{ "info": { "author": "Carlos Abad S\u00e1nchez", "author_email": "c.abad.sanchez@gmail.com", "bugtrack_url": null, "classifiers": [ "Development Status :: 2 - Pre-Alpha", "Intended Audience :: Developers", "License :: OSI Approved :: MIT License", "Natural Language :: English", "Programming Language :: Python :: 2", "Programming Language :: Python :: 2.7", "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.4", "Programming Language :: Python :: 3.5", "Programming Language :: Python :: 3.6" ], "description": "================================\nData Rhapsody UniversityHack2018\n================================\n\n\n.. image:: https://img.shields.io/pypi/v/universityhack2018.svg\n :target: https://pypi.python.org/pypi/universityhack2018\n\n.. image:: https://img.shields.io/travis/cabadsanchez/universityhack2018.svg\n :target: https://travis-ci.org/cabadsanchez/universityhack2018\n\n.. image:: https://readthedocs.org/projects/universityhack2018/badge/?version=latest\n :target: https://universityhack2018.readthedocs.io/en/latest/?badge=latest\n :alt: Documentation Status\n\n\n\n\nPackage for Data Rhapsody's UniversityHack 2018 Challenge solution.\n\n\n* Free software: MIT license\n* Documentation: https://universityhack2018.readthedocs.io.\n\n\nUsage\n-----\n\nInstall::\n\n pip install universityhack2018\n\nExample (easy peasy)::\n\n from universityhack2018.prediction import Model\n import pandas as pd\n\n clients_df = pd.read_csv('/path/to/Dataset_Salesforce_Predictive_Modelling_TEST.txt')\n clients = client_df_train.iloc[0:5, :]\n\n model = Model(clients)\n predictions = model.predict(as_df=True)\n\n print(predictions.head())\n\n # Output:\n # ID_Customer PA_Est\n # 0 TE000001 26926.541016\n # 1 TE000002 15267.800781\n # 2 TE000003 19499.935547\n # 3 TE000004 12799.532227\n # 4 TE000005 11262.253906\n\nFeatures\n--------\n\n* TODO\n\nCredits\n-------\n\nThis package was 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