Anaconda is an open data science platform powered by Python. The open source version of Anaconda is a high performance distribution of Python and R and includes over 100 of the most popular Python, R and Scala packages for data science.
Predictive Analytics Software
Anaconda is an open data science platform powered by Python. The open source version of Anaconda is a high performance distribution of Python and R and includes over 100 of the most popular Python, R and Scala packages for data science. There is also access to over 720 packages that can easily be installed with conda, the package, dependency and environment manager, that is included in Anaconda.Includes the most popular Python, R & Scala packages for stats, data mining, machine learning, deep learning, simulation & optimization, geospatial, text & NLP, graph & network, image analysis. Featured packages include: NumPy, SciPy, pandas, scikit-learn, Numba, PyTables, h5py, Matplotlib, Jupyter (formerly IPython), Spyder, Qt/PySide, VTK, Numexpr, Cython, Theano, scikit-image, NLTK, NetworkX, IRKernel, dplyr, shiny, ggplot2, tidyr, caret, nnet.
Anaconda Distribution gives superpowers to people that change the world with high performance, cross-platform Python and R that includes the best innovative data science from open source. Using over 720 packages for data preparation, data analysis, data visualization, machine learning and interactive data science applications that deliver results – everything from discovering gravitational waves to creating new revenue channels. Anaconda Repository gives data science teams a way to easily reproduce, publish and deploy data science assets. Packages, notebooks and environments can be discovered and shared to increase team productivity and ensure consistency. Anaconda Scale divides and conquers data science workloads easily to take advantage of clusters and deliver cost-effective production results.
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More Information on Predictive Analysis Process
For more information of predictive analytics process, please review the overview of each components in the predictive analytics process: data collection (data mining), data analysis, statistical analysis, predictive modeling and predictive model deployment.