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 for 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 covers 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 a neural network model to explore possible correlations. Neural network models are often used for knowledge discovery and can analyze complex relationships between many inputs and outputs.
Once you determine the factors that contribute to customer satisfaction with the call center, you will build a regression model that can be used to make predictions about staffing and other daily business decisions.
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, and 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:
Updated tutorial scenario to use a single mining structure that contains multiple copies of the numeric column, with each column discretized differently.
Added an explanation of how to use the column aliases in data mining models,
Corrected the mining model names in predictions and DDL statements to match the updated scenario.
Aded description of how to generate day of week in the data source view; added day of week to resulting models.