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What if you can
predict the future?
For example, predicting demand
for smartphones or availability
of beds in a hospital?
Availability of hotel rooms?
Accurate demand
prediction converts
to big money for businesses.
This is a common challenge
across industries.
Let's choose the bike
demand prediction
as an example use case.
Bike sharing companies
have thousands
of bikes and 100s of bikes
stations across each city.
Each station has a fixed number
of docks for parking bikes,
and a variable number of
bikes available at any time.
Real time dashboards
show the problem,
but it's too late and expensive
to resolve the problem
by rebalancing the bikes.
If only bike corporations could
predict the demand per station
per hour.
So in today's demo,
we'll show you
how this can be
solved by applying
big data and
predictive analytics.
First, let's take
a look at the data.
Here, we have historical
bike availability information
integrated with
weather condition
in each station in
15 minute intervals.
This data is stored in the
Autonomous Data Warehouse
instance, and we use the SQL
Developer as an interface
to access the data.
Next, I will show
you how easy it
is to create a
machine learning model
by using the Oracle Analytic
Cloud without writing
a single line of code.
The first thing we need to do is
to connect the Oracle Analytic
Cloud's instance to our
Autonomous Data Warehouse
for instance and
upload all the data
files we have there to our
Oracle Analytic Cloud's
instance.
Once the data is uploaded, we
can create our machine learning
model by creating a data flow.
First, we need to select
the data which we want
to use for training our model.
Then, we need to select
a subset of columns
within that data table that
we want to actually use
for training our model.
In here, we want to use all
the columns in our table,
so we select all
of those columns.
Next step is to select the class
of machine learning algorithms
that we want to use for
solving our specific problem.
In this case, we want to train
a numerical prediction model.
So we need to select
algorithms within that class.
As you can see, we have four
different machine learning
algorithms built in the
Oracle Analytic Clouds.
Let's try the linear
regression model now.
The only variable which we
need to specify in order
to have a complete
linear regression model
is the target column that we
want our model to predict.
In this case, we want to predict
the number of available bikes
in each station at each hour.
So we need to select
the available bikes
columns from our table.
As you can see, all
the other variables
are set by default
or within the system,
and we don't need to
change any of those.
We just need to save our machine
learning model and the data
flow in order to
be able to run it.
Once the model is saved,
we can run the data flow
in order to train it on the
data set that you provided.
We can create four different
machine learning models
by using each of the
existing algorithms.
After creating and
training all those models,
we can compare them, based
on the r square metrics,
to find out which model
fits our problem the best.
As you can see here, the
linear regression model
fits our problem the
best and gives us
the best precision among the
four algorithms that we try.
All right, that's good.
But let's say I want to make
this model more precise.
I want to fine tune it
to my specific problem,
and I want to train it
on the entire data rather
than some portion of the data.
In order to do that, I'm going
to use Oracle Machine Learning
tool, or OMO for
short, and build
that same linear regression
model by writing some PL SQL
code.
Oracle Machine Learning tool
is a tool within the Autonomous
Data Warehouse which enables
you to access data and apply
machine learning models
within the database.
We store all these result
within the same Autonomous Data
Warehouse instance, and
we use the Oracle Analytic
Cloud in order to
visualize these data.
Let's look at this
result. The first graph
that I show you is the result
of validating our model
and the validation
portion of the data.
In here, blue bars
shows the actual number
of available bikes at each
hour in this specific location,
and the green line shows the
number of available bikes
predicted by our model at the
same hour at the same station.
As you can see here, the
green line pretty much
follows the trend in
the blue bars, which
means our model
is able to predict
the number of available
bikes with a great precision.
So we can trust the
prediction of our model.
Let's look at the
predicted number
of available bikes
in the same station
at each hour for a future date.
In this graph, each bars showed
the number of available bikes
predicted by our model
for a future date
at each hour at
the same station.
As you can see here,
around 10:00 and 11:00 AM,
there is a shortage of bikes
in this specific station.
This information can
help the planning team
to schedule
rebalancing in advance
in order to prevent this
situation from happening
in real time.
Deriving insights from data
by applying machine learning
is an iterative process.
The ML feature built
into OAC enables
iterating through models
without writing any code.
The tool writes
the code for you.
Doing this in a self driven,
super fast, highly available
database in the cloud with built
in machine learning capability
optimizes the entire process.
Projects like these,
which could take years,
can be done in a
week or a month.
Build, train, and
operationalize by leveraging ML
algorithms running in
parallel on the entire data.
No wasting time extracting data.
Better precision due
to larger data set,
and finally, continuous
and active process
that refines the model
as new data streams in.
Thanks for watching this video.
You can try out
our cloud services
for free using the link.
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