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 NameRoleTypeDescriptionUnitsMissing Values
ChannelFeatureCategoricalno
RegionTargetCategoricalno
FreshFeatureIntegerno
MilkFeatureIntegerno
GroceryFeatureIntegerno
FrozenFeatureIntegerno
Detergents_PaperFeatureIntegerno
DelicassenFeatureIntegerno

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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

Papers Citing this Dataset

An Efficient Algorithm for Density Based Subspace Clustering with Dynamic Parameter Setting

By B. Lakshmi, Kerekar Madhuri, M. Shashi. 2017

Published in International Journal of Information Technology and Computer Science.

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Creators

Margarida Cardoso

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