‫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 bulldozers?
‫I remember when I was a kid, I used to have all these tractors like Tonka 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 going to 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 were we 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.
‫Imagine going, that's a tractor, Daniel, that's not a bulldozer.
‫And yeah, I know there is 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 an evaluation metric which is suitable for our problem, will 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 predict 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 toolset that we use
‫in the previous structured data project.
‫So we'll have pandas, map, plot, lib and numpy for data analysis will be working within a Jupyter
‫notebook using our conda environment and we'll use socket 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 downloaded installed Mini Conda.
‫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.
‫We'll 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 will be using.
‫We'll use our Jupyter Notebook as our workspace will perform data analysis and manipulation using our
‫numpy pandas 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 docstring, which will give you some information of what a 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 StackOverflow and the documentation for cyclone pandas.
‫And then if you've searched for it and you find some information, maybe you copy it from the from the
‫documentation into your Jupyter 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 and you think, Oh, I wish I knew the answer to this, I 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.
‫Post a question.
‫You can go to StackOverflow or Discord or anywhere else.
‫You 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.