Beautiful.
Now we could add Jupiter notebook set up and our tools are importing thanks to installing them Conda.
Let's give this bad boy name.
End to end bulldozer price regression.
Nice and simple.
Wonderful.
And what we're gonna do is going to start off by bringing in some headings to a notebook.
So we have some structure of what we're doing and the ones we're going to do is we're gonna bring this
framework in so we get all these six steps here from definition data evaluation which I get Kaggle.
What evaluation metric they use features modeling and then experiments.
So let's do that.
We'll create a new cell above here just a predefined notebook.
So what could we start this off with.
Give it a nice title so predicting the sale price of bulldozers using machine learning.
Let's put the merger here.
There we go frequently used.
Good one.
Turn this into markdown.
Wonderful.
So in this notebook
we're going to go through an example machine learning project with the goal of predicting the sale price
of bulldozers.
Nice and simple right.
Do you want to get into modeling and and to working on code as fast as possible.
So this is just these kind of layouts is just to help us out in the beginning.
So problem definition was an excellent data.
Number three e is so you can't even remember what the top of my head.
Let's go back Maria.
This is why we have this so we can check it out.
Evaluation had a mind like there.
But these things will happen right.
Whenever you dive into every project it's not going to be like you know everything of what to do off
by heart.
From the very start.
That's what machine learning is experimentation features.
Let's go through these and then maybe down here somewhere we'll be modeling or whatever but we'll wait
till we get to that point.
So let's define our problem in a single sentence.
We'll go back to Kaggle.
So we go to the overview.
There we go.
You could just put in that but we'll rewrite it in our own words.
That's a bit more fun isn't it.
So come here.
What could we call this
brilliant quote.
How well can we predict the future sale price of a bulldozer given its characteristics
and previous examples of how much simpler bulldozers have been sold for.
Does that make sense.
So our goal here is to use some data to build a machine learning model to predict the sale price of
bulldozers in the future.
And now data.
Now we've already imported this.
So if we have a look at you've been notebook open we've already got this open here.
We don't need next year window.
Let's have a look at data.
Blue Book for bulldozers.
There we go.
We've got trained invalid train.
All right.
So I might just put a link in here.
So a guy the data is downloaded from the Kaggle
Blue Book for bulldozers competition
and then you might come into this cable competition and they've already given us some information so
I might just put something like this so I might just copy this to remind ourselves of what train valid
and test is.
Again this would be different depending on whatever data set you're working with.
We'll put it in here.
So train valid and test just so someone who is new to this would be out goes round notebook and go Oh
Cassie what's happening there are three main data sets and we're just gonna put the link in here this
is all part of communicating your work right.
That's what you want when you're doing things machine learning or any kind of coding project.
It can be easy to sort of get lost in making your own set of projects and then someone else gets upon
it and because you've done it all in your own style they're gonna be like oh wow what's going on here.
So this is what we're trying to do.
We're trying to help our future selves as well as anyone else.
We share our work with so evaluation we can come to cargo competitions whenever a competition is posted
on Kaggle.
There's gonna be an overview there's gonna be some prizes.
It's gonna be a description of what's going on here and there's gonna be an evaluation so how do you
win the competition.
So here is where we'll be shown the evaluation metric for this competition is the R M S L E or root
mean squared log era.
Wow that's a mouthful.
Between the actual and predicted auction prices see that's what all a valuation metrics are is a comparison
between the actual and predicted auction prices.
So what we'll do is we're going to copy this in here.
There we go.
And s and don't worry we're gonna have a look at that we haven't actually gone over what our M SLA is
but we have gone over mean squared error.
Now you might be able infer from this that rude mean squared log error is just the root of the mean
squared lawyer error but we'll have a look at that when we come to evaluation.
For more on the evaluation of this project check we're going to put another link in there.
So as our goal with most regression metrics we might pull in here.
Note the goal for most regression evaluation metrics is to minimize the era for example.
Our goal for this project will be to build a machine learning model which minimizes and SLA so that's
the regression metrics right.
If you have mean absolute error and lazy or mean squared error your goal is often to minimize it and
now features.
So this is where we want to have a look at what different parts are there of the data.
So if we come to Kaggle and we have a look at data this dataset comes with a data dictionary.
And the good thing is that we've downloaded that it's in a form of an Excel file that's the extension
for Excel.
Now I don't have excel on my computer but the beautiful thing is you can open Excel files in google
sheets which is what I've done here.
So what we'll see is we haven't imported our data set yet but these are the different columns.
We'll have a look at it in second.
And what this is going to tell us is.
Okay so this is the sales idea.
That's a column saleslady.
The description is unique identifier of a particular sale of machine at auction.
Wonderful.
So I might copy this.
Anyone with a Lincoln view.
Beautiful.
And go put it in here.
Kaggle provides a data dictionary
detailing all of the features of the dataset.
You can view this data
on Google Sheets so I'll put another link there.
Beautiful.
So that way we sort of know what's happening there with what's going on in a minute.
We haven't done much at all here right.
We haven't explored these at all.
But what I prefer to do is just set out this little layout and then get cracking on the code as soon
as possible.
We'll finish this video there.
So we've gone through steps 1 before.
Nice and simple right.
We've defined our problem so we know what we're doing.
We know where the data is coming from.
We know what evaluation metric we're moving towards.
Thanks to Kaggle and thankfully Cagle have provided a data dictionary for us and these things you won't
always get them like from the start as simple as this.
So this is where it might take if he didn't have these things if he didn't have a problem definition.
If your data wasn't in one source you could easily download from a link or your evaluation metric wasn't
just given to you.
Same with the data dictionary.
These are things you might have to do some research to do.
But luckily for this particular project we have them already.
So without any further ado since our tools are ready let's import the data and get started on it.