This dataset appears to contain a variety of features related to text analysis, sentiment analysis, and psychological indicators, likely derived from posts or text data. Some features include readability indices such as Automated Readability Index (ARI), Coleman Liau Index, and Flesch-Kincaid Grade Level, as well as sentiment analysis scores like sentiment compound, negative, neutral, and positive scores. Additionally, there are features related to psychological aspects such as economic stress, isolation, substance use, and domestic stress. The dataset seems to cover a wide range of linguistic, psychological, and behavioral attributes, potentially suitable for analyzing mental health-related topics in online communities or text data.

Benefits of using this dataset:

• Insight into Mental Health: The dataset provides valuable insights into mental health by analyzing linguistic patterns, sentiment, and psychological indicators in text data. Researchers and data scientists can gain a better understanding of how mental health issues manifest in online communication.
• Predictive Modeling: With a wide range of features, including sentiment analysis scores and psychological indicators, the dataset offers opportunities for developing predictive models to identify or predict mental health outcomes based on textual data. This can be useful for early intervention and support.
• Community Engagement: Mental health is a topic of increasing importance, and this dataset can foster community engagement on platforms like Kaggle. Data enthusiasts, researchers, and mental health professionals can collaborate to analyze the data and develop solutions to address mental health challenges.
• Data-driven Insights: By analyzing the dataset, users can uncover correlations and patterns between linguistic features, sentiment, and mental health indicators. These insights can inform interventions, policies, and support systems aimed at promoting mental well-being.
• Educational Resource: The dataset can serve as a valuable educational resource for teaching and learning about mental health analytics, sentiment analysis, and text mining techniques. It provides a real-world dataset for students and practitioners to apply data science skills in a meaningful context.

Unlock the full potential of BBC broadcast data with our comprehensive dataset featuring transcripts, program schedules, headlines, topics, and multimedia resources. This all-in-one dataset is designed to empower media analysts, researchers, journalists, and advocacy groups with actionable insights for media analysis, transparency studies, and editorial assessments.

Dataset Features

Transcripts: Access detailed broadcast transcripts, including headlines, content, author details, and publication dates. Perfect for analyzing media framing, topic frequency, and news narratives across various programs. Program Schedules: Explore program schedules with accurate timing, show names, and related metadata to track news coverage patterns and identify trends. Topics and Keywords: Analyze categorized topics and keywords to understand content diversity, editorial focus, and recurring themes in news broadcasts. Multimedia Content: Gain access to videos, images, and related articles linked to each broadcast for a holistic understanding of the news presentation. Metadata: Includes critical data points like publication dates, last updates, content URLs, and unique IDs for easier referencing and cross-analysis.

Customizable Subsets for Specific Needs Our CNN dataset is fully customizable to match your research or analytical goals. Focus on transcripts for in-depth media framing analysis, extract multimedia for content visualization studies, or dive into program schedules for broadcast trend analysis. Tailor the dataset to ensure it aligns with your objectives for maximum efficiency and relevance.

Popular Use Cases

Media Analysis: Evaluate news framing, content diversity, and topic coverage to assess editorial direction and media focus. Transparency Studies: Analyze journalistic standards, corrections, and retractions to assess media integrity and accountability. Audience Engagement: Identify recurring topics and trends in news content to understand audience preferences and behavior. Market Analysis: Track media coverage of key industries, companies, and topics to analyze public sentiment and industry relevance. Journalistic Integrity: Use transcripts and metadata to evaluate adherence to reporting practices, fairness, and transparency in news coverage. Research and Scholarly Studies: Leverage transcripts and multimedia to support academic studies in journalism, media criticism, and political discourse analysis.

Whether you are evaluating transparency, conducting media criticism, or tracking broadcast trends, our BBC dataset provides you with the tools and insights needed for in-depth research and strategic analysis. Customize your access to focus on the most relevant data points for your unique needs.

Analyzing Coffee Shop Sales: Excel Insights 📈

In my first Data Analytics Project, I Discover the secrets of a fictional coffee shop's success with my data-driven analysis. By Analyzing a 5-sheet Excel dataset, I've uncovered valuable sales trends, customer preferences, and insights that can guide future business decisions. 📊☕

DATA CLEANING 🧹

• REMOVED DUPLICATES OR IRRELEVANT ENTRIES: Thoroughly eliminated duplicate records and irrelevant data to refine the dataset for analysis.

• FIXED STRUCTURAL ERRORS: Rectified any inconsistencies or structural issues within the data to ensure uniformity and accuracy.

• CHECKED FOR DATA CONSISTENCY: Verified the integrity and coherence of the dataset by identifying and resolving any inconsistencies or discrepancies.

DATA MANIPULATION 🛠️

• UTILIZED LOOKUPS: Used Excel's lookup functions for efficient data retrieval and analysis.

• IMPLEMENTED INDEX MATCH: Leveraged the Index Match function to perform advanced data searches and matches.

• APPLIED SUMIFS FUNCTIONS: Utilized SumIFs to calculate totals based on specified criteria.

• CALCULATED PROFITS: Used relevant formulas and techniques to determine profit margins and insights from the data.

PIVOTING THE DATA 𝄜

• CREATED PIVOT TABLES: Utilized Excel's PivotTable feature to pivot the data for in-depth analysis.

• FILTERED DATA: Utilized pivot tables to filter and analyze specific subsets of data, enabling focused insights. Specially used in “PEAK HOURS” and “TOP 3 PRODUCTS” charts.

VISUALIZATION 📊

• KEY INSIGHTS: Unveiled the grand total sales revenue while also analyzing the average bill per person, offering comprehensive insights into the coffee shop's performance and customer spending habits.

• SALES TREND ANALYSIS: Used Line chart to compute total sales across various time intervals, revealing valuable insights into evolving sales trends.

• PEAK HOUR ANALYSIS: Leveraged Clustered Column chart to identify peak sales hours, shedding light on optimal operating times and potential staffing needs.

• TOP 3 PRODUCTS IDENTIFICATION: Utilized Clustered Bar chart to determine the top three coffee types, facilitating strategic decisions regarding inventory management and marketing focus.

*I also used a Timeline to visualize chronological data trends and identify key patterns over specific times.

While it's a significant milestone for me, I recognize that there's always room for growth and improvement. Your feedback and insights are invaluable to me as I continue to refine my skills and tackle future projects. I'm eager to hear your thoughts and suggestions on how I can make my next endeavor even more impactful and insightful.

THANKS TO: WsCube Tech Mo Chen Alex Freberg

TOOLS USED: Microsoft Excel

DataAnalytics #DataAnalyst #ExcelProject #DataVisualization #BusinessIntelligence #SalesAnalysis #DataAnalysis #DataDrivenDecisions

About this dataset

Context

The dataset tabulates the Allison population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Allison across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Allison was 942, a 1.15% decrease year-by-year from 2022. Previously, in 2022, Allison population was 953, a decline of 1.04% compared to a population of 963 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Allison decreased by 58. In this period, the peak population was 1,026 in the year 2010. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Allison is shown in this column.
• Year on Year Change: This column displays the change in Allison population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

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 Allison Population by Year. You can refer the same here

📊 Dataset Title: Daily Active Users Dataset

📝 Description

This dataset provides detailed insights into daily active users (DAU) of a platform or service, captured over a defined period of time. The dataset includes information such as the number of active users per day, allowing data analysts and business intelligence teams to track usage trends, monitor platform engagement, and identify patterns in user activity over time.

The data is ideal for performing time series analysis, statistical analysis, and trend forecasting. You can utilize this dataset to measure the success of platform initiatives, evaluate user behavior, or predict future trends in engagement. It is also suitable for training machine learning models that focus on user activity prediction or anomaly detection.

📂 Dataset Structure

The dataset is structured in a simple and easy-to-use format, containing the following columns:

• Date: The date on which the data was recorded, formatted as YYYYMMDD.
• Number of Active Users: The number of users who were active on the platform on the corresponding date.

Each row in the dataset represents a unique date and its corresponding number of active users. This allows for time-based analysis, such as calculating the moving average of active users, detecting seasonality, or spotting sudden spikes or drops in engagement.

🧐 Key Use Cases

This dataset can be used for a wide range of purposes, including:

1. Time Series Analysis: Analyze trends and seasonality of user engagement.
2. Trend Detection: Discover peaks and valleys in user activity.
3. Anomaly Detection: Use statistical methods or machine learning algorithms to detect anomalies in user behavior.
4. Forecasting User Growth: Build forecasting models to predict future platform usage.
5. Seasonality Insights: Identify patterns like increased activity on weekends or holidays.

📈 Potential Analysis

Here are some specific analyses you can perform using this dataset:

• Moving Average and Smoothing: Calculate the moving average over a 7-day or 30-day period.
• Correlation with External Factors: Correlate daily active users with other datasets.
• Statistical Hypothesis Testing: Perform t-tests or ANOVA to determine significant differences in user activity.
• Machine Learning for Prediction: Train machine learning models to predict user engagement.

🚀 Getting Started

To get started with this dataset, you can load it into your preferred analysis tool. Here's how to do it using Python's pandas library:

import pandas as pd

# Load the dataset
data = pd.read_csv('path_to_dataset.csv')

# Display the first few rows
print(data.head())

# Basic statistics
print(data.describe())

The data sets provide the text and detailed numeric information in all financial statements and their notes extracted from exhibits to corporate financial reports filed with the Commission using eXtensible Business Reporting Language (XBRL).

Overview:

This dataset provides a unique resource for researchers and data scientists interested in the global dynamics of the COVID-19 pandemic. It focuses on the impact of different SARS-CoV-2 variants and mutations on the duration of local epidemics. By combining variant information with epidemiological data, this dataset allows for a comprehensive analysis of factors influencing the trajectory of the pandemic.

Key Features:

• Global Coverage: Includes data from multiple countries.
• Variant-Specific Information: Detailed records for various SARS-CoV-2 variants.
• Epidemic Duration: Data on the duration of local epidemics, accounting for right-censoring.
• Epidemiological Variables: Includes mortality rates, a proxy for R0, transmission proxies, and other pertinent variables.
• Geographical characteristics: Include a continent variable for exploring geographical patterns
• Time varying variables: Include the number of waves and the number of variants in the different countries for more in-depth exploration.

Data Source: The data combines information from the Johns Hopkins University COVID-19 dataset (confirmed_cases.csv and deaths_cases.csv) and the covariants.org dataset (variants.csv). The dataset you see here is the combination of two datasets from Johns Hopkins University and covariants.org.

Questions to Inspire Users:

This dataset is designed for a diverse set of analytical questions. Here are some ideas to inspire the Kaggle community:

Survival Analysis:

1. How do different SARS-CoV-2 variants influence the duration of local epidemics?
2. Which factors (mortality, R0, etc.) are most strongly associated with shorter or longer epidemic durations?
3. Does the type of variant/mutation (mutation,S, Omicron, Delta, Other) have a significant impact on epidemic duration?
4. Is there a geographical pattern to the duration of epidemics?

Epidemiological Analysis:

1. How do local transmission rates (represented by our proxy of R0) affect the duration of an epidemic?
2. Do countries with higher mortality rates have different patterns of epidemic progression?
3. How can we predict the duration of an epidemic based on its initial characteristics?
4. How does the number of epidemic waves impact the duration of an epidemic?
5. Does the number of variants in a country affect the duration of an épidémie?

Data Science/Machine Learning:

1. Can we develop a machine learning model to predict the duration of an epidemic?
2. What features have the best predictive power ?
3. Can we identify clusters of variants/regions with similar epidemic patterns?
4. Are there interactions between variables that can explain the non-linearities that we have identified ?

About this dataset

Context

The dataset tabulates the median household income in Brevard County. It can be utilized to understand the trend in median household income and to analyze the income distribution in Brevard County by household type, size, and across various income brackets.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• Brevard County, FL Median Household Income Trends (2010-2021, in 2022 inflation-adjusted dollars)
• Median Household Income Variation by Family Size in Brevard County, FL: Comparative analysis across 7 household sizes
• Income Distribution by Quintile: Mean Household Income in Brevard County, FL
• Brevard County, FL households by income brackets: family, non-family, and total, in 2022 inflation-adjusted dollars

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/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of Brevard County median household income. You can refer the same here

About this dataset

Context

The dataset tabulates the median household income in Billings. It can be utilized to understand the trend in median household income and to analyze the income distribution in Billings by household type, size, and across various income brackets.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• Billings, OK Median Household Income Trends (2010-2021, in 2022 inflation-adjusted dollars)
• Median Household Income Variation by Family Size in Billings, OK: Comparative analysis across 7 household sizes
• Income Distribution by Quintile: Mean Household Income in Billings, OK
• Billings, OK households by income brackets: family, non-family, and total, in 2022 inflation-adjusted dollars

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/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of Billings median household income. You can refer the same here

This dataset comprises an assortment of waveplot images representing diverse human actions. Waveplot images are time-amplitude representations of audio signals that encapsulate the variation of audio amplitude over time. In this dataset, the audio signals correspond to disparate human actions, such as walking, running, jumping, and dancing. The waveplot images are created by plotting the amplitude of the audio signals against time, with each image representing a segment of the audio signal.

The dataset is explicitly designed for tasks like human action recognition, classification, segmentation, and detection based on auditory cues. It serves as a valuable resource for training and evaluating machine learning models that analyze human actions predicated on audio signals. The dataset caters well to researchers and practitioners in the disciplines of signal processing, computer vision, and machine learning, who are keen on devising algorithms for human action analysis using audio signals.

Crucially, the dataset is annotated with labels that denote the type of human action represented in each waveplot image. This ensures a supervised learning environment conducive for the development and testing of prediction models.

Background Microarray technologies are emerging as a promising tool for genomic studies. The challenge now is how to analyze the resulting large amounts of data. Clustering techniques have been widely applied in analyzing microarray gene-expression data. However, normal mixture model-based cluster analysis has not been widely used for such data, although it has a solid probabilistic foundation. Here, we introduce and illustrate its use in detecting differentially expressed genes. In particular, we do not cluster gene-expression patterns but a summary statistic, the t-statistic. Results The method is applied to a data set containing expression levels of 1,176 genes of rats with and without pneumococcal middle-ear infection. Three clusters were found, two of which contain more than 95% genes with almost no altered gene-expression levels, whereas the third one has 30 genes with more or less differential gene-expression levels. Conclusions Our results indicate that model-based clustering of t-statistics (and possibly other summary statistics) can be a useful statistical tool to exploit differential gene expression for microarray data.

Refined DataCo Smart Supply Chain Geospatial Dataset

Optimized for Geospatial and Big Data Analysis

This dataset is a refined and enhanced version of the original DataCo SMART SUPPLY CHAIN FOR BIG DATA ANALYSIS dataset, specifically designed for advanced geospatial and big data analysis. It incorporates geocoded information, language translations, and cleaned data to enable applications in logistics optimization, supply chain visualization, and performance analytics.

Key Features

1. Geocoded Source and Destination Data

• Accurate latitude and longitude coordinates for both source and destination locations.
• Facilitates geospatial mapping, route analysis, and distance calculations.

2. Supplementary GeoJSON Files

• src_points.geojson: Source point geometries.
• dest_points.geojson: Destination point geometries.
• routes.geojson: Line geometries representing source-destination routes.
• These files are compatible with GIS software and geospatial libraries such as GeoPandas, Folium, and QGIS.

3. Language Translation

• Key location fields (countries, states, and cities) are translated into English for consistency and global accessibility.

4. Cleaned and Consolidated Data

• Addressed missing values, removed duplicates, and corrected erroneous entries.
• Ready-to-use dataset for analysis without additional preprocessing.

5. Routes and Points Geometry

• Enables the creation of spatial visualizations, hotspot identification, and route efficiency analyses.

Applications

1. Logistics Optimization

• Analyze transportation routes and delivery performance to improve efficiency and reduce costs.

2. Supply Chain Visualization

• Create detailed maps to visualize the global flow of goods.

3. Geospatial Modeling

• Perform proximity analysis, clustering, and geospatial regression to uncover patterns in supply chain operations.

4. Business Intelligence

• Use the dataset for KPI tracking, decision-making, and operational insights.

Dataset Content

Files Included

1. DataCoSupplyChainDatasetRefined.csv

   • The main dataset containing cleaned fields, geospatial coordinates, and English translations.

2. src_points.geojson

   • GeoJSON file containing the source points for easy visualization and analysis.

3. dest_points.geojson

   • GeoJSON file containing the destination points.

4. routes.geojson

   • GeoJSON file with LineStrings representing routes between source and destination points.

Attribution

This dataset is based on the original dataset published by Fabian Constante, Fernando Silva, and António Pereira:
Constante, Fabian; Silva, Fernando; Pereira, António (2019), “DataCo SMART SUPPLY CHAIN FOR BIG DATA ANALYSIS”, Mendeley Data, V5, doi: 10.17632/8gx2fvg2k6.5.

Refinements include geospatial processing, translation, and additional cleaning by the uploader to enhance usability and analytical potential.

Tips for Using the Dataset

• For geospatial analysis, leverage tools like GeoPandas, QGIS, or Folium to visualize routes and points.
• Use the GeoJSON files for interactive mapping and spatial queries.
• Combine this dataset with external datasets (e.g., road networks) for enriched analytics.

This dataset is designed to empower data scientists, researchers, and business professionals to explore the intersection of geospatial intelligence and supply chain optimization.

This formatted dataset originates from raw data files from the Institute of Health Metrics and Evaluation Global Burden of Disease (GBD2017). It is population weighted worldwide data on male and female cohorts ages 15-69 years including body mass index (BMI) and cardiovascular disease (CVD) and associated dietary, metabolic and other risk factors. The purpose of creating this formatted database is to explore the univariate and multiple regression correlations of BMI and CVD and other health outcomes with risk factors. Our research hypothesis is that we can successfully apply artificial intelligence to model BMI and CVD risk factors and health outcomes. We derived a BMI multiple regression risk factor formula that satisfied all nine Bradford Hill causality criteria for epidemiology research. We found that animal products and added fats are negatively correlated with CVD early deaths worldwide but positively correlated with CVD early deaths in high quantities. We interpret this as showing that optimal cardiovascular outcomes come with moderate (not low and not high) intakes of animal foods and added fats.

For questions, please email davidkcundiff@gmail.com. Thanks.

About this dataset

Context

The dataset tabulates the median household income in Woodland Park. It can be utilized to understand the trend in median household income and to analyze the income distribution in Woodland Park by household type, size, and across various income brackets.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• Woodland Park, CO Median Household Income Trends (2010-2021, in 2022 inflation-adjusted dollars)
• Median Household Income Variation by Family Size in Woodland Park, CO: Comparative analysis across 7 household sizes
• Income Distribution by Quintile: Mean Household Income in Woodland Park, CO
• Woodland Park, CO households by income brackets: family, non-family, and total, in 2022 inflation-adjusted dollars

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/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of Woodland Park median household income. You can refer the same here

This is a dataset downloaded off excelbianalytics.com created off of random VBA logic. I recently performed an extensive exploratory data analysis on it and I included new columns to it, namely: Unit margin, Order year, Order month, Order weekday and Order_Ship_Days which I think can help with analysis on the data. I shared it because I thought it was a great dataset to practice analytical processes on for newbies like myself.

About Dataset – TL;DR

Comprehensive football (soccer) data lake from Transfermarkt, clean and structured for analysis and machine learning.

• 93,000+ players worldwide
• 2,200+ clubs across all major leagues
• 5.7M+ total records across 10 categories
• 902,000+ market valuations
• 1.9M+ player performance stats
• 1.2M+ player transfer histories
• 144,000+ injuries & 93,000+ national team appearances
• 1.3M+ teammate relationships

Everything in raw CSV format – perfect for EDA, ML, and advanced football analytics.

⚽ The Most Comprehensive Transfermarkt Football Dataset

A complete football data lake covering players, teams, transfers, performances, market values, injuries, and national team stats. Perfect for analysts, data scientists, researchers, and enthusiasts.

🗺 Entity-Relationship Overview

Here’s the high-level schema to help you understand the dataset structure:

https://i.imgur.com/WXLIx3L.png" alt="Transfermarkt Dataset ER Diagram">

📊 Key Coverage

• Players: 93,000+ professional players
• Teams: 2,200+ clubs, 7,700+ club relationships
• Data Volume: 5.7M+ total records
• Global Scope: Major leagues and competitions worldwide

🗂 Data Structure

Organized into 10 well-structured CSV categories:

Player Data (7 categories)

• Player Profiles
• Performances (matches, goals, assists, cards, minutes)
• Market Values (historical valuations)
• Transfer Histories
• Injury Records
• National Team Performances
• Teammate Networks

Team Data (3 categories)

• Team Details (club info)
• Competitions & Seasons
• Parent/Child Team Relations

🔗 What’s Inside?

• 902K+ market value records to track valuation trends
• 1.1M+ transfer histories with fees and movement
• 1.9M+ performance stats across seasons and competitions
• 144K+ injury records with days and matches missed
• 93K+ national team appearances
• 1.3M+ teammate relationships for chemistry analysis

💡 Why This Dataset?

Most football datasets are pre-processed and restrictive. This one is raw, rich, and flexible:

• Build custom KPIs and models
• Perform deep exploratory analysis (EDA)
• Train machine learning and prediction pipelines
• Combine with other football data sources

🚀 Example Use Cases

• Predictive Modeling – Player ratings, transfer value forecasts, injury risk
• Data Visualization & Dashboards – Club comparisons, performance analytics
• Scouting & Recruitment – Discover undervalued talent
• Network Analysis – Teammate relationships and synergy

🖥 Technical Details

• Format: CSV files, UTF-8 encoded
• Easy to Use: Ready for Python (pandas, numpy), R, SQL, BI tools
• Scalable: 5.7M+ rows for big-data analysis

💡 Working on a Cool Project?

I’m always excited to collaborate on innovative football data projects. If you’ve got an idea, let’s make it happen together!

📬 Contact Me

• GitHub: @salimt
• Issues: Feel free to use GitHub Issues if you’ve got dataset-specific questions.

⭐ Support & Visibility

If this dataset helps you:
- Upvote on Kaggle
- Star the GitHub repo
- Share with others in the football analytics community

Tags

football analytics soccer dataset transfermarkt sports analytics machine learning football research player statistics

🔥 Analyze football like never before. Your next AI or analytics project starts here.

About this dataset

Context

The dataset tabulates the median household income in Van Meter. It can be utilized to understand the trend in median household income and to analyze the income distribution in Van Meter by household type, size, and across various income brackets.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• Van Meter, IA Median Household Income Trends (2010-2021, in 2022 inflation-adjusted dollars)
• Median Household Income Variation by Family Size in Van Meter, IA: Comparative analysis across 7 household sizes
• Income Distribution by Quintile: Mean Household Income in Van Meter, IA
• Van Meter, IA households by income brackets: family, non-family, and total, in 2022 inflation-adjusted dollars

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/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of Van Meter median household income. You can refer the same here

About this dataset

Context

The dataset tabulates the median household income in Lewis County. It can be utilized to understand the trend in median household income and to analyze the income distribution in Lewis County by household type, size, and across various income brackets.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• Lewis County, ID Median Household Income Trends (2010-2021, in 2022 inflation-adjusted dollars)
• Median Household Income Variation by Family Size in Lewis County, ID: Comparative analysis across 7 household sizes
• Income Distribution by Quintile: Mean Household Income in Lewis County, ID
• Lewis County, ID households by income brackets: family, non-family, and total, in 2022 inflation-adjusted dollars

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/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of Lewis County median household income. You can refer the same here

Overview This dataset is a synthetic representation of real-world operational data collected from an inkjet printing process. The primary focus of the dataset is to measure and analyze color accuracy in the printing process. The synthetic nature ensures data privacy while providing a structure similar to the original data for analysis and modeling purposes. Data Collection Original Data Source: The initial data was collected from an inkjet printing process, where various patches from a Media Wedge from FOGRA were printed and measured for color accuracy.

Synthetic Data Generation: Using statistical methodologies, this synthetic dataset was generated to mirror the characteristics of the original data. The generation process involved fitting distributions to the real data and then sampling from these distributions. Features: DeltaE(2000): A measure of color difference. Lower values indicate better color accuracy. C, M, Y, K: Represents the percentages of Cyan, Magenta, Yellow, and Black in the inkjet printing. L_target, a_target, b*_target: Target color values in the Lab color space. L_measured, a_measured, b*_measured: Measured color values in the Lab color space after printing.

Intended Uses: Research: The dataset can be a starting point for academic research into printing technology, color science, or machine learning methodologies tailored to printing processes.

Machine Learning: Train and test machine learning models related to predicting color accuracy or understanding the relationships between ink compositions and final print results.

Simulation: Simulate different printing scenarios or conditions to study their impact on color accuracy.

Educational Purposes: Use the dataset in workshops, training sessions, or courses related to printing technology, data science, or machine learning.

Limitations: Being a synthetic dataset, it might not capture all the nuances and intricacies of real-world data. Hence, findings derived from this dataset should be validated with real-world data or expert insights. The dataset should be seen as a tool for preliminary analysis, prototyping, and learning.

About this dataset

Context

The dataset tabulates the median household income in New Hope. It can be utilized to understand the trend in median household income and to analyze the income distribution in New Hope by household type, size, and across various income brackets.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• New Hope, AL Median Household Income Trends (2010-2021, in 2022 inflation-adjusted dollars)
• Median Household Income Variation by Family Size in New Hope, AL: Comparative analysis across 7 household sizes
• Income Distribution by Quintile: Mean Household Income in New Hope, AL
• New Hope, AL households by income brackets: family, non-family, and total, in 2022 inflation-adjusted dollars

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/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of New Hope median household income. You can refer the same here