Market Basket Analysis using ECLAT Algorithm

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.

A package for association analysis using the ECLAT method.

ECLAT Algorithm - UEH

Using ECLAT to associate items with other items for market basket analysis.

In this repository, we will explore apriori and eclat algorithms of association rule learning models for market basket optimization.

Market basket analysis on Instacart dataset. Those association rules were computed to see relationships between products, aisles and departments, using FP-Growth, Apriori, and Eclat

"Frequent Mining Algorithms" is a Python library that includes frequent mining algorithms. This library contains popular algorithms used to discover frequent items and patterns in datasets. Frequent mining is widely used in various applications to uncover significant insights, such as market basket analysis, network traffic analysis, etc.

Association Rules

Implementation of Apriori, FP-Growth, and ECLAT algorithms on natural language data

Build a Movie recommendation system based on “Association Rules”

Wolfram Language (aka Mathematica) paclet for association rule learning.

Python implementation of ECLAT algorithm for association rule mining.

Association rules (with taxonomy) mining

On the basis of users past movie watches, recommending similar movies.

Full machine learning practical with R.