How we can apply Machine Learning in Finance

Key Takeaways

hello all today we'll be discussing about how you can apply machine learning and deep learning techniques in finance finance altogether is a very important subject and it is a very crucial or department in any companies so when I talk about finance there are a lot of activities that we do with respect to finance so some of the activities I would like to note it down over here is sales predictions right then we have something like stock predictions apart from that we have something like risk analysis after risk analysis we also have something like fraud detection so these are some of the applications that machine learning and deep learning them perform with respect to any financial data now when I talk about financial data I'm basically talking about time series kind of data so when I talk about sales prediction rights so you will be seeing that our since prediction ten moves up or down based on the season as we as we go and buy the tickets like let let me consider that this is an airline company and this is the sales data of the airline company possibly we can also consider Walmart Walmart or Amazon they sales data and how it is moving up and down so based on the base season we can definitely apply different kind of machine learning and deep learning algorithms into finance and get more out of it now in this particular video we will be discussing about ml and BL out can be applied I can also show you a better example like there I will try to you know predict something or forecast something right so when I see about smooth casting that discipline that I try to find the values of the sales in the future in the future date basically I'll try to find out the sales what will happened in the future today and for that the techniques that we'll be applying will be discussed just in a while so make sure you see the complete video and get to know as much information as you can so to begin with guys I'm just going to move some of the some of the techniques that we basically use in finance is PC for process automation security underwriting and credit score algorithm trading and row by dividing these are techniques are basically used in finance and for implementing all these techniques will be using machine learning we can also use deep learning and deep learning if you remember if you know that deep learning has a very important concept as a neural network right nowadays a very advanced neural network that is an LST a neural network recurrent neural network which is a state of Rd is being currently used for doing lot of activities like structure addictions stay in sales predictions and the inner technique that is basically used is that if you want to find out what will be the operator what will be the prediction of the stock price or the sales price or the tomorrow's date that basically will be looking up in the previous 60 to 70 times then for whatever time stream that we actually find to set and then we we try to find out the behavior of the graph that our behavior of the data how it is moving up and down based on that is actually making the prediction for the next stage so this lsdm reckoning neural network this is the basically the state of our totem that is basically being used now it's so let me just give you some of the use cases that basically you can apply machine learning and deep learning in finance and then I'll go ahead and show you an example but I'll be doing some sales prediction by using a technique which is called as Rima model that is actually available in machine learning so to begin with the first automated the first use case that I want to discuss about is basically automating this management so risk management is one of the important areas for any financial company it involves the different kind of decision-making so making one of them very important tool that you will be using in a company right - in order to actually so if you want to invest in a company if you want to actually start new work right attack and how much money you have to invest this risk management biscuits is possible by using machine learning and deep learning techniques and based on that and them and how do you are actually doing this risk management the main thing is our data that is since in finance we have huge amount of data that basically the previous day back may be customer data all financial data you can actually create a machine learning model or deep learning model and can do some better risk management decisions in my upcoming videos I'll just given small small example where I'll be implementing some machine learning implementing all these things today will be discussed having a brief discussion about this and then we will be moving to will be showing an example of sales prediction the other example is basically fraud detection fraud detection is basically heavily used in banks suppose these people are applying for credit cards and suppose they have a social security number or a UID number as well in India so the shooting security number is a unique number that is provided for the people and based on that they have the credit card linkage like how much money what is the credit score they have based on that we can apply a machine learning model and then we can you know provide that particular credit card to the customer or not based on that so that is where the fraud detection is done and we can definitely link machine learning and deep learning techniques the other technique was basically I was on with the trading here this is basically your stores stop forecasting weather forecasting stock predictions for a weather forecasting is not an example in finance but yes we can also do weather so custom because it uses the same technique now in this algorithm trading we basically are doing the stock predictions for the future no stock is a very volatile subject all together you know it completely runs on the sentiment analysis of sentiments of the people so I'm not saying that we can actually calculate the net percent accuracy of the stock movement but definitely we can find out the ups and downs in the future when the stuff is just going to go up or down so here was some of the examples now let me just go and discuss about how we can actually use in basically how we can actually do a sales prediction by using a machine learning and that technique that we'll be using in this bill called as arena or seasonal arena before going ahead guys I would like to announce that I have actually created written a book on hands-on Python so finance and this book I've actually dedicate lis taken six months of time and I put up all my experience total amount of experience that I have is somewhere on eight years in as a data scientist currently I'm working as a lead data scientist and this book and I've worked in various financial data financial projects so various domains like traveling domain on stock market domains and a lot of special financial companies basically so this book basically is available this link I provide you in the description and you can buy this particular book you can basically go with the e-book or if you are actually interested with the full-frame book you can actually go it I think guys this is very very cheap and it is also available in Amazon I'll provide you both the link of the book you can also have it in the kindle format or if you want in the paperback format you can get it the reason why I'm telling you to take this book guys because this book covers all the concepts now when I say all the concept basically I have actually written this book considering Python as one of the programming language I use stat models that is one of the critical library of statistics and then I have used machine learning and deep learning if you just go and see the description of this books and this what you'll learn you'll basically learn clean financial data with data pre-processing visualize financial data using histogram color plots and the graphs perform time series analysis with pandas for forecasting estimate covariance correlation between stocks and security so basically I have actually taken stocks securities prices I have taken portfolio optimization of the better example all the data I've actually downloaded from Google Finance with respect to the stock prices I have taken Nasdaq as one of the most financial data that I explored from the Google Finance you know and then after this I have implemented the machine learning algorithms also apart from that in the final two to three chapters you will be seeing that I have implemented recurrent neural network amount and stock predictions and I have done a lot of concepts into that also so we have I have included around twelve use cases inside it and they are on two chapters and it is completely done with the help of Python so let me just show you the table of contents so in the table of contents you'll be having this many chapters you can see that we'll start with basics of Python of how we can use Python in finance okay and then we starts with getting started with numpy pandas and math loudly now the best thing is that guys as I'll explain everything from scratch you know from the basic even even a starter will be able to understand because the code are extremely clear into this and the explanation is extremely clear into this so I have started with getting started numpy pandas and matplotlib then we have fine series analysis and forecasting we have done use cases like measuring investment rates you risk then see that there are lot of lot of topics inside this portfolio diversification covariance and correlation separated risks and return and based on the security risk what is the return that you may get in the future so this is basically available in if you are trying to invest in some securities which is having some amount of risk and some amount of visibility on to it so basically how do you select those then after this I have also shown you how we can do portfolio optimization and use concept like Markowitz which is a very important concept all together in finance then I have also explained about capital asset pricing model then we have also seen regression analysis in finance so here I've actually included various kind of machine learning algorithms which will help you to solve linearly linear problems in finance and nonlinear problems in finance I also included some important chapters like Monte Carlo simulation option pricing then we have started with the deep learning with tensorflow and Cara's have included put in sir flow and Cara's and after that we have done lot of use cases on stock market analysis and forecasting case studies so in all this chapter I have included at least two use cases where which will actually give you an idea about what we are actually doing into it and I have included important topics like ARIMA I have included Monte Carlo simulation and I guess guys this is the best book to learn finance now please do support and let me know your reviews like how this book was or you can buy basically the e-book or the whole book itself which costs you around no four hundred or two in rupees so suppose people who are from us and from other places you can definitely buy it from Amazon and in the Amazon you basically have around in the Kindle format and the paperback form so both any of them you can actually use it now trust me guys have put past six months into this book I have applied my complete knowledge into this I've actually put up my whole you know time into this particular book and made sure that I have written this book in a proper format so it is around 349 pages and it is basically a 10 I was 28 minutes apart from that I'll just show you one example which I have included in this book is basically mine seasonal area map here basically I have taken some data that is basically a monthly milk production now this multi milk production is basically a kind of you know we need to find the sales prediction and you'll be seeing that after implementing and after doing all the techniques so in this I have actually done decomposition we have tested for stationary which is dickey-fuller test and then we applied differencing so these are the concepts that we basically do in remind seasonal ARIMA ARIMA basically means autoregressive moving average model and apart from that I have gone through this and up like correlation and autocorrelation and partial auto correlation plots and based on this you will be seeing that our model is basically able to do the predictions in the future time right so these are all the values and after that you can see that how the prediction is going on in the future details so this is what this is what I have actually done it and we are basically I have included all the explanation in this in this particular file make sure that you see this particular book guys trust me it is a wonderful book all together I have taken six months of time I put up my full experience into this I'll be uploading the link of the whole URL of this particular where you will be able to see the books available in these particular sites in the description make sure you buy it and just let me know your reviews and do if you like this video please do subscribe the channel and press the bell notification icon I'll make sure that I will upload more and more videos on Finance and all that other deep learning and the machine learning techniques thank you one and all have a great day ahead and please do subscribe the channel if you have not and share with all your friends my channel because I do it for the data science community I try to provide much as as possible much as knowledge from my past experience that I've gained through thank you one at all I'll see you in the next video happy learning thank you