Description of actions in the template method pattern adaption for the data standardization procedure, shown in order of operation.

Prognostics and health management (PHM) is a maturing system engineering discipline. As with most maturing disciplines, PHM does not yet have a universally accepted research methodology. As a result, most component life estimation efforts are based on ad-hoc experimental methods that lack statistical rigor. In this paper, we provide a critical review of current research methods in PHM and contrast these methods with standard research approaches in a more established discipline (medicine). We summarize the developmental steps required for PHM to reach full maturity and to generate actionable results with true business impact.

This is a simple super-shop dataset which I used for implementing various scaling technique. Here I used for scaling technique using this dataset. It contains five columns. Here I implement MinMaxScaler, RobustScaler, Standardization, Max Absolute Scaler technique.

columns are Marketing Spend, Administration, Transport, Area, Profit

With the expansion of Internet of Things (IoT) devices, security is an important issue as attacks are constantly gaining more complex. Traditional attack detection methods in IoT systems have difficulty being able to process real-time and access limitations. To address these challenges, a stacking-based Tiny Machine Learning (TinyML) models has been proposed for attack detection in IoT networks. This ensures detection efficiently and without additional computational overhead. The experiments have been conducted using the publicly available ToN-IoT dataset, comprising a total of 461,008 labeled instances with 10 types of attacks categories. Some amount of data preprocessing has been done applying methods such as label encoding, feature selection, and data standardization. A stacking ensemble learning technique uses multiple models combining lightweight Decision Tree (DT) and small Neural Network (NN) to aggregate power of the system and generalize. The performance of the model is evaluated by accuracy, precision, recall, F1-score, specificity, and false positive rate (FPR). Experimental results demonstrate that the stacked TinyML model is superior to traditional ML methods in terms of efficiency and detection performance, and its accuracy rate is 99.98%. It has an average inference latency of 0.12 ms and an estimated power consumption of 0.01 mW.

This dataset contains information about three species of Iris flowers: Setosa, Versicolour, and Virginica. It is a well-known dataset in the machine learning and statistics communities, often used for classification and clustering tasks. Each row represents a sample of an Iris flower, with measurements of its physical attributes and the corresponding target label.

Dataset Features: sepal length (cm): The length of the sepal in centimeters. sepal width (cm): The width of the sepal in centimeters. petal length (cm): The length of the petal in centimeters. petal width (cm): The width of the petal in centimeters. target: A numerical label (0, 1, or 2) indicating the flower species: 0: Setosa 1: Versicolour 2: Virginica

Purpose: This dataset can be used for: Supervised learning tasks, particularly classification. Exploratory data analysis and visualization of flower attributes. Understanding the application of machine learning algorithms like decision trees, KNN, and support vector machines.

Source: This is a modified version of the classic Iris flower dataset, often used for beginner-level machine learning projects and demonstrations.

Potential Use Cases: Training machine learning models for flower classification. Practicing data preprocessing, feature scaling, and visualization techniques. Understanding the relationships between features through scatter plots and correlation analysis.

Sickle cell anemia (SCA) is a recessively inherited disease characterized by chronic hemolytic anemia, chronic inflammation, and acute episodes of hemolysis. Hydroxyurea (HU) is widely used to increase the levels of fetal hemoglobin (HbF). The objective of this study was to standardize and validate a method for the quantification of HU in human plasma by using ultra high performance liquid chromatography (UPLC) in order to determine the plasma HU levels in adult patients with SCA who had been treated with HU. We used an analytical reverse phase column (Nucleosil C18) with a mobile phase consisting of acetonitrile/water (16.7/83.3). The retention times of HU, urea, and methylurea were 6.7, 7.7, and 11.4 min, respectively. All parameters of the validation process were defined. To determine the precision and accuracy of quality controls, HU in plasma was used at concentrations of 100, 740, and 1600 µM, with methylurea as the internal standard. Linearity was assessed in the range of 50-1600 µM HU in plasma, obtaining a correlation coefficient of 0.99. The method was accurate and precise and can be used for the quantitative determination of HU for therapeutic monitoring of patients with SCA treated with HU.

Raw data, software, standard operating procedure, and computer aided design files for the NIST-led publication "Results of an Interlaboratory Study on the Working Curve in Vat Photopolymerization II: Towards a Standardized Method"This record contains numerous supporting documents and data for "Results of an Interlaboratory Study on the Working Curve in Vat Photopolymerization II: Towards a Standardized Method".In the main .zip file, there are three subfolders and one document.The document is the Standard Operating Procedure (SOP) that was distributed to participants in this study. The SOP contains experimental details should one want to replicate the conditions of this study in their entirety.The first zip file is "CAD Files.zip", which contains two subfolders. The first is the fixtures printed by NIST for the interlaboratory study, and the second is commercial CAD files for the light source components used in this study. Each subfolder contains a readme describing each file.The second zip file is "Interlaboratory Study Raw Data.zip". This file contains separate files, designated by wavelength and participant number (matching Table 1 in the manuscript text), containing raw radiant exposure and cure depth pairs. The header of each file denotes the wavelength and identity of the light source (one of either Eldorado, Flagstaff, or SoBo). Six outlier data sets are included and their outlier status is denoted in the file name.The third zip file is "Other Working Curves.zip". This file contains separate files designated by wavelength and relate to the working curves in the manuscript that were collected on a commercial light source. The header for these files denotes whether or not the light source was filtered, the file names denote the wavelength. The 385 nm data sets also denote the irradiance used.The final zip file is "Labview Files.zip" and contains labview files used to calibrate and operate the light sources built for this study. This folder contains a readme file explaining the names and purposes of each file.NOTE: Trade names are provided only to specify the source of information and procedures adequately and do not imply endorsement by the National Institute of Standards and Technology. Similar products by other developers may be found to work as well or better.

Real-World Data Science Challenge

Business Intelligence Program Strategy — Student Success Optimization

Hosted by: Walsoft Computer Institute 📁 Download dataset 👤 Kaggle profile

Background

Walsoft Computer Institute runs a Business Intelligence (BI) training program for students from diverse educational, geographical, and demographic backgrounds. The institute has collected detailed data on student attributes, entry exams, study effort, and final performance in two technical subjects: Python Programming and Database Systems.

As part of an internal review, the leadership team has hired you — a Data Science Consultant — to analyze this dataset and provide clear, evidence-based recommendations on how to improve:

• Admissions decision-making
• Academic support strategies
• Overall program impact and ROI

Your Mission

Answer this central question:

“Using the BI program dataset, how can Walsoft strategically improve student success, optimize resources, and increase the effectiveness of its training program?”

Key Strategic Areas

You are required to analyze and provide actionable insights for the following three areas:

1. Admissions Optimization

Should entry exams remain the primary admissions filter?

Your task is to evaluate the predictive power of entry exam scores compared to other features such as prior education, age, gender, and study hours.

✅ Deliverables:

• Feature importance ranking for predicting Python and DB scores
• Admission policy recommendation (e.g., retain exams, add screening tools, adjust thresholds)
• Business rationale and risk analysis

2. Curriculum Support Strategy

Are there at-risk student groups who need extra support?

Your task is to uncover whether certain backgrounds (e.g., prior education level, country, residence type) correlate with poor performance and recommend targeted interventions.

✅ Deliverables:

• At-risk segment identification
• Support program design (e.g., prep course, mentoring)
• Expected outcomes, costs, and KPIs

3. Resource Allocation & Program ROI

How can we allocate resources for maximum student success?

Your task is to segment students by success profiles and suggest differentiated teaching/facility strategies.

✅ Deliverables:

• Performance drivers
• Student segmentation
• Resource allocation plan and ROI projection

🛠️ Dataset Overview

📊 Dataset

You are provided with a real-world messy dataset that reflects the types of issues data scientists face every day — from inconsistent formatting to missing values.

Raw Dataset (Recommended for Full Project)

Download: bi.csv

This dataset includes common data quality challenges:

• Country name inconsistencies
  e.g. Norge → Norway, RSA → South Africa, UK → United Kingdom

• Residence type variations
  e.g. BI-Residence, BIResidence, BI_Residence → unify to BI Residence

• Education level typos and casing issues
  e.g. Barrrchelors → Bachelor, DIPLOMA, Diplomaaa → Diploma

• Gender value noise
  e.g. M, F, female → standardize to Male / Female

• Missing scores in Python subject
  Fill NaN values using column mean or suitable imputation strategy

Participants using this dataset are expected to apply data cleaning techniques such as: - String standardization - Null value imputation - Type correction (e.g., scores as float) - Validation and visual verification

✅ Bonus: Submissions that use and clean this dataset will earn additional Technical Competency points.

Cleaned Dataset (Optional Shortcut)

Download: cleaned_bi.csv

This version has been fully standardized and preprocessed: - All fields cleaned and renamed consistently - Missing Python scores filled with th...

No Publication Abstract is Available

fisheries management is generally based on age structure models. thus, fish ageing data are collected by experts who analyze and interpret calcified structures (scales, vertebrae, fin rays, otoliths, etc.) according to a visual process. the otolith, in the inner ear of the fish, is the most commonly used calcified structure because it is metabolically inert and historically one of the first proxies developed. it contains information throughout the whole life of the fish and provides age structure data for stock assessments of all commercial species. the traditional human reading method to determine age is very time-consuming. automated image analysis can be a low-cost alternative method, however, the first step is the transformation of routinely taken otolith images into standardized images within a database to apply machine learning techniques on the ageing data. otolith shape, resulting from the synthesis of genetic heritage and environmental effects, is a useful tool to identify stock units, therefore a database of standardized images could be used for this aim. using the routinely measured otolith data of plaice (pleuronectes platessa; linnaeus, 1758) and striped red mullet (mullus surmuletus; linnaeus, 1758) in the eastern english channel and north-east arctic cod (gadus morhua; linnaeus, 1758), a greyscale images matrix was generated from the raw images in different formats. contour detection was then applied to identify broken otoliths, the orientation of each otolith, and the number of otoliths per image. to finalize this standardization process, all images were resized and binarized. several mathematical morphology tools were developed from these new images to align and to orient the images, placing the otoliths in the same layout for each image. for this study, we used three databases from two different laboratories using three species (cod, plaice and striped red mullet). this method was approved to these three species and could be applied for others species for age determination and stock identification.

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Online Data Science Training Programs Market Size 2025-2029

The online data science training programs market size is forecast to increase by USD 8.67 billion, at a CAGR of 35.8% between 2024 and 2029.

The market is experiencing significant growth due to the increasing demand for data science professionals in various industries. The job market offers lucrative opportunities for individuals with data science skills, making online training programs an attractive option for those seeking to upskill or reskill. Another key driver in the market is the adoption of microlearning and gamification techniques in data science training. These approaches make learning more engaging and accessible, allowing individuals to acquire new skills at their own pace. Furthermore, the availability of open-source learning materials has democratized access to data science education, enabling a larger pool of learners to enter the field. However, the market also faces challenges, including the need for continuous updates to keep up with the rapidly evolving data science landscape and the lack of standardization in online training programs, which can make it difficult for employers to assess the quality of graduates. Companies seeking to capitalize on market opportunities should focus on offering up-to-date, high-quality training programs that incorporate microlearning and gamification techniques, while also addressing the challenges of continuous updates and standardization. By doing so, they can differentiate themselves in a competitive market and meet the evolving needs of learners and employers alike.

What will be the Size of the Online Data Science Training Programs Market during the forecast period?

Request Free SampleThe online data science training market continues to evolve, driven by the increasing demand for data-driven insights and innovations across various sectors. Data science applications, from computer vision and deep learning to natural language processing and predictive analytics, are revolutionizing industries and transforming business operations. Industry case studies showcase the impact of data science in action, with big data and machine learning driving advancements in healthcare, finance, and retail. Virtual labs enable learners to gain hands-on experience, while data scientist salaries remain competitive and attractive. Cloud computing and data science platforms facilitate interactive learning and collaborative research, fostering a vibrant data science community. Data privacy and security concerns are addressed through advanced data governance and ethical frameworks. Data science libraries, such as TensorFlow and Scikit-Learn, streamline the development process, while data storytelling tools help communicate complex insights effectively. Data mining and predictive analytics enable organizations to uncover hidden trends and patterns, driving innovation and growth. The future of data science is bright, with ongoing research and development in areas like data ethics, data governance, and artificial intelligence. Data science conferences and education programs provide opportunities for professionals to expand their knowledge and expertise, ensuring they remain at the forefront of this dynamic field.

How is this Online Data Science Training Programs Industry segmented?

The online data science training programs industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. TypeProfessional degree coursesCertification coursesApplicationStudentsWorking professionalsLanguageR programmingPythonBig MLSASOthersMethodLive streamingRecordedProgram TypeBootcampsCertificatesDegree ProgramsGeographyNorth AmericaUSMexicoEuropeFranceGermanyItalyUKMiddle East and AfricaUAEAPACAustraliaChinaIndiaJapanSouth KoreaSouth AmericaBrazilRest of World (ROW)

By Type Insights

The professional degree courses segment is estimated to witness significant growth during the forecast period.The market encompasses various segments catering to diverse learning needs. The professional degree course segment holds a significant position, offering comprehensive and in-depth training in data science. This segment's curriculum covers essential aspects such as statistical analysis, machine learning, data visualization, and data engineering. Delivered by industry professionals and academic experts, these courses ensure a high-quality education experience. Interactive learning environments, including live lectures, webinars, and group discussions, foster a collaborative and engaging experience. Data science applications, including deep learning, computer vision, and natural language processing, are integral to the market's growth. Data analysis, a crucial application, is gaining traction due to the increasing demand for data-driven decisio

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AI Data Management Market Size 2025-2029

The AI data management market size is valued to increase by USD 51.04 billion, at a CAGR of 19.7% from 2024 to 2029. Proliferation of generative AI and large language models will drive the AI data management market.

Market Insights

North America dominated the market and accounted for a 35% growth during the 2025-2029.
By Component - Platform segment was valued at USD 8.66 billion in 2023
By Technology - Machine learning segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 306.58 million 
Market Future Opportunities 2024: USD 51042.00 million
CAGR from 2024 to 2029 : 19.7%

Market Summary

The market is experiencing significant growth as businesses increasingly rely on generative AI and large language models to gain insights from their data. This trend is driven by the ascendancy of data-centric AI and the industrialization of data curation. With the proliferation of data sources and the extreme complexity of managing and ensuring data quality at scale, businesses are turning to advanced AI solutions to streamline their data management processes. One real-world scenario where AI data management is making a significant impact is in supply chain optimization. In the manufacturing sector, for instance, AI algorithms are being used to analyze vast amounts of data from various sources, including production records, sales data, and external market trends.
By identifying patterns and correlations, these systems can help optimize inventory levels, improve order fulfillment, and reduce lead times. Despite the benefits, managing AI data comes with its own set of challenges. Ensuring data accuracy, security, and privacy are critical concerns, especially as more data is generated and shared across organizations. Additionally, managing data at scale requires significant computational resources and expertise. As a result, businesses are investing in advanced data management solutions that can handle the complexities of AI data and provide robust data quality assurance. In conclusion, the market is poised for continued growth as businesses seek to harness the power of AI to gain insights from their data.
From supply chain optimization to compliance and operational efficiency, the applications of AI data management are vast and varied. Despite the challenges, the benefits far outweigh the costs, making it an essential investment for businesses looking to stay competitive in today's data-driven economy.

What will be the size of the AI Data Management Market during the forecast period?

Get Key Insights on Market Forecast (PDF) Request Free Sample

The market continues to evolve, driven by the increasing adoption of advanced technologies such as machine learning, predictive modeling, and data analytics. According to recent studies, businesses are investing heavily in AI data management solutions to enhance their operations and gain a competitive edge. For instance, data governance policies have become essential for organizations to ensure data security, privacy, and compliance. Moreover, AI data management is crucial for product strategy, enabling companies to make informed decisions based on accurate and timely data.
For example, predictive modeling techniques can help businesses forecast sales trends and optimize inventory levels, while data validation rules ensure data accuracy and consistency. Furthermore, data cataloging systems facilitate efficient data discovery and access, reducing processing time and improving overall productivity. Advancements in AI data management also include model selection criteria, such as accuracy, interpretability, and fairness, which are essential for responsible AI practices. Encryption algorithms and access control policies ensure data security, while data standardization methods promote interoperability and data consistency. Additionally, edge computing infrastructure and hybrid cloud solutions enable faster data processing and analysis, making AI data management a strategic priority for businesses.

Unpacking the AI Data Management Market Landscape

In today's data-driven business landscape, effective AI data management is a critical success factor. According to recent studies, AI data management processes can reduce data integration complexities by up to 70%, enabling faster time-to-insight and improved ROI. Anomaly detection algorithms, powered by machine learning models, can identify data anomalies with 95% accuracy, ensuring regulatory compliance and reducing potential losses. Synthetic data generation can enhance model training pipelines by up to 50%, improving model accuracy and reducing reliance on labeled data. Cloud-based data platforms offer secure data access control, while model accuracy assessment techniques ensure consistent performance across model retraining schedules. Data lineage

Context

The 1985 Auto Imports Dataset captures a pivotal era in automotive history, documenting the shift toward fuel efficiency and globalization in car manufacturing. This dataset enables: * Analysis of 1980s automotive trends (MPG, engine tech, pricing) * Predictive modeling for insurance risk (symboling) and resale value

Overview

Files

Main Dataset (data.csv) * Standardized missing values (? → NaN) * Corrected dtypes (e.g., horsepower as float) * Original columns preserved with improved readability

Key Features

1. Reliable dtypes: No more ValueError issues (fixed NaN and numeric columns).
2. Historical Snapshot: Documents 1985 market trends (pre-NAFTA, pre-electronic engines).
3. Machine Learning Ready: Cleaned for regression (predict price) or classification (e.g., fuel-type).
4. Geographic Diversity: Mix of US, European, and Japanese manufacturers.

Uses

This dataset supports: * Price Prediction: Train models using engine size, MPG, and brand. * Risk Analysis: Correlate symboling with safety features. * Fuel Efficiency Studies: Compare 1985 MPG standards to modern EVs. * EDA Tutorials: Ideal for teaching pandas/seaborn (small but feature-rich).

Licensing

License: CC BY 4.0 (matches UCI’s terms).

Geotechnical laboratory tests such as grain size analysis, Atterberg limits, and residual strength and swell-consolidation testing on remolded specimens require disaggregating a sample into its constituent particles. Specimen preparation typically involves hand processing samples with a mortar and rubber-tipped pestle until they pass a designated sieve size. Ball milling is an alternative to hand processing and has the potential to expedite the preparation process and result in more complete disaggregation, leading to more accurate test results. For ball milling to become a validated specimen preparation method and gain wide acceptance it must be standardized. The research presented here seeks progress the effort to standardization by evaluating the effects of ball size, ball material, and milling duration on geomaterials including high plasticity clay, elastic silt, shale, claystone, and clayey sandstone. The research also presents results of ball milling a fine aggregate (concrete sand) to assess the potential for grain pulverization in each milling scenario. Ball mill performance is material dependent, but for all materials evaluated in this study, ball milling induced a higher degree of disaggregation than hand processing in all scenarios. Grain pulverization from metal ball milling scenarios was evident, especially in materials with higher sand contents. Parameters obtained from ball milling were normalized by hand processed results, and the trends suggest that ball mill processing causes a greater increase in liquid limit than plastic limit compared to hand processing.

Behavioral data associated with the IBL paper: A standardized and reproducible method to measure decision-making in mice.This data set contains contains 3 million choices 101 mice across seven laboratories at six different research institutions in three countries obtained during a perceptual decision making task.When citing this data, please also cite the associated paper: https://doi.org/10.1101/2020.01.17.909838This data can also be accessed using DataJoint and web browser tools at data.internationalbrainlab.orgAdditionally, we provide a Binder hosted interactive Jupyter notebook showing how to access the data via the Open Neurophysiology Environment (ONE) interface in Python : https://mybinder.org/v2/gh/int-brain-lab/paper-behavior-binder/master?filepath=one_example.ipynbFor more information about the International Brain Laboratory please see our website: www.internationalbrainlab.comBeta Disclaimer. Please note that this is a beta version of the IBL dataset, which is still undergoing final quality checks. If you find any issues or inconsistencies in the data, please contact us at info+behavior@internationalbrainlab.org .

This dataset is an automated ETL conversion of the MIMIC-IV Clinical Database Demo into the Medical Event Data Standard (MEDS). MEDS is a data schema for storing streams of medical events such as those sourced from Electronic Health Records or claims records. MEDS is intentionally a minimal standard, designed for maximum interoperability across datasets, existing tools, and model architectures. By providing a simple standardization layer between datasets and model-specific code, MEDS is intended to help make machine learning research for EHR data more reproducible, robust, computationally performant, and collaborative.

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Master Data Management (MDM) Solutions Market Size 2024-2028

The master data management (mdm) solutions market size is forecast to increase by USD 20.29 billion, at a CAGR of 16.72% between 2023 and 2028.

Major Market Trends & Insights

North America dominated the market and accounted for a 33% growth during the forecast period.
By the Deployment - Cloud segment was valued at USD 7.18 billion in 2022
By the End-user - BFSI segment accounted for the largest market revenue share in 2022

Market Size & Forecast

Market Opportunities: USD 0 billion
Market Future Opportunities: USD 0 billion
CAGR : 16.72%
North America: Largest market in 2022

Market Summary

The market is witnessing significant growth as businesses grapple with the increasing volume and complexity of data. According to recent estimates, the global MDM market is expected to reach a value of USD115.7 billion by 2026, growing at a steady pace. This expansion is driven by the growing advances in natural language processing (NLP), machine learning (ML), and artificial intelligence (AI) technologies, which enable more effective data management and analysis. Despite this progress, data privacy and security concerns remain a major challenge. A 2021 survey revealed that 60% of organizations reported data privacy as a significant concern, while 58% cited security as a major challenge. MDM solutions offer a potential solution, providing a centralized and secure platform for managing and governing data across the enterprise. By implementing MDM solutions, businesses can improve data accuracy, consistency, and completeness, leading to better decision-making and operational efficiency.

What will be the Size of the Master Data Management (MDM) Solutions Market during the forecast period?

Explore market size, adoption trends, and growth potential for master data management (mdm) solutions market Request Free SampleThe market continues to evolve, driven by the increasing complexity of managing large and diverse data volumes. Two significant trends emerge: a 15% annual growth in data discovery tools usage and a 12% increase in data governance framework implementations. Role-based access control and data security assessments are integral components of these solutions. Data migration strategies employ data encryption algorithms and anonymization methods for secure transitions. Data quality improvement is facilitated through data reconciliation tools, data stewardship programs, and data quality monitoring via scorecards and dashboards. Data consolidation projects leverage data integration pipelines and versioning control. Metadata repository design and data governance maturity are crucial for effective MDM implementation. Data standardization methods, data lineage visualization, and data profiling reports enable data integration and improve data accuracy. Data stewardship training and masking techniques ensure data privacy and compliance. Data governance KPIs and metrics provide valuable insights for continuous improvement. Data catalog solutions and data versioning control enhance data discovery and enable efficient data access. Data loss prevention and data quality dashboard are essential for maintaining data security and ensuring data accuracy.

How is this Master Data Management (MDM) Solutions Industry segmented?

The master data management (mdm) solutions industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments. DeploymentCloudOn-premisesEnd-userBFSIHealthcareRetailOthersGeographyNorth AmericaUSCanadaEuropeGermanyUKAPACChinaRest of World (ROW)

By Deployment Insights

The cloud segment is estimated to witness significant growth during the forecast period.

Master data management solutions have gained significant traction in the business world, with market adoption increasing by 18.7% in the past year. This growth is driven by the need for organizations to manage and maintain accurate, consistent, and secure data across various sectors. Metadata management, data profiling methods, and data deduplication techniques are essential components of master data management, ensuring data quality and compliance with regulations. Data stewardship roles, data warehousing solutions, and data hub architecture facilitate effective data management and integration. Cloud-based master data management solutions, which account for 35.6% of the market share, offer agility, scalability, and real-time data availability. Data virtualization platforms, data validation processes, and data consistency checks ensure data accuracy and reliability. Hybrid MDM deployments, ETL processes, and data governance policies enable seamless data integration and management. Data security protocols, data qualit

The Uncleaned Laptop Price dataset contains over 1,272 laptop listings collected from an e-commerce website. It includes details such as brand, model, screen size, processor, memory, storage, operating system, and price. The dataset has both categorical (brand, model, OS, processor type) and numerical variables (screen size, memory, storage), with price as the target variable. Since the dataset contains missing values, inconsistent formatting, and other errors, it requires cleaning and preprocessing before analysis or predictive modeling. It is suitable for projects like predicting laptop prices based on specifications.

Context

This dataset is cleaned and ready to deploy for model building.

Content

This dataset is for learning purpose and thus is simplified and is without any null values or major skewness.

Inspiration

I learned much from Kaggle and the data community and this is my contribution so that flow of knowledge never stops.

The data release contains biomarker data generated by Gas Chromatography-Single Quadrupole Mass Spectrometry in the Petroleum Geochemistry Research Laboratory. The data was used to determine the precision and accuracy of one daily operating standard for the method entitled, “Petroleum Geochemistry Research Laboratory Method for Qualitative Biomarker Analysis of Crude Oil and Rock Extracts by Gas Chromatography-Single Quadrupole Mass Spectrometry”.