‫Beautiful.
‫Now we've got our Jupyter Notebook set up and our tools are important thanks to installing them with
‫Conda.
‫Let's give this bad boy name.
‫End to end.
‫Bulldozer price regression.
‫Nice and simple.
‫Wonderful.
‫And what we're going to do is we're going to start off by bringing in some headings to our notebook.
‫So we have some structure of what we're doing.
‫And the ones we're going to do is we're going to bring this framework in.
‫So we'll get all these six steps here.
‫Problem, definition, data evaluation which check out Kaggle for what evaluation metric they use features,
‫modeling and then experiments.
‫So let's do that.
‫We'll create a new cell above here just to certify our notebook.
‫So what can we start this off with?
‫We'll give it a nice title.
‫So predicting the sale price of bulldozers, using machine learning.
‫Let's put a little emoji here.
‫There we go.
‫Frequently used.
‫Good one.
‫We'll 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.
‫We 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 or was the next one data?
‫Number three is I can't even remember what the top of my head.
‫Let's go back.
‫Remember, this is why we have this so we can check it out.
‫Evaluation had a minor 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 will 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 if 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 we'll come here.
‫What could we call this?
‫Hmm.
‫How well can we predict the future sale price of a bulldozer, given its characteristics?
‫And previous examples of how much similar 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 a notebook open, we've already got this open here.
‫We don't need an extra window.
‫Let's have a look at data blue book for bulldozers.
‫There we go.
‫So you got try an invalid train.
‫All right, so I might just put a link in here.
‫So we go.
‫The data is downloaded from the Kaggle
‫Blue Book, the Bulldozes Competition.
‫And then you might come into this cable competition and they've already given us some information,
‫so we might just put something like this, so we might just copy this to remind ourselves of what train
‫valid and test is.
‫Again, this will be different depending on whatever data set you're working with.
‫We'll put it in here.
‫So try and valid and test just so someone who is new to this would be able to go through our notebook
‫and go, Oh, okay, I see what's happening.
‫There are three main data sets and we're just going to put the link in here.
‫This is all part of communicating your work, right?
‫So that's what you want when you're doing things in 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 going to 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 would share our work with.
‫So evaluation.
‫We can come to Kaggle competitions whenever a competition is posted on Kaggle.
‫There's going to be an overview, there's going to be some prizes, it's going to be a description of
‫what's going on here and there's going to be an evaluation.
‫So how do you win the competition?
‫So here is where we'll be showing the evaluation metric for this competition is the r m s l a or root
‫mean squared log error.
‫Wow.
‫That's a mouthful between the actual and predicted auction prices.
‫See, that's what all our 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.
‫Ms..
‫Ellie.
‫And don't worry, we're going to have a look at that.
‫We haven't actually gone over what our Emsley is, but we have gone over mean squared error.
‫Now, you might be able to infer from this that rude mean squared log error is just the root of the
‫mean squared log era.
‫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.
‫Well.
‫So as our goal with most regression metrics, we might put it in here.
‫Note The goal for most regression evaluation metrics is to minimize the error.
‫For example, our goal for this project will be to build a machine learning model which minimizes and
‫slow a.
‫So that's four regression metrics, right?
‫If you had mean absolute error may 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 data set comes with a data dictionary.
‫And the good thing is that we've downloaded that it's in the 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 have an imported our data set yet, but these are the different columns.
‫We'll have a look at it in a second.
‫And what this is going to tell us is, okay, so this is the sales ID, that's a column sales ID.
‫The description is unique identifier of a particular sale of a machine at auction.
‫Wonderful.
‫So I might copy this.
‫Anyone with a link can view beautiful and go put it in here.
‫Kaggle provides a data dictionary.
‫Detailing all of the features of the data set.
‫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 to 4, 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 a valuation metric we're moving towards thanks to Kaggle and thankfully Kaggle 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 you didn't have these things, if you didn't have a problem definition, if your data wasn't in one
‫source that you could easily download from a link or your evaluation metric wasn't just given to you.
‫Same with a 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.