Hello and welcome back to our second structured data project.
Now this one's really exciting right because we're going to be predicting the sale price of bulldozers.
Who doesn't love bulldozer remember when I was a kid I used to have all these tractors like tanker trucks
and used to play with them all the time.
And this one's a little bit different.
Of course it's a regression problem because we're trying to predict a number a.k.a. the sale price of
bulldozers.
But what we're gonna do is we're going to take data and in this case the data.
See this little clock emoji.
It has a time component to it.
So that means in our previous project there was no time series it was just a big static data set whereas
this one there's a sale date component which means that a bulldozer has been sold on a certain date.
And so what we'll be doing is using sales from the past to try and predict sales in the future.
And that's our problem definition right is we'll take our data which is full of bulldozer sales we're
trying to predict the sale price.
And I mean you might be looking at this emerging going that's a tractor Daniel that's not a bulldozer
and you know there there's no bulldozer emoji sadly yet.
That may be an update coming from Apple but who knows.
Fingers crossed.
Of course we're going to use our six step machine learning framework.
We'll start by defining our problem which is of course seeing if we can predict the sale price of bulldozers
we'll have a look at some data we'll find and an evaluation metric which is suitable for our problem.
Well then have a look at the features of the data.
We'll of course try some machine learning modelling because after all that's our goal is to build a
machine learning model that helps us to breed evict the sale price of bulldozers and throughout the
entire session we'll be doing experiments now we've seen what tools we can use and we're going to be
using the exact same tools set that we used in the previous structured data project.
So we'll have Panda's map plot layer but none imply for data analysis we'll be working within the Jupiter
notebook using our Condor environment and we'll use psychic learn for any machine learning modelling
now because this is a new project will approach it as if it's a new project.
We've got our steps to take in a new project here.
But we've been through these we've got our computer we've download installed mini Condor.
This is where we're at now we're starting a new project.
So we'll create a new project folder and now of course because we're using the exact same tools as what
we did in the previous structured data project you could complete this entire project.
The bulldozer sale price prediction in that previous folder.
However for completeness we're going to create a new project folder or download our data from Kaggle.
We'll get to that in a second.
We'll create an environment which holds all of our tools that we'll be using we'll use our Jupiter notebook
as our workspace to perform data analysis and manipulation using our name pi panders and map plot lib
tools and then machine learning using psychic learn.
Now of course if you do get stuck and don't worry you probably will I get stuck all the time.
The steps that you can take is to follow along with the code as best you can try it for yourself.
Remember our motto if in doubt run the code then if you want to find out about more about what a function
is doing.
Remember to press shift and tab to read the dock string which will give you some information of water
what a function is doing.
If you're still stuck then of course don't be afraid to search for it right you'll come across resources
like stack overflow of the documentation for cyclone panders and then if you search for it and you find
some information maybe you copy it from there from the documentation into your Jupiter notebook.
Then of course try again remember if in doubt run the code and if you're still stuck never be afraid
to ask a question.
If you're sitting on something you're thinking haha I wish I knew the answer to this and wonder I can't
find it anywhere.
Just ask right if you're running into an issue like that.
More than likely someone else is running into a similar issue so don't be afraid to ask pose the question
you can go to stack overflow or discord or anywhere else can ask a question.
Never be afraid to ask a question.
All right.
With that being said let's not wait any longer.
Let's get our project folder set up and get into this second project.