Market basket analysis
Paolo Zambrano - A01721113
2022-09-19
Librerias
#install.packages("plyr")
library(plyr)
library(Matrix)
#install.packages("arules")
library(arules)##
## Attaching package: 'arules'## The following objects are masked from 'package:base':
##
## abbreviate, write#install.packages("arulesViz")
library(arulesViz)
library(datasets)Importar base de datos
bd_limpia<- read.csv("/Users/paolozambrano/Desktop/Todo/Documentos Tec/coding_bootcamp/abarrotes_bd_limpia.csv")##Ordenar de menor a mayor los tickets
Primeras 5 filas
bd_limpia<- bd_limpia[order(bd_limpia$F.Ticket),]
head(bd_limpia)## vcClaveTienda DescGiro Fecha Hora Marca
## 1 MX001 Abarrotes 0020-06-19 NA NUTRI LECHE
## 2 MX001 Abarrotes 0020-06-19 NA DAN UP
## 3 MX001 Abarrotes 0020-06-19 NA BIMBO
## 4 MX001 Abarrotes 0020-06-19 NA PEPSI
## 5 MX001 Abarrotes 0020-06-19 NA BLANCA NIEVES (DETERGENTE)
## 6 MX001 Abarrotes 0020-06-19 NA FLASH
## Fabricante Producto Precio
## 1 MEXILAC Nutri Leche 1 Litro 16.0
## 2 DANONE DE MEXICO DANUP STRAWBERRY P/BEBER 350GR NAL 14.0
## 3 GRUPO BIMBO Rebanadas Bimbo 2Pz 5.0
## 4 PEPSI-COLA MEXICANA Pepsi N.R. 400Ml 8.0
## 5 FABRICA DE JABON LA CORONA Detergente Blanca Nieves 500G 19.5
## 6 ALEN Flash Xtra Brisa Marina 500Ml 9.5
## Ult.Costo Unidades F.Ticket NombreDepartamento NombreFamilia
## 1 12.31 1 1 Abarrotes Lacteos y Refrigerados
## 2 14.00 1 2 Abarrotes Lacteos y Refrigerados
## 3 5.00 1 3 Abarrotes Pan y Tortilla
## 4 8.00 1 3 Abarrotes Bebidas
## 5 15.00 1 4 Abarrotes Limpieza del Hogar
## 6 7.31 1 4 Abarrotes Limpieza del Hogar
## NombreCategoria Estado Mts.2 Tipo.ubicación Giro
## 1 Leche Nuevo León 60 Esquina Abarrotes
## 2 Yogurt Nuevo León 60 Esquina Abarrotes
## 3 Pan Dulce Empaquetado Nuevo León 60 Esquina Abarrotes
## 4 Refrescos Plástico (N.R.) Nuevo León 60 Esquina Abarrotes
## 5 Lavandería Nuevo León 60 Esquina Abarrotes
## 6 Limpiadores Líquidos Nuevo León 60 Esquina Abarrotes
## Hora.inicio Hora.cierre Dia_de_la_semana Subtotal Utilidad
## 1 8:00 22:00 6 16.0 3.69
## 2 8:00 22:00 6 14.0 0.00
## 3 8:00 22:00 6 5.0 0.00
## 4 8:00 22:00 6 8.0 0.00
## 5 8:00 22:00 6 19.5 4.50
## 6 8:00 22:00 6 9.5 2.19Ultimas 5 filas
tail(bd_limpia)## vcClaveTienda DescGiro Fecha Hora Marca
## 107391 MX004 Carnicería 0020-10-15 11 YEMINA
## 167763 MX004 Carnicería 0020-10-15 11 DEL FUERTE
## 149426 MX004 Carnicería 0020-10-15 11 COCA COLA ZERO
## 168742 MX004 Carnicería 0020-10-15 11 DIAMANTE
## 161185 MX004 Carnicería 0020-10-15 12 PEPSI
## 112967 MX004 Carnicería 0020-10-15 12 COCA COLA
## Fabricante Producto Precio Ult.Costo
## 107391 HERDEZ PASTA SPAGHETTI YEMINA 200G 7 5.38
## 167763 ALIMENTOS DEL FUERTE PURE DE TOMATE DEL FUERTE 345G 12 9.23
## 149426 COCA COLA COCA COLA ZERO 600ML 15 11.54
## 168742 EMPACADOS ARROZ DIAMANTE225G 11 8.46
## 161185 PEPSI-COLA MEXICANA PEPSI N. R. 500ML 10 7.69
## 112967 COCA COLA COCA COLA RETORNABLE 500ML 10 7.69
## Unidades F.Ticket NombreDepartamento NombreFamilia
## 107391 2 450032 Abarrotes Sopas y Pastas
## 167763 1 450032 Abarrotes Salsas y Sazonadores
## 149426 2 450034 Abarrotes Bebidas
## 168742 1 450037 Abarrotes Granos y Semillas
## 161185 1 450039 Abarrotes Bebidas
## 112967 8 450040 Abarrotes Bebidas
## NombreCategoria Estado Mts.2 Tipo.ubicación Giro
## 107391 Fideos, Spaguetti, Tallarines Sinaloa 53 Esquina Abarrotes
## 167763 Salsa para Spaguetti Sinaloa 53 Esquina Abarrotes
## 149426 Refrescos Retornables Sinaloa 53 Esquina Abarrotes
## 168742 Arroz Sinaloa 53 Esquina Abarrotes
## 161185 Refrescos Plástico (N.R.) Sinaloa 53 Esquina Abarrotes
## 112967 Refrescos Retornables Sinaloa 53 Esquina Abarrotes
## Hora.inicio Hora.cierre Dia_de_la_semana Subtotal Utilidad
## 107391 7:00 23:00 5 14 1.62
## 167763 7:00 23:00 5 12 2.77
## 149426 7:00 23:00 5 30 3.46
## 168742 7:00 23:00 5 11 2.54
## 161185 7:00 23:00 5 10 2.31
## 112967 7:00 23:00 5 80 2.31Generar basket
library(plyr)
basket<- ddply(bd_limpia,c("F.Ticket"), function(bd_limpia)paste(bd_limpia$Marca, collapse= ","))Eliminar Numero de Ticket
basket$F.Ticket<- NULLRenombramos el nombre de la columna
colnames(basket)<- c("Marca")Exportar basket
write.csv(basket,"basket.csv", quote= FALSE, row.names = FALSE)Importar transacciones
tr<- read.transactions("/Users/paolozambrano/basket.csv", format="basket", sep=",")## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## within quoted string## Warning in asMethod(object): removing duplicated items in transactionsreglas.asociacion<- apriori(tr, parameter = list(supp=0.001, conf=0.2, maxlen=10))## Apriori
##
## Parameter specification:
## confidence minval smax arem aval originalSupport maxtime support minlen
## 0.2 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: 115
##
## set item appearances ...[0 item(s)] done [0.00s].
## set transactions ...[604 item(s), 115111 transaction(s)] done [0.02s].
## sorting and recoding items ... [207 item(s)] done [0.00s].
## creating transaction tree ... done [0.02s].
## checking subsets of size 1 2 3 done [0.00s].
## writing ... [11 rule(s)] done [0.00s].
## creating S4 object ... done [0.00s].summary(reglas.asociacion)## set of 11 rules
##
## rule length distribution (lhs + rhs):sizes
## 2
## 11
##
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 2 2 2 2 2 2
##
## summary of quality measures:
## support confidence coverage lift
## Min. :0.001016 Min. :0.2069 Min. :0.003562 Min. : 1.325
## 1st Qu.:0.001103 1st Qu.:0.2356 1st Qu.:0.004504 1st Qu.: 1.787
## Median :0.001416 Median :0.2442 Median :0.005803 Median : 3.972
## Mean :0.001519 Mean :0.2536 Mean :0.006054 Mean :17.563
## 3rd Qu.:0.001651 3rd Qu.:0.2685 3rd Qu.:0.006893 3rd Qu.:21.798
## Max. :0.002745 Max. :0.3098 Max. :0.010503 Max. :65.908
## count
## Min. :117.0
## 1st Qu.:127.0
## Median :163.0
## Mean :174.9
## 3rd Qu.:190.0
## Max. :316.0
##
## mining info:
## data ntransactions support confidence
## tr 115111 0.001 0.2
## call
## apriori(data = tr, parameter = list(supp = 0.001, conf = 0.2, maxlen = 10))inspect(reglas.asociacion)## lhs rhs support confidence coverage
## [1] {FANTA} => {COCA COLA} 0.001051159 0.2439516 0.004308884
## [2] {SALVO} => {FABULOSO} 0.001103283 0.3097561 0.003561779
## [3] {FABULOSO} => {SALVO} 0.001103283 0.2347505 0.004699811
## [4] {COCA COLA ZERO} => {COCA COLA} 0.001416025 0.2969035 0.004769310
## [5] {SPRITE} => {COCA COLA} 0.001346526 0.2069426 0.006506763
## [6] {PINOL} => {CLORALEX} 0.001016410 0.2363636 0.004300197
## [7] {BLUE HOUSE} => {BIMBO} 0.001711392 0.2720994 0.006289581
## [8] {HELLMANN´S} => {BIMBO} 0.001537646 0.2649701 0.005803094
## [9] {REYMA} => {CONVERMEX} 0.002093631 0.2441743 0.008574333
## [10] {FUD} => {BIMBO} 0.001589770 0.2183771 0.007279930
## [11] {COCA COLA LIGHT} => {COCA COLA} 0.002745176 0.2613730 0.010502906
## lift count
## [1] 1.561906 121
## [2] 65.908196 127
## [3] 65.908196 127
## [4] 1.900932 163
## [5] 1.324955 155
## [6] 25.030409 117
## [7] 4.078870 197
## [8] 3.971997 177
## [9] 18.564824 241
## [10] 3.273552 183
## [11] 1.673447 316reglas.asociacion<- sort(reglas.asociacion, by="confidence", decreasing = TRUE)
summary(reglas.asociacion)## set of 11 rules
##
## rule length distribution (lhs + rhs):sizes
## 2
## 11
##
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 2 2 2 2 2 2
##
## summary of quality measures:
## support confidence coverage lift
## Min. :0.001016 Min. :0.2069 Min. :0.003562 Min. : 1.325
## 1st Qu.:0.001103 1st Qu.:0.2356 1st Qu.:0.004504 1st Qu.: 1.787
## Median :0.001416 Median :0.2442 Median :0.005803 Median : 3.972
## Mean :0.001519 Mean :0.2536 Mean :0.006054 Mean :17.563
## 3rd Qu.:0.001651 3rd Qu.:0.2685 3rd Qu.:0.006893 3rd Qu.:21.798
## Max. :0.002745 Max. :0.3098 Max. :0.010503 Max. :65.908
## count
## Min. :117.0
## 1st Qu.:127.0
## Median :163.0
## Mean :174.9
## 3rd Qu.:190.0
## Max. :316.0
##
## mining info:
## data ntransactions support confidence
## tr 115111 0.001 0.2
## call
## apriori(data = tr, parameter = list(supp = 0.001, conf = 0.2, maxlen = 10))inspect(reglas.asociacion)## lhs rhs support confidence coverage
## [1] {SALVO} => {FABULOSO} 0.001103283 0.3097561 0.003561779
## [2] {COCA COLA ZERO} => {COCA COLA} 0.001416025 0.2969035 0.004769310
## [3] {BLUE HOUSE} => {BIMBO} 0.001711392 0.2720994 0.006289581
## [4] {HELLMANN´S} => {BIMBO} 0.001537646 0.2649701 0.005803094
## [5] {COCA COLA LIGHT} => {COCA COLA} 0.002745176 0.2613730 0.010502906
## [6] {REYMA} => {CONVERMEX} 0.002093631 0.2441743 0.008574333
## [7] {FANTA} => {COCA COLA} 0.001051159 0.2439516 0.004308884
## [8] {PINOL} => {CLORALEX} 0.001016410 0.2363636 0.004300197
## [9] {FABULOSO} => {SALVO} 0.001103283 0.2347505 0.004699811
## [10] {FUD} => {BIMBO} 0.001589770 0.2183771 0.007279930
## [11] {SPRITE} => {COCA COLA} 0.001346526 0.2069426 0.006506763
## lift count
## [1] 65.908196 127
## [2] 1.900932 163
## [3] 4.078870 197
## [4] 3.971997 177
## [5] 1.673447 316
## [6] 18.564824 241
## [7] 1.561906 121
## [8] 25.030409 117
## [9] 65.908196 127
## [10] 3.273552 183
## [11] 1.324955 155Imagen initeractiva
top10reglas<- head(reglas.asociacion, n=10, by="confidence")
plot(top10reglas, method= "graph", engine= "htmlwidget")Conclusion
Con este codigo podemos realizar un market basquet analysis el cual nos permite identificar productos que se venden juntos con un nivel de confianza deseado. Es importante poner el descuento al primer producto ya que este motiva del segundo. En este ejemplo se puede ver como la venta de coca cola light motiva la compra de la coca cola regular. Se deberia de poner en descuento la coca cola light para que se venda mas la coca cola regular.