 Hello everyone, good afternoon. My name is Rohit and I work with Visible Alpha Company in Mumbai which is basically the financial technology company. We are basically in figuring out the investment insights. We provide investment insights to our subscribers. We collect data from multiple sources like Morgan Stanley, Jeffries, etc. These are investment banks which provide the research insights. We collate that data, we apply analytics on that and we give to our subscribers. So this is basically from the business side. And for the Python instance here I can say that see the most difficult task in dealing with machine learning and this auto AI thing is basically everyone may be aware of is data extraction. So there are a lot of tools like PDF parser, tabula, KMLOD, which is find out the tables in the PDFs. I have used all of them. We basically use all of them but none of them is actually full proof. There are a lot of errors and errors in our case are simply magnitude is very high because we deal with numbers and those numbers belong to the money. They are money numbers. So we cannot afford error there. So we have to most of the time develop our own algorithm for extracting data for applying the machine learning and dealing with numbers in general. So personally I have been a Java programmer. I have 12 years of experience. And in the last three, four years I have been working with Python. And the first thing that I notice in Python is that simply the confidence it gives you to experiment basically. You can experiment a lot in Python. In other languages it is a little language has its own fuss that you have to deal with and Python is a lot more easy and it really gives me confidence to experiment in Python. And there is a drawback of that instead of actually using the built-in libraries, you tend more to write your own. That is the level of confidence that you get. Okay, this is not working. I will fix this. So this is how we have been working in dealing with our data, which we get in the formats of PDFs, in the formats of Excel, mails, etc., extracting that information from there and making sense out of it and distributing it to our subscribers. Okay, so I am a machine learning enthusiast and now a Python enthusiast as well. We have a huge infrastructure of Python, which is based on Python. And if you have any queries regarding this visible alpha, how we work, what we do in machine learning, how we in general, where we stand in industry in terms of investment decisions, you can come to our booth, which is, I think, just right before this gate. I think that's it from my side. Narin, alright. I think, yeah, thanks. You finished earlier and made your life easier. It was quick. Yeah, it was quick. You want to talk some more? No, that's all right. I think I cover the business side of the company, where we stand in the industry and what we do in Python as Python enthusiasts. If anyone is interested, like maybe a lot of the students from various institutes, which are interested in working in ML, AI in Python, they can contact us. Alright, thank you.