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Hello, all before going ahead in this session, let's have a quick recap of what we all have done in

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this project till now.

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So starting from day to day, importing data, cleaning it up, processing this group map, which is

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exactly my spatial analysis.

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And despite your box plot, this line chart be what they were, different kind of relationship, different

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kinds of analysis, all that still.

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So in this session, we have this assignment, which is exactly my last session and this last assignment

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for this project.

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So the very first assignment that we have to deal with that is exactly what is my price per night and

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person based on booking.

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And so you have to analyze this policy statement and you have to come up with some conclusion.

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So here, let's say if on this cleaned it up, if own this clean data, if I'm going to call my columns

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over there.

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So here you will observe over here.

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Here you have all the columns as market segment.

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This is Edir and this reserved room time.

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It means you have to analyze the distribution or against the price distribution of each and every category

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of market segment having different different reserved room type.

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So here either you can use distribution plot, but I'm going to use my fancy cloth, which is exactly

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my box plot.

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So for this, I'm going to say let's say either you can use Barcott or because in such scenarios, Bartra

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can be very handy.

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So here I'm going to say S.A.S. dot ba plot.

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And in this bar plot, if you will pass shiftless tab, you will get all the documentation of this function.

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So X value data, order and order and all these things.

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So on this X axis, let's say, I have to say it is exactly my market segment and definitely on line.

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I just need my price, which is exactly my ADR and definitely on in this huge parameter.

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I have this reserved the room underscore type, which is exactly this.

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So just copy, just paste over here.

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Nothing fancy.

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And after it, in your data frame, you have this all clean and a score data and there are tons of parameters

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that you can play with that.

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So just execute it and it will show you this Liswood.

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But still that looks very messy, literally very messy.

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So in such case, you can set your own side.

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So I'm going to say BLT dot of finger.

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And here I have a parameter, which is my fixie and I'm going to set my own phagocytes sides are on

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the underside of twenty cross ten.

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So again, executed and it will take some couple of seconds depending upon what its specifications you

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have.

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So this is exactly that bar plot that you need.

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You will see you will see over here.

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This is exactly that label that you have assigned.

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And here you have all the different different kind of market segment.

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Let's say I'm going to conclude for, let's say this or this online.

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So in this line, you will of over here this pinkish and it's pinkish with respect to this edge type,

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it means whenever a person is going to book via online, he or she may be opt for this Category two.

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So that's a type of analysis you can fetch from all these different different type of board charts.

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So let's go ahead with our next honoree statement, which is exactly how many bookings were canceled.

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So it means I need the data.

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We are my all bookings are cancelled bookings.

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So for this, I'm going to say data of is on this call.

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Cancel it, Callicles, to one.

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So this exact may filter.

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I have to pass this filter in my little frame so that I have my filter data from next year.

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I would say its name is canceled and just executed.

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And in this data frame, let's say if I am going to call, I had to show you how this data frame looks

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like.

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You will see you have all entries with respect to this one in this is canceled.

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Now, in this tantlinger frame, you need a data frame or you can say you need a separate value.

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Yeah.

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How many resort hotels for which bookings are canceled and how many cities hotel for which my bookings

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are canceled.

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So for this, I'm going to see my cancel all four hotel very first to have to access this hotel.

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And in this hotel you are going to say do nothing but my this resort hotel.

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I just copy from here and here you are going to say it is exactly my resort hotel.

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So this is exactly my filter.

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I have to pass this filter, just my data frame, and on this, what I am going to do, I'm just going

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to call land of it.

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So it will exactly give me some value, which is nothing but my total number of resort hotel bookings

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that gets canceled in a similar way.

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You can compute it for like a city hotel.

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So here I am going to say it is nothing but my city hotel.

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So it is nothing but my city would have just executed this is that value with respect to your city hotel.

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Now, let's say I have to plot this this value.

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Let's say I'm going to use my bank.

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Chad, whatever you want to use, you can also go ahead with your budget or whatever you want.

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It's all up to you.

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So let's say I'm going to use my pie chart.

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So I'm going to say my stop by here.

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I am going to say my values parameter.

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So my values parameter in this value is I'm going to say the very first values is this one.

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So just to copy, just to paste.

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And similarly for this one.

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Just to copy.

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Just to paste.

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And after it you have some names.

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So in names you have nothing.

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Let's say the very first name is the thing.

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But my let's say Orridge underscore cancellations.

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The second name is exactly let's say City Hotel.

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S.H. underscore cancellations after it.

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What you have to do, you can play with some multiple parameters in this class, multiple parameters.

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You will see one here.

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How much number of parameters, classes.

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So I'm just going to execute it.

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This is that visual that you exactly need.

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You will see almost seventy five percent of cancel booking are with respect to this Hotel City hotel,

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whereas this twenty five percent are with respect to this, this resort hotel bookings.

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And this third problem statement will exactly will be your assignment that you have to sort.

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That's all about this project.

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Hope you love this project very much.

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Can you have a nice day?

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Keep learning.

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Keep growing, keep practicing.