E-Commerce Dataset: Products, Customers, and Trends

Description

This dataset provides a comprehensive view of an e-commerce platform, featuring detailed information about products, customers, pricing, and sales trends. It is designed for data analysis, machine learning, and insights into online retail operations. The dataset is structured to help researchers and analysts explore various aspects of e-commerce, such as product popularity, customer preferences, and shipping performance.

Columns and Their Descriptions

• Product ID: Unique identifier for each product.
• Product Name: The name or title of the product listed in the catalog.
• Category: The category or type of the product (e.g., Electronics, Clothing, Home Decor).
• Price: The price of the product in USD.
• Discount: The discount applied to the product as a percentage of the original price.
• Tax Rate: The applicable tax rate for the product as a percentage.
• Stock Level: The number of units currently available in inventory.
• Supplier ID: A unique identifier for the supplier of the product.
• Customer Age Group: The age group of customers who frequently purchase this product (e.g., Teens, Adults, Seniors).
• Customer Location: The geographical location of customers (e.g., Country, State, or City).
• Customer Gender: The gender(s) of customers most likely to purchase this product (e.g., Male, Female, Both).
• Shipping Cost: The cost of shipping the product in USD.
• Shipping Method: The method of shipping used (e.g., Standard, Express, Overnight).
• Return Rate: The percentage of orders for this product that are returned by customers.
• Seasonality: The season(s) during which the product is most popular (e.g., Winter, Summer, All-Year).
• Popularity Index: A score indicating the product's popularity on a scale of 0 to 100.

Use Cases

This dataset is ideal for: - Exploratory Data Analysis (EDA): Analyze sales trends, product popularity, and customer preferences. - Visualization: Create insightful charts to visualize product performance, regional sales, and shipping trends. - Customer Insights: Understand customer segmentation based on demographics, preferences, and location. - Machine Learning Applications: - Regression: Predict product popularity based on price, discount, and stock level. - Clustering: Identify similar product categories for targeted marketing. - Classification: Predict whether a product will be returned based on its features.

Sample Data

E-Commerce Synthetic Dataset

This synthetic dataset simulates a large-scale e-commerce platform with 100,000 records, ideal for data analysis, machine learning, and visualization projects. It includes various data types and reflects real-world e-commerce operations, making it suitable for portfolio projects focused on user behavior analysis, sales trends, and product performance.

Dataset Overview

This dataset contains 100,000 rows with details on users, products, and transactions, as well as user engagement and transaction attributes. It is crafted to resemble actual e-commerce data, providing insights into customer demographics, purchasing patterns, and engagement.

Columns Description

1. UserID: Unique identifier for each user.
2. UserName: Simulated username for each user.
3. Age: Age of the user (ranging from 18 to 70).
4. Gender: Gender of the user, with possible values: Male, Female, and Non-Binary.
5. Country: User's country, chosen from USA, Canada, UK, Australia, India, and Germany.
6. SignUpDate: The date when the user signed up for the platform.

Product Information

1. ProductID: Unique identifier for each product.
2. ProductName: Name of the product purchased (Laptop, Smartphone, Headphones, Shoes, T-shirt, Book, Watch).
3. Category: Category of the product, including Electronics, Apparel, Books, and Accessories.
4. Price: Price of the product (randomly set between $10 and $1,000).

Transaction Details

1. PurchaseDate: Date of purchase.
2. Quantity: Number of units purchased in the transaction.
3. TotalAmount: Total amount spent on the transaction (Price * Quantity).

User Engagement Metrics

1. HasDiscountApplied: Indicates whether a discount was applied (True or False).
2. DiscountRate: Discount rate applied to the transaction (ranging from 0 to 0.5).
3. ReviewScore: User's review score for the product, ranging from 1 to 5.
4. ReviewText: Text-based review (Excellent, Good, Average, Poor).

User Behavior Metrics

1. LastLogin: Date of the user’s last login.
2. SessionDuration: Duration of the user’s session in minutes (ranging from 5 to 120 minutes).
3. DeviceType: Device type used by the user, including Mobile, Desktop, and Tablet.
4. ReferralSource: Source of referral, which could be Organic Search, Ad Campaign, Email Marketing, or Social Media.

Usage

This dataset is intended for: - Exploratory Data Analysis (EDA): Understanding customer demographics, popular products, and sales distribution. - Data Visualization: Visualizing user engagement, sales trends, and product category performance. - Machine Learning Models: Training models on customer segmentation, purchase prediction, and review rating analysis.

Notes

• Synthetic Data: This dataset is entirely synthetic and generated for educational purposes.
• No Personally Identifiable Information (PII): All names, IDs, and records are fictional.

License

This dataset is freely available for use in projects and portfolios. When sharing results derived from this dataset, please credit it as a synthetic data source.

The number of users in the e-commerce market in Indonesia was modeled to amount to ************* users in 2024. Following a continuous upward trend, the number of users has risen by ************* users since 2017. Between 2024 and 2029, the number of users will rise by ************* users, continuing its consistent upward trajectory.Further information about the methodology, more market segments, and metrics can be found on the dedicated Market Insights page on eCommerce.

The number of users in the e-commerce market in the United States was modeled to stand at ************** users in 2024. Following a continuous upward trend, the number of users has risen by ************* users since 2017. Between 2024 and 2029, the number of users will rise by ************* users, continuing its consistent upward trajectory.Further information about the methodology, more market segments, and metrics can be found on the dedicated Market Insights page on eCommerce.

😍Upvote and share this would help me alot Thank You!

Description: The E-commerce Sales Data dataset provides a comprehensive collection of information related to user profiles, product details, and user-product interactions. It is a valuable resource for understanding customer behavior, preferences, and purchasing trends on an e-commerce platform.

Dataset Structure:

User Sheet: This sheet contains user profiles, including details such as user ID, name, age, location, and other relevant information. It helps in understanding the demographics and characteristics of the platform's users.

Product Sheet: The product sheet offers insights into the various products available on the e-commerce platform. It includes product IDs, names, categories, prices, descriptions, and other product-specific attributes.

Interactions Sheet: The interactions sheet is a crucial component of the dataset, capturing the interactions between users and products. It records details of user actions, such as product views, purchases, reviews, and ratings. This data is essential for building recommendation systems and understanding user preferences.

Potential Use Cases:

Recommendation Systems: With the user-product interaction data, this dataset is ideal for building recommendation systems. It allows the development of personalized product recommendations to enhance the user experience.

Market Basket Analysis: The dataset can be used for market basket analysis to understand which products are frequently purchased together, aiding in inventory management and targeted marketing.

User Behavior Analysis: By analyzing user interactions, you can gain insights into user behavior, such as popular product categories, browsing patterns, and the impact of user reviews and ratings on purchasing decisions.

Targeted Marketing: The dataset can inform marketing strategies, enabling businesses to tailor promotions and advertisements to specific user segments and product categories.

This E-commerce Sales Data dataset is a valuable resource for e-commerce platforms and data scientists seeking to optimize the shopping experience, enhance customer satisfaction, and drive business growth through data-driven insights.

The number of users of e-commerce in the United Kingdom is forecast to continuously increase within the next years. According to this forecast, in 2030 the users will have increased to **** million.

E-commerce_dataset

This dataset is a synthetic yet realistic E-commerce retail dataset generated programmatically using Python (Faker + NumPy + Pandas).
It is designed to closely mimic real-world online shopping behavior, user patterns, product interactions, seasonal trends, and marketplace events.

You can use this dataset for:

Machine Learning & Deep Learning
Recommender Systems
Customer Segmentation
Sales Forecasting
A/B Testing
E-commerce Behaviour Analysis
Data Cleaning / Feature Engineering Practice
SQL practice

📁**Dataset Contents**

The dataset contains 6 CSV files: ~~~ File Rows Description users.csv ~10,000 User profiles, demographics & signup info products.csv ~2,000 Product catalog with rating and pricing orders.csv ~20,000 Order-level transactions order_items.csv ~60,000 Items purchased per order reviews.csv ~15,000 Customer-written product reviews events.csv ~80,000 User event logs: view, cart, wishlist, purchase ~~~

🧬 Data Dictionary

1. Users (users.csv)
Column Description
user_id Unique user identifier
name  Full customer name
email  Email (synthetic, no real emails)
gender Male / Female / Other
city  City of residence
signup_date Account creation date

2. Products (products.csv)
Column Description
product_id Unique product identifier
product_name  Product title
category  Electronics, Clothing, Beauty, Home, Sports, etc.
price  Actual selling price
rating Average product rating

3. Orders (orders.csv)
Column Description
order_id  Unique order identifier
user_id User who placed the order
order_date Timestamp of the order
order_status  Completed / Cancelled / Returned
total_amount  Total order value

4. Order Items (order_items.csv)
Column Description
order_item_id  Unique identifier
order_id  Associated order
product_id Purchased product
quantity  Quantity purchased
item_price Price per unit

5. Reviews (reviews.csv)
Column Description
review_id  Unique review identifier
user_id User who submitted review
product_id Reviewed product
rating 1–5 star rating
review_text Short synthetic review
review_date Submission date

6. Events (events.csv)
Column Description
event_id  Unique event identifier
user_id User performing event
product_id Viewed/added/purchased product
event_type view/cart/wishlist/purchase
event_timestamp Timestamp of event

🧠 Possible Use Cases (Ideas & Projects)

🔍 Machine Learning

Customer churn prediction
Review sentiment analysis (NLP)
Recommendation engines
Price optimization models
Demand forecasting (Time-series)

📦 Business Analytics

Market basket analysis
RFM segmentation
Cohort analysis
Funnel conversion tracking
A/B testing simulations

🧮 SQL Practice

Joins
Window functions
Aggregations
CTE-based funnels
Complex queries

🛠 How the Dataset Was Generated

The dataset was generated entirely in Python using:

Faker for realistic user and review generation
NumPy for probability-based event modeling
Pandas for data processing

Custom logic for:

demand variation
user behavior simulation
return/cancel probabilities
seasonal order timestamp distribution
The dataset does not include any real personal data.
Everything is generated synthetically.

⚠️ License

This dataset is released under CC BY 4.0 — free to use for:
Research
Education
Commercial projects
Kaggle competitions
Machine learning pipelines
Just provide attribution.

⭐ If you found this dataset helpful, please:

Upvote the dataset
Leave a comment
Share your notebooks/notebooks using it

In 2024, Germany ranked first by number of users in the e-commerce market among the 27 countries presented in the ranking. Germany's number of users amounted to ************* users, while France and Italy, the second and third countries, had records amounting to ************* users and ************* users, respectively.Further information about the methodology, more market segments, and metrics can be found on the dedicated Market Insights page on eCommerce.

[Caution]

This dataset is only for study, and personal portfolio. Not reccomended for a research or profitable purpose!

License: CC BY-NC-ND

Dataset Description

This dataset contains transactiononal activity in eccommrce app including product order and also customer behavior in using app. The dataset has several files represent every table

Dataset Columns

Customer Table

Containing the detailed information of registered user in ecommerce application * customer_id = customer unique id * first_name = customer's first name * last_name = customer's last name * username = customer's username * email = customer's email * gender = customer's gender (Male (M) or Female (F)) * device_type = the device type of customer when using the app * device_id = device id of customer when using app * device_version = detailed version of device used by customer * home_location_lat = customer location latitude * home_location_long =customer location longitude * home_location = customer province/region name * home_country = customer country name * first_join_date = customer first join date in app

Product Table

Containing the detailed data of product (fashion product) sold in application * id = product id * gender = target/designate products based on gender * masterCategory = Master category of product * subCategory = sub category of product * articleType = fashion product type * baseColour = base color of fashion product * season = target/designate products based on season * year = the year of production * usage = the usage type of product * productDisplayName = the display name of product in ecommerce app

Transaction Table

contains data for each transaction/product order made by the customer. Each customer can make multiple purchases on multiple products. * created_at = the timestamp when data/transaction created * customer_id = unique id of every customer * booking_id = unique id of transaction * session_id = unique session id of user when visiting the app * product_metadata = the metadata of product purchased * payment_method = the payment method used in transaction * payment_status = the payment status (Success / Failed) * promo_amount = the amount of promo in every transacation * promo_code =promo code * shipment_fee = the shipment fee of transaction (ongkir) * shipment_date_limit = the shipment limit data * shipment_location_lat = the shipment location/target latitude * shipment_location_long = the shipment location/target longitude * total_amount = total amount of money to be paid for every transaction

Click Stream Table

contains data on application usage activities carried out by users in each session or when they make a transaction * session_id = session id * event_name = the name of activity/event * event_time = the time when event occured * event_id = id of event * traffic_source = the activity source by device (mobile/web) * event_metadata = the metadata of activity / detailed activity

Notes * There is a product_metadata feature in the Transaction Table and event_metadata in the Click_Stream Table, which is in the form of a dictionary, you maybe need to extract the contents to form a new feature * subCategory parent to masterCategory * articleType is a specification of the subCategory: masterCategory => subCategory => articleType

The number of people participating in e-commerce has increased significantly over the years. In 2019, before the coronavirus pandemic, the figure stood at an estimated *** million, which has now risen to approximately *** million in 2024. According to Digital Market Insights, forecasts suggest that the number of e-commerce users in Europe will reach *** million by 2029.

The E-commerce-shopping Behavior dataset describes user behavior on an e-commerce website and records user behavior data during a gray testing process. During the gray testing, updates were made to the website's payment page, resulting in a decrease in the ratio of users placing orders and adding items to their shopping carts. The table contains information about user events within a system. It includes the User ID (UID), which uniquely identifies each user, along with details such as the type of event (EventType), event name (EventName), time of occurrence (LocalTime), duration of the event (StayTime), reason for the event (Source), user's operating system (OS), gender, age, purchasing power rating (Level), software version (Version), and whether the user is part of a gray test (isGray). The EventType can be either "browse" or "click", and the Source can be either "natural" or "push". The Level is categorized into three levels: 0, 1, and 2. The Version includes options 4.13 and 4.13b, while isGray indicates whether the user is involved in gray testing, denoted by "Y" for yes or "N" for no. The content regarding time, gender, and ID has been anonymized.

This dataset provides simulated data for user interactions on an e-commerce platform. It includes sequences of events such as page views, clicks, product views, and purchases. Each record captures user activity within sessions, making it suitable for analyzing clickstream paths and transaction sequences.

Features:

UserID: Unique identifier for each user. SessionID: Unique identifier for each session. Timestamp: Date and time of the interaction. EventType: Type of event (e.g., page view, click, product view, add to cart, purchase). ProductID: Unique identifier for products involved in interactions. Amount: Amount of the transaction (for purchases). Outcome: Target event (e.g., purchase).

This dataset can be used to discover patterns and sequences leading to specific outcomes such as product purchases or churn.

More than 90% of people regularly use a smartphone for shopping online. With over 294 million smartphone users in the US alone, approximately 232 million of them regularly use their phones to purchase online.

Asos E-Commerce Dataset - 30,845 products, text classification dataset

Using web scraping, we collected information on over 30,845 clothing items from the Asos website. The dataset can be applied in E-commerce analytics in the fashion industry. The dataset is similar to SheIn E-Commerce Dataset.

💴 For Commercial Usage: To discuss your requirements, learn about the price and buy the dataset, leave a request on our website to buy the dataset

Dataset Info

For each item, we extracted:

• url - link to the item on the website
• name - item's name
• size - sizes available on the website
• category - product's category
• price - item's price
• color - item's color
• SKU - unique identifier of the item
• date - date of web scraping; for all items - March 11, 2023
• description - additional description, including product's brand, composition, and care instructions, in JSON format
• images - photographs from the item description

🧩 This is just an example of the data. Leave a request here to learn more

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In 2024, Germany ranked first by number of users in the e-commerce market among the 27 countries presented in the ranking. Germany's number of users amounted to 44.66 million users, while France and Italy, the second and third countries, had records amounting to 30.29 million users and 19.49 million users, respectively.Further information about the methodology, more market segments, and metrics can be found on the dedicated Market Insights page on eCommerce.

I'll show you how the pandemic has changed the way people shop and give you some accurate ecommerce statistics to prove it.

According to the Digital Market Outlook, the number of e-commerce users in Poland is expected to grow to ** million in 2029. Statista’s Digital Market Outlook offers forecasts, detailed market insights, and essential performance indicators of the most significant areas in the Digital Economy, including various digital goods and services. Alongside revenue forecasts for ** countries worldwide, Statista offers additional insights into consumer trends and the demographic structure of digital consumer markets. *Regions only include countries listed in the Digital Market Outlook

This synthetic e-commerce dataset provides a detailed simulation of user interactions, designed to aid in the analysis of user behavior and performance metrics. It includes approximately 4500 unique records, capturing critical features such as session durations, click-through rates (CTR), bounce rates, conversion rates, and pages viewed. Each entry represents a unique user session, making it ideal for predictive modeling, machine learning, and user behavior analysis. The dataset is structured to simulate realistic e-commerce environments, allowing for the exploration of factors influencing user engagement, purchase decisions, and website performance. With its comprehensive coverage of key metrics, this dataset is suitable for research in digital marketing, UX optimization, and conversion rate analysis. It serves as a valuable resource for academic studies and practical applications in e-commerce strategy and user experience enhancement.

Global E-Commerce User Behavior & Conversion Dataset Overview This dataset contains 2,500 simulated e-commerce user sessions designed to resemble realistic online shopping behavior. Each row represents a single browsing session, capturing user interactions such as pages viewed, time spent on site, cart activity, and final purchase outcomes. The dataset models how different behavioral and marketing factors influence purchase conversions in an online retail environment. Logical relationships are intentionally included to mimic real-world patterns. For example: Users who view more pages tend to have a higher probability of purchasing. Adding items to cart significantly increases the likelihood of a purchase. Time spent on the website correlates with engagement and conversion probability. Order value is recorded only when a purchase occurs. This makes the dataset useful for exploratory data analysis (EDA), machine learning modeling, and marketing analytics projects.

In recent years, the global e-commerce landscape has witnessed rapid growth, with sales reaching a new peak in the past year and expected to rise further in the coming years. Amid this e-commerce boom, accurately predicting user purchase behavior has become crucial for commercial success. We introduce a novel framework integrating three innovative approaches to enhance the prediction model’s effectiveness. First, we integrate an event-based timestamp encoding within a time-series attention model, effectively capturing the dynamic and temporal aspects of user behavior. This aspect is often neglected in traditional user purchase prediction methods, leading to suboptimal accuracy. Second, we incorporate Graph Neural Networks (GNNs) to analyze user behavior. By modeling users and their actions as nodes and edges within a graph structure, we capture complex relationships and patterns in user behavior more effectively than current models, offering a nuanced and comprehensive analysis. Lastly, our framework transcends traditional learning strategies by implementing advanced meta-learning techniques. This enables the model to autonomously adjust learning parameters, including the learning rate, in response to new and evolving data environments, thereby significantly enhancing its adaptability and learning efficiency. Through extensive experiments on diverse real-world e-commerce datasets, our model demonstrates superior performance, particularly in accuracy and adaptability in large-scale data scenarios. This study not only overcomes the existing challenges in analyzing e-commerce user behavior but also sets a foundation for future exploration in this dynamic field. We believe our contributions provide significant insights and tools for e-commerce platforms to better understand and cater to their users, ultimately driving sales and improving user experiences.