All right.
So multiple linear regression data set.
Let's get straight into it.
This one's going to be fun 50 startups.
This is going to be a venture capitalist a fund challenge.
All right.
So what do we have here.
We've got we've only got five columns.
But the real interesting thing about this data set,
this isn't like a very realistic life, like, business challenge.
So you've got, 50 companies here, 50 companies in total.
And what they have is
they have some extracts from their,
profit and loss statements from their income reports.
So how much did the company in this given financial year that you're analyzing?
And for how much in that year did it spend on research and development?
How much did it spend on an administration like paying employees,
paying executives, and so on, and how much it had spent on marketing?
So those are the three major, operational spends, I guess,
and which stated works.
And and finally, what is the profit?
What was the profit of that company for that financial year?
And the challenge here is this is a dataset.
It's totally anonymized. So we don't know the companies.
And also the there's only 50 companies.
So there's a venture capitalist fund that has hired you as a data scientist
to analyze these 50 companies over, analyze this dataset and create a model
that will tell the venture capitalists fund which types of companies it should,
it is most interested in investing.
And their main criteria is the profit.
So profit is their, dependent variable.
The most important variable for them.
And these are all independent variables.
So you have to create a model which will tell you about profit
based on R&D spent in the Australian market.
Spend instead.
And bear in mind that the
the venture capitalist fund is not looking to invest in these 50 companies per se.
It's not just, giving you a data set.
And then like obviously then they would invest in this one
because it's got the highest profit, profit.
But what they're looking for is
this is a sample.
And they want to understand, for instance, where companies perform
better in New York or California, all other things held equal,
or which companies perform better if you hold these columns equal.
All right.
Will a company that spends more on marketing perform
better, or a company that spends less on marketing?
Also, they want to understand, the main thing probably I want to understand is,
how when they assess companies, do they look for company to spend more on R&D
spend or on research and development or companies that spend more on marketing?
So which of these two, spends yields
better results of profit, brings more results of profit.
And based on your model that you'll create, they will have a
they'll set up a set of guidelines for their own, venture capitalist fund.
And they'll be like, okay, so we are more interested in companies.
This is just an example that work in New York, that operate in New York
and that have a very low administration spend and a very high R&D spend, which
so the R&D spend has to be much higher than administration, marketing spend,
something like that.
So basically you helping them create a model, based off of this sample
that will allow them to, assess
where and in which into which companies
they want to invest to achieve their goal of maximizing profit.
So there you go.
It's it's already not an obvious data set.
It's got many variables, got many records.
You can't just tell off the top of your head.
You can you probably can see that they're ordered by profit here. Right.
But there is kind of a mix all over the place.
So let's get into this is going to be a fun and exciting section.