Digital Wallet Transactions Dataset
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Digital Wallet Transactions Dataset
Datasets
Digital Wallet Transactions Dataset
File
Digital Wallet Transactions Dataset
Use Case
Digital Wallet Transactions
Description
Explore a detailed Digital Wallet Transactions Dataset featuring 5,000 synthetic records. Ideal for analyzing payment behaviors, spending patterns, and developing AI models.
Description:
The Digital Wallet Transactions Dataset offers a comprehensive simulation of transactions from a digital wallet platform, mirroring the functionality of popular services like PayTm in India or Khalti in Nepal. The dataset contains 5,000 meticulously crafted synthetic records representing a variety of financial transactions across multiple product and service categories. It provides an invaluable resource for analyzing digital payment behaviors, consumer spending patterns, and the evolving trends within the digital finance ecosystem.
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Dataset Description
This dataset includes a rich array of features designed to replicate real-world digital wallet transactions:
- idx: A unique index that serves as a primary key for each record.
- transaction_id: A universally unique identifier (UUID) assigned to each transaction, ensuring distinctiveness across all records.
- user_id: A unique identifier representing each user in the dataset.
- transaction_date: The exact date and time when the transaction occurred, spanning a dynamic range over the past year to provide temporal diversity.
- product_category: The broad category under which the product or service falls, such as electronics, groceries, or utilities.
- product_name: The specific name of the product or service involved in the transaction.
- merchant_name: The name of the merchant or service provider facilitating the transaction.
- product_amount: The monetary value of the transaction, recorded in local currency.
Key Features
- Diverse Product and Service Representation: The dataset includes a variety of realistic product and service names across multiple categories, offering a holistic view of consumer preferences.
- Temporal Variability: Transaction dates span the last year, introducing seasonal and temporal patterns that can be analyzed for trends.
- Varied Payment Methods: Multiple payment methods are represented, providing a broad scope for studying consumer preferences in payment options.
- Comprehensive Transaction Statuses: The inclusion of various transaction statuses (Successful, Failed, Pending) allows for the analysis of transaction success rates and potential issues.
- Incentives and Rewards: Features like cashback and loyalty points reflect the modern features of digital wallets, offering avenues for analyzing consumer engagement strategies.
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