Good morning everybody. Welcome back to another stream. Hope you guys doing great Gonna keep this one short Feeling too well, so I'll just do it quick run down. I'll take you this probably say minutes and so I can go Sleep Hey, hey, let's talk with you. Yes. Yes was nice yesterday. I mean, well nice could be I mean it did pretty much just Did nothing in terms of consolidation stayed within sort of last week Friday range where it went up Came back down and re added to the position short that they already had here So it's a nice sort of addition there. Uh, so that's just sort of Bullish sign. I guess you could say Uh, even the yeah, just nothing more to say and nothing more to add of them. Yeah, it's a bullish sign Which is re added sort of a heavy sort of short volume where we already had one Create that double top just retested that lower area here lower third And just pushing up Potentially look for some upside. Uh, I want to see some retracements. I guess we'll see if we can come back down Test sort of the out of the range. I think we're pretty much done with it. I guess right now Uh, but I wouldn't be sort of too surprised. Uh, if we can sort of break this high Uh, we talked about yesterday about numbers we're expecting from me. Yes, if we were to break that high Uh, so I mean for shorts we're sort of looking Around here to try and pull it down Uh for longs we're sort of looking for retests. Maybe if we can sort of get it back at sort of 3978 get a retest internally and then sort of pop it up here into sort of these higher numbers. So Definitely could see uh something Okay Which you could theory was if we sort of trade here Pull it back down could be nice and could give you something. I don't think it holds, uh Too well if it moves, uh, slowly, uh, I mean too fast down Could get a nice little spike lower Take these out in terms of stops Uh, then pull itself up getting that slow burn here. Uh, it's not really uh sort of too, uh, too good in terms of uh In terms of longing Uh, just sort of air on the core side of caution. I mean we to get that sort of volume Uh, centric area here. So again, we'll look for us to be ideally closing above this level here Uh, just sort of sort of say, yeah, I like this area. We want to be about this area and we want to stay healthy Uh, let's start with sort of bitcoin now and sort of go Do the motions with it So bitcoin was trying to look for uh, 1600 overnight. Uh, it looks like we ended up revisiting 1700 first Uh, it just overextended in terms of the asia area here Uh, let's sort of go to that 15 minutes you can see sort of asia comes in just goes high Take this uh takes itself out here And now we sort of get that london trading a bit lower Uh, I mean, well this asia was here london trades a bit higher It's just trading here now where we have that new york Falling down. What do I think of salamah? Uh, I'll see you haven't looked at it. That's my thoughts right now Uh, so I mean with sort of uh Uh, us trading in this sort of higher sort of territory. We're defending this area here Looking for this sort of fell. It will sort of fell in sort of uh, 1600 or even 500 even Uh, we'll see that and uh, maybe that could be our bullish sign here because remember Tuesday, monday's range is already set. Tuesday We'll sort of trade within that Yeah, yeah, uh, yeah, we'll do other coins just let me finish up on bitcoin So this is sort of your largest sort of area here how you look at in terms of uh, bullish abourishness, uh understanding sort of premiums for ranges You zoom out this sort of a larger sort of consolidation range. This is our weekly current weekly range here from uh, monday, uh, Tuesday will give us the entire week, but Tuesday will either trade uh, here in terms of stop-hunts And then pull itself down or pull itself down and just trade internally And we'll trade if we trade internally here Wednesday comes stop-hunt Wednesday If you can get that sort of weekly here Then that could be sort of your major sort of uh, bullishness there But depending on how we sort of uh, do that if we sort of come up here Like expanding the range higher Wednesday comes in starts pulling itself down Thursday and so on and so forth Uh, that's neither here or there because uh, we'll see what happens and what we get end up getting from Tuesday ultimately But generally it's not sort of too bad just because The position where es is at and how es has been trading the es order flow. It's okay. We can expect some uprising It's considering still that uh, bitcoin is now sort of moving together with it I mean, we did get the entirety of yesterday where we did move together. It was just Uh, and then you can just see the last couple of days is repricing so Uh, moving on We'll sort of have to look at all coins now Yeah, we'll look at all coins I guess we'll need to look at the Books So we're only in trouble now as uh, there we go. There's our pop uh There's our pop high We're expecting something like that, but again, it's just adding to that sort of longs Here we'll see we want to see if we can sort of uh Subside so we just took out those stops here We look at this large sort of pop We're still looking and airing on the side Of uh a retracement downwards just purely based on That that we've got a heavy sort of offset of these longs here and it's just very overextended. Uh, this current sort of play So we want to see that potentially collapse on itself Uh, we're nine times out of ten. It's mostly uh, it's mostly what we expect Uh, just pushing itself very very much higher It's higher very much uh at sort of the points here I think is doing that with uh The help of yes, like I said, it's just following the yes because Whatever this one's doing the other one will do Uh, so we want to look at sort of figures for yes And those figures will determine whether we want to be Taking this range or not Very fast moving looks like it couldn't keep up Looks like it decided Whoa, they're not taking any prisoners. Is there gonna go with it? I mean It happens guess the short volume was enough to say, uh, you know what these longs it would all really care about them Uh, so we're just gonna take these here or they had a lot of sort of shorts Uh short volume here.  So uh this data is unknown to me. It's just the only I don't have I don't know why they don't have cloud rhythmic. It's just so jarring Like the only only sort of downside if I could get like historic data Even though I don't have it in terms of loading it up It would allow for a lot a lot better sort of uh fine-tuning But again just taking out that entire short area here Was that unknown? I guess it was more shorts than longs Like just taking those out uh Looks like they're just going for broke here. Uh, that's sort of a scary position to be in It's not really something you want to long into Just purely based on the fact that they're doing it very early on in the uh trade session So you're looking for a point Uh, probably uh subside maybe around eight Not even 8 30 and probably around a bit later Probably got a major sort of news release uh See Yeah, I've got major sort of news release. So nine o'clock. We've got another speaker So maybe we sort of push up Uh, looks like they just had Just a major sort of release news. So we can treat this as sort of false Also move uh here So maybe we get that sort of true move in a half hour And then we get that sort of push down. So again nothing sort of uh Uh Avertly wrong with anything That we've been looking at so far Just sort of got new information that it's just sort of news days And news days end up doing this So again, that's why it's sort of important to look at news.  It's uh, uh But there there's the push we needed. Uh, still looking for that to back under so Be careful Someone asked for Solana Solana sort of still within sort of uh Levels in terms of uh, we looked at from prior. I mean not Not the greatest. I mean it's fell Considerably we sort of look at that Instead of going to sort of high time frame. So we get less of these lines. There we go Uh, I felt considerably considering that uh, we had it like a lot from here to here in terms of a swing Because you can see just fell on that data level And fell to hold that weekly level here and that's sort of very important for it to continue down all the way to third of 13 and sort of found 13 as as its support Uh, it's not surprising it does uh, but Now sort of looking at 15 is resistance. Uh, it's sort of very much our range. We're looking at Uh, ideally want to see where volume is in this There we are already getting that week under Just wash out for extensions Just because they could extend it for a couple more minutes in terms of Going up high Amazon bearish news. What's up with amazon?   Uh 10 000 layoffs, uh in amazon like that's insane considering that black friday is just around the corner and usually that's uh the most uh, sort of Off the day for any sort of retail, uh shop So laying off that much just shows that there's sort of projected Demand is not really there for it. So they they're Really not sort of too hopeful on it. So If that's not a recession indicator, I don't I don't know what is This again, uh, people not spending is just will exacerbate the whole sort of argument of just oh, yeah We're not in the recession. No, we are.  Uh, no one's spending. What do we do? Uh, decrease interest rates, uh, get people spending again And so on and so on.  Uh, if it pulls under, uh, could see it come back to, uh, a bit lower a bit higher than 13 probably see it, uh The 13 0.75 if anything Uh, sort of first level you're look for mean slightly higher of 14.39 is not too bad As you can see in terms of where volume is but generally you're looking at this 15 to 13 range as an area of Little great interest Then breaking above 15 will sort of put her above, uh Into the new range, uh, so a bit higher range was probably see it in 17 areas uh, but Times for daily, uh, you're looking at sort of ideally here and again that 14 number Uh, sort of as a midpoint It's just slightly higher than 14. I would say There we go. Just slightly higher 40.34 That's the one ticks up you Let's go in ticks up you Haven't looked at this in a very long time, uh, but it looks like the markers are still up and looks like they're still holding true That's one of the reasons why I love these numbers Uh, let's start with daily. Let's see if I can add in any more new things UIDX Sure, sure Yeah, uh, wow, so you're very long time ago. So that looked at it. I think I guess on this And there you go, uh new addition here To support it perfectly if you had it in terms of looking at it Right, so nice. So it'll remind me every now and again to look at your coins You guys have these numbers and go now you guys have these numbers mark these out This is your support. This is your resistance and so on and so forth These are sort of key indications and key levels. So you want to look for It's already sort of coming down But again, that's what we sort of predicted just sort of an extension play and just sort of very much Just based off of this sort of news release here And just clearing out that short inventory that they already had prior For ours looks decent as you can see that previous area here. We had the high volume. We created that double top here Pretty thin on this sort of top side. That's not too bad If you're sort of bullish on it and You're sort of trying to hold true sort of can say that maybe we can get in further sort of Grab and reach at that sort of floral level. Definitely wouldn't recommend buying I mean, it's just generally not my style I have I have enough patience to say even if I expected to do this Just wait for a retest around sort of the 3638 level Or even just lower. I rather buy or not downswing than on a breakout just because I'm just just better than that. I know that for a fact I have enough patience to wait for Wait for it, and then I have enough pride to miss it But generally just coming back to this sort of high volume nose here creating a nice Double top if you can get it sort of trading between sort of yeah, 3638 3516 around there could be nice Could sort of then try to push up to the 0.402052 or whatever Just this number here and then we'll see what it does from there on out Pretty nice pretty easy Chart Thank you. It's still kept true Just muck all the numbers that you see on my chart on your chart and you should be fine in terms of identifying supporting resistance I don't know if I want violence or future. So let's see. I'm just going to violence futures Spot of futures Oh, I'm in so much I can't swear I held myself from swearing I'm in so much pain. You guys don't understand how much pain. I'm in right now It's not and it's not even my lower back.  It's everything. So I just feel so That looks like I did I do want that spot position. Let's see It's just an extra month. I'm not too bad Okay. Yeah, not too bad if I just look at Futures There we go. Yeah, not too bad Weekly I'm surprised I haven't marked anything on this I don't know color me surprised There's a weekly major resistance. There we go We've already sort of came and went on that So now let's sort of look at relevant ones closer to the area So there's your breakout currently this one There's sort of that resistance there and there's sort of the bottom support there I'll just skip over that one because I mean it's relevant, but less so relevant You can skip over these internal ones just just for the sake of cleaning up a bit I mean, you can always add these in if you really wanted to I mean, this is not too bad at the level But it just starts cluttering up your chart and having less is always more at some point in time Uh high sort of reaching sort of target For don't we reach that too soon? So look at sort of four hours. See if we have any sort of shorter term Targets uh reaching up to that higher sort of four target that we look at There we go. I mean this sort of is already bearish. I mean it just doesn't notice ends or buts Uh, just getting a nice little double top with both of them just Increasingly a high on sort of volume. Maybe look for it to come back down to uh, 2.3 See if that trades down very sort of I don't know what I'm saying Uh, I don't know. I mean Other than sort of trading here and trying to bounce on this level. I don't think it's sort of uh It's too bad Like this is sort of If you see a potential bounce at this level, it's not too bad to grab this here Uh, but there's also the opportunity for it to fall lower at around here I think that's sort of uh, where you sort of target main uh, and you sort of Hold out for that hope here And I think that's more feasible having that reach all the way down to two Um, and so just be careful I guess There's sort of Just got a nice little four-hour sort of pin here So I'll just grab that and just hold that there Nice little trapping tracks of that volume just up outside that four-hour sort of level always nice to sort of see There we go again targets uh intermediate to intermediate targets there you go that three seven comes in between uh us And that's sort of four. Uh, so a little sort of empty void that to build out sort of price targets Uh, but generally I look at where to come down And retest these areas here before doing anything major Invest in bitcoin looks like we're already getting that up swing here. They're just trying to take that sort of out Uh, it looks like we're already sort of building up some cell volume here on uh, yes And that's sort of large volume dot that we looked at here again just increasingly increasingly just painting a picture of bearishness Uh, we came up to that point here. I mean we let up on that just had to fish see if we can get some fishes Uh, so I don't think we have too much of a possibility I mean there is always uh the possibility of us going up and extending this a bit higher, but the chances are Increasingly slim I would say currently looking at sort of how we're sort of training Uh, unless we sort of bounce around here and try push up Not really sort of too sort of uh confident in that so more confident in the drop and and that fade from that top Look at spots. See if spots say anything different for us. Uh, let's see So we probably take a bit too much back in I mean sort of mixed bag got some buys here.  I mean just It's just really bad. It's just like you've got these major levels. There's not really too much volume outside of that Uh, sport is just I guess, uh, not where people want to pee right now They're just too hurt I mean by his relative, but again, you got those bite breakout buyers here and then Someone's had to sell here. So someone's smart sold here Again that selling here was not really one we want to sort of see some buying if we want to see some bearishness Take place. Come on. Good morning 9k end of day Uh, yeah probably not not 9k, but like we want to see it come down ideally We'll see if it sort of takes its time or sort of just fades it from here, but If it sort of takes its time uh I mean it sort of fades it here.  I should I should oh god dammit. I want burger king dammit, greg Make me want burger king hamburger Just learn when you trade on dydx you get dydx tokens basically cover your trades fee if you sell them I just craze a lot of sell pressure.  They have it's decentralized.  I'm just in so much pain Uh, yeah, I have these sort of volume buffs. I mean that sort of high comes in taste I mean, I do want to look at sort of the closes On the hours There they are pretty sort of telling that especially from clothes around here volume wants to come down These looks are nice. I mean these weeks comprise, uh, pretty much of trap sort of longs Uh, I want to see that continue But again, just waiting on the sort of the dust to settle because uh, this is uh This is not trustworthy in terms of which direction it's going to go Either we mellow it out then drop or we just sort of taper double top come back up Or we go extension high go for that play then drop it Considering we don't have too much in terms of pivot wise Uh, other than nine o'clock, uh, don't have anything sort of midday Uh, have sort of end of day sort of sort of crude oil And then tomorrow seems a lot better in terms of uh being packed. So I think we just set up to uh I'm not sure 17th What do you call it say? Oh cool. Uh, yeah, uh, if you're sort of taking profits, uh Here's where you want to sort of uh be out at so as you can see, I mean, this is nice This is always sort of very nice to be short into Uh, probably want to try take it some off at that view up here. V app traders are very aggressive Uh, on the way the traders you can see here They sort of came to it just played with it Then pulled across just very much violated a lot of traders Uh, maybe just front running a bit higher. I guess uh, would be my Hedge, uh, but you wouldn't be too wrong in terms of the traders that sort of be that Next sort of level is probably much lower at 16 650. So if you see it If you see it Not hold here or be too strong I would hold off and and just say that and you can see that look how fleeting that is They're just removing that added here because they're expecting that first drop And they probably fit in that second and probably getting that professional lower Definitely consider Holding off having any hard take profits and just look at sort of reactionary At at sort of this area. So just set alerts and just say, okay at this level Does it does it look weak enough for me to take uh for me to be greedy if it doesn't look weak Just take it off the table. That would be my bet You're ideally looking and holding for like as she's 16 500 Uh, I'm even lower but You can see this sort of it's trying to fade it Remember we were on the trouble of it being strong here So that's again, that's why you take partials and and I was like, ah, you know Well, let's take 25 percent off the table here push stops up to 1700 or seven uh 16 900 500 or wherever the next sort of higher Present is And then if I get stopped out I can re-enter and I have some profits off the table and so on and so forth Yeah, I think I haven't checked out the exchange. It probably is considering I mean, you know considering the spreads and stuff They're probably like any anyone big enough like anyone my size could probably create wicks on that exchange That's because spread But no, uh, probably everything today is probably culminating in a set up for tomorrow So we we want to pay special attention to how sort of volume is trading I'm showing you a sort of heavy short, uh already we want to see, uh We want to see some upsides, I guess, uh just to create some Get rid of shorts off of es And that's sort of the main reason. So if we sort of mellow this out Re-test that sort of view up area at 1650 Even slightly higher. We'll see Come to that daily open as well Come around this sort of area. You can start weekly view up the daily view up. It's just sort of Around around this sort of level sort of see that monday high as well So these are your sort of if you're sort of being very aggressive on a tuesday will sort of hit these Probably hit this out of a range here Then if we hit the down range we're sort of looking to do this Maybe I have some level of bullishness If we look that strong But ideally you want to see some level of logs take out the range here hang around for a bit Tuesday, uh, wednesday comes in spikes, then we drop further That's sort of a lot more ideal in terms of setting you up Go through you eat. Yeah, sure sure I realized how much love I mean not how much I don't show any love to eat Uh, I don't even know where it is Uh There we go see Nice there we go. I mean It's it's a little better in terms of uh Well, as you can see that just sort of flat lines at that resistance here Uh, let's go a bit out a bit and just look at this for a minute So we've got that daily market here. We've got it also on that four hour here But so it's for daily take president, uh, we got some retail trying to trade there we go here Trying to trade the breakout uh of this So maybe we get that sort of extension here Just to sort of grit that sort of extra sort of volume from those breakout traders Uh, but generally uh sort of that flat line on the hour showing that okay, this is sort of a Heavy sort of breakout territory. So there's always going to be volume here In terms of Our willing participants just speculators coming in So that's something that you want to see if it sort of uh stays within sort of This sort of territory is as a short Uh to maybe sort of come down and retest lower Uh If you're looking at sort of resistance, I mean look you can add these back in and just say, okay, there we go Oh, no Don't do that There we go, uh add this back in as you can see That sort of reason to say, okay, maybe around here. We sort of call it that's four hours. Uh But daily you got that volume sort of building up very weirdly here Uh, I mean it's it's played in terms of where it's displayed So you can see that sort of where your consolidation lies between this area here So you treat this as you would a flag Go that spike high outside But if you see breakouts here So a collection of volume here we're pulling further down If you see if we sort of come back down to here collect volume Around here in terms of short volume you could definitely see this pull back up into it In terms of targets for bullishness if we see that bullish side Probably looking for much higher here bearishness. You're probably looking for uh 10 22 And like you can see the next drop is a lot further than anything else at sort of five nine six I saw just essentially just halves it In terms of sort of having a major sort of support I mean, we probably have a daily level that I don't know you don't you have nothing Nothing And no, I mean you do have something in the way of uh 750 around this level but generally just very much uh an empty sort of uh void other than the sort of center I guess if you sort of drop below that 10 22 I mean we've already got a sort of price action on volume there So we know wouldn't be too surprised if we sort of stay within that area Sort of short term wise. Let's sort of look at daily. Uh, there we go Look at daily and monthly sort of volume projections. We sort of did this Somewhere, I don't I don't remember. I think on the discord. Yes on the discord Uh, you can see previous months full of heavy lungs in this area here I've culminated on the last sort of session here It ended up just falling under Just got that sort of high volume traps alongside the previous uh couple of months. I mean that came essentially at the quarter Uh, very much a maximum level where we have a bunch of sort of volume entered in and mostly short volume Good to see us reconnecting at that area. It just shows, uh, some level of strength But just coming to that sort of uh point at the join and the opposite side where we do have some, uh Long And I don't think these guys are fully out of the market As you can see they're probably entered in as a swing trade and they probably sort of, uh Missed the boat on that and decided, you know, what it's, uh It's better to eat these sort of loss and then miss out on the potential Uh Of it to recline reclaim some levels. So maybe consider It just give us some sort of scares and some shakes, but generally on the daily Uh, this sort of is decently bullish, uh, maybe Perfect 14 90 at some point Just have to be hopeful How would I do It would do the same for bitcoin in terms of monthly Uh Zoom out you can see very much different from Ethereum. Ethereum is very much A lot a lot healthier in terms of where it's trading at And it's been it's been Decently healthy. I mean it was healthy at this point as well You can see just trades to a maximum sort of uh pivot point here where we've had this sort of collection of what of trades Just sort of build up until it sort of just falls on flat on its face Uh, so it's nice to see that Uh, so in terms of resistance, we can just say like major sort of resistance is 19 1915 I think we were ready to talk about this level Uh, at least I remember this level But generally sort of building that volume here as you can see we've already got uh this month's sort of highest sort of volume, uh node here So pulling up Hey I mean, we obviously this sort of dive is not really diverting but it's sort of going together We'll see if it's sort of If it's sort of consolidated it sort of spells a bit of bad news But if it sort of tries pushing out in a couple of days, I mean But we could definitely try catch it on the lip and then retest somewhere But it's just all depending on how sort of tomorrow trades or today and tomorrow trades Uh for that weekly remember we're looking for down, uh We're looking for down then up not up then down Uh, because if we go up today, we're down tomorrow. If we go down today, we're up tomorrow majority of the time Just because the way the news is shaking And we want early mornings Early morning Wednesday to also be the inverse of the direction we ideally want it So if you want if you're praying for some more upwards movement You want it to sort of mellow itself down collect shorts then we can sort of springboard If you want sort of downwards movements, you want to you want us to sort of slowly climb And then sort of collapse on itself So I mean you could expect us to go sort of whipsaw take the range low Then pull up come back down then pull up In terms of having a double bottom and so on and so forth You could get a little wild but Other than that, like I said, I want to keep today very short just because I am and I am generally in a lot of pain Uh, so it's just like starting to become increasing increasingly just more Uncomfortable just I mean, it's Yeah Yeah, I mean I've covered majority of what I want to cover anyway. Uh, we know our targeted areas if we're sort of looking at that Looking at that from a point of view from east from bitcoin. We know where we want to target. We know Generally, we're not too sort of bullish but yes is Yes is the kind of kind of bullish I mean as long as it cleared out all the sort of shorts It's bearish enough where we can say point it down And then sort of spite it high and sort of it was a missed sort of trade because you could have uh easy caught this If I just Again, it's just looking at sort of what we had on the news. Uh docket sometimes forget but That sort of Catch that before the sort of news release hit it on that pulled it back down again We're looking for the same sort of reaction We're looking for downwards to the upwards. We're looking for up to say down Uh for the next day, and we're just building up sort of expectations for tomorrow And that's sort of ideally where you should should be at for this stage. I mean Yes, they traded decently enough where you got a stop on here got a small climb Got a major sort of stop on reach that sort of secondary level Didn't reach that tertiary level that we're looking at down here. I mean, but It is what it is. Uh, and then just pulled up across And I traded all the way to that sort of extension straight. It's very high off that news You could see it pulled down Now it's just if this pull down leads into a further sort of extension higher. That's where you're pulling out Uh got down only alert on eith at Now I pray Now you pray indeed Hopefully you get your you get it down only. I mean, it shouldn't be too bad. But uh, yes, I mean You've got that sort of larger sort of uh volume about four hour showing that showing that off here But now it's just looking at that does that lead into a retest or does that lead into some further sort of push down? Again, these short volumes are just getting increasing increasingly larger. This is just uh A minor sort of addition to the previous uh Previous one. So we'll see if we can retest that area add more short then just Climb on to that tomorrow But it's just been consolidating Actually, we've been consolidating into sort of what appears to be sort of flags And just training sort of breakouts on off that flag Let's before I go mention that And we've already traded that high. So I mean retests Are possible Uh, I wouldn't hold hold past it. But also that sort of could just back under and just go straight for the Uh, straight for our sort of bearishness here. So Again, it's all about how weak we look at this sort of level here at the retest if we look weak enough, uh, to sort of Uh Weren't you guys keeping your positions on uh keep it on Get uh, it's it's not greedy. You can just push stops up Uh, and then just look for these lower levels burst this and then you obviously have some, uh Re-additions and ability to re-add. That's why taking partial profits. It's not always Is probably always the best plan because you can always re-add without having to uh move your position too far Gone to the actual sort of price. So you have always have a nice buffer That I mean, yeah, other than that, like I said, they kept it keeping it very short Very much shorter than usual So I can go rest If you came in late or want to see want to actually go down the thoughts Uh bubble that it went through just catch the VOD on youtube Other than that, uh, I hope you If you guys have a lovely day, uh, hope you guys trade safe. Uh, it's very important And uh, just keeping what we uh keeping what we said today, uh in mind when you're trading should Should uh be fine Other than that, have a lovely day and I will catch you mother lovers tomorrow. Peace


Okay, hello buddies. So I decided I'm going to show you just some of my inventory that still needs to be listed. I cannot believe I'm doing this. But I also am going to show you an awesome Goodwill Benz haul that was picked up from my friend who is not a reseller. And I have already sold three things, three things that sold super fast.  All right, are you ready? All of these totes are full of inventory that needs listed. So I did organize it. It was a complete disaster. If I can find a screenshot, I'll pop it up. It's really embarrassing. But now I know that plush is in this one. This one's all plush. This is all loving family. These two right here. That is Fisher Priced Little People. So yeah, I may end up doing some of this stuff on whatnot. I don't know. I just keep sourcing. These are all yearbooks, which I just have no desire to mess with. I know some of them can be Bolo's. But I don't know. This here is jewelry, jewelry, jewelry, jewelry jam packed. I can't remember if that one's jewelry also, but I just have a tremendous amount of inventory. And this does not even include my garage. Let me take you out here. So here's another pile that I have that I'm thinking about bringing to what not. I've got some jewelry down here that I thought I could do like an unbagging. These are all toddler dresses. I have no idea what's in here. More dresses, just a mix of items, toys. I'm just trying to figure out this whatnot. I also went to the bins today and guess what I bought? More inventory. And I'm rocking my Cha-ching-king shirt. So definitely go subscribe to Scott, the Cha-ching-king. He is awesome. Amazing reseller channel. Man, I can't talk today. Amazing reseller channel. And he has everything from beginners up to big money Bolo's. He is a major Bolo finder. So you definitely want to sub him up. And he has fantastic merch. All right, you guys, let's get into this bins haul that my friend found for me. I shouldn't say found, sourced for me. It's going to be a lot of fun. Oh, and one more thing. Down in the description, you can follow me on what not. So I'm trying to build up my following. I'm going to sell on there eventually. Go down there, follow me. If you're new to what not, that is my referral link also. And you can get a $10 coupon to shop when you use that link. All right, you guys, let's get started. All right, you guys, we had a blast at the bins. Okay, so I had my non reseller friend source for me today at the Goodwill Bins. Here she is walking out with her cartload. Hey, fellow buddies, thanks for watching. Amazing, right? All right, let's get started. So let's see what she got me. The Goodwill Bins. And she is not a reseller, but she likes a good treasure hunt. Her cart was loaded. Check out this picture right here. And I'm going to show you what she picked out for me to resell. Now, I did see a few things that I threw back right away because they were like bigger, heavier items. And I was just not interested, but we did not go through the bag. She just put everything in the bag. I was loading my bag. So really, honestly, I'm not really sure what's in there. So we're going through this together. And she's like, well, do you want to look at this stuff? And I'm like, no, it'll be a surprise. So I'm kind of curious as to what I've gotten myself into here. So she did show me this. And I think she said this was a watch box. I don't know this brand, but I said to throw it in there because my husband likes watches. So I'll give that to my husband. He might be able to use it for something. She got this because it is sealed. It's kind of heavy. I probably would not have picked it up, but it is sealed and it's about golfing. So I'll have to see. I mean, I'm sure I can make some money on it. And what's interesting about this also is it kind of focuses or not focuses. It kind of teaches me to maybe look outside the box a little bit. I did see her pick this up. And she's like, what do you think? And I was like, well, I would typically just get the bird, but the cage isn't that heavy and with Christmas coming, it might do better as a set. So I'll have to spot clean it and put a battery in it and see if that works. If it doesn't work, I can still sell the cage. So it's a little life pack. I would have picked up the bird for sure.  I love toys. So we've got some of these are McDonald's. I won't mess with the McDonald's, but I don't know what that is. That looks like kind of like a magnet. So we've got some smalls here. So I may sell those as a lot or sell them individually. Just kind of depends. Let's see. I think some of the things fell out of the bag. All right, we've got this girl right here. And I saw her pick this up also. And she's like, this kind of looks like an American girl. And I said, I think it is. I think it's one of the smaller American girls. Are they Wellie Wishers or something like that. But it is American girl. So I was like, if you don't want that, throw it in my car. I'll take that for sure. So she's super cute. And her hair is nice. And she's got on. She's missing her pants. But this gal right here, she is an American girl. Wishers that I just talked about that I unboxed or unbagged in this video. And she had clothes on. But I decided to sell her separately. I am going to list her shoes separately. And she had a little panty and shirt. She was missing her skirt. So I felt this was the best way to maximize my profits on this. So this outfit or panty and shirt, no skirt, missing skirt will be considered replacements for this doll. And I think her hair, I don't know, it kind of looks, I don't know, a little wonky. So anyway, whatever. Okay, now she did pull out this book. Today is Monday, pictures by Eric Carlisle. But she said it's signed. But I don't know if that's actually signed or if it's just on every publication like that. So I'll have to get a loop and really zoom in on that and see. And I can also probably look at another book and see if it's just something that's already on there. So if it is signed, that could be pretty good. But popular children's artist, it does have a name up in the corner. But I just can't tell if that's actually signed or just already printed on there, if you know what I mean. All right, she snagged this out of the plush. I was going through all the plush waiting to you guys see my plush haul from this day. Oh my goodness, I'll probably do it as a separate video because I found so much plush. But she's like, I did see her open this and I'm like, what is that? That is so cute. And it has little bunnies in it. This one bunny, the stuffing is coming out. I don't know what's going on with it, honestly. But super cute. So I have to look that up. I don't see any brand. So I don't know if somebody just made it. It kind of looks handmade, doesn't it? Now that I'm looking at these bunnies. So we will put that to the side. Oh, what is this? This is a snake. It says Zuru. Oh, that's weird.  I wonder if this is like, is it battery operated? Huh, huh, Made in China.  I think let's talk about this. Alright, you remember this snake? Yeah, you just saw it in the video. Well, well, well. I don't know what's wrong with it. But it's not working. So I want to maximize my profits. So I am going to list the tail as a replacement. You think I can sell it? I mean, the tail's fine. It just needs attached to a head that works. So I guess if somebody broke the tail, they would need the tail. So we're gonna see if we can make a little money on the tail. It might be, it might do something. Okay, definitely would not have picked this guy up. But that's okay. It looks like, I don't know, we'll have to see what he does.  With Christmas coming, I mean, maybe I have to put a battery in it. Spider-Man. Okay, I also saw her pick this up. And I think she put it back down and I'm like, I think that's a bitty baby.  It's American girl. It does have some yuck on it and needs cleaned up. But 2018, so not super old. But I think I can try to clean up the head a little bit worth the thing. When she picked this up, I saw her pick this up. So I actually did see her pick some of this stuff up when we were shopping together. And she threw, she was gonna throw it back. And I'm like, no, no, no, no, no. I like horses. I always pick them up. I could not see what it was. I'm like, just throw it with my stuff that you're getting for me. Looks like it says 2017 D wall something. But even if the horse is no good, I can sell the saddles because people buy these. This horse, her main, it needed like brushed out or you know how you do all that special stuff to it. Well, I'm not that fancy. So I put a braid in it. And I'm gonna sell it with the braid. But what I did do that I wanted to share with you is I took the saddle off and I'm selling the saddle separate. Alright, so I don't know what I did here, but I lost my original audio. So I'm gonna do a voiceover for the rest of this here. There is this view master. And I was pleasantly surprised at the comparables on these. Probably something I may not have picked up and I'm glad I did. So definitely a cool item there. Alright, now one of the items that sold. I have sold these in the past, but I probably would not have picked them up because they are typically long tail. This sold super fast for $16 and I think it was 25 cents. Plus the buyer paid shipping. And it was priority mail. So nice little Bolo there actually, they're the vintage McDonald's Barbies. This is an American Girl hair thing, super lightweight, so it cost me pennies. And someone should buy it. Christmas is coming. I do feel like people are going to be buying pre-owned for Christmas more and more in the future. Alright, this right here is really, really cool. These are Hatchimals, but there were also some little pet shop and they were the littlest pet shop right here and they connect to a ring. So I went ahead and listed those and then I also listed the Hatchimals separately. So two nice lots right down in that bag right there. So she did a fantastic job. Already sold three things. Okay, Bolo buddies, here's a little bit of footage from actually when we were at the Benz. I have three other videos coming in the near future with items that I picked up this day. I spent $140. 120 was my stuff and $20 was stuff that she purchased for me, which you saw in this video. So 140 total. So stay tuned for those videos. Okay, Bolo buddies, what did you think? How did she do? I think she did fantastic. Super excited about the things that she picked up for me to list on eBay. So what she did is she sourced the items and then I paid for them and I came home and I listed them and now I get to make some money. I didn't have to do the work. She did the work for me. So I thought it would be a fun little video to see what a non reseller picked up for me to sell. So I think we're going to do this again in the future because we had a really great time. But I will tell you that the footage at the end of the video will be included in three. This is a three part. I have three other videos coming soon. They pulled out a big bin of plush. So I'm going to do that separate and then I spent $120. In addition to the $20 I spent on the stuff that she picked out. So a hundred and forty total today. Oh my goodness. So please turn on notifications. Hit that bell. Subscribe to the channel and come back and check out part two, three and four. I guess we'll call this part one. Thank you all so much for being here and watching. Leave me an emoji of anything down in the comments. Leave me your favorite emoji. All right you guys. Thanks for watching.


How's it going guys? And welcome back to another JHR review. Today we're going to be looking at these BW GH1 headphones by Blitzwolf. Now these were sent to me by Banggood and I'll have these in the link in the description below. Let's go ahead and turn this around and look at the packaging. It says the model number right there.  It took a while to come in but I think that just the shipping from UPS they did not treat it very well. Go to this side. So it doesn't have much information about it on the front or the bottom. Let's go ahead and open it up. But like I said I'll have it in the link in the description so that you can see all of the specifications. But without further ado let's open the box and see what these guys look like. I'm going to go ahead and just use some scissors right here to cut the sticker. There we go. And then we'll just pull this little latch out of here. Line this out and pull these to the side. Well this part's kind of breaking off but that's fine. Let's go ahead and slide them out and adjust the camera. Oh whoa. These are a lot more metallic than I thought they'd be. All right so we pulled them out. We got some instruction booklets. And then it comes wrapped in some plastic so let's go ahead and take that off. Looks like there's a cardboard insert so we're gonna have to push up first or maybe not. I don't know the wires in the back. Let's do that first. The backside actually. And then we have the cable that's attached to it. It's kind of interesting. It's braided but it's like a kind of like let's see if we can focus in on it. There we go. Kind of reminds me a snakeskin. It has like a metal kind of wire mesh underneath this kind of thick plastic wire. Really interesting. It looks as though this runs right off USB so that's pretty cool. I'm gonna set that down. Slide this plastic off here. There we go. Looks like we got some dust. I'll use my air compressor. There we go. Look at these guys. This is interesting. Kind of reminds me of like studio headphones. They have that kind of classic kind of wiry kind of thing at the top and then this right here too. Look at that. What if we focus in right there? These that's like actual like metal wire. Do you see that? That is crazy. That's actual metal wire. Wow that's actually pretty high quality right there and it like stretches too when you pull on it. Kind of like a pulley system. That's really cool. If we turn it around we can get a good look at the logo. Right here it says BlitzWolf and I believe they also make those wireless headphones too. Comes with an adjustable microphone. There's a little cap on it I believe. Probably can pop that off. I actually know that's an LED light probably. Has a nice wire mesh right here. We focus in. Very nice. And then they are reasonably springy. Has a pretty thick pad. I don't know if you can see that. Decently soft. Standard fabric on the inside. And they're not too heavy but they do look very very high quality. They do feel a little light but because it's not a big deal it's actually probably beneficial but let's go ahead and plug these in and do a sound test and see how it sounds compared to some of the other ones I've reviewed. I will be right back. Alright I went ahead and plugged these in and I actually didn't realize these are RGB. Look at that. That is so cool and I was right this does light up as well. That is so cool. I love RGB stuff. Let's go ahead and turn the light down a little bit and see if we can get a better look. Look at that. That is so cool. Such a nice kind of slow fade too. It'll actually match my razor keyboard pretty well. What do you guys think about that? Pretty neat. The other side glows too as you can see. Yeah that is so aesthetically pleasing. Let's go ahead and do the audio test now. As always I'm going to go ahead and listen first to kind of get a good range of you know the bass and the treble and then I'll go ahead and put the music right next to the microphone so you guys can kind of get a gauge for it as well. First thing I noticed is they're reasonably comfortable. Not too bad at all. I'm going to go ahead and press play on some music. So the music quality is not bad and the volume is really good. A little bassy but it's a decent balance. I'd say I'd give it around a 7 maybe 6.5 out of 10 from some of the other ones I've tried out. There's one thing that bothers me about these ones though and that is the fact that it doesn't look like you can adjust them up and down. It's based on this pulley system and I felt like they were sliding a little bit too far down my head. I tried adjusting these and pulling on them but I don't think that they actually move. I could be wrong but it seems like they're pretty stuck on there and I don't really see any way to adjust how much this pulls up but I could be wrong and I'll look at that later but they sound pretty good so I'm going to go ahead and put this up to the microphone so you guys can hear as well. And that was the audio test. What do you guys think? Do you think that these are a decent pair of headphones? I think that if I had the way to adjust the band up here or at least to easily, I'm not sure still whether it can be but it doesn't look like it, it would be a higher number for me but I think the RGB and the sound quality is pretty great. I'd say that these are about the quality of around a 60 to 80 dollar pair of headphones that like GameStop or something like that. I think that they'd be really good for like some PC gaming and whatnot maybe Discord. What do you guys think? Let me know in the comments below and as always, a huge shout out to my Patreons. Thank you guys so much for your support. You guys are awesome. If you enjoyed this video, don't forget to comment, rate, and subscribe and as always, I'll see you in the next video.


I am not gonna say it. No, sir I'm just gonna sing it without any introduction at all and see how you like that Well chicken a grown on a sourd mountain holding died early I day so many pretty girls I can't count them. Oh deeing died early I day My true love lives over the holler. Oh deeing died early I day. Oh, she won't come and I won't bother Oh deeing died early I day My true love is a blue-eyed daisy. Oh deeing died early I day.  Oh, see many pretty girls. I can't count them Oh deeing died early I day Friends, I want to let you in on a little trade secret. I Just found out that there's a thing in radio called an institutional commercial Now I'm going to introduce you to one of them right now if you listen carefully You'll notice that I I won't mention any of the Philco products by name I'll just talk about Philco in a sort of a general way might even refer to it as Philco the leader and if you're really paying attention You'll notice that I I won't say that any specific Philco product has got it over any other Philco product. I might just lump them all together and say that all the Philco products are famous for quality the world over But I see I have enough time to give this here Institutional commercial so I'll just go on with the program forget about it And to see in is how we got ourselves up on top of mountain in the last song. I Guess I'll sing another one this one. You've asked to hear a lot of times On top of old smoky All covered with snow from a court and to slow But a false hearted love will send you to you and the cross ties on the radius fellows who spend their time going around to the mountains Collecting the stories and songs that have been handed down From the time the mountains were first settled Then there are fellows who spend their time singing those songs and Also spend your time Listening to them on the radio and I'm one of those fellows I'm going to sing you one about a fellow that lived on Springfield Mountain and went down to the meadow one day One spring field Mountain fair to the meadow Or to the mole I guess when you're up on top of mountain you're about as high as you can go Unless you're in an airplane or a balloon or something But there is a place where even an airplane can't go It's a place way up yonder above the sky Hey birds I go even spin you can't go Buckeye gym Cast in a silver spoon you can't go and you can't go Buckeye gym dropped an old woman died of a hook and cough Buckeye gym you can't go go even spin you can't go Buckeye gym a red bird danced with a green bull frog you can't go go even spin You can't go Buckeye My Philco friend here wants to talk to you Man to man You'll find his subject mighty interesting since I've been singing about the mountains and the hollows Here's a little closing thought from here from here for so long I'll be a senior I'm just a way better stranger so long girls Say friends listen next week at this time for burlives philco's friendly troubadour Whose weekly visit is produced and transcribed in hollywood for your listening pleasure And come into our store and let us demonstrate the new philco radios and radio phonographs


Close up, close up, close up, speak closer to the microphone. Oh, now? Yes, yes. Okay. So hello everyone. I'm Akansha, and I'm from Rakuten Institute of Technology, and I'm working in the development field. And today I'm talking a bit about just giving an overview of the incredible machine learning and how we're trying to use that in our daily analysis. So quickly how the contents would be, I first gave a brief introduction about myself and the company I work for. Then I started with a bit of a story on how we actually started looking into and why we started looking into other leadership positions. Then you brief overview of the landscape as of now and as of what I know. And roughly again, we go through one of the techniques that we're using. It's called SHAT values. I'll finally sort of go through the story with some examples of how we used it. And then I'll go through some resources, which could be, some of them are really good resources, which could be useful for anyone who sort of interested in taking up further and further up. So let me real quick about me. So in my previous life, I actually worked as a software engineer for Adobe for their print analytics. Post that I actually worked as a researcher in NUS. I actually did my master's by doing Scratch at NUS. But I worked pretty much on social media stuff, so summarizing non-social media documents and so on. And for the past few years, I've been working at Drakuten and specifically in their data science unit, which is called RIT. And my focus is a lot of user behavior. So it deals with... I work a lot with marketing as well as product. So I identify drivers for customer churn, acquisition of any prediction models for the same. I do a bit of marketing, budgeting and attribution as well. And I also do a lot of growth experiments, something we recently started, where I work with design and product to sort of run these experiments and analyze them. A quick intro about Drakuten. So I'm not sure how many of you know about Drakuten, but it's a very big e-commerce player in Japan. Though it's Japanese, but it's actually pretty much a global company. The Drakuten Group itself has a lot of companies in America. It has presence in Europe and even in Asia. And Drakuten Institute of Technology is really a very... It's a very interesting position because it's a horizontal. It sort of sits across all these companies. So as when these companies have needs that require data science or some data inputs, members in RIT sort of work with them and collaborate with these individual companies. So we have really diverse projects and diverse companies. For instance, Drakuten has... It's into e-commerce, it's into payment, it's even like golf, renting, it's into travel, it's into video streaming. But all sorts of really nice data. And we have really nice people in RIT working on areas from machine translation, like we are living here with our senior scientist on machine translation. We have a voice recognition, a lot of stuff around image. We have AR, VR, like tons of stuff that you guys are interested in. Anyway, so I've been pretty much involved in this whole Drakuten system. I've been involved with RAKUT and Viki, which is essentially a video streaming platform that streams mostly Asian content. So Asian meaning Korean and Chinese. And most of their markets, so if you're wondering why in SG, you won't see a lot of shows on Viki.com, it's because most of these good content is licensed for the Americas or the Europe. So that's their main audience. But they have a very great R&D and engineering presence here in the Singapore office. And their business model is pretty much like cool within US, if you're aware of that. So they have a free platform where users sort of watch stuff for free, but they have all the money to ads. And there's also a subscription-based system where users pay a monthly recurring fee, but then let go of the ads. And so let me start off with the story. So like I said, they have a subscription-based plan. So here my focus is on the subscription-based part of the business. And now customers coming in monthly paying to watch content. So that's gay for the company that the dollar is in. But then you also have customers leaving the subscription and that's not good because, again, the dollar is out. So when one find a management decides to go to marketing and they're like, hey, why don't we start using AI to identify which customers are going to turn next. And marketing is really excited like, oh, yeah, yeah, sure, let's not do that. So then they come to me and they're like, can you help us identify who will turn next month? And I'm like sure, like, you know, I can use past data and I can try to build a model that will predict who will turn next. Fair enough.  And you train this on the past and then I come up with a probability score for the users who will turn next month. And I sort of work on this model, try out different models, and eventually a model that works on vision and vision trees and is performing better than the baseline that they have. So I'm really excited and I give this score to marketing and let's see what happens. So the time goes by like, I think almost a month or so I started looking at some other projects because I thought I'd take that and test it out and then experiment with it, but nothing's really happening. And I'm like, okay, what's happening? What's wrong? And just to mention, we've got quite a few companies which are probably just starting our data science. This is the part where POC comes out from the data team and who actually is taken and who actually sort of dies. So what we guys did was we sort of, let's see whether there's another side to the story. So we went to marketing and in terms of, the problem was really simple. They're like, okay, you're giving me this score, what do I do with it? And why should I do what I do with this score? So we realized something that we should have probably realized that if marketing is unable to action on these predictions, then the model won't be used. So what we were giving marketing was we had a model in place where our customer X, Y, Z, and I'm giving a number assigned to that. So Y, Z have a higher score to turn as compared to X. But one thing we could also sort of give an explanation along with each prediction, saying that, let's say customer Y is someone who's, you know, a fairly recent subscriber, subscribed to get this one, but that's a history of, you know, subscribing and cancelling, and another one was going to cancel because he's someone who's lost interest now. So that's when we actually started looking at a model interpretation. And this is a fairly new field in the sense that sometimes even the definition of the word model interpretation is sort of domestic. But roughly it is how humans can understand the choices that are made by the model in their decision-making process. So after this, I'll give a bit of an overview of what's there these days in terms of a model interpretation, a very high level. So typically when we talk of the landscape of interpretability, so first we have models that are inherently interpretable. So my regressions where I can interpret the weights or simple decision trees. Then we have something called thyroid models, and I'll go into that in a little more detail, which essentially what they do is we already have a very complex model train. They train another simple interpretable model on the predictions of these complex models. And then eventually there's a lot of research also going around how you visualize these explanations so that it's easy for the end user to understand. Some of these which are at a global level like your partial dependence dots and all I guess some of you might have already used. So I won't go into too much detail into this one. I'll focus more on these survey models. There's a bit of that study that's there. So typically model interpretation techniques can be classified either as local or global. So global is something that we try to explain over the entire data set whereas local is one that we try to explain for one particular prediction. You can also classify them as model specific or model agnostic. So model specific is one that's limited to specific model classes like interpreting of regression weights in the linear model. And model agnostic techniques are one that can be used on any machine model and they are usually applied post hoc after you already trained the model. So this is again a very, very high level of view of the different techniques out there. I won't touch on the model specific ones. And in the model agnostic ones I typically focus on the survey models as the last few. I have a lot of resources at the end. Anyone who is interested you can always look them up. So I go to survey models. So the basic idea behind these is that see how a really complex machine learning model you train this and they have a complex model in that. And what we then do is we train another single model on the original inputs and the predicted target value of my complex model and then that's how I interpret these decisions. One of them is it's been out there for a while it's really popular. It's called LINE which is a local intractable model agnostic explanation model. Local means it's doing it for each individual instance. Model agnostic means you can sort of plug it into any underlying complex model that's making decisions for you. And again at a very high level it's definitely more sophisticated than what I have here, but it's the interest of time. So what you do is you select your instance of interest and sort of perturb your data set and get predictions for new points. So in this case if my interest, the point of interest is the bright red one I essentially get new data points around this. And then what I do is I weight these new samples by the distance to my point of interest and then I train a weighted intractable model of this data set with these variations. And then I explain this model. The problem with this method is that actually there are quite a few I have referenced at the back which you can refer to. One of the main problem is how do you define this neighborhood around this instance. And believe it or not currently the number is hard-coded in the LINE course. If you actually go to the LINE repo and see that you can see that it's hard-coded. So, and I sort of tried playing around with it and for different sort of use cases it seemed to require some sort of tuning. So if anyone wants to play with it they already have an R library as well as a private library so I would suggest you keep this recording in mind when you're using this model and then play around with it. Another thing that sort of a problem across interpretation is correlation of features. There's nothing that's even there same simple intractable models like regression. The two features are correlated how do you sort of integrate them. And recently another work came out which is an article in the archive right now which is actually shown the instability of this model. So what I mean by instability is that the authors actually conducted experiments where two explanations that are really close the explanation, the points are really close but the explanation is very degrading. So this is something to keep to watch out for. I'll move on to another technique that's called SHAP which is the most recent work on model interpretation and what it does is it unifies several previous methods. What I mean by that is it sort of says that there are six, there are currently existing methods such as diamond deep lift and it basically says that all these methods are able to prove that they belong to a particular class, they all belong to the same class. And if this class was to satisfy certain properties, again I won't go into the details but these are properties that are important for interpretability, you guys can go through it, we can go through it later by some time. And this class has to satisfy these properties, there's only one unique solution that's possible and this unique solution they say is given by these SHAP key values. So this really big looking formula is how SHAP key values are derived but the details you can go to the next paper but I really give some sort of an intuition on how what these mean and these values actually come from game theory and what these guys, what SHAP people try to do is it tries to connect game theories with modern interpretation. At a high level what game theory does is that say I have a team that's making $400 in profit and actually people working on it how do I attribute an individual's contribution to this team. That's the question that game theory is trying to answer. And again in a very high level point of view how it does is it sort of takes all possible team interpretations as a normalizing way for them. So once we know the individual when the team is working alone and we know say a contribution of two players then I can find the intersectional game of a member when he is working in this team. So for instance A in a team that's contributing $400 I'll find this contribution as $400-100 which is actually these contribution when he is just working alone. Similarly for B these contribution would be $400-200 and who are able to actually A's contribution when he is working alone. In this way we can essentially find out what is each team member's contribution across teams of different sizes and sort of normalize this and eventually get how much you attribute to each individual member. So how does this relate to one later prediction? Well essentially you can envision these individual team members as being the features of a prediction. And the amount that they are actually contributing is my prediction that's above the base value. So my base is quantified and that's my average prediction without any using features. Then my gain is 0.7 in minus 0.5. So actually you think of this overall sales as you know the prediction score above my base value. So that's how you sort of at a very high level join analogies between theory and interpreting my model's prediction. So this is what we eventually used and this is what the results on our model play. Previously what we gave to marketing for a particular prediction was this score. So for this user you have a score of 0.71. But using model interpretation this is shape value. What you were actually able to give was a reason as to why we are saying this score is higher than the base value. So he's subscribed for less than a month. He's not viewed much content and he's someone who seems to have not in the past. Versus a user that has a lower prediction than the base value can see someone who's been there for a while and he's viewed a lot of content. In fact he's just watched content. He's been very active. And another thing you can do is if I were to take all these let's just say horizontal explanations, clip them vertically and kind of stack them together I can actually see my users together in one bow. So for instance I have these, this set of new users who are not watching any content they have my set of disengaged users over here and one thing I didn't expect it to do but it also helped me qualitatively evaluate it in my model. So there was this particular area where it didn't make sense to me. So these users are not probably likely to churn but then I see very weird features be attributed to that saying that they had a lot of drop in their minutes view, they had a lot of content in the past and it actually made me go back and revisit my model and data and I sort of realized the data was ugly my threadings were off for some of these so I actually managed to correct some of this so instead of just cranking up my accuracy I actually sort of qualitatively got to see what's happening here. So Shathi is great but again qualitative features is also an issue there are millions of attributes there yet The article that I referred to is recently being out again argued for the fact that some of the explanations even for shaft values were not stable though they were more stable than line and also sort of a trade-off between shaft and line is that shaft also builds a prediction model so you can do things like you can do regression such as if I change my feature by this much how much will the output change but for the shaft value which is an attribute you can't do that sort of modeling so if you say denying someone a loan with probably in the future with that you can say so your income is less than 10,000 but maybe if you increase it to this much your loan would be approved so there are things like that which you potentially right now came up with with shaft. Again this has a very good implementation that are likely to be available though I haven't tried it. The Python one is really good and the good thing is that the author is very active and the data is actually open to contributions as well as to answering questions So in summary what I would potentially like you guys to take away would be that it's possible if you're building stuff especially for marketing for some other business stakeholders they might especially were not familiar with how predictive modeling works so you might not take action on the predictions unless they have some more reason to trust your model and in this and in such situations model interpretation could help you Currently the method of using local surrogate models such as 9-metre shaft seems to be the way it seems to be promising but as a portion it's still in development phase so there are a lot of problems that still need to be solved so I mean you can use it to explain to a lay person what's happening for a heavily regulated system like you know answerable to the one if you're talking about why you're denying a loan I probably wouldn't use them right away yeah there's still a lot of work to do but like I said this means there's scope for us to contribute so any of you guys are actually interested in this you cannot, the authors are all very responsive and on behalf you can just, I mean one of them actually wrote down another query for me and sent me an email so they'll be in response so I'll touch on some resources and they're really good there's this book by a PhD grad student his name is Krista it's really good, in fact some of the content like the structure of how I want to do it was through this book he has also very interesting stories like black mirror kind of stories to start the book with what would happen if you don't interpret our models it's a nice way, it's also very concise and to the point and for cases when you should be going out like he refers you to papers and everything really good Kaggle also came up with a machine learning explainability course if anyone's interested but that's kind of a shot up and there are also very nice tips on getting interpretability practices in your machine learning process this is by H2O.ai very nice stuff about how you should constantly test models for sensitivity and a couple of other tips then this is the original line paper then they've also got a YouTube video explaining it in short again you have the shot value this is the guy's github code base really good, really useful have a look this is his lips paper that came out and he actually also came up with another paper specifically for ensemble trees because usually when you're doing all this feature for application there's so many features, so many kinds of it's actually come up with a faster method of doing this specifically for trees you can have a look the robustness paper that I was mentioning there's that one there have a look for that as well and if you're specifically looking for experts in Singapore I don't get any expertise in this area and sort of exploring this and trying to incorporate this in my machine learning process and see business stakeholders where is this lab called ubiquitous computer lab in the US you can check out their link they're doing some really nice stuff right from visualization to actually working on this model and the grad student who actually introduced to me was actually a former master student by research so that's his website if you're interested and funny thing I actually asked him whether I could put this on slide last week and he actually said they were artificial intelligence so if any of you are interested it's happening next week on April 4 I think he has a really small room book because he doesn't expect a lot of people it's a first need of but I guess if you guys are really interested and you get on the waitlist he's probably willing to get a bigger venue or maybe you can talk next here I think he's one of the really good experts in this he's the one sort of guy that need to start using it and definitely if you guys have questions, feedbacks or any sort of collaboration that you would want my link is already there the link is there in the meetup as well but this is my personal email as well as my company email if you guys want to get in touch I'll be more than happy to work on some problems together it's a very interesting area and finally please my manager got me here so you guys are interested it's across the board engineering just full stack engineering data scientists senior scientists across the board I guess it's a diverse company there are loads of problems so if you guys are interested please go and check it out I think this year so thank you if you guys have any questions I'll answer them to my best as much as I know of this you guys I hope I didn't bore you will you guys can reach out to me later on on thing day or like my addresses I will probably hand over this to the organizers so you can have them and I'll come to you once again some of the examples for internal but just reach out to me or to these guys at NUS who are trying to do some stuff of interpretation and have fun with it it's a nice it's a very interesting board basically what you guys made up of thank you


Hey, what's up code to welcome back to my YouTube channel? I've been uploading videos for the last five six years if this is your first time watching one of my videos Make sure hit the subscribe button if you have not talked to me yet personally and you want to connect send me a text All you need to do is check the links in the description below. You can set up a Send me a text there or you can set up a zoom call with me Really whenever you want I have a schedule there just pick a time chat with me And today I'm gonna be talking about one of the things that I Like I've talked about this before on my channel. I haven't really broken Exactly how this is set up Which is what I want to do today, but normally when I have videos like this these videos get way less views But those who execute on this information Don't don't end up doing better with their business and this is because like my whole business really is operated from email marketing Right. I capture leads It sounds like I'm a kidnapper when I say that but I capture leads Then I email leads they're on my list I own the information until they opt out and They don't opt in unless they're interested in what I'm selling Right. So this has helped me grow My in-person soccer training business. This has helped me grow online soccer skills, which is a online training business This is helping me grow Soccer parent education, which is Another online business. This is also helping me grow Another YouTube channel that I have And this has been my kind of my bread and butter with this channel It's you know, coaches watch my videos. They go to my website that you jump on my email list. I give them more free information I Have convincing stories. I have all sorts of content that I get away for free on that newsletter And without that newsletter, I would not be here continuing to make these videos, right? I built up Audience of coaches there. I've been doing email marketing. All right, just to give you some some Real context here, I've been doing emails for over 10 years now I have written thousands of them. I have paid a lot of money to learn how to write email newsletters That's something I was really weak at when I started my business and I feel very confident at this point And this is no lie or exaggeration but if I wanted to stop completely what I was doing and Go work for someone. I feel very confident that a business would pay me right around $250,000 a year just to write emails for them Right. That's because of the experience and that's because also of results right It works. So emails are not boring Maybe writing them might be boring, but what's not boring is adding revenue All right, so let me you know with all that being said, let me dig into How I believe these emails should be set up for success, right? So first thing you need to do is you need to have a way of getting Prospects to give you their email that could be a Content video that you put out that could be a free trial on your website That could be a paid ad that you run There's a million different ways to do it It doesn't really matter to me how it's done, but if it's done Right. So if you are doing that then once someone comes onto your list I break the emails into really three different pieces. So first at the top left It's the story. So We have to envision who is reading our emails, right? So if you're following my channel, right, ideally, it's probably parents that are opening your emails, right? So it's not like a bunch of kids that are reading them It's parents that are opening the email and if you are going to spend the time writing something to them Right. It needs to be interesting. It needs to be Thought-provoking they need to go away from the email feeling something and this is what a lot of coaches do though They'll say like three tips for playing better soccer and they'll say one two three do these three things your child will play better That's it. There's there's no substance to that email other than the the advice, right? Giving away free advice is great. You can still build authority doing that But what's better is if you actually connect with the reader, right? And you do that by storytelling and telling real stories, right? Not telling some Bullcrap made-up stories, right?  So where do you draw your stories from? Well The easiest way to draw your stories from first is you personally if you played that sport or with clients that you've already worked with you can draw a lot of Inspiration stories from just that All right, then you can start to tell stories about things that you've seen So maybe games that you've watched or training sessions that you've seen You can tell stories about professional players that are playing on, you know the NBA playoffs or Champions League, whatever it doesn't matter There are so many different ways you can story tell, right? I could come up with unlimited stories Just based off of my own personal experience That just comes down to sitting down turning off your phone Stop getting distracted on Instagram and just like do the work, right? So part one of the emails are stories Right and within the stories we talk about the pain point that is relevant to the person who's reading Right and this is hard to do but you have to connect with the reader and Talk about a pain point that we think and we know their child is suffering with right so for example a good a Good story to tell is talk about one of your previous clients who was getting a lack of playing time and They were getting a lack of playing time because of fill in the blank All right, this will resonate with someone who's on your email list, right? And then all right, we talk about the solution, which is the last thing and the solution is This player committed to our program for 12 months. They saw this transformation This happened on month three. Boom. Boom. Boom. Talk about their result So we make the reader feel that wow if my child Is not getting playing at time right now My child was in a very similar situation to this person i'm reading about here It gets the reader to connect the dots with your story and they feel something when they get done reading it And ideally we give them a call to action. So I put solution cta call to action That is You know if this message resonates with you text me at this number If you're interested in one of our last spots in our program Respond to this message right now like give a clear action step That the prospect takes someone who ever sees the email Takes and the reason why I love email marketing is because like you can always tell your most serious clients to Share these emails that you send on a regular basis with other parents that don't know you yet Right That's just a simple text or a simple phone call. Hey miss jones I just sent out a really awesome email. Can you forward that on to johnny's team? To all the parents Right doing things like that helps you grow your list way faster helps you grow your authority way faster And by doing something like this once a week Your business will grow If it's consistent And it's no surprise like if you If you like look in my deep inner circle of coaches that I've worked with Some of the people that are huge on instagram. They're big online now One of the key factors is their emails. They're really good at writing. They've gotten better. They do this story pain point Solution in every email and it works It draws in people to want to talk to you over the phone And the thing That you need to understand is the emails that you write If they don't connect with the reader you'll get zero response If you talk about me me me I I No one cares about that They care if you can help So by showing more examples of testimonials and case studies in your emails That is what draws in people Right, and if you don't believe me go join my email list, you will see exactly how I write A lot of coaches literally just copy and paste what I do and they plug it into their business and it works Right Go join my list if you want right and the way you join that All right, you see at the top of the screen just go to billmysportsbiz.com I think if you go to the um, if you just like stay on my website for 10 seconds, I have a pop-up That shows up when you're on the screen enter your name and email there you'll join my list and um You'll start seeing the emails that go out every week. I sent two or three that go out to coaches. They're free um A lot of the information in there will help you grow your business if you just implement it the same way these youtube videos are Um, but I have that I treat that as its own content Um, that's separate from what I do on youtube So if you want more free information go get on my list join it just go to my website And uh, you'll be able to do that and if you start writing emails Like if you haven't done that before you're gonna suck at the beginning. That's normal. That's how I was If you write one a week that goes out for the next year you get 52 of these under your belt in year one Think about how much better you're gonna be in year two how much better you're gonna be in year three and If that's a consistent thing I expect your business to grow where a lot of coaches fail with this is they'll write like Too many at the beginning and then they get burned out Right, so if you need help with emails if you have a list of like 50 to 100 people I I really do think you could probably make an extra thousand to five thousand dollars per month without doing any extra work If you have a list of people all right and You go through it story pain point solution, which is the offer All right getting getting prospects on the call close them on your program That's it for today's video. Hope this helps go to my website to join my list. See you later