Data
Oilst_Customers_Dataset

Oilst_Customers_Dataset

active ARFF Attribution-NonCommercial-ShareAlike (CC BY-NC-SA) Visibility: public Uploaded 31-05-2024 by Iwo Godzwon
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Description: The `olist_customers_dataset.csv` offers a comprehensive snapshot of customer details from the Olist e-commerce platform. This dataset encapsulates essential customer information, providing a foundation for understanding consumer demographics, regional distribution, and identifying unique shoppers on the platform. Attribute Description: - `customer_id` (String): A hexadecimal ID unique to each order placed on the Olist platform, serving as a primary key for order tracking. Examples: '827c1daa...', 'a3d6e70f...'. - `customer_unique_id` (String): A distinct identifier for each customer, irrespective of the number of orders they place. This ID helps in distinguishing individual consumers and analyzing their purchasing patterns. Examples: 'c49f6a29...', 'ad7c7b01...'. - `customer_zip_code_prefix` (Integer): The first five digits of the customer's postal code, indicating their geographical location and aiding in logistics and distribution analysis. Examples: 4363, 45436. - `customer_city` (String): The name of the city where the customer resides, providing insights into the urban or rural distribution of the clientele. Examples: 'itupeva', 'sao paulo'. - `customer_state` (String): The two-letter code representing the state within Brazil where the customer is located, essential for regional sales and marketing strategies. Examples: 'CE', 'SP', 'MG'. Use Case: This dataset is invaluable for stakeholders aiming to enhance customer relationship management (CRM) systems, improve targeted marketing strategies, and analyze regional sales trends. By leveraging the unique identifiers, businesses can track customer loyalty and recurring purchasing behaviors. Additionally, geographic attributes enable detailed market segmentation and logistics optimization. Marketers, data analysts, and supply chain specialists will find this dataset particularly useful for crafting personalized offers, identifying potential markets, and efficient resource allocation.

5 features

customer_idstring99441 unique values
0 missing
customer_unique_idstring96096 unique values
0 missing
customer_zip_code_prefixnumeric14994 unique values
0 missing
customer_citynominal4119 unique values
0 missing
customer_statenominal27 unique values
0 missing

19 properties

99441
Number of instances (rows) of the dataset.
5
Number of attributes (columns) of the dataset.
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
1
Number of numeric attributes.
2
Number of nominal attributes.
0
Percentage of binary attributes.
0
Percentage of instances having missing values.
Average class difference between consecutive instances.
0
Percentage of missing values.
0
Number of attributes divided by the number of instances.
20
Percentage of numeric attributes.
Percentage of instances belonging to the most frequent class.
40
Percentage of nominal attributes.
Number of instances belonging to the most frequent class.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the least frequent class.
0
Number of binary attributes.

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