Dataset

This dataset was created by prajapati meet

Contents

Amazon Sales Dataset Description This dataset contains 250 records of Amazon sales transactions, including details about the products sold, customers, payment methods, and order statuses.

Columns Description: Order ID - Unique identifier for each order (e.g., ORD0001).

Date - Date of the order.

Product - Name of the product purchased.

Category - Product category (Electronics, Clothing, Home Appliances, etc.).

Price - Price of a single unit of the product.

Quantity - Number of units purchased in the order.

Total Sales - Total revenue from the order (Price × Quantity).

Customer Name - Name of the customer.

Customer Location - City where the customer is based.

Payment Method - Mode of payment (Credit Card, Debit Card, PayPal, etc.).

Status - Order status (Completed, Pending, or Cancelled).

This dataset can be used for sales analysis, customer behavior insights, and revenue trends visualization. 🚀

Comprehensive dataset tracking Amazon Prime member spending patterns from 2019-2024, including comparison with non-Prime customers and demographic breakdowns

This dataset encompasses mobile app based media consumption, collected from over 150,000 first-party US Daily Active Users on Android devices. Use it for measurement, journey understanding or to trigger surveys about sentiment. Platforms include Netflix, YouTube, Disney+ & Amazon Prime Video.

It is a exploratory analysis of Amazon Prime Movies using python.

Amazon Review 2018 Common Users Dataset

  Overview

This dataset contains Amazon Review data from the 2018 version, filtered across multiple domains. It includes reviews from common users across the following categories:

Books CDs and Vinyl Digital Music Magazine Subscriptions Movies and TV Toys and Games Video Games

The data has been curated by selecting users who have reviews across multiple domains, ensuring that only relevant items and user interactions are included.… See the full description on the dataset page: https://huggingface.co/datasets/sszhong/Amazon-Users.

Dataset

This dataset was created by Shreyas Naphad

Contents

These datasets contain reviews from the Steam video game platform, and information about which games were bundled together.

Metadata includes

• reviews

• purchases, plays, recommends (likes)

• product bundles

• pricing information

Basic Statistics:

• Reviews: 7,793,069

• Users: 2,567,538

• Items: 15,474

• Bundles: 615

Context

This Dataset is an updated version of the Amazon review dataset released in 2014. As in the previous version, this dataset includes reviews (ratings, text, helpfulness votes), product metadata (descriptions, category information, price, brand, and image features), and links (also viewed/also bought graphs). In addition, this version provides the following features:

• More reviews:

  • The total number of reviews is 233.1 million (142.8 million in 2014).

• New reviews:

  • Current data includes reviews in the range May 1996 - Oct 2018.

• Metadata: - We have added transaction metadata for each review shown on the review page.

  • Added more detailed metadata of the product landing page.

Acknowledgements

If you publish articles based on this dataset, please cite the following paper:

• Jianmo Ni, Jiacheng Li, Julian McAuley. Justifying recommendations using distantly-labeled reviews and fined-grained aspects. EMNLP, 2019.

This is a dataset of users consuming streaming content on Twitch. We retrieved all streamers, and all users connected in their respective chats, every 10 minutes during 43 days.

Dataset

This dataset was created by DHARMENDRA MAURYA

Contents

About the Dataset:

Amazon is one of the biggest online retailers in the UK. With this dataset, you can get an in-depth idea of what products sell best, which SEO titles generate the most sales, the best price range for a product in a given category, and much more.

It took a lot of time and energy to prepare this original dataset, so don't forget to hit the upvote button! 😊💝

Similar Datasets:

Amazon Canada Products

Amazon India Products

Amazon USA Products

Interesting Task Ideas:

1. Uncover trending product categories and their sales performance.
2. Analyze customer ratings to find top-rated products.
3. Train a product title generator that can generate sales-worthy product titles based on products with the most sales.
4. Gain insight into the best price for any given product based on sales data and competition.
5. Identify which niches are the easiest to make sales in.
6. Gain insights into the general spending habits of online shoppers.
7. Use as a seed dataset for practicing database management and performance optimization.
8. Train (or learn to train) an AI-based search model to recommend Amazon products.

Checkout my other datasets

USA Unemployment Rates by Demographics & Race

USA Hispanic-White Wage Gap Dataset

Median and Avg Hourly Wages in the USA

Health Insurance Coverage in the USA

Black-White Wage Gap in the USA Dataset

Photo by Canonicalized

Dataset

This dataset was created by Chetan Pawar

Released under MIT

Contents

This dataset contains meticulously cleaned and structured web traffic data collected across multiple websites, including Amazon platforms and services like Amazon Prime, AWS, and AWS Support. It spans various traffic sources, user devices, key actions, and engagement metrics, making it a powerful resource for digital marketing analysis, customer behavior modeling, and time series forecasting.

Ideal for:

Web traffic analysis Conversion rate optimization Bounce rate analysis User segmentation Predictive modeling and machine learning 📌 Dataset Features: Rows: 2006 Columns: 18

Date Range: Starts from January 1st, 2019 (Exact end date can be inferred from the dataset)

🔍 Columns Overview: Country: Country of user origin

Timestamp: Full timestamp of the visit Device Category: Type of device (Desktop, Mobile, Tablet) Key Actions: User actions like Purchase, Sign Up, Subscribe Page Path: Visited page (e.g., /home, /contact) Source: Traffic source (e.g., organic search, social media) Avg Session Duration: Duration of session in seconds Bounce Rate: % of single-page sessions Conversions: Number of conversions New Users: Number of new users in session Page Views: Total page views Returning Users: Count of returning users Unique Page Views: Unique page views Average time on home page (min): Self-explanatory Website: Name of the specific Amazon service or domain Date, Time, Day: Parsed date and time information

📊 Potential Use Cases: Machine Learning: Predicting bounce rate, conversion likelihood, or segmenting user behavior. Business Intelligence: Dashboards for performance analysis by device, source, or day. Time Series Forecasting: Analyze traffic patterns over time. A/B Testing: Benchmarking traffic changes across page paths or traffic sources.

Context

Amazon.com, Inc. is an American online retailer with a wide range of products. According to its own information, Amazon, as the market leader in Internet trade, has the world's largest selection of books, CDs and videos. Via the integrated sales platform Marketplace, private individuals or other companies can also offer new and used products as part of online trading. The Amazon Kindle is sold under its own brand as a reader for electronic books, the Amazon Fire HD tablet computer, the Fire TV set-top box, the Fire TV Stick HDMI stick and the Echo speech recognition system.

With sales of $280 billion in 2019, a profit of $11.6 billion, and a market value of $1.32 trillion (June 2020), it was the third most valuable after Apple and Microsoft, and even before Google United States company.

Market cap

Market capitalization of Amazon (AMZN)

Market cap: $2.362 Trillion USD

As of February 2025 Amazon has a market cap of $2.362 Trillion USD. This makes Amazon the world's 4th most valuable company by market cap according to our data. The market capitalization, commonly called market cap, is the total market value of a publicly traded company's outstanding shares and is commonly used to measure how much a company is worth.

Revenue

Revenue for Amazon (AMZN)

Revenue in 2024 (TTM): $637.95 Billion USD

According to Amazon's latest financial reports the company's current revenue (TTM ) is $637.95 Billion USD. an increase over the revenue in the year 2023 that were of $574.78 Billion USD. The revenue is the total amount of income that a company generates by the sale of goods or services. Unlike with the earnings no expenses are subtracted.

Earnings

Earnings for Amazon (AMZN)

Earnings in 2024 (TTM): $71.02 Billion USD

According to Amazon's latest financial reports the company's current earnings are $637.95 Billion USD. , an increase over its 2023 earnings that were of $40.73 Billion USD. The earnings displayed on this page is the company's Pretax Income.

End of Day market cap according to different sources

On Feb 20th, 2025 the market cap of Amazon was reported to be:

$2.362 Trillion USD by Yahoo Finance

$2.362 Trillion USD by CompaniesMarketCap

$2.362 Trillion USD by Nasdaq

Content

Geography: USA

Time period: May 1997- February 2025

Unit of analysis: Amazon Stock Data 2025

Variables

Acknowledgements

This dataset belongs to me. I’m sharing it here for free. You may do with it as you wish.

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This dataset belongs to a single product on Amazon and contains detailed information about its reviews. Each entry in the dataset represents a review written by a customer.

reviewerid: A unique identifier for the reviewer. Each reviewer has a distinct ID that helps differentiate their reviews from others. asin: Amazon Standard Identification Number. It is a unique identifier assigned to each product on Amazon. reviewername: The name or username of the reviewer. This is the display name of the person who wrote the review. helpful: A measure indicating how many people found the review helpful. This is often shown as a ratio, e.g., [2,3] where 2 people found it helpful out of 3 total votes. reviewtext: The actual text of the review. This is the content of what the reviewer wrote about the product. overall: The rating given by the reviewer, usually on a scale of 1 to 5 stars. summary: A short summary or title of the review. This is often a brief highlight of the reviewer's opinion. unixreviewtime: The time the review was written, represented as a Unix timestamp. This is the number of seconds that have elapsed since January 1, 1970 (midnight UTC/GMT). reviewtime: The human-readable date when the review was written, typically in the format "MM DD, YYYY". day_diff: The difference in days between the review date and some reference date (often the current date or the date the dataset was compiled). This helps to understand how recent the review is. helpful_yes: The number of people who found the review helpful. This is the first number in the "helpful" ratio. total_vote: The total number of votes the review received. This is the second number in the "helpful" ratio.

The column of the dataset are shown below: Order-id-> It contain the id of the order. Order-Date->It shows the date when the customer order the item. Ship-Date->It shows the shipping date of the customer item. Status->It shows the Status of the delivering item i.e. delay or on-time. Customer-Name->It shows the name of the customer who place the order. country->It shows the country of the customer. city->It shows the city of the customer. State->It shows the state of the customer. Category->It shows the in which category the customer order. Product-Name->It shows the name of the product. Sales->It shows the sales done by the company on the product. Quantity->It shows how much the item sold by the customer. Profit->It shows the profit gain .

Amazon (AMZN) Stock Price Time-Series Dataset: May 2012 - November 2012

Dataset Overview

This dataset provides a detailed, intraday view of Amazon's stock (AMZN) price movements from May 21, 2012, to November 14, 2012. Meticulously compiled, it offers a granular perspective on market dynamics, enabling robust quantitative analysis and modeling.

Content

The dataset encompasses the following key financial metrics for each trading day:

• Date: The specific date of the trading session.
• Open: The initial price at the commencement of trading.
• High: The maximum price attained during the trading day.
• Low: The minimum price recorded during the trading day.
• Close: The final trading price at the market's close.
• Adj Close: The closing price adjusted for corporate actions like dividends and stock splits, providing a true return on investment.
• Volume: The number of shares exchanged throughout the trading day, indicating market activity and liquidity.

Intended Use Cases

This dataset is tailored for sophisticated financial analysis, model development, and academic research. Potential applications include:

• Algorithmic Trading Strategy Development: Design and back-test trading algorithms using historical price movements and volume data.
• Volatility Modeling: Analyze and forecast stock price volatility using time-series analysis techniques (e.g., GARCH models).
• Financial Forecasting: Implement machine learning models to predict future stock prices based on historical patterns.
• Event Study Analysis: Examine the impact of specific events or news announcements on Amazon's stock price.
• Risk Management: Evaluate potential risks associated with investing in Amazon stock during this period.
• Academic Research: Conduct studies on market efficiency, price discovery, and the impact of market microstructure on stock behavior.

Data Considerations

• Time Zone: Data is timestamped with Eastern Time (ET).
• Data Cleaning: The dataset has been verified for accuracy, but users are encouraged to perform their own data quality checks.

Contect info:

You can contect me for more data sets if you want any type of data to scrape

-E_mail

-Linkdin

-Kaggle

-X

-Github

This dataset contains 100,000 synthetic Amazon-style e-commerce sales transactions, designed to closely resemble real-world online retail behavior. With 20 clean and well-structured columns, it captures detailed information about customers, products, pricing, payments, logistics, and order outcomes.

Although the data is artificially generated, it reflects realistic patterns such as:

Dynamic product pricing

Varying discounts and taxes

Multiple product categories & brands

Seasonal order trends

Payment method diversity

Realistic customer names & locations

Order statuses like Delivered, Cancelled, Shipped, Returned

This makes the dataset highly suitable for analytics, machine learning, data visualization, dashboards, and business case studies.

📊 Column Overview

The dataset includes:

🧾 Order Details

OrderID

OrderDate

OrderStatus

SellerID

👤 Customer Information

CustomerID

CustomerName

City, State, Country

📦 Product Information

ProductID

ProductName

Category

Brand

Quantity

💰 Pricing & Revenue Metrics

UnitPrice

Discount

Tax

ShippingCost

TotalAmount

💳 Payment Details

PaymentMethod

Dataset

This dataset was created by Phan Nguyễn Hữu Phong

Contents