Managing multiple programming languages in a data science workflow often means jumping from one environment to another—adding friction to already complex processes. This slows down collaboration and innovation among teams. But what if there were a way to remove this friction between environments? Working in a single environment that supports multiple coding languages helps give teams time back for development, rather than managing tools. For example, being able to run Python, R, and SAS tog...| SAS Users
You have years of legacy SAS code dating back to the time of your great-great-great-great grandparents (okay, SAS hasn’t been around quite that long).| SAS Users
Building a machine learning model isn’t always as easy as running .fit() and calling it a day. Sometimes, you need to eke out a little more accuracy, because even a 1% improvement can mean a lot to the bottom line. Many machine learning models have a lot of buttons and knobs you can adjust. Changing one value here, tweaking another value there, checking the accuracy one at a time, making sure it’s generalizable and not overfitting… it’s a lot of work to find the right model. Needless ...| SAS Users
Every organization that chooses to rely on SAS solutions does so with the goal of unlocking value from data, improving business decisions, and optimizing processes. However, even in the best-designed implementations with well-trained users, a critical moment can arise: when the production system goes down. It is in these moments that the difference between reactive support and proactive support becomes truly significant. From standard to tailored: Why advanced support levels matter Standard t...| SAS Users
I am a die-hard Survivor fan. I was born the year after the show came out, and since then I have tuned in for every single episode, with season 48 being no exception. However, I’ve noticed a trend: the players I think are most deserving of the million dollars never end up winning! As a data lover, I was curious about what the stats said. What qualities make a Survivor winner? Did the players with the best stats end up winning the game? And, most importantly, can a model predict who will wi...| SAS Users
Organizations face a familiar dilemma: how can developers experiment and build models using realistic data without exposing sensitive customer information? Generative adversarial networks (GANs) offer a promising solution by creating synthetic data that mimics real datasets. In this post, we explore a practical approach by training a tabular GAN model in a secure production environment and then deploy that model in a development environment to generate synthetic data for training another mode...| SAS Users
You use VS Code all the time. Great tool! And you love the extensions. But for whatever reason you don’t have access to the up-to-date extension gallery to install directly within VS Code. Maybe you work on an air-gapped network somewhere? Maybe you’re in a deep, dark basement of a defense department contractor with cigarette smoke-stained 30-year-old carpet and no windows where they don’t let you connect to the internet (I feel ya! I’ve literally been there before). Or maybe you crea...| SAS Users
Fitting a Support Vector Machine (SVM) Model - Learn how to fit a support vector machine model and use your model to score new data In Part 6, Part 7, Part 9, Part 10, and Part 11 of this series, we fit a logistic regression, decision tree, random forest, gradient boosting and neural network model to the Home Equity data we saved in Part 4. In this post we will fit a support vector machine (SVM) model to the same data to predict who is likely to go delinquent on their home equity loan and we ...| SAS Users
Cultural heritage is defined as an expression of the ways of living developed by a community and passed on from generation to generation, including customs, practices, places, objects, artistic expressions, and values (ICOMOS, 2002).| SAS Users
