‫Now, since we're starting a new project, it makes sense to take the steps in starting a new project.
‫We're up to here.
‫What we're going to do in this video is create a project folder, get our data ready, create an environment,
‫and then launch up a Jupyter notebook and make sure that we can import our tools so they're ready to
‫use for our problem.
‫So let's get started.
‫I'm going to go to my desktop.
‫I'm going to come to my machine learning course folder that I've been using.
‫I'm going to make a new folder and this one I'm going to call let's call Bulldozer.
‫Price Prediction project.
‫Nice and simple.
‫Go to any spelling errors.
‫Nope.
‫Beautiful.
‫So we'll come into this folder.
‫Beautiful.
‫We've got an empty folder.
‫So if we go here.
‫We can take this one off.
‫So create project folder now for the data.
‫This one's a little bit different.
‫We're going to go to Kaggle to get our dataset because what the bulldozer problem is, if we go to Qualcomm
‫Blue Book for bulldozers.
‫Now, this problem that we're working through originated as a kale competition.
‫So you can see here, predict the auction sale price for a piece of heavy equipment, aka bulldozer.
‫To create a blue book for Bulldozer.
‫Now, I'm assuming a blue book means maybe historic sales events or something like that?
‫Not entirely sure.
‫So if we look here, we can see an overview of the project.
‫When this competition first came out, there was a $10,000 prize pool for this competition.
‫And if you're wondering, hey, I'm not even sure what Kaggle is.
‫Well, you can imagine it as the home for data scientists, because Kaggle is full of different competitions,
‫different data sets, different notebooks and discussions, as well as heaps of courses as well.
‫Kale is just a plethora of resources for data scientists.
‫And if you're wondering what the competitions are, well, this is one here.
‫What happens is a company might post a data set and then it'll be your job to see how well you can model
‫it or how well you can build a system to solve the problem that they're posting.
‫And that's what we're going to be working through in this project.
‫We're going to download the data, the data set from the original competition, and then we're going
‫to build an entire project to end up building a machine learning model, and we'll see how we would
‫have ended up on the leaderboard.
‫So you can see here we've got some scores and these are different teams of people around the world of
‫data scientists just like you.
‫So that's what we'll have by the end of this project.
‫But you're talking too much.
‫Let's get the data so you can come to this URL here.
‫Google.com slash C for competition, slash blue book for bulldozers, slash data.
‫You can click this download all link.
‫Now you will need an account to log in to get this data.
‫But since I'm already signed in, I'm going to download it.
‫We can see that it's going into my downloads folder.
‫So if you come back to my file browser, you can see I've actually already downloaded it here, but
‫this is just going to re download the exact same file so that we've got it there.
‫Now we could keep it here, but since we're working in our project folder, I'm going to move it into
‫the Bulldozer Price Prediction Project, the folder we just created.
‫So your folder might be differently named.
‫Now we can see here.
‫Beautiful.
‫We've got our zip file.
‫Let's unzip that.
‫Wonderful.
‫And that comes into a folder of more folders far out.
‫We've got train, train and valid train and valid CSV.
‫Hmm.
‫And if you're looking through these wondering what's going on, well, the next place to look is back
‫at Kaggle and and see what's going on here.
‫So we've got data description.
‫What I might do is zoom in.
‫There we go.
‫Pinch to zoom.
‫Come on, Daniel.
‫You know how to use a computer.
‫Okay, so this is what it's telling us.
‫The data for this competition is split into three parts.
‫And remember how I said that this is a time series problem?
‫Well, we've got trained CSV which contains data up to the end of 2011 and then we have valid CSV,
‫which is the validation set which contains data from January one, 2012 to April 30, 2012.
‫Now you notice here that the validation set has data after the training set, which is from 2011.
‫We'll have a look at this when we're working through it.
‫And then finally, the test data set, which won't be released until the last week of the competition,
‫but because this competition has already passed, we've got access to the test csv.
‫It contains data after the validation set.
‫So see here, the validation set ends on April 30, 2012, whereas this one continues from May one,
‫2012.
‫Hmm.
‫Okay.
‫So this is some information here about about the data we're going to be working with.
‫There's also a data dictionary, but we'll have a look at that in a second.
‫Let's go back to the folder we were working with.
‫And if we look here, train CSV, where's trained CSV?
‫Oh, it's still in a zip file, so we'll unzip that, try and csv.
‫Wonderful.
‫Then we've got valid csv.
‫Okay.
‫So this one must be trained and valid.
‫CSV in the one file.
‫We'll have a look at that in our Jupyter Notebook in a minute.
‫We're just familiarising ourselves with the data we've downloaded from Kaggle.
‫Then we can see data dictionary.
‫Okay, that's pretty self-explanatory what that might be.
‫And then there's tests CSV.
‫Okay, so we've got that.
‫All right.
‫Now let's go back to our project folder that we're working on.
‫What I might do is put this create sticking with the folder nomenclature, which is a fancy name for
‫naming system that we've been using.
‫We might create a data folder and just put all of our data in there and then we can delete this zip
‫file because we've got blue book for bulldozers in there.
‫Wonderful.
‫Now we've got a file to work with.
‫We come back here.
‫Bulldozer Price Prediction Project.
‫We've got our data.
‫Let's go back to our workflow.
‫We've got our data in our project folder.
‫Excellent.
‫The next is to create an environment or collection of tools using Conda.
‫Let's do that.
‫And to do that, we're going to have to use terminal.
‫Make this window a little bit bigger.
‫So I'm going to change directory into my desktop, which is where I'm storing this folder.
‫Come back in a terminal so change directory decks top eml course and then within that I've got bulldozer
‫price prediction.
‫Wonderful.
‫And so I've got to go.
‫It shows me that I have this data folder, which is exactly what we've got there.
‫Wonderful.
‫And so now we're going to create an environment so we can do that with Conda Create and because we want
‫to create a little end folder or actually I need the, the prefix tag.
‫Now we've seen this in our previous project, but we're going to create a new environment within here.
‫So let's do that.
‫So conduct create prefix.
‫MV Yes, correct.
‫And then we're going to give it pandas.
‫Num pi map plot lib Jupiter and Psychic learn because we're staying in line with the tools that we want
‫to use.
‫So pandas map plot lib num pi Jupiter Mini Condor is what we're using to create this environment and
‫psychic cyclone.
‫So let's see if it works.
‫Beautiful.
‫And we're going to press.
‫Yes.
‫And there we go.
‫So I'll let this load for about a minute or so and I'll come back once it's finished.
‫Beautiful.
‫So that took about a minute or so on my machine.
‫It'll depend on how quick your machine is or how fast your Internet connection is, because Condi has
‫to download a few things so we can see to activate this environment which we've seen before.
‫We can use a code here to activate users.
‫Daniel Desktop Machine Learning Course, Bulldozer Price Prediction Projects Slash EMF.
‫And if we look back into our folder that we created, we've got an end folder which in here is going
‫to contain all of our tools.
‫So let's activate that and see if we've got a Jupyter Notebook ready to use, which we won't, but we'll
‫start up a Jupyter Server so we can get started in our workspace.
‫Desktop slash ML Course Slash Bulldozer Price Prediction Project slash.
‫MV.
‫Wonderful.
‫So you notice here that the base has now changed to this file path here.
‫And to test if we've got Jupyter installed, we can type in Jupiter Notebook.
‫What this is going to do is create a server on our local machine.
‫Okay?
‫This computer right here running Jupiter, which is where we can start Jupiter notebooks and get our
‫workspace up and running.
‫Beautiful.
‫So that took about 30 seconds or so.
‫The first time you run it in a new environment, it may take a little while to load.
‫Now we've got the exact same folder that we created in our file Explorer or Finder, in my case, up
‫on Jupiter.
‫Wonderful.
‫And the way we can test to see if our environment is is set up correctly so we can go new notebook.
‫Wonderful.
‫And to test it quickly, we're going to go import numpy as MP.
‫We need to check that we can import all of our tools.
‫Pandas as PD import map plot lib dot py plot as plt import src learn.
‫Now, if it's worked correctly, this cell should run without any errors.
‫Now, again, this might take a little while if it's the first time running in this environment as Conder
‫and Jupiter prepare all these tools.
‫So we'll just wait patiently while this runs through.
‫Beautiful.
‫So, again, on my machine, that took about 30 seconds or so, maybe closer to a minute.
‫But what this means is we've got our tools ready to use.
‫So if we come back to our workflow, we've just imported pandas, map plot lib numpy.
‫And so I get learn and we're working within a Jupyter notebook and we've gone through the steps here.
‫So we've created a project folder, we've downloaded our data from Kaggle.
‫We've created an environment which is a collection of tools.
‫We're working in a Jupyter Notebook, and now we have access to these tools here.
‫So this is what we're going to do now, data analysis and manipulation and of course, finishing off
‫with some machine learning.
‫Well, our notebooks are ready to go.
‫This is what we're working for.
‫The next few videos.
‫So I'll see you then.