The US Family Budget Dataset provides insights into the cost of living in different US counties based on the Family Budget Calculator by the Economic Policy Institute (EPI).

This dataset offers community-specific estimates for ten family types, including one or two adults with zero to four children, in all 1877 counties and metro areas across the United States.

Interesting Task Ideas:

1. See how family budgets compare to the federal poverty line and the Supplemental Poverty Measure in different counties.
2. Look into the money challenges faced by different types of families using the budgets provided.
3. Find out which counties have the most affordable places to live, food, transportation, healthcare, childcare, and other things people need.
4. Explore how the average income of families relates to the overall cost of living in different counties.
5. Investigate how family size affects the estimated budget and find counties where bigger families have higher costs.
6. Create visuals showing how the cost of living varies across different states and big cities.
7. Check whether specific counties are affordable for families of different sizes and types.
8. Use the dataset to compare living standards and economic security in different US counties.

If you find this dataset valuable, don't forget to hit the upvote button! 😊💝

Checkout my other datasets

Employment-to-Population Ratio for USA

Productivity and Hourly Compensation

130K Kindle Books

900K TMDb Movies

USA Unemployment Rates by Demographics & Race

Photo by Alev Takil on Unsplash

This dataset is about book subjects. It has 2 rows and is filtered where the books is The cost of living : the greater common good and the end of the imagination. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

This dataset is about books. It has 3 rows and is filtered where the book subjects is Cost and standard of living-Northern Ireland. It features 9 columns including author, publication date, language, and book publisher.

As of September 2025, Mumbai had the highest cost of living among other cities in the country, with an index value of ****. Gurgaon, a satellite city of Delhi and part of the National Capital Region (NCR) followed it with an index value of ****.  What is cost of living? The cost of living varies depending on geographical regions and factors that affect the cost of living in an area include housing, food, utilities, clothing, childcare, and fuel among others. The cost of living is calculated based on different measures such as the consumer price index (CPI), living cost indexes, and wage price index. CPI refers to the change in the value of consumer goods and services. The wage price index, on the other hand, measures the change in labor services prices due to market pressures. Lastly, the living cost indexes calculate the impact of changing costs on different households. The relationship between wages and costs determines affordability and shifts in the cost of living. Mumbai tops the list Mumbai usually tops the list of most expensive cities in India. As the financial and entertainment hub of the country, Mumbai offers wide opportunities and attracts talent from all over the country. It is the second-largest city in India and has one of the most expensive real estates in the world.

**Student Data Description: ** The dataset contains data representing the financial and personal information of 200 students.

Name: The full name of the student, combining the first name and last name. Names are generated based on the gender of the student.

Age: The age of the student, randomly generated between 18 and 30 years.

Gender: The gender of the student, randomly assigned as "Male" or "Female".

Current Educational Level: The current educational level of the student, randomly assigned from "High School", "Undergraduate", or "Graduate".

Monthly Income: The monthly income of the student, randomly generated between $500 and $3000.

Rent/Room Accommodation: The monthly expense for rent or room accommodation, randomly generated between $200 and $800.

Utilities: The monthly expense for utilities, such as electricity and water, randomly generated between $50 and $200.

Groceries: The monthly expense for groceries, randomly generated between $50 and $200.

Dining Out/Eating Outside: The monthly expense for dining out or eating outside, randomly generated between $20 and $150.

Public Transportation: The monthly expense for public transportation, randomly generated between $20 and $100.

Fuel/Car Maintenance: The monthly expense for fuel and car maintenance, randomly generated between $20 and $100.

Tuition Fees: The monthly expense for tuition fees, randomly generated between $1000 and $5000.

Books and Supplies: The monthly expense for books and supplies, randomly generated between $20 and $200.

Online Courses/Subscriptions: The monthly expense for online courses and subscriptions, randomly generated between $10 and $100.

Clothing/Shoes: The monthly expense for clothing and shoes, randomly generated between $20 and $150.

Entertainment: The monthly expense for entertainment, randomly generated between $10 and $200.

Health Insurance/Medical Expenses: The monthly expense for health insurance and medical expenses, randomly generated between $20 and $200.

Gym Memberships/Physical Activities: The monthly expense for gym memberships and physical activities, randomly generated between $10 and $100.

Mobile Phone/Internet Bill: The monthly expense for mobile phone and internet bill, randomly generated between $20 and $100.

Other Miscellaneous Expenses: The monthly expense for other miscellaneous items, randomly generated between $10 and $150.

Savings/Investments Amount: The amount of money saved or invested by the student, randomly generated between $0 and $1000.

Final Monthly Expense: The calculated total monthly expense for the student, which is the sum of all individual expenses.

The dataset is intended to be used for analysis and exploration of student financial behaviors and patterns.

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Description:

A simple yet challenging project, to predict the housing price based on certain factors like house area, bedrooms, furnished, nearness to mainroad, etc. The dataset is small yet, it's complexity arises due to the fact that it has strong multicollinearity. Can you overcome these obstacles & build a decent predictive model?

Acknowledgement:

Harrison, D. and Rubinfeld, D.L. (1978) Hedonic prices and the demand for clean air. J. Environ. Economics and Management 5, 81–102. Belsley D.A., Kuh, E. and Welsch, R.E. (1980) Regression Diagnostics. Identifying Influential Data and Sources of Collinearity. New York: Wiley.

Objective:

• Understand the Dataset & cleanup (if required).
• Build Regression models to predict the sales w.r.t a single & multiple feature.
• Also evaluate the models & compare thier respective scores like R2, RMSE, etc.

In 2024, the revenue generated from children's books in the United Kingdom amounted to *** million British pounds. The revenue of children's books generally grew steadily from 2017 onwards, although gains between 2019 and 2021 were small, with the figure rising by only *** or *** percent year over year. The source suggested that the drop in 2023 could be due to cost-of-living factors as well as the publication cycles of bigger authors and the categories they are published into. Children’s book access Children’s access to books in the UK differs according to several factors. For example, children and teens with access to free school meals were the least likely group to be encouraged to read by parents or carers, or to talk about what they were reading with their family. Boys were overall less likely to read than girls, regardless of family income, and only around 18 percent said their friends helped them find things to read, compared to 30 percent of girls. Demographics also affect the likelihood of children having a book of their own at home. Whilst over 80 percent of all children aged five to eight years old owned a book, again, boys of this age were the least likely group to have one. Getting kids reading Encouragement from parents, carers, and teachers at a young age could help young children to engage with reading materials, as well as a diverse and relatable selection of books in key areas like the home, the classroom, after-school clubs, and libraries. An awareness of the amount of time spent on screens or on social media, as well as potential reduction of this, could also be worth exploring. Data shows that children and teens aged four to 18 years old in the UK spend more than *** hours with TikTok each day – and whilst social media use among young people is now the norm, reduced screen time could help when attempting to encourage kids to engage with books and other reading material.

Canonical landing page and documentation: https://www.thepricer.org/student/resources/The Student Affordability Toolkit (SEAT) v1.2 is a time-series dataset that estimates how many hours a typical U.S. student must work to cover basic monthly survival costs, 1990–2025. The dataset converts major living and academic expenses into “hours of work” using the federal minimum wage (net of typical payroll withholding) to give a comparable affordability signal across years. Costs are grouped into a “student month” basket: shared off-campus rent and utilities, basic groceries, transportation, phone/internet, and typical out-of-pocket academic costs (tuition/fees portion not covered by aid, books/supplies). Outputs include: (1) raw dollar estimates for each cost category by year; (2) the same basket expressed in hours of work per month; and (3) a Monthly Survival Hours (MSH) measure and Work-to-Essentials Index (WTEI) that summarize student burden over time. The goal is to give students, journalists, and universities a transparent way to talk about affordability without needing to parse loan jargon or inflation-adjusted dollars. SEAT is intended for public use in advising, financial aid communication, affordability dashboards, and policy discussions.

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This case study sample dataset presents an opportunity to address pay inequity concerns through data analysis at BananaByte, a fictional leading company in the augmented reality (AR) home experience industry. As the popularity of AR celebrities and historical figures soars, the recent surge in stock prices has led to an increased budget for salary and equity.

To maintain a motivated and equitable workforce, BananaByte aims to identify and rectify any pay disparities discovered. This sample dataset provides the foundation for a systematic approach to ensure fair compensation, foster employee satisfaction, and uphold BananaByte's commitment to total rewards equity.

Mission Statement:
​Unleashing the Home Metaverse: Connecting Reality and Imagination

Vision Statement:
Transforming homes with smart whole home solutions, augmented reality spatial computing, and immersive experiences that transcend time and space.

Product Offerings:

-**Smart Home Whole Home Solutions:** Seamlessly integrate technology into every corner of your home for a truly connected living experience.

-**Augmented Reality Spatial Computing:** Explore a realm where the digital and physical worlds merge, enhancing your surroundings with interactive and immersive elements.

-**Augmented Reality Social Media:** Engage with friends, family, and virtual communities in captivating and innovative ways, blurring the line between social interaction and digital realms.

-**Celebrity AI Experiences:** Interact with lifelike AI representations of your favorite celebrities, bringing them to life in your own home.

-**Historical Figures:** Step back in time and converse with iconic historical figures, uncovering their wisdom and stories through augmented reality encounters.

-**Imaginary Friends:** Let your imagination run wild as you forge connections with virtual companions, bringing the joy and wonder of imaginary friends into your reality.

Can you use HR Data Science to explore these questions?

Compensation and total rewards problems a company may face:

​ - Salary Compression: This happens when there is a smaller than normal difference in pay between employees regardless of their skills or experience. It often occurs when wage growth is flat and new employees demand higher starting pay.

• Salary Inversion: Also known as pay inversion, it occurs when new hires are paid more than experienced personnel in the same position. This can create dissatisfaction and demotivation among senior employees.

• Pay Inequity: Pay inequity arises when employees doing the same job are paid differently. This could be due to gender, race, age, or other non-job-related factors, and it's both illegal and demotivating.

• Lack of Transparency: Without transparency in the compensation process, employees may not understand the logic behind their pay, which can lead to mistrust and dissatisfaction.

• Inadequate Compensation: When companies fail to offer competitive salaries and benefits as compared to the overall market, they risk losing talented employees to better-paying competitors.

• Overcompensation: Conversely, overcompensating employees without clear performance benchmarks can lead to financial strain on the company and a lack of motivation among employees.

• Inconsistent Bonus Structures: When bonus structures are inconsistent or seem arbitrary, they can lead to confusion and dissatisfaction among employees.

• Poor Performance Management System: If a company's performance management system is not robust, it could result in biased or unfair compensation decisions, impacting employee morale and productivity.

• Geographical Pay Variations: Companies with offices in multiple locations may struggle with setting fair compensation, given the differences in cost of living and local market rates.

Dataset Overview

This dataset comprises detailed real estate listings scraped from Realtor.com, providing a snapshot of various property types across Chicago. It includes 2,000 entries with information on property characteristics such as type, size, age, price, and features. This dataset was ethically collected using an API provided by Apify, ensuring all data scraping adhered to ethical standards.

Data Science Applications

This dataset is ideal for a variety of data science applications, including but not limited to: - Predictive Modeling: Forecast property prices based on various features like location, size, and age. - Market Analysis: Understand trends in real estate, including the types of properties being sold, pricing trends, and the influence of property features on market value. - Natural Language Processing: Analyze the textual descriptions provided for each listing to extract additional features or perform sentiment analysis. - Anomaly Detection: Identify unusual listings or potential outliers in the data, which could indicate errors in data collection or unique investment opportunities.

Column Descriptors

1. type: The type of property (e.g., single-family home, condo).
2. text: A textual description of the property.
3. year_built: The year in which the property was constructed.
4. beds: The number of bedrooms.
5. baths: Total number of bathrooms (including full and half).
6. baths_full: Number of full bathrooms.
7. baths_half: Number of half bathrooms.
8. garage: Garage capacity (number of cars).
9. lot_sqft: Size of the lot in square feet.
10. sqft: Living area size in square feet.
11. stories: Number of stories/floors in the property.
12. lastSoldPrice: The price at which the property was last sold.
13. soldOn: The date on which the property was last sold.
14. listPrice: The listing price of the property at the time of data collection.
15. status: The current status of the listing (e.g., for sale, sold).

Ethically Mined Data

This dataset was responsibly and ethically mined, adhering to all legal standards of data collection. The use of Apify's API ensures that the data collection process respects privacy and the platform's terms of service.

Acknowledgements

We thank Realtor.com for maintaining a comprehensive and accessible database, and Apify for providing the tools necessary for ethical data scraping. Their contributions have been invaluable in the creation of this dataset. Credits to Dall E3 for thumbnail image.

Usage Policy

This dataset is provided for non-commercial and educational purposes only. Users are encouraged to use this data to enhance learning, contribute to academic or personal projects, and develop skills in data science and real estate market analysis.

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Context:

My dataset is a valuable collection of real estate information sourced from REALTING.com, an international affiliate sales system known for facilitating safe and convenient property transactions worldwide. REALTING.com has a strong foundation, with its founders boasting approximately 20 years of experience in creating information technologies for the real estate market. This dataset offers insights into various properties across the globe, making it a valuable resource for real estate market analysis, property valuation, and trend prediction.

Content:

The dataset contains information on a diverse range of properties, each represented by a row of data. Here are the key columns and their contents:

• Title: A brief description or name of the property listing.
• Country: The country where the property is located.
• Location: The specific address or location of the property within the country.
• Building Construction Year: The year in which the building was constructed.
• Building Total Floors: The total number of floors or stories in the building.
• Apartment Floor: The floor on which the apartment is situated within the building.
• Apartment Rooms: The total number of rooms in the apartment.
• Apartment Bedrooms: The number of bedrooms in the apartment.
• Apartment Bathrooms: The number of bathrooms in the apartment.
• Apartment Total Area: The total area of the apartment in square meters.
• Apartment Living Area: The living area of the apartment in square meters.
• Price in USD: The price of the property listed in United States Dollars (USD).
• Image: References or links to images associated with the property listing.
• URL: Web links to the full property listing or more detailed information.

This dataset is rich in real estate-related information, making it suitable for various analytical tasks such as market research, property comparison, geographical analysis, and more. The dataset's global scope and diverse property attributes provide a comprehensive view of the international real estate market, offering ample opportunities for data-driven insights and decision-making.

Approx. 4000 household budget accounts were collected. The study started as a research project by the ministry of science and research of the state North-Rhine Westphalia in the second half of the 19th century (Az: IV AG-30101386). Then it was supported by research funds of the University of Saarland in Saarbrücken.

This statistic shows the average cost to attend university per year when living on campus and studying in-state in the United States in 2018/19. Attendance cost includes tuition and fees, room and board costs, books and supplies, and other personal and transportation costs. Studying in the District of Columbia was the most expensive in the academic year of 2018/19, with attendance costs adding up to approximately 64,354 U.S. dollars per year.

The safety of ready-to-eat food sold in urban informal markets in low and middle-income countries is a pressing public health challenge, that needs to be addressed if we are to establish healthy food systems. Guided by the Capability, Opportunity, and Motivation model for Behavior change (COM-B), this qualitative study aimed to explore perceptions of street vendors on their participation in a food safety capacity building intervention, consisting of training and provision of food safety equipment. The intervention aimed to improve food safety behavior of vendors of ready-to-eat chicken in informal markets in Ouagadougou, Burkina Faso. A total of 24 vendors selling ready-to-eat chicken at street restaurants participated in semi-structured interviews after training, which focused on vendors’ stories of change related to food safety capabilities, opportunities, motivation, and behaviors. Data were thematically analyzed following COM-B components. Vendors noted improvements in psychological (i.e., knowledge, awareness, self-efficacy, perceptions) and physical capabilities (i.e., equipment useability and applicability), and motivations (perceived responsibility, reputation, client satisfaction, profits, consumer demand). Moreover, training and provision of equipment, spill-over effects to employees or neighboring outlets, and social support were perceived as key social and physical opportunities, while structural challenges such as market infrastructure, regulations, financial resources, cost of living, and outlet culture were physical barriers to implement lessons learnt. This study provides insights into the impact of engaging vendors in improving food safety behavior through training and equipment provision. Improvements in vendors’ perceived capabilities and motivation contributed to improved food safety behavior, while contextual barriers hindered the perceived adoption of food safety behaviors.

Graph and download economic data for Inflation, consumer prices for the United States (FPCPITOTLZGUSA) from 1960 to 2024 about consumer, CPI, inflation, price index, indexes, price, and USA.

In 2021, the Dutch government granted nearly 2.7 billion euros worth of loans to students. Up until 2015-2016, the government subsidized participation in education and training by providing allowances for school costs and student grants and loans. These served to partly compensate students or their parents for their expenditure on tuition fees, books and materials, public transport and the cost of living. The supplementary grant and the student travel card were conditional loans. A conditional loan will be converted to a gift on completion of the student’s studies within a given period. If a student did not complete his/her studies within this period it will remain a loan and will have to be repaid. This system (referred to as basisbeurs in the Netherlands) was abolished in 2015-2016 and replaced with with a system of regular, non-conditional loans.

Detailed breakdown of average weekly household expenditure on goods and services in the UK. Data are shown by place of purchase, income group (deciles) and age of household reference person.

EKOS Research Associates and the Canada Millennium Scholarship Foundation conducted a monthly national study of the finances of post-secondary students from September 2001 until May 2002. The study was designed to capture the expenses and income of students on a monthly basis, in order to profile the financial circumstances of Canadian post-secondary students and the adequacy of available funding. The Web based Students Financial Survey provided accurate, quantifiable results for the first time on such issues as the incidence and level of assistance, the level of debt from outstanding bank loans, personal lines of credit, and credit cards. The study also yielded up-to-date information on student assets (such as automobiles, computers, and electronics), student earnings, time usage, and types of expenses incurred. The survey featured a panel of 1,524 post-secondary students from across the country, who participated in a very brief monthly survey, either via Internet or telephone. Students were required to complete a longer baseline wave of the survey in order to participate in the study. The baseline survey asked a number of questions concerning summer income and existing debt, including credit card debt. This dataset was received from the Canada Millennium Scholarship Foundation as is. Issues with value labels and missing values were discovered and corrected as best as possible with the documentation received. The variable gasst: Do you receive any government assistance? was not corrected due to lack of documentation about this variable. Some caution should be used with this dataset. This dataset was freely received from, the Canadian Millenium Scholarship Foundation. Some work was required for the variable and value labels, and missing values. They were correct as best as possible with the documentation received. Caution should be used with this dataset as some variables are lacking information.

Subjective evaluations of living conditions, desires and priorities,as well as difference between desire and reality.Topics:residential area;year of arrival;satisfaction with place of residence;moving frequency;previous residential area, residential status and size of residence;most important reasons to move;current residential status;number of households in building;shared costs;manner of acquisition of residence or building;public support and monthly load from mortgage payments;additional costs;heating costs;maintenance costs;subsidy of expenses;residence authorization;rent costs;heating costs and hot water costs;fixed amount for heating costs or hot water costs;heating costs;manner of payment;shared costs for modernization;type of shared costs for modernization;judgement on rent costs and receipt of housing benefit;amount of housing benefit;size of residence;number of living-rooms;judgement on size of residence;detailed information on residential furnishings;judgement on residential furnishings;information on desirable furnishing characteristics for a standard residence;year of construction of building;judgement on condition of building;satisfaction with environmental conditions, residential surroundings andsatisfaction with housing (scale);description of infrastructure facilities in residential area;social differences in residential area;neighborhood relationship;satisfaction with neighborhood (scale);development of housing situation in the course of life;most important sacrifices and losses in case of leavingcurrent residential surroundings;type of building;desired building and desired area;proportion of foreigners in residential areain comparison to other residential areas;current influx of foreigners or traditional foreigner quarter;origins of foreigners as ethnic Germans from Eastern Europe or refugees;relationship with foreigners;judgement on neighborhood relationships with foreigners;preference for mixing or separate foreigner quarter;contacts with foreigners;attitudes to help getting set up and integration measures for foreigners;moving plans;reasons for moving;destination city of moving;renovation plans;renovation in the past;classification of one's residence on a scale as well asclassification of one's residence five years ago, an attainable residence,the average residence of friends, the average of the West German andthe residence the respondent is entitled to;judgement on personal economic situation;goals in life (scale);preferences in music, watching television, and reading books;life style and behaviors;preferences in furnishing style and clothing style;leisure preferences.Demography:state;degree of urbanization and type of residential area;administrative district;city size;employment;job security;commuting time to work;spatial mobility readiness;sex;age (month and year of birth);school degree;age at school degree;occupational training;college degree;employment;occupational position earlier and today;marital status;living together with a partner;number of children;self-assessment of social class;religious denomination;church closeness;behavior at the polls in the last Federal Parliament election;party preference (Sunday question);size of household;net household income;personal net income;year of birth of children;number of persons in household with their own income;place of residence before 1989;car possession;German citizenship;possession of a telephone.Interviewer rating:day of interview and month;length of interview.

Montevideo Property Rental Dataset

Description

The Montevideo Property Rental Dataset provides comprehensive information about rental properties in Montevideo, Uruguay. This dataset is a valuable resource for anyone interested in exploring the rental property market in Montevideo, including real estate professionals, data analysts, and researchers.

Key Features

• Property Price: The rental price of each property.
• Property Address: The location and address of each property.
• Last Updated: The date when the property listing was last updated.
• Common Expenses: Common expenses associated with the property, if applicable.
• Floor: The floor number of the property within the building.
• Apartments per Floor: The number of apartments on each floor in the building.
• Number of Floors: The total number of floors in the building.
• Property Type: The type of property (e.g., apartment, house, office).
• Zone: The location or zone where the property is situated.
• Condition: The condition or state of the property (e.g., good condition, excellent condition).
• Disposition: The layout or disposition of the property (e.g., front, back, interior).
• Bathrooms: The number of bathrooms in the property.
• Bedrooms: The number of bedrooms or rooms in the property.
• Built Square Meters: The total square meters of the property.
• Terrace Square Meters: The square meters of terrace space, if applicable.
• Land Square Meters: The total land square meters, if applicable.
• Year of Construction: The year when the property was constructed.
• Bedrooms: The number of bedrooms in the property.
• Garages: The number of parking spaces or garages.
• Floors: The number of floors or stories in the property.
• Sea View: Whether the property offers a sea view (True/False).
• Social Housing: Whether the property is designated as social housing (True/False).
• Living Room: The presence of a living room in the property.
• Grill: The availability of a barbecue area in the property.
• Patio: The presence of a patio area in the property.
• Balcony: The presence of a balcony in the property.
• WiFi: Availability of WiFi in the property.
• Gas Pipeline: Connection to a gas pipeline in the property.
• Barbecue: Facilities for barbecues in the property.
• Radiant Floor Heating: Radiant floor heating in the property.
• Pet-Friendly: Whether the property is pet-friendly (True/False).
• Playroom: The presence of a playroom or recreational area in the property.
• Garden: The availability of a garden area in the property.
• Heating: The type of heating system in the property.
• Furnished: Whether the property is furnished (True/False).
• Storage: Storage area or deposit in the property.
• Air Conditioning: Availability of air conditioning in the property.
• Gym: Gym facilities in the property.
• Duplex: Whether the property is a duplex (True/False).
• Pool: The presence of a swimming pool in the property.
• Furnished: Whether the property is furnished (True/False).
• Closet: Closet or wardrobe space in the property.
• Solarium: Availability of a solarium area in the property.
• Garage: Garage or parking space.

This dataset provides a wealth of information for analyzing and understanding the rental property market in Montevideo. Whether you're looking for insights into property prices, property conditions, or available amenities, this dataset offers a comprehensive view of the rental properties in the city.