R Notebook
Employee Attrition Prediction using R language
DATA SCIENCE PROJECT
Introducing a machine learning project centered on employee attrition prediction using the Python programming language. Employees are the backbone of any organization, directly impacting its success or failure. Consequently, when trained, skilled, and experienced employees depart for better opportunities, it poses significant challenges for the organization to address. To get the current working directory, we use the below command using r language.
What is Employee Attrition Prediction?
Employee attrition is downsizing in any organization where employees resign. Employees are valuable assets of any organization. It is necessary to know whether the employees are dissatisfied or whether there are other reasons for leaving their respective jobs.
Nowadays, for better opportunities, employees are eager to move from one organization to another. But if they quit their jobs unexpectedly, it can result in a huge loss for the organization. A new hire will consume money and time, and newly hired employees will also take time to make the respective organization profitable.
Retaining skilled and hardworking employees is one of the most critical challenges many organizations face. Therefore, by improving employee satisfaction and providing a desirable working environment, we can certainly reduce this problem significantly. # Machine Learning Project on Employee Attrition Prediction with R In this section, I will take you through a Machine Learning project on predicting Employee Attrition prediction with R programming language. I will start this task by importing the necessary R libraries that we need for this task:
current_directory <- getwd()We can import the required packages and modules,Here..
if (!require("ggplot2")) install.packages("ggplot2")## Loading required package: ggplot2if (!require("gridExtra")) install.packages("gridExtra")## Loading required package: gridExtraif (!require("psych")) install.packages("psych")## Loading required package: psych##
## Attaching package: 'psych'## The following objects are masked from 'package:ggplot2':
##
## %+%, alphaif (!require("stats")) install.packages("stats")
if (!require("caTools")) install.packages("caTools")## Loading required package: caToolsif (!require("randomForest")) install.packages("randomForest")## Loading required package: randomForest## randomForest 4.7-1.1## Type rfNews() to see new features/changes/bug fixes.##
## Attaching package: 'randomForest'## The following object is masked from 'package:psych':
##
## outlier## The following object is masked from 'package:gridExtra':
##
## combine## The following object is masked from 'package:ggplot2':
##
## marginif (!require("ggcorrplot")) install.packages("ggcorrplot")## Loading required package: ggcorrplotif (!require("caret")) install.packages("caret")## Loading required package: caret## Loading required package: latticeif (!require("gam")) install.packages("gam")## Loading required package: gam## Loading required package: splines## Loading required package: foreach## Loaded gam 1.22-3library(ggplot2)
library(gridExtra)
library(psych)
library(stats)
library(caTools)
library(ggcorrplot)
library(caret)
library(randomForest)
library(gam)Data collection
Importing HR Employee Attrition data in to variable ‘data’.
data <- read.csv("C:\\Users\\BIJILI MADHU\\OneDrive\\Desktop\\paper\\HR-Employee-Attrition.csv")Priview of the data
head(data)Exploratory Data Analasys
dimensions=dim(data)
print(dimensions)## [1] 1470 35we can get the number of columns using the command ncol()
number_of_columns=ncol(data)
cat("\nNumber of columns=",number_of_columns)##
## Number of columns= 35similarly we can see the number of rows in the data using nrow(data)
number_of_rows=nrow(data)
cat("\nNumber of rows=",number_of_rows)##
## Number of rows= 1470Let’s see all the features in the data using ‘names(data)’
names(data)## [1] "Age" "Attrition"
## [3] "BusinessTravel" "DailyRate"
## [5] "Department" "DistanceFromHome"
## [7] "Education" "EducationField"
## [9] "EmployeeCount" "EmployeeNumber"
## [11] "EnvironmentSatisfaction" "Gender"
## [13] "HourlyRate" "JobInvolvement"
## [15] "JobLevel" "JobRole"
## [17] "JobSatisfaction" "MaritalStatus"
## [19] "MonthlyIncome" "MonthlyRate"
## [21] "NumCompaniesWorked" "Over18"
## [23] "OverTime" "PercentSalaryHike"
## [25] "PerformanceRating" "RelationshipSatisfaction"
## [27] "StandardHours" "StockOptionLevel"
## [29] "TotalWorkingYears" "TrainingTimesLastYear"
## [31] "WorkLifeBalance" "YearsAtCompany"
## [33] "YearsInCurrentRole" "YearsSinceLastPromotion"
## [35] "YearsWithCurrManager"lets see, how is the Attrition outcomes are distributed.
attributes_count=table(data$Attrition)
print(attributes_count)##
## No Yes
## 1233 237To see the nature of the data
describe(data)#Unique values of every feature
char_cols=sapply(data,is.character)
unique_vals=lapply(data[,char_cols],unique)
for (i in seq_along(unique_vals)){
cat("unique values of feature: ", names(unique_vals)[i],"\n")
print(unique_vals[i])
}## unique values of feature: Attrition
## $Attrition
## [1] "Yes" "No"
##
## unique values of feature: BusinessTravel
## $BusinessTravel
## [1] "Travel_Rarely" "Travel_Frequently" "Non-Travel"
##
## unique values of feature: Department
## $Department
## [1] "Sales" "Research & Development" "Human Resources"
##
## unique values of feature: EducationField
## $EducationField
## [1] "Life Sciences" "Other" "Medical" "Marketing"
## [5] "Technical Degree" "Human Resources"
##
## unique values of feature: Gender
## $Gender
## [1] "Female" "Male"
##
## unique values of feature: JobRole
## $JobRole
## [1] "Sales Executive" "Research Scientist"
## [3] "Laboratory Technician" "Manufacturing Director"
## [5] "Healthcare Representative" "Manager"
## [7] "Sales Representative" "Research Director"
## [9] "Human Resources"
##
## unique values of feature: MaritalStatus
## $MaritalStatus
## [1] "Single" "Married" "Divorced"
##
## unique values of feature: Over18
## $Over18
## [1] "Y"
##
## unique values of feature: OverTime
## $OverTime
## [1] "Yes" "No"From the above information, we can conclude that the target variabe “Attrition” is binary data type which is classification type. ## converting the binary Yes, No values into 1s and 0s
#converting categorical binary values into numerical binary values
data$Attrition=ifelse(data$Attrition=="Yes",1,0)
unique(data$Attrition)## [1] 1 0data$OverTime=ifelse(data$OverTime=="Yes",1,0)
unique(data$OverTime)## [1] 1 0data$Gender=ifelse(data$Gender=="Male",1,0)
unique(data$Gender)## [1] 0 1Let’s see the data types of the features
str(data)## 'data.frame': 1470 obs. of 35 variables:
## $ Age : int 41 49 37 33 27 32 59 30 38 36 ...
## $ Attrition : num 1 0 1 0 0 0 0 0 0 0 ...
## $ BusinessTravel : chr "Travel_Rarely" "Travel_Frequently" "Travel_Rarely" "Travel_Frequently" ...
## $ DailyRate : int 1102 279 1373 1392 591 1005 1324 1358 216 1299 ...
## $ Department : chr "Sales" "Research & Development" "Research & Development" "Research & Development" ...
## $ DistanceFromHome : int 1 8 2 3 2 2 3 24 23 27 ...
## $ Education : int 2 1 2 4 1 2 3 1 3 3 ...
## $ EducationField : chr "Life Sciences" "Life Sciences" "Other" "Life Sciences" ...
## $ EmployeeCount : int 1 1 1 1 1 1 1 1 1 1 ...
## $ EmployeeNumber : int 1 2 4 5 7 8 10 11 12 13 ...
## $ EnvironmentSatisfaction : int 2 3 4 4 1 4 3 4 4 3 ...
## $ Gender : num 0 1 1 0 1 1 0 1 1 1 ...
## $ HourlyRate : int 94 61 92 56 40 79 81 67 44 94 ...
## $ JobInvolvement : int 3 2 2 3 3 3 4 3 2 3 ...
## $ JobLevel : int 2 2 1 1 1 1 1 1 3 2 ...
## $ JobRole : chr "Sales Executive" "Research Scientist" "Laboratory Technician" "Research Scientist" ...
## $ JobSatisfaction : int 4 2 3 3 2 4 1 3 3 3 ...
## $ MaritalStatus : chr "Single" "Married" "Single" "Married" ...
## $ MonthlyIncome : int 5993 5130 2090 2909 3468 3068 2670 2693 9526 5237 ...
## $ MonthlyRate : int 19479 24907 2396 23159 16632 11864 9964 13335 8787 16577 ...
## $ NumCompaniesWorked : int 8 1 6 1 9 0 4 1 0 6 ...
## $ Over18 : chr "Y" "Y" "Y" "Y" ...
## $ OverTime : num 1 0 1 1 0 0 1 0 0 0 ...
## $ PercentSalaryHike : int 11 23 15 11 12 13 20 22 21 13 ...
## $ PerformanceRating : int 3 4 3 3 3 3 4 4 4 3 ...
## $ RelationshipSatisfaction: int 1 4 2 3 4 3 1 2 2 2 ...
## $ StandardHours : int 80 80 80 80 80 80 80 80 80 80 ...
## $ StockOptionLevel : int 0 1 0 0 1 0 3 1 0 2 ...
## $ TotalWorkingYears : int 8 10 7 8 6 8 12 1 10 17 ...
## $ TrainingTimesLastYear : int 0 3 3 3 3 2 3 2 2 3 ...
## $ WorkLifeBalance : int 1 3 3 3 3 2 2 3 3 2 ...
## $ YearsAtCompany : int 6 10 0 8 2 7 1 1 9 7 ...
## $ YearsInCurrentRole : int 4 7 0 7 2 7 0 0 7 7 ...
## $ YearsSinceLastPromotion : int 0 1 0 3 2 3 0 0 1 7 ...
## $ YearsWithCurrManager : int 5 7 0 0 2 6 0 0 8 7 ...finding the relation between input variables and Target variale to observe the data distribution and relation between them.
#categorical variables
categorical_vars <- c("BusinessTravel", "Department",
"EducationField", "Gender", "JobRole",
"MaritalStatus", "OverTime")
#F-test to know the co-relation between categorical variables and target variable
for(var in categorical_vars) {
cat("fisher test for", var, ":\n")
result=fisher.test(table(data$Attrition, data[[var]]),simulate.p.value = TRUE)
print(result)
}## fisher test for BusinessTravel :
##
## Fisher's Exact Test for Count Data with simulated p-value (based on
## 2000 replicates)
##
## data: table(data$Attrition, data[[var]])
## p-value = 0.0004998
## alternative hypothesis: two.sided
##
## fisher test for Department :
##
## Fisher's Exact Test for Count Data with simulated p-value (based on
## 2000 replicates)
##
## data: table(data$Attrition, data[[var]])
## p-value = 0.004998
## alternative hypothesis: two.sided
##
## fisher test for EducationField :
##
## Fisher's Exact Test for Count Data with simulated p-value (based on
## 2000 replicates)
##
## data: table(data$Attrition, data[[var]])
## p-value = 0.01049
## alternative hypothesis: two.sided
##
## fisher test for Gender :
##
## Fisher's Exact Test for Count Data
##
## data: table(data$Attrition, data[[var]])
## p-value = 0.2778
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.8775556 1.5933584
## sample estimates:
## odds ratio
## 1.179913
##
## fisher test for JobRole :
##
## Fisher's Exact Test for Count Data with simulated p-value (based on
## 2000 replicates)
##
## data: table(data$Attrition, data[[var]])
## p-value = 0.0004998
## alternative hypothesis: two.sided
##
## fisher test for MaritalStatus :
##
## Fisher's Exact Test for Count Data with simulated p-value (based on
## 2000 replicates)
##
## data: table(data$Attrition, data[[var]])
## p-value = 0.0004998
## alternative hypothesis: two.sided
##
## fisher test for OverTime :
##
## Fisher's Exact Test for Count Data
##
## data: table(data$Attrition, data[[var]])
## p-value < 2.2e-16
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 2.799096 5.078460
## sample estimates:
## odds ratio
## 3.767353#continous variaables
continuous_vars <- c("Age", "DailyRate","DistanceFromHome","Education","EnvironmentSatisfaction",
"HourlyRate","JobInvolvement","JobLevel","JobSatisfaction","NumCompaniesWorked",
"MonthlyIncome", "MonthlyRate", "PercentSalaryHike","PerformanceRating",
"RelationshipSatisfaction","StockOptionLevel",
"TotalWorkingYears","TrainingTimesLastYear","WorkLifeBalance",
"YearsAtCompany", "YearsInCurrentRole", "YearsSinceLastPromotion",
"YearsWithCurrManager")
#T-test to know the relation between continous variables and target variable
for(var in continuous_vars) {
cat("T-test for", var, ":\n")
res=t.test(data[data$Attrition == 1, var], data[data$Attrition == 0, var])
print(res)
}## T-test for Age :
##
## Welch Two Sample t-test
##
## data: data[data$Attrition == 1, var] and data[data$Attrition == 0, var]
## t = -5.828, df = 316.93, p-value = 1.38e-08
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -5.288346 -2.618930
## sample estimates:
## mean of x mean of y
## 33.60759 37.56123
##
## T-test for DailyRate :
##
## Welch Two Sample t-test
##
## data: data[data$Attrition == 1, var] and data[data$Attrition == 0, var]
## t = -2.1789, df = 333.76, p-value = 0.03004
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -118.243100 -6.040083
## sample estimates:
## mean of x mean of y
## 750.3629 812.5045
##
## T-test for DistanceFromHome :
##
## Welch Two Sample t-test
##
## data: data[data$Attrition == 1, var] and data[data$Attrition == 0, var]
## t = 2.8882, df = 322.72, p-value = 0.004137
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.5475146 2.8870025
## sample estimates:
## mean of x mean of y
## 10.632911 8.915653
##
## T-test for Education :
##
## Welch Two Sample t-test
##
## data: data[data$Attrition == 1, var] and data[data$Attrition == 0, var]
## t = -1.2177, df = 336.95, p-value = 0.2242
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.22843290 0.05374319
## sample estimates:
## mean of x mean of y
## 2.839662 2.927007
##
## T-test for EnvironmentSatisfaction :
##
## Welch Two Sample t-test
##
## data: data[data$Attrition == 1, var] and data[data$Attrition == 0, var]
## t = -3.7513, df = 316.62, p-value = 0.0002092
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.468253 -0.146056
## sample estimates:
## mean of x mean of y
## 2.464135 2.771290
##
## T-test for HourlyRate :
##
## Welch Two Sample t-test
##
## data: data[data$Attrition == 1, var] and data[data$Attrition == 0, var]
## t = -0.26477, df = 335.98, p-value = 0.7914
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -3.188891 2.432272
## sample estimates:
## mean of x mean of y
## 65.57384 65.95215
##
## T-test for JobInvolvement :
##
## Welch Two Sample t-test
##
## data: data[data$Attrition == 1, var] and data[data$Attrition == 0, var]
## t = -4.6602, df = 312.81, p-value = 4.681e-06
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.3576727 -0.1453097
## sample estimates:
## mean of x mean of y
## 2.518987 2.770479
##
## T-test for JobLevel :
##
## Welch Two Sample t-test
##
## data: data[data$Attrition == 1, var] and data[data$Attrition == 0, var]
## t = -7.3859, df = 376.25, p-value = 9.845e-13
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.6443231 -0.3733861
## sample estimates:
## mean of x mean of y
## 1.637131 2.145985
##
## T-test for JobSatisfaction :
##
## Welch Two Sample t-test
##
## data: data[data$Attrition == 1, var] and data[data$Attrition == 0, var]
## t = -3.9261, df = 328.59, p-value = 0.0001052
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.4656797 -0.1547890
## sample estimates:
## mean of x mean of y
## 2.468354 2.778589
##
## T-test for NumCompaniesWorked :
##
## Welch Two Sample t-test
##
## data: data[data$Attrition == 1, var] and data[data$Attrition == 0, var]
## t = 1.5747, df = 317.14, p-value = 0.1163
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.07367926 0.66437603
## sample estimates:
## mean of x mean of y
## 2.940928 2.645580
##
## T-test for MonthlyIncome :
##
## Welch Two Sample t-test
##
## data: data[data$Attrition == 1, var] and data[data$Attrition == 0, var]
## t = -7.4826, df = 412.74, p-value = 4.434e-13
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -2583.050 -1508.244
## sample estimates:
## mean of x mean of y
## 4787.093 6832.740
##
## T-test for MonthlyRate :
##
## Welch Two Sample t-test
##
## data: data[data$Attrition == 1, var] and data[data$Attrition == 0, var]
## t = 0.5755, df = 330.1, p-value = 0.5653
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -709.8084 1296.8656
## sample estimates:
## mean of x mean of y
## 14559.31 14265.78
##
## T-test for PercentSalaryHike :
##
## Welch Two Sample t-test
##
## data: data[data$Attrition == 1, var] and data[data$Attrition == 0, var]
## t = -0.50424, df = 326.11, p-value = 0.6144
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.6572652 0.3890709
## sample estimates:
## mean of x mean of y
## 15.09705 15.23114
##
## T-test for PerformanceRating :
##
## Welch Two Sample t-test
##
## data: data[data$Attrition == 1, var] and data[data$Attrition == 0, var]
## t = 0.10999, df = 331.22, p-value = 0.9125
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.04784086 0.05350780
## sample estimates:
## mean of x mean of y
## 3.156118 3.153285
##
## T-test for RelationshipSatisfaction :
##
## Welch Two Sample t-test
##
## data: data[data$Attrition == 1, var] and data[data$Attrition == 0, var]
## t = -1.7019, df = 323.54, p-value = 0.08973
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.29067575 0.02102367
## sample estimates:
## mean of x mean of y
## 2.599156 2.733982
##
## T-test for StockOptionLevel :
##
## Welch Two Sample t-test
##
## data: data[data$Attrition == 1, var] and data[data$Attrition == 0, var]
## t = -5.2442, df = 329.67, p-value = 2.812e-07
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.4368288 -0.1985054
## sample estimates:
## mean of x mean of y
## 0.5274262 0.8450933
##
## T-test for TotalWorkingYears :
##
## Welch Two Sample t-test
##
## data: data[data$Attrition == 1, var] and data[data$Attrition == 0, var]
## t = -7.0192, df = 350.88, p-value = 1.16e-11
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -4.632019 -2.604401
## sample estimates:
## mean of x mean of y
## 8.244726 11.862936
##
## T-test for TrainingTimesLastYear :
##
## Welch Two Sample t-test
##
## data: data[data$Attrition == 1, var] and data[data$Attrition == 0, var]
## t = -2.3305, df = 339.56, p-value = 0.02036
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.38439273 -0.03251776
## sample estimates:
## mean of x mean of y
## 2.624473 2.832928
##
## T-test for WorkLifeBalance :
##
## Welch Two Sample t-test
##
## data: data[data$Attrition == 1, var] and data[data$Attrition == 0, var]
## t = -2.1742, df = 302.49, p-value = 0.03047
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.23393357 -0.01165453
## sample estimates:
## mean of x mean of y
## 2.658228 2.781022
##
## T-test for YearsAtCompany :
##
## Welch Two Sample t-test
##
## data: data[data$Attrition == 1, var] and data[data$Attrition == 0, var]
## t = -5.2826, df = 338.21, p-value = 2.286e-07
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -3.071629 -1.404805
## sample estimates:
## mean of x mean of y
## 5.130802 7.369019
##
## T-test for YearsInCurrentRole :
##
## Welch Two Sample t-test
##
## data: data[data$Attrition == 1, var] and data[data$Attrition == 0, var]
## t = -6.8471, df = 366.57, p-value = 3.187e-11
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -2.035355 -1.127107
## sample estimates:
## mean of x mean of y
## 2.902954 4.484185
##
## T-test for YearsSinceLastPromotion :
##
## Welch Two Sample t-test
##
## data: data[data$Attrition == 1, var] and data[data$Attrition == 0, var]
## t = -1.2879, df = 338.49, p-value = 0.1987
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.7309843 0.1525043
## sample estimates:
## mean of x mean of y
## 1.945148 2.234388
##
## T-test for YearsWithCurrManager :
##
## Welch Two Sample t-test
##
## data: data[data$Attrition == 1, var] and data[data$Attrition == 0, var]
## t = -6.6334, df = 365.1, p-value = 1.185e-10
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -1.964223 -1.065929
## sample estimates:
## mean of x mean of y
## 2.852321 4.367397FEATURE SELECTION
From the above F-test and T-test results, there are some input features which are not significant to the target variable. So, eliminating those input features from the data set as they are not showing any relation with teaget variable “Attrition”.
req_data <- subset(data, select = -c(Gender, Education,EmployeeCount, StandardHours, Over18, HourlyRate, NumCompaniesWorked,
MonthlyRate, PercentSalaryHike, EmployeeNumber, PerformanceRating,
RelationshipSatisfaction, YearsSinceLastPromotion))SELECTED FEATURES AND DIMENTION OF THE REQUIRED DATA FOR MODELING
names(req_data)## [1] "Age" "Attrition"
## [3] "BusinessTravel" "DailyRate"
## [5] "Department" "DistanceFromHome"
## [7] "EducationField" "EnvironmentSatisfaction"
## [9] "JobInvolvement" "JobLevel"
## [11] "JobRole" "JobSatisfaction"
## [13] "MaritalStatus" "MonthlyIncome"
## [15] "OverTime" "StockOptionLevel"
## [17] "TotalWorkingYears" "TrainingTimesLastYear"
## [19] "WorkLifeBalance" "YearsAtCompany"
## [21] "YearsInCurrentRole" "YearsWithCurrManager"dim(req_data)## [1] 1470 22DATA VISUALIZATION
Relation between number of people and Attrition
# Add data labels on top of each bar
text(
x=barplot(attributes_count,names.arg = c("No","Yes"), xlab = "Attrition Values",
ylab = "Number of people", col = c("GREEN","RED"),
main = "Distribution of Attrition values",
ylim = c(0, max(attributes_count) * 1.2)),
y = attributes_count + 1, labels = attributes_count, pos = 3, cex = 1.2, col = "black"
)
## Relation between Attrition vs Age
# Create the plot
p <- ggplot(data, aes(x = Age, y = factor(Attrition))) +
geom_violin() +
labs(x = "Age", y = "Attrition") +
ggtitle("Attrition vs Age") +
theme_minimal()
# Add rectangular region to highlight
p + annotate("rect", xmin = 25, xmax = 35, ymin = 1.5, ymax = 2.5, fill = "red", alpha = 0.3)+annotate("rect", xmin = 25, xmax = 45, ymin = 0.5, ymax = 1.5, fill = "lightgreen", alpha = 0.3)
Attrition Vs Business_travell
ggplot(data, aes(x = Attrition, y = factor(BusinessTravel))) +
geom_violin() +
labs(y = "BusinessTravel", x = "Attrition") +
ggtitle("Attrition vs BusinessTravel") +
theme_minimal()
Attrition Vs Environment Satisfaction
ggplot(data, aes(x = EnvironmentSatisfaction, y = factor(Attrition))) +
geom_violin() +
labs(y = "Environment Satisfaction", x = "Attrition") +
ggtitle("Attrition vs Environment Satisfaction") +
theme_minimal()
ggplot(data, aes(x = EnvironmentSatisfaction, fill = Attrition)) +
geom_bar(position = "stack") +
labs(x = "Environment Satisfaction", y = "Count") +
ggtitle("Stacked Bar Plot of Attrition vs EnvironmentSatisfaction") +
theme_minimal() ## Warning: The following aesthetics were dropped during statistical transformation: fill.
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
Attrition Vs Education Field
ggplot(data, aes(x = Attrition, fill = EducationField)) +
geom_bar(position = "stack") +
labs(x = "Attrition", y = "Count") +
ggtitle("Stacked Bar Plot of Attrition vs Education Field") +
theme_minimal()
Daily Rate
drp<-ggplot(data, aes(x = DailyRate, y = factor(Attrition))) +
geom_violin() +
labs(x = "DailyRate", y = "Attrition") +
ggtitle("Attrition vs DailyRate") +
theme_minimal()
# Add rectangular region to highlight
drp + annotate("rect", xmin = 150, xmax = 600, ymin = 1.5, ymax = 2.5, fill = "red", alpha = 0.3)+annotate("rect", xmin = 750, xmax = 1500, ymin = 0.5, ymax = 1.5, fill = "lightgreen", alpha = 0.3)
ggplot(data, aes(x = factor(Attrition), y = DailyRate, color = factor(Attrition))) +
geom_jitter(position = position_jitter(width = 0.2), alpha = 0.7) +
labs(x = "Attrition", y = "DailyRate") +
ggtitle("Attrition vs DailyRate") +
theme_minimal()
ggplot(data, aes(x = Attrition, fill = Department)) +
geom_bar(position = "stack") +
labs(x = "Attrition", y = "Count") +
ggtitle("Stacked Bar Plot of Attrition vs Department") +
theme_minimal()
##Distance from Home
ggplot(data, aes(x = DistanceFromHome, y = factor(Attrition))) +
geom_violin() +
labs(x = "DistanceFromHome", y = "Attrition") +
ggtitle("Attrition vs Distance From Home") +
theme_minimal()
ggplot(data, aes(x = Attrition, fill = Gender)) +
geom_bar(position = "dodge", width = 0.7) +
labs(x = "Attrition", y = "Count") +
ggtitle("Grouped Bar Plot of Attrition vs Gender") +
theme_minimal()## Warning: The following aesthetics were dropped during statistical transformation: fill.
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
Our data is not good to proceed because as we know that out data has
yes:no=1:5 so it is not good to go with the data here we can upscale the
diminishing feature of the output by up sampling methods like 1. ordered
list 2. item 2 + Upsampling Minority Class + Downsampling Majority Class
+ Generate Synthetic Data + Combine Upsampling & Downsampling
Techniques + Balanced Class Weight We used Upsampling Minority
Class method,which is better fit for our data. #Random Forest
##Upsampling
indices_1 <- which(req_data$Attrition == 1)
# Get the rows with 1s
rows_with_1s <- req_data[indices_1, ]
# Duplicate rows with 1s
duplicated_rows <- rows_with_1s[rep(1:nrow(rows_with_1s), times =4 ), ]##Random Forest Model
# Combine original data with duplicated rows
req_data1 <- rbind(req_data, duplicated_rows)
# Shuffle the rows
req_data1 <- req_data1[sample(nrow(req_data1)), ]
# Reset row names
rownames(req_data1) <- NULLsplit=sample.split(req_data1,SplitRatio = 0.80)
dim(split)## NULLtrain=subset(req_data1,split=="TRUE")
names(train)## [1] "Age" "Attrition"
## [3] "BusinessTravel" "DailyRate"
## [5] "Department" "DistanceFromHome"
## [7] "EducationField" "EnvironmentSatisfaction"
## [9] "JobInvolvement" "JobLevel"
## [11] "JobRole" "JobSatisfaction"
## [13] "MaritalStatus" "MonthlyIncome"
## [15] "OverTime" "StockOptionLevel"
## [17] "TotalWorkingYears" "TrainingTimesLastYear"
## [19] "WorkLifeBalance" "YearsAtCompany"
## [21] "YearsInCurrentRole" "YearsWithCurrManager"test=subset(req_data1,split=="FALSE")
names(test)## [1] "Age" "Attrition"
## [3] "BusinessTravel" "DailyRate"
## [5] "Department" "DistanceFromHome"
## [7] "EducationField" "EnvironmentSatisfaction"
## [9] "JobInvolvement" "JobLevel"
## [11] "JobRole" "JobSatisfaction"
## [13] "MaritalStatus" "MonthlyIncome"
## [15] "OverTime" "StockOptionLevel"
## [17] "TotalWorkingYears" "TrainingTimesLastYear"
## [19] "WorkLifeBalance" "YearsAtCompany"
## [21] "YearsInCurrentRole" "YearsWithCurrManager"nrow(test)## [1] 550set.seed(120)
train$Attrition <- factor(train$Attrition)
classifier_RF= randomForest(x=train[-2],
y=train$Attrition,
ntree=900)
classifier_RF##
## Call:
## randomForest(x = train[-2], y = train$Attrition, ntree = 900)
## Type of random forest: classification
## Number of trees: 900
## No. of variables tried at each split: 4
##
## OOB estimate of error rate: 2.73%
## Confusion matrix:
## 0 1 class.error
## 0 890 49 0.052183174
## 1 2 927 0.002152853Predictions
y_pred=predict(classifier_RF,newdata=test[-2])
y_pred## 1 5 9 10 16 23 27 31 32 38 45 49 53 54 60 67
## 1 0 1 1 0 0 1 0 0 0 0 0 1 1 1 0
## 71 75 76 82 89 93 97 98 104 111 115 119 120 126 133 137
## 0 0 1 1 1 0 1 0 0 1 0 0 0 1 1 1
## 141 142 148 155 159 163 164 170 177 181 185 186 192 199 203 207
## 1 0 0 0 1 0 0 0 1 0 1 1 0 0 0 0
## 208 214 221 225 229 230 236 243 247 251 252 258 265 269 273 274
## 1 0 0 1 0 0 0 0 0 1 1 0 0 1 0 1
## 280 287 291 295 296 302 309 313 317 318 324 331 335 339 340 346
## 0 1 1 0 1 1 1 1 0 0 0 1 0 0 1 1
## 353 357 361 362 368 375 379 383 384 390 397 401 405 406 412 419
## 1 1 1 0 1 1 1 1 1 1 1 0 1 1 1 1
## 423 427 428 434 441 445 449 450 456 463 467 471 472 478 485 489
## 1 1 0 0 1 0 1 0 1 0 0 1 0 0 0 0
## 493 494 500 507 511 515 516 522 529 533 537 538 544 551 555 559
## 1 1 0 0 1 1 1 1 0 0 1 0 0 0 1 0
## 560 566 573 577 581 582 588 595 599 603 604 610 617 621 625 626
## 0 1 1 1 1 1 0 0 1 0 0 0 1 1 0 0
## 632 639 643 647 648 654 661 665 669 670 676 683 687 691 692 698
## 1 0 0 0 1 0 1 1 1 0 1 1 1 0 0 1
## 705 709 713 714 720 727 731 735 736 742 749 753 757 758 764 771
## 1 1 0 1 1 1 0 0 0 1 0 0 0 1 0 1
## 775 779 780 786 793 797 801 802 808 815 819 823 824 830 837 841
## 1 1 0 0 0 0 1 1 1 1 1 1 0 0 0 1
## 845 846 852 859 863 867 868 874 881 885 889 890 896 903 907 911
## 1 1 1 0 0 0 0 0 0 1 1 0 1 0 1 1
## 912 918 925 929 933 934 940 947 951 955 956 962 969 973 977 978
## 1 0 1 1 1 0 0 0 0 0 1 1 0 0 1 1
## 984 991 995 999 1000 1006 1013 1017 1021 1022 1028 1035 1039 1043 1044 1050
## 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0
## 1057 1061 1065 1066 1072 1079 1083 1087 1088 1094 1101 1105 1109 1110 1116 1123
## 0 0 1 1 1 1 0 0 1 1 1 1 1 1 1 1
## 1127 1131 1132 1138 1145 1149 1153 1154 1160 1167 1171 1175 1176 1182 1189 1193
## 1 0 1 0 0 1 1 0 0 0 0 1 1 0 0 0
## 1197 1198 1204 1211 1215 1219 1220 1226 1233 1237 1241 1242 1248 1255 1259 1263
## 0 1 0 1 1 1 0 0 1 1 0 0 0 1 0 1
## 1264 1270 1277 1281 1285 1286 1292 1299 1303 1307 1308 1314 1321 1325 1329 1330
## 0 1 0 1 1 1 1 1 0 1 1 1 1 1 0 1
## 1336 1343 1347 1351 1352 1358 1365 1369 1373 1374 1380 1387 1391 1395 1396 1402
## 0 1 1 1 1 0 0 1 1 0 0 0 0 0 1 0
## 1409 1413 1417 1418 1424 1431 1435 1439 1440 1446 1453 1457 1461 1462 1468 1475
## 0 1 1 0 0 1 1 1 1 0 0 1 0 0 1 0
## 1479 1483 1484 1490 1497 1501 1505 1506 1512 1519 1523 1527 1528 1534 1541 1545
## 0 0 1 1 1 1 1 0 1 0 0 0 0 0 1 1
## 1549 1550 1556 1563 1567 1571 1572 1578 1585 1589 1593 1594 1600 1607 1611 1615
## 0 0 0 0 0 1 0 0 1 1 0 0 0 1 0 1
## 1616 1622 1629 1633 1637 1638 1644 1651 1655 1659 1660 1666 1673 1677 1681 1682
## 0 1 0 1 0 1 0 1 1 0 0 0 1 0 0 0
## 1688 1695 1699 1703 1704 1710 1717 1721 1725 1726 1732 1739 1743 1747 1748 1754
## 1 1 0 0 0 1 0 0 0 1 1 1 0 0 1 0
## 1761 1765 1769 1770 1776 1783 1787 1791 1792 1798 1805 1809 1813 1814 1820 1827
## 1 1 0 0 1 0 0 1 1 0 1 0 1 1 0 0
## 1831 1835 1836 1842 1849 1853 1857 1858 1864 1871 1875 1879 1880 1886 1893 1897
## 1 1 0 0 1 1 1 1 1 1 0 1 1 0 0 0
## 1901 1902 1908 1915 1919 1923 1924 1930 1937 1941 1945 1946 1952 1959 1963 1967
## 0 1 1 1 0 1 0 0 0 0 1 1 1 1 0 1
## 1968 1974 1981 1985 1989 1990 1996 2003 2007 2011 2012 2018 2025 2029 2033 2034
## 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0
## 2040 2047 2051 2055 2056 2062 2069 2073 2077 2078 2084 2091 2095 2099 2100 2106
## 0 1 1 1 0 1 1 1 0 1 1 1 0 1 0 0
## 2113 2117 2121 2122 2128 2135 2139 2143 2144 2150 2157 2161 2165 2166 2172 2179
## 0 0 1 0 1 0 0 1 1 1 0 1 1 0 0 0
## 2183 2187 2188 2194 2201 2205 2209 2210 2216 2223 2227 2231 2232 2238 2245 2249
## 0 1 1 1 0 1 0 0 1 0 0 1 0 0 0 1
## 2253 2254 2260 2267 2271 2275 2276 2282 2289 2293 2297 2298 2304 2311 2315 2319
## 1 1 1 0 0 1 0 0 0 1 1 1 1 1 1 1
## 2320 2326 2333 2337 2341 2342 2348 2355 2359 2363 2364 2370 2377 2381 2385 2386
## 0 0 0 0 0 0 0 1 1 1 1 1 0 0 1 1
## 2392 2399 2403 2407 2408 2414
## 0 1 1 1 1 1
## Levels: 0 1confusion_mtx=table(test[,2],y_pred)
confusion_mtx## y_pred
## 0 1
## 0 279 15
## 1 0 256plot(classifier_RF)
importance(classifier_RF)## MeanDecreaseGini
## Age 76.89543
## BusinessTravel 18.30494
## DailyRate 73.20719
## Department 15.51714
## DistanceFromHome 55.71008
## EducationField 26.62398
## EnvironmentSatisfaction 39.06212
## JobInvolvement 29.24384
## JobLevel 32.42759
## JobRole 39.07922
## JobSatisfaction 37.76589
## MaritalStatus 26.73549
## MonthlyIncome 96.04783
## OverTime 67.11484
## StockOptionLevel 43.87606
## TotalWorkingYears 57.32128
## TrainingTimesLastYear 33.23878
## WorkLifeBalance 27.47825
## YearsAtCompany 56.15705
## YearsInCurrentRole 38.70397
## YearsWithCurrManager 41.58885varImpPlot(classifier_RF)
##Accuracy of Random forest model
TP <- confusion_mtx[2, 2]
TN <- confusion_mtx[1, 1]
FP <- confusion_mtx[1, 2]
FN <- confusion_mtx[2, 1]
# Calculate accuracy
accuracy <- (TP + TN) / sum(confusion_mtx)
# Calculate precision
precision <- TP / (TP + FP)
# Calculate recall (sensitivity)
recall <- TP / (TP + FN)
# Calculate F1-score
f1_score <- 2 * (precision * recall) / (precision + recall)
# Print the calculated metrics
print(paste("Accuracy:", round(accuracy, 4)))## [1] "Accuracy: 0.9727"print(paste("Precision:", round(precision, 4)))## [1] "Precision: 0.9446"print(paste("Recall:", round(recall, 4)))## [1] "Recall: 1"print(paste("F1-score:", round(f1_score, 4)))## [1] "F1-score: 0.9715"#printing confusion matrix
# Define row and column names for the confusion matrix
rownames(confusion_mtx) <- c("Actual 0", "Actual 1")
colnames(confusion_mtx) <- c("Predicted 0", "Predicted 1")
# Plot the confusion matrix as a heatmap
heatmap(confusion_mtx,
col = heat.colors(10),
scale = "column",
margins = c(15,15),
main = "Confusion Matrix",
xlab = "Predicted",
ylab = "Actual"
)
# ANN Model
#Building ANN-Model
#Label encoding for categorical values, deleting unnecessary features
names(req_data)## [1] "Age" "Attrition"
## [3] "BusinessTravel" "DailyRate"
## [5] "Department" "DistanceFromHome"
## [7] "EducationField" "EnvironmentSatisfaction"
## [9] "JobInvolvement" "JobLevel"
## [11] "JobRole" "JobSatisfaction"
## [13] "MaritalStatus" "MonthlyIncome"
## [15] "OverTime" "StockOptionLevel"
## [17] "TotalWorkingYears" "TrainingTimesLastYear"
## [19] "WorkLifeBalance" "YearsAtCompany"
## [21] "YearsInCurrentRole" "YearsWithCurrManager"names(req_data)## [1] "Age" "Attrition"
## [3] "BusinessTravel" "DailyRate"
## [5] "Department" "DistanceFromHome"
## [7] "EducationField" "EnvironmentSatisfaction"
## [9] "JobInvolvement" "JobLevel"
## [11] "JobRole" "JobSatisfaction"
## [13] "MaritalStatus" "MonthlyIncome"
## [15] "OverTime" "StockOptionLevel"
## [17] "TotalWorkingYears" "TrainingTimesLastYear"
## [19] "WorkLifeBalance" "YearsAtCompany"
## [21] "YearsInCurrentRole" "YearsWithCurrManager"req_data$BusinessTravel = as.integer(factor(req_data$BusinessTravel, levels = unique(req_data$BusinessTravel)))
req_data$Department = as.integer(factor(req_data$Department, levels = unique(req_data$Department)))
req_data$EducationField = as.integer(factor(req_data$EducationField, levels = unique(req_data$EducationField)))
req_data$JobRole = as.integer(factor(req_data$JobRole, levels = unique(req_data$JobRole)))
req_data$MaritalStatus = as.integer(factor(req_data$MaritalStatus, levels = unique(req_data$MaritalStatus)))
print(req_data)## Age Attrition BusinessTravel DailyRate Department DistanceFromHome
## 1 41 1 1 1102 1 1
## 2 49 0 2 279 2 8
## 3 37 1 1 1373 2 2
## 4 33 0 2 1392 2 3
## 5 27 0 1 591 2 2
## 6 32 0 2 1005 2 2
## 7 59 0 1 1324 2 3
## 8 30 0 1 1358 2 24
## 9 38 0 2 216 2 23
## 10 36 0 1 1299 2 27
## 11 35 0 1 809 2 16
## 12 29 0 1 153 2 15
## 13 31 0 1 670 2 26
## 14 34 0 1 1346 2 19
## 15 28 1 1 103 2 24
## 16 29 0 1 1389 2 21
## 17 32 0 1 334 2 5
## 18 22 0 3 1123 2 16
## 19 53 0 1 1219 1 2
## 20 38 0 1 371 2 2
## 21 24 0 3 673 2 11
## 22 36 1 1 1218 1 9
## 23 34 0 1 419 2 7
## 24 21 0 1 391 2 15
## 25 34 1 1 699 2 6
## 26 53 0 1 1282 2 5
## 27 32 1 2 1125 2 16
## 28 42 0 1 691 1 8
## 29 44 0 1 477 2 7
## 30 46 0 1 705 1 2
## 31 33 0 1 924 2 2
## 32 44 0 1 1459 2 10
## 33 30 0 1 125 2 9
## 34 39 1 1 895 1 5
## 35 24 1 1 813 2 1
## 36 43 0 1 1273 2 2
## 37 50 1 1 869 1 3
## 38 35 0 1 890 1 2
## 39 36 0 1 852 2 5
## 40 33 0 2 1141 1 1
## 41 35 0 1 464 2 4
## 42 27 0 1 1240 2 2
## 43 26 1 1 1357 2 25
## 44 27 0 2 994 1 8
## 45 30 0 2 721 2 1
## 46 41 1 1 1360 2 12
## 47 34 0 3 1065 1 23
## 48 37 0 1 408 2 19
## 49 46 0 2 1211 1 5
## 50 35 0 1 1229 2 8
## 51 48 1 1 626 2 1
## 52 28 1 1 1434 2 5
## 53 44 0 1 1488 1 1
## 54 35 0 3 1097 2 11
## 55 26 0 1 1443 1 23
## 56 33 0 2 515 2 1
## 57 35 0 2 853 1 18
## 58 35 0 1 1142 2 23
## 59 31 0 1 655 2 7
## 60 37 0 1 1115 2 1
## 61 32 0 1 427 2 1
## 62 38 0 2 653 2 29
## 63 50 0 1 989 2 7
## 64 59 0 1 1435 1 25
## 65 36 0 1 1223 2 8
## 66 55 0 1 836 2 8
## 67 36 0 2 1195 2 11
## 68 45 0 1 1339 2 7
## 69 35 0 2 664 2 1
## 70 36 1 1 318 2 9
## 71 59 0 2 1225 1 1
## 72 29 0 1 1328 2 2
## 73 31 0 1 1082 2 1
## 74 32 0 1 548 2 1
## 75 36 0 1 132 2 6
## 76 31 0 1 746 2 8
## 77 35 0 1 776 1 1
## 78 45 0 1 193 2 6
## 79 37 0 1 397 2 7
## 80 46 0 1 945 3 5
## 81 30 0 1 852 2 1
## 82 35 0 1 1214 2 1
## 83 55 0 1 111 1 1
## 84 38 0 3 573 2 6
## 85 34 0 1 1153 2 1
## 86 56 0 1 1400 2 7
## 87 23 0 1 541 1 2
## 88 51 0 1 432 2 9
## 89 30 0 1 288 2 2
## 90 46 1 1 669 1 9
## 91 40 0 2 530 2 1
## 92 51 0 1 632 1 21
## 93 30 0 1 1334 1 4
## 94 46 0 2 638 2 1
## 95 32 0 1 1093 1 6
## 96 54 0 1 1217 2 2
## 97 24 0 1 1353 1 3
## 98 28 0 3 120 1 4
## 99 58 0 1 682 1 10
## 100 44 0 3 489 2 23
## 101 37 1 1 807 3 6
## 102 32 0 1 827 2 1
## 103 20 1 2 871 2 6
## 104 34 0 1 665 2 6
## 105 37 0 3 1040 2 2
## 106 59 0 3 1420 3 2
## 107 50 0 2 1115 2 1
## 108 25 1 1 240 1 5
## 109 25 0 1 1280 2 7
## 110 22 0 1 534 2 15
## 111 51 0 2 1456 2 1
## 112 34 1 2 658 2 7
## 113 54 0 3 142 3 26
## 114 24 0 1 1127 2 18
## 115 34 0 1 1031 2 6
## 116 37 0 1 1189 1 3
## 117 34 0 1 1354 2 5
## 118 36 0 2 1467 1 11
## 119 36 0 1 922 2 3
## 120 43 0 2 394 1 26
## 121 30 0 2 1312 2 23
## 122 33 0 3 750 1 22
## 123 56 1 1 441 2 14
## 124 51 0 1 684 2 6
## 125 31 1 1 249 1 6
## 126 26 0 1 841 2 6
## 127 58 1 1 147 2 23
## 128 19 1 1 528 1 22
## 129 22 0 1 594 2 2
## 130 49 0 1 470 2 20
## 131 43 0 2 957 2 28
## 132 50 0 2 809 1 12
## 133 31 1 1 542 1 20
## 134 41 0 1 802 1 9
## 135 26 0 1 1355 3 25
## 136 36 0 1 216 2 6
## 137 51 1 2 1150 2 8
## 138 39 0 1 1329 1 4
## 139 25 0 1 959 1 28
## 140 30 0 1 1240 3 9
## 141 32 1 1 1033 2 9
## 142 45 0 1 1316 2 29
## 143 38 0 1 364 2 3
## 144 30 0 1 438 2 18
## 145 32 0 2 689 1 9
## 146 30 0 1 201 2 5
## 147 30 0 1 1427 2 2
## 148 41 0 2 857 2 10
## 149 41 0 1 933 2 9
## 150 19 0 1 1181 2 3
## 151 40 0 2 1395 2 26
## 152 35 0 1 662 1 1
## 153 53 0 1 1436 1 6
## 154 45 0 1 194 2 9
## 155 32 0 2 967 1 8
## 156 29 0 3 1496 2 1
## 157 51 0 1 1169 2 7
## 158 58 0 1 1145 2 9
## 159 40 0 1 630 1 4
## 160 34 0 2 303 1 2
## 161 22 0 1 1256 2 19
## 162 27 0 3 691 2 9
## 163 28 0 1 440 2 21
## 164 57 0 1 334 2 24
## 165 27 0 3 1450 2 3
## 166 50 0 1 1452 2 11
## 167 41 0 1 465 2 14
## 168 30 0 1 1339 1 5
## 169 38 0 1 702 1 1
## 170 32 0 1 120 2 6
## 171 27 0 1 1157 2 17
## 172 19 1 2 602 1 1
## 173 36 0 2 1480 2 3
## 174 30 0 3 111 2 9
## 175 45 0 1 1268 1 4
## 176 56 0 1 713 2 8
## 177 33 0 1 134 2 2
## 178 19 1 1 303 2 2
## 179 46 0 1 526 1 1
## 180 38 0 1 1380 2 9
## 181 31 0 1 140 2 12
## 182 34 0 1 629 2 27
## 183 41 1 1 1356 1 20
## 184 50 0 1 328 2 1
## 185 53 0 1 1084 2 13
## 186 33 0 1 931 2 14
## 187 40 0 1 989 2 4
## 188 55 0 1 692 2 14
## 189 34 0 2 1069 2 2
## 190 51 0 1 313 2 3
## 191 52 0 1 699 2 1
## 192 27 0 1 894 2 9
## 193 35 1 1 556 2 23
## 194 43 0 3 1344 2 7
## 195 45 0 3 1195 2 2
## 196 37 0 1 290 2 21
## 197 35 0 2 138 2 2
## 198 42 0 3 926 2 21
## 199 38 0 1 1261 2 2
## 200 38 0 1 1084 2 29
## 201 27 0 2 472 2 1
## 202 49 0 3 1002 2 18
## 203 34 0 2 878 2 10
## 204 40 0 1 905 2 19
## 205 38 1 1 1180 2 29
## 206 29 1 1 121 1 27
## 207 22 0 1 1136 2 5
## 208 36 0 2 635 2 18
## 209 40 0 3 1151 2 9
## 210 46 0 1 644 2 1
## 211 32 1 1 1045 1 4
## 212 30 0 3 829 2 1
## 213 27 0 2 1242 1 20
## 214 51 0 1 1469 2 8
## 215 30 1 1 1005 2 3
## 216 41 0 1 896 1 6
## 217 30 1 2 334 1 26
## 218 29 1 1 992 2 1
## 219 45 0 3 1052 1 6
## 220 54 0 1 1147 1 3
## 221 36 0 1 1396 2 5
## 222 33 0 1 147 2 4
## 223 37 0 2 663 2 11
## 224 38 0 1 119 1 3
## 225 31 0 3 979 2 1
## 226 59 0 1 142 2 3
## 227 37 0 2 319 1 4
## 228 29 0 2 1413 1 1
## 229 35 0 2 944 1 1
## 230 29 1 1 896 2 18
## 231 52 0 1 1323 2 2
## 232 42 0 1 532 2 4
## 233 59 0 1 818 3 6
## 234 50 0 1 854 1 1
## 235 33 1 1 813 2 14
## 236 43 0 1 1034 1 16
## 237 33 1 1 465 2 2
## 238 52 0 3 771 1 2
## 239 32 0 1 1401 1 4
## 240 32 1 1 515 2 1
## 241 39 0 1 1431 2 1
## 242 32 0 3 976 1 26
## 243 41 0 1 1411 2 19
## 244 40 0 1 1300 2 24
## 245 45 0 1 252 2 1
## 246 31 0 2 1327 2 3
## 247 33 0 1 832 2 5
## 248 34 0 1 470 2 2
## 249 37 0 1 1017 2 1
## 250 45 0 2 1199 2 7
## 251 37 1 2 504 2 10
## 252 39 0 2 505 2 2
## 253 29 0 1 665 2 15
## 254 42 0 1 916 2 17
## 255 29 0 1 1247 1 20
## 256 25 0 1 685 2 1
## 257 42 0 1 269 2 2
## 258 40 0 1 1416 2 2
## 259 51 0 1 833 2 1
## 260 31 1 2 307 2 29
## 261 32 0 2 1311 2 7
## 262 38 0 3 1327 1 2
## 263 32 0 1 128 2 2
## 264 46 0 1 488 1 2
## 265 28 1 1 529 2 2
## 266 29 0 1 1210 1 2
## 267 31 0 1 1463 2 23
## 268 25 0 3 675 2 5
## 269 45 0 1 1385 2 20
## 270 36 0 1 1403 2 6
## 271 55 0 1 452 2 1
## 272 47 1 3 666 2 29
## 273 28 0 1 1158 2 9
## 274 37 0 1 228 1 6
## 275 21 0 1 996 2 3
## 276 37 0 3 728 2 1
## 277 35 0 1 1315 2 22
## 278 38 0 1 322 1 7
## 279 26 0 2 1479 2 1
## 280 50 0 1 797 2 4
## 281 53 0 1 1070 2 3
## 282 42 0 1 635 1 1
## 283 29 0 2 442 1 2
## 284 55 0 1 147 2 20
## 285 26 0 2 496 2 11
## 286 37 0 1 1372 2 1
## 287 44 1 2 920 2 24
## 288 38 0 1 688 2 23
## 289 26 1 1 1449 2 16
## 290 28 0 1 1117 2 8
## 291 49 0 2 636 2 10
## 292 36 0 1 506 2 3
## 293 31 0 2 444 1 5
## 294 26 1 1 950 1 4
## 295 37 0 2 889 2 9
## 296 42 0 2 555 1 26
## 297 18 1 1 230 2 3
## 298 35 0 1 1232 1 16
## 299 36 0 2 566 2 18
## 300 51 0 1 1302 2 2
## 301 41 0 1 334 1 2
## 302 18 0 1 812 1 10
## 303 28 0 1 1476 2 16
## 304 31 0 1 218 1 7
## 305 39 0 1 1132 2 1
## 306 36 0 3 1105 2 24
## 307 32 0 1 906 1 7
## 308 38 0 1 849 2 25
## 309 58 0 3 390 2 1
## 310 31 0 1 691 2 5
## 311 31 0 1 106 3 2
## 312 45 0 2 1249 2 7
## 313 31 0 1 192 2 2
## 314 33 0 2 553 2 5
## 315 39 0 1 117 2 10
## 316 43 0 2 185 2 10
## 317 49 0 1 1091 2 1
## 318 52 1 1 723 2 8
## 319 27 0 1 1220 2 5
## 320 32 0 1 588 1 8
## 321 27 0 1 1377 1 2
## 322 31 0 1 691 1 7
## 323 32 0 1 1018 2 2
## 324 28 1 1 1157 2 2
## 325 30 0 1 1275 2 28
## 326 31 0 2 798 2 7
## 327 39 0 2 672 2 7
## 328 39 1 1 1162 1 3
## 329 33 0 2 508 1 10
## 330 47 0 1 1482 2 5
## 331 43 0 2 559 2 10
## 332 27 0 3 210 1 1
## 333 54 0 2 928 2 20
## 334 43 0 1 1001 2 7
## 335 45 0 1 549 2 8
## 336 40 0 1 1124 1 1
## 337 29 1 1 318 2 8
## 338 29 0 1 738 2 9
## 339 30 0 1 570 1 5
## 340 27 0 1 1130 1 8
## 341 37 0 1 1192 2 5
## 342 38 0 1 343 2 15
## 343 31 0 1 1232 2 7
## 344 29 0 1 144 1 10
## 345 35 0 1 1296 2 5
## 346 23 0 1 1309 2 26
## 347 41 0 1 483 2 6
## 348 47 0 2 1309 1 4
## 349 42 0 1 810 2 23
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## 1470 3 2names(req_data)## [1] "Age" "Attrition"
## [3] "BusinessTravel" "DailyRate"
## [5] "Department" "DistanceFromHome"
## [7] "EducationField" "EnvironmentSatisfaction"
## [9] "JobInvolvement" "JobLevel"
## [11] "JobRole" "JobSatisfaction"
## [13] "MaritalStatus" "MonthlyIncome"
## [15] "OverTime" "StockOptionLevel"
## [17] "TotalWorkingYears" "TrainingTimesLastYear"
## [19] "WorkLifeBalance" "YearsAtCompany"
## [21] "YearsInCurrentRole" "YearsWithCurrManager"#scaling the req_data
attrition=req_data$Attrition
overtime=req_data$OverTime
businesstravell=req_data$BusinessTravel
department=req_data$Department
educationfield=req_data$EducationField
jobrole=req_data$JobRole
maritalstatus=req_data$MaritalStatus
req_data= req_data[,-which(names(req_data)%in% c("Attrition", "OverTime","BusinessTravel","Department",
"EducationField","JobRole","MaritalStatus"))]
scaled_data=scale(req_data)
scaled_data=cbind(Attrition=attrition, OverTime=overtime, BusinessTravel=businesstravell,
Department=department, EducationField=educationfield, jobrole=jobrole,
MaritalStatus=maritalstatus, as.data.frame(scaled_data))#ANN-model
names(scaled_data)## [1] "Attrition" "OverTime"
## [3] "BusinessTravel" "Department"
## [5] "EducationField" "jobrole"
## [7] "MaritalStatus" "Age"
## [9] "DailyRate" "DistanceFromHome"
## [11] "EnvironmentSatisfaction" "JobInvolvement"
## [13] "JobLevel" "JobSatisfaction"
## [15] "MonthlyIncome" "StockOptionLevel"
## [17] "TotalWorkingYears" "TrainingTimesLastYear"
## [19] "WorkLifeBalance" "YearsAtCompany"
## [21] "YearsInCurrentRole" "YearsWithCurrManager"set.seed(123)
newdata=sample(2,nrow(scaled_data),replace=TRUE,
prob=c(0.75,0.25))
trainingdata=scaled_data[newdata==1,]
testingdata=scaled_data[newdata==2,]
library(neuralnet)
set.seed(123)
net=neuralnet(Attrition~.,
data=trainingdata,
hidden=3,
err.fct = "ce",
linear.output = FALSE,
learningrate = 0.01,
rep=6
)plot(net)#prediction
output=compute(net,testingdata[,-1])
head(output$net.result)## [,1]
## 2 0.02286469
## 4 0.02286469
## 5 0.02286469
## 8 0.02286469
## 11 0.41711095
## 16 0.02286469head(trainingdata[1,])results=data.frame(DataActual=testingdata$Attrition,
pred_vals=output$net.result)
resultsrounded_results=sapply(results,round,digits=0)
rounded_results## DataActual pred_vals
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## [294,] 1 0
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## [300,] 1 1
## [301,] 0 0
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## [303,] 0 1
## [304,] 0 0
## [305,] 0 0
## [306,] 1 1
## [307,] 0 0
## [308,] 0 0
## [309,] 0 0
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## [311,] 1 0
## [312,] 0 0
## [313,] 1 0
## [314,] 0 1
## [315,] 0 0
## [316,] 1 0
## [317,] 0 0
## [318,] 1 0
## [319,] 0 0
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## [321,] 0 0
## [322,] 0 0
## [323,] 1 0
## [324,] 0 1
## [325,] 0 0
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## [327,] 1 1
## [328,] 0 0
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## [331,] 0 0
## [332,] 1 0
## [333,] 1 0
## [334,] 0 0
## [335,] 0 1
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## [337,] 0 1
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## [340,] 0 0
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## [342,] 0 0
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## [344,] 0 0
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## [347,] 0 0
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## [349,] 0 0
## [350,] 0 1
## [351,] 0 0
## [352,] 0 0
## [353,] 0 0#Accuracy
actual=round(testingdata$Attrition,digits = 0)
prediction_vals=round(output$net.result,digits = 0)
mtab=table(actual,prediction_vals)
mtab## prediction_vals
## actual 0 1
## 0 267 31
## 1 36 19confusion_mtx <- confusionMatrix(mtab)
# Extract the confusion matrix elements
TN <- confusion_mtx$table[1, 1]
FP <- confusion_mtx$table[1, 2]
FN <- confusion_mtx$table[2, 1]
TP <- confusion_mtx$table[2, 2]# Calculate accuracy
accuracy <- sum(diag(confusion_mtx$table)) / sum(confusion_mtx$table)
# Calculate precision
precision <- TP / (TP + FP)
# Calculate recall (sensitivity)
recall <- TP / (TP + FN)
# Calculate specificity
specificity <- TN / (TN + FP)
# Calculate F1-score
f1_score <- 2 * (precision * recall) / (precision + recall)
# Print the calculated metrics
print(paste("Accuracy:", round(accuracy, 4)))## [1] "Accuracy: 0.8102"print(paste("Precision:", round(precision, 4)))## [1] "Precision: 0.38"print(paste("Recall (Sensitivity):", round(recall, 4)))## [1] "Recall (Sensitivity): 0.3455"print(paste("Specificity:", round(specificity, 4)))## [1] "Specificity: 0.896"print(paste("F1-score:", round(f1_score, 4)))## [1] "F1-score: 0.3619"Deploying the model with highg accuracy and testing it with new input from the original dataset > Random Forest model for employee attrition prediction
temp=test[-2]
temptemp=temp[1:5,]
tempy_pred=predict(classifier_RF,newdata=temp)
y_pred## 1 5 9 10 16
## 1 0 1 1 0
## Levels: 0 1The model is predicting from the given new input variables