Dataset: 👔 Shein Men’s 1K Fashion Insights

About this Dataset

👔 Shein Men’s 1K Fashion Insights

Dataset Overview: This dataset, titled "👔 Shein Men's 1K Fashion Insights", provides a comprehensive look into the fashion trends of Shein's men's apparel. It contains 1000 entries, each representing a unique product with detailed attributes.

Data Science Applications: This dataset is a valuable resource for data scientists interested in fashion trends, consumer behavior, and retail analytics. It can be used for exploratory data analysis, trend forecasting, customer segmentation, and recommendation systems.

Column Descriptors: The dataset includes columns such as 'product_id', 'sku', 'url', 'title', 'color', 'sale_price', 'retail_price', 'discount_percentage', 'category_name', 'category_id', 'description', 'reviews_count', and 'average_rating'. Each column provides specific information about the product, from its ID and SKU to its color, price details, category, and customer reviews.

Ethically Mined Data: The data has been ethically mined, ensuring respect for privacy norms and adherence to data protection regulations. No personal customer information is included in this dataset.

Acknowledgements: We extend our gratitude to Shein for making their product data accessible, and to Kaggle for providing a platform for sharing and exploring such datasets. Their contributions to the data science community are invaluable.

Please note that this description is intended to be a concise overview. For a more detailed analysis, please refer to the dataset directly.

Tables

Shein Mens Fashion

@kaggle.kanchana1990_shein_mens_1k_fashion_insights.shein_mens_fashion

157.75 KB
1000 rows
15 columns


CREATE TABLE shein_mens_fashion (
  "product_id" BIGINT,
  "sku" VARCHAR,
  "url" VARCHAR,
  "title" VARCHAR,
  "color" VARCHAR,
  "sale_price_amount" DOUBLE,
  "sale_price_amount_with_symbol" VARCHAR,
  "retail_price_amount" DOUBLE,
  "retail_price_amount_with_symbol" VARCHAR,
  "discount_percentage" BIGINT,
  "category_name" VARCHAR,
  "category_id" BIGINT,
  "description" VARCHAR,
  "reviews_count" BIGINT,
  "average_rating" DOUBLE
);