I pretty much went for a walk and then just sat in this room.


I didn't eat food for 5 days, here's what happened

I had nine of these and I'm going up, finishing another one.


What did you say?


You're starting to appreciate food more.


Yeah, starting to appreciate food more.


25 hours without it.


Is this the longest you've been without food?


I wouldn't say the longest.


This is like I'm consciously thinking of food.


When I didn't want food, I was not thinking of it.


Whenever I feel hungry, I just second nature to get up and go to the kitchen.


I'm fighting that urge.


That's what's hard about it.


The good thing is within the next sort of...


The app, this is telling us now that we're in ketosis apparently.


Obviously, we haven't done a blood ketone check, but judging by the timeline of a general fast with no food,


I think around about 36 hours, you start getting into deeper and deeper ketosis.


By the time we wake up, potentially, body might have switched over from using glycogen as fuel to using ketones.


That hunger might go away, my friend.


I hope.


You know what?


We're going to call Dave, who's my friend.


He's also fasting.


We're going to see how he's going.


Sup, bro?


Big boy, how are you?


Good, mate.


Mate, how are you feeling?


I have recovered.


I was in worlds, mate.


I was in worlds.


I thought I was going to cave, but I bounced back.


Like a cat, mate, I've got nine lives.


Maybe I've got five days left in me.


Yes.


For context, I spoke to Dave, what was it, an hour and a half ago?


You were on the borderline, mate, weren't you?


Dude, I was going to get the camera out, film a nice quality short video,


of me knocking up some butter chicken or maybe a steak,


and totally just concede to this challenge.


But no, I'm back.


All right, so you're feeling all right now?


Basically, I didn't go for a walk.


I was thinking about going to gym or something,


but I ended up going around to my sister's boyfriend's house


to help him with his car.


I took my mind off it.


Since then, I just forgot that I was feeling hungry.


Now, I'm not even really feeling hungry.


It's all good.


I was reading about I'm about to hit 24 hours, I think,


in about half an hour, and definitely slipping into that next phase, I think.


Boom.


We're all on the next phase.


I love it.


I'm just picturing the abs.


They're coming.


I hope you're taking progress photos.


Not really.


I have one or two.


Yes.


Beautiful.


All right, brother.


Well, that's a little update.


That's day one in the books.


Day one in the books.


We'll check in tomorrow at some point.


Easy.


All right, mate.


Have a good sleep.


All right.


Peace out, brother.


Later, bro.


See you.


Davo's doing well.


Good on you, brother.


I caught him like an hour ago.


I finished Touch Football.


He was like, why should I keep doing this?


I'm like, I don't even have a good reason.


But do it for fun.


And so I'm going to go to bed.


You're going to go to bed.


And yeah, just keep doing it tomorrow.


We're up to day two.


Now I need to color this in so you can see it.


And anyway, so I woke up this morning a little bit tired.


And then I went to the bathroom and I figured out,


you don't need to see me coloring that in.


I figured out that it was probably because of dehydration.


So then, well, what I mean by I woke up tired,


I was sort of in bed and I'm like, oh, I'm feeling a bit,


but then went to the bathroom, dehydration,


had some water with some salt in it.


And now I'm feeling great.


We went for a walk this morning.


How are you feeling, Wim?


I'm feeling a lot better than I did yesterday.


That's what I want to say.


I even went to the gym this morning to just check it out


because there's a new gym in town and I'm a member of the franchise.


And I busted out a little session.


And I checked my mail this morning and check this out.


I'll hide that address.


But a food company has just sent me,


I can get 40% off my next order.


So they're like, they're tempting me.


They must know you're doing a four day, a five day fast.


But truth be told, I am feeling great.


I haven't really felt hungry at all.


I'm expecting to feel hungry maybe later this afternoon.


But 40 or so hours in, we're in deep ketosis phase.


We're going to do progress pictures and then weigh in.


I weighed in this morning at 73.1 kilos, I believe,


which is 1.8 kilos less than yesterday.


So about two kilos lost overnight.


Progress time.


Weigh in time, Nate.


How are you feeling, big boy?


Kind of excited for the first day of weigh in.


First, day two weigh in after yesterday.


You remember me, first day of full fast.


Yeah.


Well, is this the longest you've gone without eating?


Consciously, yeah.


So consciously, I reckon I've done like,


it's probably this amount.


All right, here we go.


What were you yesterday?


Look at that.


Sam, it even knows you.


What's this going to say?


Does it have a graph?


No.


So what's that down?


Almost two kilos.


Yeah, pretty much only 1.9 kilos, isn't it?


I forgot already.


Yeah, that's two kilos down.


Mate, you're going to be under 100 tomorrow.


That's...


Excuse my French.


I've been under 100 in a while.


So...


That's exciting, mate.


Well, it's your turn now.


Day two.


Oh.


Okay.


Yesterday, I was 74.9.


So yeah, I'm up 400 grams from this morning,


but that's only because I had some water.


So this morning, I weighed in at 73.1,


down 1.8 from yesterday.


So we're almost at 41.5 hours in.


What are we about to do, William?


Going to do a quick gym sesh.


We're about to go for a gym sesh.


You know what?


Because it's about 1pm, 12.1pm.


This is about the time that we would both sort of eat during the day.


And not going to lie, I feel a little bit hungry,


but I think that's mostly out of habit.


What about you, William?


How are you feeling?


Not as hungry as yesterday,


but my cravings have gone through the roof.


What?


You were saying before that not food itself,


but just like the ritual of eating food?


Well, the ritual of eating food, yeah.


And then, well, as well, like the cravings of stuff


have just gone through the roof.


What are you craving?


Sushi.


We were driving down here and he's like,


so you're telling me after this workout we can't go and get sushi?


I'm like, yep.


So, we're going to do a little sesh.


Probably go half an hour or something


and then see how we feel after.


The boys just finished the gym session.


How are you feeling after that one, buddy?


Not bad.


Yeah?


I'm still sort of hungry,


but like the ugginess has gone down.


Yeah.


And the way I think of it was talking about it


and to Sammy asked why we're doing this.


The way I think about it,


there's probably some science behind this,


but the reason why we do a weightlifting session,


we only did a small session.


We started off with incline bench and then deadlifts


and then some pull-ups and some shoulder press.


So, kind of like a whole body session, upper body focus.


But the reason why, at least I think,


or the way I think of it is,


you're stressing your muscles


and because we're not ingesting any calories or nutrients,


we're telling our body,


hey, we need to keep our muscles,


so use the fat or burn the fat.


Because if you're out in the wild or whatever


and you go a few days without food,


you need your muscles to be able to hunt a warthog.


So, that's why we're lifting weights every second day or so.


But back home, probably nap time, right William?


Yeah.


We've crossed day two, mate.


49 hours in.


How are you feeling?


I'm feeling no near as hungry as I did last night.


Probably my cognitive ability has gone down a bit


and as well my motivation to do things has gone down.


Because I'm just chill and accent.


If I do anything, I always get that second nature,


like, oh, I'm getting hungry now.


Like, I'll be doing something, so I just chill out


because I don't get hungry.


Yeah, that's what I found too, is that I can do things.


Like, I wrote a newsletter this morning that went out fine,


but usually I would do a little bit of work


and food would be the reward.


So, missing out on that reward is probably the hardest part,


but I went to jujitsu training tonight.


I'm feeling pretty damn good right now.


And we're going to do a little test.


We've got a ketone meter here, which is to test blood ketones.


Can you handle needles?


Somewhat.


If you can't handle needles, you probably want to skip ahead.


Essentially, what it does, you make a prick in your finger,


you put some blood on a strip and it goes into this little device here


and then it tells you how many or the concentration of ketones in your blood.


And that is generally an indicator of whether or not you're in ketosis,


which according to the app is saying,


at 49 hours, 18 minutes, 19 seconds in,


we are entering in ketosis, which is some information here.


I'm not going to read all that.


Oh, here we go.


Blood ketones increase.


BHB is usually 0.5 to 2 millimoles.


So we should be around that if we're in ketosis.


Let's check it out.


Let me get this set up.


All right, pass your finger, finger of choice.


Had to look away there.


I hate needles.


Here we go.


We got the countdown.


What's it going to be, mate?


I don't know.


I don't know what's good.


Three, two, between 0.5 and two.


Oh, 1.8 millimole.


Is that good?


There we go.


1.8 millimole.


Not sure if you can see that, but anyway, I'll try to take a little photo of this.


So yeah, according to the Xero app, you're in nutritional ketosis.


So you're in the fat burning stage.


Your body's starting to use fat as fuel.


I thought it had to be 0.2.


No, 0.5 to 2.0.


Oh.


All right, my turn.


It's like an exam.


Like you're studying for all this effort of like 50 hours of not eating to see if I've


even passed or not.


Have you passed the test, mate?


Just, mate.


Okay, round two.


Different hand.


I wash my hands.


So that's what they advise to do is that wash your hands before you prick them.


Oh, 0.2 millimolar.


So I would be outside the range.


I'm interested to see what that would be tomorrow morning, actually.


I think I'll be.


What do you mean you're outside the range?


So the range of nutritional ketosis is, well, according to the, it's going to be different


for different sources, but you want your blood ketone levels to be around 1.5 to 3 millimolar.


But the Xero app says 0.5 to 2.


So depending on where you take the sources from.


You mean like as a healthy ketosis?


Well, that's, so once that's kind of an indicator that you have blood, that you have a significant


amount of ketones in your blood.


And what that means is now your, your body is using those ketones as energy rather than glucose.


Yeah.


So we've spent the last 48 hours basically depleting our body of glycogen and glucose.


My levels might be a bit altered because I just trained with jiu-jitsu.


Anyway, we're getting taken away here.


We're at the end of day two, brother.


Congratulations.


Guess what?


Tomorrow morning I'm going to wake up and be close to 60 hours, basically over halfway.


You excited?


We'll check in then.


I'm over halfway.


Can't quit now.


But here we are.


Day three.


Just been for a little walk along the front with El Cameo Dave.


How are you feeling, mate?


I want to give up.


You were feeling great before.


What happened?


I think that going for a walk drained my batteries completely.


It was a pretty long walk.


It was hot.


Yeah, it was pretty hot.


Long walk.


No food.


Thinking about food.


Talking about food.


I dreamt of food.


I dreamt I ate couscous and I bit into it and had to, not couscous, yeah couscous, like


the hard bread things, had to spit it out.


That was my dream.


My nightmare.


It's not couscous, it's crouton.


Crouton.


Square crouton.


Dreams of croutons.


That could be a band name or a movie.


That was like, that was like my food wet dream.


Well, now we're going to do, now we're going to do day three progress shots against the wall


and Wham's going to weigh in.


I already weighed in this morning.


What'd you, what'd you start off with?


What'd you weigh in?


I think it was like 90 flat, maybe 91 flat.


I can't remember.


Kilos?


Yeah, yeah, yeah.


And now I'm down to, this morning I weighed in 87 and a half.


So I've lost three or four kilos in three days.


I can feel it here.


Feeling very clean.


Look at that jawline.


Get yourself a set square.


Cause that's it.


Not any degree angle.


All right.


How we looking, Mark?


Weighing in time, day three, 62 or so hours fasting.


How you feeling?


Talking about food on the wall this morning and I already want to quit.


We shouldn't have talked about food.


If we make it four days, I'll be happy.


I'll be real happy if we can make it four days.


We go on a family holiday every year and we were talking about the food that we're going


to eat there on the walk.


Bad idea.


Oh, look at that.


Did I call it?


Below a hundred.


What was that?


Yeah.


So what's that from yesterday?


You're under.


Yeah, I'm down like 800 grams.


800 grams.


Good stuff, brother.


Oh, what are you weighing in at pal?


What are you weighing in?


Get your wheels in.


Look at that.


Big boy.


Is it even registered?


Well, I don't think it's picking you up.


Oh, there we go.


Look at you.


13.9 kilos.


Wow.


Good.


We on?


All right.


It's just your local dial ketone tester.


You got a finger there, mate?


Pull it.


Nothing will happen.


Speaking of nothing happening in that run.


Yeah.


I've been to the toilet like once.


I haven't at all.


Barely in this time.


Three days.


Really sweet.


This is the least you've ever heard me fart, right?


Yes.


How do you like it?


It's great.


Okay, mate.


Finger?


Nuclear.


Ah.


Oh, my God.


Can you ever say you've done this before?


Testing your blood ketones on the side of the road.


If I'm not in it, I'm failing.


A beautiful sunny day on the Northern Beaches.


Let's see what we got.


1.2 millimolar.


I'm in it.


Congratulations.


I made it.


You're in starvation pitosis, mate.


Hey, time to end this fast.


Let's go get some clothes.


Just wanted to know if my body could get there.


got there. Fast and Cheap Guy. I'm going to end this video now. Catch you later. My turn.


We're doing ours on the side of the road too. Yeah, why not? Day 3, why don't we just...


Why don't we just start eating bark inside trees? You know what, I'm... Because there's


catilocs. You guys, I don't know, I don't get it. I'm not that hungry. At all. My mental


game has been destroyed. Why talk about tigers? Yeah. See, that's what I said before. We were


going on a family holiday. Everyone has a group. And we were talking about the yest,


the amazing banquets and how good it is to... It's just a good ritual to be around people


you love and eat good food. Anyway, it's your local roadside ketone assessment. Dude, and


like olives and like blue vein cheese, some crackers, spring rolls, curry puffs. Not 100%


salt on the Doritos, but they will be there. Maybe just the PM ones this time so we can


still intermittently fast. How we looking there? Actually, no, no, no, more. Hang on,


hang on. Sun dried tomatoes. Here we go. We're coming down. Five, four, three, two, one.


You boy. There we go. 1.5. I just have to beat you in everything, don't I? All right,


man. Dave, you want to take over the camera man? What's wrong? I hate needles. Hate needles.


Yeah. Even if it's a prick. It's fine, bro. Don't sweat it. What do you reckon you're


going to be when? I don't know. Last night I was 1.8, so I don't know, like 1.7. Based


off of what? That I'm hungry. Just having a guess. All right. Look away, mate. Duh


duh duh. He missed it. He's going to do it again. Here we go. People think we're crackheads


or something. Injectables on the side of the road. 0.1. I think we might have to retest


it, but that was 0.1. Sam's like, I'm hungry. This doesn't make sense. What does that mean?


We can do a rescan. It means you're still hungry for a reason because your body hasn't


used it. We'll skip ahead to the rescan. What does that mean? Well, for you, mate,


it means another prick. You're kidding. Thumb bleeds the most. Yeah. Oh.


You're definitely not having this, are you? Hey? You're definitely going to the pub after


this, are you? I'm so done. All right. We're trying it again. Is Sam in? Three, two, one.


2.7. It said zero. I was looking at this. I thought it was saying zero. All right. Well,


this is very opposite ends of the spectrum. I think last time in the scan we got before,


we didn't get enough blood. So this time I think you're deep in nutritional or starvation


ketosis, mate. Congratulations. Lovely. How do you feel? Shit. It's all right. It's all


right. Look, I'm surrounded by people. I can't help it. I just feel great. Right? I seriously


feel great. I don't know why you guys don't feel so good. Because we don't feed off of


other people's unhappiness. Maybe that's what it is. You're just sucking. You're the only


joy between us right now. You just suck it all. All right. We'll check back in in the


next update. All right, Wham. 69 hours in, mate. We got that there. Boom. What are you


going to do? I'm breaking it. Breaking it? Yeah. Why? How you been feeling? Feeling like


shit the whole day. What do you mean? Like hungry, hangry. Like just literally all I


can think about past like probably 48 hours is just food. Yeah. And then it's a demoralizer


how I can hear the family have dinner upstairs while I'm just down in my room fasting. There's


nothing wrong with the fast. I kind of enjoyed the first 48 hours, but this last, last you


could say 24 just sucks. What have you been doing? Just chilling in my room, drink of


water, going for walks, did a bit of the gym sesh. Just pretty much drinking water. That's


what I've been doing. I've been very chill, but that's probably a blessing and a curse


in itself. Mate, you've done 70 hours. So that's basically three full days. Swapping


out the chips for steamed veggies. What are you getting? Rib fillet. Thank you. I'm so


excited. I'm honestly over the moon. Highlight of the week right now. I'm coming here, getting


a steak, going home, getting chicken. I've been told by Daniel that I'm allowed to have


chicken, high fat, chicken, cheese and bacon. That's definitely what's going to be on my


plate tonight. That's only it. Chicken, cheese and bacon. I'm going to devour it, have a


good night, sleep well, wake up, start editing this footage. It's going to be great.


I'm having a steak. Yes, please. What is that? Oh wow, it's getting to the photograph and


everything. Yeah, he's the star of the show. The paparazzi, you know what I mean? Breaking


a three day fast. How you feel, mate? Excited. Excited. Excited. Hey, guess what? I'm proud


of you, brother. Thank you. You're welcome. And now I'll continue on with my dinner of


sparkling water. 48 hours to go for me. You know what? We're at the shopping centre right


now. Quam's getting food for dinner and I'm not feeling hungry at all. It's weird, I think


my ghrelin levels have hit that point where it's like, it's just after I've, it feels


like I've already eaten a meal. All this delicious food. Yet, I'm not hungry at all. I look at


it and I go, yeah, that's nice. That'd be nice to eat. But it's like, I don't need it.


Maybe that'll change over the next 48 hours. I don't know how you're doing it. I'm being


serious. It sounds like I'm going crazy but I'm not. You are. Third time's the charm,


mate. Oh, this is the third time, is it, mate? Yeah, it is. All right. So, this is the third,


well it is, well it is the third day. You've had your fast by the time we're done with


this clip. Yeah, I couldn't imagine. Welcome back to the fasting couch. We are here on


day four. I'm officially 73 hours into my fast. Big Wham, you ended yours earlier and


you also cooked dinner for the family, which I love the sight of. I didn't get to enjoy


it, but I'm sure it tastes amazing. It was pretty good. Pretty good? Yeah, I had two


dinners pretty much. Yeah. So, Wham, about 70 hours in, so a couple hours ago in the


clip you saw before, ended his fast. But then we went to the grocery store and he bought


some supplies and cooked the family dinner because it is Wednesday and that's his night.


Day, yeah. But speaking of being 73 hours in, apparently I'm in deep ketosis. There's


a couple of points here. So, the liver continues to produce ketones, which become the primary


fuel for the brain. IGF-1 levels are low, as is glucose and circulating insulin. BHB


production increases, so this makes sense. We actually did a blood ketone test before,


but it didn't record. So, my blood ketone level is 3.3 millimoles per litre. And we'll


put a photo up of the scan we took. And total ghrelin output remains lower than baseline.


So, ghrelin is the hunger hormone. It basically tells you that your body's hungry. And so,


this is what's really counterintuitive. The longer you go on in a fast, the lower your


ghrelin levels are. So, that hormone that tells you that you're hungry, the lower it


gets the longer you fast. Which makes sense because I was in the supermarket before and


I was just in a zen mode. I can't really explain it. It's just I wasn't hungry. I was looking


at food. I'm like, wow, that's nice. I kind of know deep down that I have to eat. But


at the same time, I was like, I feel as if I've already had a meal. That completely doesn't


make sense, but that's just how it feels. I don't have any urge to eat. It's hard to


explain. Number five is autophagy accelerates. So, that's my body breaking down old cells


and using those as fuel. But other than not eating for the last just over three days,


I feel great. I had a nap this afternoon. I did some writing for another project that


I'm working on. I had great concentration during that. I could sit there for a couple


of hours and just did it. I would say that my energy levels aren't peaking and they aren't


completely hitting rock bottom. They're kind of just flat lining it. How are you feeling?


Feeling great. I have food. But yeah, going back to your energy levels, my energy levels


before I ended the fast, which is very like below average, I would say. And I couldn't


do what he says, like keep focus and get, I didn't get into his end mode. I couldn't


keep focus. I was too micro stressing about food and stuff like that. So, like that was


mainly what was on my mind pretty much 24 seven since I started it. But yeah, it was


kind of fun. Next time when I fast point in the future, we'll do another video. I'll


probably prepare a lot more for said fast with better diet and no coffee prior because


that was the main thing that sort of ruined to me was the coffee headaches that I got.


It kind of ended today, like this morning on the third day, but I was too far gone.


I was just in micro stressing about food. I had to end it early.


It's all right, mate. Three days. I'm proud of you. Love you. And we'll check in tomorrow.


I'm going to keep going. I've got about another 48 hours or so. So, yeah, it's like when you're


young and you're waiting for your birthday. That's how I feel. You know how the days kind


of go a little bit slow and then you're just so excited for your birthday. That's how I


feel about Friday night. I'm excited. I'm cooking up a feast for everyone here. But


we'll see you tomorrow. Good morning. We are almost 84 hours in 69% either way up. So we


are 83 hours 42 minutes in. I've just woken up and a little bit dehydrated again this


morning. I think that seems to be a red thread. So I've got this glass. I just put a few sprinkles


of salt in here and I find if I sip this by the time I'm finished it I feel I'm back to


feeling normal. I have no hunger at all and an exciting thing happened this morning. I


went to the bathroom for the first time. Come on, I'll show you. No, I'm kidding. I'm not


going to show you that. But I can tell you is that it wasn't much and it wasn't solid.


So if you want to know those details that's what happens. That's the first time since


I started the fast that I went to the toilet and it was pretty uneventful. So I'm going


to test my blood ketones. Last night they were pretty high, 3.3 millimolars. Today is


just going to be as normal. It's the second last day of the fast. I'm going to enjoy it.


I said to myself, I was writing in my diary, I was like, you know what, I'm not going to


rush through this. Even though part of me is like, it would be nice to eat food. I don't


feel hungry as I said. Oh, I'm out of needles. Be back. Reload. Yeah, as I was saying, I'm


not going to rush it. I'm just going to embrace it. I'm going to go into new territory by


the end of today because the longest fast I've done before is about four days. So 96


hours. So by the end of this time tomorrow, I'll be the longest I've ever gone without


food, which is kind of exciting. Three, two, one. 3.4 millimoles. That's pretty cool. So


that's the highest I've been so far. If we go into here, according to the app, I read


it this morning, fasting for three plus days has been shown to decrease circulating insulin


and glucose by 30% or more, which can contribute to decreased risk of metabolic disease. Metabolic


disease like heart disease and other things like that, diabetes, blood, BHB. So BHB, again,


I'm not sure what that acronym is. I'll put it up on the screen, is basically blood ketones


and will continue to elevate. I think that's where I am. One study showed that BHB levels


will eventually plateau between five to six millimoles day 20 to 25 of fasting. I'm not


going to go that far. I'm going to end the fast tomorrow night. A 2020 review of scientific


literature showed that a correlation between elevated BHB levels, AKA this, and lower levels


of hunger, although the underlying mechanisms are currently unknown. But I can attest to


that because I have very low levels of hunger. Anyway, I've got some study to do today. I'm


going to go see my dad. We'll go for a walk and then treat the day as normal. Rest if


I need to rest and just embrace the last 48 hours or 40 so hours of the fast. All right,


we're on day four. Just been for a beautiful little walk. It's nice and warm out there,


not bad. It's time to do progress photos and weigh in, check in. I already weighed in this


morning. I was the lightest I've ever been so far. I think it was 71.9. But before that,


I'm going to get some nice breakfast because it's hot outside. There we go. Delicious glass


of breakfast, AKA water and ice. Let's weigh in. One more host didn't even offer you one.


You got one? Here you go, mate. Yes, sir. Glass of water with ice. Cheers. I read a superstition


once that it's bad luck to cheers with just water. All right, we prepared this earlier.


We are, let me check, 85 hours, 19 minutes in of day four. We on? Yeah, we on. Fish on.


So I'm feeling great. I said we went for a walk and you know what? I'm pretty excited


because at the start of each day, I'd write a little list of what I need to do. So I can


tick off this one. I'm not sure if you can see that, but that says fast all day. And


as you see, I didn't tick off any of the others because I usually just do them and then review


them the next day. But that's enough about what I do during the day. Let's progress shot.


I'm feeling pretty lean. Get in line with the little wall hanger. How we looking, mate?


Here we are again. Back in the bathroom. So this morning, weighting, I mean, yeah, when


I first woke up, 71.9 kilos. And by the way, it's the first of the month. So pinch and


a punch, mate, for the first of the month. That was down on yesterday by about a kilo.


So yesterday at 9.23 PM, or 72.8. So I feel like that's just water, like you sweat while


you sleep. So let's weigh in again. Few hours after waking up. Boom, 73.1. Damn. And that,


I believe, is because I've had a hell of a lot of water this morning because I just woke


up feeling thirsty as. Your turn, Wayne. So you've had food yesterday, mate?


Yes. I've had two dinners. Two dinners. Pretty much, yeah. Because I was hungry as hell.


And as well, I'm continuing fasting from, what was it? From 8 o'clock PM to 12 PM the


next day. So you're switching to a 16 and eight.


Yes. So not eating for 16 hours a day. So it's somewhat of a fast, but it's not what


you were doing. Yeah, that's fine.


Yeah, I feel lighter, but at the same time, I don't think I've lost weight since I've


ate all that food. But anyway, here goes nothing.


Get in on it, mate. What are we? 98.5, damn.


No way. That's 600 grams.


Step forward a bit. There we go. 98.7, mate. 0.6. It will eventually register. Sam. Boom.


What's the graph going to say? Just stay on there, mate.


What does it tell you the weather? I don't get it. Yeah, let me just see the weather


one real quick. Yeah, if you're weighing in the first thing


morning, you want to know what the weather's like. So you can go to the beach and show


off your tidy rig. Anyway, what are you down from yesterday?


It's always 99.1 yesterday. So that's 600 grams or something. No, 500 grams. Yeah.


Beautiful. I already forgot the number because I don't


really... So we're going to do some weightlifting later


today, I think around 11. So that's what I found helps is usually when I get hungry,


if I do some sort of movement, then I feel better after that. So, yeah. You ready?


Sure am. Get in here, man.


We need some really cool elevator music here. You can't have a one minute elevator ride.


So we just finished the workout. We've had 90 hours without food. I'm feeling great.


It was a little bit slow getting started, but the pump is actually not too bad. How


are you feeling, mate? Amazing. I actually feel really good after


that workout. Good reset, eating healthy. I can't believe this. Like if you use the


gym, you can do this. If you had told me a few years ago, as I said before, that I'd


be able to train and have an awesome session, we kept it simple. Bench press, shoulder press,


deadlifts, pull ups. You did that pull down. Finish off with some bicep curls and tricep


press sounds. Four days, no food. Fast, be fast. We've got jujitsu tonight, so we're


going to see how I go then. But otherwise, I'm feeling great.


Holy crap, am I ready for bed. But it is 9.44 PM on day five of five. Check that out. We


are officially 98 hours on day five of five of the five day fast. Today was a full on


day. After weight training, I had a little bit of a nap. That was a great afternoon nap.


And then I did a couple of hours study. So just reading a book. And after that, went


to jujitsu training, had some rolls. Shout out to Big Wham. Thank you for the roll, my


brother. It was great fun as always. But now I'm well and truly ready for bed. And I'm


going to check my ketones just before I go because I seriously, I finished jujitsu training.


It went great. I felt like I had plenty of energy. The rolls that I did weren't too high


intense. So I think that's where probably I would lack energy if I had to go really,


really intense. But these are, it certainly wasn't as if I was offering nothing. Put it


that way. This time I'll try it on the other pinky. A little bit of a sharp sting. Ten.


See this is, there's a bit of indiscrepancy with these meters. Okay, that's 0.9. I'll


take a quick photo of that. But there we go. So put it on the screen somewhere here. 0.9


millimoles. I'm feeling great. Not hungry at all. Definitely am looking forward to eating


tomorrow night though. So yeah, I guess that's it for day four leading into day five. Under


24 hours left. I'll see you tomorrow morning. Good morning. You know what? I'm really excited


because it is day five of five. We're 107 hours in. I just woke up. You know, I woke


up this morning feeling the best I felt the past few days. Again, I was a little bit dehydrated


but I put the big mug with a few sprinkles of salt in there and I've been sipping on


that for the last 10 minutes or so. I weighed in this morning at 71.4 kilos which is the


lowest of our entire time. Last night just before bed I weighed in at 72.7 kilos. Also


went to the bathroom again this morning. But as you could imagine, wasn't much again. So


that's the second time. I think it might be the magnesium. I did read somewhere that if


you have too much magnesium, so I had two magnesium tablets last night. If you have


too much magnesium in one hit, it can cause diarrhea. So maybe that was it. But let's


check the ketones. You know, I was thinking, it's very strange to me how good I feel because


I've studied nutrition. Well, I did it. I've got a degree in nutrition and I've studied


it for probably the last seven to eight years actively, like just experimenting with myself


and seeing what the best way to eat for athletic performance is basically. I still don't know.


But for weight loss, I can tell you what I've come down to is the conclusion is because


there are so many different diets and so many different dogmas of eating, I mean at the


end of the day, what works for me might not work for you. But usually the two points that


I tell people, we're not bleeding this morning very much. The two points that I tell people


are eat less often. So that's number one and it would help if I could get some blood out


to test these ketones. Anyway, eat less often and avoid refined crap. Those two are basically


universal principles. Maybe my hands are cold. There we go. Three, two, one. 1.9 millimoles.


Not a bad way to start off a Friday. I'll take a photo of that. Yeah. So if you eat


less often and avoid refined crap, that's a pretty robust way to just not put on weight.


I'm also a big fan of Michael Pollan's eat food as in real food. Not too much, mostly


plants. And so Michael Pollan is a science writer. If you want to check out a really


great book about food, check out his book, Food Rules. Highly recommend that. Otherwise


today it's going to be as normal. If it feels like Groundhog Day for you, it's because it


kind of is. I'm not doing anything. Fasting is kind of a big consumerism because when


you say you're doing a fasting diet, what supplement are you taking? None, except for


salt and water. What kind of food are you eating? None. So it's like all these diet


trends and supplements are just like off the wall. You're just doing nothing basically.


So I'm going to go for a walk and then I've got some study to do again today. And yeah,


I'm cooking dinner for the family tonight. I'm excited.


So real good tip is when you're fasting, this is my trick. This is how I've gone five days


with no food. Ready? Look at that. Absolutely empty except for some ex-peligrino. You know


this is just sparkling mineral water. We haven't probably wanted these today. Just got a bunch


of just sodium, magnesium, basically just sparkling water. So yeah, that's my trick.


Get yourself some San Pellegrino. It's the fastest breakfast of choice. Let's take some


progress shots and we'll do our daily weigh-ins. Boom. I like that little colouring tradition


every morning. You know what I realised probably would be a good idea to put the date on there.


Anyway, next time we're at October 2nd, 111 hours in. You got that? Beautiful. I'm feeling


unreal this morning actually, like a Jedi. Real calm and in tune with nature, but also


ready to hunt. How we looking? Well, fancy seeing you here again. Dying to weigh in,


bit of groundhog day. This morning I was 71.4. I've had a lot of water since then. Boom,


72.3. So, I'm about a kilo from this morning and that's from a bunch of water. So, I think


I'm still down two and a half kilos or so from the very start. I think I was 74.8 or


74.9. Big Wham, what do you reckon, mate? Do you know what you were yesterday?


I was on like 98. I did like a 19-hour fast, so I haven't eaten from lunch yesterday. So,


three o'clock yesterday. So, you only had one meal yesterday?


Yeah. Yeah. All right. Beautiful. Here we go. What do we have? 98, 94. You need to stand


forward a little bit more. There we go. 97.9. Recognises you as Sam. Mate, under 98. 97.9.


How does that feel?


Not bad.


Keep the momentum going, brother. All right. We're going to go to lift some weights later


this afternoon, but during the day, I've got a few things to do. So, actually, we've got


to get some food for tonight. I've got a good dinner. Don't ruin the surprise.


Now, this is one of my favourite places in all of the Northern Beaches.


How are you, brother?


Mate, excellent. I'm very excited. We need to load up. Tonight is steak night. I've needed


five days. We're making a little documentary here. And so, I'm going to need, I think it


is going to be at least six of us, plus two, so that's eight, plus four, 12. And as we


go, 13 of the black line, Angus lines, please, mate. Hopefully, there's enough.


Thank you, mate. Look at this. This is what you want. The primo cut.


How are you, mate? We've bought you out of me, mate. We've bought you out of me, have


we? I haven't eaten in five days. So, tonight's steak night, mate.


What's with the documentary?


Just seeing what it's like, mate, feeding the family tonight. Crews come around, too,


steak night. Have to load up on the goods.


Yeah, absolutely.


Put your pin in the right spot.


How's going on the weekend?


No, mate, probably edit some videos, do some training. Not too much. How about you?


I'm having a birthday tomorrow.


Exciting.


I've got to work in the morning, but I'm going to have two chicken wings and a couple of


beans.


What, 25?


Yeah, 25 with 23 years experience.


Happy birthday. I'll see you later. Thanks, guys. See you later.


Thanks, man.


You know what? I want to see if a workout influences my ketone levels, so I'm going


to test it before and after.


About to go to the gym, lift some weights.


And we're on.


Five, four, three, two, one.


Three millimiles.


That's exciting.


All right, let's go lift and then we'll measure this again.


That's what we forgot to do.


I said after workout, I wanted to measure my ketones.


I think this thing's on the fritz.


So before it was, I think that's what it is, every time I put it down, it turns off.


So before the workout, it was 3.0 millimiles per litre.


And now let's see after workout.


Really, I should probably be measuring blood glucose before and after a workout.


The magic countdown.


What do we got?


Not focusing.


That's 2.1.


So 2.1 millimiles.


I'll take a quick photo of that.


So interesting to see how quickly it can change.


I mean, as I said, blood glucose would probably, like my blood glucose would probably be fairly


low right now.


But then after a workout, it would probably go up because as the muscle is releasing,


I believe it, I'm going to get this wrong, but it's glycogen back into the bloodstream


and it gets turned into glucose, hence after you've worked out, you need some energy,


you need some glucose, the muscle releases it, blood glucose goes up.


But back to prepping dinner.


Guess what time it is?


Look at that, turning green.


We're 120 hours, so day six of six, 100% done.


The fast is done.


I just finished jujitsu training.


We're practicing armbars.


Big shout out to Jazz and Billy for the rolls.


Epic tonight.


But you know what?


I don't actually feel hungry.


I just had a shower and I feel, I feel ready to eat, but I'm not at all like ravenous to eat.


That being said, the barbecue, this big dog is ready to go.


We have some primo steaks for my boy Tim.


I think the crew are rocking around.


You can hear my dogs going off in the background.


Check that out.


Beautiful Black Angus sirloin.


That's what I'm going to break my fast with.


Let's get it on.


So we've got a beautiful, delicious spread here.


We've got some Black Angus sirloin steaks, feet, some cauliflower, cheese and red onion.


I'm probably going to stick with just one of these to begin with, to see how it goes.


I did read somewhere that you probably want to avoid cref-fiss-er-us.


I can't say the word.


It begins with C, type vegetables.


But this is for the fam.


I'm cooking for the fam here.


And over here we've got some broccoli slash broccolini, also with melted cheese.


Just a simple dish that needs to feed a lot of people.


Play it out, team.


You need to just start, buddy.


One day we'll get this right.


We'll get this cameraman-person relationship right.


You got the code?


So we're starting?


No code.


You didn't get the code?


We're on.


Are we?


Boom.


We're about to...


Have we started?


Yes.


Alright.


Anyway, I'm ending this.


Done.


Whatever that says.


Well done.


You've fasted for 120 hours, 42 minutes.


I've got 42 minutes to spare.


I'm taking that off the next one.


Got the fam for dinner they've already tucked in.


So I hope that's nice, team.


Lovely.


You enjoying it, old man?


I'm good.


You know what's funny?


I don't even feel like eating.


My dinner's right there.


Right there.


Thank you, cameraman.


You miss it?


Food still tastes good.


Yeah.


Real good.


Taste.


Yeah, it's real good.


Seriously.


Good.


Wash a little tear.


That.


My God.


Never again.


Hunger is the best seasoning.


Alright.


That's it.


Well done, mate.


We'll check back in later.


I'm gonna have dinner.


Here we are.


Day 6, post-fast.


What are we?


We are...


12 hours, seven minutes post-fast.


So I'm not sure if you can see that,


that's why we do the screen recording.


And I'm feeling great.


I weighed in the lightest I've weighed


this whole time this morning.


I believe I was 70.5 kilos,


which if we go back to day one,


I was five days ago,


So that's down 4.3 kilos.


And again, that was from just waking up,


might be lighter after water and whatever, sorry, heavier.


And now we're gonna test blood ketones.


So what I wanna do for the next week or so


is stay in ketosis.


And I'm not sure if the meal I had last night,


oh, probably should have got that strip ready.


So the way I'm gonna do that is eat high fat


and mediocre protein.


So if you eat too much protein,


well then by the pathway of gluconeogenesis,


it turns that protein into carbohydrates,


potentially kicking you out of ketosis.


So let's go here.


Tick tock, tick tock.


2.5 millimiles.


So still in ketosis.


Now we'll set up for some progress shots.


So I'm feeling great.


Like I trained twice yesterday,


had a big steak for dinner,


go on a training after we do this.


How we looking?


The final way in.


Don't worry, I'll never do that again.


All right, let's see.


Wonderful, so that's up 800 grams on this morning


because as I said, I had some water,


a big wham, you're weighing in.


Here we go.


How you feeling, mate?


Probably step back on.


Feel great.


I'm just a little tired


because I just wrecked from training.


What do we got there, mate?


Up 300 grams.


Up 300 grams?


That's about 97.9 yesterday.


That's all right, mate.


Well, that's gonna fluctuate in between that.


Yeah, nah, I mean, look, I'm under 100, I'm happy.


Yeah, day to day is not as important


as month to month, year to year.


All right, so that's gonna be the final segment for this.


I'm probably gonna put some sort of outro on the end,


summarizing different things.


But thanks, brother.


It was a great adventure.


I appreciate your help.


Yeah, it was really good.


Thank you.


Thanks for following along.


tså


I'll move at the speed of light and this is something


Code with me (live): How to make your first Kaggle submission from scratch!

All right, I think we might be live are we live I'm not sure


Test oh


I'm checking live. Here we go


Yes, it's a little bit delayed. There we go. All right, we're live


Sweet, sorry for the delay. I


Need to pause my own live stream so I don't hear myself echoing


All right. How we going everyone? I gotta gonna remember where to look. This is my first time doing this


Actually, I might have to keep the chat open so I can see what you guys are saying


I'm really excited. We're gonna do an exploratory data analysis on a Kaggle data set


So let me uh, let me rearrange my screen windows here. So I've got it all all sorted all hunky-dory


How you're going?


Hey


Hey Kashuk, how are you mate? Nice to meet you


Actually, I think the chat will load if I just keep this window open


Can I chat


How are you all?


It's a beautiful beautiful sunny day outside where I'm at


We'll just uh, just let this run for a little bit to uh to load everything up


If you can't make the stream if you're just checking it out, we can it's gonna live on the YouTube channel forever. So


What's the purpose of this stream


well


Since since becoming a machine learning engineer


There have been a few things that I've really had to upskill myself on and that's


Number one thing is probably how do you explore a data set from scratch, right?


So that's that's probably that's probably number one


And so that's what we're gonna do today we're gonna start with a data set where we're gonna go to Kaggle


We're gonna get the Titanic data set. We're gonna explore it. We're gonna make a Kaggle submission


So if you've never done a Kaggle submission before


You can go through the data set and you can go through the data set


You can go through the Kaggle submission before you can go through this notebook


You can go through this live stream and you can you can do your first Kaggle


Official Kaggle submission on the Titanic data set, which is which is pretty exciting


If you if you want the code it's all gonna be available on my github after this stream


So I'll make sure to put that in the description


Otherwise before we jump big shout out to Coursera. Thank you so much for for sponsoring this video


a lot of the stuff that I've I've done in this notebook has been


From the applied data science with Python course


I mean learning pandas is is fundamental to to exploratory data analysis and the applied data science with Python


By the University of Michigan bit of a mouthful is an incredible course is available on Coursera


and and


Also due to this partnership Coursera if you if you want to sign up


Any type of computer science course they're offering a one month free trial


Till December 11th


So if you sign up before December 11, you get a one month free trial of of any course in the computer science section


So be sure to check that out the links in the description as well


That's that should already be there as long as I've got this, right?


Now if if you can't hear me if you if you can't see me, right if you can't see the screen


Please leave it in a chat. Oh


No, the stream is cutting out


Oh, please tell me this is working


All right, it should it should be working from my end it should be working


All right, so if anyone watching if you give me a heads up we'll get this started because I want to make sure it's all good


So just leave a message in the chat. How are we going?


Otherwise, we might have to work out another another way to to fix this up. It's working fine


As I said, I've never done this before it might have been better to record it


But we're gonna try to do it live and if that all else fails we can do a recording and do it later


But I want to have the chat here. So


You can you can ask me questions along


All right, so I'm getting a good heads up from empiricist Academy


Thank you so much for tuning in. Let's let's get started. Shall we so the goal of this is to


Predict who would have survived on the the Titanic? So if we look at we look at Kaggle, there's a there's a project here, right?


It's called Titanic machine learning from disaster


This is where you should start if you want to jump into Kaggle competitions


So predict survival rates on the Titanic and get familiar with some machine learning basics


So there's some descriptions here. You can get this at kaggle.com slash C slash Titanic


Otherwise, if you want the data, you can download it here. There's a couple of different things, but


We will eventually be creating a submission here by the end of this stream


Right. So let's let's go through with that now we go to the we've go to the pre cooks notebook that I have here


Precooked notebook that I have here. All right, can we predict who would have survived on the Titanic? Oh, there we go


Now as I said by the end of this stream, this will be up on my github so you can go through it yourself


Expand on it


I put some challenges in here that I will explain and I'll do my best to explain each step and what I've done


Why I've done it and by no means is this an exhaustive list of things that you should do with the data set, right?


It's it's just an example to get you started


I've put a bunch of challenges through here and I mean I'm I'm still learning as well


So pull me up hit me up in the chat if if if I'm doing something wrong. All right


so


Why EDA?


So what is EDA? EDA is exploratory data analysis


So not all data comes in a neat little package ready to be modeled by the latest and greatest machine learning models


Most of the time you'll get a data set. You don't know much about so before you can make any solid predictions


You'll have to try and find out more about the data, right?


And what a prediction so predictions are running your machine learning model over over some historical data and then trying to predict the future


Based off what's happened in that historical data. All right


So this is where EDA comes in when you first get a data set


You got to start exploring it start figuring out what what is the question I want to answer and now remember this is exploratory


So we're not writing the crystal clear code


We're just trying to go through it step by step and figure think more things out about the data. Okay, so step one


Is download the data you can download the data from from Kaggle. I showed you that before


boom, you go to this little data tab and


Here it is, right so that's kaggle.com Titanic if you just Google kaggle Titanic data


You should be able to download it, but we'll get this submission tab ready. We'll go back


Neural you're awesome, man. Thanks, bro. You're awesome, too. All right


Be sure guys if something happens with the stream hit me up in the chat because I've got it paused my live version


So so I can I can keep this going so step two


Work through the notebook. All right. This is what we're gonna do. We're gonna go through step by step of


Of what you would do if you're exploring a data


Just a data set you've you've never seen before so I've got some some basic import dependencies here


All right, so you should be able to run this cell in the Jupyter notebook regardless


We're doing this matplotlib in line so we can see some graphs


In the Jupyter notebook


In the in notebook if you don't run this little if you don't run this little line here matplotlib.inline


You won't be able to see see what's see the graph in the actual notebook


Okay, and then we get some Python imports standard ones like math time random daytime


data manipulation manipulation data manipulation


Python not pandas numpy


Visualization so this is this is where the graphs come in


This is where we want to see our seaborn graphs seaborn is a is a is a graph library visualization library


It's really pretty


Pre-processing so we've got some one-hot encoding we'll get to that


Machine learning a whole bunch of different machine learning models will get to those as well


And we're gonna hide hide the warnings here that pandas sometimes throws because


We don't want to steal that red stuff throughout our code


You can you can comment this out and see what warnings they give you but comment it out and by comment it out


I mean you can just put a little little a little hash at the start, but we won't need those for now


Trust me. I've I've gone through this notebook the warnings are just


unrelated


Alright, so we're gonna run this one run this cell shift enter


Oh actually to make sure it's really live let's restart the kernel. What does that do?


So that means anything that's stored in memory is going to be deleted


So where this is as if we're starting it you downloaded this notebook on your computer


And you were starting it from fresh now if you do were to run this cell running it from fresh


You might get some errors here because you haven't installed some of these now if you get if you get an error


That says you haven't installed numpy


Google how to install numpy right how to install the thing you have and install it


In your through your terminal or something like that so you can get it running and then repeat that for each one that you need


So we're gonna first of all we're gonna load in the data


How's that how are you man?


Hamad yes, I made this notebook Merlin. How's the health app coming along still still in a very much planning process


All right, so loading in the data after we downloaded the data we need to get into the notebook


We need sorry we need to get the data in here, so this is training test gender submission


So what is this so we look back at the the kaggle data?


We go back here


You can download three different data sources right gender submission test CSV trained CSV right so these are all


CSV stands for comma separated values and now this is what it looks like right


So you'll have eight ninety eight ninety two comma zero right so that's what this gender submission file looks like this eight nine


But what is that eight ninety two comma zero?


This is why we bring it into our notebook so we can see it a bit better


So we're going to download these three files right so you've got these in your in your home directory or or wherever you you


You've downloaded them, so I've downloaded them


I'll show you here


I've downloaded them into my Titanic folder


And then you've got data here and so see these three files gender submission test submission train


Etc we've got it here right so it's in my data folder now. We're gonna put that back over there


And then we go back into our notebook


All right, so this is where where it's stored so we're importing the train data data


dot train dot CSV data slash train slash the test equals now


What's this function here PD dot read CSV so that means pandas?


Read this CSV and save it as test now


We do it for all all three of them right so now we've got them


We've got them as variables as Python objects, and they're in there


They're in a pandas data frame form now now


What's a data frame now remember that that weird way of looking at things over here? So this is this is how a CSV looks


Right, but then we move into back to here, and this is how a data frame looks so there's our gender submission


so passenger ID and


Survived so this is an example submission of what we want to what we want to get to at the end of this notebook


Alright, and this is the data. We're going to use to build that that submission


Now if we look here, this is a training data. We have passenger ID


Ahmed thank you so much


Cheers my friend


All right, so oh sorry guys


You got distracted reading the chat trying to trying to trying to maintain both of this at the same time


We've got chat and we've got notebook all right. I'm gonna. I'm gonna be bouncing in between them as best I can


So we have the training data set here, so this is what it looks like we have passenger ID now now


How long is this all right actually we need to import first need to run this cell run this cell all right?


How long is this training data set so Lang Lang is just length of the data frame?


So this is we have zero zero to four examples here, but there's actually eight hundred ninety-one rows of this


So we've got mr.. Rowans. We've got miss miss liana


We've got mr.. William Henry and now this column here survived says is zero for if they did not survive on the Titanic data set


So that means these these are actual passengers on the Titanic right so this is


It's kind of sad right we know that these people


Some of these people perished from the Titanic. It's it's a it's a tragedy, but it can help


It wasn't in vain they didn't die for no reason they can help us practice machine learning and hopefully then we can


We can use those skills to bring some value to society right so they they didn't just bought it on and and


And drown or pass away for no reason that at least we can we can learn something from this


All right, so survive so mr.. Owen Harris brawned


22 male that's that's a young young lad


tragically didn't survive


So this is this is his information here. He was in p class 3. What does that mean? We'll explore that in a sec


22 years old he saved SP. What's the best P pH?


Zero ticket as his ticket number he paid seven dollars twenty five probably a lot of money back then when Titanic was around


Cabin Nan what does Nan mean Nan means not a number right so there's no value here?


But we'll look at that in a second


Cabin C85 that's for that's for miss mrs.. John Bradley John Bradley hmm


And embarked s see SSS. What does embarked mean?


We'll explore that that's part of this exploratory data analysis, right?


But this is just an overview of what the training data look like and now we have the same for test now test or head


Head means to view


Let's do this head equals view first five lines


It's a real way real good way to view the data frame without having to see the whole thing right so if we just type in


test


It'll show us the whole thing, but what if this one only has?


418 rows what if you had one that had a thousand or actually no not even thousand because computers are fast they can do with


A thousand pretty well if you had 10 million right you might not want to look at all 10 million rows at once


Let's see the length of our test data set oh


Type that in wrong see that's the beauty of doing this live right it's going to be some errors


So there's 418 so here's what we have to do guys we have to use the 18


891 rows of


The pass of the training data set so we need to learn


information from these rows to predict on the test data set


whether these passengers survived or not


So this as you can see training has a survived column


But the test data set doesn't have a survived column so what we want to do is build a machine learning model


that is able to learn across this data set and


predict this column so survives on


The test data set right and that's what we're going to submit so our end submission is going to look something like this


So it's going to have passenger IDs


So we're going to take this column from the test data set and then use our machine learning model to predict the survived column


All right, and then we're going to submit that to our Kaggle competition, and then we're going to get


glory


Well, maybe depending on how well our submission goes all right so back to some data description


So remember how we looked at these columns we have SIBS, SIBSP, PRCH, Ticket, Fair, Cabin


What do these actually mean right that to me?


That's just numbers like is that is that anything significant why there's so many NANDs in cabin


You should stream in it. Yeah, I'm not sure what I'll have to fix it for the next stream if I'm not streaming in high enough quality


I'll make sure to fix that by next time


Because this is my first one. I'm not not quite sure


Why the quality is not that great might be my internet connection, but we'll do our best


for now


But thank you Nikitand for the for the comment alright so back to data descriptions


So remember these are the data that we're going to be using to test our data set


Data descriptions so remember these SIBSP, PRCH. Let's have a look. This is what comes in the Kaggle


Dataset so it comes with this a data dictionary, so it tells you survival is


Gives you definition gives you the key so if we look at this so SIBSP number of siblings


spouses aboard the Titanic alright


So that means if I if I'm aboard the Titanic and I have one little brother on there


This should be one for me and PRCH is number of parents slash children aboard the Titanic alright


So if I'm on there, and I have I'm a father, and I have two kids


My PRCH should be two alright and then embarked


Ticket is ticket number passenger fare is fare cabin number is cabin


Embarked port of embarkation is C equals


Cherbourg Q equals Queenstown S equals South Hampton alright now


We know a little bit more about the data. No I've put these here right. I've just manually wrote them in


So now we sort of know alright so SIBSP


Mrs.. James Wilkes must have had we look here number of siblings slash


spouses so she must have had either a husband or or a


or a


Sibling on board right so at least one, but we'll get to that


We'll get to that in a second


Maybe there was maybe there's some people had more


But we can't see that because we're only looking at the first five lines


That's where we run some some more more more more in-depth exploration


now


What you can first run is a train dot describe so this function will will just pull out all of the


All of these little information bits here


This is a function built into pandas you can run that on any data frame


So if we remember what train looks like so train is is our our data frame here


And we're going to describe that here, so if we look at this passenger ID


We don't really we don't really mind about that because that's just we know that's just labels of one to


891 so of course there's going to be 891 oh, but what's this?


714 in the age alright, so


Maybe there's some missing values in the age column. We'll check that in a second


Sib SP these all have 891 that's good right so that means there's these values in in the columns


891 values that's what we want because that's the length. That's the overall length of our data set so if we look in here


length train


891 beautiful, so this is also going to give us a mean


standard deviation mean


25% threshold 50% threshold 75% threshold and the maximum we're not really going to worry about those at the moment


We're going to do a bit more of our own exploration


But this is this is a really quick function that you can run on any data frame


To quickly get a get a bird's-eye view of what's actually going on


all right


Hey, shake what's happening, brother?


Thanks for joining in


We're doing some exploratory data analysis on the Titanic kaggle data set


But thanks for showing up man. It's good to see you all right


So first of all in any data set what I like to do is find out where the missing values are okay?


Why why would you do that because we want to figure out where the holes in our data are?


Alright because you can't make a prediction when there's no data there you have to figure out how to plug that hole


Or if you do make a prediction when there's no data there how actually valid is that prediction right?


So first things first you can run this great little function missing no dot matrix on


Train fig size equals what what is this? What is this line doing all right? We come back up here?


We imported this missing no we can just think of this is missing missing


Numbers or missing information. Let's just call it missing information for now


So missing information


aka missing no


Dot matrix dot train what's train train is our data frame here this one this information and


Fig size is just the size of this little graph here right so if we change this to maybe maybe 10


It'll be a bit bit higher. I think yeah there we go


beautiful


so we're running missing no a


Matrix feeding it the train data set train data frame, and we're telling it we want it to be


30 30 pixels wide by 10 pixels high now. What what is this?


What does this look like now again always always be looking at your data always check it out


We're going to look at it here so train


go ahead


Always keep paying attention to your data you want to make sure you're doing the right thing so


Okay, we can see we got a column here passenger ID survived p-class name


sex age oh


Hold on ages got a lot of missing values right so so if you imagine


The black columns are fully black columns no lines here


That means that there's this these have values right, but the ones that have white gaps


That's a missing number all right so our age column has a fair few missing numbers all right


So we'll have to we'll have to figure out a way to deal with that


Potentially not in this in this EDA, but in your future EDAs you'll have to go through missing numbers


And then we have Cib SP. That's that's good that has lots of data parch ticket fair cabin


Oh


Cabins, maybe not even worth looking at because look how many?


Look at that. That's just


Most of it is not there put it that way and barked that has that has two missing values


This is a really good way of visualizing quickly


Visualizing quickly where the missing data is in your in your data frame right and you can show that to someone like say you are


Working with the client you're like


You go to the client you like hey


I'm gonna run this quick function on your data and and show you this and you can you can send that to him and go


Your age column has plenty of missing values your cabin column is plenty missing values if you have that information


it would really help us for


For making predictions on your data because machine learning engineers and machine learning models love more data


alright


So we clearly see some missing values especially in the calum cabin column


It's important to visualize these missing values early so you know where the holes are in your data set so important alright


Well, we'll revisit we we might not


We'll revisit this in a second alright


Now find missing values so what I've done here is another form of this not as pretty right, but it does the job


I've wrote my own function, okay


It's called find missing values nice and simple you pass it a data frame. What's the data frame again?


It's this so train and you pass it the columns you want it to scan over so what it's going to go through


It's going to create an empty dictionary


It's going to go print number of missing or NAND values of each column, and then it's going to loop through


It's going to actually it's going to create length data frames, so that'll be


Can you remember how long our training data frame was we can figure that out really quickly alright, so length


train


So length length DF DF length is going to be 891 so then it's going to loop through columns


So what's the columns so so we passed it train dot columns?


So it's going to maybe we pass through this


It's going to loop through all the columns


So for column in columns, so it's going to go total column values equals DF column dot value counts dot sum


Alright, then the missing column values is going to be the total length


Minus the column values that it's found so this total column values. What will that do alright?


So that's going to go through the column alright, so maybe it goes through the age column


Okay, and it's going to total up the amount of black lines, okay?


It's going to total them up, and then it's going to sum sum all those black lines up alright, so this might for age


For age it might might turn out to be


Who knows maybe there's a hundred and fifty missing values here?


But we can run this function to find out and then it's going to going to create a dictionary


It's going to update the dictionary to say DF length the missing values column


DF length is going to equal the total length of the data frame so this this total length


Minus the number of values in the column


Okay, so let's run this and see what happens


Okay


So here's our dictionary


We revisit up here. This should be zero for the first five columns. Yeah


We have one two three four five yep zero and now age


Should have a fairly high amount of missing values. Let's check that


age


One hundred and seventy seven missing values out of eight hundred and ninety one okay?


We're going to have to do something about that, and then we have see best PPSH


Okay, these all have have values there and now cabin


Six hundred eighty seven missing values out of eight hundred ninety one


A lot of missing values there and then embarked has two two missing values, so not that bad alright


So so maybe we we can easily discard those or fill them up, but we'll have a look at that in a second


Alright, so we've gone through the missing values of the data frame. We've worked out where the holes are okay?


So now what we're going to do is go through each column and


Check out more in depth what the actual data contains so the information that is there


Right or isn't


What can we do with that?


Alright is there stuff that we can we can manipulate it in some way? What's what's what's holding us back from running a machine learning model?


Because that's that's what we want to do right


Alright, so to perform our data analysis. We're going to create two separate data frames


one for discreeted


discretized continuous variables


And another one for continuous variables, so what does this mean? What's a discrete variable?


Well a discretized continuous variable is


Is a continuous variable that's just been chopped up into bins alright, so let's just say


What's a continuous variable first of all a continuous variable?


Is is say like fair alright say fair you have


Values we can do a simple example


We don't even need to use to use fair so say you have fair


values range from from 0 to 100


Right so you could have any value in between 0 and 100 for fair


And that would be a continuous variable, but if you wanted to


Discretize that you could bin them up


Into separate buckets, so what does that mean so maybe you have a section that's 0 to 10 you have a section that's


10 to 20 and you have a section that's


20 to 30


so now the continuous variable would be


all numbers between


0 and 100


And then you have a continuous variable


0 and 100 but it would be binned in a


discretized


Continuous variable if you put all the numbers between 0 and 10


In in this bucket and have a label of


1 so now you would change your values from being 0 to 100 to being


Values from 1 to 10 and if it gets a number 1 it means it's it's from 0 to 10 if it gets a number


2 it's from 10 to 20 and if it gets a number 3


It's it's 20 or 30 and so on all right, so that's what we're gonna


Do we're gonna make two types of data frames?


We won't necessarily need both of them for this type of analysis


But it's good to separate your values, and you'll see how we separate them in a second


It's good to separate them into two sub data frames so you can potentially run


Different types of models over over each data frame, so we'll create those


And now what data types are in the data frame before we do that. We'll check the chat. What's up Stefan? How you doing man?


Discrete labels


We are still survived, please make a short video give a capital competition in future


Great idea, I will definitely do that


But I want to make this one an extended one just to go through this whole notebook


But we'll make a shorter version of this in the future


What data types are in the data frame so you can do that train dot D types remember train is our main data frame?


We're looking at now so we're gonna do that


So we're gonna create a new data frame and we're gonna call it train dot D types remember train is our main data frame


We're looking at now so passenger ID that's an integer survived


That's an integer P class an integer name is an object sex object float int int


Object float object object all right. That's what I we got we got a good mixture of integers objects floats


Okay, let's explore each of these features


Individually so we're gonna start with survived all right, so survived


What columns are there what what information is there because we don't want to lose track of the problem?


We're trying to solve we're trying to predict whether someone survived the Titanic based on this information


Okay on the test data set always define your problem at the start of any exploratory data analysis and use that problem


To guide your your exploration all right you might not find the answer


But you might also raise different questions that will help you figure out


What's not the answer which is almost as valuable as as figuring out the exact answer you're looking for all right?


So you're eliminating things that that aren't the answer


So we're gonna interact we're gonna go through this in an iterative process now


There is that is the what an EDA should be an iterative process you're not writing pure clean code


You should strive to as best you can


But we just want to figure out as much as we can about the data frame as quickly as we can all right


We're not as quickly as we can but sometimes you have project deadlines right Kaggle competitions don't last forever this one lasts forever


not a good example, but when you're doing something like a


Client work for for a customer you may have six to eight weeks


So you don't have all the time in the world to to explore I know for myself at Max Kelson a lot of our projects


Go for six to eight weeks right so we we have to get this exploratory data analysis done in the first week


So we can start to build models and get them back to the client


so


first column first


Make sense we're gonna go through the target feature


Which is survived so description whether the passengers survived or not and it's really good to communicate this stuff


Okay, so so the reason being is is say your colleague or say one of your teammates had to read your code


Can they understand what you've done all right? That's what I need to really work on as well


I'm gonna. I think I've communicated this one pretty well


you can give me some advice or some feedback once you see it on github and


And and yeah, it's important to communicate always important to communicate


This is a variable we want our machine learning model to predict so we're gonna make a little plot here


This is using the seaborne library remember we imported that way back up here


Import seaborne as SNS so we can use SNS whenever we want now we come down here


How many people survived the Titanic now this might be a bit of a graphic?


Yeah, it's gonna be it is going to be a graphic


But it might be a bit graphic considering. I've seen the data before and it's it's it's not the best-case scenario


Boom there we go, so what are we what are we actually printing here?


Now we're we're getting


SNS


Count plot so the y-value web we're giving it is is survived so the y-value on here is going to be 0 or 1


Now count plot just shows the total amount of numbers and the data is our training data frame


So this is a data frame here, and so it's going to bring back the total number of zeros in this column right


549 but you can you can slightly see that I've printed out the the value counts here


So you can see the exact number now. That's just trained or survived so train survived value counts so


549 people


In the training data set did not survive the Titanic crashing so rest in peace to those


and then we have


342 so


Thankfully a fairly large amount of people did survive so then we can get see that here


So now we sort of know what the ratio is of how many people survived how many people didn't so so we know in the future


If our machine learning model is predicting on the test data set that more people survived


Then didn't then we probably got something wrong right why?


Because we can see from this training data set that the number of people who didn't survive is


Clearly larger than the number of people who did all right


So that's that's just a little tidbit to remember when we're doing our first predictions


So we're going to add add this column to our subset data frames so now we've got we've got two


Two subset data frames that we can use later


Boom DF bin and DF con


All right, so they just look the same right now


All right now. We're going through the next next column so p-class bomb there


We go p-class what values are in here the key the ticket class of the passenger now


Key is number one is first class so those people a bit fancy right


Fancy first class. I'm not sure really


I'm just making this up second class third class nice and simple way of labeling things


This is this is unaltered from how we download it from the kaggle data set so first of all we're going to plot the distribution


Now why would we plot the distribution look at the distribution of each first each each feature first?


If we can understand what kind of spread there is across the data set now what I mean by spread


All right now we can do that. There won't be many outliers in here


And what do I mean by an outlier okay? So we've got a little highlight of this graph already


All right, but say you had some some column way out here right and it's it's ten instead of


instead of one two or three


Do we need ten in our data set?


And this is not a good example right because the data is is pretty pretty well close


But ten is out here imagine if that had one you had one variable that had ten out here


All right out of out of eight hundred and ninety one


Rows in our training data frame if you had one that that is totally out out here when everything else is here


You might not want to use that in your data set okay, so that's the concept of outliers


We won't dive too much into that, but that's something that's a little challenge. That's challenge number one check out what an outlier is


We're going to have challenges all throughout this notebook


Because I don't want to just just give you the way of how to do everything because really there is no way of how to do


Every single thing you need to explore. That's the role of a machine learning engineer


That's the role of a good data scientist is to figure these things out


Okay, so we can see with this feature the values are numerical one two and three so the numerical


But they are also categorical right why because


What is that if you so a numerical feature you could say?


Say this these were numerical so that means if you're in class one and


Someone else was in class two and you add those two people together they equal someone in class three


Does that make sense?


No, it doesn't make sense right so they're categories so you have one two three


They're not necessarily related to each other because they're they're separate separate categories, okay?


So how many missing values around our P class?


Zero how we figure that out because we got our missing values dictionary up here


All right, we're just calling this dictionary missing values P class zero so we come down


Go go to that and we're going to add our first


Feature that is not our predictor variable to our subset data frames


So now we've got survived and P class we're going to keep working through the training data set feature by feature


All right, it's it's as I said iterative process sometimes it can be tedious


But it's important to understand the data you're working with


Here we go


What's up, Daniel? Hey Raymond good to see you my man. Good morning Mohit put this up on your channel


Shake I'll definitely put it up there


It's called a nominal variable when the categories are not ranked if they can be ranked then it's an audio or no variable


Ranked then it's an audio or no variable. I'm art is 100% correct. Yes, I did not


The actual names of the variables escaped me so if we come up here


So so this is these are nominal variables


All right as as some odd said when the categories are not ranked if they can be ranked then it's an order ordinal variable


Okay, so like a order all right these are nominal


That's that's homework. That's homework number two figure out what the difference is between nominal and ordinal


And I'm half saying that because I need to work it out completely in my head, too


Okay feature name


Description the name of the passenger all right how many different names are there?


Whoa actually this doesn't show us exactly


All right look how many different names there are so value counts is a beautiful easy easy to use function


that will give you the counts number of appearances a certain value appeared in a in a in a


In a column all right so train from the train data frame get the name column


count how many values and how many times they appeared so all of these are going to be one because


No one has the same name so there's eight hundred and ninety one different names, and how many rows did we have?


We check it


Eight hundred ninety one so every single row has a different name all right, and what can we do?


So every row is a unique name. This is equivalent to the passenger ID, but name could possibly be used differently


can you think you could reduce the number of different names or


Create new features out of the names now


We're not going to go through that at the moment because we want to we want to get through this EDA fast


Now because there are so many different names


To keep this EDA fast we won't move forward with using the name variable


So we're not going to add it to our subset, but I'll give you a little hint. This is challenge number three


I'm gonna lose count of the number of challenges


See how all of these have mr.. Mr.. Mr.. Mr.. Mr.. Mrs.. Mrs.. Mr..


Could you


Create another column right on this data frame?


Which uses the name column?


and then creates a class so like p class


That if you have say you have mister you get a number one or if you get miss you get a number two


If you have misses you get number three can you do that all right? So your column might look like name?


one two four five


three two


Depending on what what what what numbers you give these guys you got a master there you got master


That's a little bit of a homework all right?


So it won't use name, but now we're up to sex


Okay, let's check it out sex is nice and simple because it was just male and female


We're doing the same thing as we did for survive. We're plotting the count all right?


So we have we have a few fair few more males on there


So that's it just just under 600 so 575 ish and count for females is just over


375 ish and count for females is just over 300 and then we have


Zero missing values in this X column which is great


So since this is already a binary variable male or female let's add it straight to our subset data frames beautiful


So sex we can add it straight there. We're gonna add it to DF bin as


one or zero so


How do we do that we use MP where so that's gonna go through the


The DF bin so actually let's just let's just check out the train train sex column


All right


We'll just print the head of that column so this is going to say DF bin sex column MP


Where so DF bin sex we just added it here. Just added it to the to the to the DF bin


Where it equals female give it a one and where it doesn't give it a zero so that means we'll have a column that goes


zero one one one zero


Okay, let's do that. Oh


and now


Now we've added it to our subset data frames


We can use the sex variable and see how it looks like compared to survival


Why because they're both binaries, okay?


You can do this with other type of variables


But this is pretty easy to do with with binaries and what's a binary a binary is simply zero or one


So we're gonna plot the distribution here


Let's check it out. So we've got two plots happening here


If it equals one in DF bin for survived, let's look at DF bin actually, okay


So DF bin dot head


All right, so this is what we're doing we're grabbing from DF bin


we're grabbing this column and we're comparing it to this column sex and we're giving the


We're giving it the plot two little labels here. So this is what this this is doing now


What does this look like? Oh, and by the way, this is the size of your graph here


So sex is down here. So zero we have zero this this this scale is not that great


You could probably improve that. Okay, that's another challenge improve the scale on here. So maybe these columns are a bit closer together


So we have zero right? What's zero? So zero for sex. What do we give that?


That was males


wonderful, and


Then we have one so this is females. So males females now we have two colours. All right predicting


Showing us sorry survived or did not survive. So if we look at this zero males survived


Look at the proportion of males that survived not very many right? That's that's that's if you're a male on Titanic


You had a you your chances of survival were far lower because look at this did not survive is right up here


Okay, and now we got females. So females are pretty close some of them still perished


So not not all of them more more did not survive than actually survived


But the ratio is certainly a lot closer. All right, so if we look at this this is survived did not survived


Way bigger and this is the same over here. So these are pretty close


That's that's really good for the for the females not so good for the males


Not many people survived but all those who did more were female, okay


So we've gone through the sex the sex column now


We're gonna go to age now you remember how many missing values today our age column had 177 way back up here


All right, we have this graph like so many missing values. Oh


Now we quickly look at age so out of a total of 891 that's almost a quarter of the data set but


to make this quicker where


I'm gonna give you a big challenge. This is your first big big challenge right along with the name column


How would you fill in the missing variables of the age column?


Could you replace them with the average?


But what's the pros and cons of just replacing the age values with the average what I mean by that?


So you have a hundred and seventy seven missing values


what if


You just took all the information from this this one here


These these black lines and filled up the white ones with the average of the black lines


Can you do that? That's pretty easy to do but


What's wrong with that or would you get rid of the age rows the whole row that doesn't have values probably not


So we won't answer these questions in our initial look to EDA because we're trying to iterate over


Every column as fast as we can to run some models and make some early predictions then we can add an age later


But once age has been fixed up there's some code here that you can add it to the two sub data frames


So this is your first big challenge. See how it's in bold


How would you fill the missing values in the age column? I'll leave that for you to check out


Now we're going to create a little function here before we check that we'll check the chat


Daniel it's not good practice to use sex as a label


You should use male or female


Depending on what is one?


Hmm


Are you talking about the graph there maybe maybe that's important. Yeah


I'm not not quite sure what you mean. Oh


Okay. Yes. Sorry, so it should be


male female you're right you're right yeah that should be a better way to graph it.


Alright so we've got a little function here not the best the best code to do it's just simply


a binary that says if if we have a true value here use BindDF else don't use it this is just


for some graphing you'll be able to check that out we won't go too far in depth essentially it's


just going to create two different types of graphs side by side to each other. Now how many missing


values we're up to the Sib SP column how many missing values does that have it has zero beautiful


alright now what other value counts are there most of them are zero alright then we have one


then we have 28 then we have 18 then we have 16 7 alright so Sib or Spouse so there's some people


on there with seven seven people on there have eight siblings or spouses on there wow so this


whole family's on there probably but a lot of people are just there with with no spouse or


sibling that's strange I would have thought a lot of couples go on Titanic but but apparently not


the data remember this is the description here the number of siblings spouses the passenger has


aboard the Titanic alright now let's add Sib SP to our subset data frames we'll do that as usual


you're used to that now and then we're going to run a visualization here now this is this is what


this function does here this big messy function but we're going to run this graph a couple of


times so we don't want to be rewriting the same code over and over again wherever you can to save


yourself writing the same code over and over again create a small little function and use that to to


to run similar similar snippets of code over and over again it's good practice to start


functionizing your work so oh no we forgot to define our function so we'll define it here and


then we'll uh we'll plot it so what what is this graph on the right tell us so we got the counts


here we can see that that's that tapers off that that that mimics this little a little little graph


here I mean little table here what does this one tell us so this is on the on the x-axis we have


0 2 4 6 8 so these are these values here and we have survived and did not survive as well so did


not survive so 0 have a hot much higher ratio of people who did not survive compared to people who


did survive and then here if you did have a have one spouse or one sibling it seems like more of


them survived than then didn't so what does that tell us well maybe if if you had a wife on board


or if you had yeah I think that might be it maybe if you had a wife on board your chances of survival


were higher or if your husband was on board maybe I'm not entirely sure but we'll see so that's it


that's another good way to visualize your binary variables remember survived is just a binary


variable and and start to see the distribution of of the different column values versus the two


values you're trying to predict so next we're up to P arch so the description is the number of


parents the children the passenger has aboard the Titanic all right so since this feature is similar


to see SP we'll do a similar analysis right because it's a number of parents of children


that passengers aboard the Titanic and this is the number of sibling spouses the passenger has


aboard the Titanic so we're gonna missing values none value counts same same sort of distribution


as before watch this same graphs right that's why we made a function plot count dist you feed it a


data frame you give it a bin column you give it the label column which is this one here you give


it a target column which is P arch which is down here and then you tell it how big you want this


figure to be all right now that is our function up here all right that's what you can check out


yourself so what's this what's the story here similar similar case scenario right you got zero


if you if you had zero number if you have zero parents or children aboard the Titanic your


chances of survive of dying did not survive is higher than your chances of surviving yeah if you


had one or two your chances of survival is higher than if you have a few if you didn't right sorry


your chances of survival is higher than not so you can see that survived did not survive all right


so that's some some information there we've added them to our we've added both of these to our subset


data frame seed CBSP and pH now they're both in our subset data frame we can have a look at that


here so we've got DF con dot head beautiful all right so we're see how we've got it we've got


DF train we're slowly working through the data frame train we're slowly working through column


by column adding it to adding it to a DF a subset data frame to start making some predictions all


right so we've got a few columns left to go but ideally this is going to be filled out with


similar columns to what's on here in a similar way then we can run some machine learning models


on our subset data frame all right because we want to keep train as the ground truth you want


to keep that as it as it as you would download it all right because we don't want to alter that


because we need to figure out if in case we have to go back to what our original file was we want


to keep that original that's why we're making subset data frame so we can or supplementary


data frames so we can alter those and not not destroy our ground truth information here now


ticket how many missing values to ticket have zero beautiful now how many how many different


types of tickets were there now this plot might take a while the one zero data is perfectly fine


if the data one is male then you should just label that variable male if you label it sex


or gender we don't know if oh yes I see yeah one in zero four for male and female yep I think


that's what we got okay we go down now so this way to as you can see this is this is clogged up


why because there's so many different types of tickets now oh actually I think we do that here


we don't need to screw that so that doesn't look too good what about another way of looking at this


all right let's let's see all the value counts of ticket Wow right so remember our name variable


okay remember our name variable had 891 different types ticket has 681 different types so that's


still a lot of and what this is is is um high high dimensionality all right so that's a curse


of high dimensionality you have have one variable so one feature ticket but it has 681 different


types of things that it could be within it so the idea of machine learning model is to reduce that


right so that's what we're trying to reduce it back to but is there a way you can quickly see


that on here I can't really maybe some that were were we could group them by if they had CA there's


a few with CA there we could group them by that or if they if they were over they were over 20,000


or maybe if they were over a hundred thousand I can't so essentially we're trying to group these


into different into different smaller a smaller amount of categories than 681 right because that's


just too many across 891 rows that might be a challenge I think it is a challenge how would


you reduce the ticket feature is it even possible if you can't see a clear way immediately you can


probably skip over it in your initial EDA so remember we're just we're just running over this


quickly to try and get a prediction to get no idea of how well we can we can build a model first okay


so there's 681 unique ticket values that's too much we're not going to use the ticket data data


ticket column for now there may be some way to reduce this down but I can't see one immediately


you might be able to see one and that that is definitely a challenge for anyone who decides


to work on this this notebook themselves now we're up to fair path Dave how to get an internship in


AI and business great question my friend the best way I've that worked for me is to develop some


skills so whether it be machine learning data science and be good at those skills and communicate


those skills online and then apply rigorously to to as many internships as you can there's no


structured way to do it but there are two things you will definitely need you'll definitely need


the skills and you'll definitely need to be able to communicate those skills and so they're the two


things you can communicate them through through what I'm doing you could you could build something


like this and show show someone how well you can go through a data set from scratch you could enter


a carry competition you can make a video like this you could do a blog post but first of all you need


the skills you need to be able to communicate those and then you need to apply you can do that


through messaging people on LinkedIn or going through a company's website but the two things


you definitely need are skills and communication skills back to this all the best path so we have


a feature fair no missing values beautiful we're doing a count now as you can see a lot of this


process is is similar right so we've done it missing values zero we've done the counts we've


done the data type we're gonna continually do this for each column right because that's that's what


we do we do high quality work and we leave no stone unturned all right you go through a data


frame iteratively that's that's there's there's really no other way to do it if you have a better


way let me know but of the problems I've worked on it's just a matter of putting in the time and


putting in the work to explore the data as best you can so there's 248 unique fair values now what


we're gonna do is we're gonna we're gonna create our first bucket but because fair fair is a float


number let's add it to our continuous sub data frame and add it to our categorical sub data


frame and to add it to our cat the good of course sub data frame will cut it into bins now what does


this mean all right so if we go here so we've done a bin we've we've PD cut train fare into


five different bins so this is bins equals five but as for DF con we've just added the column why


would we cut it here all right well remember being is our oh no this is this is not discretize this


one is discretize this is a beauty of doing it live right you think you've checked the notebook


for errors but you find some all right so that means it's cut into bins so if we look at look


at DF bin dot head so now we've got different groupings for fare whereas in DF con we've got


there just exactly how it is so remember how I was saying we'll have fare zero between negative


that's weird someone's fare was negative 50 cents that's not sure what that values in there that


might be an outlier you want to remove so we have fares between zero fifty negative fifty cents and


one hundred and two dollars now if we have a look at at how many different times there are here value


counts all right so we have negative negative fifty cents to one hundred two dollars is eight


hundred and thirty thirty eight now these bins aren't exactly they might not be the best so what


fares bins does it just I'm not actually sure how how it decides to cut them into bins because I


haven't used that function very often you might want to look into that a bit more as a little