Description: Dive into the world of wholesale customer data and uncover insights into purchasing behavior across different channels and regions. This dataset offers a comprehensive view of annual spending on various product categories, providing valuable information for market segmentation and customer classification.

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Features: - Region: Categorical feature indicating the region of purchase, with options including Lisbon, Oporto, or Other.

• Fresh: Continuous feature representing annual spending (in monetary units) on fresh products.

• Milk: Continuous feature representing annual spending (in monetary units) on milk products.

• Grocery: Continuous feature representing annual spending (in monetary units) on grocery products.

• Frozen: Continuous feature representing annual spending (in monetary units) on frozen products.

• Detergents_Paper: Continuous feature representing annual spending (in monetary units) on detergents and paper products.

• Delicassen: Continuous feature representing annual spending (in monetary units) on delicatessen products.

• Target Feature: Channel - Categorical feature indicating the type of channel, either Horeca (Hotel/Restaurant/Cafe) or Retail channel.

How the data can be used: - Market Segmentation: Analyze spending patterns to segment customers based on their purchasing behavior, helping businesses tailor their marketing strategies accordingly. - Channel Classification: Develop machine learning models to classify customers into Hotel/Restaurant/Cafe (HoReCa) or Retail channels, enabling businesses to optimize their supply chain and distribution strategies. - Predictive Analytics: Utilize the dataset to build predictive models that forecast future spending trends, aiding businesses in inventory management and demand forecasting. - Customer Insights: Gain insights into the preferences and buying habits of wholesale customers, allowing businesses to identify growth opportunities and improve customer satisfaction.

Dataset

This dataset was created by Shabbir Ahmad

Released under Apache 2.0

Contents

The dataset refers to clients of a wholesale distributor. It includes the annual spending in monetary units (m.u.) on diverse product categories

Source: UCI Wholesale customers Data Set

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This dataset was created by Nurai16

Released under MIT

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This dataset was created by kKamal_2003

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This dataset was created by Wael Rahhal

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This dataset was created by Margaret Wangari

Released under MIT

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This dataset was created by Aleksandr Myznikov

Released under CC0: Public Domain

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This dataset was created by Ligin Thomas CK

Released under Database: Open Database, Contents: © Original Authors

Contents

The data set refers to clients of a wholesale distributor. It includes the annual spending in monetary units (m.u.) on diverse product categories.

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

This dataset was created by Kirollos Ashraf

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This dataset was created by Prins Kumar

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This dataset was created by Emanuel

Contents

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This dataset was created by nguyen17

Released under CC0: Public Domain

Contents

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This dataset was created by Sanchi Batra

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This dataset was created by zhou shixiang

Released under Apache 2.0

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The customer segments data is included as a selection of 440 data points collected on data found from clients of a wholesale distributor in Lisbon, Portugal. More information can be found on the UCI Machine Learning Repository.

Note (m.u.) is shorthand for monetary units.

Features 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: {Hotel/Restaurant/Cafe - 1, Retail - 2} (Nominal) 8) Region: {Lisbon - 1, Oporto - 2, or Other - 3} (Nominal)

The data set refers to clients of a wholesale distributor. It includes the annual spending in monetary units (m.u.) on diverse product categories.

The dataset for this project can be found on the UCI Machine Learning Repository. For the purposes of this project, the features 'Channel' and 'Region' will be excluded in the analysis — with focus instead on the six product categories recorded for customers.

Description of Categories

FRESH: annual spending (m.u.) on fresh products (Continuous) MILK: annual spending (m.u.) on milk products (Continuous) GROCERY: annual spending (m.u.) on grocery products (Continuous) FROZEN: annual spending (m.u.)on frozen products (Continuous) DETERGENTS_PAPER: annual spending (m.u.) on detergents and paper products (Continuous) DELICATESSEN: annual spending (m.u.) on and delicatessen products (Continuous) "A store selling cold cuts, cheeses, and a variety of salads, as well as a selection of unusual or foreign prepared foods."

Dataset

This dataset was created by Aditya Raj Sisodiya

Released under GPL 2

Contents

The Wholesale Trade Survey is a statistical survey conducted by government agencies or statistical organizations to collect data and measure the performance and characteristics of the wholesale trade sector within a specific country or region. It aims to provide valuable information about wholesale trade activities, trends, and economic indicators.

The survey typically collects data from wholesale businesses, which are involved in buying goods in large quantities from manufacturers or producers and selling them to retailers, institutional clients, or other businesses. The data collected in the survey can include:

Sales and revenue: Information about the total sales volume and revenue generated by wholesale businesses within a specified period, such as monthly, quarterly, or annually.

Inventory levels: Data on the inventory or stock levels held by wholesale businesses, including the value and quantity of goods held in storage or warehouses.

Purchases: Information about the quantity and value of goods purchased by wholesale businesses from manufacturers or producers. This data helps in understanding the demand for different products in the market.

Employment: Statistics on the number of people employed in the wholesale trade sector, including full-time and part-time workers.

Industry-specific data: Depending on the survey's scope, additional data may be collected for specific industries or sectors within wholesale trade, such as electronics, pharmaceuticals, clothing, or automotive.

Geographic information: The survey may also gather data on the geographic distribution of wholesale trade activities, including the number and location of wholesale businesses in different regions or cities.

The Wholesale Trade Survey provides insights into the overall performance, growth, and trends in the wholesale trade sector. It helps policymakers, researchers, businesses, and investors to analyze market conditions, identify opportunities, make informed business decisions, and assess the economic health of the wholesale trade industry. The data collected from the survey is often used in economic analysis, policy formulation, forecasting, and market research.