>> The data science life
cycle made a lot of sense.
Now I know that I want to define
my problem before I do anything else.
So my app is a service-based app.
This means that it depends
on not only usage data,
but also external factors
like weather and date.
For example, how people
rent bikes might
look very different on a
holiday compared to a workday.
I know that I want to use
the anonymized data that
I collected my app,
as well as the external factors
like date and weather to make
better informed decisions about how
and where I place my
bikes around the city.
I know that this
initial analysis that
we do today is just the first step.
I'm going to take
what I learn and I'm
going to improve what data I
collect and what questions I
ask so I can be more
successful in my app.
So what does success look like to me?
Well, for me it means more
people renting bikes.
Now I could spend a lot more money on
marketing but another way to do
that is to make sure
that my inventory,
my bikes, are in the right
place at the right time.
Now that's a pretty complex matrix,
so let's dial it down a
little bit more and focus on
how many bikes I might need in a
certain area in the next hour,
or a day, or a few minutes even.
So that's going to be my question.
I want to predict how
many bikes will be
rented in the next hour
in a certain place,
then maybe I can move my inventory to
that area and more
bikes will be rented.
So it looks like I've got
a pretty good grasp of my
business understanding.
I don't really want to mess
with my data quite yet.
I want to get an initial analysis
and understanding of
what's going on first.
So the next step is
going to be modeling.
Then I do want to try to deploy
this as a web service
because I want to try it
out in the wild and see if I can make
any predictions and
improve this overtime.
So let's check back in with
Francesca so that we can
learn a little bit more
about machine learning
models and maybe find
out which one we should
use for our problem.
