Dataset

This dataset was created by ninaadps

Released under Other (specified in description)

Contents

The variety of products that can be purchased online is continuously growing. Among U.S. consumers the two most popular categories for online purchases are ******** and *****. ** percent and ** percent of consumers respectively chose these answers in our representative online survey. The survey was conducted online among 15,492 respondents in the United States, in 2025. Looking to gain valuable insights about customers of online shops across the globe? Check out our reports about consumers of online shops worldwide. These reports offer the readers a comprehensive overview of customers of eCommerce brands: who they are; what they like; what they think; and how to reach them.

Between January 1 and June 14, 2023, fashion and accessories were featured more than any other product on Instagram posts among consumers in the United States. Almost ************* posts were related to fashion and accessory products in the examined period. Lifestyle products racked up *** million posts, whilst food and beverages were posted about on Instagram *** million times in the U.S. in the examined period.

Business Context: The client is one of the leading online market place in India and would like partner with Analytixlabs. Client wants help in measuring, managing and analysing performance of business. Analytixlabs has hired you as an analyst for this project where client asked you to provide data driven insights about business and understand customer, seller behaviors, product behavior and channel behavior etc... While working on this project, you are expected to clean the data (if required) before analyze it. Available Data: Data has been provided for the period of Sep 2016 to Oct 2018 and the below is the data model. Tables: Customers: Customers information Sellers: Sellers information Products: Product information Orders: Orders info like ordered, product id, status, order dates etc.. Order_Items: Order level information Order_Payments: Order payment information Order_Review_Ratings: Customer ratings at order level Geo-Location: Location details Data Model: https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F13449746%2Fdd2a9639372124fb12bfd630fd43e473%2FScreenshot%202024-01-29%20210318.png?generation=1706542441423684&alt=media" alt="">

Business Objective: The below are few Sample business questions to be addressed as part of this analysis. However this is not exhaustive list and you can add as many as analysis and provide insights on the same. 1. Perform Detailed exploratory analysis a. Define & calculate high level metrics like (Total Revenue, Total quantity, Total products, Total categories, Total sellers, Total locations, Total channels, Total payment methods etc…) b. Understanding how many new customers acquired every month c. Understand the retention of customers on month on month basis d. How the revenues from existing/new customers on month on month basis e. Understand the trends/seasonality of sales, quantity by category, location, month, week, day, time, channel, payment method etc… f. Popular Products by month, seller, state, category. g. Popular categories by state, month h. List top 10 most expensive products sorted by price 2. Performing Customers/sellers Segmentation a. Divide the customers into groups based on the revenue generated b. Divide the sellers into groups based on the revenue generated 3. Cross-Selling (Which products are selling together) Hint: We need to find which of the top 10 combinations of products are selling together in each transaction. (combination of 2 or 3 buying together) 4. Payment Behaviour a. How customers are paying? b. Which payment channels are used by most customers? 5. Customer satisfaction towards category & product a. Which categories (top 10) are maximum rated & minimum rated? b. Which products (top10) are maximum rated & minimum rated? c. Average rating by location, seller, product, category, month etc. Etc..

The variety of products that can be purchased online is continuously growing. Among UK consumers the two most popular categories for online purchases are ******** and *****. ** percent and ** percent of consumers respectively chose these answers in our representative online survey. The survey was conducted online among 6,174 respondents in the UK, in 2025. Looking to gain valuable insights about customers of online shops across the globe? Check out our reports about consumers of online shops worldwide. These reports offer the readers a comprehensive overview of customers of eCommerce brands: who they are; what they like; what they think; and how to reach them.

About this Dataset

A downloadable list of Amazon Bestseller categories and subcategories, along with the top 10 products in each category/subcategory.

Category

Product Lists

Keywords

ecommerce

Row Count

361650

Price

$30.00

Context

Google Play stores top 500 app data based on their rankings on January 2022 for all the available categories. Link to scraping code: https://github.com/Shakthi-Dhar/AppPin Link to backup datafiles: github data files

Content

The dataset contains the top 500 android apps available on the google play store for the following categories: All Categories, Art & Design, Auto & Vehicles, Beauty, Books & Reference, Business, Comics, Communication, Education, Entertainment, Events, Finance, Food & Drink, Health & Fitness, House & Home, Libraries & Demo, Lifestyle, Maps & Navigation, Medical, Music & Audio, News & Magazines, Parenting, Personalization, Photography, Productivity, Shopping, Social, Sports, Tools, Travel & Local, and Video Players & Editors.

The app rankings are based on google play store app rankings for January 2022.

Abbreviations

In Review and Downloads, the alphabet T, L, Cr represents Thousands, Lakhs, Crores as per the google play store naming convention. They are similar to M, B which represent millions, billions. 1L (1 Lakh) = 100T (100 Thousand) 10L (10 Lakhs) = 1M (1 Million) 1Cr( 1 Crore) = 10M (10 Million)

Acknowledgements

This data is not provided directly by Google, so I used Appium an automation tool with python to scrape the data from the google play store app.

Inspiration

Inspired by Fortune500. Fortune500 provides data on top companies in the world, so why not have a data source for top apps in the world.

The dataset contains counts for the Top Five inpatient diagnosis groups based on Major Diagnostic Categories (MDCs) from the Patient Discharge Data (PDD) for each California hospital. Each MDC corresponds to a major organ system (e.g., Respiratory System, Circulatory System, Digestive System) rather than a specific disease (e.g., cancer, sepsis). The MDCs are also generally associated with a particular medical specialty. Therefore, the MDCs can be used to help identify what types of health care specialists are needed at each facility. For instance, a facility with “Circulatory System, Disease and Disorders” as one of their Top Five MDC diagnosis groups is more likely to have a greater need for cardiac specialists. The data will be updated on an annual basis.

Context

A 11.11 sale was going on Daraz within all categories. This dataset contains some product data from all categories such as health & beauty, men's & boys' fashion, groceries and others. Each product data carries information like product title, original price, discount, seller name and some more .

• The scraper program for data collection can be found at Daraz 11.11 Top Selling Product Data Analysis git repo.
• The Tableau visualization is available at 11.11 sale data analysis.

Dataset Containings

File Name: [All the .csv files within categorical_data folder]

• Category: Category of the product.
• SubCategory: Link to the relatable sub-category page.
• Title: The given name of the product given by seller.
• Original Price: Price before 11.11 sale.
• Discount Price: Price running on 11.11 sale.
• Discount: The discount(%) offered by the seller.
• Seller Name: The shop name selling the product.
• Number of Ratings: The total number of ratings given for the product.
• Positive Seller Ratings: Seller’s positive ratings percentage.
• Ship On Time: Seller’s on time shipping percentage.
• Chat Response Rate: Seller’s message reply percentage.
• Delivery Type: Whether the product is a “Free Delivery” product or “Standard Delivery” one?
• Flagship Store: Whether the shop is a flagship store or not? Yes/ No
• Cash On Delivery: Whether cash on delivery option is available or not?

File Name: subategories.csv

• Category: Category Name
• SubCategory Name: The subcategory name.
• SubCategory Link: Url of the subccategory page

File Name: Top_Selling_Product_Data.csv

• Category: Category of the product.
• SubCategory: Subcategory of the product within the category.
• Title: The given name of the product given by seller.
• Original Price: Price before 11.11 sale.
• Discount Price: Price running on 11.11 sale.
• Discount: The discount(%) offered by the seller.
• Seller Name: The shop name selling the product.
• Number of Ratings: The total number of ratings given for the product.
• Positive Seller Ratings: Seller’s positive ratings percentage.
• Ship On Time: Seller’s on time shipping percentage.
• Chat Response Rate: Seller’s message reply percentage.
• Delivery Type: Whether the product is a “Free Delivery” product or “Standard Delivery” one?
• Flagship Store: Whether the shop is a flagship store or not? Yes/ No
• No. of products to be sold: Total products to be sold in discount price to reach a break-even point equal to the sell of 50 products in original price. (Hypothetical Situation)
• Sell percentage to increase: Sell percentage to increase than the normal sale to reach the break-even point. (Hypothetical Situation)

Acknowledgements

The website daraz was used to scrape the dataset. If you use the data research purpose, don't forget add a citation.

Inspiration

This dataset can be used for traditional machine learning based project and also natural language processing workings.

In 2025, around ** percent of consumers in the United States planned to treat themselves to groceries. Apparel and travel rounded off the top three most popular categories that consumers wanted to splurge on.

The Top Websites in the World

How They Change Over Time

About this dataset

This dataset consists of the top 50 most visited websites in the world, as well as the category and principal country/territory for each site. The data provides insights into which sites are most popular globally, and what type of content is most popular in different parts of the world

How to use the dataset

This dataset can be used to track the most popular websites in the world over time. It can also be used to compare website popularity between different countries and categories

Research Ideas

• To track the most popular websites in the world over time
• To see how website popularity changes by region
• To find out which website categories are most popular

Acknowledgements

Dataset by Alexa Internet, Inc. (2019), released on Kaggle under the Open Data Commons Public Domain Dedication and License (ODC-PDDL)

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: df_1.csv | Column name | Description | |:--------------------------------|:---------------------------------------------------------------------| | Site | The name of the website. (String) | | Domain Name | The domain name of the website. (String) | | Category | The category of the website. (String) | | Principal country/territory | The principal country/territory where the website is based. (String) |

This dataset includes the top 100 bestselling audiobooks for all 24 categories including their ratings and the top 3 reviews. To be specific, each entry includes the audiobook's: Title, Category, Author, Narrator(s), Series if applicable, Length in hours and minutes, release date, price in USD, amount of ratings, Overall ratings and its breakdown, Performance ratings and its breakdown, Story ratings and its breakdown, the first 3 reviews, and their helpful number.

Please let me know if there is anything I missed so that I can improve this dataset. Hopefully this helps

This horizontal bar chart displays sites by category using the aggregation count in the United States. The data is about sites.

According to a survey conducted in Southeast Asia from June to August 2024, around ** percent of respondents reported having purchased beauty items due to recommendations from an influencer or celebrity. In comparison, around ** percent of respondents said they had purchased products in the travel category based on an influencer or celebrity's recommendation.

This horizontal bar chart displays sites by category using the aggregation count in Finland. The data is about sites.

Clothing and accessories were the most popular online products for Dutch consumers in 2022. ** percent of respondents in the Netherlands said that they purchased fashion items in the last six months. Food and beverages were also an important product category for the Dutch, as nearly half of consumers bought this online.

Boosted top tagging is an essential binary classification task for experiments at the Large Hadron Collider (LHC) to measure the properties of the top quark. The ATLAS Top Tagging Open Data Set is a publicly available data set for the development of Machine Learning (ML) based boosted top tagging algorithms. The data are split into two orthogonal sets, named train and test and stored in the HDF5 file format, containing 42 million and 2.5 million jets respectively. Both sets are composed of equal parts signal (jets initiated by a boosted top quark) and background (jets initiated by light quarks or gluons). For each jet, the data set contains:

• The four vectors of constituent particles
• 15 high level summary quantities evaluated on the jet
• The four vector of the whole jet
• A training weight
• A signal (1) vs background (0) label.

There is one rule in using this data set: the contribution to a loss function from any jet should always be weighted by the training weight. Apart from this a model should separate the signal jets from background by whatever means necessary.

Updated on July 26th 2024. This dataset has been superseeded by a new dataset which also includes systematic uncertainties. Please use the new dataset instead of this one.

A 2024 survey revealed that global consumers research the product category of electronics the most through user-generated content (UGC). Approximately ** percent of those surveyed used UGC to research electronics products. Another popular product category to research through UGC was apparel, which was done by roughly ** percent of consumers. Next, with about ** percent of respondents, was the health and beauty category. Global shoppers prioritize the value for money, the product's suitability for their intended purpose, and the delivery services offered when evaluating UGC.

The variety of products that can be purchased online is continuously growing. Among Brazilian consumers the two most popular categories for online purchases are ******** and *****. ** percent and ** percent of consumers respectively chose these answers in our representative online survey. The survey was conducted online among 2,743 respondents in Brazil, in 2025. Looking to gain valuable insights about customers of online shops across the globe? Check out our reports about consumers of online shops worldwide. These reports offer the readers a comprehensive overview of customers of eCommerce brands: who they are; what they like; what they think; and how to reach them.

In the 52 weeks ended February 20, 2022, ground beef and chicken breast were the top two fresh food categories in online sales in the U.S., accounting for *** percent and *** percent of online fresh food sales share, respectively. Berries and bacon were the next leading categories.