Hello, all before going ahead in this session, let's have a quick recap of what we all have done in
this project till now.
So starting from day to day, importing data, cleaning it up, processing this group map, which is
exactly my spatial analysis.
And despite your box plot, this line chart be what they were, different kind of relationship, different
kinds of analysis, all that still.
So in this session, we have this assignment, which is exactly my last session and this last assignment
for this project.
So the very first assignment that we have to deal with that is exactly what is my price per night and
person based on booking.
And so you have to analyze this policy statement and you have to come up with some conclusion.
So here, let's say if on this cleaned it up, if own this clean data, if I'm going to call my columns
over there.
So here you will observe over here.
Here you have all the columns as market segment.
This is Edir and this reserved room time.
It means you have to analyze the distribution or against the price distribution of each and every category
of market segment having different different reserved room type.
So here either you can use distribution plot, but I'm going to use my fancy cloth, which is exactly
my box plot.
So for this, I'm going to say let's say either you can use Barcott or because in such scenarios, Bartra
can be very handy.
So here I'm going to say S.A.S. dot ba plot.
And in this bar plot, if you will pass shiftless tab, you will get all the documentation of this function.
So X value data, order and order and all these things.
So on this X axis, let's say, I have to say it is exactly my market segment and definitely on line.
I just need my price, which is exactly my ADR and definitely on in this huge parameter.
I have this reserved the room underscore type, which is exactly this.
So just copy, just paste over here.
Nothing fancy.
And after it, in your data frame, you have this all clean and a score data and there are tons of parameters
that you can play with that.
So just execute it and it will show you this Liswood.
But still that looks very messy, literally very messy.
So in such case, you can set your own side.
So I'm going to say BLT dot of finger.
And here I have a parameter, which is my fixie and I'm going to set my own phagocytes sides are on
the underside of twenty cross ten.
So again, executed and it will take some couple of seconds depending upon what its specifications you
have.
So this is exactly that bar plot that you need.
You will see you will see over here.
This is exactly that label that you have assigned.
And here you have all the different different kind of market segment.
Let's say I'm going to conclude for, let's say this or this online.
So in this line, you will of over here this pinkish and it's pinkish with respect to this edge type,
it means whenever a person is going to book via online, he or she may be opt for this Category two.
So that's a type of analysis you can fetch from all these different different type of board charts.
So let's go ahead with our next honoree statement, which is exactly how many bookings were canceled.
So it means I need the data.
We are my all bookings are cancelled bookings.
So for this, I'm going to say data of is on this call.
Cancel it, Callicles, to one.
So this exact may filter.
I have to pass this filter in my little frame so that I have my filter data from next year.
I would say its name is canceled and just executed.
And in this data frame, let's say if I am going to call, I had to show you how this data frame looks
like.
You will see you have all entries with respect to this one in this is canceled.
Now, in this tantlinger frame, you need a data frame or you can say you need a separate value.
Yeah.
How many resort hotels for which bookings are canceled and how many cities hotel for which my bookings
are canceled.
So for this, I'm going to see my cancel all four hotel very first to have to access this hotel.
And in this hotel you are going to say do nothing but my this resort hotel.
I just copy from here and here you are going to say it is exactly my resort hotel.
So this is exactly my filter.
I have to pass this filter, just my data frame, and on this, what I am going to do, I'm just going
to call land of it.
So it will exactly give me some value, which is nothing but my total number of resort hotel bookings
that gets canceled in a similar way.
You can compute it for like a city hotel.
So here I am going to say it is nothing but my city hotel.
So it is nothing but my city would have just executed this is that value with respect to your city hotel.
Now, let's say I have to plot this this value.
Let's say I'm going to use my bank.
Chad, whatever you want to use, you can also go ahead with your budget or whatever you want.
It's all up to you.
So let's say I'm going to use my pie chart.
So I'm going to say my stop by here.
I am going to say my values parameter.
So my values parameter in this value is I'm going to say the very first values is this one.
So just to copy, just to paste.
And similarly for this one.
Just to copy.
Just to paste.
And after it you have some names.
So in names you have nothing.
Let's say the very first name is the thing.
But my let's say Orridge underscore cancellations.
The second name is exactly let's say City Hotel.
S.H. underscore cancellations after it.
What you have to do, you can play with some multiple parameters in this class, multiple parameters.
You will see one here.
How much number of parameters, classes.
So I'm just going to execute it.
This is that visual that you exactly need.
You will see almost seventy five percent of cancel booking are with respect to this Hotel City hotel,
whereas this twenty five percent are with respect to this, this resort hotel bookings.
And this third problem statement will exactly will be your assignment that you have to sort.
That's all about this project.
Hope you love this project very much.
Can you have a nice day?
Keep learning.
Keep growing, keep practicing.