Scatterplot for online furniture retailer data of product price vs shipping price
Online Furniture Retailer Data: comprehensive synthetic dataset tailored for online furniture retail analytics. This dataset captures real-world scenarios across product pricing, delivery windows, shipping logistics, assembly services, payment behavior, and customer satisfaction—ideal.
Key features
1,938 rows, 14 columns
~60% numerical, ~40% categorical for balanced analytics
Realistic furniture categories, brands, and delivery statuses
Delivery windows (1–14 days), shipping and assembly cost logic
Optional assembly services with complexity-based pricing
Customer ratings with natural sparsity for real-world analysis
https://www.kaggle.com/datasets/pratyushpuri/online-furniture-orders-delivery-and-assembly-2025/data
| Column | Type | Description |
|---|---|---|
| order_id | Quantitative | Unique order identifier per transaction. |
| customer_id | Quantitative | Unique customer identifier for cohort and LTV analysis. |
| product_category | Categorical | High-level category (Living Room, Bedroom, Dining Room, Office, Kitchen, Outdoor). |
| product_subcategory | Categorical | Specific item type (e.g., Sofa, Bed Frame, Desk, Patio Set). |
| brand | Categorical | Furniture brand (e.g., IKEA, West Elm, Pottery Barn). May contain nulls. |
| product_price | Quantitative | Base product price in USD, ranges tuned by category. |
| shipping_cost | Quantitative | Shipping fee; free or reduced for higher price points. May contain nulls. |
| assembly_service_requested | Categorical | True/False flag indicating if assembly was requested. |
| assembly_cost | Quantitative | Assembly fee driven by complexity tiers; zero when not requested. May contain nulls. |
| total_amount | Quantitative | All-in order value: product_price + shipping_cost + assembly_cost. |
| delivery_window_days | Quantitative | Promised delivery window in days (1–14). |
| delivery_status | Categorical | Fulfillment status (Pending, In Transit, Delivered, Failed Delivery, Rescheduled, Cancelled). |
| payment_method | Categorical | Payment mode (Credit/Debit Card, PayPal, Apple/Google Pay, Bank Transfer, COD). |
| customer_rating | Quantitative | Post-delivery rating (1.0–5.0) with natural missingness. |
MIT Licensed
Comments
Whoah this is fascinating! Try color and see if you can figure out what segments those are with the box shapes in the scatter plot. If you filter out those groups, I wonder if those ones at the very bottom would spread out upward. This will be fund to explore!
Another idea: a tree visualization based on the hierarchy created by category and subcategory.
I would suggest not to use this dataset, because it's synthetic. Not interesting at all, it's just made up fake data.