Melodie Rush is a Principal Data Scientist for the Customer Success Technical Team at SAS Institute. Melodie received both her B.S. in Statistics and her Masters in Science of Management from North Carolina State University. Since joining SAS, Melodie has developed presentations and methodology for doing many types of analysis, including data mining, forecasting, data exploration and visualization, quality control and marketing. She has spent more than 20 years helping companies identify and solve problems in each of these analytical areas.
Learn how to split your data into a training and validation data set to be used for modeling. In part 3 of this series, we replaced the missing values with imputed values. Our final step in preparing the data for modeling is to split the data into a training and
In part 1 of this series, we examined our data before building any models. Among the discoveries were missing values in some of our columns. Missing values are an inevitable part of data analysis. Whether it's due to a faulty sensor, human error, or simply the absence of information, missing
In part 1 of this series, we examined our data before building any models. Among the discoveries was a column that seemed to contain a SAS date value. Here, we will discuss what exactly is meant by a 'SAS date', how to format it correctly, and how to create a
Welcome to my series on getting started with Python integration to SAS Viya for predictive modeling. Exploring Data - Learn how to explore the data before fitting a model Working with Dates - Learn how to format a SAS Date and calculate a new column Imputing Missing Values - Learn
Welcome to the first post in my series Getting Started with Python Integration to SAS Viya for Predictive Modeling. I'm going to dive right into the content assuming you have minimal knowledge on SAS Cloud Analytic Services (CAS), CAS Actions and Python. For some background on these subjects, refer to
