About this dataset

Context

The dataset presents median household incomes for various household sizes in Upper St. Clair Township, Pennsylvania, as reported by the U.S. Census Bureau. The dataset highlights the variation in median household income with the size of the family unit, offering valuable insights into economic trends and disparities within different household sizes, aiding in data analysis and decision-making.

Key observations

• Of the 7 household sizes (1 person to 7-or-more person households) reported by the census bureau, Upper St. Clair township did not include 7-person households. Across the different household sizes in Upper St. Clair township the mean income is $175,211, and the standard deviation is $72,205. The coefficient of variation (CV) is 41.21%. This high CV indicates high relative variability, suggesting that the incomes vary significantly across different sizes of households. Please note that the U.S. Census Bureau uses $250,001 as a JAM value to report incomes of $250,000 or more. In the case of Upper St. Clair township, there were 1 household sizes where the JAM values were used. Thus, the numbers for the mean and standard deviation may not be entirely accurate and have a higher possibility of errors. However, to obtain an approximate estimate, we have used a value of $250,001 as the income for calculations, as reported in the datasets by the U.S. Census Bureau.
• In the most recent year, 2023, The smallest household size for which the bureau reported a median household income was 1-person households, with an income of $55,843. It then further increased to $250,001 for 6-person households, the largest household size for which the bureau reported a median household income.

Content

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

Household Sizes:

• 1-person households
• 2-person households
• 3-person households
• 4-person households
• 5-person households
• 6-person households
• 7-or-more-person households

Variables / Data Columns

• Household Size: This column showcases 7 household sizes ranging from 1-person households to 7-or-more-person households (As mentioned above).
• Median Household Income: Median household income, in 2023 inflation-adjusted dollars for the specific household size.

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 Upper St. Clair township median household income. You can refer the same here

The Forbes published "The Definitive Ranking of the Wealthiest Americans In 2022". And here is the top 200 list including additional information on each of the billionaires.

About Dataset

This dataset contains the top 200 richest American based on their net worth.

Columns Attributes

| Column | Meaning | | -- | -- | | rank | their rank | | name | their name | | net worth | their net worth | | age | their age | | title | their title (e.g. CEO, Chairman etc.) | | source of wealth | the source of how they've managed to get this much money | | self made score | shows how far each of these billionaires has climbed to make it to the top. According to Forbes, The score ranges from 1 to 10, with 1 through 5 indicating someone who inherited some or all of his or her fortune; while 6 through 10 are for those who built their company or established a fortune on his or her own. | | philanthropy score | this score shows how much these billionaires donates on nonprofits foundations | | residence | their residence | | marital status | their marital status | | children | their children | | education | their education |

Methodology

This dataset was acquired using a web scraping tool called Beautiful soup and scraped Forbes website

Image by pch.vector on Freepik

Description

This dataset contains statistics on the world's billionaires, including information about their businesses, industries, and personal details. It provides insights into the wealth distribution, business sectors, and demographics of billionaires worldwide.

Key Features

• rank: The ranking of the billionaire in terms of wealth.
• finalWorth: The final net worth of the billionaire in U.S. dollars.
• category: The category or industry in which the billionaire's business operates.
• personName: The full name of the billionaire.
• age: The age of the billionaire.
• country: The country in which the billionaire resides.
• city: The city in which the billionaire resides.
• source: The source of the billionaire's wealth.
• industries: The industries associated with the billionaire's business interests.
• countryOfCitizenship: The country of citizenship of the billionaire.
• organization: The name of the organization or company associated with the billionaire.
• selfMade: Indicates whether the billionaire is self-made (True/False).
• status: "D" represents self-made billionaires (Founders/Entrepreneurs) and "U" indicates inherited or unearned wealth.
• gender: The gender of the billionaire.
• birthDate: The birthdate of the billionaire.
• lastName: The last name of the billionaire.
• firstName: The first name of the billionaire.
• title: The title or honorific of the billionaire.
• date: The date of data collection.
• state: The state in which the billionaire resides.
• residenceStateRegion: The region or state of residence of the billionaire.
• birthYear: The birth year of the billionaire.
• birthMonth: The birth month of the billionaire.
• birthDay: The birth day of the billionaire.
• cpi_country: Consumer Price Index (CPI) for the billionaire's country.
• cpi_change_country: CPI change for the billionaire's country.
• gdp_country: Gross Domestic Product (GDP) for the billionaire's country.
• gross_tertiary_education_enrollment: Enrollment in tertiary education in the billionaire's country.
• gross_primary_education_enrollment_country: Enrollment in primary education in the billionaire's country.
• life_expectancy_country: Life expectancy in the billionaire's country.
• tax_revenue_country_country: Tax revenue in the billionaire's country.
• total_tax_rate_country: Total tax rate in the billionaire's country.
• population_country: Population of the billionaire's country.
• latitude_country: Latitude coordinate of the billionaire's country.
• longitude_country: Longitude coordinate of the billionaire's country.

Potential Use Cases

• Wealth distribution analysis: Explore the distribution of billionaires' wealth across different industries, countries, and regions.
• Demographic analysis: Investigate the age, gender, and birthplace demographics of billionaires.
• Self-made vs. inherited wealth: Analyze the proportion of self-made billionaires and those who inherited their wealth.
• Economic indicators: Study correlations between billionaire wealth and economic indicators such as GDP, CPI, and tax rates.
• Geospatial analysis: Visualize the geographical distribution of billionaires and their wealth on a map.
• Trends over time: Track changes in billionaire demographics and wealth over the years.

If this was helpful, a vote is appreciated 🙂❤️

How sensitive to business cycles are the earnings of top earners? And, how does the business cycle sensitivity of top earners vary by industry? We use a confidential dataset on earnings histories of US males from the Social Security Administration. On average, individuals in the top 1 percent of the earnings distribution are slightly more cyclical than the population average. But there are large differences across sectors; top earners in Finance, Insurance, and Real Estate (FIRE) and Construction face substantial business cycle volatility, whereas those in Services (who make up 40 percent of individuals in the top 1 percent) have earnings that are less cyclical than the average worker.

About this dataset

Context

The dataset presents median household incomes for various household sizes in Upper Nyack, NY, as reported by the U.S. Census Bureau. The dataset highlights the variation in median household income with the size of the family unit, offering valuable insights into economic trends and disparities within different household sizes, aiding in data analysis and decision-making.

Key observations

• Of the 7 household sizes (1 person to 7-or-more person households) reported by the census bureau, Upper Nyack did not include 6, or 7-person households. Across the different household sizes in Upper Nyack the mean income is $188,531, and the standard deviation is $92,073. The coefficient of variation (CV) is 48.84%. This high CV indicates high relative variability, suggesting that the incomes vary significantly across different sizes of households. Please note that the U.S. Census Bureau uses $250,001 as a JAM value to report incomes of $250,000 or more. In the case of Upper Nyack, there were 1 household sizes where the JAM values were used. Thus, the numbers for the mean and standard deviation may not be entirely accurate and have a higher possibility of errors. However, to obtain an approximate estimate, we have used a value of $250,001 as the income for calculations, as reported in the datasets by the U.S. Census Bureau.
• In the most recent year, 2023, The smallest household size for which the bureau reported a median household income was 1-person households, with an income of $28,333. It then further increased to $250,001 for 5-person households, the largest household size for which the bureau reported a median household income.

Content

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

Household Sizes:

• 1-person households
• 2-person households
• 3-person households
• 4-person households
• 5-person households
• 6-person households
• 7-or-more-person households

Variables / Data Columns

• Household Size: This column showcases 7 household sizes ranging from 1-person households to 7-or-more-person households (As mentioned above).
• Median Household Income: Median household income, in 2023 inflation-adjusted dollars for the specific household size.

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 Upper Nyack median household income. You can refer the same here

Income of individuals by age group, sex and income source, Canada, provinces and selected census metropolitan areas, annual.

A dataset listing the richest zip codes in California per the most current US Census data, including information on rank and average income.

Content

This dataset consists of top most billionaires in the world and respective their names, whether it is a finance company or any software company, how much money they have ,these all the details which are in the dataset

Researchers have compiled a multi-decade database of the super-rich. Building off the Forbes World’s Billionaires lists from 1996-2014, scholars at Peterson Institute for International Economics have added a couple dozen more variables about each billionaire - including whether they were self-made or inherited their wealth. (Roughly half of European billionaires and one-third of U.S. billionaires got a significant financial boost from family, the authors estimate.)

Acknowledgements

Reference : https://corgis-edu.github.io/corgis/csv/billionaires/

Inspiration

Some of the datasets which I have seen in the kaggle or somewhere but it is limited to less number of columns . Kagglers are not able to get an insights from very low amount of data. so that I decided that to be more helpful to them or we can able to get an more insights from this dataset

This dataset contains 1,000 financial records with five key features and one target variable, Loan Default Risk. It is designed for credit risk analysis, helping to predict whether a customer is likely to default on a loan based on financial attributes.

Income: The individual's annual income. Credit Score: A credit rating score ranging from 300 to 850, where higher values indicate better creditworthiness. Spending Score: A normalized score between 0 and 100, representing the individual's spending habits. Transaction Count: The number of transactions made by the individual in a given period. Savings Ratio: The ratio of savings to income, ranging from 0 to 1. Loan Default Risk (Target): 0: Low risk (likely to repay the loan). 1: High risk (likely to default on the loan).

Feel free to use this dataset for research, projects, or educational purposes. If you use it in a publication, kindly provide attribution.

This dataset was synthetically generated. The features were adjusted to resemble real-world financial data, but they do not represent actual individuals or real financial records.

This dataset provides information on customers, including demographics, spending habits, and interactions with marketing campaigns. Key variables include customer ID, birth year, education, marital status, income, household composition, enrollment date, recency of purchases, complaints, campaign acceptances, and various spending categories. Analysis of this data can yield insights into customer behavior, campaign effectiveness, and overall business performance.

Columns in this dataset:

Data Dictionary

ID: Customer's unique identifier Year_Birth: Customer's birth year Education: Customer's education level Marital_Status: Customer's marital status Income: Customer's yearly household income Kidhome: Number of children in customer's household Teenhome: Number of teenagers in customer's household Dt_Customer: Date of customer's enrollment with the company Recency: Number of days since customer's last purchase Complain: 1 if the customer complained in the last 2 years, 0 otherwise MntWines: Amount spent on wine in last 2 years MntFruits: Amount spent on fruits in last 2 years MntMeatProducts: Amount spent on meat in last 2 years MntFishProducts: Amount spent on fish in last 2 years MntSweetProducts: Amount spent on sweets in last 2 years MntGoldProds: Amount spent on gold in last 2 years NumDealsPurchases: Number of purchases made with a discount AcceptedCmp1: 1 if customer accepted the offer in the 1st campaign, 0 otherwise AcceptedCmp2: 1 if customer accepted the offer in the 2nd campaign, 0 otherwise AcceptedCmp3: 1 if customer accepted the offer in the 3rd campaign, 0 otherwise AcceptedCmp4: 1 if customer accepted the offer in the 4th campaign, 0 otherwise AcceptedCmp5: 1 if customer accepted the offer in the 5th campaign, 0 otherwise Response: 1 if customer accepted the offer in the last campaign, 0 otherwise NumWebPurchases: Number of purchases made through the company’s website NumCatalogPurchases: Number of purchases made using a catalogue NumStorePurchases: Number of purchases made directly in stores NumWebVisitsMonth: Number of visits to company’s website in the last month

County-Level Socioeconomic and Cancer Clinical Trial Data

Examining Possible Correlations

By Noah Rippner [source]

About this dataset

This dataset offers an unprecedented opportunity to explore the relationship between socioeconomic status and cancer clinical trials. By combining data from multiple open sources, this project will reveal how factors such as poverty rate, population estimate, median income, cancer incidence rate and mortality rate can impact the number of cancer-related clinical trials in different counties across the United States. By studying these powerful correlations and associations at both individual county and zip code level, we’ll gain better insight into finding solutions for reducing disparities in healthcare regarding access to cancer treatments. We hope that by encourging researchers to use this valuable dataset in their own studies for furthering efforts on understanding how these various socioeconomic factors play a role in impacting overall care accessibility for those affected by cancers of all types across America

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset includes data from multiple sources such as the US Census Bureau, ClinicalTrials.gov, and other sources for counties in the US from 2010-2015. The data includes fields related to population estimates; median household income; poverty estimates and percentages; cancer incidence rates per capita; cancer mortality rates per capita; number of clinical trials in each county (studyCount); number of clinical trials per capita (StudyPerCap); binned median income deciles (binnedInc), summarization level (SUMLEV), region of the US (REGION), division of the US (DIVISION), state of the US (STATE), county name(CTYNAME).

Using this dataset you can explore relationships between socioeconomic status and cancer by combining data from different sources into one cohesive package. You can compare factors such as poverty rates with average annual count or death rate per 100,000 people in each county. You can also look at correlations between binned median household incomes deciles across different states ad regions within a state as well as look at it on a nationwide basis by comparing level sumlevs 1(National)and 40(County). Additionally an interesting analysis could be done using census population estimates over time compared with net migration over time to examine changes in population size vs foreign vs domestic movement into/out of areas based on economic conditions etc.

Here are some steps you could take to analyze this dataset :

• Download all available datasets including 'data dictionary' file (.csv & .xml) containing study_fields related info from ClinicalTrails.gov

• Futher consolidate all census/income by county information into a single csv file ('cen_income')

• Consolidate FIPS/ZCTA information('fips_zip_x')

• Aggregate census population ('census_county_population')by county over time into 'countyData' csv file with features aggregated either by mean or count dependent upon feature type

• loading all files into pandas . DataFrames

• Scanning through summary statistics for fields like studyCount , avgAnnCount , avgDeathsPerYear , deathRate , incindence rate etc.. finding correlation coefficients between these

Research Ideas

• Using this dataset, one could develop personalized cancer treatment plans for patients based on factors such as the patient's poverty level, population size of the county, median household income, and number of clinical trials in the area.
• This dataset can be used to create an interactive map of cancer mortality rates by county. These maps could provide valuable information to epidemiologists looking to quantify disease prevalence across different regions and demographic groups in different parts of the country.
• This dataset could be leveraged to compare the incidence and mortality of various types of cancers in different counties to better inform public health prevention measures at a local level. For example, counties with high levels of lung cancer incidence/mortality might consider targeted smoking cessation campaigns or other interventions specific to that population rather than adopting a generic one size fits all approach

Acknowledgements

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

License

License: Dataset copyright by authors - You are free t...

About this dataset

Context

The dataset presents median household incomes for various household sizes in Upper Darby Township, Pennsylvania, as reported by the U.S. Census Bureau. The dataset highlights the variation in median household income with the size of the family unit, offering valuable insights into economic trends and disparities within different household sizes, aiding in data analysis and decision-making.

Key observations

• Of the 7 household sizes (1 person to 7-or-more person households) reported by the census bureau, all of the household sizes were found in Upper Darby township. Across the different household sizes in Upper Darby township the mean income is $86,071, and the standard deviation is $21,321. The coefficient of variation (CV) is 24.77%. This high CV indicates high relative variability, suggesting that the incomes vary significantly across different sizes of households.
• In the most recent year, 2023, The smallest household size for which the bureau reported a median household income was 1-person households, with an income of $40,929. It then further increased to $85,810 for 7-person households, the largest household size for which the bureau reported a median household income.

Content

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

Household Sizes:

• 1-person households
• 2-person households
• 3-person households
• 4-person households
• 5-person households
• 6-person households
• 7-or-more-person households

Variables / Data Columns

• Household Size: This column showcases 7 household sizes ranging from 1-person households to 7-or-more-person households (As mentioned above).
• Median Household Income: Median household income, in 2023 inflation-adjusted dollars for the specific household size.

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 Upper Darby township median household income. You can refer the same here

Context:

This dataset is a modified version of the California Housing Data used in the research paper by Pace and Barry, titled "Sparse spatial autoregressions" (1997). It serves as an excellent introductory dataset for implementing machine learning algorithms due to its need for basic data cleaning, clear variable names, and an optimal size that strikes a balance between simplicity and complexity.

The data provides information from the 1990 California census. While it may not be suitable for predicting current housing prices like the Zillow Zestimate dataset, it does offer an accessible platform for teaching the fundamentals of machine learning.

Several modifications were made to the original data. A "Cities" column was added using Geopandas. Additionally, distances were calculated using the Haversine formula based on longitude and latitude coordinates, providing measurements in kilometers.

The dataset contains details about houses in specific California districts, along with summary statistics derived from the 1990 census. The columns are self-explanatory:

1. Median House Value: Median value of houses in a block (in US Dollars) [$]
2. Median Income: Median income of households in a block (in tens of thousands of US Dollars) [10k$]
3. Median Age: Median age of houses in a block (in years)
4. Total Rooms: Total number of rooms in a block
5. Total Bedrooms: Total number of bedrooms in a block
6. Population: Total number of residents in a block
7. Households: Total number of households in a block
8. Latitude: Indicates how far north a house is (higher value means farther north) [°]
9. Longitude: Indicates how far west a house is (higher value means farther west) [°]
10. Distance to Coast: Distance to the nearest coastal point [m]
11. Distance to Los Angeles: Distance to the center of Los Angeles [m]
12. Distance to San Diego: Distance to the center of San Diego [m]
13. Distance to San Jose: Distance to the center of San Jose [m]
14. Distance to San Francisco: Distance to the center of San Francisco [m]
15. The county in which the household resides.

• The dataset was enhanced by a data scientist, fedesoriano, who added five new features and performed data cleaning. The original dataset (without distance features) can be found at the provided link.

Source:

The one who added the 5 new features and cleaned the data: https://www.kaggle.com/datasets/fedesoriano/california-housing-prices-data-extra-features fedesoriano Data Scientist at Kaggle Madrid, Community of Madrid, Spain

The original data (without the distance features) was initially featured in the following paper: Pace, R. Kelley, and Ronald Barry. "Sparse spatial autoregressions." Statistics & Probability Letters 33.3 (1997): 291-297.

The original dataset can be found under the following link: https://www.dcc.fc.up.pt/~ltorgo/Regression/cal_housing.html

The Western Interior Plains aquifer system is located in parts of Arkansas, Colorado, Kansas, Missouri, Nebraska, New Mexico, Oklahoma, South Dakota, Texas, and Wyoming and covers an area of more than 220,800 square miles. The thickness of the aquifer system ranges from 500 feet in eastern Colorado (HA 730-D) to as much as 10,000 feet in western Oklahoma (PP_1414B). This aquifer system consists of water-bearing dolostone, limestone, and shale and overlies the basement confining unit in the western part of the Interior Plains physiographic division. This aquifer system consists of the upper aquifer unit (A1), a middle confining unit, and the lower aquifer unit (A2). The aquifer system is overlain by the Western Interior Plains confining system and is underlain by the Basement confining unit (PP_1414B). This product provides source data for the Western Interior Plains aquifer system framework, including: Georeferenced images: 1. i_63WIP_A1_top.tif: Digitized figure of extent and altitude contour lines representing the top of the Western Interior Plains aquifer system upper unit. The original figure was from PP_1414B plate 6. 2. i_63WIP_A1_bot.tif: Digitized figure of altitude contour lines of the top of the confining unit between the upper and lower units of the Western Interior Plains aquifer system. This figure was used to construct the bottom contour lines of the upper aquifer unit. The original figure was from PP_1414B plate 5. 3. i_63WIP_A2_top.tif: Digitized figure of extent and altitude contour lines representing the top of the Western Interior Plains aquifer system lower unit. The original figure was from PP_1414B plate 4. 4. i_63WIP_A2_bot.tif: Digitized figure of altitude contour lines of the top of the basement confining unit of the Western Interior Plains aquifer system. This figure was used to construct the bottom contour lines of the lower aquifer unit. The original figure was from PP_1414B plate 3. Extent shapefiles: 1. p_63WIP.shp: Polygon shapefile containing the areal extent of the Western Interior Plains aquifer system sourced from PP_1414B plates 4 and 6. The extent file contains the upper and lower subunits A1 and A2. Contour line shapefiles: 1. c_63WIP_A1_top.shp: Contour line dataset containing altitude values, in feet reference to National Geodetic Vertical Datum of 1929 (NGVD29), of the top of the Western Interior Plains aquifer system upper unit. These data were used to create the ra_63WIP_A1_top.tif dataset. 2. c_63WIP_A1_bot.shp: Contour line dataset containing altitude values, in feet reference to NGVD29, of the bottom of the Western Interior Plains aquifer system upper unit. These data were used to create the ra_63WIP_A1_bot.tif dataset. 3. c_63WIP_A2_top.shp: Contour line dataset containing altitude values, in feet reference to NGVD29, of the top of the Western Interior Plains aquifer system lower unit. These data were used to create the ra_63WIP_A2_top.tif dataset. 4. c_63WIP_A2_bot.shp: Contour line dataset containing altitude values, in feet reference to NGVD29, of the bottom of the Western Interior Plains aquifer system lower unit. These data were used to create the ra_63WIP_A2_bot.tif dataset. Altitude raster files: 1. ra_63WIP_A1_top.tif: Altitude raster dataset of the top of the Western Interior Plains aquifer system upper unit. The altitude values are in meters reference to North American Vertical Datum of 1988 (NAVD88). This raster was interpolated from contour line shapefile c_63WIP_A1_top.shp. 2. ra_63WIP_A1_bot.tif: Altitude raster dataset of the bottom of the Western Interior Plains aquifer system upper unit. The altitude values are in meters reference to NAVD88. This raster was interpolated from contour line shapefile c_63WIP_A1_bot.shp. 3. ra_63WIP_A2_top.tif: Altitude raster dataset of the top of the Western Interior Plains aquifer system lower unit. The altitude values are in meters reference to NAVD88. This raster was interpolated from contour line shapefile c_63WIP_A2_top.shp. 4. ra_63WIP_A2_bot.tif: Altitude raster dataset of the bottom of the Western Interior Plains aquifer system lower unit. The altitude values are in meters reference to NAVD88. This raster was interpolated from contour line shapefile c_63WIP_A2_bot.shp. Depth raster files: 1. rd_63WIP_A1_top.tif: Depth raster dataset of the top of the Western Interior Plains aquifer system upper unit. The depth values are in meters below land surface (NED, 100-meter). 2. rd_63WIP_A1_bot.tif: Depth raster dataset of the bottom of the Western Interior Plains aquifer system upper unit. The depth values are in meters below land surface (NED, 100-meter). 3. rd_63WIP_A2_top.tif: Depth raster dataset of the top of the Western Interior Plains aquifer system lower unit. The depth values are in meters below land surface (NED, 100-meter). 4. rd_63WIP_A2_bot.tif: Depth raster dataset of the bottom of the Western Interior Plains aquifer system lower unit. The depth values are in meters below land surface (NED, 100-meter).

1. Data Collection and Integration Objective: • Combine the monthly sales data into a single dataset. Tasks: • Load all CSV files representing the 12 months of sales data. • Merge these files into a unified dataset. • Conduct an initial inspection to ensure data is combined correctly.
2. Data Cleaning Objective: • Prepare the dataset by resolving inconsistencies and removing irrelevant data. Tasks: • Identify and handle any missing or duplicated data. • Ensure all data types are consistent and appropriate. • Filter out any erroneous entries or irrelevant data.
3. Data Preprocessing Objective: • Enhance the dataset with additional features to facilitate analysis. Tasks: • Extract useful features like month, city, and sales amounts from the existing data. • Categorize and format data to optimize it for analysis. • Sort and organize the data for easier access and analysis.
4. Exploratory Data Analysis (EDA) Objective: • Explore the data to uncover trends, distributions, and patterns. Tasks: • Generate basic statistics to understand the distribution of the data. • Analyze sales trends over time and by product category. • Investigate geographical sales distribution across different cities. Key Questions: • Which month had the highest sales? • Which product generated the most revenue? • Which city had the highest sales?
5. Data Analysis Objective: • Answer specific business-related questions using the dataset. Key Questions: Question 1: What was the best months for sales ? How much was earned that month ? Question 2: What city had the highest number of sales ? Question 3: What time should we display advertisements to maximize likelihood of customer's buying product? Question 4: What products are most often sold together ? Question 5: What Product sold the most or least ? Why do you think it sold the most/least ? Add more questions based on your understanding of the data to generate more insights that could help the business grow.
6. Data Visualization Objective: • Create visual representations of the data to communicate findings clearly. Tasks: • Visualize sales trends over time. • Highlight top-performing cities and products. • Explore the relationship between time of day, day of week, and sales through visualizations. • Try to find the longitude and latitude of the cities and make a map that shows top cities in sales.
7. Insights and Recommendations Objective: • Summarize the findings and suggest actionable business strategies. Tasks: • Provide a clear summary of the key insights gained from the analysis. • Offer recommendations for business strategies based on the data insights.

A global data set of soil types is available at 0.5-degree latitude by 0.5-degree longitude resolution. There are 106 soil units, based on Zobler?s (1986) assessment of the FAO/UNESCO Soil Map of the World. This data set is a conversion of the Zobler 1-degree resolution version to a 0.5-degree resolution. The resolution of the data set was not actually increased. Rather, the 1-degree squares were divided into four 0.5-degree squares with the necessary adjustment of continental boundaries and islands. The computer code used to convert the original 1-degree data to 0.5-degree is provided as a companion file. A JPG image of the data is provided in this document. The Zobler data (1-degree resolution) as distributed by Webb et al. (1993) [http://www.ngdc.noaa.gov/seg/eco/cdroms/gedii_a/datasets/a12/wr.htm#top] contains two columns, one column for continent and one column for soil type. The Soil Map of the World consists of 9 maps that represent parts of the world. The texture data that Webb et al.(1993) provided allowed for the fact that a soil type in one part of the world may have different properties than the same soil in a different part of the world. This continent-specific information is retained in this 0.5-degree resolution data set, as well as the soil type information which is the second column. A code was written (one2half.c) to take the file CONTIZOB.LER distributed by Webb et al. (1993) [http://www.ngdc.noaa.gov/seg/eco/cdroms/gedii_a/datasets/a12/wr.htm#top] and simply divide the 1-degree cells into quarters. This code also reads in a land/water file (land.wave) that specifies the cells that are land at 0.5 degrees. The code checks for consistency between the newly quartered map and the land/water map to which the quartered map is to be registered. If there is a discrepancy between the two, an attempt was made to make the two consistent using the following logic. If the cell is supposed to be water, it is forced to be water. If it is supposed to be land but was resolved to water at 1 degree, the code looks at the surrounding 8 cells and picks the most frequent soil type and assigns it to the cell. If there are no surrounding land cells then it is kept as water in the hopes that on the next pass one or more of the surrounding cells might be converted from water to a soil type. The whole map is iterated 5 times. The remaining cells that should be land but couldn't be determined from surrounding cells (mostly islands that are resolved at 0.5 degree but not at 1 degree) are printed out with coordinate information. A temporary map is output with -9 indicating where data is required. This is repeated for the continent code in CONTIZOB.LER as well. A separate map of the temporary continent codes is produced with -9 indicating required data. A nearly identical code (one2half.c) does the same for the continent codes. The printout allows one to consult the printed versions of the soil map and look up the soil type with the largest coverage in the 0.5-degree cell. The program manfix.c then will go through the temporary map and prompt for input to correct both the soil codes and the continent codes for the map. This can be done manually or by preparing a file of changes (new_fix.dat) and redirecting stdin. A new complete version of the map is outputted. This is in the form of the original CONTIZOB.LER file (contizob.half) but four times larger. Original documentation and computer codes prepared by Post et al. (1996) are provided as companion files with this data set. Image of 106 global soil types available at 0.5-degree by 0.5-degree resolution. Additional documentation from Zobler?s assessment of FAO soil units is available from the NASA Center for Scientific Information.

Dataset: Leading Companies in Market Capitalization https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F14839888%2Fe7df5ac47c282cc6288f211b698233c0%2FAdd%20a%20heading%20(5).png?generation=1716157027189074&alt=media" alt="">

Introduction: This dataset provides comprehensive information on the leading companies globally by market capitalization. It includes various key metrics and identifiers for each company, facilitating detailed analysis and comparisons. This dataset is gathered from companies market capital website. below i have given the details of the dataset and columns after that i have given some information about the use cases of this dataset.

About Dataset Columns: Below is a detailed description of each column in the dataset:

1-Rank: -Description: This column shows the ranking number of the company based on its market capitalization. The rankings are in ascending order, with rank 1 representing the company with the highest market capitalization. -Data Type: Integer -Example Values: 1, 2, 3, ...

2-Company: -Description: This column displays the full name of the company. It helps identify the company being analyzed. -Data Type: String -Example Values: "Apple Inc.", "Microsoft Corporation", "Amazon.com Inc."

3-Stock Symbol: -Description: This column contains the stock symbols (ticker symbols) of the companies, which are used for trading on stock exchanges. This is essential for identifying the company's stock in financial markets. -Data Type: String -Example Values: "AAPL", "MSFT", "AMZN"

4-Market Cap (USD): -Description: This column provides the market capitalization of the company in trillion US dollars. Market capitalization is calculated as the share price times the number of outstanding shares, representing the company's total market value. -Data Type: Float (to handle large values with precision) -Example Values: 2.43, 1.87, 1.76

5-Share Price: -Description: This column contains the current share price of the respective company in US dollars. It represents the price at which a single share of the company is traded on the stock market. -Data Type: Float -Example Values: 145.09, 250.35, 3400.50

6-Company Origin: -Description: This column provides the country name where the company is headquartered. It helps in understanding the geographical distribution of the leading companies. -Data Type: String -Example Values: "United States", "China", "Germany

Use Cases of the Leading Companies in Market Capitalization Dataset

This dataset is a treasure of information for anyone interested in the financial world. Here’s how different people and professionals might use it:

1-Investors and Traders: - Stock Picking: Investors can use the dataset to identify top-performing companies by market cap. This helps them make informed decisions about where to put their money. - Comparative Analysis: Traders can compare the share prices and market caps to find potential investment opportunities and trends.

2-Financial Analysts: -Performance Tracking: Analysts can track the performance of leading companies over time, helping them to forecast future trends and provide investment recommendations. -Sector Analysis: By examining the companies and their origins, analysts can identify which sectors and countries are leading the market.

3-Business Students and Educators: -Case Studies: Students can use the dataset for case studies and projects, analyzing the financial health and market position of global giants. -Learning Tool: Educators can use the data to teach about market capitalization, stock markets, and financial metrics.

4-Economists and Researchers: -Economic Indicators: The dataset can help economists understand the economic impact of leading companies on their respective countries and the global market. -Market Dynamics: Researchers can study the market dynamics and how large companies influence economic trends.

5-Journalists and Media: -Reporting: Journalists can use the data to report on the financial health of major companies, industry trends, and economic forecasts. -Insights: Media can provide insights into the rise and fall of company rankings, helping the public stay informed about market changes.

6-Corporate Strategists: -Benchmarking: Companies can benchmark their performance against the leaders in their industry, identifying areas for improvement. -Strategic Planning: Strategists can use the data to develop long-term plans, aiming to enhance their market position.

7-General Public: -Personal Finance: Individuals interested in personal finance can use the dataset to learn more about the companies behind the brands they use daily. -Educational: For anyone curious about how global markets work, this dataset provides a straightforward way to see which companies are at the top and why.

These two datasets represent sensor events collected in the CASAS smart apartment testbed at Washington State University. In both sets of data, ambient sensor readings are collected while 20 participants performing five ADL activities in the apartment. This resource is valuable for designing and validating activity recognition algorithms. Further, this resource provides data for detecting errors that are helpful in assessing and intervening for functional independence.

Note: Other CASAS smart home and smartwatch datasets are also available, look for more at https://zenodo.org/communities/casas.

In the adl_noerror dataset, the five tasks are:

1. Make a phone call. The participant moves to the phone in the dining room, looks p a specific number in the phone book, dials the number, and listens to the message. The recorded message provides cooking directions, which the participant summarizes on a notepad.
2. Wash hands. The participant moves into the kitchen sink and washes his/her hands in the sink, using hand soap and drying their hands with a paper towel.
3. Cook. The participant cooks a pot of oatmeal according to the directions given in the phone message. To cook the oatmeal the participant must measure water, pour the water into a pot and boil it, add oats, then put the oatmeal into a bowl with raisins and brown sugar.
4. Eat. The participant takes the oatmeal and a medicine container to the dining room and eats the food.
5. Clean. The participant takes all of the dishes to the sink and cleans them with water and dish soap in the kitchen.

In the adl_error dataset, a scripted error is introduced. The errors are:

1. Make a phone call. Error: The participant initially dials the wrong number and has to redial.
2. Wash hands. Error: The participant does not turn the water off after washing his/her hands.
3. Cook. Error: The participant does not turn the burner off after cooking the oatmeal.
4. Eat. Error: The participant does not bring the medicine container with them to the dining room.
5. Clean. Error: The participant does not use water to clean the dishes.

The files are named according to the participant number and task number (e.g., p01.t1.csv contains sensor data for participant 1 performing task 1). There is one sensor reading in each row with fields date, time, sensor, and message.

A floorplan of the smart apartment is provided in Chinook.png, together with the locations of the sensors. A zoomed-in look at the Chinook cabinet with sensors is provided in Chinook_Cabinet.png. The sensors are categorized (and named) as:

• M01 - M026: PIR motion detectors (ON when detected motion starts and OFF when it stops)
• I01 - I08: item use sensors for (in order) oatmeal, raisins, brown sugar, bowl, measuring spoon, medicine container, pot, phone book (PRESENT or ABSENT indicating item is on sensor or not)
• D01: door sensor on kitchen cabinet (OPEN or CLOSE)
• AD1-A and AD1-B: water sensors for kitchen sink (value indicates level)
• AD1-C: burner sensor (value indicates level)
• asterisk: phone use sensor

SABA Core dataset This Syria core dataset comprises 14 quantitative indicators based on publicly available information from humanitarian organisations. It is updated on a monthly basis, and it covers the whole country. This dataset brings together data from a range of sources to provide a greater overall and comparative understanding of the current situation and context inside each district. The core dataset indicators cover a range of categories including agriculture, commodity prices (food and fuel), conflict, displacement, exchange rate, food security, humanitarian access severity, health, people in need per sector, and rainfall.
When analysing and interpreting the data, please be aware that while we aim to include district-level data that is updated monthly, some indicators are updated on only a quarterly or annual basis and some data is only available on admin 1 level. Please ensure you check the details in the ‘indicator list’ tab and the references for each indicator before conducting analysis.
The Syria Area Based Analysis (SABA) team recommends that this dataset is used only as a starting point. It will enable you to quickly examine and compare cross-sectoral, quantitative data at the district level in Syria. However, for operational decision making, we recommend you consult ACAPS analysis products on Syria available here: https://www.acaps.org/en/countries/syria ACAPS conducts random quality checking of a sample of entries to try to limit errors. However, it is likely that errors remain. For sensitive analysis, we recommend you cross check findings with the source data in the list of indicators and at the top row of each column. Do you have ideas to make this data set more useful? Do you see mistakes or disagree with our opinions or assumptions? Contact us at info@acaps.org. Your feedback helps us do better.

Top1

## Overview

Top1 is a dataset for instance segmentation tasks - it contains Box annotations for 370 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).