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Hello my friends, and welcome

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to this new practical activity
on multiple linear regression.

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So in this new section

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we're going to learn together how to build
a multiple linear regression model

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on the same data set that was introduced
by Kirill in the previous lectures.

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And just before we start,

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I just want to make sure
that everyone here is on the same page.

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This is the whole machine learning dataset
folder containing all the codes

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and data sets.

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And right before this tutorial,
I give you again the link to this folder.

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So make sure to connect to that link.

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And now we should be all on the same page
ready to start this new machine

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learning model,
which is of course in part two regression.

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And now we're going to go of course
to multiple linear regression folder.

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And we're going to start with Python
to implement this model.

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All right so this is the data set.

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And this is the Python
implementation in Ipynb format

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which you can either open with Google
Collaboratory or Jupyter Notebook.

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Make sure you also have the folder

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downloaded on your machine
so that you can indeed get these files.

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All right.

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So before we start the implementation,

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let me just explain again
what this data set is about.

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So remember venture
capital is funds hired you

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as a data scientist
to train a machine learning model.

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And actually a multiple linear
regression model to understand

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the correlations between these features,

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which are the spend in R&D,
administration and marketing.

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And as well as the state
and the profit of what, of 50 startups.

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So in this data set, it's

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very important to understand that
each row corresponds to a certain startup.

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And for each startup, well, you data
scientist collected the following data.

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R&D spend, administration
spend, marketing spend

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and the state of the startups.

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And of course their profit.

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Because the goal for this VC fund

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is to figure out in which startup
to invest based on these information.

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So these are all information
that we already know from 50 startups.

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And therefore, if you manage to train a
machine learning model that can understand

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well these correlations, well,
for the next step,

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you'll be able to deploy this model
on these features

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to predict what sort of profit
this new startup might generate.

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Okay.

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So for this fund, you definitely
want to build an accurate model.

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All right.

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And now we can start the implementation.

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But before we start

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I would like you to figure out

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what are going to be the first steps
of this implementation.

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You know, before I show it to you.

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All right.

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So first,
I hope that the first thing that came to

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your mind is that indeed the first step
is the data preprocessing phase.

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And remember, in the data preprocessing
phase we start by importing the libraries.

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That's for sure.

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Then we're going to import the data set.

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That's even more for sure.

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And then we will split the data set
between the training set and the data set.

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Because indeed
we want to train separately our model

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and evaluate its performance
on a separate set.

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Okay. So that's always required.

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But then is there
something else that we have to do here.

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Well, to answer this question
let's have a look

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at the columns
one by one, starting with R&D spend.

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So R&D spent is an empirical column
you know containing numerical values.

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And when we scroll down
you know we can scroll down

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because there are only 50 observations
corresponding to 50 startups.

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And we can see that here
there is no missing data.

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So all good then second column
the administration spent

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you know, all the spending administration
like paying employee salaries

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or anything else.

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So this column is once again
numerical with numerical values.

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And there is once again no missing data.

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Perfect.

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So so far are three steps of the data
preprocessing template

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argued the next one
column in marketing spend.

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Well same numerical column
with no missing data.

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Oh, good.

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And now this column.

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You know the last feature.

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Notice once again

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that all the features
are in the first columns

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and the dependent variable which you want
to predict in the last column.

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So back to this stage feature
what reflexes you have in your mind.

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Now you should have the reflex.

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If you paid attention to parts one
they depressing.

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Basically, the question I'm asking now is
do we need to apply

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a certain tool of our data
preprocessing toolkit,

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which we built in part
one into this data set?

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Well, here the answer is obviously yes,

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because indeed this state
column is not numerical.

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It actually has some categories.

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It has three categories which are New
York, California and Florida.

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And therefore

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that's exactly the same situation
as in parts when data preprocessing.

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There is no order relationship
between these

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three states
New York, California and Florida.

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And therefore we will have to apply
one hot encoding to that state column,

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and therefore will have to grab a tool
of our data preprocessing toolkit.

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And that's why I prepared it here
in order to indeed one hot encode

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that categorical variable,
the state variable.

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All right.

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And then the profit is fine.

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It is numerical.

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And besides there is once again
no missing data.

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So that's what you know you must do.

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First
you need to have a look at your data set.

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If it is not too long, you can check that
there is no missing data

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like we just did.

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If it is too long,
then I recommend to apply your data

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preprocessing tool that handles missing
data and deploy it on this data set.

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And then you must check
if any feature is categorical.

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And here we could check that very easily.

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The state is categorical.

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So we're going to apply our one hot
encoding tool

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of our data preprocessing toolkit
on this state column.

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And then of course
we will apply all the rest of the three

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essential steps of our data
preprocessing template.

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And once again we will do that
in a flashlight because this is a template

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where we only have one thing to change,
which is the name of the dataset.