This exercise introduces you to association rule mining. The Excel data set baskets1ntrans.xlsx has.

This exercise introduces you to
association rule mining. The Excel data set baskets1ntrans.xlsx has around
2,800 observations/records of supermarket transaction products data. Each
record contains the customers ID and products that they have purchased. Use
this data set to understand the relationships among products (i.e., which
products are purchased together). Look for interesting relationships and add
screenshots of any subtle association patterns that you might find. More
specifically, answer the following questions.

Which association rules do you think are
most important?

This exercise introduces you to
association rule mining. The Excel data set baskets1ntrans.xlsx has around
2,800 observations/records of supermarket transaction products data. Each
record contains the customers ID and products that they have purchased. Use
this data set to understand the relationships among products (i.e., which
products are purchased together). Look for interesting relationships and add
screenshots of any subtle association patterns that you might find. More
specifically, answer the following questions.

Which association rules do you think are
most important?

Based on some of the association rules
you found, make at least three business recommendations that might be
beneficial to the company. These recommendations can include ideas about shelf
organization, up-selling, or cross-selling products. (Bonus points will be
given to new/innovative ideas.)

What is the Support, Confidence, and Lift
values for the following rule? Wine, Canned Veg S Frozen Meal 6. 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
behaviour 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 neighbour, 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 pre-process
the data (if you need to/want to).

Download CustomerChurnData.csv data file
from the books 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 (use 12-point Times New Roman fonts, and 1.5- line
spacing).

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