ishaansehgal99/kubernetes-reformatted-remove-outliers dataset hosted on Hugging Face and contributed by the HF Datasets community

ABSTRACT The considerable volume of data generated by sensors in the field presents systematic errors; thus, it is extremely important to exclude these errors to ensure mapping quality. The objective of this research was to develop and test a methodology to identify and exclude outliers in high-density spatial data sets, determine whether the developed filter process could help decrease the nugget effect and improve the spatial variability characterization of high sampling data. We created a filter composed of a global, anisotropic, and an anisotropic local analysis of data, which considered the respective neighborhood values. For that purpose, we used the median to classify a given spatial point into the data set as the main statistical parameter and took into account its neighbors within a radius. The filter was tested using raw data sets of corn yield, soil electrical conductivity (ECa), and the sensor vegetation index (SVI) in sugarcane. The results showed an improvement in accuracy of spatial variability within the data sets. The methodology reduced RMSE by 85 %, 97 %, and 79 % in corn yield, soil ECa, and SVI respectively, compared to interpolation errors of raw data sets. The filter excluded the local outliers, which considerably reduced the nugget effects, reducing estimation error of the interpolated data. The methodology proposed in this work had a better performance in removing outlier data when compared to two other methodologies from the literature.

Ordinary least square (OLS) estimation of a linear regression model is well-known to be highly sensitive to outliers. It is common practice to (1) identify and remove outliers by looking at the data and (2) to fit OLS and form confidence intervals and p-values on the remaining data as if this were the original data collected. This standard “detect-and-forget” approach has been shown to be problematic, and in this article we highlight the fact that it can lead to invalid inference and show how recently developed tools in selective inference can be used to properly account for outlier detection and removal. Our inferential procedures apply to a general class of outlier removal procedures that includes several of the most commonly used approaches. We conduct simulations to corroborate the theoretical results, and we apply our method to three real datasets to illustrate how our inferential results can differ from the traditional detect-and-forget strategy. A companion R package, outference, implements these new procedures with an interface that matches the functions commonly used for inference with lm in R. Supplementary materials for this article are available online.

Dataset

This dataset was created by Riaz Ansari

Released under CC0: Public Domain

Contents

Understanding the statistics of fluctuation driven flows in the boundary layer of magnetically confined plasmas is desired to accurately model the lifetime of the vacuum vessel components. Mirror Langmuir probes (MLPs) are a novel diagnostic that uniquely allow us to sample the plasma parameters on a time scale shorter than the characteristic time scale of their fluctuations. Sudden large-amplitude fluctuations in the plasma degrade the precision and accuracy of the plasma parameters reported by MLPs for cases in which the probe bias range is of insufficient amplitude. While some data samples can readily be classified as valid and invalid, we find that such a classification may be ambiguous for up to 40% of data sampled for the plasma parameters and bias voltages considered in this study. In this contribution, we employ an autoencoder (AE) to learn a low-dimensional representation of valid data samples. By definition, the coordinates in this space are the features that mostly characterize valid data. Ambiguous data samples are classified in this space using standard classifiers for vectorial data. In this way, we avoid defining complicated threshold rules to identify outliers, which require strong assumptions and introduce biases in the analysis. By removing the outliers that are identified in the latent low-dimensional space of the AE, we find that the average conductive and convective radial heat fluxes are between approximately 5% and 15% lower as when removing outliers identified by threshold values. For contributions to the radial heat flux due to triple correlations, the difference is up to 40%.

Enthalpies of formation and reaction are important thermodynamic properties that have a crucial impact on the outcome of chemical transformations. Here we implement the calculation of enthalpies of formation with a general-purpose ANI‑1ccx neural network atomistic potential. We demonstrate on a wide range of benchmark sets that both ANI-1ccx and our other general-purpose data-driven method AIQM1 approach the coveted chemical accuracy of 1 kcal/mol with the speed of semiempirical quantum mechanical methods (AIQM1) or faster (ANI-1ccx). It is remarkably achieved without specifically training the machine learning parts of ANI-1ccx or AIQM1 on formation enthalpies. Importantly, we show that these data-driven methods provide statistical means for uncertainty quantification of their predictions, which we use to detect and eliminate outliers and revise reference experimental data. Uncertainty quantification may also help in the systematic improvement of such data-driven methods.

Dataset

This dataset was created by Sally Ahmed

Contents

This work reports pure component parameters for the PCP-SAFT equation of state for 1842 substances using a total of approximately 551 172 experimental data points for vapor pressure and liquid density. We utilize data from commercial and public databases in combination with an automated workflow to assign chemical identifiers to all substances, remove duplicate data sets, and filter unsuited data. The use of raw experimental data, as opposed to pseudoexperimental data from empirical correlations, requires means to identify and remove outliers, especially for vapor pressure data. We apply robust regression using a Huber loss function. For identifying and removing outliers, the empirical Wagner equation for vapor pressure is adjusted to experimental data, because the Wagner equation is mathematically rather flexible and is thus not subject to a systematic model bias. For adjusting model parameters of the PCP-SAFT model, nonpolar, dipolar and associating substances are distinguished. The resulting substance-specific parameters of the PCP-SAFT equation of state yield in a mean absolute relative deviation of the of 2.73% for vapor pressure and 0.52% for liquid densities (2.56% and 0.47% for nonpolar substances, 2.67% and 0.61% for dipolar substances, and 3.24% and 0.54% for associating substances) when evaluated against outlier-removed data. All parameters are provided as JSON and CSV files.

Melbourne Housing Market (Cleaned)

This dataset is a cleaned and enhanced version of the original Melbourne Housing Market dataset by Anthony Pino, licensed under CC BY-NC-SA 4.0. It has been preprocessed to facilitate exploratory data analysis and house price prediction modeling.

Key Improvements

1) Cleaned Missing Data: Removed missing and null values to ensure data integrity.

2) Outlier Removal: Eliminated unrealistic price and land size outliers to better reflect Melbourne's housing market.

3) Data Type Optimization: Converted Date and BuiltYear columns from float to appropriate datetime formats for easier analysis.

Acknowledgments

This dataset is derived from the original work by Anthony Pino, available here. Please credit the original source when using this dataset.

Project Data Analytics of a Sales company start to use OSEMN steps -Obtain the data -Scrub and clean the data by removing outliers and duplicates and dealing with missing values -Explore the data by doing some analysis and statistical formulas - sorting filtering and more -Model the data to predict the sales for the next month -iNterpret - using Tableau create a dashboard to show the result in a simple way and to show the the stakeholder

Recovering 3-D information from several 2-D images is one of the most important topics in computer vision. There are a lot of applications in different areas such as TV contents, games, medical applications, robot navigation and special effects, etc. Multi-stereo and Structure-from-Motion methods aim to recover the 3-D camera pose and scene structure for a rigid scene from an uncalibrated sequence of 2-D images. The 3-D camera pose can be estimated as the principle projection ray for a camera observing a rigid scene. The line of principal ray defines the collineation of all viewpoints observing the same scene. However, the projective camera geometry is involved in a number of distorted images for a Euclidean geometric object since the projection is a non-metrical form of geometry. This means that the collineation of projective ray is not always satisfied in metrically distorted pixels in the viewpoints, and the distortion is the image form of divergent rays on the 3-D surfaces. The estimation of dense scene geometry is a process to recover the metric geometry and to adjust the global ray projection passing through each 2-D image point to a 3-D scene point on real object surfaces. The generalization of 3-D video analysis depends on the density and robustness of the scene geometry estimation. In this dissertation, the 3-D sceneflow method that analyzes jointly stereo and motion is proposed for retrieving the camera geometry and reconstructing dense scene geometry accurately. The stereo viewpoints provide more robust 3-D information against noises, and the viewpoints of the camera motion increase the spatial density of the 3-D information. This method utilizes a brightness-invariance constraint that a set of spatio-temporal correspondences do not change for a 3-D scene point. Due to physical imperfections in the imaging sensors and bad locations of detected features and false matches, image data contain a lot of outliers. A unified scheme of robust estimation is proposed to eliminate outliers both from feature-based camera geometry estimates and dense scene geometry estimates. With a robust error weight, the error distribution of estimates can be restricted in a smoothly invariant support region, and the anisotropic diffusion regularization efficiently eliminates outliers in the regional structure. Finally, the structure-preserving dense 3-D sceneflow is obtained from stereo-motion sequences for a 3-D natural scene.

Data-set Information

This Data-set is launched by google in 2014 Here is the link Google Dataset Link

Files Information In Dataset

in google data-set we are only able to get first 1000 images which is not enough for the classification so I just resize the all the 3500 images and uploaded it in zip file so that you guys are able to work on this data-set on kaggle kernel. Thank google for this awesome data-set

DataFrame:

Data Frame File Include all the information of images e.g patient number level of diabetic Ratinopathy and images names. There are separate Data frames for every level of Diabetic Rateinpahthy (0,1,2,3,4)

Zip Files:

In Zip files there are compressed images. Images Dimension are 224,224,3

Outlier Zip File:

Outliers Data set is added to make your classification more accurate by removing outliers you can clean your data set by using these outlier data set

Geographic coverage

National, regional

Analysis unit

Households

Kind of data

Sample survey data [ssd]

Sampling procedure

The 2020 Vietnam COVID-19 High Frequency Phone Survey of Households (VHFPS) uses a nationally representative household survey from 2018 as the sampling frame. The 2018 baseline survey includes 46,980 households from 3132 communes (about 25% of total communes in Vietnam). In each commune, one EA is randomly selected and then 15 households are randomly selected in each EA for interview. We use the large module of to select the households for official interview of the VHFPS survey and the small module households as reserve for replacement. After data processing, the final sample size for Round 2 is 3,935 households.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

The questionnaire for Round 2 consisted of the following sections

Section 2. Behavior Section 3. Health Section 5. Employment (main respondent) Section 6. Coping Section 7. Safety Nets Section 8. FIES

Cleaning operations

Data cleaning began during the data collection process. Inputs for the cleaning process include available interviewers’ note following each question item, interviewers’ note at the end of the tablet form as well as supervisors’ note during monitoring. The data cleaning process was conducted in following steps: • Append households interviewed in ethnic minority languages with the main dataset interviewed in Vietnamese. • Remove unnecessary variables which were automatically calculated by SurveyCTO • Remove household duplicates in the dataset where the same form is submitted more than once. • Remove observations of households which were not supposed to be interviewed following the identified replacement procedure. • Format variables as their object type (string, integer, decimal, etc.) • Read through interviewers’ note and make adjustment accordingly. During interviews, whenever interviewers find it difficult to choose a correct code, they are recommended to choose the most appropriate one and write down respondents’ answer in detail so that the survey management team will justify and make a decision which code is best suitable for such answer. • Correct data based on supervisors’ note where enumerators entered wrong code. • Recode answer option “Other, please specify”. This option is usually followed by a blank line allowing enumerators to type or write texts to specify the answer. The data cleaning team checked thoroughly this type of answers to decide whether each answer needed recoding into one of the available categories or just keep the answer originally recorded. In some cases, that answer could be assigned a completely new code if it appeared many times in the survey dataset.
• Examine data accuracy of outlier values, defined as values that lie outside both 5th and 95th percentiles, by listening to interview recordings. • Final check on matching main dataset with different sections, where information is asked on individual level, are kept in separate data files and in long form. • Label variables using the full question text. • Label variable values where necessary.

Maryland Department of Transportation’s Maryland Transit Administration Bus Stops including CityLink, LocalLink, Express BusLink, Commuter Bus & Intercity Bus. This data is based on the Summer 2019 schedule and reflects bus stop changes through June 23, 2019. Automatic Passenger Counting (APC) system data reflects average daily weekday bus stop ridership (boarding, alighting, and total) from the Spring 2019 schedule period and does not exclude outliers.

All data are prone to error and require data cleaning prior to analysis. An important example is longitudinal growth data, for which there are no universally agreed standard methods for identifying and removing implausible values and many existing methods have limitations that restrict their usage across different domains. A decision-making algorithm that modified or deleted growth measurements based on a combination of pre-defined cut-offs and logic rules was designed. Five data cleaning methods for growth were tested with and without the addition of the algorithm and applied to five different longitudinal growth datasets: four uncleaned canine weight or height datasets and one pre-cleaned human weight dataset with randomly simulated errors. Prior to the addition of the algorithm, data cleaning based on non-linear mixed effects models was the most effective in all datasets and had on average a minimum of 26.00% higher sensitivity and 0.12% higher specificity than other methods. Data cleaning methods using the algorithm had improved data preservation and were capable of correcting simulated errors according to the gold standard; returning a value to its original state prior to error simulation. The algorithm improved the performance of all data cleaning methods and increased the average sensitivity and specificity of the non-linear mixed effects model method by 7.68% and 0.42% respectively. Using non-linear mixed effects models combined with the algorithm to clean data allows individual growth trajectories to vary from the population by using repeated longitudinal measurements, identifies consecutive errors or those within the first data entry, avoids the requirement for a minimum number of data entries, preserves data where possible by correcting errors rather than deleting them and removes duplications intelligently. This algorithm is broadly applicable to data cleaning anthropometric data in different mammalian species and could be adapted for use in a range of other domains.

Lazarus_2012_Paleobiology_AppendicesAppendices 1 and 2. 1-tabular data extracted from or calculated from the Neptune database for Cenozoic radiolarian occurrences. 2-histograms (pdf plates) of occurences in time and effects of pacman data trimming for selected species of radiolarians. Details of table formats in readme.txt file included. File is zip archive, created with OS X 10.6 system 'compress'.

To perform accurate engineering predictions, a method which accounts for both Gaussian process regression (GPR) and possibilistic fuzzy c-means clustering (PFCM) is developed in this paper, where the Gaussian process regression method is used in relationship regressions and the corresponding prediction errors are utilised to determine the memberships of the training samples. On the basis of its memberships and the prediction errors of the clusters, the typicality of each training sample is computed and used to determine the existence of outliers. In actual applications, the identified outliers should be eliminated and predictive model could be developed with the rest of the training samples. In addition to the method of predictive model construction, the influence of key parameters on the model accuracy is also investigated using two numerical problems. The results indicate that compared with standard outlier detection approaches and Gaussian process regression, the proposed approach is able to identify outliers with more precision and generate more accurate prediction results. To further identify the ability and feasibility of the method proposed in this paper in actual engineering applications, a predictive model was developed which can be used to predict the inlet pressure of a nuclear control valve on the basis of its in-situ data. The findings show that the proposed approach outperforms Gaussian process regression. In comparison to the traditional Gaussian process regression, the proposed approach reduces the detrimental impact of outliers and generates a more precise prediction model.

Data Analysis is the process that supports decision-making and informs arguments in empirical studies. Descriptive statistics, Exploratory Data Analysis (EDA), and Confirmatory Data Analysis (CDA) are the approaches that compose Data Analysis (Xia & Gong; 2014). An Exploratory Data Analysis (EDA) comprises a set of statistical and data mining procedures to describe data. We ran EDA to provide statistical facts and inform conclusions. The mined facts allow attaining arguments that would influence the Systematic Literature Review of DL4SE.

The Systematic Literature Review of DL4SE requires formal statistical modeling to refine the answers for the proposed research questions and formulate new hypotheses to be addressed in the future. Hence, we introduce DL4SE-DA, a set of statistical processes and data mining pipelines that uncover hidden relationships among Deep Learning reported literature in Software Engineering. Such hidden relationships are collected and analyzed to illustrate the state-of-the-art of DL techniques employed in the software engineering context.

Our DL4SE-DA is a simplified version of the classical Knowledge Discovery in Databases, or KDD (Fayyad, et al; 1996). The KDD process extracts knowledge from a DL4SE structured database. This structured database was the product of multiple iterations of data gathering and collection from the inspected literature. The KDD involves five stages:

Selection. This stage was led by the taxonomy process explained in section xx of the paper. After collecting all the papers and creating the taxonomies, we organize the data into 35 features or attributes that you find in the repository. In fact, we manually engineered features from the DL4SE papers. Some of the features are venue, year published, type of paper, metrics, data-scale, type of tuning, learning algorithm, SE data, and so on.

Preprocessing. The preprocessing applied was transforming the features into the correct type (nominal), removing outliers (papers that do not belong to the DL4SE), and re-inspecting the papers to extract missing information produced by the normalization process. For instance, we normalize the feature “metrics” into “MRR”, “ROC or AUC”, “BLEU Score”, “Accuracy”, “Precision”, “Recall”, “F1 Measure”, and “Other Metrics”. “Other Metrics” refers to unconventional metrics found during the extraction. Similarly, the same normalization was applied to other features like “SE Data” and “Reproducibility Types”. This separation into more detailed classes contributes to a better understanding and classification of the paper by the data mining tasks or methods.

Transformation. In this stage, we omitted to use any data transformation method except for the clustering analysis. We performed a Principal Component Analysis to reduce 35 features into 2 components for visualization purposes. Furthermore, PCA also allowed us to identify the number of clusters that exhibit the maximum reduction in variance. In other words, it helped us to identify the number of clusters to be used when tuning the explainable models.

Data Mining. In this stage, we used three distinct data mining tasks: Correlation Analysis, Association Rule Learning, and Clustering. We decided that the goal of the KDD process should be oriented to uncover hidden relationships on the extracted features (Correlations and Association Rules) and to categorize the DL4SE papers for a better segmentation of the state-of-the-art (Clustering). A clear explanation is provided in the subsection “Data Mining Tasks for the SLR od DL4SE”. 5.Interpretation/Evaluation. We used the Knowledge Discover to automatically find patterns in our papers that resemble “actionable knowledge”. This actionable knowledge was generated by conducting a reasoning process on the data mining outcomes. This reasoning process produces an argument support analysis (see this link).

We used RapidMiner as our software tool to conduct the data analysis. The procedures and pipelines were published in our repository.

Overview of the most meaningful Association Rules. Rectangles are both Premises and Conclusions. An arrow connecting a Premise with a Conclusion implies that given some premise, the conclusion is associated. E.g., Given that an author used Supervised Learning, we can conclude that their approach is irreproducible with a certain Support and Confidence.

Support = Number of occurrences this statement is true divided by the amount of statements Confidence = The support of the statement divided by the number of occurrences of the premise

##Vehicle-insurance

Vehicle Insurance data: This dataset contains multiple features according to the customer’s vehicle and insurance type.

OBJECTIVE: Business requirement is to increase the clv (customer lifetime value) that means clv is the target variable.

Data Cleansing:

This dataset is pretty clean already, a few outliers are there. Remove the outliers.

Why remove Outliers? Outliers are unusual values in dataset, and they can distort statistical analyses and violate their assumptions.

Feature selection:

This step is required to remove unwanted features.

VIF and Correlation Coefficient can be used to find important features.

VIF: Variance Inflation Factor It is a measure of collinearity among predictor variables within a multiple regression. It is calculated by taking the the ratio of the variance of all a given model's betas divide by the variance of a single beta if it were fit alone.

Correlation Coefficient: A positive Pearson coefficient mean that one variable's value increases with the others. And a negative Pearson coefficient means one variable decreases as other variable decreases. Correlations coefficients of -1 or +1 mean the relationship is exactly linear.

Log transformation and Normalisation: Many ML algorithms perform better or converge faster when features are on a relatively similar scale and/or close to normally distributed.

Applying different ML Algorithms to the dataset for predictions. Their accuracies are in notebook.

Please see my work. And I am open to suggestion.

The data contain bathymetric data from the Namibia continental slope. The data were acquired on R/V Meteor research expeditions M76/1 in 2008, and R/V Maria S. Merian expedition MSM19/1c in 2011. The purpose of the data was the exploration of the Namibian continental slope and espressially the investigation of large seafloor depressions. The bathymetric data were acquired with the 191-beam 12 kHz Kongsberg EM120 system. The data were processed using the public software package MBSystems. The loaded data were cleaned semi-automatically and manually, removing outliers and other erroneous data. Initial velocity fields were adjusted to remove artifacts from the data. Gridding was done in 10x10 m grid cells for the MSM19-1c dataset and 50x50 m for the M76 dataset using the Gaussian Weighted Mean algorithm.