Customer Purchase Behavior - Electronic Sales Data

Description:
This dataset contains sales transaction records for an electronics company over a one-year period, spanning from September 2023 to September 2024. It includes detailed information about customer demographics, product types, and purchase behaviors.

Key Features:

• Customer ID: Unique identifier for each customer.
• Age: Age of the customer (numeric)
• Gender: Gender of the customer (Male or Female)
• Loyalty Member: (Yes/No) (Values change by time, so pay attention to who cancelled and who signed up)
• Product Type: Type of electronic product sold (e.g., Smartphone, Laptop, Tablet)
• SKU: a unique code for each product.
• Rating: Customer rating of the product (1-5 stars) (Should have no Null Ratings)
• Order Status: Status of the order (Completed, Cancelled)
• Payment Method: Method used for payment (e.g., Cash, Credit Card, Paypal)
• Total Price: Total price of the transaction (numeric)
• Unit Price: Price per unit of the product (numeric)
• Quantity: Number of units purchased (numeric)
• Purchase Date: Date of the purchase (format: YYYY-MM-DD)
• Shipping Type: Type of shipping chosen (e.g., Standard, Overnight, Express)
• Add-ons Purchased: List of any additional items purchased (e.g., Accessories, Extended Warranty)
• Add-on Total: Total price of add-ons purchased (numeric)

NOTE: The original generated data had exactly 50% counts for both gender and loyalty. I have adjusted this for a more realistic distribution. Additionally, I ensured that all customer information is consistent across all their orders.

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Data Stats:

Total Rows: 20,000
Time Period: September 2023 to September 2024
Product Types: Includes various electronics such as Smartphones, Laptops, Tablets, and Smartwatches.

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Disclaimer:
This dataset is generated. It does not represent real-world transactions or actual customer data.
All values, including customer IDs, ages, product types, and ratings, are fictitious and created for the purpose of analysis and modeling.