{"id":25376496,"url":"https://github.com/tuni56/retail_transaction_analysis","last_synced_at":"2026-04-28T18:32:44.664Z","repository":{"id":277609644,"uuid":"932936654","full_name":"tuni56/retail_transaction_analysis","owner":"tuni56","description":"Ever wondered why some products are frequently bought together? Using the Apriori algorithm, I analyzed real-world retail transactions to uncover hidden shopping patterns and enhance product recommendations.","archived":false,"fork":false,"pushed_at":"2025-02-14T21:52:54.000Z","size":22564,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-02-14T22:30:01.990Z","etag":null,"topics":["apriori-algorithm","machine-learning-algorithms","python","streamlit"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/tuni56.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2025-02-14T19:51:22.000Z","updated_at":"2025-02-14T21:55:35.000Z","dependencies_parsed_at":"2025-02-14T22:30:07.243Z","dependency_job_id":null,"html_url":"https://github.com/tuni56/retail_transaction_analysis","commit_stats":null,"previous_names":["tuni56/retail_transaction_analysis"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tuni56%2Fretail_transaction_analysis","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tuni56%2Fretail_transaction_analysis/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tuni56%2Fretail_transaction_analysis/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tuni56%2Fretail_transaction_analysis/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/tuni56","download_url":"https://codeload.github.com/tuni56/retail_transaction_analysis/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248017997,"owners_count":21034042,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["apriori-algorithm","machine-learning-algorithms","python","streamlit"],"created_at":"2025-02-15T04:28:04.875Z","updated_at":"2026-04-28T18:32:44.646Z","avatar_url":"https://github.com/tuni56.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# 🛍️ Retail Market Basket Analysis with Apriori\n\n\n\n## 📌 Overview\nDiscover purchasing patterns and optimize product recommendations using the **Apriori algorithm**! This project analyzes retail transactions to identify frequently bought-together items and generate personalized suggestions for customers.\n\n## 🎯 Objectives\n- Extract valuable **associations** between products.\n- Identify **top-selling items** and trends.\n- Create an **interactive dashboard** for visualizing insights.\n- Provide **personalized product recommendations**.\n\n## 📂 Dataset\nWe use the **Online Retail Dataset**, which contains real-world transactions from a UK-based retailer. It includes:\n✔️ Transaction IDs  \n✔️ Products Purchased  \n✔️ Customer Information  \n✔️ Purchase Date \u0026 Payment Method  \n✔️ Discounts \u0026 Promotions  \n\n## 🚀 Tech Stack\n- **Python** 🐍\n- **Pandas** for data processing 📊\n- **Mlxtend** for Apriori algorithm 🛒\n- **Plotly** \u0026 **Seaborn** for visualization 📈\n- **Streamlit** for an interactive dashboard ⚡\n\n## 📊 Key Insights\n✅ **Top 10 Best-Selling Products**  \n✅ **Product Associations** (e.g., \"Customers who buy A also buy B\")  \n✅ **Seasonal Shopping Trends**  \n✅ **Personalized Product Recommendations**  \n\n## 🏗️ How to Run the Project\n### 1️⃣ Install Dependencies\n```bash\npip install -r requirements.txt\n```\n\n### 2️⃣ Run Streamlit Dashboard\n```bash\nstreamlit run app.py\n```\n\n### 3️⃣ Explore the Insights!\nThe dashboard will open in your browser, allowing you to analyze shopping trends and recommendations interactively.\n\n## 💡 Future Improvements\n- Implement **Deep Learning** for enhanced recommendations 🤖\n- Integrate with **E-commerce APIs** for real-time data 🌍\n- Deploy the dashboard as a web app 🚀\n\n## 🙌 Contributing\nWant to improve this project? Fork it, create a branch, and submit a PR! 🤝\n\n## 📬 Contact\n📧 **Email:** tunidev56@gmail.com\n🔗 **LinkedIn:** https://linkedin.com/in/rociobaigorria\n\n---\n\n⭐ **If you like this project, don't forget to give it a star!** ⭐\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftuni56%2Fretail_transaction_analysis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ftuni56%2Fretail_transaction_analysis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftuni56%2Fretail_transaction_analysis/lists"}