https://github.com/tuni56/retail_transaction_analysis
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.
https://github.com/tuni56/retail_transaction_analysis
apriori-algorithm machine-learning-algorithms python streamlit
Last synced: 8 months ago
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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.
- Host: GitHub
- URL: https://github.com/tuni56/retail_transaction_analysis
- Owner: tuni56
- Created: 2025-02-14T19:51:22.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2025-02-14T21:52:54.000Z (10 months ago)
- Last Synced: 2025-02-14T22:30:01.990Z (10 months ago)
- Topics: apriori-algorithm, machine-learning-algorithms, python, streamlit
- Language: Python
- Homepage:
- Size: 21.5 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# 🛍️ Retail Market Basket Analysis with Apriori
## 📌 Overview
Discover 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.
## 🎯 Objectives
- Extract valuable **associations** between products.
- Identify **top-selling items** and trends.
- Create an **interactive dashboard** for visualizing insights.
- Provide **personalized product recommendations**.
## 📂 Dataset
We use the **Online Retail Dataset**, which contains real-world transactions from a UK-based retailer. It includes:
✔️ Transaction IDs
✔️ Products Purchased
✔️ Customer Information
✔️ Purchase Date & Payment Method
✔️ Discounts & Promotions
## 🚀 Tech Stack
- **Python** 🐍
- **Pandas** for data processing 📊
- **Mlxtend** for Apriori algorithm 🛒
- **Plotly** & **Seaborn** for visualization 📈
- **Streamlit** for an interactive dashboard ⚡
## 📊 Key Insights
✅ **Top 10 Best-Selling Products**
✅ **Product Associations** (e.g., "Customers who buy A also buy B")
✅ **Seasonal Shopping Trends**
✅ **Personalized Product Recommendations**
## 🏗️ How to Run the Project
### 1️⃣ Install Dependencies
```bash
pip install -r requirements.txt
```
### 2️⃣ Run Streamlit Dashboard
```bash
streamlit run app.py
```
### 3️⃣ Explore the Insights!
The dashboard will open in your browser, allowing you to analyze shopping trends and recommendations interactively.
## 💡 Future Improvements
- Implement **Deep Learning** for enhanced recommendations 🤖
- Integrate with **E-commerce APIs** for real-time data 🌍
- Deploy the dashboard as a web app 🚀
## 🙌 Contributing
Want to improve this project? Fork it, create a branch, and submit a PR! 🤝
## 📬 Contact
📧 **Email:** tunidev56@gmail.com
🔗 **LinkedIn:** https://linkedin.com/in/rociobaigorria
📂 **Portfolio:** https://tuni56.netlify.app/
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