This dataset provides a synthetic representation of user behavior on a fictional dating app. It contains 50,000 records with 19 features capturing demographic details, app usage patterns, swipe tendencies, and match outcomes. The data was generated programmatically to simulate realistic user interactions, making it ideal for exploratory data analysis (EDA), machine learning modeling (e.g., predicting match outcomes), or studying user behavior trends in online dating platforms.

Key features include gender, sexual orientation, location type, income bracket, education level, user interests, app usage time, swipe ratios, likes received, mutual matches, and match outcomes (e.g., "Mutual Match," "Ghosted," "Catfished"). The dataset is designed to be diverse and balanced, with categorical, numerical, and labeled variables for various analytical purposes.

Usage

This dataset can be used for:

Exploratory Data Analysis (EDA): Investigate correlations between demographics, app usage, and match success. Machine Learning: Build models to predict match outcomes or user engagement levels. Social Studies: Analyze trends in dating app behavior across different demographics. Feature Engineering Practice: Experiment with transforming categorical and numerical data.

Lovoo v3 Dating App User Profiles and Statistics

Revealing popular user traits and behavior

By Jeffrey Mvutu Mabilama [source]

About this dataset

When Dating apps like Tinder began to become more popular, users wanted to create the best profiles possible in order to maximize their chances of being noticed and gain more potential encounters. Unlike traditional dating platforms, these new ones required mutual attraction before allowing two people to chat, making it all the more important for users to create a great profile that would give them an advantage over others.

It was amidst this scene that we Humans began paying attention at how charismatic and inspiring people presented themselves online. The most charismatic individuals tended to be the ones with the most followers or friends on social networks. This made us question what makes a great user profile and how one could make a lasting first impression in order ensure finding true love or even just some new friendships? How do we recognize a truly charismatic person from their presentation on social media? Is there any way of quantifying charisma?

In 2015 I set out with researching all this using Lovoo's newest dating app version -V3 (the iOS version), gathering user profile data such as age demographics, interest types (friendship, chatting or dating), language preferences etc., as well as usually unavailable metrics like number of profile visits, kisses received etc. I was also able to collect pictures of those user profiles in order discern any correlations between appeal and reputation that may have existed at that time amongst Lovoo's population base.

My goal is forthis dataset will help you answer those questions related not just romantic success but also popularity/charisma censes/demographic studies and even detect influential figures both within & outside Lovoo's platform . A starter analysis is available accompanying this dataset which can be used as a reference point when working with the data here. Using this dataset you can your own investigations into:

  * What type of person has attracted more visitors or potential matches than others?   
  * Which criteria can be used when determining someone’s charm/likability among others    ?
  * How does one optimize his/her dating app profile visibility so he/she won’t remain unseen among other users? 

Grab this amazing opportunity now! Kick-start your journey towards understanding the inner workings behind success in online relationships today!

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How to use the dataset

To get started with this dataset first you need to download it from Kaggle. Once downloaded you should take a look at the column names in order to get an idea of what information is available. This data includes fields such as gender, age name (and nickname), number of pictures uploaded/profile visits/kisses /fans/gifts received and flirt interests (chatting or making friends). It also contains language specifics like detected languages for each user as well as country & city of residence.

The most interesting section for your research is likely the number of details that have been filled in for each user – such as whether they are interested in chatting or making friends. Usually these information points allow us to infer more about a person’s character – from jokester to serious individualist (or anything else!). The same holds true for their language preferences which might reveal aspects regarding their cultures orientation or habits.

You may also want collected data which was left out here - imagery associated with users' profiles - so please contact JfreexDatasets_bot on Telegram if you would like access to this imagery that has not yet been uploaded here on Kaggle but is intregral part of understanding what makes a great user profile attractive on these platforms according Aesthetics Theory applied in an uthentic way when considering how each image adds sentimental appeal value by its perspective content focus - be it visually descriptive; emotive narrative; personality coupled with expression mood association.. etcetera... Or simple just download relevant images yourself using automated scripts ready made via webiste Grammak where Github Repo exists: https://github.com/grammak580542008/Lovoo-v3-Profiles-Data # 1 year ago...

Finally moving ahead — keep in mind that there are other ways data can be gathered possible besides just downloading it from Kaggle – such us Messenger Bots or Customer Relationship Management systems which help companies serve...

Don't forget to hit the upvote🙏🙂

This dataset contains simulated data from a DATING app. It is designed to explore user behaviors, preferences, and demographics trends.

Columns Description:

User ID: Unique identifier for each user.

Age: Age of the user (range: 18-35 years).

Gender: Gender of the user (Male/Female).

Height: User's height in feet.

Interests: A list of user interests (Sports, Reading, Movies).

Looking For: The type of relationship the user is seeking (Casual Dating, Marriage).

Children: Whether the user has children (Yes/No).

Education Level: User's highest education qualification (High School, Ph.D.).

Occupation: Current occupation of the user (Doctor, Engineer, Artist).

Swiping History: Total number of swipes by the user.

Frequency of Usage: How often the user uses the app (Daily, Weekly, Monthly).

This research conducted an online survey to investigate the relationship between dating app use and hookup intention. It measured dating app use, perceived descriptive norms, injunctive norms, fear of negative evaluation, hookup intention, and demographic information including age, gender, sexual orientation, and relationship status.

Context

Hinge is a dating app that bills itself as the only dating app that emphasizes long-term connections between users over superficiality and building relationships. It seeks to attract a younger demographic than Match.com and eHarmony, such as the demographic using Tinder. The app was fully owned by Match Group as of February 2019.. [Source: Wikipedia]

This dataset belongs to the app Hinge available on the Google Play Store. The Dataset mostly has user reviews and the various comments made by the users.

Content

The content of the various columns is listed below. Please find the description for each column.

Acknowledgements

Banner image - Hinge

BackgroundUniversity campus clinics provide crucial sexual health services to students, including STI/HIV screening, testing, contraception, and counseling. These clinics are essential for engaging young adults who may lack access to primary care or have difficulty reaching off-campus services. Dating apps are widely used by young adults, yet there is a lack of studies on how they affect sexual practices. This study aimed to evaluate the use of dating apps, engagement in condomless sexual activity, and the prevalence of STIs among young adult college students in Northern Texas.MethodsA cross-sectional survey was conducted from August to December 2022 among undergraduate and graduate students aged 18–35 at a large university in Northern Texas. A total of 122 eligible participants completed the survey, which assessed demographics, sexual behaviors, dating app use, and STI/HIV testing practices. Descriptive statistics, bivariate analyses, and multivariate Poisson regression analyses with robust variance were performed to identify factors associated with dating app use and condomless sexual activity.ResultsTwo-thirds of participants reported using dating apps. Significant differences were found between app users and non-users regarding demographic factors and unprotected sexual behaviors. Dating app users were more likely to report multiple sexual partners, inconsistent condom use, and a higher likelihood of engaging in unprotected sex. Poisson regression analysis indicated that app use was associated with residing in large urban areas, frequent use of campus STI/HIV screening services, and having multiple sexual partners (p 

Foreword

This dataset is a preview of a bigger dataset. My Telegram bot will answer your queries for more data and also allow you to contact me.

Context

When Dating apps like Tinder were becoming viral, people wanted to have the best profile in order to get more matches and more potential encounters. Unlike other previous dating platforms, those new ones emphasized on the mutuality of attraction before allowing any two people to get in touch and chat. This made it all the more important to create the best profile in order to get the best first impression.

Parallel to that, we Humans have always been in awe before charismatic and inspiring people. The more charismatic people tend to be followed and listened to by more people. Through their metrics such as the number of friends/followers, social networks give some ways of "measuring" the potential charisma of some people.

In regard to all that, one can then think: - what makes a great user profile ? - how to make the best first impression in order to get more matches (and ultimately find love, or new friendships) ? - what makes a person charismatic ? - how do charismatic people present themselves ?

In order to try and understand those different social questions, I decided to create a dataset of user profile informations using the social network Lovoo when it came out. By using different methodologies, I was able to gather user profile data, as well as some usually unavailable metrics (such as the number of profile visits).

Content

The dataset contains user profile infos of users of the website Lovoo.

The dataset was gathered during spring 2015 (april, may). At that time, Lovoo was expanding in european countries (among others), while Tinder was trending both in America and in Europe. At that time the iOS version of the Lovoo app was in version 3.

Accessory image data

The dataset references pictures (field pictureId) of user profiles. These pictures are also available for a fraction of users but have not been uploaded and should be asked separately.

The idea when gathering the profile pictures was to determine whether some correlations could be identified between a profile picture and the reputation or success of a given profile. Since first impression matters, a sound hypothesis to make is that the profile picture might have a great influence on the number of profile visits, matches and so on. Do not forget that only a fraction of a user's profile is seen when browsing through a list of users.

https://s1.dmcdn.net/v/BnWkG1M7WuJDq2PKP/x480" alt="App preview of browsing profiles">

Details about collection methodology

In order to gather the data, I developed a set of tools that would save the data while browsing through profiles and doing searches. Because of this approach (and the constraints that forced me to develop this approach) I could only gather user profiles that were recommended by Lovoo's algorithm for 2 profiles I created for this purpose occasion (male, open to friends & chats & dates). That is why there are only female users in the dataset. Another work could be done to fetch similar data for both genders or other age ranges.

Regarding the number of user profiles It turned out that the recommendation algorithm always seemed to output the same set of user profiles. This meant Lovoo's algorithm was probably heavily relying on settings like location (to recommend more people nearby than people in different places or countries) and maybe cookies. This diminished the number of different user profiles that would be presented and included in the dataset.

Inspiration

As mentioned in the introduction, there are a lot of questions we can answer using a dataset such as this one. Some questions are related to - popularity, charisma - census and demographic studies. - Statistics about the interest of people joining dating apps (making friends, finding someone to date, finding true love, ...). - Detecting influencers / potential influencers and studying them

Previously mentioned: - what makes a great user profile ? - how to make the best first impression in order to get more matches (and ultimately find love, or new friendships) ? - what makes a person charismatic ? - how do charismatic people present themselves ?

Other works: - A starter analysis is available on my data.world account, made using a SQL query. Another file has been created through that mean on the dataset page. - The kaggle version of the dataset might contain a starter kernel.

In 2024, 'Better in person' was the leading type of communication style on Tinder in the United States, followed by 'Big time texter' and 'Bad texter'. Launched in 2012, Tinder is the most popular dating app worldwide.

This is a public dataset called OKCupid, collected by Kirkegaard and Bjerrekaer. The dataset is composed of 68,371 records and 2,626 variables. It is shared for educational purposes. Formatted in Arrow Parquet.Description from the authors:"A very large dataset (N=68,371, 2,620 variables) from the dating site OKCupid is presented and made publicly available for use by others. As an example of the analyses one can do with the dataset, a cognitive ability test is constructed from 14 suitable items. To validate the dataset and the test, the relationship of cognitive ability to religious beliefs and political interest/participation is examined. Cognitive ability is found to be negatively related to all measures of religious belief (latent correlations -.26 to -.35), and found to be positively related to all measures of political interest and participation (latent correlations .19 to .32). To further validate the dataset, we examined the relationship between Zodiac sign and every other variable. We found very scant evidence of any influence (the distribution of p-values from chi square tests was flat). Limitations of the dataset are discussed."

The rise of online dating apps has transformed how Gen Z in India explores relationships, social interactions, and casual dating. This analysis investigates dating app usage patterns, preferences, and challenges faced by individuals aged 18-25 across major Indian cities.

: ✅ Most popular dating apps ✅ Frequency & reasons for usage ✅ User satisfaction levels ✅ Challenges like safety concerns & time-wasting ✅ Preferences for features & communication methods

The study employs data visualization, statistical insights, and correlation analysis to understand the evolving landscape of online dating in India. 🚀\

User_ID: Unique identifier for each participant.

Age: Age of the user (18-25 range).

Gender: Gender identity (Male, Female, Non-binary, etc.).

Location: City of residence (e.g., Delhi, Mumbai).

Education: Education level (Undergraduate, Graduate, Postgraduate).

Occupation: Occupation type (e.g., Student, Freelancer, Intern).

Primary_App: The main dating app used by the user (e.g., Tinder, Bumble, Hinge).

Secondary_Apps: Other dating apps used, if any.

Usage_Frequency: How often they use dating apps (Daily, Weekly, Monthly).

Daily_Usage_Time: Time spent daily on dating apps (e.g., 1 hour, 2 hours).

Reason_for_Using: Purpose for using the apps (e.g., Casual Dating, Finding a Partner).

Satisfaction: Satisfaction level with the primary app (e.g., 1 to 5 scale).

Challenges: Challenges faced during usage (e.g., Safety Concerns, Lack of Matches).

Desired_Features: Features users want in dating apps (e.g., Video Calls, Compatibility Insights).

Preferred_Communication: Communication preferences (e.g., Text, Voice Notes, Video Calls).

Partner_Priorities: Attributes prioritized in a partner (e.g., Personality > Interests > Appearance).

According to a survey conducted in March 2025, approximately one in five Tinder users in the United States considered themselves as being a part of the LGBTQ+ community. Overall, 80 percent of users did not identify as part of the community, and three percent did not say.

This data dictionary provides information about archived demographic trend data for people receiving COVID-19 vaccinations in the United States at the national level. Data represents all vaccine partners including jurisdictional partner clinics, retail pharmacies, long-term care facilities, dialysis centers, Federal Emergency Management Agency and Health Resources and Services Administration partner sites, and federal entity facilities. These data have been archived to provide historical demographic trend data for COVID-19 vaccine recipients prior to CDC converting the Vaccination Demographic Trends site to using data based on the date of vaccine administration. Previously, the Vaccination Demographic Trends site presented trend data by the date the vaccination was reported to CDC.

In August of 2018, FSSA’s Office of Healthy Opportunities deployed a social risk assessment survey. The 10-question survey was made available to anyone applying online through FSSA for health coverage, the Supplemental Nutritional Assistance Program or Temporary Assistance for Needy Families. The results of this survey are aggregated and presented below and can help communities better understand the social risk factors affecting the health of those applying for our services. Please read and review the following information regarding the use of this data prior to viewing the tool. This survey was made available to those individuals who applied online ONLY and does not represent anyone who applied in-person, by telephone, by mail or any other method. In 2018, online applications accounted for 79% of those who applied for SNAP, TANF or health coverage. Survey completion is voluntary and does not impact eligibility for SNAP, TANF or health coverage. Applications are filed at a household level and may represent several individuals. The application process identifies a primary contact person for the household, and that individual’s demographics are represented on the dashboard; for example, person’s gender, race and education level. An individual who completes more than one application and survey over any given time period is represented once for each instance, and the survey answers and demographic details are based on each application’s responses. For example, an applicant’s age, education level and survey answers can change over time, and the reporting reflects any such changes. All information is presented in aggregate to ensure personally identifiable information is protected. To protect the privacy of individuals, data representing 20 or less individuals in any county will not be displayed. I.e. it will show as blank

Link to github repo: https://github.com/Ashley-Xu/AIPIFinalDataset

The dataset is intended for research, analysis, and innovation. All efforts have been made to anonymize the data to ensure that no individual can be identified or harmed by its use.

Every effort has been made to uphold the privacy and confidentiality of the individuals whose data is represented in the dataset.

The dataset may only be used for ethical, responsible, and non-exploitative purposes. Users of the dataset are expected to:

Avoid any attempt to re-identify individuals or misuse the data in ways that could lead to harm, discrimination, or stigmatization. Adhere to applicable laws, regulations, and ethical guidelines relevant to data usage and research in their jurisdiction. Cite the dataset responsibly in any derivative work to maintain transparency about its source and limitations.

The dataset may reflect biases inherent in the original data source and population. Researchers are encouraged to be aware of these limitations and to take steps to address or acknowledge biases in their work to prevent misleading conclusions or harmful applications.

While every effort has been made to anonymize the data and protect privacy, no anonymization process is entirely risk-free. Users of the dataset assume full responsibility for ensuring that their research complies with ethical and legal standards.

The data was collected from https://www.swipestats.io/ with appropriate permissions from the owner of the website.

Open source license Community Data License Agreement – Sharing – Version 1.0

The number of users in the 'Online Dating' segment of the eservices market in the United States was forecast to continuously increase between 2024 and 2028 by in total *** million users (+**** percent). After the ninth consecutive increasing year, the indicator is estimated to reach ***** million users and therefore a new peak in 2028. Notably, the number of users of the 'Online Dating' segment of the eservices market was continuously increasing over the past years.Find further information concerning revenue in the United States and revenue growth in Indonesia. The Statista Market Insights cover a broad range of additional markets.

Demographic information of participants.

This dataset is about books. It has 2 rows and is filtered where the book is Probability and statistics with reliability queuing, and computer science applications. It features 7 columns including author, publication date, language, and book publisher.

Abstract

fiNd HoT SiNgLeS iN yOuR aReA. Not really, this dataset is annonymous, but you can explore dating aspects though.

About this dataset

OkCupid is a mobile dating app. It sets itself apart from other dating apps by making use of a pre computed compatibility score, calculated by optional questions the users may choose to answer.

In this dataset, there are 60k records containing structured information such as age, sex, orientation as well as text data from open ended descriptions.

How to use

• Lover Recommendation with Unsupervised Learning
• Explore dating profiles and preferences

Acknowledgements

If you use this dataset in your research, please credit the authors.

Citation

@article{article, author = {Kim, Albert and Escobedo-Land, Adriana}, year = {2015}, month = {07}, pages = {}, title = {OkCupid Data for Introductory Statistics and Data Science Courses}, volume = {23}, journal = {Journal of Statistics Education}, doi = {10.1080/10691898.2015.11889737} }

Notes

• Permission to use this data set was explicitly granted by OkCupid. (source)
• Usernames and pictures are not included.
• The open text fields are somewhat unique, here is a description about them.

License

License was not specified at the source

Splash banner

Photo by Giorgio Trovato on Unsplash

Splash icon

Logo by OkCupid available for download on their website.

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The main goal of POI data is to identify a place of interest, establish its accurate location, and help businesses understand the happenings around that place to make better, well-informed decisions. POI can be essential in assessing competition, improving operational efficiency, planning the expansion of your business, and more.

It can be used by businesses to power their apps and platforms for last-mile delivery, navigation, mapping, logistics, and more. Combined with mobility data, POI data can be employed by retail outlets to monitor traffic to one of their sites or of their competitors. Logistics businesses can save costs and improve customer experience with accurate address data. Real estate companies use POI data for site selection and project planning based on market potential. Governments can use POI data to enforce regulations, monitor public health and well-being, plan public infrastructure and services, and more. A few common and widespread use cases of POI data are:

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Context

Table CMS17, page 281 of Morning Consult.

Content

Survey by Morning Consult: "And are you using online dating apps or services now more or less amid the COVID-19 pandemic (coronavirus)?"

Acknowledgements

Morning consult https://morningconsult.com/wp-content/uploads/2020/04/200473_crosstabs_CONTENT_CORONAVIRUS_Adults_v2_JB.pdf

Photo by Mika Baumeister on Unsplash

Inspiration

Dating Apps