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E-commerce Predictive Analytics Data
E-commerce & Online Transactions
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About
This dataset contains a collection of fictional e-commerce transaction data designed for data analysis, machine learning, and predictive modelling. It includes various attributes such as product categories, prices, discounts, payment methods, and purchase dates. The data simulates typical e-commerce behaviour, providing insights into pricing trends, payment method distributions, and customer purchase patterns, making it ideal for practising data science techniques.
Columns
- User_ID: A unique identifier for each user.
- Product_ID: A unique identifier for each product.
- Category: The product category, including "Home & Kitchen", "Books", and "Other" (with "Home & Kitchen" and "Books" each representing 15% of records).
- Price (Rs.): The original price of the product in Rupees, ranging from 10.1 to 500, with a mean of 255.
- Discount (%): The discount applied to the product, ranging from 0% to 50%, with a mean of 18.8%.
- Final_Price(Rs.): The final price of the product after discounts, ranging from 5.89 to 497, with a mean of 207.
- Payment_Method: The method used for payment, including "Credit Card", "UPI", and "Other" (with "Credit Card" and "UPI" each representing 21% of records).
- Purchase_Date: The date of the transaction, with "21-08-2024" being the most common date.
Distribution
The dataset is provided as an ecommerce_dataset_updated.csv file, approximately 248.64 kB in size. It consists of 8 columns and contains 3660 records/rows. The expected update frequency for this dataset is annually.
Usage
This dataset is ideal for data scientists and analysts looking to practice and develop skills in:
- Regression analysis to predict prices or final prices.
- Classification tasks related to product categories or payment methods.
- Time-series forecasting to analyse purchasing trends over time.
- Gaining insights into pricing trends, payment method distributions, and customer purchase patterns in an e-commerce context.
Coverage
As a fictional dataset designed for simulation, it does not represent specific real-world geographic regions, time periods, or demographic groups. The purchase dates are simulated, with a particular focus on typical e-commerce transaction behaviour rather than actual historical coverage.
License
CC BY-NC-SA 4.0
Who Can Use It
- Data Scientists for building and testing predictive models.
- Machine Learning Engineers for developing and evaluating algorithms.
- Data Analysts for exploring transaction patterns and generating business insights.
- Students and Researchers for educational purposes and academic projects in data science and e-commerce analytics.
Dataset Name Suggestions
- Fictional E-commerce Transactions
- E-commerce Predictive Analytics Data
- Simulated Retail Sales Data
- Online Purchase Patterns Dataset
Attributes
Original Data Source: E-commerce Predictive Analytics Data
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