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The datasets are classified, based on the licenses, as Open data and Non-Open data. The datasets from various governmental-bodies are presented in List of open government data sites. The datasets are ported on open data portals. They are made available for searching, depositing and accessing through interfaces like Open API. The datasets are ...
Kaggle is a data science competition platform and online community for data scientists and machine learning practitioners under Google LLC.Kaggle enables users to find and publish datasets, explore and build models in a web-based data science environment, work with other data scientists and machine learning engineers, and enter competitions to solve data science challenges.
Download the XML database dump (*.xml.bz2) of your favorite wiki. Run WikiTaxi_Importer.exe to import the database dump into a WikiTaxi database. The importer takes care to uncompress the dump as it imports, so make sure to save your drive space and do not uncompress beforehand.
Various plots of the multivariate data set Iris flower data set introduced by Ronald Fisher (1936). [1]A data set (or dataset) is a collection of data.In the case of tabular data, a data set corresponds to one or more database tables, where every column of a table represents a particular variable, and each row corresponds to a given record of the data set in question.
C4.5 is an algorithm used to generate a decision tree developed by Ross Quinlan. [1] C4.5 is an extension of Quinlan's earlier ID3 algorithm.The decision trees generated by C4.5 can be used for classification, and for this reason, C4.5 is often referred to as a statistical classifier.
The SDMX converter is an open source application that offers the ability to convert DSPL (Google's Dataset Publishing Language) messages to SDMX-ML, and vice versa. The output file of a DSPL dataset is a zip file containing data (in the form of CSV files) and metadata (as an XML file). Datasets in this format can be visualized in the Google ...
Kernel density estimation of 100 normally distributed random numbers using different smoothing bandwidths.. In statistics, kernel density estimation (KDE) is the application of kernel smoothing for probability density estimation, i.e., a non-parametric method to estimate the probability density function of a random variable based on kernels as weights.
In 2008, developers of LabPlot and SciDAVis (another Origin clone, forked from QtiPlot) "found their project goals to be very similar" and decided to merge their code into a common backend while maintaining two frontends: LabPlot, integrated with the KDE desktop environment (DE); and SciDAVis, written in DE-independent Qt with fewer dependencies for easier cross-platform use.