# Define the URLs
#urls <- c(
# "https://excelbianalytics.com/wp/wp-content/uploads/2017/07/1000000%20Sales%20Records.zip",
# "https://excelbianalytics.com/wp/wp-content/uploads/2017/07/1500000%20Sales%20Records.zip"
#)
#
## Define the filenames to save the zip files locally
#file_names <- c("1000000_Sales_Records.zip", "1500000_Sales_Records.zip")
#
## Download, extract, and import CSV files individually
#for (i in 1:length(urls)) {
# # Download the zip file
# download.file(urls[i], destfile = file_names[i], mode = "wb")
# cat("Downloaded:", file_names[i], "\n")
#
# # Extract the downloaded zip file
# unzip_dir <- paste0("extracted_", i)
# unzip(file_names[i], exdir = unzip_dir)
# cat("Extracted to folder:", unzip_dir, "\n")
#
# # Get the CSV file path (assuming only one CSV file per ZIP)
# csv_file <- list.files(unzip_dir, pattern = "\\.csv$", full.names = TRUE)
#
# # Import the CSV file into R with a unique data frame name
# if (length(csv_file) > 0) {
# df_name <- paste0("sales_data_", i) # e.g., "sales_data_1", "sales_data_2"
# assign(df_name, read.csv(csv_file[1], stringsAsFactors = FALSE))
# cat("Imported CSV file as data frame:", df_name, "\n")
# } else {
# cat("No CSV file found in", unzip_dir, "\n")
# }
#}
## incase above code is not working due to slow internet
library(readr)
sales_data_1 <- read_csv("~/GIT/Data/1000000 Sales Records.csv")
## Rows: 1000000 Columns: 14
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (7): Region, Country, Item Type, Sales Channel, Order Priority, Order Da...
## dbl (7): Order ID, Units Sold, Unit Price, Unit Cost, Total Revenue, Total C...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
sales_data_2 <- read_csv("~/GIT/Data/1500000 Sales Records.csv")
## Rows: 1500000 Columns: 14
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (7): Region, Country, Item Type, Sales Channel, Order Priority, Order Da...
## dbl (7): Order ID, Units Sold, Unit Price, Unit Cost, Total Revenue, Total C...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
head(sales_data_1)
## # A tibble: 6 × 14
## Region Country `Item Type` `Sales Channel` `Order Priority` `Order Date`
## <chr> <chr> <chr> <chr> <chr> <chr>
## 1 Sub-Saharan… South … Fruits Offline M 7/27/2012
## 2 Middle East… Morocco Clothes Online M 9/14/2013
## 3 Australia a… Papua … Meat Offline M 5/15/2015
## 4 Sub-Saharan… Djibou… Clothes Offline H 5/17/2017
## 5 Europe Slovak… Beverages Offline L 10/26/2016
## 6 Asia Sri La… Fruits Online L 11/7/2011
## # ℹ 8 more variables: `Order ID` <dbl>, `Ship Date` <chr>, `Units Sold` <dbl>,
## # `Unit Price` <dbl>, `Unit Cost` <dbl>, `Total Revenue` <dbl>,
## # `Total Cost` <dbl>, `Total Profit` <dbl>
head(sales_data_2)
## # A tibble: 6 × 14
## Region Country `Item Type` `Sales Channel` `Order Priority` `Order Date`
## <chr> <chr> <chr> <chr> <chr> <chr>
## 1 Sub-Saharan… South … Fruits Offline M 7/27/2012
## 2 Middle East… Morocco Clothes Online M 9/14/2013
## 3 Australia a… Papua … Meat Offline M 5/15/2015
## 4 Sub-Saharan… Djibou… Clothes Offline H 5/17/2017
## 5 Europe Slovak… Beverages Offline L 10/26/2016
## 6 Asia Sri La… Fruits Online L 11/7/2011
## # ℹ 8 more variables: `Order ID` <dbl>, `Ship Date` <chr>, `Units Sold` <dbl>,
## # `Unit Price` <dbl>, `Unit Cost` <dbl>, `Total Revenue` <dbl>,
## # `Total Cost` <dbl>, `Total Profit` <dbl>
colnames(sales_data_1)
## [1] "Region" "Country" "Item Type" "Sales Channel"
## [5] "Order Priority" "Order Date" "Order ID" "Ship Date"
## [9] "Units Sold" "Unit Price" "Unit Cost" "Total Revenue"
## [13] "Total Cost" "Total Profit"
str(sales_data_1)
## spc_tbl_ [1,000,000 × 14] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ Region : chr [1:1000000] "Sub-Saharan Africa" "Middle East and North Africa" "Australia and Oceania" "Sub-Saharan Africa" ...
## $ Country : chr [1:1000000] "South Africa" "Morocco" "Papua New Guinea" "Djibouti" ...
## $ Item Type : chr [1:1000000] "Fruits" "Clothes" "Meat" "Clothes" ...
## $ Sales Channel : chr [1:1000000] "Offline" "Online" "Offline" "Offline" ...
## $ Order Priority: chr [1:1000000] "M" "M" "M" "H" ...
## $ Order Date : chr [1:1000000] "7/27/2012" "9/14/2013" "5/15/2015" "5/17/2017" ...
## $ Order ID : num [1:1000000] 4.43e+08 6.68e+08 9.41e+08 8.81e+08 1.75e+08 ...
## $ Ship Date : chr [1:1000000] "7/28/2012" "10/19/2013" "6/4/2015" "7/2/2017" ...
## $ Units Sold : num [1:1000000] 1593 4611 360 562 3973 ...
## $ Unit Price : num [1:1000000] 9.33 109.28 421.89 109.28 47.45 ...
## $ Unit Cost : num [1:1000000] 6.92 35.84 364.69 35.84 31.79 ...
## $ Total Revenue : num [1:1000000] 14863 503890 151880 61415 188519 ...
## $ Total Cost : num [1:1000000] 11024 165258 131288 20142 126302 ...
## $ Total Profit : num [1:1000000] 3839 338632 20592 41273 62217 ...
## - attr(*, "spec")=
## .. cols(
## .. Region = col_character(),
## .. Country = col_character(),
## .. `Item Type` = col_character(),
## .. `Sales Channel` = col_character(),
## .. `Order Priority` = col_character(),
## .. `Order Date` = col_character(),
## .. `Order ID` = col_double(),
## .. `Ship Date` = col_character(),
## .. `Units Sold` = col_double(),
## .. `Unit Price` = col_double(),
## .. `Unit Cost` = col_double(),
## .. `Total Revenue` = col_double(),
## .. `Total Cost` = col_double(),
## .. `Total Profit` = col_double()
## .. )
## - attr(*, "problems")=<externalptr>
str(sales_data_2)
## spc_tbl_ [1,500,000 × 14] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ Region : chr [1:1500000] "Sub-Saharan Africa" "Middle East and North Africa" "Australia and Oceania" "Sub-Saharan Africa" ...
## $ Country : chr [1:1500000] "South Africa" "Morocco" "Papua New Guinea" "Djibouti" ...
## $ Item Type : chr [1:1500000] "Fruits" "Clothes" "Meat" "Clothes" ...
## $ Sales Channel : chr [1:1500000] "Offline" "Online" "Offline" "Offline" ...
## $ Order Priority: chr [1:1500000] "M" "M" "M" "H" ...
## $ Order Date : chr [1:1500000] "7/27/2012" "9/14/2013" "5/15/2015" "5/17/2017" ...
## $ Order ID : num [1:1500000] 4.43e+08 6.68e+08 9.41e+08 8.81e+08 1.75e+08 ...
## $ Ship Date : chr [1:1500000] "7/28/2012" "10/19/2013" "6/4/2015" "7/2/2017" ...
## $ Units Sold : num [1:1500000] 1593 4611 360 562 3973 ...
## $ Unit Price : num [1:1500000] 9.33 109.28 421.89 109.28 47.45 ...
## $ Unit Cost : num [1:1500000] 6.92 35.84 364.69 35.84 31.79 ...
## $ Total Revenue : num [1:1500000] 14863 503890 151880 61415 188519 ...
## $ Total Cost : num [1:1500000] 11024 165258 131288 20142 126302 ...
## $ Total Profit : num [1:1500000] 3839 338632 20592 41273 62217 ...
## - attr(*, "spec")=
## .. cols(
## .. Region = col_character(),
## .. Country = col_character(),
## .. `Item Type` = col_character(),
## .. `Sales Channel` = col_character(),
## .. `Order Priority` = col_character(),
## .. `Order Date` = col_character(),
## .. `Order ID` = col_double(),
## .. `Ship Date` = col_character(),
## .. `Units Sold` = col_double(),
## .. `Unit Price` = col_double(),
## .. `Unit Cost` = col_double(),
## .. `Total Revenue` = col_double(),
## .. `Total Cost` = col_double(),
## .. `Total Profit` = col_double()
## .. )
## - attr(*, "problems")=<externalptr>
summary(sales_data_1)
## Region Country Item Type Sales Channel
## Length:1000000 Length:1000000 Length:1000000 Length:1000000
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## Order Priority Order Date Order ID Ship Date
## Length:1000000 Length:1000000 Min. :100001180 Length:1000000
## Class :character Class :character 1st Qu.:323962950 Class :character
## Mode :character Mode :character Median :548652350 Mode :character
## Mean :549352019
## 3rd Qu.:774598097
## Max. :999999892
## Units Sold Unit Price Unit Cost Total Revenue
## Min. : 1 Min. : 9.33 Min. : 6.92 Min. : 9
## 1st Qu.: 2502 1st Qu.: 81.73 1st Qu.: 35.84 1st Qu.: 277867
## Median : 4998 Median :154.06 Median : 97.44 Median : 784445
## Mean : 4999 Mean :266.03 Mean :187.52 Mean :1329563
## 3rd Qu.: 7496 3rd Qu.:421.89 3rd Qu.:263.33 3rd Qu.:1822444
## Max. :10000 Max. :668.27 Max. :524.96 Max. :6682700
## Total Cost Total Profit
## Min. : 7 Min. : 2.4
## 1st Qu.: 161729 1st Qu.: 95104.8
## Median : 466782 Median : 281054.9
## Mean : 937267 Mean : 392295.6
## 3rd Qu.:1196327 3rd Qu.: 565307.6
## Max. :5249600 Max. :1738700.0
summary(sales_data_2)
## Region Country Item Type Sales Channel
## Length:1500000 Length:1500000 Length:1500000 Length:1500000
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## Order Priority Order Date Order ID Ship Date
## Length:1500000 Length:1500000 Min. :100001180 Length:1500000
## Class :character Class :character 1st Qu.:324686288 Class :character
## Mode :character Mode :character Median :549792182 Mode :character
## Mean :550068060
## 3rd Qu.:775627851
## Max. :999999892
## Units Sold Unit Price Unit Cost Total Revenue
## Min. : 1 Min. : 9.33 Min. : 6.92 Min. : 9
## 1st Qu.: 2501 1st Qu.: 81.73 1st Qu.: 35.84 1st Qu.: 277719
## Median : 4998 Median :154.06 Median : 97.44 Median : 785329
## Mean : 4999 Mean :266.05 Mean :187.55 Mean :1329888
## 3rd Qu.: 7498 3rd Qu.:421.89 3rd Qu.:263.33 3rd Qu.:1821933
## Max. :10000 Max. :668.27 Max. :524.96 Max. :6682700
## Total Cost Total Profit
## Min. : 7 Min. : 2.4
## 1st Qu.: 161818 1st Qu.: 95066.4
## Median : 467357 Median : 281370.4
## Mean : 937488 Mean : 392399.9
## 3rd Qu.:1196572 3rd Qu.: 565425.2
## Max. :5249600 Max. :1738700.0
# Get the shape of sales_data_1
shape_sales_data_1 <- dim(sales_data_1)
# Get the shape of sales_data_2
shape_sales_data_2 <- dim(sales_data_2)
# Print the shapes
print(paste("Shape of sales_data_1:", shape_sales_data_1[1], "rows and", shape_sales_data_1[2], "columns"))
## [1] "Shape of sales_data_1: 1000000 rows and 14 columns"
print(paste("Shape of sales_data_2:", shape_sales_data_2[1], "rows and", shape_sales_data_2[2], "columns"))
## [1] "Shape of sales_data_2: 1500000 rows and 14 columns"
sales_data_1
## # A tibble: 1,000,000 × 14
## Region Country `Item Type` `Sales Channel` `Order Priority` `Order Date`
## <chr> <chr> <chr> <chr> <chr> <chr>
## 1 Sub-Sahara… South … Fruits Offline M 7/27/2012
## 2 Middle Eas… Morocco Clothes Online M 9/14/2013
## 3 Australia … Papua … Meat Offline M 5/15/2015
## 4 Sub-Sahara… Djibou… Clothes Offline H 5/17/2017
## 5 Europe Slovak… Beverages Offline L 10/26/2016
## 6 Asia Sri La… Fruits Online L 11/7/2011
## 7 Sub-Sahara… Seyche… Beverages Online M 1/18/2013
## 8 Sub-Sahara… Tanzan… Beverages Online L 11/30/2016
## 9 Sub-Sahara… Ghana Office Sup… Online L 3/23/2017
## 10 Sub-Sahara… Tanzan… Cosmetics Offline L 5/23/2016
## # ℹ 999,990 more rows
## # ℹ 8 more variables: `Order ID` <dbl>, `Ship Date` <chr>, `Units Sold` <dbl>,
## # `Unit Price` <dbl>, `Unit Cost` <dbl>, `Total Revenue` <dbl>,
## # `Total Cost` <dbl>, `Total Profit` <dbl>
library(ggplot2)
# Boxplot for Total Revenue in sales_data_1
ggplot(sales_data_1, aes(x = `Item Type`, y = `Total Revenue`)) +
geom_boxplot() +
ggtitle("Boxplot of Total Revenue by Item Type (1M Records)") +
xlab("Item Type") +
ylab("Total Revenue") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
# Boxplot for Total Revenue in sales_data_2
ggplot(sales_data_2, aes(x = `Item Type`, y = `Total Revenue`)) +
geom_boxplot() +
ggtitle("Boxplot of Total Revenue by Item Type (1.5M Records)") +
xlab("Item Type") +
ylab("Total Revenue") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
# Histogram for Total Revenue in sales_data_1
ggplot(sales_data_1, aes(x = `Total Revenue`)) +
geom_histogram(bins = 50, fill = "blue", alpha = 0.7) +
ggtitle("Histogram of Total Revenue (1M Records)") +
xlab("Total Revenue") +
ylab("Frequency")
# Histogram for Total Revenue in sales_data_2
ggplot(sales_data_2, aes(x = `Total Revenue`)) +
geom_histogram(bins = 50, fill = "red", alpha = 0.7) +
ggtitle("Histogram of Total Revenue (1.5M Records)") +
xlab("Total Revenue") +
ylab("Frequency")
library(reshape2)
# Calculate the correlation matrix for sales_data_1
cor_matrix_1 <- cor(sales_data_1[, sapply(sales_data_1, is.numeric)], use = "complete.obs")
# Reshape the correlation matrix for heatmap
cor_melted_1 <- melt(cor_matrix_1)
# Heatmap for sales_data_1
ggplot(cor_melted_1, aes(Var1, Var2, fill = value)) +
geom_tile() +
scale_fill_gradient2(low = "red", high = "green", mid = "white",
midpoint = 0, limit = c(-1,1), space = "Lab",
name="Correlation") +
theme_minimal() +
ggtitle("Correlation Heatmap (1M Records)")
# Calculate the correlation matrix for sales_data_2
cor_matrix_2 <- cor(sales_data_2[, sapply(sales_data_2, is.numeric)], use = "complete.obs")
# Reshape the correlation matrix for heatmap
cor_melted_2 <- melt(cor_matrix_2)
# Heatmap for sales_data_2
ggplot(cor_melted_2, aes(Var1, Var2, fill = value)) +
geom_tile() +
scale_fill_gradient2(low = "blue", high = "red", mid = "green",
midpoint = 0, limit = c(-1,1), space = "Lab",
name="Correlation") +
theme_minimal() +
ggtitle("Correlation Heatmap (1.5M Records)")
Scatterplots can help visualize relationships between two numeric variables.
# Scatterplot for Total Revenue vs. Units Sold in sales_data_1
ggplot(sales_data_1, aes(x = `Units Sold`, y = `Total Revenue`)) +
geom_point(alpha = 0.5) +
ggtitle("Total Revenue vs. Units Sold (1M Records)") +
xlab("Units Sold") +
ylab("Total Revenue")
# Scatterplot for Total Revenue vs. Units Sold in sales_data_2
ggplot(sales_data_2, aes(x = `Units Sold`, y = `Total Revenue`)) +
geom_point(alpha = 0.5) +
ggtitle("Total Revenue vs. Units Sold (1.5M Records)") +
xlab("Units Sold") +
ylab("Total Revenue")
You can also visualize the correlation matrix using the
corrplot
package.
library(corrplot)
## corrplot 0.94 loaded
# Correlation plot for sales_data_1
corrplot(cor_matrix_1, method = "circle", type = "upper", tl.col = "black", tl.srt = 45,
title = "Correlation Plot (1M Records)")
# Correlation plot for sales_data_2
corrplot(cor_matrix_2, method = "circle", type = "upper", tl.col = "black", tl.srt = 45,
title = "Correlation Plot (1.5M Records)")
# Load necessary packages
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, union
# Convert Order.Date and Ship.Date to Date type
sales_data_1 <- sales_data_1 %>%
mutate(Order.Date = as.Date(`Order Date`, format="%m/%d/%Y"),
Ship.Date = as.Date(`Ship Date`, format="%m/%d/%Y"))
sales_data_2 <- sales_data_2 %>%
mutate(Order.Date = as.Date(`Order Date`, format="%m/%d/%Y" ),
Ship.Date = as.Date(`Ship Date`, format="%m/%d/%Y"))
# Linear regression for sales_data_1
linear_model_1 <- lm(`Total Revenue` ~ `Units Sold`, data = sales_data_1)
summary(linear_model_1)
##
## Call:
## lm(formula = `Total Revenue` ~ `Units Sold`, data = sales_data_1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2565044 -789981 -183891 576713 4024356
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1383.0580 2505.5845 0.552 0.581
## `Units Sold` 265.6961 0.4341 612.052 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1253000 on 999998 degrees of freedom
## Multiple R-squared: 0.2725, Adjusted R-squared: 0.2725
## F-statistic: 3.746e+05 on 1 and 999998 DF, p-value: < 2.2e-16
# Linear regression for sales_data_2
linear_model_2 <- lm(`Total Revenue` ~ `Units Sold`, data = sales_data_2)
summary(linear_model_2)
##
## Call:
## lm(formula = `Total Revenue` ~ `Units Sold`, data = sales_data_2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2565492 -790049 -184307 576454 4023908
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1353.5169 2046.1659 0.661 0.508
## `Units Sold` 265.7438 0.3545 749.672 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1253000 on 1499998 degrees of freedom
## Multiple R-squared: 0.2726, Adjusted R-squared: 0.2726
## F-statistic: 5.62e+05 on 1 and 1499998 DF, p-value: < 2.2e-16
# Multiple regression for sales_data_1
multiple_model_1 <- lm(`Total Revenue` ~ `Units Sold` + `Unit Price` + `Order Priority`, data = sales_data_1)
summary(multiple_model_1)
##
## Call:
## lm(formula = `Total Revenue` ~ `Units Sold` + `Unit Price` +
## `Order Priority`, data = sales_data_1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2012853 -360893 400 360634 2013941
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.329e+06 1.824e+03 -728.739 <2e-16 ***
## `Units Sold` 2.659e+02 2.169e-01 1225.943 <2e-16 ***
## `Unit Price` 5.001e+03 2.884e+00 1734.140 <2e-16 ***
## `Order Priority`H 1.018e+02 1.769e+03 0.058 0.954
## `Order Priority`L -2.086e+03 1.769e+03 -1.179 0.238
## `Order Priority`M 9.552e+02 1.770e+03 0.540 0.589
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 625700 on 999994 degrees of freedom
## Multiple R-squared: 0.8185, Adjusted R-squared: 0.8185
## F-statistic: 9.017e+05 on 5 and 999994 DF, p-value: < 2.2e-16
# Multiple regression for sales_data_2
multiple_model_2 <- lm(`Total Revenue` ~ `Units Sold` + `Unit Price` + `Order Priority`, data = sales_data_2)
summary(multiple_model_2)
##
## Call:
## lm(formula = `Total Revenue` ~ `Units Sold` + `Unit Price` +
## `Order Priority`, data = sales_data_2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2012679 -361133 119 360734 2013047
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.329e+06 1.490e+03 -891.977 <2e-16 ***
## `Units Sold` 2.659e+02 1.771e-01 1501.407 <2e-16 ***
## `Unit Price` 5.001e+03 2.355e+00 2123.977 <2e-16 ***
## `Order Priority`H -4.836e+02 1.445e+03 -0.335 0.738
## `Order Priority`L -2.026e+03 1.445e+03 -1.402 0.161
## `Order Priority`M -3.162e+02 1.445e+03 -0.219 0.827
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 625800 on 1499994 degrees of freedom
## Multiple R-squared: 0.8185, Adjusted R-squared: 0.8185
## F-statistic: 1.353e+06 on 5 and 1499994 DF, p-value: < 2.2e-16
# Compare the results
cat("Linear Model Summary for Sales Data 1:\n")
## Linear Model Summary for Sales Data 1:
print(summary(linear_model_1))
##
## Call:
## lm(formula = `Total Revenue` ~ `Units Sold`, data = sales_data_1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2565044 -789981 -183891 576713 4024356
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1383.0580 2505.5845 0.552 0.581
## `Units Sold` 265.6961 0.4341 612.052 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1253000 on 999998 degrees of freedom
## Multiple R-squared: 0.2725, Adjusted R-squared: 0.2725
## F-statistic: 3.746e+05 on 1 and 999998 DF, p-value: < 2.2e-16
cat("\nLinear Model Summary for Sales Data 2:\n")
##
## Linear Model Summary for Sales Data 2:
print(summary(linear_model_2))
##
## Call:
## lm(formula = `Total Revenue` ~ `Units Sold`, data = sales_data_2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2565492 -790049 -184307 576454 4023908
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1353.5169 2046.1659 0.661 0.508
## `Units Sold` 265.7438 0.3545 749.672 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1253000 on 1499998 degrees of freedom
## Multiple R-squared: 0.2726, Adjusted R-squared: 0.2726
## F-statistic: 5.62e+05 on 1 and 1499998 DF, p-value: < 2.2e-16
cat("\nMultiple Model Summary for Sales Data 1:\n")
##
## Multiple Model Summary for Sales Data 1:
print(summary(multiple_model_1))
##
## Call:
## lm(formula = `Total Revenue` ~ `Units Sold` + `Unit Price` +
## `Order Priority`, data = sales_data_1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2012853 -360893 400 360634 2013941
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.329e+06 1.824e+03 -728.739 <2e-16 ***
## `Units Sold` 2.659e+02 2.169e-01 1225.943 <2e-16 ***
## `Unit Price` 5.001e+03 2.884e+00 1734.140 <2e-16 ***
## `Order Priority`H 1.018e+02 1.769e+03 0.058 0.954
## `Order Priority`L -2.086e+03 1.769e+03 -1.179 0.238
## `Order Priority`M 9.552e+02 1.770e+03 0.540 0.589
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 625700 on 999994 degrees of freedom
## Multiple R-squared: 0.8185, Adjusted R-squared: 0.8185
## F-statistic: 9.017e+05 on 5 and 999994 DF, p-value: < 2.2e-16
cat("\nMultiple Model Summary for Sales Data 2:\n")
##
## Multiple Model Summary for Sales Data 2:
print(summary(multiple_model_2))
##
## Call:
## lm(formula = `Total Revenue` ~ `Units Sold` + `Unit Price` +
## `Order Priority`, data = sales_data_2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2012679 -361133 119 360734 2013047
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.329e+06 1.490e+03 -891.977 <2e-16 ***
## `Units Sold` 2.659e+02 1.771e-01 1501.407 <2e-16 ***
## `Unit Price` 5.001e+03 2.355e+00 2123.977 <2e-16 ***
## `Order Priority`H -4.836e+02 1.445e+03 -0.335 0.738
## `Order Priority`L -2.026e+03 1.445e+03 -1.402 0.161
## `Order Priority`M -3.162e+02 1.445e+03 -0.219 0.827
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 625800 on 1499994 degrees of freedom
## Multiple R-squared: 0.8185, Adjusted R-squared: 0.8185
## F-statistic: 1.353e+06 on 5 and 1499994 DF, p-value: < 2.2e-16
# Create a binary outcome variable for sales_data_1
sales_data_1 <- sales_data_1 %>%
mutate(Is_Profitable = ifelse(`Total Revenue` > mean(`Total Revenue`), 1, 0))
# Create a binary outcome variable for sales_data_2
sales_data_2 <- sales_data_2 %>%
mutate(Is_Profitable = ifelse(`Total Revenue` > mean(`Total Revenue`), 1, 0))
# Load necessary packages for logistic regression
library(dplyr)
library(caret) # For confusionMatrix
## Loading required package: lattice
# Logistic regression for sales_data_1
logistic_model_1 <- glm(Is_Profitable ~ `Units Sold` + `Unit Price` + `Order Priority`,
data = sales_data_1,
family = binomial)
# Logistic regression for sales_data_2
logistic_model_2 <- glm(Is_Profitable ~ `Units Sold` + `Unit Price` + `Order Priority`,
data = sales_data_2,
family = binomial)
summary
## function (object, ...)
## UseMethod("summary")
## <bytecode: 0x5555f84bff60>
## <environment: namespace:base>
# Predictions for sales_data_1
predictions_1 <- ifelse(predict(logistic_model_1, type = "response") > 0.5, 1, 0)
# Predictions for sales_data_2
predictions_2 <- ifelse(predict(logistic_model_2, type = "response") > 0.5, 1, 0)
# Create confusion matrices to assess accuracy
confusion_matrix_1 <- confusionMatrix(as.factor(predictions_1), as.factor(sales_data_1$Is_Profitable))
confusion_matrix_2 <- confusionMatrix(as.factor(predictions_2), as.factor(sales_data_2$Is_Profitable))
# Display accuracy
accuracy_1 <- confusion_matrix_1$overall['Accuracy']
accuracy_2 <- confusion_matrix_2$overall['Accuracy']
cat("Accuracy for Logistic Regression Model 1:", accuracy_1, "\n")
## Accuracy for Logistic Regression Model 1: 0.920284
cat("Accuracy for Logistic Regression Model 2:", accuracy_2, "\n")
## Accuracy for Logistic Regression Model 2: 0.9203953
mol <- colnames(sales_data_1)
mol
## [1] "Region" "Country" "Item Type" "Sales Channel"
## [5] "Order Priority" "Order Date" "Order ID" "Ship Date"
## [9] "Units Sold" "Unit Price" "Unit Cost" "Total Revenue"
## [13] "Total Cost" "Total Profit" "Order.Date" "Ship.Date"
## [17] "Is_Profitable"
# Load necessary libraries
library(class)
library(caret) # For confusionMatrix and other functions
# Preprocess the data for KNN
preprocess_knn <- function(data) {
# Convert categorical variables to factors if needed
data$Order.Priority <- as.factor(data$`Order Priority`)
# Create a binary response variable (e.g., profitable or not)
data$Is_Profitable <- ifelse(data$`Total Profit` > mean(data$`Total Profit`), 1, 0) # Define profitable
# Select only numeric columns for KNN (including response variable)
knn_data <- data[, c("Units Sold" , "Unit Price", "Unit Cost" , "Total Revenue", "Total Cost" , "Total Profit" , "Is_Profitable")]
# Scale the data (except for the response variable)
knn_data_scaled <- scale(knn_data[, -ncol(knn_data)]) # Scale features only
# Bind the scaled features with the response variable
knn_data_final <- data.frame(knn_data_scaled, Is_Profitable = knn_data$Is_Profitable)
return(knn_data_final)
}
# Preprocess both datasets
knn_data_1 <- preprocess_knn(sales_data_1)
knn_data_2 <- preprocess_knn(sales_data_2)
# Split the data into training and testing sets (80% train, 20% test)
set.seed(123) # For reproducibility
train_index_1 <- sample(1:nrow(knn_data_1), 0.8 * nrow(knn_data_1))
train_index_2 <- sample(1:nrow(knn_data_2), 0.8 * nrow(knn_data_2))
train_data_1 <- knn_data_1[train_index_1, ]
test_data_1 <- knn_data_1[-train_index_1, ]
train_data_2 <- knn_data_2[train_index_2, ]
test_data_2 <- knn_data_2[-train_index_2, ]
# Fit KNN Model
k_value <- 5 # You can adjust the value of K
# Predictions for sales_data_1
predictions_1 <- knn(train = train_data_1[, -ncol(train_data_1)],
test = test_data_1[, -ncol(test_data_1)],
cl = train_data_1$Is_Profitable,
k = k_value)
# Predictions for sales_data_2
predictions_2 <- knn(train = train_data_2[, -ncol(train_data_2)],
test = test_data_2[, -ncol(test_data_2)],
cl = train_data_2$Is_Profitable,
k = k_value)
# Evaluate the results
confusion_matrix_1 <- confusionMatrix(as.factor(predictions_1), as.factor(test_data_1$Is_Profitable))
confusion_matrix_2 <- confusionMatrix(as.factor(predictions_2), as.factor(test_data_2$Is_Profitable))
# Print the accuracy
cat("Accuracy for Sales Data 1:", confusion_matrix_1$overall['Accuracy'], "\n")
## Accuracy for Sales Data 1: 1
cat("Accuracy for Sales Data 2:", confusion_matrix_2$overall['Accuracy'], "\n")
## Accuracy for Sales Data 2: 0.99999
# Install necessary packages if you haven't already
install.packages("e1071")
## Installing package into '/home/dragon/R/x86_64-pc-linux-gnu-library/4.4'
## (as 'lib' is unspecified)
install.packages("caret")
## Installing package into '/home/dragon/R/x86_64-pc-linux-gnu-library/4.4'
## (as 'lib' is unspecified)
install.packages("ggplot2")
## Installing package into '/home/dragon/R/x86_64-pc-linux-gnu-library/4.4'
## (as 'lib' is unspecified)
# Load the libraries
library(e1071)
library(caret)
library(ggplot2)
# Add Is_Profitable column based on Total.Profit being greater than 0
sales_data_1$Is_Profitable <- ifelse(sales_data_1$`Total Profit` > mean(sales_data_1$`Total Profit`), 1, 0)
sales_data_2$Is_Profitable <- ifelse(sales_data_2$`Total Profit` > mean(sales_data_2$`Total Profit`), 1, 0)
# Convert target variable to factor for both datasets
sales_data_1$Is_Profitable <- as.factor(sales_data_1$Is_Profitable)
sales_data_2$Is_Profitable <- as.factor(sales_data_2$Is_Profitable)
# Split data into train and test sets for both datasets
set.seed(123) # For reproducibility
trainIndex_1 <- createDataPartition(sales_data_1$Is_Profitable, p = 0.8, list = FALSE)
train_data_1 <- sales_data_1[trainIndex_1, ]
test_data_1 <- sales_data_1[-trainIndex_1, ]
trainIndex_2 <- createDataPartition(sales_data_2$Is_Profitable, p = 0.8, list = FALSE)
train_data_2 <- sales_data_2[trainIndex_2, ]
test_data_2 <- sales_data_2[-trainIndex_2, ]
# Train Naive Bayes model on both datasets
naive_model_1 <- naiveBayes(Is_Profitable ~ ., data = train_data_1)
naive_model_2 <- naiveBayes(Is_Profitable ~ ., data = train_data_2)
# Make predictions on test sets
predictions_1 <- predict(naive_model_1, test_data_1)
predictions_2 <- predict(naive_model_2, test_data_2)
# Confusion matrix and accuracy for sales_data_1
conf_matrix_1 <- confusionMatrix(predictions_1, test_data_1$Is_Profitable)
accuracy_1 <- conf_matrix_1$overall['Accuracy']
print(paste("Accuracy for Dataset 1: ", round(accuracy_1, 4)))
## [1] "Accuracy for Dataset 1: 0.9114"
print(conf_matrix_1)
## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 111580 9619
## 1 8103 70697
##
## Accuracy : 0.9114
## 95% CI : (0.9101, 0.9126)
## No Information Rate : 0.5984
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.8151
##
## Mcnemar's Test P-Value : < 2.2e-16
##
## Sensitivity : 0.9323
## Specificity : 0.8802
## Pos Pred Value : 0.9206
## Neg Pred Value : 0.8972
## Prevalence : 0.5984
## Detection Rate : 0.5579
## Detection Prevalence : 0.6060
## Balanced Accuracy : 0.9063
##
## 'Positive' Class : 0
##
# Confusion matrix and accuracy for sales_data_2
conf_matrix_2 <- confusionMatrix(predictions_2, test_data_2$Is_Profitable)
accuracy_2 <- conf_matrix_2$overall['Accuracy']
print(paste("Accuracy for Dataset 2: ", round(accuracy_2, 4)))
## [1] "Accuracy for Dataset 2: 0.9106"
print(conf_matrix_2)
## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 167350 14639
## 1 12170 105840
##
## Accuracy : 0.9106
## 95% CI : (0.9096, 0.9117)
## No Information Rate : 0.5984
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.8134
##
## Mcnemar's Test P-Value : < 2.2e-16
##
## Sensitivity : 0.9322
## Specificity : 0.8785
## Pos Pred Value : 0.9196
## Neg Pred Value : 0.8969
## Prevalence : 0.5984
## Detection Rate : 0.5578
## Detection Prevalence : 0.6066
## Balanced Accuracy : 0.9054
##
## 'Positive' Class : 0
##
# Function to plot confusion matrix
plot_confusion_matrix <- function(conf_matrix, title) {
cm_df <- as.data.frame(conf_matrix$table)
ggplot(cm_df, aes(x = Reference, y = Prediction, fill = Freq)) +
geom_tile() +
geom_text(aes(label = Freq), color = "white") +
labs(title = title, x = "Actual", y = "Predicted") +
scale_fill_gradient(low = "white", high = "blue") +
theme_minimal()
}
# Plot confusion matrices for both datasets
plot_confusion_matrix(conf_matrix_1, "Confusion Matrix for Sales Data 1")
plot_confusion_matrix(conf_matrix_2, "Confusion Matrix for Sales Data 2")
library(rpart)
library(rpart.plot)
# Fit regression tree for the first dataset
reg_tree_1 <- rpart(`Total Profit` ~ ., data = sales_data_1, method = "anova")
# Fit regression tree for the second dataset
reg_tree_2 <- rpart(`Total Profit` ~ ., data = sales_data_2, method = "anova")
# Plot regression tree for sales_data_1
rpart.plot(reg_tree_1, type = 2, extra = 1, main = "Regression Tree for Sales Data 1")
# Plot regression tree for sales_data_2
rpart.plot(reg_tree_2, type = 2, extra = 1, main = "Regression Tree for Sales Data 2")
# Summary of the first regression tree model
summary(reg_tree_1)
## Call:
## rpart(formula = `Total Profit` ~ ., data = sales_data_1, method = "anova")
## n= 1000000
##
## CP nsplit rel error xerror xstd
## 1 0.63951099 0 1.00000000 1.00000277 0.0018072122
## 2 0.12513982 1 0.36048901 0.36049190 0.0008060948
## 3 0.04489665 2 0.23534919 0.23547971 0.0005011016
## 4 0.03514567 3 0.19045255 0.19067830 0.0003891094
## 5 0.02928676 4 0.15530687 0.15554000 0.0003936206
## 6 0.02228934 5 0.12602011 0.12608169 0.0002488589
## 7 0.01683394 6 0.10373077 0.10369276 0.0001906571
## 8 0.01544967 7 0.08689683 0.08690546 0.0001544073
## 9 0.01000000 8 0.07144716 0.07149608 0.0001113936
##
## Variable importance
## Total Revenue Is_Profitable Total Cost Item Type Units Sold
## 23 21 20 12 10
## Unit Price Unit Cost
## 10 3
##
## Node number 1: 1000000 observations, complexity param=0.639511
## mean=392295.6, MSE=1.435044e+11
## left son=2 (598416 obs) right son=3 (401584 obs)
## Primary splits:
## Is_Profitable splits as LR, improve=0.6395110, (0 missing)
## Total Revenue < 1413792 to the left, improve=0.5538571, (0 missing)
## Total Cost < 779086.9 to the left, improve=0.5136881, (0 missing)
## Unit Price < 429.545 to the left, improve=0.3421216, (0 missing)
## Item Type splits as LLLLRLRLRLLL, improve=0.3421216, (0 missing)
## Surrogate splits:
## Total Revenue < 957289.9 to the left, agree=0.900, adj=0.751, (0 split)
## Total Cost < 623819.8 to the left, agree=0.872, adj=0.681, (0 split)
## Units Sold < 5376.5 to the left, agree=0.761, adj=0.406, (0 split)
## Item Type splits as RLRLRLRLRLLL, agree=0.744, adj=0.363, (0 split)
## Unit Price < 429.545 to the left, agree=0.720, adj=0.302, (0 split)
##
## Node number 2: 598416 observations, complexity param=0.03514567
## mean=144129, MSE=1.311986e+10
## left son=4 (323235 obs) right son=5 (275181 obs)
## Primary splits:
## Total Revenue < 399198.3 to the left, improve=0.6423985, (0 missing)
## Total Cost < 310432.2 to the left, improve=0.5194542, (0 missing)
## Item Type splits as RLRRRLRRRRRR, improve=0.2881855, (0 missing)
## Unit Cost < 33.815 to the left, improve=0.2881855, (0 missing)
## Unit Price < 64.59 to the left, improve=0.2881855, (0 missing)
## Surrogate splits:
## Total Cost < 267461 to the left, agree=0.954, adj=0.901, (0 split)
## Item Type splits as RLRLRLRRRRRR, agree=0.722, adj=0.395, (0 split)
## Unit Cost < 46.255 to the left, agree=0.722, adj=0.395, (0 split)
## Unit Price < 130.93 to the left, agree=0.718, adj=0.388, (0 split)
## Units Sold < 2596.5 to the left, agree=0.651, adj=0.240, (0 split)
##
## Node number 3: 401584 observations, complexity param=0.1251398
## mean=762098.2, MSE=1.092688e+11
## left son=6 (296582 obs) right son=7 (105002 obs)
## Primary splits:
## Total Revenue < 3560905 to the left, improve=0.4092493, (0 missing)
## Unit Price < 429.545 to the left, improve=0.3630894, (0 missing)
## Item Type splits as L-LLR-RLR-LL, improve=0.3630894, (0 missing)
## Total Cost < 1331272 to the left, improve=0.3267937, (0 missing)
## Unit Cost < 211.375 to the left, improve=0.2416675, (0 missing)
## Surrogate splits:
## Total Cost < 2870495 to the left, agree=0.942, adj=0.777, (0 split)
## Item Type splits as L-LLL-RLR-LL, agree=0.819, adj=0.306, (0 split)
## Unit Price < 544.205 to the left, agree=0.819, adj=0.306, (0 split)
## Unit Cost < 433.615 to the left, agree=0.819, adj=0.306, (0 split)
## Units Sold < 9999.5 to the left, agree=0.739, adj=0.000, (0 split)
##
## Node number 4: 323235 observations
## mean=59422.51, MSE=2.845411e+09
##
## Node number 5: 275181 observations
## mean=243627.6, MSE=6.860361e+09
##
## Node number 6: 296582 observations, complexity param=0.02928676
## mean=636272.7, MSE=4.511273e+10
## left son=12 (247437 obs) right son=13 (49145 obs)
## Primary splits:
## Item Type splits as L-LLR-LLL-LL, improve=0.3141179, (0 missing)
## Total Revenue < 1662664 to the left, improve=0.3091881, (0 missing)
## Total Cost < 974114.3 to the left, improve=0.2906839, (0 missing)
## Unit Price < 429.545 to the left, improve=0.1490113, (0 missing)
## Unit Cost < 107.275 to the left, improve=0.1341467, (0 missing)
## Surrogate splits:
## Unit Price < 429.545 to the left, agree=0.850, adj=0.095, (0 split)
## Units Sold < 2736.5 to the right, agree=0.838, adj=0.021, (0 split)
## Order ID < 100023700 to the right, agree=0.834, adj=0.000, (0 split)
##
## Node number 7: 105002 observations, complexity param=0.04489665
## mean=1117497, MSE=1.194537e+11
## left son=14 (50617 obs) right son=15 (54385 obs)
## Primary splits:
## Item Type splits as ----R-RLL---, improve=0.5136671, (0 missing)
## Unit Price < 429.545 to the left, improve=0.4102918, (0 missing)
## Unit Cost < 314.01 to the right, improve=0.3034319, (0 missing)
## Total Cost < 2655668 to the right, improve=0.3034319, (0 missing)
## Total Revenue < 5342013 to the left, improve=0.1620376, (0 missing)
## Surrogate splits:
## Unit Cost < 513.75 to the right, agree=0.877, adj=0.744, (0 split)
## Unit Price < 659.74 to the left, agree=0.853, adj=0.695, (0 split)
## Total Cost < 3054590 to the right, agree=0.661, adj=0.297, (0 split)
## Order Date splits as RRRRLLLLRRRRRRRLLLRLLRLLLRLRRRLRRLRRLRRRLLLRRRLRRRLRRLRRRLRRLRLRLLRLRLRRLRRRRRLLRRLRRRRLLLRLRRRRLRRLRRRRRRRRLLLRRRLLRRRRRLRRLRRRRRLRLRRLRRRRRLRLRRRRRRLRLRLLRRRRRRRLRLLRRRRRRRRRLRRRLRLRRLLRRRRLRRLLRRLRRRLLRRRLRRRLLRRRRRRLRRRRLRRRRLLRRRRRLRRRRRRRRLLLLRLRRLRRRLLLRRRLRRLLRRRLLRRRRRLRRLLRRLRLRRLLRLLLLRLLRRRRLLRRRRLRRLRRLLRRRLLRRRLLRRRRLRRRRLRLRRRRRRLLRLRRRRLRRLLLRLLRLLRLLRLLRRRLRRLRRRLLRRRRRRRRRLLRLRRLRRRRRRRLLRLRRRRLRLLRRRLLRLRRRRLLLRLRRLLRRRLLRRRLRLRRRRRRRRRLLRLLRRLRRRRRRRRRLLRLLRRRRRRRRRLLLLLLLRLRRRLLLLLRRRRRLRLRLRRRLRLRRLLLRRLRLRRLLRLLLRRRRRRRLRLLRRRRRRLLRRRRLRRRLRLRLRLLRLRRLLRLLLLRLRLRLLRRRRLRRLRRLLLLRRRLLRLRRLLLRLRLRLRRRRRLRLRLLLRLLRRLLLRRRRRRRRLRRLRLLLRLRRRRRRLRRLLLLLRLLRRLLRRRLRRLRRRRRRRLRLLRRRRRRLRRRLRLLRLRLRRLRRLRRRRRRRLLLLRRRLRLRLRRRRRRRRRRLLRRLRLRRLRLRLRLRRRRLLLRRRLRRRLRLRRLRRLLRRRRLRRLRRRRLRRRRLRRRRRRRRRLRLRRLRLRLRRRRLRRRLRLRLLLLRLLRRLLLRLRRRRRLRLRRRRRLRRLLLRLLRLRRLLRLLRLRRRLLLRRRRRLRLLRLRRRRRRLRRRRLRRRLRRRLRRRRRLRRRRRLLRLRRLRLRLRLRRRRRLLLLRRRRRRRRLLLRRLRLRRLRRRRRRRRRRRLLRLLRRLRRRRLLRRRRRLLRLRRRRRRLLLRRRLRRRLLLLLLLLRRLRRRRRLLLRRRRRRRLRRLRRRRRLRLLLLRRLLLRRRRLRLLRLLRRRRRRLRRRLRRRLRRRLRRLLRLLLRRLLRRLRLLLRRRRRRRLRLRRRRLRRLRRLLRLLLLLRRRRRLRLRRRLRLLRLRRLRRRLLLRLRLRRRLRRLLLRRLRLRRRLRLRRRRRLLRRLRRLRLRRLLLRRRRLLLLLLLRLLLRLLLLRRRLRRLRLLRRRRLRLRLRRLLLRLRLLRRLRRLLLRRRRLRRRLRLRRLLRRRLRRLRLRRRRRLLRLRRRRRRLLLLLRRRLRLLRRRRLRRLRRLRRRLLRLLLLRRRRRLRRRLRLRRLRRLRRLLLRLRRRRRLRRLLRRLRRRRLRRLRRRLRRLRRLRLRRLRRRLRLRRRLRLRLRRRRLLRRRRRRLRRRLRRRLRLRRLLRLRLLRRRRRLRRLRRLRRLLRRLRRRRLLRRRRRRLLLRLLRLLLLRLRLLRRRLLRRLLRRLRLRRLRLRRRRRLLRRRLRLRRRRLLRRLLLRLRLLRLRRRRLLRRLLLRLRRLRRRRLRRLLLRRRRRRLLRRLLRLRLRLRLRLLRLRLLLRLLLRRLLLRRLRRLLRRLRLRRRRRRRRRLRRRRLLRLRRRLRRLRRRRLRRRRLLRRRLRRLLLRRRLLRRRRRRRLRRLLLLRRRRRRRRLRLLRRLRRRLRRRRLLRRRLRRRLLRLLRLRRRRRLRRRRLRLRRLRRRRLLLRLLLRRRRRLLRLLRRLLRLRRRRRRLLLRLRLRLLLLRRLRLRRRRRRLRLRRLLRRLRLRRRLRLRRRRLLRRRRRLRRRRLLLRRRRRRLLRRRRRRRRRRRRRLRRLRLLRRRRRRRRLRRLRRRLLLRLLLLRLLRLRRLLRRLRRLLRRRRRLRRRLRRLRRRLLLRRLLRRLLRRRRRRLRLRRRRRRRLRLLRLLRRRLRRLLRRLLRRLRLRRRLLRRRRRRRLRRRLRRRRLRRRRRRRRLRRRLRLRLLRRLLLLRRRRRRRLRLRRRRLRRLLRRLRLLRLRRRLRRRRRRLLRRRRRLLLRRRLLRRRLRLRRRLLRRRLRRRRLRRRRLRLRRLRLRLLLRRLRLRRRLLLLRRLRRLRRRLLRLRRRRRLRRRRRLLLLRRRRLRRRLRLLRRRLRRRRRLLLRLRRRRRLLLRLLRLRLRLRRRRLRLRRLLRRLLLLRRRLRLRRLRLRRLRRRRRRRRLRLRRLRRLRRLRRLRLLRLLLRLRRRLRLLLRRRRRLLLRLRRRLRRRRRRRRRRRLRRRLRRRRRRRRLLLRRLLRRRLLRRRLRLLRRLRRRRRRLLRRRRRRRRLRLLLRRRLRLRRRLLLRRRRRRRRLRRLLLRRRRRRLRRRLRRRLLRRLLLLRRLLRRLLLRLRRLRRRRRLLRRLRRLLLRRLRRRRLRRLRLRLRRRLRLRRLRRRRRRRRRLRRLRRRLLLRLLLLRLRRRLLLLLRLRRRRRRRLLRRRLRLRLLLLRRRRRRLLLLRLRRRRLRRRRRRRRRRLRLLRLLLLLRRLRLRRRRRRRRLLRLRRRRRLLLRLRRRLRRRRRLRLLRRLRRRRRLLRRRRRRRRRRLRLRRRLLRLLRRLLRRRRLRRRRLRRLRLLRLRRLRLLLRRLRRLRLRLLRRLRRRRRRLRRRLLRLLLLRRRRRRLLLLLLRRRRLLRRRLLRRRRRRLLLRRRRRRRRRRRLRLRRRRRLRRRRRRRRRLLLLLRRLRRRRLLRLLRRRRRRRLRLRLRLRR, agree=0.565, adj=0.097, (0 split)
## Ship Date splits as -LRRRRRLLRRLLLLLLRLRLLRRRRRLRRLRRRRRLLRRLLLRRRLRRRLRLRRLLRRLLRLRRRRLRRRRLRRLLRRRLLLRLRRRLLLRRLRRRLLLRRRLLRRRLRRLRRLRRLLLLRRRRRLLRRRRLRLLRLLLLLLLLLLRRRRLRRRLLRRLLRRLLRRLRRLRRRLRLLRRRLLRLRRRRRLLRRRRRRLLLLRLRLRLLRLRRLLL-LLRLLRRLLLLLLLLLRLRLLRRLRRRRLLLLRRLRLRRLRRLLRRRLLRRRRLRLRRLRLRRRRRRRLRRRRRRLRRRLRRLRRLRRRRRRRLRLLRRRLLLRRRRRRRRLRRRLRLRLLLLRLLLRRRLLRRLLRRRRRRRLRLRRRLLLRRRRRRRLLLRRLLLRRRLLRRRLLRRRRRRLRLRRRLRRLLRRLRRRLRRRLRRLLLRRLRRLRRLLLLLRLLLRLRLRRRRRLRRRLRRRLLLRLLLRRRRRRRLLRLRRRRRLRLRRRLLRRRRLRRRLRRLLRLLLRRRRRRRLRRLLRLRLLRLRRRRRLRRRLRLLLRRRLLRRRLLRRLLLRRRRLRLLRRRRRLLRRRRRLLRRRLRRLRLRLRLRRRLLRRLRRRLRLRRRLRLLRLLRLLRRRLRRRRLRRLLLLRLRRLRLLLRRRRRLLRRLLRRRLLRLRRRRRRRLRRLRRRRRRRRRLLRRLRRRRLRRLRRLLLRRRRRLLLLRRRLRRRLLLLRLLRLRRLLRRRRLLRRRRLLRLRLRRRLLRLLRLRLLLRRRLRLRLLRRRRRLLRRRRLRRLRRLRLRRRRRLRLRLRRRRRRLRRLRLLRRRLLLLRRLRRRLLLRRRRLRLLLRLLRRRLRRRRRRLRRRRRLRLRRRRLLRLRRLLLRLRRLLRRRLRLLRRRLRRRLRRRLRRRLRLLRRRLRRRRRRRRRRRRLRLLLRLRRRRRRRLRLRRLRRRRLRLRRLRRLRLLRRRRRLLRRRRRRRRLRLLRLRRRRRLRLRRRLRRRRLRRRLRRRLLRRLRRLRRRLRLRRLRLLLLRRLLRRRRLRRRRRRRLLRRRRRLRRRRRLRRRRRRRRRRRRRRRRRLRRLLRLRRRRLLRLRLRLRLRLRRRRLRRRRRRRLLRRLLLLRRRRRLRRRRLRLLRRRRRRRRLLRRRRLRRLLRRRRRLLRRRRLRLRRRRLLLRLLRRLRRRLRRLLLRLRRRRLLLRRLLRRRRRRRRLRRRRRRLLLLLLRRLRLRLLLRRRRRRLLRRLRLRLRRRRRRRRRLLRLLRRRRRLRLRRLRRLLRRLRRLRRLLLRRRLRRRLLRLLRRRRRLLRRRRRRLRRRRRRLRRRRRLRLRLRRRLLRRLLLLRLLLRRRLLRLLRRRLLRLRLRLLLRLLLRRLRRRRRLLLRLLRLLRLRLLLLRRRLLLLLRRRRLRRLRRRRRRLLRRLRRRLRLRRRRLRRRLLRRRLRRRRRRLRRRLRRRRLRRRRRRRRRRRRRLLLLLRLRRLRRRLRRRLLLLRLRLLRLLRLRLLLLLLLRRRLLLRLLLRLRRRRLRRLLRRRRRRRLRRLLLRRRLLRLLRLRRRLLLLLRRLRRRRRRRRRLLLLLRRRLLRLRRLRLLRLRRLLLLLLLRLRRRRRLRRRLRRRRLRLRRRRLRLLLLRRRRLLLRRLRLRLRRRRLLLRLRRRRLRLLLRLRLLRRRLRRRRRRLRLRRRRRRRLRRRLRLLRLRRRRRRRLRLRRRRLRRRLRLLLRRLLRRRRLLLRRRLRRLRRRRRRRRLRRLRLRRRLRLRRLRLLRRLLRRLLRLRRRRRRRRRRRRLRLLLRLRLRLRLRRLLLRRRRRLLLRRLRLLRLRLRRLRRRLRLLLLRLLRLLLRLRRRRRLRLRLRLLRLRRLRRRRLRRRRRLLRLLRRRLRRRRLRLLRLLLLRRRRRRRRRLLLRRRLRLLRLRLRRLRRRRRLRLLRLLLRRLRRLRLRLLLRLRRRRRLRRLRRRLLRRLRLRRRRRLLRLRRRRLRRRRLRRLLRRRLRLRLLRRRRLLLRLRLRRLLRRLRRLLRRLLRLLLRRRLRRLRRRRRRLLLLRLRRLLRRRRRLRRLRLLLLLRRRRLLRLLRRRRRRLLRRLRRLLRRRRLRRLRLRLLRRLLRRRRRRRRRRRRRLRLRRLRLRRLRRRRRRRLRLRRLLRLRLLRRLRRLRLRRRRRRRRLLRRRLLRLRLRLRLRRRLLRLRLRRLLRLRRRRLLLRRLRRRRRLLLLRLRRRRRRLLRRRLRLLRRRLRRRLRRLRRRRLRRRRLRLRRRLLLRRLLLRLLRLLRLRRRRRRLRLLLLLRRLRLLRLRRLRRRRLRRRLRRRRRLRLLRLRLRLRRRRRRRLRLRLRLRRLRRRLRRRLRRRLRLLLLRLRRRRLRRRRRRLRRLRRRLLLLRLRRLLRRRLRRLRRRLLLRRRRRLLLRRLRRLRLRLRRRLLLRLLRLLLRRRRRRLRRRRLLRLRRLRLRRLRRRRLRRLLLLLRRRRRLLRRRRRRRLLRRLRRLLLRLRLRRLLLRLRLRRRRRRLLLLLRLRRRRRLRLRRRRRRRLLRRLRRLLLLRRLRLLRRRRRRLLLRRLLLRRLRRRRRLLRRRLRLLLRRLLRRRLRRRRRRLLLRLRRRRLLRRLRRRRRLRLRRLRRLRLRLRLRLLLLRRRRRRLRRRLRLRLRRLRRLR-RLRRRRL-RLRRRRR-LRLLRLRRRRRRLLRRRRLRLLRLRRRRRRLRRLRRRRRLRRLRRLLRRLLLLLRRLRLRRLLRRRRRLLRLLLRRLLRLRRRRRRRRLLRLRRRRRRLLLLRRLRRRRRLRRLRRRLLLLRLLRRRRRRRRRLLRRRRRRLLRRLRRRLRRLRR, agree=0.563, adj=0.094, (0 split)
##
## Node number 12: 247437 observations
## mean=583220.5, MSE=1.982406e+10
##
## Node number 13: 49145 observations, complexity param=0.02228934
## mean=903381.7, MSE=8.691906e+10
## left son=26 (24602 obs) right son=27 (24543 obs)
## Primary splits:
## Units Sold < 5197.5 to the left, improve=0.74880380, (0 missing)
## Total Revenue < 2272347 to the left, improve=0.74880380, (0 missing)
## Total Cost < 1368658 to the left, improve=0.74880380, (0 missing)
## Ship Date splits as -RRLRRRR-LRRLRLL-RRLLLLL-RLLLLLRRLRRRRRLRRRLRLRLLRLRRLLLRLLRRRLRLRRRLRRLLLLRLLLRLRLRRRLL-RRRLRLLLRRRLLLLRLLRRRRLLLLLLLRLLRRRLRLRRRLRRRLLLLLRRRRRRRRLLLLLRLLLLRLLRLLRRLLRRRRLLLLL-LLLLLLRLLLLRRRRLLRRLLLL-LLLLRRL-RRLRRLL-LLLLRRR-LLLRLLL-RRLLRRR-RLRRRRRLLLLLRLRRLLRRLLLLRRLRRLRRRLLRRLRLRLLRLRLRRRRLLLRLRRLLLRLLLLRLLRRRLRLRLRRLLLLRLRLRRRRLLRLLLLRRRRLLRRRLLLRLLLRRRRRLLRRLRRRLRRRLLLRLRLRRRRRLLRRRRLLLLLRLRRRLRRRLLLRLRLLRLRRLRLRLLRRRRRLLRLLRLRRLLLRLLRRRLLRLRRLLRRRLRLRRLLRRLLRLLLRLLRRLRRLRLRLRRRRRLRRRLLRRLRLLLRLRLLRRRLLLRRLRRLRRRRLRRRRRRRLRRLLLRRRLLRRRRRRRRRRLLLRRRRRLRLRLRRRLRRLRRLLLRLRRLLLLRLRRLLLLRRRRRRLLLLRRLLRRLLRRRLRLRRRRRRRRLLRLRLLRLLLRRLRRRRLRRLRRLRLRLLLRRLLRLRRRLLLLRRRLRLRRRRRRRLLLRLLLLRRLLRLRRLLLLLLLLLLRLLRLRRLRRRRRLLLLLLLLLRRLLLLRLRRRLLRLLLRRRRRLLLRRRLLLRRRRRRRLLLRRLLRLRLLRLRRLLLRLLLLRLLLLRRLRRRRRRLLRRLLRRLLRRLLLRRLLLRLRRRLLLRLRRLRRLLLRRLLLRRRRRRLRLRRRLRRRRLLLRLLRLLRRRRLLRLLRRRLLLRLLRRLLRRLRRRLLLLLRLRLRRLLLLLRRLLRRRLLLLLRRRLLRLRRLLLLRLLRRRLLLLRLRRRRLLRRLLLRRLRRRRRRLLRLRLRLRLLRRLLLRLLRRLRLLRLRLRRLRLRLRLLRLLRLLLRLLRLRRLRRLRLRRLLLLRLRLLLRLRLRRRLRRRRLRRLRLRRRRRRRRLLRRLRLRLLRRRLRLLLLRRRRRLLLLLRLLRRRLLLLLLRRRRLLLLRRLRLLLRRLLLLLRLRRRRRRLLLLLLLLLLLRLLLLRRRRRLRLLRLLLLRRLLLLLRLLRLRRLRRRLRRLRLRLRRLRLLLLRRLRRRLLRRLRLRRRLRRLLLRRLLRRLRLLRRRLLLLRRLLRLLRRLLLLRLLLLLRRLRRRRLRLLRRLLRLRLRLLRRRRRRRLLLLLLRLLRLLLRLRRRLLRLLRLLLLRRLLLRLLLLRRRRLLRLRRRLLLRLRLRRRRRRRRLLLRRLRLLRLRRLRLLRRLLRRRRLLLRRLLRLLLRLLLLRLLLRLLRRLLRRRRRLLRLLRLLRRRLLRLRRRRRLLRRLRRRRRLLLRLRLLRLLRLLLRRRRLRLRLLLLLLLRRLLLRLLLRRRRLLRRLLRLRLLRLLLRRRLLRRLRRRLLLRRLRRLLLLLRRLLLLRRRRLRLLRRLLRRRLRRLLLLLLLLRRLLLRRRRRRLLLRLRLRRRLLLRRRRRRRRLRRRLRRRLLLRLRLLLRRRRRLLRRRLLLLRLLRLLLRLLLRLLLLLLLRRLRLLRRLLLRLRLLLLRLLLLRLLLLLRLRRRRLLRLRRLLLLLRRRRLRRLLLLRLRLRLRLLRLRRLLLLLLLRRRLLRLLRRLRLRLRLLLLRLRLLRRLLRLLLRRLLLRRRLLLRRRRLRRRRLRLLRLRRRRRLLRRLRLRLLLLRRLRLRLRLRRLLRRRLRRLRLRLLRLLRLLLLRRLLRRLRRLLRRLRRRLRRRLRLLRLLRLLLRLRLLRLLLLRLRRLLLRLLLLLLRLLRLRLRLLRRLLLLRLLRLLRLRLLLRRRLRLRRLRLRRRLLLLLLRLLRRLRLRRRRLLLLRRRRLLLRRLLRRRRRRLLRLRRLRRRRRLRRRRLLRRRLLRRRRRRRRRRRLRRRLLLLRRLLLRLRRRRLLRRRLLLRRLLLRRLLRRLLLRRRRRRRLLRLLRRRLLLRRLLLRRLLLRLLRRRRRRRRLRRRRRLRRRRLRRLLRLRRLLLRRLRRLRRLLLRLRRLLRLLRRRLLRLRLLRRLLLLLRLLRLLRLRRLLLLRLLLRRRLLRLLLRLRRRRLLRRRRRLRRLLLLRRLRRLLLRLLLLRLLLRRRLLLRRLRRRLLRLLRRRRLRLRRLLRRRLRLRLLLRRLRRRLLLRLLLLRRRRLRRRLLRRRRRLRRLLRLRRRLRRRLLRRRRRRLLRRRRRLLRLLLLLLLLLLLLRLLLRLRLRRRRRLLLLRRLLLLRLRRRRRLLLLLRRRLLRLLLLLLRRRLLLLRRRLRRRLRRLLLLRLLLLRLLRRRRRRRRRRLLLLRLLLRRLLRRLRLRLRRRRLRRRLLRLRRLLRRLLRLLRLRLLRRRRRLRRLLLLLLRLRRLRRLLLRRLLRLRRRLRRRRRRLRRLLRLLRRLRRLRRRRRRLRRLLLLLRLLRRRRRRLLRRRRRLLRRLLLRLLRRLRRLLLLRLLLLLRLLRRRLRRRRLRLRLLLLRRLRRRLLLRRLRLLLLLLRLRLLLLRRR-LLRRRLL-RRRRLLR-RRLLLLL-LLRRLRL-RRRRLLR-RLLLLRL-LLRRLLR-LRLLLLLRLLRRRRLLRRLLLRRLRLRRLRRLRLRLRLLLLRLLLRRLRLRRRRLRLRRRLLLLRLRLLLRRRLRLRRRLRLRRLLLRRLLRRRLRRRLRLRRLLRRRRLRRLLLLLRRLRRRLRLLRLLLRLLLLLLRLRLRRRLLRRLLRLLL, improve=0.03795124, (0 missing)
## Order Date splits as RRLLRLLRRRLRLLRRLRRRLRLRRRRRRRRLRLRLRRLLRRRRLLRRRRRLLRLRRLLLRRLRLLLRRRLLRRLRLLLRRLRLLRLRLLRRLLRRLRRRLRRRLLRRLRRLLLLLRRLLRRRRRLRRRLRRLRRRLRRRRRLRLLLRRLLRLRRLRLRLRLRLRLLRLLLLLLRRLLRLLLLLRRRLRLRLLRLLRLRLRLLRLLLLLLRRLLLRLLRRLRLLLRRRLRRRLLRRLRRLRRRLRLRRRLRRLRLLRRRRLRRLLLLLRRLLLRLRRLRLLRRRLLRRRLRLRRLRLRRRRLRLRLRRLLLRLRLRLLLRLLRLLLRLRRLLRLLRRRRLRRRRRLLRLRLLRRLRRLRRRLLRRLRRLLLLRLLRRLRRRRRRLLLLRRRRRRRRRRLRRRLRRLLLRRRRRLLLRRLRLLRRRLRRRLRRLRLRLRLLRLLRLRLLLRRLRLLRRLRRRRLRRLRRRLRRLLLLRRRRRLLRLLRLLRLLLLLRLRRLRRRLLRRRRRLLRLRRRRLLRRRLRLRLLLLLRLLLLLRRRRLRRRRRLRRRLLRLRLLRRLRRRRLLLRRLRRLRRRRRLLLLLRLLRLLRLRRLRLRRLRLLRRRRLLLRLRRLRLRLLLLLRRRLLRRRRRLRRRRRLRLRLLRLRRRRRLRRLLLRLRLRLRRLRRRRRRRRLLRLLRLLRLRRLRLRRLLRLRLLRRRLLLRRRRLRRRLLRLRRRRRRRRLLLRRLRLLLRLLLLRRRLLLLLRLRRLRRLLLRLLRRLLRRLLRLRLLLLRRLLRRRLLLLRRLLRRRRRLRLRRLLRRLRLLRLLLRRLLRRRRLLLRLRRRLRLLRLLRLLLRRLRLLRRLLRRRLRRRLLRLRLRRLRLRRLRRLLLLRLRLRRLRLLRRRLRRRLRRLRRRLRLRRRRRLLRLRLRRRLLRLRRRLLLLLRLRLLRLLRLRRRLLRRLRLRLRRRLLLRRRLLRLRRRRRRRLLRLRLLRRRLRLLLRLRLLRLRLLRLLRRRLLLRLRRLLLRRLRLLRRRRRRRLLLRRLLRLLLLRLLLRLRRRRRRRRRRRLRRLRLRRRLLRLLRLRRLRRLLRLLLRLLLRRLRLRRRLLLRLLRLRRRRRLRLLRRRRLLRLLRRLLRRLRRRRLLRLRRLLRRRRLLRRRRRLLRRRRRLRRLLLLRRRRLLRRRLLRLRLRLRLRRLLLLRLLLLRRRRLLRLRLLLRRLRLLLRLLLRRLLRRRRLRRRRRLRLLRRRRRRRLLRLRLRLRRRRLRRRLLLRRRLLLRRLRRLRRLLLLLRRRLLLLRRLRLLRRRLLLRLRRLLRLRRLLRRRRRLLLLRLRLLRLRRLLLRLRLRLRLLRRLRRRLLRRRRRRRLLRRLRRRRLLLRLRRRLRRLLRRRRRRLRRRLLRLLRLRLLRLRRRRLRRRRLLLRLLLRRLRRRRLRLLRRRLRRLLLLRRLRRLLRRRRRLRRLLRLLRRLLLLRLLRLLLRRLLRRLRRLLRLLLLRLRLRLRLRRLLLRRLRRRLRLLLLRLLRRRRLRLLLRRLRLLLLLRLLLRRLLRLRRRLRLLRLRRLRLLLLRLLLRRRLLLRRRLRLRRRLRRLRRRLRLLRLLLRLRRRLLLRLRRLRRRLRRLLRRRRRLRLLRRRLLRRLRRLRRRRRLLLLRLRRLLLRRLRRRRRLRLRLLRLRLRLLRLLLRLLRRRLRRRRLLRRLRRLRRLRRLRRLRRLLRRRRLRRLLRLRLRLRRLRLRRRLRLRLLLRRLRLLRRLLRLRLRRRRRRRRLRLRRRLLRLLRRLLRRRLRLRRRLRRLLRRLLRRRRLRLRLRLLLLRLRLLLRLLLLLRRLLLLLLLRRRRLRRLRLLRLLRRRLRLLRLLLLLLLLRLRRRLLLRLRLRLLRLRLLRLRRLRLRLLLLLRLLRRRRRLRRLLRRLRRLRRLRLRLLLLLRRLRLRRRRRRRLRLLLLRLRRRRRLLRRLRRRRRRLRLRRLRRRRRLRLLLRRRRRRLRLRRLRRLRLRLRLLRLRRLLLLRRLLLRRLLRLLLLLLRLRRRLLRRRLLRRLRRLRRRLRRLLRLLRLRLLLRRLLRLLRLRLRLRRLLLRLRLRRLRLRRRRLRLLLRRRRRLRRRRRRLLRLRRLRLRRLRRRRRLRRLRLLLRLRRRLRRLRLRLLLLRRLLRRRLRRRLLRRRRLRLLRRLRRRLRLRRLLLLLLLRRLRLRRRLRRRLRLLRLRRRRLRRRRRRRRRLLRLRRLRLRRRLRRRLRRRRRLLLRRLLRLRLLRLLLRRLRLRRLLRRRRRLRRLRRRLLLLLLRLRLLLLRRLLLRRRRRLRLRRRRLLLRLLRLRLLLLRRLRRLLLLRRLRLLRLLLRRLLLRLLRRLRRRRLLRRLRLRLRLRLLRRRRRRRRLLLRLRRRLLRLLRRLLLRLLRLRLRRRRLRLLRRLRRRRLLRRRLRRLLRLRRRRRRRRRLLLLLRLRLLRRRLLLLLRLLLRRRRRRRRRLLRLLRRLRLRLLLRRRLLLRLRRRLRLRLRLLLRRLLRLRLRLLLLRLLLLRRRRLRLLRLLRRLLRRLLLLLLLRRRLRLLLRLRRLRRLRLRRLRRRLRLRLLLLLLLLLLRRRLLRRRRLRLLRRRRRLRLRRRLLLLLLRLLLRRLLRRRLLLLRRLRLLRRLLLRLRRLLLRRLLLRRLRRRLLLLRRLRLLLLRRLLRRRLRRLLLRLLLRLLRRRRRRRRLRLLRLRLRLLLLLLRRLLRLRLRLRRRRLRRLRLRLLRLRRRLLLLLLRRLRRLLLLLRLRLLRRLRRLLRRRR, improve=0.02787308, (0 missing)
## Surrogate splits:
## Total Revenue < 2272347 to the left, agree=1.000, adj=1.000, (0 split)
## Total Cost < 1368658 to the left, agree=1.000, adj=1.000, (0 split)
## Ship Date splits as -RRLRRRL-LRRLLLL-RRLLLLR-RLLRLLRLLRRRRRLRRRLRLRLLLLRLLLLLLLRLRLRLRRRLLRLRLLRLLRRLRLLRRLL-RRLLRLLRRRRLLLLRLLLLRRLLLLLRLRLLRRLLRLRRRLLRRLLLLLLRRRRRRRLLLLLRLLLLRLLRLLRRLLRRRRLLLLL-LLLLLRRLLLLRRRRLLRLLLLL-LLLLRRL-RRLRLLL-LLLLLRR-LLLRRLL-RRLLRLR-LLRRRRRLLLLLRLRRLLRRLLLLRRLRLLRLRLLRRLLLRLLLLRLRRLRLLLRLRRLLLLLLLLRLLRRRLLLRLRRLLLLRLRLRRRRLLRLLLLRRRRLLLRRRLRRRLLRRRRRLLRRLRRRLRRRLLLLLRLRRRRRLLRLRRLLLLLLRRRLLRRRRLLRLRLLRLRLLRLRRLRRRLRLLRLLLLRRLLLRLLLRRLLRLLLLLRRRRRLRRLLRLLLLLLLRLLRRLRRLRRRLLLRRRLRRRLLRRLRLLLRRRLLRRLLLLLRLLRLRLLRRRRRRLRRRRRLLLRLLLLRRRRRRLRRLLLLRRRLRLRLRLRRRRLRRRRLLLRLRRLLLLLLRLLLLLRRRRRRLLLLLRLLRLLLRRRLLLRRRRRRRLLLRLRLLRLLLRRLRRRRLRLLRRLLRRRLRRRLLRLRRRLLLLLRRLRLRLRRRRRLLLRLRLLRRLLRLRRLLLLLLLLLLRRLLLLRRRRLRRLLLLLLLLLRRLLLLRLLRLLLRLLLRRRRRLRRRLRLLLRLRRRRRLLLRRLLLLRLLRLRLLLLRLLRLRLLLLRLRRLLRRRLLRRLLLRLLRRLLLLRLLLRLRRRRLLLLRRLRLRLLLLLLLRLRRRRLLLLLLLRRRRLRLRLLRLLRRLLLLRLRRRRLLLRLLRRLLLLRRRRLLLLLRRLLRRLLLLLLRLLRRLLLLLRLRRLLRLRRLLLLLLLRRRLLLLRLRRRRLRRRLLLRRLRLRRRRLLRLRLRLRLRRLLLLRLLRRLRLLRLRLRRLLLRLRLLRLRRLLLRRLRLRRLLRLLLLRRLLLRLRLLLRLRLRRRLLRRLLLLLRLRRRRRLRRLLRRLRLRLLRRRLRLRLLLRRRRLLRLLRLLRRRLLLLLRRRRRLRLLRRLRLLLRRLLRLLLLRRRRRRRLLRLRLLLLLRLLLRRRRRLLRLLRLLLLRRLLLLLLLLRLRRLRRRLRRLLLRLRLLRRLLLRRRRRRLLRRLRLRLRLLLRLRRLLLRRLLLRRRRLLLLRLRLRLLRRLRLLRLLLRLRRLRRRRLLLRLRLLLLLLRLLRLRRLRRRLLLLLRLLRLRLRLRRRRLRLLRLLLLRRLLLRLLLLRRRRLLLLRRRLLLRLLLRRRRRRRRRLLRLLRLLRLRLLLLLRRLLRRRRLLLLRLLRLLRRLLLLRLLLRRLRRLLRRRRRLRRLLLLLLRRRLRLRRRRRLLRRLRRRRRLLLLLRLLRLLRLLLLRRRLRLRLLLLLLLRRLLLRLLLRRRRLLRRLLRLRLLRLLLRRRLLRRLRRRLLLRRLRRLLLLLRLLLLLRRRRLLRLRRLLLLRLRLLLLRRLLLRRLLLRRRRRRLLLRLRLRRRLLLRRRRRRRRLRLLRRRRLLLRLRLLLRRLRLLLRRRLLLLRLLLLLLRLLLRLLLLLRLRRLRLRLRLLLRLRLLLLRLLLLRLLRLLLLRRRRRLRLRRRLLLLLLLRLRRLLRLRLRLRLRLLRLRRLLLLLLLRRLLLRLLRRLLLRLRLLLLRLRLLRRLLRLLLRRLLLRRRLLLRRRRLRLLRLRLLRLLLRRRLLRRLRLRLLLLRRLRLRLRLRRLLRRRRRLLRLRLLLLLRLLRLRRLLRLRRRLLRRLRRRLRRRLRLLLLLRLLLRRRLRRLLLLRRLLLLLLLLLLLLRLLRLRLRLLRRLLLLRLLRLLRRLLLRLRRLRRRLLLLRRRLLLLLLRLLRRLRLRRLRLRLLRRRRLLLRRLRRRRRRRLLRLLLLRRRRLLLRRLLLRRLLLRRRRRRRLRRRLRRRLLRLLRLLRRRLRRRLLRRRLLLRLLLLRRLLRRLLLRRRRLRRLLRLLRRRLLLRRLRLRRLRLRLLRRRLLLLRLRRRRLLRLLLLRRLRRLRRLLRLRRRRRLRLLLLLRLLLRLLLRRLLRLRRLRRLLLLLRLLRRRRRLRLLRLLLLLRRRLRRLRLLLRRRLLLRRRRRRRRRLLLRLLRRLLLRLLLLLLLLRRRLLLRLRRRRLLLLLRRRRLRLRLLLRRRLRLRRLRRRLLRRRLRLLRLLRLRRLRRRRLRRLRLLRLLRRLRRRLRRRLLRRRRLRLLLRRLRLLRRLLLLLLRLLLLRLLLRRRLRLRRRLLLLRLLLLLRLRRRRRRLLLLRRRLLRLLLLLLLRRLLLLRRRLRRRLRRLLLLLLLLLRLLRRRRRRRRRRLLLLLRLLRRLLRRLRLLLRRRRRRRLLLRLRRLLLRLRLLLRRRLLRRRLRLRRLLLLLLRLRRLRRRLLRRLRRLRLLLRRRRRRLLRLLRLLRRLRRLRLLRLRLRRLLLLRRLLRRRRRRLLRRRRRLLLRLLLRRLLRLRLLLLLRLLLLLRLLRRRLRRLRLRLLLLLLLRLRLRLLLRLLRLLLLRLRLRLLLLRRR-LLLRRLL-RRRLRLL-RRLLLLL-LLLRLRL-RLRRLRR-RLLLLRL-LLRRRLL-RRLLLRLRLLLLLRLLRRLLLRRLRLRRLRRLRLRLRLRLLLLLLRRLLLRRRLLLLRRRLLLLRLRLLLRRRLLLRRRLLLRLLLLRRLLRRRLLRRLRRRRLLLRRRLRRRLLLLRRRRLRLRLLRLLLRLRLLLLRLRLRRRLLRRLLRLLL, agree=0.596, adj=0.190, (0 split)
## Order Date splits as RRLLRLLRRLLRLLLLRRLLLLLRRRLRRRLLRLRLRRLLRRLLLRLRRRRLLRLLRLLLRRLRLRLLRRLLRLLRLLRRRLRLLRLRLRRRRLRRLRRLLLRRLLLRLLRRLLLLLRLLRRRRLLLRRLRRLRLRLRLRRLLRLLLRRLLRLRLLRLRLRLRLRRLLLLLLLLRRLLRLLLLLRRRLLLRLLRLRLLRLRRLRLRRLLLRRLLLLLLRRLLRLLRRRLRRRLLLLLLRRRRRLLLRLLRRRLRLLRLRRLRRLLLLLRLRRLRLRRLRRLLRRLLRRLLRLRLLLLRRRRLRLRLRLLRLRLRLRLLRRLRRRLLRLLRLLLLLRRRLLRLLRLLLRRRLLRLLRRLRRRLRRRLLLLLLLRLLLRLRRRLRRLLLLLRLRLLRRRRLLRRLRRLLLRLLRLLLLLRLRLLRLRRRLRLRRLRLLRRLLRLLRLRLRLRLLRLLRRLLRRRLRLLRRRRRRLLLLRRRLLLRRLLRLLLLLLLLLLRRLRRRLLRRLRRLLLLRRRRLRLRLLRLRLLLLLRRLLLLRRRRLRRRRLLRRLLLRLLLLRRLRLRRLLLRLLLRLRRRRRLLLRLRLLLLLRLLRLRLRLLRLLRLRRLLLRRRRLRLRLLRLLRLRLLRLRRRLRRRRRRRLRLLRRRRRRRLRLRLLRRRLRLRRLRRRRLRRRLLRLRRLLRLRLLRLLRLLLLRLRRRRLLRRRRRLRRLLLRLRRLRRLRLLLLRLLRLLRLLLLLRRRLRLLLRLRRLRLLLLRLLRRLLRRRLRLRLLLLRRLLRRRLLLLRRLLLRRRRLRRRRLLRLLLLLRLLLLRLLRRLRLLLRLLRRLRLLRLLRLLLRRLRLLRRLLRRLLLRRLLRLRLRRLRLRLLRRLLLLLLRLLRRRLLLRRLLRLLRRLRRRLRLRRLRRLRRRRRRRLLLRLRRRLLLLLRLLLLRLLLLRRRLLLRLRLRLRRRRLLLRLLLLLRRRLRRRLLRLRLLLRRLRLLLRLLLRRLLLLRLLRRRLLLRLRLLLLLRLRLLRRRRRLRLLLRRLLRLLRLRLLLRLRLLRRRRRRRLLRRLRLRRRLLRLLRLRLLRLRLRLLRRLLLRRLRRLRRLLLRLLRLRRRRRLRLLRRRRLRLRLLRLLRRLLRRRLLRLRRLLRLRLLLRRRRLLLLRRLRLRLLLLLRRRLRLRRRLLRLRLRLLLRRLLLLRLLLLRRRRLRRLRLLRRRLRLLLRLLLRRLLLRRRRLRLRRLLLLRRRRRRRLLRRRLRRRRRLLRRRLLLRRRLLRRRLRRLLRLLLRLLRRRLLLRRRRRRRLRLLLRLRRLRLRRLLRRLRRRLLLLRLRLLLLLRLLLRLRLRLRLLLRLRLLLLRRRRRRRLLLLLLLRRRLLLLRRRLRRLLRLRLRRRLRRLLRLLRLRLRRLLLRRLLRRLLLRRLLLRLLLRRRLRLLRRRLRRRLLLRRLRRLLRLRRRLRRLLRLLRRLLLLLLLRLLLRRLLRLLRLLLLLLLLRLRLRRRRLRLLLRRLRRRLRLLLLRLLLRLRLRLLLRRLRLLLLLLLLLLRLLRLRLRLRLLRRRRLLLLLLRLLLRRRLLLLLRLRLLLRLRRLLRRLRLLLLLLRLRRRLLLRRLRLRRRLLLLLLLRRRLRLLRRLLLRRLRLLRRLRLLLLLRLLRRLRLRLLRRLRLRLRLLRLRLRRLRLLLRLRRRRLRRRRLLRRLRRLLRLRRLRRLRRLRRRRRLLRLLRRLLLLRRRRLLLRLRLRLLLRRLLLLRRLLRLRLRLRRRRRRLRLRLLLLLLLRLLLLRRLRLRLLLRRLLRLLLRLLRLRLRLRLLLLRLRLLLRLRLLLRRLLLLLLLRLRRLLRLRLLRLLRLRLLLLRLRLLLLLLRLRLRLLLRLRLRLLRLRLLRLRLLLLRLLLLLRRLLRLLRLRRLLRRRRRLLRRRLLLLLLLRLLRLRRLRRRRLRLLLLRLLRRRLLLRRLRRLRRRLRLRRLLRRRRLRLLLRRRLRRLRLLLLRRLLLRLLLLRLRRLRLLLLLLRLLLLLLLLRLLRLRRRRLLRLLLRRLRRLRLRLRRLLRLLLLRLLLRRLLLLLRLLLRRRLRLLRLRRRRLRRRRRLLRLLLRRRLRRRLLRLRLLRLRLLRLRRLLRRRRLRRLRLLLRLRRRLRLLRLLLLLLRLRLRRRLLRLRLLRRRRRLLRLRLRRLLLRRLLLLLLLRRLRLLLLLRRRLRLRLLRRRRLRRRRRRRLLLLRLRRLRLLLRLLRRLRRRRRLLLRRLLRLLRLRRLLRRLRLRRLLRRRLRLLRLRLRLLLLLLRLLLLLLLRRLLRRRRRLRLRRRRLLLLLLLRLLLLLRLRRRLLLLRRLRLLLLLLRLLLLRLRRLLRRRRLLRRLRLLLRLRLLRRRLRRRRLRLRLLRLLLRLLRRRLLRLLRRRLRRRRLRLLLRLRRLRLLRRRLRRLLRLRLRLRRRRRLRLLLLLRLRRRRLLLLLRLLLRRRRRRRLRLLRLLRRLRLRLLLRRRLLLLRRRRLLLRLRLLLLRLLLLLLRLLLLLLLLLLRRRLRLLRLLRRLLRRLLLLLLLLLRLRLLLRLRRLRRLLLRLLRLLLRLRRLLLLLLRLLRRRLLRRRRLRRLRRRRLLRLRLRLLLLLLRLLRRRLLRLRLLLLLRLRLLLRLLLRLRRLLLLRLLLLRLRRRLLLLRRLRLLLLRRLLRLRLRRLLLRLLLRRRRRRRLRRRLLLRRLRLRLLLLLLLRLLRLRLRLRLRRLRRLLLRLLRLRRRLRLLRLLRLRRLLLLLRLRLLLRLRRLLRRRL, agree=0.583, adj=0.166, (0 split)
## Country splits as LRLLLRLLRRLLLLRLRRLRRRRRRLLLLLRLRLLLLRLLRRRRRRRLLLRRRLLRRLLLLRRLLLLLLLLLRLLRRRRLLRRRLLLRLRRLLRLRRLLRRRRRLLLLRRLLRRLRRLRLRRRRLRRLRLLLLRRLLRLRLLLRLRRRLRLRRLLRLRRLLRRRRRLLLRLLRRLRLRRLLLRRL, agree=0.522, adj=0.043, (0 split)
##
## Node number 14: 50617 observations, complexity param=0.01544967
## mean=860734, MSE=5.87797e+10
## left son=28 (23160 obs) right son=29 (27457 obs)
## Primary splits:
## Total Revenue < 4347804 to the left, improve=0.7451790, (0 missing)
## Total Cost < 3647161 to the left, improve=0.7402085, (0 missing)
## Item Type splits as -------LR---, improve=0.6499716, (0 missing)
## Unit Cost < 444.825 to the left, improve=0.6499716, (0 missing)
## Unit Price < 536.55 to the left, improve=0.6499716, (0 missing)
## Surrogate splits:
## Total Cost < 3647161 to the left, agree=0.955, adj=0.902, (0 split)
## Item Type splits as -------LR---, agree=0.799, adj=0.560, (0 split)
## Unit Price < 536.55 to the left, agree=0.799, adj=0.560, (0 split)
## Unit Cost < 444.825 to the left, agree=0.799, adj=0.560, (0 split)
## Units Sold < 6676.5 to the left, agree=0.744, adj=0.440, (0 split)
##
## Node number 15: 54385 observations, complexity param=0.01683394
## mean=1356470, MSE=5.745636e+10
## left son=30 (21734 obs) right son=31 (32651 obs)
## Primary splits:
## Units Sold < 7924.5 to the left, improve=0.7730963, (0 missing)
## Unit Price < 552.735 to the right, improve=0.3325343, (0 missing)
## Unit Cost < 382.935 to the right, improve=0.3325343, (0 missing)
## Total Cost < 2655668 to the right, improve=0.3325343, (0 missing)
## Item Type splits as ----R-L-----, improve=0.3325343, (0 missing)
## Surrogate splits:
## Total Revenue < 5295706 to the left, agree=0.716, adj=0.289, (0 split)
## Total Cost < 3982378 to the left, agree=0.716, adj=0.289, (0 split)
## Item Type splits as ----R-L-----, agree=0.684, adj=0.208, (0 split)
## Unit Price < 552.735 to the right, agree=0.684, adj=0.208, (0 split)
## Unit Cost < 382.935 to the right, agree=0.684, adj=0.208, (0 split)
##
## Node number 26: 24602 observations
## mean=648569.6, MSE=2.18352e+10
##
## Node number 27: 24543 observations
## mean=1158806, MSE=2.183228e+10
##
## Node number 28: 23160 observations
## mean=632856.6, MSE=1.530951e+10
##
## Node number 29: 27457 observations
## mean=1052949, MSE=1.469892e+10
##
## Node number 30: 21734 observations
## mean=1098146, MSE=1.559367e+10
##
## Node number 31: 32651 observations
## mean=1528422, MSE=1.133526e+10
# Summary of the second regression tree model
summary(reg_tree_2)
## Call:
## rpart(formula = `Total Profit` ~ ., data = sales_data_2, method = "anova")
## n= 1500000
##
## CP nsplit rel error xerror xstd
## 1 0.63920977 0 1.00000000 1.00000124 1.476472e-03
## 2 0.12570714 1 0.36079023 0.36079172 6.589069e-04
## 3 0.04450517 2 0.23508309 0.23508483 4.091770e-04
## 4 0.03518954 3 0.19057792 0.19057940 3.183940e-04
## 5 0.02958268 4 0.15538838 0.15539202 3.220563e-04
## 6 0.02243535 5 0.12580570 0.12580985 2.029243e-04
## 7 0.01670105 6 0.10337035 0.10337400 1.550197e-04
## 8 0.01522044 7 0.08666930 0.08667278 1.257354e-04
## 9 0.01000000 8 0.07144886 0.07145263 9.095928e-05
##
## Variable importance
## Total Revenue Is_Profitable Total Cost Item Type Units Sold
## 23 21 20 12 10
## Unit Price Unit Cost
## 10 3
##
## Node number 1: 1500000 observations, complexity param=0.6392098
## mean=392399.9, MSE=1.435788e+11
## left son=2 (897601 obs) right son=3 (602399 obs)
## Primary splits:
## Is_Profitable splits as LR, improve=0.6392098, (0 missing)
## Total Revenue < 1414181 to the left, improve=0.5532301, (0 missing)
## Total Cost < 782993.4 to the left, improve=0.5132426, (0 missing)
## Unit Price < 429.545 to the left, improve=0.3419590, (0 missing)
## Item Type splits as LLLLRLRLRLLL, improve=0.3419590, (0 missing)
## Surrogate splits:
## Total Revenue < 958355.6 to the left, agree=0.899, adj=0.750, (0 split)
## Total Cost < 603213.4 to the left, agree=0.872, adj=0.681, (0 split)
## Units Sold < 5669.5 to the left, agree=0.761, adj=0.406, (0 split)
## Item Type splits as RLRLRLRLRLLL, agree=0.744, adj=0.363, (0 split)
## Unit Price < 429.545 to the left, agree=0.720, adj=0.302, (0 split)
##
## Node number 2: 897601 observations, complexity param=0.03518954
## mean=144219.6, MSE=1.314264e+10
## left son=4 (484508 obs) right son=5 (413093 obs)
## Primary splits:
## Total Revenue < 399198.3 to the left, improve=0.6424350, (0 missing)
## Total Cost < 310432.2 to the left, improve=0.5197164, (0 missing)
## Item Type splits as RLRRRLRRRRRR, improve=0.2885250, (0 missing)
## Unit Cost < 33.815 to the left, improve=0.2885250, (0 missing)
## Unit Price < 64.59 to the left, improve=0.2885250, (0 missing)
## Surrogate splits:
## Total Cost < 267461 to the left, agree=0.954, adj=0.901, (0 split)
## Item Type splits as RLRLRLRRRRRR, agree=0.722, adj=0.396, (0 split)
## Unit Cost < 46.255 to the left, agree=0.722, adj=0.396, (0 split)
## Unit Price < 130.93 to the left, agree=0.719, adj=0.389, (0 split)
## Units Sold < 2594.5 to the left, agree=0.651, adj=0.242, (0 split)
##
## Node number 3: 602399 observations, complexity param=0.1257071
## mean=762199.5, MSE=1.094057e+11
## left son=6 (445801 obs) right son=7 (156598 obs)
## Primary splits:
## Total Revenue < 3570598 to the left, improve=0.4107876, (0 missing)
## Unit Price < 429.545 to the left, improve=0.3633362, (0 missing)
## Item Type splits as L-LLR-RLR-LL, improve=0.3633362, (0 missing)
## Total Cost < 1331272 to the left, improve=0.3267723, (0 missing)
## Unit Cost < 211.375 to the left, improve=0.2418755, (0 missing)
## Surrogate splits:
## Total Cost < 2878872 to the left, agree=0.942, adj=0.778, (0 split)
## Item Type splits as L-LLL-RLR-LL, agree=0.819, adj=0.304, (0 split)
## Unit Price < 544.205 to the left, agree=0.819, adj=0.304, (0 split)
## Unit Cost < 433.615 to the left, agree=0.819, adj=0.304, (0 split)
## Units Sold < 9999.5 to the left, agree=0.740, adj=0.000, (0 split)
##
## Node number 4: 484508 observations
## mean=59374, MSE=2.839479e+09
##
## Node number 5: 413093 observations
## mean=243733.2, MSE=6.880752e+09
##
## Node number 6: 445801 observations, complexity param=0.02958268
## mean=636552.8, MSE=4.530087e+10
## left son=12 (372029 obs) right son=13 (73772 obs)
## Primary splits:
## Item Type splits as L-LLR-LLL-LL, improve=0.3154798, (0 missing)
## Total Revenue < 1662664 to the left, improve=0.3075029, (0 missing)
## Total Cost < 974436.2 to the left, improve=0.2893031, (0 missing)
## Unit Price < 429.545 to the left, improve=0.1500607, (0 missing)
## Unit Cost < 107.275 to the left, improve=0.1332107, (0 missing)
## Surrogate splits:
## Unit Price < 429.545 to the left, agree=0.850, adj=0.092, (0 split)
## Units Sold < 2799.5 to the right, agree=0.838, adj=0.019, (0 split)
##
## Node number 7: 156598 observations, complexity param=0.04450517
## mean=1119889, MSE=1.190143e+11
## left son=14 (75318 obs) right son=15 (81280 obs)
## Primary splits:
## Item Type splits as ----R-RLL---, improve=0.5142883, (0 missing)
## Unit Price < 429.545 to the left, improve=0.4083765, (0 missing)
## Unit Cost < 314.01 to the right, improve=0.3032946, (0 missing)
## Total Cost < 2659437 to the right, improve=0.3032946, (0 missing)
## Total Revenue < 5347951 to the left, improve=0.1614648, (0 missing)
## Surrogate splits:
## Unit Cost < 513.75 to the right, agree=0.878, adj=0.747, (0 split)
## Unit Price < 659.74 to the left, agree=0.854, adj=0.696, (0 split)
## Total Cost < 3086668 to the right, agree=0.659, adj=0.291, (0 split)
## Order Date splits as RRRRLRLLRRRRRRRLRLRRLRLRLRLLRRLRRRRLLRLRLLLRRRLRRRRLRLRRRLRRLRLLLRRLRLRRRRRRRLLRRRLRLRRLLRRLRRRRLRRLRRRLRRRLLLLLLRLLRRRRRLRRLRRRLRRRLRRLRRRRRLRLRRRRLRLLLRLLLRRRRLRLLRRRRRRRRRRRRRRRLRLRRLLRRRRRRLRRRLLRRRLRRRRRRLRLLRRRRRRRRRLRLRRRRLRRRLRRRRRRRRLRRRLRLLLRRRRLLLRLRLRLRRLRLLRLLRRRRRLRRLLRRLRRRRLRLLLLLLLLRLRLLRRRRRLRRRRRLLRRRRLRRRRLRRRRLRRRRLRLRRRRRRRLRLRRRRLRRRLLRLLRLLRLLRLLRRRLRRRRRLLRRRRRRRRRLLLRLRRLRRRRRRRLLRRRRLRLRLRRRRRLLLRRRRLRRRRRRLLRRRLLRRRRRLRRRRRRRLRLLRLRRRRRRLRRRRRRLLLRLRRRRRRRRLRLLRLLRLLRLRRLLLLRRLRRLRLRLRRRLRRRRRLLRLLRLRRLLRLLLRRLRRLRLRLRLRRRRRLLRRRLRRRRRLRRLRLLRLRRRLRLRRLRLRLRRRRRRRLRRLRLLLRLRRRRLLRRRRLRRLRLRRRRRRRLRLRRLLLLRRRRLLRLRLRRRRLRRLRLLRLLRLRRRLRLRRRRRRRLRRRRRRRRRRRRRLRRLRRRRLLRRRRRRLLRRLRLRRRRLRRLRRRRRRRRRRRLLLRLLRRRRLRRRRRRRRRRLLLRLRLRRLRRRRRRRRRRLLLLRRLRRRRLLRRLRRLLRRLRRRRLRRRRLRRRRLLRLRRRRRRLRRRRRRLLRLLRRRRRRRRLRRRRRRLLRRLLLRLRRRRRRRRRLRRLLRRLLLRLLRLRRLLRLLRRLRLLRLRRRRRLLLLRLRRRRRRRRRLRLLRRRRLRRRRRRRLRRRRRRLRLRRLRLRLRRRRRRRRLLLRRRRRRRRLLLRLLRLRRRRRRRRRRRRRRLRRLLRRLRRRRRLRRRLRRLRRRRRLLRLLRRRRLRLRLLLLLLLLRRLLLRRRRRLRRRRRRRLRLLRRRRRRRLLRLRLRLLRRRRLLLLLLRRRRRLRLRRRRRRRRRRRLRRLRRLLRRRRRLRLLLLLRRRRRRRLRLRLRRLRRLRRLLRRLLLLRRRRRLRRRRRRRLLRLRRLRRRLLLRRRLLRRLRRLLLRRLRLRRRRRRLRRRRRLRLLRRLRLRRLLRRLRRLLRLLRLRLLLLRLLLRRRLRRRRLRRRLRLRRLLRRLLLRLRLLRRLRLRLRLRRRRRRRRRLRRLLRLRRRRLRLRLRRLLRRLRLRRLRRRLLLRRRLRRRRRRRLLRLLRRLLRLLLLLLLLRLRRLRRRLRLRRLRRLRRLLRRLRRRRRLRRLLRRLLRRRLRRRRRLLRRRRRLRLRRLRRRLRLRRRLRRLLRLLRRLRRRRRRRRRRLRRRRRLRRLRRLRRLRRRRLLRRLRRLRRLRRRLRRLRLLRRRRRRRLLRLLRLRLLLRRLLLRRLLRRLLRRLRLRRLRLRRRLRRLRRRLRLRRLRLRRRLLRLLRLLLLRRRLRLRRLLRRLRRLRRLRRRRRLLRLRRRRRRRRLRRRRRRLRLRRLRLRRRRRLLLRLLLLRRLRRLRRRLRRRRRRRRRRRLRLRRLLRLRRLLRRLRRRRLRRRRLRRRRLRRRRLRRRLRRLRRRRRLRRLLRRRRRRLRRRRRLLRRLLRLLRRLRLLRRRLRRRRLLRRRLLLRRRLLLRLRRLRRLRLLLRRLRLRLRRLRRLLRLLLRLLRLRRRRLRLLLRLLLRLLLLRLLRRRRRRRRLRLRRLLRRLRLRRRLRRLRRRLRRRRRRLRRRLLLLRRRRRRRLRRRLRRRRRRRRRRRRLRRLRRRRRRLRLRRLRRLLRLRLLLLRLRRLRRLLRRLRRLRRRRRRLRRRLRRLRRRLRLRRLLLRLRRRRRRRLRRLRRLRRRRRRLRLLRRRRRRRLRRLRRRLRLLRRLLRRRLRRRLRLRLRRRRLRRRRRRRRLRRRRRLRLLRRRLLLLRRRRRRLRLRRLRLRRLLRRLRLLRLRRRLRLRRRRLRRRRRRLLLRLRLLRRRLRLRRRLLRRRLRRRRRRRLRLRLRRRRLRLRLRRRRLRRRLLLLRRLLRLRRRLLRLRRRRRLRRRRRRRRLRRRRRRRRLRLLRRRLRLRRLLLRRRRRRRRRLRLLLRLRRRRRRLRRRRRRLRRRLRLRLRRLRLRRLRRRRRLRRRRRRRLRRLRLRRLRRLRRLRLLRRLLRLRRRLRLLLRRRRRLRLRLRRRLRRRRRRRRLRRLRRLLLLRRRRRRLRRLRLRRRRLLRRLRLLLRRLRLLLLRLLRRRRRRRLLLLLRRLRLRLRRRLRLRRLRRRRRLRRLLLRRRRRRRRRRLRRRRLLRLLLLRLRLLLLLRRLRRLRRRRRRLRRLLRLLLLRLRRRRLRLRLLRLRRLLRLRRRRLRRLRRRRLRRLRRRRLRRLLLLRLLRRLLLLRLRRRRRRRRLLLLRLRLRLLLRRRRRRRLLLLRLRRRRLRRRRRRRRRRLRLRRLLLLLRLLRLRRRRRRRRLLLLRRRRRLLRLRRLRLRRRRRLLRRRRLRLRLRLLLRRRRRRLRRLLLLRRLRRRLRLRLRRRRLRRRRLRRLRLRRLRRRRLRLRRLRRLRLRLLRRLLRRRRRRRRRRLLLLLLRRRRRRLLLLLLRRLRLRRRRRLRRRRRRLLRRLRRRRRRRRRLLRRRLRRLRRRRRLRRRLLLLLRRLRRRLRLRLLRRRRRRRLLLRLRLLR, agree=0.555, adj=0.076, (0 split)
## Ship Date splits as -LLRRRRLLRRLRLRLLRLRLLRRRRRLLRLRLRRRRLLRLRRRRRRRRLRLLRLRLRRLLRRRRRRRRRRRLRRLLRRRLLLLLRRRLLRRRLLRRRLLRRRRRRRRLRRLRRRLRRRLLLRRRRLRRRRLLRLRRRRLRRRLLLLRRRRLRRRLLRRLRRLLLRRLRRRRRRRL-RRRLLLRLRRRRRLRRRRRRRLLLLRLRLRLLRLRRLRR-LLRLRRRLLLRLLRRLLRRRLRRLRRRRLRRLLRLRLRLLRRLLRRRLRLLRRLRLRRLRLRRRRRRRLRLRLRRLLRRLLRRRRRRLRLLRRLRLLRRRLRLRRRRLRRRLLRRRRLLLLLLRRLRRLRLLRRLLRRRRRLLRLLRRRLLLRRRRRRRLLLRRLLRRRRLLRRRRLRRRRLRRRLRRRRRRLLRRLRRRLRRLLRRLRLRRLRRLRRRRLLRRRLLRLRLRRLRRLRRRLRRRLLRRLLLLLLLRRRLLRLRRRLRLRRLLRRLRRRLLRRRLRRRRRLLLRRRRRRRLLRRLRRRRRRLRRRRRLRRRLRLLLRRRLRRRRLLRRRLLLLRRRLRLRRLRLRLRRRLRRLRRRLRRLRRRRRLRLRLRRRLRRRLRRLRRLRLLLRRLLLRRRLRRRRLRRRLRLRRRRLRLLLRRRRRLLLRLLRRRLRRLRRRRRRRLRLRRRLRRRRRRLRRRLRRRRRRLLRRLLRRRRRRRLLLRRRLRRRLLRRRRLRLRRLLRRRRLLRRRRLRRLRRRRRRLRLLRLRLLLRRLLRLRLLRRRRRLLRRRRLRRRRLRRLRRRRRLRRRLRRRRRRRRRRLRLRRRLLRRRRLRRRLRRRRRLLRLLRRLRRRLLRRRRRLLRRLRRLRRRRRRRLRLRRRLRRLRRLLRRRLRRRRLRLRRRLRRRLRRRLLLLLRLLRRRRRRRRLRLRLRLRLRLRRRRLRRLLLLLLRRRLRRRRLLRRRRLLRRLLRLLRRRRLRRRRRLLRLRRRRRRRRRRLLRRRRLRRRLRRRLLRRLRRRRRRLRRRRLRLLLLRRLLRRRRLRRRRLRRLRLRLRRRRRRRRRLRRRRLRRRLLRRRRRRRRRLLRLLRRRLLRLRLRRRRRLRRRRLRRLRRRRLLRRLRRRRRRRRRRLRRRRRLRRRRRRRRLRRRRRLRRLLLLRRRLLLRRRLRRRRRRLLRLLLRRLRRRLRRLLLRLLRRLLLLRRRLRRRRRRRRRLRRRRRRLLLLRRRLRLRRLRRRRRRRLLRRLLLRRRRRRLRRRRRLLLLRRLRRLRLRRLRRRLRRLRRRRRLLLLRRLRRRRLRLRRRRRRLLRRRLRRLRRRRRRLRRRRRLRRRLRLRLLRRRLLRRRLLLRRRLRRLLRLLLRRRLRLLRRRRLRRLRRRRLLRLRLLRRLRLLRLLLRRLLLLLLRRRRRRRLRRLRRLLLRRLLRRLRLRRRRLRRRRLRRRLLRRRRRRRRRLLRRRRLLRRRRRRRRRRRLLRLLRLRRLLRRLRRRLLLLLRRRLRLLRLRLLLRLLRRLRRLLRLLLLLRRRRLRRLRRLRRLRRLRRLRLRRRLLRLLRRLRRRLLLLRRLRRRRLLRRRLLLLLRRRLLRLRRRRRRRLRLRLLRRLRRRLLRRRRRRRLLRRLLRLRRLRLRLRLLRLRRLLLRRRRLLLLRLRLLLLRRRRRLRLRRLLRRLLRRRRRRRRRLRLRRRRRRRLRRRLRRLRLLRRRRRRRRLRRRRLRRRRRLLLRRLLRRRRLLRRRRLRRLRRRRRRRRRLRRRRRLLLRRRRRLLLRRRLRRLLRLRRRLRRRRRRRRLRRLRRLLLRLRLRRRLLRRRRRLRLRRLRLLRLRLRRLRRRLRLLLLRLRRRLLRLRRRRRRLRRLLLLRLRRRRRRRLRRRRLRRRRRRLLLRRRRLRRLLLLLRRLRRRRRRRLRLRRRLRLLRLRRRRLRRRRRLRLLRRRLLRLRRLLRRRLLRLRRRRRLRRLRRRRLRRLRLRRRRRRLRRRRRRLRRRRLRRLLRRRLRLRLLLRRRRLRRLRRRRLLRRLRRLLRRRLRLRLRRRLLRRRRRRRRLLLLRLLRLLRRRRRLRRLRLLLLRRLRRLRRLLRRRRRRLLRRLRRLLRRRRLRRLRRLLLRRLRRRRLRLLRLRRRRLLLRRLRLRRLRRRRRRRRRLRLLLRLRLLRRLRRLRRRRRRRRRLRLRRRRLRLRLRLLLRRRLRRRRLRRLRRLRRRRLLRRLLRRRRRLLRLRLRRRRRRLLRRRLRRRRRRLRRRLRRRLRRLLRRRRLLRRRRLLRRRLRLRLLRLLRRRRRLLRLRLLRLRRRRLLLRRRRLRRRRLRRRLLRRLRLRRRLRRRRLLLRRRRRLRRRLRRRRLRRRLRRRLRLRRRLLLRRLRRLLRRRRRLRRRRRLRRRLRLRLLRLLRRLLRRRRRRLLLRRLRRRLLRRLRRLRLRLRRRLLRRLLRLLRRLRRLRRRLRRLLLLRRLRLRRLRRRLLRRRLLLLRRRLRRLRRRRLRLRLLRLRRRLRRRRLRRLLRRLLRRRRLRRRRRLRRLRRRRRRRLLRRRRRRLLLRRRRRLLLRRRRLRLLRRRRRRRRRLRLLRRRRRRRLLRRRLRLLRRLLRRRRLRRRRRRLRRRLRRRRLLRRRRRRRRLRLRRLRLLLLLLRLRRLLLRRRRLRLRRRLRLLLRLLRRLR-LLRRRRRLRRRRRRR-LRRLRLLRLLRRLRRRRRLRRLRRLRRLRRLRRLRRRRRRRLLRRLLLLLLLRLRRLRLRRLLRRRLRLLLLLLRRLLRLRRRRRRRRLLRLLRRRRRLLRLRLLRRRRRLRRRRRLLRLLRLLRRRRRRRLRLLLLRRRRLLRRLLRRRRRLRR, agree=0.551, adj=0.066, (0 split)
##
## Node number 12: 372029 observations
## mean=583317.9, MSE=1.981776e+10
##
## Node number 13: 73772 observations, complexity param=0.02243535
## mean=905014.1, MSE=8.744813e+10
## left son=26 (37000 obs) right son=27 (36772 obs)
## Primary splits:
## Units Sold < 5209.5 to the left, improve=0.74898370, (0 missing)
## Total Revenue < 2277593 to the left, improve=0.74898370, (0 missing)
## Total Cost < 1371818 to the left, improve=0.74898370, (0 missing)
## Ship Date splits as -LLLRRRR-LRRRRLL-RRLLLLL-RLRLRLRRLRLRRRRRRRLLLRLLRLRRRLLLLLRRRLRRRRLLRRRLLLRLRRRLLLRLRLL-RRRLLLLRRRRLLRLRLLRRRRLLLLRLLLRRRRLLRRLRRLRRRRLLLLLRRRRRRRLLLRLRLLLRRLLLLLRRLLRRRRLLRLL-LLLLRRRLLLLRRRLLLRLLLLR-LRLLRRR-RRLRRLL-LLLLLRL-RLLLLLR-RRLLLLR-LLRRRRLLLLLRRLRLLLRRRLLLRRLRRRLRRLRRLLRLRLLLRRLRRRRLLLLRRRLRLRLLLRRLLRRRLRLRRRRLRRRRLLRRRRRLLRLRLLRRRRLLRLRLLRRLLRRRRRRLRRRLRRRLLRRLLLRLRLRLRRRLLRRLRLLLLLRRRRRLRRRLLLLRRLLRLRRRRLRLRRRRLLLLLLLRLRRRLRRLLLRRLLLRRRLLRRRRRRRRLLLRLLRLRLRRRRRRRRLRLRLLRRRRLRRRLLRRLLLLRRLLLLRRLLLRLRLRRLLLRRLRRRRRRLLRRLLLRLRLLRRRRRRRRRRRLLRLRLLLRLLLRRRRRRLRLLLLRRRRRRLLLLRRLLRLLRLRRRLLRLRLRRRRRLLRRRLLRRRRRRRRLRRLLLLRLLRRRLRLRRLRLRRRLRRRLLLRRRRRLRRRLLRRRRLRRLRLRRRLRLLRRRRRLRLLLRLRRLRRRLLLLLRRLLRLRRLRRRRRLLLLLRLLRRRLLLLLLRRLLLRLLLRRRRRLRLLLRLLLRRRLRRRLLRRLLLRLRLLRRRRLLRRLRLLRLLLRRLLRLRRRLLRRRLLRRLRRRRRLRRLRLRLRRRRLRLLRRLRRRLLRRRLLRLRLRRLRLRRRLLRRRLLLRLLLLRRRLLRRRLLRRRLLRRLLRRLLRLRRRRLLLLLRLLLRRRLLLLLLLLLRRLLLLLRRRLRRLRRLLLRRLLRRRLLLLRLRRRRLRRLLLLRRLLRRRRLLLRLRRLRRRRRRLLRRLRRRLRLRRLRRRRLLLRLRLRRLRRLLLLRLRLRRLRRLLLRRLLLLRLLLLLRRRLRRRLRLRRRLRLLLLRLLRLRRLLRRLRLRLLRLLLRLLLRLRLRRLLLLRLLLRRRRLLLLRLLRRRLLRRRLRLLLRRRLLLLRLRLRRRRLLLLLLLLLRRRLLLLRRLRLLRLLRLLLLRRLLLLLLLLRRRLRRRRRRLRRLRLRRLRLLLLRLRRRRLLRRRLLRRRLRLRRRLRLLRRLLLLRRRLLRLRRLLRRLRRLLLLRLLLLLRRLRRRLLLLRRRLLRLRLRLRRRRRLRRLLLLLLRLRRLLLRRRRRLLRLLRLLLRRRLLLRLLLRRRRRLLLRRRRLRLRRRLLRRRRLRRRLLLLRRLLRLRRRRLLRRRLRRRRLLLRRLLRLLLRLLLLRRLLRRRRRLLRRRRRLRRLRLLLRRRLLLLRRLLRRLRRLRRRRLLLLRRRRLLLLRLLRRRRRLLLRLRRLLRRLLLLLRRLLRRRRLLLRRRRLRRRRLLLRRRLLRRRRRLLRLRRLRLRLLLLRRRLLLRRRLRLRRRRLLRRLLRRLRLRLLLLRRRLLRRRRRRLLLRLRLRRLLLLRRRRRRRRLRLLLRRRLLLRRRLLRRRRRRLLRRRLLLLRLRRLLLRLLLRRLLLLLRRRLLLLRRLLRRRRLRRRRRLLRRLLLLLRLRLRRRRRLLLLRLRLRRRRLRRRLLLLLRLRRRLRRLRRLLLLLLLLRRLLLLRRRLLLRLRRLLLRRRLLRLLLRRLLRRLLLRRRLLLLLRLLRRRRLRLLRLLRRRRLLRRRRLRRLLLRRLRLRLRRRRLLRRRRRRRRLLLLLLLLLLLRLLLLRLRRLLLRRRRLRLRRRLRLLLLLLLLRLRRRRRLLRLRRLLLLLRLLRRLRRLLRLLLRLLRRLLLLRLLRLRRLLLLRRRRLRRRRLRLRRRLLLRLRRLLRRLLRRRRRLRLLRRRRLLLRRLLLRRRRLLLLLLLLLRRRLLRRRRLLRRRLLLLLLLRRRRRRLLLRLLLLRRLLLRLLRRRLRRRRLLLRRLLLRRLLLRLLRRRRLRRRLLRLRRRRLLLRLLLLLRLLLRLLRRRLRRRRLLRRRRLRRRRRRRLRRLRRRRLRRRRRLRRRLLLLRRLLRLRRRRLRRLLRRRRLLLRRRLLRRRRLRLLLRLRLLLRRRLLRLLLRLRRRLLLLRRLRRLRRLLLRRLRRLLLRLLLLLRLLRRRLLRRRLRRRLLLRLRLRRLRLRLLLRRRRRLRRLLLRRLRLLLLRLLLLRRLRLRRRLLRLLRRLRRLLLLRLRRRRLLLLRRRLRRLLRRLRLRRRLLLRLLRLRLRRLLLLRRRLRRRRLLLRLRLLRLRLRLRRLLLLLLRRRLLLLLLLLRRRLLLLRRRRLRRRLLLLRLLRLLLRLLLRRRLRRRRRLLLLLRLLLRRLLLRLRRLLRRRRLRRRRRRLRRLRRRLRLLLRLLRLRRRLRLRRLLLRLRRRRRLRRLRLRRLLRLRLLLRRRRRLLLRLLRLLRRLRRRRRLLLRLLLLLRLRRLLRRRRLRRLRRRRRLRLRLRLRRLRRLRLLLLRLRRLLLRLLRRRLRRRRLLLLLLRLRRLRLLLLLRLLRRLLRRRRLRRLLLLRR-LLRRRLL-RRRLLLR-LRLRRRL-LLRRLLR-RRRRLLR-RLLLLRL-LLRRLLR-RRLLLLLRLLRLRRLLRLLLLRRLRLRRLRRLRLRLRLLLRRLLLRRLLLRRLRLRLRRRRLRLLLRLLLRRRLLLRRRLRRRRLLLRRRLRRRLLLLRLLRRLLRRLRLRRRLLLLRRLRRRLRLLLLLLRLLLLLRRLRRRRRRRLRLLRLLL, improve=0.02686264, (0 missing)
## Order Date splits as LRLLRLRLRRRRLLLRLRRRLRLLLRLRRLRLRRRLRRLLRRLLLRLRRRRLLRLRRLLLRRLRLLLLLRLLRRLRLRRRRLRLLRLRLLRLLLRRLRRRLLRRLRRRLRRRLLLLLLRLLRRRLRLRRLRRRRLRLRRRRLLLLLLRRLLRLRRRLLRLRLRLLRRRLLLLLRRRLLRLLLLLRRRLRRRLLRLLRLRLRLLRRLLLLLRRLLRLLLLRLRLLLRRLLRRLLLRLLLLLLRRLLLRRRLRRLRRLRLRRLRRLRRLLRRLLLLLLLLRLLRRRLLRLLLRLRRLRLRRRRLRLLLRRLLLRLRLRLLLRRLRLLRRLLLLLRLLRRRRRRLRRRLLRLRLLRRLRRRRRRLRRRLRLLLRLRLLRRRRRRRRRRLLLRRRRRRRRLRLRRRLRRRLLRRLRRLLLRLRRLLRRRLRRRRLRLLRLRLRLRLLRLRLLRRRLRLLRRLRRRRLLLLRRRRRLRLLLRRRRRRLLLLRRLRLLLLRLLRRLLLRLRRLRRRLLLLRRRRLRRRRLLLRLLLLLRLLRLLRRRLLRRRRRLRLRLLLRRLLRRLRLRLLLLRRLLRLRRRRRLLLLLRLLLLLRRRRLLRRLLLLLLRRLLLLRRRRLRLRLLLLLRRRLLRRRRRLRRRRRRRLLLLRLRLRRRLRRLLLRLRRRLLRLRRRRLRLRLLRLRRLLRLRLRRLLRRLRLRLLRRRLLLRLRRLRRLLRLLRLLRLLRRLRLLLLRLLLRLLLLLRLLLLLLRLRRLRLRRLRLRRRRRRLRLRLRRLRLRRLLRLRLRRRRRLLLRRLRLLLLRLLRRLLRRRRLLRLRRRRLLRLLRLLRRLRLLRLLRLLLRRLLLLRRLLLLRLLRRLLRLRLRRLRLRRLRRLLLLRLRRRRLRLRLRRLRLRLLRLRLRLRLLRRRRLRRRRLLRLLLRLRLRLLRLRRLRLLRRRRLRRRLLLRLLLRLRRRLLLRRRLLLRRRRRRRRLLLLRRRRRRLRLRRRLLLLRLRRLLLLRRLLLLRLRRLLLRLLRLLRRLRRLRRLLRRLRLLLRLRLLLRLRLRRRRRRLRRLRRLLLLLLLLRLLRLRRLRLRLRLLLRLLLRRLRLRRRRLLRLLRRRRLRRRRLRRRLRLLRLLRRLRRRLRLRRLLRLRRLLRRRLLLRRRLRLLRRRLRLRLLLLLLRRRLLRRRLLRLRRRLRRRRRLLLRLLRRLRRRLRRLRLLLRRLLLLLRLLLRRLLLRRRLLRRRRLLLLRRRRRLLLLLLRLRLRRLRRRRRLLLRRRRLRRRLRRLLRLLLLLLRRRLLRRRLRRLRLRRLLRLRRLRRRRLLLLRRRLLLLLRLRLLRLRRLLLRLRLRLRLLRRLRLLLRRRLRRRRRLRLLRRRRLRLRRRRRLRRLRRLRRRLLRRRLLRLLLLRLLRLLLRLLRRRRLLRLRLLRRRLRRLLRLLRRRRLRRLRLRLLRLLLLLRLLLRLLRRRLRLRLLLLLLRLRLRLLLRRLLRLLLLLLLRLRLRRRRRLRLLLLLLRRLRLLRLRLLRRRRRRRLLRRLRRLLLLLLLLRRLLRLRLRRRLLLLRRRRLLLRRLLLLRRLLLLRRLRLRLLLRRLRRRLRLLRRLLRLRRLRLRRLRLLLRRLRRLLRLRRRLRLLRRRLLRRLRRLRLRRLLLLRRRLRLRRLRLLRRLRRRLRLLRLRLLLLLLLLRLLRLRLLRRLLRRLLRRLRRLRRRRRLRRLRRLRRRLRLLRRRLRLRLLRLLRRLRRRRLLRRLRLLRLLRRLRLRLRRRRRRLRLLRLLLRLLRRLRLRRLRLRRLLRLLRRRLLRRRLLRLRLRLLRLLRRRLLRLLLLRRLLLLLLLLRRRRLRRLRLLRLLRRRLLLLRLLLLLLLLRRRRLLLLRLRLRLLRRRLLLLRRLRLRRLLLLRRLRLRLRLRRLLRRLRRLRRRRLRLRLRLRRRRLRRRRRRRLRRLLLRLRRRRRLRRRLRLLRLRLRLLLLRLRLRLRLLRRRLRRRLRLLRLLRLLRRLRLRLLRRLRLLLRLRRRRLRLLLLLLLRLRRLLLRRRLLLLLRRLRRLRLRLLRLLLLRRLLRRLLLLLRLLLRLRLLLLRLLRRRLRRRRLRRRLLRRRLLRLRLLLRRRLRLRRLRLRLLRRRLLLRRLRLLLRRRRRLRRRLLRLLLRRLLRRRRRRRRRLRRRLRLLLRLLRRLLRLRRLLLLLLRRLRRRRLRLRRLLRRLRLRRLRRRRRLRLRRLLLRRRLLRRLRRLRLLLLRRRRLLRRRLLRLLLLRLLLLRLRLRLLLRRRRLLRLLRLRRLLLLLRLRLLLLLRLRRRLRRRLRLLRRRLLRRRLLRRRLLLRRLRRRRRLRLLRRLLLLRRRRLLRLLRLLRRRLLLRRLRLLLRLRLLRLRRRRRRLRLRLLRRLLRLRRRRRLRRLRRRRLRLRLRLLRLLLRLRLLRRRLRLLLRLRRRLRRRRRLLRLLLLRLLRRLLRLRLRLRLRLRRRRLLRLLRLLRLRRLRRLLRRRLRLLLLLRLLLRLRLLLRRLLRLRLRLLRLLLLRLLRRRLRLLRLLRRRLRRLRLLRLLLLRLRLLLRLRRLRRLRLRLRRRLLRLRRLLRLRRLLLLRRLLRLRRLRLLRRLLLRRLRLLLLLRLLRLLLRLLRRRRRLLRRLLRLRLLLRLRLRRLLLRRRLLLRLLLRLLLLRRLRLRLLRRLRRRRLLRLLRRLLLRRRRRLRRLRRLLLLRLRLRLLLLLLRRLRLLRRLLRRRRLLRRRRRLLRLRRRRLRLRLLRLLRLLLLLRLRRLRRLRRLLRRRR, improve=0.01488431, (0 missing)
## Surrogate splits:
## Total Revenue < 2277593 to the left, agree=1.000, adj=1.000, (0 split)
## Total Cost < 1371818 to the left, agree=1.000, adj=1.000, (0 split)
## Ship Date splits as -RLRRLLR-LRRLLRL-RRLLLLL-RLRLLLLLRRLRRLRRRRLRLLLLLLRRRLLLLLRLRLRRRRLLLRLLLLRLLRRLRLLLLLR-RRLLLLLRRRRLLRLRLLRLRRLLLLRRLLRLRRLLRRLRRLRRRRLLLLLRRRRRRRLLLLLRLLLLLLLLLLRRLLRRRRLLRLL-LLLLRRRLLLLRRRLLLRLLLLR-LLLLRRL-RRLRRLL-LLLLLRL-LLLLLLR-RRLLLLR-LLRRRRLLLLLRRLRLLLRRRLLLRRLRRRLRLLLRLLRLRLLLRRLRRLRLLLLRRRLLLRLLLLRLLRRRLRLRRRRLLLRRLLRRRRRLLRLLLLRRRRLLLLRLLRLLLLRRRRRLLRLRRRRLLRRLLLLLRLLLRRRLLLRRRLLLLLRRRRRRRRRRLLLLRLLLLRRLRLRRRRRRLLLLLLLLLRRRLRRLLLRLLLLRLLLLRRLLRRRLLLRRLLRLRLLRRRRRRRLRLLLLRRRRLRRRLLRRLLLLLRLLLRRLLLLLLLLRRRRLLRRRLRRRLLRRRRLLRLLLLRRRRRRRRRLLLLLLRLLLRLRLRRRRLRRRLLLLRRLRRRLLLLRLLLRLLRLRRRLLLLLLRLRRLLRRRLLLRRRRRRRRLLRLLLLRLLLRRLRLRRLRLRRRLRRRRLLRRLLRLRLLLLRRLRRLRLRLRRRLRLLLRLRRLRLLLRLRRLRLRLLLRLRRLLRRLRLRRLLRLLLLLRLLRRRLLLLLRRRLLLLLLLRRRRLLRLLLLLLLRRLLRRRLLRLLLLLLRLLRRRLLLRRLRLRRRLLLRLLRLLRRLLRRRLLLRLRLRLLLLRLRLLLRRRRLRLLRRLRLRLLLRRLLRLRRRRLLLLLLLLRRRLLLRLLLRLRRRLRLRLLRRRLLRRLLRRLLRLRRRRLLLLLRLLLRRLLLLLLLLLRRRLLLLRRRRLRRLRRLLLLRLLRRRLLLLRLRRRRLLRRLLLRRLLLRRRLLLRLRLRRRRRRRLLRRLRRRLRLRRLRRRRLLLRLRLRRLRRLLLLRLRLRRLRRLLLLRRLLLRLLLLLRLLLRRRLLLRLLLRLRLLRLLRLRRLLRRLRLLLLRLLLRLLLRLRRRRLLLLRLLLRRRRLLLLRLLRRRRLLRRLRLLLRRRLRLLRLRLRRRRRLLLLLLLLLRRRLLRRRRRLLRLLLLLLLRRLLLLLLLLRLRRRRRRLLLLLLRLRRLRLLLLRRRRRRLLRRLLLRLRLRLRRRLLLLRRLLLLRRRLLRLRLLLRRLRRLRLLLLLLLLRRLRRRLLLLRRRLRLLRLRRLRRRRLLRRLLLLLRLRRLLLRLRRRLLRLLRLLLLRRLLLLLLLRRRRRLLRRRRRLRLRLRLLRRRLLRRRLLLLLRLLRLRLLLLLRRRLRRRRLLLLRLLRLLRRLLLLRLLLRRLRRLLRRRRRLRRLRLLLRRRLRRLRRLLRLLRRLRRRRRLLLRLRRLLLLRLLRRRRRLLLRLLLLLRRRLLLLRLLLRRRRLLRRRLRLRRRRLLLRRRLLRLRRRLLRRRRLRLRLLLLRLRLLRRRRRRLRRRRLLRRLLRRLLLRRLLLRRRLRLRRLRRLLRRLRLRRLLLLRRRRRRRRLRLLLRRRLLLRLLLLRRRRRLLLRRRLLLLRRLLLLLRLLLRRLLLLRRRRLRLRRRLLRRRRLRRLRLLLRRLRLLLRLRLRRRLRLLLLLLLLRRRLRRRRLLLLLRLRRRLLRLRRLLLLLLLLRRLLLLRRRLLLRLRRLLLRRRLLLLLRRLLLRRLLLRRRLRLLRRLLRRLRLRLLLLLRRRRLLRRRRLRRLLLRRLRLRLRLRRLLRRRRRLRRLLLLLLLLLLRLLLLLRLLRRLLRRRRLRLRRRLRLLLLLRLLLLRRLRLLLRLRRLLLLLLLLLLRRRLLRLRLRLLLRLLLLLLLRLRRLLLLLRRRLRRRLLLLRRRLLRRLRRLLRRLLLRRRRLRLRRRRRLLLLRLLLRRRRLLLLLLLLLRRRLLRRRRLLRRRRLLLLLRRRRRRRLLRRLLLLRRLLLRLLRRRLRRRRLLLRLLLLRRLLLRLLRRRRRRRLLRRLRRRRLLLRLLRLLRLRLRLLRRRLLRRRLLRRRLLRRRRRLRLLRRLRRLLLRRRRRRRLLLLLRLLLRLRLLRLRRLLRLRRLLLLRLLLRRRRRRLLLRRLLLLRRLLLRLRLRLRRRLLLLRRLLRLRRLLRRLLRRLLLRLLLLLRLLRRRLLRRRRRRRLLLRLLLRRRRLRLLLLRLLRLRRLLLLRLLRLLLRLRLLRRLRLRRRRLLLLLRLRRLRLLRRRRRRLLLLRRRLRRLLRRLRRRRRLLLRLLRLRLRRLLLLRRRLLRRRLLLLRLLLRLRLRRRRRRLLLLRRRLLLLLLLLRRRLLLLRRRLLRRRLLLLRLLLLRLRLLLRRRLRRRRRLLLLRRRLLRRLLRRLRLLLRRRRLRRRRLRLRRLLLLLRRLLRLLRLRRRLRLRRLLLLLLRRRRLRRRRLRRLLRLRLLLRRRRRLLLRLLRLLRLLRRRRLLLLRLLLLLRLRRLLLRRRRRRLLRRRRLRLRLLLLRLRRLRLRLLLLRRRLLRLLRRRLRLRRLLLLLLRLLRLLLRLLLRLLLLLLLRRRLRRLLLRRL-LLRRRLL-RRRLLLR-LRLRRLL-LLLRLLR-RRRRLRR-RLLLLRL-LLRRLRR-RRLLLRLRLLLLLRLLRRLLLLRLRLRRLRRLLLRLRLLLRRLLLRRLLLRRLRLLRRRRRLRLLLRLLLLRRLLLLRRLLRRRLLLRRRLRRRLLRLRRRRRLLLRRRLRRRLRLLRRRRRRLRLLRLLLLLLLLLLRLRRRRRLRRRLLLLLL, agree=0.581, adj=0.160, (0 split)
## Order Date splits as LRLLRLRLRLLRLLLRRRRRLLLLLRLRRLLLRRRLLRLLRRLLLRLRRRRLLRLRRLLLRRLRLLLLLLLLRLLRLLRRRLLLLLLRLRRLRLRRRRRRLLRRLLRRLRLRRLLLLLRLRRRRLRLRRLRRRRLLLRLRRLLRLLLRRLLRLRLLLLRLRLLLRRLRLLLLRRRRLLLLLLLLRRRLLRRLLRLRLLRLRRLRLRLLLLRRLLLLLLRRLRLLLRRLLRRRLLRLLLLLLLLLLLRLLLRRLRLLRLRRLRRLRRLLRLRRLRLLRLRLLLRLLLLRLLRLRLLLLLRRRLRLLLRLLRLRLRLRLLRRRLRLLLRLLRLLLLLRRRLRRLRRLLLRRRLLRLLRRRLRRLLRRLLLLLRLLLLLRLRRRLRRLLLLLRRRRLRRRRLLRRLRRRLLRRLRLLRLRRRRLLRLRLRRRLLRLLRLRLLLRLLRLRLRRRLLRLLLRLLRRRLLLLRRRLLLLLLLRRRRRRRLLLRRLLLLLLLLLRLRLLRLRRLLRRLLLLLRRRLRLRRLLLRLLLLLRRLLLLRRRLLRRRRLLRRLLRRRLLRRRLRLRLLLLRRLLRLRLRRRLLLLLLLLLLLRRLRLLRRLLRLLLLRLLLLRRLRLRLRLLRLLRLLLLLRLRLLRRRRLRRLLLLRLLLLRRLRRRLLRLRRRLLRLRRRRLRLRLLRLRRLLRLRLRRLLLRLRRRLLRRLLLRRLLRLRRLLRLLRLLLRLRLLLLLLRRLLLRLLLLLRLRLLLRRLRRLRLLLLRLRRRRRRRRLLLRRLLLRLRLLLRLLLRRLLLLRRLRLLLRRLLRRLLLRRLLLLLLRLRRRRLLRLLRRLRLLRLLRLLLRRLLLLRRLLRLLLLRRLLRLLLRRLLLRRLLRLLRLRLRRLRRRLRLRRLRLLLLRLRRRLLLRRLLRLLRRRLLRLLLRLRRRLLLLRRLRLLLLLRRRRRLLLLLLRRLRRLRLLRRLLRLRRRRLRRRLRLLRRLRRRLLLLRLLLLLRLRRLLLLRRLLLLLLLLLLLLLLRLLRRRRRLRRLLLRLRLLLRLRLLLLLRLRRRRRRRRLLRRLLLRLLLLLLLRLRLLRLRLRLLLRLLLRLLRLLRLRLLRLLRLRRRRLRRLLRLLRLLRRRLRLRRRLRLRRLLLLRRLLRRRLRLRRRLRLLRLRLRLRLLLLLLRRLLLRRRLLRLRRRLRRRRRLLLRLRRRLRRRLRRLRLLRLRLLLLLLLLLRLLLLRRRRLRLRRLLLLRRLRRLLLLLLRLRLRRLLRRRRLLRRRLLLRRRLRRLLRLLLRLLRRRLLLRRLRRRRLRLLLRLRRLRRRRRLRLLRRRLLLLRLRLLRRLRLRLRLRLRRRLRLRLLLLLRRRLRRRRLLRLLLRRRRLLLLRRLLRRLLRLRRRRLLRRLLRLLLLRLLRLLLRLLLLRLLLRRRLLRLRLRRLLRLLRRRRRRRLRLRLLRLLLLLRLLLRRLRRRLRLLLLLLLLRLRLRLLLLLLLLLLLRLLLRLLLRRRRLLRLLLLLLRRLRLLRLRLLRRLLRRLLRRRLRLLLLLLLLLLRLLRLLLLLRLLLRLRRLLLLLRRLLRLRLLLLLRLRLLLLLRRLLRRLRLLLLLLRLRLLRLRRLRLLLRRLRRLLRLRRRLRLLRRLLLRRLLLLRLRLLLLLRRRLRLLRLRLLRRLLRRLRLLRLRLLLLLLLLRLLRLRLRRRLLRRLLRRLRRLRRRRRLRRLRRLRRRLRLLRRLLRLRRLRLLLRLRRRLLLRRLLLLRLLLRLRLRLRRLRRRLRLRLLLLRLLRLLRLRRLRRRLLLLLLRRLLLRRLLLRLRLRLLLLLLLRLLRLLLLRRLLLLLRLRRLRRLLRLRLLRRLRRLRLLLRLLLLLLLLRRRLRLLLRRRLRLLRRRLLLLRLLRLRRLLRLRRLLLRLRLRRLLRRRRRLLRRRLRLRLRLRRLRLRRLRRRRLRLLLLRLRRRRLLRLRLRLLRLRLRLLLLLLRRRLRLLRLRLLRRLRLLLLRRLLRRLRLLRLLRLRLLLRLLRRRLRLLLLLLLRLLRLRLLRLLLRRLRRRRRLRRRLLRLLLLRRLLLRLLLLLRLLLRLRLLLLRLLRRRLRRRRLLRRLLRLRLLLLRLLRRRLLRLRLLRLRLLLRRLLLRRLLLLRRLRRRLRLLLLLLRLRRLRRRRRRRRRRLRRRLRLLLRLLRRRLRLRRLLLLLLRRLLRRRLLLRLRLRRRRLRRLRLLRRRRRRRLLLRLLRLRRLLRLLRRLLRLRLLLLLRLLRLLLLRLLLRRLLLLRLLRRRLLLLLLRLLLLLRLLRLLLLLLLRRLRRLLRLLRLRRRRLRLRRLLRRRLLRRRLRRRRLLRLLRRLLLLLRRLLLLLLRLRLRRLLLRRLRLLLRLRLLRRRRRRRRLRLRLLRLLLRLLRRRLLRLLRRLRLRLRLLLLLLLLRLRLLRRRLLRLLRLRRRLRRRRRLLRRLLRRLRRRLLLLLLRLLLRLRRRLRLRLLLLLRLRRLRRLLRRRLLLRLLLRLLLRLRLLLRRLLRLLRRLLLLLLRLLLRRRLRLLRLLRRRLRRLLLLLLLLLRLRLLLRLRRLRRLLLRLRRRLLRLRRLLRLLRLLLLRRLLRLRRLRRLRRRLLLRLRLLLLLLLLRRLLRLRLRLRRLLLRLLLLLLLLRLRLRRLLLLRLLLLRLLRRLLLLRRLRLLLLRRLRLRRLLRLLRRLLLRRRLRLRRRRRLLLRRLRLRLLLLLLRRLRLLRLLLRLRRLRRLLRLLLRLRRRRRLLRLLRLLRLLLLLRLLRLLRLRRLRLRRR, agree=0.561, adj=0.120, (0 split)
## Country splits as LRLLLRLRRRRLLLRLRRLRLRLLLLLLLLLLRLLRLRLRLRLRRRRLRLRRRLLRLLLLLRRLLRLLLLRLRLLRLLRLLRLRLLRRLLLLLRRRLLLRRLLLRLLLRRLLRLLRRLRLRRRLLRRLRLLLLRRLLLLRRLLRLRLRLRLRRRLLLRRLLRLRLRLLLRLLRRRRLLRLLRRRL, agree=0.514, adj=0.026, (0 split)
##
## Node number 14: 75318 observations, complexity param=0.01522044
## mean=862881.7, MSE=5.849142e+10
## left son=28 (34115 obs) right son=29 (41203 obs)
## Primary splits:
## Total Revenue < 4349106 to the left, improve=0.7440772, (0 missing)
## Total Cost < 3647161 to the left, improve=0.7392278, (0 missing)
## Unit Price < 536.55 to the left, improve=0.6503606, (0 missing)
## Item Type splits as -------LR---, improve=0.6503606, (0 missing)
## Unit Cost < 444.825 to the left, improve=0.6503606, (0 missing)
## Surrogate splits:
## Total Cost < 3645827 to the left, agree=0.955, adj=0.901, (0 split)
## Item Type splits as -------LR---, agree=0.800, adj=0.559, (0 split)
## Unit Price < 536.55 to the left, agree=0.800, adj=0.559, (0 split)
## Unit Cost < 444.825 to the left, agree=0.800, adj=0.559, (0 split)
## Units Sold < 6678.5 to the left, agree=0.747, adj=0.441, (0 split)
##
## Node number 15: 81280 observations, complexity param=0.01670105
## mean=1358044, MSE=5.717211e+10
## left son=30 (32636 obs) right son=31 (48644 obs)
## Primary splits:
## Units Sold < 7939.5 to the left, improve=0.7740294, (0 missing)
## Unit Cost < 382.935 to the right, improve=0.3346489, (0 missing)
## Unit Price < 552.735 to the right, improve=0.3346489, (0 missing)
## Total Cost < 2659437 to the right, improve=0.3346489, (0 missing)
## Item Type splits as ----R-L-----, improve=0.3346489, (0 missing)
## Surrogate splits:
## Total Revenue < 5305730 to the left, agree=0.718, adj=0.298, (0 split)
## Total Cost < 3989916 to the left, agree=0.718, adj=0.298, (0 split)
## Item Type splits as ----R-L-----, agree=0.683, adj=0.212, (0 split)
## Unit Price < 552.735 to the right, agree=0.683, adj=0.212, (0 split)
## Unit Cost < 382.935 to the right, agree=0.683, adj=0.212, (0 split)
##
## Node number 26: 37000 observations
## mean=649879.6, MSE=2.196317e+10
##
## Node number 27: 36772 observations
## mean=1161731, MSE=2.193856e+10
##
## Node number 28: 34115 observations
## mean=633611.9, MSE=1.534094e+10
##
## Node number 29: 41203 observations
## mean=1052711, MSE=1.466157e+10
##
## Node number 30: 32636 observations
## mean=1101220, MSE=1.547089e+10
##
## Node number 31: 48644 observations
## mean=1530352, MSE=1.120726e+10