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 Today
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
Context 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
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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