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  "fetched_at": "2026-10-20T08:03:13Z",
  "source_url": "https://www.kaggle.com/competitions/bdaio-2024-segment-conquer/data",
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      "name": "data-description",
      "content": "The dataset contains various features that describe customer demographics, purchasing behavior, and preferences. Here’s a detailed explanation of each column:\n\n**1. CustomerID:** Unique identifier for each customer. This column is used to distinguish each customer record in the dataset.\n**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.\n**3. Gender:** The gender of the customer (Male or Female). This feature can be used to analyze purchasing behavior differences between genders.\n**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.\n**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.\n**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.\n**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.\n**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.\n**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.\n**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.\n**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.\n**12. Customer Loyalty Score:** A score indicating the loyalty of the customer. This feature helps in identifying loyal customers who consistently make purchases.\n**13. Online Purchases:** The number of purchases made by the customer online. This feature helps in understanding the online shopping behavior of customers.\n**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.\n**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.\n**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.\n**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.\n**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.\n**19. Customer Feedback Score:** The feedback score given by the customer. This feature helps in understanding customer satisfaction and identifying areas for improvement.\n**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.\n**21. Cluster:** The cluster to which the customer belongs. This feature is the result of the clustering algorithm and indicates the customer segment.\n\n## Files\n\n*   **train.csv** - the training set\n*   **test.csv** - the test set\n*   **sample_submission.csv** - a sample submission file in the correct format\n\n\n",
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      "name": "Description",
      "content": "Your task is to analyze a dataset containing various features related to customer demographics, purchasing behavior, and feedback. Using this data, you will apply clustering techniques to group customers into meaningful segments. These segments will then be used to derive actionable insights for the retail store.",
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      "name": "Evaluation",
      "content": "The Adjusted Rand Index (ARI) will be used as the evaluation metric for this problem. The ARI is a measure of the similarity between two clusterings, and it ranges from -1 to 1. A value of 1 indicates perfect agreement between the two clusterings, while a value of -1 indicates perfect disagreement.\n## Submission File\nFor each ID in the test set, you must predict a probability for the TARGET variable. The file should contain a header and have the following format:\n\n```python\n    CustomerID,Cluster\n    2,0\n    5,0\n    6,0\n```\n  ",
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      "name": "rules",
      "content": "This is a page where you can include rules that participants must accept before joining. You may wish to include rules like:\n\n- Don't cheat!\n- Any kind of misconduct will result in disqualification\n",
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    {
      "name": "abstract",
      "content": "Suppose you're a salesman at a bustling retail store. Every day, you interact with a wide variety of customers, each with their own unique preferences and shopping habits. You notice that some customers frequently buy high-end electronics, while others are always on the lookout for the best deals in the clothing section. Some visit the store weekly, while others make their purchases online. As a keen observer, you realize that understanding these diverse behaviours could unlock tremendous opportunities for the store. Now, imagine you have access to a treasure trove of customer data and the skills of a data scientist. Your mission: to uncover hidden patterns and insights from customer purchasing behaviour, segmenting customers into distinct groups to revolutionize the store's marketing and customer experience strategies.",
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    {
      "name": "Documentation",
      "content": "To check documentation from command line, refer to this notebook.\nhttps://www.kaggle.com/code/promachowdhury/check-library-documentation",
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