https://www.kaggle.com/code/mithilesh9/amazon-sales-data-analysis-using-python

Dataset Description This dataset contains a 100 rows of sales data for Amazon, including the region, country, item type, sales channel, order priority, order date, order ID, ship date, units sold, unit price, unit cost, total revenue, total cost, and total profit.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F19501062%2F5d10a624d07eefb2240c474ca00114b6%2FScreenshot%202024-06-25%20135139.png?generation=1719303822906805&alt=media" alt="">

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. 🚀

Don't forget to Upvote the Dataset🙏.

This Data is a Amazon Product Sales. This Dataset about Amazon Sales Contain 3204 Rows and 9 Columns. You Can Apply Various thing you can make DashBoard and perform Analysis many more..

Column Description:

Order Date - Order_Date.

Ship Date - Shipping Date.

Email_ID - Email_ID of Users

Geography - Location of Orders by Users.

Category - Product Category

Product Name - Product Name of Amazon

Sales - Amazon Product Sales

Quantity - how many units of a particular product are available.

Profit - Amazon Sales Profit

Problem Statement: Sales management has gained importance to meet increasing competition and the need for improved methods of distribution to reduce cost and to increase profits. Sales management today is the most important function in a commercial and business enterprise. We need to extract all the Amazon sales datasets, transform them using data cleaning and data preprocessing and then finally loading it for analysis. We need to visualize sales trend month-wise, year-wise and yearly-month wise. Moreover, we need to find key metrics and factors and show meaningful relationships between attributes.

Approach The main goal of the project is to find key metrics and factors and then show meaningful relationships between them based on different features available in the dataset.

Data Collection : Imported data from various datasets available in the project using Pandas library.

Data Cleaning : Removed missing values and created new features as per insights.

Data Preprocessing : Modified the structure of data in order to make it more understandable and suitable and convenient for statistical analysis.

Data Analysis : I started analyzing dataset using Pandas,Numpy,Matplotlib and Seaborn.

Data Visualization : Plotted graphs to get insights about dependent and independent variables. Also used Tableau and PowerBI for data visulization.

According to forecasts, net sales of electrical products on Amazon are forecast at over *** billion U.S. dollars. With a compound annual growth rate of **** percent, this figure is expected to exceed *** billion dollars by 2026. Yet, the category expected to grow the strongest on the e-commerce platform is health and beauty.

1) Data Introduction • The Amazon Sales Dataset includes e-commerce product and consumer feedback data, including details on more than 1,000 products collected from Amazon's official website, discount prices, ratings, reviews, and categories.

2) Data Utilization (1) Amazon Sales Dataset has characteristics that: • The dataset includes a variety of product and review-related attributes, including product ID, product name, category, real and discounted prices, discount rates, ratings, rating numbers, product descriptions, user reviews, images, and product links. (2) Amazon Sales Dataset can be used to: • Product Rating and Review Analysis: Use rating and review data to analyze consumer satisfaction, popular products, review trends, and develop marketing strategies for each product. • Development of Price Policy and Recommendation System: Based on price information such as actual price, discount price, and discount rate, it can be used for price policy analysis, product recommendation system, consumer purchasing behavior prediction, etc.

The "Amazon Sales Data" dataset is a comprehensive collection of 12 CSV files, each representing a month's worth of Amazon sales data. This dataset provides valuable insights into the ordering patterns, product details, quantities, prices, order dates, and purchase addresses associated with Amazon orders.

The dataset contains the following columns:

1. Order ID: Unique identification number for each order.
2. Product: Description of the product being ordered.
3. Quantity Ordered: The quantity of the product ordered in a particular transaction.
4. Price Each: The price of each product unit.
5. Order Date: The date and time when the order was placed.
6. Purchase Address: The address where the order was delivered.

By analyzing this dataset, users can gain a deep understanding of Amazon sales trends over the course of 12 months. It provides a valuable resource for market research, sales forecasting, product analysis, and customer behavior analysis. Researchers, data scientists, and business professionals can leverage this dataset to uncover patterns, identify popular products, understand pricing strategies, and derive actionable insights for optimizing sales and business strategies.

Please note that this dataset is anonymized and does not contain any personally identifiable information (PII) of customers or sensitive financial details. It is intended for educational and analytical purposes.

1) Data Introduction • The Amazon Products Sales Dataset 2023 is a large e-commerce dataset that summarizes various product information in a tabular format, including product name, price, rating, discount information, images, and links by 142 major categories collected from Amazon's website.

2) Data Utilization (1) Amazon Products Sales Dataset 2023 has characteristics that: • Each row contains 10 key attributes, including product name, main/subcategory, image, Amazon link, rating, number of ratings, discount price, and actual price. • The data encompasses a wide range of products and is structured to enable multi-faceted analysis such as price policy, customer evaluation, and trend by category. (2) Amazon Products Sales Dataset 2023 can be used to: • Product Recommendation and Marketing Strategy: Use rating, price, and category data to develop a customized recommendation system, analyze popular products, and establish a category-specific marketing strategy. • Price and Discount Policy Analysis—Based on discounted prices and actual prices, ratings, reviews, etc., it can be applied to effective pricing policies, promotion strategies, market competitiveness analyses, and more.

Amazon is one of the most recognisable brands in the world, and the third largest by revenue. It was the fourth tech company to reach a $1 trillion market cap, and a market leader in e-commerce,...

Comprehensive statistical analysis of Amazon seller monthly revenue, profit margins, and business performance metrics based on survey data from nearly 2,000 sellers across 100+ countries

Comprehensive Amazon India sales dataset featuring 15,000 synthetic e-commerce transactions from 2025. This cleaned and validated dataset captures real-world shopping patterns including customer behavior, product preferences, payment methods, delivery metrics, and regional sales distribution across Indian states.

Key Features: - 15,000 orders across multiple product categories (Electronics, Clothing, Home & Kitchen, Beauty) - Daily OHLCV-style transactional data from January to December 2025 - Complete customer journey: Order placement, payment, delivery, and review - Geographic coverage across major Indian states - Payment method diversity: Credit Card, Debit Card, UPI, Cash on Delivery - Delivery status tracking: Delivered, Pending, Returned - Customer review ratings and sentiment analysis

Dataset Columns (14): Order_ID, Date, Customer_ID, Product_Category, Product_Name, Quantity, Unit_Price_INR, Total_Sales_INR, Payment_Method, Delivery_Status, Review_Rating, Review_Text, State, Country

Use Cases: - E-commerce sales analysis and forecasting - Customer behavior and segmentation studies - Payment method preference analysis - Regional market trends and geographic insights - Delivery optimization and logistics planning - Product performance and category analysis - Customer satisfaction and review analysis - SQL practice and business intelligence training

Data Quality: - Cleaned and validated for analysis - No missing values in critical fields - Consistent data types and formatting - Ready for immediate SQL/Python analysis

Perfect for data analysts, SQL learners, business intelligence projects, and e-commerce analytics practice!

This repository was created for my Master's thesis in Computational Intelligence and Internet of Things at the University of Córdoba, Spain. The purpose of this repository is to store the datasets found that were used in some of the studies that served as research material for this Master's thesis. Also, the datasets used in the experimental part of this work are included.

Below are the datasets specified, along with the details of their references, authors, and download sources.

----------- STS-Gold Dataset ----------------

The dataset consists of 2026 tweets. The file consists of 3 columns: id, polarity, and tweet. The three columns denote the unique id, polarity index of the text and the tweet text respectively.

Reference: Saif, H., Fernandez, M., He, Y., & Alani, H. (2013). Evaluation datasets for Twitter sentiment analysis: a survey and a new dataset, the STS-Gold.

File name: sts_gold_tweet.csv

----------- Amazon Sales Dataset ----------------

This dataset is having the data of 1K+ Amazon Product's Ratings and Reviews as per their details listed on the official website of Amazon. The data was scraped in the month of January 2023 from the Official Website of Amazon.

Owner: Karkavelraja J., Postgraduate student at Puducherry Technological University (Puducherry, Puducherry, India)

Features:

• product_id - Product ID
• product_name - Name of the Product
• category - Category of the Product
• discounted_price - Discounted Price of the Product
• actual_price - Actual Price of the Product
• discount_percentage - Percentage of Discount for the Product
• rating - Rating of the Product
• rating_count - Number of people who voted for the Amazon rating
• about_product - Description about the Product
• user_id - ID of the user who wrote review for the Product
• user_name - Name of the user who wrote review for the Product
• review_id - ID of the user review
• review_title - Short review
• review_content - Long review
• img_link - Image Link of the Product
• product_link - Official Website Link of the Product

License: CC BY-NC-SA 4.0

File name: amazon.csv

----------- Rotten Tomatoes Reviews Dataset ----------------

This rating inference dataset is a sentiment classification dataset, containing 5,331 positive and 5,331 negative processed sentences from Rotten Tomatoes movie reviews. On average, these reviews consist of 21 words. The first 5331 rows contains only negative samples and the last 5331 rows contain only positive samples, thus the data should be shuffled before usage.

This data is collected from https://www.cs.cornell.edu/people/pabo/movie-review-data/ as a txt file and converted into a csv file. The file consists of 2 columns: reviews and labels (1 for fresh (good) and 0 for rotten (bad)).

Reference: Bo Pang and Lillian Lee. Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales. In Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL'05), pages 115–124, Ann Arbor, Michigan, June 2005. Association for Computational Linguistics

File name: data_rt.csv

----------- Preprocessed Dataset Sentiment Analysis ----------------

Preprocessed amazon product review data of Gen3EcoDot (Alexa) scrapped entirely from amazon.in
Stemmed and lemmatized using nltk.
Sentiment labels are generated using TextBlob polarity scores.

The file consists of 4 columns: index, review (stemmed and lemmatized review using nltk), polarity (score) and division (categorical label generated using polarity score).

DOI: 10.34740/kaggle/dsv/3877817

Citation: @misc{pradeesh arumadi_2022, title={Preprocessed Dataset Sentiment Analysis}, url={https://www.kaggle.com/dsv/3877817}, DOI={10.34740/KAGGLE/DSV/3877817}, publisher={Kaggle}, author={Pradeesh Arumadi}, year={2022} }

This dataset was used in the experimental phase of my research.

File name: EcoPreprocessed.csv

----------- Amazon Earphones Reviews ----------------

This dataset consists of a 9930 Amazon reviews, star ratings, for 10 latest (as of mid-2019) bluetooth earphone devices for learning how to train Machine for sentiment analysis.

This dataset was employed in the experimental phase of my research. To align it with the objectives of my study, certain reviews were excluded from the original dataset, and an additional column was incorporated into this dataset.

The file consists of 5 columns: ReviewTitle, ReviewBody, ReviewStar, Product and division (manually added - categorical label generated using ReviewStar score)

License: U.S. Government Works

Source: www.amazon.in

File name (original): AllProductReviews.csv (contains 14337 reviews)

File name (edited - used for my research) : AllProductReviews2.csv (contains 9930 reviews)

----------- Amazon Musical Instruments Reviews ----------------

This dataset contains 7137 comments/reviews of different musical instruments coming from Amazon.

This dataset was employed in the experimental phase of my research. To align it with the objectives of my study, certain reviews were excluded from the original dataset, and an additional column was incorporated into this dataset.

The file consists of 10 columns: reviewerID, asin (ID of the product), reviewerName, helpful (helpfulness rating of the review), reviewText, overall (rating of the product), summary (summary of the review), unixReviewTime (time of the review - unix time), reviewTime (time of the review (raw) and division (manually added - categorical label generated using overall score).

Source: http://jmcauley.ucsd.edu/data/amazon/

File name (original): Musical_instruments_reviews.csv (contains 10261 reviews)

File name (edited - used for my research) : Musical_instruments_reviews2.csv (contains 7137 reviews)

Utilize our Amazon reviews dataset for diverse applications to enrich business strategies and market insights. Analyzing this dataset can aid in understanding customer behavior, product performance, and market trends, empowering organizations to refine their product and marketing strategies. Access the entire dataset or tailor a subset to fit your requirements. Popular use cases include: Product Performance Analysis: Analyze Amazon reviews to assess product performance, uncovering customer satisfaction levels, common issues, and highly praised features to inform product improvements and marketing messages. Customer Behavior Insights: Gain insights into customer behavior, purchasing patterns, and preferences, enabling more personalized marketing and product recommendations. Demand Forecasting: Leverage Amazon reviews to predict future product demand by analyzing historical review data and identifying trends, helping to optimize inventory management and sales strategies. Accessing and analyzing the Amazon reviews dataset supports market strategy optimization by leveraging insights to analyze key market trends and customer preferences, enhancing overall business decision-making.

APISCRAPY's Amazon Data extraction is a sophisticated solution that leverages AI & web scraping skills to supply organizations with critical data from the Amazon platform. By scraping Amazon you get a product-related Amazon database, including product names, descriptions, pricing, ratings & reviews

Gain access to a structured dataset featuring thousands of products listed on Amazon India. This dataset is ideal for e-commerce analytics, competitor research, pricing strategies, and market trend analysis.

Dataset Features:

• Product Details: Name, Brand, Category, and Unique ID

• Pricing Information: Current Price, Discounted Price, and Currency

• Availability & Ratings: Stock Status, Customer Ratings, and Reviews

• Seller Information: Seller Name and Fulfillment Details

• Additional Attributes: Product Description, Specifications, and Images

Dataset Specifications:

• Format: CSV

• Number of Records: 50,000+

• Delivery Time: 3 Days

• Price: $149.00

• Availability: Immediate

This dataset provides structured and actionable insights to support e-commerce businesses, pricing strategies, and product optimization. If you're looking for more datasets for e-commerce analysis, explore our E-commerce datasets for a broader selection.

From 2004 to 2024, the net revenue of Amazon e-commerce and service sales has increased tremendously. In the fiscal year ending December 31, the multinational e-commerce company's net revenue was almost 638 billion U.S. dollars, up from 575 billion U.S. dollars in 2023.Amazon.com, a U.S. e-commerce company originally founded in 1994, is the world’s largest online retailer of books, clothing, electronics, music, and many more goods. As of 2024, the company generates the majority of it's net revenues through online retail product sales, followed by third-party retail seller services, cloud computing services, and retail subscription services including Amazon Prime. From seller to digital environment Through Amazon, consumers are able to purchase goods at a rather discounted price from both small and large companies as well as from other users. Both new and used goods are sold on the website. Due to the wide variety of goods available at prices which often undercut local brick-and-mortar retail offerings, Amazon has dominated the retailer market. As of 2024, Amazon’s brand worth amounts to over 185 billion U.S. dollars, topping the likes of companies such as Walmart, Ikea, as well as digital competitors Alibaba and eBay. One of Amazon's first forays into the world of hardware was its e-reader Kindle, one of the most popular e-book readers worldwide. More recently, Amazon has also released several series of own-branded products and a voice-controlled virtual assistant, Alexa. Headquartered in North America Due to its location, Amazon offers more services in North America than worldwide. As a result, the majority of the company’s net revenue in 2023 was actually earned in the United States, Canada, and Mexico. In 2023, approximately 353 billion U.S. dollars was earned in North America compared to only roughly 131 billion U.S. dollars internationally.

This dataset provides comprehensive real-time data from Amazon's global marketplaces. It includes detailed product information, reviews, seller profiles, best sellers, deals, influencers, and more across all Amazon domains worldwide. The data covers product attributes like pricing, availability, specifications, reviews and ratings, as well as seller information including profiles, contact details, and performance metrics. Users can leverage this dataset for price monitoring, competitive analysis, market research, and building e-commerce applications. The API enables real-time access to Amazon's vast product catalog and marketplace data, helping businesses make data-driven decisions about pricing, inventory, and market positioning. Whether you're conducting market analysis, tracking competitors, or building e-commerce tools, this dataset provides current and reliable Amazon marketplace data. The dataset is delivered in a JSON format via REST API.

The Amazon Diwali India Sales Dataset (2025) is a synthetic dataset containing 15,000 e-commerce transactions designed to simulate real-world sales on Amazon India. It captures customer purchasing behavior, product details, payment preferences, and feedback patterns across various product categories throughout the year 2025.

1. Order_ID: Unique identifier for each order

2. Date: Date on which the order was placed

3. Customer_ID: Unique customer identifier

4. Product_Category: Main category of the product purchased.

5. Product_Name: Specific product within the category.

6. Quantity: Number of units ordered for that item.

7. Unit_Price_INR: Price per unit in Indian Rupees (₹).

8. Total_Sales_INR: Total sales value = Quantity × Unit_Price_INR.

9. Payment_Method: Mode of payment used for the order.

10. Delivery_Status: Indicates if the order was delivered, pending, or returned.

11. Review_Rating: Customer rating for the product

12. Review_Text: Short review text consistent with the rating’s sentiment.

13. State: The Indian state where the order was delivered.

14. Country: Country of the order

The Amazon Sales Dataset (2019-2024) provides a comprehensive overview of sales transactions over a five-year period, covering key metrics essential for business intelligence and performance analysis. It includes 5000 records of sales data across five major regions: North America, Europe, Asia, South America, and Australia.

The dataset contains 13 key attributes, including Order ID, Order Date, Customer ID, Customer Name, Region, Product Category, Product Name, Quantity Sold, Unit Price, Discount Percentage, Total Sales, Profit Margin, Payment Method, and Order Status. These attributes provide valuable insights into revenue trends, customer behavior, regional performance, and discount effectiveness.

This dataset is ideal for visualization in Tableau, allowing analysts to explore sales performance, track profit margins, analyze the impact of discounts, and assess order fulfillment trends. With its structured format and diverse sales insights, the dataset serves as a powerful resource for data-driven decision-making. 🚀

Comprehensive dataset analyzing Amazon product review counts across categories, including 40 reviews average, category-specific benchmarks, and reviews-to-sales ratios based on analysis of 31,900 brands and 12 million product reviews.