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    \title{Risk classification - LIC}
    
    
    

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    \begin{document}
    
    
    \maketitle
    
    

    
    \subsection{Risk Classification - Life Insurance
Data}\label{risk-classification---life-insurance-data}

\emph{Risk Classification} is the task of classifies risk of the person

The below are the topics which the notebook covered,

\begin{verbatim}
- <a href="#readDataset">Read Dataset</a><br>
- <a href="#labelCount">Response Vs No of records</a><br>
- <a href="#preprocess">Preprocessing</a><br>
- <a href="#featureSelection">Feature Selection</a><br>
- <a href="#buildModel">Build Model</a><br>
- <a href="#lime">Understanding Model Predictions with LIME</a></ul>
\end{verbatim}

    \begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}62}]:} \PY{c+c1}{\PYZsh{}Necessary packages}
         \PY{k+kn}{import} \PY{n+nn}{pandas} \PY{k}{as} \PY{n+nn}{pd}
         \PY{k+kn}{import} \PY{n+nn}{seaborn} \PY{k}{as} \PY{n+nn}{sns}
         \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}{sklearn}\PY{n+nn}{.}\PY{n+nn}{feature\PYZus{}selection} \PY{k}{import} \PY{n}{SelectKBest}
         \PY{k+kn}{from} \PY{n+nn}{sklearn}\PY{n+nn}{.}\PY{n+nn}{feature\PYZus{}selection} \PY{k}{import} \PY{n}{chi2}
         \PY{k+kn}{from} \PY{n+nn}{sklearn}\PY{n+nn}{.}\PY{n+nn}{preprocessing} \PY{k}{import} \PY{n}{LabelEncoder}
         \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}{metrics} \PY{k}{import} \PY{n}{accuracy\PYZus{}score}\PY{p}{,}\PY{n}{confusion\PYZus{}matrix}
         \PY{k+kn}{from} \PY{n+nn}{sklearn}\PY{n+nn}{.}\PY{n+nn}{model\PYZus{}selection} \PY{k}{import} \PY{n}{cross\PYZus{}val\PYZus{}score}\PY{p}{,}\PY{n}{GridSearchCV}
         \PY{k+kn}{from} \PY{n+nn}{sklearn}\PY{n+nn}{.}\PY{n+nn}{ensemble} \PY{k}{import} \PY{n}{RandomForestClassifier}
         \PY{k+kn}{import} \PY{n+nn}{xgboost} \PY{k}{as} \PY{n+nn}{xgb}
         \PY{k+kn}{from} \PY{n+nn}{sklearn}\PY{n+nn}{.}\PY{n+nn}{utils}\PY{n+nn}{.}\PY{n+nn}{multiclass} \PY{k}{import} \PY{n}{unique\PYZus{}labels}
         \PY{k+kn}{from} \PY{n+nn}{sklearn}\PY{n+nn}{.}\PY{n+nn}{metrics} \PY{k}{import} \PY{n}{classification\PYZus{}report}
\end{Verbatim}


     \#\#\# Read Dataset

    \begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}2}]:} \PY{c+c1}{\PYZsh{} construct dataframe using pandas}
        \PY{n}{df} \PY{o}{=} \PY{n}{pd}\PY{o}{.}\PY{n}{read\PYZus{}csv}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{data/Training.csv}\PY{l+s+s2}{\PYZdq{}}\PY{p}{)}
\end{Verbatim}


    \begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}3}]:} \PY{n+nb}{print} \PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{Dimension of the dataset}\PY{l+s+s2}{\PYZdq{}}\PY{p}{,}\PY{n}{df}\PY{o}{.}\PY{n}{shape}\PY{p}{)}
        \PY{n}{df}\PY{o}{.}\PY{n}{head}\PY{p}{(}\PY{p}{)}
\end{Verbatim}


    \begin{Verbatim}[commandchars=\\\{\}]
Dimension of the dataset (59381, 128)

    \end{Verbatim}

\begin{Verbatim}[commandchars=\\\{\}]
{\color{outcolor}Out[{\color{outcolor}3}]:}    Id  Product\_Info\_1 Product\_Info\_2  Product\_Info\_3  Product\_Info\_4  \textbackslash{}
        0   2               1             D3              10        0.076923   
        1   5               1             A1              26        0.076923   
        2   6               1             E1              26        0.076923   
        3   7               1             D4              10        0.487179   
        4   8               1             D2              26        0.230769   
        
           Product\_Info\_5  Product\_Info\_6  Product\_Info\_7   Ins\_Age        Ht  \textbackslash{}
        0               2               1               1  0.641791  0.581818   
        1               2               3               1  0.059701  0.600000   
        2               2               3               1  0.029851  0.745455   
        3               2               3               1  0.164179  0.672727   
        4               2               3               1  0.417910  0.654545   
        
             {\ldots}     Medical\_Keyword\_40  Medical\_Keyword\_41  Medical\_Keyword\_42  \textbackslash{}
        0    {\ldots}                      0                   0                   0   
        1    {\ldots}                      0                   0                   0   
        2    {\ldots}                      0                   0                   0   
        3    {\ldots}                      0                   0                   0   
        4    {\ldots}                      0                   0                   0   
        
           Medical\_Keyword\_43  Medical\_Keyword\_44  Medical\_Keyword\_45  \textbackslash{}
        0                   0                   0                   0   
        1                   0                   0                   0   
        2                   0                   0                   0   
        3                   0                   0                   0   
        4                   0                   0                   0   
        
           Medical\_Keyword\_46  Medical\_Keyword\_47  Medical\_Keyword\_48  Response  
        0                   0                   0                   0         8  
        1                   0                   0                   0         4  
        2                   0                   0                   0         8  
        3                   0                   0                   0         8  
        4                   0                   0                   0         8  
        
        [5 rows x 128 columns]
\end{Verbatim}
            
     \#\# Response Vs No of records

    \begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}4}]:} \PY{c+c1}{\PYZsh{} Get the unique target variable}
        \PY{n}{target\PYZus{}variable} \PY{o}{=} \PY{n}{df}\PY{o}{.}\PY{n}{Response}\PY{o}{.}\PY{n}{unique}\PY{p}{(}\PY{p}{)}
        \PY{n+nb}{print} \PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{No of classes available }\PY{l+s+si}{\PYZpc{}s}\PY{l+s+se}{\PYZbs{}n}\PY{l+s+s2}{target variables }\PY{l+s+si}{\PYZpc{}s}\PY{l+s+s2}{ }\PY{l+s+s2}{\PYZdq{}} \PY{o}{\PYZpc{}} \PY{p}{(}\PY{n+nb}{len}\PY{p}{(}\PY{n}{target\PYZus{}variable}\PY{p}{)}\PY{p}{,}\PY{n}{target\PYZus{}variable} \PY{p}{)}\PY{p}{)}
        \PY{c+c1}{\PYZsh{} Identify the no of records available for each target variable}
        \PY{n}{class\PYZus{}distribution} \PY{o}{=} \PY{n}{df}\PY{p}{[}\PY{p}{[}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{Id}\PY{l+s+s2}{\PYZdq{}}\PY{p}{,}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{Response}\PY{l+s+s2}{\PYZdq{}}\PY{p}{]}\PY{p}{]}\PY{o}{.}\PY{n}{groupby}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{Response}\PY{l+s+s2}{\PYZdq{}}\PY{p}{)}\PY{o}{.}\PY{n}{agg}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{count}\PY{l+s+s2}{\PYZdq{}}\PY{p}{)}\PY{o}{.}\PY{n}{rename}\PY{p}{(}\PY{n}{index}\PY{o}{=}\PY{n+nb}{str}\PY{p}{,}\PY{n}{columns}\PY{o}{=}\PY{p}{\PYZob{}}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{Id}\PY{l+s+s2}{\PYZdq{}}\PY{p}{:}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{count}\PY{l+s+s2}{\PYZdq{}}\PY{p}{\PYZcb{}}\PY{p}{)}
        \PY{n}{class\PYZus{}distribution}\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}{)}
        \PY{n}{plt}\PY{o}{.}\PY{n}{xlabel}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{Response}\PY{l+s+s2}{\PYZdq{}}\PY{p}{)}
        \PY{n}{plt}\PY{o}{.}\PY{n}{ylabel}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{No of Records}\PY{l+s+s2}{\PYZdq{}}\PY{p}{)}
        \PY{n}{plt}\PY{o}{.}\PY{n}{title}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{Independent (Target) variable distribution}\PY{l+s+s2}{\PYZdq{}}\PY{p}{)}
        \PY{n}{plt}\PY{o}{.}\PY{n}{show}\PY{p}{(}\PY{p}{)}
\end{Verbatim}


    \begin{Verbatim}[commandchars=\\\{\}]
No of classes available 8
target variables [8 4 1 6 2 7 3 5] 

    \end{Verbatim}

    \begin{center}
    \adjustimage{max size={0.9\linewidth}{0.9\paperheight}}{output_6_1.png}
    \end{center}
    { \hspace*{\fill} \\}
    
    The above diagram shows the distribution of independent variable -
Response 8 has high frequency compared to other responses - Response 3
and 4 has very low frequency - These both responses makes problem while
building model due to imbalance in the class variables - Need to be
careful while splitting data for training and testing because the way we
split should be cater for each class with minimum no of records.
Randomly split data is not a best option for this

     \#\# Preprocessing

    \begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}5}]:} \PY{c+c1}{\PYZsh{} Check any missing values available in the data}
        \PY{n}{df}\PY{o}{.}\PY{n}{isnull}\PY{p}{(}\PY{p}{)}\PY{o}{.}\PY{n}{sum}\PY{p}{(}\PY{p}{)}\PY{p}{[}\PY{n}{df}\PY{o}{.}\PY{n}{isnull}\PY{p}{(}\PY{p}{)}\PY{o}{.}\PY{n}{sum}\PY{p}{(}\PY{p}{)} \PY{o}{\PYZgt{}} \PY{l+m+mi}{0}\PY{p}{]}
\end{Verbatim}


\begin{Verbatim}[commandchars=\\\{\}]
{\color{outcolor}Out[{\color{outcolor}5}]:} Employment\_Info\_1         19
        Employment\_Info\_4       6779
        Employment\_Info\_6      10854
        Insurance\_History\_5    25396
        Family\_Hist\_2          28656
        Family\_Hist\_3          34241
        Family\_Hist\_4          19184
        Family\_Hist\_5          41811
        Medical\_History\_1       8889
        Medical\_History\_10     58824
        Medical\_History\_15     44596
        Medical\_History\_24     55580
        Medical\_History\_32     58274
        dtype: int64
\end{Verbatim}
            
    Oh! There are huge values are missing in the data.

    \begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}6}]:} \PY{c+c1}{\PYZsh{} Check how much percentage of data has been missing in the data}
        \PY{n}{missing\PYZus{}df}\PY{o}{=} \PY{n}{df}\PY{o}{.}\PY{n}{isnull}\PY{p}{(}\PY{p}{)}\PY{o}{.}\PY{n}{sum}\PY{p}{(}\PY{p}{)}\PY{p}{[}\PY{n}{df}\PY{o}{.}\PY{n}{isnull}\PY{p}{(}\PY{p}{)}\PY{o}{.}\PY{n}{sum}\PY{p}{(}\PY{p}{)} \PY{o}{!=}\PY{l+m+mi}{0}\PY{p}{]}
        \PY{n}{missing\PYZus{}df}\PY{o}{=}\PY{n}{pd}\PY{o}{.}\PY{n}{DataFrame}\PY{p}{(}\PY{n}{missing\PYZus{}df}\PY{o}{.}\PY{n}{reset\PYZus{}index}\PY{p}{(}\PY{p}{)}\PY{p}{)}
        \PY{n}{missing\PYZus{}df}\PY{o}{.}\PY{n}{rename}\PY{p}{(}\PY{n}{columns}\PY{o}{=}\PY{p}{\PYZob{}}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{index}\PY{l+s+s1}{\PYZsq{}}\PY{p}{:}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{features}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,}\PY{l+m+mi}{0}\PY{p}{:}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{missing\PYZus{}count}\PY{l+s+s1}{\PYZsq{}}\PY{p}{\PYZcb{}}\PY{p}{,}\PY{n}{inplace}\PY{o}{=}\PY{k+kc}{True}\PY{p}{)}
        \PY{n}{missing\PYZus{}df}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{missing\PYZus{}count\PYZus{}percentage}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]}\PY{o}{=}\PY{p}{(}\PY{p}{(}\PY{n}{missing\PYZus{}df}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{missing\PYZus{}count}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]}\PY{p}{)}\PY{o}{/}\PY{n+nb}{len}\PY{p}{(}\PY{n}{df}\PY{p}{)}\PY{p}{)}\PY{o}{*}\PY{l+m+mi}{100}
        \PY{n}{missing\PYZus{}df}\PY{o}{.}\PY{n}{index} \PY{o}{=} \PY{n}{missing\PYZus{}df}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{features}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]}
        \PY{n}{missing\PYZus{}df}\PY{p}{[}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{missing\PYZus{}count\PYZus{}percentage}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{features}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]}\PY{p}{]}\PY{o}{.}\PY{n}{plot}\PY{p}{(}\PY{n}{kind}\PY{o}{=}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{barh}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
        \PY{n}{plt}\PY{o}{.}\PY{n}{ylabel}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{Missing columns}\PY{l+s+s2}{\PYZdq{}}\PY{p}{)}
        \PY{n}{plt}\PY{o}{.}\PY{n}{xlabel}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+si}{\PYZpc{} o}\PY{l+s+s2}{f records}\PY{l+s+s2}{\PYZdq{}}\PY{p}{)}
        \PY{n}{plt}\PY{o}{.}\PY{n}{title}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{Missing values in the columns}\PY{l+s+s2}{\PYZdq{}}\PY{p}{)}
        \PY{n}{plt}\PY{o}{.}\PY{n}{show}\PY{p}{(}\PY{p}{)}
        
        \PY{c+c1}{\PYZsh{}\PYZsh{}\PYZsh{}\PYZsh{}\PYZsh{}\PYZsh{}\PYZsh{}\PYZsh{}\PYZsh{}\PYZsh{}\PYZsh{}\PYZsh{}}
        \PY{n+nb}{print}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{In terms of percentage in the overall dataset, There are many columns have more than 50}\PY{l+s+si}{\PYZpc{} r}\PY{l+s+s2}{ecords are missing}\PY{l+s+s2}{\PYZdq{}}\PY{p}{)}
        \PY{n+nb}{print}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{Let}\PY{l+s+s2}{\PYZsq{}}\PY{l+s+s2}{s see how we can impute those values}\PY{l+s+s2}{\PYZdq{}}\PY{p}{)}
\end{Verbatim}


    \begin{center}
    \adjustimage{max size={0.9\linewidth}{0.9\paperheight}}{output_11_0.png}
    \end{center}
    { \hspace*{\fill} \\}
    
    \begin{Verbatim}[commandchars=\\\{\}]
In terms of percentage in the overall dataset, There are many columns have more than 50\% records are missing
Let's see how we can impute those values

    \end{Verbatim}

    The below are data type of missing values columns - Employment\_Info\_1,
Employment\_Info\_4, Employment\_Info\_6,Insurance\_History\_5,
Family\_Hist\_2, Family\_Hist\_3, Family\_Hist\_4, Family\_Hist\_5 are
continous - Medical\_History\_1, Medical\_History\_10,
Medical\_History\_15, Medical\_History\_24, Medical\_History\_32 are
discrete

Let's start with continous variable first

    \begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}7}]:} \PY{n+nb}{len}\PY{p}{(}\PY{n}{df}\PY{p}{[}\PY{n}{df}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{Medical\PYZus{}History\PYZus{}1}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]}\PY{o}{.}\PY{n}{isnull}\PY{p}{(}\PY{p}{)}\PY{p}{]}\PY{p}{)}
\end{Verbatim}


\begin{Verbatim}[commandchars=\\\{\}]
{\color{outcolor}Out[{\color{outcolor}7}]:} 8889
\end{Verbatim}
            
    \begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}8}]:} \PY{c+c1}{\PYZsh{} Let\PYZsq{}s draw box plot for Employment Info columns}
        \PY{n}{boxplot} \PY{o}{=} \PY{n}{df}\PY{o}{.}\PY{n}{boxplot}\PY{p}{(}\PY{n}{column}\PY{o}{=}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{Employment\PYZus{}Info\PYZus{}1}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{Employment\PYZus{}Info\PYZus{}4}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{Employment\PYZus{}Info\PYZus{}6}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]}\PY{p}{)}
\end{Verbatim}


    \begin{center}
    \adjustimage{max size={0.9\linewidth}{0.9\paperheight}}{output_14_0.png}
    \end{center}
    { \hspace*{\fill} \\}
    
    The above box-plot drawn for Employment info because it has very less
missing values. The following are key points,

Employment\_Info\_1: - It has lot of outlier. So if we take mean value
to fill the missing values, It won't makes sense. Should prefer the
Median value

Employment\_Info\_4: - lot of outlier. So we should prefer median value
to fill missing values

Employment\_Info\_6: - No outliers. Looks good with mean, 25\% value and
75\% value. There is not much variance in the data.So we should go with
mean value to fill missing values

    \begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}9}]:} \PY{n}{df}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{Employment\PYZus{}Info\PYZus{}1}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]}\PY{o}{.}\PY{n}{fillna}\PY{p}{(}\PY{n}{df}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{Employment\PYZus{}Info\PYZus{}1}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]}\PY{o}{.}\PY{n}{median}\PY{p}{(}\PY{p}{)}\PY{p}{,}\PY{n}{inplace}\PY{o}{=}\PY{k+kc}{True}\PY{p}{)}
        \PY{n}{df}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{Employment\PYZus{}Info\PYZus{}4}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]}\PY{o}{.}\PY{n}{fillna}\PY{p}{(}\PY{n}{df}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{Employment\PYZus{}Info\PYZus{}4}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]}\PY{o}{.}\PY{n}{median}\PY{p}{(}\PY{p}{)}\PY{p}{,}\PY{n}{inplace}\PY{o}{=}\PY{k+kc}{True}\PY{p}{)}
        \PY{n}{df}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{Employment\PYZus{}Info\PYZus{}6}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]}\PY{o}{.}\PY{n}{fillna}\PY{p}{(}\PY{n}{df}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{Employment\PYZus{}Info\PYZus{}6}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]}\PY{o}{.}\PY{n}{mean}\PY{p}{(}\PY{p}{)}\PY{p}{,}\PY{n}{inplace}\PY{o}{=}\PY{k+kc}{True}\PY{p}{)}
\end{Verbatim}


    \begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}10}]:} \PY{n+nb}{print} \PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{Check whether null values are present in the columns}\PY{l+s+s2}{\PYZdq{}}\PY{p}{)}
         \PY{n}{df}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{Employment\PYZus{}Info\PYZus{}1}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]}\PY{o}{.}\PY{n}{isnull}\PY{p}{(}\PY{p}{)}\PY{o}{.}\PY{n}{sum}\PY{p}{(}\PY{p}{)}\PY{p}{,}\PY{n}{df}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{Employment\PYZus{}Info\PYZus{}4}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]}\PY{o}{.}\PY{n}{isnull}\PY{p}{(}\PY{p}{)}\PY{o}{.}\PY{n}{sum}\PY{p}{(}\PY{p}{)}\PY{p}{,}\PY{n}{df}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{Employment\PYZus{}Info\PYZus{}6}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]}\PY{o}{.}\PY{n}{isnull}\PY{p}{(}\PY{p}{)}\PY{o}{.}\PY{n}{sum}\PY{p}{(}\PY{p}{)}
\end{Verbatim}


    \begin{Verbatim}[commandchars=\\\{\}]
Check whether null values are present in the columns

    \end{Verbatim}

\begin{Verbatim}[commandchars=\\\{\}]
{\color{outcolor}Out[{\color{outcolor}10}]:} (0, 0, 0)
\end{Verbatim}
            
    \begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}11}]:} \PY{c+c1}{\PYZsh{} Medical History 1 has less missing values. So trying to replace with maximum no of frequency data}
         \PY{n+nb}{print}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{No of unique values }\PY{l+s+s2}{\PYZdq{}}\PY{p}{,}\PY{n+nb}{len}\PY{p}{(}\PY{n}{df}\PY{o}{.}\PY{n}{Medical\PYZus{}History\PYZus{}1}\PY{o}{.}\PY{n}{unique}\PY{p}{(}\PY{p}{)}\PY{p}{)}\PY{p}{,}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+se}{\PYZbs{}n}\PY{l+s+s2}{\PYZdq{}}\PY{p}{,}\PY{n}{df}\PY{o}{.}\PY{n}{Medical\PYZus{}History\PYZus{}1}\PY{o}{.}\PY{n}{unique}\PY{p}{(}\PY{p}{)}\PY{p}{)}
\end{Verbatim}


    \begin{Verbatim}[commandchars=\\\{\}]
No of unique values  172 
 [  4.   5.  10.   0.  nan   6.   1.  39.  61.   8.   2.   3.  22.  11.
  19.   7.  16.  12.  49.   9.  13.  15.  47.  57.  24.  23.  62.  28.
  44.  32.  17.  18.  34.  30. 153.  21.  86.  14.  25.  36.  42.  26.
  20.  64.  29.  55. 124.  27. 114.  40.  31.  41.  46.  76.  51.  65.
  66.  78. 154. 147.  37.  80.  71.  85. 105.  53.  56.  68.  52.  77.
  33.  50.  73.  35.  70. 182.  95.  43.  72.  84.  54.  48.  38.  45.
 112. 134.  88.  63.  69. 158. 120.  59. 100. 119.  74.  82.  58.  97.
  90.  96. 123.  94. 160. 156. 113. 176.  60.  75.  91.  79.  81. 121.
 103.  83. 240. 128.  93. 136. 122.  87.  92. 110. 178. 175. 171.  89.
  67. 169.  99. 229. 223. 109. 146. 118. 141. 107. 104. 117. 130. 159.
 172. 108. 191. 201.  98. 161. 115. 173. 102. 106. 162. 150. 125. 179.
 148. 132. 116. 138. 126. 193. 101. 239. 185. 145. 143. 111. 155. 131.
 180. 127. 228. 187.]

    \end{Verbatim}

    \begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}12}]:} \PY{c+c1}{\PYZsh{} Fill medical history with most frequent used in the data}
         \PY{n}{most\PYZus{}frequent\PYZus{}medicalHistory} \PY{o}{=} \PY{n+nb}{int}\PY{p}{(}\PY{n}{df}\PY{p}{[}\PY{p}{[}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{Medical\PYZus{}History\PYZus{}1}\PY{l+s+s2}{\PYZdq{}}\PY{p}{,}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{Medical\PYZus{}History\PYZus{}2}\PY{l+s+s2}{\PYZdq{}}\PY{p}{]}\PY{p}{]}\PY{o}{.}\PY{n}{groupby}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{Medical\PYZus{}History\PYZus{}1}\PY{l+s+s2}{\PYZdq{}}\PY{p}{)}\PY{o}{.}\PY{n}{agg}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{count}\PY{l+s+s2}{\PYZdq{}}\PY{p}{)}\PY{o}{.}\PY{n}{sort\PYZus{}values}\PY{p}{(}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{Medical\PYZus{}History\PYZus{}2}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]}\PY{p}{,} \PY{n}{ascending}\PY{o}{=}\PY{k+kc}{False}\PY{p}{)}\PY{o}{.}\PY{n}{reset\PYZus{}index}\PY{p}{(}\PY{p}{)}\PY{p}{[}\PY{l+m+mi}{0}\PY{p}{:}\PY{l+m+mi}{1}\PY{p}{]}\PY{p}{[}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{Medical\PYZus{}History\PYZus{}1}\PY{l+s+s2}{\PYZdq{}}\PY{p}{]}\PY{p}{)}
         \PY{n}{df}\PY{p}{[}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{Medical\PYZus{}History\PYZus{}1}\PY{l+s+s2}{\PYZdq{}}\PY{p}{]}\PY{o}{.}\PY{n}{fillna}\PY{p}{(}\PY{n}{most\PYZus{}frequent\PYZus{}medicalHistory}\PY{p}{,}\PY{n}{inplace}\PY{o}{=}\PY{k+kc}{True}\PY{p}{)}
\end{Verbatim}


    \begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}13}]:} \PY{n+nb}{print}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{No of unique values }\PY{l+s+s2}{\PYZdq{}}\PY{p}{,}\PY{n+nb}{len}\PY{p}{(}\PY{n}{df}\PY{o}{.}\PY{n}{Medical\PYZus{}History\PYZus{}1}\PY{o}{.}\PY{n}{unique}\PY{p}{(}\PY{p}{)}\PY{p}{)}\PY{p}{,}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+se}{\PYZbs{}n}\PY{l+s+s2}{\PYZdq{}}\PY{p}{,}\PY{n}{df}\PY{o}{.}\PY{n}{Medical\PYZus{}History\PYZus{}1}\PY{o}{.}\PY{n}{unique}\PY{p}{(}\PY{p}{)}\PY{p}{)}
\end{Verbatim}


    \begin{Verbatim}[commandchars=\\\{\}]
No of unique values  171 
 [  4.   5.  10.   0.   1.   6.  39.  61.   8.   2.   3.  22.  11.  19.
   7.  16.  12.  49.   9.  13.  15.  47.  57.  24.  23.  62.  28.  44.
  32.  17.  18.  34.  30. 153.  21.  86.  14.  25.  36.  42.  26.  20.
  64.  29.  55. 124.  27. 114.  40.  31.  41.  46.  76.  51.  65.  66.
  78. 154. 147.  37.  80.  71.  85. 105.  53.  56.  68.  52.  77.  33.
  50.  73.  35.  70. 182.  95.  43.  72.  84.  54.  48.  38.  45. 112.
 134.  88.  63.  69. 158. 120.  59. 100. 119.  74.  82.  58.  97.  90.
  96. 123.  94. 160. 156. 113. 176.  60.  75.  91.  79.  81. 121. 103.
  83. 240. 128.  93. 136. 122.  87.  92. 110. 178. 175. 171.  89.  67.
 169.  99. 229. 223. 109. 146. 118. 141. 107. 104. 117. 130. 159. 172.
 108. 191. 201.  98. 161. 115. 173. 102. 106. 162. 150. 125. 179. 148.
 132. 116. 138. 126. 193. 101. 239. 185. 145. 143. 111. 155. 131. 180.
 127. 228. 187.]

    \end{Verbatim}

    \begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}14}]:} \PY{c+c1}{\PYZsh{}lets see Insurance\PYZus{}History\PYZus{}5}
         \PY{n+nb}{print}\PY{p}{(}\PY{n}{df}\PY{o}{.}\PY{n}{Insurance\PYZus{}History\PYZus{}5}\PY{o}{.}\PY{n}{describe}\PY{p}{(}\PY{p}{)}\PY{p}{)}
         \PY{n}{boxplot} \PY{o}{=} \PY{n}{df}\PY{o}{.}\PY{n}{boxplot}\PY{p}{(}\PY{n}{column}\PY{o}{=}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{Insurance\PYZus{}History\PYZus{}5}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]}\PY{p}{)}
\end{Verbatim}


    \begin{Verbatim}[commandchars=\\\{\}]
count    33985.000000
mean         0.001733
std          0.007338
min          0.000000
25\%          0.000400
50\%          0.000973
75\%          0.002000
max          1.000000
Name: Insurance\_History\_5, dtype: float64

    \end{Verbatim}

    \begin{center}
    \adjustimage{max size={0.9\linewidth}{0.9\paperheight}}{output_21_1.png}
    \end{center}
    { \hspace*{\fill} \\}
    
    Even Insurance\_History\_5 has lot of outlier in the data. It has 40\%
more missing values. So Will remove this feature

    \begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}15}]:} \PY{c+c1}{\PYZsh{}lets drop features with high number of missing values }
         \PY{n}{df}\PY{o}{.}\PY{n}{drop}\PY{p}{(}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{Medical\PYZus{}History\PYZus{}10}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{Medical\PYZus{}History\PYZus{}15}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{Medical\PYZus{}History\PYZus{}24}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{Medical\PYZus{}History\PYZus{}32}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{Insurance\PYZus{}History\PYZus{}5}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,}
                          \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{Family\PYZus{}Hist\PYZus{}2}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{Family\PYZus{}Hist\PYZus{}3}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{Family\PYZus{}Hist\PYZus{}4}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{Family\PYZus{}Hist\PYZus{}5}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]}\PY{p}{,}
                         \PY{n}{axis}\PY{o}{=}\PY{l+m+mi}{1}\PY{p}{,}\PY{n}{inplace}\PY{o}{=}\PY{k+kc}{True}\PY{p}{)}
\end{Verbatim}


    \begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}16}]:} \PY{c+c1}{\PYZsh{} convert string into numerical value for product info}
         \PY{n}{le}\PY{o}{=}\PY{n}{LabelEncoder}\PY{p}{(}\PY{p}{)}
         \PY{n}{df}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{Product\PYZus{}Info\PYZus{}2}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]}\PY{o}{=}\PY{n}{le}\PY{o}{.}\PY{n}{fit\PYZus{}transform}\PY{p}{(}\PY{n}{df}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{Product\PYZus{}Info\PYZus{}2}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]}\PY{p}{)}
\end{Verbatim}


    \begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}17}]:} \PY{n+nb}{print}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{Final dataframe feature columns }\PY{l+s+s2}{\PYZdq{}}\PY{p}{,}\PY{n}{df}\PY{o}{.}\PY{n}{shape}\PY{p}{)}
\end{Verbatim}


    \begin{Verbatim}[commandchars=\\\{\}]
Final dataframe feature columns  (59381, 119)

    \end{Verbatim}

     \#\# Feature Selection

    \begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}18}]:} \PY{n}{X} \PY{o}{=} \PY{n}{df}\PY{o}{.}\PY{n}{iloc}\PY{p}{[}\PY{p}{:}\PY{p}{,}\PY{l+m+mi}{1}\PY{p}{:}\PY{o}{\PYZhy{}}\PY{l+m+mi}{1}\PY{p}{]}  \PY{c+c1}{\PYZsh{} features without ID}
         \PY{n}{y} \PY{o}{=} \PY{n}{df}\PY{o}{.}\PY{n}{iloc}\PY{p}{[}\PY{p}{:}\PY{p}{,}\PY{o}{\PYZhy{}}\PY{l+m+mi}{1}\PY{p}{]}    \PY{c+c1}{\PYZsh{}target column i.e price range}
         
         \PY{c+c1}{\PYZsh{}apply SelectKBest class to extract top 80 best features}
         \PY{n}{bestfeatures} \PY{o}{=} \PY{n}{SelectKBest}\PY{p}{(}\PY{n}{score\PYZus{}func}\PY{o}{=}\PY{n}{chi2}\PY{p}{,} \PY{n}{k}\PY{o}{=}\PY{l+m+mi}{80}\PY{p}{)}
         \PY{n}{fit} \PY{o}{=} \PY{n}{bestfeatures}\PY{o}{.}\PY{n}{fit}\PY{p}{(}\PY{n}{X}\PY{p}{,}\PY{n}{y}\PY{p}{)}
         \PY{n}{dfscores} \PY{o}{=} \PY{n}{pd}\PY{o}{.}\PY{n}{DataFrame}\PY{p}{(}\PY{n}{fit}\PY{o}{.}\PY{n}{scores\PYZus{}}\PY{p}{)}
         \PY{n}{dfcolumns} \PY{o}{=} \PY{n}{pd}\PY{o}{.}\PY{n}{DataFrame}\PY{p}{(}\PY{n}{X}\PY{o}{.}\PY{n}{columns}\PY{p}{)} 
         \PY{n}{featureScores} \PY{o}{=} \PY{n}{pd}\PY{o}{.}\PY{n}{concat}\PY{p}{(}\PY{p}{[}\PY{n}{dfcolumns}\PY{p}{,}\PY{n}{dfscores}\PY{p}{]}\PY{p}{,}\PY{n}{axis}\PY{o}{=}\PY{l+m+mi}{1}\PY{p}{)}
         \PY{n}{featureScores}\PY{o}{.}\PY{n}{columns} \PY{o}{=} \PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{Features}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{Score}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]}  
         
         \PY{n+nb}{print}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{\PYZsh{}\PYZsh{}\PYZsh{}\PYZsh{}\PYZsh{} Important features based on SelectKBest using chi\PYZhy{}square value}\PY{l+s+s2}{\PYZdq{}}\PY{p}{)}
         \PY{n+nb}{print}\PY{p}{(}\PY{n}{featureScores}\PY{o}{.}\PY{n}{nlargest}\PY{p}{(}\PY{l+m+mi}{80}\PY{p}{,}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{Score}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}\PY{p}{)}  
\end{Verbatim}


    \begin{Verbatim}[commandchars=\\\{\}]
\#\#\#\#\# Important features based on SelectKBest using chi-square value
                Features        Score
32     Medical\_History\_1  9949.778760
83    Medical\_Keyword\_15  5639.405401
71     Medical\_Keyword\_3  4339.025870
33     Medical\_History\_2  2749.627783
52    Medical\_History\_23  2186.232874
116   Medical\_Keyword\_48  1650.551405
12     Employment\_Info\_2  1462.347229
35     Medical\_History\_4  1181.399564
91    Medical\_Keyword\_23  1123.683820
93    Medical\_Keyword\_25   987.312671
1         Product\_Info\_2   826.736193
3         Product\_Info\_4   716.825284
106   Medical\_Keyword\_38   714.827753
2         Product\_Info\_3   614.718816
45    Medical\_History\_16   543.331296
110   Medical\_Keyword\_42   534.524096
69     Medical\_Keyword\_1   524.587484
10                   BMI   485.966874
13     Employment\_Info\_3   465.641372
22         InsuredInfo\_6   450.313429
105   Medical\_Keyword\_37   420.561268
92    Medical\_Keyword\_24   410.946069
7                Ins\_Age   404.887633
66    Medical\_History\_39   369.614400
9                     Wt   346.416302
108   Medical\_Keyword\_40   339.499925
90    Medical\_Keyword\_22   247.918094
115   Medical\_Keyword\_47   246.045661
78    Medical\_Keyword\_10   243.143632
111   Medical\_Keyword\_43   242.133121
..                   {\ldots}          {\ldots}
86    Medical\_Keyword\_18    98.786492
87    Medical\_Keyword\_19    89.882964
104   Medical\_Keyword\_36    85.106587
80    Medical\_Keyword\_12    83.267842
102   Medical\_Keyword\_34    76.817414
81    Medical\_Keyword\_13    73.454116
96    Medical\_Keyword\_28    67.506438
31         Family\_Hist\_1    65.841289
82    Medical\_Keyword\_14    64.800995
79    Medical\_Keyword\_11    49.280217
75     Medical\_Keyword\_7    48.501127
23         InsuredInfo\_7    46.963694
89    Medical\_Keyword\_21    45.244921
56    Medical\_History\_28    44.557732
30   Insurance\_History\_9    43.543800
40     Medical\_History\_9    38.136147
109   Medical\_Keyword\_41    37.197363
85    Medical\_Keyword\_17    36.527097
21         InsuredInfo\_5    35.043426
97    Medical\_Keyword\_29    33.746022
76     Medical\_Keyword\_8    33.583362
24   Insurance\_History\_1    32.395642
11     Employment\_Info\_1    31.940887
73     Medical\_Keyword\_5    31.858163
94    Medical\_Keyword\_26    30.618919
112   Medical\_Keyword\_44    29.538399
61    Medical\_History\_34    29.430290
47    Medical\_History\_18    29.389412
15     Employment\_Info\_5    28.512267
67    Medical\_History\_40    27.564225

[80 rows x 2 columns]

    \end{Verbatim}

    The above features are more important than other features based on
selection.

Let's filter these column in main dataframe

    \begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}19}]:} \PY{c+c1}{\PYZsh{}\PYZsh{} Filter important features from the dataframe}
         \PY{n}{feature\PYZus{}names\PYZus{}list} \PY{o}{=} \PY{n}{featureScores}\PY{o}{.}\PY{n}{nlargest}\PY{p}{(}\PY{l+m+mi}{80}\PY{p}{,}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{Score}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}\PY{p}{[}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{Features}\PY{l+s+s2}{\PYZdq{}}\PY{p}{]}\PY{o}{.}\PY{n}{tolist}\PY{p}{(}\PY{p}{)}
         \PY{n}{feature\PYZus{}names\PYZus{}list}\PY{o}{.}\PY{n}{append}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{Response}\PY{l+s+s2}{\PYZdq{}}\PY{p}{)}
         \PY{n}{df\PYZus{}important\PYZus{}features} \PY{o}{=} \PY{n}{df}\PY{p}{[}\PY{n}{feature\PYZus{}names\PYZus{}list}\PY{p}{]}
         \PY{n+nb}{print} \PY{p}{(}\PY{n}{df\PYZus{}important\PYZus{}features}\PY{o}{.}\PY{n}{shape}\PY{p}{)}
         \PY{n+nb}{print}\PY{p}{(}\PY{n}{df\PYZus{}important\PYZus{}features}\PY{o}{.}\PY{n}{columns}\PY{p}{)}
\end{Verbatim}


    \begin{Verbatim}[commandchars=\\\{\}]
(59381, 81)
Index(['Medical\_History\_1', 'Medical\_Keyword\_15', 'Medical\_Keyword\_3',
       'Medical\_History\_2', 'Medical\_History\_23', 'Medical\_Keyword\_48',
       'Employment\_Info\_2', 'Medical\_History\_4', 'Medical\_Keyword\_23',
       'Medical\_Keyword\_25', 'Product\_Info\_2', 'Product\_Info\_4',
       'Medical\_Keyword\_38', 'Product\_Info\_3', 'Medical\_History\_16',
       'Medical\_Keyword\_42', 'Medical\_Keyword\_1', 'BMI', 'Employment\_Info\_3',
       'InsuredInfo\_6', 'Medical\_Keyword\_37', 'Medical\_Keyword\_24', 'Ins\_Age',
       'Medical\_History\_39', 'Wt', 'Medical\_Keyword\_40', 'Medical\_Keyword\_22',
       'Medical\_Keyword\_47', 'Medical\_Keyword\_10', 'Medical\_Keyword\_43',
       'Medical\_Keyword\_46', 'Medical\_Keyword\_33', 'Insurance\_History\_4',
       'Insurance\_History\_7', 'Medical\_Keyword\_35', 'Insurance\_History\_3',
       'Medical\_Keyword\_16', 'Medical\_History\_13', 'Medical\_Keyword\_27',
       'Medical\_Keyword\_31', 'Medical\_History\_33', 'Medical\_History\_29',
       'Employment\_Info\_6', 'Medical\_History\_41', 'Medical\_History\_6',
       'Medical\_Keyword\_30', 'Medical\_Keyword\_9', 'InsuredInfo\_1',
       'Insurance\_History\_2', 'Medical\_Keyword\_4', 'Medical\_Keyword\_18',
       'Medical\_Keyword\_19', 'Medical\_Keyword\_36', 'Medical\_Keyword\_12',
       'Medical\_Keyword\_34', 'Medical\_Keyword\_13', 'Medical\_Keyword\_28',
       'Family\_Hist\_1', 'Medical\_Keyword\_14', 'Medical\_Keyword\_11',
       'Medical\_Keyword\_7', 'InsuredInfo\_7', 'Medical\_Keyword\_21',
       'Medical\_History\_28', 'Insurance\_History\_9', 'Medical\_History\_9',
       'Medical\_Keyword\_41', 'Medical\_Keyword\_17', 'InsuredInfo\_5',
       'Medical\_Keyword\_29', 'Medical\_Keyword\_8', 'Insurance\_History\_1',
       'Employment\_Info\_1', 'Medical\_Keyword\_5', 'Medical\_Keyword\_26',
       'Medical\_Keyword\_44', 'Medical\_History\_34', 'Medical\_History\_18',
       'Employment\_Info\_5', 'Medical\_History\_40', 'Response'],
      dtype='object')

    \end{Verbatim}

     \#\# Build Model

    Spliting data: - We are not using randomized split since class variables
are imbalanced - Trying to use Stratify split data, to make sure each
class has same \% of split

    \begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}20}]:} \PY{n}{X\PYZus{}features} \PY{o}{=} \PY{n}{df\PYZus{}important\PYZus{}features}\PY{o}{.}\PY{n}{iloc}\PY{p}{[}\PY{p}{:}\PY{p}{,}\PY{p}{:}\PY{o}{\PYZhy{}}\PY{l+m+mi}{1}\PY{p}{]}
         \PY{n}{y\PYZus{}lables} \PY{o}{=} \PY{n}{df\PYZus{}important\PYZus{}features}\PY{p}{[}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{Response}\PY{l+s+s2}{\PYZdq{}}\PY{p}{]}
         
         \PY{c+c1}{\PYZsh{} split train and test data using Stratifed Fashion 80\PYZhy{}20 split}
         \PY{n}{X\PYZus{}train}\PY{p}{,}\PY{n}{X\PYZus{}test}\PY{p}{,}\PY{n}{y\PYZus{}train}\PY{p}{,}\PY{n}{y\PYZus{}test}\PY{o}{=}\PY{n}{train\PYZus{}test\PYZus{}split}\PY{p}{(}\PY{n}{X\PYZus{}features}\PY{p}{,}\PY{n}{y\PYZus{}lables}\PY{p}{,}\PY{n}{stratify}\PY{o}{=}\PY{n}{y\PYZus{}lables}\PY{p}{,}\PY{n}{test\PYZus{}size}\PY{o}{=}\PY{l+m+mf}{0.20}\PY{p}{)}
         \PY{n+nb}{print} \PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{Train and test data split}\PY{l+s+s2}{\PYZdq{}}\PY{p}{)}
         \PY{n+nb}{print}\PY{p}{(}\PY{n}{X\PYZus{}train}\PY{o}{.}\PY{n}{shape}\PY{p}{,}\PY{n}{X\PYZus{}test}\PY{o}{.}\PY{n}{shape}\PY{p}{,}\PY{n}{y\PYZus{}train}\PY{o}{.}\PY{n}{shape}\PY{p}{,}\PY{n}{y\PYZus{}test}\PY{o}{.}\PY{n}{shape}\PY{p}{)}
\end{Verbatim}


    \begin{Verbatim}[commandchars=\\\{\}]
Train and test data split
(47504, 80) (11877, 80) (47504,) (11877,)

    \end{Verbatim}

    \begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}21}]:} \PY{n}{parameters} \PY{o}{=} \PY{p}{\PYZob{}}
             \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{n\PYZus{}estimators}\PY{l+s+s1}{\PYZsq{}}      \PY{p}{:} \PY{p}{[}\PY{l+m+mi}{320}\PY{p}{,}\PY{l+m+mi}{330}\PY{p}{,}\PY{l+m+mi}{340}\PY{p}{]}\PY{p}{,}
             \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{max\PYZus{}depth}\PY{l+s+s1}{\PYZsq{}}         \PY{p}{:} \PY{p}{[}\PY{l+m+mi}{8}\PY{p}{,} \PY{l+m+mi}{9}\PY{p}{,} \PY{l+m+mi}{10}\PY{p}{,} \PY{l+m+mi}{11}\PY{p}{,} \PY{l+m+mi}{12}\PY{p}{]}\PY{p}{,}
             \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{random\PYZus{}state}\PY{l+s+s1}{\PYZsq{}}      \PY{p}{:} \PY{p}{[}\PY{l+m+mi}{0}\PY{p}{]}
         \PY{p}{\PYZcb{}}
\end{Verbatim}


    \begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}22}]:} \PY{c+c1}{\PYZsh{} use random forest classifier}
         \PY{n}{clf} \PY{o}{=} \PY{n}{GridSearchCV}\PY{p}{(}\PY{n}{RandomForestClassifier}\PY{p}{(}\PY{p}{)}\PY{p}{,} \PY{n}{parameters}\PY{p}{,} \PY{n}{cv}\PY{o}{=}\PY{l+m+mi}{10}\PY{p}{,} \PY{n}{n\PYZus{}jobs}\PY{o}{=}\PY{o}{\PYZhy{}}\PY{l+m+mi}{1}\PY{p}{)}
         \PY{n}{clf}\PY{o}{.}\PY{n}{fit}\PY{p}{(}\PY{n}{X\PYZus{}train}\PY{p}{,}\PY{n}{y\PYZus{}train}\PY{p}{)}
\end{Verbatim}


\begin{Verbatim}[commandchars=\\\{\}]
{\color{outcolor}Out[{\color{outcolor}22}]:} GridSearchCV(cv=10, error\_score='raise',
                estimator=RandomForestClassifier(bootstrap=True, class\_weight=None, criterion='gini',
                     max\_depth=None, max\_features='auto', max\_leaf\_nodes=None,
                     min\_impurity\_decrease=0.0, min\_impurity\_split=None,
                     min\_samples\_leaf=1, min\_samples\_split=2,
                     min\_weight\_fraction\_leaf=0.0, n\_estimators=10, n\_jobs=1,
                     oob\_score=False, random\_state=None, verbose=0,
                     warm\_start=False),
                fit\_params=None, iid=True, n\_jobs=-1,
                param\_grid=\{'n\_estimators': [320, 330, 340], 'max\_depth': [8, 9, 10, 11, 12], 'random\_state': [0]\},
                pre\_dispatch='2*n\_jobs', refit=True, return\_train\_score='warn',
                scoring=None, verbose=0)
\end{Verbatim}
            
    \begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}23}]:} \PY{n}{predictions} \PY{o}{=} \PY{n}{clf}\PY{o}{.}\PY{n}{predict}\PY{p}{(}\PY{n}{X\PYZus{}test}\PY{p}{)}
         \PY{n+nb}{print}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{Overall accurracy }\PY{l+s+s2}{\PYZdq{}}\PY{p}{,}\PY{n}{accuracy\PYZus{}score}\PY{p}{(}\PY{n}{y\PYZus{}test}\PY{p}{,}\PY{n}{predictions}\PY{p}{)}\PY{p}{)}
\end{Verbatim}


    \begin{Verbatim}[commandchars=\\\{\}]
Overall accurracy  0.5221015407931295

    \end{Verbatim}

    Let define function to plot confusion matrix

    \begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}48}]:} \PY{k}{def} \PY{n+nf}{plot\PYZus{}confusion\PYZus{}matrix}\PY{p}{(}\PY{n}{y\PYZus{}true}\PY{p}{,} \PY{n}{y\PYZus{}pred}\PY{p}{,} \PY{n}{classes}\PY{p}{,}
                                   \PY{n}{normalize}\PY{o}{=}\PY{k+kc}{False}\PY{p}{,}
                                   \PY{n}{title}\PY{o}{=}\PY{k+kc}{None}\PY{p}{,}
                                   \PY{n}{cmap}\PY{o}{=}\PY{n}{plt}\PY{o}{.}\PY{n}{cm}\PY{o}{.}\PY{n}{Blues}\PY{p}{)}\PY{p}{:}
             \PY{l+s+sd}{\PYZdq{}\PYZdq{}\PYZdq{}}
         \PY{l+s+sd}{    This function prints and plots the confusion matrix.}
         \PY{l+s+sd}{    Normalization can be applied by setting `normalize=True`.}
         \PY{l+s+sd}{    \PYZdq{}\PYZdq{}\PYZdq{}}
             \PY{k}{if} \PY{o+ow}{not} \PY{n}{title}\PY{p}{:}
                 \PY{k}{if} \PY{n}{normalize}\PY{p}{:}
                     \PY{n}{title} \PY{o}{=} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{Normalized confusion matrix}\PY{l+s+s1}{\PYZsq{}}
                 \PY{k}{else}\PY{p}{:}
                     \PY{n}{title} \PY{o}{=} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{Confusion matrix, without normalization}\PY{l+s+s1}{\PYZsq{}}
         
             \PY{c+c1}{\PYZsh{} Compute confusion matrix}
             \PY{n}{cm} \PY{o}{=} \PY{n}{confusion\PYZus{}matrix}\PY{p}{(}\PY{n}{y\PYZus{}true}\PY{p}{,} \PY{n}{y\PYZus{}pred}\PY{p}{)}
             \PY{c+c1}{\PYZsh{} Only use the labels that appear in the data}
             \PY{c+c1}{\PYZsh{}classes = [1,2,3,4,5,6,7,8]\PYZsh{}classes[unique\PYZus{}labels(y\PYZus{}true, y\PYZus{}pred)]}
             \PY{k}{if} \PY{n}{normalize}\PY{p}{:}
                 \PY{n}{cm} \PY{o}{=} \PY{n}{cm}\PY{o}{.}\PY{n}{astype}\PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{float}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)} \PY{o}{/} \PY{n}{cm}\PY{o}{.}\PY{n}{sum}\PY{p}{(}\PY{n}{axis}\PY{o}{=}\PY{l+m+mi}{1}\PY{p}{)}\PY{p}{[}\PY{p}{:}\PY{p}{,} \PY{n}{np}\PY{o}{.}\PY{n}{newaxis}\PY{p}{]}
                 \PY{n+nb}{print}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{Normalized confusion matrix}\PY{l+s+s2}{\PYZdq{}}\PY{p}{)}
             \PY{k}{else}\PY{p}{:}
                 \PY{n+nb}{print}\PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{Confusion matrix, without normalization}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
         
             \PY{n+nb}{print}\PY{p}{(}\PY{n}{cm}\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}{im} \PY{o}{=} \PY{n}{ax}\PY{o}{.}\PY{n}{imshow}\PY{p}{(}\PY{n}{cm}\PY{p}{,} \PY{n}{interpolation}\PY{o}{=}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{nearest}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{n}{cmap}\PY{o}{=}\PY{n}{cmap}\PY{p}{)}
             \PY{n}{ax}\PY{o}{.}\PY{n}{figure}\PY{o}{.}\PY{n}{colorbar}\PY{p}{(}\PY{n}{im}\PY{p}{,} \PY{n}{ax}\PY{o}{=}\PY{n}{ax}\PY{p}{)}
             \PY{c+c1}{\PYZsh{} We want to show all ticks...}
             \PY{n}{ax}\PY{o}{.}\PY{n}{set}\PY{p}{(}\PY{n}{xticks}\PY{o}{=}\PY{n}{np}\PY{o}{.}\PY{n}{arange}\PY{p}{(}\PY{n}{cm}\PY{o}{.}\PY{n}{shape}\PY{p}{[}\PY{l+m+mi}{1}\PY{p}{]}\PY{p}{)}\PY{p}{,}
                    \PY{n}{yticks}\PY{o}{=}\PY{n}{np}\PY{o}{.}\PY{n}{arange}\PY{p}{(}\PY{n}{cm}\PY{o}{.}\PY{n}{shape}\PY{p}{[}\PY{l+m+mi}{0}\PY{p}{]}\PY{p}{)}\PY{p}{,}
                    \PY{c+c1}{\PYZsh{} ... and label them with the respective list entries}
                    \PY{n}{xticklabels}\PY{o}{=}\PY{n}{classes}\PY{p}{,} \PY{n}{yticklabels}\PY{o}{=}\PY{n}{classes}\PY{p}{,}
                    \PY{n}{title}\PY{o}{=}\PY{n}{title}\PY{p}{,}
                    \PY{n}{ylabel}\PY{o}{=}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{True label}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,}
                    \PY{n}{xlabel}\PY{o}{=}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{Predicted label}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
         
             \PY{c+c1}{\PYZsh{} Rotate the tick labels and set their alignment.}
             \PY{n}{plt}\PY{o}{.}\PY{n}{setp}\PY{p}{(}\PY{n}{ax}\PY{o}{.}\PY{n}{get\PYZus{}xticklabels}\PY{p}{(}\PY{p}{)}\PY{p}{,} \PY{n}{rotation}\PY{o}{=}\PY{l+m+mi}{45}\PY{p}{,} \PY{n}{ha}\PY{o}{=}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{right}\PY{l+s+s2}{\PYZdq{}}\PY{p}{,}
                      \PY{n}{rotation\PYZus{}mode}\PY{o}{=}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{anchor}\PY{l+s+s2}{\PYZdq{}}\PY{p}{)}
         
             \PY{c+c1}{\PYZsh{} Loop over data dimensions and create text annotations.}
             \PY{n}{fmt} \PY{o}{=} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{.2f}\PY{l+s+s1}{\PYZsq{}} \PY{k}{if} \PY{n}{normalize} \PY{k}{else} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{d}\PY{l+s+s1}{\PYZsq{}}
             \PY{n}{thresh} \PY{o}{=} \PY{n}{cm}\PY{o}{.}\PY{n}{max}\PY{p}{(}\PY{p}{)} \PY{o}{/} \PY{l+m+mf}{2.}
             \PY{k}{for} \PY{n}{i} \PY{o+ow}{in} \PY{n+nb}{range}\PY{p}{(}\PY{n}{cm}\PY{o}{.}\PY{n}{shape}\PY{p}{[}\PY{l+m+mi}{0}\PY{p}{]}\PY{p}{)}\PY{p}{:}
                 \PY{k}{for} \PY{n}{j} \PY{o+ow}{in} \PY{n+nb}{range}\PY{p}{(}\PY{n}{cm}\PY{o}{.}\PY{n}{shape}\PY{p}{[}\PY{l+m+mi}{1}\PY{p}{]}\PY{p}{)}\PY{p}{:}
                     \PY{n}{ax}\PY{o}{.}\PY{n}{text}\PY{p}{(}\PY{n}{j}\PY{p}{,} \PY{n}{i}\PY{p}{,} \PY{n+nb}{format}\PY{p}{(}\PY{n}{cm}\PY{p}{[}\PY{n}{i}\PY{p}{,} \PY{n}{j}\PY{p}{]}\PY{p}{,} \PY{n}{fmt}\PY{p}{)}\PY{p}{,}
                             \PY{n}{ha}\PY{o}{=}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{center}\PY{l+s+s2}{\PYZdq{}}\PY{p}{,} \PY{n}{va}\PY{o}{=}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{center}\PY{l+s+s2}{\PYZdq{}}\PY{p}{,}
                             \PY{n}{color}\PY{o}{=}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{white}\PY{l+s+s2}{\PYZdq{}} \PY{k}{if} \PY{n}{cm}\PY{p}{[}\PY{n}{i}\PY{p}{,} \PY{n}{j}\PY{p}{]} \PY{o}{\PYZgt{}} \PY{n}{thresh} \PY{k}{else} \PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{black}\PY{l+s+s2}{\PYZdq{}}\PY{p}{)}
             \PY{n}{fig}\PY{o}{.}\PY{n}{tight\PYZus{}layout}\PY{p}{(}\PY{p}{)}
             \PY{k}{return} \PY{n}{ax}
\end{Verbatim}


    \begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}68}]:} 
\end{Verbatim}


\begin{Verbatim}[commandchars=\\\{\}]
{\color{outcolor}Out[{\color{outcolor}68}]:} array([1, 2, 3, 4, 5, 6, 7, 8])
\end{Verbatim}
            
    \begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}69}]:} \PY{c+c1}{\PYZsh{} Plot non\PYZhy{}normalized confusion matrix}
         \PY{n}{plot\PYZus{}confusion\PYZus{}matrix}\PY{p}{(}\PY{n}{y\PYZus{}test}\PY{p}{,} \PY{n}{predictions}\PY{p}{,} \PY{n}{classes}\PY{o}{=}\PY{n}{np}\PY{o}{.}\PY{n}{sort}\PY{p}{(}\PY{n}{y\PYZus{}train}\PY{o}{.}\PY{n}{unique}\PY{p}{(}\PY{p}{)}\PY{p}{)}\PY{p}{,}
                               \PY{n}{title}\PY{o}{=}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{Confusion matrix, without normalization \PYZhy{} Random Forest classifier}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
\end{Verbatim}


    \begin{Verbatim}[commandchars=\\\{\}]
Confusion matrix, without normalization
[[ 197  155    0    0   88  332  100  369]
 [  84  319    0    0  142  334  110  321]
 [   3   11    0    0   57   94    4   34]
 [   4    0    0    0    0   91   11  180]
 [  24   90    2    0  544  252   37  137]
 [  67   65    0    0   96 1077  198  744]
 [  14    2    0    0    8  432  465  685]
 [   3    0    0    0    3  225   68 3599]]

    \end{Verbatim}

\begin{Verbatim}[commandchars=\\\{\}]
{\color{outcolor}Out[{\color{outcolor}69}]:} <matplotlib.axes.\_subplots.AxesSubplot at 0x7fa1abc00a58>
\end{Verbatim}
            
    \begin{center}
    \adjustimage{max size={0.9\linewidth}{0.9\paperheight}}{output_39_2.png}
    \end{center}
    { \hspace*{\fill} \\}
    
    \begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}61}]:} \PY{n+nb}{print}\PY{p}{(}\PY{n}{classification\PYZus{}report}\PY{p}{(}\PY{n}{y\PYZus{}test}\PY{p}{,} \PY{n}{predictions}\PY{p}{,} \PY{n}{target\PYZus{}names}\PY{o}{=}\PY{p}{[}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{1}\PY{l+s+s2}{\PYZdq{}}\PY{p}{,}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{2}\PY{l+s+s2}{\PYZdq{}}\PY{p}{,}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{3}\PY{l+s+s2}{\PYZdq{}}\PY{p}{,}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{4}\PY{l+s+s2}{\PYZdq{}}\PY{p}{,}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{5}\PY{l+s+s2}{\PYZdq{}}\PY{p}{,}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{6}\PY{l+s+s2}{\PYZdq{}}\PY{p}{,}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{7}\PY{l+s+s2}{\PYZdq{}}\PY{p}{,}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{8}\PY{l+s+s2}{\PYZdq{}}\PY{p}{]}\PY{p}{)}\PY{p}{)}
\end{Verbatim}


    \begin{Verbatim}[commandchars=\\\{\}]
             precision    recall  f1-score   support

          1       0.50      0.16      0.24      1241
          2       0.50      0.24      0.33      1310
          3       0.00      0.00      0.00       203
          4       0.00      0.00      0.00       286
          5       0.58      0.50      0.54      1086
          6       0.38      0.48      0.42      2247
          7       0.47      0.29      0.36      1606
          8       0.59      0.92      0.72      3898

avg / total       0.49      0.52      0.48     11877


    \end{Verbatim}

    \begin{Verbatim}[commandchars=\\\{\}]
/home/narendra/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.
  'precision', 'predicted', average, warn\_for)

    \end{Verbatim}

    The above statistics value not much impressive though.

\begin{verbatim}
- If you look at class 3 and 4 has 0 precision 
- Model is not able to classify those classes.
\end{verbatim}

    \begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}24}]:} \PY{n}{xgb\PYZus{}model} \PY{o}{=} \PY{n}{xgb}\PY{o}{.}\PY{n}{XGBClassifier}\PY{p}{(}\PY{n}{objective}\PY{o}{=}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{multi:softprob}\PY{l+s+s2}{\PYZdq{}}\PY{p}{,} \PY{n}{random\PYZus{}state}\PY{o}{=}\PY{l+m+mi}{42}\PY{p}{)}
\end{Verbatim}


    \begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}25}]:} \PY{n}{xgb\PYZus{}model}\PY{o}{.}\PY{n}{fit}\PY{p}{(}\PY{n}{X\PYZus{}train}\PY{p}{,}\PY{n}{y\PYZus{}train}\PY{p}{)}
\end{Verbatim}


\begin{Verbatim}[commandchars=\\\{\}]
{\color{outcolor}Out[{\color{outcolor}25}]:} XGBClassifier(base\_score=0.5, booster='gbtree', colsample\_bylevel=1,
                colsample\_bytree=1, gamma=0, learning\_rate=0.1, max\_delta\_step=0,
                max\_depth=3, min\_child\_weight=1, missing=None, n\_estimators=100,
                n\_jobs=1, nthread=None, objective='multi:softprob', random\_state=42,
                reg\_alpha=0, reg\_lambda=1, scale\_pos\_weight=1, seed=None,
                silent=True, subsample=1)
\end{Verbatim}
            
    \begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}37}]:} \PY{n}{y\PYZus{}pred} \PY{o}{=} \PY{n}{xgb\PYZus{}model}\PY{o}{.}\PY{n}{predict}\PY{p}{(}\PY{n}{X\PYZus{}test}\PY{p}{)}
         \PY{n+nb}{print}\PY{p}{(}\PY{n}{accuracy\PYZus{}score}\PY{p}{(}\PY{n}{y\PYZus{}test}\PY{p}{,}\PY{n}{y\PYZus{}pred}\PY{p}{)}\PY{p}{)}
         \PY{n+nb}{print}\PY{p}{(}\PY{n}{confusion\PYZus{}matrix}\PY{p}{(}\PY{n}{y\PYZus{}test}\PY{p}{,} \PY{n}{y\PYZus{}pred}\PY{p}{,} \PY{n}{y\PYZus{}train}\PY{o}{.}\PY{n}{unique}\PY{p}{(}\PY{p}{)}\PY{p}{)}\PY{p}{)}
\end{Verbatim}


    \begin{Verbatim}[commandchars=\\\{\}]
0.5276584996211164
[[3465  114  304    5    5    3    2    0]
 [ 610  554  407    1    8   25    1    0]
 [ 613  254 1123   71  117   67    2    0]
 [ 265  124  334  300  172  113    2    0]
 [ 124   49  206   94  582   28    0    3]
 [ 330  111  309  144  111  234    0    2]
 [ 155    9  112    0    0    5    5    0]
 [  25    3   95    9   62    5    0    4]]

    \end{Verbatim}

    \begin{Verbatim}[commandchars=\\\{\}]
/home/narendra/anaconda3/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.
  if diff:

    \end{Verbatim}

    \begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}47}]:} \PY{c+c1}{\PYZsh{} Plot non\PYZhy{}normalized confusion matrix}
         \PY{n}{plot\PYZus{}confusion\PYZus{}matrix}\PY{p}{(}\PY{n}{y\PYZus{}test}\PY{p}{,} \PY{n}{y\PYZus{}pred}\PY{p}{,} \PY{n}{classes}\PY{o}{=}\PY{n}{y\PYZus{}train}\PY{o}{.}\PY{n}{unique}\PY{p}{(}\PY{p}{)}\PY{p}{,}
                               \PY{n}{title}\PY{o}{=}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{Confusion matrix, without normalization}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
\end{Verbatim}


    \begin{Verbatim}[commandchars=\\\{\}]
Confusion matrix, without normalization
[[ 234  144    2    0  111  309  111  330]
 [ 113  300    0    2  172  334  124  265]
 [   5    9    4    0   62   95    3   25]
 [   5    0    0    5    0  112    9  155]
 [  28   94    3    0  582  206   49  124]
 [  67   71    0    2  117 1123  254  613]
 [  25    1    0    1    8  407  554  610]
 [   3    5    0    2    5  304  114 3465]]

    \end{Verbatim}

\begin{Verbatim}[commandchars=\\\{\}]
{\color{outcolor}Out[{\color{outcolor}47}]:} <matplotlib.axes.\_subplots.AxesSubplot at 0x7fa19b0bf6d8>
\end{Verbatim}
            
    \begin{center}
    \adjustimage{max size={0.9\linewidth}{0.9\paperheight}}{output_45_2.png}
    \end{center}
    { \hspace*{\fill} \\}
    
    \begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}63}]:} \PY{n+nb}{print}\PY{p}{(}\PY{n}{classification\PYZus{}report}\PY{p}{(}\PY{n}{y\PYZus{}test}\PY{p}{,} \PY{n}{y\PYZus{}pred}\PY{p}{,} \PY{n}{target\PYZus{}names}\PY{o}{=}\PY{p}{[}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{1}\PY{l+s+s2}{\PYZdq{}}\PY{p}{,}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{2}\PY{l+s+s2}{\PYZdq{}}\PY{p}{,}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{3}\PY{l+s+s2}{\PYZdq{}}\PY{p}{,}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{4}\PY{l+s+s2}{\PYZdq{}}\PY{p}{,}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{5}\PY{l+s+s2}{\PYZdq{}}\PY{p}{,}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{6}\PY{l+s+s2}{\PYZdq{}}\PY{p}{,}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{7}\PY{l+s+s2}{\PYZdq{}}\PY{p}{,}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{8}\PY{l+s+s2}{\PYZdq{}}\PY{p}{]}\PY{p}{)}\PY{p}{)}
\end{Verbatim}


    \begin{Verbatim}[commandchars=\\\{\}]
             precision    recall  f1-score   support

          1       0.49      0.19      0.27      1241
          2       0.48      0.23      0.31      1310
          3       0.44      0.02      0.04       203
          4       0.42      0.02      0.03       286
          5       0.55      0.54      0.54      1086
          6       0.39      0.50      0.44      2247
          7       0.45      0.34      0.39      1606
          8       0.62      0.89      0.73      3898

avg / total       0.51      0.53      0.49     11877


    \end{Verbatim}

    Even though this model accurracy is close to same as previous. But,
statistics value tells us compartively good as previous model.

\begin{verbatim}
- Class 3 and 4 has been able to identify from the model
- Overall precision and recall just increased
\end{verbatim}

So, We can deploy this model as Rest API to test

    \begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}70}]:} \PY{c+c1}{\PYZsh{} Save model to pkl file }
         
         \PY{k+kn}{import} \PY{n+nn}{pickle}
         \PY{c+c1}{\PYZsh{} Dump the trained decision tree classifier with Pickle}
         \PY{n}{random\PYZus{}forest\PYZus{}pkl\PYZus{}filename} \PY{o}{=} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{random\PYZus{}forest\PYZus{}classifier\PYZus{}gridcv.pkl}\PY{l+s+s1}{\PYZsq{}}
         \PY{c+c1}{\PYZsh{} Open the file to save as pkl file}
         \PY{n}{random\PYZus{}forest\PYZus{}pkl} \PY{o}{=} \PY{n+nb}{open}\PY{p}{(}\PY{n}{random\PYZus{}forest\PYZus{}pkl\PYZus{}filename}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{wb}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
         \PY{n}{pickle}\PY{o}{.}\PY{n}{dump}\PY{p}{(}\PY{n}{clf}\PY{p}{,} \PY{n}{random\PYZus{}forest\PYZus{}pkl}\PY{p}{)}
         \PY{c+c1}{\PYZsh{} Close the pickle instances}
         \PY{n}{random\PYZus{}forest\PYZus{}pkl}\PY{o}{.}\PY{n}{close}\PY{p}{(}\PY{p}{)}
\end{Verbatim}


    \begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}71}]:} \PY{c+c1}{\PYZsh{} Dump the trained decision tree classifier with Pickle}
         \PY{n}{xgboost\PYZus{}classifier\PYZus{}pkl\PYZus{}filename} \PY{o}{=} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{xgboost\PYZus{}classifier.pkl}\PY{l+s+s1}{\PYZsq{}}
         \PY{c+c1}{\PYZsh{} Open the file to save as pkl file}
         \PY{n}{xgboost\PYZus{}classifier\PYZus{}model\PYZus{}pkl} \PY{o}{=} \PY{n+nb}{open}\PY{p}{(}\PY{n}{xgboost\PYZus{}classifier\PYZus{}pkl\PYZus{}filename}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{wb}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
         \PY{n}{pickle}\PY{o}{.}\PY{n}{dump}\PY{p}{(}\PY{n}{xgb\PYZus{}model}\PY{p}{,} \PY{n}{xgboost\PYZus{}classifier\PYZus{}model\PYZus{}pkl}\PY{p}{)}
         \PY{c+c1}{\PYZsh{} Close the pickle instances}
         \PY{n}{xgboost\PYZus{}classifier\PYZus{}model\PYZus{}pkl}\PY{o}{.}\PY{n}{close}\PY{p}{(}\PY{p}{)}
\end{Verbatim}


     \#\# Understanding Model with LIME Explainer

Once we build the machine learning model is not our job done.

Machine learning models are black box models. How do we trust our model?

Oh yes. There is Local Interpretable Model-agnostic Explanations (LIME)
technique

The technique attempts to understand the model by perturbing the input
of data samples and understanding how the predictions change.


    % Add a bibliography block to the postdoc
    
    
    
    \end{document}