This project is a part of my work on association rule mining; it demonstrates a pipeline for market basket analysis aimed at identifying product associations to optimize retail promotions and bundle deals. Although the project was designed using a real company's dataset, the provided example uses simulated data.

Tools:

• Python with mlxtend for association rule mining; pandas, json, random
• SQLite to simulate a real-world database storing transactional data
• D3.js for network graph visualization of the product associations

Structure

.
├── README.md
├── main.py
├── d3_visualization.html
└── data
    └── (generated SQLite DB and JSON output files)

• main.py contains the Python code to generate example transaction data, store it in an SQLite database, apply the Apriori algorithm to extract frequent itemsets and association rules, and generate a JSON file for network visualization
• d3_visualization.html: a D3.js based HTML file that loads the generated JSON data and displays a network graph of product associations
• data/: directory where the SQLite database (transactions.db) and the JSON file (associations.json) are saved

Features

1. Data generation and SQL storage

   • simulated transactional data is created with random transactions and a list of example products
   • the transactions are stored in an SQLite database to mimic a real-world scenario
   • a SQL query retrieves the transactions to feed the association rule mining process

2. Association rule mining part

   • the Apriori algorithm (from the mlxtend package) is applied on one-hot encoded transaction data
   • association rules are extracted based on defined support, confidence and lift thresholds

3. Visualization part

   • the extracted rules (with single-item antecedents and consequents) are converted into a network graph JSON format
   • the D3.js visualization uses force-directed graph techniques to display nodes (products) and edges (associations), allowing interactive exploration of the results

How to run

1. Ensure you have Python3 and install the required packages:

   pip install pandas mlxtend

2. Execute:

   python main.py

3. To view the visualization, open d3_visualization.html in your browser. Ensure the JSON file associations.json is in the same directory or adjust the file path in the HTML file accordingly.

Notes

• The simulated data in this project is for demonstration purposes only
• Parameters for the Apriori algorithm (such as support and confidence thresholds) can be adjusted in main.py to fine-tune the association rule mining process.

License

MIT