausage} 0.09395973 924
## [44] {shopping bags} 0.09853569 969
## [45] {citrus fruit} 0.08267236 813
## [46] {pastry} 0.08897702 875
## [47] {pip fruit} 0.07565589 744
## [48] {whipped/sour cream} 0.07169005 705
## [49] {fruit/vegetable juice} 0.07230018 711
## [50] {domestic eggs} 0.06345333 624
## [51] {newspapers} 0.07982510 785
## [52] {butter} 0.05541997 545
## [53] {margarine} 0.05847061 575
## [54] {brown bread} 0.06487696 638
## [55] {bottled beer} 0.08053691 792
## [56] {frankfurter} 0.05897905 580
## [57] {pork} 0.05765711 567
## [58] {napkins} 0.05236933 515
## [59] {curd} 0.05328452 524
## [60] {beef} 0.05247102 516
## [61] {frozen vegetables} 0.04809843 473
## [62] {coffee} 0.05806386 571
## [63] {chocolate} 0.04962375 488
## [64] {white bread} 0.04209884 414
## [65] {chicken} 0.04291234 422
## [66] {cream cheese} 0.03965833 390
## [67] {canned beer} 0.07768965 764
## [68] {dessert} 0.03711613 365
## [69] {long life bakery product} 0.03742119 368
## [70] {waffles} 0.03843807 378
## [71] {sugar} 0.03386211 333
## [72] {salty snack} 0.03782794 372
## [73] {hygiene articles} 0.03294692 324
## [74] {hamburger meat} 0.03325198 327
## [75] {berries} 0.03325198 327
## [76] {onions} 0.03101485 305
## [77] {oil} 0.02806589 276
## [78] {UHT-milk} 0.03345536 329
## [79] {ham} 0.02603213 256
## [80] {candy} 0.02989628 294
## [81] {butter milk} 0.02796421 275
## [82] {frozen meals} 0.02837096 279
## [83] {meat} 0.02582876 254
## [84] {specialty chocolate} 0.03040472 299
## [85] {misc. beverages} 0.02837096 279
## [86] {specialty bar} 0.02735408 269
## [87] {ice cream} 0.02501525 246
## [88] {beverages} 0.02603213 256round(support(items(freq.items), trans1) , 2)## [1] 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03
## [16] 0.03 0.03 0.03 0.04 0.04 0.03 0.05 0.05 0.03 0.04 0.03 0.04 0.03 0.06 0.04
## [31] 0.03 0.06 0.04 0.07 0.26 0.19 0.18 0.14 0.17 0.11 0.10 0.11 0.09 0.10 0.08
## [46] 0.09 0.08 0.07 0.07 0.06 0.08 0.06 0.06 0.06 0.08 0.06 0.06 0.05 0.05 0.05
## [61] 0.05 0.06 0.05 0.04 0.04 0.04 0.08 0.04 0.04 0.04 0.03 0.04 0.03 0.03 0.03
## [76] 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03freq.rules<-ruleInduction(freq.items, trans1, confidence=0.4)
freq.rules## set of 9 rulesOnly assuming confidence at the 0.4 level, one can inspect the rules. This means that the strenght of the determined rules is between 100% and 40%. Let’s now look at the mined rules.
inspect(freq.rules)## lhs rhs support confidence lift
## [1] {curd} => {whole milk} 0.02613382 0.4904580 1.919285
## [2] {butter} => {whole milk} 0.02755745 0.4972477 1.945855
## [3] {domestic eggs} => {whole milk} 0.02999797 0.4727564 1.850015
## [4] {whipped/sour cream} => {whole milk} 0.03223510 0.4496454 1.759575
## [5] {whipped/sour cream} => {other vegetables} 0.02887940 0.4028369 2.081712
## [6] {tropical fruit} => {whole milk} 0.04230222 0.4031008 1.577435
## [7] {root vegetables} => {whole milk} 0.04891194 0.4486940 1.755852
## [8] {root vegetables} => {other vegetables} 0.04738662 0.4347015 2.246376
## [9] {yogurt} => {whole milk} 0.05603010 0.4016035 1.571575
## itemset
## [1] 1
## [2] 3
## [3] 5
## [4] 7
## [5] 8
## [6] 19
## [7] 22
## [8] 23
## [9] 29plot(freq.rules, method="matrix", measure=c("support","confidence"))## Itemsets in Antecedent (LHS)
## [1] "{root vegetables}" "{butter}" "{whipped/sour cream}"
## [4] "{curd}" "{domestic eggs}" "{tropical fruit}"
## [7] "{yogurt}"
## Itemsets in Consequent (RHS)
## [1] "{whole milk}" "{other vegetables}"
APRIORI
rules.trans1<-apriori(trans1, parameter=list(supp=0.005, conf=0.6)) ## Apriori
##
## Parameter specification:
## confidence minval smax arem aval originalSupport maxtime support minlen
## 0.6 0.1 1 none FALSE TRUE 5 0.005 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: 49
##
## set item appearances ...[0 item(s)] done [0.00s].
## set transactions ...[169 item(s), 9834 transaction(s)] done [0.00s].
## sorting and recoding items ... [120 item(s)] done [0.00s].
## creating transaction tree ... done [0.00s].
## checking subsets of size 1 2 3 4 done [0.00s].
## writing ... [22 rule(s)] done [0.00s].
## creating S4 object ... done [0.00s].rules.by.conf<-sort(rules.trans1, by="confidence", decreasing=TRUE)
inspect(head(rules.by.conf))## lhs rhs support confidence coverage lift count
## [1] {root vegetables,
## tropical fruit,
## yogurt} => {whole milk} 0.005694529 0.7000000 0.008135042 2.739276 56
## [2] {other vegetables,
## pip fruit,
## root vegetables} => {whole milk} 0.005491153 0.6750000 0.008135042 2.641444 54
## [3] {butter,
## whipped/sour cream} => {whole milk} 0.006711409 0.6600000 0.010168802 2.582746 66
## [4] {pip fruit,
## whipped/sour cream} => {whole milk} 0.005999593 0.6483516 0.009253610 2.537163 59
## [5] {butter,
## yogurt} => {whole milk} 0.009355298 0.6388889 0.014643075 2.500133 92
## [6] {butter,
## root vegetables} => {whole milk} 0.008236730 0.6377953 0.012914379 2.495853 81Second part of the analysis
What makes people buy a yogurt? A yogurt is defined in the RHS, i.e. as a consequence of other purchases
Generating rules
rules.yogurt<-apriori(data=trans1, parameter=list(supp=0.001,conf = 0.08),
appearance=list(default="lhs", rhs="yogurt"), control=list(verbose=F))Sorting and displaying the rules
rules.yogurt.byconf<-sort(rules.yogurt, by="confidence", decreasing=TRUE)
inspect(head(rules.yogurt.byconf))## lhs rhs support confidence coverage lift count
## [1] {butter,
## cream cheese,
## root vegetables} => {yogurt} 0.001016880 0.9090909 0.001118568 6.516035 10
## [2] {butter,
## sliced cheese,
## tropical fruit,
## whole milk} => {yogurt} 0.001016880 0.9090909 0.001118568 6.516035 10
## [3] {cream cheese,
## curd,
## other vegetables,
## whipped/sour cream} => {yogurt} 0.001016880 0.9090909 0.001118568 6.516035 10
## [4] {butter,
## other vegetables,
## tropical fruit,
## white bread} => {yogurt} 0.001016880 0.9090909 0.001118568 6.516035 10
## [5] {pip fruit,
## sausage,
## sliced cheese} => {yogurt} 0.001220256 0.8571429 0.001423632 6.143690 12
## [6] {butter,
## curd,
## tropical fruit,
## whole milk} => {yogurt} 0.001220256 0.8571429 0.001423632 6.143690 12What people will buy if they have a yogurt in their basket?
A yogurt is defined in the LHS as a developer of other purchases
rules.yogurt<-apriori(data=trans1, parameter=list(supp=0.001,conf = 0.08),
appearance=list(default="rhs",lhs="yogurt"), control=list(verbose=F))
rules.yogurt.byconf<-sort(rules.yogurt, by="confidence", decreasing=TRUE)
inspect(head(rules.yogurt.byconf))## lhs rhs support confidence coverage lift
## [1] {yogurt} => {whole milk} 0.05603010 0.4016035 0.139516 1.571575
## [2] {yogurt} => {other vegetables} 0.04342079 0.3112245 0.139516 1.608293
## [3] {} => {whole milk} 0.25554200 0.2555420 1.000000 1.000000
## [4] {yogurt} => {rolls/buns} 0.03437055 0.2463557 0.139516 1.339227
## [5] {yogurt} => {tropical fruit} 0.02928615 0.2099125 0.139516 2.000271
## [6] {yogurt} => {soda} 0.02735408 0.1960641 0.139516 1.124253
## count
## [1] 551
## [2] 427
## [3] 2513
## [4] 338
## [5] 288
## [6] 269trans1.closed<-apriori(trans1, parameter=list(target="closed frequent itemsets", support=0.15))## Apriori
##
## Parameter specification:
## confidence minval smax arem aval originalSupport maxtime support minlen
## NA 0.1 1 none FALSE TRUE 5 0.15 1
## maxlen target ext
## 10 closed frequent itemsets TRUE
##
## Algorithmic control:
## filter tree heap memopt load sort verbose
## 0.1 TRUE TRUE FALSE TRUE 2 TRUE
##
## Absolute minimum support count: 1475
##
## set item appearances ...[0 item(s)] done [0.00s].
## set transactions ...[169 item(s), 9834 transaction(s)] done [0.00s].
## sorting and recoding items ... [4 item(s)] done [0.00s].
## creating transaction tree ... done [0.00s].
## checking subsets of size 1 2 done [0.00s].
## filtering closed item sets ... done [0.00s].
## sorting transactions ... done [0.00s].
## writing ... [4 set(s)] done [0.00s].
## creating S4 object ... done [0.00s].inspect(trans1.closed)## items support count
## [1] {soda} 0.1743950 1715
## [2] {rolls/buns} 0.1839536 1809
## [3] {other vegetables} 0.1935123 1903
## [4] {whole milk} 0.2555420 2513is.closed(trans1.closed)## {soda} {rolls/buns} {other vegetables} {whole milk}
## TRUE TRUE TRUE TRUEfreq.closed<-eclat(trans1, parameter=list(supp=0.15, maxlen=15, target="closed frequent itemsets"))## Eclat
##
## parameter specification:
## tidLists support minlen maxlen target ext
## FALSE 0.15 1 15 closed frequent itemsets TRUE
##
## algorithmic control:
## sparse sort verbose
## 7 -2 TRUE
##
## Absolute minimum support count: 1475
##
## create itemset ...
## set transactions ...[169 item(s), 9834 transaction(s)] done [0.01s].
## sorting and recoding items ... [4 item(s)] done [0.00s].
## creating bit matrix ... [4 row(s), 9834 column(s)] done [0.00s].
## writing ... [4 set(s)] done [0.00s].
## Creating S4 object ... done [0.00s].inspect(freq.closed)## items support count
## [1] {whole milk} 0.2555420 2513
## [2] {other vegetables} 0.1935123 1903
## [3] {rolls/buns} 0.1839536 1809
## [4] {soda} 0.1743950 1715is.closed(freq.closed)## {whole milk} {other vegetables} {rolls/buns} {soda}
## TRUE TRUE TRUE TRUEfreq.max<-eclat(trans1, parameter=list(supp=0.15, maxlen=15, target="maximally frequent itemsets"))## Eclat
##
## parameter specification:
## tidLists support minlen maxlen target ext
## FALSE 0.15 1 15 maximally frequent itemsets TRUE
##
## algorithmic control:
## sparse sort verbose
## 7 -2 TRUE
##
## Absolute minimum support count: 1475
##
## create itemset ...
## set transactions ...[169 item(s), 9834 transaction(s)] done [0.00s].
## sorting and recoding items ... [4 item(s)] done [0.00s].
## creating bit matrix ... [4 row(s), 9834 column(s)] done [0.00s].
## writing ... [4 set(s)] done [0.00s].
## Creating S4 object ... done [0.00s].inspect(freq.max)## items support count
## [1] {whole milk} 0.2555420 2513
## [2] {other vegetables} 0.1935123 1903
## [3] {rolls/buns} 0.1839536 1809
## [4] {soda} 0.1743950 1715Not clear output, soda is not more frequent than individual baskets
is.significant(rules.yogurt, trans1) ## [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [13] TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE
## [25] TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE FALSE TRUE TRUE
## [37] TRUEis.maximal(rules.yogurt) ## {bottled beer} {pastry}
## TRUE FALSE
## {citrus fruit} {shopping bags}
## FALSE FALSE
## {sausage} {bottled water}
## FALSE FALSE
## {tropical fruit} {root vegetables}
## FALSE FALSE
## {soda} {rolls/buns}
## FALSE FALSE
## {other vegetables} {whole milk}
## FALSE FALSE
## {cream cheese,yogurt} {frozen vegetables,yogurt}
## TRUE TRUE
## {beef,yogurt} {curd,yogurt}
## TRUE TRUE
## {napkins,yogurt} {frankfurter,yogurt}
## TRUE TRUE
## {brown bread,yogurt} {margarine,yogurt}
## TRUE TRUE
## {butter,yogurt} {newspapers,yogurt}
## TRUE TRUE
## {domestic eggs,yogurt} {fruit/vegetable juice,yogurt}
## TRUE TRUE
## {whipped/sour cream,yogurt} {pip fruit,yogurt}
## TRUE TRUE
## {pastry,yogurt} {citrus fruit,yogurt}
## TRUE TRUE
## {shopping bags,yogurt} {sausage,yogurt}
## TRUE TRUE
## {bottled water,yogurt} {tropical fruit,yogurt}
## TRUE TRUE
## {root vegetables,yogurt} {soda,yogurt}
## TRUE TRUE
## {rolls/buns,yogurt} {other vegetables,yogurt}
## TRUE TRUE
## {whole milk,yogurt}
## TRUEinspect(rules.yogurt[is.maximal(rules.yogurt)==TRUE]) ## lhs rhs support confidence coverage
## [1] {} => {bottled beer} 0.08053691 0.08053691 1.000000
## [2] {yogurt} => {cream cheese} 0.01240594 0.08892128 0.139516
## [3] {yogurt} => {frozen vegetables} 0.01240594 0.08892128 0.139516
## [4] {yogurt} => {beef} 0.01169412 0.08381924 0.139516
## [5] {yogurt} => {curd} 0.01728696 0.12390671 0.139516
## [6] {yogurt} => {napkins} 0.01230425 0.08819242 0.139516
## [7] {yogurt} => {frankfurter} 0.01118568 0.08017493 0.139516
## [8] {yogurt} => {brown bread} 0.01454139 0.10422741 0.139516
## [9] {yogurt} => {margarine} 0.01423632 0.10204082 0.139516
## [10] {yogurt} => {butter} 0.01464308 0.10495627 0.139516
## [11] {yogurt} => {newspapers} 0.01535489 0.11005831 0.139516
## [12] {yogurt} => {domestic eggs} 0.01433801 0.10276968 0.139516
## [13] {yogurt} => {fruit/vegetable juice} 0.01871060 0.13411079 0.139516
## [14] {yogurt} => {whipped/sour cream} 0.02074436 0.14868805 0.139516
## [15] {yogurt} => {pip fruit} 0.01799878 0.12900875 0.139516
## [16] {yogurt} => {pastry} 0.01769372 0.12682216 0.139516
## [17] {yogurt} => {citrus fruit} 0.02165955 0.15524781 0.139516
## [18] {yogurt} => {shopping bags} 0.01525320 0.10932945 0.139516
## [19] {yogurt} => {sausage} 0.01962579 0.14067055 0.139516
## [20] {yogurt} => {bottled water} 0.02298149 0.16472303 0.139516
## [21] {yogurt} => {tropical fruit} 0.02928615 0.20991254 0.139516
## [22] {yogurt} => {root vegetables} 0.02582876 0.18513120 0.139516
## [23] {yogurt} => {soda} 0.02735408 0.19606414 0.139516
## [24] {yogurt} => {rolls/buns} 0.03437055 0.24635569 0.139516
## [25] {yogurt} => {other vegetables} 0.04342079 0.31122449 0.139516
## [26] {yogurt} => {whole milk} 0.05603010 0.40160350 0.139516
## lift count
## [1] 1.000000 792
## [2] 2.242184 122
## [3] 1.848736 122
## [4] 1.597439 115
## [5] 2.325379 170
## [6] 1.684047 121
## [7] 1.359380 110
## [8] 1.606540 143
## [9] 1.745164 140
## [10] 1.893835 144
## [11] 1.378743 151
## [12] 1.619611 141
## [13] 1.854916 184
## [14] 2.074040 204
## [15] 1.705204 177
## [16] 1.425336 174
## [17] 1.877868 213
## [18] 1.109542 150
## [19] 1.497137 193
## [20] 1.490236 226
## [21] 2.000271 288
## [22] 1.698302 254
## [23] 1.124253 269
## [24] 1.339227 338
## [25] 1.608293 427
## [26] 1.571575 551is.redundant(rules.yogurt)## [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [13] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [25] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [37] FALSEinspect(rules.yogurt[is.redundant(rules.yogurt)==FALSE])## lhs rhs support confidence coverage
## [1] {} => {bottled beer} 0.08053691 0.08053691 1.000000
## [2] {} => {pastry} 0.08897702 0.08897702 1.000000
## [3] {} => {citrus fruit} 0.08267236 0.08267236 1.000000
## [4] {} => {shopping bags} 0.09853569 0.09853569 1.000000
## [5] {} => {sausage} 0.09395973 0.09395973 1.000000
## [6] {} => {bottled water} 0.11053488 0.11053488 1.000000
## [7] {} => {tropical fruit} 0.10494204 0.10494204 1.000000
## [8] {} => {root vegetables} 0.10900956 0.10900956 1.000000
## [9] {} => {soda} 0.17439496 0.17439496 1.000000
## [10] {} => {rolls/buns} 0.18395363 0.18395363 1.000000
## [11] {} => {other vegetables} 0.19351230 0.19351230 1.000000
## [12] {} => {whole milk} 0.25554200 0.25554200 1.000000
## [13] {yogurt} => {cream cheese} 0.01240594 0.08892128 0.139516
## [14] {yogurt} => {frozen vegetables} 0.01240594 0.08892128 0.139516
## [15] {yogurt} => {beef} 0.01169412 0.08381924 0.139516
## [16] {yogurt} => {curd} 0.01728696 0.12390671 0.139516
## [17] {yogurt} => {napkins} 0.01230425 0.08819242 0.139516
## [18] {yogurt} => {frankfurter} 0.01118568 0.08017493 0.139516
## [19] {yogurt} => {brown bread} 0.01454139 0.10422741 0.139516
## [20] {yogurt} => {margarine} 0.01423632 0.10204082 0.139516
## [21] {yogurt} => {butter} 0.01464308 0.10495627 0.139516
## [22] {yogurt} => {newspapers} 0.01535489 0.11005831 0.139516
## [23] {yogurt} => {domestic eggs} 0.01433801 0.10276968 0.139516
## [24] {yogurt} => {fruit/vegetable juice} 0.01871060 0.13411079 0.139516
## [25] {yogurt} => {whipped/sour cream} 0.02074436 0.14868805 0.139516
## [26] {yogurt} => {pip fruit} 0.01799878 0.12900875 0.139516
## [27] {yogurt} => {pastry} 0.01769372 0.12682216 0.139516
## [28] {yogurt} => {citrus fruit} 0.02165955 0.15524781 0.139516
## [29] {yogurt} => {shopping bags} 0.01525320 0.10932945 0.139516
## [30] {yogurt} => {sausage} 0.01962579 0.14067055 0.139516
## [31] {yogurt} => {bottled water} 0.02298149 0.16472303 0.139516
## [32] {yogurt} => {tropical fruit} 0.02928615 0.20991254 0.139516
## [33] {yogurt} => {root vegetables} 0.02582876 0.18513120 0.139516
## [34] {yogurt} => {soda} 0.02735408 0.19606414 0.139516
## [35] {yogurt} => {rolls/buns} 0.03437055 0.24635569 0.139516
## [36] {yogurt} => {other vegetables} 0.04342079 0.31122449 0.139516
## [37] {yogurt} => {whole milk} 0.05603010 0.40160350 0.139516
## lift count
## [1] 1.000000 792
## [2] 1.000000 875
## [3] 1.000000 813
## [4] 1.000000 969
## [5] 1.000000 924
## [6] 1.000000 1087
## [7] 1.000000 1032
## [8] 1.000000 1072
## [9] 1.000000 1715
## [10] 1.000000 1809
## [11] 1.000000 1903
## [12] 1.000000 2513
## [13] 2.242184 122
## [14] 1.848736 122
## [15] 1.597439 115
## [16] 2.325379 170
## [17] 1.684047 121
## [18] 1.359380 110
## [19] 1.606540 143
## [20] 1.745164 140
## [21] 1.893835 144
## [22] 1.378743 151
## [23] 1.619611 141
## [24] 1.854916 184
## [25] 2.074040 204
## [26] 1.705204 177
## [27] 1.425336 174
## [28] 1.877868 213
## [29] 1.109542 150
## [30] 1.497137 193
## [31] 1.490236 226
## [32] 2.000271 288
## [33] 1.698302 254
## [34] 1.124253 269
## [35] 1.339227 338
## [36] 1.608293 427
## [37] 1.571575 551Conclusions
Based on the analysis of the store’s sales data, the following recommendations can be made to improve the overall strategy of the store and increase sales and profitability.
Promoting low-selling products: There are several products that are not selling well and may require more promotion to increase sales. These items include baby food, sound storage medium, preservation products, bags, kitchen utensils, and baby cosmetics. Store management can consider implementing various marketing strategies, such as discounts, special offers, or in-store displays to increase visibility and attract customers.
Deleting unselling products: Some items may not be selling well and may not be worth keeping in the store, considering the costs associated with storing them in the warehouse. Store managers should regularly assess the sales of each product and make informed decisions on whether to continue stocking them or remove them from the store.
Enhancing product placement: Product placement can play a significant role in increasing sales. In this case, store management should consider placing a booth with dairy products near sections that sell fruits, vegetables, baked goods, and sausages. This can increase visibility and attract customers looking for complementary items.
Continuously monitoring sales: Finally, it is essential to continuously monitor sales data and make informed decisions based on the results. This will allow store management to regularly assess the performance of different products and adjust their strategy accordingly.
In conclusion, implementing these recommendations can help the store improve its strategy and increase sales and profitability.