The dataset represents retail transactional data. It contains information about customers, their purchases, products, and transaction details. The data includes various attributes such as customer ID, name, email, phone, address, city, state, zipcode, country, age, gender, income, customer segment, last purchase date, total purchases, amount spent, product category, product brand, product type, feedback, shipping method, payment method, and order status.
Includes customer details like ID, name, email, phone, address, city, state, zipcode, country, age, and gender. Customer segments are categorized into Premium, Regular, and New.
Transaction-specific data such as transaction ID, last purchase date, total purchases, amount spent, total purchase amount, feedback, shipping method, payment method, and order status.
Contains product-related details such as product category, brand, and type. Products are categorized into electronics, clothing, grocery, books, and home decor.
Contains location details including city, state, and country. Available for various countries including USA, UK, Canada, Australia, and Germany.
Last purchase date is provided along with separate columns for year, month, date, and time. Allows analysis based on temporal patterns and trends.
Some rows contain null values, and others are duplicates, which may need to be handled during data preprocessing. Null values are randomly distributed across rows. Duplicate rows are available at different parts of the dataset.
- Customer segmentation analysis based on age, gender, income etc.
- Sales trend analysis over time to identify peak seasons or trends.
- Product performance analysis to determine popular categories, brand and type.
- Geographic analysis to understand regional preferences.