Feature Selection
Collins Bett
2022-04-06
CarreFour Kenya Sale Increment Strategies.
1. Research Question
Carrefour Kenya and are currently undertaking a project that will inform the marketing department on the most relevant marketing strategies that will result in the highest no. of sales (total price including tax).This project is aimed at doing analysis on the dataset provided by carrefour and create insights on how to achieve highest sales.
2. Metric of Success
Achieving a set of variables that are important and influences the sales variable.
3. Understanding the context.
CarreFour is an International chain of retail supemarkets in the world, It was set up in Kenya in the year 2016 and has been performing well over the years. This project is aimed at creating insights from existing and current trends to develop marketing strategies that will enable the marketing team achieve higher sales.
4. Recording the Experimental Design
- Data Loading
- Data Cleaning and preprocessing
- Exploratory Data Analysis
- Implementation o solution.
- Recommendations and Conclusions.
5. Data Relevance.
The provided data is relevant for this study since it’s been sourced from CarreFour database and is a reflection of current transactions.
Data Preview
Loading the libraries
library(modelr)
library(broom)##
## Attaching package: 'broom'## The following object is masked from 'package:modelr':
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## bootstraplibrary(caret)## Loading required package: ggplot2## Loading required package: latticelibrary(rpart)
library(ggplot2)
library(Amelia)## Loading required package: Rcpp## ##
## ## Amelia II: Multiple Imputation
## ## (Version 1.8.0, built: 2021-05-26)
## ## Copyright (C) 2005-2022 James Honaker, Gary King and Matthew Blackwell
## ## Refer to http://gking.harvard.edu/amelia/ for more information
## ##library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
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## intersect, setdiff, setequal, unionlibrary(data.table)##
## Attaching package: 'data.table'## The following objects are masked from 'package:dplyr':
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## between, first, lastlibrary(tidyverse)## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --## v tibble 3.1.6 v purrr 0.3.4
## v tidyr 1.2.0 v stringr 1.4.0
## v readr 2.1.2 v forcats 0.5.1## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x data.table::between() masks dplyr::between()
## x broom::bootstrap() masks modelr::bootstrap()
## x dplyr::filter() masks stats::filter()
## x data.table::first() masks dplyr::first()
## x dplyr::lag() masks stats::lag()
## x data.table::last() masks dplyr::last()
## x purrr::lift() masks caret::lift()
## x purrr::transpose() masks data.table::transpose()Loading the data
carrefour <- fread('http://bit.ly/CarreFourDataset')
carrefour## Invoice ID Branch Customer type Gender Product line Unit price
## 1: 750-67-8428 A Member Female Health and beauty 74.69
## 2: 226-31-3081 C Normal Female Electronic accessories 15.28
## 3: 631-41-3108 A Normal Male Home and lifestyle 46.33
## 4: 123-19-1176 A Member Male Health and beauty 58.22
## 5: 373-73-7910 A Normal Male Sports and travel 86.31
## ---
## 996: 233-67-5758 C Normal Male Health and beauty 40.35
## 997: 303-96-2227 B Normal Female Home and lifestyle 97.38
## 998: 727-02-1313 A Member Male Food and beverages 31.84
## 999: 347-56-2442 A Normal Male Home and lifestyle 65.82
## 1000: 849-09-3807 A Member Female Fashion accessories 88.34
## Quantity Tax Date Time Payment cogs
## 1: 7 26.1415 1/5/2019 13:08 Ewallet 522.83
## 2: 5 3.8200 3/8/2019 10:29 Cash 76.40
## 3: 7 16.2155 3/3/2019 13:23 Credit card 324.31
## 4: 8 23.2880 1/27/2019 20:33 Ewallet 465.76
## 5: 7 30.2085 2/8/2019 10:37 Ewallet 604.17
## ---
## 996: 1 2.0175 1/29/2019 13:46 Ewallet 40.35
## 997: 10 48.6900 3/2/2019 17:16 Ewallet 973.80
## 998: 1 1.5920 2/9/2019 13:22 Cash 31.84
## 999: 1 3.2910 2/22/2019 15:33 Cash 65.82
## 1000: 7 30.9190 2/18/2019 13:28 Cash 618.38
## gross margin percentage gross income Rating Total
## 1: 4.761905 26.1415 9.1 548.9715
## 2: 4.761905 3.8200 9.6 80.2200
## 3: 4.761905 16.2155 7.4 340.5255
## 4: 4.761905 23.2880 8.4 489.0480
## 5: 4.761905 30.2085 5.3 634.3785
## ---
## 996: 4.761905 2.0175 6.2 42.3675
## 997: 4.761905 48.6900 4.4 1022.4900
## 998: 4.761905 1.5920 7.7 33.4320
## 999: 4.761905 3.2910 4.1 69.1110
## 1000: 4.761905 30.9190 6.6 649.2990Checking on the first 6 entries
head(carrefour, 6)## Invoice ID Branch Customer type Gender Product line Unit price
## 1: 750-67-8428 A Member Female Health and beauty 74.69
## 2: 226-31-3081 C Normal Female Electronic accessories 15.28
## 3: 631-41-3108 A Normal Male Home and lifestyle 46.33
## 4: 123-19-1176 A Member Male Health and beauty 58.22
## 5: 373-73-7910 A Normal Male Sports and travel 86.31
## 6: 699-14-3026 C Normal Male Electronic accessories 85.39
## Quantity Tax Date Time Payment cogs gross margin percentage
## 1: 7 26.1415 1/5/2019 13:08 Ewallet 522.83 4.761905
## 2: 5 3.8200 3/8/2019 10:29 Cash 76.40 4.761905
## 3: 7 16.2155 3/3/2019 13:23 Credit card 324.31 4.761905
## 4: 8 23.2880 1/27/2019 20:33 Ewallet 465.76 4.761905
## 5: 7 30.2085 2/8/2019 10:37 Ewallet 604.17 4.761905
## 6: 7 29.8865 3/25/2019 18:30 Ewallet 597.73 4.761905
## gross income Rating Total
## 1: 26.1415 9.1 548.9715
## 2: 3.8200 9.6 80.2200
## 3: 16.2155 7.4 340.5255
## 4: 23.2880 8.4 489.0480
## 5: 30.2085 5.3 634.3785
## 6: 29.8865 4.1 627.6165checking on last 6 entries
tail(carrefour, 6)## Invoice ID Branch Customer type Gender Product line Unit price
## 1: 652-49-6720 C Member Female Electronic accessories 60.95
## 2: 233-67-5758 C Normal Male Health and beauty 40.35
## 3: 303-96-2227 B Normal Female Home and lifestyle 97.38
## 4: 727-02-1313 A Member Male Food and beverages 31.84
## 5: 347-56-2442 A Normal Male Home and lifestyle 65.82
## 6: 849-09-3807 A Member Female Fashion accessories 88.34
## Quantity Tax Date Time Payment cogs gross margin percentage
## 1: 1 3.0475 2/18/2019 11:40 Ewallet 60.95 4.761905
## 2: 1 2.0175 1/29/2019 13:46 Ewallet 40.35 4.761905
## 3: 10 48.6900 3/2/2019 17:16 Ewallet 973.80 4.761905
## 4: 1 1.5920 2/9/2019 13:22 Cash 31.84 4.761905
## 5: 1 3.2910 2/22/2019 15:33 Cash 65.82 4.761905
## 6: 7 30.9190 2/18/2019 13:28 Cash 618.38 4.761905
## gross income Rating Total
## 1: 3.0475 5.9 63.9975
## 2: 2.0175 6.2 42.3675
## 3: 48.6900 4.4 1022.4900
## 4: 1.5920 7.7 33.4320
## 5: 3.2910 4.1 69.1110
## 6: 30.9190 6.6 649.2990checking on data types
str(carrefour)## Classes 'data.table' and 'data.frame': 1000 obs. of 16 variables:
## $ Invoice ID : chr "750-67-8428" "226-31-3081" "631-41-3108" "123-19-1176" ...
## $ Branch : chr "A" "C" "A" "A" ...
## $ Customer type : chr "Member" "Normal" "Normal" "Member" ...
## $ Gender : chr "Female" "Female" "Male" "Male" ...
## $ Product line : chr "Health and beauty" "Electronic accessories" "Home and lifestyle" "Health and beauty" ...
## $ Unit price : num 74.7 15.3 46.3 58.2 86.3 ...
## $ Quantity : int 7 5 7 8 7 7 6 10 2 3 ...
## $ Tax : num 26.14 3.82 16.22 23.29 30.21 ...
## $ Date : chr "1/5/2019" "3/8/2019" "3/3/2019" "1/27/2019" ...
## $ Time : chr "13:08" "10:29" "13:23" "20:33" ...
## $ Payment : chr "Ewallet" "Cash" "Credit card" "Ewallet" ...
## $ cogs : num 522.8 76.4 324.3 465.8 604.2 ...
## $ gross margin percentage: num 4.76 4.76 4.76 4.76 4.76 ...
## $ gross income : num 26.14 3.82 16.22 23.29 30.21 ...
## $ Rating : num 9.1 9.6 7.4 8.4 5.3 4.1 5.8 8 7.2 5.9 ...
## $ Total : num 549 80.2 340.5 489 634.4 ...
## - attr(*, ".internal.selfref")=<externalptr>Checking on dataset description
summary(carrefour)## Invoice ID Branch Customer type Gender
## Length:1000 Length:1000 Length:1000 Length:1000
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## Product line Unit price Quantity Tax
## Length:1000 Min. :10.08 Min. : 1.00 Min. : 0.5085
## Class :character 1st Qu.:32.88 1st Qu.: 3.00 1st Qu.: 5.9249
## Mode :character Median :55.23 Median : 5.00 Median :12.0880
## Mean :55.67 Mean : 5.51 Mean :15.3794
## 3rd Qu.:77.94 3rd Qu.: 8.00 3rd Qu.:22.4453
## Max. :99.96 Max. :10.00 Max. :49.6500
## Date Time Payment cogs
## Length:1000 Length:1000 Length:1000 Min. : 10.17
## Class :character Class :character Class :character 1st Qu.:118.50
## Mode :character Mode :character Mode :character Median :241.76
## Mean :307.59
## 3rd Qu.:448.90
## Max. :993.00
## gross margin percentage gross income Rating Total
## Min. :4.762 Min. : 0.5085 Min. : 4.000 Min. : 10.68
## 1st Qu.:4.762 1st Qu.: 5.9249 1st Qu.: 5.500 1st Qu.: 124.42
## Median :4.762 Median :12.0880 Median : 7.000 Median : 253.85
## Mean :4.762 Mean :15.3794 Mean : 6.973 Mean : 322.97
## 3rd Qu.:4.762 3rd Qu.:22.4453 3rd Qu.: 8.500 3rd Qu.: 471.35
## Max. :4.762 Max. :49.6500 Max. :10.000 Max. :1042.65checking the size/shape of a dataframe
dim(carrefour)## [1] 1000 16Data Preprocessing.
i. Completeness
This is achieved by checking for missing values if any imputed to ensure correct predictions are made.
is.null(carrefour)## [1] FALSETotal number of null valuesin dataset
total_null <- sum(is.na(carrefour))
total_null## [1] 0ii. Consistency.
Consistency is achieved when all the duplicated rows are done away with.
duplicated_rows <- carrefour[duplicated(carrefour), ]
duplicated_rows## Empty data.table (0 rows and 16 cols): Invoice ID,Branch,Customer type,Gender,Product line,Unit price...anyDuplicated(carrefour)## [1] 0iii. Relevance.
Relevance is achieved by ensuring all the features provided for the analysis are relevant to the objective Which in this case all provided features are.
iv. Accuracy.
Checking that all entries are correct.
Outliers
We can visualize any outliers in a dataset using boxplots
colnames(carrefour)## [1] "Invoice ID" "Branch"
## [3] "Customer type" "Gender"
## [5] "Product line" "Unit price"
## [7] "Quantity" "Tax"
## [9] "Date" "Time"
## [11] "Payment" "cogs"
## [13] "gross margin percentage" "gross income"
## [15] "Rating" "Total"Renaming column names
# Rename column where names
names(carrefour)[names(carrefour) == "Invoice ID"] <- "Invoice_ID"
names(carrefour)[names(carrefour) == "Customer type"] <- "Customer_type"
names(carrefour)[names(carrefour) == "Product line"] <- "Product_line"
names(carrefour)[names(carrefour) == "gross margin percentage"] <- "gross_margin_percentage"
names(carrefour)[names(carrefour) == "Unit price"] <- "Unit_price"
names(carrefour)[names(carrefour) == "gross income"] <- "gross_income"
colnames(carrefour)## [1] "Invoice_ID" "Branch"
## [3] "Customer_type" "Gender"
## [5] "Product_line" "Unit_price"
## [7] "Quantity" "Tax"
## [9] "Date" "Time"
## [11] "Payment" "cogs"
## [13] "gross_margin_percentage" "gross_income"
## [15] "Rating" "Total"str(carrefour)## Classes 'data.table' and 'data.frame': 1000 obs. of 16 variables:
## $ Invoice_ID : chr "750-67-8428" "226-31-3081" "631-41-3108" "123-19-1176" ...
## $ Branch : chr "A" "C" "A" "A" ...
## $ Customer_type : chr "Member" "Normal" "Normal" "Member" ...
## $ Gender : chr "Female" "Female" "Male" "Male" ...
## $ Product_line : chr "Health and beauty" "Electronic accessories" "Home and lifestyle" "Health and beauty" ...
## $ Unit_price : num 74.7 15.3 46.3 58.2 86.3 ...
## $ Quantity : int 7 5 7 8 7 7 6 10 2 3 ...
## $ Tax : num 26.14 3.82 16.22 23.29 30.21 ...
## $ Date : chr "1/5/2019" "3/8/2019" "3/3/2019" "1/27/2019" ...
## $ Time : chr "13:08" "10:29" "13:23" "20:33" ...
## $ Payment : chr "Ewallet" "Cash" "Credit card" "Ewallet" ...
## $ cogs : num 522.8 76.4 324.3 465.8 604.2 ...
## $ gross_margin_percentage: num 4.76 4.76 4.76 4.76 4.76 ...
## $ gross_income : num 26.14 3.82 16.22 23.29 30.21 ...
## $ Rating : num 9.1 9.6 7.4 8.4 5.3 4.1 5.8 8 7.2 5.9 ...
## $ Total : num 549 80.2 340.5 489 634.4 ...
## - attr(*, ".internal.selfref")=<externalptr>- Unit Price.
a <- carrefour$Unit_price
boxplot(a)
b.Quantity
quantity <- carrefour$Quantity
boxplot(quantity)
- cogs
cogs <- carrefour$cogs
boxplot(cogs)
- Gross margin percentage
b <- carrefour$gross_margin_percentage
boxplot(b)
- Gross income
gross_income <- carrefour$gross_income
boxplot(gross_income)
- Rating
rating <- carrefour$Rating
boxplot(rating)
- Total
total <- carrefour$Total
boxplot(total)
The total and gross income have outliers .
To see the number of outliers
Gross Income
a <- carrefour$gross_income
boxplot.stats(a)$out## [1] 47.790 49.490 49.650 47.720 48.605 49.260 48.750 48.685 48.690The outlier entries are 9.
Total
a <- carrefour$Total
boxplot.stats(a)$out## [1] 1003.590 1039.290 1042.650 1002.120 1020.705 1034.460 1023.750 1022.385
## [9] 1022.490The outlier entries are 9.
Exploratory Data Analysis.
Univariate Analysis.
- Unit Price
mean(carrefour$Unit_price, trim = 0, na.rm=FALSE)## [1] 55.67213median(carrefour$Unit_price,na.rm=FALSE)## [1] 55.23range(carrefour$Unit_price,na.rm=FALSE, finite=FALSE)## [1] 10.08 99.96quantile(carrefour$Unit_price, probs=seq(0, 1,0.25), na.rm=FALSE, names=TRUE, type=7)## 0% 25% 50% 75% 100%
## 10.080 32.875 55.230 77.935 99.960var(carrefour$Unit_price)## [1] 701.9653sd(carrefour$Unit_price,na.rm=FALSE)## [1] 26.49463mode
getmode <- function(v){
uniqv <- unique(v)
uniqv[which.max(tabulate(match(v, uniqv)))]
}unit_price_mode <- getmode(carrefour$Unit_price)
unit_price_mode## [1] 83.77Visualizing Unit Price.
d<-hist(carrefour$Unit_price, breaks=10, col="red", xlab="Unit price",main="Unit price")
plot(d)
The highest unit prices are between 20-30, 70-80 and 90-100.
head(carrefour)## Invoice_ID Branch Customer_type Gender Product_line Unit_price
## 1: 750-67-8428 A Member Female Health and beauty 74.69
## 2: 226-31-3081 C Normal Female Electronic accessories 15.28
## 3: 631-41-3108 A Normal Male Home and lifestyle 46.33
## 4: 123-19-1176 A Member Male Health and beauty 58.22
## 5: 373-73-7910 A Normal Male Sports and travel 86.31
## 6: 699-14-3026 C Normal Male Electronic accessories 85.39
## Quantity Tax Date Time Payment cogs gross_margin_percentage
## 1: 7 26.1415 1/5/2019 13:08 Ewallet 522.83 4.761905
## 2: 5 3.8200 3/8/2019 10:29 Cash 76.40 4.761905
## 3: 7 16.2155 3/3/2019 13:23 Credit card 324.31 4.761905
## 4: 8 23.2880 1/27/2019 20:33 Ewallet 465.76 4.761905
## 5: 7 30.2085 2/8/2019 10:37 Ewallet 604.17 4.761905
## 6: 7 29.8865 3/25/2019 18:30 Ewallet 597.73 4.761905
## gross_income Rating Total
## 1: 26.1415 9.1 548.9715
## 2: 3.8200 9.6 80.2200
## 3: 16.2155 7.4 340.5255
## 4: 23.2880 8.4 489.0480
## 5: 30.2085 5.3 634.3785
## 6: 29.8865 4.1 627.6165- Quantity
hist(carrefour$Quantity, breaks=12, col="skyblue",xlab="Quantity", main='Quantity of Products')
Most product quantities bought are 1 or 2.
- Tax.
d <- density(carrefour$Tax, xlab="Tax")## Warning: In density.default(carrefour$Tax, xlab = "Tax") :
## extra argument 'xlab' will be disregardedplot(d)
- cogs
cogs <- hist(carrefour$cogs, xlab="cogs")
plot(cogs)
The highest number of cogs is at zero but the occurence reduces as the
value of cogs increases.
- gross margin percentage
#plot(gross_margin)The gross margin percentage range between 2 to 8.
head(carrefour)## Invoice_ID Branch Customer_type Gender Product_line Unit_price
## 1: 750-67-8428 A Member Female Health and beauty 74.69
## 2: 226-31-3081 C Normal Female Electronic accessories 15.28
## 3: 631-41-3108 A Normal Male Home and lifestyle 46.33
## 4: 123-19-1176 A Member Male Health and beauty 58.22
## 5: 373-73-7910 A Normal Male Sports and travel 86.31
## 6: 699-14-3026 C Normal Male Electronic accessories 85.39
## Quantity Tax Date Time Payment cogs gross_margin_percentage
## 1: 7 26.1415 1/5/2019 13:08 Ewallet 522.83 4.761905
## 2: 5 3.8200 3/8/2019 10:29 Cash 76.40 4.761905
## 3: 7 16.2155 3/3/2019 13:23 Credit card 324.31 4.761905
## 4: 8 23.2880 1/27/2019 20:33 Ewallet 465.76 4.761905
## 5: 7 30.2085 2/8/2019 10:37 Ewallet 604.17 4.761905
## 6: 7 29.8865 3/25/2019 18:30 Ewallet 597.73 4.761905
## gross_income Rating Total
## 1: 26.1415 9.1 548.9715
## 2: 3.8200 9.6 80.2200
## 3: 16.2155 7.4 340.5255
## 4: 23.2880 8.4 489.0480
## 5: 30.2085 5.3 634.3785
## 6: 29.8865 4.1 627.6165Bivariate Analysis.
Covariance Covariance is the statistical representation of the degree to which two variables vary from each other.
Covariance.
carrefour_cov <- carrefour[,c(6,7,8,12,14,16)]
cov(carrefour_cov)## Unit_price Quantity Tax cogs gross_income
## Unit_price 701.9653313 0.8347785 196.66834 3933.3668 196.66834
## Quantity 0.8347785 8.5464464 24.14957 482.9914 24.14957
## Tax 196.6683401 24.1495704 137.09659 2741.9319 137.09659
## cogs 3933.3668019 482.9914076 2741.93188 54838.6377 2741.93188
## gross_income 196.6683401 24.1495704 137.09659 2741.9319 137.09659
## Total 4130.0351420 507.1409780 2879.02848 57580.5695 2879.02848
## Total
## Unit_price 4130.035
## Quantity 507.141
## Tax 2879.028
## cogs 57580.570
## gross_income 2879.028
## Total 60459.598Correlation.
carrefour.cor <- cor(carrefour_cov, method=c('spearman'))visualizing
#install.packages('corrplot')
library(corrplot)## corrplot 0.92 loadedcorrplot(carrefour.cor)
cogs,gross income, tax and total are highly correlated to each
other.
IMPLEMENTATION.
- Feature Selection.
carrefour_1 <- carrefour
head(carrefour_1)## Invoice_ID Branch Customer_type Gender Product_line Unit_price
## 1: 750-67-8428 A Member Female Health and beauty 74.69
## 2: 226-31-3081 C Normal Female Electronic accessories 15.28
## 3: 631-41-3108 A Normal Male Home and lifestyle 46.33
## 4: 123-19-1176 A Member Male Health and beauty 58.22
## 5: 373-73-7910 A Normal Male Sports and travel 86.31
## 6: 699-14-3026 C Normal Male Electronic accessories 85.39
## Quantity Tax Date Time Payment cogs gross_margin_percentage
## 1: 7 26.1415 1/5/2019 13:08 Ewallet 522.83 4.761905
## 2: 5 3.8200 3/8/2019 10:29 Cash 76.40 4.761905
## 3: 7 16.2155 3/3/2019 13:23 Credit card 324.31 4.761905
## 4: 8 23.2880 1/27/2019 20:33 Ewallet 465.76 4.761905
## 5: 7 30.2085 2/8/2019 10:37 Ewallet 604.17 4.761905
## 6: 7 29.8865 3/25/2019 18:30 Ewallet 597.73 4.761905
## gross_income Rating Total
## 1: 26.1415 9.1 548.9715
## 2: 3.8200 9.6 80.2200
## 3: 16.2155 7.4 340.5255
## 4: 23.2880 8.4 489.0480
## 5: 30.2085 5.3 634.3785
## 6: 29.8865 4.1 627.61651. Filter Method.
Applies a metric to assign a score to each feature, then features are then ranked by the score. It uses a correlation function therefore removes redundancy using correlation.
carrefour_2 <- carrefour_1[,c(6,7,8,12,14,15,16)]
head(carrefour_2)## Unit_price Quantity Tax cogs gross_income Rating Total
## 1: 74.69 7 26.1415 522.83 26.1415 9.1 548.9715
## 2: 15.28 5 3.8200 76.40 3.8200 9.6 80.2200
## 3: 46.33 7 16.2155 324.31 16.2155 7.4 340.5255
## 4: 58.22 8 23.2880 465.76 23.2880 8.4 489.0480
## 5: 86.31 7 30.2085 604.17 30.2085 5.3 634.3785
## 6: 85.39 7 29.8865 597.73 29.8865 4.1 627.6165Libraries
library(caret)
library(corrplot)Calculating the correlation matrix
correlationMatrix <- cor(carrefour_2)
# Find attributes that are highly correlated
highlyCorrelated <- findCorrelation(correlationMatrix, cutoff=0.75)
head(highlyCorrelated)## [1] 4 7 3head(carrefour_2)## Unit_price Quantity Tax cogs gross_income Rating Total
## 1: 74.69 7 26.1415 522.83 26.1415 9.1 548.9715
## 2: 15.28 5 3.8200 76.40 3.8200 9.6 80.2200
## 3: 46.33 7 16.2155 324.31 16.2155 7.4 340.5255
## 4: 58.22 8 23.2880 465.76 23.2880 8.4 489.0480
## 5: 86.31 7 30.2085 604.17 30.2085 5.3 634.3785
## 6: 85.39 7 29.8865 597.73 29.8865 4.1 627.6165Variables that are highly correlated are at index 4,7 and 3 they include; tax, cogs and total.
We will remove these variables then compare the results graphically.
# Removing Redundant Features
carrefour_22 <-carrefour_2[,-c(3,4,7)]
head(carrefour_22)## Unit_price Quantity gross_income Rating
## 1: 74.69 7 26.1415 9.1
## 2: 15.28 5 3.8200 9.6
## 3: 46.33 7 16.2155 7.4
## 4: 58.22 8 23.2880 8.4
## 5: 86.31 7 30.2085 5.3
## 6: 85.39 7 29.8865 4.1correlationmatrix <- cor(carrefour_22)
# Performing our graphical comparison
par(mfrow = c(1, 2))
corrplot(correlationMatrix, order = "hclust")
corrplot(correlationmatrix, order = "hclust")
Conclusion.
Important features for this research are unit price, quantity, gross income and rating.