Obesity percentages for Lake County, Illinois. Explanation of field attributes: Pct_Obese – The percent of people in the zip code who are considered obese, defined as having a BMI greater than or equal to 30. ObsOrOvrwt –The percent of people in the zip code who are considered overweight (defined as having a BMI greater than or equal to 25 but less than 30) or obese (defined as having a BMI greater than or equal to 30).

National Obesity Percentages by State. Explanation of Field Attributes:Obesity - The percent of the state population that is considered obese from the 2015 CDC BRFSS Survey.

SUMMARYThis analysis, designed and executed by Ribble Rivers Trust, identifies areas across England with the greatest levels of obesity in adults (aged 18+). Please read the below information to gain a full understanding of what the data shows and how it should be interpreted.ANALYSIS METHODOLOGYThe analysis was carried out using Quality and Outcomes Framework (QOF) data, derived from NHS Digital, relating to obesity in adults (aged 18+).This information was recorded at the GP practice level. However, GP catchment areas are not mutually exclusive: they overlap, with some areas covered by 30+ GP practices. Therefore, to increase the clarity and usability of the data, the GP-level statistics were converted into statistics based on Middle Layer Super Output Area (MSOA) census boundaries.The percentage of each MSOA’s adult population (aged 18+) that are obese was estimated. This was achieved by calculating a weighted average based on:The percentage of the MSOA area that was covered by each GP practice’s catchment areaOf the GPs that covered part of that MSOA: the percentage of registered patients that have that illness The estimated percentage of each MSOA’s adult population that are obese was then combined with Office for National Statistics Mid-Year Population Estimates (2019) data for MSOAs, to estimate the number of people in each MSOA that are obese, within the relevant age range.Each MSOA was assigned a relative score between 1 and 0 (1 = worst, 0 = best) based on:A) the PERCENTAGE of the adult population within that MSOA who are estimated to be obeseB) the NUMBER of adults within that MSOA who are estimated to be obeseAn average of scores A & B was taken, and converted to a relative score between 1 and 0 (1= worst, 0 = best). The closer to 1 the score, the greater both the number and percentage of the population in the MSOA that are estimated to be obese compared to other MSOAs. In other words, those are areas where it’s estimated a large number of people are obese, and where those people make up a large percentage of the population, indicating there is a real issue with obesity within the adult population and the investment of resources to address that issue could have the greatest benefits.LIMITATIONS1. GP data for the financial year 1st April 2018 – 31st March 2019 was used in preference to data for the financial year 1st April 2019 – 31st March 2020, as the onset of the COVID19 pandemic during the latter year could have affected the reporting of medical statistics by GPs. However, for 53 GPs (out of 7670) that did not submit data in 2018/19, data from 2019/20 was used instead. Note also that some GPs (997 out of 7670) did not submit data in either year. This dataset should be viewed in conjunction with the ‘Health and wellbeing statistics (GP-level, England): Missing data and potential outliers’ dataset, to determine areas where data from 2019/20 was used, where one or more GPs did not submit data in either year, or where there were large discrepancies between the 2018/19 and 2019/20 data (differences in statistics that were > mean +/- 1 St.Dev.), which suggests erroneous data in one of those years (it was not feasible for this study to investigate this further), and thus where data should be interpreted with caution. This dataset also shows rural areas (with little or no population) that do not officially fall into any GP catchment area and for which there were no statistics regarding adult obesity (although this will not affect the results of this analysis if there are no people living in those areas).2. It was not feasible to incorporate ultra-fine-scale geographic distribution of populations that are registered with each GP practice or who live within each MSOA. Populations might be concentrated in certain areas of a GP practice’s catchment area or MSOA and relatively sparse in other areas. Therefore, the dataset should be used to identify general areas where there are high levels of adult obesity, rather than interpreting the boundaries between areas as ‘hard’ boundaries that mark definite divisions between areas with differing levels of adult obesity.TO BE VIEWED IN COMBINATION WITH:This dataset should be viewed alongside the following datasets, which highlight areas of missing data and potential outliers in the data:Health and wellbeing statistics (GP-level, England): Missing data and potential outliersLevels of obesity, inactivity and associated illnesses (England): Missing dataDOWNLOADING THIS DATATo access this data on your desktop GIS, download the ‘Levels of obesity, inactivity and associated illnesses: Summary (England)’ dataset.DATA SOURCESThis dataset was produced using:Quality and Outcomes Framework data: Copyright © 2020, Health and Social Care Information Centre. The Health and Social Care Information Centre is a non-departmental body created by statute, also known as NHS Digital.GP Catchment Outlines. Copyright © 2020, Health and Social Care Information Centre. The Health and Social Care Information Centre is a non-departmental body created by statute, also known as NHS Digital. Data was cleaned by Ribble Rivers Trust before use.COPYRIGHT NOTICEThe reproduction of this data must be accompanied by the following statement:© Ribble Rivers Trust 2021. Analysis carried out using data that is: Copyright © 2020, Health and Social Care Information Centre. The Health and Social Care Information Centre is a non-departmental body created by statute, also known as NHS Digital.CaBA HEALTH & WELLBEING EVIDENCE BASEThis dataset forms part of the wider CaBA Health and Wellbeing Evidence Base.

This data set summarizes obesity and the number of deaths caused by it in each country

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💡I have already divided these into TRAIN data, TEST data, and ANSWER data so you guys can start working on the regression problem right away.

• train.csv: Obesity and deaths data from 1990 to 2013
• test.csv: The explanatory variable in 2014
• answer.csv: The objective variable in 2014

These data were created with the assumption that the number of deaths due to obesity in 2014 will be estimated from data from 1990 to 2013.

There is also something called HINT data(hint.csv). This is data for 2015 and beyond. I have left it out of the train or test data because it has many missing values, but it may be useful for forecasting and for those who are interested in more recent data.

📖 About Dataset

🌎 Overview

This dataset provides cleaned and structured information from the Behavioral Risk Factor Surveillance System (BRFSS) conducted by the CDC. It focuses on nutrition, physical activity, and obesity trends across U.S. states and national averages from 2011 to 2023.

The data originates from the Division of Nutrition, Physical Activity, and Obesity (DNPAO) and has been pre-processed to remove missing values, redundant columns, and inconsistencies, making it ready for analysis.

📊 Contents

The dataset contains 29 columns and over 106,000 rows of observations, including:

• Year: Start and end years of data collection (2011–2023)
• Location: State abbreviation, state name, and geographic coordinates
• Class & Topic: High-level categories such as Obesity/Weight Status, Physical Activity, Fruits and Vegetables
• Question: Specific health behavior measured (e.g., % of adults with BMI ≥30)
• Data_Value: The main metric (percentage or proportion)
• Confidence Intervals: Statistical lower and upper bounds
• Sample Size: Number of participants
• Demographics: Age, sex, income, education, race/ethnicity

✅ Cleaning Process

• Removed fully empty columns (e.g., Total, Data_Value_Unit)
• Imputed missing numeric values using median replacement
• Categorical variables (Age, Sex, Education, Income, Race/Ethnicity) filled with Unknown
• Dropped non-essential ID columns (ClassID, TopicID, etc.) to simplify analysis
• Final dataset contains no missing values

🎯 Use Cases

This dataset is highly valuable for:

• Public Health Research: Tracking obesity and physical activity trends
• Policy Evaluation: Comparing state-level health initiatives
• Data Science & ML: Building predictive models on obesity & lifestyle behaviors
• Visualization Projects: Heatmaps, time series, and demographic comparisons

📌 Example Questions You Can Answer

• How have obesity rates changed from 2011–2023 across U.S. states?
• Which states report the highest vs lowest physical activity levels?
• What is the relationship between income, education, and obesity?
• How do dietary habits (fruit & vegetable intake) correlate with weight status?

📂 File Information

• File Name: Nutrition_Physical_Activity_Obesity_Clean.csv
• Rows: 106,260
• Columns: 29
• Format: CSV (comma-separated)

🏛 Source

• Centers for Disease Control and Prevention (CDC)
• Division of Nutrition, Physical Activity, and Obesity (DNPAO)
• Original dataset: Data.gov – BRFSS Nutrition, Physical Activity, and Obesity

💡 Citation

If you use this dataset in your work, please cite: Centers for Disease Control and Prevention (CDC). Behavioral Risk Factor Surveillance System (BRFSS), 2011–2023.

✨ This cleaned version was prepared for easy exploration, analysis, and machine learning applications on Kaggle.

Data Series: Proportion of adults who are obese, by sex Indicator: III.7 - Proportion of adults who are obese, by sex Source year: 2022 This dataset is part of the Minimum Gender Dataset compiled by the United Nations Statistics Division. Domain: Health and related services

West Virginia, Mississippi, and Arkansas are the U.S. states with the highest percentage of their population who are obese. The states with the lowest percentage of their population who are obese include Colorado, Hawaii, and Massachusetts. Obesity in the United States Obesity is a growing problem in many countries around the world, but the United States has the highest rate of obesity among all OECD countries. The prevalence of obesity in the United States has risen steadily over the previous two decades, with no signs of declining. Obesity in the U.S. is more common among women than men, and overweight and obesity rates are higher among African Americans than any other race or ethnicity. Causes and health impacts Obesity is most commonly the result of a combination of poor diet, overeating, physical inactivity, and a genetic susceptibility. Obesity is associated with various negative health impacts, including an increased risk of cardiovascular diseases, certain types of cancer, and diabetes type 2. As of 2022, around 8.4 percent of the U.S. population had been diagnosed with diabetes. Diabetes is currently the eighth leading cause of death in the United States.

The adult obesity rate, or the percentage of the county population (age 18 and older*) that is obese, or has a Body Mass Index (BMI) equal to or greater than 30 [kg/m2], is illustrative of a serious health problem, in Champaign County, statewide, and nationally.

The adult obesity rate data shown here spans from Reporting Years (RY) 2015 to 2024. Champaign County’s adult obesity rate fluctuated during this time, peaking in RY 2022. The adult obesity rates for Champaign County, Illinois, and the United States were all above 30% in RY 2024, but the Champaign County rate was lower than the state and national rates. All counties in Illinois had an adult obesity rate above 30% in RY 2024, but Champaign County's rate is one of the lowest among all Illinois counties.

Obesity is a health problem in and of itself, and is commonly known to exacerbate other health problems. It is included in our set of indicators because it can be easily measured and compared between Champaign County and other areas.

This data was sourced from the University of Wisconsin’s Population Health Institute’s and the Robert Wood Johnson Foundation’s County Health Rankings & Roadmaps. Each year’s County Health Rankings uses data from the most recent previous years that data is available. Therefore, the 2024 County Health Rankings (“Reporting Year” in the table) uses data from 2021 (“Data Year” in the table). The survey methodology changed in Reporting Year 2015 for Data Year 2011, which is why the historical data shown here begins at that time. No data is available for Data Year 2018. The County Health Rankings website notes to use caution if comparing RY 2024 data with prior years.

*The percentage of the county population measured for obesity was age 20 and older through Reporting Year 2021, but starting in Reporting Year 2022 the percentage of the county population measured for obesity was age 18 and older.

Source: University of Wisconsin Population Health Institute. County Health Rankings & Roadmaps 2024. www.countyhealthrankings.org.

This dataset provides state-level obesity percentages across the United States, offering insights into the prevalence of obesity based on data from the 2015 Behavioral Risk Factor Surveillance System (BRFSS) survey conducted by the Centers for Disease Control and Prevention (CDC).

Each record represents the percentage of the population in a given state considered obese, helping researchers, policymakers, and public health professionals analyze and address obesity trends nationwide.

Decrease the percentage of adults who are obese from 32.2% in 2012 to 29.7% by 2017.

Pct_Obese – The percent of people in the zip code who are considered obese, defined as having a BMI greater than or equal to 30. ObsOrOvrwt –The percent of people in the zip code who are considered overweight (defined as having a BMI greater than or equal to 25 but less than 30) or obese (defined as having a BMI greater than or equal to 30).

The spreadsheet contains regional level obesity trend data from the the HSE, BMI data from Understanding Society, and adjusted prevalence of underweight, healthy weight, overweight, and obesity by local authority from the Active People Survey. Understanding Society data shows the percentage of the population aged 10 and over by their Body Mass Index Classification, covering underweight, normal weight, overweight, and three classes of obesity. Questions on self-reported height and weight were added to the Sport England Active People Survey (APS) in January 2012 to provide data for monitoring excess weight (overweight including obesity, BMI ≥25kg/m2) in adults (age 16 and over) at local authority level for the Public Health Outcomes Framework (PHOF). Health Survey for England (HSE) results at a national level are available on the NHS Information Centre website. Other NHS indicators on obesity are available for Strategic Health Authorities (SHA). Relevant links: http://discover.ukdataservice.ac.uk/series/?sn=2000053 http://www.noo.org.uk/visualisation/adult_obesity

Problem Statement Obesity is a complex disease involving an excessive amount of body fat. Obesity isn't just a cosmetic concern. It is a medical problem that increases your risk of other diseases and health problems, such as heart disease, diabetes, high blood pressure and certain cancers.

Obesity is a worldwide problem which causes a lot of serious medical problems. Obesity will be increased, about 45% out of the whole population by 2035. The proportion of morbid obese and the actual costs on healthcare will be increased. Implementation of a system that could estimate the percentage of obese population for particular time duration given the age range, income range, location, high confidence level and low confidence level of obesity, education, gender, the class level, etc. of the population can help in fight against obesity.

Objective Build a machine learning model that would help us estimate the percentage of obese population.

About the Dataset This dataset includes data on adult's diet, physical activity, and weight status from the Behavioral Risk Factor Surveillance System. This data is used for DNPAO's Data, Trends, and Maps database, which provides national and state specific data on obesity, nutrition, physical activity, and breastfeeding. There are 29 variables in the train dataset including target variable. Some of the variables are listed below with their description:

YearStart & YearEnd are the starting and ending year for which the obesity percentage is to be estimated Sample_Size is the sample of population LocationDesc is the location description and LocationAbbr is the location abbreviation Topic and Question are the topic and question category under which sample population falls Age(years) is the age range to which sample population belong Data_VAlue is the population obesity percentage (the target variable)

This report presents information on obesity, physical activity and diet drawn together from a variety of sources for England. More information can be found in the source publications which contain a wider range of data and analysis. Each section provides an overview of key findings, as well as providing links to relevant documents and sources. Some of the data have been published previously by NHS Digital. A data visualisation tool (link provided within the key facts) allows users to select obesity related hospital admissions data for any Local Authority (as contained in the data tables), along with time series data from 2013/14. Regional and national comparisons are also provided. The report includes information on: Obesity related hospital admissions, including obesity related bariatric surgery. Obesity prevalence. Physical activity levels. Walking and cycling rates. Prescriptions items for the treatment of obesity. Perception of weight and weight management. Food and drink purchases and expenditure. Fruit and vegetable consumption. Key facts cover the latest year of data available: Hospital admissions: 2018/19 Adult obesity: 2018 Childhood obesity: 2018/19 Adult physical activity: 12 months to November 2019 Children and young people's physical activity: 2018/19 academic year

Percentage of adults who are obese, with a body mass index (BMI) of 30.0 or higher.

In 2023, it was estimated that around 37 percent of adults with an annual income of less than 15,000 U.S. dollars were obese, compared to 29 percent of those with an annual income of 75,000 dollars or more. This statistic shows the percentage of U.S. adults who were obese in 2023, by income.

This is historical data. The update frequency has been set to "Static Data" and is here for historic value. Updated on 8/14/2024 Adults who are not overweight or obese - This indicator shows the percentage of adults who are not overweight or obese. In Maryland in 2015, of adults considered obese, 52% had high blood pressure, 44% had high cholesterol, and 21% had diabetes. Healthy weight can aid in the control of these conditions if they develop. Link to Data Details

This dataset shows the levels of overweight and obese people by country. Data is provided for 2002 and 2010 as a percentage of the total population and is also broken down by sex. Rates of change between 2002 and 2010 are also provided. The data was collated by the World Health Organisation (WHO)(http://www.who.int/gho/ncd/risk_factors/overweight/en/index.html) and was downloaded via the Guardian website (http://www.theguardian.com/news/datablog/interactive/2013/feb/19/obesity-map-of-world-weight). GIS vector data. This dataset was first accessioned in the EDINA ShareGeo Open repository on 2014-01-03 and migrated to Edinburgh DataShare on 2017-02-22.

This data is a summary of the prevalence of residents in Travis County whose Body Mass Index (BMI) is categorized as overweight or obese. The data is calculated from self reported measures of height and weight from the Behavioral Risk Factor Surveillance System (BRFSS) survey from the CDC. View more details and insights related to this data set on the story page: https://data.austintexas.gov/stories/s/HE-A-3-Percentage-of-adults-whose-body-mass-index-/7xkh-64hz/

SUMMARYThis analysis, designed and executed by Ribble Rivers Trust, identifies areas across England with the greatest levels of physical illnesses that are linked with obesity and inactivity. Please read the below information to gain a full understanding of what the data shows and how it should be interpreted.ANALYSIS METHODOLOGYThe analysis was carried out using Quality and Outcomes Framework (QOF) data, derived from NHS Digital, relating to:- Asthma (in persons of all ages)- Cancer (in persons of all ages)- Chronic kidney disease (in adults aged 18+)- Coronary heart disease (in persons of all ages)- Diabetes mellitus (in persons aged 17+)- Hypertension (in persons of all ages)- Stroke and transient ischaemic attack (in persons of all ages)This information was recorded at the GP practice level. However, GP catchment areas are not mutually exclusive: they overlap, with some areas covered by 30+ GP practices. Therefore, to increase the clarity and usability of the data, the GP-level statistics were converted into statistics based on Middle Layer Super Output Area (MSOA) census boundaries.For each of the above illnesses, the percentage of each MSOA’s population with that illness was estimated. This was achieved by calculating a weighted average based on:- The percentage of the MSOA area that was covered by each GP practice’s catchment area- Of the GPs that covered part of that MSOA: the percentage of patients registered with each GP that have that illnessThe estimated percentage of each MSOA’s population with each illness was then combined with Office for National Statistics Mid-Year Population Estimates (2019) data for MSOAs, to estimate the number of people in each MSOA with each illness, within the relevant age range.For each illness, each MSOA was assigned a relative score between 1 and 0 (1 = worst, 0 = best) based on:A) the PERCENTAGE of the population within that MSOA who are estimated to have that illnessB) the NUMBER of people within that MSOA who are estimated to have that illnessAn average of scores A & B was taken, and converted to a relative score between 1 and 0 (1= worst, 0 = best). The closer to 1 the score, the greater both the number and percentage of the population in the MSOA predicted to have that illness, compared to other MSOAs. In other words, those are areas where a large number of people are predicted to suffer from an illness, and where those people make up a large percentage of the population, indicating there is a real issue with that illness within the population and the investment of resources to address that issue could have the greatest benefits.The scores for each of the 7 illnesses were added together then converted to a relative score between 1 – 0 (1 = worst, 0 = best), to give an overall score for each MSOA: a score close to 1 would indicate that an area has high predicted levels of all obesity/inactivity-related illnesses, and these are areas where the local population could benefit the most from interventions to address those illnesses. A score close to 0 would indicate very low predicted levels of obesity/inactivity-related illnesses and therefore interventions might not be required.LIMITATIONS1. GPs do not have catchments that are mutually exclusive from each other: they overlap, with some geographic areas being covered by 30+ practices. This dataset should be viewed in combination with the ‘Health and wellbeing statistics (GP-level, England): Missing data and potential outliers’ dataset to identify where there are areas that are covered by multiple GP practices but at least one of those GP practices did not provide data. Results of the analysis in these areas should be interpreted with caution, particularly if the levels of obesity/inactivity-related illnesses appear to be significantly lower than the immediate surrounding areas.2. GP data for the financial year 1st April 2018 – 31st March 2019 was used in preference to data for the financial year 1st April 2019 – 31st March 2020, as the onset of the COVID19 pandemic during the latter year could have affected the reporting of medical statistics by GPs. However, for 53 GPs (out of 7670) that did not submit data in 2018/19, data from 2019/20 was used instead. Note also that some GPs (997 out of 7670) did not submit data in either year. This dataset should be viewed in conjunction with the ‘Health and wellbeing statistics (GP-level, England): Missing data and potential outliers’ dataset, to determine areas where data from 2019/20 was used, where one or more GPs did not submit data in either year, or where there were large discrepancies between the 2018/19 and 2019/20 data (differences in statistics that were > mean +/- 1 St.Dev.), which suggests erroneous data in one of those years (it was not feasible for this study to investigate this further), and thus where data should be interpreted with caution. Note also that there are some rural areas (with little or no population) that do not officially fall into any GP catchment area (although this will not affect the results of this analysis if there are no people living in those areas).3. Although all of the obesity/inactivity-related illnesses listed can be caused or exacerbated by inactivity and obesity, it was not possible to distinguish from the data the cause of the illnesses in patients: obesity and inactivity are highly unlikely to be the cause of all cases of each illness. By combining the data with data relating to levels of obesity and inactivity in adults and children (see the ‘Levels of obesity, inactivity and associated illnesses: Summary (England)’ dataset), we can identify where obesity/inactivity could be a contributing factor, and where interventions to reduce obesity and increase activity could be most beneficial for the health of the local population.4. It was not feasible to incorporate ultra-fine-scale geographic distribution of populations that are registered with each GP practice or who live within each MSOA. Populations might be concentrated in certain areas of a GP practice’s catchment area or MSOA and relatively sparse in other areas. Therefore, the dataset should be used to identify general areas where there are high levels of obesity/inactivity-related illnesses, rather than interpreting the boundaries between areas as ‘hard’ boundaries that mark definite divisions between areas with differing levels of these illnesses. TO BE VIEWED IN COMBINATION WITH:This dataset should be viewed alongside the following datasets, which highlight areas of missing data and potential outliers in the data:- Health and wellbeing statistics (GP-level, England): Missing data and potential outliersDOWNLOADING THIS DATATo access this data on your desktop GIS, download the ‘Levels of obesity, inactivity and associated illnesses: Summary (England)’ dataset.DATA SOURCESThis dataset was produced using:Quality and Outcomes Framework data: Copyright © 2020, Health and Social Care Information Centre. The Health and Social Care Information Centre is a non-departmental body created by statute, also known as NHS Digital.GP Catchment Outlines. Copyright © 2020, Health and Social Care Information Centre. The Health and Social Care Information Centre is a non-departmental body created by statute, also known as NHS Digital. Data was cleaned by Ribble Rivers Trust before use.COPYRIGHT NOTICEThe reproduction of this data must be accompanied by the following statement:© Ribble Rivers Trust 2021. Analysis carried out using data that is: Copyright © 2020, Health and Social Care Information Centre. The Health and Social Care Information Centre is a non-departmental body created by statute, also known as NHS Digital.CaBA HEALTH & WELLBEING EVIDENCE BASEThis dataset forms part of the wider CaBA Health and Wellbeing Evidence Base.