Question

Association Rules and Clustering

The data we use is from a major web-intelligence vendor for the apparel industry. Each instance records a customer’s web purchasing history: what web sites she has purchased from. In addition, the data also consists of demographic information and the dollar amount the customer spent. Your job is to use association rules and clustering implemented in python to solve the business problem you identified.
This is an open project. You are supposed to come up with your own business problem and apply Python to solve it. Specifically, you need to understand the dataset and figure out what the data could tell you. Suppose you are working for a firm appearing in our dataset. You are asked to identify business problems (opportunities) that association rule and clustering could help solve. In grading, I will pay attention to the significance and interestingness of the problem you identified, as well as how you use the knowledge you learned so far (association rule and clustering) to discover business intelligence for the firm.
In the end, you need to submit a report stating clearly what the problem you are solving, the procedures you apply using association rule and clustering to solve it, and your recommendations based on the results (after analyzing the data). Through the project, you are supposed to demonstrate your ability of using association rule and clustering in Python. The following are a couple of items I would like you to include in your report to demonstrate your proficiency.
I. Association rules
Choose appropriate level of support & confidence and run Python. Report a few sample rules Python generates.
Contrast two association rules that have reverse body and head. In particular, describe the meaning of the support, confidence and lift values in the context of the two rules.
Specify different levels of support and confidence (try at least 5 different combinations) and run Python for each combination. Report ONLY the number of rules you got under each setting. Analyze the relationship between the number of rules and the support/confidence level.

Answer 1

Association rules and clustering can be used to analyze customer purchasing patterns based on web purchasing history and demographic information. This allows businesses to make data-driven recommendations for marketing and personalized product offerings.

Step-by-step explanation:

Association rules and clustering can be used to solve business problems in various industries, including the apparel industry mentioned in the question. For example, a business problem could be to identify the purchasing patterns of customers based on their web purchasing history and demographic information.

By applying association rule and clustering techniques in Python, patterns and relationships can be discovered, such as which websites are frequently visited together and which customer segments exhibit similar purchasing behavior. This information can then be used to make data-driven recommendations for targeted marketing strategies or personalized product recommendations.