{ "info": { "author": "Manav Sehgal", "author_email": "new@speedml.com", "bugtrack_url": null, "classifiers": [ "Development Status :: 2 - Pre-Alpha", "Intended Audience :: Developers", "Intended Audience :: Information Technology", "Intended Audience :: Science/Research", "License :: OSI Approved :: MIT License", "Programming Language :: Python", "Programming Language :: Python :: 2", "Programming Language :: Python :: 2.7", "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.6", "Topic :: Scientific/Engineering :: Artificial Intelligence", "Topic :: Software Development :: Libraries :: Python Modules" ], "description": "======================================\nSpeedml Machine Learning Speed Start\n======================================\n\nSpeedml is a Python package for speed starting machine learning projects.\n\nThe speedml.com_ website documents API use cases, behind-the-scenes implementation, features, best practices, and demos in detail.\n\n To see Speedml in action run or download the notebook_ `Titanic Solution Using Speedml` which walks through an end-to-end machine learning solution documenting features of the Speedml API.\n\nLatest Speedml release is always available on the PyPi_ website.\n\nInstall Speedml package using `pip` like so::\n\n pip install speedml\n\nWe manage the project on GitHub.\n\n- Demo notebooks_\n- GitHub repo_\n- Project roadmap_\n- Issues_ tracking\n\nWe are authoring Speedml API with four goals in mind.\n\nPopular. Best packages together\n--------------------------------\n\nSpeedml already imports and properly initializes the popular ML packages including pandas, numpy, sklearn, xgboost, and matplotlib. All you need to do is import speedml to get started::\n\n from speedml import Speedml\n\nRapid. Machine learning speed start\n------------------------------------\n\nCoding is up to 3X faster when using Speedml because of (1) iterative development, (2) linear workflow, and (3) component-based API.\n\nThese three lines of Speedml code (a) load the training, test datasets, (b) define the target and unique id features, (c) plot the feature correlation matrix heatmap for numerical features, (d) perform a detailed EDA returning 10-15 observations and next steps for making the datasets model ready::\n\n sml = Speedml('train.csv', 'test.csv',\n target='Survived', uid='PassengerId')\n sml.plot.correlate()\n sml.eda()\n\nEasy. Concise commands with sensible defaults\n----------------------------------------------\n\nA notebook using Speedml reduces coding required by up to 70%. Speedml API implements methods requiring zero to minimal number of parameters, working on sensible defaults.\n\nCall to this single method replaces empty values in the entire dataframe with median value for numerical features and most common values for text features::\n\n sml.feature.impute()\n\nProductive. Intuitive linear workflow\n---------------------------------------\n\nUnderstanding machine learning fundamentals is a breeze with Speedml as we have designed the API to follow a linear workflow with sensible prerequisites and intuitive next steps.\n\nThese three lines of Speedml code perform feature engineering by replacing null values, extracting a new feature matching a regular expression, and dropping a feature that is no longer required::\n\n sml.feature.fillna(a='Cabin', new='Z')\n sml.feature.extract(new='Deck', a='Cabin', regex='([A-Z]){1}')\n sml.feature.drop(['Cabin'])\n\nHope you enjoy using Speedml in your projects. Watch this space as we intend to update Speedml frequently with more cool features.\n\n.. _PyPi: https://pypi.python.org/pypi/speedml\n.. _documentation: http://pythonhosted.org/speedml/\n.. _speedml.com: https://speedml.com\n.. _repo: https://github.com/Speedml/speedml\n.. _roadmap: https://github.com/Speedml/speedml/projects/1\n.. _notebooks: https://github.com/Speedml/notebooks\n.. _Issues: https://github.com/Speedml/speedml/issues\n.. _notebook: https://github.com/Speedml/notebooks/blob/master/titanic/titanic-solution-using-speedml.ipynb\n\n\n", "description_content_type": null, "docs_url": "https://pythonhosted.org/speedml/", "download_url": "", "downloads": { "last_day": -1, "last_month": -1, "last_week": -1 }, "home_page": "https://speedml.com", "keywords": "machine-learning data-science jupyter-notebook", "license": "MIT", "maintainer": "", "maintainer_email": "", "name": "speedml", "package_url": "https://pypi.org/project/speedml/", "platform": "", "project_url": 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