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none;\n    box-shadow: none;\n  }\n\n  .markdown-body .progress {\n    border: 1px solid #ddd;\n  }\n\n  .markdown-body .progress-bar {\n    height: 22px;\n    border-right: 1px solid #ddd;\n  }\n\n  .markdown-body tr,\n  .markdown-body img {\n    page-break-inside: avoid;\n  }\n\n  .markdown-body img {\n    max-width: 100% !important;\n  }\n\n  .markdown-body p,\n  .markdown-body h2,\n  .markdown-body h3 {\n    orphans: 3;\n    widows: 3;\n  }\n\n  .markdown-body h2,\n  .markdown-body h3 {\n    page-break-after: avoid;\n  }\n}\n\u003c/style\u003e\u003ctitle\u003eREADME\u003c/title\u003e\u003c/head\u003e\u003cbody\u003e\u003carticle class=\"markdown-body\"\u003e\u003ch1 id=\"simple-logistic-regression-tutorial-using-python\"\u003e\u003ca name=\"user-content-simple-logistic-regression-tutorial-using-python\" href=\"#simple-logistic-regression-tutorial-using-python\" class=\"headeranchor-link\" aria-hidden=\"true\"\u003e\u003cspan class=\"headeranchor\"\u003e\u003c/span\u003e\u003c/a\u003eSimple Logistic Regression Tutorial using Python\u003c/h1\u003e\n\u003cp\u003e\u003cstrong\u003eLogistic Regression\u003c/strong\u003e is a statistical technique capable of predicting a binary outcome and commonly applied in disciplines from credit and finance to medicine and other social sciences.\u003c/p\u003e\n\u003ch2 id=\"predicting-a-students-admission-rate\"\u003e\u003ca name=\"user-content-predicting-a-students-admission-rate\" href=\"#predicting-a-students-admission-rate\" class=\"headeranchor-link\" aria-hidden=\"true\"\u003e\u003cspan class=\"headeranchor\"\u003e\u003c/span\u003e\u003c/a\u003ePredicting a student\u0026rsquo;s admission rate\u003c/h2\u003e\n\u003cp\u003eA researcher is interested in how variables, such as GRE (Graduate Record Exam scores), GPA (grade point average) and prestige of the undergraduate institution, effect admission into graduate school. The response variable, admit/don’t admit, is a binary variable.\u003c/p\u003e\n\u003cp\u003eFor this tutorial, we will use \u003ccode\u003epandas\u003c/code\u003e, \u003ccode\u003estatsmodels\u003c/code\u003e, and \u003ccode\u003enumpy\u003c/code\u003e to predict each student\u0026rsquo;s success (admission) rate given their gre, gpa scores and the rank of her alma matter upon application.\u003c/p\u003e\n\u003ch1 id=\"what-it-includes\"\u003e\u003ca name=\"user-content-what-it-includes\" href=\"#what-it-includes\" class=\"headeranchor-link\" aria-hidden=\"true\"\u003e\u003cspan class=\"headeranchor\"\u003e\u003c/span\u003e\u003c/a\u003eWhat it includes\u003c/h1\u003e\n\u003cp\u003eThis repo contains the dataset required for this tutorial, the Jupyter notebook, as well as HTML and Python code for this exercise.  \u003c/p\u003e\n\u003ch3 id=\"credits\"\u003e\u003ca name=\"user-content-credits\" href=\"#credits\" class=\"headeranchor-link\" aria-hidden=\"true\"\u003e\u003cspan class=\"headeranchor\"\u003e\u003c/span\u003e\u003c/a\u003eCredits\u003c/h3\u003e\n\u003cp\u003eCredits to the original tutorial on \u003ca href=\"http://blog.yhat.com/posts/logistic-regression-python-rodeo.html\"\u003eyHat\u003c/a\u003e, which is adapted from the R tutorial published by \u003ca href=\"http://stats.idre.ucla.edu/r/dae/logit-regression/\"\u003eUCLA\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThe code for our cartesian function originally found on StackOverflow \u003ca href=\"http://stackoverflow.com/questions/1208118/using-numpy-to-build-an-array-of-all-combinations-of-two-arrays\"\u003ehere\u003c/a\u003e\u003c/p\u003e\u003c/article\u003e\u003c/body\u003e\u003c/html\u003e","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fonlyphantom%2Flogisticregressionpy","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fonlyphantom%2Flogisticregressionpy","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fonlyphantom%2Flogisticregressionpy/lists"}