#Kira Plastinina customer behaviour analysis
1.Objective:
a.) Understanding Customer Behavior.
b.) Understanding the characteristics of customer groups.2.Problem Statement.
Businesses in this day and time have learnt the power of leveraging data for product optimization and also to channel resources where they are needed. Unorthodox methods of marketing and assessing customer behavior based on gut feeling and guesses have led to losses in companies and also many have lost customers to companies that understand their customers in a deeper level.In this scope of analysis we will delve in understanding customer behavior and cluster them to groups for optimum product development through effective marketing strategies.
3.) Data Sourcing.
This data was sourced from kira Plastina’s ecommerce website as the product is sold through a chain of retail stores in Russia,Ukraine,Kazakhstan,Belarus,Chins,Philipines and Armenia.
4. Experimental Design
This scope of analysis will involve reviewing data to inspect its fittness to carry out analysis. This will later be followed by a deep analysis of the variables present in our data while understanding customers behaviour from these results. The solution will finaly be implemented by segemting the customers into similar clusters and reviewing the unique characteristics of these groups.
5.) Checking the data.
# Viewing the data structures and its constituents
library(data.table)
customer_df<-fread("http://bit.ly/EcommerceCustomersDataset")
head(customer_df)## Administrative Administrative_Duration Informational Informational_Duration
## 1: 0 0 0 0
## 2: 0 0 0 0
## 3: 0 -1 0 -1
## 4: 0 0 0 0
## 5: 0 0 0 0
## 6: 0 0 0 0
## ProductRelated ProductRelated_Duration BounceRates ExitRates PageValues
## 1: 1 0.000000 0.20000000 0.2000000 0
## 2: 2 64.000000 0.00000000 0.1000000 0
## 3: 1 -1.000000 0.20000000 0.2000000 0
## 4: 2 2.666667 0.05000000 0.1400000 0
## 5: 10 627.500000 0.02000000 0.0500000 0
## 6: 19 154.216667 0.01578947 0.0245614 0
## SpecialDay Month OperatingSystems Browser Region TrafficType
## 1: 0 Feb 1 1 1 1
## 2: 0 Feb 2 2 1 2
## 3: 0 Feb 4 1 9 3
## 4: 0 Feb 3 2 2 4
## 5: 0 Feb 3 3 1 4
## 6: 0 Feb 2 2 1 3
## VisitorType Weekend Revenue
## 1: Returning_Visitor FALSE FALSE
## 2: Returning_Visitor FALSE FALSE
## 3: Returning_Visitor FALSE FALSE
## 4: Returning_Visitor FALSE FALSE
## 5: Returning_Visitor TRUE FALSE
## 6: Returning_Visitor FALSE FALSE# The data is comprised of 10 numerical and 8 categorical variables. In them contains the nature of their operation in the e commerce website.str(customer_df)## Classes 'data.table' and 'data.frame': 12330 obs. of 18 variables:
## $ Administrative : int 0 0 0 0 0 0 0 1 0 0 ...
## $ Administrative_Duration: num 0 0 -1 0 0 0 -1 -1 0 0 ...
## $ Informational : int 0 0 0 0 0 0 0 0 0 0 ...
## $ Informational_Duration : num 0 0 -1 0 0 0 -1 -1 0 0 ...
## $ ProductRelated : int 1 2 1 2 10 19 1 1 2 3 ...
## $ ProductRelated_Duration: num 0 64 -1 2.67 627.5 ...
## $ BounceRates : num 0.2 0 0.2 0.05 0.02 ...
## $ ExitRates : num 0.2 0.1 0.2 0.14 0.05 ...
## $ PageValues : num 0 0 0 0 0 0 0 0 0 0 ...
## $ SpecialDay : num 0 0 0 0 0 0 0.4 0 0.8 0.4 ...
## $ Month : chr "Feb" "Feb" "Feb" "Feb" ...
## $ OperatingSystems : int 1 2 4 3 3 2 2 1 2 2 ...
## $ Browser : int 1 2 1 2 3 2 4 2 2 4 ...
## $ Region : int 1 1 9 2 1 1 3 1 2 1 ...
## $ TrafficType : int 1 2 3 4 4 3 3 5 3 2 ...
## $ VisitorType : chr "Returning_Visitor" "Returning_Visitor" "Returning_Visitor" "Returning_Visitor" ...
## $ Weekend : logi FALSE FALSE FALSE FALSE TRUE FALSE ...
## $ Revenue : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
## - attr(*, ".internal.selfref")=<externalptr># Checking the dimension of my dataset
dim(customer_df)## [1] 12330 18# My dataset has 12330 rows and 18 columns.6.) Cleaning data
Duplicated values
# Duplicated values
length(which(duplicated.data.frame(customer_df)))## [1] 119# 119 rows are duplicated# Dealing with duplicated rows
customer_df<-customer_df[!duplicated(customer_df),]
head(customer_df)## Administrative Administrative_Duration Informational Informational_Duration
## 1: 0 0 0 0
## 2: 0 0 0 0
## 3: 0 -1 0 -1
## 4: 0 0 0 0
## 5: 0 0 0 0
## 6: 0 0 0 0
## ProductRelated ProductRelated_Duration BounceRates ExitRates PageValues
## 1: 1 0.000000 0.20000000 0.2000000 0
## 2: 2 64.000000 0.00000000 0.1000000 0
## 3: 1 -1.000000 0.20000000 0.2000000 0
## 4: 2 2.666667 0.05000000 0.1400000 0
## 5: 10 627.500000 0.02000000 0.0500000 0
## 6: 19 154.216667 0.01578947 0.0245614 0
## SpecialDay Month OperatingSystems Browser Region TrafficType
## 1: 0 Feb 1 1 1 1
## 2: 0 Feb 2 2 1 2
## 3: 0 Feb 4 1 9 3
## 4: 0 Feb 3 2 2 4
## 5: 0 Feb 3 3 1 4
## 6: 0 Feb 2 2 1 3
## VisitorType Weekend Revenue
## 1: Returning_Visitor FALSE FALSE
## 2: Returning_Visitor FALSE FALSE
## 3: Returning_Visitor FALSE FALSE
## 4: Returning_Visitor FALSE FALSE
## 5: Returning_Visitor TRUE FALSE
## 6: Returning_Visitor FALSE FALSE# Confirming the operation
length(which(duplicated.data.frame(customer_df)))## [1] 0# No duplicated values presentMissing values
# Checking for missing values
length(which(is.na.data.frame(customer_df)))## [1] 96# We have 96 missing valuesChecking specific columns with the missing values
# Checking the missing number count
customer_df[!complete.cases(customer_df),]## Administrative Administrative_Duration Informational Informational_Duration
## 1: NA NA NA NA
## 2: NA NA NA NA
## 3: NA NA NA NA
## 4: NA NA NA NA
## 5: NA NA NA NA
## 6: NA NA NA NA
## 7: NA NA NA NA
## 8: NA NA NA NA
## 9: NA NA NA NA
## 10: NA NA NA NA
## 11: NA NA NA NA
## 12: NA NA NA NA
## ProductRelated ProductRelated_Duration BounceRates ExitRates PageValues
## 1: NA NA NA NA 0
## 2: NA NA NA NA 0
## 3: NA NA NA NA 0
## 4: NA NA NA NA 0
## 5: NA NA NA NA 0
## 6: NA NA NA NA 0
## 7: NA NA NA NA 0
## 8: NA NA NA NA 0
## 9: NA NA NA NA 0
## 10: NA NA NA NA 0
## 11: NA NA NA NA 0
## 12: NA NA NA NA 0
## SpecialDay Month OperatingSystems Browser Region TrafficType
## 1: 0 Mar 2 2 2 1
## 2: 0 Mar 1 1 1 2
## 3: 0 Mar 2 4 5 1
## 4: 0 Mar 2 2 1 2
## 5: 0 Mar 3 2 1 1
## 6: 0 Mar 2 2 1 1
## 7: 0 Mar 1 1 6 1
## 8: 0 Mar 2 2 3 1
## 9: 0 Mar 1 1 2 3
## 10: 0 Mar 3 2 4 1
## 11: 0 Mar 3 2 4 15
## 12: 0 May 2 2 4 13
## VisitorType Weekend Revenue
## 1: Returning_Visitor FALSE FALSE
## 2: Returning_Visitor FALSE FALSE
## 3: Returning_Visitor FALSE FALSE
## 4: Returning_Visitor FALSE FALSE
## 5: Returning_Visitor FALSE FALSE
## 6: Returning_Visitor TRUE FALSE
## 7: Returning_Visitor TRUE FALSE
## 8: Returning_Visitor FALSE FALSE
## 9: Returning_Visitor FALSE FALSE
## 10: Returning_Visitor FALSE FALSE
## 11: Returning_Visitor TRUE FALSE
## 12: Returning_Visitor FALSE FALSE# the are quite a lot to get rid off and they carry much data involving site informationUsing the mice function to get a better understanding of missing data.
library(mice)##
## Attaching package: 'mice'## The following objects are masked from 'package:base':
##
## cbind, rbindmd.pattern(customer_df,plot = TRUE,rotate.names = TRUE)
## PageValues SpecialDay Month OperatingSystems Browser Region TrafficType
## 12199 1 1 1 1 1 1 1
## 12 1 1 1 1 1 1 1
## 0 0 0 0 0 0 0
## VisitorType Weekend Revenue Administrative Administrative_Duration
## 12199 1 1 1 1 1
## 12 1 1 1 0 0
## 0 0 0 12 12
## Informational Informational_Duration ProductRelated
## 12199 1 1 1
## 12 0 0 0
## 12 12 12
## ProductRelated_Duration BounceRates ExitRates
## 12199 1 1 1 0
## 12 0 0 0 8
## 12 12 12 96# We get to understand that there are a total of 96 missing values in the dataset and they are mainly found in the administrative,Informational,product related ,bounce rates and exit rates sites. Their proportions also are similar in magnitude.Imputing them using the mice function
# Assessing the imputation methods for mice
methods(mice)## Warning in .S3methods(generic.function, class, envir): function 'mice' appears
## not to be S3 generic; found functions that look like S3 methods## [1] mice.impute.2l.bin mice.impute.2l.lmer mice.impute.2l.norm
## [4] mice.impute.2l.pan mice.impute.2lonly.mean mice.impute.2lonly.norm
## [7] mice.impute.2lonly.pmm mice.impute.cart mice.impute.jomoImpute
## [10] mice.impute.lda mice.impute.logreg mice.impute.logreg.boot
## [13] mice.impute.mean mice.impute.midastouch mice.impute.mnar.logreg
## [16] mice.impute.mnar.norm mice.impute.norm mice.impute.norm.boot
## [19] mice.impute.norm.nob mice.impute.norm.predict mice.impute.panImpute
## [22] mice.impute.passive mice.impute.pmm mice.impute.polr
## [25] mice.impute.polyreg mice.impute.quadratic mice.impute.rf
## [28] mice.impute.ri mice.impute.sample mice.mids
## [31] mice.theme
## see '?methods' for accessing help and source codeImputing using the mice function
# Using random forest as the imputation method as most of our missing values are discrete
customer_df_full<-mice(customer_df,m=5,
method="rf",
maxit = 10,seed = 500)##
## iter imp variable
## 1 1 Administrative Administrative_Duration Informational Informational_Duration ProductRelated ProductRelated_Duration BounceRates ExitRates
## 1 2 Administrative Administrative_Duration Informational Informational_Duration ProductRelated ProductRelated_Duration BounceRates ExitRates
## 1 3 Administrative Administrative_Duration Informational Informational_Duration ProductRelated ProductRelated_Duration BounceRates ExitRates
## 1 4 Administrative Administrative_Duration Informational Informational_Duration ProductRelated ProductRelated_Duration BounceRates ExitRates
## 1 5 Administrative Administrative_Duration Informational Informational_Duration ProductRelated ProductRelated_Duration BounceRates ExitRates
## 2 1 Administrative Administrative_Duration Informational Informational_Duration ProductRelated ProductRelated_Duration BounceRates ExitRates
## 2 2 Administrative Administrative_Duration Informational Informational_Duration ProductRelated ProductRelated_Duration BounceRates ExitRates
## 2 3 Administrative Administrative_Duration Informational Informational_Duration ProductRelated ProductRelated_Duration BounceRates ExitRates
## 2 4 Administrative Administrative_Duration Informational Informational_Duration ProductRelated ProductRelated_Duration BounceRates ExitRates
## 2 5 Administrative Administrative_Duration Informational Informational_Duration ProductRelated ProductRelated_Duration BounceRates ExitRates
## 3 1 Administrative Administrative_Duration Informational Informational_Duration ProductRelated ProductRelated_Duration BounceRates ExitRates
## 3 2 Administrative Administrative_Duration Informational Informational_Duration ProductRelated ProductRelated_Duration BounceRates ExitRates
## 3 3 Administrative Administrative_Duration Informational Informational_Duration ProductRelated ProductRelated_Duration BounceRates ExitRates
## 3 4 Administrative Administrative_Duration Informational Informational_Duration ProductRelated ProductRelated_Duration BounceRates ExitRates
## 3 5 Administrative Administrative_Duration Informational Informational_Duration ProductRelated ProductRelated_Duration BounceRates ExitRates
## 4 1 Administrative Administrative_Duration Informational Informational_Duration ProductRelated ProductRelated_Duration BounceRates ExitRates
## 4 2 Administrative Administrative_Duration Informational Informational_Duration ProductRelated ProductRelated_Duration BounceRates ExitRates
## 4 3 Administrative Administrative_Duration Informational Informational_Duration ProductRelated ProductRelated_Duration BounceRates ExitRates
## 4 4 Administrative Administrative_Duration Informational Informational_Duration ProductRelated ProductRelated_Duration BounceRates ExitRates
## 4 5 Administrative Administrative_Duration Informational Informational_Duration ProductRelated ProductRelated_Duration BounceRates ExitRates
## 5 1 Administrative Administrative_Duration Informational Informational_Duration ProductRelated ProductRelated_Duration BounceRates ExitRates
## 5 2 Administrative Administrative_Duration Informational Informational_Duration ProductRelated ProductRelated_Duration BounceRates ExitRates
## 5 3 Administrative Administrative_Duration Informational Informational_Duration ProductRelated ProductRelated_Duration BounceRates ExitRates
## 5 4 Administrative Administrative_Duration Informational Informational_Duration ProductRelated ProductRelated_Duration BounceRates ExitRates
## 5 5 Administrative Administrative_Duration Informational Informational_Duration ProductRelated ProductRelated_Duration BounceRates ExitRates
## 6 1 Administrative Administrative_Duration Informational Informational_Duration ProductRelated ProductRelated_Duration BounceRates ExitRates
## 6 2 Administrative Administrative_Duration Informational Informational_Duration ProductRelated ProductRelated_Duration BounceRates ExitRates
## 6 3 Administrative Administrative_Duration Informational Informational_Duration ProductRelated ProductRelated_Duration BounceRates ExitRates
## 6 4 Administrative Administrative_Duration Informational Informational_Duration ProductRelated ProductRelated_Duration BounceRates ExitRates
## 6 5 Administrative Administrative_Duration Informational Informational_Duration ProductRelated ProductRelated_Duration BounceRates ExitRates
## 7 1 Administrative Administrative_Duration Informational Informational_Duration ProductRelated ProductRelated_Duration BounceRates ExitRates
## 7 2 Administrative Administrative_Duration Informational Informational_Duration ProductRelated ProductRelated_Duration BounceRates ExitRates
## 7 3 Administrative Administrative_Duration Informational Informational_Duration ProductRelated ProductRelated_Duration BounceRates ExitRates
## 7 4 Administrative Administrative_Duration Informational Informational_Duration ProductRelated ProductRelated_Duration BounceRates ExitRates
## 7 5 Administrative Administrative_Duration Informational Informational_Duration ProductRelated ProductRelated_Duration BounceRates ExitRates
## 8 1 Administrative Administrative_Duration Informational Informational_Duration ProductRelated ProductRelated_Duration BounceRates ExitRates
## 8 2 Administrative Administrative_Duration Informational Informational_Duration ProductRelated ProductRelated_Duration BounceRates ExitRates
## 8 3 Administrative Administrative_Duration Informational Informational_Duration ProductRelated ProductRelated_Duration BounceRates ExitRates
## 8 4 Administrative Administrative_Duration Informational Informational_Duration ProductRelated ProductRelated_Duration BounceRates ExitRates
## 8 5 Administrative Administrative_Duration Informational Informational_Duration ProductRelated ProductRelated_Duration BounceRates ExitRates
## 9 1 Administrative Administrative_Duration Informational Informational_Duration ProductRelated ProductRelated_Duration BounceRates ExitRates
## 9 2 Administrative Administrative_Duration Informational Informational_Duration ProductRelated ProductRelated_Duration BounceRates ExitRates
## 9 3 Administrative Administrative_Duration Informational Informational_Duration ProductRelated ProductRelated_Duration BounceRates ExitRates
## 9 4 Administrative Administrative_Duration Informational Informational_Duration ProductRelated ProductRelated_Duration BounceRates ExitRates
## 9 5 Administrative Administrative_Duration Informational Informational_Duration ProductRelated ProductRelated_Duration BounceRates ExitRates
## 10 1 Administrative Administrative_Duration Informational Informational_Duration ProductRelated ProductRelated_Duration BounceRates ExitRates
## 10 2 Administrative Administrative_Duration Informational Informational_Duration ProductRelated ProductRelated_Duration BounceRates ExitRates
## 10 3 Administrative Administrative_Duration Informational Informational_Duration ProductRelated ProductRelated_Duration BounceRates ExitRates
## 10 4 Administrative Administrative_Duration Informational Informational_Duration ProductRelated ProductRelated_Duration BounceRates ExitRates
## 10 5 Administrative Administrative_Duration Informational Informational_Duration ProductRelated ProductRelated_Duration BounceRates ExitRates## Warning: Number of logged events: 2# Getting a summary of the full set
summary(customer_df_full) ## Class: mids
## Number of multiple imputations: 5
## Imputation methods:
## Administrative Administrative_Duration Informational
## "rf" "rf" "rf"
## Informational_Duration ProductRelated ProductRelated_Duration
## "rf" "rf" "rf"
## BounceRates ExitRates PageValues
## "rf" "rf" ""
## SpecialDay Month OperatingSystems
## "" "" ""
## Browser Region TrafficType
## "" "" ""
## VisitorType Weekend Revenue
## "" "" ""
## PredictorMatrix:
## Administrative Administrative_Duration Informational
## Administrative 0 1 1
## Administrative_Duration 1 0 1
## Informational 1 1 0
## Informational_Duration 1 1 1
## ProductRelated 1 1 1
## ProductRelated_Duration 1 1 1
## Informational_Duration ProductRelated
## Administrative 1 1
## Administrative_Duration 1 1
## Informational 1 1
## Informational_Duration 0 1
## ProductRelated 1 0
## ProductRelated_Duration 1 1
## ProductRelated_Duration BounceRates ExitRates
## Administrative 1 1 1
## Administrative_Duration 1 1 1
## Informational 1 1 1
## Informational_Duration 1 1 1
## ProductRelated 1 1 1
## ProductRelated_Duration 0 1 1
## PageValues SpecialDay Month OperatingSystems Browser
## Administrative 1 1 0 1 1
## Administrative_Duration 1 1 0 1 1
## Informational 1 1 0 1 1
## Informational_Duration 1 1 0 1 1
## ProductRelated 1 1 0 1 1
## ProductRelated_Duration 1 1 0 1 1
## Region TrafficType VisitorType Weekend Revenue
## Administrative 1 1 0 1 1
## Administrative_Duration 1 1 0 1 1
## Informational 1 1 0 1 1
## Informational_Duration 1 1 0 1 1
## ProductRelated 1 1 0 1 1
## ProductRelated_Duration 1 1 0 1 1
## Number of logged events: 2
## it im dep meth out
## 1 0 0 constant Month
## 2 0 0 constant VisitorType# The imputation was successful with five complete data sets using random forest method.Getting the first dataset and visualizing the imputed values against my observed values to check for plausibility of the imputations
complete_df<-complete(customer_df_full,1)
# Checking for missing values
length(which(is.na.data.frame(complete_df)))## [1] 0# No missing value presenttable(complete_df$Administrative)##
## 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
## 5647 1355 1114 915 766 575 432 338 287 225 153 105 86 56 44 38
## 16 17 18 19 20 21 22 23 24 26 27
## 24 16 12 6 2 2 4 3 4 1 1plot(table(complete_df$Administrative))
# Checking the unique values of distinct values in the original table with missing values
original<-customer_df$Administrative
# Checking the imputed values
customer_df_full$imp$Administrative## 1 2 3 4 5
## 1050 0 3 0 0 1
## 1116 0 1 1 2 0
## 1117 4 0 2 0 0
## 1118 0 0 1 0 0
## 1119 0 0 0 0 4
## 1443 0 0 0 0 0
## 1444 0 0 0 0 0
## 1445 0 0 0 0 0
## 1446 0 0 0 0 0
## 1996 0 0 0 0 0
## 1997 1 0 0 0 0
## 2706 0 0 0 0 0# Checking the new value
imputed<-complete_df$Administrativeboxplot(original,imputed,main="Checking for distribution of original vs imputed",xlab="original and imputed",ylab="Distribution within set")
# The distribution is similar, that confirms the plausibility of the imputed values# Checking the unique values of distinct values in the original table with missing values
original_ex<-customer_df$ExitRates
# Checking the imputed values
customer_df_full$imp$ExitRates## 1 2 3 4 5
## 1050 0.20000000 0.01176471 0.20000000 0.025000000 0.03287281
## 1116 0.20000000 0.04000000 0.00000000 0.009090909 0.01666667
## 1117 0.00952381 0.01818182 0.05000000 0.022222222 0.02500000
## 1118 0.03600000 0.04166667 0.03333333 0.100000000 0.02000000
## 1119 0.06500000 0.05777778 0.20000000 0.034074074 0.02571429
## 1443 0.02500000 0.02222222 0.10000000 0.040000000 0.10000000
## 1444 0.01777778 0.10000000 0.10000000 0.008333333 0.04000000
## 1445 0.06666667 0.06666667 0.02500000 0.007407407 0.06666667
## 1446 0.01666667 0.03750000 0.20000000 0.100000000 0.05000000
## 1996 0.20000000 0.13194444 0.03333333 0.200000000 0.00000000
## 1997 0.10000000 0.04000000 0.03333333 0.008695652 0.20000000
## 2706 0.01666667 0.04000000 0.06666667 0.075000000 0.06666667# Checking the new value
imputed_ex<-complete_df$ExitRates# Checking the distribution between the original with null values and the imputed values to check for similarity
boxplot(original_ex,imputed_ex,main="Checking for distribution of original vs imputed",xlab="original and imputed",ylab="Distribution within set")
### Checking for Outliers and anomalies
# Rechecking the clean and complete dataset
head(complete_df)## Administrative Administrative_Duration Informational Informational_Duration
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 -1 0 -1
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## ProductRelated ProductRelated_Duration BounceRates ExitRates PageValues
## 1 1 0.000000 0.20000000 0.2000000 0
## 2 2 64.000000 0.00000000 0.1000000 0
## 3 1 -1.000000 0.20000000 0.2000000 0
## 4 2 2.666667 0.05000000 0.1400000 0
## 5 10 627.500000 0.02000000 0.0500000 0
## 6 19 154.216667 0.01578947 0.0245614 0
## SpecialDay Month OperatingSystems Browser Region TrafficType
## 1 0 Feb 1 1 1 1
## 2 0 Feb 2 2 1 2
## 3 0 Feb 4 1 9 3
## 4 0 Feb 3 2 2 4
## 5 0 Feb 3 3 1 4
## 6 0 Feb 2 2 1 3
## VisitorType Weekend Revenue
## 1 Returning_Visitor FALSE FALSE
## 2 Returning_Visitor FALSE FALSE
## 3 Returning_Visitor FALSE FALSE
## 4 Returning_Visitor FALSE FALSE
## 5 Returning_Visitor TRUE FALSE
## 6 Returning_Visitor FALSE FALSENumerical varables
# Rechecking numeric datatypes
str(complete_df)## 'data.frame': 12211 obs. of 18 variables:
## $ Administrative : int 0 0 0 0 0 0 0 1 0 0 ...
## $ Administrative_Duration: num 0 0 -1 0 0 0 -1 -1 0 0 ...
## $ Informational : int 0 0 0 0 0 0 0 0 0 0 ...
## $ Informational_Duration : num 0 0 -1 0 0 0 -1 -1 0 0 ...
## $ ProductRelated : int 1 2 1 2 10 19 1 1 2 3 ...
## $ ProductRelated_Duration: num 0 64 -1 2.67 627.5 ...
## $ BounceRates : num 0.2 0 0.2 0.05 0.02 ...
## $ ExitRates : num 0.2 0.1 0.2 0.14 0.05 ...
## $ PageValues : num 0 0 0 0 0 0 0 0 0 0 ...
## $ SpecialDay : num 0 0 0 0 0 0 0.4 0 0.8 0.4 ...
## $ Month : chr "Feb" "Feb" "Feb" "Feb" ...
## $ OperatingSystems : int 1 2 4 3 3 2 2 1 2 2 ...
## $ Browser : int 1 2 1 2 3 2 4 2 2 4 ...
## $ Region : int 1 1 9 2 1 1 3 1 2 1 ...
## $ TrafficType : int 1 2 3 4 4 3 3 5 3 2 ...
## $ VisitorType : chr "Returning_Visitor" "Returning_Visitor" "Returning_Visitor" "Returning_Visitor" ...
## $ Weekend : logi FALSE FALSE FALSE FALSE TRUE FALSE ...
## $ Revenue : logi FALSE FALSE FALSE FALSE FALSE FALSE ...A function to plot a box plot for numerical values and detect outliers
# Boxplot of the individual variables
box_plot<-function(data,var,main){
boxplot(data[[var]],ylab="Distribution of values",main=main)
}a.)
# A box plot of Administrative distribution
box_plot(complete_df,1,"A box plot of the distribution of Administrative page")
# This represent the number of administrative pages visited by a visited in that session.
# Most customers visited less than 5 administrative sites. It is also possible for them to visit more than 25 pages . This set wont be treated as an outlier. Users if this page could be actual administrators and maybe customers.Checking the extent of outliers
# Viewing the distribution of outliers
outs<-boxplot.stats(complete_df$Administrative)$out
print(length(outs))## [1] 404plot(outs,main="Administration of Outliers",col="blue")
# 404 data points are considered outliers out of the 12,330 points.box_plot(complete_df,2,"A box plot of the distribution of Administrative Duration")
# The outliers present here are absolutely extreme. Its however important to note that time spent on site could be in seconds thus 3600 translates to an hour. Thus , for now, the data wont be treated as an outlier. Users of this page could be users on an administrative levels and can validly spend time there.# Checking for the count of outliers
outs1<-boxplot.stats(complete_df$Administrative_Duration)$out
print(length(outs1))## [1] 1149# This are too many to be removed# Outliers detection in admin pages and duration
min(complete_df$Administrative)## [1] 0min(complete_df$Administrative_Duration)## [1] -1max(complete_df$Administrative_Duration)## [1] 3398.75# -1 for a duration is an illegitimate data point thus this anomaly will be dealt with by imputing them with values given from their quantile distributionmin(complete_df$Administrative_Duration)## [1] -1#max(complete_df$Administrative_Duration)The operation to remove the lower quantile of outliers did not change the minimum from -1 and replacing the values past the 3rd quantile drops them to lower than 300. This greately tampers with the actual distribution of the data.
# Informational pages
box_plot(complete_df,3,"A box plot of the distribution of Informational page")
# The pages visited vary from 0 to 20 . The points stated as outliers will be treated as legitimate points because informational pages can genuinely be 20 . Its however evident that most people dont spent time in the informational pageout2<-boxplot.stats(complete_df$Informational)$out
print(length(out2))## [1] 2630# These are too many to be removed. They wont be replaced as id consider them legitimate data points# Checking for outliers in the time spent in the informational page
box_plot(complete_df,4,"A box plot of the distribution of Informational Duration")
# As stated formally in the administrative duration the units could be small enough and time is a continuously growing variable thus will not be removedbox_plot(complete_df,5,"A box plot of the distribution of Product Related pages")
# Depending on the amount of product this e commerce website seem to have a lot of pages. Its extreme to have 700 pages visited per session but considering that some visitors rarely log out of these pages .# Duration spent per page
box_plot(complete_df,6,"A box plot of the distribution of Product Related Duration")
# The high numbers stated here are totally valid as this time is measured per page and considering that these pages are alot the time spent on each page would considerable be expected to be high.# Checking for an anomaly in product duration
min(complete_df$ProductRelated_Duration)## [1] -1# This is an anomaly to be dealt with# Distribution of bounce rates.These are the % of visitors who enter the site and then drop off without triggering any request.
box_plot(complete_df,7,"A box plot of the distribution of Bounce Rates")
# They vary from 0 to 20 %. The points are genuine thus will not be eliminated.min(complete_df$BounceRates)## [1] 0# % of considerably last session
box_plot(complete_df,8,"A box plot of the distribution of Exit Rates")
# The higher the exit rate, the more likely it is for one to leave the session.min(complete_df$ExitRates)## [1] 0# Page value for a webpage that a user visited before completing the e commerce transaction.
box_plot(complete_df,9,"A box plot of the distribution of Page Values")
# Its interesting to discover that page value range from 0 to more than 300. The choice NOT to remove the product pages now totally makes sense.# These is the closeness of the site visiting time to a special day
box_plot(complete_df,10,"A box plot of the distribution of Special Days")
# Its maximum when we are close to a special day and close to 1 and minimum when not close to a soecial day (0).We can therefore conclude from the outliers that.
- The pages visited depend on the content posted in the e commerce website. The extreme values detected thus cannot be treated as outliers.
- A challenge can however be seen in minimum duration being - 1 and thus can be treated as outliers and will be handles by replacing them by zero since imputation by quantile values did not work.
Dealing with anomalies
# Replacing duration of -1 with zero
complete_df[complete_df$Administrative_Duration==-1,"Administrative_Duration"]<-0
# Confirming the operation
min(complete_df$Administrative_Duration)## [1] 0complete_df[complete_df$Informational_Duration==-1,"Informational_Duration"]<-0
# Confirming the operation
min(complete_df$Informational_Duration)## [1] 0complete_df[complete_df$ProductRelated_Duration==-1,"ProductRelated_Duration"]<-0
# Confirming the operation
min(complete_df$ProductRelated_Duration)## [1] 0Outlier or anomaly detection in Discrete values
unique(complete_df$Month)## [1] "Feb" "Mar" "May" "Oct" "June" "Jul" "Aug" "Nov" "Sep" "Dec"# We don't have the month of January and April. That's an anomaly that we are constrained on replacing for this scope of the analysis# Checking for anomalies in the visitor type page
unique(complete_df$VisitorType)## [1] "Returning_Visitor" "New_Visitor" "Other"# There are three categories Regulars,New_visitors and other who i would consider to be developers or administrators etc# Weekday weekend status
unique(complete_df$Weekend)## [1] FALSE TRUE# No anomaly detected# Revenue status
unique(complete_df$Revenue)## [1] FALSE TRUE# No anomaly detected. I would however guess that this are the users that add revenue into the company.# Checking for outliers in the OS,Browser, and traffic type columns
unique(complete_df$OperatingSystems)## [1] 1 2 4 3 7 6 8 5# No anomaly detected. This would be considered ad the different types of operating systems used.# Checking for anomalies in the browser type and region factors
unique(complete_df$Browser)## [1] 1 2 3 4 5 6 7 10 8 9 12 13 11unique(complete_df$Region)## [1] 1 9 2 3 4 5 6 7 8unique(complete_df$TrafficType)## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 18 19 16 17 20# No anomalies detected7.) Exploratory data Analysis.
a.) Univariate Analysis.
Numerical Continuous values
A function to plot Density plot
density_plot<-function(data,var,main){
plot(density(data[[var]]),ylab="Density of visitors",main=main)
polygon(density(data[[var]]),col = "blue",border="red")
}# Administrative plots
density_plot(complete_df,1,"Distribution of visitors in Administration page")
# The distribution of the number of pages visited in the administration section of the site
# The distribution is not gausian. The values are skewed to the left indicating many visitors don't visit many pages in the administration pages. The same is expected to be reflected in the duration distribution# Duration distribution
density_plot(complete_df,2,"Distribution of Duration per Administration page")
# Time spent in the administration pages is skewed to the left. Most visitors dont spent time here.# Informative page distribution
density_plot(complete_df,3,"Distribution of vistors in informative pages")
# The distribution is multi modal but skewed to the left as many people don't visit informative pages but they are considerably more than those who visit the admin pages. Some people go to as far as 25 pages in information# Informative page distribution
density_plot(complete_df,4,"Distribution of time spent in informative pages")
# The distribution is skewed to to left and time spent is seen to be on the lower side# Product page distribution
density_plot(complete_df,5,"Distribution of visitors on Product related pages")
# Though skewed to the left. The number of people is considerable higher than the rest of the pages. The product seem to be very concentrated as pages go to as high as 700 .# Product page distribution
density_plot(complete_df,6,"Distribution of time spent on Product related pages")
# This is also skewed to the left meaning its concentrated on the lower end. We however have visitors spending as much as 60,000 units(unspecified) of time on the product related page# Distribution of Bounce rates
density_plot(complete_df,7,"Distribution of Bounce rates")
# Alot of users don't bounce off the site as soon as they visit it as the rate is skewed to the left. We have a few individuals however who bounce off site considerably at a rate of 20 % .# Distribution of Exit rates. calculated by (pages visited/ Total pages)
density_plot(complete_df,8,"Distribution of Exit rates")
# The distribution is fairly Gaussian with irregular mode values. The highest rate however is on the lower side.This could only mean that many people peruse more pages of our site, especially considering the denisty of pages in the product related site. Its important to note that the exit rate can be used to denote the likelihood of the page being the last. The higher the rate the higher the chances of that particular page being the page being the last. We will however view this in the bi-variate analysis while assessing its relation with other variables.# Distribution of Page values
density_plot(complete_df,9,"Distribution of Page values")
# This is the average value of a webpage that a user visited before making a complete transaction. The higher the page value the higher the rate of complete transactions# Distribution of Special days- closeness of a sites visiting time to a special day
density_plot(complete_df,10,"Distribution of Special day proximity")
# Very few sites are close to special days. We will see their relationship with the months in bivariate analysis.Discrete Variables
barplot(table(complete_df$Month),main="Distribution of months",col = "blue",ylab = "count of visitors")
# May has the highest visitors while February has the lowest count of users. That is an interesting discovery.barplot(table(complete_df$VisitorType),main="Distribution of Visitor Type",col = "blue",ylab = "Count of visitors")
# Returning customers are higher in count than new customers. # Weekday status distribution
barplot(table(complete_df$Weekend),main="Weekend status",col = "blue",ylab = "Visitor count")
# More users tend to visit the site during the weekdays as compared to weekends # Here we get to determine whether the users boil down to out traffic or not
barplot(table(complete_df$Revenue),main="Revenue Status",col = "blue",ylab = "Visitor count")
# Our users are not translating their activity on site to revenue, which is the main reason for our analysis. Taking a deeper look at our analysis we wll know who generates more revenue and why we are not generating revenue generaly.Bivariate analysis
Numerical- Numerical
# Fishing out numerical columns to assess their correlation
library("dplyr") ## Warning: package 'dplyr' was built under R version 4.0.5##
## Attaching package: 'dplyr'## The following objects are masked from 'package:data.table':
##
## between, first, last## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionnum_complete<-select_if(complete_df,is.numeric)
str(num_complete)## 'data.frame': 12211 obs. of 14 variables:
## $ Administrative : int 0 0 0 0 0 0 0 1 0 0 ...
## $ Administrative_Duration: num 0 0 0 0 0 0 0 0 0 0 ...
## $ Informational : int 0 0 0 0 0 0 0 0 0 0 ...
## $ Informational_Duration : num 0 0 0 0 0 0 0 0 0 0 ...
## $ ProductRelated : int 1 2 1 2 10 19 1 1 2 3 ...
## $ ProductRelated_Duration: num 0 64 0 2.67 627.5 ...
## $ BounceRates : num 0.2 0 0.2 0.05 0.02 ...
## $ ExitRates : num 0.2 0.1 0.2 0.14 0.05 ...
## $ PageValues : num 0 0 0 0 0 0 0 0 0 0 ...
## $ SpecialDay : num 0 0 0 0 0 0 0.4 0 0.8 0.4 ...
## $ OperatingSystems : int 1 2 4 3 3 2 2 1 2 2 ...
## $ Browser : int 1 2 1 2 3 2 4 2 2 4 ...
## $ Region : int 1 1 9 2 1 1 3 1 2 1 ...
## $ TrafficType : int 1 2 3 4 4 3 3 5 3 2 ...# Assessing the correlation
library(corrplot)## corrplot 0.84 loadedcorrplot(cor(num_complete),method = "circle",type="upper")
# There is generally a high correlation between the site pages and their duration.
# There can be seen to be a slightly negative correlation between administrative pages visited and exit rate. The columns that i will visualize are those with a correlation between the whether positive or negative.It is inevitably exciting how these number reveal relationships.
- There is a considerable positive relationship between pages and their duration meaning there is a corresponding increase in duration when there is a rise in the pages visited, Which we would expect as the more we flip into a page the more time we get to spend on the same page. This transcends to (administration,Informational and product related pages).
- Another positive correlation is seen in the Exit rates and bounce rates. As i had explained earlier the higher the exit rate the higher the chances that that is the user’s last page. This would eventually cause them to bounce or leave the site.Very interesting discovery.
- An interestingly negative correlation is seen in the exit rates and the administrative page. It leads me to thinking that probably the administrative page is the first we encounter on a sight and thus the more the administrative pages the less likely it is that that is the users last page.
# Plotting these to see relationships
plot(complete_df$BounceRates,complete_df$ExitRates,main="Relationship between Bounce rates and Exit rates",col = "blue",xlab = "Bounce rates",ylab = "Exit rates")
# As was explained by the correlation map. The relationship is linear and positive.# Because there was a linear correlation we need to see the relationship between administrative and exit rate
plot(complete_df$Administrative,complete_df$ExitRates,main="Relationship between Administration and Exit rates",col = "blue",xlab = "Administrative Page",ylab = "Exit rates")
# As expected the relationship is inversely corresponding which leads me to thinking its the first page we encounter when we get into the sight. Numerical_Categorical
# Rechecking the datatypes
str(complete_df)## 'data.frame': 12211 obs. of 18 variables:
## $ Administrative : int 0 0 0 0 0 0 0 1 0 0 ...
## $ Administrative_Duration: num 0 0 0 0 0 0 0 0 0 0 ...
## $ Informational : int 0 0 0 0 0 0 0 0 0 0 ...
## $ Informational_Duration : num 0 0 0 0 0 0 0 0 0 0 ...
## $ ProductRelated : int 1 2 1 2 10 19 1 1 2 3 ...
## $ ProductRelated_Duration: num 0 64 0 2.67 627.5 ...
## $ BounceRates : num 0.2 0 0.2 0.05 0.02 ...
## $ ExitRates : num 0.2 0.1 0.2 0.14 0.05 ...
## $ PageValues : num 0 0 0 0 0 0 0 0 0 0 ...
## $ SpecialDay : num 0 0 0 0 0 0 0.4 0 0.8 0.4 ...
## $ Month : chr "Feb" "Feb" "Feb" "Feb" ...
## $ OperatingSystems : int 1 2 4 3 3 2 2 1 2 2 ...
## $ Browser : int 1 2 1 2 3 2 4 2 2 4 ...
## $ Region : int 1 1 9 2 1 1 3 1 2 1 ...
## $ TrafficType : int 1 2 3 4 4 3 3 5 3 2 ...
## $ VisitorType : chr "Returning_Visitor" "Returning_Visitor" "Returning_Visitor" "Returning_Visitor" ...
## $ Weekend : logi FALSE FALSE FALSE FALSE TRUE FALSE ...
## $ Revenue : logi FALSE FALSE FALSE FALSE FALSE FALSE ...# Assessing the proportion of true and false
table(complete_df$Revenue)##
## FALSE TRUE
## 10303 1908What are the factors that directly affect the revenue status of the e-commerce site?
library(ggplot2)## Warning: package 'ggplot2' was built under R version 4.0.5ggplot(complete_df,
aes(x = Administrative,
fill =Revenue)) +
geom_density(alpha = 0.4) +
labs(title = "Administrative page distribution with revenue status")
# Since we can see the distribution of administrative pages and revenue status. It is evident that when the pages are less, the revenue is densely populated in the FALSE meaning that at less administrative pages the revenue is mostly at false status. A paradigm shift is seen at higher pages where the revenue status is trueggplot(complete_df,
aes(x = Administrative_Duration,
fill =Revenue)) +
geom_density(alpha = 0.4) +
labs(title = "Duration in administrative page's relationship with revenue status")
# The status is the same as the correlation is positive. ggplot(complete_df,
aes(x = ProductRelated,
fill =Revenue)) +
geom_density(alpha = 0.4) +
labs(title = "Product related page relationship with revenue status")
# The product related page poses a clear relationship with revenue status. This clearly shows that as one moves more in the product related pages the count of TRUE revenues increase.# The relationship between bounce rates and revenue
ggplot(complete_df,
aes(x = BounceRates,
fill =Revenue)) +
geom_density(alpha = 0.4) +
labs(title = "Bounce rate's relationship with revenue status")
# It is evident that the less bounce rates translate to TRUE revenues and more bounce rates translate to NO /FALSE revenues.# The relationship between exit rates and revenue
ggplot(complete_df,
aes(x = ExitRates,
fill =Revenue)) +
geom_density(alpha = 0.4) +
labs(title = "Exit rate's relationship with revenue status")
# Low exit rate means that the visitor is less likely to be in their last page and thus the status is populated at TRUE unlike when the exit rate is high which means that the probability of being in their last page is high thus the revenue status is false# Relationship between page value and revenue status. Since the page value is the average value of the page a user visited before completing a transaction
ggplot(complete_df,
aes(x = PageValues,
fill =Revenue)) +
geom_density(alpha = 0.4) +
labs(title = "Page Value's relationship with revenue status")
# Higher page values translate to a TRUE revenue.Which months have a high special day rate
ggplot(complete_df,
aes(x = SpecialDay,
y=Month)) +
geom_boxplot() +
labs(title = "Special days relationship with months")
# May and Feb seem to have high special day rates. Multivariate analysis
str(complete_df)## 'data.frame': 12211 obs. of 18 variables:
## $ Administrative : int 0 0 0 0 0 0 0 1 0 0 ...
## $ Administrative_Duration: num 0 0 0 0 0 0 0 0 0 0 ...
## $ Informational : int 0 0 0 0 0 0 0 0 0 0 ...
## $ Informational_Duration : num 0 0 0 0 0 0 0 0 0 0 ...
## $ ProductRelated : int 1 2 1 2 10 19 1 1 2 3 ...
## $ ProductRelated_Duration: num 0 64 0 2.67 627.5 ...
## $ BounceRates : num 0.2 0 0.2 0.05 0.02 ...
## $ ExitRates : num 0.2 0.1 0.2 0.14 0.05 ...
## $ PageValues : num 0 0 0 0 0 0 0 0 0 0 ...
## $ SpecialDay : num 0 0 0 0 0 0 0.4 0 0.8 0.4 ...
## $ Month : chr "Feb" "Feb" "Feb" "Feb" ...
## $ OperatingSystems : int 1 2 4 3 3 2 2 1 2 2 ...
## $ Browser : int 1 2 1 2 3 2 4 2 2 4 ...
## $ Region : int 1 1 9 2 1 1 3 1 2 1 ...
## $ TrafficType : int 1 2 3 4 4 3 3 5 3 2 ...
## $ VisitorType : chr "Returning_Visitor" "Returning_Visitor" "Returning_Visitor" "Returning_Visitor" ...
## $ Weekend : logi FALSE FALSE FALSE FALSE TRUE FALSE ...
## $ Revenue : logi FALSE FALSE FALSE FALSE FALSE FALSE ...Converting the strings to numeric columns to enable clustering is introducting alot of null values thus in this scope i will work with numerical columns
8.) Implementing the solution
# Selecting my features.
str(num_complete)## 'data.frame': 12211 obs. of 14 variables:
## $ Administrative : int 0 0 0 0 0 0 0 1 0 0 ...
## $ Administrative_Duration: num 0 0 0 0 0 0 0 0 0 0 ...
## $ Informational : int 0 0 0 0 0 0 0 0 0 0 ...
## $ Informational_Duration : num 0 0 0 0 0 0 0 0 0 0 ...
## $ ProductRelated : int 1 2 1 2 10 19 1 1 2 3 ...
## $ ProductRelated_Duration: num 0 64 0 2.67 627.5 ...
## $ BounceRates : num 0.2 0 0.2 0.05 0.02 ...
## $ ExitRates : num 0.2 0.1 0.2 0.14 0.05 ...
## $ PageValues : num 0 0 0 0 0 0 0 0 0 0 ...
## $ SpecialDay : num 0 0 0 0 0 0 0.4 0 0.8 0.4 ...
## $ OperatingSystems : int 1 2 4 3 3 2 2 1 2 2 ...
## $ Browser : int 1 2 1 2 3 2 4 2 2 4 ...
## $ Region : int 1 1 9 2 1 1 3 1 2 1 ...
## $ TrafficType : int 1 2 3 4 4 3 3 5 3 2 ...# Criterion used to select points
# 1.) Independence- two variables that are correlated were not selected
# 2) Numerical variables- Because a challenge was experience in encoding the string columns, Null values were being formed
feat<-num_complete[,c(1,3,5,7,9,10)]
# These are the numeric features
# Selecting the labels
label<-complete_df[,"Revenue"]
feat## Administrative Informational ProductRelated BounceRates PageValues
## 1 0 0 1 0.200000000 0.00000000
## 2 0 0 2 0.000000000 0.00000000
## 3 0 0 1 0.200000000 0.00000000
## 4 0 0 2 0.050000000 0.00000000
## 5 0 0 10 0.020000000 0.00000000
## 6 0 0 19 0.015789474 0.00000000
## 7 0 0 1 0.200000000 0.00000000
## 8 1 0 1 0.200000000 0.00000000
## 9 0 0 2 0.000000000 0.00000000
## 10 0 0 3 0.000000000 0.00000000
## 11 0 0 3 0.000000000 0.00000000
## 12 0 0 16 0.018750000 0.00000000
## 13 0 0 7 0.000000000 0.00000000
## 14 0 0 6 0.000000000 0.00000000
## 15 0 0 2 0.000000000 0.00000000
## 16 2 0 23 0.008333333 0.00000000
## 17 0 0 1 0.200000000 0.00000000
## 18 0 0 13 0.000000000 0.00000000
## 19 0 0 2 0.000000000 0.00000000
## 20 0 0 20 0.000000000 0.00000000
## 21 0 0 8 0.000000000 0.00000000
## 22 0 0 1 0.200000000 0.00000000
## 23 0 0 3 0.000000000 0.00000000
## 24 0 0 2 0.000000000 0.00000000
## 25 0 0 1 0.200000000 0.00000000
## 26 0 0 5 0.000000000 0.00000000
## 27 4 0 32 0.002857143 0.00000000
## 28 0 0 4 0.050000000 0.00000000
## 29 0 0 4 0.000000000 0.00000000
## 30 1 1 45 0.043478261 54.17976426
## 31 0 0 2 0.000000000 0.00000000
## 32 0 0 3 0.000000000 0.00000000
## 33 0 0 8 0.000000000 0.00000000
## 34 0 0 10 0.040000000 0.00000000
## 35 0 0 14 0.014285714 0.00000000
## 36 0 0 52 0.015384615 0.00000000
## 37 0 0 8 0.025000000 0.00000000
## 38 2 0 5 0.000000000 0.00000000
## 39 0 0 7 0.028571429 0.00000000
## 40 0 0 9 0.000000000 0.00000000
## 41 1 0 46 0.000000000 0.00000000
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## 43 0 0 15 0.000000000 0.00000000
## 44 0 0 2 0.000000000 0.00000000
## 45 0 0 4 0.000000000 0.00000000
## 46 0 0 22 0.003030303 0.00000000
## 47 0 0 14 0.017142857 0.00000000
## 48 0 0 3 0.066666667 0.00000000
## 49 0 0 11 0.000000000 0.00000000
## 50 0 0 1 0.200000000 0.00000000
## 51 0 0 1 0.200000000 0.00000000
## 52 0 0 12 0.011111111 0.00000000
## 53 0 0 2 0.000000000 0.00000000
## 54 0 0 4 0.000000000 0.00000000
## 55 0 0 4 0.050000000 0.00000000
## 56 0 0 1 0.200000000 0.00000000
## 57 0 0 1 0.200000000 0.00000000
## 58 4 2 36 0.000000000 19.44707913
## 59 0 0 4 0.000000000 0.00000000
## 60 2 0 16 0.011764706 0.00000000
## 61 0 0 6 0.000000000 0.00000000
## 62 0 0 8 0.050000000 0.00000000
## 63 12 0 42 0.009000000 38.30849268
## 64 0 0 14 0.000000000 0.00000000
## 65 0 0 1 0.200000000 0.00000000
## 66 3 0 27 0.000000000 22.91603570
## 67 4 0 90 0.002150538 0.00000000
## 68 0 0 1 0.200000000 0.00000000
## 69 0 0 18 0.000000000 0.00000000
## 70 0 0 1 0.200000000 0.00000000
## 71 0 0 1 0.200000000 0.00000000
## 72 0 0 20 0.025000000 0.00000000
## 73 0 0 2 0.000000000 0.00000000
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## 75 0 0 3 0.000000000 0.00000000
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## 77 10 0 36 0.004347826 11.43941195
## 78 0 0 2 0.000000000 0.00000000
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## 84 0 0 12 0.016666667 0.00000000
## 85 0 0 1 0.200000000 0.00000000
## 86 0 0 1 0.200000000 0.00000000
## 87 0 0 7 0.057142857 0.00000000
## 88 0 0 3 0.000000000 0.00000000
## 89 0 0 7 0.028571429 0.00000000
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## 91 2 0 15 0.000000000 0.00000000
## 92 0 0 1 0.200000000 0.00000000
## 93 0 0 2 0.000000000 0.00000000
## 94 0 0 13 0.000000000 0.00000000
## 95 0 0 27 0.003703704 0.00000000
## 96 0 0 2 0.000000000 0.00000000
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## 98 0 0 4 0.000000000 0.00000000
## 99 0 1 7 0.038095238 0.00000000
## 100 0 0 16 0.004166667 0.00000000
## 101 3 0 38 0.000000000 0.00000000
## 102 4 0 19 0.000000000 17.53595893
## 103 0 0 6 0.000000000 0.00000000
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## 105 3 0 17 0.007058824 0.00000000
## 106 0 0 2 0.150000000 0.00000000
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## 110 6 4 128 0.000854701 0.00000000
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## 126 0 0 1 0.200000000 0.00000000
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## 130 0 0 3 0.133333333 0.00000000
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## 136 0 0 6 0.000000000 0.00000000
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## 172 0 0 3 0.066666667 0.00000000
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## 175 3 0 10 0.000000000 0.00000000
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## 178 0 0 6 0.033333333 0.00000000
## 179 0 0 17 0.000000000 0.00000000
## 180 0 0 1 0.200000000 0.00000000
## 181 0 0 1 0.200000000 0.00000000
## 182 0 0 8 0.008333333 0.00000000
## 183 0 16 5 0.003174603 0.00000000
## 184 0 0 20 0.011111111 8.00074074
## 185 2 0 50 0.000000000 0.00000000
## 186 10 2 96 0.001960784 0.00000000
## 187 9 1 49 0.000000000 1.70601497
## 188 3 1 68 0.007142857 46.53017511
## 189 0 0 2 0.200000000 0.00000000
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## 196 3 0 13 0.013068182 0.00000000
## 197 0 0 17 0.000000000 109.17600000
## 198 3 2 55 0.000000000 96.25511582
## 199 1 0 50 0.000000000 153.44324780
## 200 0 0 5 0.000000000 0.00000000
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## 204 3 0 16 0.000000000 0.00000000
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## 206 2 0 5 0.000000000 0.00000000
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## 1972 0 0 8 0.000000000 0.00000000
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## 1994 0 0 24 0.008333333 0.00000000
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## 12211 0.0Normalization of all the numerical features using the maxmin method
normalize <- function(x){
return ((x-min(x)) / (max(x)-min(x)))
}# Scaling the features
normal_features<-normalize(feat)
summary(normal_features)## Administrative Informational ProductRelated BounceRates
## Min. :0.000000 Min. :0.000000 Min. :0.00000 Min. :0.000e+00
## 1st Qu.:0.000000 1st Qu.:0.000000 1st Qu.:0.01135 1st Qu.:0.000e+00
## Median :0.001418 Median :0.000000 Median :0.02553 Median :4.157e-06
## Mean :0.003317 Mean :0.000721 Mean :0.04544 Mean :2.905e-05
## 3rd Qu.:0.005674 3rd Qu.:0.000000 3rd Qu.:0.05390 3rd Qu.:2.364e-05
## Max. :0.038298 Max. :0.034043 Max. :1.00000 Max. :2.837e-04
## PageValues SpecialDay
## Min. :0.000000 Min. :0.000e+00
## 1st Qu.:0.000000 1st Qu.:0.000e+00
## Median :0.000000 Median :0.000e+00
## Mean :0.008435 Mean :8.782e-05
## 3rd Qu.:0.000000 3rd Qu.:0.000e+00
## Max. :0.513140 Max. :1.418e-03library(factoextra)## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBaresult<-kmeans(normal_features,5,iter.max = 10)
# Visualizing the result
fviz_cluster(result,data=normal_features)
# This visual is a bit hard to interpret.Using the elbow method to obtain the optimum number of clusters to use.# Elbow method to point to check the best number of clusters to use
fviz_nbclust(x=normal_features,FUNcluster = kmeans,method="silhouette")
# 2 is the best clustering number to useresult1<-kmeans(normal_features,2,iter.max = 5)
# Visualizing the result
fviz_cluster(result1,data=normal_features)
# Clusters are still overlapping. The K-means is hard to interpret especially when used with lots of data sets.Hierarchical clustering
This clustering has an advantage of letting us be free with the number of clusters to choose. Its less messy as compared to K-means and easily interprateable
# Obtaining the distance to be used to locate points
dist_d<-dist(normal_features,method = "euclidian")
# Hierarchical model
h_model<-hclust(dist_d,method = "complete")
# Plotting the dendrogram to visualize the similarity between points
h_model##
## Call:
## hclust(d = dist_d, method = "complete")
##
## Cluster method : complete
## Distance : euclidean
## Number of objects: 12211# The dendrogram was not visually interprateable.9.) Challenging the solution
In more details the clusters need to be studied to verify their validity for analysis and use by the company for segmentation. The methods of clustering need to be improved and tweaked to give accurate results. ## 10.) Follow up Questions Was the data sufficient for this scope of analysis? Yes, a challenge was however experienced in encoding categorical columns to numeric columns for clustering purposes # Did we have the right question? Yes we did. The only reamaining thing is to learn more about clustering methods.