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Shop Customer Clustering.pdf (21085184, 2023-11-24)

# Shop-Customer-Clustering Shop Customer Data is a comprehensive dataset that provides a detailed analysis of a hypothetical shop's ideal customers. By collecting and analyzing customer data through membership cards, this dataset provides valuable insights that can help a business better understand its customers. The dataset includes 2000 records and 8 columns, providing a wealth of information about the shop's customer base. Each column represents a specific aspect of the customer's profile, including their unique Customer ID, Gender, Age, Annual Income, Spending Score, Profession, Work Experience, and Family Size. By analyzing this data, businesses can gain valuable insights into their customers' preferences, behaviors, and purchasing habits. For example, they can segment customers by age, income, or family size to better understand how these factors impact their purchasing decisions. Customer ID: A unique identifier assigned to each customer in the dataset. It is used to differentiate between individual customers and to keep track of their purchases and other behaviors. Gender: The gender of the customer, either male or female. Gender can be used to analyze purchasing behavior and preferences between genders. Age: The age of the customer, usually measured in years. Age can be used to segment customers into different age groups, which can help identify purchasing patterns and preferences among different age groups. Annual Income: The annual income of the customer, usually measured in dollars or another currency. Annual income can be used to segment customers into different income groups, which can help identify purchasing patterns and preferences among different income levels. Spending Score: A score assigned by the shop based on the customer's behavior and spending nature. This score can be used to segment customers based on their purchasing patterns, such as high-spending customers, low-spending customers, and customers who are likely to make impulse purchases. Profession: The occupation or profession of the customer. Profession can be used to analyze purchasing patterns and preferences among different professions. Work Experience: The number of years of work experience of the customer. This feature can be used to segment customers based on their level of experience, which can help identify purchasing patterns and preferences among different experience levels. Family Size: The size of the customer's family, usually measured in terms of the number of family members. Family size can be used to analyze purchasing patterns and preferences among different family sizes, such as families with children versus families without children.

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