Apple_Mango

Frommlxtend.preprocessingimportTransactionEncoder
Frommlxtend.frequent_patternsimportapriori
#Createthedataset
TID={1:[“apple”,”mango”,”banana”],
2:[“mango”,”banana”,”cabbage”,”carrots”],
3:[“mango”,”banana”,”carrots”],
4:[“mango”,”carrots”]}
#Convertthecategoricalvaluesintonumericformat
Te=TransactionEncoder()
Te_ary=te.fit([TID[i]foriinTID]).transform([TID[i]foriinTID])
Df=pd.DataFrame(te_ary,columns=te.columns_)
#Applytheapriorialgorithmwithdifferentmin_supvalues
Min_sup_values=[0.25,0.5,0.75]
Formin_supinmin_sup_values:
Frequent_itemsets=apriori(df,min_support=min_sup,use_colnames=True)
Print(“Frequentitemsetswithmin_sup=”,min_sup)
Print(frequent_itemsets)
Print(“\n”