Long before going deep dive into the session, let's have a quick recap of what we have done in our
previous session.
So no previous session, we have this distribution, which is nothing but a distribution of price of
each and every room type tonight and per person.
So this is exactly the distribution that we have come of it in a similar way.
We have this analysis, which is exactly a room price per night over the air with respect to my resort
hotel as well as Cost City Hotel as so in the session.
We have this assignment in which I have to find what is our distribution of nights spent at hotels by
different different market segment and hotel type Lexcen.
Let's see if I'm going to call our pad on my date of now.
You will see over here, this is the data fee that you have to consider for your analysis purposes.
So here you have a column, Naem, which is exactly something known as market segment, which is exactly
this one, which is exactly this one.
And this is exactly your second column, which is my stays in weekend.
And it means it means you have to find a distribution of each and every market segment or you can say
you have to find distribution of it stays in weekend night in each and every market segment.
So for this, we have a very handy plot.
So I'm going to say asanas dot box plot.
And in this box plot, I have to play with different different parameters.
So here I'm going to say on X axis, I have this I have this feature.
So here I'm just going to do copy paste and basically on y axis or y axis, I have this column, so
I have to just copy.
I have to just paste over there.
Let me remove the special character and after it, what I have to do, I have to say what exactly is
my little frame, which is my data and simple.
I had to split this box plot on the basis of this hotel, on this basis, all on the basis of this hotel
and after it.
What I have to do, let's say I have to set my own window site for this.
I'm going to say Paltalk figure.
And here I have something which is exactly Fixit and I have to assign my physical side of that Cifas
Kincumber 10.
So after what I have to do, I have to just execute it.
And this is exactly your box plot with respect to each and every different different market segment
you will see with respect to this stock market segment.
You have this distribution, whereas with respect to all these different, different kind of market
segments, you have this kind of situation.
So from this, if I have to conclude that, I can say almost most of the groups are normally distributed.
Yeah, some of have some skewness.
You will see this this data point, which are exactly outliers.
It means some of have some positive skewness in my data.
So if looking at this box plot or I can see looking at the distribution, I can see most people do not
seem to prefer to stay at the hotel more than one weeks.
And you will observe over here in case of resolved people are going to stay more.
It means it is always that whenever people go to resort hotels, they prefer to stay more.
So that's a basic conclusion, how you can fetch from your data.
So let's go ahead with our second problem statement, which is I have to analyze preference of guest
what they basically prefer.
So I have to analyze their behavior, what basically the PAFA.
So here I'm going to say let's say, well, let me show you a thing.
So very first, I have to excuse my middle column.
And on this, I have to say I could just call this the loop, because you will see over here this this
Beeby has highest count.
It means most of the guests are basically going to prep for this meeting.
Let me show this thing.
Why am I pie chart?
Or you can use some handy personal picture like me, use Pockley.
So I'm going to say B X dot by and in this, if you will, plus tab, you will get all your stuff.
So here what I'm going to do very first, I have to pass my Netafim, which is exactly my data.
After what I have to do here in my names, I have to just copy this and I have to just paste over there
and.
In this index, I have to say DOT index of this one, and after what I have to do, are likely brashest
plus tapu check all the parameters.
It has names.
So let me just do some modifications.
And this is exactly my names.
It is not index.
So here I'm going to say it is nothing, but it is just my values.
And if you will shift crosstab, you will see here you have name, here you have values.
And let's say I have to use something known as donor chart.
So for this I have to just blow this whole parameter.
And in this whole parameter I have to set some value.
Let's say I have to set a value of five five.
And if you will, execute the self executed and this is your beautiful donnacha and if you will hold
your mouth, you will see all these have this discount.
So you will see this this breakfast has that much higher ground.
And if you have to conclude from this visual, you will see this is exactly on a chart.
And almost 90 percent of the bookings are definitely reserved for breakfast.
So that's the type of analysis that's type of conclusion you can fetch from data.
You can also consider your pie chart instead of this chart.
You can also consider Barcia, but you done it and by will play a very handy role whenever you have
some left number of Category four.
The idea behind when to use a lot means that that's all up to what scenario text all up to what use
we have.
So I hope you love the session very much.
Thank you.
Have a nice day.
Keep learning.
Keep growing.
Keep practicing.