Government Debt in Canada increased to 1223.62 CAD Billion in 2024 from 1173.01 CAD Billion in 2023. This dataset provides - Canada Government Debt- actual values, historical data, forecast, chart, statistics, economic calendar and news.

🏦 Synthetic Loan Approval Dataset

A Realistic, High-Quality Dataset for Credit Risk Modelling

🎯 Why This Dataset?

Most loan datasets on Kaggle have unrealistic patterns where:

1. ❌ Credit scores don't matter
2. ❌ Approval logic is backwards
3. ❌ Models learn nonsense patterns

Unlike most loan datasets available online, this one is built on real banking criteria from US and Canadian financial institutions. Drawing from 3 years of hands-on finance industry experience, the dataset incorporates realistic correlations and business logic that reflect how actual lending decisions are made. This makes it perfect for data scientists looking to build portfolio projects that showcase not just coding ability, but genuine understanding of credit risk modelling.

📊 Dataset Overview

🔑 Key Features

Identifier:

-Customer ID (unique identifier for each application)

Demographics:

-Age, Occupation Status, Years Employed

Financial Profile:

-Annual Income, Credit Score, Credit History Length -Savings/Assets, Current Debt

Credit Behaviour:

-Defaults on File, Delinquencies, Derogatory Marks

Loan Request:

-Product Type, Loan Intent, Loan Amount, Interest Rate

Calculated Ratios:

-Debt-to-Income, Loan-to-Income, Payment-to-Income

💡 What Makes This Dataset Special?

1️⃣ Real-World Approval Logic The dataset implements actual banking criteria: - DTI ratio > 50% = automatic rejection - Defaults on file = instant reject - Credit score bands match real lending thresholds - Employment verification for loans ≥$20K

2️⃣ Realistic Correlations - Higher income → Better credit scores - Older applicants → Longer credit history - Students → Lower income, special treatment for small loans - Loan intent affects approval (Education best, Debt Consolidation worst)

3️⃣ Product-Specific Rules - Credit Cards: More lenient, higher limits - Personal Loans: Standard criteria, up to $100K - Line of Credit: Capped at $50K, manual review for high amounts

4️⃣ Edge Cases Included - Young applicants (age 18) building first credit - Students with thin credit files - Self-employed with variable income - High debt-to-income ratios - Multiple delinquencies

🎓 Perfect For - Machine Learning Practice: Binary classification with real patterns - Credit Risk Modelling: Learn actual lending criteria - Portfolio Projects: Build impressive, explainable models - Feature Engineering: Rich dataset with meaningful relationships - Business Analytics: Understand financial decision-making

📈 Quick Stats

Approval Rates by Product - Credit Card: 60.4% more lenient) - Personal Loan: 46.9 (standard) - Line of Credit: 52.6% (moderate)

Loan Intent (Best → Worst Approval Odds) 1. Education (63% approved) 2. Personal (58% approved) 3. Medical/Home (52% approved) 4. Business (48% approved) 5. Debt Consolidation (40% approved)

Credit Score Distribution - Mean: 644 - Range: 300-850 - Realistic bell curve around 600-700

Income Distribution - Mean: $50,063 - Median: $41,608 - Range: $15K - $250K

🎯 Expected Model Performance

With proper feature engineering and tuning: - Accuracy: 75-85% - ROC-AUC: 0.80-0.90 - F1-Score: 0.75-0.85

Important: Feature importance should show: 1. Credit Score (most important) 2. Debt-to-Income Ratio 3. Delinquencies 4. Loan Amount 5. Income

If your model shows different patterns, something's wrong!

🏆 Use Cases & Projects

Beginner - Binary classification with XGBoost/Random Forest - EDA and visualization practice - Feature importance analysis

Intermediate - Custom threshold optimization (profit maximization) - Cost-sensitive learning (false positive vs false negative) - Ensemble methods and stacking

Advanced - Explainable AI (SHAP, LIME) - Fairness analysis across demographics - Production-ready API with FastAPI/Flask - Streamlit deployment with business rules

⚠️ Important Notes

This is SYNTHETIC Data - Generated based on real banking criteria - No real customer data was used - Safe for public sharing and portfolio use

Limitations - Simplified approval logic (real banks use 100+ factors) - No temporal component (no time series) - Single country/currency assumed (USD) - No external factors (economy, market conditions)

Educational Purpose This dataset is designed for: - Learning credit risk modeling - Portfolio projects - ML practice - Understanding lending criteria

NOT for: - Actual lending decisions - Financial advice - Production use without validation

🤝 Contributing

Found an issue? Have suggestions? - Open an issue on GitHub - Suggest i...

About this dataset

Context

The dataset presents median household incomes for various household sizes in New Canada, Maine, 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, New Canada town did not include 6, or 7-person households. Across the different household sizes in New Canada town the mean income is $103,835, and the standard deviation is $59,699. The coefficient of variation (CV) is 57.49%. This high CV indicates high relative variability, suggesting that the incomes vary significantly across different sizes of households.
• In the most recent year, 2021, The smallest household size for which the bureau reported a median household income was 1-person households, with an income of $16,552. It then further increased to $136,465 for 5-person households, the largest household size for which the bureau reported a median household income.

https://i.neilsberg.com/ch/new-canada-me-median-household-income-by-household-size.jpeg" alt="New Canada, Maine median household income, by household size (in 2022 inflation-adjusted dollars)">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 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 2022 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 New Canada town median household income. You can refer the same here

Introducing the Indeed Job Postings Index

Hiring Lab's Job Postings Tracker is being re-released as the Indeed Job Postings Index. By Chris Glynn

Indeed Hiring Lab is re-releasing our Job Postings Tracker as the Indeed Job Postings Index, a daily measure of labor market activity that is updated and will continue to be released weekly. Covering seven national markets in the US, Canada, United Kingdom, Ireland, France, Germany, and Australia, the Indeed Job Postings Index meets one of Hiring Lab’s primary goals: produce high quality and high frequency labor market metrics using Indeed’s proprietary data.

The primary difference between the Indeed Job Postings Index and the legacy Job Postings Tracker is the level. The Indeed Job Postings Index is set to 100 on February 1, 2020, and this effectively provides a uniform level shift of 100 to the existing Job Postings Tracker across all time points.The Job Postings Tracker measured the percent change in postings from February 1st, 2020. For example, if the Job Postings Tracker were 40%, the corresponding Indeed Job Postings Index on the same date would be 140. Additionally, we are now including year-over-year and month-over-month percent changes in the Indeed Job Postings Index as part of our data portal on hiringlab.org/data and on our GitHub page. Month-over-month changes are calculated as 28 day (4 week) differences to control for day of week.

As Covid-19 fades from the global labor market discussion, moving to an index better reflects current economic conditions. The Indeed Job Postings Index allows us to compare job postings more naturally across flexible date ranges as opposed to comparing to the pre-pandemic baseline. It also places Indeed’s job postings metric in a broader class of macroeconomic indexes such as the Case Shiller Index that measures house price appreciation and the Consumer Price Index that measures inflation.

Data Schema Each market covered by a Hiring Lab economist has a folder in this repo. Each folder contains the following files:

aggregate_job_postings_{country_code}.csv This file contains the % change in seasonally-adjusted postings since February 1, 2020 for total job postings and new jobs postings (on Indeed for 7 days or fewer) for that market, as well as non-seasonally adjusted postings since February 1, 2020 for total job postings.

job_postings_by_sector_{country_code}.csv This file contains the % change in seasonally-adjusted postings since February 1, 2020 for occupational sectors for that market. We do not share sectoral data for Ireland.

For certain markets, we also share subnational job postings trends. In the United States, we provide:

metro_job_postings_us.csv This file contains the % change in seasonally-adjusted postings since February 1, 2020 for total job postings in US metropolitan areas with a population of at least 500,000 people.

state_job_postings_us.csv This file contains the % change in seasonally-adjusted postings since February 1, 2020 for total job postings in the US states and the District of Columbia.

In Canada, we provide:

provincial_postings_ca.csv This file contains the % change in seasonally-adjusted postings since February 1, 2020 for total job postings in each Canadian provinces. In the United Kingdom, we provide:

regional_postings_gb.csv This file contains the % change in seasonally-adjusted postings since February 1, 2020 for total job postings in each region in the UK.

city_postings_gb.csv This file contains the % change in seasonally-adjusted postings since February 1, 2020 for total job postings in each city in the UK.

Github link: https://github.com/hiring-lab/job_postings_tracker#data-schema Hiring Lab Link: https://www.hiringlab.org/2022/12/15/introducing-the-indeed-job-postings-index/

About this dataset

Context

The dataset presents median household incomes for various household sizes in Little Canada, MN, 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, Little Canada did not include 7-person households. Across the different household sizes in Little Canada the mean income is $100,789, and the standard deviation is $44,773. The coefficient of variation (CV) is 44.42%. This high CV indicates high relative variability, suggesting that the incomes vary significantly across different sizes of households.
• In the most recent year, 2021, The smallest household size for which the bureau reported a median household income was 1-person households, with an income of $40,881. It then further increased to $110,552 for 6-person households, the largest household size for which the bureau reported a median household income.

https://i.neilsberg.com/ch/little-canada-mn-median-household-income-by-household-size.jpeg" alt="Little Canada, MN median household income, by household size (in 2022 inflation-adjusted dollars)">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 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 2022 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 Little Canada median household income. You can refer the same here

About Dataset UPDATE: Source code used for collecting this data released here

Context YouTube (the world-famous video sharing website) maintains a list of the top trending videos on the platform. According to Variety magazine, “To determine the year’s top-trending videos, YouTube uses a combination of factors including measuring users interactions (number of views, shares, comments and likes). Note that they’re not the most-viewed videos overall for the calendar year”. Top performers on the YouTube trending list are music videos (such as the famously virile “Gangam Style”), celebrity and/or reality TV performances, and the random dude-with-a-camera viral videos that YouTube is well-known for.

This dataset is a daily record of the top trending YouTube videos.

Note that this dataset is a structurally improved version of this dataset.

Content This dataset includes several months (and counting) of data on daily trending YouTube videos. Data is included for the US, GB, DE, CA, and FR regions (USA, Great Britain, Germany, Canada, and France, respectively), with up to 200 listed trending videos per day.

EDIT: Now includes data from RU, MX, KR, JP and IN regions (Russia, Mexico, South Korea, Japan and India respectively) over the same time period.

Each region’s data is in a separate file. Data includes the video title, channel title, publish time, tags, views, likes and dislikes, description, and comment count.

The data also includes a category_id field, which varies between regions. To retrieve the categories for a specific video, find it in the associated JSON. One such file is included for each of the five regions in the dataset.

For more information on specific columns in the dataset refer to the column metadata.

Acknowledgements This dataset was collected using the YouTube API.

Inspiration Possible uses for this dataset could include:

Sentiment analysis in a variety of forms Categorising YouTube videos based on their comments and statistics. Training ML algorithms like RNNs to generate their own YouTube comments. Analysing what factors affect how popular a YouTube video will be. Statistical analysis over time. For further inspiration, see the kernels on this dataset!

Techsalerator’s Import/Export Trade Data for Canada

Techsalerator’s Import/Export Trade Data for Canada provides a comprehensive and insightful collection of information on international trade activities involving Canadian companies. This dataset offers a detailed examination of trade transactions, documenting and classifying imports and exports across various industries within Canada. ** To obtain Techsalerator’s Import/Export Trade Data for Canada, please reach out to info@techsalerator.com or to https://www.techsalerator.com/contact-us **

with your specific requirements. Techsalerator will provide a customized quote based on your data needs, with delivery available within 24 hours. Ongoing access options can also be discussed.

Techsalerator's Import/Export Trade Data for Canada delivers a thorough analysis of trade activities, integrating data from customs reports, trade agreements, and shipping records. This comprehensive dataset helps businesses, investors, and trade analysts understand Canada’s trade landscape in detail.

Key Data Fields

Company Name: Lists the companies involved in trade transactions. This information helps identify potential partners or competitors and track industry-specific trade patterns. Trade Volume: Details the quantity or value of goods traded, providing insights into the scale and economic impact of trade activities. Product Category: Specifies the types of goods traded, such as raw materials or finished products, aiding in understanding market demand and supply chain dynamics. Import/Export Country: Identifies the countries of origin or destination for traded goods, offering insights into regional trade relationships and market access. Transaction Date: Records the date of transactions, revealing seasonal trends and shifts in trade dynamics over time. Top Trade Trends in Canada

Trade Balance Dynamics: Canada’s trade balance fluctuates with major partners such as the United States and China. Ongoing trade negotiations and policy adjustments aim to address imbalances and foster more equitable trade relationships. U.S.-Canada Trade Relations: The trade relationship with the U.S. remains central, influenced by agreements like the USMCA. This partnership shapes significant aspects of Canada's trade policy and practices. Expansion of Global Trade Networks: Canada is increasingly diversifying its trade partners and markets beyond traditional partners, reflecting a trend toward broader global trade engagement. Growth in Resource Exports: Canada continues to see substantial trade in natural resources, including oil, minerals, and timber, which play a critical role in its export economy. Emphasis on Sustainable Trade Practices: There is a growing focus on integrating sustainability into trade policies, promoting environmentally friendly practices and technologies. Notable Companies in Canadian Trade Data

Shopify Inc.: A leading e-commerce company that has a significant impact on international trade through its global platform for online retail. Bombardier Inc.: A major player in aerospace, known for exporting aircraft and components, contributing significantly to Canada’s trade in the aerospace sector. Suncor Energy Inc.: A major exporter of energy products, including crude oil and refined products, impacting Canada's energy trade. Loblaw Companies Limited: A major retailer involved in both importing and exporting a range of consumer goods, reflecting its significant role in Canada’s trade dynamics. Nutrien Ltd.: A leading exporter of agricultural products and fertilizers, highlighting Canada’s role in global agriculture and food production. Accessing Techsalerator’s Data

To obtain Techsalerator’s Import/Export Trade Data for Canada, please contact us at info@techsalerator.com with your requirements. We will provide a customized quote based on the number of data fields and records needed, with delivery available within 24 hours. Ongoing access options can also be discussed.

Included Data Fields:

Company Name Trade Volume Product Category Import/Export Country Transaction Date Shipping Details Customs Codes Trade Value For detailed insights into Canada’s import and export activities and trends, Techsalerator’s dataset is an invaluable resource for staying informed and making strategic decisions.

The Border Crossing Entry Data, provided by the Bureau of Transportation Statistics (BTS), offers summary statistics on inbound crossings at U.S. land ports along the 🇨🇦 Canada-U.S. and 🇲🇽 Mexico-U.S. borders. The dataset includes counts for 🚛 trucks, 🚂 trains, 📦 containers, 🚌 buses, 🚗 personal vehicles, 🧍 passengers, and 🚶 pedestrians entering the United States.

📊 Data Collection & Coverage 🔹 Originator: 🛂 U.S. Customs and Border Protection (CBP) 🔹 Scope: Captures the number of vehicles, containers, passengers, or pedestrians entering the U.S. 🔹 Limitations:

🚫 CBP does not collect data on outbound crossings. 🔍 Users seeking outbound data should refer to bridge operators, border state governments, or the Mexican and Canadian governments. 🔹 Level of Reporting: Data is reported at the 📍 port level, aggregating multiple entry points within each port. 🔓 Access & Use ✅ Public Availability: This dataset is open for public access and use. 📝 License: No license information was provided. However, if created by a U.S. government officer or employee, it is considered a U.S. Government Work.

🔗 Source & More Information: 📂 Data.gov - Border Crossing Entry Data

Techsalerator’s Import/Export Trade Data for the United States

Techsalerator’s Import/Export Trade Data for the United States offers a comprehensive and insightful collection of information on international trade activities involving U.S. companies. This dataset provides a detailed examination of trade transactions, documenting and classifying imports and exports across various industries within the U.S.

To obtain Techsalerator’s Import/Export Trade Data for the United States, please reach out to info@techsalerator.com or visit Techsalerator Contact Us with your specific requirements. Techsalerator will provide a customized quote based on your data needs, with delivery available within 24 hours. Ongoing access options can also be discussed.

Techsalerator's Import/Export Trade Data for the United States delivers a thorough analysis of trade activities, integrating data from customs reports, trade agreements, and shipping records. This comprehensive dataset helps businesses, investors, and trade analysts understand the U.S. trade landscape in detail.

Key Data Fields

• Company Name: Lists the companies involved in trade transactions. This information helps identify potential partners or competitors and track industry-specific trade patterns.
• Trade Volume: Details the quantity or value of goods traded, providing insights into the scale and economic impact of trade activities.
• Product Category: Specifies the types of goods traded, such as raw materials or finished products, aiding in understanding market demand and supply chain dynamics.
• Import/Export Country: Identifies the countries of origin or destination for traded goods, offering insights into regional trade relationships and market access.
• Transaction Date: Records the date of transactions, revealing seasonal trends and shifts in trade dynamics over time.

Top Trade Trends in the United States

• Trade Balance Dynamics: The U.S. trade balance fluctuates with major partners such as China, Canada, and Mexico. Ongoing trade negotiations and policy adjustments aim to address imbalances and foster more equitable trade relationships.
• U.S.-China Trade Relations: The trade relationship with China remains central, influenced by agreements and tariffs. This partnership shapes significant aspects of the U.S. trade policy and practices.
• Expansion of Global Trade Networks: The United States continues to diversify its trade partners and markets beyond traditional partners, reflecting a trend toward broader global trade engagement.
• Growth in Technology Exports: The U.S. sees substantial trade in technology products, including electronics and software, which play a critical role in its export economy.
• Emphasis on Sustainable Trade Practices: There is a growing focus on integrating sustainability into trade policies, promoting environmentally friendly practices and technologies.

Notable Companies in U.S. Trade Data

• Apple Inc.: A leading technology company involved in exporting electronics and importing components from various global suppliers.
• Boeing: A major aerospace manufacturer engaged in importing and exporting aircraft and aerospace products, impacting U.S. trade in the transportation sector.
• Cargill: A key player in agriculture, known for exporting and importing agricultural products, impacting the U.S. trade in commodities.
• Amazon: A significant e-commerce operator involved in the import and export of a wide range of goods, reflecting its role in the U.S. trade dynamics.
• General Motors: An important automotive manufacturer that engages in global trade of vehicles and automotive parts, highlighting the U.S. role in the automotive sector.

Accessing Techsalerator’s Data

To obtain Techsalerator’s Import/Export Trade Data for the United States, please contact us at info@techsalerator.com with your requirements. We will provide a customized quote based on the number of data fields and records needed, with delivery available within 24 hours. Ongoing access options can also be discussed.

Included Data Fields:

• Company Name
• Trade Volume
• Product Category
• Import/Export Country
• Transaction Date
• Shipping Details
• Customs Codes
• Trade Value

For detailed insights into the United States’ import and export activities and trends, Techsalerator’s dataset is an invaluable resource for staying informed and making strategic decisions.

Techsalerator’s Import/Export Trade Data for North America

Techsalerator’s Import/Export Trade Data for North America delivers an exhaustive and nuanced analysis of trade activities across the North American continent. This extensive dataset provides detailed insights into import and export transactions involving companies across various sectors within North America.

Coverage Across All North American Countries

The dataset encompasses all key countries within North America, including:

1. United States

The dataset provides detailed trade information for the United States, the largest economy in the region. It includes extensive data on trade volumes, product categories, and the key trading partners of the U.S. 2. Canada

Data for Canada covers a wide range of trade activities, including import and export transactions, product classifications, and trade relationships with major global and regional partners. 3. Mexico

Comprehensive data for Mexico includes detailed records on its trade activities, including exports and imports, key sectors, and trade agreements affecting its trade dynamics. 4. Central American Countries:

Belize Costa Rica El Salvador Guatemala Honduras Nicaragua Panama The dataset covers these countries with information on their trade flows, key products, and trade relations with North American and international partners. 5. Caribbean Countries:

Bahamas Barbados Cuba Dominica Dominican Republic Grenada Haiti Jamaica Saint Kitts and Nevis Saint Lucia Saint Vincent and the Grenadines Trinidad and Tobago Trade data for these Caribbean nations includes detailed transaction records, sector-specific trade information, and their interactions with North American trade partners. Comprehensive Data Features

Transaction Details: The dataset includes precise details on each trade transaction, such as product descriptions, quantities, values, and dates. This allows for an accurate understanding of trade flows and patterns across North America.

Company Information: It provides data on companies involved in trade, including names, locations, and industry sectors, enabling targeted business analysis and competitive intelligence.

Categorization: Transactions are categorized by industry sectors, product types, and trade partners, offering insights into market dynamics and sector-specific trends within North America.

Trade Trends: Historical data helps users analyze trends over time, identify emerging markets, and assess the impact of economic or political events on trade flows in the region.

Geographical Insights: The data offers insights into regional trade flows and cross-border dynamics between North American countries and their global trade partners, including significant international trade relationships.

Regulatory and Compliance Data: Information on trade regulations, tariffs, and compliance requirements is included, helping businesses navigate the complex regulatory environments within North America.

Applications and Benefits

Market Research: Companies can leverage the data to discover new market opportunities, analyze competitive landscapes, and understand demand for specific products across North American countries.

Strategic Planning: Insights from the data enable companies to refine trade strategies, optimize supply chains, and manage risks associated with international trade in North America.

Economic Analysis: Analysts and policymakers can monitor economic performance, evaluate trade balances, and make informed decisions on trade policies and economic development strategies.

Investment Decisions: Investors can assess trade trends and market potentials to make informed decisions about investments in North America's diverse economies.

Techsalerator’s Import/Export Trade Data for North America offers a vital resource for organizations involved in international trade, providing a thorough, reliable, and detailed view of trade activities across the continent.

Snapshot img

AI Training Dataset Market Size 2025-2029

The ai training dataset market size is valued to increase by USD 7.33 billion, at a CAGR of 29% from 2024 to 2029. Proliferation and increasing complexity of foundational AI models will drive the ai training dataset market.

Market Insights

North America dominated the market and accounted for a 36% growth during the 2025-2029.
By Service Type - Text segment was valued at USD 742.60 billion in 2023
By Deployment - On-premises segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 479.81 million 
Market Future Opportunities 2024: USD 7334.90 million
CAGR from 2024 to 2029 : 29%

Market Summary

The market is experiencing significant growth as businesses increasingly rely on artificial intelligence (AI) to optimize operations, enhance customer experiences, and drive innovation. The proliferation and increasing complexity of foundational AI models necessitate large, high-quality datasets for effective training and improvement. This shift from data quantity to data quality and curation is a key trend in the market. Navigating data privacy, security, and copyright complexities, however, poses a significant challenge. Businesses must ensure that their datasets are ethically sourced, anonymized, and securely stored to mitigate risks and maintain compliance. For instance, in the supply chain optimization sector, companies use AI models to predict demand, optimize inventory levels, and improve logistics. Access to accurate and up-to-date training datasets is essential for these applications to function efficiently and effectively. Despite these challenges, the benefits of AI and the need for high-quality training datasets continue to drive market growth. The potential applications of AI are vast and varied, from healthcare and finance to manufacturing and transportation. As businesses continue to explore the possibilities of AI, the demand for curated, reliable, and secure training datasets will only increase.

What will be the size of the AI Training Dataset Market during the forecast period?

Get Key Insights on Market Forecast (PDF) Request Free SampleThe market continues to evolve, with businesses increasingly recognizing the importance of high-quality datasets for developing and refining artificial intelligence models. According to recent studies, the use of AI in various industries is projected to grow by over 40% in the next five years, creating a significant demand for training datasets. This trend is particularly relevant for boardrooms, as companies grapple with compliance requirements, budgeting decisions, and product strategy. Moreover, the importance of data labeling, feature selection, and imbalanced data handling in model performance cannot be overstated. For instance, a mislabeled dataset can lead to biased and inaccurate models, potentially resulting in costly errors. Similarly, effective feature selection algorithms can significantly improve model accuracy and reduce computational resources. Despite these challenges, advances in model compression methods, dataset scalability, and data lineage tracking are helping to address some of the most pressing issues in the market. For example, model compression techniques can reduce the size of models, making them more efficient and easier to deploy. Similarly, data lineage tracking can help ensure data consistency and improve model interpretability. In conclusion, the market is a critical component of the broader AI ecosystem, with significant implications for businesses across industries. By focusing on data quality, effective labeling, and advanced techniques for handling imbalanced data and improving model performance, organizations can stay ahead of the curve and unlock the full potential of AI.

Unpacking the AI Training Dataset Market Landscape

In the realm of artificial intelligence (AI), the significance of high-quality training datasets is indisputable. Businesses harnessing AI technologies invest substantially in acquiring and managing these datasets to ensure model robustness and accuracy. According to recent studies, up to 80% of machine learning projects fail due to insufficient or poor-quality data. Conversely, organizations that effectively manage their training data experience an average ROI improvement of 15% through cost reduction and enhanced model performance.

Distributed computing systems and high-performance computing facilitate the processing of vast datasets, enabling businesses to train models at scale. Data security protocols and privacy preservation techniques are crucial to protect sensitive information within these datasets. Reinforcement learning models and supervised learning models each have their unique applications, with the former demonstrating a 30% faster convergence rate in certain use cases.

Data annot

National-scale geologic, geophysical, and mineral resource raster and vector data covering the United States, Canada, and Australia are provided in this data release. The data were compiled as part of the tri-national Critical Minerals Mapping Initiative (CMMI). The CMMI, established in 2019, is an international science collaboration between the U.S. Geological Survey (USGS), Geoscience Australia (GA), and the Geological Survey of Canada (GSC). One aspect of the CMMI is to use national- to global-scale earth science data to map where critical mineral prospectivity may exist using advanced machine learning approaches (Kelley, 2020). The geoscience information presented in this report include the training and evidential layers that cover all three countries and underpin the resultant prospectivity models for basin-hosted Pb-Zn mineralization described in Lawley and others (2021). It is expected that these data layers will be useful to many regional- to continental-scale studies related to a wide range of earth science research. Therefore, the data layers are organized using widely accepted GIS formats in the same map projection to increase efficiency and effectiveness of future studies. All datasets have a common geographic projection in decimal degrees using a WGS84 datum. Data for the various training and evidential layers were either derived for this study or were extracted from previous national to global-scale compilations. Data from outside work are provided here as a courtesy for completeness of the model and should be cited as the original source. Original references are provided on each child page. Where possible, data for the United States were merged to data for Canada to provide composite data that allow for continuity and seamless analyses of the earth science data across the two countries. Earth science data provided in this report include training data for the models. Training data include a mineral resource database of Pb-Zn deposits and occurrences related to either carbonate-hosted (Mississippi Valley type-MVT) or clastic-dominated (aka sedex) Pb-Zn mineralization. Evidential layers that were used as input to the models include GeoTIFF grid files consisting of ground, airborne, and satellite geophysical data (magnetic, gravity, tomography, seismic) and several related derivative products. Geologic layers incorporated into the models include shapefiles of modified lithology and faults for the United States, Canada and Australia. A global database of ancient and modern passive margins is provided here as well as a link to a database mapping the global distribution of black shale units from a previous USGS study. GeoTIFF grids of the final prospectivity models for MVT and for clastic-dominated Pb-Zn mineralization across the US, Canada, and Australia from Lawley and others (2021) are also included. Each child page describes the particular data layer and related derivative products if applicable. Kelley, K.D., 2020, International geoscience collaboration to support critical mineral discovery: U.S. Geological Survey Fact Sheet 2020–3035, 2 p., https://doi.org/10.3133/fs20203035. Lawley, C.J.M., McCafferty, A.E., Graham, G.E., Huston, D.L., Kelley, K.D., Czarnota, K., Paradis, S., Peter, J.M., Hayward, N., Barlow, M., Emsbo, P., Coyan, J., San Juan, C.A., and Gadd, M.G., 2022, Data-driven prospectivity modelling of sediment-hosted Zn-Pb mineral systems and their critical raw materials: Ore Geology Reviews, v. 141, no. 104635, https://doi.org/10.1016/j.oregeorev.2021.104635.

About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in Little Canada, MN, as reported by the U.S. Census Bureau. The dataset highlights the variation in mean household income across quintiles, offering valuable insights into income distribution and inequality.

Key observations

• Income disparities: The mean income of the lowest quintile (20% of households with the lowest income) is 16,271, while the mean income for the highest quintile (20% of households with the highest income) is 270,999. This indicates that the top earners earn 17 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 530,349, which is 195.70% higher compared to the highest quintile, and 3259.47% higher compared to the lowest quintile.

https://i.neilsberg.com/ch/little-canada-mn-mean-household-income-by-quintiles.jpeg" alt="Mean household income by quintiles in Little Canada, MN (in 2022 inflation-adjusted dollars))">

Content

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

Income Levels:

• Lowest Quintile
• Second Quintile
• Third Quintile
• Fourth Quintile
• Highest Quintile
• Top 5 Percent

Variables / Data Columns

• Income Level: This column showcases the income levels (As mentioned above).
• Mean Household Income: Mean household income, in 2022 inflation-adjusted dollars for the specific income level.

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 Little Canada median household income. You can refer the same here

IMDB Movie Metadata Multivariate Analysis

Exploring the Impact of Types, Ratings, and Genres on Audience Engagement

By Addi Ait-Mlouk [source]

About this dataset

This amazing IMDB movie metadata dataset has everything you need to uncover the secrets of cinema! Featuring a comprehensive set of variables that range from budget, revenue, and cast size to title types and genres - it holds the keys for unlocking lucrative insights about your favorite films. This dataset contains an array of information on over 10,000 film titles from 6 different countries (USA, UK, Germany, Canada, India and Japan). Its detailed columns include Movie ID (unique identifier for each film), Title Type (TV Series/Movie/Video), Production Budget & Revenue figures in USD along with a breakdown into Country-specific Currency Units such as EUR or GBP. Additionally, each entry features the Primary Genre Category it was classified under along with secondary Genres if applicable. Finally this collection includes text fields giving insight into plot keywords as well as Cast & Crew credits including names of Actors/Actresses in main roles plus other important personnel working on set such as Directors or Writers. Together all these features create an invaluable resource suitable for detailed analysis aiming to understand movie trends over time – How budgets compare across nations? What genres are doing better internationally all these fascinating questions addressed by this incredible dataset!

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For more datasets, click here.

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How to use the dataset

This dataset contain 2000 films from IMDb, containing basic metadata on the films such as genre, budget and gross box office performance. It has a variety of columns which provide valuable insights when attempting to do multivariate analysis on this data. The data is divided into metacritic score (which is a measure of critics ratings), budget (in dollars), revenue (in dollars) and eight other stats related to movies

• Metacritic Score: A metric based on critical reviews ranging from 0 - 100 indicating how well the movie was reviewed by critics.
• Budget: Total budget in US dollars for each movie. This can be used to better understand why certain films are successful or not so successful when analyzing against other metrics in the dataset
• Revenue: Total revenue earned in US dollars for each movie at Box Office and outside digital downloads/streams & TV airplay etc… we could use this to calculate revenue per budget for movies as well as success rates of certain genres over others given higher budgets
• Release Date: The release date given in string format mainly used for understanding seasonality effects between various releases in different times throughout the year e.g summer vs winter releases
  5 .Runtime: Length of time that a film plays for expressed in minutes could be useful measuring watchability/ engagement potential or correlations with average ticket prices at theatre pricing timeframes(matinees vs evening shows)

6 Genres : Genres associated with a particular film e.g comedy ,drama , horror etc … Value information taken from freedb collection created by IMDB,these categories can also help further narrow down what people like and disliked about certain films or companies producing them

7 Directors : Each directors name usually one director per film which might give us some insight into directing decisions made which have an impact on box-office performance

8 Writers : Names of writers associated with a particular project same reasoning applies here that making different decisions around who write content reflects audience response

9 Actors/Actresses : Names of some key actors associate with major roles like leads/support takes who worked on stories will give more insight into why viewers preferred them OVER OTHER PROJECTS THEY MAY HAVE BEEN ASSOCIATED WITH along their career paths

10 Countries apart from USA origin countries are identified Here since many american productions try new approaches keeping an eye out may point us towards effective techniques being implemented abroad that have potential application to American markets ADR

Research Ideas

• Creating movie recommendation systems based on user preferences: This dataset can be used to uncover patterns in user movie watching habits and preferences by factoring in multiple variables such as genre, release year, director, cast, popularity/ratings etc., and recommending similar movies accordingly.
• Cost vs Profit predictive models: This dataset can be utilized to construct a p...

Access B2B Contact Data for North American Small Business Owners with Success.ai—your go-to provider for verified, high-quality business datasets. This dataset is tailored for businesses, agencies, and professionals seeking direct access to decision-makers within the small business ecosystem across North America. With over 170 million professional profiles, it’s an unparalleled resource for powering your marketing, sales, and lead generation efforts.

Key Features of the Dataset:

Verified Contact Details

Includes accurate and up-to-date email addresses and phone numbers to ensure you reach your targets reliably.

AI-validated for 99% accuracy, eliminating errors and reducing wasted efforts.

Detailed Professional Insights

Comprehensive data points include job titles, skills, work experience, and education to enable precise segmentation and targeting.

Enriched with insights into decision-making roles, helping you connect directly with small business owners, CEOs, and other key stakeholders.

Business-Specific Information

Covers essential details such as industry, company size, location, and more, enabling you to tailor your campaigns effectively. Ideal for profiling and understanding the unique needs of small businesses.

Continuously Updated Data

Our dataset is maintained and updated regularly to ensure relevance and accuracy in fast-changing market conditions. New business contacts are added frequently, helping you stay ahead of the competition.

Why Choose Success.ai?

At Success.ai, we understand the critical importance of high-quality data for your business success. Here’s why our dataset stands out:

Tailored for Small Business Engagement Focused specifically on North American small business owners, this dataset is an invaluable resource for building relationships with SMEs (Small and Medium Enterprises). Whether you’re targeting startups, local businesses, or established small enterprises, our dataset has you covered.

Comprehensive Coverage Across North America Spanning the United States, Canada, and Mexico, our dataset ensures wide-reaching access to verified small business contacts in the region.

Categories Tailored to Your Needs Includes highly relevant categories such as Small Business Contact Data, CEO Contact Data, B2B Contact Data, and Email Address Data to match your marketing and sales strategies.

Customizable and Flexible Choose from a wide range of filtering options to create datasets that meet your exact specifications, including filtering by industry, company size, geographic location, and more.

Best Price Guaranteed We pride ourselves on offering the most competitive rates without compromising on quality. When you partner with Success.ai, you receive superior data at the best value.

Seamless Integration Delivered in formats that integrate effortlessly with your CRM, marketing automation, or sales platforms, so you can start acting on the data immediately.

Use Cases: This dataset empowers you to:

Drive Sales Growth: Build and refine your sales pipeline by connecting directly with decision-makers in small businesses. Optimize Marketing Campaigns: Launch highly targeted email and phone outreach campaigns with verified contact data. Expand Your Network: Leverage the dataset to build relationships with small business owners and other key figures within the B2B landscape. Improve Data Accuracy: Enhance your existing databases with verified, enriched contact information, reducing bounce rates and increasing ROI. Industries Served: Whether you're in B2B SaaS, digital marketing, consulting, or any field requiring accurate and targeted contact data, this dataset serves industries of all kinds. It is especially useful for professionals focused on:

Lead Generation Business Development Market Research Sales Outreach Customer Acquisition What’s Included in the Dataset: Each profile provides:

Full Name Verified Email Address Phone Number (where available) Job Title Company Name Industry Company Size Location Skills and Professional Experience Education Background With over 170 million profiles, you can tap into a wealth of opportunities to expand your reach and grow your business.

Why High-Quality Contact Data Matters: Accurate, verified contact data is the foundation of any successful B2B strategy. Reaching small business owners and decision-makers directly ensures your message lands where it matters most, reducing costs and improving the effectiveness of your campaigns. By choosing Success.ai, you ensure that every contact in your pipeline is a genuine opportunity.

Partner with Success.ai for Better Data, Better Results: Success.ai is committed to delivering premium-quality B2B data solutions at scale. With our small business owner dataset, you can unlock the potential of North America's dynamic small business market.

Get Started Today Request a sample or customize your dataset to fit your unique...

Land cover information is necessary for a large range of environmental applications related to climate impacts and adaption, emergency response, wildlife habitat, etc. In Canada, a 2008 user survey indicated that the most practical land cover data is provided in a nationwide 30 m spatial resolution format, with an update frequency of five years. In response to this need, the Canada Centre for Remote Sensing (CCRS) has generated a 30 m land cover map of Canada for the base year 2010, as well as this 2015 land cover map. This land cover dataset is also the Canadian contribution to the 30 m spatial resolution 2015 Land Cover Map of North America, which is produced by Mexican, American and Canadian government institutions under a collaboration called the North American Land Change Monitoring System (NALCMS). This land cover dataset for Canada is produced using observation from Operational Land Imager (OLI) Landsat sensor. An accuracy assessment based on 806 randomly distributed samples shows that land cover data produced with this new approach has achieved 79.90% accuracy with no marked spatial disparities. - Land Cover of Canada - Cartographic Product Collection

This is the accompanying dataset to the following paper https://www.nature.com/articles/s41597-023-01975-w

Caravan is an open community dataset of meteorological forcing data, catchment attributes, and discharge daat for catchments around the world. Additionally, Caravan provides code to derive meteorological forcing data and catchment attributes from the same data sources in the cloud, making it easy for anyone to extend Caravan to new catchments. The vision of Caravan is to provide the foundation for a truly global open source community resource that will grow over time.

If you use Caravan in your research, it would be appreciated to not only cite Caravan itself, but also the source datasets, to pay respect to the amount of work that was put into the creation of these datasets and that made Caravan possible in the first place.

All current development and additional community extensions can be found at https://github.com/kratzert/Caravan

Channel Log:

23 May 2022: Version 0.2 - Resolved a bug when renaming the LamaH gauge ids from the LamaH ids to the official gauge ids provided as "govnr" in the LamaH dataset attribute files.

24 May 2022: Version 0.3 - Fixed gaps in forcing data in some "camels" (US) basins.

15 June 2022: Version 0.4 - Fixed replacing negative CAMELS US values with NaN (-999 in CAMELS indicates missing observation).

1 December 2022: Version 0.4 - Added 4298 basins in the US, Canada and Mexico (part of HYSETS), now totalling to 6830 basins. Fixed a bug in the computation of catchment attributes that are defined as pour point properties, where sometimes the wrong HydroATLAS polygon was picked. Restructured the attribute files and added some more meta data (station name and country).

16 January 2023: Version 1.0 - Version of the official paper release. No changes in the data but added a static copy of the accompanying code of the paper. For the most up to date version, please check https://github.com/kratzert/Caravan

10 May 2023: Version 1.1 - No data change, just update data description.

17 May 2023: Version 1.2 - Updated a handful of attribute values that were affected by a bug in their derivation. See https://github.com/kratzert/Caravan/issues/22 for details.

16 April 2024: Version 1.4 - Added 9130 gauges from the original source dataset that were initially not included because of the area thresholds (i.e. basins smaller than 100sqkm or larger than 2000sqkm). Also extended the forcing period for all gauges (including the original ones) to 1950-2023. Added two different download options that include timeseries data only as either csv files (Caravan-csv.tar.xz) or netcdf files (Caravan-nc.tar.xz). Including the large basins also required an update in the earth engine code

16 Jan 2025: Version 1.5 - Added FAO Penman-Monteith PET (potential_evaporation_sum_FAO_PENMAN_MONTEITH) and renamed the ERA5-LAND potential_evaporation band to potential_evaporation_sum_ERA5_LAND. Also added all PET-related climated indices derived with the Penman-Monteith PET band (suffix "_FAO_PM") and renamed the old PET-related indices accordingly (suffix "_ERA5_LAND").

This dataset contains a (mostly) complete set of results from marathons across the United States and Canada in 2024.

The dataset is restricted to races with more than 200 finishers. Some races are therefore excluded, but they account for a small share of the total number of finishers.

The dataset is also restricted to races that are USATF-certified. Most of the races are road marathons, although some trail races are included. But these are "road-like" trail marathons, where times are similar to the road and can be used for Boston qualifying purposes.

This dataset is similar to the one I created with results from 2023. The two datasets can be combined, but the race names differ in some cases. You'll have to clean up the race names to get them to group correctly.

I initially collected these results to prepare the dataset for the 2026 Boston Marathon Cutoff Time Tracker. I also used it to update my percentile-based age grade calculator, to calculate the average marathon times for each age group, to identify a list of the largest races in the United States, and to support various other analyses.

If time permits, I plan to update this dataset to include additional information about each race - including the location and the weather on race day.

Derived from over 150 years of lexical research, these comprehensive textual and audio data, focused on American English, provide linguistically annotated data. Ideal for NLP applications, LLM training and/or fine-tuning, as well as educational and game apps.

One of our flagship datasets, the American English data is expertly curated and linguistically annotated by professionals, with annual updates to ensure accuracy and relevance. The below datasets in American English are available for license:

1. American English Monolingual Dictionary Data
2. American English Synonyms and Antonyms Data
3. American English Pronunciations with Audio

Key Features (approximate numbers):

1. American English Monolingual Dictionary Data

Our American English Monolingual Dictionary Data is the foremost authority on American English, including detailed tagging and labelling covering parts of speech (POS), grammar, region, register, and subject, providing rich linguistic information. Additionally, all grammar and usage information is present to ensure relevance and accuracy.

• Headwords: 140,000
• Senses: 222,000
• Sentence examples: 140,000
• Format: XML and JSON format
• Delivery: Email (link-based file sharing) and REST API
• Updated frequency: annually

1. American English Synonyms and Antonyms Data

The American English Synonyms and Antonyms Dataset is a leading resource offering comprehensive, up-to-date coverage of word relationships in contemporary American English. It includes rich linguistic details such as precise definitions and part-of-speech (POS) tags, making it an essential asset for developing AI systems and language technologies that require deep semantic understanding.

• Synonyms: 600,000
• Antonyms: 22,000
• Format: XML and JSON format
• Delivery: Email (link-based file sharing) and REST API
• Updated frequency: annually

1. American English Pronunciations with Audio (word-level)

This dataset provides IPA transcriptions and clean audio data in contemporary American English. It includes syllabified transcriptions, variant spellings, POS tags, and pronunciation group identifiers. The audio files are supplied separately and linked where available for seamless integration - perfect for teams building TTS systems, ASR models, and pronunciation engines.

• Transcriptions (IPA): 250,000
• Audio files: 180,000
• Format: XLSX (for transcriptions), MP3 and WAV (audio files)
• Updated frequency: annually

Use Cases:

We consistently work with our clients on new use cases as language technology continues to evolve. These include NLP applications, TTS, dictionary display tools, games, translation machine, AI training and fine-tuning, word embedding, and word sense disambiguation (WSD).

If you have a specific use case in mind that isn't listed here, we’d be happy to explore it with you. Don’t hesitate to get in touch with us at Growth.OL@oup.com to start the conversation.

Pricing:

Oxford Languages offers flexible pricing based on use case and delivery format. Our datasets are licensed via term-based IP agreements and tiered pricing for API-delivered data. Whether you’re integrating into a product, training an LLM, or building custom NLP solutions, we tailor licensing to your specific needs.

Contact our team or email us at Growth.OL@oup.com to explore pricing options and discover how our language data can support your goals. Please note that some datasets may have rights restrictions. Contact us for more information.

About the sample:

To help you explore the structure and features of our dataset on this platform, we provide a sample in CSV and/or JSON formats for one of the presented datasets, for preview purposes only, as shown on this page. This sample offers a quick and accessible overview of the data's contents and organization.

Our full datasets are available in various formats, depending on the language and type of data you require. These may include XML, JSON, TXT, XLSX, CSV, WAV, MP3, and other file types. Please contact us (Growth.OL@oup.com) if you would like to receive the original sample with full details.

The North American Rail Network (NARN) Rail Lines: CPKC View dataset is from the Federal Railroad Administration (FRA) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). This dataset is a subset of the NARN Rail Lines dataset that represents the ownership and trackage rights for the Class I railroad “Canadian Pacific Kansas City (CPKC).” PLEASE NOTE: “Canadian Pacific (CP)” and “Kansas City Southern (KCS)” have merged per a business prospective to form “Canadian Pacific Kansas City (CPKC).” However, this is not yet reflected in the North American Rail Network (NARN) until the dispatching is unified. This view layer has combined “Canadian Pacific (CP)” and “Kansas City Southern (KCS)” per their ownerships and trackage rights as stipulated in the NARN. It is derived from the North American Rail Network (NARN) Lines dataset, and for more information please consult, https://doi.org/10.21949/1519415. The NARN Rail Lines dataset is a database that provides ownership, trackage rights, type, passenger, STRACNET, and geographic reference for North America's railway system at 1:24,000 or better within the United States. The data set covers all 50 States, the District of Columbia, Mexico, and Canada. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/1528950