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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{Data Analysis (Lending Club)}
% 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}}}
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\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{introduction}{%
\section{Introduction}\label{introduction}}
Author: Saleban Olow Title: Apply analysis technique and machine
learning models on Lending Club data Data From:
https://www.kaggle.com/wendykan/lending-club-loan-data Personal Blog:
Will be hosting this notebook on my blog soon, saleban-olow.com
TODO:
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}94}]:} \PY{c+c1}{\PYZsh{} libraries used on this notebook}
\PY{k+kn}{import} \PY{n+nn}{warnings}
\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{k+kn}{import} \PY{n+nn}{matplotlib}\PY{n+nn}{.}\PY{n+nn}{pyplot} \PY{k}{as} \PY{n+nn}{plt}
\PY{o}{\PYZpc{}}\PY{k}{matplotlib} inline
\PY{k+kn}{import} \PY{n+nn}{seaborn} \PY{k}{as} \PY{n+nn}{sns}
\PY{c+c1}{\PYZsh{}from wordcloud import WordCloud, STOPWORDS}
\PY{k+kn}{from} \PY{n+nn}{matplotlib} \PY{k}{import} \PY{n}{rcParams}
\PY{n}{rcParams}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{figure.figsize}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]} \PY{o}{=} \PY{l+m+mi}{8}\PY{p}{,}\PY{l+m+mi}{6}
\PY{c+c1}{\PYZsh{} Sklearn}
\PY{k+kn}{from} \PY{n+nn}{sklearn} \PY{k}{import} \PY{n}{linear\PYZus{}model}\PY{p}{,}\PY{n}{svm}
\PY{k+kn}{from} \PY{n+nn}{sklearn}\PY{n+nn}{.}\PY{n+nn}{metrics} \PY{k}{import} \PY{n}{average\PYZus{}precision\PYZus{}score}
\PY{k+kn}{from} \PY{n+nn}{sklearn}\PY{n+nn}{.}\PY{n+nn}{metrics} \PY{k}{import} \PY{n}{precision\PYZus{}recall\PYZus{}curve}
\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}{ensemble} \PY{k}{import} \PY{n}{RandomForestClassifier}
\PY{k+kn}{from} \PY{n+nn}{sklearn}\PY{n+nn}{.}\PY{n+nn}{neighbors} \PY{k}{import} \PY{n}{KNeighborsClassifier}
\PY{k+kn}{from} \PY{n+nn}{sklearn}\PY{n+nn}{.}\PY{n+nn}{metrics} \PY{k}{import} \PY{n}{accuracy\PYZus{}score}
\PY{k+kn}{from} \PY{n+nn}{sklearn} \PY{k}{import} \PY{n}{preprocessing}
\PY{k+kn}{from} \PY{n+nn}{sklearn}\PY{n+nn}{.}\PY{n+nn}{svm} \PY{k}{import} \PY{n}{SVC}
\PY{k+kn}{from} \PY{n+nn}{sklearn}\PY{n+nn}{.}\PY{n+nn}{model\PYZus{}selection} \PY{k}{import} \PY{n}{learning\PYZus{}curve}
\PY{k+kn}{from} \PY{n+nn}{sklearn}\PY{n+nn}{.}\PY{n+nn}{model\PYZus{}selection} \PY{k}{import} \PY{n}{ShuffleSplit}
\PY{k+kn}{from} \PY{n+nn}{sklearn} \PY{k}{import} \PY{n}{metrics}
\PY{k+kn}{import} \PY{n+nn}{itertools}
\PY{k+kn}{from} \PY{n+nn}{sklearn}\PY{n+nn}{.}\PY{n+nn}{metrics} \PY{k}{import} \PY{n}{confusion\PYZus{}matrix}
\PY{k+kn}{from} \PY{n+nn}{sklearn}\PY{n+nn}{.}\PY{n+nn}{feature\PYZus{}selection} \PY{k}{import} \PY{n}{RFE}
\PY{k+kn}{from} \PY{n+nn}{sklearn}\PY{n+nn}{.}\PY{n+nn}{decomposition} \PY{k}{import} \PY{n}{PCA}
\PY{k+kn}{from} \PY{n+nn}{sklearn}\PY{n+nn}{.}\PY{n+nn}{grid\PYZus{}search} \PY{k}{import} \PY{n}{RandomizedSearchCV}
\PY{k+kn}{from} \PY{n+nn}{sklearn}\PY{n+nn}{.}\PY{n+nn}{grid\PYZus{}search} \PY{k}{import} \PY{n}{GridSearchCV}
\PY{k+kn}{from} \PY{n+nn}{sklearn} \PY{k}{import} \PY{n}{model\PYZus{}selection}
\PY{k+kn}{from} \PY{n+nn}{sklearn}\PY{n+nn}{.}\PY{n+nn}{ensemble} \PY{k}{import} \PY{n}{BaggingClassifier}
\PY{k+kn}{from} \PY{n+nn}{sklearn}\PY{n+nn}{.}\PY{n+nn}{ensemble} \PY{k}{import} \PY{n}{AdaBoostClassifier}
\PY{k+kn}{from} \PY{n+nn}{sklearn}\PY{n+nn}{.}\PY{n+nn}{neural\PYZus{}network} \PY{k}{import} \PY{n}{MLPClassifier}
\PY{n}{sns}\PY{o}{.}\PY{n}{set}\PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{talk}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{whitegrid}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{dark}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{n}{font\PYZus{}scale}\PY{o}{=}\PY{l+m+mi}{1}\PY{p}{,} \PY{n}{font}\PY{o}{=}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{Ricty}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,}\PY{n}{rc}\PY{o}{=}\PY{p}{\PYZob{}}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{lines.linewidth}\PY{l+s+s2}{\PYZdq{}}\PY{p}{:} \PY{l+m+mi}{2}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{grid.linestyle}\PY{l+s+s1}{\PYZsq{}}\PY{p}{:} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{\PYZhy{}\PYZhy{}}\PY{l+s+s1}{\PYZsq{}}\PY{p}{\PYZcb{}}\PY{p}{)}
\PY{n}{warnings}\PY{o}{.}\PY{n}{filterwarnings}\PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{ignore}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
\end{Verbatim}
\hypertarget{load-data}{%
\subsubsection{Load data}\label{load-data}}
Handy function for data loading.
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}95}]:} \PY{k}{def} \PY{n+nf}{load\PYZus{}data}\PY{p}{(}\PY{n}{filename}\PY{p}{)}\PY{p}{:}
\PY{l+s+sd}{\PYZdq{}\PYZdq{}\PYZdq{}Read from local file\PYZdq{}\PYZdq{}\PYZdq{}}
\PY{k}{return} \PY{n}{pd}\PY{o}{.}\PY{n}{read\PYZus{}csv}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{data/}\PY{l+s+s2}{\PYZdq{}} \PY{o}{+} \PY{n}{filename} \PY{o}{+} \PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{.csv}\PY{l+s+s2}{\PYZdq{}}\PY{p}{,} \PY{n}{low\PYZus{}memory}\PY{o}{=}\PY{k+kc}{False}\PY{p}{)}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}96}]:} \PY{n}{for\PYZus{}analysis} \PY{o}{=} \PY{n}{load\PYZus{}data}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{loan}\PY{l+s+s2}{\PYZdq{}}\PY{p}{)}
\PY{n}{for\PYZus{}ml} \PY{o}{=} \PY{n}{for\PYZus{}analysis}\PY{o}{.}\PY{n}{copy}\PY{p}{(}\PY{p}{)}
\end{Verbatim}
\hypertarget{making-dataframe-faster}{%
\subsubsection{Making DataFrame Faster}\label{making-dataframe-faster}}
Let's decrease our memory usage by at least 50\%, 501.0+ MB to 217.5 MB!
The data type we have in our data is: dtypes: float64(49), int64(2),
object(23)
There are about 887379 entries and 74 columns this data. In our data
type there are 23 objects or strings, some of those columns have unique
value that are less than 100 out of 887379. It would not be efficiency
to keep columns that has few rows keep repeating more than 800K, so what
we would do is convert columns that has less than 150 unique values to
category. The key to converting to category is to ensure that there are
few categories to save memory usage. If there are too many unique on
each column, we should not covert.
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}98}]:} \PY{k}{def} \PY{n+nf}{reduce\PYZus{}memory\PYZus{}usage}\PY{p}{(}\PY{n}{data}\PY{p}{)}\PY{p}{:}
\PY{k}{for} \PY{n}{x} \PY{o+ow}{in} \PY{n}{data}\PY{o}{.}\PY{n}{columns}\PY{p}{:}
\PY{n}{n} \PY{o}{=} \PY{n}{data}\PY{p}{[}\PY{n}{x}\PY{p}{]}\PY{o}{.}\PY{n}{dtypes}
\PY{k}{if} \PY{n}{n} \PY{o}{==} \PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{object}\PY{l+s+s2}{\PYZdq{}}\PY{p}{:}
\PY{n}{cat} \PY{o}{=} \PY{n}{data}\PY{p}{[}\PY{n}{x}\PY{p}{]}\PY{o}{.}\PY{n}{nunique}\PY{p}{(}\PY{p}{)}
\PY{k}{if} \PY{n}{cat} \PY{o}{\PYZlt{}} \PY{l+m+mi}{150}\PY{p}{:}
\PY{n}{data}\PY{p}{[}\PY{n}{x}\PY{p}{]} \PY{o}{=} \PY{n}{data}\PY{p}{[}\PY{n}{x}\PY{p}{]}\PY{o}{.}\PY{n}{astype}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{category}\PY{l+s+s2}{\PYZdq{}}\PY{p}{)}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}99}]:} \PY{n}{reduce\PYZus{}memory\PYZus{}usage}\PY{p}{(}\PY{n}{for\PYZus{}analysis}\PY{p}{)}
\end{Verbatim}
\hypertarget{slicing-long-string.}{%
\paragraph{Slicing Long String.}\label{slicing-long-string.}}
There are many ways we can working with string in our data. We have to
keep in mind that in this data there are few rows in ``loan\_status''
column that has long name, we want to slice that and keep everything
after `colon'.
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}100}]:} \PY{k}{def} \PY{n+nf}{remove\PYZus{}long\PYZus{}names}\PY{p}{(}\PY{n}{v}\PY{p}{)}\PY{p}{:}
\PY{k}{if} \PY{n+nb}{isinstance}\PY{p}{(}\PY{n}{v}\PY{p}{,} \PY{n+nb}{str}\PY{p}{)}\PY{p}{:}
\PY{k}{return} \PY{n}{v}\PY{o}{.}\PY{n}{replace}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{Does not meet the credit policy. Status:}\PY{l+s+s2}{\PYZdq{}}\PY{p}{,} \PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{\PYZdq{}}\PY{p}{)}
\PY{k}{else}\PY{p}{:}
\PY{k}{raise} \PY{n+ne}{TypeError}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{String Type is required}\PY{l+s+s2}{\PYZdq{}}\PY{p}{)}
\end{Verbatim}
\hypertarget{using-seaborn-library-for-visulazing-nan-values}{%
\paragraph{Using Seaborn library for visulazing `NaN'
values}\label{using-seaborn-library-for-visulazing-nan-values}}
We'll be using heatmap in Seaborn library, heatmap is a two-dimensional
graphical representation of data where the individual values that are
contained in a matrix are represented as colors.
Output: I would say 1/3 of our data contains null values
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}101}]:} \PY{k}{def} \PY{n+nf}{visualise\PYZus{}null}\PY{p}{(}\PY{n}{data}\PY{p}{)}\PY{p}{:}
\PY{n}{sns}\PY{o}{.}\PY{n}{set}\PY{p}{(}\PY{n}{font\PYZus{}scale}\PY{o}{=}\PY{l+m+mf}{1.3}\PY{p}{)}
\PY{n}{fig}\PY{p}{,} \PY{n}{ax} \PY{o}{=} \PY{n}{plt}\PY{o}{.}\PY{n}{subplots}\PY{p}{(}\PY{n}{figsize}\PY{o}{=}\PY{p}{(}\PY{l+m+mi}{8}\PY{p}{,}\PY{l+m+mi}{5}\PY{p}{)}\PY{p}{)}
\PY{n}{sns}\PY{o}{.}\PY{n}{heatmap}\PY{p}{(}\PY{n}{data}\PY{o}{.}\PY{n}{isnull}\PY{p}{(}\PY{p}{)}\PY{p}{,} \PY{n}{yticklabels}\PY{o}{=}\PY{k+kc}{False}\PY{p}{,} \PY{n}{cbar}\PY{o}{=}\PY{k+kc}{False}\PY{p}{,} \PY{n}{cmap}\PY{o}{=}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{viridis}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
\PY{n}{plt}\PY{o}{.}\PY{n}{xticks}\PY{p}{(}\PY{n+nb}{range}\PY{p}{(}\PY{n+nb}{len}\PY{p}{(}\PY{n}{data}\PY{o}{.}\PY{n}{columns}\PY{p}{)}\PY{p}{)}\PY{p}{,} \PY{n}{data}\PY{o}{.}\PY{n}{columns}\PY{p}{,} \PY{n}{rotation}\PY{o}{=}\PY{l+m+mi}{60}\PY{p}{)}
\PY{n}{plt}\PY{o}{.}\PY{n}{show}\PY{p}{(}\PY{p}{)}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}102}]:} \PY{n}{visualise\PYZus{}null}\PY{p}{(}\PY{n}{for\PYZus{}analysis}\PY{p}{)}
\end{Verbatim}
\begin{center}
\adjustimage{max size={0.9\linewidth}{0.9\paperheight}}{output_12_0.png}
\end{center}
{ \hspace*{\fill} \\}
\hypertarget{drop-columns}{%
\paragraph{Drop columns}\label{drop-columns}}
We'll filter out the columns with more than 80\% NULL values and then
drop those columns from the DataFrame. In my opioin I don't thinking
keeping a column that contains less than 20\% information gives us good
accuracy score. Otherwise, it's your take!
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}103}]:} \PY{k}{def} \PY{n+nf}{PercentageMissin}\PY{p}{(}\PY{n}{Dataset}\PY{p}{)}\PY{p}{:}
\PY{l+s+sd}{\PYZdq{}\PYZdq{}\PYZdq{}}
\PY{l+s+sd}{ This function will return the percentage of missing values in a dataset }
\PY{l+s+sd}{ If column has over 80\PYZpc{} missing value, del that column}
\PY{l+s+sd}{ \PYZdq{}\PYZdq{}\PYZdq{}}
\PY{k}{if} \PY{n+nb}{isinstance}\PY{p}{(}\PY{n}{Dataset}\PY{p}{,}\PY{n}{pd}\PY{o}{.}\PY{n}{DataFrame}\PY{p}{)}\PY{p}{:}
\PY{n}{adict}\PY{o}{=}\PY{p}{\PYZob{}}\PY{p}{\PYZcb{}} \PY{c+c1}{\PYZsh{}a dictionary conatin keys columns names and values percentage of missin value in the columns}
\PY{n}{column} \PY{o}{=} \PY{p}{[}\PY{p}{]}
\PY{k}{for} \PY{n}{col} \PY{o+ow}{in} \PY{n}{Dataset}\PY{o}{.}\PY{n}{columns}\PY{p}{:}
\PY{n}{adict}\PY{p}{[}\PY{n}{col}\PY{p}{]}\PY{o}{=}\PY{p}{(}\PY{n}{np}\PY{o}{.}\PY{n}{count\PYZus{}nonzero}\PY{p}{(}\PY{n}{Dataset}\PY{p}{[}\PY{n}{col}\PY{p}{]}\PY{o}{.}\PY{n}{isnull}\PY{p}{(}\PY{p}{)}\PY{p}{)}\PY{o}{*}\PY{l+m+mi}{100}\PY{p}{)}\PY{o}{/}\PY{n+nb}{len}\PY{p}{(}\PY{n}{Dataset}\PY{p}{[}\PY{n}{col}\PY{p}{]}\PY{p}{)}
\PY{k}{if} \PY{n}{adict}\PY{p}{[}\PY{n}{col}\PY{p}{]} \PY{o}{\PYZgt{}} \PY{l+m+mi}{80}\PY{p}{:}
\PY{k}{del} \PY{n}{adict}\PY{p}{[}\PY{n}{col}\PY{p}{]}
\PY{k}{else}\PY{p}{:}
\PY{n}{column}\PY{o}{.}\PY{n}{append}\PY{p}{(}\PY{n}{col}\PY{p}{)}
\PY{n}{Dataset} \PY{o}{=} \PY{n}{Dataset}\PY{o}{.}\PY{n}{loc}\PY{p}{[}\PY{p}{:}\PY{p}{,} \PY{n}{column}\PY{p}{]}
\PY{k}{return} \PY{n}{Dataset}
\PY{k}{else}\PY{p}{:}
\PY{k}{raise} \PY{n+ne}{TypeError}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{can only be used with panda dataframe}\PY{l+s+s2}{\PYZdq{}}\PY{p}{)}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}104}]:} \PY{n}{for\PYZus{}analysis}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{loan\PYZus{}status}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]} \PY{o}{=} \PY{n}{for\PYZus{}analysis}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{loan\PYZus{}status}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]}\PY{o}{.}\PY{n}{apply}\PY{p}{(}\PY{n}{remove\PYZus{}long\PYZus{}names}\PY{p}{)}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}105}]:} \PY{n}{for\PYZus{}analysis} \PY{o}{=} \PY{n}{PercentageMissin}\PY{p}{(}\PY{n}{for\PYZus{}analysis}\PY{p}{)}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}106}]:} \PY{n}{for\PYZus{}analysis}\PY{o}{.}\PY{n}{head}\PY{p}{(}\PY{p}{)}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{outcolor}Out[{\color{outcolor}106}]:} id member\_id loan\_amnt funded\_amnt funded\_amnt\_inv term \textbackslash{}
0 1077501 1296599 5000.0 5000.0 4975.0 36 months
1 1077430 1314167 2500.0 2500.0 2500.0 60 months
2 1077175 1313524 2400.0 2400.0 2400.0 36 months
3 1076863 1277178 10000.0 10000.0 10000.0 36 months
4 1075358 1311748 3000.0 3000.0 3000.0 60 months
int\_rate installment grade sub\_grade {\ldots} next\_pymnt\_d \textbackslash{}
0 10.65 162.87 B B2 {\ldots} NaN
1 15.27 59.83 C C4 {\ldots} NaN
2 15.96 84.33 C C5 {\ldots} NaN
3 13.49 339.31 C C1 {\ldots} NaN
4 12.69 67.79 B B5 {\ldots} Feb-2016
last\_credit\_pull\_d collections\_12\_mths\_ex\_med mths\_since\_last\_major\_derog \textbackslash{}
0 Jan-2016 0.0 NaN
1 Sep-2013 0.0 NaN
2 Jan-2016 0.0 NaN
3 Jan-2015 0.0 NaN
4 Jan-2016 0.0 NaN
policy\_code application\_type acc\_now\_delinq tot\_coll\_amt tot\_cur\_bal \textbackslash{}
0 1.0 INDIVIDUAL 0.0 NaN NaN
1 1.0 INDIVIDUAL 0.0 NaN NaN
2 1.0 INDIVIDUAL 0.0 NaN NaN
3 1.0 INDIVIDUAL 0.0 NaN NaN
4 1.0 INDIVIDUAL 0.0 NaN NaN
total\_rev\_hi\_lim
0 NaN
1 NaN
2 NaN
3 NaN
4 NaN
[5 rows x 55 columns]
\end{Verbatim}
\hypertarget{section-1}{%
\subsection{Section 1}\label{section-1}}
\hypertarget{analysis}{%
\subsubsection{Analysis}\label{analysis}}
\hypertarget{what-can-we-learn-from-this-data}{%
\subsubsection{What can we learn from this
data?}\label{what-can-we-learn-from-this-data}}
A lot!, We'll do some basic and advance analysis. We'll ask ourself
questions like, What kind of interest rate are borrowers paying? What
are the Loan Status Distribution? How long are the loan terms? How much
are people borrowing? for what purpose? Why do people take out loans?
A bulk of this notebook will cover on machine learning aspect, we'll
test different models as we explore further.
\hypertarget{question-1-what-kind-of-interest-rate-are-borrowers-paying}{%
\subsubsection{Question 1: What kind of interest rate are borrowers
paying?}\label{question-1-what-kind-of-interest-rate-are-borrowers-paying}}
Output: the common interest rate borrowers paying is 13.24\% To
understand loan calculation \textgreater{}\textgreater{}\textgreater{}
https://mozo.com.au/interest-rates/guides/calculate-interest-on-loan
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}107}]:} \PY{n}{for\PYZus{}analysis} \PY{o}{=} \PY{n}{for\PYZus{}analysis}\PY{p}{[}\PY{n}{for\PYZus{}analysis}\PY{o}{.}\PY{n}{int\PYZus{}rate}\PY{o}{.}\PY{n}{notnull}\PY{p}{(}\PY{p}{)}\PY{p}{]}
\PY{n}{for\PYZus{}analysis}\PY{o}{.}\PY{n}{int\PYZus{}rate}\PY{o}{.}\PY{n}{describe}\PY{p}{(}\PY{p}{)}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{outcolor}Out[{\color{outcolor}107}]:} count 887379.000000
mean 13.246740
std 4.381867
min 5.320000
25\% 9.990000
50\% 12.990000
75\% 16.200000
max 28.990000
Name: int\_rate, dtype: float64
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}108}]:} \PY{c+c1}{\PYZsh{}Let\PYZsq{}s visualize the distribution of \PYZsq{}interest rate\PYZsq{}}
\PY{n}{sns}\PY{o}{.}\PY{n}{distplot}\PY{p}{(}\PY{n}{for\PYZus{}analysis}\PY{o}{.}\PY{n}{int\PYZus{}rate}\PY{p}{,} \PY{n}{axlabel} \PY{o}{=} \PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{Interest Rate}\PY{l+s+s2}{\PYZdq{}}\PY{p}{,} \PY{n}{color}\PY{o}{=}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{blue}\PY{l+s+s2}{\PYZdq{}}\PY{p}{)}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{outcolor}Out[{\color{outcolor}108}]:} <matplotlib.axes.\_subplots.AxesSubplot at 0x4eb254e0>
\end{Verbatim}
\begin{center}
\adjustimage{max size={0.9\linewidth}{0.9\paperheight}}{output_22_1.png}
\end{center}
{ \hspace*{\fill} \\}
\hypertarget{question-2-what-are-the-loan-status-distribution}{%
\subsubsection{Question 2: What are the Loan Status
Distribution?}\label{question-2-what-are-the-loan-status-distribution}}
We'll visualize the loan\_status using countplot to further understand
and compare that with `loan\_amnt' using violinplot
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}109}]:} \PY{k}{def} \PY{n+nf}{plot\PYZus{}distribution}\PY{p}{(}\PY{n}{data}\PY{p}{)}\PY{p}{:}
\PY{n}{plt}\PY{o}{.}\PY{n}{figure}\PY{p}{(}\PY{n}{figsize}\PY{o}{=}\PY{p}{(}\PY{l+m+mi}{10}\PY{p}{,}\PY{l+m+mi}{8}\PY{p}{)}\PY{p}{)}
\PY{n}{plt}\PY{o}{.}\PY{n}{subplot}\PY{p}{(}\PY{l+m+mi}{311}\PY{p}{)}
\PY{n}{cntplt} \PY{o}{=} \PY{n}{sns}\PY{o}{.}\PY{n}{countplot}\PY{p}{(}\PY{n}{x}\PY{o}{=}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{loan\PYZus{}status}\PY{l+s+s2}{\PYZdq{}}\PY{p}{,} \PY{n}{data}\PY{o}{=}\PY{n}{data}\PY{p}{)}
\PY{n}{cntplt}\PY{o}{.}\PY{n}{set\PYZus{}xticklabels}\PY{p}{(}\PY{n}{cntplt}\PY{o}{.}\PY{n}{get\PYZus{}xticklabels}\PY{p}{(}\PY{p}{)}\PY{p}{,} \PY{n}{rotation} \PY{o}{=} \PY{l+m+mi}{15}\PY{p}{)}
\PY{n}{cntplt}\PY{o}{.}\PY{n}{set\PYZus{}xlabel}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{\PYZdq{}}\PY{p}{,} \PY{n}{fontsize} \PY{o}{=} \PY{l+m+mi}{10}\PY{p}{)}
\PY{n}{cntplt}\PY{o}{.}\PY{n}{set\PYZus{}ylabel}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{Count}\PY{l+s+s2}{\PYZdq{}}\PY{p}{,} \PY{n}{fontsize} \PY{o}{=} \PY{l+m+mi}{15}\PY{p}{)}
\PY{n}{cntplt}\PY{o}{.}\PY{n}{set\PYZus{}title}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{Loan Status Distribution}\PY{l+s+s2}{\PYZdq{}}\PY{p}{,} \PY{n}{fontsize} \PY{o}{=} \PY{l+m+mi}{18}\PY{p}{)}
\PY{n}{plt}\PY{o}{.}\PY{n}{subplot}\PY{p}{(}\PY{l+m+mi}{312}\PY{p}{)}
\PY{n}{vplt} \PY{o}{=} \PY{n}{sns}\PY{o}{.}\PY{n}{violinplot}\PY{p}{(}\PY{n}{x}\PY{o}{=}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{loan\PYZus{}status}\PY{l+s+s2}{\PYZdq{}}\PY{p}{,} \PY{n}{y}\PY{o}{=}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{loan\PYZus{}amnt}\PY{l+s+s2}{\PYZdq{}}\PY{p}{,} \PY{n}{data}\PY{o}{=}\PY{n}{data}\PY{p}{)}
\PY{n}{vplt}\PY{o}{.}\PY{n}{set\PYZus{}xticklabels}\PY{p}{(}\PY{n}{vplt}\PY{o}{.}\PY{n}{get\PYZus{}xticklabels}\PY{p}{(}\PY{p}{)}\PY{p}{,}\PY{n}{rotation} \PY{o}{=} \PY{l+m+mi}{15}\PY{p}{)}
\PY{n}{vplt}\PY{o}{.}\PY{n}{set\PYZus{}xlabel}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{Duration Distribuition}\PY{l+s+s2}{\PYZdq{}}\PY{p}{,} \PY{n}{fontsize}\PY{o}{=}\PY{l+m+mi}{10}\PY{p}{)}
\PY{n}{vplt}\PY{o}{.}\PY{n}{set\PYZus{}ylabel}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{Count}\PY{l+s+s2}{\PYZdq{}}\PY{p}{,} \PY{n}{fontsize}\PY{o}{=}\PY{l+m+mi}{15}\PY{p}{)}
\PY{n}{vplt}\PY{o}{.}\PY{n}{set\PYZus{}title}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{Loan Amount}\PY{l+s+s2}{\PYZdq{}}\PY{p}{,} \PY{n}{fontsize}\PY{o}{=}\PY{l+m+mi}{18}\PY{p}{)}
\PY{n}{plt}\PY{o}{.}\PY{n}{subplots\PYZus{}adjust}\PY{p}{(}\PY{n}{wspace} \PY{o}{=} \PY{l+m+mf}{0.2}\PY{p}{,} \PY{n}{hspace} \PY{o}{=} \PY{l+m+mf}{0.7}\PY{p}{,}\PY{n}{top} \PY{o}{=} \PY{l+m+mf}{0.9}\PY{p}{)}
\PY{n}{plt}\PY{o}{.}\PY{n}{show}\PY{p}{(}\PY{p}{)}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}110}]:} \PY{n}{plot\PYZus{}distribution}\PY{p}{(}\PY{n}{for\PYZus{}analysis}\PY{p}{)}
\end{Verbatim}
\begin{center}
\adjustimage{max size={0.9\linewidth}{0.9\paperheight}}{output_25_0.png}
\end{center}
{ \hspace*{\fill} \\}
\hypertarget{question-3-how-long-are-the-loan-terms}{%
\subsubsection{Question 3: How long are the loan
terms?}\label{question-3-how-long-are-the-loan-terms}}
Output: about 70 percent of all borrows choose 36 months loan terms.
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}111}]:} \PY{n}{for\PYZus{}analysis}\PY{o}{.}\PY{n}{term}\PY{o}{.}\PY{n}{value\PYZus{}counts}\PY{p}{(}\PY{l+m+mi}{1}\PY{p}{)}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{outcolor}Out[{\color{outcolor}111}]:} 36 months 0.699955
60 months 0.300045
Name: term, dtype: float64
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}112}]:} \PY{n}{for\PYZus{}analysis}\PY{o}{.}\PY{n}{term}\PY{o}{.}\PY{n}{value\PYZus{}counts}\PY{p}{(}\PY{l+m+mi}{1}\PY{p}{)}\PY{o}{.}\PY{n}{plot}\PY{p}{(}\PY{n}{kind}\PY{o}{=}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{pie}\PY{l+s+s2}{\PYZdq{}}\PY{p}{,} \PY{n}{fontsize}\PY{o}{=}\PY{l+m+mi}{16}\PY{p}{)}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{outcolor}Out[{\color{outcolor}112}]:} <matplotlib.axes.\_subplots.AxesSubplot at 0xf67bb70>
\end{Verbatim}
\begin{center}
\adjustimage{max size={0.9\linewidth}{0.9\paperheight}}{output_28_1.png}
\end{center}
{ \hspace*{\fill} \\}
\hypertarget{question-4a-how-much-are-people-borrowing}{%
\subsubsection{Question 4(a): How much are people
borrowing?}\label{question-4a-how-much-are-people-borrowing}}
Output: the average amount people borrowing are \$14,755
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}113}]:} \PY{n}{for\PYZus{}analysis}\PY{o}{.}\PY{n}{loan\PYZus{}amnt}\PY{o}{.}\PY{n}{describe}\PY{p}{(}\PY{p}{)}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{outcolor}Out[{\color{outcolor}113}]:} count 887379.000000
mean 14755.264605
std 8435.455601
min 500.000000
25\% 8000.000000
50\% 13000.000000
75\% 20000.000000
max 35000.000000
Name: loan\_amnt, dtype: float64
\end{Verbatim}
\hypertarget{question-4b-for-what-purpose}{%
\subsubsection{Question 4(b): for what
purpose?}\label{question-4b-for-what-purpose}}
Output: most people are borrowing money for `debt', `small business',
and `credit card'
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}114}]:} \PY{n}{for\PYZus{}analysis}\PY{o}{.}\PY{n}{groupby}\PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{purpose}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}\PY{o}{.}\PY{n}{agg}\PY{p}{(}
\PY{p}{\PYZob{}}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{loan\PYZus{}amnt}\PY{l+s+s1}{\PYZsq{}}\PY{p}{:}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{mean}\PY{l+s+s1}{\PYZsq{}}\PY{p}{\PYZcb{}}\PY{p}{)}\PY{o}{.}\PY{n}{sort\PYZus{}values}\PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{loan\PYZus{}amnt}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{n}{ascending}\PY{o}{=}\PY{k+kc}{False}\PY{p}{)}\PY{o}{.}\PY{n}{plot}\PY{p}{(}\PY{n}{kind}\PY{o}{=}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{bar}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{outcolor}Out[{\color{outcolor}114}]:} <matplotlib.axes.\_subplots.AxesSubplot at 0x1b52b080>
\end{Verbatim}
\begin{center}
\adjustimage{max size={0.9\linewidth}{0.9\paperheight}}{output_32_1.png}
\end{center}
{ \hspace*{\fill} \\}
\hypertarget{question-5-why-do-people-take-out-loans}{%
\subsubsection{Question 5: Why do people take out
loans?}\label{question-5-why-do-people-take-out-loans}}
As you see above graph, over 500K people take loan because of debt
repayment.
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}115}]:} \PY{n}{loan\PYZus{}purposes} \PY{o}{=} \PY{n}{for\PYZus{}analysis}\PY{o}{.}\PY{n}{purpose}\PY{o}{.}\PY{n}{value\PYZus{}counts}\PY{p}{(}\PY{p}{)}
\PY{n}{loan\PYZus{}purposes}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{outcolor}Out[{\color{outcolor}115}]:} debt\_consolidation 524215
credit\_card 206182
home\_improvement 51829
other 42894
major\_purchase 17277
small\_business 10377
car 8863
medical 8540
moving 5414
vacation 4736
house 3707
wedding 2347
renewable\_energy 575
educational 423
Name: purpose, dtype: int64
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}117}]:} \PY{n}{loan\PYZus{}purposes}\PY{o}{.}\PY{n}{plot}\PY{o}{.}\PY{n}{barh}\PY{p}{(}\PY{n}{figsize}\PY{o}{=}\PY{p}{(}\PY{l+m+mi}{10}\PY{p}{,}\PY{l+m+mi}{5}\PY{p}{)}\PY{p}{,} \PY{n}{color}\PY{o}{=}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{orange}\PY{l+s+s2}{\PYZdq{}}\PY{p}{)}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{outcolor}Out[{\color{outcolor}117}]:} <matplotlib.axes.\_subplots.AxesSubplot at 0xe77d8d0>
\end{Verbatim}
\begin{center}
\adjustimage{max size={0.9\linewidth}{0.9\paperheight}}{output_35_1.png}
\end{center}
{ \hspace*{\fill} \\}
\hypertarget{wordcloud}{%
\subsubsection{WordCloud}\label{wordcloud}}
A word-cloud is a visual representation of word frequeucy. The more
commonly the term appears within the text being analysed.
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}118}]:} \PY{l+s+sd}{\PYZdq{}\PYZdq{}\PYZdq{}titles = for\PYZus{}analysis.title.str.cat(sep=\PYZsq{},\PYZsq{})}
\PY{l+s+sd}{wordcloud = WordCloud(stopwords=STOPWORDS, background\PYZus{}color=\PYZsq{}white\PYZsq{}, max\PYZus{}font\PYZus{}size=35, relative\PYZus{}scaling=0.2)}
\PY{l+s+sd}{wordcloud.generate(titles)}
\PY{l+s+sd}{plt.figure(figsize=(12,6))}
\PY{l+s+sd}{plt.imshow(wordcloud)}
\PY{l+s+sd}{plt.axis(\PYZdq{}off\PYZdq{})}
\PY{l+s+sd}{plt.show()\PYZdq{}\PYZdq{}\PYZdq{}}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{outcolor}Out[{\color{outcolor}118}]:} 'titles = for\_analysis.title.str.cat(sep=\textbackslash{}',\textbackslash{}')\textbackslash{}nwordcloud = WordCloud(stopwords=STOPWORDS, background\_color=\textbackslash{}'white\textbackslash{}', max\_font\_size=35, relative\_scaling=0.2)\textbackslash{}nwordcloud.generate(titles)\textbackslash{}nplt.figure(figsize=(12,6))\textbackslash{}nplt.imshow(wordcloud)\textbackslash{}nplt.axis("off")\textbackslash{}nplt.show()'
\end{Verbatim}
\hypertarget{create-a-crossbat-table-by-loan-purpose-and-loan-status}{%
\paragraph{Create a crossbat table by loan purpose and loan
status}\label{create-a-crossbat-table-by-loan-purpose-and-loan-status}}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}119}]:} \PY{c+c1}{\PYZsh{}Exploring the loan\PYZus{}status x purpose}
\PY{n}{purp\PYZus{}loan}\PY{o}{=} \PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{purpose}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{loan\PYZus{}status}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]}
\PY{n}{cm} \PY{o}{=} \PY{n}{sns}\PY{o}{.}\PY{n}{light\PYZus{}palette}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{green}\PY{l+s+s2}{\PYZdq{}}\PY{p}{,} \PY{n}{as\PYZus{}cmap}\PY{o}{=}\PY{k+kc}{True}\PY{p}{)}
\PY{n}{pd}\PY{o}{.}\PY{n}{crosstab}\PY{p}{(}\PY{n}{for\PYZus{}analysis}\PY{p}{[}\PY{n}{purp\PYZus{}loan}\PY{p}{[}\PY{l+m+mi}{0}\PY{p}{]}\PY{p}{]}\PY{p}{,} \PY{n}{for\PYZus{}analysis}\PY{p}{[}\PY{n}{purp\PYZus{}loan}\PY{p}{[}\PY{l+m+mi}{1}\PY{p}{]}\PY{p}{]}\PY{p}{)}\PY{o}{.}\PY{n}{style}\PY{o}{.}\PY{n}{background\PYZus{}gradient}\PY{p}{(}\PY{n}{cmap} \PY{o}{=} \PY{n}{cm}\PY{p}{)}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{outcolor}Out[{\color{outcolor}119}]:} <pandas.io.formats.style.Styler at 0x1fc87358>
\end{Verbatim}
\hypertarget{create-a-crossbat-table-by-loan-status-and-grade}{%
\paragraph{Create a crossbat table by loan status and
grade}\label{create-a-crossbat-table-by-loan-status-and-grade}}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}120}]:} \PY{n}{loan\PYZus{}grade} \PY{o}{=} \PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{loan\PYZus{}status}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{grade}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]}
\PY{n}{cm} \PY{o}{=} \PY{n}{sns}\PY{o}{.}\PY{n}{light\PYZus{}palette}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{orange}\PY{l+s+s2}{\PYZdq{}}\PY{p}{,} \PY{n}{as\PYZus{}cmap}\PY{o}{=}\PY{k+kc}{True}\PY{p}{)}
\PY{n}{pd}\PY{o}{.}\PY{n}{crosstab}\PY{p}{(}\PY{n}{for\PYZus{}analysis}\PY{p}{[}\PY{n}{loan\PYZus{}grade}\PY{p}{[}\PY{l+m+mi}{0}\PY{p}{]}\PY{p}{]}\PY{p}{,} \PY{n}{for\PYZus{}analysis}\PY{p}{[}\PY{n}{loan\PYZus{}grade}\PY{p}{[}\PY{l+m+mi}{1}\PY{p}{]}\PY{p}{]}\PY{p}{)}\PY{o}{.}\PY{n}{style}\PY{o}{.}\PY{n}{background\PYZus{}gradient}\PY{p}{(}\PY{n}{cmap} \PY{o}{=} \PY{n}{cm}\PY{p}{)}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{outcolor}Out[{\color{outcolor}120}]:} <pandas.io.formats.style.Styler at 0x128bdd30>
\end{Verbatim}
\hypertarget{create-a-crossbat-table-by-term-and-loan-status}{%
\paragraph{Create a crossbat table by term and loan
status}\label{create-a-crossbat-table-by-term-and-loan-status}}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}121}]:} \PY{n}{loan\PYZus{}grade} \PY{o}{=} \PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{term}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{loan\PYZus{}status}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]}
\PY{n}{cm} \PY{o}{=} \PY{n}{sns}\PY{o}{.}\PY{n}{light\PYZus{}palette}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{orange}\PY{l+s+s2}{\PYZdq{}}\PY{p}{,} \PY{n}{as\PYZus{}cmap}\PY{o}{=}\PY{k+kc}{True}\PY{p}{)}
\PY{n}{pd}\PY{o}{.}\PY{n}{crosstab}\PY{p}{(}\PY{n}{for\PYZus{}analysis}\PY{p}{[}\PY{n}{loan\PYZus{}grade}\PY{p}{[}\PY{l+m+mi}{0}\PY{p}{]}\PY{p}{]}\PY{p}{,} \PY{n}{for\PYZus{}analysis}\PY{p}{[}\PY{n}{loan\PYZus{}grade}\PY{p}{[}\PY{l+m+mi}{1}\PY{p}{]}\PY{p}{]}\PY{p}{)}\PY{o}{.}\PY{n}{style}\PY{o}{.}\PY{n}{background\PYZus{}gradient}\PY{p}{(}\PY{n}{cmap} \PY{o}{=} \PY{n}{cm}\PY{p}{)}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{outcolor}Out[{\color{outcolor}121}]:} <pandas.io.formats.style.Styler at 0xd77f588>
\end{Verbatim}
\hypertarget{section-2}{%
\section{Section 2}\label{section-2}}
\hypertarget{machine-learning}{%
\subsection{Machine Learning}\label{machine-learning}}
In this section, we'll work on testing many ensemble models which allows
us to run two or more related but different analytical models and the
synthesizing the results into a single score or spread in order to
improve the accuracy score.
In our data the `loan\_status' feature tells us more about if a borrower
is `Fully Paid' paid back loan or `Charged off'. So, we'll use
`loan\_status' for our independent variable.
We'll start off preparing our data for machine learning, which means our
data has to be all numeric.
We'll select what features to use in our models and drop less
informative features. We'll then encode features that has category
values, for example, `grade' column contains letter grade.
We'll apply heatmap on features we select to see if they contain missing
values, that will tell us what features to drop.
We'll apply feature scaling, using `MinMaxScaler' from sckit-learn
library. Which scales and translates each feature individually such that
it is in the given range on the training set, i.e.~between zero and one.
We'll split our data into train and test set using the sckit-learn
library and specifically the `train\_test\_split' function.
We'll apply learning curve to our data, which produces a graph that
compares the performance of a model on training and testing data over a
varying number of training. We can learn a lot of our data from this
graph. \#\#\#\# Types of learning curves: High Bais: Bad Learning Curve
* When both training and testing data converge. * No matter how much
data we feed the model, its still performing poor. * Poor generalization
High Variance: Bad Learning Curve * When there is a large gap between
the errors * Needs more data
Ideal: Learning Curve * Model generalizes to new data * Training and
Testing learning curves converge at similar values * Performance good
We'll apply two other functions to further understand the performance of
our model. \#\#\#\# The Roc Curve gives us several things: * It shows
the tradeoff between sensitivity and specificity. * The closer the curve
follows the left-hand border - the more accurate the test * The closer
the curve comes to the 45-degree diagonal of the ROC space, the less
accurate the test.
\hypertarget{the-confusion-matrix}{%
\paragraph{The Confusion Matrix}\label{the-confusion-matrix}}
A confusion matrix is a summary of prediction results on a
classification problem. It gives us insight not only into the errors
being made by our model but more importantly the types of errors that
are being made.
We'll use `Recursive Feature Elimination' which assigns weights to
features based on feature informative.
We'll apply PCA to reduce the dimensionality of our data set.
Finally, we'll test few machine learning models!
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}122}]:} \PY{k}{def} \PY{n+nf}{remove\PYZus{}percent\PYZus{}sign}\PY{p}{(}\PY{n}{v}\PY{p}{)}\PY{p}{:}
\PY{k}{if} \PY{n+nb}{isinstance}\PY{p}{(}\PY{n}{v}\PY{p}{,} \PY{n+nb}{float}\PY{p}{)}\PY{p}{:}
\PY{k}{return} \PY{n}{v}\PY{p}{[}\PY{p}{:}\PY{o}{\PYZhy{}}\PY{l+m+mi}{1}\PY{p}{]}
\PY{k}{else}\PY{p}{:}
\PY{n+ne}{TypeError}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{Required float type}\PY{l+s+s2}{\PYZdq{}}\PY{p}{)}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}123}]:} \PY{k}{def} \PY{n+nf}{loan\PYZus{}format}\PY{p}{(}\PY{n}{data}\PY{p}{)}\PY{p}{:}
\PY{k}{if} \PY{n+nb}{isinstance}\PY{p}{(}\PY{n}{data}\PY{p}{,} \PY{n}{pd}\PY{o}{.}\PY{n}{DataFrame}\PY{p}{)}\PY{p}{:}
\PY{n}{data} \PY{o}{=} \PY{n}{data}\PY{p}{[}\PY{p}{(}\PY{n}{data}\PY{p}{[}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{loan\PYZus{}status}\PY{l+s+s2}{\PYZdq{}}\PY{p}{]} \PY{o}{==} \PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{Fully Paid}\PY{l+s+s2}{\PYZdq{}}\PY{p}{)} \PY{o}{|}
\PY{p}{(}\PY{n}{data}\PY{p}{[}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{loan\PYZus{}status}\PY{l+s+s2}{\PYZdq{}}\PY{p}{]} \PY{o}{==} \PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{Charged Off}\PY{l+s+s2}{\PYZdq{}}\PY{p}{)}\PY{p}{]}
\PY{n}{onehotd} \PY{o}{=} \PY{p}{\PYZob{}}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{Fully Paid}\PY{l+s+s2}{\PYZdq{}}\PY{p}{:}\PY{l+m+mi}{0}\PY{p}{,} \PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{Charged Off}\PY{l+s+s2}{\PYZdq{}}\PY{p}{:}\PY{l+m+mi}{1}\PY{p}{\PYZcb{}}
\PY{n}{data} \PY{o}{=} \PY{n}{data}\PY{o}{.}\PY{n}{replace}\PY{p}{(}\PY{p}{\PYZob{}}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{loan\PYZus{}status}\PY{l+s+s2}{\PYZdq{}}\PY{p}{:} \PY{n}{onehotd}\PY{p}{\PYZcb{}}\PY{p}{)}
\PY{k}{return} \PY{n}{data}
\PY{k}{else}\PY{p}{:}
\PY{n+ne}{TypeError}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{Required dataframe type}\PY{l+s+s2}{\PYZdq{}}\PY{p}{)}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}124}]:} \PY{n}{for\PYZus{}ml} \PY{o}{=} \PY{n}{loan\PYZus{}format}\PY{p}{(}\PY{n}{for\PYZus{}ml}\PY{p}{)}
\PY{n}{for\PYZus{}ml}\PY{o}{.}\PY{n}{shape}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{outcolor}Out[{\color{outcolor}124}]:} (252971, 74)
\end{Verbatim}
This will the features we'll be using for our machine learning models
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}125}]:} \PY{n}{features\PYZus{}used} \PY{o}{=} \PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{funded\PYZus{}amnt}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{emp\PYZus{}length}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{annual\PYZus{}inc}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{home\PYZus{}ownership}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{grade}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,}
\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{last\PYZus{}pymnt\PYZus{}amnt}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{mort\PYZus{}acc}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{pub\PYZus{}rec}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{int\PYZus{}rate}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{open\PYZus{}acc}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{num\PYZus{}actv\PYZus{}rev\PYZus{}tl}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,}
\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{mo\PYZus{}sin\PYZus{}rcnt\PYZus{}rev\PYZus{}tl\PYZus{}op}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{mo\PYZus{}sin\PYZus{}old\PYZus{}rev\PYZus{}tl\PYZus{}op}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{bc\PYZus{}util}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{bc\PYZus{}open\PYZus{}to\PYZus{}buy}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,}
\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{avg\PYZus{}cur\PYZus{}bal}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{acc\PYZus{}open\PYZus{}past\PYZus{}24mths}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{loan\PYZus{}status}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]}
\PY{n}{for\PYZus{}ml} \PY{o}{=} \PY{n}{for\PYZus{}ml}\PY{o}{.}\PY{n}{loc}\PY{p}{[}\PY{p}{:}\PY{p}{,} \PY{n}{features\PYZus{}used}\PY{p}{]}
\PY{n}{for\PYZus{}ml} \PY{o}{=} \PY{n}{for\PYZus{}ml}\PY{o}{.}\PY{n}{reset\PYZus{}index}\PY{p}{(}\PY{n}{drop}\PY{o}{=}\PY{k+kc}{True}\PY{p}{)}
\PY{n}{for\PYZus{}ml}\PY{o}{.}\PY{n}{shape}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{outcolor}Out[{\color{outcolor}125}]:} (252971, 18)
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}126}]:} \PY{c+c1}{\PYZsh{}for\PYZus{}ml.loc[:, features\PYZus{}used]}
\PY{n}{ready\PYZus{}data} \PY{o}{=} \PY{n}{for\PYZus{}ml}\PY{o}{.}\PY{n}{copy}\PY{p}{(}\PY{p}{)}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}127}]:} \PY{n}{ready\PYZus{}data}\PY{o}{.}\PY{n}{head}\PY{p}{(}\PY{p}{)}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{outcolor}Out[{\color{outcolor}127}]:} funded\_amnt emp\_length annual\_inc home\_ownership grade last\_pymnt\_amnt \textbackslash{}
0 5000.0 10+ years 24000.0 RENT B 171.62
1 2500.0 < 1 year 30000.0 RENT C 119.66
2 2400.0 10+ years 12252.0 RENT C 649.91
3 10000.0 10+ years 49200.0 RENT C 357.48
4 5000.0 3 years 36000.0 RENT A 161.03
mort\_acc pub\_rec int\_rate open\_acc num\_actv\_rev\_tl \textbackslash{}
0 NaN 0.0 10.65 3.0 NaN
1 NaN 0.0 15.27 3.0 NaN
2 NaN 0.0 15.96 2.0 NaN
3 NaN 0.0 13.49 10.0 NaN
4 NaN 0.0 7.90 9.0 NaN
mo\_sin\_rcnt\_rev\_tl\_op mo\_sin\_old\_rev\_tl\_op bc\_util bc\_open\_to\_buy \textbackslash{}
0 NaN NaN NaN NaN
1 NaN NaN NaN NaN
2 NaN NaN NaN NaN
3 NaN NaN NaN NaN
4 NaN NaN NaN NaN
avg\_cur\_bal acc\_open\_past\_24mths loan\_status
0 NaN NaN 0
1 NaN NaN 1
2 NaN NaN 0
3 NaN NaN 0
4 NaN NaN 0
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}128}]:} \PY{n}{ready\PYZus{}data}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{loan\PYZus{}status}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]}\PY{o}{.}\PY{n}{value\PYZus{}counts}\PY{p}{(}\PY{p}{)}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{outcolor}Out[{\color{outcolor}128}]:} 0 207723
1 45248
Name: loan\_status, dtype: int64
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}129}]:} \PY{k}{def} \PY{n+nf}{data\PYZus{}encoding}\PY{p}{(}\PY{n}{data}\PY{p}{)}\PY{p}{:}
\PY{k}{if} \PY{n+nb}{isinstance}\PY{p}{(}\PY{n}{data}\PY{p}{,} \PY{n}{pd}\PY{o}{.}\PY{n}{DataFrame}\PY{p}{)}\PY{p}{:}
\PY{n}{data}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{grade}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]} \PY{o}{=} \PY{n}{data}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{grade}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]}\PY{o}{.}\PY{n}{map}\PY{p}{(}\PY{p}{\PYZob{}}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{A}\PY{l+s+s1}{\PYZsq{}}\PY{p}{:}\PY{l+m+mi}{7}\PY{p}{,}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{B}\PY{l+s+s1}{\PYZsq{}}\PY{p}{:}\PY{l+m+mi}{6}\PY{p}{,}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{C}\PY{l+s+s1}{\PYZsq{}}\PY{p}{:}\PY{l+m+mi}{5}\PY{p}{,}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{D}\PY{l+s+s1}{\PYZsq{}}\PY{p}{:}\PY{l+m+mi}{4}\PY{p}{,}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{E}\PY{l+s+s1}{\PYZsq{}}\PY{p}{:}\PY{l+m+mi}{3}\PY{p}{,}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{F}\PY{l+s+s1}{\PYZsq{}}\PY{p}{:}\PY{l+m+mi}{2}\PY{p}{,}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{G}\PY{l+s+s1}{\PYZsq{}}\PY{p}{:}\PY{l+m+mi}{1}\PY{p}{\PYZcb{}}\PY{p}{)}
\PY{n}{data}\PY{p}{[}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{home\PYZus{}ownership}\PY{l+s+s2}{\PYZdq{}}\PY{p}{]} \PY{o}{=} \PY{n}{data}\PY{p}{[}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{home\PYZus{}ownership}\PY{l+s+s2}{\PYZdq{}}\PY{p}{]}\PY{o}{.}\PY{n}{map}\PY{p}{(}\PY{p}{\PYZob{}}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{MORTGAGE}\PY{l+s+s2}{\PYZdq{}}\PY{p}{:}\PY{l+m+mi}{6}\PY{p}{,}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{RENT}\PY{l+s+s2}{\PYZdq{}}\PY{p}{:}\PY{l+m+mi}{5}\PY{p}{,}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{OWN}\PY{l+s+s2}{\PYZdq{}}\PY{p}{:}\PY{l+m+mi}{4}\PY{p}{,}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{OTHER}\PY{l+s+s2}{\PYZdq{}}\PY{p}{:}\PY{l+m+mi}{3}\PY{p}{,}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{NONE}\PY{l+s+s2}{\PYZdq{}}\PY{p}{:}\PY{l+m+mi}{2}\PY{p}{,}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{ANY}\PY{l+s+s2}{\PYZdq{}}\PY{p}{:}\PY{l+m+mi}{1}\PY{p}{\PYZcb{}}\PY{p}{)}
\PY{n}{data}\PY{p}{[}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{emp\PYZus{}length}\PY{l+s+s2}{\PYZdq{}}\PY{p}{]} \PY{o}{=} \PY{n}{data}\PY{p}{[}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{emp\PYZus{}length}\PY{l+s+s2}{\PYZdq{}}\PY{p}{]}\PY{o}{.}\PY{n}{replace}\PY{p}{(}\PY{p}{\PYZob{}}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{years}\PY{l+s+s1}{\PYZsq{}}\PY{p}{:}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{year}\PY{l+s+s1}{\PYZsq{}}\PY{p}{:}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{\PYZsq{}}\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{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{\PYZlt{}}\PY{l+s+s1}{\PYZsq{}}\PY{p}{:}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{\PYZbs{}}\PY{l+s+s1}{+}\PY{l+s+s1}{\PYZsq{}}\PY{p}{:}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{n/a}\PY{l+s+s1}{\PYZsq{}}\PY{p}{:}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{0}\PY{l+s+s1}{\PYZsq{}}\PY{p}{\PYZcb{}}\PY{p}{,} \PY{n}{regex} \PY{o}{=} \PY{k+kc}{True}\PY{p}{)}
\PY{n}{data}\PY{p}{[}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{emp\PYZus{}length}\PY{l+s+s2}{\PYZdq{}}\PY{p}{]} \PY{o}{=} \PY{n}{data}\PY{p}{[}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{emp\PYZus{}length}\PY{l+s+s2}{\PYZdq{}}\PY{p}{]}\PY{o}{.}\PY{n}{apply}\PY{p}{(}\PY{k}{lambda} \PY{n}{x}\PY{p}{:}\PY{n+nb}{int}\PY{p}{(}\PY{n}{x}\PY{p}{)}\PY{p}{)}
\PY{n}{data}\PY{o}{.}\PY{n}{dropna}\PY{p}{(}\PY{n}{how}\PY{o}{=}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{any}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
\PY{k}{return} \PY{n}{data}\PY{o}{.}\PY{n}{head}\PY{p}{(}\PY{p}{)}
\PY{k}{else}\PY{p}{:}
\PY{n+ne}{TypeError}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{Required DataFrame}\PY{l+s+s2}{\PYZdq{}}\PY{p}{)}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}130}]:} \PY{n}{new\PYZus{}data} \PY{o}{=} \PY{n}{ready\PYZus{}data}\PY{o}{.}\PY{n}{copy}\PY{p}{(}\PY{p}{)}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}131}]:} \PY{n}{data\PYZus{}encoding}\PY{p}{(}\PY{n}{new\PYZus{}data}\PY{p}{)}
\end{Verbatim}