Lesson 5: Building Neural Network and Logistic Regression Models (Intermediate Data Mining Tutorial)
The Operations department of Adventure Works is engaged in a project to improve customer satisfaction with their call center. They hired a vendor to manage the call center and to report metrics on call center effectiveness, and have asked you to analyze some preliminary data provided by the vendor. They want to know if there are any interesting findings. In particular, they would like to know if the data suggests any problems with staffing or ways to improve response time.
The data set is small and covers only a 30-day period in the operation of the call center. The data tracks the number of operators in each shift, the number of calls and orders, response time, and a service grade metric based on abandon rate, which is an indicator of customer frustration.
As you do not have any prior expectations about what the data will show, you decide to use data mining to explore possible correlations. Neural network models are often used for exploration because they can analyze complex relationships between many inputs and outputs.
In this lesson, you will use the neural network algorithm to build a model that you and the Operations team can use to understand the data and the trends in it. As part of this lesson, you will explore the data and try to answer the following questions:
What factors affect customer satisfaction?
What can the call center do to improve service grade?
Based on the results, you will then build a logistic regression model that you can use for predictions. The predictions will be used by the Operations team as an aid in planning call center operation.
This lesson contains the following topics: