10 Market basket analysis
Aldo Fabián Rodríguez Almaguer - A01570685
2022-09-19
Introducción
Se hará un market basket analysis para determinar las tendencias de los clientes al momento de comprar en una tienda.
Librerías
library(plyr)## Warning: package 'plyr' was built under R version 4.2.1library(Matrix)
library(arules)## Warning: package 'arules' was built under R version 4.2.1##
## Attaching package: 'arules'## The following objects are masked from 'package:base':
##
## abbreviate, writelibrary(arulesViz)## Warning: package 'arulesViz' was built under R version 4.2.1library(datasets)Importar base de datos
df <- read.csv("C:\\Users\\aldof\\Documents\\1 Tec\\Semana Tec 1 - Bootcamp de Programación\\Cuarta Clase\\abarrotes_limpia.csv")Ordenar Tickets
Se ordenan los Tickets de mayor a menor y se visualiza que se haya hecho correctamente con las funciones head() y tail()
df <- df[order(df$F.Ticket),]
head(df)## vcClaveTienda DescGiro Fecha Hora Marca
## 1 MX001 Abarrotes 2020-06-19 8 NUTRI LECHE
## 2 MX001 Abarrotes 2020-06-19 8 DAN UP
## 3 MX001 Abarrotes 2020-06-19 8 BIMBO
## 4 MX001 Abarrotes 2020-06-19 8 PEPSI
## 5 MX001 Abarrotes 2020-06-19 8 BLANCA NIEVES (DETERGENTE)
## 6 MX001 Abarrotes 2020-06-19 8 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_laSemana Dia_de_la_Semana Subtotal Utilidad
## 1 08:00 22:00 6 6 16.0 3.69
## 2 08:00 22:00 6 6 14.0 0.00
## 3 08:00 22:00 6 6 5.0 0.00
## 4 08:00 22:00 6 6 8.0 0.00
## 5 08:00 22:00 6 6 19.5 4.50
## 6 08:00 22:00 6 6 9.5 2.19tail(df)## vcClaveTienda DescGiro Fecha Hora Marca
## 107394 MX004 Carnicería 2020-10-15 11 YEMINA
## 167771 MX004 Carnicería 2020-10-15 11 DEL FUERTE
## 149429 MX004 Carnicería 2020-10-15 11 COCA COLA ZERO
## 168750 MX004 Carnicería 2020-10-15 11 DIAMANTE
## 161193 MX004 Carnicería 2020-10-15 12 PEPSI
## 112970 MX004 Carnicería 2020-10-15 12 COCA COLA
## Fabricante Producto Precio Ult.Costo
## 107394 HERDEZ PASTA SPAGHETTI YEMINA 200G 7 5.38
## 167771 ALIMENTOS DEL FUERTE PURE DE TOMATE DEL FUERTE 345G 12 9.23
## 149429 COCA COLA COCA COLA ZERO 600ML 15 11.54
## 168750 EMPACADOS ARROZ DIAMANTE225G 11 8.46
## 161193 PEPSI-COLA MEXICANA PEPSI N. R. 500ML 10 7.69
## 112970 COCA COLA COCA COLA RETORNABLE 500ML 10 7.69
## Unidades F.Ticket NombreDepartamento NombreFamilia
## 107394 2 450032 Abarrotes Sopas y Pastas
## 167771 1 450032 Abarrotes Salsas y Sazonadores
## 149429 2 450034 Abarrotes Bebidas
## 168750 1 450037 Abarrotes Granos y Semillas
## 161193 1 450039 Abarrotes Bebidas
## 112970 8 450040 Abarrotes Bebidas
## NombreCategoria Estado Mts.2 Tipo.ubicación Giro
## 107394 Fideos, Spaguetti, Tallarines Sinaloa 53 Esquina Abarrotes
## 167771 Salsa para Spaguetti Sinaloa 53 Esquina Abarrotes
## 149429 Refrescos Retornables Sinaloa 53 Esquina Abarrotes
## 168750 Arroz Sinaloa 53 Esquina Abarrotes
## 161193 Refrescos Plástico (N.R.) Sinaloa 53 Esquina Abarrotes
## 112970 Refrescos Retornables Sinaloa 53 Esquina Abarrotes
## Hora.inicio Hora.cierre Dia_de_laSemana Dia_de_la_Semana Subtotal
## 107394 07:00 23:00 5 5 14
## 167771 07:00 23:00 5 5 12
## 149429 07:00 23:00 5 5 30
## 168750 07:00 23:00 5 5 11
## 161193 07:00 23:00 5 5 10
## 112970 07:00 23:00 5 5 80
## Utilidad
## 107394 1.62
## 167771 2.77
## 149429 3.46
## 168750 2.54
## 161193 2.31
## 112970 2.31Generar basket
basket <- ddply(df, c("F.Ticket"), function(df)paste(df$Marca, collapse = ","))Eliminar número de ticket
basket$F.Ticket <- NULLRenombrar columnas
Renombramos las columnas, en esta ocasión se utiliza un vector para que puedan ser varias las renombradas en caso de ser necesario.
colnames(basket) <- c("Marca")Exportar basket
Se exporta la nueva base de datos llamada basket para después importarla y poder trabajar con ella sin perder la original, se pudo haber hecho nombrandola como variable pero exportarla nos da una mayor organización
write.csv(basket, "basket.csv", quote = FALSE, row.names = FALSE)importar transacciones
tr <- read.transactions("C:\\Users\\aldof\\Documents\\1 Tec\\Semana Tec 1 - Bootcamp de Programación\\Cuarta Clase\\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 transactionsAsociaciones
Se hacen reglas de asociación para determinar los productos que se compran juntos normalmente y mediante los argumentos de las funciones apriori() y sort() podemos determinar las reglas más comunes que se derivan de estos tickets de la canasta, después se hace una variable que se llama top10reglas que contiene la función head() para saber cuáles son las reglas con mayor confianza que nos permitan tener un conocimiento más produndo y confiable de las transacciones. Y se grafica para una mejor visualización.
reglas.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 [0.07s].
## sorting and recoding items ... [207 item(s)] done [0.00s].
## creating transaction tree ... done [0.10s].
## checking subsets of size 1 2 3 done [0.01s].
## writing ... [11 rule(s)] done [0.00s].
## creating S4 object ... done [0.02s].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 155top10reglas <- head(reglas.asociacion, n=10, by = "confidence")
plot(top10reglas, method = "graph", engine = "htmlwidget")Conclusión
Analizando los resultados de un market basket analysis nos podemos dar cuenta las tendencias de los clientes al comprar en una tienda, qué es más probable que compren si ya compraron algo y qupe es menos probable, con ello podemos determinar descuentos que se puedan hacer o mejorar los productos que vendemos o armar combos. Por ejemplo si vemos que la gente que compra tortillas también compra huevo y compra chorizo y queso, pues podemos pensar que es para hacer tacos de huevo con chorizo y queso, por lo que podemos empezar a vender el producto ya hecho y así ahorrarle tiempo al cliente y ganar a su vez un mayor rendimiento.