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🛒 E-Commerce Orders 50 sales orders - dates, categories, revenue & regions 🚚 Logistics & Deliveries 50 shipments - routes, carriers, costs, delivery times & ratings
You are viewing a sample dataset:  Logistics & Deliveries. Upload your own CSV above to analyze your real data.
Overview - sample2.csv
50
Rows
14
Columns
0
Missing Values
4 / 8 / 2
Numeric / Categorical / Date
0.0%
Completeness gap
Column Analysis
shipment_id Categorical
Count50
Missing0 (0.00%)
Unique values50
SH001 1
SH002 1
SH003 1
SH004 1
SH005 1
SH006 1
SH007 1
SH008 1
SH009 1
SH010 1
shipment_date Date
Count50
Missing0 (0.00%)
Span4.6 months
2023-01-03 → 2023-05-22 4.6 months
2023-01 2023-05
delivery_date Date
Count50
Missing0 (0.00%)
Span4.6 months
2023-01-06 → 2023-05-26 4.6 months
2023-01 2023-05
origin_city Categorical
Count50
Missing0 (0.00%)
Unique values11
New York 6
Chicago 6
Los Angeles 5
Houston 5
Miami 5
Seattle 5
Phoenix 4
Dallas 4
Boston 4
Atlanta 4
destination_city Categorical
Count50
Missing0 (0.00%)
Unique values48
Las Vegas 2
San Francisco 2
Boston 1
Seattle 1
Detroit 1
Dallas 1
Atlanta 1
Chicago 1
Houston 1
Portland 1
carrier Categorical
Count50
Missing0 (0.00%)
Unique values3
QuickShip 18
FastFreight 16
SwiftCargo 16
service_type Categorical
Count50
Missing0 (0.00%)
Unique values3
Standard 25
Express 15
Same-Day 10
weight_kg Numeric
Count50
Missing0 (0.00%)
Min2.50
Max52.10
Mean18.13
Median14.10
Std Dev13.45
Sum906.60
2.5 52.1
distance_km Numeric
Count50
Missing0 (0.00%)
Min25.00
Max1,544.00
Mean440.36
Median389.00
Std Dev339.66
Sum22,018.00
25.0 1,544.0
shipping_cost Numeric
Count50
Missing0 (0.00%)
Min31.50
Max118.60
Mean56.27
Median52.05
Std Dev20.23
Sum2,813.50
31.5 118.6
status Categorical
Count50
Missing0 (0.00%)
Unique values1
Delivered 50
on_time Categorical
Count50
Missing0 (0.00%)
Unique values2
Yes 38
No 12
damage_reported Categorical
Count50
Missing0 (0.00%)
Unique values2
No 45
Yes 5
customer_rating Numeric
Count50
Missing0 (0.00%)
Min2.00
Max5.00
Mean4.00
Median4.00
Std Dev0.94
Sum200.00
2.0 5.0
Data Preview (first 10 rows)
shipment_idshipment_datedelivery_dateorigin_citydestination_citycarrierservice_typeweight_kgdistance_kmshipping_coststatuson_timedamage_reportedcustomer_rating
SH0012023-01-032023-01-06New YorkBostonFastFreightExpress12.534648.50DeliveredYesNo5
SH0022023-01-052023-01-11Los AngelesSeattleSwiftCargoStandard34.0154487.20DeliveredYesNo4
SH0032023-01-082023-01-12ChicagoDetroitFastFreightStandard8.245732.10DeliveredNoNo3
SH0042023-01-102023-01-11HoustonDallasQuickShipSame-Day5.039665.00DeliveredYesNo5
SH0052023-01-122023-01-18MiamiAtlantaSwiftCargoStandard22.7109361.40DeliveredNoYes2
SH0062023-01-152023-01-17PhoenixLas VegasQuickShipExpress9.847942.30DeliveredYesNo4
SH0072023-01-172023-01-24New YorkChicagoFastFreightStandard45.31272102.80DeliveredYesNo4
SH0082023-01-192023-01-20DallasHoustonQuickShipSame-Day3.539658.00DeliveredYesNo5
SH0092023-01-222023-01-29SeattlePortlandSwiftCargoStandard18.028039.60DeliveredNoNo3
SH0102023-01-252023-01-28BostonPhiladelphiaFastFreightExpress11.248351.70DeliveredYesNo4
What Can We Do With Your Data?
📈
Sales & Revenue Forecasting
73
Readiness score
Good fit
🎯
Lead Scoring & Risk Detection
60
Readiness score
Good fit
👥
Customer Profiling & Segmentation
81
Readiness score
Ready to go
📅
Trend Analysis & Demand Planning
75
Readiness score
Ready to go

✦ Positive Signals

✦ No missing values - clean data with no gaps - ready to use as-is.
✦ 4 numeric columns - rich set of measurable columns - great for forecasting and profiling.
✦ 5 low-cardinality categorical column(s) (2–20 classes) - ideal columns for scoring and risk detection.
◈ 8 categorical columns - useful for grouping customers and building audience profiles.
✦ 14 columns - enough data columns to support multi-factor analysis.
✦ 4 numeric columns show meaningful variance (CV > 15%) - good variation in the data - well suited for forecasting.
✦ Date/time column detected alongside numeric data - strong candidate for trend analysis and demand planning. (2 date column(s) confirmed)

⚠ Issues & Warnings

⚠ Small dataset (50 rows) - may limit accuracy. Consider collecting more records before proceeding.
⚠ 2 high-cardinality column(s) detected - may need grouping or simplifying before use in analysis.
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Your data is ready
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Your data is ready for customer profiling & audience segmentation and trend analysis & demand planning and sales forecasting & revenue prediction. Get in touch - our team can turn it into real business intelligence.
Customer profiling & audience segmentation Trend analysis & demand planning Sales forecasting & revenue prediction
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