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These are the user uploaded subtitles that are being translated: 1 00:00:00,960 --> 00:00:06,840 Today we're going to continue working our data mining workbook which contains the bank churn data set 2 00:00:06,840 --> 00:00:07,160 . 3 00:00:07,200 --> 00:00:12,580 However we will start looking at a bit of a different rule. 4 00:00:12,630 --> 00:00:15,560 Subtle looking at our daughter from a bit of a different angle. 5 00:00:15,930 --> 00:00:23,280 Let's create a new work tap and this one will call and well we won't call it anything yet. 6 00:00:23,280 --> 00:00:30,690 What we want to visualize here is we want to understand how our customers are distributed by age so 7 00:00:31,020 --> 00:00:35,260 our old customers predominately older or younger what age groups are they in. 8 00:00:35,370 --> 00:00:36,240 And so on. 9 00:00:36,690 --> 00:00:39,510 And as you can see we already have an age variable here. 10 00:00:39,810 --> 00:00:43,900 So the first thing that comes to mind is let's say age and drag it into columns. 11 00:00:44,110 --> 00:00:47,240 And now let's take number of records and drag it into rows. 12 00:00:47,490 --> 00:00:52,860 And now we have one big dot over here at the top. 13 00:00:52,860 --> 00:00:54,100 Why do we have one big dog. 14 00:00:54,120 --> 00:01:04,260 Because well what happened is Tablo took some of Age of all of your customers and it got just over 380000 15 00:01:04,260 --> 00:01:09,840 years and then it took some more records so the toll on them are at it's 10000. 16 00:01:09,840 --> 00:01:14,400 Before we continue I'm just going to fix up the format here so we can see better. 17 00:01:15,330 --> 00:01:21,800 So you can see here three eighty thousand not for 90000 and 10000 customers. 18 00:01:21,810 --> 00:01:23,370 So how do we avoid doing that. 19 00:01:23,370 --> 00:01:30,690 Well in my course on Tablo I talk about aggregation and granularity. 20 00:01:30,690 --> 00:01:32,330 We won't go into details about that yet. 21 00:01:32,340 --> 00:01:34,880 I'll just show you how to avoid this happening. 22 00:01:34,890 --> 00:01:43,470 So if you go to some age and you just change this from a measure to a dimension then what will happen 23 00:01:43,470 --> 00:01:47,250 is it's not aggregating age anymore it's using it as a dimension. 24 00:01:47,280 --> 00:01:52,430 And as you can see you've got this nice shape of a curve. 25 00:01:52,680 --> 00:01:59,430 So basically it just tells you how many customers at every given age they are and it's uploading it 26 00:01:59,430 --> 00:02:00,890 with a line. 27 00:02:00,900 --> 00:02:07,170 So that's good but the problem here is that this is a nice data set so I'm going to Right-Click and 28 00:02:07,170 --> 00:02:12,180 I'll show you the data such if you Right-Click a data set up here and click your data and you go in 29 00:02:12,180 --> 00:02:15,110 here you will see the details of your data. 30 00:02:15,360 --> 00:02:16,930 So there we go. 31 00:02:16,930 --> 00:02:17,900 There's age. 32 00:02:18,150 --> 00:02:23,250 So here you can see that this is a nice data set in the sense that age has been around for you in a 33 00:02:23,250 --> 00:02:30,250 way it's not good because you don't have the details how exactly how old the person is is he for 29 34 00:02:30,250 --> 00:02:34,890 years between nine and a half or Z 29 and 11 months and a couple of days old. 35 00:02:34,890 --> 00:02:41,520 So you don't have that decimal point you can tell how exactly all the Is and therefore you've lost some 36 00:02:41,520 --> 00:02:44,090 information but on the other hand you can't do anything about it. 37 00:02:44,100 --> 00:02:49,170 If this is how the data said was provided to well then you've got to work on it and you might as well 38 00:02:49,170 --> 00:02:54,990 just enjoy it because that means it's a nice data set and you have one less thing to worry about you 39 00:02:54,990 --> 00:02:57,120 don't have the decimal point to worry about. 40 00:02:57,120 --> 00:02:59,320 So what does that say to us. 41 00:02:59,340 --> 00:03:06,230 Well that says to us that yes in this case because they're rounded you Tablo will be able to group customers 42 00:03:06,240 --> 00:03:10,860 for instance there's a person who's 36 and there is a person who's 36 and there's a person who is 36 43 00:03:11,220 --> 00:03:16,520 and there's a couple more people who are 36 and then you go to this chart and then you find 46. 44 00:03:16,550 --> 00:03:24,240 Let me just close this and you find 36 and you find that there's is 456 people that have thirty six 45 00:03:24,540 --> 00:03:26,470 in that column. 46 00:03:26,550 --> 00:03:33,090 What you will find is with other data sets when it's not rounded for you then you'll have people who 47 00:03:33,090 --> 00:03:34,170 are three six and a half. 48 00:03:34,170 --> 00:03:38,640 You'll have the people of thirty six point to thirty six point three and that will cause this charge 49 00:03:38,640 --> 00:03:45,030 to go to create very strange patterns because you might have a lot of people who have three six and 50 00:03:45,390 --> 00:03:51,600 a half but you might have very few people or three 6.6 so it will be very spiky and you don't want that 51 00:03:51,600 --> 00:03:53,340 in your body you can really see a spike over here. 52 00:03:53,340 --> 00:03:54,410 This is not normal right. 53 00:03:54,420 --> 00:04:00,820 So 30 29 and then 31 so 348 people foreign for people on this island. 54 00:04:00,870 --> 00:04:06,170 Just you know just so happened maybe these people are about to turn 30 or these people just turned 31 55 00:04:06,570 --> 00:04:10,280 and therefore you have this spike and you'll see a lot more of them. 56 00:04:10,290 --> 00:04:15,810 If your daughter is I would say more proper in the sense it's more precise. 57 00:04:15,960 --> 00:04:17,530 So you have to find a way around that. 58 00:04:17,550 --> 00:04:24,360 And we have to find a way to visualize our distribution of age regardless of the way our daughter is 59 00:04:24,360 --> 00:04:25,280 presented to us. 60 00:04:25,320 --> 00:04:32,760 And this is where we can use bins. This is where bin's come in and this is where we start thinking about 61 00:04:32,850 --> 00:04:37,970 converting our numeric variables to categorical variables in two dimensions. 62 00:04:38,160 --> 00:04:40,920 So let's start by having a look at that. 63 00:04:40,950 --> 00:04:46,820 I'm going to Right-Click age and what I want to do is I want to group people into bands. 64 00:04:46,950 --> 00:04:53,730 So I want to group people into for instance 5 year bans anybody between 20 and 25 will go into one group 65 00:04:53,730 --> 00:04:56,190 of 25 to 30 will go to the next group and so on. 66 00:04:56,430 --> 00:05:02,490 Well it's very easy to do it and Tablo all you have to go do is right click your variable and then here 67 00:05:02,550 --> 00:05:06,700 select Create and then select bins. 68 00:05:07,140 --> 00:05:12,460 And here you want an age been so we don't want size to be 10 one size to be five. 69 00:05:12,510 --> 00:05:17,470 So every five years there'll be a new bin will click OK. 70 00:05:17,850 --> 00:05:20,030 And here you can see Age state as a measure. 71 00:05:20,040 --> 00:05:24,160 But now we have a dimension which is also age but it's called Age been. 72 00:05:24,540 --> 00:05:29,190 And now what we'll do is we'll take age pin and will drag it in to replace age. 73 00:05:29,970 --> 00:05:35,460 Well actually I need to take age out and then put age and been there because one's a dimension that 74 00:05:35,460 --> 00:05:41,400 is a measure and what you can see right away is a nice distribution just like the ones we are used to 75 00:05:42,000 --> 00:05:46,130 seeing in economics and in mathematics because what happened here. 76 00:05:46,170 --> 00:05:53,730 We've grouped our records by category or by these bins and the bins are acting as categories so just 77 00:05:53,730 --> 00:06:01,440 like let's say gender or the country that people live in a similar way so it's not a recognisable is 78 00:06:01,440 --> 00:06:06,930 no longer recognising these as numbers these are categories so people in this category are between 20 79 00:06:06,930 --> 00:06:10,980 and 25 people in this category between 25 and 30 by the way. 80 00:06:10,980 --> 00:06:18,300 It includes the starting numbers so starting from 25 up to the last number before 30 in our case it's 81 00:06:18,300 --> 00:06:27,720 29 so 25 29 30 to 34 35 to 39 and so on and right away you can see how many people they are in each 82 00:06:27,720 --> 00:06:29,550 of these categories. 83 00:06:29,760 --> 00:06:31,850 So what else would we want to do here. 84 00:06:31,860 --> 00:06:37,700 Maybe let's give it a color so it looks a bit more pleasant. 85 00:06:38,040 --> 00:06:42,540 Let's take some number records hold down control and ragged on to color. 86 00:06:42,550 --> 00:06:45,140 Personally I use green for money. 87 00:06:45,190 --> 00:06:48,810 So let's use a different color. 88 00:06:48,840 --> 00:06:52,970 How about we go with kind of a brownish color. 89 00:06:53,590 --> 00:07:00,690 Something that apply and maybe we want to add a label as well so we don't have to visually check every 90 00:07:00,690 --> 00:07:03,590 single time where this is sitting. 91 00:07:03,750 --> 00:07:09,960 So we'll go and use some number of records as a label as well. 92 00:07:10,080 --> 00:07:11,650 And now you can see that. 93 00:07:11,880 --> 00:07:15,510 Exactly how many people you have in each of these categories. 94 00:07:15,540 --> 00:07:22,980 So and this approach allows you to assess your daughter a bit more so now you understand from this sample 95 00:07:22,980 --> 00:07:28,860 which is quite a significant sample 10000 people in the sample So it's very representative of the overall 96 00:07:28,860 --> 00:07:35,480 population of this bank and what you can see is that most people are age 35 to 40. 97 00:07:35,670 --> 00:07:42,060 Quite a few people between 30 to 40 so basically this is telling you what is the demographic of the 98 00:07:42,060 --> 00:07:43,290 bank like they are. 99 00:07:43,460 --> 00:07:53,760 Let's say more younger people say in their 30s that most customers are presented by age groups between 100 00:07:53,760 --> 00:07:55,590 30 and 45. 101 00:07:55,590 --> 00:07:59,550 So you have the most of your customers are in those age groups. 102 00:07:59,550 --> 00:08:05,100 And in fact if you want to see the percentages you can easily convert this so you can just go here and 103 00:08:05,250 --> 00:08:08,220 add table calculation like we did previously. 104 00:08:08,220 --> 00:08:11,040 You can even at a quick table calculation in this case. 105 00:08:11,040 --> 00:08:13,190 So you just need a percent of total. 106 00:08:13,380 --> 00:08:16,150 And there you go so you have the exact percentages. 107 00:08:16,170 --> 00:08:25,060 This gives you 43 plus 43 plus 16 gives you 59 or 59 plus. 108 00:08:25,200 --> 00:08:32,740 There's like a percentage so that gives you 60 percent 60 percent of your customers are in these H-back 109 00:08:33,030 --> 00:08:35,370 age bands and the rest there's like a long tail. 110 00:08:35,370 --> 00:08:39,440 So this is a right skewed distribution. 111 00:08:39,480 --> 00:08:46,560 So that is a thing to remember as well so maybe another tip that when that skewed means where the tail 112 00:08:46,560 --> 00:08:50,280 is going if the tells going to the right then it's a right skewed. 113 00:08:50,280 --> 00:08:55,260 This is not a left skewed a lot of people get it wrong left skewed would be if the tail was going to 114 00:08:55,260 --> 00:09:01,590 the left so that's a statistical term to keep in mind it's a rice food distribution got a long tail 115 00:09:01,590 --> 00:09:04,050 to the right you and have customers at 90 years old. 116 00:09:04,350 --> 00:09:07,390 And so what are we going to do now. 117 00:09:07,470 --> 00:09:11,560 That's that's pretty much what we want to do with this chart. 118 00:09:11,580 --> 00:09:15,480 I'll just say one quick thing you can change the bins very easily. 119 00:09:15,750 --> 00:09:20,800 You can just go to age and then you go to Edit. 120 00:09:21,060 --> 00:09:24,470 And here let's say we want a 10 year band. 121 00:09:24,810 --> 00:09:31,170 See right away it's changed and now you can see that you got most customers between 30 and 40 and then 122 00:09:31,170 --> 00:09:35,600 40 to 50 you have 26 and so rich controls at that. 123 00:09:35,910 --> 00:09:38,760 And that's all we wanted to do here. 124 00:09:38,760 --> 00:09:43,680 The last thing you might want to do is change this access because it's not consistent with your percentage 125 00:09:43,680 --> 00:09:48,890 you might want to change a percentage but that's details we're just investigating so we won't be doing 126 00:09:48,890 --> 00:09:49,560 it right now. 127 00:09:49,770 --> 00:09:51,600 And that's all for today. 128 00:09:51,600 --> 00:09:53,160 I look forward to seeing you next time. 129 00:09:53,160 --> 00:09:54,780 And until then happy analyzing 14202