hello and welcome back to data science Wednesday my name is Tessa Jones and I'm
a data scientist with decisive data and today we're going to talk about
predictive analytics and what it can do for you
predictive analytics fits into the spectrum of analytics that we've talked
about before starting with descriptive which is the most basic of the analytics
it is basically just cleaning relating summarizing and visualizing your data
really getting to the questions about what's happening in my business and then
there's diagnostic which is really getting down to why things are happening
what's causing my revenue to decline or to increase how are things related
things like that so if you've got a good base in both of these then we're ready
to move into predictive analytics which is gonna dive into what's gonna happen
in the future which is super powerful if you're a business person and you want to
be able to make good business questions if you have at least an idea of what
might happen in the future your your answers are already gonna be a little
bit better so let's dive in so let's go with an example cuz that just makes it
easier to kind of flow through what's actually happening here so let's pretend
that we are grocery store owners and if we're already talking about predictive
analytics you should have a pretty good grasp on descriptive and predictive and
diagnostic analytics so you probably already have a decent dashboard that
really tells you what's happening in your business right now so something
like this where you have you know something here that tells you revenue by
different departments like foods meats or foods and pastry or how your sales
changes by product or overtime things like that so you have an idea of what's
happening in your business but now you really want to know what's going to
happen in my business so one really common question is how many of a given
product am I going to sell for every store because this can really give you
quite answer questions around how you're going to support supply chain processes
or how you're going to manage the profits that you're going to have things
like that so the first thing we need to do is talk about what happened
past we really can't do anything or predict very easily unless we know or at
least have an idea of what's happened in the past so here we have three lines in
black that represent basically historical data each line here is one
year worth of sales for a given product and then the green line here is the
current year and here's today and if we build a predictive model it's going to
tell us what's going to happen for the rest of the year so if this is all set
up and we build a model basically we mix this information with all the data
that's really clean and well-organized we mash it together with a bunch of
mathematics and coding and basically we pop out some results and it shows up in
a visual like this where you have these are the cells that we have had and these
are the cells that we think we're going to have so a business person can look at
this chart and say wow we need to put a lot more products to this store because
I see sales are going to increase or our profit margins are going to be way
higher than we thought so we can start a new program things like that you can
really start to get innovative with your business decisions so let's pretend
we've built this model and it's been running for a year and now we want to
know how well is this model actually performing so down here we have a chart
that shows in black what we actually sold and then in green what we thought
we were going to sell and we see that there's some a couple of pretty big
misses right here we sold way more than we thought we would which leaves risk to
you know missing out on inventory or here we predicted we would sell more way
more than we did so both of these are kind of misses and so we need to go back
and look at the data and understand what assumptions we we applied that we're
maybe a little bit wrong or applied incorrectly or look at the data maybe we
weren't accounting for something and we kind of reorganize that and incorporate
it into the model and then we redeploy it and then the then we have a better
model this cycle can you know happen a couple times or it can happen many
it really depends on the data it really depends on the objective it depends on a
lot of different things but we do try to minimize the number of times that we're
having to iterate through this before we can have a really sound predictive model
so that's predictive analytics in a nutshell basically once you have a solid
foundation of descriptive and diagnostic analytics we can really start pushing
forward with predictive analytics and then next week we're going to start
talking about prescriptive analytics which really gets to the questions of
okay now that we know what's happening in the future what do we do about it I'm
Tessa Jones and that's a reindeer