Sharonda McDaniel BAN 600: Advanced Business Analytics Professor Shawnee McDaniel October 23, 2023
Week 8 SAS Miner 5.4 Exercises
A.
In preparation for a neural network model, is imputation of missing values needed? Why
or why not?
Yes, imputation of missing values is needed to create a more reliable model. Missing values are data points that are not available and are therefore not taken into account when calculating the output of the model. Imputation of missing values helps to
ensure that the model is more accurate by making the data more complete. Without imputation, the model would be unable to take into account data that is missing from its
training set, leading to inaccurate predictions.
B.
In preparation for a neural network model, is data transformation generally needed? Why or why not?
If the data is well-structured, then data transformation may not be necessary. However, if the data contains missing values, then data transformation may be necessary to prepare the data for the neural network. Data transformation involves organizing the data into a format that is suitable for the model, such as normalizing or aggregating the data.
