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Apriori Algorithm for Finding Frequent Itemsets with PySpark

Implement the Apriori algorithm to find frequent itemsets for market basket analysis.

Apriori Algorithm for Finding Frequent Itemsets with PySpark

You will learn to:

Use PySpark to build distributed computing projects.

Implement the Apriori algorithm for mining frequent itemsets.


Data Science

Distributed Architecture

Data Mining


Intermediate Python coding skills

Familiarity with distributed computing concepts

Basic working knowledge of PySpark




Project Description

Let’s say we run a grocery store and have a good amount of data from the point of sale. We want the sets of items frequently bought together to be placed on shelves near each other to boost sales and increase customer convenience. To achieve this, we can use the Apriori algorithm. It’s much faster than its brute-force variant and can be implemented in a distributed computing scenario.

We’ll first write the Python code for the parallel processing of dataset partitions at the worker nodes. We’ll then write the final central itemset frequency check by the master node. The code we’ll write can be run on a compute cluster for a full flavor of distributed computing.

Project Tasks


Getting Started

Task 0: Introduction

Task 1: Import the Libraries and Set Up the Environment


Distributed Combination Generation

Task 2: Generate Combinations—Parent Intersection Property

Task 3: Generate Combinations—Subset Frequency Property

Task 4: Count Check

Task 5: Generate k-Size Combinations

Task 6: Generate Singles

Task 7: The Worker Partition Mapper


Filtering at the Master Node

Task 8: Load Data and Preprocess

Task 9: The Distributed Transform

Task 10: Auxiliary Function to Check Presence

Task 11: Count Check at Master