# BDAIO2024: Segment & Conquer — Data

来源：https://www.kaggle.com/competitions/bdaio-2024-segment-conquer/data

采集时间：2026-10-20T08:03:13Z

The dataset contains various features that describe customer demographics, purchasing behavior, and preferences. Here’s a detailed explanation of each column:

**1. CustomerID:** Unique identifier for each customer. This column is used to distinguish each customer record in the dataset.
**2. Age:** The age of the customer. This feature can help in understanding the age distribution of the customer base and identifying age-specific purchasing patterns.
**3. Gender:** The gender of the customer (Male or Female). This feature can be used to analyze purchasing behavior differences between genders.
**4. Marital Status:** The marital status of the customer (Single, Married, Divorced, Widowed). This feature can provide insights into the purchasing behavior of customers based on their marital status.
**5. Annual Income (k$):** The annual income of the customer in thousand dollars. This feature is crucial for understanding the spending capacity of customers and segmenting them based on their income levels.
**6. Spending Score (1-100):** A score assigned to the customer based on their spending behavior and patterns. This score ranges from 1 to 100 and helps in identifying high spenders.
**7. Number of Purchases:** The total number of purchases made by the customer. This feature helps in understanding the frequency of purchases and identifying frequent shoppers.
**8. Days Since Last Purchase:** The number of days since the customer last made a purchase. This feature can help in identifying inactive customers and potential churn.
**9. Average Purchase Value:** The average value of the purchases made by the customer. This feature helps in understanding the typical purchase amount for each customer.
**10. Total Purchase Value:** The total value of all purchases made by the customer. This feature helps in identifying high-value customers who contribute significantly to the revenue.
**11. Membership Level:** The membership level of the customer (Basic, Silver, Gold, Platinum). This feature can be used to analyze the benefits and behavior associated with different membership levels.
**12. Customer Loyalty Score:** A score indicating the loyalty of the customer. This feature helps in identifying loyal customers who consistently make purchases.
**13. Online Purchases:** The number of purchases made by the customer online. This feature helps in understanding the online shopping behavior of customers.
**14. Store Visits:** The number of visits made by the customer to the physical store. This feature helps in understanding the in-store shopping behavior of customers.
**15. Average Discount Received:** The average discount received by the customer on their purchases. This feature helps in understanding the sensitivity of customers to discounts and promotions.
**16. Total Discount Received:** The total discount received by the customer on their purchases. This feature helps in identifying customers who frequently take advantage of discounts.
**17. Preferred Store Section:** The preferred section of the store for the customer (Electronics, Clothing, Groceries, Home & Kitchen). This feature helps in understanding customer preferences for different store sections.
**18. Payment Method:** The preferred payment method of the customer (Credit Card, Debit Card, Cash, Online Payment). This feature helps in understanding the payment preferences of customers.
**19. Customer Feedback Score:** The feedback score given by the customer. This feature helps in understanding customer satisfaction and identifying areas for improvement.
**20. Last Purchase Amount:** The amount spent by the customer on their last purchase. This feature helps in understanding the recent spending behaviour of customers.
**21. Cluster:** The cluster to which the customer belongs. This feature is the result of the clustering algorithm and indicates the customer segment.

## Files

*   **train.csv** - the training set
*   **test.csv** - the test set
*   **sample_submission.csv** - a sample submission file in the correct format