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This dataset contains a cleaned version of this dataset https://www.kaggle.com/rikdifos/credit-card-approval-prediction on credit cards.

The size of the Data Classification market was valued at USD 748.7 million in 2023 and is projected to reach USD 1750.51 million by 2032, with an expected CAGR of 12.9% during the forecast period.

The Data Classification Market is booming, with a 24% CAGR projected to 2033! Learn about key drivers, market trends, segments (Software, Cloud, BFSI), and top companies like AWS & Microsoft. Explore regional growth and forecast data in our comprehensive analysis. Key drivers for this market are: , Government Regulations and Compliance for Privacy & Data Security; Concern for Data Theft due to Mismanagement; Surge in Analytics Applications with Stored Data. Potential restraints include: , Government Regulations and Compliance for Privacy & Data Security; Concern for Data Theft due to Mismanagement; Surge in Analytics Applications with Stored Data. Notable trends are: Surge in Data Security Solutions for Increased Malware Infection Rates in Computers.

Problem Statement: We’re excited to launch a unique challenge in the lead-up to MLDS 2025, where your skills in fine-tuning Small language models (SLMs) will be tested. This hackathon focuses on multi-class classification—your task is to fine-tune an SLM to classify data into multiple categories using the provided dataset accurately.

In this competition, you will have to develop an algorithm for automatic categorization of products by their name and attributes, even in conditions of incomplete marking.

The category system is arranged in the form of a hierarchical tree (up to 5 levels of nesting), and product data comes from many trading platforms, which creates a number of difficulties:

• Product marking is incomplete and imperfect.
• The attributes of the same products may differ on different platforms or be absent altogether.
• As the catalog expands, new platforms and categories may appear that did not exist before.

In this competition, we invite participants to try their hand at setting up a task as close to the real one as possible:

• The task is complicated by the fact that in the training sample, categories are marked only for half of the products, and in the test sample there will be categories for which there are no marked examples.
• At the same time, you are provided with a complete list of possible categories in advance - will you be able to take them all into account and accurately assign products to the right classes?
• LLMs (Large Language Models) look promising for such tasks – but will they really help? This is a great chance to test different approaches!

Number and value of loans by category

The two available datasets were used to conduct the quantitative analysis of the text classification area. The set, such as:

biblio.bib contains all articles that are grouped in categories

biblio.csv contains processed records from biblio.bib, based on it were built the statistics presented in the article

Idea

The vision behind creating this dataset is to have a data set for classifying animal species. A lot of animal species can be included in this data set, which is why it gets revised regularly. This will help to create a machine-learning model that can accurately classify animal species.

Class Distribution

This is Animal Classification Data-set made for the Multi-Class Image Recognition Task. The dataset contains 15 Classes, these classes are :

1. Beetle
2. Butterfly
3. Cat
4. Cow
5. Dog
6. Elephant
7. Gorilla
8. Hippo
9. Lizard
10. Monkey
11. Mouse
12. Panda
13. Spider
14. Tiger
15. Zebra

Data Distribution

The data is split into 6 directories:

Interesting Data * As the name suggests, this folder contains 5 interesting images per class. These are called Interesting images because it will be fascinating to know which class the model allocates to these shots. Based on the model's prediction, we can understand the model's understanding of that class.

Testing Data * This folder is filled with a random number of images per class. As the name indicates this folder is purposefully made to incorporate testing images, that is images on which the model will be tested after training.

TFRecords Data * This folder contains the data in Tensorflow records format. All the images present in TF records format have already been resized to 256 x 256 pixels and normalized.

Train Augmented * This time, an additional train augmented data is added to the data set. As per the name, this directory contains augmented images per class. 5 augmented images per original image, in total each class has 10,000 augmented images. This is done to increase the data set size because, With the increase in the total number of classes, the model complexity increases. And thus we require more data to train the model. The best way to get more data is data augmentation. It is highly recommended to shuffle the data before/after loading it.

Training Images * Each class is filled with 2000 images for training purpose. This is the data that is used for training the model. In this case, all the images are resized to 256 by 256 pixels and normalized to have the input pixel range of 0 to 1.

Validation Images * This folder contains 100/200 images per class, this is intentionally created for validation purposes. Images from this directory will be used at the time of training for validating the model's performance.

DeepNets

Data Classification for Public Sector Market Outlook

According to our latest research, the global Data Classification for Public Sector market size reached USD 1.95 billion in 2024, with robust growth driven by increasing regulatory requirements and the rapid digital transformation of public sector organizations. The market is projected to expand at a CAGR of 21.7% from 2025 to 2033, reaching a forecasted value of USD 13.8 billion by 2033. The primary growth factor fueling this trajectory is the escalating need for robust data governance and compliance frameworks across government agencies, law enforcement, public healthcare, and educational institutions worldwide.

One of the most significant growth drivers for the Data Classification for Public Sector market is the intensifying regulatory landscape. Governments across the globe are enacting stringent data privacy and protection laws, such as GDPR in Europe, CCPA in the United States, and similar mandates in Asia Pacific and Latin America. These regulations require public sector organizations to implement advanced data classification solutions to ensure sensitive information is properly identified, labeled, and managed. The risk of severe penalties for non-compliance, coupled with the necessity to maintain public trust, is compelling agencies to invest in sophisticated data classification tools. Moreover, the proliferation of digital services and e-governance initiatives has led to exponential growth in data volumes, further necessitating robust classification and management strategies.

Technological advancements are also playing a pivotal role in market expansion. The integration of artificial intelligence (AI) and machine learning (ML) into data classification software is enabling more accurate and automated identification of sensitive data. These technologies help public sector organizations reduce manual intervention, minimize human error, and enhance operational efficiency. Furthermore, the adoption of cloud-based solutions is providing scalability, flexibility, and cost-effectiveness, making it easier for government bodies of all sizes to deploy and manage data classification systems. As cyber threats become more sophisticated, the demand for proactive risk management and real-time data visibility is accelerating, pushing public sector entities to upgrade their data governance frameworks.

Another critical growth factor is the increasing focus on risk management and cyber resilience. Public sector organizations are prime targets for cyberattacks due to the sensitive nature of the data they handle. High-profile breaches and ransomware incidents have underscored the need for comprehensive data classification policies as a foundational layer of defense. By accurately categorizing and prioritizing data, agencies can implement more effective access controls, monitor data flows, and respond swiftly to security incidents. This proactive approach not only mitigates risks but also supports compliance with internal and external audit requirements. As a result, the market is witnessing heightened investments in both software and services tailored to the unique needs of the public sector.

From a regional perspective, North America currently leads the market, accounting for the largest share in 2024, closely followed by Europe and Asia Pacific. This dominance is attributed to early adoption of digital technologies, well-established regulatory frameworks, and substantial government spending on cybersecurity and data protection. However, the Asia Pacific region is expected to witness the highest CAGR over the forecast period, driven by rapid digitalization initiatives, expanding government IT budgets, and the emergence of new data protection laws. Latin America and the Middle East & Africa are also showing promising growth, supported by increasing awareness and gradual regulatory developments. The global landscape is becoming increasingly interconnected, with cross-border data flows and collaborative governance efforts shaping the future of data classification in the public sector.

Component Analysis

The Component segment of the Data Classification for Public Sector market is bifurcated into Software and Services, each playing a distinct yet complementary role in addressing the complex needs of public sector organizations. Software solutions form the backbone of data classification initiatives, offering automated tools for identifying, labeling, and managing sen

This policy document, approved by the City's Data Governance Committee in December 2019, outlines the organization's data classification levels and process for determining the level of risk posed by the contents of a dataset. The purpose of this document and associated process is to ensure that staff are using a consistent process that protects the City and its residents from risk of accidentally making public or otherwise sharing with inappropriate parties data that may cause harm to a specific organization or individual.The City has four levels of data classification: public; internal use; sensitive; and restricted. The definitions and examples of these levels are provided in the document.This document also provides a workflow diagram staff can use when classifying data. This may be helpful for members of the public to better understand how classification decisions are made by staff and the Data Governance Committee.

Wave statistics computed using output from the NOAA WWIII hindcast simulations, spanning thirty years from 1980 to 2009. The statistics are computed based on frequency-directional variance density spectra every three hours for 1951 locations in US waters.

Ecologists use classifications of individuals in categories to understand composition of populations and communities. These categories might be defined by demographics, functional traits, or species. Assignment of categories is often imperfect, but frequently treated as observations without error. When individuals are observed but not classified, these “partial” observations must be modified to include the missing data mechanism to avoid spurious inference.

We developed two hierarchical Bayesian models to overcome the assumption of perfect assignment to mutually exclusive categories in the multinomial distribution of categorical counts, when classifications are missing. These models incorporate auxiliary information to adjust the posterior distributions of the proportions of membership in categories. In one model, we use an empirical Bayes approach, where a subset of data from one year serves as a prior for the missing data the next. In the other approach, we use a small random sample of data within a year to inform the distribution of the missing data.

We performed a simulation to show the bias that occurs when partial observations were ignored and demonstrated the altered inference for the estimation of demographic ratios. We applied our models to demographic classifications of elk (Cervus elaphus nelsoni) to demonstrate improved inference for the proportions of sex and stage classes.

We developed multiple modeling approaches using a generalizable nested multinomial structure to account for partially observed data that were missing not at random for classification counts. Accounting for classification uncertainty is important to accurately understand the composition of populations and communities in ecological studies.

📄 Paper: Efficient Few-shot Learning for Multi-label Classification of Scientific Documents with Many Classes (ICNLSP 2024) 💻 GitHub: https://github.com/sebischair/FusionSent This is a dataset of scientific documents derived from arXiv metadata. The arXiv metadata provides information about more than 2 million scholarly articles published in arXiv from various scientific fields. We use this metadata to create a dataset of 203,961 titles and abstracts categorized into 130 different classes.… See the full description on the dataset page: https://huggingface.co/datasets/TimSchopf/arxiv_categories.

This data set consists of land use classification data derived from Landsat 5 data for the Soil Moisture Experiment 2003 (SMEX03).

The data contains 300 skulls. The data is divided into three groups with 100 scans in three categories Females, Males, and Mix. The purpose of the project was to generate a machine learning algorithm to be able reconstruct missing parts of the skull for cranioplasty. Each scan was verified to ensure normal skull shape. To ensure data privacy the faces have been deblurred with an in-house developed algorithm.

1) Data Introduction • The Mushroom dataset focuses on identifying whether mushrooms are edible or poisonous based on their physical characteristics. This dataset, sourced from the UCI Machine Learning Repository, consists of 8124 instances detailing attributes like cap color, gill size, stalk shape, and odor, which are crucial for classification.

2) Data Utilization (1) Mushrooms data has characteristics that: • It includes comprehensive attributes from gilled mushrooms, categorized into various classes based on edibility. • The dataset is essential for developing machine learning models that can accurately classify mushrooms, which is critical for both educational purposes and practical applications. (2) Mushrooms data can be used to: • Educational Resource: Used in academic settings to teach students about biological data classification and the importance of accurate classification in environmental biology. • Food Safety: Assists in the development of automated systems to help foragers and consumers distinguish between safe and toxic mushrooms.

Discover the booming GRC Data Classification market, projected to reach $15 billion by 2025 with a 12% CAGR. This comprehensive analysis explores market drivers, trends, restraints, and key players like IBM & Microsoft, offering insights into regional market share and future growth potential across BFSI, Government, and other sectors. Learn more about data classification strategies for enhanced security and regulatory compliance.

This dataset lists categories of open datasets from 40 European open data catalogs. The 40 European data catalogues were taken from four countries (France, Germany, Spain, and the United Kingdom) at a rate of 10 per country. These categories are an indicator of the topics of interests in the respective countries (back in 2016), the types of questions data publishers assume users will ask, and ultimately the types of questions citizens can ask.