Transaction Dataset Pattern and Quality Analysis

📊 Dataset Explorer Report: /tmp/transactions.csv

I’ve completed a comprehensive analysis of your transactions dataset. Here’s what I found:

Overview

• 100 transactions across 6 columns (transaction_id, amount, category, date, customer_age, region)
• 99.3% complete (only 4 missing values)
• Date range: January 4 - October 25, 2024 (~10 months)
• Total revenue: $254,395.28

🎯 Key Patterns Discovered

1. Balanced Product Mix

All 5 categories have fairly even distribution (17-24% each):

• Electronics leads with 24 transactions
• Food, Home, Clothing, and Sports are well-represented
• No single category dominates

2. Regional Performance Gap

• East region: 34% of transactions (highest volume)
• North region: 18% of transactions (lowest volume)
• BUT: North has the highest average transaction value ($3,151 vs $2,328 in East)
• This suggests North may be lower volume but higher value market

3. Transaction Values

• Range: $20.29 to $4,967.51
• Mean: $2,543.95 | Median: $2,715.55
• Symmetric distribution (no skew) = healthy mix of low/high value sales
• 14 transactions exceed $4,500 (high-value opportunities)

4. Customer Demographics

• Age range: 18-75 years
• Average: 46.2 years (mature customer base)
• Good spread across all age brackets
• 26-35 age group has highest average spend ($3,063)

⚠️ Quality Issues Found

Minor Issues (Easy Fixes)

1. 4 missing customer ages (4% of data):
   • TX0026, TX0052, TX0055, TX0065
   • Two of these are high-value transactions (>$4,700)

Excellent Data Quality

✅ No duplicate transactions
✅ All transaction IDs follow consistent format (TX####)
✅ No negative amounts
✅ No outliers or suspicious values
✅ All ages in reasonable range (18-100)
✅ Consistent date formatting

💡 Key Insights

Interesting Finding: North Region Paradox

• North has fewest transactions (18) but highest average value ($3,151)
• This could indicate:
  • Premium customer segment in North
  • Less frequent but higher-value purchases
  • Market opportunity for increased volume

Category Performance

Electronics dominates revenue (26% of total) despite being only 24% of transactions:

• Average Electronics transaction: $2,752
• Suggests higher price points in this category

Age-Based Spending Patterns

Younger customers (26-35) spend most per transaction ($3,063), followed by mature customers (46-55 at $2,729)

📈 Visualizations Generated

I’ve created comprehensive visualizations showing:

1. Transaction amount distribution (fairly symmetric bell curve)
2. Category and regional breakdowns
3. Customer age demographics
4. Box plots revealing spending patterns by category/region
5. Temporal trends (March and September show peaks)
6. Heatmap of average amounts by category × region
7. Data completeness chart (99.3% complete!)

The visualization above shows all 9 analysis charts in one comprehensive dashboard.

🎯 Recommendations

Immediate Actions:

1. ✅ Collect missing age data for 4 transactions (especially high-value ones)
2. ✅ Investigate North region strategy:
   • Why lower volume but higher value?
   • Opportunity to increase transaction frequency?

Analysis Opportunities:

3. Temporal analysis - I see peaks in March and September; investigate seasonal patterns
4. Category-region optimization - Heatmap shows Electronics perform best in East/South
5. Customer segmentation - Focus on 26-35 age group (highest spenders)
6. High-value transaction patterns - Study the 14 transactions >$4,500 for upsell insights

✅ Dataset Assessment

Overall Quality: ⭐⭐⭐⭐ EXCELLENT

Status: READY FOR ANALYSIS

This dataset is clean, well-structured, and suitable for:

• Sales performance analysis
• Regional market analysis
• Customer demographic profiling
• Product category optimization
• Temporal trend analysis
• Revenue forecasting

The only limitation is the small sample size (100 transactions), but the data quality is outstanding and sufficient for meaningful insights.