In this assignment you will use a free/open-source data mining tool, KNIME (knime.org) to build predictive models for a relatively small Customer Churn Analysis data set. You are to analyze the given data set (about the customer retention/attrition behavior for 1,000 customers) to develop and compare at least three prediction (i.e., classification) models. For example, you can include decision trees, neural networks, SVM, k nearest neighbor, and/or logistic regression models in your comparison. Here are the specifics for this assignment:

• Install and use the KNIME software tool from (knime.org).

• You can also use MS Excel to preprocess the data (if you need to/want to).

• Download CustomerChurnData.csv data file from the book’s Web site.

• The data is given in CSV (Comma Separated Value)

format. This format is the most common flat-file format that many software tools can easily open/

handle (including KNIME and MS Excel).

• Present your results in a well-organized professional document.

• Include a cover page (with proper information about you and the assignment).

• Make sure to nicely integrate figures (graphs, charts, tables, screenshots) within your textual description in a professional manner. The report should have six main sections (resembling CRISP-DM phases).

• Try not to exceed 15 pages in total, including the cover (use 12-point Times New Roman fonts, and 1.5-line spacing).