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 are given in comma-separated value (CSV) 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.