Wondering if any of you have thoughts on my code. I've borrowed this from other posts and I am trying to remove quote marks and end of file indicators in a csv file prior to import so SAS can read several comment fields. I think i've had this working previously, but now Export2 has no data. Not sure why this doesn't work - any thoughts are appreciated.    code: %let repA=' '; %let repD=' '; %let dsnnme="\\path\export.csv"; %let dsnnme1="\\path\export2.csv";   data _null_; infile &dsnnme recfm=n sharebuffers ; file &dsnnme1 recfm=n; retain open 0; input a $char1.; if a = '"' then open = ^(open); if open then do; if a= '0D'x then put &repD; else if a = '0A'x then put &repA; end; run;     log: NOTE: UNBUFFERED is the default with RECFM=N. NOTE: The infile "\\Path\export.csv" is: Filename=\\Path\export.csv, RECFM=N,LRECL=256,File Size (bytes)=1127506, NOTE: UNBUFFERED is the default with RECFM=N. NOTE: The file "\\path\export2.csv" is: Filename=\\path\export2.csv, RECFM=N,LRECL=256,File Size (bytes)=0, ... View more

This post is part 3 in a four-part series on feature extraction techniques in SAS Model Studio, where I’m exploring practical methods for preparing data for predictive modeling. In Part 1, I introduced Principal Components Analysis, or PCA, as a way to reduce dimensionality and handle correlated inputs. In Part 2, I discussed Robust PCA, which extends PCA by separating the underlying structure in the data from unusual or extreme values.   In this post, I’ll introduce autoencoders, a feature extraction method based on neural networks. While PCA and Robust PCA construct features using linear combinations of the original inputs, autoencoders can capture more complex, nonlinear relationships in the data. I’ll discuss the basic idea behind autoencoders and then provide an example of implementing one with the Feature Extraction node in SAS Model Studio. ... View more

Team Name Kepler Track Machine Learning and Health Use Case Develop a Machine Learning (ML) model capable of accurately differentiating between MS and NMO Technology SAS Viya; Python Region LATAM Team lead João Pedro Team members @joaocorreia014 ; @dcmacedo ; @felipesr ; @MoahSouza ; @felipepuziol ; @klaivercoutinho;   Social media handles * * Is your team interested in participating in an interview? *N* Optional: Expand on your technology expertise     ... View more

Team Name KeyLucent Track Public Sector Use Case The dynamic disaster mobility intelligence system will utilize commercial off-the-shelf drones, photogrammetry software, and AI to help disaster-response teams identify viable evacuation routes and prioritize route-clearance missions as conditions change. Technology COTS drone, ArcGIS, SAS Viya, Python/ArcPy Region Americas Team lead David Domingo @Dave_Domingo  Team members Ned Babbott (profile TBP) Matt Strand @matt_strand  Social media handles Dave's LinkedIn  Ned's LinkedIn  Matt's LinkedIn  Is your team interested in participating in an interview? Y Optional: Expand on your technology expertise Dave - Real Estate, HA/DR, Army Civil Affairs and Intelligence Ned - Designer, Ham Radio Operator, Drone Operator Matt - Senior Software Engineer, Full Stack Developer, Air Force   ... View more

The purpose of this post is to show how to compare alert data to non-alert data in SAS Field Quality Analytics. As a reminder SAS Field Quality Analytics generates alerts when it identifies higher than usual events. A natural progression after an alert is generated is to compare data to where the issue is known to data where the issue is not present. This allows for the root cause to be identified quicker, leading to possible reduction in warranty-related costs. ... View more