A minimalistic framework for Numerical Association Rule Mining

Analyzing heart data via association rules

Breast Cancer Pattern Recognition through Association Rule Mining

Final Project for Cpt_S 315 at Washington State University.

An overview of how to perform Sales Market Basket Analysis using PySpark, focusing on the steps from data preprocessing to association rule mining. It is a method used by retailers to uncover patterns in customer purchasing behavior, involves analyzing the items that customers frequently buy together and associations between products

Here we are performing real time market basket analysis using hive for dynamic updating data

Projects of the Introduction to Machine Learning course at the Lebanese American University

This in-depth market basket analysis goes through a complete project cycle towards extracting valuable insights that the business can implement allowing them to scale. From preprocessing the data, to exploratory data analysis, association rule mining, interpretation and insights, and recommendations. This project was made to tackle these problems.

R code to analyze streaming video platforms (code developed by Maitha Almemari and Mariam Almuhairi)

The project offers RFM segmentation, analyzing Recency, Frequency, and Monetary value. These metrics are vital for understanding customer behavior, influencing both retention and lifetime value.

Hadoop Ecosystem - 대규모 빈발 패턴 마이닝을 위한 하둡 클러스터 환경 구축

This project is a Market Basket Analysis App that analyzes customer purchase patterns to generate association rules and offer personalized product recommendations.

The project dives into transaction records of an online retail business to uncover hidden relationships between products. The overall goal is a data-driven approach to enhance the customer shopping experience, improve loyalty, boost profitability, tailor marketing strategies, and optimize inventory management via strategic business decisions.

Numerical Association Rule Mining in Julia

I use R to analyze shopping transaction data, identify which supermarket items are frequently purchased together using association rule mining and the strength of those relationships, visualize the findings, and evaluate potential profit margins for different items.

Using Customer Buying Patterns to Improve Warehouse Layout

Empirical Investigation of the Relationship Between Design Smells and Role Stereotypes

universal Association Rule Mining Solver