Welcome to the Canvas Data Science Meetup Group.
You're in for a treat today.
It could easily be the best meetup to date.
So we'll cover a lot of ground today.
So fasten your seatbelts because it's going to be a good one.
So before we kick it off though,
Kay and Travis.
So Kay just told me that you're interested in data science,
particularly R. Travis,
would you be interested in introducing yourself and
what questions about cannabis and the cannabis industry
you may be interested in answering with data science?
Hi. I'm just trying to get into the industry
and getting to be in a data scientist.
I just want to learn as much as I can right now.
Awesome. You're in the right place.
Well, without further ado,
I typically end up just trying to present some of my latest work,
and then just would like to pause frequently throughout
to answer any questions you may have,
and hopefully it spark a discussion.
So I'll go ahead and share my screen.
Awesome. So welcome to the meetup.
You can find all of the source code on GitHub here.
Here's a good regular Varus Graham who will be joining momentarily.
Welcome Graham. It's good to see you today.
Hey.
Well, we've got a lot of questions.
Awesome. So just making sure everybody's on board.
So here's the source code.
And essentially thought we could start with a question
and spend most of the day doing data science today.
So Graham will be getting to producers and processors
in next week's meetup.
For today, I thought we could answer the question,
how can you run a profitable lab?
So we've got this rich data set of lab analyses in Washington state.
And so I was thinking, is there any way we can use this data set to help others
either in Washington state or across the country,
think about how they can run a profitable lab?
So always helps to start with a good question.
So this is our question of the day.
So without further ado, the first step is really just to read in the data.
So I've already read in the data just to save us a minute or so.
But it's simple enough to read in the packages.
And here I just defined a few useful helper functions.
And then to save ourselves some bandwidth,
I'm just reading in one variable.
So we've got this awesome data set of lab results in Washington state.
And so you can find the link here above.
And just to show you where I've stored the data,
so in the cannabis data science repository,
you can create a folder at your root.
Called data sets, dot data sets.
And here I've stored these three sets of lab results,
as well as the licensees data.
So as you can see, we're working with about 2.2 or so gigabytes of data.
So a nice good chunk.
And there's so many data points here.
We've got all these solvents and all the pesticide data is blank.
So you can't use it anyways.
You do have the cannabinoid data.
So rich data set here.
But I'll show you what you can do with just this one data point, the global ID.
So here I've got the
global ID, which is this one data point, the global ID.
So we'll do a lot of legwork today with just this one variable.
So basically, I've already read in the data here.
This doesn't take very long, but maybe 30 seconds or so to read in this data set.
And then we clean it.
So we can identify all of the labs in Washington state.
And this is sort of supplementary.
We don't really need this step.
But you can also find out information about the various labs
by running it, by reading in the licensees data.
That way we can, for example, look at specific factors.
And then here, just add day, month, year variables.
So a lot of cleaning and organizing to do.
But we can get onto the data because that's what's more interesting to look at.
So we can calculate our first data point.
So just using the total number of samples,
we can see which lab tested each sample based on the global ID.
So for example, you can see that the global ID contains the lab's identifier.
So these five lab results were tested by lab 10.
So we can simply just do a count of how many samples all the various labs were testing.
And since we know the total number of samples tested,
we can see the proportion that each lab is testing.
And so I just call that the market share here,
so that the number of samples that lab tested versus the total.
So without further ado, let's go ahead and print out our first statistics for the day.
And here we have them.
So these are a handful of laboratories that have or are operating in Washington state.
And I just printed out the city, so that way you can just get a geographical perspective
of where the labs may be.
And just to sort these, I'm just going to essentially sort them
by their total number of samples.
That way, we can see what's the market share by laboratory
and where in the state are they located.
So you can see, so Poles bow is what you would call Western Washington.
So it's interesting the breakdown of Washington.
It's really a different climate in Eastern versus Western Washington.
And so I always thought it was interesting about where producers may be located.
And I thought, OK, maybe laboratories can locate anywhere.
But of course, people prefer a shorter distance for sending their samples.
So there is a laboratory in Poles bow, and they have the highest market share.
Bellingham is in Northeastern Washington, so they're capturing that segment of the market.
Redmond is near Seattle.
And then, of course, Olympia is on the board and in a couple of places.
So just a statistic, there's the market share and there's the cities they're in.
So we always like to try to do some plots here.
So here is simply a plot of samples by day.
Just checking, am I still coming in OK for everyone?
Just making sure we're still on the view.
Awesome.
So like I said, we've got a lot of ground to cover, so sorry if I talk a lot.
So here we have samples by day, by lab.
Quite noisy, so a little too noisy.
So here I'm just doing the same plot, but by month.
So we're just aggregating all the samples by month.
So here you can see we've got labs operating all over the board with some testing,
you know, almost some months more, but, you know, some up to around 3,000 samples a month.
And then we can plot by year.
Just to reduce a bit of the noise even further.
And so here you see samples tested by lab by year.
Just really just sort of adding statistics upon statistics here.
So, right, we started with, OK, what's the total number of samples?
Tested by lab, now we want to try to break that out by year.
So doing that, and, you know, we're seeing, right, we've got some labs testing around a little less than 30,000 samples per year.
Looks like the blue lab, light blue, is, you know, on the trajectory towards 30,000.
Then you've got a handful of labs testing around 15,000 or so samples a year.
And then we've got another handful that are testing below 10,000 per year with this gray on a slight positive trajectory.
So nothing too interesting yet.
Just, you know, these are just the samples being tested.
So where things get interesting in data science is when you can start adding supplementary statistics.
So for those of you that are ambitious, I'll let you calculate these extra statistics on your own.
So this is you can start to break down samples tested by the various analyses.
That they may be doing, or you could even look at the failure rate.
So these are quality assurance or quality control tests that samples need to go through to make it onto the shelves.
So it would be interesting from a producer processor point of view to know what the failure rate may be.
So that may be for a bit more for next week. So next week, we'll look at the profitability of producers and processors.
So that'll come into play then.
So we'll start supplementing this with data here in a second, but we can start using some of our statistical techniques.
So, for example, we've done forecasting with ARIMA models.
So here we can essentially forecast the number of samples tested by lab.
And I was thinking.
We've been talking about the long term here, so I figured let's just go ahead and do a long term forecast.
So we'll forecast today through the next five years.
So through 2027.
Cecilia joining.
So I'll go ahead and run this forecast since this forecasting model takes a second to run.
And I'll talk about some of the next data that we'll collect.
So where am I going with all of this?
Right. We're getting the samples tested per year, but I'm essentially interested in, OK, how do you run a profitable lab?
Just because you're testing a lot of samples doesn't necessarily mean you're profitable or not.
So I was thinking we could supplement this with some of the data that we've collected.
And we could supplement this with the price data in Washington state.
So essentially what I've done is I've just collected a big data set here of laboratories.
And I just went to the laboratory's website, went to their pricing page and.
And grabbed.
The price of an eye 502 panel, so that would be your quality assurance testing panel.
Some of the labs have different prices, depending on if it's a flower or if it's a concentrate or what have you.
So I just chose the simplest number.
So that's sort of an imperfection.
So the price may be overstated because, for example.
And the products such as sodas, I believe.
I may walk that back, but certain products don't need as many tests as others, so their prices are cheaper.
So I generally got the price for a flower or concentrate test.
And so I collected all the prices for the various labs.
And since can litics is here to make all this data easily accessible for you.
You can actually access all of this data.
Through an API, so you can ping can litics dot com forward slash API forward slash labs and you can get all of the lab data.
And haven't built in very many queries yet, but we can at least query by the state.
So we'll do that here momentarily, but it looks like our forecasts are finished.
So here's a forecast for the next five years of samples tested by the various laboratories in Washington state.
So as you can see, we're forecasting there is a group of laboratories that are testing around 500 or so samples a month.
Another set testing between a thousand and two thousand samples per month.
And then there's a couple of labs here at the top that are testing around 2500 samples per month.
But keep in mind this these are samples tested, not necessarily profitability.
So we needed to keep in mind price and cost if we want to really get a deep dive on profitability.
So let's read in this place price data.
So simple enough. So here I'm just making a request to can litics API.
Forward slash labs in passing the parameter that I want the state to be Washington since we just want to look at Washington labs here.
So just going to make a quick request.
And it should already have executed.
And there are all of our labs.
And in particular, we just want to look at the prices that the labs are charging for their panels.
And so.
Here you see, you know, we've got a range.
I couldn't find prices for all the labs. That's OK.
We'll just look at the labs where we do have price data.
As you can see, we've got a range here, 80 to 120.
It looks like around 100 is around the average.
Not certain if we can just take a quick average here.
Yeah, so the average is is 99.
So this is a lot lower of a price than you'll see in many other states because Washington state doesn't mandate yet pesticide or heavy metal testing, whereas a lot of the other states do.
And I'll explain further on.
As to a reason why, but it's really hard to change the rules once you've implemented them.
So a lot of states, their initial rules required heavy metal or pesticide testing.
Washington State, one of the earlier labs, I mean, one of the earlier states to start laboratory testing just started with some of the more basic tests.
So cannabinoids that screen for micro toxins and microbiology, foreign matter.
And they do test concentrates for residual solvents.
So not as many tests that are required.
So you're looking at around $100 for a quality assurance test.
Well, assuming that all of the labs keep their prices the same, then what would their revenue be for the next five years?
So this is where things get interesting, right?
So we now get to combine price data with data on the total number of samples tested.
So we can do just that.
And we can have a forecast of monthly.
This should actually read monthly revenue by labs in Washington.
So let's go ahead and make that quick.
Adjustment. OK, so here we have it.
So revenue is ranging anywhere from below 50,000 a month to above 300 or not above certain months.
But really, like above 250,000 for certain laboratories here.
A lot of times we would like to think about this on a year to year basis so we can plot this by year.
So we've talked about our forecasting model in the past.
I don't think this package PMD-AutoAReMI is handling month effects correctly.
And so I'm having to include all of the months.
And so you can't really interpret the regression.
So long story short, just keep in mind that I don't have full confidence in our forecasting model here.
But some forecasts are better than no forecasts.
So keep that in mind. Just want to bring that up that.
This isn't estimating how I would prefer to estimate our forecast.
So hopefully I'll find some time this spring or later this winter to redo this forecast.
But long story short, we can still get some approximations for today.
So we've got our prices, we've got our year forecast.
So we're forecasting some labs will be cresting 3 million in revenue per year.
Where, you know, you're looking at, you know, this lab.
It's just a smaller lab and they've just they're going to have a little less than half a million in revenue.
And then the second that we can measure a little less than one million in revenue.
And then you see where the others fall out as well.
The only main major prediction so far is that this lab, lab number 22,
for which for whatever reason, our forecasting model predicts that their revenue is greatly going to diminish after 2023.
So that would be the only real thing I would really point out so far is, you know,
if you're running lab 22, I would look at your fundamentals and see maybe it's just the forecasting model.
Something's odd with the forecasting model, but that's that's sort of.
Something's going on there because you see the other forecasts are relatively staffed.
15 million, three lab three around 14.6 million and then down the line.
So long story short.
I find it interesting that the lab with the second highest predicted revenue doesn't actually have the second highest forecasted total samples tested.
So this would be sort of my my second major.
Observation is. Lab number 21 is forecasted to test almost.
150,000 samples over the next five years, but they have the lowest price.
And. We need to do a bit more research, but it's not clear to me that price.
Is a major factor for boosting revenue.
So, for example, these are mandated quality control tests. So producers and processors demand for tests,
maybe fairly not maybe I mean, to a large extent, it could.
We will include the maybe it may be fairly inelastic.
Right, you have to get the tests one way or the other, so you pretty much have to pay your price.
And, you know, maybe you can go and travel to another lab, but these labs are.
You saw they have different geographic locations, so you may not necessarily want to.
Have to work out logistics to send your samples to a lab across the state.
So that could play a factor. So there's a lot of factors that.
May suggest there may be price inelasticity.
For quality control testing, so.
I think there's more research to be done, but I would naively suggest to lab number 21,
you know that they may want to think about increasing their price, right? Because.
The top two labs have higher prices and.
You know, it's not clear that lowering their price is, you know, increasing their revenue.
It may, you know, their low price may be one of the reasons why they're testing a lot of samples.
I just think it would be interesting to see, OK, you know, if they did raise their price, you know, could they?
So long story short.
I'm not convinced that price necessarily shakes out with revenue.
It does seem like there's a pretty strong correlation here between the number of samples you're testing and your revenue.
But once again, not a perfect correlation there.
So.
Now on to I think the most interesting statistic of the day.
And that is profitability, and so we'll need to take into account fixed costs.
And what I was really trying to do was collect instrument data for various instruments that you would expect to see at a laboratory.
However, it can be tricky to find prices for instruments, so prices vary and you may get a.
It wouldn't surprise me if you get a lab by lab quote on a price.
So long story short. Don't have a good measure on instrument prices, so I'm just going to have to do my best.
And use my Bayesian prior on what total fixed costs may be, and we'll sort of do like they do in finance and do a couple of different projections, a low, a medium and a high.
So I think, you know, was there a question or comment?
So long story short, we'll start with the low projection.
So keep in mind, Washington State doesn't have pesticide and heavy metals mandated, which would increase the cost of testing.
So if you need those additional instruments, your fixed costs are going to increase.
But let's start with the low projection. Just say, OK, let's say.
It costs one million to increase to to open a lab.
Well, essentially, what I've done here is I've taken the projected revenue that you would.
The lab would earn over the next five years.
Assuming that the instruments fully depreciate over five years.
So here we've factored in the total fixed costs.
And so that may not necessarily be the case. These instruments do see a lot of wear and tear in the technology is always increasing.
People use scientific instruments from way back, though.
So it's not uncommon to get a used scientific instrument.
So there could be some resale value of these, but we'll just assume they fully depreciate over five years.
So what I'm basically estimating here is the maximum variable cost per sample.
So we're subtracting out fixed costs.
So that means the rest is you basically write you need to keep your revenue above your fixed costs plus your variable costs.
So here is a measure of what a lab's maximum variable cost could be to remain profitable.
And keep in mind, we don't have a measure of prices for these labs, so just ignore these.
So these are what we're focusing on. So.
So long story short.
The this would include all of your variable costs, so all of your supplies,
say, plus your instrument fixes, so say your instrument breaks down and needs a repair.
That would be a variable cost plus all of your labor.
So you need a well-educated labor force to run your laboratory.
So that can often come with high hourly wages for your employees.
So long story short, you know, these labs will need to keep their variable costs per sample under these dollar amounts to remain profitable.
So as you can see. As.
Labs can test more and more samples.
The. Room for increased variable costs increases.
So this has a couple implications here.
One profit, right? So if let's say all of the labs have just picking out a number,
say they're all operating with sixty dollars variable cost per sample.
Well. You know, lab 18 wouldn't be profitable if variable costs are sixty dollars per sample,
whereas, you know, lab, you know, for.
At sixty dollars per sample cost, they could, you know, you know, they could borderline.
Well, I guess they're just testing a lot fewer samples, but they could have a higher profit margin than lab 22.
So, you know, they've got, you know, a bit bit more room for their variable costs there.
We're just going to say, let's just redo these.
These estimates with a bit higher of a fixed cost.
So here we'll assume a fixed cost of five million dollars to open a laboratory.
You know, today, so say all of these labs had to expend five million dollars today.
You know, what would their variable costs need to be over the next five years?
So we see that if each lab had to expend five million dollars today,
then, you know, lab four and 18, you know, they couldn't be profitable in over the next five years.
That's a high fixed cost. But it just would be, you know, sort of a lesson to people thinking about opening a lab.
You know, if you're looking at fixed costs of around five million, you know,
you're going to need to test, you know, more than, you know, more than 30,000 samples a year.
And, you know, you may want to start looking at, you know, at these labs as to what may be feasible.
Keep in mind that they need to keep their variable costs low.
So, you know, if they had five million dollars of fixed costs, you know, they'll need to keep their variable costs,
you know, below, you know, twenty four and twenty eight dollars per sample.
So that may be getting pretty tight with all the labor you need to employ.
And as you can see, you know, these labs have slightly, lab 21 has slightly higher.
And then, you know, lab three and seven have a good, you know, a good room there.
So, you know, the lower they can keep their variable costs, the higher their profit will be.
And then, you know, for example, in I've heard an estimate that in Massachusetts,
you know, it would cost 15 million to open a laboratory.
And, you know, at 15 million, really, none of the laboratories in Washington would be profitable at their given price.
So keep in mind that in Massachusetts, they also have.
Pesticide and heavy metal screening.
So if you did need high capital expenditure to do heavy metal and pesticide screening,
then that's that's why labs in states such as Massachusetts charge higher higher prices.
So so that's the low, medium and high.
And we'll just stick with the medium forecast for now.
And we've been doing a lot of numbers, so we'll just look at a few plots here and then I'll share my takeaway with you.
And we can start to talk about this because I know it was a lot of number crunching for one day.
So sorry if it's been.
Been a little abstract or much, but here is just a quick regression.
We only have seven observations, so, you know, make of it what you will.
But these were the four tests that we made for the various labs over the next five years.
Regressed on their price.
And basically what I can show you is right here, you know, two labs, you know,
where they each have a price of $100 per test.
But, you know, one lab is forecasted to make less than 2.5 million over the next five years,
where the other lab is projected to make.
You know, around 15 million in the next five years.
So long story short is I don't know how much price competition will work in boosting your sales.
So.
So long story short, you know, if I was doing pricing, I mean, why not, you know, just crank it up, right?
Because that's what it looks like some labs have done, and they've just they've just chosen a price of 120.
Because it looks like they've just maybe inelastic demand.
I'm not telling anyone what the price.
I'm just saying, you know, if I was running a lab.
So you can't compete on price or at least I'm not certain you can.
So at least not to a great extent.
So, you know, what are some of the other factors you can compete on?
Well, here is essentially, you know, the maximum variable cost per sample regressed against revenue.
And so here we see a positive correlation.
Keep in mind, we only have seven observations, but essentially what I'm starting to kind of gather from this data is, you know,
it looks like, you know, if you're able to sort of give yourself a bit more cushion and, you know,
have room to have a higher variable cost and still make a profit, then that can increase your revenue.
And just to kind of show a few more figures here to flesh out the story.
Here is total samples against variable costs.
So it looks like, you know, if you are able to have a higher variable cost, it looks like you're able to test more samples.
And it also looks, I mean, I would assume there is sort of simultaneous causation here in that.
The more samples you test.
That's not the best plot ever, but the more samples you test, the.
You know, the higher your variable cost can be.
So let's just print this back out here.
And I'll try to get to my takeaway here.
So so basically.
We're starting to get a little abstract and a little imperfect here, but basically what I'm trying to argue is.
You know, to run a profitable lab.
I think focusing on your variable costs is one of your most important.
Factors that's under your control, so you can only do so many pricing gains.
And if you look at the laboratory's websites, you see that they are engaged in pricing strategies.
They do, for example, they do bulk deals.
There's various discounts and packages, so they're engaged in pricing strategies,
but I'm not certain that those are effective strategies.
And. I would like to just introduce a concept to you.
Price as a signal.
So, you know, we think, oh, yes, just increasing the price or decreasing the price, and that'll change quantity.
But retailers are hip to this and.
It may make sense for a laboratory to think about this price can be a signal of quality.
So if you charge a higher price, you know that that could just be a signal to others that your higher quality.
And so, you know, if you're charging a price of 80, you know, and other people are charging a price of 120.
That may be signaling to people that you're doing poor quality tests,
whereas you're just trying that pricing strategy out because you think, you know, it may be profitable.
So so just, you know, that's a factor.
Of course, you know, the higher your price is, you know, the more wiggle room you have on.
You know, to keep your variable costs under control, under control while making a profit.
So so that's sort of my main takeaway from today is.
And that was sort of the lesson I was trying to say is, you know, it's important to keep your variable costs right.
Gloves, solvents, disposables, it's important to keep those low.
And in economics, there's a mathematical proof to it.
But essentially, there's an argument that cost minimization results in the same outcome as profit maximization.
So instead of just. Question or comment.
Yes, there was a question about the recording. Yes, there will be a recording available.
I'm sorry. I'm sorry, actually, you know, I'm running into two calls and I wanted to ask a lot of questions there.
But just because of like my office work results there.
So I could not. But at the last, like I thought, OK, maybe it's only 10 minutes.
And if you guys disconnect, I need to have that video like audio, some recording maybe.
Because next week I will come to, you know, join this call.
And with a lot of questions. Awesome.
We'll love to have you next week and I'll get the recording up.
And then next week we'll start talking.
Yes, so bring your bring your questions next time and we'll make sure to spend a ton of time answering your questions.
And we'll be focusing on producers and processors next time.
But long story short is, you know, just instead of just being obsessed with profit and prices and,
you know, how many total samples are you testing and this and that,
you could also be just as successful by trying to be efficient.
So this is where I say, you know, just if you just have this laser focus on efficiencies and optimizing your workflow,
then you can, you know, you can run a profitable lab here.
Right. I mean, you know, it's hard to imagine that these laboratories aren't going to be profitable.
It's possible. Right. It's expensive to operate a laboratory.
So, for example, you know, lab 21, you know, they could bring in 12 million dollars, you know, in the next five years.
But if they can't keep their variable costs under forty six dollars per sample, then they're not going to be profitable.
And forty six dollars per sample, I mean, we're talking about things like disposables and variable costs include things like your employee labor.
Then, you know, every, you know, centers matters, especially on things like your like, you know, your disposables.
And basically, you know, I do know, you know, there's no need to cut corners because it does look like.
You can make a profit with room to spare. But but that's sort of my my main takeaway is,
you know, rather than, you know, just try to just be laser focused on price or, you know, how just just thinking, oh, how can we bring in more and more samples?
You know, maybe you should think about costs, you know, you know, maybe there are ways you can improve your workflow or, you know,
I always think there is endless opportunities for just becoming more and more efficient, just just doing things better and better and better.
And that's why I started, you know, analytics.
So, you know, just quick shout out to you know, analytics for making this all possible. And, you know, if any of you are interested in helping laboratories
keep their variable costs as low as possible, then check out the analytics software, the analytics engine.
So here is software specifically designed to help laboratories keep their costs as low as possible.
Right. If you're having your analyst enter data, that's increasing your variable costs.
And there's many better things that your analysts can be doing.
So I've seen in the past where people have gotten their time freed up and they're able to enroll in courses part time and they just become more educated and just their careers blossom.
So, you know, there's a lot of ways that you can just become more efficient over time.
And then, you know, that way, you know, you don't have to compete on price.
And, you know, if you get more efficient, I would argue that you're going to be able to test more samples.
You can test more samples. That'll give you a bit more wiggle room and you can do a better and better job.
So they can actually increase the quality of their tests. Right.
So if you've got a bit more room, you can do even more stringent quality control.
So I think it's just a win win for everyone.
So focus on costs, test more samples, test your samples better.
Increase your price because you're doing quality work. Right.
It's just that that's how I would go about running a profitable lab.
So sort of a long winded long winded story for today, but.
But I think it was an interesting one.
So are there any thoughts, comments? I'll just put up a plot.
But any thoughts or comments about the labs in Washington state or across the country or really any questions whatsoever?
Maybe maybe not related to maybe not to the Washington state thing.
I was, you know, I'm back from my office. I'm sorry.
I winded up like, OK, they wind up for five minutes. So I have a lot of questions.
I'm joining this session from last three consecutive times.
I'm trying to get a hold of this thing.
Number one is like, OK, this cannabis is a kind of like the commercial tool or what?
Or we are we are most of time talking about like, OK, these labs,
because I am trying to where I'm trying to pick up your brain is I'm into the data science.
Just just stepping into this one. And I am finding like, OK, any of the existing forums which are talking about more of like
like today you were talking about the forecasting and all that stuff and definitely maybe not the forecasting.
And you for for for only for this one, it will be applicable to anything else as well.
If it is my right understanding, like this this forum is specific to a certain need or is it really the generalized version of it,
like which we can implement anywhere else in the industry?
Really, you know, these are tools you can use really anywhere.
So this forecasting model I used is if you want to learn more,
tune in, I'd recommend checking out Saturday morning statistics when we can go in depth on this.
But we really just use what's called a REMA forecasting.
And so this just uses historic observations.
So we literally just use.
The historic right. So these are the historic, the historic trends we've seen from the various labs.
And with the REMA forecasting, all we use is the one time series.
I always also use the month so you can add in other variables.
But really, all you're using is the past historic trends of these labs.
And you're forecasting it forward. So to not only to you can.
That's what I love a REMA forecasting is you can forecast any time series,
any data that you can track over time, you can forecast with the REMA.
There's a few stipulations like it's good to have like a consistent interval and it's best not to have missing data.
So there's and there's like some statistical checks you can do to make sure.
You're not leaving any variation left on the table, so to speak.
But it's it's powerful because a lot of people stop here.
Right. I see a lot of people, they they do like historic analysis, which is useful.
Right. It's it's awesome to know how many samples were tested by month by the labs.
But you can just play it forward. Right. You can just use statistical techniques, forecast this forward.
It's not going to be perfect. We're not going to hit everything perfectly.
But, you know, at least now you have a forecast for the future.
And, you know, like I was saying. You know, yes.
Yeah. So one more question here. So I get your point.
Like when you said, OK, it is that's good for forecasting.
Let me let me say, like, in other words, like what my expectations, if it is like if it is useful for me, would be is
we deal with a lot of volume of data, like which is coming daily.
And you make you can say like, OK, millions of records.
Right. And let's say it's the claims data, like millions of claims we are getting day in and out.
Right. And I want to be in a position like where I can say, OK, hey, for because let's say I for this year,
I think we churned out like almost close to maybe 30 million of records.
We churned out that one. And that is just growing.
And we are just started like three years back. It is not like two old data.
We don't have much data from the from the longevity perspective, but we have like, OK, within the short time,
we have grown enormously, like from like, OK, from few hundred thousands from few thousands towards the hundred thousands.
And now we are churning out approximately 300 to 400 thousand in a day and which will be giving us a huge amount.
I don't know where we are going. And I want to see be in a position like, OK, hey, I have the, you know,
a prediction that based on like whatever the data from the last three years.
Because you were talking about the samples as well. I was just since I was into the two calls,
like I was thinking this sample, which you're talking about, like the sample of the population.
And I think you were talking about the sample about like the lab testing. Right.
So somewhat I could differentiate. But then I think, OK, maybe maybe I need a dedicated call for this one.
Maybe with you. And questions were how often you guys are doing this session so that I can be regular here
and I can pick up some brain for like, OK, these kind of things can be applied at my side.
Second thing, is it a commercial tool or is it just like an open source Python like kind of library?
Awesome questions. So it's all open source. So it's under the MIT license.
So if you just, you know, give credit to Caneletics and that's awesome.
And then to answer your second your other questions, we do meet weekly.
So there's a group Saturday morning statistics where we specifically do statistics.
And then we do the Wednesday cannabis data science each week.
And then as to your question with data, the time series forecasting can be done.
Wait, did I do that here? Yes, this model, it can be done on any time series.
So here you see this data. It's just. It's, you know, it's yearly data.
I wonder. But but as long.
You will be lost, too, I guess. Hello.
Is it only me or anybody else? OK, another three more people.
Yes, can you listen to me, Dharma? Paul, I can hear you, but I might as well answer the question for Keegan.
I think you're you described like huge data sets, like millions of data points.
And if you're looking for humongous data sets with millions of data points, you're in the wrong space.
But because cannabis has very few data points, a lot of this situation is primarily if we're going to go into data science realm,
unsupervised learning where you have the bootstrap data samples and supplement data samples and everything like that.
They're the only big data sets within the cannabis space is held in private businesses because cannabis is big business.
And it particularly relies with cannabis genome sequencing and to tie it into this cannabis conversation.
That is particularly why Eastern Washington.
Is very well known for their testing sample, because that is the forefront of cannabis research in terms of growing and testing and sampling.
Got it. Appreciate that. Thank you.
And this sample which you have created, like is it available somewhere in the GitHub or somewhere?
Or is it shareable? I mean, first thing I should ask, like, is it shareable?
Yes. So check out the Cannabix Data Science GitHub repository.
And I've got a link here in this latest script lab profitability to the data sets.
So that way you can download those. Can you please put into the message?
Just fork the repo, dude. If you're a real data scientist, you know what a fork on GitHub.
Here we are.
So. Thank you. And here's the link to the actual data.
And the largest data there is going to be the sales data. And Graham was right.
We've got to work with the data points we can get.
So but I would just just push back slightly in that I think the the models, the ARIMA models,
they work on small and large data sets.
I think as long as you've got a timestamp, you can start to aggregate by time.
So say you're looking at sales over time or you were looking at claims.
So you can just look at number of claims over time.
And any frequency, right? So here.
Right, we already showed you one with yearly and this should be monthly.
So really anything that you can put a timestamp on.
And so you could run your ARIMA model.
Minute by minute, hour by hour, so you could get, you know, same day predictions.
I found they're useful for like week, like one week ahead, one month ahead forecasts.
But if you're ambitious and you want to do them in real time with your data,
there's really no reason why you can't.
All you just all you need is just a series of values that you're keeping track of over time.
And you're off to the races.
And one more question. So this data cleanup, the data which you have used,
like you would send me both of the links.
So the data is already cleaned up or you are doing it like in your program somewhere?
Oh, yes. All the cleanings done here.
So basically, so here I just read in all of the data.
And if you're reading it in, you can just read it in sort of bit by bit at a time.
And in here, you know, you can pick and choose your variables.
Got it.
I just scraped the tip of the iceberg.
But like I said, if you're feeling ambitious, then check out the sales data.
There is. I really appreciate that.
I will not say like, OK, the millions of the data points, I would say the data is too much.
But yeah, the features are limited.
Like, OK, it's not expanding any further.
It's like merely like 10 to 20 features, I would be saying.
But yes, there is a huge diversification in terms of like one data is segmented into
this type of process.
Second is into this type of process.
Like, as I said, like the claims.
So it could be like, OK, this guy is having this kind of claim.
That guy is having that kind of claim.
So in a nutshell, like I'm getting like the variety of the claims basically,
maybe 10 to 20 types of that one.
And then eventually it turns into like millions of records.
So I was thinking like, OK, like to be very honest, like I even started this one like
some time back and then I think, OK, what actually like would be would be helpful for me?
And I am honestly like going from kind of the door to door.
People say, OK, if you have too much of data, OK, go ahead.
Do the deep learning.
OK, if you are like, OK, this no, no, no, you do this supervised learning.
And today, again, like, OK, it's not going anywhere.
So let me let me start with some point like how at least they start handling these things.
And, you know, that's where I am.
And I appreciate that, like you guys, at least, you know, sharing this information so that I can
next time probably I will see you with a lot of questions what I have and what I don't.
Or probably I'll try to grab some sample, maybe not the sample from the original data,
but I will just create a replica of like with some dummy values so that we can,
if you guys can, you know, think about like, yeah, you can you can guide me.
We lost you again.
Yes.
I don't really yet.
We lost you for for last 15 seconds.
You have to repeat what I said.
I'm sorry.
Oh, yes.
So my background is in economics.
So that's why I use.
A lot of tools that economists may use.
So I'm just as eager as you are to learn some of these interesting,
say, machine learning tools or other data science tools.
So just because I haven't talked about it doesn't mean it's not useful.
So if you find an awesome, cool data science tool, machine learning tool,
then please share it because I'd be.
Yeah.
Drilled to explore it and apply it to some of this cannabis data.
Yep.
For sure.
Thank you very much.
Awesome.
Well, thank you all for coming.
I'm going to go ahead and stop the presentation for today.
I hope I hope you all were able to get something out of it.
It was a little long.
It was a little long winded today.
So next week, I'll I'll try to make it a bit more simple and a bit more handy.
So I'll try to make it a little bit more simple and a bit more handy.
I'll try to make it a little bit more simple and a bit more hands on,
a bit more discussion based.
So I think next week will be a good one, too.
I appreciate that.
Thank you.
Great.
Awesome.
Thank you all for coming.
Have a productive week.