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

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 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.

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.

The Area Level Index of Age Diversity (ALIAD) is based on the Simpson's Index of Diversity. It is commonly used in ecological studies to quantify the biodiversity of a habitat as it takes into account both the richness, i.e. the number of species present, and the evenness, i.e. the abundance of each species, within an environment. As species richness and evenness increase, so diversity increases. The index represents the probability that two randomly selected individuals will belong to different groups. It ranges from 0 and 100, with higher values representing greater diversity. ALIAD was computed for each Lower Super Output Area (LSOA) in England and Wales (E&W), each Data Zone (DZ) in Scotland and each Super Output Area (SOA) in Northern Ireland from 2002 to 2019. It is based on the mid-year population estimates (MYPE) for each area for each year. This is information is freely available in accordance with version 3.0 of the Open Government Licence. However, the different national statistical agencies compute MYPE for different age groups. In England and Wales estimates are provided for single-year age groups, i.e. the number of people aged 0, 1, 2, 3, etc. In Scotland estimates are provided for quinary age groups, i.e. the number of people aged 0-4, 5-9, 10-14, etc. In Northern Ireland (NI) estimates are provided for four larger age groups, i.e. 0-15, 16-39, 40-64 and 65+. It was decided to match the age groups to the NI classification as i) this would provide the greatest geographical coverage, ii) the estimates ought to be more robust and iii) in discussions with policy and practice stakeholders these age groups were seen as more meaningful than single-year or quinary age groups. An exact match was possible between the E&W and NI age groups. However, because of the use of quinary age groups it is not possible to get an exact match for all age groups in Scotland. Hence, the age groups used on Scotland are 0-14, 15-39, 40-64 and 65+. The final dataset contains the computed ALIAD values for each of the 34,753 LSOAs, the 6,976 DZs and the 890 SOAs from 2002-2019. ALIAD has a range of 0-100. On this scale 0 would represent total age concentration, i.e. every member of the area is in the same age group, and 100 would represent complete age diversity within the area.There is evidence that Britain is becoming more and more generationally divided. A major part of this is that the places where we live have become increasingly 'age segregated'. This means younger people tend to live in places where there are more younger people and older people tend to live in places where there are more older people. Deep generational divisions can have implications for social cohesion and effective societal functioning. Policy makers are concerned that this could have negative health, economic, social and political costs. Indeed, a recent report by the Resolution Foundation estimated that age-segregation could cost the UK economy £6 billion per year. However, there is currently no research in Britain that has been able to directly test whether living in areas with a greater mix of ages has an impact on people. By linking information on the number of people in different age groups at the local level with information from a long running survey, our project will be the first to do this. We will create a new measure, called the 'area level index of age diversity', for all the residential areas in Great Britain (these are called Lower Super Output Areas in England and Wales and Data Zones in Scotland). Unlike existing measures which tend to focus just on younger versus older adults, this new measure will use information from people of all ages to get a better idea of the mix of age groups in an area. The first thing we intend to do with this information is to produce a series of maps of Britain to show which local areas are more or less age diverse. This information will be very useful for local government, councils, city planners and the like. Once we have done this, we will then link our new measure of age diversity to information on around 50,000 people living in Britain who have been part of a long running study (called the UK Household Longitudinal Survey). This will enable us to see whether living in areas that have people from a wide (or narrow) range of age groups impacts on people's health (e.g. whether the person has an illness or chronic condition), well-being (e.g. loneliness), civic participation (e.g. whether someone volunteers or not), and neighbourhood quality (e.g. whether people trust their neighbours). Our findings will provide a much needed evidence base on the extent of local area level age diversity in Britain and what effect (if any) this has on people's lives. ALIAD was computed for each Lower Super Output Area (LSOA) in England and Wales (E&W), each Data Zone (DZ) in Scotland and each Super Output Area (SOA) in Northern Ireland from 2002 to 2019. It is based on the mid-year population estimates (MYPE) for each area for each year.

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.

Age-Specific Text Simplification Dataset

  Dataset Description

This dataset contains complex texts simplified into age-appropriate versions for children aged 3, 4, and 5 years old. Each original text has been professionally adapted to match the cognitive development, vocabulary, and comprehension abilities of each specific age group.

  Dataset Summary

Total Examples: 17,177 Training Split: 15,459 examples Validation Split: 1,718 examples Languages: English Task:… See the full description on the dataset page: https://huggingface.co/datasets/lumees/age-specific-text-simplification.

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

Table from the American Community Survey (ACS) B01001A-I sex by age by race - data is grouped into three age group categories for each race, under 18, 18-64 and 65 and older. These are multiple, nonoverlapping vintages of the 5-year ACS estimates of population and housing attributes starting in 2010 shown by the corresponding census tract vintage. Also includes the most recent release annually.Data on total number of people by each race alone and in combination by each census tract has been transposed to support dashboard visualizations.King County, Washington census tracts with nonoverlapping vintages of the 5-year American Community Survey (ACS) estimates starting in 2010. Vintage identified in the "ACS Vintage" field.The census tract boundaries match the vintage of the ACS data (currently 2010 and 2020) so please note the geographic changes between the decades. Tracts have been coded as being within the City of Seattle as well as assigned to neighborhood groups called "Community Reporting Areas". These areas were created after the 2000 census to provide geographically consistent neighborhoods through time for reporting U.S. Census Bureau data. This is not an attempt to identify neighborhood boundaries as defined by neighborhoods themselves.Vintages: 2010, 2015, 2020, 2021, 2022, 2023ACS Table(s): B01001Data downloaded from: Census Bureau's Explore Census Data The United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2020 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

Interpretations of paleoclimate records from ocean sediment cores rely on age models to provide estimates of age as a function of core depth. As one metric of age model uncertainty, we compare four dating methods using high-resolution data over the last glacial cycle from ten Iberian Margin cores. The first method uses radiocarbon data and the Bayesian statistical software Bacon [Blaauw and Christen, 2011] . The second method aligns benthic δ18O to a target core using the probabilistic alignment algorithm HMM-Match [Lin et al., 2014] . The third and fourth methods are alignment of planktonic δ18O and sea surface temperature (SST), respectively, using the deterministic alignment software Match [Lisiecki and Lisiecki, 2002] . Where three or more age model types overlap in a core, the average range of age estimates is 1.7 kyr, and 90% of age ranges are less than 3 kyr. For the two probabilistic methods, HMM-Match and Bacon, age estimates agree to within uncertainty, and the highest resolution records yield 95% confidence interval widths of 1-2 kyr. However, HMM-Match appears to underestimate alignment uncertainty near benthic δ18O gaps in one core, as indicated by disagreement with the core's planktonic δ18O and SST alignments. Because planktonic δ18O and SST proxies appear to provide reliable alignments on the Iberian Margin, probabilistic alignment algorithms should be developed for these proxies. Ultimately, the most reliable age models would incorporate information from as many proxies as possible.

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

Matching (E5) by region, occupational group (SSYK), sex/age, table content and year

Table from the American Community Survey (ACS) B01001 of total population count by sex and age group. These are multiple, nonoverlapping vintages of the 5-year ACS estimates of population and housing attributes starting in 2010 shown by the corresponding census tract vintage. Also includes the most recent release annually.King County, Washington census tracts with nonoverlapping vintages of the 5-year American Community Survey (ACS) estimates starting in 2010. Vintage identified in the "ACS Vintage" field.The census tract boundaries match the vintage of the ACS data (currently 2010 and 2020) so please note the geographic changes between the decades. Tracts have been coded as being within the City of Seattle as well as assigned to neighborhood groups called "Community Reporting Areas". These areas were created after the 2000 census to provide geographically consistent neighborhoods through time for reporting U.S. Census Bureau data. This is not an attempt to identify neighborhood boundaries as defined by neighborhoods themselves.Vintages: 2010, 2015, 2020, 2021, 2022, 2023ACS Table(s): B01001Data downloaded from: Census Bureau's Explore Census Data The United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2020 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

ATP Tennis Rankings, Results, and Stats

This contains my master ATP player file, historical rankings, results, and match stats.

The player file columns are player_id, first_name, last_name, hand, birth_date, country_code, and height (cm).

The columns for the ranking files are ranking_date, ranking, player_id, and ranking_points (where available).

ATP rankings are mostly complete from 1985 to the present. 1982 is missing, and rankings from 2001-2022020 are only intermittent.

Results and stats: There are up to three files per season: One for tour-level main draw matches (e.g. 'atp_matches_2014.csv'), one for tour-level qualifying and challenger main-draw matches, and one for futures match.

Most of the columns in the results files are self-explanatory. I've also included a matches_data_dictionary.txt file to spell things out a bit more.

To make the results files easier for more people to use, I've included a fair bit of redundancy with the biographical and ranking files: each row contains several columns of biographical information, along with ranking and ranking points, for both players. Ranking data, as well as age, areas of tourney_date, which is almost always the Monday at or near the beginning of the event.

MatchStats a are included where I have them. In general, that means 1991-present for tour-level matches, 2008-present for challengers, and 2011-present for tour-level qualifying. The MatchStats columns should be self-explanatory, but they might not be what you're used to seeing; it's all integer totals (e.g. 1st serves in, not 1st serve percentage), from which traditional percentages can be calculated.

There are some tour-level matches with missing stats. Some are missing because ATP doesn't have them. Others I've deleted because they didn't pass some sanity check (loser won 60% of points, or match time was under 20 minutes, etc). Also, Davis Cup matches are included in the tour-level files, but there are no stats for Davis Cup matches until the last few seasons.

Doubles I've added tour-level doubles back to 2000. Filenames follow the convention atp_matches_doubles_yyyy.csv. I may eventually be able to add tour-level doubles from before 2000, as well as lower-level doubles for some years. Most of the columns are the same, though in a different order.

Doubles updates are temporarily suspended as of late 2020.

Contributing If you find a bug, please file an issue, and be as specific as possible.

Feel free to correct bugs or fill in missing data via pull requests, but be aware that I will not merge PRs. But if that's the most convenient way for you to submit improvements to the data, that's fine; I can work with that.

If you'd like to contribute to the project, I post "help wanted" issues, starting with a plea to fill in biographical data such as date of birth.

Also, I encourage everyone to pitch into the Match Charting Project by charting pro matches. It's not a direct contribution to this repo, but it is a great way to improve the existing state of tennis data.

Attention Please read, understand, and abide by the license below. It seems like a reasonable thing to ask, given the hundreds of hours I've put into amassing and maintaining this dataset. Unfortunately, a few bad apples have violated the license, and when people do that, it makes me considerably less motivated to continue updating.

Also, if you're using this for academic/research purposes (great!), take a minute and cite it properly. It's not that hard, it helps others find a useful resource, and let's face it, you should be doing it anyway.

License Creative Commons License Tennis databases, files, and algorithms by Jeff Sackmann / Tennis Abstract is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Based on a work at https://github.com/JeffSackmann.

BackgroundDepression is common among hospitalized patients and poses a significant threat to their quality of life. Patient engagement (PE) in healthcare has been shown to be associated with positive health outcomes. However, the relationship between PE and depression among hospitalized patients, with and without chronic conditions, has not yet been explored. This study aimed to investigate the association between patients' satisfaction with PE and self-reported depression in Chinese public hospitals.MethodA multi-centered, cross-sectional survey was conducted in seven tertiary-level public hospitals in Guangdong province, China. Twelve items from a patient-centered care questionnaire and the Patient Health Questionnaire 2-item version were used were used to assess patients' satisfaction with PE and self-reported depression, respectively. Propensity score matching (PSM) approach was used to reduce selection bias and potential baseline differences between patients with and without chronic conditions. The relationship between satisfaction with PE and depression was assessed, using univariate and multivariate logistic regression analyses, respectively.ResultsA total of 1,974 hospitalized patients participated in the survey. After the PSM procedure, 604 patients were assigned to the chronic condition group, and another 604 patients were successfully matched in the comparison group with no differences in sex, age, educational level, and PE-related characteristics. Univariate logistic regression analysis indicated that high satisfaction with PE-related approaches significantly decreased the probability of developing depressive status. Multivariate logistic regression analysis further indicated that, after adjusting all PE-related approaches, “patient education” and “involvement in discharge planning” could significantly decrease the probability of patients developing depression.ConclusionsOur results indicate that encouraging PE and improving patients' satisfaction with PE interventions in clinical practice led to improved mental health outcomes among hospitalized patients in China.

Description: The dataset is intentionally provided for data cleansing and applying EDA techniques. This brings fun exploring and wrangling for data geeks. The data is very original so dive-in and Happy Exploring.

Features: In total the dataset contains 121 Features. Details given below.

SK_ID_CURR ID of loan in our sample TARGET Target variable (1 - client with payment difficulties: he/she had late payment more than X days on at least one of the first Y installments of the loan in our sample, 0 - all other cases) NAME_CONTRACT_TYPE Identification if loan is cash or revolving CODE_GENDER Gender of the client FLAG_OWN_CAR Flag if the client owns a car FLAG_OWN_REALTY Flag if client owns a house or flat CNT_CHILDREN Number of children the client has AMT_INCOME_TOTAL Income of the client AMT_CREDIT Credit amount of the loan AMT_ANNUITY Loan annuity AMT_GOODS_PRICE For consumer loans it is the price of the goods for which the loan is given NAME_TYPE_SUITE Who was accompanying client when he was applying for the loan NAME_INCOME_TYPE Clients income type (businessman, working, maternity leave,…) NAME_EDUCATION_TYPE Level of highest education the client achieved NAME_FAMILY_STATUS Family status of the client NAME_HOUSING_TYPE What is the housing situation of the client (renting, living with parents, ...) REGION_POPULATION_RELATIVE Normalized population of region where client lives (higher number means the client lives in more populated region) DAYS_BIRTH Client's age in days at the time of application DAYS_EMPLOYED How many days before the application the person started current employment DAYS_REGISTRATION How many days before the application did client change his registration DAYS_ID_PUBLISH How many days before the application did client change the identity document with which he applied for the loan OWN_CAR_AGE Age of client's car FLAG_MOBIL Did client provide mobile phone (1=YES, 0=NO) FLAG_EMP_PHONE Did client provide work phone (1=YES, 0=NO) **FLAG_WORK_PHONE ** Did client provide home phone (1=YES, 0=NO) FLAG_CONT_MOBILE Was mobile phone reachable (1=YES, 0=NO) FLAG_PHONE Did client provide home phone (1=YES, 0=NO) FLAG_EMAIL Did client provide email (1=YES, 0=NO) OCCUPATION_TYPE What kind of occupation does the client have CNT_FAM_MEMBERS How many family members does client have REGION_RATING_CLIENT Our rating of the region where client lives (1,2,3) REGION_RATING_CLIENT_W_CITY Our rating of the region where client lives with taking city into account (1,2,3) WEEKDAY_APPR_PROCESS_START On which day of the week did the client apply for the loan HOUR_APPR_PROCESS_START Approximately at what hour did the client apply for the loan REG_REGION_NOT_LIVE_REGION Flag if client's permanent address does not match contact address (1=different, 0=same, at region level) REG_REGION_NOT_WORK_REGION Flag if client's permanent address does not match work address (1=different, 0=same, at region level) LIVE_REGION_NOT_WORK_REGION Flag if client's contact address does not match work address (1=different, 0=same, at region level) REG_CITY_NOT_LIVE_CITY Flag if client's permanent address does not match contact address (1=different, 0=same, at city level) REG_CITY_NOT_WORK_CITY Flag if client's permanent address does not match work address (1=different, 0=same, at city level) LIVE_CITY_NOT_WORK_CITY Flag if client's contact address does not match work address (1=different, 0=same, at city level) ORGANIZATION_TYPE Type of organization where client works EXT_SOURCE_1 Normalized score from external data source EXT_SOURCE_2 Normalized score from external data source EXT_SOURCE_3 Normalized score from external data source APARTMENTS_AVG Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor BASEMENTAREA_AVG Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor YEARS_BEGINEXPLUATATION_AVG Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor YEARS_BUILD_AVG Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MED...

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-00051439

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-00054332