https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F4967510%2F7a48d40629d55734e63d9fc4580782e1%2Fmichele-blackwell-evRB-x0TJkM-unsplash%20(1).jpg?generation=1671349108165530&alt=media" alt=""> Photo by Michele Blackwell on Unsplash

The chocolate rating dataset was scraped from flavours of cacao. The dataset comprises various chocolate bars with their ingredients. These determine the overall taste and flavour of the chocolates, which consequently affect their ratings.

The chocolate reviews are between 2006 and 2022. The dataset was last updated on June 26, 2022.

Rating Scale

The ratings are between 1 and 5 with 1 considered the lowest rating and 5 as the highest rating possible.

• 4.0 - 5.0 = Outstanding
• 3.5 - 3.9 = Highly Recommended
• 3.0 - 3.49 = Recommended
• 2.0 - 2.9 = Disappointing
• 1.0 - 1.9 = Unpleasant

Review Guide

Ratings assigned to the chocolate bars are based on the following aspects: - Flavor is the most important component of the Flavors of Cacao ratings. Diversity, balance, intensity and purity of flavors are all considered. - Texture has a great impact on the overall experience and it is also possible for texture-related issues to impact flavour. - Aftermelt is the experience after the chocolate has melted. Higher quality chocolate will linger and be long-lasting and enjoyable. - Overall Opinion is really where the ratings reflect a subjective opinion. - Other Notes- These are topics that may be interesting to discuss but may not necessarily impact the flavour or experience. For example, appearance, snap, packaging, cost etc.

Variables

There are ten variables in the dataset as follows: - REF (reference number). The highest REF numbers were the last entries made. They are not unique values - Company name or manufacturer - Company location (Country) - Date of review of the chocolate ratings - Origin of bean (Country) - Specific bean origin or bar name - Cocoa percent - Ingredients: Represents the number of ingredients in the chocolate; B = Beans, S = Sugar, S* = Sweetener other than white cane or beet sugar, C = Cocoa Butter, V = Vanilla, L = Lecithin, Sa = Salt) - Most memorable characteristics - Rating

About this dataset :chocolate_bar:

Chocolate is paramount in our society, it is a food product made from roasted and ground cacao pods, that is available as a liquid, solid or paste, on its own or as a flavoring agent in other foods. Cacao has been consumed in some form since at least the Olmec civilization (19th-11th century BCE) and the majority of Mesoamerican people - including the Maya and Aztecs - made chocolate beverages.

The first solid chocolate bar put into production was made by J. S. Fry & Sons of Bristol, England in 1847 and today it is estimated to be an USD 130.56 billion dollar industry.

In light of all of this, this dataset contains reviews on more than 2400 different chocolate bars along with metadata and information on US and Canadian based producers. A rating scale is provided and is defined as follows: - 4.0 - 5.0 = Outstanding - 3.5 - 3.9 = Highly Recommended - 3.0 - 3.4 = Recommended - 2.0 - 2.9 = Disappointing - 1.0 - 1.9 = Unpleasant

How to use this dataset

• Create a regression model to predict chocolate bar rating;
• Explore the most memorable features/ingredients associated with chocolate bars.

Highlighted Notebooks

• Your kernel can be featured here!
• More datasets

Acknowledgements

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

Citation

Manhattan Chocolate Society, Flavors of Cacao [Internet]. Available from: http://flavorsofcacao.com/

License

Public Domain

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Icon by Freepik. Photo by Universal Eye available on Unsplash.

About this dataset

Context

The dataset tabulates the population of Cocoa by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Cocoa. The dataset can be utilized to understand the population distribution of Cocoa by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Cocoa. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Cocoa.

Key observations

Largest age group (population): Male # 0-4 years (824) | Female # 60-64 years (861). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Cocoa population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Cocoa is shown in the following column.
• Population (Female): The female population in the Cocoa is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Cocoa for each age group.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Cocoa Population by Gender. You can refer the same here

Abstract Background: Cardiovascular diseases, such as acute myocardial infarction, are the main causes of death in the world. The flavonoids present in chocolate can have benefits for people who have risk factors to the development of cardiovascular diseases and have a coadjuvant effect on known therapies. Objective: To analyze the association between chocolate consumption, severity of coronary lesions, risk factors and severity of the first infarction in patients attended at the Cardiology Institute of Santa Catarina and other hospitals in the State of Santa Catarina. Methods: Subanalysis of the Catarina Heart Study cohort, evaluated 350 patients with first myocardial infarction. We evaluated clinical, echocardiographic, hemodynamic laboratorial variables. We used chi square test to evaluate qualitative variables, t student test in the case of parametric variables and U Mann Whitney test in non-parametric variables. We considered significant p < 0,05. Results: Lower prevalence of hypertension (43.2% % vs. 62.3% p = 0.003), diabetes mellitus (13.5% vs. 25.7%, p = 0.027) and smoking (24.3% vs. 37.7%, p = 0.032) among those who consume chocolate. Higher use of alcohol (40.5% vs. 26.4%, p = 0.018) and drugs (9.5% vs. 3.3%, p = 0.023) among those who consumed chocolate. Among the patients who consumed chocolate, there was a negative correlation between amount consumed and Syntax (r = -0.296, p = 0.019). Conclusion: There was association between chocolate consumption and lower prevalence of hypertension, diabetes and smoking. There was no association between amount of chocolate consumed and post-infarction ventricular function and TIMI frame count. Higher prevalence of alcohol and drug use among those who consume chocolate. Negative correlation between Syntax and the amount of chocolate consumed.

About this dataset

Context

The dataset presents the detailed breakdown of the count of individuals within distinct income brackets, categorizing them by gender (men and women) and employment type - full-time (FT) and part-time (PT), offering valuable insights into the diverse income landscapes within Cocoa. The dataset can be utilized to gain insights into gender-based income distribution within the Cocoa population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within Cocoa, among individuals aged 15 years and older with income, there were 7,034 men and 7,784 women in the workforce. Among them, 3,513 men were engaged in full-time, year-round employment, while 3,202 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 10.53% fell within the income range of under $24,999, while 11.81% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 28.47% of men in full-time roles earned incomes exceeding $100,000, while 6.46% of women in full-time positions earned within this income bracket.
• Refer to the research insights for more key observations on more income brackets ( Annual income under $24,999, Annual income between $25,000 and $49,999, Annual income between $50,000 and $74,999, Annual income between $75,000 and $99,999 and Annual income above $100,000) and employment types (full-time year-round and part-time)

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Income brackets:

• $1 to $2,499 or loss
• $2,500 to $4,999
• $5,000 to $7,499
• $7,500 to $9,999
• $10,000 to $12,499
• $12,500 to $14,999
• $15,000 to $17,499
• $17,500 to $19,999
• $20,000 to $22,499
• $22,500 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $54,999
• $55,000 to $64,999
• $65,000 to $74,999
• $75,000 to $99,999
• $100,000 or more

Variables / Data Columns

• Income Bracket: This column showcases 20 income brackets ranging from $1 to $100,000+..
• Full-Time Males: The count of males employed full-time year-round and earning within a specified income bracket
• Part-Time Males: The count of males employed part-time and earning within a specified income bracket
• Full-Time Females: The count of females employed full-time year-round and earning within a specified income bracket
• Part-Time Females: The count of females employed part-time and earning within a specified income bracket

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Cocoa median household income by race. You can refer the same here

Context

Chocolate is one of the most popular candies in the world. Each year, residents of the United States collectively eat more than 2.8 billion pounds. However, not all chocolate bars are created equal! This dataset contains expert ratings of over 1,700 individual chocolate bars, along with information on their regional origin, percentage of cocoa, the variety of chocolate bean used, and where the beans were grown.

Flavors of Cacao Rating System:

Rating Scale

4.0 - 5.0 = Outstanding 3.5 - 3.9 = Highly Recommended 3.0 - 3.49 = Recommended 2.0 - 2.9 = Disappointing 1.0 - 1.9 = Unpleasant

*Not all the bars in each range are considered equal, so to show variance from bars in the same range I have assigned .25, .50 or .75.

Each chocolate is evaluated from a combination of both objective qualities and subjective interpretation. A rating here only represents an experience with one bar from one batch. Batch numbers, vintages, and review dates are included in the database when known. I would recommend people to try all the chocolate on the database regardless of the rating and experience for themselves.

The database is narrowly focused on plain dark chocolate to appreciate the flavors of the cacao when made into chocolate. The ratings do not reflect health benefits, social missions, or organic status.

The flavor is the most important component of the Flavors of Cacao ratings. Diversity, balance, intensity, and purity of flavors are all considered. A straight forward single note chocolate can rate as high as a complex flavor profile that changes throughout. Genetics, terroir, post-harvest techniques, processing, and storage can all be discussed when considering the flavor component.

Texture has a great impact on the overall experience and it is also possible for texture related issues to impact flavor. It is a good way to evaluate the makers' vision, attention to detail, and level of proficiency.

Aftermelt is the experience after the chocolate has melted. Higher quality chocolate will linger and be long-lasting and enjoyable. Since the after melt is the last impression you get from the chocolate, it receives equal importance in the overall rating.

Overall Opinion is really where the ratings reflect a subjective opinion. Ideally, it is my evaluation of whether or not the components above worked together and opinion on the flavor development, character, and style. It is also here where each chocolate can usually be summarized by the most prominent impressions that you would remember about each chocolate

Acknowledgements

These ratings were compiled by Brady Brelinski, Founding Member of the Manhattan Chocolate Society. For up-to-date information, as well as additional content (including interviews with craft chocolate makers), please see his website: Flavors of Cacao

Inspiration

We have multiple questions to answer, in the below list we answer most important pieces of information that possible to answer.
1. Where are the best cocoa beans grown? 2. Which countries produce the highest-rated bars? 3. Who creates the best Chocolate bars? 4. What is Favorite taste? 5. Which company has highest Rate?

About this dataset

Context

The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in Cocoa. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

Key observations: Insights from 2023

Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Cocoa, the median income for all workers aged 15 years and older, regardless of work hours, was $39,255 for males and $30,331 for females.

These income figures indicate a substantial gender-based pay disparity, showcasing a gap of approximately 23% between the median incomes of males and females in Cocoa. With women, regardless of work hours, earning 77 cents to each dollar earned by men, this income disparity reveals a concerning trend toward wage inequality that demands attention in thecity of Cocoa.

- Full-time workers, aged 15 years and older: In Cocoa, among full-time, year-round workers aged 15 years and older, males earned a median income of $53,058, while females earned $44,500, leading to a 16% gender pay gap among full-time workers. This illustrates that women earn 84 cents for each dollar earned by men in full-time roles. This analysis indicates a widening gender pay gap, showing a substantial income disparity where women, despite working full-time, face a more significant wage discrepancy compared to men in the same roles.

Remarkably, across all roles, including non-full-time employment, women displayed a similar gender pay gap percentage. This indicates a consistent gender pay gap scenario across various employment types in Cocoa, showcasing a consistent income pattern irrespective of employment status.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Gender classifications include:

• Male
• Female

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Variables / Data Columns

• Year: This column presents the data year. Expected values are 2010 to 2023
• Male Total Income: Annual median income, for males regardless of work hours
• Male FT Income: Annual median income, for males working full time, year-round
• Male PT Income: Annual median income, for males working part time
• Female Total Income: Annual median income, for females regardless of work hours
• Female FT Income: Annual median income, for females working full time, year-round
• Female PT Income: Annual median income, for females working part time

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Cocoa median household income by race. You can refer the same here

Baseline demographic status of 15 healthy, pain-free women and 15 healthy, pain-free age-matched men, i.e. before injection of any of the substances.

About this dataset

Context

The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in Cocoa Beach. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

Key observations: Insights from 2023

Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Cocoa Beach, the median income for all workers aged 15 years and older, regardless of work hours, was $59,472 for males and $34,247 for females.

These income figures highlight a substantial gender-based income gap in Cocoa Beach. Women, regardless of work hours, earn 58 cents for each dollar earned by men. This significant gender pay gap, approximately 42%, underscores concerning gender-based income inequality in the city of Cocoa Beach.

- Full-time workers, aged 15 years and older: In Cocoa Beach, among full-time, year-round workers aged 15 years and older, males earned a median income of $87,882, while females earned $68,750, leading to a 22% gender pay gap among full-time workers. This illustrates that women earn 78 cents for each dollar earned by men in full-time roles. This analysis indicates a widening gender pay gap, showing a substantial income disparity where women, despite working full-time, face a more significant wage discrepancy compared to men in the same roles.

Surprisingly, the gender pay gap percentage was higher across all roles, including non-full-time employment, for women compared to men. This suggests that full-time employment offers a more equitable income scenario for women compared to other employment patterns in Cocoa Beach.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Gender classifications include:

• Male
• Female

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Variables / Data Columns

• Year: This column presents the data year. Expected values are 2010 to 2023
• Male Total Income: Annual median income, for males regardless of work hours
• Male FT Income: Annual median income, for males working full time, year-round
• Male PT Income: Annual median income, for males working part time
• Female Total Income: Annual median income, for females regardless of work hours
• Female FT Income: Annual median income, for females working full time, year-round
• Female PT Income: Annual median income, for females working part time

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Cocoa Beach median household income by race. You can refer the same here

Chocolate Chip Cookie Recipes

Ingredients, Instructions, & Ratings

By data.world's Admin [source]

About this dataset

This dataset contains all of the data used in The Pudding essay Baking the Most Average Chocolate Chip Cookie, exploring three unique recipes for crafting the perfect chocolate chip cookie. Through scraping and text processing methods, we uncover predictive text, neural network and mathematical average cookies. With a variety of measurements, units and ingredients, this dataset offers insight into an exploration of a classic dessert treat favorite.

The ingredients listed are precise and must be adhered to in order to receive an accurate representation of the taste of each individual recipe. Those attempting to replicate these recipes may find that some uncertainty is involved as instructions sometimes reference ingredients which were never listed or that were not used as part of the overall instruction set. However despite this risk, users can still access a variety detailed information about each recipe to follow when attempting their creations at home!

Making use of this data will enable users to explore various aspects about chocolate chip cookies; from rescaling servings to unlock unique attributes about each recipe or analyzing ratings for results, there is something here for people from all walks on life who enjoy baking or just eating sweets! So take a plunge into our extensive dataset donned with comprehensive details - you may just discover your new favorite cookie recipe! Data available under MIT License; contact Elle O'Brien with any questions regarding this dataset

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

How to Use This Dataset

The dataset includes 6 columns: 'Ingredient', 'Rating', 'Quantity', 'Unit'. Each column provides detailed information on several unique cookie recipes.

The Ingredient column contains the names of each ingredient that corresponds to a recipe in the data set. The Quantity column lists how much of that particular ingredient is needed in order for a recipe to successfully turn out. The Unit field specifies what unit (teaspoon, cup etc.) should be used when measuring out each ingredient. Finally, the Rating field shows how testers rated any given recipe; each rating is based on a scale from -4 (very bad) to 4 (very good).

Using this dataset can help you create yummy chocolate chip cookies without fail! Now let's get baking!

Research Ideas

• Using machine learning models to suggest new recipes based on existing ones or predict the rating of a recipe given its ingredients and instructions.
• Creating an interactive tool that allows users to easily find a particular cookie recipe such as one with specific dietary requirements, specific ingredient quantities, or even limit searching for cookies according to their ratings

Acknowledgements

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

License

See the dataset description for more information.

Columns

File: choc_chip_cookie_ingredients.csv | Column name | Description | |:---------------|:-----------------------------------------------------------| | Ingredient | The name of the ingredient used in the recipe. (String) | | Text | The instructions for the recipe. (String) | | Rating | The rating given to the recipe. (Integer) | | Quantity | The amount of the ingredient used in the recipe. (Integer) | | Unit | The unit of measurement used for the ingredient. (String) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit data.world's Admin.

This dataset provides a comprehensive overview of consumer preferences and willingness to pay for Fair Trade chocolate versus normal chocolate. The data is sourced from a survey conducted in 2023 and offers detailed insights into buying behavior, attitudes towards Fair Trade products, and demographic factors. The dataset is structured to include key metrics such as buying frequency, reasons for selecting Fair Trade or normal chocolate, important purchase criteria, willingness to pay more for Fair Trade products, and demographic information, providing a robust foundation for analyzing consumer behavior and market trends.

Key Features

• Buying Frequency: The dataset includes information on how frequently respondents buy bar chocolate, offering insights into purchase patterns.
• Reasons for Selection: Information is presented by reasons for selecting Fair Trade or normal chocolate, allowing for detailed analysis of consumer attitudes and motivations.
• Purchase Criteria: Data is segmented by important purchase criteria, such as brand, taste, country of origin, price, and sustainable behavior, providing insights into consumer priorities.
• Willingness to Pay: The dataset includes metrics on how much more respondents are willing to spend on Fair Trade chocolate, offering insights into price sensitivity and willingness to support sustainable products.
• Demographic Information: Information is categorized by gender, income, and country of origin, enabling analysis of demographic influences on consumer behavior.

Potential Uses

• Market Research: Assist in understanding consumer preferences and market trends for Fair Trade products, which is crucial for product development and marketing strategies.
• Consumer Behavior Analysis: Support consumer behavior analysis by providing detailed data on buying behavior, attitudes, and motivations.
• Policy Development: Inform policymakers in developing and adjusting policies related to Fair Trade and sustainable products.
• Strategic Planning: Provide insights into consumer behavior and market trends, informing strategic planning for businesses and policymakers.
• Academic Research: Enable academic research on consumer behavior, market trends, and the impact of Fair Trade products.

About this dataset

Context

The dataset presents the detailed breakdown of the count of individuals within distinct income brackets, categorizing them by gender (men and women) and employment type - full-time (FT) and part-time (PT), offering valuable insights into the diverse income landscapes within Cocoa Beach. The dataset can be utilized to gain insights into gender-based income distribution within the Cocoa Beach population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within Cocoa Beach, among individuals aged 15 years and older with income, there were 4,944 men and 4,508 women in the workforce. Among them, 1,930 men were engaged in full-time, year-round employment, while 1,411 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 8.65% fell within the income range of under $24,999, while 7.73% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 39.27% of men in full-time roles earned incomes exceeding $100,000, while 22.54% of women in full-time positions earned within this income bracket.
• Refer to the research insights for more key observations on more income brackets ( Annual income under $24,999, Annual income between $25,000 and $49,999, Annual income between $50,000 and $74,999, Annual income between $75,000 and $99,999 and Annual income above $100,000) and employment types (full-time year-round and part-time)

https://i.neilsberg.com/ch/cocoa-beach-fl-income-distribution-by-gender-and-employment-type.jpeg" alt="Cocoa Beach, FL gender and employment-based income distribution analysis (Ages 15+)">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Income brackets:

• $1 to $2,499 or loss
• $2,500 to $4,999
• $5,000 to $7,499
• $7,500 to $9,999
• $10,000 to $12,499
• $12,500 to $14,999
• $15,000 to $17,499
• $17,500 to $19,999
• $20,000 to $22,499
• $22,500 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $54,999
• $55,000 to $64,999
• $65,000 to $74,999
• $75,000 to $99,999
• $100,000 or more

Variables / Data Columns

• Income Bracket: This column showcases 20 income brackets ranging from $1 to $100,000+..
• Full-Time Males: The count of males employed full-time year-round and earning within a specified income bracket
• Part-Time Males: The count of males employed part-time and earning within a specified income bracket
• Full-Time Females: The count of females employed full-time year-round and earning within a specified income bracket
• Part-Time Females: The count of females employed part-time and earning within a specified income bracket

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Cocoa Beach median household income by gender. You can refer the same here

Context

What’s the best (or at least the most popular) Halloween candy? That was the question this dataset was collected to answer. Data was collected by creating a website where participants were shown presenting two fun-sized candies and asked to click on the one they would prefer to receive. In total, more than 269 thousand votes were collected from 8,371 different IP addresses.

Content

candy-data.csv includes attributes for each candy along with its ranking. For binary variables, 1 means yes, 0 means no. The data contains the following fields:

• chocolate: Does it contain chocolate?
• fruity: Is it fruit flavored?
• caramel: Is there caramel in the candy?
• peanutalmondy: Does it contain peanuts, peanut butter or almonds?
• nougat: Does it contain nougat?
• crispedricewafer: Does it contain crisped rice, wafers, or a cookie component?
• hard: Is it a hard candy?
• bar: Is it a candy bar?
• pluribus: Is it one of many candies in a bag or box?
• sugarpercent: The percentile of sugar it falls under within the data set.
• pricepercent: The unit price percentile compared to the rest of the set.
• winpercent: The overall win percentage according to 269,000 matchups.

Acknowledgements:

This dataset is Copyright (c) 2014 ESPN Internet Ventures and distributed under an MIT license. Check out the analysis and write-up here: The Ultimate Halloween Candy Power Ranking. Thanks to Walt Hickey for making the data available.

Inspiration:

• Which qualities are associated with higher rankings?
• What’s the most popular candy? Least popular?
• Can you recreate the 538 analysis of this dataset?

Content

Candy Power Ranking

This folder contains the data behind the story The Ultimate Halloween Candy Power Ranking.

candy-data.csv includes attributes for each candy along with its ranking. For binary variables, 1 means yes, 0 means no.

The data contains the following fields:

Header	Description
chocolate	Does it contain chocolate?
fruity	Is it fruit flavored?
caramel	Is there caramel in the candy?
peanutalmondy	Does it contain peanuts, peanut butter or almonds?
nougat	Does it contain nougat?
crispedricewafer	Does it contain crisped rice, wafers, or a cookie component?
hard	Is it a hard candy?
bar	Is it a candy bar?
pluribus	Is it one of many candies in a bag or box?
sugarpercent	The percentile of sugar it falls under within the data set.
pricepercent	The unit price percentile compared to the rest of the set.
winpercent	The overall win percentage according to 269,000 matchups.

Context

This is a dataset from FiveThirtyEight hosted on their GitHub. Explore FiveThirtyEight data using Kaggle and all of the data sources available through the FiveThirtyEight organization page!

• Update Frequency: This dataset is updated daily.

Acknowledgements

This dataset is maintained using GitHub's API and Kaggle's API.

This dataset is distributed under the Attribution 4.0 International (CC BY 4.0) license.

Cover photo by Jeff Frenette on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

✍️ Personal Note: While I'm presenting this dataset for analysis and insights, I want to emphasize the importance of ethical sourcing and consumption, especially in commodities like cocoa and coffee which have known ethical concerns in their supply chains.

About This Dataset:

This dataset delivers an extensive and current assortment of futures related to soft commodities. Futures are financial contracts obligating the buyer to purchase, and the seller to sell, a specified amount of a particular commodity at a predetermined price on a set date in the future.

Use Cases: 1. Price Forecasting: Harness machine learning to predict the price dynamics of commodities like coffee and cocoa, aiding stakeholders in their decision-making. 2. Supply Chain Analysis: Evaluate the correlation between futures prices and global events, offering insights into potential supply chain disruptions. 3. Demand Projections: Utilize deep learning techniques to correlate historical consumption patterns with price movements, projecting future demand.

Dataset Image Source: Photo by Tom Fisk from Pexels: https://www.pexels.com/photo/aerial-shot-of-green-milling-tractor-1595108/

Column Descriptions: 1. Date: The date when the data was recorded. Format: YYYY-MM-DD. 2. Open: The opening market price for the day. 3. High: Maximum price achieved during the trading session. 4. Low: Lowest traded price during the session. 5. Close: Market's concluding price. 6. Volume: Count of contracts traded throughout the session. 7. Ticker: Distinct market quotation symbol for the commodity future. 8. Commodity: Indicates the type of soft commodity the futures contract pertains to (e.g., Cocoa, Coffee).

Remember to link to the correct image source for your dataset's image!

Context:

This data was gathered as part of the data mining project for General Assembly Data Science Immersive course.

Dataset Description:

For this project, I choose the Iherb website. this website is famous around the world for groceries and health care products for a reasonable price. One of the benefits of these products it has good customer services and you can find also a lot of customer's experience with its products. On this website, the data were gathered from 12 different categories under the groceries section: Tea, Coffee, Cocoa, Coconut, Chocolate & Candies, Snack Bars, Vinegar & Oils, Seeds & Nuts, Spices, Breakfast, Fruit & & Vegetables, and Flour mixs

The quality column was self-explanatory, from this link How to Measure the Quality of Your Product, I defined the quality of the products based on the customer's satisfaction and rating on the products

Data Dictionary:

• product id: The product unique id
• product name: The product name
• product company: The product company name
• product flavour: The type of the product and what it's contained
• category: The product category
• product size: The product size in grams
• product price: The product price or if it's not available
• product rate: The product rate based on customer satisfaction from 1 to 5
• one star: Number of customers who rate the product one star
• two star: Number of customers who rate the product two stars
• three star: Number of customers who rate the product three stars
• four star: Number of customers who rate the product four stars
• five star: Number of customers who rate the product five stars
• reviews number: The total number of customers review in for product
• product quality: The quality of the products based on customers satisfaction and the rate of the product

Inspiration:

• Product Quality: Do you enjoy a good quality product? why you don't let the machine discover which products are excellent for you.
• prices prediction: why not play around more and predict the prices of the products based on the giving features
• Feature Engineering: be creative and create new features from this dataset

Acknowledgement:

thanks to Iherb website for this amazing data.

Contract months: Mar (H), May (K), Jul (N), Sep (U), Dec (Z)

Contract description can be found at: https://www.theice.com/products/7/Cocoa-Futures

Note that Open Interest is always reported for the previous trading day, to avoid lookahead bias.