- In This Assignment You Will Use Python S Mlxtend Frequent Patterns To Find The Association Rules Satisfying Given Lift 1 (73.76 KiB) Viewed 10 times
In this assignment, you will use Python's mlxtend.frequent_patterns to find the association rules satisfying given lift, confidence and support threshold for the list of transactions available at 'online retail.csv' file. To see an example of how to work with Python's mlxtend, you can watch the video posted earlier in the previous module. • Step 1 (35 points): Clean the data by • removing rows whose StockCode or Invoice values contain non-digit characters removing rows whose Price values are less than 10 0 • removing rows whose country values are not equal to "United Kingdom", "Italy", "France", "Germany", "Norway", "Finland", "Austria", "Belgium", "European Community", "Cyprus", "Greece", "Iceland", "Malta", "Netherlands", "Portugal", "Spain", "Sweden", or "Switzerland". 0 removing rows whose quantity values are negative. 0 trimming the description using string.strip function • Step 2 (30 points) Find the frequent itemsets with min_support = 0.01 • Step 3 (35 points) Find the association rules with confidence greater than 10%. Among them, which rule(s) has the highest value of lift? Deliverables Submit a zip file containing your Python code and a "report.pdf" answering the question asked in step 3.