Predicción de ventas con modelo lineal
MARIA FERNANDA MEJIA ANGELES
2024-10-31
#1 Cargar paquetes necesarios
#2 Cargar base de datos
df <- read.csv("~/PRACTICAS/DATA/Sample - Superstore.csv")
head(df)## Row.ID Order.ID Order.Date Ship.Date Ship.Mode Customer.ID
## 1 1 CA-2016-152156 11/8/2016 11/11/2016 Second Class CG-12520
## 2 2 CA-2016-152156 11/8/2016 11/11/2016 Second Class CG-12520
## 3 3 CA-2016-138688 6/12/2016 6/16/2016 Second Class DV-13045
## 4 4 US-2015-108966 10/11/2015 10/18/2015 Standard Class SO-20335
## 5 5 US-2015-108966 10/11/2015 10/18/2015 Standard Class SO-20335
## 6 6 CA-2014-115812 6/9/2014 6/14/2014 Standard Class BH-11710
## Customer.Name Segment Country City State
## 1 Claire Gute Consumer United States Henderson Kentucky
## 2 Claire Gute Consumer United States Henderson Kentucky
## 3 Darrin Van Huff Corporate United States Los Angeles California
## 4 Sean O'Donnell Consumer United States Fort Lauderdale Florida
## 5 Sean O'Donnell Consumer United States Fort Lauderdale Florida
## 6 Brosina Hoffman Consumer United States Los Angeles California
## Postal.Code Region Product.ID Category Sub.Category
## 1 42420 South FUR-BO-10001798 Furniture Bookcases
## 2 42420 South FUR-CH-10000454 Furniture Chairs
## 3 90036 West OFF-LA-10000240 Office Supplies Labels
## 4 33311 South FUR-TA-10000577 Furniture Tables
## 5 33311 South OFF-ST-10000760 Office Supplies Storage
## 6 90032 West FUR-FU-10001487 Furniture Furnishings
## Product.Name Sales
## 1 Bush Somerset Collection Bookcase 261.9600
## 2 Hon Deluxe Fabric Upholstered Stacking Chairs, Rounded Back 731.9400
## 3 Self-Adhesive Address Labels for Typewriters by Universal 14.6200
## 4 Bretford CR4500 Series Slim Rectangular Table 957.5775
## 5 Eldon Fold 'N Roll Cart System 22.3680
## 6 Eldon Expressions Wood and Plastic Desk Accessories, Cherry Wood 48.8600
## Quantity Discount Profit
## 1 2 0.00 41.9136
## 2 3 0.00 219.5820
## 3 2 0.00 6.8714
## 4 5 0.45 -383.0310
## 5 2 0.20 2.5164
## 6 7 0.00 14.1694#3 Modificacion de columnas
df_mod <-df %>%
filter(State == "Colorado") %>%
select(Order.Date, Sales) %>% # Selecionamos dos columnas Order.Date y Sales
mutate(Order.Date = mdy(Order.Date)) %>% #Cambiar a tipo de fecha
mutate(Fecha = floor_date(Order.Date, unit = "month")) %>% #Regresar a inicio de mes
group_by(Fecha) %>% # Agrupar por mes
summarise(Ventas = sum(Sales)) %>% #Hacer la suma
mutate(Mes = seq_along(Fecha)) #Crear columna x para la regresion
head(df_mod)## # A tibble: 6 × 3
## Fecha Ventas Mes
## <date> <dbl> <int>
## 1 2014-03-01 720. 1
## 2 2014-06-01 233. 2
## 3 2014-07-01 210. 3
## 4 2014-08-01 646. 4
## 5 2014-09-01 14.6 5
## 6 2014-11-01 3376. 6#4 Visualizacion
df_mod %>%
ggplot(., aes(x=Mes, y=Ventas)) +
geom_point() +
geom_line() +
geom_smooth(method = "lm", formula = "y ~ x")
#5 Grafica de caja y bigotes
#Esta grafica me sirve para encontrar valores atipicos
df_mod %>%
ggplot(., aes(x=Ventas)) +
geom_boxplot()
#6 Revisar si la variable y tiene una distribucion aprox normal
df_mod %>%
ggplot(., aes(x=Ventas)) +
geom_histogram(bins=30, aes(y=..density..)) +
geom_density()## Warning: The dot-dot notation (`..density..`) was deprecated in ggplot2 3.4.0.
## ℹ Please use `after_stat(density)` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
# 7 Analisis de correlacion
# Calculamos coeficiente de correlacion de Pearson, con Mes como x
with(df_mod, cor.test(Mes, Ventas))##
## Pearson's product-moment correlation
##
## data: Mes and Ventas
## t = 1.0485, df = 34, p-value = 0.3018
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1609068 0.4777420
## sample estimates:
## cor
## 0.176985#La correlacion va de -1 a 1
#Valores extremos indican correlacion mas fuerte
#Puede ser positiva (suben o bajan juntas) o
#negativas(si una sube la otra baja viceversa)#8 Modelo de regresion
modelo_lineal <- lm(Ventas ~ Mes, data = df_mod)
stargazer(modelo_lineal, type = "text")##
## ===============================================
## Dependent variable:
## ---------------------------
## Ventas
## -----------------------------------------------
## Mes 15.948
## (15.210)
##
## Constant 596.849*
## (322.711)
##
## -----------------------------------------------
## Observations 36
## R2 0.031
## Adjusted R2 0.003
## Residual Std. Error 948.032 (df = 34)
## F Statistic 1.099 (df = 1; 34)
## ===============================================
## Note: *p<0.1; **p<0.05; ***p<0.01#El modelo es y= a + bx
#Cosntant =a
#Pendiente =b
#Mes =x #9 Coeficiente del modelo
#Obtener los coeficientes del modelo, es decir a y b
coeficientes <- coef(modelo_lineal)
intercepto <- coeficientes["(Intercept"]
pendiente <- coeficientes["Mes"]
#Crear el texto de la ecuacion
ecuacion <- paste0("y = ", round(intercepto, 2),
" + ",
round(pendiente, 2), "x")
ecuacion## [1] "y = NA + 15.95x"#10 Prediccion
#Extender la serie temporal para 6 meses
df_futuro <- data.frame(Fecha =seq.Date(from = max(df_mod$Fecha)+ months(1),
by = "month",
length.out = 6),
Mes = seq(from = 49, to= 54,by= 1))
#Generar las predicciones para 6 meses con intervalos de confianza
predicciones <- predict(modelo_lineal,
newdata = df_futuro,
interval = "confidence")
predicciones## fit lwr upr
## 1 1378.315 382.3662 2374.264
## 2 1394.263 369.0064 2419.520
## 3 1410.212 355.5550 2464.868
## 4 1426.160 342.0197 2510.300
## 5 1442.108 328.4069 2555.809
## 6 1458.056 314.7228 2601.390#11 Juntar dtaos
#Convertir prediccion en un data frame
df_predicciones <- as.data.frame(predicciones)
colnames(df_predicciones) <- c("Ventas", "Bajo", "Alto")
#Unir las predicciones y los intervalos de confianza con las fechas de futuro
df_futuro <- cbind(df_futuro, df_predicciones) # Cbind une por columnas
#Unir con la base de datos original
df_total <- bind_rows(df_mod, df_futuro)
tail(df_total, 7)## # A tibble: 7 × 5
## Fecha Ventas Mes Bajo Alto
## <date> <dbl> <dbl> <dbl> <dbl>
## 1 2017-12-01 3440. 36 NA NA
## 2 2018-01-01 1378. 49 382. 2374.
## 3 2018-02-01 1394. 50 369. 2420.
## 4 2018-03-01 1410. 51 356. 2465.
## 5 2018-04-01 1426. 52 342. 2510.
## 6 2018-05-01 1442. 53 328. 2556.
## 7 2018-06-01 1458. 54 315. 2601.#12 Vizualizacion
#Grafica con valores pasados y prediccion
df_total %>%
ggplot(., aes(x = Fecha, y = Ventas)) +
geom_point(data = df_mod) +
geom_line(data = df_mod) +
geom_smooth(method = "lm", formula = y ~ x, color ="blue", data = df_mod) +
geom_ribbon(data = df_futuro, aes(ymin = Bajo, ymax = Alto),
fill = "lightblue") +
geom_point(data = df_futuro, aes(y = Ventas), color ="red") +
geom_line(data = df_futuro, aes(y = Ventas, color ="red",
linetype = "dashed"))