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...

This data contains the same information as TDH Vaccine County Age Groups Census (https://www.chattadata.org/dataset/TDH-Vaccine-County-Age-Groups-Census/4giv-dvmp/), but 85+ categories have been aggregated to match census age groups.

Vaccine County Age Group Census file from TDH website: https://www.tn.gov/health/cedep/ncov/data/downloadable-datasets.html

The dataset contains roughly 225.000 matches played in Age of Empires 2: Definitive Edition in different granularity and connected Master Data. The current version contains 3 levels: -Match Level: featuring Match Id, Map, Map Size, Duration, Mean Elo, Civilizations, Starting Positions and Outcomes with one row per game -Time Slice Level: contains the aggregated commands of type "Queue","Build" and "Research" made until a certain time in the game, with one row per game and one file per time slice. The games are sliced in 120 second slices. -Input Level: contains data about all made decisions in a game, with one row per input and one file per game.

The information were collected by scraping and parsing AoE2:DE matches, using https://github.com/happyleavesaoc/aoc-mgz. The code for the underlying work can be found in https://github.com/nicoelbert/rtsgamestates.

Stay posted, for any questions feel free to get in touch.

Context

This data is Age of Empires II DE match data, one of the OG RTS video games. Data is anonymized match level data pulled from aoe2.net with some post-processing to determine match winners and to ensure data integrity. This is the backend data that powers aoestats.io.

Content

Note that due to data integrity issues this is not a comprehensive list of all matches played on the AoE II DE

The data is broken up into two files...

matches.csv

Each row in this file contains a match played between 2 or more players. Matches are categorized by map, rating, and ladder (1v1 or team).

match_players.csv

Each row contains a given player in a match. Meaning each row has a many to one relationship with those in matches.csv. You can figure out which players go with with matches based on the match column (match_players.csv) and token column (matches.csv).

Acknowledgements

This data conforms to Microsoft's Game Content Usage Rules. It would not be possible to have this data without Microsoft and I am grateful to them for creating this game and for re-energizing the AoEII scene.

I also want to thank aoe2.net who have done the dirty deed of reverse engineering the game protocols so that this data is available to developers like myself

The SIVEP-Gripe database contains 3,395,398 records with 166 attributes, covering the years 2020 to 2022. These records document cases of Severe Acute Respiratory Syndrome (SARS) caused by COVID-19, Influenza, other etiological agents, various respiratory viruses, and unspecified cases. Of the total records, 1,872,106 are related to SARS due to COVID-19, and 21,490 are related to SARS due to Influenza, highlighting the need for class balancing.

Four datasets were created with different balancing configurations: * Balanced by age range (1BAR): The majority class was reduced to match the number of records in the minority class, based on age ranges. Specifically, records from the majority class were selected to match the minimum and maximum age ranges of the minority class. * Balanced by age, sex, and same distribution (2BASD): For each record in the minority class, an equal number of records with the same sex and age were selected from the majority class. * Balanced by age, sex, region, and same distribution (3BARD): This approach included balancing by region, in addition to age and sex. * Balanced by age, sex, outcome, and same distribution (4BASED): This method balanced records by age, sex, and outcome (recovery or death) to maintain consistent distributions of these factors across both classes.

After preprocessing, all datasets retained 24 attributes and one target class, "classi_fin", where 1 represents SARS due to influenza and 5 represents SARS due to COVID-19. These subsets were created to evaluate the performance of machine learning models during training.

Employed persons by sex, age, educational attainment level, work experience while studying and match between education and job

Initial Claims for UI released by the CT Department of Labor. Initial Claims are applications for Unemployment Benefits. Initial Claims may not result in receiving UI benefits if the individual doesn't qualify. Claims data can be access directly from CT DOL here: https://www1.ctdol.state.ct.us/lmi/claimsdata.asp

The initial claims reported in these tables are "processed" claims to the extent that duplicates and "reopened" claims have been eliminated. The claim counts in this dataset may not match claim counts from other sources.

Claims are disaggregated by age, education, industry, race/national origin, sex, and wages.

The claim counts in this dataset may not match claim counts from other sources.

Unemployment claims tabulated in this dataset represent only one component of the unemployed. Claims do not account for those not covered under the Unemployment system (e.g. federal workers, railroad workers or religious workers) or the unemployed self-employed.

Claims filed for a particular week will change as time goes on and the backlog is addressed.

Continued Claims for UI released by the CT Department of Labor. Continued Claims are total number of individuals being paid benefits in any particular week.

Claims are disaggregated by age, education, industry, race/national origin, sex, and wages.

The claim counts in this dataset may not match claim counts from other sources.

Unemployment claims tabulated in this dataset represent only one component of the unemployed. Claims do not account for those not covered under the Unemployment system (e.g. federal workers, railroad workers or religious workers) or the unemployed self-employed.

Claims filed for a particular week will change as time goes on and the backlog is addressed.

For data on initial claims at the town level, see the dataset "Initial Claims for Unemployment Benefits by Town," here: https://data.ct.gov/Government/Initial-Claims-for-Unemployment-Benefits-by-Town/twvc-s7wy

For data on continued claims see the following two datasets:

"Continued Claims for Unemployment Benefits in Connecticut," https://data.ct.gov/Government/Continued-Claims-for-Unemployment-Benefits-in-Conn/f9e5-rn42

"Continued Claims for Unemployment Benefits by Town," https://data.ct.gov/Government/Continued-Claims-for-Unemployment-Benefits-by-Town/r83t-9bjm

These data include gridded estimates of population at approximately 1km for 2021 and 2022. These datasets results were produced based on using the spatial distribution of unconstrained and constrained population datasets for individual countries for 2020 datasets with country totals were adjusted to match the corresponding official United Nations population estimates, that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (World Population Prospects 2022) for the relevant years, and broken down by gender and age groups.

Our analysis focuses on seven metropolitan cities across Italy. Here, we report the number of spatial cells of the mobile phone network and the population (in thousands) of each of these cities split across 6 age groups. Population data is retrieved from the 2011 Italian census and comprises all the census sections within the phone cells considered for each city. It is important to highlight that in each cell of the network there can be several mobile phone users, thus we cannot estimate the fraction of the census population included in our data set. Note that the age groups provided by the Italian census do not perfectly match those of the Telecom Italia dataset.

Analysis of ‘International football results from 1872 to 2021’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/martj42/international-football-results-from-1872-to-2017 on 13 February 2022.

--- Dataset description provided by original source is as follows ---

Context

Well, what happened was that I was looking for a semi-definite easy-to-read list of international football matches and couldn't find anything decent. So I took it upon myself to collect it for my own use. I might as well share it.

Content

This dataset includes 43,170 results of international football matches starting from the very first official match in 1972 up to 2019. The matches range from FIFA World Cup to FIFI Wild Cup to regular friendly matches. The matches are strictly men's full internationals and the data does not include Olympic Games or matches where at least one of the teams was the nation's B-team, U-23 or a league select team.

results.csv includes the following columns:

• date - date of the match
• home_team - the name of the home team
• away_team - the name of the away team
• home_score - full-time home team score including extra time, not including penalty-shootouts
• away_score - full-time away team score including extra time, not including penalty-shootouts
• tournament - the name of the tournament
• city - the name of the city/town/administrative unit where the match was played
• country - the name of the country where the match was played
• neutral - TRUE/FALSE column indicating whether the match was played at a neutral venue

shootouts.csv includes the following columns:

• date - date of the match
• home_team - the name of the home team
• away_team - the name of the away team
• winner - winner of the penalty-shootout

Note on team and country names: For home and away teams the current name of the team has been used. For example, when in 1882 a team who called themselves Ireland played against England, in this dataset, it is called Northern Ireland because the current team of Northern Ireland is the successor of the 1882 Ireland team. This is done so it is easier to track the history and statistics of teams.

For country names, the name of the country at the time of the match is used. So when Ghana played in Accra, Gold Coast in the 1950s, even though the names of the home team and the country don't match, it was a home match for Ghana. This is indicated by the neutral column, which says FALSE for those matches, meaning it was not at a neutral venue.

Acknowledgements

The data is gathered from several sources including but not limited to Wikipedia, rsssf.com and individual football associations' websites.

Inspiration

Some directions to take when exploring the data:

• Who is the best team of all time
• Which teams dominated different eras of football
• What trends have there been in international football throughout the ages - home advantage, total goals scored, distribution of teams' strength etc
• Can we say anything about geopolitics from football fixtures - how has the number of countries changed, which teams like to play each other
• Which countries host the most matches where they themselves are not participating in
• How much, if at all, does hosting a major tournament help a country's chances in the tournament
• Which teams are the most active in playing friendlies and friendly tournaments - does it help or hurt them

The world's your oyster, my friend.

Contribute

If you notice a mistake or the results are being updated fast enough for your liking, you can fix that by submitting a pull request on Github: https://github.com/martj42/international_results

--- Original source retains full ownership of the source dataset ---

DATASET: Alpha version 2014 estimates of number of people in each 5-year age group per grid square, with national totals adjusted to match UN population division estimates (http://esa.un.org/wpp/). REGION: Africa SPATIAL RESOLUTION: 0.00833333 decimal degrees (approx 100m at the equator) PROJECTION: Geographic, WGS84 UNITS: Estimated number of people in each 5-year age group per grid square MAPPING APPROACH: Land cover based, as described in: Linard, C., Gilbert, M., Snow, R.W., Noor, A.M. and Tatem, A.J., 2012, Population distribution, settlement patterns and accessibility across Africa in 2010, PLoS ONE, 7(2): e31743. 5-YEAR AGE PROPORTIONS: Tatem, Andrew J., Garcia, Andres J., Snow, Robert W., Noor, Abdisalan M., Gaughan, Andrea E.,Gilbert, Marius and Linard, Catherine, 2013, Millennium development health metrics: where do Africa's children and women of childbearing age live? Population Health Metrics, 11, (1), 11. FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org) FILENAMES: Example - BEN14_A0005_adjv1 = Benin (BEN) population count between 0 and 5 years old map (A0005) for 2014 (14) adjusted to match UN national estimates (adj), version 1 (v1). DATE OF PRODUCTION: August 2014

This data collection is comprised of responses from the March and April installments of the 2008 Current Population Survey (CPS). Both the March and April surveys used two sets of questions, the basic CPS and a separate supplement for each month.The CPS, administered monthly, is a labor force survey providing current estimates of the economic status and activities of the population of the United States. Specifically, the CPS provides estimates of total employment (both farm and nonfarm), nonfarm self-employed persons, domestics, and unpaid helpers in nonfarm family enterprises, wage and salaried employees, and estimates of total unemployment.In addition to the basic CPS questions, respondents were asked questions from the March supplement, known as the Annual Social and Economic (ASEC) supplement. The ASEC provides supplemental data on work experience, income, noncash benefits, and migration. Comprehensive work experience information was given on the employment status, occupation, and industry of persons 15 years old and older. Additional data for persons 15 years old and older are available concerning weeks worked and hours per week worked, reason not working full time, total income and income components, and place of residence on March 1, 2007. The March supplement also contains data covering nine noncash income sources: food stamps, school lunch program, employer-provided group health insurance plan, employer-provided pension plan, personal health insurance, Medicaid, Medicare, CHAMPUS or military health care, and energy assistance. Questions covering training and assistance received under welfare reform programs, such as job readiness training, child care services, or job skill training were also asked in the March supplement.The April supplement, sponsored by the Department of Health and Human Services, queried respondents on the economic situation of persons and families for the previous year. Moreover, all household members 15 years of age and older that are a biological parent of children in the household that have an absent parent were asked detailed questions about child support and alimony. Information regarding child support was collected to determine the size and distribution of the population with children affected by divorce or separation, or other relationship status change. Moreover, the data were collected to better understand the characteristics of persons requiring child support, and to help develop and maintain programs designed to assist in obtaining child support. These data highlight alimony and child support arrangements made at the time of separation or divorce, amount of payments actually received, and value and type of any property settlement.The April supplement data were matched to March supplement data for households that were in the sample in both March and April 2008. In March 2008, there were 4,522 household members eligible, of which 1,431 required imputation of child support data. When matching the March 2008 and April 2008 data sets, there were 170 eligible people on the March file that did not match to people on the April file. Child support data for these 170 people were imputed. The remaining 1,261 imputed cases were due to nonresponse to the child support questions. Demographic variables include age, sex, race, Hispanic origin, marital status, veteran status, educational attainment, occupation, and income. Data on employment and income refer to the preceding year, although other demographic data refer to the time at which the survey was administered.

Initial Claims for UI released by the CT Department of Labor. Initial Claims are applications for Unemployment Benefits. Initial Claims may not result in receiving UI benefits if the individual doesn't qualify. Claims data can be access directly from CT DOL here: https://www1.ctdol.state.ct.us/lmi/claimsdata.asp

The initial claims reported in these tables are "processed" claims to the extent that duplicates and "reopened" claims have been eliminated. The claim counts in this dataset may not match claim counts from other sources.

Claims are disaggregated by age, education, industry, race/national origin, sex, and wages.

The claim counts in this dataset may not match claim counts from other sources.

Unemployment claims tabulated in this dataset represent only one component of the unemployed. Claims do not account for those not covered under the Unemployment system (e.g. federal workers, railroad workers or religious workers) or the unemployed self-employed.

Claims filed for a particular week will change as time goes on and the backlog is addressed.

Continued Claims for UI released by the CT Department of Labor. Continued Claims are total number of individuals being paid benefits in any particular week.

Claims are disaggregated by age, education, industry, race/national origin, sex, and wages.

The claim counts in this dataset may not match claim counts from other sources.

Unemployment claims tabulated in this dataset represent only one component of the unemployed. Claims do not account for those not covered under the Unemployment system (e.g. federal workers, railroad workers or religious workers) or the unemployed self-employed.

Claims filed for a particular week will change as time goes on and the backlog is addressed.

For data on initial claims at the town level, see the dataset "Initial Claims for Unemployment Benefits by Town," here: https://data.ct.gov/Government/Initial-Claims-for-Unemployment-Benefits-by-Town/twvc-s7wy

For data on continued claims see the following two datasets:

"Continued Claims for Unemployment Benefits in Connecticut," https://data.ct.gov/Government/Continued-Claims-for-Unemployment-Benefits-in-Conn/f9e5-rn42

"Continued Claims for Unemployment Benefits by Town," https://data.ct.gov/Government/Continued-Claims-for-Unemployment-Benefits-by-Town/r83t-9bjm

This dataset contains comprehensive information on the teams participating in the UEFA Euro 2024 tournament. It includes details about each team, their group stage placement, FIFA rankings, captains, head coaches, pre-tournament forms, and average player age.

Columns Description:

• FIFA Ranking: Pre-tournament FIFA ranking of the team
• Team: Name of the team
• Total Points: Points in the pre-tournament FIFA ranking
• Previous Points: Points in the previous FIFA ranking
• Change in Points: Difference between points in Total Points and Previous Points
• Base Camp: Location for training and residency for the duration of UEFA Euro 2024
• Training Ground: Home training ground for the duration of UEFA Euro 2024
• Qualified as: Status under which the team has qualified for UEFA Euro 2024
• Previous appearances: Number of times the team has qualified for previous editions of UEFA Euro Championship
• Manager Name: Name of the manager of the team
• Installation (in years): Number of years the current manager has been coaching the team (rounded down an integer number of years)
• Group: Group in UEFA Euro 2024
• Average Age: Average age of the squad
• Captain: Captain of the team
• Recent Form: Record of wins, draws and losses in the 10 most recent matches played by the team (Friendlies included)

This find is registered at Portable Antiquities of the Netherlands with number PAN-00068974

This find is registered at Portable Antiquities of the Netherlands with number PAN-00011781

This find is registered at Portable Antiquities of the Netherlands with number PAN-00054332