Our AI Agent autonomously uncovers trends, patterns, and seasonality in data, delivering natural-language forecasts, performance monitoring, and anomaly detection – fully automated and future-ready.
The AI Agent instantly reveals frequencies, variability, and outliers, translating raw distributions into clear, expert-level insights. Patterns emerge automatically, empowering faster, smarter decisions.
By analyzing correlations and associations between variables, the AI Agent uncovers hidden drivers and connections. Everything is explained in natural language – automatic, actionable, and transformative.
Detailed EDA Interpretation
Only the POSTAL_CODE column contains missing values (41,296 entries)
This represents approximately 80.5% of all records, which is significant
For classification tasks, this feature may need to be dropped or imputed
Mean sales: 246.49 (high standard deviation: 487.57)
Highly skewed distribution (75% of sales below $251.05)
Potential outliers in upper range require investigation
Quantity: 1 – 14 units per order
Average quantity: 3.48 items (std: 2.28)
Discounts: 0% – 85%
Median discount: 0%, indicating many orders have no discount
Negative profits observed (min: -6,599.98)
Maximum profit: 8,399.98
Shipping cost range: 1.00 – 933.57
Mean shipping cost: 26.48
Strong Positive Correlations
Sales & Shipping Cost (ρ=0.909, r=0.768)
Higher value orders incur higher shipping costs
Profit & Sales (ρ=0.490, r=0.485)
Higher sales generally lead to higher profits
Notable Negative Correlations
Discount & Profit (ρ=-0.596, r=-0.316)
Higher discounts reduce profitability
Discount & Sales (ρ=-0.100, r=-0.087)
Minimal impact of discounts on sales volume
Order Priority
Medium priority: 57.39%
High priority: 30.22%
Critical: 7.67%, Low: 4.73%
Product Categories
Office Supplies: 61%
Technology: ~19%
Furniture: ~20%
Sub-Categories
Top 5 sub-categories = ~45% of orders
Binders: 11.98%, Storage: 9.84%
Other sub-categories evenly distributed
Address missing POSTAL_CODE values
Feature engineer based on strong correlations
Manage class imbalance in categorical variables
Handle outliers in sales and profit
Normalize numerical features due to varying scales
We optimize AI models tailored to your domain, ensuring solutions that align perfectly with your business needs and drive measurable impact.
“What is cool actually is startups - like Benjamin's startup can really scale actually, so the good thing is if you have an idea, you just take ecosystems like Snowflake and there's no concern about scaling servers and hardware anymore.”
Univ.-Prof. Dr. Stefan Thalmann