DS_Association-Rules-on-Business-Problem
🥈SilverContext A real online retail transaction data set of two years. Content This is a transnational data set which contains all the transactions occurring between 01/12/2010 and 09/12/2011 for a UK-based and registered non-store online retail.The company mainly sells unique all-occasion gifts. Many customers of the company are wholesalers. Column Descriptors InvoiceNo: Invoice number. Nominal, a 6-digit integral number uniquely assigned to each transaction. If this code starts with letter 'c', it
300Updated 2mo ago
Intermediate30min to implementmarketing
Saves ~15 min per use
Quick InstallView Source
git clone https://github.com/mathchi/DS_Association-Rules-on-Business-Problem.gitWorks with:
Claude
Overview
About This Skill
This skill applies association rule mining to a real UK online retail dataset containing over two years of transactions from a non-store gift retailer. The dataset includes invoice details, product codes, quantities, pricing, and customer information across multiple countries, enabling analysis of which products are frequently purchased together. By mining association rules from this multivariate, time-series transaction data, marketing teams can uncover product relationships, optimize cross-selling strategies, and segment customers based on purchasing behavior. The skill supports RFM model-based customer segmentation and market analysis for precision marketing and business decision-making.
Use Cases
Identify frequently co-purchased products for cross-selling recommendations
Segment wholesale customers based on transaction patterns and order values
Analyze product affinity to optimize gift bundle offerings
Detect seasonal purchasing trends across the 24-month transaction history
Setup & Installation
Quick Install
No install command available. Check the GitHub repository for manual installation instructions.
Alternative Install (Git Clone)
git clone https://github.com/mathchi/DS_Association-Rules-on-Business-ProblemRequirements
- Claude Code or compatible AI agent
- Works with: Claude
Quick Start Guide
1
Install the Skill
Copy the install command above and run it in your terminal.
2
Open Your AI Agent
Launch Claude Code, Cursor, or your preferred AI coding agent.
3
Try It Out
Use the prompt template or examples below to test the skill.
4
Customize
Adapt the skill to your specific use case and workflow.
Usage Examples
Prompt Template
Analyze the provided retail transaction dataset to identify association rules that can help [COMPANY] improve their marketing strategy. Focus on frequent itemsets and rules with a minimum support of 0.01 and confidence of 0.5. Highlight any interesting patterns or insights related to [INDUSTRY] trends or customer behavior.
Example Output
## Association Rules Analysis for Online Retail Dataset
### Top 5 Frequent Itemsets
1. {22456, 22457, 22458}
2. {22456, 22457}
3. {22456, 22458}
4. {22457, 22458}
5. {22456}
### Top 5 Association Rules
1. {22456} → {22457} (Support: 0.012, Confidence: 0.65)
2. {22457} → {22458} (Support: 0.011, Confidence: 0.60)
3. {22456, 22457} → {22458} (Support: 0.010, Confidence: 0.70)
4. {22456} → {22458} (Support: 0.009, Confidence: 0.55)
5. {22457} → {22456} (Support: 0.008, Confidence: 0.50)
### Insights
- The dataset shows a strong association between products 22456, 22457, and 22458, suggesting they are often purchased together.
- Product 22456 appears to be a key driver, frequently leading to the purchase of other products.
- The rules indicate potential bundling opportunities for the company's marketing campaigns.Apply to these tools
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