Everybody Matt here for late in sports cards ripping 2022 Panini one football hobby. This is 10 box break number 31 pick your team We have Adam M. Bengals Jags Alexandros S. Bills Anthony G. Texans Bradley R. Steelers and Niners Brandon J. Lyons Brian C. Packers Cesar D. Bears Chance L. Panthers David M. Jets Frank A Falcons Henrik J Broncos Hunter R. Bucks Jake B. Browns James D. Ravens Chiefs Joel B. Cardinals Jose S. Dolphins Josh N. Patriots Kyle M. Vikings Ram Saints Matt P. Raiders Nicholas T. Colts Chargers Seahawks Paul D. Cowboys Tony H. Commanders Victor F. Titans Wallace S. Eagles and UW Giants. All right, here we go Good luck everybody. Let's see some big hits How about our biggest hit of the day here? You go with that. Oh, yeah Nice start. This is Chargers one-man show auto two of six Justin Herbert congrats Chargers. That's pretty nice That's pretty nice Let's start more of that. All right. Yeah Yeah, you're good Tyler forest whichever one's doing that Kyle Yeah, I don't need it anymore Yeah, he's my fancy quarterback man believe in This is Patriots Rookie patch auto to 199 Taekwondo Thornton Yeah, let's see one Wallace. What is this? I've not pulled one of these before I think I think I see Surge Justin Herbert That is awesome looking yeah Chargers once again. Yeah, I remember we hit like an It is a case hit a case. That's why yeah, yeah, yeah, yeah, I haven't done a full case up in anyone That's awesome. Congrats Chargers. Who are the Chargers? Nicholas T Hey, there he is. All right one-man show auto Steelers 25 of 25 TJ Watt one-man show It's what they call Jake around here. He could sort he can ship he can run the shop to the top learn Take a break if you wanted to I mean if I had to it was dire If everyone every other breaker was Existent This is commanders rookie patch auto Johan Dotson precision. This is 17 of 75 Those are so nice looking they're on the board for too long Yeah That's right.  Yeah Gonna say go and get Just start breaking Not call for help or anything. That is Lions rookie patch auto to 99 Aiden Hutchinson 54 of 99 Chris Spielman jersey number. I don't know if that means anything Nice one Lions can half the break His Falcons last team rookie dual patch auto 85 of 99 break London. We got London on the track Nice taking Sam's Here Four boxes to go say sorry I was looking for some bears. Yeah, let's get you it's gonna use a bear This is Colts rookie patch auto precision Alec Pierce 43 of 99 I'll be jumping off the stream pretty much right away after this break Don't need to do a hit recap everything we're showing is a hit that is jets day one rookie auto two of ten a mod sauce gardener Nice sauce sauce on that too Come on two more. How about a shield pull the Kyler? Let's do that pull the Kyler rookie shield auto Telling stories my happen stories. That is Packers rookie patch auto 116 of 199 Romeo Dobbs Thanks, you appreciate the follow alright No panther so far. Here's our last box. I guess this will do to close out our day This is Steelers rookie dual patch auto gold to 10 Kenny pick it. Let's go Congrats Steelers. Come on Here we go Love to see it man white patch in a letter patch Love to see it. Awesome hit for the Steelers to close out the day. That was a great case That will do it for the break. Thank you everybody appreciate it


Into the wall oh My What's up everybody welcome back to the channel i'm uvr rabbit here i'm forza horizon 3 joined by mk6 djf 102 hamster joins in later on we're here in an open lobby in the parking lot just having a little fun drifting Getting some tandems going messing around playing some drift limbo We have a lot of fun drifting with the little couple members of the team as well as some randoms So if you guys want to join in on the twitch feeds when i do host them It is in the link the description box below just follow me on twitch So let's get to some of the drifting action We had a lot of fun drifting in the parking lot with you know people we didn't normally drift with so hope you guys enjoy it You have ripped the handbrake and pray i like that I like that that analogy rips handbrake breaks handbrake. Good job, andy. Hey, I've only done that That actually be me. I've I've broken my handbrake twice On the same drift zone mind set up set up same drift zone. All right Get some wet tandems and only two turns away from being the same turn too Let me turn. I gotta turn. Wow. I gotta turn kazi on kazi medic Cosmetic My street my twitch is not living Here's mine mine's black To get to get a handbrake and break it just making sure it works like seeing if it like initiates the break or not And I can't drive a car for my life. Oh my I forgot it's wet. Oh my gosh. How much power does that thing have? I'll tell you here a minute I'll tell you here in a minute. You can go ahead and meet the mk6. You're way better than i am Okay, just completely extended like like you guys are A lot of throttle. I can pull and drift sometimes. Yeah, that's what I do, but I either spin out or like I just Like you know, I mainly spin out. I just got my wheel back like because I've had so much strong problems with it Hey, whoa, where are you sending it? You start somewhere Hey, you're streaming on twitch. Just give me the notification Okay, six. I can't really really don't even have to counter steer. It's like the only time I spin I always remember you're using my tune for the s15. He's using the high-end edition tune, yeah Oh Not for the s15 No, the lexus rcf was but not my s15 my s15 is a self self-tune You did help me on the the rcf. I'll give you credit for that Oh, sorry, what? You just can't you know help you just can't be happy at the fact that i'm starting to be just as good at you as making tunes Or you just spun out dark house tonight I'm I'm super excited for tomorrow. You have no idea Well, it's tomorrow. I am going to Pennsylvania Disney giant car show drift event season opener It's going to be glorious nice Where are you going? I'm going to PA for midwest drift union And soft grip season opener car show drift event. It's like 50 some registered drivers Nice So you know, there will be videos going to the channel from the events I'm I have the notifications turned on. Oh, jeez. Hey, you lost it. Bye Yeah, finally, I started getting freaking dizzy I kind of want to throw rally suspension on this thing right now Are you gonna jelly? Thank you obstacle. Yeah, I like the stock wheels.  And that's another thing is easier or harder to drift the force feedback on Easier You have more feel Yeah, and remember when you said too much throttle, I light off the throttle completely Kind of steering the opposite way and it's still spun out Yeah, there's a there's some time that there's the past coming or returning don't need to regard this Yeah If you get past that point where it's like, all right, if he lets off here, he's fine. We can save it and then it's to the point of Well, if he lets off here, we're still going around And to a tree. I don't know where I'm going. I wouldn't follow It's definitely a high speed little thing Yeah, hurry up.   Oh, wow I actually like drifting this thing Until I hit a curve it's been out When you inadvertently find a new drift favorite I think this thing drifted pretty friggin' low Excuse me, is this little teamers passing?  Just to tap the tip, is this the tip?   Talk to him like almost a month, I feel bad You haven't talked to me?  Are we going to do this?  Here's that drink you asked for Yo, I'm thirsty Can you pass the drink up here?  Who was that? My screen, I went right past you, it didn't touch you Yeah, well, Mike Oh I'm going through forward because you're at a great place right now This dude's great forward Scrapper Alright, so everybody, yo, everybody go in front of me I'm going to follow I want to record everybody going through this Yeah, that's what I'm getting Hold on, I'm just inviting some people I don't know if you guys will be able to fit through this But I want to see you guys try Alright, screw it, I'm trying it I'm doing it, I'm sending it It's kind of hard I don't know if you can squeeze by Alright, here you go This is my Miata You don't want to try me with my Miata Oh, yeah, this is Miata, nothing And those are the three best drifters On the team Alright, and you want to see the worst Go in third gear, go in third gear Just cap Fifth gear Alright, so, I actually want to try this So probably you stay here Everyone has a car, it won't help you against our cop cars Poor GT in there Oh, so is Oh, my wheels, not right I want to drive this thing with my wheels so bad Ah, I hit it Okay, well that's going to do it here For this episode here on Forza Horizon 3 Big shout out to everybody who came out To drift with us in the, you know, open lobby That I was hosting on Twitch Like I said, you guys, if you want to get in on the Twitch Lobbies when I host them, don't forget to follow me On Twitch in the description box below As well as you can also follow me on Facebook Instagram and Twitter Also found in the description box below So I hope you guys enjoyed this episode here on Horizon 3 You know, having a little fun in an open lobby Just getting some random drifting going Doing a little drift limbo Like I said, if you guys want to join in, follow me on Twitch Until next time guys, I'd like to thank you guys For watching, I'm EvilRabbit I'm out


We are now getting to what is probably my favorite award in a year like 2020. Our scumbag of the year. And let me tell you, narrowing it down to just four choices was almost impossible. There are so many people who deserve this title in a year like 2020 that I really struggled to just choose four. And when you see how many honorable mentions there are on this list, you will truly grasp how difficult this was for me. So I chose four somehow and these are my nominations. The first nominee for scumbag of the year is Donald Trump. Now I think that to me this was a no-brainer. He did so much damage in this year, more so than other years that he couldn't not be here and this is actually who I think deserves the title overall. So first, he fumbled when it comes to COVID-19 and as a result of his negligence, hundreds of thousands of Americans are dead. That's on Donald Trump. Additionally, he became more and more authoritarian this year, more so than other years. He sent militarized police to Portland to kidnap protesters. On top of that, he threatened to use the Insurrection Act. I mean, these are authoritarian things. He also used his postmaster general, Louis DeJoy, to cripple the US Postal Service during a pandemic knowing that millions of people would opt for mail-in voting because he thought that that would give him an edge in this election. And when he actually lost this election, he's refusing to concede. He tried to steal the election and if he was successful, this would end democracy in the United States as we know it and he's still lying and he got enough people in this country to think that he's the rightful winner of this election.  So he's the one who I had to nominate and I hope he wins. Now, of course, Mitch McConnell, that's my nomination. And so long as he is the Senate Majority Leader, it's easy to put him in this category because he does so much damage. He really is the lowest common denominator. Like when talks regarding stimulus and economic relief fails, nine times out of ten, Mitch McConnell is the culprit. He's the reason why it didn't get passed. And when we need people to have relief, when they're struggling, when they're going hungry, now is not the time to be playing politics. Because if you do, if you obstruct, if you don't let votes pass, you're playing politics with people's lives and that's a unique evil. So of course, Mitch McConnell is a nominee and this might be a controversial choice, but I chose Matt Gaetz to be a nominee. Now, let me tell you specifically why this is the case. There's a lot of Republican politicians that are garbage, but he did something that I think is so beyond the pale that he deserves to be nominated. He literally called for American citizens to be hunted down like terrorists and murdered as if they were terrorists in the Middle East. I'm referring, of course, to his tweet where he directly calls for Antifa to be targeted and he said this and then he doubled down. Now Tom Cotton also called for this, but nobody was as direct and explicit as Matt Gaetz. And to have a sitting member of Congress call for the extra judicial murders of American citizens, this is disgusting.   So I nominated him. Now this next one is going to probably get me in hot water with any remaining lives that watch this channel, Barack Obama. Now let me explain to you why he's a nominee. He killed not just one, but two major social movements. Had he not picked up the phone in March, we would very likely see a different president going into 2021. He single-handedly killed Bernie Sanders' movement by getting Pete but a judge and Amy Klobuchar to drop out. And then when he basically had Bernie Sanders on the ropes, he had a private conversation with Bernie Sanders and possibly encouraged Bernie to drop out as well.  But still, you deprived us single-handedly perhaps of a president who actually cares about us. And on top of that, when the NBA was planning to strike over the Black Life's Matter movement, he got them to stop a meeting with Obama, got the NBA to back down. And on top of that, after he did all of this, he was parading around the country as if he were a hero promoting his shitty new book. Now he's already a multi-millionaire. He has a mansion. Why he thinks it's important for us to read like the 20th biography that he's writing about himself. It tells you how narcissistic he is. But I mean, people need to realize that Obama is not your friend. He is your enemy and he's the final boss of liberalism. And you have to fight him if you truly want change. Having said that, though, those are my four nominees. But let me tell you my honorable mentions. Derek Chauvin for obvious reasons. Kyle Rittenhouse, Lindsey Graham, Amy Coney Barrett, William Barr, Elizabeth Warren, Mike Bloomberg, and Pete Buttigieg. But before we get to the results, let's look at the history of the Scumbag Award. So back in 2019, the winner of the Scumbag title was Mitch McConnell, and the runner up was Donald Trump. Now in 2018, Donald Trump was actually the Humanist Report Scumbag of the year. And Paul Ryan was the runner up specifically because he blocked a vote on ending Saudi Arabia's genocide in Yemen, or more specifically, us ending our support to Saudi Arabia as they carried out a genocide in Yemen. And in 2017, Ajit Pai was the Scumbag after he repealed net neutrality. And Trump was the runner up. And in 2016, hilariously enough, Hilary Clinton was the Scumbag of the year because she lost what should have been a really easy race. Her hubris led to us getting Donald Trump as president. And I think that was scumbag worthy. And the audience agreed. And the runner up was corporate media for assisting her ultimately in this loss. Now in 2015, Donald Trump was the Scumbag of the year. And the runner up was Debbie Wasserman Schultz because at the time, she was doing everything in her power to sabotage Bernie Sanders when she was supposed to be a neutral arbiter. But having said that, the results are in.  So in fourth place, we have Matt Gaetz with 150 votes. He made a buy this time, but he will win this title in the future if he keeps going the way he's been going. In third place, we have Barack Obama with more than 2,100 votes. And that means that the scumbag of 2020 came between Donald Trump and Mitch McConnell. But the Humanist Report has decided that the biggest scumbag of 2020 is, once again, Mitch McConnell with more than 7,000 votes, which means that Donald Trump is the runner up with just under 4,000 votes, meaning that Mitch McConnell won decisively. Now, when it comes to our Twitter poll, Mitch McConnell is the winner still with 38%. Obama came in second with 34.2%. Trump came in third with 25.7%. And Matt Gaetz came in fourth with 2.2%. On Patreon, Barack Obama actually is the individual who won this poll with 59%. Mitch McConnell came in a distant second with 28%. Donald Trump came in third with 13%. And Matt Gaetz came in fourth with no votes. Now, when it comes to our YouTube audience, where most toll votes were cast, Mitch McConnell came in first with 57%. Trump came in second with 28%. Obama came in third with 14%. Matt Gaetz came in fourth with 1%. And when you look at the results overall, you can see that the audience was actually somewhat divided. You have Patreon voters being the biggest renegades and choosing Obama as the biggest scumbag, which I respect and admire. But on Twitter and YouTube, voters were pretty much in agreement that Mitch McConnell was the biggest scumbag, although the YouTube audience was much more decisive there. I'm now getting to our comments. Stoned gentleman on YouTube says, Trump's gonna trump. Mitch McConnell is a genuinely evil and irredeemable turtle man. The difference between the two is McConnell is actually competent and therefore far more dangerous. I see that. My toes are cold on YouTube says, Rudy Leakes fluid and Farts Giuliani would have been a contender for certain. Brian on YouTube says, this is tough, but now that Trump is executing people to make himself feel tough, while 3,000 people a day are dying, gotta go with Trump. Yep, totally agree. Jose Cadena on YouTube says, Obama stopped us from having the most progressive president in modern history. Gunnen valid on Patreon says, Obama is the only reason Bernie isn't president right now. Bloody Monday is the only reason thousands of more people are going to enter medical bankruptcy and our planet is going to be uninhabitable in a decade. Chaotic Human on Twitter says, not sure if I should vote for Trump or for Obama. Sure, I think Trump is by far the greater evil here, especially because of his mishandling of the corona pandemic, but I'm still angry at Obama for his interference in the primaries to stop Bernie. Yeah, and honestly, like you can make the argument that all of them are scumbags, but for me, I actually thought that Trump was the biggest scumbag overall worse than Mitch McConnell because even though I totally agree with the case that was made here that McConnell is more competent, so therefore he's more evil, Trump was uniquely evil, uniquely competent in the sense that he really made moves to do long-term damage to the country. Hence why I thought that it should have been Trump. Having said that though, I only get to nominate. I don't get to decide. I can vote like everyone else, although I didn't vote in these polls. But yeah, there you have it. Our biggest scumbag for the second year in a row is Mitch McConnell. And even though I think Trump probably deserved it more, you can't say that Mitch McConnell didn't earn it.


Hey everyone, welcome to another episode and today we are going to talk about ROC curves and precision recall curves Understand why they're important what they do and even come up with an implementation from scratch in Python of how to just come About both precision recall curves and ROC curves. So let's get to it All right, so in this notebook importing of basic Libraries pandas data frame manipulation library NumPy is our math library make classification is Used to create our classification dummy data set just to create dummy data test train split to split test and train sets And I have like a bunch of built-in metrics here for Creating well the meat of this entire video, which is precision recall curves as well as ROC curves We'll also code these out from scratch individually. So that's more to come below And then we have logistic regression just a built-in classifier That'll be the example that we're using for the model in this video Okay, so now I kind of split this up into two cases of balanced and unbalanced classes Because you can really see the difference between ROC curves and precision recall curves for both of these cases So let's get to that So in this first little chunk of cell right here, I'm just creating our dummy data Basically 10,000 samples four features all of them good numerical features Where the positive and negative classes are evenly split 5,000 each And then I'm creating a bunch of I mean just creating column names for each called feet one to feet four And uh converting it to a data frame and that's it and you get here's like an example Sample from that data frame of 10,000 rows. So you have four features numeric and label Okay, so now what we're doing is We'll basically split our data set into test and train sets with like a 90 10 split I'm putting shuffle is equal to false here because This is very application dependent. Uh, you know certain situations data has Time stamps or date stamps and you don't want to shuffle them together because you'll be training potentially on data That's in your test set and also vice versa, which could lead to data leakage. We don't want that Um, but depending on the situation you can you can change this And then yeah, just splitting into tests and train sets And with that data, we are fitting a typical logistic regression classifier Nothing too fancy here Now for evaluating our model, uh, we have a little function here called the evaluation where we're passing in the classifier that we fitted Along with some x which will be a set of features and y which will be the true ground labels Now in this first line, we are just getting predictions from our model pass all the x's in get the predicted y's Which will be in the form of a probability and hence I called it y predict proba Right, uh, and with this first line right here We're calling the psychic learns precision recall curve by passing in the ground label as well as the set of predictions that we've made And using this it'll give us three items back all three of them are lists or arrays So let's say like, um, this will be a list of precisions This will be a list of recalls and this will be the list of thresholds at which we got each precision and each Recall so one of these could be 50%. Um, maybe like For the corresponding, let's say the 10th entry is 50 precision in the 10th entry and the precision array would be the precision at 50% Uh, and the 10 and then at the 50 threshold and same for recall So what essentially this is doing is like you have this model, right? And it's really hard to determine sometimes what threshold is Suitable typically we go at 50% but in some cases 30% may be better some cases 70% may be better And by using this function here We can get the value of precision and recall at each of the thresholds that we could potentially choose And this is kind of an edge over simply arbitrarily choosing a specific precision and specific recall Because now like by just computing the area under that curve We have a single metric that could take into account any threshold And is not based on or dependent on any source of arbitrariness that we would you know We would use in modeling and hence, you know a a ucs are pretty useful in general Um, I think I skipped this line and this is just used for The same thing instead of a precision recall curve where you know the y-axis precision x-axis is recall We have the typical roc curves where you know the y-axis could be like True positive rate and the x-axis could be false positive rate Again, we pass in the ground labels and the predictions and we get similar arrays of False positive rates true positive rates and the thresholds at which they exist Um just returning a json dictionary for uh, he just come now we can with a uc again It's a built-in function that takes a list of all the x-axis the y-axis and the x-axis And then it is used well just to compute the area under the corresponding curve created by that graph, right? It's pretty neat very simple functions that will return a number And so if we just call our functions on our training test set we get the training test a ucs for both cases Now just looking at the test set. Well, I mean they're all like 95 precision 95 percent recall So sorry 95 percent. Um a uc under the roc curve 95 percent a uc under the pr precision recall curve So yeah all in all pretty good model. Yeah pretty well performing Now there's not really much of a difference between these two until we actually look at case two Which isn't using this on an unbalanced data set So like before we create a classification data set But this time the weights are like a 90 percent 90 We're going to create like 90 percent of the labels We're just 9 000 of the samples will be of the negative class and only 1 000 will be of the positive class And we split our data set in the same way But this time when training our model we're going to pass in this parameter called class weight is equal to balanced With this we're telling logistic regression to say hey because there are nine times more Negative samples and there are positive samples when training We want to weight the samples of the positive class nine times more than that of the negative class This is kind of required this and potentially under sampling would also be required because you need your model to be able to predict Or rather interpret and detect these small instances of positive classes or positive labels that occur in your data Without it. It's just going to perform much worse Now uh looking at uh We're just calling the same evaluation function And it's only here now that you can see the true difference between just a simple roc curve versus a precision recall curve Right while the roc values might be super high and they they get you really excited This 70 percent is more likely how your model is currently performing Right because my hunch is and you can also check this with the data is that there is so Because of the few positive classes your model finds it a little hard to detect them And so our precision is tanked but our recall is pretty high And that's why this might be a more indicative of actual performance and the kind of performance that we would be looking for anyways So yeah, that's also uh kind of what we called out here So why use precision recall curves over roc curves in some cases? It's because of class imbalances Okay, that's like the main takeaway from this section right here that we just went through but all this is just using you know, these black box You know functions that are built in so well in scikit-learn that you should be using by the way Uh, but let's pick it apart a little bit and try to implement those functions ourselves So let's create them so that you get a better understanding Now the first thing to note though I kind of put this under a big note right here is that the area under a curve in scikit-learn It's typically found by using the trapezoid rule So if you and as a proof like you can kind of go into scikit-learn's documentation and you see okay This is like uh, this is the auc function that we've been using before And if you scroll down here, you'll kind of see this little highlighted thing that I've made here Where it's calling a numpy dot trap z and passing in y and x y and x being like lists of values like List of precision lists of recalls or a list of true positives a list of false positives like I mentioned before And with this it's basically going to perform the trapezoid rule if I mean, I'm not sure if you remember that but Um, if you kind of remember in high school, there is this little let me try to open it. Ah These are Yeah I might have closed it here, but let me actually just google a trapezoid rule real quick and get back to you Okay, so this is a super simple example where you know, maybe your curve might be like A little bit more smooth than this but with the trapezoid rule what we can do is you know kind of create Very distinct lines and it's an it's a method of finding an area by splitting up your entire curve Into you know, probably vertical sections and then finding the area of those components And typically those components will be like, you know from 0.25 From 0.25 to 0.5 It looks like a trapezoid and you can use like the trapezium formula in order the trapezium area formula or area within a trapezium To just find, you know, this area and similarly would find this big chunk area And then we would find this little triangle area and then add them all up to get the overall corresponding area of this curve And that's how you would kind of find uh area under a curve, right? And that's kind of how you would use a trapezoid rule in general, right? And that's kind of what's going on here Now what we're going to do, uh, we're going to kind of use that logic too. Just for context So first ROC AUC manual is our manual, uh, it's this is going to be the manual function that we use which inputs Uh, ground a set of ground labels and a set of predictions kind of like what we did before right over here Yeah, same inputs, but the outputs are going to be well I'm just going to output the area under the curve, which is the AUC right So first of all what we're doing is we're creating data frames for A set of predictions and a set of labels two columns in that data frame And are initializing all the false positives and true positives that occur to be just empty lists And let's say that we want to evaluate this for 100 thresholds between zero and one at, you know intervals of like zero zero point zero one zero point zero two So if we had used those thresholds like one percent two percent all the way up to 100 What would have been the corresponding false positive rates and true positive rates? That we're doing in this function here. So for every one of those thresholds I'm computing true and false positives true and false negatives A false false positives and false negatives sorry and then appending it to our list And what I'm doing right here is I'm saying like direction is either negative or positive Because there are situations where you know your precision Because of the nature of the curve it might be in one direction or in like sloping upwards or just like coming downwards that The the value that's returned by and numpy's trap c could be negative So we just want the absolute value of that and we're returning that as the main area All right, so now that that's taken care of for Um, I'm going to do the same thing now for precision recall. Basically, we're taking in ground labels We're taking in the predictions We are constructing data frame a data frame of two columns of predictions and the corresponding label the predictions being in terms of probability values, by the way And then we're going to say, okay Let's create two empty lists of precision recall the thresholds We're going to do from zero to 100 and we are going to compute precision and recall So precision recall will end up to be like a list of 100 elements at different time at different items It'll be like the um the precision recall at different thresholds And now we have well, and then we compute the area which is using the trapezoid rule numpy has a built-in function for it Now what we can do is well in our new evaluation function that i'm creating it takes in it's has the same Input arguments as a trained classifier, which is our logistic regression trained classifier Set of x's and actual ground truth label y's So with that we're making a prediction using the built-in functions to To determine the area under the curve for the precision recall curves, which we did before But i'm also adding in manual calls to our roc and precision recall so that we can compare all these values together with that Let's take a look at how our um values look so for the balanced data set case It looks like well The the built-in roc and the roc here they agree with each other same with the built-in precision recall metrics They also agree with each other, which means that we implemented it pretty good And with the unbalanced data set case as well You kind of see that these the values that are to be matched from you know the built-in scikit-learn version as well as our version They do match up pretty well, you know Now you might see like there are like subtle differences between you know like these values for precision It's mostly because the precision recall curves like the way that it is determined is not exactly the way that I have typed it out And in fact, let's see if I could pull that up um If I can pull that up right here, right? So we have this uh function, right that's built in the precision recall curve But what it does is it in reality? It makes a clock call to this binary clf curve which basically it outputs a list of false positives and true positives It's kind of like the base uh function for also the roc curve that's built in you can check it out on scikit-learn And it's from that that we're doing a bunch of like other processing here And because of slightly different methods of computing the same thing You'll see that there are slightly different values that are outputted But more or less this is still the overall logic. That's also very you know intuitive to us Uh, the main takeaways that I want you to get out of this though are It is also one first of all It's also better to use a uc over just specific precision recall scores Because when you're calculating precision and recall at like 50 thresholds, like you don't know if that threshold is the best And also there is a sense of arbitrariness in just choosing that threshold And computing an area under a curve removes that sense of arbitrariness because we can see how the model's performing at all thresholds So it's a better single metric that removes a sense of arbitrariness and also like I mentioned up here Wherever I mentioned it here. Yeah Precision recall curves can be very useful in evaluating models where there's high data imbalances So you might want to check that out too. I'll put all of this code in the description down below It's a link on my github. Uh, I encourage you to check out, uh, probably the trapezoid rule certain other things like, uh, You know the the actual coding documentation for each one of these libraries Now one last thing a tidbit before we leave is that I have this function here called average precision score that I'd never really used But this is an alternative case to computing, um area under the pr curve, right? And why it's an alternative case as well if you look at the documentation here You know what, um, when you when you look at, uh, the AUC and how it's computed with the trapezoid rule, there's Interpolation that is actually happening. So let's say you look at this particular, you know, ROC curve, right? So it's zero. It's zero at 0.25 for a false positive rate. It's a 0.5 True positive rate But in these values, it kind of looks like well at 0.2 The value would be something around 0.3 for a true positive rate, which honestly isn't necessarily the case right And this could be a difference. It's only because here in this case There's only like four thresholds that we're actually using and in my example, there were a hundred thresholds So the more number of thresholds that you use the more accurate will be the representation With the ROC implementation that we made But um, you know, sometimes in this case, it might be a little too Optimistic and in which case we might want to use like step function Why so it would go kind of like how my cursor is being traced, right? And we only want to use step function wise characteristics What it would do is it would actually decrease though the value of the area under the curve And I think if you read this documentation It's exactly what it says because the linear interpolation Linear interpolation Just connecting two points could be too optimistic Is why this might be another alternative solution to evaluating how a model performs Now when you're comparing two models together It kind of makes um, doesn't make too much of a difference because you're only concerned with what is one is one greater than the other But this might be still a good Method or a good function to use in scikit-learn and you can just check out the source documentation here Along with everything else. I'll just put it in the description down below So that's all I have for you today. If you like what you saw, please hit the like Please subscribe hit that bell and yeah, just have a good day. Happy coding. Happy design thing. Take care


Going on everyone Sam here with late and sports cards rip in 23 Panini Donor's optic basketball fanatics blaster 20 box case random team style Look again at your random results as always you can find those on the website under the results tab as well as the link in chat So good luck everybody Again 19 to go a prism draft choice 19 to go breaks next here. We go Why does it go bro?  Maybe not Maybe it's the mega I'm thinking of these aren't that bad actually winter stays Steph Arabia KG. Oh nice green shock Trey man. Nice one there short print Victor Olibipo Jalen Suggs Christian Brown green shock my house chat home group Those are the one-on-one checkerboards in this Not super easy to hit but they're there Better than not being there for us. All right keep moving Ah Keegan Murray rated rookie and D book my house Jimmy Sohan and expressly in a luka Benedict Matthew in green shock of tie-tie Washington's First Fortmatch with Steve reviews is a W. Good Lord, dude Where what who are we playing against? Who are we playing against? That's what I want to know down in Kaminga green shock. Hello, Trey Murphy Unless there's an auto or something or not something numbered steal a trap to AJ Griffin. Okay cutting him Yeah, any any AJ griffons need to be Yeah Moonburger 15 Yeah, there you go. That's a job right there, dude. I see that I am Static God glass nasties When our stays of the honest that guy's a bomb holy crap, dude Malachi brand them green shock Jalen green express lane Green shock of D'Angelo Russell Alex Caruso hollow the crew show Hopefully dude shade on sharp rookie and Katie we have nobody hurt nobody on the injury report, which is huge In Baltimore to we already beat them in Cleveland I mean they're favored by like six, I think yeah, I think you're like minus 200. Yeah Oh shy Baji Trey. I mean granted. It is a division game. They're always gonna play closer Like we always talk about but window, which I feel like they are yeah Brian P with two spots in choice Tony Z with a spot in NT so we were down to 17 to go everybody Tony shoot us an email shoot us an email and I will forward it to sorting and shipping I will forward it to sorting and shipping for you Here send it to this sports cards Send it to that just put it include your apart include what your full address should be and then we will We will send I will send it over to sorting and shipping and they will they'll fix it for you 17 to go on prism draft choice on prism Draft choice everyone 17 to go. Let's get that filled up. We're almost there 17 to go 17 to go now breaks next and Three to go on versions divisions one draft guys link to that too as over halfway sold out We have five divisions off the board three remain three to go on that one 700 spot Origins bitties Steve no views more like Steve no Mike. That's all she says that he leaves Yeah, dude We're probably under a probably being used as a coaster for her she's Seeing her she didn't link recurrently a coaster for a very very expensive coach there Jabari Smith hollow rated rookie Kennedy Chandler. I know Central Florida destroyed Oklahoma. They always been close to this was a blowout Walker Kessler. Yeah, right Who'd you have lose to green shock Roy Hachimora and Herbert Jones one for the row the USC game on yes Jalen Williams and splash of James Harden. I know Florida State one Magic beat the box Hawks lost to the freaking heat dude Yeah, chat home grin Or stinks find the game Georgia just blew out Ole Miss to this one's probably gonna be a blowout to Press lane Jalen Brown a few SC defense things like they always do That's like last week Washington Yeah, that was free money Johnny Davis so on green shock nice one there for the spoils San Antonio spoils green shock of Anthony Davis T minus hollow is Trey young Jake is a bot Bottom tonight Just look in you'd be like you'd be like shorting out Kevin Durant Yeah, that one's stunk dude Oh shy Vaji Tim Duncan winner stays The one-on-one checkerboards the one-on-one checkerboards John Collins hollow of Damian Lillard Plus they have like autos in here and stuff to Harrison sporadically thrown in jail and during the honest my house not super like Common, but they are in there Then like hollows of course as well green shock Wendell Moore Jr. No, yeah With football they're in one or with basketball. They're in one-on-one. They're much higher end They're much more sought after and much more expensive Same with kabooms of the same deal There's Brandon Ingram watching a couple of kabooms on One And last time the Hustle Washington was ten and I was 91 What a thought during the Browning years they would have gone ten and I once he was so good there and Locker to Locker was really good there as well Feel it Gabriel. That's kind of insane Jalen Durin express lane of giddy Hello, Steve. Hello, Mr. No views. No Mike Steve.  What are we doing? What are we doing green shock? Shea Alfred Shengun? Right. I'm green shock Jason Tatum. Oh Steve has a mic and quite Leonard Sean came in and said Steve. No views more like Steve. No Mike and then left So I don't know what that was all about.  I'm gonna make sure that guy mixes the water in Yeah, dude, that guy needs some water. We got a checkerboard. That's David Roddy of Alec Burke's nice one there Congrats on that. She's like, I guess it's not you got a dub more tomorrow. Hell, yeah Green shock of Tyrese Halliburton silver Dougie McBuckets. I'm in dude Tomorrow You're running God James Harden Usman shang steves got God dude want to play with Steve mill views get God, bro Hey, have a good one Steve have a good night. Yeah, we can play tomorrow man. I'm in I'm done That is a checkerboard auto porter. I just went there on the short print like Wesley Payton Watson green shock optical illusions of Embedon Harden green shock Gary Harris hollow Kade March on Bochamp Palo from the start to get to me here now It's got a pip in and T minus green shock of mr. Russ Brody Well, that's you There you go from dust all day Along the chrome dust the night those guy that guy needs to mix in a whole hundred Green shock of it's often hollow of winter stays of Shaq Oh, yeah, he's buzzing off there. She's yeah, Williams winter stays green shock of Anthony Davis splash Hey, man, let's press lane Zion In an ivy green shock of Mark Williams Keegan-Marie rookie winter stays monogen overly Green shock of kuzma. I think you can Yeah, I don't think it's super easy. Yeah. Oh, sorry everybody. Jesus. I came out of nowhere Sohan and Kevin Durant She's Yeah, gold the bra in my house, right? I might have just been an error to be honest to bed eggs mattering Oh, did that? Okay Yeah, maybe not because either the fanatics ones these like the fanatics or sleuthing ones I'm pretty sure so I don't know on these What's up putting gold in That's kind of nuts dude. Yeah, that is kind of unfathomable 17 to go prism draft choice get it sold out everybody 17 to go on prism draft choice Almost there. Let's get it sold out everybody Let's get it sold out Jadon ivy rookie. I feel you shorts. I'm just wait. I know dude Keegan-Marie my house Trey Jeremy so on and you on its own time to come up Green shock Josh Christopher hollow Marcus smart Benedict Matherin winner stays Kareem Ty Ty and Russell Westbrook homeboy over there But that proved to be false Prove to be false So Thank you I know Oregon game is gonna take like four hours in the scoring J. Dub or no J. Will not J. Dub. They will Chandler T-minus Tatum Jadon ivy rated rookie splash of Ant-Man Green shock miles Turner hollow winner stays LeBron Keegan-Marie and express lane green shock there in Fox Really Or at least just switch to the freaking like shorts, I just switch to the Oregon Good There's Jadon ivy Person brown and Dyson Daniels. I know shorts lives in Portland and they can't have even switched over there That's that's bad express lane of game Green shock Spencer didn't we T-minus why so you know freaking what's it called throw it about the times West Virginia So it's probably a ton of incompletions and the clock's gonna I did he needs nine touchdowns, bro, they have 16 NFL scouts at the USC Oregon. Yeah Nick's and Willings well, I guess you kind like Scotty Pippin, I guess with the way that The USC has been playing. I don't know if that's gonna reflect on the draft stock of Caleb Williams as well as the way that How demanding he is with his drafting stuff But my my wonder is I guess I wonder if there's got like teams There's Andrew Wiggins green shock and hollow PJ Washington There's teams that are like in like the three two three four rage that are sending their scouts for Williams to in case he doesn't go One at the average T-minus Well or somebody Harrison or not necessarily QB He was I think he'll still be the first QB off the board regardless But he might not go number one overall really it all depends on who picks there because I don't know why the bear Is gonna take him. I don't know maybe not maybe Yeah, I mean they got to first they got to they got the Panthers first to us. They're gonna probably take one and two And Caleb I know it's scary That's really scary Vince Williams Kauai and Paul George Jane Hardy and be memory There's an error on TV That is Donovan Mitchell and Yaakov Pirtle Pirtle Palo rookie green shock T-minus of the Rosen You know, let's get origins divisions sold out as well. Everybody. That's at three to go Trevor Kiehl's express lane of Zach Levine three to go on origins divisions over halfway sold out there Thanks again Appreciate that very much very much very much very much So Han rookie express lane Donovan Mitchell one sec Okay Get everybody 17 to go for some draft choice And it's Matherin look at Donchich Ty Ty Washington. Hello, Ryan Ryan Rollins rated rookie Quad are right there Ryan Rollins rated rookie AJ Griffin steal the draft That's our D Griff. So D Griff. That's Trey young and now you have PS buff this year to pierce you Already made that nickname And Fred Van Vleet Justin sits Caleb good traffic. Yeah, maybe I Don't know man. I don't know what what the bears are gonna do.  None of those Recently at least the jets they're good now. Don't get me wrong. They're fine now, but since like Sanchez they've been terrible. I mean look at like the laundry list. You have Gino Then you have petty then you have Hackenburg and then you have darnold then you have Wilson like Call a spade a spade there But also if you go to a good franchise and you stink that's kind of on you. Oh shy poshie T-minus Yanis Jalen Duran and KD Nothing Chicago keeps fields. They might not honestly. They really might not That is a green shock of Darius Garland and Winters stays hollow a KD KD I know he was really good last year thousand Russian Shade on sharp and Jason Tatum Jalen Brown a lot of those games one worn on prime time in big big scenarios, too They weren't really like super they were kind of like already out of it Malachi Branham and Kyrie That is gonna do it for the break everybody. Thanks again for the film up next prison draft choice


Welcome everyone to the generation zeitgeist podcast by Lostra UM India. This is a series of podcasts where we discuss all things Gen Z. This is the episode 6 of the series of the podcast and today we will be discussing different Gen Z personas because not one size fits all. If you are new to the podcast and you've discovered this podcast for the first time and you like listening to this one, you can go back and listen to the previous ones. We've recorded around 5 of them previously. I'm Kulla and you will be hearing my voice through the podcast and as always I have with me Lostra UM CEO, our Strad Guru, our Mark Darshak, Aditi Mishra, hi Aditi. Hi Kulla. Hi everyone who's tuned in today to our podcast. A quick few words of introduction on how we have done this Gen Z podcast and what's the source. So to understand Gen Z, we actually adopted a little different approach. We recruited an army of insiders who would actually go and tell us more about their peers, tell us more about their behaviors, mindsets and habits. And this was conducted over three phases over the last two years. So we've captured things like the impact of COVID, how that has changed the way they take decisions, how they look at life and also looked at this across a huge study which was quantitative as well as qualitative and understanding what's on their devices. So all of this has been now summed up into a series of six podcasts and today is really our final episode where we are going to look at how they are split into different personas or personality types and how this can help us as brand stakeholders or market leaders to connect with them better to understand them better. Over to you Kulla. Yep. So we'll straight over dive into it. Today's podcast, if you have listened to the previous one, this will be slightly different because we'll be discussing personas per se. So we'll start with the overview of the personas that we've found. We have looked at a particular framework to put all of those different personas in different buckets just like a two by two, four quadrant XY axis which we've kind of plotted those personas. And after going through that framework, we'll look at each of the personas a little bit in depth and we'll hear from each of the personas a little bit representative of them. So firstly, when we plot those X in Y axis, we've looked at from an X axis perspective, we've looked at people who are self-focused to people who are more sense of belonging or group affiliation. And in the Y axis, we've plotted people who are more pragmatic to people who are more hope driven or more optimistic. So that's the kind of framework that we've looked at. The largest audience in that two by two framework is the one who is more optimistic as well as have more group affiliation. So they are what we are calling them as the rooted clients. The second largest cohort is the people who are again more in terms of group affiliation but more pragmatic in their mindset. So they are comfort zoners. The third cohort, which is in terms of the largeness of the cohort, they are self-focused but they're more optimistic and positive in their mindset, more hope driven. So they are the balanced trendseeker. And lastly is the one who are the smallest cohort. They are the sensitive reserve to there in the cross section of people who are self-focused and more pragmatic in their mindset. So these are the four cohorts that we've looked at while everybody is typically distributed well across all the regions, all the demography. But we've found some of the skews and like for example, the rooted clients, they seem to be skewed slightly more towards the female versus the comfort zoners who are skewed heavily towards the male. Like the rooted clients again, so they are skewed towards the north of the country and comfort zoners are distributed in east and south region more. The balanced seekers are very much skewed towards the west of the country. The sensitive reserve are distributed in the east, west and south of the country, not so much in north. In terms of the metro audiences, they are more the comfort zoners who have more pragmatic and sense of belonging sort of mindset. And again, the comfort zoners are more younger relative to the other audiences who are skewing more towards the old Gen Zs. This is like an overview of the subgroups. This kind of helps will help us if any brand tomorrow decides to kind of focus on a particular let's say within the Gen Z because Gen Z is still a huge chunk of audience if you want to focus on a particular region. What kind of mindset in that region of the country the Gen Zs might have? Or what kind of a mindset a younger Gen Z versus an older Gen Z can have? So that's the kind of like an overview I just wanted to throw up. Yeah, so you know as we know archetypes or personas can be created across multiple facets of parameters. The reason we sort of chose these two parameters to map the audience in terms of their focus inwards as well as outwards and to look at you know how they grade themselves in terms of you know a very functional aspect of life versus a more future forward, hopeful kind of a view to life is because all through this study what we found is that you know Gen Zs are a lot more aware than generations before. They have access to a lot more information. Sometimes they're actually getting drowned in that information and therefore it's very important for them to sort of tease out what's important and in a way Gen Zs today more than ever are demanding a lot more from themselves in terms of you know how they want to shape their lives, how they want to live their lives and at the same time there is a lot of expectation from the world at large how the world should be, what is the right way for things to be and therefore we felt this whole thing of inward and outward balanced and also what they do versus what can happen in the future. We thought these were two key parameters for us to understand their mindset and as we went along this journey it was very interesting to see that there are people from a certain part of a country who are more in tune with a certain kind of thinking, they are more in tune with a certain way of being which we thought was quite interesting for us to look at from a brand lens in the future. That different kinds of brand promises, different kind of activities that brands may undertake will resonate better with certain kinds of audiences and somewhere it could even give us nuances saying that if you want the same brand to have a translation in the north versus the east perhaps you need to tweak things a bit, perhaps you need to look at what would cut through much better for a agency. Yeah so yeah we'll start with the individual personas and then we'll start with the biggest cohort which is the rooted clients. The kind of mindset that they have if you see their position in that quadrant mapping is all about like they are more in terms of group affiliation they have they seek a sense of belonging and they are more hope driven, they are more optimistic, future forward as they said so they are in that quadrant so they are very much extrovert they like to share with their friends, family they like to be part of a particular team when they work and very much sharing their own emotion and also give respect and expect respect of others so that's the kind of broad mindset that they have they obviously seek a mix of convenience with good experience they are quite value seeking in terms of their mindset and a good thing about these audiences are these audiences are the most likely to be influenced by marketing activities or advertising that's what they've said they do believe that advertising kind of has a certain impact how digital media is kind of influences them so they many of them have kind of said that these personas are skewed slightly more towards the female audiences and they're found much in the north they're very much skewed towards the north of the country those are the two really stand out skew of the audiences that we can find any thoughts anything yeah so you know the way I was looking at this is you know this is an audience which is impactful and impacted and that's an interesting skew because this is the largest share of people we have and I think it sums up well the way Gen Z's typically are that they they want to have a print on the world and they definitely want to share that or spread that amongst their peers amongst people through social and so on so visible they want to be visible in a way you know visible for themselves and visible for the impact that they bring to the world so that that was an interesting aspect the other thing also is you know and which came out perhaps in the courts we'll share some with you is there are a lot more emotional there there are a lot more happy talking about their emotions so you know as we've been seeing in the last few podcasts certain issues like mental health or you know how other people feel has been something that has been a thread running through much more than what the previous generations whether it's a millennial generation or the ones before but comfortable speaking about things this generation is a lot more comfortable and and this group is a lot more specifically comfortable about these kind of aspects surely surely and we'll listen to Ritika she speaks about how she likes to share her feelings and emotions yeah my secret can begin with that my feelings I'll share with you with them I'll also be speaking it's not just friends which we can talk to or sometimes they are very important like it depends on you know the parenting style like my friends they are not so accommodating they're very friendly with me so I would think that there is no clear cut between the two if I've seen that I can open up to anyone like I don't know for my values or permission I look through my feelings I want some other issues with them so that is not some you know the foundation that I love to share it with my friends only who can be anybody yes I do I strongly do because if you have a strong advertising campaign or something which touches the right kind of code with the audience then definitely changes perception I mean like I really love to watch cricket matches and after each over there is an advertisement which appears on your screen if that advertisement is something which is irritating me then I will never buy that product because I am watching that advertisement again and again and again there's not a lot of time between two over so there are there's a little less possibility of switching to the other channels I would watch those advertisements as well and at the same time is that advertisement is as good as the advertisement of I would say February quick arts then I will surely get indulged into buying that product for me and adhesives which is required to stick things which are not which cannot be brought together with the help of normal glue it had to be a work of February quick I don't even know the names of the competitors so advertising really helps a lot so you can hear that she likes to share with her friends as well as her parents but also the fact that she don't want to be judged while she would open herself and she would only share to those with those people who kind of respect her feelings and emotions so that's kind of a give and take sort of a relationship that they expect and you rightly said they're very emotional they're very true to their emotions and really like to express and share with their near and dear ones so the brands have this and like I just mentioned previously that they are open to influence so brand probably will need to appeal to their emotion side of this audience much more than the other ones yeah and obviously as expected because they want to share and and you know impact social media plays a very critical role in their lives so they're highly social they are on multiple platforms they leverage those platforms both for sharing their emotions as well as for you know spreading the message that they would want I think the other key thing to remember about them is that you know in a way they are the quintessential way we look at Indian audiences very value-focused so while they love to shop and and there is a thread of you know consumerism running through but highly highly focused on the value of things that they are able to get so so there is a tough negotiation in their minds in terms of the need for buying something vis-a-vis the value that it can bring yeah for sure for sure so now we'll move over to the next audience who was the comfort owner there's a second largest audience so in terms of the placement in the quadrant so they are in that sense of belonging or group affiliation versus more pragmatic in their mindset versus the previous one when when we look at them so they are they they have a surrounding of comfort on my like a shell of trust that they surround themselves like they have people who are they like to consult before taking any key decision like the family and the friends so it's more like an advisory role that they have they would very much focus on their basics getting their basics right so they they definitely focus a lot more in terms of education earning enough money and also very much in mental satisfaction plays a very key role in their life they also while they would look for values but also they would seek some bit of style or a quotient that would kind of influence their decision in their lives and and again a mix of indulgence and entertainment kind of influences them a lot more from a media perspective so that's what they kind of seek in their consumption of media in terms of the skews they are relatively more male focused they are spread in east and south or much higher skewed more towards the top four cities of the country and relatively younger audiences compared to other cohorts so that's the kind of audience the comforts owners are yeah so you know if you really pull back and see the difference from the previous audience this group is is a lot more rooted grounded I would say in terms of the kind of things that they adopt in and and see the the regions that they come from and also the age group since it's a younger age group that seems quite natural that you know they are still a lot more in the family cocoon I would say the other thing and that's reflective of the same is is this need for security or the sense of security of the familiar security of what I know so unlike the previous group which is all about impacting the larger world this is more about being comfortable with themselves and the world around them and therefore their path to success appears to be a lot more traditional in terms of you know education playing a very very big role earning enough money you know making sure that I have enough for for my needs those kind of things are important the fact that they seek more deals they their their translation of you know value is more about deals and and quick quick returns or quick gains is something that that we can see yeah for sure and this audience plays definitely high importance on mental satisfaction a lot where we'll hear Priyanka talking about how mental satisfaction is very important for her I think this for me uh yeah it's the part of a fortune doesn't matter if it's a complex So as you can see, I mean, very, very practical, you know, very, very practical in terms of the overall approach and how she is looking at life, what is her priority in life and what she thinks is important. And in terms of media consumption also, you know, when we see this audience, of course, social remains a longer thread, but they are also investing a lot of time and, you know, their focus is on things for themselves. So music streaming, for example, comes up with a high skew. Similarly, OTT content wherein they are consuming for themselves and not so much in terms of sharing or showing. So there is a little bit of an advance there as well. Yeah. So that's the audience in terms of the comfort zone as we just heard. Now we'll move over to the third audience who are in the cross section of people who are optimistic driven by hope. And on the other side, it's more focused on self, what we call them as the balance trend seekers. They're very much independent compared to the other ones. So they like to take their decision themselves in terms of thinking on their own. And after, let's say, doing their own research quite a bit, while they do that they're independent, they are also very much because they constantly hunt information on their own, they would like to stay on top of trends. And what is the next cool and smart thing to do that also influences in terms of their media decision as well, where they are influenced on the cool side of the media, things which are more cool, they get attracted by that. So that's the brief summary of balance trend seekers. So they are distributed very much strongly in the western part of the country. And also they're not so much in terms of the metros, but in the remaining part of the country and distributed well in the older age group. So that's the kind of audiences that we are talking about when we talk about the balance trend seekers. Yeah, so I think the interesting take that we have here is these are mostly people from smaller towns. And I think it connects well with this whole need for an identity for self, very personal, maybe they are breaking out of what the rest of their peer group or their family has been about. And hence a strong sense of self wanting to establish their decisions independently, looking at things through their own lens. And in that journey, they balance out a bit of what's for everyone versus indulgence for themselves. So there is that interesting choice I think which they make across this path. And that's why also this thing of how they are seen as cool and maybe associating with things which are perceived as better or things to be done. So there is a nuance of that, which is also emerging in this audience. Yeah, true. And you mentioned about that how they are not in the larger cities, but more in the smaller towns, which kind of shows the aspirational value of the audience. So they see which is more cool thing to do and more in thing to do, they would be attracted by that. Similarly, when it comes to media also, when something which is like more appealing, more cool, in any media activities, they'll be easily attracted by that. So that's the audience will listen from Piyush. He talks about how he tries in focusing on individual tasks. What I can think of is I think it's become a very soft, very traditional culture if I can say that because we are very much influenced by the TV series or the music culture. So it's very prevalent there to have the race, the confectionaries all the way throughout, especially in the morning after lunch or during the, during the evening time. Certainly it kind of has a lot of influence over it. And we are very proud that he has been accustomed to drinking tea. So we all know that. So it kind of has, kind of collaborating, everything is collaborating together to force us to do such things. So I think it's, there's an influence that has been going on for ages. So I thought, right, and the idea comes to have it eventually. So I think okay. So there he talks about individual own decision, own way of working, where he's not answerable to too many people. So they can decide on their own and make their own decision, make their own mistakes and then move on, like we've discussed earlier as well. Yeah. So that's the audience in terms of the balance trend seeker. And lastly is the sensitive reserved audience who are in the cross-section between who are more self-focused and have a very pragmatic mindset. These audiences are more, in terms of they are like, like the name suggests, so they are very much cautious in their approach. So they would evaluate things much deeply. They are more focused and driven in their decision. Education is a key thing for them. Their drive is to contribute towards the society. On one side, they're more reserved, but yet they have this sensitive side that they want to focus more on terms of the career. And while doing this, so they can contribute to the society. While they are more reserved, but probably they are not interacting too much with their peer groups or the near and dear ones, but they're consuming a lot of media. Their consumption of media is very high. In terms of the index, it's almost like 110 compared to other audiences. So their consumption of media is much higher. The reason could be that they are not interacting with so much with the peer ones, but taking more information from media and other surroundings. So that's the kind of audiences in sensitive reserves. They're well distributed, not in the north of the country, but rather in the west, east and south of the country. And again, they're not so much focused in terms of the top cities, but rather in the smaller towns again. So this was something which was interesting. The people who are more sense of belonging, they're more skewed slightly more towards the metros or larger towns, versus people who are more self-focused. They seem to be more from the smaller towns. So that was an interesting thing that how people are focusing in the smaller towns more about self and their own goal towards the future. Yeah. And that sort of fits in, I think, with the larger understanding, because that's where probably the change in lifestyle, the change in people coming in for different jobs or education is the highest. So it makes sense for them to articulate their aspirations or their personalities in that way. And I think if you look at the sensitive reserve, what you really find is they have faith in themselves. They're fairly sure about what they want to achieve, how they want to achieve, and then they don't see too much of merit tom-tomming about it or just talking about it. So in a sense, they are more careful. They are more focused in terms of what they want to achieve. And there is a social or I would say a larger angle to it, wherein they want to do things which are for everyone. They want to contribute back to society. And a lot of their consumption of media is led by that, wherein they're trying to understand what is happening in the world around them, what is going on and how they can decode that into what it would mean for them. And in terms of the kind of things that they choose to do, I would say a lot more led by simplicity, unlike one of the earlier groups that we spoke about where consumerism was sort of a driver's thread. So it's quite distinct in terms of the mindset and how they look at things. And one of the things because the consumption of media is so high, they're also very conscious about this information overload and the fact that there are a lot of things around, you know, whether they are, so they're in that sense, I think a lot more evaluative as well. Yes, yes, certainly. And that's exactly the Jaya will talk about that, how she has, while she has very high in terms of consumption, but also this balance approach of how to evaluate things, evaluate all the information and then take decision maybe. So two factors.  So in terms of better salaries, if you can get, because you are in job is primary, so you want some kind of a money out of an organization. So one thing is money. And the second thing is growth. So in whichever organization you're working, so I will prefer growth. So if they provide me a conducive environment to growth, so I will prefer to go there. So money and growth. Okay, so basically, I will put second, my mind is second. First is growth and third is the money. So there she talks clearly about what you just spoke about, that she would evaluate things, she would listen to a lot of conversation and a lot of things on social media as well and digital media and then she would decide her own on her own. She has this filter, very evaluative as rightly said, of a very filter which are fake news, which one should stay away from versus one information which is good for you and which you can take it forward. So that's the sensitive reserve, very cautious in their approach. So these are the four audiences, client rooted, somebody who is female and more not driven phenomenon. And they are very much driven by hope, group affiliation. So they are very much emotional comfort zoners who are relatively more practical and more deal seeking in their mindset. They are more pragmatic yet having the sense of belonging that they seek. Balance trend seekers, they are the like the names such as they are, they want to stay on top of trends. They are also optimist, but they are more focused on self. They would like to take decision on their own versus the sensitive reserve who are more cautious in terms of their decision making, their focus on self and very much pragmatic and rational thought process that they have. So these are the four audiences that we were looking at. Any final thoughts? So I hope you enjoyed looking at the GenC through this lens, which we felt offers us a much more deeper view of how they are looking at the world and how we can engage with them, what kind of messaging can resonate with them, what kind of brand or product offers would be more meaningful for them. Because while pretty much all of these audiences exist across the country, but there are very clear skews that we see and we feel that brands and marketers can gain a lot if they were to engage with the GenC keeping this lens in mind. Yeah, for sure. And this podcast is the episode 6 of the series and for some time this will be the final episode in this series of podcast that we've done. Any final thoughts for overall looking back? So I think it's been quite interesting. I think looking at GenC, a lot of things that we have learned across the six podcasts, we eat in terms of how they view media, how they are in some ways similar but in some ways quite distinct from generations before and the fact that we need to really deep dive into their mindset and make brands and marketers look at them as distinct groups rather than a one brush for everyone. Because while at large they are defined by a certain age group and a life stage, but actually there are distinct clusters, there are very very clear guidelines in terms of how we should engage with them and I think that's the next stage for us as we get into the next round of research with them. So we will hopefully circle back soon once we have some more information on our hands and we have more GenC insiders talking to us. And so like I said, so this is the episode 6 of the series, so if you want if you like listening to this one, if you've discovered this one for the first time, please go back and listen to the previous one. So we've discussed the GenC from different different lenses, so hopefully you'll enjoy all of them. Thank you so much. Have a nice day. Thanks for listening everyone. Have a great time and we look forward to hearing from you. Yep. Thank you so much. Bye-bye.


Hey everybody, Forrest here, ripping 21-22, NT and Prism, basketball hobby, two box mixer, number one. I'll leave the results on screen here. I did drop a Lincoln chat for you as well. Let's get started. Let's see something crazy here, man. I want something crazy every break now. Good luck, start Prism. Get to NT in just a bit. I feel like J is a tough sound for like, to like learn as like an infant though. Like D and B's that like easy to say like Buh Duh. Like that's easier to sound out. I don't know. I'm not a linguist. I just pretend to know everything apparently. Starting off with the blue ice. Sixers, Danny Green out of 125. We'll focus here. Come on now. Nice hit nuggets here. Michael Porter Jr. Sensational signatures, non-numbered. What's up, Johnny? Oh, that was a thick sleep. Sorry. I don't have any things over here. Oh, you're good. I was like, I'm not gonna need those today. There's a Scotty based rookie for the Raptors. Kaminga Warriors rookie base. Warriors, Silver Clay Thompson. Nice. Spurs, Hyperbren, Forbes. Nets, Rookie, Cam, Thomas. Pulsar, Bucks, Jordan, Nwara. 75th anniversary, parallel Pelicans, Brandon Ingram to 75. Nice one there. It's like their secondary color match. Got Edwards, Fearless Insert. Say Jackson, Ruby Wave, Spurs, Keldon Johnson. Orange Wave, Jazz, Carmel Lone to 60. NTK11, yeah, that'll be tonight with Brad. Hopefully it'll be worth the wait though. Santa, Davion Mitchell Kings Base. Raptors, Silver, Vince Carter. Jazz, Purple Color Match, Pistol Pete to 99. Cherry West, Insert. Lincoln, Chad guys for 5 p.m. The first break still has spots left for tonight. There's a K based rookie, Pistons. NT and Prism, two box number five. He's got spots left. Again, Brad's live at five. Ruby Wave, Sixers, Jeannie Green. Piper, Wizards, Bradley Beal. That's right. Brad, making another sports shift. I wouldn't get used to it too much guys. Maybe during the national he'll be breaking some sports, but yeah, he's been on gaming for like a month now. Franz Wagner, Sundra Rookie, Silver for the Dubs, Andrew Wiggins, Silver Prismatic, Sons, Devin Booker. Jalen Green RPA, that would be freaking huge, Johnny. I'm pulling for you, man. Huge card. Primo, Blue Prism, Hawks, Cam Reddish, 99. Bucks, Silver, Jordan Diwara. Blazers, Hyper, Norman Powell. Silver Rookie, Clippers, Jason Preston, Red Prism, Bulls, Collar Match, Pat Williams, 299. Bass Rookie, Dubs, Moses, Moody. Greg Brown, Nick's, Ruby Wave, New Orleans, Noel. Bucks, Hyper, Ray Allen. Book Knight, Bass for the Hornets, the Rookie. Garuba. Blazers, Silver, Yusuf Nerkic, Second Auto, the Box. Is a Rookie for the Nets, Dearon Sharp. Oh, there was something on the back of that Nerkic, sorry guys. Stuff there, Silver Fireworks, Jazz, Donovan Mitchell. Sorry about that. All right, that'll do for Prism. Decent Box for Prism. Let's see some big hits in NT to close it out. I think Kenzie breaks it again Friday. Daytime up, I'm not mistaken. It should be back real soon. All right, we've got a Kara Silver for the Caps, the 75, Bass Parallel. That is an RPA printing plate for the Sixers Isaiah Joe. Printing plate of the RPA, not an RPA printing plate. Rookie Jersey Warriors, Jonathan Kaminga to 99. There you go, dubs. Nice two color, Game Use Patch, son of three color Game Use Patch, Devin Booker to 25. Nice one, Phoenix. Redemption, and it's a nice one. Signatures, Redemption, Celtics, Bill Russell. Good stuff, congrats, Cs. That'd be really nice. Good go. True RPA for the Bulls. Marco Samanovich to 99. Samanovich Bulls, true RPA. Triple Jersey, Grizzlies, Rookie, Zyre Williams to 99. Hornets, or Rookie Jembo Jersey, James Booknight to 99. Definitive Ink Pacers, Malcolm Brogdon, 18 to 49. The new Celtic, Malcolm Brogdon. And on the back is a Jazz Definitive Ink, Mike Conley, 7 to 49. Real quick recap here. Relics, James Booknights, Zyre Williams, Devin Booker, Jonathan Kaminga. Autos, we have the Bill Russell Redemption for the Celtics. Nice. Mike Conley Jazz, Malcolm Brogdon Pacers, RPA Bulls, Marco Samanovich. Prism Autos, Dayrunstarp, Nets Rookie, and Michael Porter Jr. Nuggets. That'll do it, thanks again everybody. We've got more coming up.


Good afternoon everyone and welcome. This is Melissa Arma with the stocks. What should I wanted to review the spy and Here we have it a beautiful Gap up another Beautiful bullish gap in the market. We have had three bullish gaps in the last three days of the market We have a hundred percent retraced the drop-off that happened the sell-off that happened Friday of last week and Monday of this week And we did it in no time flat and literally we flipped completely. In fact, I am breathing We're even up here after hours. I think a little bit. Well, let's just say we closed it to a 950. No, we're down a little bit Here we were up it 53 433 So this is terrific. I don't know how we start off July tomorrow's July 1st, but I know that this is absolutely Exactly what the market needed to do the market needed to recover from that drop-off very quickly And I knew it when I saw Monday not not Friday Friday was really a neutral day the way I looked at it Now we were bearish we were bearish in the day we gap down But the way we traded on the day because we rallied up huge and then fell into the close was really neutral But the gap down in Monday. I mean that was there was selling that happened I mean I looked at I said well, that's what see what happens and over the weekend I said this is going to be a very Interesting week for the market. In fact, I said this is going to be a big week for the market I said that over the weekend after the gap down that happened on Friday after the sell-off is it's going to be a big week For the market. What did we do? Let's just look at the week because I don't think tomorrow brings really necessarily anything It's going to be a light training day and low volume and a day for people to take off trading right before the holiday of July 4th, but It was a big week for the market. We retrace The gap we retraced it It was it's almost as if it didn't even happen Because it's really so as if it didn't even happen because we closed this is Thursday night I just listen here. This is before the Brexit vote came out, which was not as as expected Okay, we closed that night at 210 81 Where are we today in the clothes? Well, let's just look at the high 219 57. Okay. Look at this This is amazing. It's like a dollar fifty difference, which is next to nothing in the spy So we it's it's really almost like it didn't even happen except for except for people Panicked and sold people panicked and sold in these two days And there were triggers that were hit because we came on a fell under 205 and 200 is Because really we're done at 198 65 here in Monday at one point below. I Didn't want to see us get anywhere near this number in here This number in here was very important for the spy and I really didn't want to see us I really really really didn't want to see us completely retracing here Just didn't want to see it and we didn't do it So the bottom line is that we are set to make the brand new all-time high if we get over to 1378 that was the last number it was more than a year ago now You know, I thought we'd do it here Of course, no one could expect that this would have happened here with the Brexit vote So, you know in the market looks higher. That's the best prediction that I can give This activity in the last three days is extremely bullish to gap up and hold every day in the last three days It was a gaping up that made the buying come in Rallied gapping up here and buying came in rallied gapping up here and buying came in that rally Huge big bullish gaps with big rallies. It's fine. This is institutional buying. I know the day this happened here I talked about in the room I said the only way we're gonna get up and out of this the only way is with a gap up and and and buying and real buying and I said beware that institutional money will come in at any time and Buy the market up if it wants to support it and that is exactly what happened Not just in the live day in the last three days But in the gaps too and I kind of joked and said, you know banks rule the world meaning that the banks are in the market long So if they want to support it after the drop-off, then they will buy back in that's exactly what happened So a beautiful bullish week after all for the market despite the drop-off and Friday and Monday Because this really was selling that happened on Monday I mean it was and then we could have just fallen off the planet We could have the only way we're gonna get out of it is a gap up which happened here on Tuesday and Then a beautiful gap up here and Wednesday Follow-through and another beautiful gap up on Thursday more follow-through I mean what more could you ask for from the market in fact? Let's just look at the rally of the move and from the low on Monday morning This is the movie the market had them buying that committed to the market this week And just listen to what I'm saying the low here on Monday was one ninety eight sixty five High was two oh nine fifty seven. Let's just be 100 percent exact Well, let's take the close. What did I say the clothes was two one nine fifty And this is just Monday through Thursday. Let's just be 100% exact here The market ran up ten dollars and eighty five cents and the last From really the low on Monday See the time that was set until the closed today. One was the low on Monday hit. Let me just see the time That's a big. That's a big move That's a big move after that drop-off to happen people 315 we retested the low so from 315 close 45 minutes into the close of Monday night So basically 45 minutes into the close of Monday and then after hours Monday night Into Tuesday Tuesday Wednesday Thursday into the close the market rallied up almost 11 dollars So really really really nice move here in the market just a show of strength an incredible show of strength and so You know as everyone knows I prefer to short I like to shore but I'm very good at reading things in the correct Direction whether they be long or shorts and the case of the market. I have not seen this market be a short I've never seen this market be assured me over a longer-term trend. So look for follow-through here I don't think we've looked seen much of anything activity tomorrow We'll see how the week of the month starts out really in July the first full week next week Actually, we're closed Monday, so it'll be Tuesday Tuesday Wednesday Thursday Friday next week We'll see what the market does and we'll see you know when exactly we're gonna get over this high I I don't know if it's in July or not. I hope that it is it looks like it's going to attempt it when I talked about it here Before I said, you know what if we get back over anywhere near this area after this after this pull in here We're gonna we're gonna get over it I really don't think we get back up here and not get over this area In other words, we could get up over this area retest it pull back in again slush slush around I don't think it's gonna happen. I think we get back up here We're going right on over it right on over it and look for the short squeeze to happen over some of these bigger numbers and some people shorted the market Friday into Monday and even a Monday probably into the clothes thinking follow-through lower and there are people short this market But I haven't I haven't seen this much buying Let's just look if there's even anything remotely like the last three days if I can just let's just go back the last two years Because I can't remember my mind seeing something like the last two years. This is kind of close Well, no, it's not because it was a gap down. No, that doesn't even count Well, this could kind of be something like it and actually This was close to where we hit up over the high. This is when we hit up over the high in 2014 Got up almost to 213. It was a big day here a gap up and a big move in this day and this was a gap up Here this is no, this isn't even as good as what we saw the last three days Let me just let's just go back. Let's go back five years. I Just want to try to see if I can find any bullish activity in a three-day span that looks anywhere near like what we just saw Maybe This is February 2014 sort of again really not as bullish as what we just saw That's the closest thing I can see This is a very bullish move in the market to have this week after that drop-off. It's not just it's not just the fact that it dropped off It's it's the fact it's it's two things one bullish activity Even if we hide and dropped off we just if we had acted like we did the last three days That would have been considered bullish no matter what but after the fall-off to recover like that Extremely bullish people. I mean that is a feat of strength. I've ever seen one Activity in the last three days is the most bullish I've seen in them. I've seen yep 2009. Wow So there it is people. It's I just went back in the chart trying to see something that even remotely Coincided with the kind of bullish activity we saw in the last three days, and there's nothing nothing going back to 2010 at least So the market's higher. This is this is real money. It came in and bought it It's continuing to buy it all up through so as I've been predicting the market is gonna hold the uptrend follow through higher and We'll see where we go. Have a great holiday weekend everyone Email me at molestathestockswitch.com if you'd like to sign up for the Golden Gap class and have a great day everyone You


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