Walmart
José Ramón Vázquez Guzmán A01625203
09-09-2022
Importar base de datos
stores <- read.csv("/Users/joseramonvazquezguzman/Documents/BDWAL/stores (2).csv")
features <- read.csv("/Users/joseramonvazquezguzman/Documents/BDWAL/features (6).csv")
test <- read.csv("/Users/joseramonvazquezguzman/Documents/BDWAL/test (2).csv")
train <- read.csv("/Users/joseramonvazquezguzman/Documents/BDWAL/train (2).csv")Instalar paquetes y llamar librerías
# install.packages("dyplr")
library(dbplyr)Entender las bases de datos
summary(stores)## Store Type Size
## Min. : 1 Length:45 Min. : 34875
## 1st Qu.:12 Class :character 1st Qu.: 70713
## Median :23 Mode :character Median :126512
## Mean :23 Mean :130288
## 3rd Qu.:34 3rd Qu.:202307
## Max. :45 Max. :219622# count(stores, Type, sort = TRUE)
str(stores)## 'data.frame': 45 obs. of 3 variables:
## $ Store: int 1 2 3 4 5 6 7 8 9 10 ...
## $ Type : chr "A" "A" "B" "A" ...
## $ Size : int 151315 202307 37392 205863 34875 202505 70713 155078 125833 126512 ...summary(features)## Store Date Temperature Fuel_Price
## Min. : 1 Length:8190 Min. : -7.29 Min. :2.472
## 1st Qu.:12 Class :character 1st Qu.: 45.90 1st Qu.:3.041
## Median :23 Mode :character Median : 60.71 Median :3.513
## Mean :23 Mean : 59.36 Mean :3.406
## 3rd Qu.:34 3rd Qu.: 73.88 3rd Qu.:3.743
## Max. :45 Max. :101.95 Max. :4.468
##
## MarkDown1 MarkDown2 MarkDown3 MarkDown4
## Min. : -2781 Min. : -265.76 Min. : -179.26 Min. : 0.22
## 1st Qu.: 1578 1st Qu.: 68.88 1st Qu.: 6.60 1st Qu.: 304.69
## Median : 4744 Median : 364.57 Median : 36.26 Median : 1176.42
## Mean : 7032 Mean : 3384.18 Mean : 1760.10 Mean : 3292.94
## 3rd Qu.: 8923 3rd Qu.: 2153.35 3rd Qu.: 163.15 3rd Qu.: 3310.01
## Max. :103185 Max. :104519.54 Max. :149483.31 Max. :67474.85
## NA's :4158 NA's :5269 NA's :4577 NA's :4726
## MarkDown5 CPI Unemployment IsHoliday
## Min. : -185.2 Min. :126.1 Min. : 3.684 Mode :logical
## 1st Qu.: 1440.8 1st Qu.:132.4 1st Qu.: 6.634 FALSE:7605
## Median : 2727.1 Median :182.8 Median : 7.806 TRUE :585
## Mean : 4132.2 Mean :172.5 Mean : 7.827
## 3rd Qu.: 4832.6 3rd Qu.:213.9 3rd Qu.: 8.567
## Max. :771448.1 Max. :229.0 Max. :14.313
## NA's :4140 NA's :585 NA's :585str(features)## 'data.frame': 8190 obs. of 12 variables:
## $ Store : int 1 1 1 1 1 1 1 1 1 1 ...
## $ Date : chr "05/02/2010" "12/02/2010" "19/02/2010" "26/02/2010" ...
## $ Temperature : num 42.3 38.5 39.9 46.6 46.5 ...
## $ Fuel_Price : num 2.57 2.55 2.51 2.56 2.62 ...
## $ MarkDown1 : num NA NA NA NA NA NA NA NA NA NA ...
## $ MarkDown2 : num NA NA NA NA NA NA NA NA NA NA ...
## $ MarkDown3 : num NA NA NA NA NA NA NA NA NA NA ...
## $ MarkDown4 : num NA NA NA NA NA NA NA NA NA NA ...
## $ MarkDown5 : num NA NA NA NA NA NA NA NA NA NA ...
## $ CPI : num 211 211 211 211 211 ...
## $ Unemployment: num 8.11 8.11 8.11 8.11 8.11 ...
## $ IsHoliday : logi FALSE TRUE FALSE FALSE FALSE FALSE ...summary(test)## Store Dept Date IsHoliday
## Min. : 1.00 Min. : 1.00 Length:115064 Mode :logical
## 1st Qu.:11.00 1st Qu.:18.00 Class :character FALSE:106136
## Median :22.00 Median :37.00 Mode :character TRUE :8928
## Mean :22.24 Mean :44.34
## 3rd Qu.:33.00 3rd Qu.:74.00
## Max. :45.00 Max. :99.00str(test)## 'data.frame': 115064 obs. of 4 variables:
## $ Store : int 1 1 1 1 1 1 1 1 1 1 ...
## $ Dept : int 1 1 1 1 1 1 1 1 1 1 ...
## $ Date : chr "02/11/2012" "09/11/2012" "16/11/2012" "23/11/2012" ...
## $ IsHoliday: logi FALSE FALSE FALSE TRUE FALSE FALSE ...summary(train)## Store Dept Date Weekly_Sales
## Min. : 1.0 Min. : 1.00 Length:421570 Min. : -4989
## 1st Qu.:11.0 1st Qu.:18.00 Class :character 1st Qu.: 2080
## Median :22.0 Median :37.00 Mode :character Median : 7612
## Mean :22.2 Mean :44.26 Mean : 15981
## 3rd Qu.:33.0 3rd Qu.:74.00 3rd Qu.: 20206
## Max. :45.0 Max. :99.00 Max. :693099
## IsHoliday
## Mode :logical
## FALSE:391909
## TRUE :29661
##
##
## str(train)## 'data.frame': 421570 obs. of 5 variables:
## $ Store : int 1 1 1 1 1 1 1 1 1 1 ...
## $ Dept : int 1 1 1 1 1 1 1 1 1 1 ...
## $ Date : chr "05/02/2010" "12/02/2010" "19/02/2010" "26/02/2010" ...
## $ Weekly_Sales: num 24924 46039 41596 19404 21828 ...
## $ IsHoliday : logi FALSE TRUE FALSE FALSE FALSE FALSE ...Observaciones
1. “features”, “test” y “train” tienen la fecha como caracter
2. “features”, hay NA’s en más de la mitad de los registros de MarkDown (del 1 al 5)
3. En “features”, hay 585 NA’s en CPI y Unemployment, y hay 585 registros de IsHoliday = TRUE ¿Tiene relación? R: Si
4. “train” tiene ventas semanales negativas.
Herramienta “El Generador de Valor de Datos”
Paso 1. Definir el área del negocio que buscamos impactaro mejorar su KPI.
El departamento de mercadotecnia de EUA (con muestra de 45 tiendas) en el indicador de ventas semanales
Paso 2. Seleccionar plantilla (-s) para crear valor a partir de los datos de los clientes
Visión ** / segmentación / Personalización / Contextualización
Paso 3. Generar ideas o conceptos espesificos
Elaborar un modelo predictivo de ventas semanales
Paso 4. Reunir los datos requeridos
Elaborar una base de datos con la variable dependiente (ventas semanales) y las variables independietes
Mercadotecnia elaborará plan desplegar modelo predictivo en fases:
Fase 1. Piloto(San Antonio xTX).
Fase 2. Texas
Fase 3. EUA
Sistemas asegurará la captura del Markdown en las bases de datos
Nota: Eliminar lo que no sirve, después transformar
Creación de la base de datos consoldiada
# Agregar "STORES" a "TRAIN"
bd <- merge(train, stores, by= "Store")
# Agregar "Features" a "BD"
bd1 <-bd
bd1 <- merge(bd1, features)
# Eliminar columnas
bd2 <- bd1
bd2 <- subset(bd2, select = -c (MarkDown1,MarkDown2,MarkDown3,MarkDown4,MarkDown5))
summary(bd2)## Store Date IsHoliday Dept
## Min. : 1.0 Length:421570 Mode :logical Min. : 1.00
## 1st Qu.:11.0 Class :character FALSE:391909 1st Qu.:18.00
## Median :22.0 Mode :character TRUE :29661 Median :37.00
## Mean :22.2 Mean :44.26
## 3rd Qu.:33.0 3rd Qu.:74.00
## Max. :45.0 Max. :99.00
## Weekly_Sales Type Size Temperature
## Min. : -4989 Length:421570 Min. : 34875 Min. : -2.06
## 1st Qu.: 2080 Class :character 1st Qu.: 93638 1st Qu.: 46.68
## Median : 7612 Mode :character Median :140167 Median : 62.09
## Mean : 15981 Mean :136728 Mean : 60.09
## 3rd Qu.: 20206 3rd Qu.:202505 3rd Qu.: 74.28
## Max. :693099 Max. :219622 Max. :100.14
## Fuel_Price CPI Unemployment
## Min. :2.472 Min. :126.1 Min. : 3.879
## 1st Qu.:2.933 1st Qu.:132.0 1st Qu.: 6.891
## Median :3.452 Median :182.3 Median : 7.866
## Mean :3.361 Mean :171.2 Mean : 7.960
## 3rd Qu.:3.738 3rd Qu.:212.4 3rd Qu.: 8.572
## Max. :4.468 Max. :227.2 Max. :14.313# Cambiar formato de fecha
bd2$Date <- as.Date(bd2$Date, format = "%d/%m/%Y")
str(bd2)## 'data.frame': 421570 obs. of 11 variables:
## $ Store : int 1 1 1 1 1 1 1 1 1 1 ...
## $ Date : Date, format: "2011-04-01" "2011-04-01" ...
## $ IsHoliday : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
## $ Dept : int 49 26 81 34 59 30 7 85 8 28 ...
## $ Weekly_Sales: num 13168 5947 28545 9950 317 ...
## $ Type : chr "A" "A" "A" "A" ...
## $ Size : int 151315 151315 151315 151315 151315 151315 151315 151315 151315 151315 ...
## $ Temperature : num 59.2 59.2 59.2 59.2 59.2 ...
## $ Fuel_Price : num 3.52 3.52 3.52 3.52 3.52 ...
## $ CPI : num 215 215 215 215 215 ...
## $ Unemployment: num 7.68 7.68 7.68 7.68 7.68 ...# install.packages("wordspace")
library(wordspace)## Loading required package: Matrixsigncount(bd2$Weekly_Sales)## pos zero neg
## 420212 73 1285# Eliminar Ventas menores que 0
bd3 <- bd2
bd3 <- bd3[bd3$Weekly_Sales > 0, ]
summary (bd3)## Store Date IsHoliday Dept
## Min. : 1.0 Min. :2010-02-05 Mode :logical Min. : 1.00
## 1st Qu.:11.0 1st Qu.:2010-10-08 FALSE:390652 1st Qu.:18.00
## Median :22.0 Median :2011-06-17 TRUE :29560 Median :37.00
## Mean :22.2 Mean :2011-06-18 Mean :44.24
## 3rd Qu.:33.0 3rd Qu.:2012-02-24 3rd Qu.:74.00
## Max. :45.0 Max. :2012-10-26 Max. :99.00
## Weekly_Sales Type Size Temperature
## Min. : 0 Length:420212 Min. : 34875 Min. : -2.06
## 1st Qu.: 2120 Class :character 1st Qu.: 93638 1st Qu.: 46.68
## Median : 7662 Mode :character Median :140167 Median : 62.09
## Mean : 16033 Mean :136750 Mean : 60.09
## 3rd Qu.: 20271 3rd Qu.:202505 3rd Qu.: 74.28
## Max. :693099 Max. :219622 Max. :100.14
## Fuel_Price CPI Unemployment
## Min. :2.472 Min. :126.1 Min. : 3.879
## 1st Qu.:2.933 1st Qu.:132.0 1st Qu.: 6.891
## Median :3.452 Median :182.4 Median : 7.866
## Mean :3.361 Mean :171.2 Mean : 7.960
## 3rd Qu.:3.738 3rd Qu.:212.4 3rd Qu.: 8.567
## Max. :4.468 Max. :227.2 Max. :14.313# Agregar número de la semana
bd4 <- bd3
bd4$week_number <- strftime(bd4$Date, format = "%V")
str(bd4)## 'data.frame': 420212 obs. of 12 variables:
## $ Store : int 1 1 1 1 1 1 1 1 1 1 ...
## $ Date : Date, format: "2011-04-01" "2011-04-01" ...
## $ IsHoliday : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
## $ Dept : int 49 26 81 34 59 30 7 85 8 28 ...
## $ Weekly_Sales: num 13168 5947 28545 9950 317 ...
## $ Type : chr "A" "A" "A" "A" ...
## $ Size : int 151315 151315 151315 151315 151315 151315 151315 151315 151315 151315 ...
## $ Temperature : num 59.2 59.2 59.2 59.2 59.2 ...
## $ Fuel_Price : num 3.52 3.52 3.52 3.52 3.52 ...
## $ CPI : num 215 215 215 215 215 ...
## $ Unemployment: num 7.68 7.68 7.68 7.68 7.68 ...
## $ week_number : chr "13" "13" "13" "13" ...bd4$week_number <- as.integer(bd4$week_number)
str(bd4)## 'data.frame': 420212 obs. of 12 variables:
## $ Store : int 1 1 1 1 1 1 1 1 1 1 ...
## $ Date : Date, format: "2011-04-01" "2011-04-01" ...
## $ IsHoliday : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
## $ Dept : int 49 26 81 34 59 30 7 85 8 28 ...
## $ Weekly_Sales: num 13168 5947 28545 9950 317 ...
## $ Type : chr "A" "A" "A" "A" ...
## $ Size : int 151315 151315 151315 151315 151315 151315 151315 151315 151315 151315 ...
## $ Temperature : num 59.2 59.2 59.2 59.2 59.2 ...
## $ Fuel_Price : num 3.52 3.52 3.52 3.52 3.52 ...
## $ CPI : num 215 215 215 215 215 ...
## $ Unemployment: num 7.68 7.68 7.68 7.68 7.68 ...
## $ week_number : int 13 13 13 13 13 13 13 13 13 13 ...summary(bd4)## Store Date IsHoliday Dept
## Min. : 1.0 Min. :2010-02-05 Mode :logical Min. : 1.00
## 1st Qu.:11.0 1st Qu.:2010-10-08 FALSE:390652 1st Qu.:18.00
## Median :22.0 Median :2011-06-17 TRUE :29560 Median :37.00
## Mean :22.2 Mean :2011-06-18 Mean :44.24
## 3rd Qu.:33.0 3rd Qu.:2012-02-24 3rd Qu.:74.00
## Max. :45.0 Max. :2012-10-26 Max. :99.00
## Weekly_Sales Type Size Temperature
## Min. : 0 Length:420212 Min. : 34875 Min. : -2.06
## 1st Qu.: 2120 Class :character 1st Qu.: 93638 1st Qu.: 46.68
## Median : 7662 Mode :character Median :140167 Median : 62.09
## Mean : 16033 Mean :136750 Mean : 60.09
## 3rd Qu.: 20271 3rd Qu.:202505 3rd Qu.: 74.28
## Max. :693099 Max. :219622 Max. :100.14
## Fuel_Price CPI Unemployment week_number
## Min. :2.472 Min. :126.1 Min. : 3.879 Min. : 1.00
## 1st Qu.:2.933 1st Qu.:132.0 1st Qu.: 6.891 1st Qu.:14.00
## Median :3.452 Median :182.4 Median : 7.866 Median :26.00
## Mean :3.361 Mean :171.2 Mean : 7.960 Mean :25.83
## 3rd Qu.:3.738 3rd Qu.:212.4 3rd Qu.: 8.567 3rd Qu.:38.00
## Max. :4.468 Max. :227.2 Max. :14.313 Max. :52.00# Separar Año, Mes y Día
bd5 <- bd4
bd5 <- bd5 %>%
dplyr::mutate(year = lubridate::year(Date), month = lubridate::month(Date), day = lubridate::day(Date))Generar regresión lineal
regresion <- lm(Weekly_Sales ~ Store + Dept + IsHoliday + Type + Size + Temperature + Fuel_Price + CPI + Unemployment + week_number + year + month + day, data=bd5)
summary(regresion)##
## Call:
## lm(formula = Weekly_Sales ~ Store + Dept + IsHoliday + Type +
## Size + Temperature + Fuel_Price + CPI + Unemployment + week_number +
## year + month + day, data = bd5)
##
## Residuals:
## Min 1Q Median 3Q Max
## -34331 -12895 -5852 5626 671540
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.110e+06 2.999e+05 3.701 0.000214 ***
## Store -1.426e+02 3.087e+00 -46.198 < 2e-16 ***
## Dept 1.108e+02 1.097e+00 101.013 < 2e-16 ***
## IsHolidayTRUE 8.511e+02 1.391e+02 6.119 9.45e-10 ***
## TypeB -3.133e+02 1.078e+02 -2.908 0.003642 **
## TypeC 5.836e+03 1.840e+02 31.709 < 2e-16 ***
## Size 9.920e-02 9.584e-04 103.511 < 2e-16 ***
## Temperature 3.701e+00 2.133e+00 1.735 0.082688 .
## Fuel_Price 4.791e+02 1.480e+02 3.237 0.001207 **
## CPI -2.340e+01 9.996e-01 -23.409 < 2e-16 ***
## Unemployment -2.538e+02 2.062e+01 -12.308 < 2e-16 ***
## week_number 7.678e+02 4.566e+02 1.682 0.092648 .
## year -5.485e+02 1.485e+02 -3.695 0.000220 ***
## month -3.167e+03 1.988e+03 -1.594 0.111036
## day -1.281e+02 6.539e+01 -1.959 0.050115 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 21690 on 420197 degrees of freedom
## Multiple R-squared: 0.08982, Adjusted R-squared: 0.08979
## F-statistic: 2962 on 14 and 420197 DF, p-value: < 2.2e-16Construir un modelo de predicción
datos_nuevos <- data.frame(Store=1, IsHoliday= TRUE, Dept=1, Type="A", Size= 151315, week_number =1, Temperature = 59.17, Fuel_Price = 3.524, CPI= 214.8372, Unemployment = 7.682, year =2012, month = 1, day=1)
predict(regresion,datos_nuevos)## 1
## 14667.94Propuestas
Crear estrategias de ventas con base a la festividad para aumentar las ventas semanales, involucrar al área de marketing con promociones que atraigan a los consumidores tanto frecuentes como no e incluyendo la cantidad de dinero que gastan en la tienda en sus visitas.
Conclusiones
Se realizo una limpieza de base de datos, para poder desarrollar una regresión lineal, esta nos va a permitir conocer el comportamiento de la variable dependiente que buscamos analizar desde otras independientes. Se trabajaron 4 bases de dato, finalmente concluimos después de realizar el análisis se pudo pronosticar las ventas semanales con la magnitud que una empresa como walmart puede tener y en base a estas tener mejores estrategias que los lleven a mantener al cliente consumiendo constantemente; quedarse en el subconsciente de las personas y mantener o mejorar sus ventas.
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