Hello.
My
name
is
Najmeh
Dehghani.
I'm
a
Data
Engineer
at
JLL.
I
have
my
Master
Degree
in Analytics
from
Texas
A&M.
My
capstone
project
is
in
RFQ
Process
Optimization
for
Pre-engineered
Metal
Buildings.
I
get
to
know
JMP
and
SAS
in
school
at
Texas
A&M.
I
founded
a
wonderful
tool
to
do
analysis
and
perform
analysis
with
that.
But
actually,
my
capstone
project
is
in
both
in
JMP
and
also
Python.
I
have
done
it
in
both.
Basically,
the
objective
of
my
project
is
to
predicting
the
weight
of
pre-engineered
metal
building
by
considering
various
geometric
and
geographic
requirements.
Let's
have
an
introduction
to
the
metal
buildings.
Metal
buildings,
basically
pre-engineered
metal
buildings
are
structures
that
utilize
steel
frames
to
support
the
roof
and
wall
panel.
We
can
do
the
customizations
on
them
based
on
the
customer's
requirement.
They
can
be
commercial
or
residential.
They
have
an
accuracy
because
they
are
pre-engineered
and
the
dimensions
are
precise.
They
have
flexibility.
We
can
change
the
design
based
on
the
customer
needs
and
customer
requirements.
What
exactly
on
this
project
we
have
to
do
is
that
estimate
the
weight
to
predict
the
price.
The
steel
structure
is
a
critical
factor
on
the
metal
building
pricing.
We
want
to
see
if
we
can
predict
that
weight.
Eventually,
we
can
predict
the
price
and
eventually,
we
can
work
on
the
RFQ
process.
Basically,
let's
take
a
look
at
the
RFQ
process.
What
is
the
RFQ
process
that
we
want
to
optimize
and
what
is
exactly
their
current
system
is
doing.
As
I
mentioned,
the
business
challenges
on
hand
on
this
project
is
to
improve
the
RFQ
process
for
metal
buildings
and
to
see
if
we
can
improve
the
response
time,
reduce
the
server
cost,
and
mitigate
the
missing
opportunities
for
the
company.
To
send
out
the
RFQ
to
their
client,
basically,
they
are
going
to
choose
some
process,
some
steps.
They
review
the
inquiries
that
coming
to
the
company,
specify
the
purpose,
size,
layout,
design
requirement
of
the
building.
They
check
all
of
the
technical
specification,
the
load
capacity,
roofing
and
insulation
and
other
factors
to
prepare
accurate
quotes
that
cover
all
of
the
materials,
labor
and
cost
for
them,
calculate
the
price
and
send
out
the
RFQ
back
to
the
customers.
Their
current
system
that
processing
the
RFQ
for
them
basically
is
like
this.
They
are
right
now
having
three
servers.
Each
server
is
processing
about
480
RFQ
per
day.
The
time
each
RFQ
will
take
to
process
is
about
180
seconds.
The
cost
per
RFQ
is
approximately
75
cents.
Dividing
all
of
these
together,
we
can
come
up
with
a
number
of
$0.004
per
second
to
process
RFQ.
To
consider
480
per
day
per
server,
it's
going
to
be
$360
per
day
to
process
the
RFQs.
They
have
three
servers.
Totally,
it's
going
to
be
about
$1,000
per
day
for
all
three
servers
to
process
the
RFQs.
If
we
consider
365
days
in
a
year,
it's
going
to
be
about
$400,000
server
and
maintenance
cost
for
the
company
to
process
all
of
the
RFQs.
Basically,
we
want
to
see
if
we
can
improve
this
process
for
the
company
by
predicting
the
weight
based
of
the
historical
data.
The
methods
I
have
used
on
this
project
probably
based
all
of
the
machine
learning
projects
for
predicting.
It's
going
to
be
data
collection
and
preprocessing,
feature
engineering
and
selection,
and
model
selection.
Let's
take
a
look.
We
started
by
defining
exactly
what
is
the
business
problem.
The
business
problem
is
that
predicting
the
weight
of
the
metal building.
The
data
collection
and
pre-processing
is
that
how
I
handle
this
part
is
that
about
182
SQL
tables
were
provided
to
me
as
a
part
of
subset
of
historical
data
from
the
manufacturer.
Around
500,000
record
was
included
in
those
tables,
more
than
500
features,
US
data
only.
I
have
performed
the
ETL
process
and
cleaning
my
data
set
fine
abnormal
data
in
SAS,
Python
and
JMP
and
integrated
all
of
my
data
in
one
table
with
the
help
of
these
three
tools.
My
next
step
was
to,
the
feature
selection
or
extraction.
The
preliminary
feature
selection,
I
performed
it
starting
industrial
study
and
also
getting
the
expertise
judgment
to
eliminate
the
features
that
it
was
really
unrelated
to
our
target.
Then
I
started
correlation
analysis
to
examine
the
correlation
and
find
the
multicollinearity
between
my
features
to
eliminate
more
features.
Then
descriptive
analysis
and
visualization
to
check
all
of
the
relationship
between
my
data
as
I
have
done
in
JMP,
Python,
and
Tableau.
The
next
step
for
me
was
to
start
figuring
out
or
exploring
the
models,
the
algorithms
based
on
the
characteristic
of
my
data.
As
my
data
was,
my
target
is
an
integral
target,
so
I
started
to
look
into
the
linear
regression
stepwise,
random
forest,
decision
tree,
and
some
other
more
algorithm.
Eventually,
I
trained
and
evaluate
different
models
in
both
Python
and
JMP.
I
compared
the
performance
metrics
and
did
the
cross
validation
and
k-fold
validation
for
all
of
my
models.
If
we
take
a
look
at
here
in
the
middle,
we
can
see
the
models
that
I
trained
my
data
on
it.
Some
of
them
are
in
Python,
some
of
them
are
in
JMP.
To
compare
this
model
to
see
which
one
is
the
best,
I
had
to
compare
the
models
based
on
the
ASE,
and
also
to
check
which
model
is
performing
better
and
also
check
the
R-squared
to
see
how
close
our
forecast,
our
prediction
going
to
be
to
the
actual
data.
This
is
the
residual
plot
for
all
of
the
models.
If
you
go
back
here,
you
can
see
out
of
all
of
the
models
that
I
had
run,
I
have
two
models
that
are
the
best,
a
decision
tree
in
Python
and
boosted
tree
in
JMP.
If
we
take
a
look
at
both
of
them,
we
can
see
most
of
the
features
that
both
selected
are
the
same,
even
the
importance
are
close
to
each
other.
Looking
at
the
ASE,
we
can
see
the
ASE
for
decision
tree
is
about
4,000,
but
for
the
boosted
tree
is
about
2,000,
which
is
much
more
better.
Also,
R-squared
for
the
boosted
tree
is
a
little
bit
higher.
The
residual,
if
we
look
at
the
residuals
from
both
softwares,
we
can
see
the
residual
is
really
good,
close
to
the
actual
values,
or
from
Python
is
close
to
is
around
zero
values.
Both
model
are
really
good
and
they're
predicting
good.
It
means
that
we
can
predict
the
weight
of
the
building
based
of
this
historical
data
that
we
have.
Let's
see
how
it's
going
to
impact
the
business
here.
Okay,
we
have
a
good
model.
This
model
is
performing
well.
Let's
see
if
the
business
want
to
implement
this
model,
what
are
the
benefits
for
them?
The
first
chart
we
can
see
was
the
chart
that
I
already
presented
to
you
as
their
current
system.
We
know
they
are
each
server,
they
have
three
servers
and
each
server
is
processing
480
RFQ
per
day
that
totally
it
costs
$360
per
day
for
each
server.
Consider
that
we
want
to
process
the
same
amount
of
the
RFQs,
480.
But
at
this
time,
with
implementing
the
model,
we
can
lower
down
the
process
time
to
one
second
because
the
model
going
to
just
instantly
running
and
giving
the
price
to
us.
Considering
the
cost
going
to
be
per
RFQ
going
to
be
the
same,
cost
per
second
going
to
be
the
same,
just
processing
time
will
be
one
second
out
of
180
seconds.
The
total
cost
will
lower
down
to
$2
per
day
per
server.
Then
they
have
three
servers,
it's
going
to
be
$6.
If
we
consider,
it's
going
to
be
365
days
per
year
that
all
three
servers
are
running,
it's
going
to
be
about
$2,000.
Comparing
with
current
cost,
they
are
paying
for
the
server
and
processing
time.
It's
almost
99%
cost
saving
for
the
business
and
the
company.
But
surprisingly,
this
is
not
the
only
cost
they
can
save.
The
other
cost
they
can
save
is
or
they
can
bring
more
opportunities.
They
are
missing
some
opportunities.
Right
now,
they
are
processing
480
RFQs
per
day,
one
server.
But
if
we
lower
down
the
processing
time
to
one
second,
they
can
process
80,000
RFQs.
Considering
the
win
rate
for
them
will
be
4%.
Right
now,
the
total
number
they
can
win
per
day
is
going
to
be
19.
But
with
implementing
the
model
because
they
are
processing
more
RFQ,
so
they
can
win
more
and
they
can
bring
more
opportunities.
Actually,
basically
they
are
missing
about
3,000
opportunities
per
day.
Probably
this
is
going
to
be
more
than
their
capacity,
but
this
is
one
of
the
points
that
they
can
think
about
their
business
development.
This
is
my
project
about
they
could
have
predict
a
good
model
and
eventually
we
can
see
they're
going
to
be
a
huge
business
impact
for
the
company.
I hope
you
enjoy
it
and
thank
you
so
much
to
Texas
A&M
and
Dr.
Jones
as
my
capstone
coach.
