So I may share this.
And we will get started.
Is it recording?
Yes, we are.
This one.
OK can everybody see the screen.
No, I cannot.
Papa James you can see it.
And Frank is yes.
Yes, again.
OK, so some people do so it might be just on its way to
coming up.
Are this is taking a minute?
The anterior share.
And again OK.
OK let's get started.
Hi my name's Angelica.
So Google Sweet consists of free cloud computing programs that's
powered by Google.
And then all the programs can be accessed through a web browser
or download it on apps on either iOS or Android and then some of
the programs that's on there are Gmail Calendar, Google meets
sheets Google Docs slides chat and then drive.
So use the programs in Google Suite is free, which needs to
have a Google account in order to do that. So here I'm showing
you how to create that account so you will go to whatever
search engine. You're using and then I just enter Gmail and then
so it'll be the first it should be the first one, that comes up.
But what you should click on is the one that says Gmail e-mail
from Google.
And then from there.
It'll take you to the next image. Where in the bottom left
corner, you can select create an account.
And then it'll give you a few options of who you're creating
account for you have the option of for myself for my child or to
manage my business and then after you select what you're
trading, it for you will select next, but for the purposes of
this for an isolated myself, I'm.
So after you select who you are creating the account for it'll
take you to this first image where you begin to get your
account. You enter your name and then you enter the username that
you would like which would be your Gmail like your e-mail
account and then if the name you selected is free. Then it,
you'll get a green check mark. I believe it is, and then you can
just proceed. But if it's taken.
It'll let you know that and then it'll like bring up some.
Some suggestions underneath it and then you create your
password and then you move on to the next page.
And then you need some more information.
And then under gender, they of course have male female rather
not say, and then they have custom and then you know you
can.
It's like what you want from there and then at the bottom
this hyperlink. While we ask this information. They provide
you with information with of course, why they ask for it if
you care to know.
And then here is the last step in creating your account. You
just have to agree to the privacy and terms and then I
didn't.
Scroll all the way down to show you where you would agree to it,
but I mean. You just scroll through and read this and at the
bottom you will select I agree.
So after you agreed to the privacy and terms. You'll be
taken to this green which is your Gmail account and then you
have your first welcoming e-mail from Google and then of course,
you can see on the left, you can compose an e-mail. Then you have
your different folders. You can search for emails at the top
after you start getting more.
And then at the top right corner is where you can access the
other programs and that dotted square where the Red Arrows,
pointing but before we get to that part at the bottom. I guess
more so the left side of the screen. It shows that you have
one GB of data to use.
So the first one, I have to like it was calendar. And here it
helps you like stay really organized by allowing you to see
everything you have scheduled you can also add color
coordinate different things that I didn't show. But I should
have.
Umm they can color coordinate different meetings or whatever
you have on your calendar to help you stay organized as well.
So you can schedule events meetings. You can search for
people and then you can see where you can search for people
on the left hand side in the middle where I have the Red
Arrow, pointing this is more beneficial. I think for like
businesses because.
I'm pretty sure unless somebody's calendar is publicly
available like you can't just search. Anybody who's calendar
on the free version.
And then you can.
So before we get to this screen, which needs to do on the
previous screen is double click anywhere on the calendar in
order to add a meeting or event and then so you will add a title
and this is what people will see if you are going to invite
anybody to this meeting and their e-mail.
That's what they'll see.
And then if they ask to select it, you know this is what they
see on their calendar and what you was doing your calendar as
well, and then underneath that you have the start date start
time the end time and the end date and then you can select on
these to change them as you need to, and then you can also add a
video link. If you need to, and then you can add a physical
location if you wanted to as well.
You can also ask for the notifications.
And then you can make teleport maybe, as well. If you need to
right next to my name. There's a blue circle. You can click on
that drop down and then change it to whatever code needs to be
and then you can also add the description.
And then to the right you can see this is where you will be
adding guests if that's what you want to do for this meeting.
So here we have Google Docs and this is the word processor where
you can create documents upload them. Edit them share them and
some other things, I feel like it's pretty comparable to
Microsoft Word but the basic version so maybe I should have
said that but it will get the job done nonetheless and then
they also have like templates that I've actually used for to
get.
My current job and my third interview that they didn't even
I don't know.
look over what I created but whatever, so they have like,
some useful things here with the templates and then at the bottom
here. I showed you some of the options that they have like when
you actually click on a blank document or any of the documents
you can click on.
Some of the editing actions that they have similar to word. But
like I said, just not as.
Extensive.
So here is Google Sheets and it's like the very basic version
of Excel.
You can basically use it, you know as a calculator you can use
it to create list budget.
And.
And pretty much like anything else will like numbers.
But there were like some interesting things that I've
done with Excel before like creating like different images
like not just graphs.
That I thought I'd be able to do here, but I cannot because you
know like I said it is like the very basic version.
And here is actually where I thought I was going to come and
blow. Everybody's minds away with what I could do in sheets
because I know what I can do and Excel even as like a basic you
know user of Excel but I do not know how to do that, or I can't
do it here at least from what I know.
So.
Yeah.
Thank you for bearing with me through this presentation like I
said in the beginning, I feel like I chose the wrong degree.
So it was kind of hard for me to figure out of everything that I
learned what do I feel like I could explain?
You know well and I checked on my million times before I did
this, and obviously ended up on this and it was a bad decision.
But I do appreciate the internship for allowing me to
get more clarity on you know where I feel like I would rather
be where I think I fit better as opposed to Analytics. So thank
you for that. Thank you for all of your help.
And your time and.
Yeah, that's it for me. Bye.
Well, I think she did a great job.
Yeah, I think so, too I think she did a great job.
I know it's hard to decide on what to do, I've had a few
people that were having trouble deciding on? What to present and
like I said resent anything we appreciate anything.
There might have been some people who don't know a lot
about Google.
Uh you know.
Things that you can use on Google so.
All right, so our next person in line to present.
Is day?
Ohh, right. Yes, how's it going? Everybody glad you're all here?
I'm gonna go ahead and present. This presentation here on
Wireshark? I guess I should probably share my screen first.
I'd probably be a good idea.
Let's do this quick.
OK can everyone see this alright.
Yes.
OK, wonderful, yeah, let's go ahead and get started so I just
wanna have forward did have some background noise. I did my best
to try and minimize it, but just bear with me OK.
This screen here.
If you're speaking we can't hear anything?
Yes, I'm I'm not speaking. I'm I'm not sure if you guys could
hear the sound if the presentation.
But.
We cannot.
I'm sorry.
No there's no sound.
No, I cannot.
OK, well. That's that's a problem we're going to have to
go and fix that.
My apologies guys I had no idea you couldn't hear it and I guess
we'll go back to the beginning.
How would I
do this?
Yeah, let's try this real quick.
OK, guys, hopefully you all can.
Sure, this time sorry about that.
OK.
Track back here.
On Wireshark my name is daelano and I'm excited to have you all
here with me today. Everyone here this OK presentation on
Wireshark. My name is daelano and I'm excited to have you all
Yes.
here with me today so with that being said. Let's go and take a
look at today's agenda.
So First things first we're going to take a moment to talk
about the basics of Wireshark. Much like all software tools. It
is critical to establish a foundation of what we're working
with before we dive in.
Next let's follow that up with how we actually get started. The
Wireshark, including where we go to install a program and some of
the first things you're greeted with as we open it up.
We'll then take it one step further and talk about Wireshark
in the context of security with a focus on adversary
investigations with Nmap unencrypted transmissions and
finally log 4J as a case study.
Lastly, we'll send it out the bang and open the floor up to
any questions.
So if all that sounds good to you. Let's take a look at the
most common questions. I get by far which is what is Wireshark
well?
Wireshark is an open source packet sniffer and inspection
tool is used to capture network traffic from Ethernet Bluetooth
wireless and frame relay protocols just name, a few.
Why do we use Wireshark well Wireshark offers cybersecurity
professionals like blue team defenders enhanced visibility
over the security of their company assets visibility is an
important because it allows company leaders to make more
informed business decision, which in turn helps Dr
innovation. Big picture thinking and is overall critical for the
cultivation of the better cyber security posture overall.
But let's take a moment to talk about what it isn't.
Monitoring for security threats is not a one sided operation. It
is a strategic process that requires other sophisticated
tools and equally talented defenders to conclusively
document and determine adversary activity.
So all that out of the way why don't we get into the fun stuff
alright so First things first let's take a look at the
installation and execution.
Very simple here, we just had to Wireshark Org you'll download
the appropriate installer for your machine. Download any
necessary libraries, such as NPC cap or USB cap and then finally
you'll execute wireshark.exe.
That's it, you're good to go.
Finally, once we got everything situated you'll be greeted with
this startup screen just keep in mind yours might look different.
Now, although the screen is displaying is that it's a
different places where we can capture traffic. You can see
which ones are active based on the electrocardiogram looking
feature parallel to the interface name.
In my case, I'm gonna go ahead and capture on interface EN0.
Once doing so, you may or may not notice a surge of new
information. Just flooding the screen, but don't be alarmed
instead.
Simply Press, the Red Stop Button, located near the top of
the screen to halt the capture.
Now there are quite a few other buttons that we can take a look
at here such as the blue fin button, which will prompt you to
save the capture before starting a new one or the green, one
which will simply restart the capture during the recording
removing any previously unsaved packets.
Now let's be good time to talk about the difference between
capture filters and display filters. So a capture filter is
set right before the capture it cannot be changed during the
capture. It is generally used for capturing a specific type of
traffic.
Whereas the display filter is the type of filter to refine the
packet of stirring or after the capture itself. It is generally
used for hiding nonessential traffic.
So now we talked about what different filters are available
to you. Let's take a look at a few examples as you can see
these are some common capture filters, including filters for
IPS protocols and hosts.
And likewise for display filters as well.
Now I be doing you a disservice if I don't mention the
invaluable statistics tab. This is where Wireshark stores. It's
fast and precise calculations that are made against various
important attributes of the collected data of course. This
is done to save us. Analysts lots of time.
So for instance, let's look at the conversations window.
This feature will give you all the information about
conversations between 2 endpoints in your capture as you
can see it will provide data about the packet lengths and the
time it took to start and end in relation to the capture and much
more.
Similar to the conversations window, the endpoints window one
step provide information about specific host.
As you can see this will include data related to transmission and
reception on a host this is great for performance.
In my case, I have a database called Max mind DB installed and
it is configured to provide additional geolocation
information of each host, including the autonomous system
number or asn and its respective organization.
Diving deeper the protocol history reveals a tree of all
protocols and all packets observed within capture where
each rolled holds statistical values of each protocol.
And how many packets actually follow that protocol, however,
Please note packets off contain multiple different protocols. In
one, so if the packet is not 1% just one protocol. Wireshark
will breakdown at some gradients instead.
Moving on so last one will be showing off here is the IPV 4.
IPV 6 to statistics window, which will as the name suggests,
reveal in depth information about the Internet Protocol
version of your choosing.
Alright so with all that out of the way we're going to be taking
a look at a very common case of an adversary using nmap to scan
hosts on our network for valuable information, but first.
What is in map?
And Mapor Network Mapper is an open source network utility
tool, which facilitates host discovery and security auditing
and map uses raw. IP packets in novel ways to determine host
information such as the active services that are running active
operating systems that are running an even active packet
filters or firewalls that are running as well.
OK, so First things first despite its beautiful neon
coloration. There is an attacker actively scanning hosts on our
network for information in this screenshot. That's first find
out how we can be confident in this claim, though, although it
just keep in mind. In an ideal security setting enterprise soar
and Sam Technology will often alert of and ultimately try to
block this from happening, but let's just say that this is
happening to keep things simple for now.
So First things first I wanna join your eyes to these
highlighted points here.
Here.
And here.
1st.
While source IP's sending packets to one destination. IP
is not inherently suspicious. The fact that this IP address is
actively sending one handed sin flags over many different parts
of the same time is suspicious of course. This is a common
example of a sin self scan with the preset and map switches.
And again in the next example. We have a case of the same
thing. However, this time, the attacker is using nmap to send
various flags and an attempt to discover how the operating
system will respond when they receive them you can see these
flags here.
Down there at the bottom.
In this example, we have a common end map null scan taking
place here. However, this time, the attacker attempted to scan
the entire network and find all open ports based on their RST
flag reply and you can see that down there at the bottom.
OK moving on just wanna take a few moments to talk about
another critical security feature mark. It's called
protocol. Following I have created 2 examples for
demonstration.
In this video we have a Web Server on the right called Meta
Exploitable 2, which I briefly connects to you for the sake of
creating a file called flag TXT and showing its IP address of
192168183, now on the left, I have mind MacBook that will
attempt to connect to it via telnet, but tortiously insecure
protocol. I demonstrate how someone on the network can view
the entire session in plain text by following the TCP stream
let's watch.
OK.
OK, in the next video. We'll be taking this concept to very
insecure, and depreciated protocol called HTTP like last
time I'll demonstrate how we can use the file stream feature to
observe plain text information and indeed you'll notice how I'm
able to search for specific strings within this graphical
user interface, allowing me to potentially even find sensitive
data in no time let's watch.
Going take a look at log 4 J and log 4. J is an open source
logging framework that allows software developers to log
various types of data within their applications. It is part
of the Apache. Logging services log 4. J is still used by
thousands of websites and applications to enable routine
functions, namely logging information, which can be used
for debugging and other purposes.
Logging is a very critical aspect of software development
and organized logs can offer a quick debugging easy maintenance
and control storage of an applications runtime
information.
So the lock for Jenny vulnerability was classified as
CVE 202144228. It was also known as log for Shell and log jam. It
was a zero day remote code execution vulnerability in the
log 4. J framework thread. Actors made use of a server that
is running the unpatched labor jsoftware to execute any
software that they wanted to including malware like
ransomware.
So I just wanted to create this log for Jay attack life cycle to
break down things a little bit further. We have several stages.
The Recon delivery exploitation installation and finally the
actions on objectives. First, the attacker will sweep the
Internet for vulnerable log for J severs. They will go ahead and
attempt to send a malicious HTTP request to the server.
There, the exploitation will occur from bad strings and
queries to the malicious ldap server. The attacker will
download and execute malicious payloads and then of course,
they will enable things like Crypto mining or ransomware
remote shells. They'll just do basic data is we'll trace an et
cetera et cetera.
So this attack map specifically was created with the goal of
showing you all how widespread this attack was and but just in
relation to the small packet capture that I'm using here for
the demonstration. But you can just imagine that on a much
larger scale. Just how many attackers and defenders were
attempting to interact with this one ability.
Let's go ahead and take a look and see how this works at the
packet level.
So phase one begins with packet #404, revealing that an attacker
sends a base 64 encoded post request to our server running
log for J.
In phase two, when we decode the string. We can see that the
attacker wants our server to install Linux malware from a
known malicious IP address.
Now we know that it's malicious because it has a negative
reputation report from virustotal.net.
We can even compound that information against Eurohouse, a
business dedicated to identifying and reporting
command and control malware, loading servers.
And in phase four we can obviously check to see if our
server responds or beacons out to that specific malicious IP
address. But in this case, we're retrieving this 0 results of
that happening, however, in the real world. It would definitely
be worth investigating at just to make sure that there's no
additional penetration. We can use some sort of log analysis
technology to ensure that this particular search was not a
fluke. That's outside the scope of this presentation.
Alright I do hope that that was informative and I do. Thank you
for sticking around to the end of this presentation. If you do
have any questions, please. Feel free to reach out to me after
this recording.
OK, great, so hopefully.
Everyone can still hear me OK.
Just a little.
Just a little laggy Here at the moment.
Hopefully everyone could have seen that.
Stuff that's real quick.
OK can everyone hear me OK going once going twice.
Yes.
OK, Wonderful Alright Thanks guys appreciate.
From good job.
Thank you very much hope it was a good presentation thanks.
Yeah, it was great.
You and then I guess now we have a certain of us.
Upshot uh let me share my screen.
OK.
I hope you're gonna be able to see my screen and hear me right.
Yes, we can hear you and see your screen.
Thank you hello everyone, my name is Srinivas and today I
will be talking about data Analytics.
The different types of data Analytics. It's importance for
the within the business community and I will also demo
one of the techniques commonly used in the data Analytics.
That's the agenda.
And I will start right away so data analytics data. Analytics
is a.
Read the signs of analyzing the data.
Uh in the business to derive meaning meaningful and
actionable insights.
So I mean, uh, yeah, I'll tics.
Uh how comes I mean, it's a bigger process, so there are
many things that need to be done before you analyze your data so
data. Analytics also sometimes involve ingesting the data. I
mean, getting gathering all the relevant data data cleansing is
bigger part of the data and it takes because many times.
Incorrect data or
yeah will actually skew your analytics to one side which may
not be right. So it's very important to make sure that that
is cleansed and you have trust on your data before you perform
any kind of data Analytics.
Uh and and why they don't text is important data. Analytics is
very important because without Analytics. You will not know
like how business? How your business is performing so for
example, if you want to know like if you are if you are a a
company, which publishes games or sales games, you would like
to know how your users are feeling about your game or if
you want to, if you go deep if you dig a little bit deeper,
you'll also.
I don't know like what parts of my game are interesting.
Kind of which sections of my game are longer and boring for
my users.
And if you can also see how your game is monetized so whether
whether the users are buying the stuff that you are putting on
the game like the weapons all the stuff that you gonna put in
for the cell. I mean, whether they are buying it or not you
would like to know all that stuff data. I'll text is also
useful in the supply chain, so for example, supply chain is a
good use case so you'd like to know like how your devices are
selling what part of your I mean? What kind of devices are
selling faster so that you can.
Improve your purchasing plans.
That's that's one of the use case that generally people use
and.
Let's go to types of data Analytics. So there are four
types of data analytics called descriptive diagnostic
predictive and Prescriptive.
This script to analytics is all about understanding your data so
it will tell you like? What is happening in your data set so I
mean, some of the examples would be like have the numbers of the
views gone up, I mean? What I mean? How are the sales looking
in the past six months? What kind of products are selling
faster these are all descriptive errors you, you just understand
the data.
And the next level of analytics is diagnostic analysis
diagnostic. Analytics talks about so I mean, it is pinpoint
on.
Uh what is causing what? So, for example, if I want to, if I'm
looking at the hospital data. I would like to know like? What is
making my patients spend more time in the ICU?
So like I mean, you can talk about you can verify that stats
of your workstations and see like yeah, yeah, whether the
edge is causing them to spend more time in the hospital or is
it like underlying symptoms, causing them to spend more time
in the hospital. You can all that's one good example of the
Diagnostic Analytics.
The other kind of analytics is predictive predictive is the is
like using the diagnostic. Analytics and understanding and
predicting what gonna happen. So for example, if I want to
understand if I diagnose myself.
Will I be able to predict the sales in the future?
That's one kind of analysis that we can do in creative.
And the prescriptive analysis is like what will we do, if
something happens, so for example.
And it can lead play action so if I if I'm talking about the
same example. I listed here. If you're measuring if they're
analyzing the weather patterns. So one such prescriptive.
Analytics would be like, so if my weather is above average.
What should I do should I actually I mean in case of this
bravery example should we add an extra shift or should we order
more beer into the house, so those are the prescription
examples.
So I have some I'm in the notes I also added some of the
techniques that we're gonna use in the kind of Analytics. So for
example, I mean, all descriptive analysis. We're gonna use
somebody plots histograms. These are all kinds of Descriptive
Analytics. So I mean, all of these are these underlying
statistics and math to it, but when we are using in the actual
technology world. All of them are actually wrapped under the
functions so if we take any of the softwares available for
example, power BI.
A title or hot all of them have functions available for these.
A statistical functions are models, so descriptive is
talking about like you can Instagram user stagram or plot
to understand the data and for diagnostics to understand what
happened. There are many things to do, I mean for example,
regression analysis can be done to understand like? What are our
variables are affecting my sales and there are test like test or
ANOVA which can help you understand like? What is the
context that you have in?
So then variables to predict this outcome.
And prediction it also has a methods available all
encapsulated in the algorithms. So I'm not going to go deeper
into the stacks and math behind it, but I gonna demo. One such
example where we're gonna break something based on a model that
we build.
I'll go to that next step. So some of the common techniques
used in the data. Analytics are regression models regression
models usually tell you like if I have 10 variables.
Ohh, how what is the contents of those 10 variables to determine
the outcome and the classification models are there
time series analysis? Is there and there are Monte Carlo
simulations. Also, there so there are many such techniques
that commonly used so I'm using one such technique here. It's
called a decision tree so decision tree will help you
understand like so if I have an example of insurance payments so
it will tell you like.
Uh if how is your premium depend upon the number of factors
whether he has a ticket whether he has more activity happening
on his account of what his age what his his how many tickets.
He has in the previous 12 months. I mean, we can. All we
can based on those factors. We can identify whether a certain
person should have a higher premium or do we have the whole
should we have more conditions on his premium whether an
underwriting is required so for example.
There is a live example of this so one of the insurance
companies use this theoretical techniques to understand whether
underwriting is required for all the insurers whether it's worth
the effort or where the money that they are that they spend on
these underwriting and then when they did. This analysis, they
identified that in 85% of the cases underwriting is not even
required. It's straightforward you can directly give them the
premiums and there will be OK for only 15% they need to be
more they need to scrutinize more on the.
Ohh driving patterns and then do a stricter underwriting.
That's one of the things so.
I'll take an example, so I'm trying to build an?
Uh a model here using the insurance data and then I will
determine whether the new whether a person needs to be
insured or whether he needs to have more conditions applied for
insurance. So I will go to art. I mean, I'm using R to build my
model. But there are many ways of doing it. So a lot of people
use Python. A lot of people use power BI. I mean, there are many
other tools to build. These models and demonstrate it. I'm
just using R I'm I mean?
For the sake of this demo, I mean to get faster so I have
written all these.
Ohh libraries already so I don't go into uh why I am using these
libraries. It's it's pretty simple. So I mean, if you are
using pop Python. You might need to import different libraries,
so that you are a plotting and the functions work better
because all these functions statistical functions. They are
included in packages and for those packages to work. We need
to load less the that's the reason I am using those
libraries, so for example.
If I see here I am using 2.
Uh datasets one is the policy holders and one is policy
buyers. I'm using policy holders to train my model and then use
policy bias to determine what kind of incidents should I give
it to them so for example, I loaded the policy buyers here.
If you see here so I have a lot of entries here and I have
gender status. What is account activity whether he's employed
or not? How is it paying? How many tickets he has and
everything and then?
And even that they have policy policy here, so if you see for
policy holders. It is the instance categories. It will
tell whether it's we should ensure them whether we should
give best terms whether we should give higher premiums for
them. So this is the policy holders. I gonna train my model
using this policy holders and then I'm gonna use this training
data to predict the outcome of the policy buyers. So I'll go
back here so I have a policy holders loaded and then.
The.
So I'm using decision tree here, so decision tree is one such
example. One such method, which can predict that which can
analyze that this is the diagnostic one and then we're
gonna use this for the prediction so for example, I did
this, I use deep three what I'm trying to do is like I'm trying.
The model to identify the incidence category based on all
these different variables and then I use summary.
And then if you see somebody here what I will tell you is
like there are certain elements in the summary. I mean, there
are many things here, but I will be focusing on only certain
things that will help you understand so for example, if
you see the variable importance. It says what variables are most
important to predict the outcome so for example, tickets within
12 months age and account activity are three significant
factors that will determine the insurance.
So that's what our model says So what we are gonna do is like I
mean. I actually visualize this model also here and if you see
the model here.
This will tell it if you say account activity is high. Yes,
or no and whether the ticket account is if account activity?
Is no and the ticket. There are no tickets then we should be
giving best terms for the insurance and who is the high
risk insurance here and for example, do not ensure see if
there is high activity and if there are tickets within 12
months. And if the age is less than 57. We are simply saying do
not ensure the model says do not ensure.
So this is how the model uh comes up So what I'm gonna do
was like what I did was I used this model and then apply it on
the.
My policy buyer so if you see the policy buyers here. My
policy buyers doesn't have the incidence category. Yet so I
gonna use this model to predict that so I'll go back to the.
My script here, So what I'll do was like I mean, there are some.
Basically, what you are doing is like you are reading the policy
buyers into the.
Ohh into the your model and then you are trying to use the
predict function.
Uh and apply the model on the buyers so when we apply the
model on the buyers.
It will come something like this.
So what it says is
So for each of these uh account ID.
Uh it will tell you what is the model so for example.
The model says that for this account, ID, it says do not
ensure.
And similarly if we talk about that on some accounts, it says
ensure with high premiums ensure with risk teams and then there
are and then there will be items, which says insured best
terms so that's how we're gonna use the model to break the
outcomes. I'll go back to presentation because I got there
are some important things that you wanna discuss so I mean,
it's the summary. So basically what the summary says is like
when you look at the decision tree the best premiums must be
given to the customers who don't have high account activity.
And doesn't have any ticket within the last 12 months, which
makes sense. So that's what the model says, and then we do the
same thing to our.
Fiction I mean, the data that we need to predict and then if you
see here when we apply the model are you gonna expect sorry I
will.
So it says when we apply the model. It says that it never
data set. It says that there are 184 customers should be insured
with summary systems 137, should be should not be insured at all.
82 customers should be insured with high premiums and 70
customers should be insured with best terms, so that's how the
models are used. I mean, this is a simplistic example with a lot
of assumptions, but these decision. Three models are
really applied on the real examples as well in our
insurance companies.
Ohh, that's all I have, I don't think I I mean put so much. I
mean bored you waited so much technical details. I tried to
put more in English terms. Let me know if you have any
questions.
I stopped my presentation.
So Srinivas did you just actually give us the secrets
behind insurance companies and how we get higher premiums or
lower premiums?
Yeah.
Yeah, that's what happened, I mean, there might be using more.
Yeah, you did so as long as we don't claim or bother them.
They might
We're gonna get lower insurance rates.
Yeah, if you are younger.
I'm liking it, I I I I just hit that Mark I'm over 57 people.
I I qualify there.
Right.
So don't claim don't bother them.
That's awesome.
Yeah, yeah, I mean, I took a simplistic example. But they
That was great.
might be using a lot of advanced techniques. Other than it but
this is what happens and I mean?
Well.
Well, that's
fantastic showing us the analytics that you've learned at
that's awesome. That's really great.
I love that.
Yeah, thank you guys.
Any questions.
Just a tiny bit. I mean, they cannot use it for the sales
because there are laws in many, many states against the price
optimization. So they cannot use these kind of models to give you
the price. So basically if I say like based on your.
Ohh so social media habits are your history of buying they
cannot determine the price.
That's not allowed.
But I mean, something like that would allow you to do like an
instant quote like they have on.
The Web, you can just go and I mean, it's not 100% but they'll
give you an instant quote based on a few pieces of information
Yep.
and it's that simple, model basically behind scenes.
Yep.
Rejects have to people.
It's good very good interesting.
Excellent excellent job everybody yes. Thank you very
much.
OK and that we do not have David I'm not sure.
Umm.
But that's it. That's all of our presentations this evening.
Thank you, you guys did a great job.
And really appreciate you know where you guys are coming from
with your information. I mean, there's a lot, that we get out
of these.
Looking forward to next week's as well. Thank you everybody.
You guys have a great weekend.
Have a good night everyone.
Hi guys,
Goodnight bye guys.