mlextend

Feature selection techniques in machine learning is a process of automatically or manually selecting the subset of most appropriate and relevant features to be used in model building. Here we are taking a machine learning regression problem and shows the different steps in feature selection process

Market Basket Analysis is one of the key techniques used by large retailers to uncover associations between items. It works by looking for combinations of items that occur together frequently in transactions. To put it another way, it allows retailers to identify relationships between the items that people buy. Association Rules are widely used …

Association Rules Data Mining (Groceries). Converting the data frame into a list of lists, Using Transactionencoder to transform this dataset into a logical data frame, Building the data frame: rows are logical and columns are the items that have been purchased, Print Column names, We need to drop nan column from the data frame, Most popular ite…

It's a data science project for classification of Fraudulent and Non-Fraudulent transactions.

Using Unsupervised Association Rule Base Learning -Apriori Algorithm for market basket analysis

Tableau Prep+Python:Basket Case Analysis with Superstore. Setup: People who bought product X and product Y might be interested in product Z. By analyzing a lot of transactional data we try to distill association rules to make such statements. The output table out the Tableau Prep flow can be implemented in various ways. Tools: Tableau Prep + Pyt…

A brief overview of popular Association Rule Mining algorithms: Apriori, FPGrowth and ECLAT on the UCI Student Performance Dataset. We attempt to identify antecedents (interpretable categorical student descriptors) which have as a consequent certain categories of the final grade.