HR Analytics Project EEB

HR Analytics: Why are our best and most
experienced employees leaving prematurely?
Erik Bebernes

Introduction

This
project
uses
a
dataset
I
found
on
kaggle,
where
a
company
has
been
experiencing
difficulty

retaining
their
best
and
most
experienced
employees.
The
data
frame
consists
of
15,000

observations
of
10
variables,
which
are:

names(hr)

[1]
"satisfaction_level"

"last_evaluation"

"number_project"

[4]
"average_montly_hours"

"time_spend_company"

"Work_accident"

[7]
"left"

"promotion_last_5years"
"sales"

[10]
"salary"

Satisfaction
Level
–
employees
overall
job
satisfaction
level
based
on
a
survey

Last
Evaluation
–
employees
performance
score
given
by
their
manager

Number
of
projects
–
how
many
projects
an
employee
has
been
involved
in

Average
monthly
hours-‐
mean
hours
worked
by
employee
per
month

Time
spend
company
–
years
employee
has
worked
for
the
company

Work
accident
–
binary
variable
indicating
if
1,
the
employee
has
had
an
accident
in
the

workplace

Left-‐
indicated
if
1,
the
employee
has
left
or
0,
the
employee
is
still
at
the
company

Promotion
last
5
years
–
binary
variable
signaling
if
the
employee
has
been
promoted

Sales-‐
categorical
variable
on
job
type

Salary-‐
categorical
variable
(low,
medium,
high)
of
how
much
the
employee
is
paid
annually

My
approach
to
this
project
can
be
summarized
in
the
following
steps:

1.) Clean
and
structure
the
data
set,
including
imputing
missing
values
if
necessary

2.) Create
subsets
between
the
best
employees
that
left
and
stayed

3.) Create
discrete
factor
variables
and
perform
association
rules
analysis

4.) Classify
employees
through
decision
tree
analysis

5.) Find
any
significant
correlations,
and
differences
in
correlations
between
said
subsets.

6.) Exploratory
visualization
analysis
in
an
attempt
to
explain
any
discrepancies
in

correlations.

7.) Run
a
random
forest
algorithm
to
confirm
significant
relationships
between
the

variables,
as
well
as
a
logistic
regression

8.) Provide
conclusions
and
recommendations
for
management

HR_comma_sep
<-‐
read.csv("~/Downloads/HR_comma_sep.csv",
header=TRUE)

View(HR_comma_sep)

hr<-‐HR_comma_sep

Cleaning
and
structuring
the
dataset

At
first
glance
the
dataset
seems
clean,
but
to
make
sure
I’m
going
to
use
the
“amelia”
package

to
identify
any
missingness.

library(Amelia)

missmap(hr)

This
shows
that
there
is
no
missing
data.

>
str(hr)

'data.frame':
14999
obs.
of

10
variables:

$
satisfaction_level

:
num

0.38
0.8
0.11
0.72
0.37
0.41
0.1
0.92
0.89
0.42
...

$
last_evaluation

:
num

0.53
0.86
0.88
0.87
0.52
0.5
0.77
0.85
1
0.53
...

$
number_project

:
int

2
5
7
5
2
2
6
5
5
2
...

$
average_montly_hours
:
int

157
262
272
223
159
153
247
259
224
142
...

$
time_spend_company

:
int

3
6
4
5
3
3
4
5
5
3
...

$
Work_accident

:
int

0
0
0
0
0
0
0
0
0
0
...

$
left

:
int

1
1
1
1
1
1
1
1
1
1
...

$
promotion_last_5years:
int

0
0
0
0
0
0
0
0
0
0
...

$
sales

:
Factor
w/
10
levels
"accounting","hr",..:
8
8
8
8
8
8
8
8
8
8
...

$
salary

:
Factor
w/
3
levels
"high","low","medium":
2
3
3
2
2
2
2
2
2
2
...

Subsets

hrbestleft<-‐hr[which(hr$Last_eval>.72
&
hr$Left
==
1),]

#employees
with
high
evaluations
and
who
left
the
company

hrbeststay<-‐hr[which(hr$Last_eval>.72
&
hr$Left
==
'0'),]

#employees
with
high
evaluations
that
left
the
company

Creating
Discrete
Variables
and
Association
Rules
Analysis

quantile(hr$average_montly_hours,
.33)

quantile(hr$average_montly_hours,
.67)

hr$Hours_Discrete[hr$average_montly_hours
<=
69]<-‐
'low'

>69

&
hr$average_montly_hours
<
134]<-‐

'average'

>=134]<-‐
'high'

quantile(hr$satisfaction_level,
.33)

.67)

.8)

hr$Sat_Discrete[hr$satisfaction_level
<=
43]<-‐
'low'

>43

&
hr$satisfaction_level
<
68]<-‐
'average'

>=68]<-‐
'high'

library(arules)

hr$Work_accident<-‐as.factor(hr$Work_accident)

hr$left<-‐as.factor(hr$left)

hr$promotion_last_5years<-‐as.factor(hr$promotion_last_5years)

hr$Hours_Discrete<-‐as.factor(hr$Hours_Discrete)

hr$Sat_Discrete<-‐as.factor(hr$Sat_Discrete)

names(hr)

hrassoc<-‐hr[,c(6,7,8,9,10,11,12)]

rules<-‐apriori(hrassoc,
parameter
=
list(support
=
.2,
confidence
=
.7))

#since
the
majority
of
employees
haven't
left,
it
will
be
a
good
idea
to
reduce
support
and

increase
confidence

rules<-‐apriori(hrassoc,
parameter
=
list(support
=
.05,
confidence
=
.95))

#still
not
getting
any
interesting
rules,
so
I'll
make
a
new
dataset
with
only
left
=1

hrleft<-‐hr[which(hrassoc$left==1),]

hrleft<-‐hrleft[,c(6:12)]

rules<-‐apriori(hrleft,
parameter
=
list(support
=
.3,
confidence
=
1))

inspect(rules)

lhs

rhs

support
confidence
lift

[1]

{}

=>
{left=1}

1.0000000

1

1

[2]

{}

=>
{Sat_Discrete=low}
1.0000000

1

1

[3]

{salary=medium}

=>
{left=1}

0.3688043

1

1

[4]

{salary=medium}

=>
{Sat_Discrete=low}
0.3688043

1

1

[5]

{salary=low}

=>
{left=1}

0.6082330

1

1

[6]

{salary=low}

=>
{Sat_Discrete=low}
0.6082330

1

1

[7]

{Hours_Discrete=high}

=>
{left=1}

0.9106693

1

1

[8]

{Hours_Discrete=high}

=>
{Sat_Discrete=low}
0.9106693

1

1

[9]

{Work_accident=0}

=>
{left=1}

0.9526743

1

1

[10]
{Work_accident=0}

=>
{Sat_Discrete=low}
0.9526743

1

1

[11]
{promotion_last_5years=0}
=>
{left=1}

0.9946794

1

1

[12]
{promotion_last_5years=0}
=>
{Sat_Discrete=low}
0.9946794

1

1

[13]
{left=1}

=>
{Sat_Discrete=low}
1.0000000

1

1

[14]
{Sat_Discrete=low}

=>
{left=1}

1.0000000

1

1

[15]
{salary=medium,

Hours_Discrete=high}

=>
{left=1}

0.3385606

1

1

[16]
{salary=medium,


=>
{Sat_Discrete=low}
0.3385606

1

1

[17]
{Work_accident=0,

salary=medium}

=>
{left=1}

0.3480818

1

1

[18]
{Work_accident=0,

salary=medium}

=>
{Sat_Discrete=low}
0.3480818

1

1

[19]
{promotion_last_5years=0,

salary=medium}

=>
{left=1}

0.3674041

1

1

[20]

salary=medium}

=>
{Sat_Discrete=low}
0.3674041

1

1

[21]
{left=1,

salary=medium}

=>
{Sat_Discrete=low}
0.3688043

1

1

[22]
{salary=medium,

Sat_Discrete=low}

=>
{left=1}

0.3688043

1

1

[23]
{salary=low,


=>
{left=1}

0.5527863

1

1

[24]
{salary=low,


=>
{Sat_Discrete=low}
0.5527863

1

1

[25]
{Work_accident=0,

salary=low}

=>
{left=1}

0.5816298

1

1

[26]
{Work_accident=0,

salary=low}

=>
{Sat_Discrete=low}
0.5816298

1

1

[27]

salary=low}

=>
{left=1}

0.6043125

1

1

[28]

salary=low}

=>
{Sat_Discrete=low}
0.6043125

1

1

[29]
{left=1,

salary=low}

=>
{Sat_Discrete=low}
0.6082330

1

1

[30]
{salary=low,

Sat_Discrete=low}

=>
{left=1}

0.6082330

1

1

Most
Interesting
rules:

1.)
of
the
people
who
left,
99%
never
received
a
promotion

2.)
95%
never
had
an
accident

3.)
60%
were
low
salary

4.)
100%
had
low
job
satisfaction

These
rules
signify
a
few
important
relationships
between
the
variables
that
may
explain
why

some
employees
are
leaving.
Of
the
employees
who
left,
99%
never
had
an
accident,
60%
were

low
salary
and
an
astonishing
100%
had
low
job
satisfaction.
This
must
mean
satisfaction
is

significant
in
determining
leaving
vs.
staying.
Next
I’m
going
to
look
at
correlations
between

satisfaction
and
the
numeric
variables.

Correlation
Analysis

Using
all
employees
in
the
dataset:

cor(hr[,1:5])

satisfaction_level
last_evaluation
number_project

satisfaction_level

1.00000000

0.1050212

-‐0.1429696

-‐0.02004811

last_evaluation

0.10502121

1.0000000

0.3493326

0.33974180

number_project

-‐0.14296959

0.3493326

1.0000000

0.41721063


-‐0.02004811

0.3397418

0.4172106

1.00000000

time_spend_company

-‐0.10086607

0.1315907

0.1967859

0.12775491

time_spend_company

satisfaction_level

-‐0.1008661

last_evaluation

0.1315907

number_project

0.1967859


0.1277549

time_spend_company

1.0000000

The
above
plot
and
output
shows
correlations
between
numeric
variables
of
all
employees.

Managers
seem
to
give
higher
evaluation
scores
to
employees
who
work
more
hours
and
who

have
more
projects,
however
there
is
a
negative
correlation
between
employee
satisfaction
and

number
of
projects.
It
should
be
interesting
to
see
how
this
compares
to
correlations
using
just

the
best
employees.

Correlations
using
just
the
best
employees
and
most
experienced
employees
that
left:

>
hrbestleft<-‐hr[which(hr$last_evaluation
>=
.72
&
hr$left
==
1),]

>
cor(hrbestleft[,1:5])

satisfaction_level
last_evaluation
number_project

satisfaction_level

1.0000000

0.3611564

-‐0.7370609

last_evaluation

0.3611564

1.0000000

-‐0.2150533

number_project

-‐0.7370609

-‐0.2150533

1.0000000


-‐0.4771749

-‐0.1261519

0.5217016

time_spend_company

0.6582700

0.3147566

-‐0.3644283

time_spend_company

satisfaction_level

-‐0.4771749

0.6582700

last_evaluation

-‐0.1261519

0.3147566

number_project

0.5217016

-‐0.3644283


1.0000000

-‐0.1572702

time_spend_company

-‐0.1572702

1.0000000

There
are
some
very
notable
differences
here,
including
the
massive
negative
correlations

between
number
of
projects
and
satisfaction
level
and
the
large
negative
correlation
between

average
monthly
hours
and
satisfaction
level.
This
probably
means
that
managers
are

overworking
their
best
employees,
which
leads
to
lower
satisfaction
levels.
It’s
worth
looking
at

the
data
visually
to
see
if
this
is
in
fact
the
case.
I’ll
also
run
a
decision
tree
analysis
which
may

serve
as
a
confirmation.

Interpreting
Correlation
Differences
Visually

Do
the
best
employees
work
more
hours?

Comparing
these
histograms,
it’s
clear
that
employees
that
score
higher
on
manager

evaluations
are
working
considerably
more
hours
than
the
workforce
as
a
whole.

Do
the
best
employees
work
on
more
projects?

Yes,
the
best
employees
usually
have
more
projects.
There
is
a
downward
trend
as
the
number

of
projects
increase
when
you
look
at
the
workforce
as
a
whole,
and
the
opposite
can
almost
be

said
for
the
best
employees
(until
you
get
to
6
projects).

Have
the
best
employees
been
working
at
the
company
for
a
longer
period
of
time?

Almost
all
of
the
best
employees
have
been
at
the
company
for
at
least
four
years,
perhaps
this

can
be
related
to
“learning
by
doing.”
It’s
also
a
sufficient
amount
of
time
to
prove
to
managers

that
they
are
high
performing.
The
dataset
as
a
whole
shows
that
there
are
an
abundance
of

employees
who
have
been
there
for
2
and
3
years.
Let’s
see
if
anyone
is
being
promoted.

As
you
can
see
above,
of
the
best
performing
employees…hardly
any
of
them
have
been

promoted
in
the
last
five
years.
In
fact,
it’s
only
.2%.
It
must
be
discouraging
to
these
employees

to
be
highly
evaluated
and
not
be
rewarded
for
it.

Next
I’m
going
to
look
at
the
relationship
between
job
type
and
salary.
Are
there
noticeable

differences
in
pay
between
different
departments
of
the
company?
And
how
many
employees

are
in
each
department?

A
couple
of
things
I
noticed
while
looking
at
this
graph
are
that
a
majority
of
the
good

employees
are
on
the
low
end
of
the
salary
spectrum
and
most
of
them
are
working
in
sales,

support
in
technical
roles.
However
I
made
the
same
graph
using
the
dataset
as
a
whole
and

didn’t
see
much
of
a
difference,
so
I’ll
put
these
observations
aside
for
now.

As
I
mentioned
earlier
during
my
association
rules
analysis,
satisfaction
is
most
likely
significant

in
determining
why
the
best
employees
are
leaving.
The
plot
below
is
an
attempt
to
see
that

relationship
visually,
where
the
green
density
is
the
subset
of
the
best
employees
that
left,
the

red
density
are
the
best
employees
that
have
stayed,
and
the
blue
density
is
the
entire
dataset.

p1<-‐ggplot()+geom_density(data
=
hrbestleft,
aes(satisfaction_level),
fill
=
'green',
alpha
=
.3)+

geom_density(data
=
hrbeststay,
fill
=
'red',
alpha
=
.3)+

geom_density(data
=
hr,
fill
=
'blue',
alpha
=
.3)+theme_light(base_size

=
16)+xlab("Satisfaction
Level")+ylab("")+

ggtitle("Satisfaction
Levels
of
Subsets")

The
best
employees
that
left
(green)
is
what
really
stands
out
here.
Many
of
them
have
very

low
satisfaction
levels
(<.25),
then
there
is
a
lull,
and
then
another
group
with
satisfaction
levels

greater
than
.6.
It’s
difficult
to
say
why
this
might
be.
Perhaps
there
is
a
difference
in
how
the

employees
interpret
satisfaction.
It’s
possible
that
they
still
enjoyed
their
job
despite
being
over

worked
and
not
being
promoted.
I
think
the
best
way
to
figure
this
out
is
through
a
decision

tree
analysis,
where
those
who
left
will
be
classified
more
accurately.
But
first,
I
want
to

combine
average
monthly
hours
and
satisfaction
into
a
plot.
Since
I
noticed
earlier
that
the

good
employees
that
left
were
working
a
lot
more
hours,
there
should
be
a
strong
relationship

between
the
two.

plot6<-‐ggplot(hr,
aes(satisfaction_level,
average_montly_hours,
color
=
left,
alpha
=

.3))+geom_point()+ggtitle("Hours
and
Satisfaction")

These
distributions
are
very
tight,
which
tells
me
that
the
decision
tree
will
be
a
great
addition

to
my
analysis.
The
blue
box
must
be
underperforming
employees,
those
that
have
not
been

working
many
hours
and
aren’t
that
satisfied.
Where
the
other
two
blue
distributions,
judging

by
the
density
plots
on
the
previous
page,
are
high
performing
employees.
My
next
plot
is

another
confirmation
of
that
hypothesis,
but
this
time
I’m
adding
years
spent
at
the
company.

The
cluster
on
the
right
has
a
lot
of
employees
that
have
been
at
the
company
for
a
long
time,
I

think
the
lack
of
promotions
may
have
something
to
do
with
them
leaving.

Decision
Tree
Analysis

Decision
trees
are
best
used
on
small
datasets,
so
in
order
to
get
a
few
simple
rules
(and
to

avoid
over-‐fitting
the
model)
I
made
a
small
sample
of
the
data
(2%).

install.packages("party")

library(party)

set.seed(421)

ind<-‐sample(2,
nrow(hr),
replace
=
TRUE,
prob
=
c(0.02,0.3))

traindata<-‐hr[ind==1,]

testdata<-‐hr[ind==2,]

form<-‐left~satisfaction_level+average_montly_hours+time_spend_company+last_evaluation

hrtree<-‐ctree(form,
data
=
traindata,
controls
=
ctree_control(maxsurrogate
=
3))

table(predict(hrtree),
traindata$left)

plot(hrtree,
type
=
"simple")

?ctree

print(hrtree)

Using
the
variables
time
spent
at
company,
satisfaction,
average
monthly
hours
and
last

evaluation
(what
I
think
are
the
most
important
variables
based
on
the
visualizations
I
made)
I

was
able
to
come
up
with
a
few
rules
that
help
classify
employees
into
the
leaving
and
staying

categories.
Here
are
my
key
takeaways:

1.) Employees
with
low
satisfaction
levels,
but
haven’t
been
at
the
company
long
will
generally

stay.

satisfaction_level
p < 0.001
1
≤ 0.46 > 0.46
time_spend_company
p < 0.001
2
≤ 4 > 4
time_spend_company
p = 0.001
3
≤ 2 > 2
n = 21
y = (0.952, 0.048)
4
n = 217
y = (0.258, 0.742)
5
n = 46
y = (0.891, 0.109)
6
time_spend_company
p < 0.001
7
≤ 4 > 4
n = 562
y = (0.984, 0.016)
8
last_evaluation
p < 0.001
9
≤ 0.8 > 0.8
n = 61
y = (0.951, 0.049)
10
p < 0.001
11
≤ 216 > 216
n = 18
y = (1, 0)
12
time_spend_company
p = 0.001
13
≤ 5 > 5
n = 37
y = (0.081, 0.919)
14
n = 22
y = (0.273, 0.727)
15

2.) Employees
with
low
satisfaction
levels
and
who
have
been
at
the
company
between
2
and

5
years
leave.

3.) Employees
with
high
satisfaction
levels
who
have
been
working
for
less
than
or
equal
to
4

years
stay.

4.) High
performing
employees
with
high
satisfaction
and
who
have
been
at
the
company
>4

years
leave
when
they
are
working
too
many
hours.

This
analysis
is
91.5%
accurate,
which
is
pretty
good
considering
how
simple
the
tree
is.
If
I

were
to
show
management
one
graph
it
would
be
this,
it
identifies
clear
cut
patterns
and

confirms
much
of
what
I
had
been
hypothesizing
with
my
previous
analyses.

Random
Forest
and
Logistic
Regression

Before
offering
my
final
advice
to
management,
I
want
to
see
how
accurately
I
can
predict
who

is
going
to
leave.
An
accurate
machine
learning
algorithm
will
allow
the
company
to
focus
on

specific
employees…perhaps
offering
them
a
raise
or
reducing
their
hours
before
they
decide
to

leave.
First
I’m
going
to
try
a
logistic
regression,
which
determines
probabilities
of
a
binary

dependent
variable
for
each
observation.
Any
probability
greater
than
.5
will
mean
the

employee
will
leave.
Let’s
see
how
it
goes:

Logistic
Regression:

#creating
a
test
and
training
set
using
dplyr

set.seed(142)

train<-‐sample_frac(hr,
.7)

sid<-‐as.numeric(rownames(train))

test<-‐hr[-‐sid,]

fitted.results<-‐predict(glmmodel,
newdata
=
test,
type
=
"response")

#type
=
response
converts
logits
to
predicted
probabilities

new<-‐mutate(test,
fitted.results)

predicted.to.leave<-‐filter(new,
fitted.results
>
.5)

predicted.to.stay<-‐filter(new,
fitted.results
<
.5)

View(predicted.to.stay)

summary(predicted.to.stay$left)

summary(predicted.to.leave$left)

The
model
ended
up
being
only
79.4%
accurate.
Which
is
okay,
but
considering
the
decision

tree
was
91%,
I
think
I
can
come
up
with
a
better
model.
Random
forest
works
by
averaging
the

results
of
many
decision
trees
and
can
work
very
well.
Let’s
try
that:

randindex<-‐
sample(1:dim(hr)[1])

cutpoint2_3<-‐floor(2*dim(hr)[1]/3)

traindata<-‐hr[randindex[1:cutpoint2_3],]

testdata<-‐
hr[randindex[(cutpoint2_3+1):dim(hr)[1]],]

library(randomForest)

rfmodel
<-‐
randomForest(factor(left)
~
satisfaction_level
+
number_project
+

+

time_spend_company
+
promotion_last_5years
+
last_evaluation,

data
=
traindata)

plot9<-‐plot(rfmodel,
ylim=c(0,0.36))

The
false
positive
and
false
negative
errors
are
very
low,
which
is
a
good
sign.
Let’s
see
how

accurate
the
model
is
when
I
try
it
on
a
test
set.

prediction<-‐predict(rfmodel,
testdata)

confusionMatrix(prediction,
testdata$left)

Confusion
Matrix
and
Statistics

Reference

Prediction

0

1

0
3786

48

1

10
1156

Accuracy
:
0.9884

95%
CI
:
(0.985,
0.9912)

No
Information
Rate
:
0.7592

P-‐Value
[Acc
>
NIR]
:
<
2.2e-‐16

Kappa
:
0.9679

Mcnemar's
Test
P-‐Value
:
1.184e-‐06

Sensitivity
:
0.9974

Specificity
:
0.9601

Pos
Pred
Value
:
0.9875

Neg
Pred
Value
:
0.9914

Prevalence
:
0.7592

Detection
Rate
:
0.7572

Detection
Prevalence
:
0.7668

Balanced
Accuracy
:
0.9787

'Positive'
Class
:
0

The
model
is
98.84%
accurate,
this
will
prove
to
be
very
beneficial
in
identifying
employees
that

are
likely
to
be
leaving
in
the
future.
What
variables
are
most
important
in
leaving
vs.
staying?

importance(rfmodel)

MeanDecreaseGini

satisfaction_level

1226.048093

number_project

665.390311


536.922188

time_spend_company

664.193153

promotion_last_5years

4.487941

last_evaluation

430.694068

According
to
the
random
forest
model,
satisfaction,
number
of
projects
and
time
spent
at
the

company
are
the
three
most
significant
variables.

Conclusion
and
Recommendations

I
very
much
enjoyed
learning
more
about
this
dataset.
I
performed
so
many
types
of
analyses

because
retaining
a
company’s
best
employees
is
extremely
important.
High
turnover
is
costly,

and
if
a
company
wants
to
grow
you
need
the
right
people
leading
the
way.
I’ve
worked
for

organizations
in
the
past
that
have
had
high
turnover
rates,
and
while
you
want

underperforming
employees
to
leave,
you
want
your
best
workers
to
grow
with
you.

What
I
found
most
useful
in
this
project
were
visualizations,
the
decision
tree
and
the
random

forest
algorithm.
They
all
can
be
used
in
different
ways.
If
management
wants
a
basic

understanding
of
what’s
going
on,
I
would
show
them
the
visuals,
if
they
want
to
know
what

patterns
are
harming
them,
I
would
go
over
the
decision
tree,
and
if
they
want
to
know
what

employees
will
leave
in
the
future,
the
random
forest
model
would
be
helpful.
Based
on
all
of

those,
here
are
the
two
key
points
management
should
know
concerning
why
their
best
and

most
experienced
employees
are
leaving
prematurely:

1.) They
are
being
overworked
–
it’s
common
for
managers
to
take
advantage
of
employees

who
do
a
good
job
by
giving
them
a
heavier
workload.
This
is
costing
the
company,

because
they
are
deciding
to
leave.

2.) They
aren’t
being
promoted-‐
good
employees
expect
to
be
rewarded.
There
is
a
large

group
of
employees
with
high
satisfaction
levels
who
have
been
at
the
company
for

more
than
four
years,
but
they
decided
to
leave
because
there
isn’t
any
career
growth.

There
are
a
couple
of
simple,
obvious
actions
management
can
take.
They
shouldn’t
work
their

best
employees
more
than
anyone
else,
and
they
should
be
promoted
after
3
or
4
years.
In

time,
I
think
they
will
find
that
although
the
company
will
be
less
productive
in
the
short
run,

reducing
their
turnover
rate
of
their
best
employees
will
lead
to
incremental
growth.

HR Analytics Project EEB

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