Enhanced Product Dataset with Reviews
Group Category: Dataset
Product Category: Database Design & Development
Sub Category: PostgreSQL
Understand customer sentiment, track product feedback, and drive intelligent merchandising with review-enriched product datasets.
Business Overview:
The Enhanced Product Dataset with Reviews integrates a rich product catalog with real customer feedback and behavioral data, offering a complete view of product performance across both supply and demand dimensions. This dataset is perfect for analysts, data scientists, and engineers working in e-commerce, consumer analytics, or product intelligence. It brings together structured inventory data and user-generated reviews to support deeper insights into product satisfaction, category trends, and customer sentiment — all within a PostgreSQL-compatible format.
Product Highlights:
- Includes 5 CSV files:
products.csv,subcategories.csv,categories.csv,reviews.csv, andusers.csv - Merges catalog structure with customer feedback for powerful sentiment and satisfaction analysis
- Fully documented with column-level data dictionaries across all tables
- Ideal for prototyping review-based recommendation systems and sentiment-driven dashboards
- Supports end-to-end SQL practice for analytics, data modeling, and ETL
Learning Outcomes:
By working with this dataset, you will:
- Analyze product reviews to identify satisfaction levels and quality trends
- Perform sentiment classification and category-level review aggregation
- Link user demographics with feedback behavior to uncover persona-based patterns
- Build customer review pipelines, scoring logic, and collaborative filtering models
- Simulate real-world SQL use cases involving
JOINs across product, user, and review tables
products
| CSVContains detailed product-level data including identifiers, descriptions, pricing, stock levels, and subcategory associations.
This file supports inventory management, pricing analysis, and catalog optimization use cases.
Key Features:
- 965 product records with pricing and inventory
- Direct linkage to subcategories via foreign key
- Ideal for performing product segmentation, stock analysis, or listing operations
subcategories
| CSVLists subcategories under each product category, providing mid-level grouping for product classification.
Supports hierarchical navigation and filtering within the catalog structure.
Key Features:
- 68 unique subcategories
- Includes category linkage and descriptive metadata
- Enables faceted search, classification logic, and category-level rollups
categories
| CSVCaptures top-level product groupings with general descriptions. Serves as the parent layer for subcategories and products.
Useful for high-level analysis, KPI tracking by department, and product tree structuring.
Key Features:
- 10 main product categories
- Forms the root hierarchy for the catalog schema
- Can be used to generate filters, summary views, or landing pages by category
Dataset Document
| PDFThis documentation explains a comprehensive product dataset enriched with user-generated reviews and customer metadata. It integrates product information with feedback, ratings, and user profiles to enable full-spectrum analysis across marketing, customer satisfaction, and recommendation systems.
Highlights:
- Covers 5 integrated tables: products, subcategories, categories, reviews, and users
- Enables sentiment analysis, rating trends, and customer segmentation
- Ideal for analysts, data scientists, and ML engineers working on product feedback systems or review analytics
reviews
| CSVContains detailed customer reviews of products, including rating values, review texts, and relational mappings to both users and products. This file powers sentiment scoring, review distribution analysis, and satisfaction measurement.
Key Features:
- 18,421 reviews covering thousands of products
- Includes star ratings (1 to 5), review descriptions, and timestamps
- Supports review trend analysis, feedback mining, and sentiment model training
users
| CSVStores demographic and account-related information of users who submit reviews. Data includes names, contact details, and user classifications, useful for segmenting customers or analyzing review behaviors across user types.
Key Features:
- 6,104 unique users with full profiles
- Enables customer segmentation and behavioral cohorting
- Supports integrations with review and transaction data for user-centric analytics
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About this Dataset:
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