All examples for Categorical Variable Profiler

Focused Regional Satisfaction Association

Targeted analysis examining the relationship between specific categorical variables (Region and Satisfaction). Demonstrates the skill's flexibility in handling user-directed analysis requests, performing chi-square independence tests with effect size interpretation, and delivering focused insights with clear regional performance breakdown and urgent recommendations for business action.

Input

What you provide to the skill

Analyze /tmp/test_customer_data.csv focusing on Region and Satisfaction association

Output

What the skill generates for you

Analysis Complete: Region and Satisfaction Association

I’ve successfully executed the categorical profiler skill on your customer data. Here are the key findings:

🎯 Main Discovery: Significant Regional Association

The analysis reveals a statistically significant association between Region and Satisfaction:

  • Chi-square test: χ² = 20.20, p = 0.0167 (significant at α = 0.05)
  • Effect size: Cramér’s V = 0.474 (moderate-large effect)

📊 Regional Performance Breakdown

🏆 South (Best Performer)

  • 100% positive satisfaction
  • 75% Satisfied, 25% Very Satisfied
  • Zero negative feedback

🔶 East & North (Moderate)

  • ~70-75% positive satisfaction
  • No dissatisfied customers
  • Some neutral responses

⚠️ West (Critical Issue)

  • Only 14.3% positive satisfaction
  • 57.1% Dissatisfied (4 out of 7 customers)
  • ALL dissatisfied customers come from West

📈 Overall Metrics

  • 66.7% positive satisfaction overall
  • Balanced regional distribution (23-27% each)
  • No data quality issues detected

⚡ Urgent Recommendations

  1. Investigate West Region - The 57% dissatisfaction rate demands immediate attention
  2. Document South’s Success - Identify and replicate their exceptional practices
  3. Expand Sample Size - Current n=30 is small; target ≥100 per region for robust conclusions

📊 Visualizations Created

Three charts were generated showing:

  1. Region distribution - Balanced across all regions
  2. Satisfaction distribution - “Satisfied” is most common (46.7%)
  3. Heatmap - Clearly shows West’s dissatisfaction concentration (red area)

⚠️ Statistical Caveat

The small sample size (n=30) violates chi-square assumptions (expected frequencies < 5 in all cells). However, the pattern is too stark to ignore - the practical significance is clear even if statistical confidence is limited.

View all examples for Categorical Variable Profiler

About This Skill

Automated categorical variable analysis for datasets. Generates frequency tables, percentage breakdowns, bar chart visualizations, rare category identification, cross-tabulations between categorical pairs, and chi-square association tests.

View Skill Details

More Examples

Customer Satisfaction Regional Analysis

Comprehensive analysis of 30-customer survey dataset with Region, Age_Group, Satisfaction, and Product_Category variables. Demonstrates complete workflow including frequency tables, chi-square tests with multiple testing correction, effect size calculations (Cramér's V), and visualization generation. Identifies critical West region dissatisfaction issue (57% vs 0% in South) and strong statistical associations between variables.

Rare Category Detection and Data Quality Check

Analysis focused on identifying rare categories (below 1% threshold) and comprehensive data quality assessment. Demonstrates the skill's ability to flag small categories, detect data quality issues, and provide exploratory insights with appropriate statistical caveats for small sample sizes. Shows proper handling of chi-square assumption violations with transparent reporting.

View all examples