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OpenCog, a GPL-licensed framework for artificial intelligence written in C++, Python and Scheme. [15] PolyAnalyst: A commercial tool for data mining, text mining, and knowledge management. [89] RapidMiner, an environment for machine learning and data mining, now developed commercially. [90]
scikit-learn: machine learning library in Python; Weka: A similar project by the University of Waikato, with a focus on classification algorithms; RapidMiner: An application available commercially (a restricted version is available as open source) KNIME: An open source platform which integrates various components for machine learning and data ...
Orange is an open-source software package released under GPL and hosted on GitHub.Versions up to 3.0 include core components in C++ with wrappers in Python.From version 3.0 onwards, Orange uses common Python open-source libraries for scientific computing, such as numpy, scipy and scikit-learn, while its graphical user interface operates within the cross-platform Qt framework.
KNIME (/ n aɪ m / ⓘ), the Konstanz Information Miner, [2] is a free and open-source data analytics, reporting and integration platform.KNIME integrates various components for machine learning and data mining through its modular data pipelining "Building Blocks of Analytics" concept.
Data mining is a particular data analysis technique that focuses on statistical modeling and knowledge discovery for predictive rather than purely descriptive purposes, while business intelligence covers data analysis that relies heavily on aggregation, focusing mainly on business information. [4]
Predictive analytics statistical techniques include data modeling, machine learning, AI, deep learning algorithms and data mining. Often the unknown event of interest is in the future, but predictive analytics can be applied to any type of unknown whether it be in the past, present or future.
catboost.ai CatBoost [ 6 ] is an open-source software library developed by Yandex . It provides a gradient boosting framework which, among other features, attempts to solve for categorical features using a permutation-driven alternative to the classical algorithm. [ 7 ]
The difference between data analysis and data mining is that data analysis is used to test models and hypotheses on the dataset, e.g., analyzing the effectiveness of a marketing campaign, regardless of the amount of data. In contrast, data mining uses machine learning and statistical models to uncover clandestine or hidden patterns in a large ...