Wholesale customers
Donated on 3/30/2014
The data set refers to clients of a wholesale distributor. It includes the annual spending in monetary units (m.u.) on diverse product categories
Dataset Characteristics
Multivariate
Subject Area
Business
Associated Tasks
Classification, Clustering
Feature Type
Integer
# Instances
440
# Features
7
Dataset Information
Has Missing Values?
No
Variables Table
| Variable Name | Role | Type | Description | Units | Missing Values |
|---|---|---|---|---|---|
| Channel | Feature | Categorical | no | ||
| Region | Target | Categorical | no | ||
| Fresh | Feature | Integer | no | ||
| Milk | Feature | Integer | no | ||
| Grocery | Feature | Integer | no | ||
| Frozen | Feature | Integer | no | ||
| Detergents_Paper | Feature | Integer | no | ||
| Delicassen | Feature | Integer | no |
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Additional Variable Information
1) FRESH: annual spending (m.u.) on fresh products (Continuous); 2) MILK: annual spending (m.u.) on milk products (Continuous); 3) GROCERY: annual spending (m.u.)on grocery products (Continuous); 4) FROZEN: annual spending (m.u.)on frozen products (Continuous) 5) DETERGENTS_PAPER: annual spending (m.u.) on detergents and paper products (Continuous) 6) DELICATESSEN: annual spending (m.u.)on and delicatessen products (Continuous); 7) CHANNEL: customers’ Channel - Horeca (Hotel/Restaurant/Café) or Retail channel (Nominal) 8) REGION: customers’ Region – Lisnon, Oporto or Other (Nominal) Descriptive Statistics: (Minimum, Maximum, Mean, Std. Deviation) FRESH ( 3, 112151, 12000.30, 12647.329) MILK (55, 73498, 5796.27, 7380.377) GROCERY (3, 92780, 7951.28, 9503.163) FROZEN (25, 60869, 3071.93, 4854.673) DETERGENTS_PAPER (3, 40827, 2881.49, 4767.854) DELICATESSEN (3, 47943, 1524.87, 2820.106) REGION Frequency Lisbon 77 Oporto 47 Other Region 316 Total 440 CHANNEL Frequency Horeca 298 Retail 142 Total 440
Dataset Files
| File | Size |
|---|---|
| Wholesale customers data.csv | 14.7 KB |
Papers Citing this Dataset
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By B. Lakshmi, Kerekar Madhuri, M. Shashi. 2017
Published in International Journal of Information Technology and Computer Science.
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pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset wholesale_customers = fetch_ucirepo(id=292) # data (as pandas dataframes) X = wholesale_customers.data.features y = wholesale_customers.data.targets # metadata print(wholesale_customers.metadata) # variable information print(wholesale_customers.variables)
Cardoso, M. (2013). Wholesale customers [Dataset]. UCI Machine Learning Repository. https://doi.org/10.24432/C5030X.
Creators
Margarida Cardoso
DOI
License
This dataset is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
This allows for the sharing and adaptation of the datasets for any purpose, provided that the appropriate credit is given.