Please download chalrsebookclub dataset from the link – https://www3.nd.edu/~busiforc/problems/DataMining/dataminingdatasets.html (since i cannot attach the csv)
This dataset examines associations among transactions involving various types of books. The database includes 2000 transactions, and there are 11 different types of books. Please use jupyter notebook and python
a) Drop the following columns ‘Seq#’, ‘ID#’, ‘Gender’, ‘M’, ‘R’, ‘F’, ‘FirstPurch’, ‘Related Purchase’, ‘Mcode’, ‘Rcode’, ‘Fcode’, ‘Yes_Florence’, ‘No_Florence’. Plot the support count for each item in the dataset using barplot.
b) Find the frequent itemsets with the min_support =5%.
d) Define min_confidence= 0.5 and print the 25 rules with the highest lift alongside their corresponding support, confidence, and lift.
e) Filter rules by number of antecedents (maximum 2) and consequents (maximum 1) and print the rules with their support, confidence, and lift.
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