Online Retail Transaction Data

UK Online Retail Sales and Customer Transaction Data

By UCI [source]

About this dataset

Comprehensive Dataset on Online Retail Sales and Customer Data

Welcome to this comprehensive dataset offering a wide array of information related to online retail sales. This data set provides an in-depth look at transactions, product details, and customer information documented by an online retail company based in the UK. The scope of the data spans vastly, from granular details about each product sold to extensive customer data sets from different countries.

This transnational data set is a treasure trove of vital business insights as it meticulously catalogues all the transactions that happened during its span. It houses rich transactional records curated by a renowned non-store online retail company based in the UK known for selling unique all-occasion gifts. A considerable portion of its clientele includes wholesalers; ergo, this dataset can prove instrumental for companies looking for patterns or studying purchasing trends among such businesses.

The available attributes within this dataset offer valuable pieces of information:

• InvoiceNo: This attribute refers to invoice numbers that are six-digit integral numbers uniquely assigned to every transaction logged in this system. Transactions marked with 'c' at the beginning signify cancellations - adding yet another dimension for purchase pattern analysis.

• StockCode: Stock Code corresponds with specific items as they're represented within the inventory system via 5-digit integral numbers; these allow easy identification and distinction between products.

• Description: This refers to product names, giving users qualitative knowledge about what kind of items are being bought and sold frequently.

• Quantity: These figures ascertain the volume of each product per transaction – important figures that can help understand buying trends better.

• InvoiceDate: Invoice Dates detail when each transaction was generated down to precise timestamps – invaluable when conducting time-based trend analysis or segmentation studies.

• UnitPrice: Unit prices represent how much each unit retails at — crucial for revenue calculations or cost-related analyses.

Finally,

• Country: This locational attribute shows where each customer hails from, adding geographical segmentation to your data investigation toolkit.

This dataset was originally collated by Dr Daqing Chen, Director of the Public Analytics group based at the School of Engineering, London South Bank University. His research studies and business cases with this dataset have been published in various papers contributing to establishing a solid theoretical basis for direct, data and digital marketing strategies.

Access to such records can ensure enriching explorations or formulating insightful hypotheses about consumer behavior patterns among wholesalers. Whether it's managing inventory or studying transactional trends over time or spotting cancellation patterns - this dataset is apt for multiple forms of retail analysis

How to use the dataset

1. Sales Analysis:

Sales data forms the backbone of this dataset, and it allows users to delve into various aspects of sales performance. You can use the Quantity and UnitPrice fields to calculate metrics like revenue, and further combine it with InvoiceNo information to understand sales over individual transactions.

2. Product Analysis:

Each product in this dataset comes with its unique identifier (StockCode) and its name (Description). You could analyse which products are most popular based on Quantity sold or look at popularity per transaction by considering both Quantity and InvoiceNo.

3. Customer Segmentation:

If you associated specific business logic onto the transactions (such as calculating total amounts), then you could use standard machine learning methods or even RFM (Recency, Frequency, Monetary) segmentation techniques combining it with 'CustomerID' for your customer base to understand customer behavior better. Concatenating invoice numbers (which stand for separate transactions) per client will give insights about your clients as well.

4. Geographical Analysis:

The Country column enables analysts to study purchase patterns across different geographical locations.

Practical applications

Understand what products sell best where - It can help drive tailored marketing strategies. Anomalies detection – Identify unusual behaviors that might lead frau...

Context

The World Bank is an international financial institution that provides loans to countries of the world for capital projects. The World Bank's stated goal is the reduction of poverty. Source: https://en.wikipedia.org/wiki/World_Bank

Content

This dataset combines key education statistics from a variety of sources to provide a look at global literacy, spending, and access.

For more information, see the World Bank website.

Fork this kernel to get started with this dataset.

Acknowledgements

https://bigquery.cloud.google.com/dataset/bigquery-public-data:world_bank_health_population

http://data.worldbank.org/data-catalog/ed-stats

https://cloud.google.com/bigquery/public-data/world-bank-education

Citation: The World Bank: Education Statistics

Dataset Source: World Bank. This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - http://www.data.gov/privacy-policy#data_policy - and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

Banner Photo by @till_indeman from Unplash.

Inspiration

Of total government spending, what percentage is spent on education?

The online banking penetration rate in India was forecast to continuously increase between 2024 and 2029 by in total 19.3 percentage points. After the fifteenth consecutive increasing year, the online banking penetration is estimated to reach 64.34 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 Pakistan and Bangladesh.

Description of the Dataset: A Comprehensive Record of Personal Savings Account Transactions

This dataset provides a detailed record of an individual's savings bank account transactions spanning the years 2022 and 2023. It encompasses a total of 10 columns, consisting of 6 primary columns and 4 secondary columns.

The 6 Primary Columns Are: 1. Date: This column records the date of each transaction. 2. Debit or Credit: It signifies whether each transaction involves a debit (money withdrawn) or a credit (money deposited). 3. Amount: This column documents the monetary value associated with each transaction. 4. Balance: It records the account balance after each transaction. 5. Method of Transaction: This column specifies the method or channel through which the transaction was executed. 6. Name of Person: It identifies the person involved in the transaction.

In addition to these primary columns, there is an essential secondary column: i.e., Tday (Transaction Day): This column is designed to highlight specific days with multiple transactions. It indicates the occurrence of multiple transactions on the same date, providing valuable information about account activity.

This dataset comprises a total of 509 transactions, and within these transactions, there are 313 unique transaction dates. This unique date count underscores that the dataset includes days with multiple transactions, and the 'Tday' column is utilized to distinguish and track such instances.

Refer the data for more insights. You will also be able to see Pareto distribution (Find it out!!)

The diversification index is based on counting how many activities, out of a list of 12, have been realised at least once in the previous months. It is computed at individual level for those individuals having used internet in the last 3 months.

Notes

The 12 activities included in the index are: sending/receiving e-mails, information about goods and services, reading online newspapers/news, information on travel/accommodation services, posting messages to social media, interaction with public authorities, internet banking, telephoning or video calls, selling goods or services, purchases of content (films,music,software,etc), purchase of goods, purchase of services.

Original source

Eurostat, Table isoc_bde15cua: Internet use and activities:

http://ec.europa.eu/eurostat/web/products-datasets/-/isoc_bde15cua

Parent dataset

This dataset is part of of another dataset:

http://digital-agenda-data.eu/datasets/digital_agenda_scoreboard_key_indicators

• Agriculture, Hunting, Forestry and Fishing refers to loans granted for the purpose of financing customers in the cultivation of crops, livestock farming, timber extraction, forest management, poultry, farming, fishing and agricultural services. • Mining and Quarrying refers to loans granted to finance coal mining, crude petroleum and natural gas production, metal ore mining and quarrying. • Manufacturing refers to loans granted to finance customers in the manufacturing of a multitude of goods, including processing of food, rubber, palm oil, etc., manufacture of wearing apparel, leather goods, wood and wood products, paper and paper products, printing, publishing, manufacture of chemical and chemical products, petroleum, coal, rubber and plastic products, manufacture of iron and steel products, manufacture of fabricated metal products, machinery and equipment, etc. • Electricity, Gas and Water refers to loans granted to finance customers in generation, transmission and distribution of electrical energy for sale to households, industrial and commercial users, production of gas in gas works, distribution of manufactured gas and natural gas. • Wholesale and Retail Trade, Restaurants and Hotels refers to loans granted to finance customers in wholesale trade, retail trade and those operating restaurants and hotels. • Broad Property Sector Of which:  Construction refers to loans granted to finance customers in general contracting including civil engineering work, special contracting work, construction of industrial buildings and factories, construction of infrastructure, commercial complexes, residential dwellings and other construction activity.  Residential Property refers to loans granted for the purchase or refinancing the purchase of residential property which were classified as low cost (RM25, 000 and below), lower medium cost (RM25, 001 -RM60, 000), medium cost (RM60,001-RM100,000), higher medium cost (RM100,001 -RM150,000) and higher cost houses (more that RM150,000).  Non-Residential Property refers to loans granted for the purchase and refinancing of the purchase of non-residential property. Non-residential means landed property, which are not used for human dwelling purposes. It includes industrial buildings, factories, land, commercial complexes, warehouses and other structures not meant for human dwelling.  Real Estate refers to loans granted to companies involved in letting and operating real estate services on own account. Include renting of land to others, development and sale of land on own account, sub-dividing real property etc. Include real estate agents, brokers and managers engaged in renting, buying, selling and managing real estate for others for a fee and commission.  Transport, Storage and Communication refers to loans granted to finance customers in the provision of transport, storage and communication services to others. • Finance, Insurance and Business Services Of which:  Finance refers to loans granted to banking institutions and non-bank financial institutions.  Insurance refers to life insurance, reinsurance and general insurance services, insurance broking and loss assessing for insurance claims purposes.  Business Services refer to loans extended for provision of legal services, accounting services, auditing services, data collection etc. • Consumption Credit Of which:  Personal Uses refer to loans granted to individuals only for private use, exclude loans to purchase securities, consumer durables, transport vehicles, residential property, non-residential property and loans or credit obtained through the use of credit cards.  Purchase of Consumer Durables refers to loans granted for the acquisition of consumer durable goods such as televisions, refrigerators, washing machines etc.  Purchase of Passenger Cars refers to loans for the purchase of motor vehicles which are used primarily to carry a limited number of people, and includes multipurpose vehicles fitted to carry passengers.  Credit Cards refer to credit extended to customers using credit/charge cards issued by a reporting institution and includes cash withdrawals through such cards.  Purchase of Securities refers to loans granted to finance both primary and secondary market purchases of securities. Include loans granted to substitute for another loan granted previously by another party for the purchase of securities.  Purchase of Transport Vehicles refers to loans granted for the purposes of financing the purchases of motor vehicles other than passenger cars and other transport vehicles.  Community, Social and Personal Services refer to loans granted to finance customers for services such as public administration and defense, sanitary and similar services, social and related community services, recreation and cultural services and personal and household services. No. of Views : 555

ExactOne delivers unparalleled consumer transaction insights to help investors and corporate clients uncover market opportunities, analyze trends, and drive better decisions.

Dataset Highlights - Source: Debit and credit card transactions from 600K+ active users and 2M accounts connected via Open Banking. Scale: Covers 250M+ annual transactions, mapped to 1,800+ merchants and 400+ tickers. Historical Depth: Over 6 years of transaction data. Flexibility: Analyse transactions by merchant/ticker, category/industry, or timeframe (daily, weekly, monthly, or quarterly).

ExactOne data offers visibility into key consumer industries, including: Airlines - Regional / Budget Airlines - Cargo Airlines - Full Service Autos - OEMs Communication Services - Cable & Satellite Communication Services - Integrated Telecommunications Communication Services - Wireless Telecom Consumer - Services Consumer - Health & Fitness Consumer Staples - Household Supplies Energy - Utilities Energy - Integrated Oil & Gas Financial Services - Insurance Grocers - Traditional Hotels - C-corp Industrial - Misc Industrial - Tools And Hardware Internet - E-commerce Internet - B2B Services Internet - Ride Hailing & Delivery Leisure - Online Gambling Media - Digital Subscription Real Estate - Brokerage Restaurants - Quick Service Restaurants - Fast Casual Restaurants - Pubs Restaurants - Specialty Retail - Softlines Retail - Mass Merchants Retail - European Luxury Retail - Specialty Retail - Sports & Athletics Retail - Footwear Retail - Dept Stores Retail - Luxury Retail - Convenience Stores Retail - Hardlines Technology - Enterprise Software Technology - Electronics & Appliances Technology - Computer Hardware Utilities - Water Utilities

Use Cases

For Private Equity & Venture Capital Firms: - Deal Sourcing: Identify high-growth opportunities. - Due Diligence: Leverage transaction data to evaluate investment potential. - Portfolio Monitoring: Track performance post-investment with real-time data.

For Consumer Insights & Strategy Teams: - Market Dynamics: Compare sales trends, average transaction size, and customer loyalty. - Competitive Analysis: Benchmark market share and identify emerging competitors. - E-commerce vs. Brick & Mortar Trends: Assess channel performance and strategic opportunities. - Demographic & Geographic Insights: Uncover growth drivers by demo and geo segments.

For Investor Relations Teams: - Shareholder Insights: Monitor brand performance relative to competitors. - Real-Time Intelligence: Analyse sales and market dynamics for public and private companies. - M&A Opportunities: Evaluate market share and growth potential for strategic investments.

Key Benefits of ExactOne - Understand Market Share: Benchmark against competitors and uncover emerging players. - Analyse Customer Loyalty: Evaluate repeat purchase behavior and retention rates. - Track Growth Trends: Identify key drivers of sales by geography, demographic, and channel. - Granular Insights: Drill into transaction-level data or aggregated summaries for in-depth analysis.

With ExactOne, investors and corporate leaders gain actionable, real-time insights into consumer behaviour and market dynamics, enabling smarter decisions and sustained growth.

Internet use in the UK annual estimates by age, sex, disability, ethnic group, economic activity and geographical location, including confidence intervals.

Context

There's a story behind every dataset and here's your opportunity to share yours.

Content

What's inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too.

Acknowledgements

We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

Sri Lanka LK: Internet Users: Individuals: % of Population data was reported at 32.051 % in 2016. This records an increase from the previous number of 29.989 % for 2015. Sri Lanka LK: Internet Users: Individuals: % of Population data is updated yearly, averaging 1.625 % from Dec 1990 (Median) to 2016, with 24 observations. The data reached an all-time high of 32.051 % in 2016 and a record low of 0.000 % in 1990. Sri Lanka LK: Internet Users: Individuals: % of Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Sri Lanka – Table LK.World Bank: Telecommunication. Internet users are individuals who have used the Internet (from any location) in the last 3 months. The Internet can be used via a computer, mobile phone, personal digital assistant, games machine, digital TV etc.; ; International Telecommunication Union, World Telecommunication/ICT Development Report and database.; Weighted average; Please cite the International Telecommunication Union for third-party use of these data.

Envestnet®| Yodlee®'s Online Purchase Data (Aggregate/Row) Panels consist of de-identified, near-real time (T+1) USA credit/debit/ACH transaction level data – offering a wide view of the consumer activity ecosystem. The underlying data is sourced from end users leveraging the aggregation portion of the Envestnet®| Yodlee®'s financial technology platform.

Envestnet | Yodlee Consumer Panels (Aggregate/Row) include data relating to millions of transactions, including ticket size and merchant location. The dataset includes de-identified credit/debit card and bank transactions (such as a payroll deposit, account transfer, or mortgage payment). Our coverage offers insights into areas such as consumer, TMT, energy, REITs, internet, utilities, ecommerce, MBS, CMBS, equities, credit, commodities, FX, and corporate activity. We apply rigorous data science practices to deliver key KPIs daily that are focused, relevant, and ready to put into production.

We offer free trials. Our team is available to provide support for loading, validation, sample scripts, or other services you may need to generate insights from our data.

Investors, corporate researchers, and corporates can use our data to answer some key business questions such as: - How much are consumers spending with specific merchants/brands and how is that changing over time? - Is the share of consumer spend at a specific merchant increasing or decreasing? - How are consumers reacting to new products or services launched by merchants? - For loyal customers, how is the share of spend changing over time? - What is the company’s market share in a region for similar customers? - Is the company’s loyal user base increasing or decreasing? - Is the lifetime customer value increasing or decreasing?

Additional Use Cases: - Use spending data to analyze sales/revenue broadly (sector-wide) or granular (company-specific). Historically, our tracked consumer spend has correlated above 85% with company-reported data from thousands of firms. Users can sort and filter by many metrics and KPIs, such as sales and transaction growth rates and online or offline transactions, as well as view customer behavior within a geographic market at a state or city level. - Reveal cohort consumer behavior to decipher long-term behavioral consumer spending shifts. Measure market share, wallet share, loyalty, consumer lifetime value, retention, demographics, and more.) - Study the effects of inflation rates via such metrics as increased total spend, ticket size, and number of transactions. - Seek out alpha-generating signals or manage your business strategically with essential, aggregated transaction and spending data analytics.

Use Cases Categories (Our data provides an innumerable amount of use cases, and we look forward to working with new ones): 1. Market Research: Company Analysis, Company Valuation, Competitive Intelligence, Competitor Analysis, Competitor Analytics, Competitor Insights, Customer Data Enrichment, Customer Data Insights, Customer Data Intelligence, Demand Forecasting, Ecommerce Intelligence, Employee Pay Strategy, Employment Analytics, Job Income Analysis, Job Market Pricing, Marketing, Marketing Data Enrichment, Marketing Intelligence, Marketing Strategy, Payment History Analytics, Price Analysis, Pricing Analytics, Retail, Retail Analytics, Retail Intelligence, Retail POS Data Analysis, and Salary Benchmarking

1. Investment Research: Financial Services, Hedge Funds, Investing, Mergers & Acquisitions (M&A), Stock Picking, Venture Capital (VC)

2. Consumer Analysis: Consumer Data Enrichment, Consumer Intelligence

3. Market Data: AnalyticsB2C Data Enrichment, Bank Data Enrichment, Behavioral Analytics, Benchmarking, Customer Insights, Customer Intelligence, Data Enhancement, Data Enrichment, Data Intelligence, Data Modeling, Ecommerce Analysis, Ecommerce Data Enrichment, Economic Analysis, Financial Data Enrichment, Financial Intelligence, Local Economic Forecasting, Location-based Analytics, Market Analysis, Market Analytics, Market Intelligence, Market Potential Analysis, Market Research, Market Share Analysis, Sales, Sales Data Enrichment, Sales Enablement, Sales Insights, Sales Intelligence, Spending Analytics, Stock Market Predictions, and Trend Analysis

Context

The World Bank is an international financial institution that provides loans to countries of the world for capital projects. The World Bank's stated goal is the reduction of poverty. Source: https://en.wikipedia.org/wiki/World_Bank

Content

This dataset combines key health statistics from a variety of sources to provide a look at global health and population trends. It includes information on nutrition, reproductive health, education, immunization, and diseases from over 200 countries.

Update Frequency: Biannual

For more information, see the World Bank website.

Fork this kernel to get started with this dataset.

Acknowledgements

https://datacatalog.worldbank.org/dataset/health-nutrition-and-population-statistics

https://cloud.google.com/bigquery/public-data/world-bank-hnp

Dataset Source: World Bank. This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - http://www.data.gov/privacy-policy#data_policy - and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

Citation: The World Bank: Health Nutrition and Population Statistics

Banner Photo by @till_indeman from Unplash.

Inspiration

What’s the average age of first marriages for females around the world?

Iran IR: Internet Users: Individuals: % of Population data was reported at 60.416 % in 2017. This records an increase from the previous number of 53.227 % for 2016. Iran IR: Internet Users: Individuals: % of Population data is updated yearly, averaging 8.100 % from Dec 1990 (Median) to 2017, with 25 observations. The data reached an all-time high of 60.416 % in 2017 and a record low of 0.000 % in 1990. Iran IR: Internet Users: Individuals: % of Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Iran – Table IR.World Bank: Telecommunication. Internet users are individuals who have used the Internet (from any location) in the last 3 months. The Internet can be used via a computer, mobile phone, personal digital assistant, games machine, digital TV etc.; ; International Telecommunication Union, World Telecommunication/ICT Development Report and database.; Weighted average; Please cite the International Telecommunication Union for third-party use of these data.

Annual data on internet usage in Great Britain, including frequency of internet use, internet activities and internet purchasing.

The Survey of Consumer Finances (SCF) is normally a triennial cross-sectional survey of U.S. families. The survey data include information on families' balance sheets, pensions, income, and demographic characteristics.

Aktivitäten in der Freizeit. Internet- und Mobiltelefonnutzung. Vor- und Nachteile der Internet- und Mobiltelefonnutzung. Themen: Häufigkeit von Freizeitaktivitäten (Sport, Kino, Fernsehen, Gaststättenbesuche, Hobby, Informationssuche, Freundeskontakte); Häufigkeit aktiver Teilnahme an Vereinsaktivitäten; Personenvertrauen; Häufigkeit der Internetnutzung zu privaten Zwecken; Art der Internetnutzung; Verbesserungen durch das Internet: Bekanntschaften machen, Verwaltung von Finanzen, Umgang mit Behörden, Erhalt gesundheitsbezogener Informationen, Ausführung der Arbeit, Tätigung von Einkäufen, Gelegenheit zum Lernen, Ausüben von Hobbies, Information über aktuelle Themen, Kontakte zu Familienmitgliedern und Freunden sowie Gelegenheit zum kulturellen Austausch; Nachteile und Vorteile durch eine Nicht-Nutzung des Internets: geringere Gelegenheit zur persönlichen Kontaktpflege, Nachteile in Bezug auf die beruflichen Perspektiven, Risiko, altmodisch zu werden, weniger Offenheit gegenüber der Außenwelt, geringere Informiertheit, mehr Zeit für Freunde und Familie, geringes Risiko Opfer von Online-Betrug zu werden, höherer Schutz der persönlichen Daten, schlechtere Erreichbarkeit zu beruflichen Zwecken, geringeres Risiko der Frustration durch komplizierte Technologien; Internetnutzung über Freunde oder Verwandte; Nutzung eines Mobiltelefons; Vorteile der Mobiltelefonnutzung: Kontaktpflege mit Familie und Freunden, bessere Informiertheit, Organisation der Freizeit, Austausch von Ideen und Materialien, gesteigertes Sicherheitsgefühl, zu Arbeitszwecken; Auswirkungen durch eine Nicht-Nutzung von Mobiltelefonen: verpasste Gelegenheiten der Kontaktpflege, schlechtere Erreichbarkeit, Kostenersparnis, weniger Stress. Demographie: Geschlecht; Alter; Alter bei Beendigung der Ausbildung; Beruf; berufliche Stellung; Region; Urbanisierungsgrad; Haushaltszusammensetzung und Haushaltsgröße. Zusätzlich verkodet wurde: Befragten-ID; Interviewer-ID; Interviewsprache; Land; Interviewdatum; Interviewdauer (Interviewbeginn und Interviewende); Interviewmodus (Mobiltelefon oder Festnetz); Gewichtungsfaktor. Attitudes towards the benefits of internet and mobile phone use. Topics: frequency of the following leisure activities: sport, cultural activities, watch TV, go out, pursue a hobby, keep oneself informed, meet friends; frequency of participating in activities of organisations; trust in other people; frequency of internet use; online activities: send or receive emails or instant messages, purchase goods or services, internet banking, download multimedia content or software, use electronic forms of public administration, learn, use social networks, look for information, read or watch news, upload content, do daily work, transfer content to other devices; assessment of the improvement in selected areas due to the internet: opportunity to meet new people, way to manage finances, way to deal with public authorities, way to get health-related information, way to perform job, way to shop, opportunity to learn, way to pursue hobbies, capability to be informed, personal relationships, opportunity to access culture; attitude towards the following statements on people that don’t use the internet: miss opportunity of greater contact to people, are at disadvantage in career prospects, are at risk of becoming old-fashioned, miss opportunity of finding good bargains, are less open to the world, are less informed, have more time, are not at risk of online fraud, are not at risk of others finding out personal information about them, are less reachable for professional purposes, avoid frustration of dealing with complicated technologies; asked other person in the last year to send email, get information from the internet or make online purchase for oneself; frequency of mobile phone use; assessment of the improvement in selected areas due to mobile phones: keep contact with people, capability to be informed, way to manage free time, share content, feel more secure, work; attitude towards the following statements on people that don’t use mobile phones: miss opportunity of greater contact to people, are less reachable, are saving money, have less stress. Demography: sex; age; age at end of education; occupation; professional position; region; type of community; household composition and household size. Additionally coded was: respondent ID; interviewer ID; language of the interview; country; date of interview; time of the beginning of the interview; duration of the interview; type of phone line; weighting factor.

This dataset is about books. It has 1 row and is filtered where the book is Denying democracy : how the IMF and World Bank take power from people. It features 7 columns including author, publication date, language, and book publisher.

Dataset Description

The dataset includes 1,000 records with information about loan applications, including variables related to the applicant's financial status, credit history, and loan details. The goal is to analyze patterns in credit risk or build models to predict loan defaults.

Columns:

• checking_balance: Customer's current account balance in deutschmarks, classified as < 0 DM (negative balance), 1 - 200 DM, > 200 DM, or unknown (unknown).
• months_loan_duration: Duration of the loan in months.
• credit_history: Credit history of the applicant.
• purpose: Purpose of the loan.
• amount: Loan amount.
• savings_balance: Savings account balance.
• employment_duration: Length of employment.
• percent_of_income: Percentage of income allocated to loan repayment.
• years_at_residence: Years at the current residence.
• age: Applicant's age.
• other_credit: Presence of other credit agreements.
• housing: Housing status (e.g., rent, own).
• existing_loans_count: Number of existing loans.
• job: Job type or classification.
• dependents: Number of dependents.
• phone: Availability of a telephone.
• default: Target variable indicating loan default ("yes" or "no").

Inspiration

This dataset can be used for: - Building predictive models for loan default. - Exploring relationships between financial variables and credit risk. - Enhancing your understanding of credit risk analysis.

License

This dataset is published under the CC BY-NC-SA 4.0 license: - Permitted: Educational, research, and personal use. - Restricted: Commercial use is not allowed. - Attribution: Credit to Universidad de Santiago de Chile is required. - Sharing: Derivative works must use the same license.

This dataset was originally provided by the Universidad de Santiago de Chile as part of the course "Machine Learning for Management". I am not the original creator of the data, and my role is solely to share this resource for educational and research purposes. All rights to the original data belong to the university and/or the original authors.

This dataset may not be used for commercial purposes or in contexts that violate the copyright or policies of the institution that created it. Users are responsible for complying with the terms of use specified in the accompanying license and should ensure they provide appropriate credit.

Additional Notes If you are a student or researcher interested in using this dataset, please make sure to give proper credit to the original source in your publications or projects.

Abstract

The fourth edition of the Global Findex offers a lens into how people accessed and used financial services during the COVID-19 pandemic, when mobility restrictions and health policies drove increased demand for digital services of all kinds.

The Global Findex is the world's most comprehensive database on financial inclusion. It is also the only global demand-side data source allowing for global and regional cross-country analysis to provide a rigorous and multidimensional picture of how adults save, borrow, make payments, and manage financial risks. Global Findex 2021 data were collected from national representative surveys of about 128,000 adults in more than 120 economies. The latest edition follows the 2011, 2014, and 2017 editions, and it includes a number of new series measuring financial health and resilience and contains more granular data on digital payment adoption, including merchant and government payments.

The Global Findex is an indispensable resource for financial service practitioners, policy makers, researchers, and development professionals.

Geographic coverage

Excluded populations living in Northeast states and remote islands and Jammu and Kashmir. The excluded areas represent less than 10 percent of the total population.

Analysis unit

Individual

Kind of data

Observation data/ratings [obs]

Sampling procedure

In most developing economies, Global Findex data have traditionally been collected through face-to-face interviews. Surveys are conducted face-to-face in economies where telephone coverage represents less than 80 percent of the population or where in-person surveying is the customary methodology. However, because of ongoing COVID-19 related mobility restrictions, face-to-face interviewing was not possible in some of these economies in 2021. Phone-based surveys were therefore conducted in 67 economies that had been surveyed face-to-face in 2017. These 67 economies were selected for inclusion based on population size, phone penetration rate, COVID-19 infection rates, and the feasibility of executing phone-based methods where Gallup would otherwise conduct face-to-face data collection, while complying with all government-issued guidance throughout the interviewing process. Gallup takes both mobile phone and landline ownership into consideration. According to Gallup World Poll 2019 data, when face-to-face surveys were last carried out in these economies, at least 80 percent of adults in almost all of them reported mobile phone ownership. All samples are probability-based and nationally representative of the resident adult population. Phone surveys were not a viable option in 17 economies that had been part of previous Global Findex surveys, however, because of low mobile phone ownership and surveying restrictions. Data for these economies will be collected in 2022 and released in 2023.

In economies where face-to-face surveys are conducted, the first stage of sampling is the identification of primary sampling units. These units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. To increase the probability of contact and completion, attempts are made at different times of the day and, where possible, on different days. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used. Respondents are randomly selected within the selected households. Each eligible household member is listed, and the hand-held survey device randomly selects the household member to be interviewed. For paper surveys, the Kish grid method is used to select the respondent. In economies where cultural restrictions dictate gender matching, respondents are randomly selected from among all eligible adults of the interviewer's gender.

In traditionally phone-based economies, respondent selection follows the same procedure as in previous years, using random digit dialing or a nationally representative list of phone numbers. In most economies where mobile phone and landline penetration is high, a dual sampling frame is used.

The same respondent selection procedure is applied to the new phone-based economies. Dual frame (landline and mobile phone) random digital dialing is used where landline presence and use are 20 percent or higher based on historical Gallup estimates. Mobile phone random digital dialing is used in economies with limited to no landline presence (less than 20 percent).

For landline respondents in economies where mobile phone or landline penetration is 80 percent or higher, random selection of respondents is achieved by using either the latest birthday or household enumeration method. For mobile phone respondents in these economies or in economies where mobile phone or landline penetration is less than 80 percent, no further selection is performed. At least three attempts are made to reach a person in each household, spread over different days and times of day.

Sample size for India is 3000.

Mode of data collection

Face-to-face [f2f]

Research instrument

Questionnaires are available on the website.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in Demirgüç-Kunt, Asli, Leora Klapper, Dorothe Singer, Saniya Ansar. 2022. The Global Findex Database 2021: Financial Inclusion, Digital Payments, and Resilience in the Age of COVID-19. Washington, DC: World Bank.

Attitudes towards the benefits of internet and mobile phone use. Topics: frequency of the following leisure activities: sport, cultural activities, watch TV, go out, pursue a hobby, keep oneself informed, meet friends; frequency of participating in activities of organisations; trust in other people; frequency of internet use; online activities: send or receive emails or instant messages, purchase goods or services, internet banking, download multimedia content or software, use electronic forms of public administration, learn, use social networks, look for information, read or watch news, upload content, do daily work, transfer content to other devices; assessment of the improvement in selected areas due to the internet: opportunity to meet new people, way to manage finances, way to deal with public authorities, way to get health-related information, way to perform job, way to shop, opportunity to learn, way to pursue hobbies, capability to be informed, personal relationships, opportunity to access culture; attitude towards the following statements on people that don’t use the internet: miss opportunity of greater contact to people, are at disadvantage in career prospects, are at risk of becoming old-fashioned, miss opportunity of finding good bargains, are less open to the world, are less informed, have more time, are not at risk of online fraud, are not at risk of others finding out personal information about them, are less reachable for professional purposes, avoid frustration of dealing with complicated technologies; asked other person in the last year to send email, get information from the internet or make online purchase for oneself; frequency of mobile phone use; assessment of the improvement in selected areas due to mobile phones: keep contact with people, capability to be informed, way to manage free time, share content, feel more secure, work; attitude towards the following statements on people that don’t use mobile phones: miss opportunity of greater contact to people, are less reachable, are saving money, have less stress. Demography: sex; age; age at end of education; occupation; professional position; region; type of community; household composition and household size. Additionally coded was: respondent ID; interviewer ID; language of the interview; country; date of interview; time of the beginning of the interview; duration of the interview; type of phone line; weighting factor. Aktivitäten in der Freizeit. Internet- und Mobiltelefonnutzung. Vor- und Nachteile der Internet- und Mobiltelefonnutzung. Themen: Häufigkeit von Freizeitaktivitäten (Sport, Kino, Fernsehen, Gaststättenbesuche, Hobby, Informationssuche, Freundeskontakte); Häufigkeit aktiver Teilnahme an Vereinsaktivitäten; Personenvertrauen; Häufigkeit der Internetnutzung zu privaten Zwecken; Art der Internetnutzung; Verbesserungen durch das Internet: Bekanntschaften machen, Verwaltung von Finanzen, Umgang mit Behörden, Erhalt gesundheitsbezogener Informationen, Ausführung der Arbeit, Tätigung von Einkäufen, Gelegenheit zum Lernen, Ausüben von Hobbies, Information über aktuelle Themen, Kontakte zu Familienmitgliedern und Freunden sowie Gelegenheit zum kulturellen Austausch; Nachteile und Vorteile durch eine Nicht-Nutzung des Internets: geringere Gelegenheit zur persönlichen Kontaktpflege, Nachteile in Bezug auf die beruflichen Perspektiven, Risiko, altmodisch zu werden, weniger Offenheit gegenüber der Außenwelt, geringere Informiertheit, mehr Zeit für Freunde und Familie, geringes Risiko Opfer von Online-Betrug zu werden, höherer Schutz der persönlichen Daten, schlechtere Erreichbarkeit zu beruflichen Zwecken, geringeres Risiko der Frustration durch komplizierte Technologien; Internetnutzung über Freunde oder Verwandte; Nutzung eines Mobiltelefons; Vorteile der Mobiltelefonnutzung: Kontaktpflege mit Familie und Freunden, bessere Informiertheit, Organisation der Freizeit, Austausch von Ideen und Materialien, gesteigertes Sicherheitsgefühl, zu Arbeitszwecken; Auswirkungen durch eine Nicht-Nutzung von Mobiltelefonen: verpasste Gelegenheiten der Kontaktpflege, schlechtere Erreichbarkeit, Kostenersparnis, weniger Stress. Demographie: Geschlecht; Alter; Alter bei Beendigung der Ausbildung; Beruf; berufliche Stellung; Region; Urbanisierungsgrad; Haushaltszusammensetzung und Haushaltsgröße. Zusätzlich verkodet wurde: Befragten-ID; Interviewer-ID; Interviewsprache; Land; Interviewdatum; Interviewdauer (Interviewbeginn und Interviewende); Interviewmodus (Mobiltelefon oder Festnetz); Gewichtungsfaktor.