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FP-growth Frequent Pattern Growth, is used as an analytical process that finds frequent patterns or associations from data sets. It greatly reduces the size of the itemset in the database by one simple principle which is if an itemset is frequent, then all of its subsets must also be frequent. It only scans the database twice and used a tree structure(FP-tree) to store all the information. The r..

mlxtend Machine Learning Extensions, a Python library of useful tools for the day-to-day data science tasks. - TransactionEncoder We can transform this dataset into an array format suitable for typical machine learning APIs. The NumPy array is boolean for the sake of memory efficiency when working with large datasets. fit : the TransactionEncoder learns the unique labels in the dataset transform..

Apriori - An algorithm for extracting frequent itemsets with applications in association rule learning. - The apriori algorithm has been designed to operate on databases containing transactions, such as purchases by customers of a store. - An itemset is considered as "frequent" if it meets a user-specified support threshold. For instance, if the support threshold is set to 0.5 (50%), a frequent ..

read_csv - sep : Separator - names : List of column names to use. - header : To use as the column names, and the start of the data. If column names are passed explicitly then the behavior is identical to header=None. - low_memory : It results mixed type inference, so set False to ensure no mixed type. columns=['user_id', 'item_id', 'rating', 'timestamp'] df=pd.read_csv('./u.data',sep='\t', names..

array Create an array. dtype="object" a_object = np.array([1, 0.1, 'one'], dtype=object) print(a_object) print(a_object.dtype) >>> [1 0.1 'one'] >>> object print(type(a_object[0])) print(type(a_object[1])) print(type(a_object[2])) >>> >>> >>> It stores pointers to Python objects. Since the data of each element itself is stored in each memory area, it can have multiple data types (pointers to) in..