Watch this Ask the Expert session to learn what’s new about SAS Analytics Pro Advanced Programming – for new and current users.    Watch the webinar   You will learn: How to deploy SAS Analytics Pro. How SAS Analytics Pro runs in different environments, using containers and in the cloud. How SAS Analytics Pro integrates with Python, GitHub and databases.   The questions from the Q&A segment held at the end of the webinar are listed below and the slides from the webinar are attached.   Q&A What is difference between advanced programming and pro advanced programming? There is the analytics pro and there is the analytics Pro advanced programming. Analytics Pro is pretty much the same product but with less packages. It would be the SAS base, SAS stat and graph with all the access engines and SAS studio. Then there is this SAS Analytics Pro advanced programming, which is what we were talking about here today, which is those same base stat graph Studio products plus the IML, QC, OR and ETS. It's pretty much the same. What it adds is more capabilities for different use cases.   Analytics Pro: BASE SAS SAS/STAT® SAS/GRAPH® SAS/SECURE™ SAS/ACCESS®* Analytics Pro Advanced Programming: BASE SAS SAS/STAT® SAS/GRAPH® SAS/SECURE™ SAS/ACCESS®* With additional capabilities:   SAS/ETS® SAS/OR® SAS/QC® SAS/IML® *all SAS Viya access engines included. This blog shares additional information as well! https://blogs.sas.com/content/sgf/2022/06/06/introducing-analytics-pro-advanced-programming/   Are there SAS Analytics Pro Advanced programming courses provided by SAS Institute on the SAS YouTube channel? Check out some of the training and support resources here! https://support.sas.com/en/software/analytics-pro-support.html   What is the annual license fee of the product? If I don't need all packages, can I choose a subset of packages within the product and pay for them only? Unfortunately, we can't answer pricing questions ourselves. Please contact your SAS account manager and they will respond to your question. Also, if you're not sure who or your organization doesn't have an account manager, you can submit your question here: https://www.sas.com/en_us/software/how-to-buy/request-price-quote.html   I use a Mac and SAS is not supported on MacOS, right? It used to be right, but it's not anymore. With this product, since it runs on container and you can have the Docker container running in different operating systems, you can deploy it on Mac OS as well. The only caveat there is that it doesn't run on the newer versions of MacBook that come with the M1 and M2 ship chips. If you have the Intel versions of MacBook, you can run it. I ran it on this demonstration that you just saw, and it runs seamlessly. There is no difference between what you would do in Python and Windows or Mac or Linux.   Am I limited to the number of cores I initially license? No, you're not limited to the number of cores. You're free to choose what you want to do. In my example, since the software is licensed by number of users, it's a license on end user basis, you don't have any threshold in the number of cores that you can deploy. That's why in my demonstration, I have a small development environment on my computer just using a sample of the data. When I push it to Azure and I have the software deployed there, I can have 80 cores if I want to run the software in there. I run the process that I need to at the end. If I don't need 80 cores anymore, I can downgrade it to four cores if I want. SAS is not going to monitor that, so you're free to choose how many cores you want to use.   Since you mentioned Containers, do I need to be a Kubernetes expert to get the software up and running? Not necessarily, unless you want to deploy the software using Co BERNADIS. If you want to have a single container running and add multiple users to that container, you can do that easily with the quick start, and you can get the software up and running in like 5 to 10 minutes depending on your Internet connection. You don't necessarily need to be an expert.   How do I deploy the software on the cloud? There are two options to do that or maybe three. When you go to the sales documentation, SAS is going to give you 2 main options. First you have the option to deploy the software using a quick start. The quick start uses a tool that was created to help you deploy the software. It means you're going to have to have a virtual machine on the cloud of preference. Inside that virtual machine, you're going to download the Docker images for the containers and then you're going to run this quick start, which is going to spin up the container and get the software up and running. The second option is to do a custom deployment if you want to add and mount to different volumes. If you want to add different users and you want to create, for example, active directory, or if you want to connect to a Python environment or to different databases, all those things you can do on the custom deployment and you can do it the way you want. SAS will provide you the step by step on how to do it. The third option would be to deploy the software as a Kubernetes application. To do that, you need to know a little bit of Kubernetes and you need to know a little bit of the cloud environment that you're deploying the software on.   Can I code in Python using this offer? Yes, we saw an example on the demonstration on how to do it. You can bring in and use Proc Python. You paste your Python code, and you have this mechanism of sending data in and out of the Python process to assess library as well. It's very easy to use and configure. You can maintain the Python environment separately. You also have the option to use SASpy as the Python package to access the container or the Analytics Pro deployment as well, so there is a lot that can be done with Python in this product.   Can I use SAS Enterprise Guide with this product? Unfortunately, the answer is no, you cannot use enterprise guide with this offering. We do have a 9.4 environment offering that allows you to use enterprise guide. You can continue to use that, but unfortunately this product that we demonstrated today is not capable of connecting to enterprise guide.   When SAS Analytics Pro operates in the cloud, are sensitive data or work products stored in the cloud? it depends. If the sensitive data that you have is on the data center or on the cloud or anywhere that it's hosted and you can run SAS close to it, it doesn't necessarily need to be the cloud. SAS, in this case, is going to where the data is after you bring that data to the SAS platform. So, it's either cloud or desktop. It's going to get stored on the temporary location that you specified for the work library. Or you can use the software to store data on the cloud, process it in there, save it in there. It really depends on the type of scenarios that you have.   Is there no way to run the code "locally" when you don't have access to network? In the first part of the demo, I was running the software on my local computer, on my MacBook, so it was connected to the Internet because I was bringing in data from a remote location and I was sending code to GitHub. But, for example, if I had all the code and all the data that I needed for my analysis on my computer, I don't need to connect to the Internet. So, I could be on an airplane and running SAS code, which would be fancy. But it really depends on the scenario that you have. If you already have everything in one in your computer, you don't need access to the Internet. If you need to share things to the world or if you need to access data that is saved remotely, then you need the Internet, but it works fine without it.   Is this the same for SAS Viya environment? For the SAS Viya environment, the software also runs on Kubernetes and containers, but it comes with different products that are more visual. All the visual analytics, visual data mining and machine learning, intelligent decisioning, visual text analytics, all these products that are visual and contains different types of processes and use cases that you can rely on. Those are part of the SAS Viya deployment, which is going to be a bigger deployment and you can only run it on a server. In this case, it's a small scoped part of the platform that allows you to deploy either on your computer or on the servers or on the cloud.   Cost per user, say 10 then 100? For licensing questions and pricing, I recommend getting in contact with your account representative if you are a current customer. If you are a new customer, you can contact us through the website, and they will assign an account executive to help you.   If SAS Analytics is updated monthly, how can the system be kept in a validated state? We would need to clarify what validated state means in this case. But if you mean to have the software concisely running on the server, SAS is making sure that is happening as part of what we call the CI CD process, or the continuous integration, continuous delivery. We will make sure that all the tests run when you migrate to a newer version and that all the tasks are backward compatible. SAS is doing that work for you now if you think about it on like the validation from a life sciences or health care perspective where you need to have a validated environment that unfortunately I don't think it's possible to have today on this product specifically.   Can PRO be customized to only have the packages I need? You have two options. One is the analytics pro only with the stats-based graph. The other option is the advanced programming. That's what we saw today. You cannot customize and license in this case for this specific version, just like for example the IML package. We don't allow that on this specific package. We do allow that on the previous version, which is the SAS Analytics Pro 94. 99.4 version there you can license things separately.   Is this product available outside Viya, i.e., running on SAS 9.4? This product is available outside of Viya. We say that there is a Viya component to it because, internally, in terms of the code that SAS R& D is developing, it's pretty much coming from the same place. But it's not a Viya product itself, though there is a big distinction between what Viya is and what you can do there and here in Analytics Pro advanced programming. There are some similarities, but in this case they're very different in terms of scope.   Are there different licenses for pro and Viya? Yes, there are. They are completely different pricing metrics and deployments and functionality wise.   Is it possible to have XCMD option enabled? Yes, you can run X command in the software. You just need to change one thing when you're spinning out the software. And that's very simple. It's well documented on how to do that. SAS Help Center: Enable Use of the X Command   Does Environment Manager ship with APro? Not this version. It doesn't come with the environment manager. All the management of the platform is done through Docker container or Kubernetes, depending on what you're using.   Is there API to integrate SAS Analytics Pro with another business application? If not, then how to integrate SAS Analytics Pro with other applications? That's a great question and I'm happy to say that I have an answer for that because I wrote a couple of blogs a couple months ago on how to do that. Running CMS Risk Adjustment Models via API with SAS Analytics Pro (Viya) on Azure - SAS Users The analytics pro that we saw today doesn't have an API layer out-of-the-box. But since it's a container and you can connect to the container via the SAS Python package, if you know a little bit of Python, you can hack it a little bit to create, for example, a flask or a fast API application that connects to the sales environment, and you can pass data back and forth through API calls to the container and I'm happy to share the link.     Recommended Resources Analytics Pro modernization brochure SAS Analytics Pro Analytics Pro Blogs Running CMS Risk Adjustment Models via API with SAS Analytics Pro (Viya) on Azure Running CMS-HCC Risk Adjustment models with SAS Analytics Pro on Viya SAS Analytics Pro on Viya Installation Introducing Analytics Pro Advanced Programming: The power of SAS on your desktop Moving from SAS®9 to SAS® Viya® Please see additional resources in the attached slide deck.   Want more tips? Be sure to subscribe to the Ask the Expert board to receive follow up Q&A, slides and recordings from other SAS Ask the Expert webinars.   ... View more

Hi SAS Viya Community, I’m presenting a live “Ask the Expert” webinar on December 15 th , 11 AM – Noon ET entitled What Are the New Capabilities of SAS® Analytics Pro Advanced Programming? I’ll cover what’s new about SAS Analytics Pro Advanced Programming – for both new and current users. You’ll also get a full end-to-end demo of the software. Bring your questions. I’ll host a live Q&A session at the end of the webinar.   You will learn: How to deploy SAS Analytics Pro. How SAS Analytics Pro runs in different environments, using containers and in the cloud. How SAS Analytics Pro integrates with Python, GitHub and databases. Reserve your spot for the webinar.   Want more tips? Be sure to subscribe to the Ask the Expert board to receive follow up Q&A, slides and recordings from this and other SAS Ask the Expert webinars.   Can't join the live event? You can view this and other Ask the Expert sessions on-demand here.  ... View more

Think about digital transformation. It’s a constant and inevitable. You have a lot of code that you developed producing great calculations, statistics and graphs. So, what is the best way to present all of your work for other to consume? Do you still need to login and run your code every time? What if you automate it with APIs or jobs?   If you are a software engineer, you could easily create an application that would automate your code, but not everyone has that skill. Luckily, SAS has created functionality that is very friendly for business analysts to create automation and code execution from a HTML form. It is called SAS Job Execution.   If you have used SAS in the past, you probably have come across something called Stored Process on SAS 9. This allows you to export your code and create a web page allowing you run code through an HTML page. SAS Job Execution on SAS Viya presents a similar idea, with a couple of improvements.   SAS Job Execution – defined  SAS Job Execution is a web application that comes with SAS Viya. It allows users to create SAS code and tie it to an HTML form that can then pass parameters to the job, resulting in an output. This output takes one of many forms. In this article we explore the SAS Output page with plots and tables.   One of the good things about this web application is when you create SAS Code in a Job Execution project, it automatically becomes an API to which you pass parameters.   If you are curious on how SAS Job Execution works behind the scenes, consider looking at the overview of the SAS Job Execution Web Application page.     Create Job Execution application Now that we understand what it is, let's go through our first SAS Job Execution app. We will follow these steps:   Upload the data Generate the model Astore Create the SAS Job Execution file – SAS code Create an HTML form that ties to our SAS code Call the Job using the form and browser.   The scenario For this example, think about a loan company that needs to calculate the likelihood of default on loans. The bank loan representative is responsible for aquiring and entering potential customer data into an application. The application, in turn, returns the default probability of default right away. The decision variables used are: LOAN - Requested loan amount MORTDUE - Amount due on existing mortgage VALUE - Value of current property YOJ - Years at present job REASON - DebtCon = debt consolidation / HomeImp = home improvement JOB - Occupational category   Our main asset in this project will be the pre-trained model generated by a data scientist. This file - ASTORE - represents the model binary. We will use this model to score the input data, and then, will generate a couple of plots to show the applicant how their inputs compare with the historical data - the HMEQ table.   You can find all the assets in this Github repository.   Upload the data In SASDrive, go to Manage Data and import the HMEQ dataset to the Public Caslib.   Generate the model Astore Go to SAS Studio and run the code jobexec_forest_train.sas. Verify the table named forest_astore exists in the Public Caslib.   Create the SAS Job Execution file Go to http://yourserver.com/SASJobExecution Create a new folder inside your personal user folder -> usually it is under /Users/yourname Inside this folder, right click it and select New File Provide a name and accept the defaults for File type (Job Definition) and Server (Compute). Open the file and paste the contents of jobexec.sas and save. Right click the Job Execution file and go to Properties -> Parameters – add values from the table below.   Name Default value Field type Required p_loan   Numeric Yes p_mortdue   Numeric Yes p_value   Numeric Yes p_yoj   Numeric Yes P_reason   Character Yes P_job   Character Yes _action Form Character Yes _CASHOST <your SAS server> Character Yes     Create an HTML form Right click the Job Execution file, go to Edit and select New HTML form. Paste the contents of the index.html file in the new tab. Find and update the value parameter in the following section to match the file path for your environment: <!-- Change the path to match your job execution file. --> <input type="hidden" name="_program" value="/Users/ludepa/JES/scoringJob"> Save the file.   Call the Job On the Job Execution file tab, click submit and you should see something like the image below. Loan application form   Fill in sample data in the form and click Run Code. Your output should resemble the following: Loan application results     Note: the first time I submitted the form, I received an error. Referencing to the logs (the gear icon on the Job Execution screen) alerted me of a connection error. It turns out in my multi-node environment, I had to provide the fully qualified URL for the CAS controller in the _CASHOST_ parameter. This will vary from deployment to deployment.   As you can see in the response, this application is approved. The generated SGPLOTs represent how the scored data compares against the historical data used to train this model. Let’s take the first graph as an example. The title is: “You requested $2000 – here is how it compares to historical data.” This input value ($2000) is represented with a red dotted line. Here, about 0.5 percent of the loan requestors, asked for a loan of $2000.   The goal of these graphs is to show to the analyst how the request compares against the population this model knows. It helps as a guide providing feedback for the data scientists or to explain to the customer why they might have been denied a loan.   The video below outlines the step-by-step process of the example explained in this article. I added some comments on the video so you can follow along.     Finally As we saw in this article, you can take your code and wrap it around an API using the SAS Job Execution. You can pass parameters to your code through macro variables and create HTML forms that connects to your code. Job Execution enables you to call SAS code from other applications or use the generated form to embed it into applications. This is an easy and intuitive way to organize your codes as well as share them.   In the next article, we will learn other features from SAS Job Execution - the SAS Job Flow Scheduler, where we will schedule a job to run using the scheduler.   Related resources SAS Job Execution documentation Running Your SAS Code from the Web Using the SAS Job Execution Web Application - SASGF 2020 super demo by @Vince_SAS  APIs in action: build an automated profiler with SAS Viya -  SAS Community article by @Clare_Casey          ... View more

There is not one solution or programming language that will deliver everything you need. That is why customizing the platform you already master is where you find value. In order to accomplish this, I've seen customers move directly to coding, in lieu of SAS user interfaces. This provides freedom and full control on how things get executed. This holds true for analytics and SAS Viya. In this post I'll discuss an application, written in R Shiny, as a low-code solution demonstrating SAS integration through APIs with open source languages.    Utilizing SAS APIs as an interface allows you to: utilize the knowledge you already know, for example R, Python, Lua, etc build things the way you imagined, to what makes sense for you integrate SAS processes to your systems: embed SAS scoring capabilities on a website run SAS processing in real time for data quality control mix/match language specific capabilities with the SAS engine incorporate all platform capabilities - from data access to deployment to integration. Let's first introduce SAS Viya and how it is designed to facilitate the analytics life cycle using APIs.   SAS Viya The entire analytics life cycleYou have probably heard of the analytical life cycle, which is the way we define analytics at SAS. SAS Viya is a platform capable of executing the full life cycle seamlessly. It is an in memory engine that offers capabilities on data access, data preparation/quality and data governance. Let's dive a little bit deeper on a couple of phases.   Data preparation It is not just about offering a basic set of self-service data preparation capabilities like visual joins, filter data, column transformation, profiling, etc. Focus more on required capabilities for analytical model development like variable selection/treatment, handling missing values, training-validation data partitioning and more.   Explore Visual exploration is key for any data scientist. We do not want you to explore biased hypothesis, miss key findings, and draw incorrect or incomplete conclusions. Otherwise it will adversely affect decisions and outcomes. Hence, visual exploration is key to the analytical life cycle – before and after you build the model.   SAS Viya also offers statistical procedures ranging from classic statistics to machine learning with automated machine learning, forecasting and optimization, as well as visualization capabilities.   Deployment This phase is where customers assemble everything they build in the the data prep and explore steps. Deployment includes putting a model into production, setting up a full ETL + Visualization process, etc.   Deployment potentially creates headaches for IT and Analysts. This is due to the monitoring and upkeep requirements once a model is put into production. SAS has model management capabilities to alert when a model decays in performance and setup retraining. Not only that, but it will also help IT govern tables used in projects, project lineage and access to system.   Now that we know what SAS Viya offers, let's see how we can integrate with open source.   Open source integration There any many ways to interact with the platform. This article by my SAS colleague Mark Thomas is an excellent overview of open source integration points with SAS Viya.   In his article, Mark breaks down SAS Viya interaction with open source in four categories: open-source applications leveraging SAS execution engines SAS applications leveraging open-source execution engines application development/integration data integration For more details, I highly recommend reading more in Mark's article.   The effort to integrate is part of the API-first approach which has in recent years changed how we consume information and also a shift on education. Nowadays, access to programming skills are much easier than years ago. Suffice it to say, SAS recognizes the value of openness and provides tools for integration through its APIs.   Today we will focus on R integration. We'll create an R Shiny application and interface with SAS.   R Shiny R Shiny is a package that allows you to build interactive web applications straight from R. The library is extendable with CSS themes, HTML widgets, and JavaScript actions. I won’t go into details in this post on the details of R Shiny but getting set up and started is quite easy from an RStudio session.   For our use case in this post, we’ll use R Shiny to write and test code on the RStudio interface to create the application. The R code backing the application sends requests to SAS using the SAS Scripting Wrapper for Analytics Transfer (SWAT) package for R. SAS acts as the source for data storage and processes any computational or analytical requests, returning the results back to the application.   We can also choose from a variety of themes depending on what we are trying to build. R Shiny provides the ability to render tables, plots, filters, input fields and create different screens, like a regular application without even bothering with HTML, CSS, and/or JS. I recommend the R Shiny tutorial for more information on set up and functionality.   Now that we are familiar with our tool set, let's dive into the example.   The final application To introduce our app, let’s start at the end. Below is the application login screen once the app is up and running.   The R Shiny app   The app is built to upload a data set, perform some data management tasks, and train models. How do we get there? It’s really pretty simple. Let’s find out. The following video takes you through the entire application build process. You may also follow the details, with sample code through the rest of this article.      The code We break the R Shiny code down into three distinct sections: R code – functions used make calls to SAS via SWAT as well as data wrangling and model architecture setup. Design the UI – this is pure R code to setup the Shiny UI. We define tabs, buttons, fields and what gets rendered on screen. Define the server – define what to do with response outputs from SAS.   R code This is the section for R code for connecting, data manipulation, model training and visualization. The R code here uses SWAT to connect to SAS and run CAS Actions, which is a set of functions to perform operations in SAS.   I will not go through the whole code on this blog, but you can check the full code on the repository in GitHub.   This code you will see below is a subset of the full code just to demonstrate how to tie the back end (SAS Viya) with the front end (R Shiny).         ##################################### # R code for data manipulation, # model training and visualization ##################################### # Connect function connect <- function(username, password, lib) { conn <<- CAS('your-host', port=8777, caslib = 'casuser', username = username, password = password, protocol = "http", no_gen_actions=1) # load actionsets gen.functions(conn, 'table') gen.functions(conn, 'simple') gen.functions(conn, 'builtins') return(cas.builtins.serverStatus(conn)) } # Generate summay statistics for a specific CAS Table explore_tbl <- function(tbl) { cols <- c("Column", "Min", "Max", "N", "NMiss", "Mean", "Sum") summary_tbl <- data.frame(cas.simple.summary(conn, table={name=tbl}))[1:7] colnames(summary_tbl) <- cols return(summary_tbl) } # Upload SAS dataset to CAS upload_tbl <- function(tbl_path) { str1 <- gsub("\\","/", tbl_path["datapath"], fixed=TRUE) name = unlist(strsplit(as.character(tbl_path["name"]), split='.', fixed=TRUE))[1] cas.table.dropTable(conn, caslib="casuser", name=name, quite=TRUE) tbl <- cas.read.sas7bdat(conn, str1, casOut=list(name=name, caslib="casuser", replace = TRUE)) tbl <- defCasTable(conn, table=name) tbl_sum <- explore_tbl(name) return(tbl_sum) }       The UI This R Shiny code below is the beginning of the code creating the user interface we displayed earlier. This is the page layout for the tabs Login, Import Data, etc.       ######################################## # Defining the UI # Creating the panels and tabs ######################################## if (interactive()) { ui <- fluidPage( titlePanel("Viya App"), tabsetPanel( tabPanel(title = "Login", sidebarLayout( sidebarPanel( tags$head(tags$style(type="text/css", " #loadmessage { position: fixed; top: 50%; left: 50%; transform: translate(-50%, -50%); width: 100%; padding: 5px 0px 5px 0px; text-align: center; font-weight: bold; font-size: 100%; color: #000000; background-color: rgba(0,255,0,0.5); }") ), textInput(label = "What is your username?", inputId = "username"), passwordInput(label= "What is your password?", inputId = "pwd"), submitButton("Login", icon("sign-in-alt")), conditionalPanel(condition="$('html').hasClass('shiny-busy')", tags$div("Loading...",id="loadmessage")) ), mainPanel( tableOutput("connection"), ) ) ), tabPanel(title = "Import Data", sidebarLayout( sidebarPanel( fileInput(inputId = "csv_file", label="File", multiple = FALSE, accept = NULL, width = NULL) ), mainPanel( tableOutput("tbl_import"), ) ) ) ) ) ######################################## # Defining R Shiny Server # Creating calls to R code and # rendering it on the panels # defined above on the UI ######################################## < insert code from the section below here > }       The R Shiny server And finally, we need to bring everything together by defining the server. The code snippet below outlines how to render the information returned from SAS, in the application.       ######################################## # Defining R Shiny Server # Creating calls to R code and # rendering it on the panels # defined above on the UI ######################################## server <- function(input, output, session) { output$connection <- renderTable ({ validate(need(input$username, '')) validate(need(input$pwd, '')) connect(input$username, input$pwd, 'CASUSER') }) output$tbl_import <- renderTable({ validate(need(input$csv_file, '')) upload_tbl(input$csv_file) }) output$tbl_profile <- renderTable({ validate(need(input$tbl_to_profile, '')) explore_tbl(input$tbl_to_profile) }) output$tbl_list <- renderTable({ validate(need(input$library_to_explore, '')) list_tables(input$library_to_explore)}) # then, render the output table output$tbl_import <- renderTable({ validate(need(input$csv_file, '')) upload_tbl(input$csv_file) }) shinyApp(ui, server)       Application details For the final section of this post let’s briefly look at the application, how it functions and what it can do.   The application is comprised of six tabs: Login, Import Data, Verify Data, Explore Data, Feature Engineering and Auto ML. Let’s explore each tab.   Login The login tab is used for authentication to the SAS server. When credentials are provided, you can see the calls being invoked in RStudio. This holds true for any action taken on any tab.   Import Data In this section you will be able to import data and view summary statistics (min, max, Nobs, missing, etc)   Verify Data You provide the name of the library where the data lives and see which tables you have available in memory, as well as run summary statistics for each of them.   Explore Data You will be able to analyze the data using Cardinality and Missing Percentage graphs . These graphs will be generated using the table name you fill in. The plots can be explored using zoom in/out and selection pane.   Feature Engineering One of the steps the data scientist can go through is creating new features. SAS has developed the Feature Machine Action. In this section you will be able to select from a range of options to generate features by analyzing:  High cardinality High kurtosis High skewness Low entropy Low indices of qualitative variation Missing values Outliers   Auto ML In the final step of the project, you will run the models using the generated features. You can select one or more models. SAS will train those models, score the data set and generate the ROC and misclassification metrics for you to compare those models.   Conclusion In this article we saw how to build a simple application using R Shiny and SAS Viya. This is just the beginning, but demonstrates how to take advantage of the APIs SAS offers. If you want to see the full code, go to our GitHub repository. There you will find the code and and a copy of the video with the demonstration of the app we created. The full app will follow through the analytical life cycle and implement Data Exploration, Feature Engineering and Machine Learning training and model comparison.   ... View more