The monthly card payment statistics provide data in relation to credit and debit card transactions undertaken by Irish resident households. The data includes the monthly value and volume of transactions across both credit and debit cards by Irish households. The data is collected from issuers of credit and debit cards and specifically from reporting agents that are resident in Ireland (including established foreign branches). The aggregate data is further broken down into, remote and non-remote card spending; contactless and mobile wallet card spending; sectoral card spending; domestic and non-domestic card spending; regional card spending in Ireland; and cash withdrawals. A breakdown of the number of credit & debit cards currently issued to Irish residents is also provided. Note, only Personal Cards are in scope for this reporting, business cards and cards issued to non-Irish residents are not included. Additionally, data files uploaded here follow the SDMX –ML format where Series Key are the primary identifier for a reporting period (Date for which the data is reported is represented in the Reporting Period field). For example : PCI.M.IE.W2.PCS_ALL.11.PN is the series key and each element/dimension between the delimiter “.” is expanded with a description in subsequent columns ending with the subscript “DESC” to understand the meaning of each element/dimension. The Observation_free column represents the value (€ EUR) or Volume (PN) of transactions depending on the last element/dimension, EUR or PN. For further information on the Payment Statistics Monthly, the reporting instructions in the Landing page link has additional details about the table and the column names used in this data collection.

Daily, weekly and monthly data showing seasonally adjusted and non-seasonally adjusted UK spending using debit and credit cards. These are official statistics in development. Source: CHAPS, Bank of England.

Turkey Credit Card Transaction: Domestic: Value: Domestic Cards: Cash Adv data was reported at 6,035.550 TRY mn in Sep 2018. This records an increase from the previous number of 5,979.190 TRY mn for Aug 2018. Turkey Credit Card Transaction: Domestic: Value: Domestic Cards: Cash Adv data is updated monthly, averaging 1,693.490 TRY mn from Jan 2002 (Median) to Sep 2018, with 201 observations. The data reached an all-time high of 6,527.270 TRY mn in May 2018 and a record low of 180.050 TRY mn in Feb 2002. Turkey Credit Card Transaction: Domestic: Value: Domestic Cards: Cash Adv data remains active status in CEIC and is reported by The Interbank Card Center. The data is categorized under Global Database’s Turkey – Table TR.KA012: Credit and Debit Cards Statistics.

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Data is the new oil, and this dataset is a wellspring of knowledge waiting to be tapped😷!

Don't forget to upvote and share your insights with the community. Happy data exploration!🥰

** For more related datasets: ** https://www.kaggle.com/datasets/rajatsurana979/fifafcmobile24 https://www.kaggle.com/datasets/rajatsurana979/most-streamed-spotify-songs-2023 https://www.kaggle.com/datasets/rajatsurana979/comprehensive-credit-card-transactions-dataset https://www.kaggle.com/datasets/rajatsurana979/hotel-reservation-data-repository https://www.kaggle.com/datasets/rajatsurana979/percent-change-in-consumer-spending https://www.kaggle.com/datasets/rajatsurana979/fast-food-sales-report/data

Description: Welcome to the world of credit card transactions! This dataset provides a treasure trove of insights into customers' spending habits, transactions, and more. Whether you're a data scientist, analyst, or just someone curious about how money moves, this dataset is for you.

Features: - Customer ID: Unique identifiers for every customer. - Name: First name of the customer. - Surname: Last name of the customer. - Gender: The gender of the customer. - Birthdate: Date of birth for each customer. - Transaction Amount: The dollar amount for each transaction. - Date: Date when the transaction occurred. - Merchant Name: The name of the merchant where the transaction took place. - Category: Categorization of the transaction.

Why this dataset matters: Understanding consumer spending patterns is crucial for businesses and financial institutions. This dataset is a goldmine for exploring trends, patterns, and anomalies in financial behavior. It can be used for fraud detection, marketing strategies, and much more.

Acknowledgments: We'd like to express our gratitude to the contributors and data scientists who helped curate this dataset. It's a collaborative effort to promote data-driven decision-making.

Let's Dive In: Explore, analyze, and visualize this data to uncover the hidden stories in the world of credit card transactions. We look forward to seeing your innovative analyses, visualizations, and applications using this dataset.

Turkey Credit Card Transaction: Domestic: Vol: Domestic Cards: Cash Advance data was reported at 8,664,979.000 Unit in Mar 2018. This records an increase from the previous number of 7,584,631.000 Unit for Feb 2018. Turkey Credit Card Transaction: Domestic: Vol: Domestic Cards: Cash Advance data is updated monthly, averaging 6,982,175.000 Unit from Jan 2002 (Median) to Mar 2018, with 195 observations. The data reached an all-time high of 8,959,264.000 Unit in May 2011 and a record low of 2,518,262.000 Unit in Feb 2003. Turkey Credit Card Transaction: Domestic: Vol: Domestic Cards: Cash Advance data remains active status in CEIC and is reported by The Interbank Card Center. The data is categorized under Global Database’s Turkey – Table TR.KA012: Credit and Debit Cards Statistics.

Statistical data on the number of credit cards in circulation, amount of credit card transactions, cash advances, and revolving credit amounts.

Turkey Number of Transactions: Domestic: Credit Cards: Cash Advance data was reported at 103.853 Unit mn in 2017. This records an increase from the previous number of 97.225 Unit mn for 2016. Turkey Number of Transactions: Domestic: Credit Cards: Cash Advance data is updated yearly, averaging 89.184 Unit mn from Dec 2000 (Median) to 2017, with 18 observations. The data reached an all-time high of 103.853 Unit mn in 2017 and a record low of 39.000 Unit mn in 2002. Turkey Number of Transactions: Domestic: Credit Cards: Cash Advance data remains active status in CEIC and is reported by The Interbank Card Center. The data is categorized under Global Database’s Turkey – Table TR.KA013: Credit and Debit Cards Statistics: Annual.

The credit card penetration in Africa was forecast to continuously increase between 2024 and 2029 by in total 0.2 percentage points. According to this forecast, in 2029, the credit card penetration will have increased for the eighth consecutive year to 2.92 percent. The penetration rate refers to the share of the total population who use credit cards.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the credit card penetration in countries like Caribbean and North America.

Turkey Credit Card Transaction: Intl: Vol: Domestic Cards: Cash Advance data was reported at 58,203.000 Unit in Mar 2018. This records an increase from the previous number of 56,290.000 Unit for Feb 2018. Turkey Credit Card Transaction: Intl: Vol: Domestic Cards: Cash Advance data is updated monthly, averaging 60,060.000 Unit from Jan 2002 (Median) to Mar 2018, with 195 observations. The data reached an all-time high of 164,165.000 Unit in Nov 2006 and a record low of 19,769.000 Unit in Mar 2002. Turkey Credit Card Transaction: Intl: Vol: Domestic Cards: Cash Advance data remains active status in CEIC and is reported by The Interbank Card Center. The data is categorized under Global Database’s Turkey – Table TR.KA012: Credit and Debit Cards Statistics.

This dataset provides access to data about general purpose credit cards, which are open-end loans used by consumers to pay for day-to-day expenses, finance purchases, or provide cash advances.

Payment Statistics Quarterly. Published by Central Bank of Ireland. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).Payment Statistics Quarterly is reported by payment service providers, it records non-cash payments by non-monetary financial institutions which include Credit Transfers, Direct Debits, Card based Payment Transactions, E-money Transactions and Cheques....

Turkey Credit Card Transaction: Domestic: Value: Intl Cards: Cash Advance data was reported at 680.960 TRY mn in Sep 2018. This records a decrease from the previous number of 874.270 TRY mn for Aug 2018. Turkey Credit Card Transaction: Domestic: Value: Intl Cards: Cash Advance data is updated monthly, averaging 116.530 TRY mn from Jan 2003 (Median) to Sep 2018, with 189 observations. The data reached an all-time high of 874.270 TRY mn in Aug 2018 and a record low of 18.370 TRY mn in Feb 2004. Turkey Credit Card Transaction: Domestic: Value: Intl Cards: Cash Advance data remains active status in CEIC and is reported by The Interbank Card Center. The data is categorized under Global Database’s Turkey – Table TR.KA012: Credit and Debit Cards Statistics.

This dataset was created to simulate a market basket dataset, providing insights into customer purchasing behavior and store operations. The dataset facilitates market basket analysis, customer segmentation, and other retail analytics tasks. Here's more information about the context and inspiration behind this dataset:

Context:

Retail businesses, from supermarkets to convenience stores, are constantly seeking ways to better understand their customers and improve their operations. Market basket analysis, a technique used in retail analytics, explores customer purchase patterns to uncover associations between products, identify trends, and optimize pricing and promotions. Customer segmentation allows businesses to tailor their offerings to specific groups, enhancing the customer experience.

Inspiration:

The inspiration for this dataset comes from the need for accessible and customizable market basket datasets. While real-world retail data is sensitive and often restricted, synthetic datasets offer a safe and versatile alternative. Researchers, data scientists, and analysts can use this dataset to develop and test algorithms, models, and analytical tools.

Dataset Information:

The columns provide information about the transactions, customers, products, and purchasing behavior, making the dataset suitable for various analyses, including market basket analysis and customer segmentation. Here's a brief explanation of each column in the Dataset:

• Transaction_ID: A unique identifier for each transaction, represented as a 10-digit number. This column is used to uniquely identify each purchase.
• Date: The date and time when the transaction occurred. It records the timestamp of each purchase.
• Customer_Name: The name of the customer who made the purchase. It provides information about the customer's identity.
• Product: A list of products purchased in the transaction. It includes the names of the products bought.
• Total_Items: The total number of items purchased in the transaction. It represents the quantity of products bought.
• Total_Cost: The total cost of the purchase, in currency. It represents the financial value of the transaction.
• Payment_Method: The method used for payment in the transaction, such as credit card, debit card, cash, or mobile payment.
• City: The city where the purchase took place. It indicates the location of the transaction.
• Store_Type: The type of store where the purchase was made, such as a supermarket, convenience store, department store, etc.
• Discount_Applied: A binary indicator (True/False) representing whether a discount was applied to the transaction.
• Customer_Category: A category representing the customer's background or age group.
• Season: The season in which the purchase occurred, such as spring, summer, fall, or winter.
• Promotion: The type of promotion applied to the transaction, such as "None," "BOGO (Buy One Get One)," or "Discount on Selected Items."

Use Cases:

• Market Basket Analysis: Discover associations between products and uncover buying patterns.
• Customer Segmentation: Group customers based on purchasing behavior.
• Pricing Optimization: Optimize pricing strategies and identify opportunities for discounts and promotions.
• Retail Analytics: Analyze store performance and customer trends.

Note: This dataset is entirely synthetic and was generated using the Python Faker library, which means it doesn't contain real customer data. It's designed for educational and research purposes.

Turkey Credit Card Transaction: Domestic: Vol: Intl Cards: Cash Advance data was reported at 490,951.000 Unit in Jun 2018. This records a decrease from the previous number of 539,899.000 Unit for May 2018. Turkey Credit Card Transaction: Domestic: Vol: Intl Cards: Cash Advance data is updated monthly, averaging 301,607.000 Unit from Jan 2002 (Median) to Jun 2018, with 198 observations. The data reached an all-time high of 1,274,871.000 Unit in Aug 2017 and a record low of 74,764.000 Unit in Feb 2004. Turkey Credit Card Transaction: Domestic: Vol: Intl Cards: Cash Advance data remains active status in CEIC and is reported by The Interbank Card Center. The data is categorized under Global Database’s Turkey – Table TR.KA012: Credit and Debit Cards Statistics.

Don't forget to hit the Upvote

This dataset represents a detailed compilation of trips made using yellow taxis in New York City. The data encapsulates a wide range of information, from pickup and drop_off times to fare amounts and payment types, offering a comprehensive view into urban mobility and the economics of taxi rides within the city. This dataset is invaluable for anyone interested in urban transportation trends, fare analysis, geographic movement patterns within New York City, and the study of temporal variations in taxi usage.

Column Description:

VendorID: A code indicating the provider associated with the trip record.

tpep_pickup_datetime: The date and time when the meter was engaged.

tpep_dropoff_datetime: The date and time when the meter was disengaged.

passenger_count: The number of passengers in the vehicle. This is a driver-entered value.

trip_distance: The distance of the trip measured in miles.

RatecodeID: The final rate code in effect at the end of the trip.

store_and_fwd_flag: Indicates whether the trip record was held in vehicle memory before sending to the vendor, Y=store and forward, N=not a store and forward trip.

PULocationID: The Taxi and Limousine Commission (TLC) Taxi Zone ID for the pickup location.

DOLocationID: The Taxi and Limousine Commission (TLC) Taxi Zone ID for the dropoff location.

payment_type: A numeric code signifying how the passenger paid for the trip.

fare_amount: The time-and-distance fare calculated by the meter.

extra: Miscellaneous extras and surcharges.

mta_tax: $0.50 MTA tax that is automatically triggered based on the metered rate in use.

tip_amount: Tip amount – This field is automatically populated for credit card tips. Cash tips are not included.

tolls_amount: Total amount of all tolls paid in trip.

improvement_surcharge: $0.30 improvement surcharge assessed trips at the flag drop. The surcharge began in 2015.

total_amount: The total amount charged to passengers. Does not include cash tips.

congestion_surcharge: A surcharge applied on trips that start, end, or pass through certain areas at specific times.

Usage Ideas

Suggest several research questions or project ideas that could be explored using the dataset. For example:

-Analyzing the impact of weather conditions on taxi usage.

-Exploring the correlation between trip distances and fares to identify pricing patterns.

-Investigating the effect of different times of day or days of the week on taxi demand.

This dataset provides simulated retail transaction data, offering valuable insights into customer purchasing behaviour and store operations. It is designed to facilitate market basket analysis, customer segmentation, and a variety of other retail analytics tasks. Each row captures detailed transaction information, including a unique identifier, the date and time of purchase, customer details, a list of purchased products, total items, total cost, payment method, and location details such as city and store type. Furthermore, it includes indicators for discounts and promotions applied, along with a customer category based on background or age group, and the season of purchase. This dataset is entirely synthetic, generated using the Python Faker library, making it a safe and versatile resource for researchers, data scientists, and analysts to develop and test algorithms, models, and analytical tools without using real customer data.

Columns

• Transaction_ID: A unique 10-digit identifier for each individual transaction, ensuring each purchase can be uniquely identified.
• Date: The precise date and time when each transaction occurred, providing a timestamp for every purchase.
• Customer_Name: The name of the customer who completed the purchase, offering a means to identify individual customers.
• Product: A detailed list of all products included in a specific transaction.
• Total_Items: The total quantity of items purchased within a single transaction.
• Total_Cost: The overall financial value of the transaction, denominated in currency.
• Payment_Method: The chosen payment method for the transaction, such as credit card, debit card, cash, or mobile payment.
• City: The geographical location (city) where the transaction took place.
• Store_Type: The classification of the store where the purchase was made, e.g., supermarket, convenience store, department store.
• Discount_Applied: A boolean indicator (True/False) showing whether a discount was applied to the transaction.
• Customer_Category: A categorisation of the customer based on their background or age group.
• Season: The season (e.g., spring, summer, autumn, winter) in which the purchase was made.
• Promotion: The specific type of promotion applied to the transaction, if any (e.g., "None", "BOGO", "Discount on Selected Items").

Distribution

This dataset is typically provided in a CSV file format. It contains approximately 1 million individual transaction records. The data spans a time range from 2020-01-01 to 2024-05-19. There are 329,738 unique customer names and 571,947 unique product entries. Payment methods are distributed with 25% Cash, 25% Debit Card, and 50% Other. Transaction locations include Boston (10%), Dallas (10%), and other cities (80%). Store types are categorised as Supermarket (17%), Pharmacy (17%), and other types (67%). Discounts were applied to approximately 50% of the transactions.

Usage

This dataset is ideally suited for: * Market Basket Analysis: Uncovering associations between products and identifying common buying patterns. * Customer Segmentation: Grouping customers based on their purchasing behaviour to target specific offers. * Pricing Optimisation: Developing strategies to optimise pricing and identify opportunities for discounts and promotions. * Retail Analytics: Analysing overall store performance and emerging customer trends. * Algorithmic Development: Testing and refining machine learning models for retail forecasting or recommendation systems.

Coverage

The dataset's geographic coverage includes transactions from various cities, such as Boston and Dallas, representing a broad, though simulated, global scope. The time range of the transactions extends from 1st January 2020 to 19th May 2024. Demographic insights are provided through the Customer_Category column, which classifies customers based on background or age group, allowing for demographic-based analyses. As a synthetic dataset, specific real-world demographic notes are not applicable.

License

CC0

Who Can Use It

This dataset is beneficial for a wide range of users, including: * Researchers: For academic studies on consumer behaviour and retail economics. * Data Scientists: To develop and validate predictive models, such as recommender systems or churn prediction models. * Analysts: For performing in-depth retail analytics, market basket analysis, and customer segmentation to inform business decisions. * Students: As a practical, realistic dataset for learning and applying data analysis techniques in a retail context.

Dataset Name Suggestions

• Retail Transactions Dataset
• Customer Purchasing Behaviour Data
• Market Basket Analysis Data
• Synthetic Retail Transactions
• E-commerce Transaction Log

Attributes

Original Dat

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Which of the four factors directly impact your total cost of using the credit card?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Graph and download economic data for Personal Saving Rate (PSAVERT) from Jan 1959 to May 2025 about savings, personal, rate, and USA.

Thailand Credit Card: Spending Value: Cash Advance: CB: Bank Card data was reported at 0.000 THB mn in Oct 2018. This stayed constant from the previous number of 0.000 THB mn for Sep 2018. Thailand Credit Card: Spending Value: Cash Advance: CB: Bank Card data is updated monthly, averaging 0.000 THB mn from Jan 2012 (Median) to Oct 2018, with 82 observations. Thailand Credit Card: Spending Value: Cash Advance: CB: Bank Card data remains active status in CEIC and is reported by Bank of Thailand. The data is categorized under Global Database’s Thailand – Table TH.KA012: Credit Card Statistics.

The online banking penetration rate in Nigeria was forecast to continuously increase between 2024 and 2029 by in total 4.5 percentage points. After the fifteenth consecutive increasing year, the online banking penetration is estimated to reach 8.75 percent and therefore a new peak in 2029. Notably, the online banking penetration rate of was continuously increasing over the past years.Shown is the estimated percentage of the total population in a given region or country, which makes use of online banking.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the online banking penetration rate in countries like Ivory Coast and Senegal.