Walmart
Isaac Díaz A01540543
2022-09-22
Importar base de datos
#file.choose()
stores <- read.csv("/Users/isaacdiazruizdechavez/Downloads/stores.csv")
features <- read.csv("/Users/isaacdiazruizdechavez/Downloads/features (1).csv")
train <- read.csv("/Users/isaacdiazruizdechavez/Downloads/train.csv")
test <- read.csv("/Users/isaacdiazruizdechavez/Downloads/test.csv")Instalar paquetes y llamar librerias
#install.packages("dplyr")
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionEntender la base 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 ...Observaciones
#1. “features”, “test” y “train” tiene 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 ¿Tienen relación? R: Si #4. “train” tiene fechas negativas
Herramienta “El generador de valor de datos”
Paso 1. Definir el área del negocio que buscamos impactar o mejorar y 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
Vision | Segmentación | Personalización | Contextualización
Paso 3. Generar ideas o conceptos específicos
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 independientes
Paso 5. Plan de ejecucion
Mercadotecnia elaborará plan para desplegar modelo predictivo en fases: Fase 1. Piloto (San Antonio TX) Fase 2. Texas Fase 3. EUA Sistemas asegurará la captura del MarkDown en las bases de datos.
Creación de la base de datos consolidada
Agregar “Stores” a “train”
bd <- merge(train, stores, by= "Store")Agregar “Features” a “BD”
bd1 <- bd
bd1 <- merge(bd1, features)Eliminar columnas
bd2 <- bd1
str(bd2)## 'data.frame': 421570 obs. of 16 variables:
## $ Store : int 1 1 1 1 1 1 1 1 1 1 ...
## $ Date : chr "01/04/2011" "01/04/2011" "01/04/2011" "01/04/2011" ...
## $ 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 ...
## $ 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 215 215 215 215 215 ...
## $ Unemployment: num 7.68 7.68 7.68 7.68 7.68 ...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. :14.313Cambiar 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$Weekly_Sales)## pos zero neg
## 420212 73 1285Eliminar ventas menores que 0
bd3 <- bd2
bd3 <- bd3[bd3$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. :14.313Agregar 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. :52.00Separar Año, mes y día
bd5 <- bd4
bd5 <- bd5 %>%
dplyr::mutate(year= lubridate::year(Date),
month= lubridate::month(Date),
day= lubridate::day(Date)) Generar regresion
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 predictivo
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
## 14667.94Conclusiones
En Walmart el modelo predictivo nos permite visualizar con el apoyo de datos un pronóstico para resultados futuros para precisar toma de decisiones. Al igual que en ejercicios anteriores, el proceso fue por medio de la regresión lineal para posteriormente llenar los datos del modelo predictivo según lo necesitamos/deseamos.
Como dato interesante, previamente se había considerado al clima como un factor relevante, lo cual en la regresión no resultó verdadero pues es de las pocas variables que no impactan en el modelo predictivo.
Walmart al ser una tienda departamental tan grande, tiene R2 menores, pues en la regresión con un 8% resultaría ver modificaciones, pero al tener un flujo tan grande, tendríamos que tener muchas más variables para porcentajes más grandes en la r2.
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