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).

Data:

The Dataset provides a comprehensive view into the dynamics of online matchmaking interactions. It captures essential variables that influence the likelihood of successful matches across different genders. This dataset allows researchers and analysts to explore how factors such as VIP subscription status, income levels, parental status, age, and self-perceived attractiveness contribute to the outcomes of online dating endeavors.

Variables:

• Gender: 0 (Male), 1 (Female)
• PurchasedVIP: 0 (No), 1 (Yes)
• Income: Annual income in USD
• Children: Number of children
• Age: Age of the user
• Attractiveness: Subjective rating of attractiveness (1-10)
• Matches: Number of matches obtained based on criteria

Target Variable:

• Matches: Number of matches received, indicative of success rate in online dating

Usage:

• Analyze gender-specific dating preferences and behaviors.
• Predict match success.

Explanation of Zero Matches for Some Users:

The occurrence of zero matches for certain users within the dataset can be attributed to the presence of "ghost users." These are users who create an account but subsequently abandon the app without engaging further. Consequently, their profiles do not participate in any matching activities, leading to a recorded match count of zero. This phenomenon should be taken into account when analyzing user activity and match data, as it impacts the overall interpretation of user engagement and match success rates.

Disclaimer:

This dataset contains 1000 records, which is considered relatively low within this category of datasets. Additionally, the dataset may not accurately reflect reality as it was captured intermittently over different periods of time.

Furthermore, certain match categories are missing due to confidentiality constraints, and several other crucial variables are also absent for the same reason. Consequently, the machine learning models employed may not achieve high accuracy in predicting the number of matches.

It is important to acknowledge these limitations when interpreting the results derived from this dataset. Careful consideration of these factors is advised when drawing conclusions or making decisions based on the findings of any analyses conducted using this data.

Warning:

Due to confidentiality constraints, only a small amount of data was collected. Additionally, only users with variables showing high correlation with the matching variable were included in the dataset.

As a result, the high performance of machine learning models on this dataset is primarily due to the data collection method (i.e., only high-correlation data was included).

Therefore, the findings you may derive from manipulating this dataset are not representative of the real dating world.

Data Source:

The source of this dataset is confidential, and it may be released in the future. For the present, this dataset can be utilized under the terms of the license visible on the dataset's card.

Users are advised to review and adhere to the terms specified in the dataset's license when using the data for any purpose.

Conclusion:

This dataset provides insights into the dynamics of online dating interactions, allowing for predictive modeling and analysis of factors influencing matchmaking success.

Dataset Usage and Attribution Notice

This dataset, shared by Rabie El Kharoua, is original and has never been shared before. It is made available under the CC BY 4.0 license, allowing anyone to use the dataset in any form as long as proper citation is given to the author. A DOI is provided for proper referencing. Please note that duplication of this work within Kaggle is not permitted.

Exclusive Synthetic Dataset

This dataset is synthetic and was generated for educational purposes, making it ideal for data science and machine learning projects. It is an original dataset, owned by Mr. Rabie El Kharoua, and has not been previously shared. You are free to use it under the license outlined on the data card. The dataset is offered without any guarantees. Details about the data provider will be shared soon.

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.

Context

"Dating apps have revolutionized the dating game for a new generation of singles. Making finding dates and future love theoretically easier than ever before, it's no surprise that 'online' has become the most common way for people to meet nowadays."

https://www.statista.com/chart/9173/online-dating-service-users-by-country/?utm_source=Statista+Newsletters&utm_campaign=29c7ad3358-All_InfographTicker_daily_COM_AM_KW03_2022_Tu_COPY&utm_medium=email&utm_term=0_662f7ed75e-29c7ad3358-315801217

Content

"The Statista Global Consumer Survey reveals that the country with the largest share of singles utilizing this modern matchmaking method is Sweden. Here, 25 percent said they used online dating services. Brazil was close to the top of the ranking with 22 percent. People in South Korea and Russia seem to get along fine without much help from online dating however, with just 7 and 4 percent of singles saying they were using such services."

https://www.statista.com/chart/9173/online-dating-service-users-by-country/?utm_source=Statista+Newsletters&utm_campaign=29c7ad3358-All_InfographTicker_daily_COM_AM_KW03_2022_Tu_COPY&utm_medium=email&utm_term=0_662f7ed75e-29c7ad3358-315801217

Acknowledgements

https://www.statista.com/chart/9173/online-dating-service-users-by-country/?utm_source=Statista+Newsletters&utm_campaign=29c7ad3358-All_InfographTicker_daily_COM_AM_KW03_2022_Tu_COPY&utm_medium=email&utm_term=0_662f7ed75e-29c7ad3358-315801217

Photo by Mika Baumeister on Unsplash

Inspiration

Users that have been fooled (financially) by other users that they met on the dating service site. Which (has no fault) since it's just another tool to people make what they are used to make before Internet times.

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!

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

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...

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.

Total-Revenue Time Series for Bumble Inc. Bumble Inc. provides online dating and social networking applications in North America, Europe, internationally. It owns and operates websites and applications that offers subscription and in-app purchases of products. The company operates apps, including Bumble, a dating app built with women at the center, where women make the first move; Badoo, the web and mobile free-to-use dating app; Bumble BFF and Bumble Bizz Modes that have a format similar to the date mode requiring users to set up profiles and matching users through yes and no votes, similar to the dating platform; and Bumble for Friends, a friendship app where people in all stages of life can meet people nearby and create meaningful platonic connections, as well as Geneva app where users can create and join chat, forum, audio, video, and broadcast rooms. The company was founded in 2020 in and is headquartered in Austin, Texas.

Reddit: /r/Tinder

Examining User Behaviors and Attitudes

By Reddit [source]

About this dataset

This dataset provides an in-depth exploration of the world of online dating, based on data mined from Reddit's Tinder subreddit. Through analysis of the six columns titled title, score, id, url, comms_num and created (which include information such as social norms and user behaviors related to online dating), this dataset can teach us valuable insights into how people are engaging with digital media and their attitudes towards it. Unveiling potential dangers such as safety risks and scams that can arise from online dating activities is also possible with this data. Its findings are paramount for anyone interested in understanding how relationships develop on a digital platform – both for researchers uncovering the sociotechnical aspects of online dating behavior and for companies seeking further insight into their user's perspectives. All in all, this dataset might just hold all the missing pieces to understanding our current relationship dynamic!

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset provides a comprehensive overview of online dating trends and behaviors observed on Reddit's Tinder subreddit. This data can be used to analyze user opinions, investigate user experiences, and discover online dating trends. To utilize this dataset effectively, there are several steps an individual can take to gain insights from the data:

Research Ideas

• Using the dataset to examine how online dating trends vary geographically and by demographics (gender, age, race etc.)
• Analyzing the language used in posts for insights into user attitudes towards online dating.
• Creating a machine learning model to predict a post's score based on its title, body and other features of the data set can help digital media companies better target their marketing efforts towards more successful posts on Tinder subreddits

Acknowledgements

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

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: Tinder.csv | Column name | Description | |:--------------|:--------------------------------------------------------| | title | The title of the post. (String) | | score | The number of upvotes the post has received. (Integer) | | url | The URL of the post. (String) | | comms_num | The number of comments the post has received. (Integer) | | created | The date and time the post was created. (DateTime) | | body | The body of the post. (String) | | timestamp | The timestamp of the post. (Integer) |

Acknowledgements

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

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 

Most people seek to establish romantic or intimate relationships in life, including people with mental health problems. However, this has been a neglected topic in mental health practice and research. This study aimed to investigate views of mental health and social care staff about the appropriateness of helping service users with romantic relationships, barriers to doing this, and suggestions for useful ways to support this. An online survey comprising both closed, multiple response and free-text questions was circulated to mental health organisations across the U.K. via social media, professional networks and use of snowballing sampling. A total of 63 responses were received. Quantitative data were analysed using descriptive statistics, and are reported as frequencies and percentages. Qualitative data were interpreted using thematic analysis, using an inductive approach. Although most participants reported that ‘finding a relationship’ conversations were appropriate in their job role, many barriers to supporting service users were identified, including: a lack of training; concerns about professional boundaries; concerns about service user capacity and vulnerability; and concerns about being intrusive. Participant suggestions for future support included educating service users on safe dating behaviours, and practical interventions such as assisting service users to use dating sites and engage with social activities to develop social skills and meet others. Staff were willing to help service users seek an intimate relationship but may need specific training or guidance to facilitate this confidently and safely. This study elucidates the need for further research in this area, particularly in understanding service user perspectives, and in developing resources to support staff in this work.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F13364933%2F23694fae55e2e76299358693ba6f32b9%2Flv-share.jpg?generation=1684843825246772&alt=media" alt=""> ➡️ There are total 3 datasets containing valuable information. ➡️ Understand people's fame and behavior's on a dating app platform. | Column Name | Description | |---------------------|------------------------------| | Age | The age of the user. | | Number of Users | The total number of users. | | Percent Want Chats | Percentage of users who want chats. | | Percent Want Friends| Percentage of users who want friendships. | | Percent Want Dates | Percentage of users who want romantic dates. | | Mean Kisses Received| Average number of kisses received by users. | | Mean Visits Received| Average number of profile visits received by users. | | Mean Followers | Average number of followers for each user. | | Mean Languages Known| Average number of languages known by users. | | Total Want Chats | Total count of users interested in chats. | | Total Want Friends | Total count of users looking for friendships. | | Total Want Dates | Total count of users seeking romantic dates. | | Total Kisses Received| Overall count of kisses received by users. | | Total Visits Received| Overall count of profile visits received by users. | | Total Followers | Overall count of followers for all users. | | Total Languages Spoken| Total count of languages spoken by all users. |

SUMMARY

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

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 pr...

Goodwill Time Series for Bumble Inc. Bumble Inc. provides online dating and social networking applications in North America, Europe, internationally. It owns and operates websites and applications that offers subscription and in-app purchases of products. The company operates apps, including Bumble, a dating app built with women at the center, where women make the first move; Badoo, the web and mobile free-to-use dating app; Bumble BFF and Bumble Bizz Modes that have a format similar to the date mode requiring users to set up profiles and matching users through yes and no votes, similar to the dating platform; and Bumble for Friends, a friendship app where people in all stages of life can meet people nearby and create meaningful platonic connections, as well as Geneva app where users can create and join chat, forum, audio, video, and broadcast rooms. The company was founded in 2020 in and is headquartered in Austin, Texas.

Context

I was interested in learning the growing trend of what dating apps are used for in India over the years.

Content

The data is from 2017-2022. I acquired the data using google_play_scraper from google playstore online. The data I received was more than just the column shown here but were unnecessary.

Inspiration

1. Are dating apps being downloaded more and more over the years? Won't get a close estimate of it using this data but will be able to still have a relative idea.
2. Which app has more favorable responses? Have those favorable responses stayed consistent through the years or have they increased/decreased? Does the first question change if we consider the last two years as the appropriate timeline to consider the favorability of one of the considered apps?
3. What are the common issues for those who rate the app below 3/5?
4. Do users find relationships? Do people find what they are looking for? (hard to answer using text analytics? maybe, but it is an interesting and crucial question)
5. User enthusiasm over the app is linked to their rating?
6. Are top rated reviews being found more useful to other users/potential users or the reverse?
7. Any common user of the three apps? Which app they favor? Which app stands in favor in case there are common users across the three?

Online dating services have increased in popularity around the world, but a lack of quality data hinders our understanding of their role in family formation. This paper studies the effect of online dating services on marital sorting, using a novel dataset with verified information on people and their spouses. Estimates based on matching techniques suggest that, relative to other spouse search methods, online dating promotes marriages that exhibit weaker sorting along occupation and geographical proximity but stronger sorting along education and other demographic traits. Sensitivity analysis, including the Rosenbaum Bounds approach, suggests that online dating's impact on marital sorting is robust to potential selection bias.

Other-Current-Liabilities Time Series for Bumble Inc. Bumble Inc. provides online dating and social networking applications in North America, Europe, internationally. It owns and operates websites and applications that offers subscription and in-app purchases of products. The company operates apps, including Bumble, a dating app built with women at the center, where women make the first move; Badoo, the web and mobile free-to-use dating app; Bumble BFF and Bumble Bizz Modes that have a format similar to the date mode requiring users to set up profiles and matching users through yes and no votes, similar to the dating platform; and Bumble for Friends, a friendship app where people in all stages of life can meet people nearby and create meaningful platonic connections, as well as Geneva app where users can create and join chat, forum, audio, video, and broadcast rooms. The company was founded in 2020 in and is headquartered in Austin, Texas.

Net-Borrowings Time Series for Bumble Inc. Bumble Inc. provides online dating and social networking applications in North America, Europe, internationally. It owns and operates websites and applications that offers subscription and in-app purchases of products. The company operates apps, including Bumble, a dating app built with women at the center, where women make the first move; Badoo, the web and mobile free-to-use dating app; Bumble BFF and Bumble Bizz Modes that have a format similar to the date mode requiring users to set up profiles and matching users through yes and no votes, similar to the dating platform; and Bumble for Friends, a friendship app where people in all stages of life can meet people nearby and create meaningful platonic connections, as well as Geneva app where users can create and join chat, forum, audio, video, and broadcast rooms. The company was founded in 2020 in and is headquartered in Austin, Texas.

Note: DPH is updating and streamlining the COVID-19 cases, deaths, and testing data. As of 6/27/2022, the data will be published in four tables instead of twelve.

The COVID-19 Cases, Deaths, and Tests by Day dataset contains cases and test data by date of sample submission. The death data are by date of death. This dataset is updated daily and contains information back to the beginning of the pandemic. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Cases-Deaths-and-Tests-by-Day/g9vi-2ahj.

The COVID-19 State Metrics dataset contains over 93 columns of data. This dataset is updated daily and currently contains information starting June 21, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-State-Level-Data/qmgw-5kp6 .

The COVID-19 County Metrics dataset contains 25 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-County-Level-Data/ujiq-dy22 .

The COVID-19 Town Metrics dataset contains 16 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Town-Level-Data/icxw-cada . To protect confidentiality, if a town has fewer than 5 cases or positive NAAT tests over the past 7 days, those data will be suppressed.

This dataset includes a count and rate per 100,000 population for COVID-19 cases, a count of COVID-19 molecular diagnostic tests, and a percent positivity rate for tests among people living in community settings for the previous two-week period. Dates are based on date of specimen collection (cases and positivity).

A person is considered a new case only upon their first COVID-19 testing result because a case is defined as an instance or bout of illness. If they are tested again subsequently and are still positive, it still counts toward the test positivity metric but they are not considered another case.

Percent positivity is calculated as the number of positive tests among community residents conducted during the 14 days divided by the total number of positive and negative tests among community residents during the same period. If someone was tested more than once during that 14 day period, then those multiple test results (regardless of whether they were positive or negative) are included in the calculation.

These case and test counts do not include cases or tests among people residing in congregate settings, such as nursing homes, assisted living facilities, or correctional facilities.

These data are updated weekly and reflect the previous two full Sunday-Saturday (MMWR) weeks (https://wwwn.cdc.gov/nndss/document/MMWR_week_overview.pdf).

DPH note about change from 7-day to 14-day metrics: Prior to 10/15/2020, these metrics were calculated using a 7-day average rather than a 14-day average. The 7-day metrics are no longer being updated as of 10/15/2020 but the archived dataset can be accessed here: https://data.ct.gov/Health-and-Human-Services/COVID-19-case-rate-per-100-000-population-and-perc/s22x-83rd

As you know, we are learning more about COVID-19 all the time, including the best ways to measure COVID-19 activity in our communities. CT DPH has decided to shift to 14-day rates because these are more stable, particularly at the town level, as compared to 7-day rates. In addition, since the school indicators were initially published by DPH last summer, CDC has recommended 14-day rates and other states (e.g., Massachusetts) have started to implement 14-day metrics for monitoring COVID transmission as well.

With respect to geography, we also have learned that many people are looking at the town-level data to inform decision making, despite emphasis on the county-level metrics in the published addenda. This is understandable as there has been variation within counties in COVID-19 activity (for example, rates that are higher in one town than in most other towns in the county).

Additional notes: As of 11/5/2020, CT DPH has added antigen testing for SARS-CoV-2 to reported test counts in this dataset. The tests included in this dataset include both molecular and antigen datasets. Molecular tests reported include polymerase chain reaction (PCR) and nucleic acid amplicfication (NAAT) tests.

The population data used to calculate rates is based on the CT DPH population statistics for 2019, which is available online here: https://portal.ct.gov/DPH/Health-Information-Systems--Reporting/Population/Population-Statistics. Prior to 5/10/2021, the population estimates from 2018 were used.

Data suppression is applied when the rate is <5 cases per 100,000 or if there are <5 cases within the town. Information on why data suppression rules are applied can be found online here: https://www.cdc.gov/cancer/uscs/technical_notes/stat_methods/suppression.htm

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.

More Datasets

‘Methods of Support’ themes, sub-themes, example quotes and number of contributing participants.

This dataset is about countries per year in Polynesia. It has 3 rows and is filtered where the date is 2021. It features 4 columns: country, tax revenue, and individuals using the Internet.