The graph displays the estimated number of homeless people in the United States from 2007 to 2024. The x-axis represents the years, ranging from 2007 to 2023, while the y-axis indicates the number of homeless individuals. The estimated homeless population varies over this period, ranging from a low of 57,645 in 2014 to a high of 771,000 in 2024. From 2007 to 2013, there is a general decline in numbers from 647,258 to 590,364. In 2014, the number drops significantly to 57,645, followed by an increase to 564,708 in 2015. The data shows fluctuations in subsequent years, with another notable low of 55,283 in 2018. From 2019 onwards, the estimated number of homeless people generally increases, reaching its peak in 2024. This data highlights fluctuations in homelessness estimates over the years, with a recent upward trend in the homeless population.

In 2023, there were about ******* homeless people estimated to be living in the United States, the highest number of homeless people recorded within the provided time period. In comparison, the second-highest number of homeless people living in the U.S. within this time period was in 2007, at *******. How is homelessness calculated? Calculating homelessness is complicated for several different reasons. For one, it is challenging to determine how many people are homeless as there is no direct definition for homelessness. Additionally, it is difficult to try and find every single homeless person that exists. Sometimes they cannot be reached, leaving people unaccounted for. In the United States, the Department of Housing and Urban Development calculates the homeless population by counting the number of people on the streets and the number of people in homeless shelters on one night each year. According to this count, Los Angeles City and New York City are the cities with the most homeless people in the United States. Homelessness in the United States Between 2022 and 2023, New Hampshire saw the highest increase in the number of homeless people. However, California was the state with the highest number of homeless people, followed by New York and Florida. The vast amount of homelessness in California is a result of multiple factors, one of them being the extreme high cost of living, as well as opposition to mandatory mental health counseling and drug addiction. However, the District of Columbia had the highest estimated rate of homelessness per 10,000 people in 2023. This was followed by New York, Vermont, and Oregon.

The graph displays the top 15 states by an estimated number of homeless people in the United States for the year 2025. The x-axis represents U.S. states, while the y-axis shows the number of homeless individuals in each state. California has the highest homeless population with 187,084 individuals, followed by New York with 158,019, while Hawaii places last in this dataset with 11,637. This bar graph highlights significant differences across states, with some states like California and New York showing notably higher counts compared to others, indicating regional disparities in homelessness levels across the country.

When analyzing the ratio of homelessness to state population, New York, Vermont, and Oregon had the highest rates in 2023. However, Washington, D.C. had an estimated ** homeless individuals per 10,000 people, which was significantly higher than any of the 50 states. Homeless people by race The U.S. Department of Housing and Urban Development performs homeless counts at the end of January each year, which includes people in both sheltered and unsheltered locations. The estimated number of homeless people increased to ******* in 2023 – the highest level since 2007. However, the true figure is likely to be much higher, as some individuals prefer to stay with family or friends - making it challenging to count the actual number of homeless people living in the country. In 2023, nearly half of the people experiencing homelessness were white, while the number of Black homeless people exceeded *******. How many veterans are homeless in America? The  number of homeless veterans in the United States has halved since 2010. The state of California, which is currently suffering a homeless crisis, accounted for the highest number of homeless veterans in 2022. There are many causes of homelessness among veterans of the U.S. military, including post-traumatic stress disorder (PTSD), substance abuse problems, and a lack of affordable housing.

Analysis of ‘Natural Disasters Data Explorer’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/mathurinache/natural-disasters-data-explorer on 28 January 2022.

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

Context

Disasters include all geophysical, meteorological and climate events including earthquakes, volcanic activity, landslides, drought, wildfires, storms, and flooding. Decadal figures are measured as the annual average over the subsequent ten-year period.

Content

Thanks to Our World in Data, you can explore death from natural disasters by country and by date.

Acknowledgements

https://www.acacamps.org/sites/default/files/resource_library_images/naturaldisaster4.jpg" alt="Natural Disasters">

Inspiration

List of variables for inspiration: Number of deaths from drought Number of people injured from drought Number of people affected from drought Number of people left homeless from drought Number of total people affected by drought Reconstruction costs from drought Insured damages against drought Total economic damages from drought Death rates from drought Injury rates from drought Number of people affected by drought per 100,000 Homelessness rate from drought Total number of people affected by drought per 100,000 Number of deaths from earthquakes Number of people injured from earthquakes Number of people affected by earthquakes Number of people left homeless from earthquakes Number of total people affected by earthquakes Reconstruction costs from earthquakes Insured damages against earthquakes Total economic damages from earthquakes Death rates from earthquakes Injury rates from earthquakes Number of people affected by earthquakes per 100,000 Homelessness rate from earthquakes Total number of people affected by earthquakes per 100,000 Number of deaths from disasters Number of people injured from disasters Number of people affected by disasters Number of people left homeless from disasters Number of total people affected by disasters Reconstruction costs from disasters Insured damages against disasters Total economic damages from disasters Death rates from disasters Injury rates from disasters Number of people affected by disasters per 100,000 Homelessness rate from disasters Total number of people affected by disasters per 100,000 Number of deaths from volcanic activity Number of people injured from volcanic activity Number of people affected by volcanic activity Number of people left homeless from volcanic activity Number of total people affected by volcanic activity Reconstruction costs from volcanic activity Insured damages against volcanic activity Total economic damages from volcanic activity Death rates from volcanic activity Injury rates from volcanic activity Number of people affected by volcanic activity per 100,000 Homelessness rate from volcanic activity Total number of people affected by volcanic activity per 100,000 Number of deaths from floods Number of people injured from floods Number of people affected by floods Number of people left homeless from floods Number of total people affected by floods Reconstruction costs from floods Insured damages against floods Total economic damages from floods Death rates from floods Injury rates from floods Number of people affected by floods per 100,000 Homelessness rate from floods Total number of people affected by floods per 100,000 Number of deaths from mass movements Number of people injured from mass movements Number of people affected by mass movements Number of people left homeless from mass movements Number of total people affected by mass movements Reconstruction costs from mass movements Insured damages against mass movements Total economic damages from mass movements Death rates from mass movements Injury rates from mass movements Number of people affected by mass movements per 100,000 Homelessness rate from mass movements Total number of people affected by mass movements per 100,000 Number of deaths from storms Number of people injured from storms Number of people affected by storms Number of people left homeless from storms Number of total people affected by storms Reconstruction costs from storms Insured damages against storms Total economic damages from storms Death rates from storms Injury rates from storms Number of people affected by storms per 100,000 Homelessness rate from storms Total number of people affected by storms per 100,000 Number of deaths from landslides Number of people injured from landslides Number of people affected by landslides Number of people left homeless from landslides Number of total people affected by landslides Reconstruction costs from landslides Insured damages against landslides Total economic damages from landslides Death rates from landslides Injury rates from landslides Number of people affected by landslides per 100,000 Homelessness rate from landslides Total number of people affected by landslides per 100,000 Number of deaths from fog Number of people injured from fog Number of people affected by fog Number of people left homeless from fog Number of total people affected by fog Reconstruction costs from fog Insured damages against fog Total economic damages from fog Death rates from fog Injury rates from fog Number of people affected by fog per 100,000 Homelessness rate from fog Total number of people affected by fog per 100,000 Number of deaths from wildfires Number of people injured from wildfires Number of people affected by wildfires Number of people left homeless from wildfires Number of total people affected by wildfires Reconstruction costs from wildfires Insured damages against wildfires Total economic damages from wildfires Death rates from wildfires Injury rates from wildfires Number of people affected by wildfires per 100,000 Homelessness rate from wildfires Total number of people affected by wildfires per 100,000 Number of deaths from extreme temperatures Number of people injured from extreme temperatures Number of people affected by extreme temperatures Number of people left homeless from extreme temperatures Number of total people affected by extreme temperatures Reconstruction costs from extreme temperatures Insured damages against extreme temperatures Total economic damages from extreme temperatures Death rates from extreme temperatures Injury rates from extreme temperatures Number of people affected by extreme temperatures per 100,000 Homelessness rate from extreme temperatures Total number of people affected by extreme temperatures per 100,000 Number of deaths from glacial lake outbursts Number of people injured from glacial lake outbursts Number of people affected by glacial lake outbursts Number of people left homeless from glacial lake outbursts Number of total people affected by glacial lake outbursts Reconstruction costs from glacial lake outbursts Insured damages against glacial lake outbursts Total economic damages from glacial lake outbursts Death rates from glacial lake outbursts Injury rates from glacial lake outbursts Number of people affected by glacial lake outbursts per 100,000 Homelessness rate from glacial lake outbursts Total number of people affected by glacial lake outbursts per 100,000 Total economic damages from disasters as a share of GDP Total economic damages from drought as a share of GDP Total economic damages from earthquakes as a share of GDP Total economic damages from extreme temperatures as a share of GDP Total economic damages from floods as a share of GDP Total economic damages from landslides as a share of GDP Total economic damages from mass movements as a share of GDP Total economic damages from storms as a share of GDP Total economic damages from volcanic activity as a share of GDP Total economic damages from volcanic activity as a share of GDP Entity Year deaths_rate_per_100k_storm injured_rate_per_100k_storm total_affected_rate_per_100k_all_disasters

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

This layer contains detailed Point in Time counts of homeless populations from 2018, 2013, and 2008. A 2019 version is now available!Layer is symbolized to show the count of the overall homeless population in 2018, with overall counts from 2008 and 2013 in the pop-up, as well as a pie chart of breakdown of type of shelter. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. The Point-in-Time (PIT) count is a count of sheltered and unsheltered homeless persons on a single night in January. HUD requires that Continuums of Care Areas (CoCs) conduct an annual count of homeless persons who are sheltered in emergency shelter, transitional housing, and Safe Havens on a single night. CoCs also must conduct a count of unsheltered homeless persons every other year (odd numbered years). Each count is planned, coordinated, and carried out locally.The Point-in-Time values were retrieved from HUD's Historical Data site. The 2018, 2013, and 2008 sheets within the "2007 - 2018 PIT Counts within CoCs.xlsx" (downloaded on 2/7/2019) file were combined and joined to the CoC boundaries available from HUD's Open Data site. As noted in the "Mergers" sheet in the PIT Excel file, some CoC Areas have merged over the years. Use caution when comparing numbers in these CoCs across years. Data note: MO-604 covers territory in both Missouri and Kansas. The record described in this file represents the CoC's total territory, the sum of the point-in-time estimates the CoC separately reported for the portions of its territory in MO and in KS.For more information and attributes on the CoC Areas themselves, including contact information, see this accompanying layer.Suggested Citation: U.S. Department of Housing and Urban Development (HUD)'s Point in Time (PIT) counts for Continuum of Care Grantee Areas, accessed via ArcGIS Living Atlas of the World on (date).

This layer contains detailed Point in Time counts of homeless populations from 2019. This layer is modeled after a similar layer that contains data for 2018, 2013, and 2008.Layer is symbolized to show the count of the overall homeless population in 2019, with a pie chart of breakdown of type of shelter. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. The Point-in-Time (PIT) count is a count of sheltered and unsheltered homeless persons on a single night in January. HUD requires that Continuums of Care Areas (CoCs) conduct an annual count of homeless persons who are sheltered in emergency shelter, transitional housing, and Safe Havens on a single night. CoCs also must conduct a count of unsheltered homeless persons every other year (odd numbered years). Each count is planned, coordinated, and carried out locally.The Point-in-Time values were retrieved from HUD's Historical Data site. Original source is the 2019 sheet within the "2007 - 2019 PIT Counts by CoCs.xlsx" (downloaded on 3/10/2020) file. Key fields were kept and joined to the CoC boundaries available from HUD's Open Data site.Data note: MO-604 covers territory in both Missouri and Kansas. The record described in this file represents the CoC's total territory, the sum of the point-in-time estimates the CoC separately reported for the portions of its territory in MO and in KS.For more information and attributes on the CoC Areas themselves, including contact information, see this accompanying layer.Suggested Citation: U.S. Department of Housing and Urban Development (HUD)'s Point in Time (PIT) 2019 counts for Continuum of Care Grantee Areas, accessed via ArcGIS Living Atlas of the World on (date).

This map shows the percent of population who are veterans. This pattern is shown by states, counties, and tracts. The data is from the most current American Community Survey (ACS) data from the U.S. Census Bureau. Veterans are men and women who have served (even for a short time), but are not currently serving, on active duty in the U.S. Army, Navy, Air Force, Marine Corps, or the Coast Guard, or who served in the U.S. Merchant Marine during World War II. People who served in the National Guard or Reserves are classified as veterans only if they were ever called or ordered to active duty.The pop-up highlights the breakdown of veterans by gender.Zoom to any area in the country to see a local or regional pattern, or use one of the bookmarks to see distinct patterns of poverty through the US. Data is available for the 50 states plus Washington D.C. and Puerto Rico.The data comes from this ArcGIS Living Atlas of the World layer, which is part of a wider collection of layers that contain the most up-to-date ACS data from the Census. The layers are updated annually when the ACS releases their most current 5-year estimates. Visit the layer for more information about the data source, vintage, and download date for the data.

Project Overview This study used a community-based participatory approach to identify and investigate the needs of people experiencing homelessness in Dublin, Ireland. The project had several stages: A systematic review on health disparities amongst people experiencing homelessness in the Republic of Ireland; Observation and interviews with homeless attendees of a community health clinic; and Interviews with community experts (CEs) conducted from September 2022 to March 2023 on ongoing work and gaps in the research/health service response. This data deposit stems from stage 3, the community expert interview aspect of this project. Stage 1 of the project has been published (Ingram et al., 2023.) and associated data are available here. De-identified field note data from stage 2 of the project are planned for sharing upon completion of analysis, in January 2024. Data and Data Collection Overview A purposive, criterion-i sampling strategy (Palinkas et al., 2015) – where selected interviewees meet a predetermined criterion of importance – was used to identify professionals working in homeless health and/or addiction services in Dublin, stratified by occupation type. Potential CEs were identified through an internet search of homeless health and addiction services in Dublin. Interviewed CEs were invited to recommend colleagues they felt would have relevant perspectives on community health needs, expanding the sample via snowball strategy. Interview questions were based on World Health Organization Community Health Needs Assessment guidelines (Rowe at al., 2001). Semi-structured interviews were conducted between September 2022 and March 2023 utilising ZOOM™, the phone, or in person according to participant preference. Carolyn Ingram, who has formal qualitative research training, served as the interviewer. CEs were presented with an information sheet and gave audio recorded, informed oral consent – considered appropriate for remote research conducted with non-vulnerable adult participants – in the full knowledge that interviews would be audio recorded, transcribed, and de-identified, as approved by the researchers’ institutional Human Research Ethics Committee (LS-E-125-Ingram-Perrotta-Exemption). Interviewees also gave permission for de-identified transcripts to be shared in a qualitative data archive. Shared Data Organization 16 de-identified transcripts from the CE interviews are being published. Three participants from the total sample (N=19) did not consent to data archival. The transcript from each interviewee is named based on the type of work the interviewee performs, with individuals in the same type of work being differentiated by numbers. The full set of professional categories is as follows: Addiction Services Government Homeless Health Services Hospital Psychotherapist Researcher Social Care Any changes or removal of words or phrases for de-identification purposes are flagged by including [brackets] and italics. The documentation files included in this data project are the consent form and the interview guide used for the study, this data narrative and an administrative README file. References Ingram C, Buggy C, Elabbasy D, Perrotta C. (2023) “Homelessness and health-related outcomes in the Republic of Ireland: a systematic review, meta-analysis and evidence map.” Journal of Public Health (Berl). https://doi.org/10.1007/s10389-023-01934-0 Palinkas LA, Horwitz SM, Green CA, Wisdom JP, Duan N, Hoagwood K. (2015) “Purposeful sampling for qualitative data collection and analysis in mixed method implementation research.” Administration and Policy in Mental Health. Sep;42(5):533–44. https://doi.org/10.1007/s10488-013-0528-y Rowe A, McClelland A, Billingham K, Carey L. (2001) “Community health needs assessment: an introductory guide for the family health nurse in Europe” [Internet]. World Health Organization. Regional Office for Europe. Available at: https://apps.who.int/iris/handle/10665/108440

The re-emergence of tuberculosis (TB) in the mid-1980s in many parts of the world, including the United States, is often attributed to the emergence and rapid spread of human immunodeficiency virus (HIV) and acquired immunodeficiency syndrome (AIDS). Although it is well established that TB transmission is particularly amplified in populations with high HIV prevalence, the epidemiology of interaction between TB and HIV is not well understood. This is partly due to the scarcity of HIV-related data, a consequence of the voluntary nature of HIV status reporting and testing, and partly due to current practices of screening high risk populations through separate surveillance programs for HIV and TB. The San Francisco Department of Public Health, TB Control Program, has been conducting active surveillance among the San Francisco high-risk populations since the early 1990s. We present extensive TB surveillance data on HIV and TB infection among the San Francisco homeless to investigate the association between the TB cases and their HIV+ contacts. We applied wavelet coherence and phase analyses to the TB surveillance data from January 1993 through December 2005, to establish and quantify statistical association and synchrony in the highly non-stationary and ostensibly non-periodic waves of TB cases and their HIV+ contacts in San Francisco. When stratified by homelessness, we found that the evolution of TB cases and their HIV+ contacts is highly coherent over time and locked in phase at a specific periodic scale among the San Francisco homeless, but no significant association was observed for the non-homeless. This study confirms the hypothesis that the dynamics of HIV and TB are significantly intertwined and that HIV is likely a key factor in the sustenance of TB transmission among the San Francisco homeless. The findings of this study underscore the importance of contact tracing in detection of HIV+ individuals that may otherwise remain undetected, and thus highlights the ever-increasing need for HIV-related data and an integrative approach to monitoring high-risk populations with respect to HIV and TB transmission.

The research, entitled Homelessness during COVID-19: Homeless Migrants in a Global Crisis, took a biographical life story approach to understand the experiences of 43 non-UK nationals who experienced homelessness during the COVID-19 pandemic. In the first phase of the project, and in order to gain insight into the homelessness sector, we conducted semi-structured interviews with 37 people across nine homelessness organisations. The focus of the interviews was on migrant homelessness before and during the pandemic. Due to ethical reasons, we are not able to upload data from the life story interviews that we conducted with migrants experiencing homelessness. However, the data from the semi-structured interviews with staff in the homelessness sector that we have submitted to the UK Data Service helped us to frame our research and provided much-needed contextual information during the pandemic.

People experiencing homelessness are disproportionately impacted by coronavirus. Despite government efforts to place rough sleepers in hotels to contain the spread of the disease, many migrants sleeping rough with No Recourse to Public Funds (NRPF) have been left behind at the height of a global pandemic. This project, involving researchers from University of Portsmouth, University of Sussex and St Mungo's, the homeless charity, will produce an 18-month qualitative-based study of migrant homelessness framed by the wider global and national context. Working with two of St Mungo's migrant services, Street Legal, St Mungo's legal team and Routes Home, a service supporting people sleeping rough from outside of the UK, a particular focus of the study will be the experience of non-UK nationals and their attempts, during the crisis, to resolve their immigration status. Many of these migrants are at the sharpest end of homelessness: almost 1,000 rough sleepers housed in emergency accommodation in London have NRPF (Heath, 2020).

Most migrant homeless clients are faced with multiple everyday challenges; they experience the hostility and aggression directed toward homeless people, compounded with often intense experiences of racism. Migrant homeless clients are also likely to be afraid of 'authorities' for various reasons including fear of deportation by the Home Office and personal histories of violent persecution by state actors in their original countries of belonging. During the pandemic, increased numbers of police on the streets have created high anxiety for refugees/asylum seekers and destitute migrants who report being retriggered with PTSD symptoms, with no access to NHS mental health services that are now delivered primarily remotely and are restricted access except to those patients who have access to free or cheap wifi, or unlimited phone credit (Munt 2020). A cultural miasma of fear and anxiety due to pandemic can affect such vulnerable minority groups particularly forcefully, with public attitudes generating direct aggression toward perceived 'outsiders' as harbingers of disease. Historically, the discourse of the 'stranger' (Ahmed 1991) or foreigner as bringer of disease has been well recognised within cultural sociology (Munt 2007), and as cultural suspicion grows under such conditions, feelings of alienation and estrangement amongst vulnerable groups intensifies.

The project will innovate by examining the biographical and life history narratives of St Mungo's clients in London in relation to their experiences of homelessness during the coronavirus crisis. Alongside semi-structured interviews, we will use participatory research methods including peer research, autoethnographic diaries, mobile phone photo-ethnographies and life history narratives in order to capture the rich and emotive narratives of those experiencing crisis. In doing so, we will examine the intersection of personal histories, complex global processes and the dynamics of the particular situation (Stewart, 2012, 2013). Researching vulnerable groups requires ethical sensitivity. It carries the danger of risking more disappointment among the respondents and exacerbating intense feelings of loneliness and isolation. To avoid this, and to make a positive intervention, we will seek to engage clients with services and support as part of the research project. Based on its findings, and working with St Mungo's partners, the project will make recommendations for measures that can be taken across the UK and elsewhere to support the homeless, particularly those most vulnerable, during times of crisis.

This dataset "Global hotspots of climate related disasters" shows the number of people impacted by climate-related disasters recorded in the EM-DAT database between 2000 and 2020. This dataset was used to prepare the maps and the analysis of the paper Donatti C.I., Nicholas K., Fedele G., Delforge D., Speybroeck N., Moraga P., Blatter J., Below R., Zvoleff A. 2024. Global hotspots of climate-related disasters. International Journal of Disaster Risk Reduction. https://doi.org/10.1016/j.ijdrr.2024.104488. This dataset includes information on people impacted by Drought, tropical cyclones, flash flood, riverine flood, forest fire, land fire, heat wave, landslide and mudslide. Data on coastal flood was not included because the database only had recordings until 2013. Data on disaster sub-types “landslides” and “mudslides” as presented in the EM-DAT were further combined as one single climate-related disaster (“land and mudslides”) for the analyses. Likewise, data on disaster sub-types “forest fire” and “land fire” were further combined as one climate-related disaster (“wildfire”). The data was accessed directly from the EM-DAT database and then summarized as show in the dataset. We used this database, downloaded on June 2nd 2021, to access data on “total affected” people and the “total deaths” per disaster event impacting a country (i.e., an entry in the EM-DAT), which were combined in this study to create the variable “total people impacted”. In the EM-DAT database, “total affected” represents the sum of people “injured,” “affected,” and “homeless” resulting from a particular event. “Injured” were considered those that have suffered from physical injuries, trauma, or an illness requiring immediate medical assistance, including people hospitalized, as a direct result of a disaster, “affected” were considered people requiring immediate assistance during an emergency and “homeless” were considered those whose homes were destroyed or heavily damaged and therefore needed shelter after an event. “Total deaths” include people that have died or were considered missing, those whose whereabouts since the disaster were unknown and presumed dead based on official figures. More details can be found under “documentation, data structure and content description” at emdat.be. In the dataset, "ADM-CODE" refers to the code used to identify each administrative area, which refers to the code of FAO's Global Administrative Unit Layer, GAUL.

This map shows Point in Time counts of the overall homeless populations from 2019. Layer is symbolized to show the count of the overall homeless population in 2019, with a pie chart of breakdown of type of shelter. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. The Point-in-Time (PIT) count is a count of sheltered and unsheltered homeless persons on a single night in January. HUD requires that Continuums of Care Areas (CoCs) conduct an annual count of homeless persons who are sheltered in emergency shelter, transitional housing, and Safe Havens on a single night. CoCs also must conduct a count of unsheltered homeless persons every other year (odd numbered years). Each count is planned, coordinated, and carried out locally.The Point-in-Time values were retrieved from HUD's Historical Data site. Original source is the 2019 sheet within the "2007 - 2019 PIT Counts by CoCs.xlsx" (downloaded on 3/10/2020) file. Key fields were kept and joined to the CoC boundaries available from HUD's Open Data site.Data note: MO-604 covers territory in both Missouri and Kansas. The record described in this file represents the CoC's total territory, the sum of the point-in-time estimates the CoC separately reported for the portions of its territory in MO and in KS.For more information and attributes on the CoC Areas themselves, including contact information, see this accompanying layer.Suggested Citation: U.S. Department of Housing and Urban Development (HUD)'s Point in Time (PIT) 2019 counts for Continuum of Care Grantee Areas, accessed via ArcGIS Living Atlas of the World on (date).

Problem statement Having a pet is one of life’s most fulfilling experiences. Your pets spoil you with their love, compassion, and loyalty. And dare anyone lay a finger on you in your pet’s presence, they are in for a lot of trouble. Thanks to social media, videos of clumsy and fussy (yet adorable) pets from across the globe entertain you all day long. Their love is pure and infinite. So, in return, all pets deserve a warm and loving family, indeed. And occasional boops, of course.

Numerous organizations across the world provide shelter to all homeless animals until they are adopted into a new home. However, finding a loving family for them can be a daunting task at times. This International Homeless Animals Day, we present a Machine Learning challenge to you: Adopt a buddy.

The brighter side of the pandemic is an increase in animal adoption and fostering. To ensure that their customers stay indoors, a leading pet adoption agency plans on creating a virtual-tour experience, showcasing all animals available in their shelter. To enable that, you have been tasked to build a Machine Learning model that determines the type and breed of the animal-based on its physical attributes and other factors.

Dataset The dataset consists of parameters such as a unique ID assigned to each animal that is up for adoption, the date on which they arrived at the shelter, their physical attributes such as color, length, and height, among other factors.

The benefits of practicing this problem by using Machine Learning techniques are as follows:

This challenge will help you to actively enhance your knowledge of multi-label classification. It is one of the basic building blocks of Machine Learning We challenge you to build a predictive model that detects the type and breed of an animal-based on its condition, appearance, and other factors.

Prizes Considering these unprecedented times that the world is facing due to the Coronavirus pandemic, we wish to do our bit and contribute the prize money for the welfare of society.

Overview Machine Learning is an application of Artificial Intelligence (AI) that provides systems with the ability to automatically learn and improve from experiences without being explicitly programmed. Machine Learning is a Science that determines patterns in data. These patterns provide a deeper meaning to problems. First, it helps you understand the problems better and then solve the same with elegance.

Here is the new HackerEarth Machine Learning Challenge—Adopt a buddy

This challenge is designed to help you improve your Machine Learning skills by competing and learning from fellow participants.

Why should you participate? To analyze and implement multiple algorithms, and determine which is more appropriate for a problem. To get hands-on experience of Machine Learning problems.

Who should participate? Working professionals. Data Science or Machine Learning enthusiasts. College students (if you understand the basics of predictive modeling).

Out of all OECD countries, Cost Rica had the highest poverty rate as of 2022, at over 20 percent. The country with the second highest poverty rate was the United States, with 18 percent. On the other end of the scale, Czechia had the lowest poverty rate at 6.4 percent, followed by Denmark.

The significance of the OECD

The OECD, or the Organisation for Economic Co-operation and Development, was founded in 1948 and is made up of 38 member countries. It seeks to improve the economic and social well-being of countries and their populations. The OECD looks at issues that impact people’s everyday lives and proposes policies that can help to improve the quality of life.

Poverty in the United States

In 2022, there were nearly 38 million people living below the poverty line in the U.S.. About one fourth of the Native American population lived in poverty in 2022, the most out of any ethnicity. In addition, the rate was higher among young women than young men. It is clear that poverty in the United States is a complex, multi-faceted issue that affects millions of people and is even more complex to solve.