Create README.md

README.md

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+---
+language:
+- en
+pretty_name: Amazon Best-Sellers – Ratings and Reviews Dataset
+license: cc-by-nc-2.0
+tags:
+- amazon
+- ecommerce
+- reviews
+- sentiment-analysis
+- recommendation-systems
+- product-analysis
+- nlp
+---
+
+This dataset provides a free trial sample of best-selling products and their customer reviews from a leading e-commerce platform, designed to support product intelligence, sentiment analysis, and market trend evaluation. This sample is provided for evaluation purposes only. It includes a curated subset of the full dataset.
+To access the complete dataset, request additional attributes, or explore alternative product segments, please contact the data provider directly.
+
+## Key Features
+- **2,700** top product records across diverse categories.
+- **256,000+** individual customer reviews and ratings.
+- Rich, structured metadata for both products and reviews.
+- Suitable for supervised and unsupervised machine learning tasks.
+
+The dataset is delivered as plain CSV files (one per table) with a consistent schema for easy ingestion into analysis pipelines or ML workflows.
+
+## Dataset Description
+
+- **Access:** Free sample dataset  
+- **Curated by:** https://datahive.ai  
+- **Language(s) (NLP):** English  
+- **License:** Creative Commons Non-Commercial 4.0 (CC BY-NC 4.0)  
+
+### Files Included
+1. **products.csv** – Product-level data  
+2. **reviews.csv** – Review-level data  
+
+### PRODUCTS CSV Fields
+- `url` – Direct link to the product page on Amazon.  
+- `asin` – Amazon Standard Identification Number.  
+- `title` – Product title.  
+- `description` – Product description text.  
+- `category_breadcrumbs` – Full category path for the product.  
+- `main_image` – URL of the main product image.  
+- `alt_images` – JSON array of URLs for alternate product images.  
+- `rating` – Average product rating (numeric).  
+- `number_of_ratings` – Total number of ratings for the product.  
+
+### REVIEWS CSV Fields
+- `review_text` – Full text of the customer review.  
+- `star_rating` – Numeric star rating (1–5).  
+- `helpfulness_count` – Number of users who marked the review as helpful.  
+- `verified_purchase` – Boolean indicating verified purchase status.  
+- `submission_date` – Date the review was posted.  
+- `user_id` – Anonymized reviewer identifier.  
+
+### Source Data
+Data was collected from publicly accessible Amazon Best-Seller listings and customer reviews that did not require paywalls, or proprietary access. Only content available in the public domain was used, and all data is provided for research and trial purposes under CC BY-NC 4.0 licensing.
+
+## Uses
+- **Sentiment Analysis** – Train models to classify sentiment or detect product satisfaction drivers.  
+- **Recommendation Systems** – Build recommender pipelines using ratings, reviews, and product similarity.  
+- **Market Analysis** – Track product popularity, review trends, and pricing indicators.  
+- **Pricing Analysis** – Use review volume and sentiment to correlate customer satisfaction with price perception.  
+
+## Dataset Card Contact
+[email protected]