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notebook.tex
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notebook.tex
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% Default to the notebook output style
% Inherit from the specified cell style.
\documentclass[11pt]{article}
\usepackage[T1]{fontenc}
% Nicer default font (+ math font) than Computer Modern for most use cases
\usepackage{mathpazo}
% Basic figure setup, for now with no caption control since it's done
% automatically by Pandoc (which extracts  syntax from Markdown).
\usepackage{graphicx}
% We will generate all images so they have a width \maxwidth. This means
% that they will get their normal width if they fit onto the page, but
% are scaled down if they would overflow the margins.
\makeatletter
\def\maxwidth{\ifdim\Gin@nat@width>\linewidth\linewidth
\else\Gin@nat@width\fi}
\makeatother
\let\Oldincludegraphics\includegraphics
% Set max figure width to be 80% of text width, for now hardcoded.
\renewcommand{\includegraphics}[1]{\Oldincludegraphics[width=.8\maxwidth]{#1}}
% Ensure that by default, figures have no caption (until we provide a
% proper Figure object with a Caption API and a way to capture that
% in the conversion process - todo).
\usepackage{caption}
\DeclareCaptionLabelFormat{nolabel}{}
\captionsetup{labelformat=nolabel}
\usepackage{adjustbox} % Used to constrain images to a maximum size
\usepackage{xcolor} % Allow colors to be defined
\usepackage{enumerate} % Needed for markdown enumerations to work
\usepackage{geometry} % Used to adjust the document margins
\usepackage{amsmath} % Equations
\usepackage{amssymb} % Equations
\usepackage{textcomp} % defines textquotesingle
% Hack from http://tex.stackexchange.com/a/47451/13684:
\AtBeginDocument{%
\def\PYZsq{\textquotesingle}% Upright quotes in Pygmentized code
}
\usepackage{upquote} % Upright quotes for verbatim code
\usepackage{eurosym} % defines \euro
\usepackage[mathletters]{ucs} % Extended unicode (utf-8) support
\usepackage[utf8x]{inputenc} % Allow utf-8 characters in the tex document
\usepackage{fancyvrb} % verbatim replacement that allows latex
\usepackage{grffile} % extends the file name processing of package graphics
% to support a larger range
% The hyperref package gives us a pdf with properly built
% internal navigation ('pdf bookmarks' for the table of contents,
% internal cross-reference links, web links for URLs, etc.)
\usepackage{hyperref}
\usepackage{longtable} % longtable support required by pandoc >1.10
\usepackage{booktabs} % table support for pandoc > 1.12.2
\usepackage[inline]{enumitem} % IRkernel/repr support (it uses the enumerate* environment)
\usepackage[normalem]{ulem} % ulem is needed to support strikethroughs (\sout)
% normalem makes italics be italics, not underlines
% Colors for the hyperref package
\definecolor{urlcolor}{rgb}{0,.145,.698}
\definecolor{linkcolor}{rgb}{.71,0.21,0.01}
\definecolor{citecolor}{rgb}{.12,.54,.11}
% ANSI colors
\definecolor{ansi-black}{HTML}{3E424D}
\definecolor{ansi-black-intense}{HTML}{282C36}
\definecolor{ansi-red}{HTML}{E75C58}
\definecolor{ansi-red-intense}{HTML}{B22B31}
\definecolor{ansi-green}{HTML}{00A250}
\definecolor{ansi-green-intense}{HTML}{007427}
\definecolor{ansi-yellow}{HTML}{DDB62B}
\definecolor{ansi-yellow-intense}{HTML}{B27D12}
\definecolor{ansi-blue}{HTML}{208FFB}
\definecolor{ansi-blue-intense}{HTML}{0065CA}
\definecolor{ansi-magenta}{HTML}{D160C4}
\definecolor{ansi-magenta-intense}{HTML}{A03196}
\definecolor{ansi-cyan}{HTML}{60C6C8}
\definecolor{ansi-cyan-intense}{HTML}{258F8F}
\definecolor{ansi-white}{HTML}{C5C1B4}
\definecolor{ansi-white-intense}{HTML}{A1A6B2}
% commands and environments needed by pandoc snippets
% extracted from the output of `pandoc -s`
\providecommand{\tightlist}{%
\setlength{\itemsep}{0pt}\setlength{\parskip}{0pt}}
\DefineVerbatimEnvironment{Highlighting}{Verbatim}{commandchars=\\\{\}}
% Add ',fontsize=\small' for more characters per line
\newenvironment{Shaded}{}{}
\newcommand{\KeywordTok}[1]{\textcolor[rgb]{0.00,0.44,0.13}{\textbf{{#1}}}}
\newcommand{\DataTypeTok}[1]{\textcolor[rgb]{0.56,0.13,0.00}{{#1}}}
\newcommand{\DecValTok}[1]{\textcolor[rgb]{0.25,0.63,0.44}{{#1}}}
\newcommand{\BaseNTok}[1]{\textcolor[rgb]{0.25,0.63,0.44}{{#1}}}
\newcommand{\FloatTok}[1]{\textcolor[rgb]{0.25,0.63,0.44}{{#1}}}
\newcommand{\CharTok}[1]{\textcolor[rgb]{0.25,0.44,0.63}{{#1}}}
\newcommand{\StringTok}[1]{\textcolor[rgb]{0.25,0.44,0.63}{{#1}}}
\newcommand{\CommentTok}[1]{\textcolor[rgb]{0.38,0.63,0.69}{\textit{{#1}}}}
\newcommand{\OtherTok}[1]{\textcolor[rgb]{0.00,0.44,0.13}{{#1}}}
\newcommand{\AlertTok}[1]{\textcolor[rgb]{1.00,0.00,0.00}{\textbf{{#1}}}}
\newcommand{\FunctionTok}[1]{\textcolor[rgb]{0.02,0.16,0.49}{{#1}}}
\newcommand{\RegionMarkerTok}[1]{{#1}}
\newcommand{\ErrorTok}[1]{\textcolor[rgb]{1.00,0.00,0.00}{\textbf{{#1}}}}
\newcommand{\NormalTok}[1]{{#1}}
% Additional commands for more recent versions of Pandoc
\newcommand{\ConstantTok}[1]{\textcolor[rgb]{0.53,0.00,0.00}{{#1}}}
\newcommand{\SpecialCharTok}[1]{\textcolor[rgb]{0.25,0.44,0.63}{{#1}}}
\newcommand{\VerbatimStringTok}[1]{\textcolor[rgb]{0.25,0.44,0.63}{{#1}}}
\newcommand{\SpecialStringTok}[1]{\textcolor[rgb]{0.73,0.40,0.53}{{#1}}}
\newcommand{\ImportTok}[1]{{#1}}
\newcommand{\DocumentationTok}[1]{\textcolor[rgb]{0.73,0.13,0.13}{\textit{{#1}}}}
\newcommand{\AnnotationTok}[1]{\textcolor[rgb]{0.38,0.63,0.69}{\textbf{\textit{{#1}}}}}
\newcommand{\CommentVarTok}[1]{\textcolor[rgb]{0.38,0.63,0.69}{\textbf{\textit{{#1}}}}}
\newcommand{\VariableTok}[1]{\textcolor[rgb]{0.10,0.09,0.49}{{#1}}}
\newcommand{\ControlFlowTok}[1]{\textcolor[rgb]{0.00,0.44,0.13}{\textbf{{#1}}}}
\newcommand{\OperatorTok}[1]{\textcolor[rgb]{0.40,0.40,0.40}{{#1}}}
\newcommand{\BuiltInTok}[1]{{#1}}
\newcommand{\ExtensionTok}[1]{{#1}}
\newcommand{\PreprocessorTok}[1]{\textcolor[rgb]{0.74,0.48,0.00}{{#1}}}
\newcommand{\AttributeTok}[1]{\textcolor[rgb]{0.49,0.56,0.16}{{#1}}}
\newcommand{\InformationTok}[1]{\textcolor[rgb]{0.38,0.63,0.69}{\textbf{\textit{{#1}}}}}
\newcommand{\WarningTok}[1]{\textcolor[rgb]{0.38,0.63,0.69}{\textbf{\textit{{#1}}}}}
% Define a nice break command that doesn't care if a line doesn't already
% exist.
\def\br{\hspace*{\fill} \\* }
% Math Jax compatability definitions
\def\gt{>}
\def\lt{<}
% Document parameters
\title{Hospital Dataset Analysis}
% Pygments definitions
\makeatletter
\def\PY@reset{\let\PY@it=\relax \let\PY@bf=\relax%
\let\PY@ul=\relax \let\PY@tc=\relax%
\let\PY@bc=\relax \let\PY@ff=\relax}
\def\PY@tok#1{\csname PY@tok@#1\endcsname}
\def\PY@toks#1+{\ifx\relax#1\empty\else%
\PY@tok{#1}\expandafter\PY@toks\fi}
\def\PY@do#1{\PY@bc{\PY@tc{\PY@ul{%
\PY@it{\PY@bf{\PY@ff{#1}}}}}}}
\def\PY#1#2{\PY@reset\PY@toks#1+\relax+\PY@do{#2}}
\expandafter\def\csname PY@tok@w\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.73,0.73,0.73}{##1}}}
\expandafter\def\csname PY@tok@c\endcsname{\let\PY@it=\textit\def\PY@tc##1{\textcolor[rgb]{0.25,0.50,0.50}{##1}}}
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\expandafter\def\csname PY@tok@ge\endcsname{\let\PY@it=\textit}
\expandafter\def\csname PY@tok@gs\endcsname{\let\PY@bf=\textbf}
\expandafter\def\csname PY@tok@gp\endcsname{\let\PY@bf=\textbf\def\PY@tc##1{\textcolor[rgb]{0.00,0.00,0.50}{##1}}}
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\expandafter\def\csname PY@tok@vg\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.10,0.09,0.49}{##1}}}
\expandafter\def\csname PY@tok@vi\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.10,0.09,0.49}{##1}}}
\expandafter\def\csname PY@tok@vm\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.10,0.09,0.49}{##1}}}
\expandafter\def\csname PY@tok@sa\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.73,0.13,0.13}{##1}}}
\expandafter\def\csname PY@tok@sb\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.73,0.13,0.13}{##1}}}
\expandafter\def\csname PY@tok@sc\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.73,0.13,0.13}{##1}}}
\expandafter\def\csname PY@tok@dl\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.73,0.13,0.13}{##1}}}
\expandafter\def\csname PY@tok@s2\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.73,0.13,0.13}{##1}}}
\expandafter\def\csname PY@tok@sh\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.73,0.13,0.13}{##1}}}
\expandafter\def\csname PY@tok@s1\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.73,0.13,0.13}{##1}}}
\expandafter\def\csname PY@tok@mb\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.40,0.40,0.40}{##1}}}
\expandafter\def\csname PY@tok@mf\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.40,0.40,0.40}{##1}}}
\expandafter\def\csname PY@tok@mh\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.40,0.40,0.40}{##1}}}
\expandafter\def\csname PY@tok@mi\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.40,0.40,0.40}{##1}}}
\expandafter\def\csname PY@tok@il\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.40,0.40,0.40}{##1}}}
\expandafter\def\csname PY@tok@mo\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.40,0.40,0.40}{##1}}}
\expandafter\def\csname PY@tok@ch\endcsname{\let\PY@it=\textit\def\PY@tc##1{\textcolor[rgb]{0.25,0.50,0.50}{##1}}}
\expandafter\def\csname PY@tok@cm\endcsname{\let\PY@it=\textit\def\PY@tc##1{\textcolor[rgb]{0.25,0.50,0.50}{##1}}}
\expandafter\def\csname PY@tok@cpf\endcsname{\let\PY@it=\textit\def\PY@tc##1{\textcolor[rgb]{0.25,0.50,0.50}{##1}}}
\expandafter\def\csname PY@tok@c1\endcsname{\let\PY@it=\textit\def\PY@tc##1{\textcolor[rgb]{0.25,0.50,0.50}{##1}}}
\expandafter\def\csname PY@tok@cs\endcsname{\let\PY@it=\textit\def\PY@tc##1{\textcolor[rgb]{0.25,0.50,0.50}{##1}}}
\def\PYZbs{\char`\\}
\def\PYZus{\char`\_}
\def\PYZob{\char`\{}
\def\PYZcb{\char`\}}
\def\PYZca{\char`\^}
\def\PYZam{\char`\&}
\def\PYZlt{\char`\<}
\def\PYZgt{\char`\>}
\def\PYZsh{\char`\#}
\def\PYZpc{\char`\%}
\def\PYZdl{\char`\$}
\def\PYZhy{\char`\-}
\def\PYZsq{\char`\'}
\def\PYZdq{\char`\"}
\def\PYZti{\char`\~}
% for compatibility with earlier versions
\def\PYZat{@}
\def\PYZlb{[}
\def\PYZrb{]}
\makeatother
% Exact colors from NB
\definecolor{incolor}{rgb}{0.0, 0.0, 0.5}
\definecolor{outcolor}{rgb}{0.545, 0.0, 0.0}
% Prevent overflowing lines due to hard-to-break entities
\sloppy
% Setup hyperref package
\hypersetup{
breaklinks=true, % so long urls are correctly broken across lines
colorlinks=true,
urlcolor=urlcolor,
linkcolor=linkcolor,
citecolor=citecolor,
}
% Slightly bigger margins than the latex defaults
\geometry{verbose,tmargin=1in,bmargin=1in,lmargin=1in,rmargin=1in}
\begin{document}
\maketitle
\hypertarget{data-mining-on-hospital-dataset}{%
\subsubsection{Data Mining on Hospital
Dataset}\label{data-mining-on-hospital-dataset}}
This notebook analyses hospital records of diabetic patients and
predicts the possibilty of readmittance.
\hypertarget{imports}{%
\subsubsection{Imports}\label{imports}}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}86}]:} \PY{k+kn}{from} \PY{n+nn}{sklearn}\PY{n+nn}{.}\PY{n+nn}{metrics} \PY{k}{import} \PY{n}{accuracy\PYZus{}score}\PY{p}{,} \PY{n}{confusion\PYZus{}matrix}\PY{p}{,} \PY{n}{classification\PYZus{}report}
\PY{k+kn}{from} \PY{n+nn}{sklearn}\PY{n+nn}{.}\PY{n+nn}{naive\PYZus{}bayes} \PY{k}{import} \PY{n}{GaussianNB}\PY{p}{,} \PY{n}{MultinomialNB}
\PY{k+kn}{from} \PY{n+nn}{sklearn}\PY{n+nn}{.}\PY{n+nn}{neural\PYZus{}network} \PY{k}{import} \PY{n}{MLPClassifier}
\PY{k+kn}{from} \PY{n+nn}{sklearn}\PY{n+nn}{.}\PY{n+nn}{model\PYZus{}selection} \PY{k}{import} \PY{n}{train\PYZus{}test\PYZus{}split}
\PY{k+kn}{from} \PY{n+nn}{sklearn}\PY{n+nn}{.}\PY{n+nn}{tree} \PY{k}{import} \PY{n}{DecisionTreeClassifier}\PY{p}{,} \PY{n}{export\PYZus{}graphviz}
\PY{k+kn}{from} \PY{n+nn}{sklearn}\PY{n+nn}{.}\PY{n+nn}{ensemble} \PY{k}{import} \PY{n}{RandomForestClassifier}\PY{p}{,} \PY{n}{RandomForestRegressor}
\PY{k+kn}{from} \PY{n+nn}{sklearn}\PY{n+nn}{.}\PY{n+nn}{linear\PYZus{}model} \PY{k}{import} \PY{n}{LogisticRegression}\PY{p}{,} \PY{n}{LinearRegression}
\PY{k+kn}{from} \PY{n+nn}{sklearn}\PY{n+nn}{.}\PY{n+nn}{naive\PYZus{}bayes} \PY{k}{import} \PY{n}{GaussianNB}\PY{p}{,} \PY{n}{MultinomialNB}
\PY{k+kn}{from} \PY{n+nn}{sklearn}\PY{n+nn}{.}\PY{n+nn}{neural\PYZus{}network} \PY{k}{import} \PY{n}{MLPClassifier}
\PY{k+kn}{from} \PY{n+nn}{sklearn}\PY{n+nn}{.}\PY{n+nn}{preprocessing} \PY{k}{import} \PY{n}{Imputer}
\PY{k+kn}{from} \PY{n+nn}{sklearn}\PY{n+nn}{.}\PY{n+nn}{metrics} \PY{k}{import} \PY{n}{roc\PYZus{}curve}
\PY{k+kn}{import} \PY{n+nn}{matplotlib}\PY{n+nn}{.}\PY{n+nn}{pyplot} \PY{k}{as} \PY{n+nn}{plt}
\PY{k+kn}{from} \PY{n+nn}{IPython}\PY{n+nn}{.}\PY{n+nn}{display} \PY{k}{import} \PY{n}{Markdown}\PY{p}{,} \PY{n}{display}
\PY{k+kn}{import} \PY{n+nn}{matplotlib}\PY{n+nn}{.}\PY{n+nn}{patches} \PY{k}{as} \PY{n+nn}{mpatches}
\PY{k+kn}{from} \PY{n+nn}{IPython}\PY{n+nn}{.}\PY{n+nn}{display} \PY{k}{import} \PY{n}{HTML}
\PY{k+kn}{from} \PY{n+nn}{operator} \PY{k}{import} \PY{n}{itemgetter}
\PY{k+kn}{import} \PY{n+nn}{pandas} \PY{k}{as} \PY{n+nn}{pd}
\PY{k+kn}{import} \PY{n+nn}{numpy} \PY{k}{as} \PY{n+nn}{np}
\PY{c+c1}{\PYZsh{} pd.options.mode.chained\PYZus{}assignment = None }
\end{Verbatim}
\hypertarget{helper-functions}{%
\subsubsection{Helper functions}\label{helper-functions}}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}87}]:} \PY{k}{def} \PY{n+nf}{print\PYZus{}bold}\PY{p}{(}\PY{n}{text}\PY{p}{)}\PY{p}{:}
\PY{n}{display}\PY{p}{(}\PY{n}{Markdown}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{**}\PY{l+s+si}{\PYZob{}\PYZcb{}}\PY{l+s+s2}{**}\PY{l+s+s2}{\PYZdq{}}\PY{o}{.}\PY{n}{format}\PY{p}{(}\PY{n}{text}\PY{p}{)}\PY{p}{)}\PY{p}{)}
\PY{k}{def} \PY{n+nf}{print\PYZus{}color}\PY{p}{(}\PY{n}{text}\PY{p}{,} \PY{n}{color}\PY{o}{=}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{red}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}\PY{p}{:}
\PY{n}{display}\PY{p}{(}\PY{n}{HTML}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{\PYZlt{}text style=color:}\PY{l+s+si}{\PYZob{}\PYZcb{}}\PY{l+s+s2}{\PYZgt{}}\PY{l+s+si}{\PYZob{}\PYZcb{}}\PY{l+s+s2}{\PYZlt{}/text\PYZgt{}}\PY{l+s+s2}{\PYZdq{}}\PY{o}{.}\PY{n}{format}\PY{p}{(}\PY{n}{color}\PY{p}{,} \PY{n}{text}\PY{p}{)}\PY{p}{)}\PY{p}{)}
\PY{k}{def} \PY{n+nf}{display\PYZus{}confusion\PYZus{}matrix}\PY{p}{(}\PY{n}{target\PYZus{}test}\PY{p}{,} \PY{n}{target\PYZus{}predict}\PY{p}{)}\PY{p}{:}
\PY{n}{print\PYZus{}bold}\PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{Confusion Matrix}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
\PY{n}{y\PYZus{}actu} \PY{o}{=} \PY{n}{pd}\PY{o}{.}\PY{n}{Series}\PY{p}{(}\PY{n}{target\PYZus{}test}\PY{p}{,} \PY{n}{name}\PY{o}{=}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{Actual}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
\PY{n}{y\PYZus{}pred} \PY{o}{=} \PY{n}{pd}\PY{o}{.}\PY{n}{Series}\PY{p}{(}\PY{n}{target\PYZus{}predict}\PY{p}{,} \PY{n}{name}\PY{o}{=}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{Predicted}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
\PY{n+nb}{print}\PY{p}{(}\PY{n}{pd}\PY{o}{.}\PY{n}{crosstab}\PY{p}{(}\PY{n}{y\PYZus{}actu}\PY{p}{,} \PY{n}{y\PYZus{}pred}\PY{p}{)}\PY{p}{)}
\PY{c+c1}{\PYZsh{} Basic graph plotting utility}
\PY{k}{def} \PY{n+nf}{plot\PYZus{}roc\PYZus{}curve}\PY{p}{(}\PY{n}{confusion\PYZus{}matrix\PYZus{}list}\PY{p}{,} \PY{n}{models}\PY{p}{,} \PY{n}{x\PYZus{}label}\PY{p}{,} \PY{n}{y\PYZus{}label}\PY{p}{,} \PY{n}{model\PYZus{}names}\PY{o}{=}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}\PY{p}{:}
\PY{n}{tpr\PYZus{}fpr\PYZus{}list} \PY{o}{=} \PY{p}{[}\PY{p}{]}
\PY{k}{for} \PY{n}{idx}\PY{p}{,} \PY{n}{cm} \PY{o+ow}{in} \PY{n+nb}{enumerate}\PY{p}{(}\PY{n}{confusion\PYZus{}matrix\PYZus{}list}\PY{p}{)}\PY{p}{:}
\PY{n}{tp} \PY{o}{=} \PY{n}{cm}\PY{p}{[}\PY{l+m+mi}{1}\PY{p}{,} \PY{l+m+mi}{1}\PY{p}{]}
\PY{n}{tn} \PY{o}{=} \PY{n}{cm}\PY{p}{[}\PY{l+m+mi}{0}\PY{p}{,} \PY{l+m+mi}{0}\PY{p}{]}
\PY{n}{fp} \PY{o}{=} \PY{n}{cm}\PY{p}{[}\PY{l+m+mi}{0}\PY{p}{,} \PY{l+m+mi}{1}\PY{p}{]}
\PY{n}{fn} \PY{o}{=} \PY{n}{cm}\PY{p}{[}\PY{l+m+mi}{1}\PY{p}{,} \PY{l+m+mi}{0}\PY{p}{]}
\PY{n}{tpr} \PY{o}{=} \PY{n}{tp} \PY{o}{/} \PY{p}{(}\PY{n}{tp} \PY{o}{+} \PY{n}{fn}\PY{p}{)}
\PY{n}{fpr} \PY{o}{=} \PY{n}{fp} \PY{o}{/} \PY{p}{(}\PY{n}{fp} \PY{o}{+} \PY{n}{tn}\PY{p}{)}
\PY{n+nb}{print}\PY{p}{(}\PY{n}{models}\PY{p}{[}\PY{n}{idx}\PY{p}{]}\PY{p}{[}\PY{l+m+mi}{0}\PY{p}{]}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{TPR:}\PY{l+s+s1}{\PYZsq{}} \PY{o}{+} \PY{n+nb}{str}\PY{p}{(}\PY{n}{tpr}\PY{p}{)}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{FPR:}\PY{l+s+s1}{\PYZsq{}} \PY{o}{+} \PY{n+nb}{str}\PY{p}{(}\PY{n}{fpr}\PY{p}{)}\PY{p}{)}
\PY{n}{tpr\PYZus{}fpr\PYZus{}list}\PY{o}{.}\PY{n}{append}\PY{p}{(}\PY{p}{(}\PY{n+nb}{round}\PY{p}{(}\PY{n}{fpr}\PY{p}{,} \PY{l+m+mi}{2}\PY{p}{)}\PY{p}{,} \PY{n+nb}{round}\PY{p}{(}\PY{n}{tpr} \PY{p}{,} \PY{l+m+mi}{2}\PY{p}{)}\PY{p}{)}\PY{p}{)}
\PY{n}{tpr\PYZus{}fpr\PYZus{}list}\PY{o}{.}\PY{n}{sort}\PY{p}{(}\PY{n}{key}\PY{o}{=}\PY{n}{itemgetter}\PY{p}{(}\PY{l+m+mi}{0}\PY{p}{)}\PY{p}{)}
\PY{n}{handles} \PY{o}{=} \PY{p}{[}\PY{p}{]}
\PY{k}{for} \PY{n}{model\PYZus{}name}\PY{p}{,} \PY{n}{model}\PY{p}{,} \PY{n}{color} \PY{o+ow}{in} \PY{n}{models}\PY{p}{:}
\PY{n}{patch} \PY{o}{=} \PY{n}{mpatches}\PY{o}{.}\PY{n}{Patch}\PY{p}{(}\PY{n}{color}\PY{o}{=}\PY{n}{color}\PY{p}{,} \PY{n}{label}\PY{o}{=}\PY{n}{model\PYZus{}name}\PY{p}{)}
\PY{n}{handles}\PY{o}{.}\PY{n}{append}\PY{p}{(}\PY{n}{patch}\PY{p}{)}
\PY{n}{fpr\PYZus{}list}\PY{p}{,} \PY{n}{tpr\PYZus{}list} \PY{o}{=} \PY{n+nb}{zip}\PY{p}{(}\PY{o}{*}\PY{n}{tpr\PYZus{}fpr\PYZus{}list}\PY{p}{)}
\PY{n}{fig}\PY{p}{,} \PY{n}{ax} \PY{o}{=} \PY{n}{plt}\PY{o}{.}\PY{n}{subplots}\PY{p}{(}\PY{p}{)}
\PY{n}{plt}\PY{o}{.}\PY{n}{plot}\PY{p}{(}\PY{n}{fpr\PYZus{}list}\PY{p}{,} \PY{n}{tpr\PYZus{}list}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{bs\PYZhy{}}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
\PY{n}{plt}\PY{o}{.}\PY{n}{xlabel}\PY{p}{(}\PY{n}{x\PYZus{}label}\PY{p}{)}
\PY{n}{plt}\PY{o}{.}\PY{n}{ylabel}\PY{p}{(}\PY{n}{y\PYZus{}label}\PY{p}{)}
\PY{k}{for} \PY{n}{idx}\PY{p}{,} \PY{n}{\PYZus{}} \PY{o+ow}{in} \PY{n+nb}{enumerate}\PY{p}{(}\PY{n}{tpr\PYZus{}list}\PY{p}{)}\PY{p}{:}
\PY{n}{tpr} \PY{o}{=} \PY{n}{tpr\PYZus{}list}\PY{p}{[}\PY{n}{idx}\PY{p}{]}
\PY{n}{fpr} \PY{o}{=} \PY{n}{fpr\PYZus{}list}\PY{p}{[}\PY{n}{idx}\PY{p}{]}
\PY{c+c1}{\PYZsh{} plt.plot([fpr, fpr], [0, tpr], \PYZsq{}ks\PYZhy{}\PYZsq{}) \PYZsh{} vertical}
\PY{c+c1}{\PYZsh{} plt.plot([0, fpr], [tpr, tpr], \PYZsq{}ks\PYZhy{}\PYZsq{}) \PYZsh{} horizontal}
\PY{n}{plt}\PY{o}{.}\PY{n}{plot}\PY{p}{(}\PY{p}{[}\PY{n}{fpr}\PY{p}{]}\PY{p}{,} \PY{p}{[}\PY{n}{tpr}\PY{p}{]}\PY{p}{,} \PY{n}{marker}\PY{o}{=}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{o}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{n}{markersize}\PY{o}{=}\PY{l+m+mi}{10}\PY{p}{,} \PY{n}{color}\PY{o}{=}\PY{n}{models}\PY{p}{[}\PY{n}{idx}\PY{p}{]}\PY{p}{[}\PY{l+m+mi}{2}\PY{p}{]}\PY{p}{)}
\PY{n}{plt}\PY{o}{.}\PY{n}{legend}\PY{p}{(}\PY{n}{handles}\PY{o}{=}\PY{n}{handles}\PY{p}{)}
\PY{n}{plt}\PY{o}{.}\PY{n}{show}\PY{p}{(}\PY{p}{)}
\end{Verbatim}
\hypertarget{read-dataset}{%
\subsubsection{Read dataset}\label{read-dataset}}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}88}]:} \PY{n}{DATASET\PYZus{}NAME} \PY{o}{=} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{datasets/diabetes\PYZus{}hospital.csv}\PY{l+s+s1}{\PYZsq{}}
\PY{n}{df} \PY{o}{=} \PY{n}{pd}\PY{o}{.}\PY{n}{read\PYZus{}csv}\PY{p}{(}\PY{n}{DATASET\PYZus{}NAME}\PY{p}{)}
\PY{n}{df}\PY{o}{.}\PY{n}{head}\PY{p}{(}\PY{p}{)}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{outcolor}Out[{\color{outcolor}88}]:} encounter\_id patient\_nbr race gender age weight \textbackslash{}
0 2278392 8222157 Caucasian Female [0-10) ?
1 149190 55629189 Caucasian Female [10-20) ?
2 64410 86047875 AfricanAmerican Female [20-30) ?
3 500364 82442376 Caucasian Male [30-40) ?
4 16680 42519267 Caucasian Male [40-50) ?
admission\_type\_id discharge\_disposition\_id admission\_source\_id \textbackslash{}
0 6 25 1
1 1 1 7
2 1 1 7
3 1 1 7
4 1 1 7
time\_in\_hospital {\ldots} citoglipton insulin glyburide-metformin \textbackslash{}
0 1 {\ldots} No No No
1 3 {\ldots} No Up No
2 2 {\ldots} No No No
3 2 {\ldots} No Up No
4 1 {\ldots} No Steady No
glipizide-metformin glimepiride-pioglitazone metformin-rosiglitazone \textbackslash{}
0 No No No
1 No No No
2 No No No
3 No No No
4 No No No
metformin-pioglitazone change diabetesMed readmitted
0 No No No NO
1 No Ch Yes >30
2 No No Yes NO
3 No Ch Yes NO
4 No Ch Yes NO
[5 rows x 50 columns]
\end{Verbatim}
\hypertarget{find-out-missing-values-in-the-dataset}{%
\subsubsection{Find out missing values in the
dataset}\label{find-out-missing-values-in-the-dataset}}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}89}]:} \PY{n}{attributes} \PY{o}{=} \PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{race}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{gender}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{age}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{medical\PYZus{}specialty}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{change}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{diabetesMed}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{readmitted}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]}
\PY{k}{for} \PY{n}{attr} \PY{o+ow}{in} \PY{n}{attributes}\PY{p}{:}
\PY{n+nb}{print}\PY{p}{(}\PY{n}{attr}\PY{p}{,}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{: }\PY{l+s+s2}{\PYZdq{}}\PY{p}{,} \PY{n+nb}{sum}\PY{p}{(}\PY{n}{df}\PY{p}{[}\PY{n}{attr}\PY{p}{]} \PY{o}{==} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{?}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}\PY{p}{)}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
race : 2273
gender : 0
age : 0
medical\_specialty : 49949
change : 0
diabetesMed : 0
readmitted : 0
\end{Verbatim}
\hypertarget{show-statistics-of-the-dataset}{%
\subsubsection{Show statistics of the
dataset}\label{show-statistics-of-the-dataset}}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}90}]:} \PY{n}{df}\PY{o}{.}\PY{n}{describe}\PY{p}{(}\PY{p}{)}\PY{o}{.}\PY{n}{transpose}\PY{p}{(}\PY{p}{)}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{outcolor}Out[{\color{outcolor}90}]:} count mean std min \textbackslash{}
encounter\_id 101766.0 1.652016e+08 1.026403e+08 12522.0
patient\_nbr 101766.0 5.433040e+07 3.869636e+07 135.0
admission\_type\_id 101766.0 2.024006e+00 1.445403e+00 1.0
discharge\_disposition\_id 101766.0 3.715642e+00 5.280166e+00 1.0
admission\_source\_id 101766.0 5.754437e+00 4.064081e+00 1.0
time\_in\_hospital 101766.0 4.395987e+00 2.985108e+00 1.0
num\_lab\_procedures 101766.0 4.309564e+01 1.967436e+01 1.0
num\_procedures 101766.0 1.339730e+00 1.705807e+00 0.0
num\_medications 101766.0 1.602184e+01 8.127566e+00 1.0
number\_outpatient 101766.0 3.693572e-01 1.267265e+00 0.0
number\_emergency 101766.0 1.978362e-01 9.304723e-01 0.0
number\_inpatient 101766.0 6.355659e-01 1.262863e+00 0.0
number\_diagnoses 101766.0 7.422607e+00 1.933600e+00 1.0
25\% 50\% 75\% max
encounter\_id 84961194.0 152388987.0 2.302709e+08 443867222.0
patient\_nbr 23413221.0 45505143.0 8.754595e+07 189502619.0
admission\_type\_id 1.0 1.0 3.000000e+00 8.0
discharge\_disposition\_id 1.0 1.0 4.000000e+00 28.0
admission\_source\_id 1.0 7.0 7.000000e+00 25.0
time\_in\_hospital 2.0 4.0 6.000000e+00 14.0
num\_lab\_procedures 31.0 44.0 5.700000e+01 132.0
num\_procedures 0.0 1.0 2.000000e+00 6.0
num\_medications 10.0 15.0 2.000000e+01 81.0
number\_outpatient 0.0 0.0 0.000000e+00 42.0
number\_emergency 0.0 0.0 0.000000e+00 76.0
number\_inpatient 0.0 0.0 1.000000e+00 21.0
number\_diagnoses 6.0 8.0 9.000000e+00 16.0
\end{Verbatim}
\hypertarget{drop-columns}{%
\subsubsection{Drop columns}\label{drop-columns}}
Drop attributes like IDs and other irrelevant attributes
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}91}]:} \PY{n}{bad\PYZus{}features} \PY{o}{=} \PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{encounter\PYZus{}id}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{patient\PYZus{}nbr}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{weight}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{payer\PYZus{}code}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{admission\PYZus{}type\PYZus{}id}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,}
\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{discharge\PYZus{}disposition\PYZus{}id}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{admission\PYZus{}source\PYZus{}id}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{number\PYZus{}outpatient}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{number\PYZus{}emergency}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,}
\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{number\PYZus{}inpatient}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{diag\PYZus{}1}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{diag\PYZus{}2}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{diag\PYZus{}3}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{number\PYZus{}diagnoses}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{examide}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,}
\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{citoglipton}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{glyburide\PYZhy{}metformin}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{glipizide\PYZhy{}metformin}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{glimepiride\PYZhy{}pioglitazone}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,}
\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{metformin\PYZhy{}rosiglitazone}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{metformin\PYZhy{}pioglitazone}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{max\PYZus{}glu\PYZus{}serum}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{A1Cresult}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,}
\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{metformin}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{repaglinide}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{nateglinide}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{chlorpropamide}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{glimepiride}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{acetohexamide}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,}
\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{glipizide}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{glyburide}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{tolbutamide}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{pioglitazone}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{rosiglitazone}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{acarbose}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,}
\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{miglitol}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{troglitazone}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{tolazamide}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{examide}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{citoglipton}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]}
\PY{n}{df}\PY{o}{.}\PY{n}{drop}\PY{p}{(}\PY{n}{bad\PYZus{}features}\PY{p}{,} \PY{n}{inplace}\PY{o}{=}\PY{k+kc}{True}\PY{p}{,} \PY{n}{axis} \PY{o}{=}\PY{l+m+mi}{1}\PY{p}{)}
\end{Verbatim}
\hypertarget{replace-missing-values}{%
\subsubsection{Replace missing values}\label{replace-missing-values}}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}92}]:} \PY{n}{df}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{medical\PYZus{}specialty}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]} \PY{o}{=} \PY{n}{df}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{medical\PYZus{}specialty}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]}\PY{o}{.}\PY{n}{replace}\PY{p}{(}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{?}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{General}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
\PY{n}{df}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{race}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]} \PY{o}{=} \PY{n}{df}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{race}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]}\PY{o}{.}\PY{n}{replace}\PY{p}{(}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{?}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{Other}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
\PY{n}{df}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{readmitted}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]} \PY{o}{=} \PY{n}{df}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{readmitted}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]}\PY{o}{.}\PY{n}{replace}\PY{p}{(}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{\PYZlt{}30}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{YES}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
\PY{n}{df}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{readmitted}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]} \PY{o}{=} \PY{n}{df}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{readmitted}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]}\PY{o}{.}\PY{n}{replace}\PY{p}{(}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{\PYZgt{}30}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{YES}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
\PY{n}{df}\PY{o}{.}\PY{n}{transpose}\PY{p}{(}\PY{p}{)}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{outcolor}Out[{\color{outcolor}92}]:} 0 1 2 \textbackslash{}
race Caucasian Caucasian AfricanAmerican
gender Female Female Female
age [0-10) [10-20) [20-30)
time\_in\_hospital 1 3 2
medical\_specialty Pediatrics-Endocrinology General General
num\_lab\_procedures 41 59 11
num\_procedures 0 0 5
num\_medications 1 18 13
insulin No Up No
change No Ch No
diabetesMed No Yes Yes
readmitted NO YES NO
3 4 5 6 7 \textbackslash{}
race Caucasian Caucasian Caucasian Caucasian Caucasian
gender Male Male Male Male Male
age [30-40) [40-50) [50-60) [60-70) [70-80)
time\_in\_hospital 2 1 3 4 5
medical\_specialty General General General General General
num\_lab\_procedures 44 51 31 70 73
num\_procedures 1 0 6 1 0
num\_medications 16 8 16 21 12
insulin Up Steady Steady Steady No
change Ch Ch No Ch No
diabetesMed Yes Yes Yes Yes Yes
readmitted NO NO YES NO YES
8 9 {\ldots} 101756 \textbackslash{}
race Caucasian Caucasian {\ldots} Other
gender Female Female {\ldots} Female
age [80-90) [90-100) {\ldots} [60-70)
time\_in\_hospital 13 12 {\ldots} 2
medical\_specialty General InternalMedicine {\ldots} General
num\_lab\_procedures 68 33 {\ldots} 46
num\_procedures 2 3 {\ldots} 6
num\_medications 28 18 {\ldots} 17
insulin Steady Steady {\ldots} Steady
change Ch Ch {\ldots} No
diabetesMed Yes Yes {\ldots} Yes
readmitted NO NO {\ldots} YES
101757 101758 101759 101760 \textbackslash{}
race Caucasian Caucasian Caucasian AfricanAmerican
gender Female Female Male Female
age [70-80) [80-90) [80-90) [60-70)
time\_in\_hospital 5 5 1 6
medical\_specialty General General General General
num\_lab\_procedures 21 76 1 45
num\_procedures 1 1 0 1
num\_medications 16 22 15 25
insulin Steady Up Up Down
change No Ch Ch Ch
diabetesMed Yes Yes Yes Yes
readmitted NO NO NO YES
101761 101762 101763 \textbackslash{}
race AfricanAmerican AfricanAmerican Caucasian
gender Male Female Male
age [70-80) [80-90) [70-80)
time\_in\_hospital 3 5 1
medical\_specialty General General General
num\_lab\_procedures 51 33 53
num\_procedures 0 3 0
num\_medications 16 18 9
insulin Down Steady Down
change Ch No Ch
diabetesMed Yes Yes Yes
readmitted YES NO NO
101764 101765
race Caucasian Caucasian
gender Female Male
age [80-90) [70-80)
time\_in\_hospital 10 6
medical\_specialty Surgery-General General
num\_lab\_procedures 45 13
num\_procedures 2 3
num\_medications 21 3
insulin Up No
change Ch No
diabetesMed Yes No
readmitted NO NO
[12 rows x 101766 columns]
\end{Verbatim}
\hypertarget{categorical-to-numeric}{%
\subsubsection{Categorical to numeric}\label{categorical-to-numeric}}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}93}]:} \PY{n}{dummy\PYZus{}columns} \PY{o}{=} \PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{race}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{gender}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{age}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{insulin}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{medical\PYZus{}specialty}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]}
\PY{n}{df\PYZus{}dummies} \PY{o}{=} \PY{n}{pd}\PY{o}{.}\PY{n}{get\PYZus{}dummies}\PY{p}{(}\PY{n}{df}\PY{p}{,} \PY{n}{columns}\PY{o}{=}\PY{n}{dummy\PYZus{}columns}\PY{p}{)}
\PY{n}{df\PYZus{}dummies}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{readmitted}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]} \PY{o}{=} \PY{n}{pd}\PY{o}{.}\PY{n}{Categorical}\PY{p}{(}\PY{n}{df\PYZus{}dummies}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{readmitted}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]}\PY{p}{)}\PY{o}{.}\PY{n}{codes}
\PY{n}{df\PYZus{}target} \PY{o}{=} \PY{n}{df\PYZus{}dummies}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{readmitted}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]}
\PY{c+c1}{\PYZsh{} Drop the target variable from dataframe}
\PY{n}{df\PYZus{}dummies}\PY{o}{.}\PY{n}{drop}\PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{readmitted}\PY{l+s+s1}{\PYZsq{}} \PY{p}{,} \PY{n}{inplace} \PY{o}{=} \PY{k+kc}{True}\PY{p}{,} \PY{n}{axis} \PY{o}{=}\PY{l+m+mi}{1}\PY{p}{)}
\PY{n}{df\PYZus{}attr} \PY{o}{=} \PY{n}{df\PYZus{}dummies}
\PY{c+c1}{\PYZsh{} Concvert binary categorical values to numeric}
\PY{n}{df\PYZus{}attr}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{diabetesMed}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]} \PY{o}{=} \PY{n}{pd}\PY{o}{.}\PY{n}{Categorical}\PY{p}{(}\PY{n}{df\PYZus{}attr}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{diabetesMed}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]}\PY{p}{)}\PY{o}{.}\PY{n}{codes}
\PY{n}{df\PYZus{}attr}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{change}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]} \PY{o}{=} \PY{n}{pd}\PY{o}{.}\PY{n}{Categorical}\PY{p}{(}\PY{n}{df\PYZus{}attr}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{change}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]}\PY{p}{)}\PY{o}{.}\PY{n}{codes}
\PY{n}{df\PYZus{}attr}\PY{o}{.}\PY{n}{head}\PY{p}{(}\PY{p}{)}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{outcolor}Out[{\color{outcolor}93}]:} time\_in\_hospital num\_lab\_procedures num\_procedures num\_medications \textbackslash{}
0 1 41 0 1
1 3 59 0 18
2 2 11 5 13
3 2 44 1 16
4 1 51 0 8
change diabetesMed race\_AfricanAmerican race\_Asian race\_Caucasian \textbackslash{}
0 1 0 0 0 1
1 0 1 0 0 1
2 1 1 1 0 0
3 0 1 0 0 1
4 0 1 0 0 1
race\_Hispanic {\ldots} \textbackslash{}
0 0 {\ldots}
1 0 {\ldots}
2 0 {\ldots}
3 0 {\ldots}
4 0 {\ldots}
medical\_specialty\_Surgery-General medical\_specialty\_Surgery-Maxillofacial \textbackslash{}
0 0 0
1 0 0
2 0 0
3 0 0
4 0 0
medical\_specialty\_Surgery-Neuro medical\_specialty\_Surgery-Pediatric \textbackslash{}
0 0 0
1 0 0
2 0 0
3 0 0
4 0 0
medical\_specialty\_Surgery-Plastic \textbackslash{}
0 0
1 0
2 0
3 0
4 0
medical\_specialty\_Surgery-PlasticwithinHeadandNeck \textbackslash{}
0 0
1 0
2 0
3 0
4 0
medical\_specialty\_Surgery-Thoracic medical\_specialty\_Surgery-Vascular \textbackslash{}
0 0 0
1 0 0
2 0 0
3 0 0
4 0 0
medical\_specialty\_SurgicalSpecialty medical\_specialty\_Urology
0 0 0
1 0 0
2 0 0
3 0 0
4 0 0
[5 rows x 101 columns]
\end{Verbatim}
\hypertarget{split-attribues-for-training-and-testing}{%
\subsubsection{Split attribues for training and
testing}\label{split-attribues-for-training-and-testing}}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}94}]:} \PY{n}{attr\PYZus{}train}\PY{p}{,} \PY{n}{attr\PYZus{}test}\PY{p}{,} \PY{n}{target\PYZus{}train}\PY{p}{,} \PY{n}{target\PYZus{}test} \PY{o}{=} \PY{n}{train\PYZus{}test\PYZus{}split}\PY{p}{(}\PY{n}{df\PYZus{}attr} \PY{p}{,} \PY{n}{df\PYZus{}target}\PY{p}{)}
\end{Verbatim}
\hypertarget{models}{%
\subsubsection{Models}\label{models}}
Fit various models and check results
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}95}]:} \PY{n}{models} \PY{o}{=} \PY{p}{[}\PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{Decision Tree}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{n}{DecisionTreeClassifier}\PY{p}{(}\PY{n}{max\PYZus{}depth}\PY{o}{=} \PY{l+m+mi}{5}\PY{p}{)}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{red}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}\PY{p}{,}
\PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{Logistic Regression}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{n}{LogisticRegression}\PY{p}{(}\PY{p}{)}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{green}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}\PY{p}{,}
\PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{Random Forest}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{n}{RandomForestClassifier}\PY{p}{(}\PY{p}{)}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{yellow}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}\PY{p}{,}
\PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{Naive Bayes}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{n}{GaussianNB}\PY{p}{(}\PY{p}{)}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{magenta}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}\PY{p}{,}
\PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{Neural Network}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{n}{MLPClassifier}\PY{p}{(}\PY{n}{hidden\PYZus{}layer\PYZus{}sizes} \PY{o}{=} \PY{l+m+mi}{3}\PY{p}{,} \PY{n}{max\PYZus{}iter}\PY{o}{=} \PY{l+m+mi}{500}\PY{p}{)}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{blue}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}\PY{p}{]}
\PY{n}{predicted\PYZus{}results} \PY{o}{=} \PY{p}{[}\PY{p}{]}
\PY{n}{time\PYZus{}elapsed} \PY{o}{=} \PY{p}{[}\PY{p}{]}
\PY{k}{for} \PY{n}{model\PYZus{}names}\PY{p}{,} \PY{n}{model}\PY{p}{,} \PY{n}{\PYZus{}} \PY{o+ow}{in} \PY{n}{models}\PY{p}{:}
\PY{n}{start} \PY{o}{=} \PY{n}{time}\PY{o}{.}\PY{n}{time}\PY{p}{(}\PY{p}{)}
\PY{n}{model}\PY{o}{.}\PY{n}{fit}\PY{p}{(}\PY{n}{attr\PYZus{}train}\PY{p}{,} \PY{n}{target\PYZus{}train}\PY{p}{)}
\PY{n}{target\PYZus{}predict} \PY{o}{=} \PY{n}{model}\PY{o}{.}\PY{n}{predict}\PY{p}{(}\PY{n}{attr\PYZus{}test}\PY{p}{)}
\PY{n}{end} \PY{o}{=} \PY{n}{time}\PY{o}{.}\PY{n}{time}\PY{p}{(}\PY{p}{)}
\PY{n}{time\PYZus{}elapsed}\PY{o}{.}\PY{n}{append}\PY{p}{(}\PY{n}{end} \PY{o}{\PYZhy{}} \PY{n}{start}\PY{p}{)}
\PY{n}{predicted\PYZus{}results}\PY{o}{.}\PY{n}{append}\PY{p}{(}\PY{n}{target\PYZus{}predict}\PY{p}{)}
\end{Verbatim}
\hypertarget{results}{%
\subsubsection{Results}\label{results}}
Show the results of various models
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}96}]:} \PY{n}{fpr\PYZus{}list} \PY{o}{=} \PY{p}{[}\PY{p}{]}
\PY{n}{tpr\PYZus{}list} \PY{o}{=} \PY{p}{[}\PY{p}{]}
\PY{n}{confusion\PYZus{}matrix\PYZus{}list} \PY{o}{=} \PY{p}{[}\PY{p}{]}
\PY{k}{for} \PY{n}{idx}\PY{p}{,} \PY{p}{(}\PY{n}{model\PYZus{}name}\PY{p}{,} \PY{n}{model}\PY{p}{,} \PY{n}{\PYZus{}}\PY{p}{)} \PY{o+ow}{in} \PY{n+nb}{enumerate}\PY{p}{(}\PY{n}{models}\PY{p}{)}\PY{p}{:}
\PY{n}{print\PYZus{}bold}\PY{p}{(}\PY{n}{model\PYZus{}name}\PY{p}{)}
\PY{n}{print\PYZus{}bold}\PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{Accuracy Score}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
\PY{n+nb}{print}\PY{p}{(}\PY{n}{accuracy\PYZus{}score}\PY{p}{(}\PY{n}{target\PYZus{}test}\PY{o}{.}\PY{n}{values}\PY{p}{,} \PY{n}{predicted\PYZus{}results}\PY{p}{[}\PY{n}{idx}\PY{p}{]}\PY{p}{)}\PY{p}{,} \PY{n}{end}\PY{o}{=}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+se}{\PYZbs{}n}\PY{l+s+se}{\PYZbs{}n}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
\PY{n}{print\PYZus{}bold}\PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{Time taken}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
\PY{n+nb}{print}\PY{p}{(}\PY{n+nb}{round}\PY{p}{(}\PY{n}{time\PYZus{}elapsed}\PY{p}{[}\PY{n}{idx}\PY{p}{]}\PY{p}{,} \PY{l+m+mi}{3}\PY{p}{)} \PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{ms}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
\PY{n}{display\PYZus{}confusion\PYZus{}matrix}\PY{p}{(}\PY{n}{target\PYZus{}test}\PY{o}{.}\PY{n}{values}\PY{p}{,} \PY{n}{predicted\PYZus{}results}\PY{p}{[}\PY{n}{idx}\PY{p}{]}\PY{p}{)}
\PY{n}{cm} \PY{o}{=} \PY{n}{confusion\PYZus{}matrix}\PY{p}{(}\PY{n}{target\PYZus{}test}\PY{o}{.}\PY{n}{values}\PY{p}{,} \PY{n}{predicted\PYZus{}results}\PY{p}{[}\PY{n}{idx}\PY{p}{]}\PY{p}{)}
\PY{n}{confusion\PYZus{}matrix\PYZus{}list}\PY{o}{.}\PY{n}{append}\PY{p}{(}\PY{n}{cm}\PY{p}{)}
\PY{n}{print\PYZus{}bold}\PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{Classification Report}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
\PY{n+nb}{print}\PY{p}{(}\PY{n}{classification\PYZus{}report}\PY{p}{(}\PY{n}{target\PYZus{}test}\PY{p}{,} \PY{n}{predicted\PYZus{}results}\PY{p}{[}\PY{n}{idx}\PY{p}{]}\PY{p}{)}\PY{p}{)}
\end{Verbatim}
\textbf{Decision Tree}
\textbf{Accuracy Score}
\begin{Verbatim}[commandchars=\\\{\}]
0.562888137725
\end{Verbatim}
\textbf{Time taken}
\begin{Verbatim}[commandchars=\\\{\}]
0.286 ms
\end{Verbatim}
\textbf{Confusion Matrix}
\begin{Verbatim}[commandchars=\\\{\}]
Predicted 0 1
Actual
0 10656 3183
1 7938 3665
\end{Verbatim}
\textbf{Classification Report}
\begin{Verbatim}[commandchars=\\\{\}]
precision recall f1-score support
0 0.57 0.77 0.66 13839
1 0.54 0.32 0.40 11603
avg / total 0.56 0.56 0.54 25442
\end{Verbatim}
\textbf{Logistic Regression}
\textbf{Accuracy Score}
\begin{Verbatim}[commandchars=\\\{\}]
0.5663469853
\end{Verbatim}
\textbf{Time taken}
\begin{Verbatim}[commandchars=\\\{\}]
2.076 ms
\end{Verbatim}
\textbf{Confusion Matrix}
\begin{Verbatim}[commandchars=\\\{\}]
Predicted 0 1
Actual
0 9815 4024
1 7009 4594
\end{Verbatim}
\textbf{Classification Report}
\begin{Verbatim}[commandchars=\\\{\}]
precision recall f1-score support
0 0.58 0.71 0.64 13839
1 0.53 0.40 0.45 11603
avg / total 0.56 0.57 0.56 25442
\end{Verbatim}
\textbf{Random Forest}
\textbf{Accuracy Score}
\begin{Verbatim}[commandchars=\\\{\}]
0.546537221917
\end{Verbatim}
\textbf{Time taken}
\begin{Verbatim}[commandchars=\\\{\}]
2.023 ms
\end{Verbatim}
\textbf{Confusion Matrix}
\begin{Verbatim}[commandchars=\\\{\}]
Predicted 0 1
Actual
0 9424 4415
1 7122 4481
\end{Verbatim}
\textbf{Classification Report}
\begin{Verbatim}[commandchars=\\\{\}]
precision recall f1-score support
0 0.57 0.68 0.62 13839
1 0.50 0.39 0.44 11603
avg / total 0.54 0.55 0.54 25442
\end{Verbatim}
\textbf{Naive Bayes}
\textbf{Accuracy Score}
\begin{Verbatim}[commandchars=\\\{\}]
0.486596965647
\end{Verbatim}
\textbf{Time taken}
\begin{Verbatim}[commandchars=\\\{\}]
0.372 ms
\end{Verbatim}
\textbf{Confusion Matrix}
\begin{Verbatim}[commandchars=\\\{\}]
Predicted 0 1
Actual
0 1511 12328
1 734 10869
\end{Verbatim}
\textbf{Classification Report}
\begin{Verbatim}[commandchars=\\\{\}]
precision recall f1-score support
0 0.67 0.11 0.19 13839
1 0.47 0.94 0.62 11603
avg / total 0.58 0.49 0.39 25442
\end{Verbatim}
\textbf{Neural Network}
\textbf{Accuracy Score}
\begin{Verbatim}[commandchars=\\\{\}]
0.547519849068
\end{Verbatim}
\textbf{Time taken}
\begin{Verbatim}[commandchars=\\\{\}]
5.012 ms
\end{Verbatim}
\textbf{Confusion Matrix}
\begin{Verbatim}[commandchars=\\\{\}]
Predicted 0 1
Actual
0 6148 7691
1 3821 7782
\end{Verbatim}
\textbf{Classification Report}
\begin{Verbatim}[commandchars=\\\{\}]
precision recall f1-score support
0 0.62 0.44 0.52 13839
1 0.50 0.67 0.57 11603
avg / total 0.56 0.55 0.54 25442
\end{Verbatim}
\hypertarget{analysis}{%
\subsubsection{Analysis}\label{analysis}}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}97}]:} \PY{n}{plot\PYZus{}roc\PYZus{}curve}\PY{p}{(}\PY{n}{confusion\PYZus{}matrix\PYZus{}list}\PY{p}{,} \PY{n}{models}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{False Positive Rate}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{True Positive Rate}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
\end{Verbatim}