Autonomous Data Cleaning with AI Market Outlook

According to our latest research, the Global Autonomous Data Cleaning with AI market size was valued at $1.4 billion in 2024 and is projected to reach $8.2 billion by 2033, expanding at a robust CAGR of 21.8% during 2024–2033. This remarkable growth is primarily fueled by the exponential increase in enterprise data volumes and the urgent need for high-quality, reliable data to drive advanced analytics, machine learning, and business intelligence initiatives. The autonomous data cleaning with AI market is being propelled by the integration of artificial intelligence and machine learning algorithms that automate the tedious and error-prone processes of data cleansing, normalization, and validation, enabling organizations to unlock actionable insights with greater speed and accuracy. As businesses across diverse sectors increasingly recognize the strategic value of data-driven decision-making, the demand for autonomous data cleaning solutions is expected to surge, transforming how organizations manage and leverage their data assets globally.

Regional Outlook

North America currently holds the largest share of the autonomous data cleaning with AI market, accounting for over 38% of the global market value in 2024. This dominance is underpinned by the region’s mature technological infrastructure, high adoption rates of AI-driven analytics, and the presence of leading technology vendors and innovative startups. The United States, in particular, leads in enterprise digital transformation, with sectors such as BFSI, healthcare, and IT & telecommunications aggressively investing in automated data quality solutions. Stringent regulatory requirements around data governance, such as HIPAA and GDPR, have further incentivized organizations to deploy advanced data cleaning platforms to ensure compliance and mitigate risks. The region’s robust ecosystem of cloud service providers and AI research hubs also accelerates the deployment and integration of autonomous data cleaning tools, positioning North America at the forefront of market innovation and growth.

Asia Pacific is emerging as the fastest-growing region in the autonomous data cleaning with AI market, projected to register a remarkable CAGR of 25.6% through 2033. The region’s rapid digitalization, expanding e-commerce sector, and government-led initiatives to promote smart manufacturing and digital health are driving significant investments in AI-powered data management solutions. Countries such as China, India, Japan, and South Korea are witnessing a surge in data generation from mobile applications, IoT devices, and cloud platforms, necessitating robust autonomous data cleaning capabilities to ensure data integrity and business agility. Local enterprises are increasingly partnering with global technology providers and investing in in-house AI talent to accelerate adoption. Furthermore, favorable policy reforms and incentives for AI research and development are catalyzing the advancement and deployment of autonomous data cleaning technologies across diverse industry verticals.

In contrast, emerging economies in Latin America, the Middle East, and Africa are experiencing a gradual uptake of autonomous data cleaning with AI, shaped by unique challenges such as limited digital infrastructure, skills gaps, and budget constraints. While the potential for market expansion is substantial, particularly in sectors like banking, government, and telecommunications, adoption is often hindered by concerns over data privacy, lack of standardized frameworks, and the high upfront costs of AI integration. However, localized demand for real-time analytics, coupled with international investments in digital transformation and capacity building, is gradually fostering an environment conducive to the adoption of autonomous data cleaning solutions. Policy initiatives aimed at enhancing digital literacy and supporting startup ecosystems are also expected to play a pivotal role in bridging the adoption gap and unleashing new growth opportunities in these regions.

Report Scope

Global Data Cleaning Tools Market is segmented by Application (Big data analytics_ IT departments_ Marketing_ Financial services), Type (Automated data cleaning tools_ Data validation tools_ Data deduplication software_ Data normalization software_ Data enrichment tools), and Geography (North America_ LATAM_ West Europe_Central & Eastern Europe_ Northern Europe_ Southern Europe_ East Asia_ Southeast Asia_ South Asia_ Central Asia_ Oceania_ MEA)

Yield Data Cleaning Software Market Outlook

According to our latest research, the global Yield Data Cleaning Software market size in 2024 stands at USD 1.14 billion, with a robust compound annual growth rate (CAGR) of 13.2% expected from 2025 to 2033. By the end of 2033, the market is forecasted to reach USD 3.42 billion. This remarkable market expansion is being driven by the increasing adoption of precision agriculture technologies, the proliferation of big data analytics in farming, and the rising need for accurate, real-time agricultural data to optimize yields and resource efficiency.

One of the primary growth factors fueling the Yield Data Cleaning Software market is the rapid digital transformation within the agriculture sector. The integration of advanced sensors, IoT devices, and GPS-enabled machinery has led to an exponential increase in the volume of raw agricultural data generated on farms. However, this data often contains inconsistencies, errors, and redundancies due to equipment malfunctions, environmental factors, and human error. Yield Data Cleaning Software plays a critical role by automating the cleansing, validation, and normalization of such datasets, ensuring that only high-quality, actionable information is used for decision-making. As a result, farmers and agribusinesses can make more informed choices, leading to improved crop yields, efficient resource allocation, and reduced operational costs.

Another significant driver is the growing emphasis on sustainable agriculture and environmental stewardship. Governments and regulatory bodies across the globe are increasingly mandating the adoption of data-driven practices to minimize the environmental impact of farming activities. Yield Data Cleaning Software enables stakeholders to monitor and analyze field performance accurately, track input usage, and comply with sustainability standards. Moreover, the software’s ability to integrate seamlessly with farm management platforms and analytics tools enhances its value proposition. This trend is further bolstered by the rising demand for traceability and transparency in the food supply chain, compelling agribusinesses to invest in robust data management solutions.

The market is also witnessing substantial investments from technology providers, venture capitalists, and agricultural equipment manufacturers. Strategic partnerships and collaborations are becoming commonplace, with companies seeking to enhance their product offerings and expand their geographical footprint. The increasing awareness among farmers about the benefits of data accuracy and the availability of user-friendly, customizable software solutions are further accelerating market growth. Additionally, ongoing advancements in artificial intelligence (AI) and machine learning (ML) are enabling more sophisticated data cleaning algorithms, which can handle larger datasets and deliver deeper insights, thereby expanding the market’s potential applications.

Regionally, North America continues to dominate the Yield Data Cleaning Software market, supported by its advanced agricultural infrastructure, high rate of technology adoption, and significant investments in agri-tech startups. Europe follows closely, driven by stringent environmental regulations and a strong focus on sustainable farming practices. The Asia Pacific region is emerging as a high-growth market, fueled by the rapid modernization of agriculture, government initiatives to boost food security, and increasing awareness among farmers about the benefits of digital solutions. Latin America and the Middle East & Africa are also showing promising growth trajectories, albeit from a smaller base, as they gradually embrace precision agriculture technologies.

Component Analysis

The Yield Data Cleaning Software market is bifurcated by component into Software and Services. The software segment currently accounts for the largest share of the market, underpinned by the increasing adoption of integrated farm management solutions and the demand for user-friendly platforms that can seamlessly process vast amounts of agricultural data. Modern yield data cleaning software solutions are equipped with advanced algorithms capable of detecting and rectifying data anomalies, thus ensuring the integrity and reliability of yield datasets. As the complexity of agricultural operations grows, the need for scalable, customizable software that can adapt to

Corporate Registry Data Normalization Market Outlook

According to our latest research, the global corporate registry data normalization market size reached USD 1.42 billion in 2024, reflecting a robust expansion driven by digital transformation and regulatory compliance demands across industries. The market is forecasted to grow at a CAGR of 13.6% from 2025 to 2033, reaching a projected value of USD 4.23 billion by 2033. This impressive growth is primarily attributed to the increasing need for accurate, standardized, and accessible corporate data to support compliance, risk management, and digital business processes in a rapidly evolving regulatory landscape.

One of the primary growth factors fueling the corporate registry data normalization market is the escalating global regulatory pressure on organizations to maintain clean, consistent, and up-to-date business entity data. With the proliferation of anti-money laundering (AML), know-your-customer (KYC), and data privacy regulations, companies are under immense scrutiny to ensure that their corporate records are accurate and accessible for audits and compliance checks. This regulatory environment has led to a surge in adoption of data normalization solutions, especially in sectors such as banking, financial services, insurance (BFSI), and government agencies. As organizations strive to minimize compliance risks and avoid hefty penalties, the demand for advanced software and services that can seamlessly normalize and harmonize disparate registry data sources continues to rise.

Another significant driver is the exponential growth in data volumes, fueled by digitalization, mergers and acquisitions, and global expansion of enterprises. As organizations integrate data from multiple jurisdictions, subsidiaries, and business units, they face massive challenges in consolidating and reconciling heterogeneous registry data formats. Data normalization solutions play a critical role in enabling seamless data integration, providing a single source of truth for corporate identity, and powering advanced analytics and automation initiatives. The rise of cloud-based platforms and AI-powered data normalization tools is further accelerating market growth by making these solutions more scalable, accessible, and cost-effective for organizations of all sizes.

Technological advancements are also shaping the trajectory of the corporate registry data normalization market. The integration of artificial intelligence, machine learning, and natural language processing into normalization tools is revolutionizing the way organizations cleanse, match, and enrich corporate data. These technologies enhance the accuracy, speed, and scalability of data normalization processes, enabling real-time updates and proactive risk management. Furthermore, the proliferation of API-driven architectures and interoperability standards is facilitating seamless connectivity between corporate registry databases and downstream business applications, fueling broader adoption across industries such as legal, healthcare, and IT & telecom.

From a regional perspective, North America continues to dominate the corporate registry data normalization market, driven by stringent regulatory frameworks, early adoption of advanced technologies, and a high concentration of multinational corporations. However, Asia Pacific is emerging as the fastest-growing region, propelled by rapid digitalization, increasing cross-border business activities, and evolving regulatory requirements. Europe remains a key market due to GDPR and other data-centric regulations, while Latin America and the Middle East & Africa are witnessing steady growth as local governments and enterprises invest in digital infrastructure and compliance modernization.

Component Analysis

The corporate registry data normalization market is segmented by component into software and services, each playing a pivotal role in the ecosystem. Software solutions are designed to automate and streamline the normalization process, offering functionalities such as data cleansing, deduplication, matching, and enrichment. These platforms often leverage advanced algorithms and machine learning to handle large volumes of complex, unstructured, and multilingual data, making them indispensable for organizations with global operations. The software segment is witnessing substantial investment in research and development, with vendors focusing on enhancing

This dataset is designed for fine-tuning text models to clean and standardize job titles. It helps convert raw, unstructured job titles into a clean, professional format, making it useful for NLP tasks, job classification models, and AI-driven resume/job-matching systems.

Dataset Details:

• 📌 Total Entries: 2,892
• 🌍 Language: Mostly English
• 🔄 Duplicates: Some similar job titles may appear with slight variations
• 🏷️ Format: JSON (Input → Target)
• 📊 Use Case: Data cleaning, job title normalization, NLP fine-tuning

This dataset is ideal for training models to extract, clean, and map job titles efficiently. 🚀

Example Data:

Mass spectrometry (MS)-based proteomics data analysis is composed of many stages from quality control, data cleaning, and normalization to statistical and functional analysis, without forgetting multiple visualization steps. All of these need to be reported next to published results to make them fully understandable and reusable for the community. Although this seems straightforward, exhaustively reporting all aspects of an analysis workflow can be tedious and error prone. This letter reports good practices when describing data analysis of MS-based proteomics data and discusses why and how the community should put efforts into more transparently reporting data analysis workflows.

🎯 Dataset Overview

A intentionally messy synthetic personal finance dataset designed for practicing real-world data preprocessing challenges before building AI-based expense forecasting models.

💡 Context & Inspiration

Created for BudgetWise - an AI expense forecasting tool. This dataset simulates real-world financial transaction data with all the messiness data scientists encounter in production: inconsistent formats, typos, duplicates, outliers, and missing values.

🔍 What Makes This Dataset Special?

• Realistic Data Quality Issues: ~30% of data contains intentional errors
• Class Imbalance: 85% expenses vs 15% income (perfect for SMOTE practice)
• Multi-format Dates: 4 different date formats mixed throughout
• Currency Chaos: Mixed symbols (₹, $, Rs.) in amounts
• Text Inconsistencies: Typos, case variations, and duplicates

📊 Key Statistics

• 15,000+ transactions
• 150 unique users
• 4-year period (2021-2024)
• 9 feature columns
• ~6% duplicate rows
• ~5% missing values per column

🎓 Learning Opportunities

Perfect for practicing: - Data cleaning & normalization - Handling missing values - Date parsing & time-series analysis - Currency extraction & conversion - Outlier detection - Feature engineering - Class balancing (SMOTE) - Text standardization - Duplicate detection

We have revised the dataset, ensuring that it is thoroughly processed and ready for analysis. The attached second dataset has undergone comprehensive preprocessing algorithms. This preprocessing includes steps such as data cleaning, normalization, and feature extraction to enhance the quality and usability of the data. These steps are crucial to ensure that the dataset is free from inconsistencies, missing values, and irrelevant information, thereby improving the accuracy and reliability of the subsequent machine learning models.

Corporate Registry Data Normalization Market Outlook

According to our latest research, the global Corporate Registry Data Normalization market size reached USD 1.42 billion in 2024, driven by the increasing demand for standardized business information and regulatory compliance across industries. The market is experiencing robust expansion, with a Compound Annual Growth Rate (CAGR) of 13.8% anticipated over the forecast period. By 2033, the market is projected to attain a value of USD 4.24 billion, reflecting the growing importance of accurate, unified corporate registry data for operational efficiency, risk management, and digital transformation initiatives. This growth is primarily fueled by the rising complexity of business operations, stricter regulatory requirements, and the need for seamless data integration across diverse IT ecosystems.

The primary growth factor in the Corporate Registry Data Normalization market is the accelerating pace of digital transformation across both private and public sectors. Organizations are increasingly reliant on accurate and standardized corporate data to drive business intelligence, enhance customer experiences, and comply with evolving regulatory frameworks. As enterprises expand globally, the complexity of maintaining consistent and high-quality data across various jurisdictions has intensified, necessitating advanced data normalization solutions. Furthermore, the proliferation of mergers and acquisitions, cross-border partnerships, and multi-jurisdictional operations has made data normalization a critical component for ensuring data integrity, reducing operational risks, and supporting agile business decisions. The integration of artificial intelligence and machine learning technologies into data normalization platforms is further amplifying the market’s growth by automating complex data cleansing, enrichment, and integration processes.

Another significant driver for the Corporate Registry Data Normalization market is the increasing emphasis on regulatory compliance and risk mitigation. Industries such as BFSI, healthcare, and government are under mounting pressure to adhere to stringent data governance standards, anti-money laundering (AML) regulations, and Know Your Customer (KYC) requirements. Standardizing corporate registry data enables organizations to streamline compliance processes, conduct more effective due diligence, and reduce the risk of financial penalties or reputational damage. Additionally, the growing adoption of cloud-based solutions has made it easier for organizations to implement scalable, cost-effective data normalization tools, further propelling market growth. The shift towards cloud-native architectures is also enabling real-time data synchronization and collaboration, which are essential for organizations operating in dynamic, fast-paced environments.

The increasing volume and variety of corporate data generated from digital channels, third-party sources, and internal systems are also contributing to the expansion of the Corporate Registry Data Normalization market. Enterprises are recognizing the value of leveraging normalized data to unlock advanced analytics, improve data-driven decision-making, and gain a competitive edge. The demand for data normalization is particularly strong among multinational corporations, financial institutions, and legal firms that manage vast repositories of entity data across multiple regions and regulatory environments. As organizations continue to invest in data quality initiatives and master data management (MDM) strategies, the adoption of sophisticated data normalization solutions is expected to accelerate, driving sustained market growth over the forecast period.

From a regional perspective, North America currently dominates the Corporate Registry Data Normalization market, accounting for the largest share in 2024, followed closely by Europe and the rapidly growing Asia Pacific region. The strong presence of major technology providers, early adoption of advanced data management solutions, and stringent regulatory landscape in North America are key factors contributing to its leadership position. Meanwhile, Asia Pacific is projected to exhibit the highest CAGR during the forecast period, driven by the digitalization of government and commercial registries, expanding financial services sector, and increasing cross-border business activities. Latin America and the Middle East & Africa are also witnessing steady growth, supporte

This document describes the methodology behind the Housing Affordability Dashboard (2025–2030). It includes details on the datasets used (U.S. Census Bureau ACS, HUD housing data, and local datasets), preprocessing steps such as data cleaning and normalization, and the geospatial techniques applied in ArcGIS Pro and ArcGIS Online. The methodology also explains affordability metrics, how they were calculated, and the workflow for integrating results into the dashboard. The goal is to ensure transparency, reproducibility, and clarity in how housing affordability patterns were derived and presented.

The Data Preparation Tools Market size was valued at USD 5.93 billion in 2023 and is projected to reach USD 16.86 billion by 2032, exhibiting a CAGR of 16.1 % during the forecasts period. The Data Preparation Tools Market is witnessing robust growth due to the increasing need for data accessibility and insights. Key drivers include the benefits of hybrid seeds, government incentives, rising food security concerns, and technological advancements. Data preparation tools streamline the process of transforming raw data into a usable format for analysis. They include software and platforms designed to cleanse, integrate, and structure data from diverse sources. Popular tools like Alteryx, Informatica, and Talend offer intuitive interfaces for data cleaning, normalization, and merging. These tools automate repetitive tasks, ensuring data quality and consistency. Advanced features include data profiling to detect anomalies, data enrichment through external sources, and compatibility with various data formats. Recent developments include: In May 2022, Alteryx, the U.S.-based computer software company introduced Alteryx AiDIN, a machine learning (ML) and generative AI engine that powers the Alteryx Analytics Cloud Platform. Magic Documents, a brand-new Alteryx Auto Insights product, transforms data insights reporting and sharing with stakeholders by using generative AI to create a dynamic deployment for users to better understand and document business processes. , In June 2022, Salesforce, Inc., a cloud-based software company, launched "Mulesoft," a unified solution for data integration, vertical programming interface (APIs), and automation. The solution enables organizations to automate their workflow, create a unified view of data, and easily connect it with any system. .

1.1 Industry Landscape of Fast Delivery Services in India

India’s fast delivery ecosystem is characterized by intense competition among multiple players offering expedited grocery and food delivery services with promised delivery windows as low as 10 to 30 minutes. Companies such as Blinkit, Zepto, Swiggy Instamart, and JioMart have emerged as frontrunners, leveraging vast logistic networks, technology-driven supply chains, and extensive consumer data analytics (Bain & Company, 2025; Expert Market Research, 2024). The sector’s growth trajectory is robust, with the online food delivery market alone valued at USD 48.07 billion in 2024 and projected to grow at a CAGR of over 27% through 2034 (Expert Market Research, 2024).

1.2 Importance of Customer Ratings and Reviews

Customer reviews and ratings provide granular feedback on delivery agents’ punctuality, professionalism, order accuracy, and communication. These metrics are crucial for operational refinements, agent training, capacity planning, and enhancing customer experience (Kaggle dataset: VivekAttri, 2025). Sentiment analysis applied to textual reviews further uncovers nuanced customer emotions and service pain points, enabling predictive insights and proactive service improvements.

1.3 Dataset Overview

The focal dataset includes structured customer reviews and numerical ratings collected for fast delivery agents across India’s leading quick-commerce platforms. Key variables encompass agent identity, delivery timestamps, rating scores (typically on a 1-5 scale), customer comments, and transactional metadata (VivekAttri, 2025). This dataset serves as the foundation for exploratory data analysis, machine learning modeling, and visualization aimed at performance benchmarking and predictive analytics.

2. Data Handling and Preprocessing Methodologies

2.1 Data Acquisition and Integration

The dataset is sourced from Kaggle repositories aggregating customer feedback across platforms, with metadata ensuring temporal, geographic, and service-specific contextualization. Effective data ingestion involves automated pipelines utilizing Python libraries such as Pandas for dataframes and requests for API interfacing (MinakshiDhhote, 2025).

2.2 Data Cleaning and Normalization

Critical preprocessing steps include:

• Removal of Redundant and Irrelevant Columns: Columns unrelated to delivery agent performance (e.g., user identifiers when anonymized) are discarded to streamline analysis.

• Handling Missing Values: Rows with null or missing ratings/reviews are either imputed using domain-specific heuristics or removed to maintain data integrity.

• Duplicate Records Elimination: To prevent bias, identical reviews or ratings are deduplicated.

• Text Cleaning for Reviews: Natural language preprocessing (NLP) techniques such as tokenization, stopword removal, lemmatization, and spell correction are applied to textual data to prepare for sentiment analysis.

• Standardization of Rating Scales: Ensuring uniformity when ratings come from different sources with varying scales.

2.3 Feature Engineering

Derived features enhance modeling capabilities:

• Sentiment Scores: Using models like VADER or BERT-based classifiers to convert textual reviews into quantifiable sentiment metrics.

• Delivery Time Buckets: Categorization of delivery durations into intervals (e.g., under 15 minutes, 15-30 minutes) to analyze performance impact.

• Agent Activity Levels: Number of deliveries per agent to assess workload-performance correlation.

• Temporal Features: Time of day, day of week, and seasonal effects considered for delivery performance trends.

3. Exploratory Data Analysis (EDA) and Visualization

3.1 Rating Distribution and Statistical Summary

A comprehensive statistical summary outlines mean ratings, variance, skewness, and kurtosis to understand central tendencies and rating dispersion among delivery agents.

Table 1: Rating Summary Statistics for Delivery Agents (2025 Dataset Sample)

|| Metric | Value | |----------------------|----------------| | Mean Rating | 3.8 ± 0.15 | | Median Rating | 4.0 | | Standard Deviation | 0.75 | | Skewness | -0.45 | | Kurtosis | 2.1 | | Number of Ratings | 250,000+ | | | | --- | --- | | | |

Data validated with 95% confidence interval from Kaggle 2025 dataset (VivekAttri, 2025).

3.2 Geographical and Platform-Based Ratings Comparison

Heatmaps and bar charts illustrate rating variations across cities and platforms. For instance, Blinkit shows higher average ratings in metropolitan regions compared to tier-2 cities, reflecting infrastructural disparities.

3.3 Service Attributes and Rating Correlations

Scatter plots and corr...

This dataset, named URL-Phish, is designed for phishing detection research. It contains 111,660 unique URLs divided into: • 100,000 benign samples (label = 0), collected from trusted sources including educational (.edu), governmental (.gov), and top-ranked domains. The benign dataset was obtained from the Research Organization Registry [1]. • 11,660 phishing samples (label = 1), obtained from the PhishTank repository [2] between November 2024 and September 2025. Each URL entry was automatically processed to extract 22 lexical and structural features, such as URL length, domain length, number of subdomains, digit ratio, entropy, and HTTPS usage. In addition, three reference columns (url, dom, tld) are preserved for interpretability. One label column is included (0 = benign, 1 = phishing). A data cleaning step removed duplicates and empty entries, followed by normalization of features to ensure consistency. The dataset is provided in CSV format, with 22 numerical feature columns, 3 string reference columns, and 1 label column (total = 26 columns).

References [1] Research Organization Registry, “ROR Data.” Zenodo, Sept. 22, 2025. doi: 10.5281/ZENODO.6347574. [2] PhishTank, “PhishTank: Join the fight against phishing.” [Online]. Available: https://phishtank.org

Timeline follow-back by trial summarizing days of use, out of 28 days, for people who used each substance.

Dataset Description

Context and Methodology

• Research Domain/Project:
  This dataset was created for a machine learning experiment aimed at developing a classification model to predict outcomes based on a set of features. The primary research domain is disease prediction in patients. The dataset was used in the context of training, validating, and testing.

• Purpose of the Dataset:
  The purpose of this dataset is to provide training, validation, and testing data for the development of machine learning models. It includes labeled examples that help train classifiers to recognize patterns in the data and make predictions.

• Dataset Creation:
  Data preprocessing steps involved cleaning, normalization, and splitting the data into training, validation, and test sets. The data was carefully curated to ensure its quality and relevance to the problem at hand. For any missing values or outliers, appropriate handling techniques were applied (e.g., imputation, removal, etc.).

Technical Details

• Structure of the Dataset:
  The dataset consists of several files organized into folders by data type:

  • Training Data: Contains the training dataset used to train the machine learning model.

  • Validation Data: Used for hyperparameter tuning and model selection.

  • Test Data: Reserved for final model evaluation.

  Each folder contains files with consistent naming conventions for easy navigation, such as train_data.csv, validation_data.csv, and test_data.csv. Each file follows a tabular format with columns representing features and rows representing individual data points.

• Software Requirements:
  To open and work with this dataset, you need VS Code or Jupyter, which could include tools like:

  • Python (with libraries such as pandas, numpy, scikit-learn, matplotlib, etc.)

Further Details

• Reusability:
  Users of this dataset should be aware that it is designed for machine learning experiments involving classification tasks. The dataset is already split into training, validation, and test subsets. Any model trained with this dataset should be evaluated using the test set to ensure proper validation.

• Limitations:
  The dataset may not cover all edge cases, and it might have biases depending on the selection of data sources. It's important to consider these limitations when generalizing model results to real-world applications.

AEOLUS master fileThis zipped file includes all the files needed to load a copy of AEOLUS into a RDBMSaeolus_v1.zip

This dataset contains a collection of SQL scripts and techniques developed by business data analyst to assist with data optimization and cleaning tasks. The scripts cover a range of data management operations, including:

1) Data cleansing: Identifying and addressing issues such as missing values, duplicate records, formatting inconsistencies, and outliers. 2) Data normalization: Designing optimized database schemas and normalizing data structures to minimize redundancy and improve data integrity. 3) Data transformation and ETL: Developing efficient Extract, Transform, and Load (ETL) pipelines to integrate data from multiple sources and perform complex data transformations. 4) Reporting and dashboarding: Creating visually appealing and insightful reports, dashboards, and data visualizations to support informed decision-making.

The scripts and techniques in this dataset are tailored to the needs of business data analysts and can be used to enhance the quality, efficiency, and value of data-driven insights.

转载 pszemraj/text2image-multi-prompt

  text2image multi-prompt(s): a dataset collection

collection of several text2image prompt datasets data was cleaned/normalized with the goal of removing "model specific APIs" like the "--ar" for Midjourney and so on data de-duplicated on a basic level: exactly duplicate prompts were dropped (after cleaning and normalization)

  contents

DatasetDict({ train: Dataset({ features: ['text', 'src_dataset'], num_rows:… See the full description on the dataset page: https://huggingface.co/datasets/JohnTeddy3/text2image-multi-prompt.

Number and percent of positive urine drug screening results by trial.

Education, employment, living arrangements and relationship status by trial.