Hi,
my
name
is
Shalika Siddique
My
name
is
Anand Manivannan
and
we're
both
students
from
Oklahoma S
tate
University
and
we
currently
pursuing
a
business
analytics
and
data
science
degree.
Today
we
are
presenting
a
boaster
where
we
explore
euthanasia
in animal
shelters,
and
we
hope
to
understand
why
cats
and
dogs
are
being
put
down.
Every
year
we
lose
about
920,000
animals
annually.
Using
JMP Pro,
we
would
like
to
identify
the
key
factors,
that
lead
to
euthanization
of
cats
and
dogs.
Once
we
identify
these
key factors,
funds
can
be
channeled
to
relevant
sectors
to
prevent
euthanization
of
animals
that
could
have
been
saved.
In
addition
to
this,
we
aim
to
make
predictions
to
identify
which
animals
are
most
likely
to
be
euthanized.
A
little
information
about
our
data
set
here,
we
source
the
data
from
Austin's
data
portal,
and
the
animal
shelter
that
we
use
for
analysis
is
located
in
Austin,
Texas.
Overall,
we
had
about
130,000
records.
After
cleaning
and
filtering,
we
focused
on
about
67,000
records
that
were
specific
to
cats
and
dogs.
Prior
to
our
analysis
we
explode
the
data
set
and
attempted
to
derive
insights.
We
use
JMPs,
graph
builder
to
create
visualizations
such
as
bar
graphs.
From
the
67,000
records,
there
were
about
3,171
animals
which
were
euthanized.
Which
is
about
4.7
%
animals
of
the
shelter.
In
comparison
to
animals
surrendered
to
the
shelter
by
the
owner,
stray
animals
were
most
prone
to
euthanasia.
When
we
compare
the
age
of
the
animals,
we
notice
that
kittens
under
the
age
of
15 months,
contribute
to
25
%
of
euthanasia,
while
pups
contributed
to
13
%
of
euthanization
This
bar
graph
here,
is
an
example
of
one
of
the
visualizations
created
using
JMPs
builder.
The
lavender
bar
represents
cats,
while
the purple
bar
represents
dogs.
We
can
see
that
intact
males
followed
by
intact
females
are
more
prone
to
euthanization,
in compared
to neutered
animals.
Next A
nand
will
go
over
in
detail
over
the
modeling.
Thank
you Shalika.
Yes,
I'd
like
to
talk
a
bit
more
about
our
approach
towards
modeling
using
JMP.
Before
we
could
start
modeling,
we
performed
a
few
data
preprocessing
steps
to
prepare
our
data.
We
did
things
like
standardizing
units
for
certain
variables,
such
as
age,
which
was
in
weeks,
months
and
years.
We
wanted...
We
converted
that
to
just
months.
We
bend
on
the
age
variable
so
we
could
convert
it
into
a
categorical
variable.
It
looked
in
the...
It
looked
like
age
ranges
like
10-
15
and
15-
25.
We
grouped
rare breeds
and
colors
to
reduce
the
number
of
categories.
Additionally,
we
also
filtered
just
cats
and
dogs
from
all
the
other
animals
that
went
through
the
shelter.
During
a
modeling
phase
we
noticed
something
very
peculiar.
We
noticed
that
class
imbalance
in
our
target
variable,
which
talked
about
whether
an
animal
was
adopted,
and
whether
an
animal
was
euthanized.
About
64,000
records,
out
of
67,000
records
were
adopted
animals,
and
only
3,000
animals
were
euthanized
animal.
Since
a
model
was
to
focus
on
predicting
euthanasia,
we
had
to
resolve
this
issue,
and
hence
we
used
JMPs
Bootstrap
model
and
Boosted Forest
to
resolve
this
issue.
It
used
the
concepts
of
bagging
and
boosting
to
do
this.
Since
bagging
and
boosting
models
don't
really
give
a
lot
of
room
for
interpretation
in
terms
of
what
the
variables
do,
we
used
logistic
regression
to
interpret
these
variables
as
well.
After
modeling,
we
tuned
up
parameters
to
get
the
best
results.
We
chose
a
few
certain
metrics,
to
choose
the
best
model
based
on
its
performance
on
validation
data.
We
used
a
70:30
%
validation
split,
and
prior
to
modeling,
we
also
tested
the
assumptions
for
logistic
regression.
Or
over
to
the
top
right,
you
can
see
that
we
tested
for
multicollinearity
and
independence
among
variables
using
JMPs
contingency
analysis,
which
spread
out
a
muse
plot,
and
gave
us
a
P
and
correlation
value,
that
basically
told
us
which
variable
was
correlated
with
each
other.
Now
I'd
like
to
dig
a
bit
deep
into
each
model
and
how
we
selected
our
models.
Over
to
the
top
left,
you
would
see
that
we
chose
metrics
like
specificity,
this
classification
area
under
the
Cove
and
R-S
quare
to
choose
which
model
performed
the
best.
These
metrics
were
chosen
for
a
particular
reason
that
aligned
with
our
goal.
Our goal
was
to
predict
which
animals
would
want
to
be
euthanized.
The
cost
of
our
model,
incorrectly
predicting
a
euthanized
animal,
as
a
non-
euthanized
animal,
would
mean
that
animal
would
probably
die
and
not
be
saved.
Hence
we
wanted
to
focus
on
increasing
the
accuracy
of
euthanized
animals
and
reducing
the
misclassifications.
Hence
these
particular
metrics, were chosen
First we
ran
the
nominal
logistic
regression
model,
which
you
can
see
over
to
the
bottom
left.
The
Log
worth
immediately
gave
us
which
variables
were
the
most
important
in
predicting
euthanasia.
Turns
out
it
was
sex
of
the
animal
intake
condition,
intake
type
and
outcome age
.
A
lot
of
these
are
not
surprising,
and
it
matched
with
what
research
shows.
The
whole
model
turned
out
to
be
significant
as
well,
the
P-
value
less
than
0.001
Following
that,
we
ran
the
Bootstrap F
orest
model,
which
was
tuned
to
have
a
hundred
trees
and
feature
selection
criteria
value
of
three
Bootstrap.
We used receiving
operating
characteristic
or
the
AUC
curve,
to
determine
which
classification
threshold
gave
us
the
best
classification
results.
We
ended
up
using
0.1
or
10
%
as
our
classification
threshold.
Over
to
the
right,
you
would
see
that
we
ran
the
Boosted Forest
model,
with
parameters
of
87
layers
and
a
learning
rate
of
0.179
Over
at
the
bottom,
we
used
the
decision
matrix
for
all
three
models
to
calculate
the
specificity
of
each
particular
model.
Which you
give
us
how
accurately
the
euthanized
animals
were
being
predicted.
We
also
use
misclassification
rate
and
R-S
quare
from
the
overall
statistic
tab
of
JMP.
In
every
metric,
we
found
that
our
Bootstrap
model
outperformed
the
other
models,
and
hence
we
chose
that
as
the
winning
model
to
make
predictions
on
euthanasia.
Next,
I
would
like
to
go
over,
some
important
results
that
logistic
regression
gave
us.
With
regards
to
sex,
we
found
that
intact
cats
and
intact
dogs,
were
way
more
likely
to
be
euthanized
than
neuter spayed
animals.
With
regards
to
breed,
we
found
that
mixed
cat
breeds,
and
Pit Bull
dog
mixed
breeds,
were
more
likely
to
be
euthanized
than
all
other
breeds.
With
regards
to age
we
found
that
cats
that
are
4.5
to 6
years,
are
more
likely
to
be
euthanized than
younger
cats.
Dogs
under
1.2
years
are
the
least
likely
to be euthanized.
This was
widely
surprising
because
it's
contradictory
to
what
we
found
during
a
data
exploration phase.
Similarly,
with
regard
to
intake
type,
we
found
that
older
surrendered
animals,
are
twice
more
likely
to
be
euthanized
than
stray
animals.
This
is
completely,
again,
contradictory
to
what
we
found
in
the
data
exploration
phase.
That
goes
to
show
that
what
the
power
of
statistical
analysis
and
unbeating
true
facts.
Next,
I
will
be
handing
it
off
to
Shalika
again
to
go
what
recommendations
we
can
make
to
these
animal
shelters.
Thank
you Anand.
Based
on
our
analysis,
we
have
a
few
recommendations
that
animal
shelters
could
use
to
lower
euthanizations.
We
believe
that
animals
taken
into
the
shelter
should
be
neutered
or
spayed
This
is
in
accordance
with
medical
research,
which
proves
that
intact
animals
are
more
prone
to
diseases.
Animal
shelters
could
also
use
our
Bootstrap
Forest
model
to
prioritize
which
animals
needs
to
be
saved,
in
case
a
difficult
decision
needs
to
be
made.
In
support
of
that,
here
are
some
recommendations
from
Austin's
animal
shelter.
This
particular
shelter
would
need
to
prioritize
cats
over
dogs
as
they
are
more
prone
to
euthanizations.
With
regards
to
age,
cats
aged
between
4.5-6
years,
and
dogs
over
1.2
years
would
require
more
attention.
Owner
surrendered
dogs
need
to
be
prioritized
over
stray
animals.
Finally,
when
it
comes
to
breeds,
Pit Bull
mix
dog
breeds
and
mixed
cat
breeds,
are
more
prone
to euthanization
and
would
likely
require
more
attention.
That
brings
us
to
the
end
of
our
presentation.
We
hope
that
animal
shelters
could
use
this
analysis,
to
reduce
the
need
for
an
animal
to
be
euthanized.
Thank you.
