Market_basket_analysis-uding-ECLAT

This repository demonstrates how to perform Market Basket Analysis using the ECLAT (Equivalence Class Clustering and bottom-up Lattice Traversal) algorithm to find frequent itemsets in transactional datasets.

🧠 Project Overview

Market Basket Analysis is a common data mining technique used to discover associations between products or items in transaction datasets. Rather than scanning pairs like Apriori, the ECLAT algorithm uses a vertical data format and intersection operations to efficiently find frequent itemsets based on support.

This project contains a Jupyter Notebook that:

• Loads and preprocesses transactional data
• Applies the ECLAT algorithm to find frequent itemsets
• Enables further exploration of patterns in purchase behavior

📁 Repository Contents

• Market_basket_analysis_uding_ECLAT.ipynb — The main notebook showing ECLAT implementation and results.

📦 Dependencies

Make sure you have the following Python packages installed:

pip install pandas mlxtend jupyter
(You can list additional packages if required by the notebook.)

🚀 How to Run
Clone this repository:

bash
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git clone https://github.com/DhanushN2005/Market_basket_analysis-uding-ECLAT.git
Navigate into the project folder:

bash
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cd Market_basket_analysis-uding-ECLAT
Open the notebook:

bash
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jupyter notebook Market_basket_analysis_uding_ECLAT.ipynb