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 fit a logistic regression and use your model to score new data. In part 4 of this series, we created and saved our modeling data set with all our updates from imputing missing values and assigning rows to training and validation data. Now we will use this
Learn how to fit a linear regression and use your model to score new data. In part 4 of this series, we created our modeling dataset by including a column to identify the rows to be used for training and validating our model. Here, we will create our first model
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
