Name: Online Store Dataset
Creator: Kaggle
License: https://opensource.org/licenses/MIT

About this Dataset

Online Store Dataset

Dataset Name: Online Store Dataset

Description: The Online Store dataset is a comprehensive collection of 500 rows of synthetic e-commerce product data. Designed to simulate an online retail environment similar to major e-commerce platforms like Amazon, this dataset includes a diverse range of attributes for each product. The dataset provides valuable insights into product characteristics, pricing, stock levels, and customer feedback, making it ideal for analysis, machine learning, and data visualization projects.

Features:

ID: Unique identifier for each product.
Product_Name: Name of the product, generated using random words to simulate real-world product names.
Category: Product category (e.g., Electronics, Clothing, Books, Home, Toys, Sports).
Price: Product price, ranging from $10 to $500.
Stock: Number of items available in stock.
Rating: Customer rating of the product (1 to 5 stars).
Reviews: Number of customer reviews.
Brand: Brand of the product.
Date_Added: Date when the product was added to the catalog.
Discount: Percentage discount applied to the product.
Use Cases:

Data Analysis: Explore trends and patterns in e-commerce product data.
Machine Learning: Build and train models for product recommendation, pricing strategies, or customer segmentation.
Data Visualization: Create visualizations to analyze product categories, pricing distribution, and customer reviews.
Notes:

The data is synthetic and randomly generated, reflecting typical attributes found in e-commerce platforms.
This dataset can be used for educational purposes, practice, and experimentation with various data analysis and machine learning techniques.

Tables

Amazon Like Dataset

@kaggle.chakilamvishwas_online_store_dataset.amazon_like_dataset

27.98 kB
500 rows
10 columns

CREATE TABLE amazon_like_dataset (
  "id" BIGINT,
  "product_name" VARCHAR,
  "category" VARCHAR,
  "price" DOUBLE,
  "stock" BIGINT,
  "rating" DOUBLE,
  "reviews" BIGINT,
  "brand" VARCHAR,
  "date_added" VARCHAR,
  "discount" DOUBLE
);