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\title{Analyzing Political Opinions and Prediction of Voting Patterns in the US Election with Data Mining Approaches}
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\begin{document}

             \author[1]{Md. Sohel  Ahammed}

             \author[2]{Md. Nahid  Newaz}

             \author[3]{Arunavo  Dey}

             \affil[1]{  Bangladesh University of Business and Technology (BUBT)}

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\date{\small \em Received: 16 December 2018 Accepted: 5 January 2019 Published: 15 January 2019}

\maketitle


\begin{abstract}
        


Data is the precious resources. Data contains the useful patterns which provide the crucial information about the prediction of what is going to be happened in the next. In this paper, we aim to identify the political preferences and tendency of the US populations using classification and data mining techniques. To provide the usefulness of proposed model we analyze the electoral data sets in US election obtained from the official website which contains the information about 1984 United States Congressional voting records. This paper shows the classification techniques that can be used to predicting voting patterns in the US House of Representatives and shows the close correspondence between election results and extracted opinion. This paper also shows the political support of the voters and prediction the characteristics of the voter with their political tendency.

\end{abstract}


\keywords{}

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\let\tabcellsep& 	 	 		 
\section[{Introduction}]{Introduction}\par
lection is important because it allows the electorate to decide who's going to make decision for their country for the next couple of years. But this election can be forecasted with a reasonable accuracy. Forecasting election using small polling system is very common approach but this often do not produce reasonable accuracy.\par
Data mining is a process that examines large preexisting databases in order to generate new information. There are also various works that uses data mining approaches to predict various types of results such as weather forecasting, sports result prediction, future buying decision prediction, etc. But there are very few works that uses data mining approaches to predict voting patterns on election. In this work, we uses data mining approaches to predict voting patterns in USA election. For this study we uses data preprocessing for removing missing value, identifying best attributes and removing duplicate values. We split the dataset into training datasets and test datasets. Then we applied four algorithms Tree J48, Naïve Bayes Classifier, Trees Random Forest and Rules zero or Classifier for predicting voting patterns and also compares the results of those model and finds the best models from those models. 
\section[{II.}]{II.} 
\section[{Related Works}]{Related Works}\par
Gregg R. Murray and Anthony Scime uses data mining approaches to predict individual voting behavior including abstention with the intent of segmenting the electorate in useful and meaningful ways \hyperref[b0]{[1]}. Gregg R. Murray, Chris Riley, and Anthony Scime, in another study, uses iterative expert data mining to build a likely voter model for presidential election in USA \hyperref[b1]{[2]}. Bae, Jung-Hwan, Ji-Eun, Song, Min uses Twitter data for predicting trends in South Korea Presidential Election by Text Mining techniques \hyperref[b2]{[3]}. Tariq Mahmood, TasmiyahIqbal, Farnaz Amin, WaheedaLohanna, Atika Mustafa uses Twitter data to predict 2013 Pakistan Election winner \hyperref[b3]{[4]}. 
\section[{III.}]{III.}\par
Data Preprocessing  
\section[{Experimental Methodology}]{Experimental Methodology}\par
We used 4 algorithms and 8 models (2 models for each algorithm) to predict the voting pattern in the US election. We then analyse and compare the results of those models and finds the best models with most accuracy. The algorithms which are applied for generating models are given below.\par
i. Trees J48 ii.\par
Naive   From the above table, the best model was identified based on the value of the parameters accuracy, precision, recall, sensitivity, and specificity. The higher the value of accuracy, precision, recall and (sensitivity> specificity), the higher the rank. 
\section[{VI.}]{VI.} 
\section[{Conclusion}]{Conclusion}\par
Though there are lot of techniques and methods for predicting voting patterns, data mining is the most efficient and effective methods in this fields. In our study, we clearly found that among various data mining algorithms Trees Random Forest performs the best with 98.17\% accuracy. In future, we will expand our research in most recent dataset for validating our findings with recent ones. \begin{figure}[htbp]
\noindent\textbf{1} \par 
\begin{longtable}{P{0.0009941520467836257\textwidth}P{0.7525730994152047\textwidth}P{0.09643274853801169\textwidth}}
\tabcellsep \tabcellsep Year 2 019\\
\tabcellsep \tabcellsep 37\\
E\tabcellsep I. Handling with Missing Attributes: In this section, we uses the technique of replacing missing values with mean, median or mode. We uses this approach because it is better approach when the dataset is small and it can prevent data loss. II. Removing Duplicates: We used WEKA tools for removing duplicates from the datasets. We used Remove Duplicates () function in WEKA for removing duplicates. III. Best Attributes Selection: We used Gain Ratio Attribute Eval which evaluates the worth of an attribute by measuring the gain ratio with respect to the class and Ranker which Ranks attributes by their individual evaluations. The top 12 attributes from the whole dataset according to rank from the attributes are presented in Figure 1.\tabcellsep Volume XIX Issue II Version I ( ) C Global Journal of Computer Science and Technology\\
\tabcellsep © 2019 Global Journals\tabcellsep \end{longtable} \par
 
\caption{\label{tab_0}Table 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{2} \par 
\begin{longtable}{P{0.5777056277056276\textwidth}P{0.24101731601731602\textwidth}P{0.03127705627705628\textwidth}}
iii.\tabcellsep \multicolumn{2}{l}{Trees RandomForest}\\
iv.\tabcellsep \multicolumn{2}{l}{Rules ZeroOR Classifier}\\
\multicolumn{2}{l}{a) Trees J48}\tabcellsep \\
\tabcellsep \multicolumn{2}{l}{We used Model 1 for training dataset and Model}\\
\multicolumn{3}{l}{2 for test dataset evaluation.}\\
\multicolumn{3}{l}{Evaluation of Model 1 Training dataset is given below:}\\
Bayes classifier\tabcellsep \tabcellsep \\
Correctly Classified Instances\tabcellsep 421\tabcellsep 96.7816\%\\
Incorrectly Classified Instances\tabcellsep 14\tabcellsep 3.2184\%\\
Kappa statistics\tabcellsep 0.9324\tabcellsep \\
Mean Absolute Error\tabcellsep 0.0582\tabcellsep \\
Root Mean Squared Error\tabcellsep 0.1706\tabcellsep \\
Relative Absolute Error\tabcellsep 12.2709\%\tabcellsep \\
Root Relative Squared Error\tabcellsep 35.0341\%\tabcellsep \\
Total Number of Instances\tabcellsep 435\tabcellsep \end{longtable} \par
 
\caption{\label{tab_1}Table 2 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{3} \par 
\begin{longtable}{P{0.6081690140845071\textwidth}P{0.028732394366197178\textwidth}P{0.03352112676056338\textwidth}P{0.028732394366197178\textwidth}P{0.03591549295774648\textwidth}P{0.01915492957746479\textwidth}P{0.03352112676056338\textwidth}P{0.03112676056338028\textwidth}P{0.03112676056338028\textwidth}}
TP Rate\tabcellsep FP Rate\tabcellsep Precision\tabcellsep Recal-l\tabcellsep F measures\tabcellsep MCC\tabcellsep Rock Area\tabcellsep PRC area\tabcellsep Class\\
0.966\tabcellsep 0.030\tabcellsep 0.981\tabcellsep 0.966\tabcellsep 0.974\tabcellsep 0.933\tabcellsep 0.975\tabcellsep 0.973\tabcellsep democrat\\
\multicolumn{6}{l}{Sensitivity \& Specificity Calculation for Training Data (Model 1)}\tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{4}{l}{Formula of Sensitivity = TP/ (TP+FN)}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{4}{l}{Formula of Specificity = TN/ (TN+FP)}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{6}{l}{So Sensitivity = TP Rate = 0.966 \& Specificity = 0.030}\tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{5}{l}{Evaluation of Model 2 test dataset is given below}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_2}Table 3 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{4} \par 
\begin{longtable}{P{0.6525590551181102\textwidth}P{0.1405511811023622\textwidth}P{0.056889763779527563\textwidth}}
Correctly Classified Instances\tabcellsep 105\tabcellsep 96.3303\%\\
Incorrectly Classified Instances\tabcellsep 4\tabcellsep 3.6697\%\\
Kappa statistics\tabcellsep 0.921\tabcellsep \\
Mean Absolute Error\tabcellsep 0.0619\tabcellsep \\
Root Mean Squared Error\tabcellsep 0.1894\tabcellsep \\
Relative Absolute Error\tabcellsep 13.2259\%\tabcellsep \\
Root Relative Squared Error\tabcellsep 39.4312\%\tabcellsep \\
Total Number of Instances\tabcellsep 109\tabcellsep \end{longtable} \par
 
\caption{\label{tab_3}Table 4 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{6} \par 
\begin{longtable}{P{0.6525590551181102\textwidth}P{0.1405511811023622\textwidth}P{0.056889763779527563\textwidth}}
Correctly Classified Instances\tabcellsep 395\tabcellsep 90.8046\%\\
Incorrectly Classified Instances\tabcellsep 40\tabcellsep 9.1954\%\\
Kappa statistics\tabcellsep 0.8094\tabcellsep \\
Mean Absolute Error\tabcellsep 0.0965\tabcellsep \\
Root Mean Squared Error\tabcellsep 0.2921\tabcellsep \\
Relative Absolute Error\tabcellsep 20.34\%\tabcellsep \\
Root Relative Squared Error\tabcellsep 59.9863\%\tabcellsep \\
Total Number of Instances\tabcellsep 435\tabcellsep \end{longtable} \par
 
\caption{\label{tab_4}Table 6 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{7} \par 
\begin{longtable}{P{0.546643109540636\textwidth}P{0.06607773851590105\textwidth}P{0.015017667844522969\textwidth}P{0.03303886925795053\textwidth}P{0.045053003533568906\textwidth}P{0.02402826855123675\textwidth}P{0.042049469964664306\textwidth}P{0.03904593639575972\textwidth}P{0.03904593639575972\textwidth}}
TP Rate\tabcellsep \multicolumn{2}{l}{FP Rate Precision}\tabcellsep Recall\tabcellsep F measures\tabcellsep MCC\tabcellsep Rock Area\tabcellsep PRC area\tabcellsep Class\\
0.895\tabcellsep 0.071\tabcellsep 0.952\tabcellsep 0.895\tabcellsep 0.923\tabcellsep 0.812\tabcellsep 0.972\tabcellsep 0.983\tabcellsep democrat\\
\multicolumn{6}{l}{Sensitivity \& Specificity Calculation for Training Data (Model 3)}\tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{6}{l}{So Sensitivity = TP Rate = 0.895 \& Specificity = 0.071}\tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{5}{l}{Evaluation of Model 4 test dataset is given below}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_5}Table 7 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{8} \par 
\begin{longtable}{P{0.6499999999999999\textwidth}P{0.14333333333333334\textwidth}P{0.056666666666666664\textwidth}}
Correctly Classified Instances\tabcellsep 99\tabcellsep 90.8257\%\\
Incorrectly Classified Instances\tabcellsep 10\tabcellsep 9.1743\%\\
Kappa statistics\tabcellsep 0.8069\tabcellsep \\
Mean Absolute Error\tabcellsep 0.0978\tabcellsep \\
Root Mean Squared Error\tabcellsep 0.2934\tabcellsep \\
Relative Absolute Error\tabcellsep 20.9083\%\tabcellsep \\
Root Relative Squared Error\tabcellsep 61.0861\%\tabcellsep \\
Total Number of Instances\tabcellsep 109\tabcellsep \end{longtable} \par
 
\caption{\label{tab_6}Table 8 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{9} \par 
\begin{longtable}{P{0.5647651006711409\textwidth}P{0.03422818791946308\textwidth}P{0.03993288590604027\textwidth}P{0.031375838926174494\textwidth}P{0.04278523489932885\textwidth}P{0.022818791946308724\textwidth}P{0.03993288590604027\textwidth}P{0.03708053691275168\textwidth}P{0.03708053691275168\textwidth}}
TP Rate\tabcellsep FP Rate\tabcellsep Precision\tabcellsep Recall\tabcellsep F measures\tabcellsep MCC\tabcellsep Rock Area\tabcellsep PRC area\tabcellsep Class\\
0.886\tabcellsep 0.051\tabcellsep 0.969\tabcellsep 0.886\tabcellsep 0.925\tabcellsep 0.812\tabcellsep 0.969\tabcellsep 0.984\tabcellsep democrat\\
\multicolumn{5}{l}{Sensitivity \& Specificity Calculation for Model 4}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{6}{l}{Sensitivity = TP Rate = 0.886 \& Specificity = 0.051}\tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{2}{l}{c) Trees Random Forest}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{6}{l}{Evaluation on Training Data set: Trees Random Forest algorithm}\tabcellsep \tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_7}Table 9 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{10} \par 
\begin{longtable}{P{0.5376826722338205\textwidth}P{0.07453027139874739\textwidth}P{0.23778705636743214\textwidth}}
\multicolumn{3}{l}{Analyzing Political Opinions and Prediction of Voting Patterns in the US Election with Data}\\
\multicolumn{2}{l}{Mining Approaches}\tabcellsep \\
\tabcellsep \tabcellsep Year 2 019\\
\tabcellsep \tabcellsep 39\\
\tabcellsep \tabcellsep Volume XIX Issue II Version I\\
\tabcellsep \tabcellsep ( ) C\\
\tabcellsep \tabcellsep Global Journal of Computer Science and Technology\\
Correctly Classified Instances\tabcellsep 427\tabcellsep 98.1609\%\\
Incorrectly Classified Instances\tabcellsep 8\tabcellsep 1.8391\%\\
Kappa statistics\tabcellsep 0.9613\tabcellsep \\
Mean Absolute Error\tabcellsep 0.0376\tabcellsep \\
Root Mean Squared Error\tabcellsep 0.1222\tabcellsep \\
Relative Absolute Error\tabcellsep 7.9365\%\tabcellsep \\
Root Relative Squared Error\tabcellsep 25.0915\%\tabcellsep \\
Total Number of Instances\tabcellsep 435\tabcellsep \\
\tabcellsep \tabcellsep © 2019 Global Journals\end{longtable} \par
 
\caption{\label{tab_8}Table 10 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{5} \par 
\begin{longtable}{P{0.5696808510638298\textwidth}P{0.015070921985815604\textwidth}P{0.042198581560283686\textwidth}P{0.033156028368794326\textwidth}P{0.045212765957446804\textwidth}P{0.024113475177304965\textwidth}P{0.042198581560283686\textwidth}P{0.03918439716312056\textwidth}P{0.03918439716312056\textwidth}}
\multicolumn{2}{l}{TP Rate FP Rate}\tabcellsep Precision\tabcellsep Recall\tabcellsep F measures\tabcellsep MCC\tabcellsep Rock Area\tabcellsep PRC area\tabcellsep Class\\
0.981\tabcellsep 0.018\tabcellsep 0.989\tabcellsep 0.981\tabcellsep 0.985\tabcellsep 0.961\tabcellsep 0.998\tabcellsep 0.999\tabcellsep democrat\\
\multicolumn{6}{l}{Sensitivity \& Specificity Calculation for Training Data (Model 5)}\tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{6}{l}{So Sensitivity = TP Rate = 0.981\& Specificity = 0.018}\tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{5}{l}{Evaluation of Model 6 test dataset is given below}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_9}Table 5 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{12} \par 
\begin{longtable}{P{0.6525590551181102\textwidth}P{0.1405511811023622\textwidth}P{0.056889763779527563\textwidth}}
Correctly Classified Instances\tabcellsep 106\tabcellsep 97.2477\%\\
Incorrectly Classified Instances\tabcellsep 03\tabcellsep 2.7523\%\\
Kappa statistics\tabcellsep 0.9404\tabcellsep \\
Mean Absolute Error\tabcellsep 0.0432\tabcellsep \\
Root Mean Squared Error\tabcellsep 0.1508\tabcellsep \\
Relative Absolute Error\tabcellsep 9.2437\%\tabcellsep \\
Root Relative Squared Error\tabcellsep 31.408\%\tabcellsep \\
Total Number of Instances\tabcellsep 109\tabcellsep \end{longtable} \par
 
\caption{\label{tab_10}Table 12 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{13} \par 
\begin{longtable}{P{0.570358306188925\textwidth}P{0.060912052117263846\textwidth}P{0.013843648208469055\textwidth}P{0.030456026058631923\textwidth}P{0.041530944625407164\textwidth}P{0.02214983713355049\textwidth}P{0.03876221498371336\textwidth}P{0.03599348534201954\textwidth}P{0.03599348534201954\textwidth}}
TP Rate\tabcellsep \multicolumn{2}{l}{FP Rate Precision}\tabcellsep Recall\tabcellsep F measures\tabcellsep MCC\tabcellsep Rock Area\tabcellsep PRC area\tabcellsep Class\\
0.971\tabcellsep 0.026\tabcellsep 0.986\tabcellsep 0.971\tabcellsep 0.978\tabcellsep 0.941\tabcellsep 0.996\tabcellsep 0.997\tabcellsep democrat\\
\multicolumn{5}{l}{Sensitivity \& Specificity Calculation for Model 6}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{6}{l}{Sensitivity = TP Rate = 0.971 \& Specificity = 0.026}\tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{2}{l}{d) Rules ZeroOR Classifier}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{7}{l}{Evaluation on Training Data set: Rules ZeroOR Classifier algorithm}\tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_11}Table 13 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{14} \par 
\begin{longtable}{P{0.676530612244898\textwidth}P{0.11102040816326532\textwidth}P{0.06244897959183674\textwidth}}
Correctly Classified Instances\tabcellsep 267\tabcellsep 61.3793\%\\
Incorrectly Classified Instances\tabcellsep 168\tabcellsep 38.6207\%\\
Kappa statistics\tabcellsep 0\tabcellsep \\
Mean Absolute Error\tabcellsep 0.4742\tabcellsep \\
Root Mean Squared Error\tabcellsep 0.4869\tabcellsep \\
Relative Absolute Error\tabcellsep 100\%\tabcellsep \\
Root Relative Squared Error\tabcellsep 100\%\tabcellsep \\
Total Number of Instances\tabcellsep 435\tabcellsep \end{longtable} \par
 
\caption{\label{tab_12}Table 14 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{15} \par 
\begin{longtable}{P{0.5582089552238806\textwidth}P{0.031716417910447756\textwidth}P{0.044402985074626866\textwidth}P{0.028544776119402985\textwidth}P{0.04757462686567164\textwidth}P{0.012686567164179104\textwidth}P{0.044402985074626866\textwidth}P{0.04123134328358209\textwidth}P{0.04123134328358209\textwidth}}
TP Rate\tabcellsep FP Rate\tabcellsep Precision\tabcellsep Recall\tabcellsep F measures\tabcellsep MCC\tabcellsep Rock Area\tabcellsep PRC area\tabcellsep Class\\
1.0\tabcellsep 1.0\tabcellsep 0.614\tabcellsep 1.0\tabcellsep 0.761\tabcellsep -\tabcellsep 0.500\tabcellsep 0.614\tabcellsep democrat\\
\multicolumn{6}{l}{Sensitivity \& Specificity Calculation for Training Data (Model 7)}\tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{5}{l}{So Sensitivity = TP Rate = 1.0 \& Specificity = 1.0}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{5}{l}{Evaluation of Model 8 test dataset is given below}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_13}Table 15 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{16} \par 
\begin{longtable}{P{0.37676659528907924\textwidth}P{0.38586723768736614\textwidth}P{0.05460385438972162\textwidth}P{0.032762312633832974\textwidth}}
\multicolumn{4}{l}{Analyzing Political Opinions and Prediction of Voting Patterns in the US Election with Data}\\
\tabcellsep \multicolumn{2}{l}{Mining Approaches}\tabcellsep \\
Year 2 019\tabcellsep \tabcellsep \tabcellsep \\
40\tabcellsep \tabcellsep \tabcellsep \\
Volume XIX Issue II Version I\tabcellsep \tabcellsep \tabcellsep \\
)\tabcellsep \tabcellsep \tabcellsep \\
( C\tabcellsep \tabcellsep \tabcellsep \\
Global Journal of Computer Science and Technology\tabcellsep Correctly Classified Instances\tabcellsep 70\tabcellsep 64.2202\%\\
\tabcellsep Incorrectly Classified Instances\tabcellsep 39\tabcellsep 35.7798\%\\
\tabcellsep Kappa statistics\tabcellsep 0\tabcellsep \\
\tabcellsep Mean Absolute Error\tabcellsep 0.4678\tabcellsep \\
\tabcellsep Root Mean Squared Error\tabcellsep 0.4802\tabcellsep \\
\tabcellsep Relative Absolute Error\tabcellsep 100\%\tabcellsep \\
\tabcellsep Root Relative Squared Error\tabcellsep 100\%\tabcellsep \\
\tabcellsep Total Number of Instances\tabcellsep 109\tabcellsep \\
© 2019 Global Journals\tabcellsep \tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_14}Table 16 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{11} \par 
\begin{longtable}{P{0.5459615384615385\textwidth}P{0.06538461538461539\textwidth}P{0.016346153846153847\textwidth}P{0.029423076923076923\textwidth}P{0.04903846153846154\textwidth}P{0.013076923076923078\textwidth}P{0.04576923076923077\textwidth}P{0.0425\textwidth}P{0.0425\textwidth}}
TP Rate\tabcellsep \multicolumn{2}{l}{FP Rate Precision}\tabcellsep Recall\tabcellsep F measures\tabcellsep MCC\tabcellsep Rock Area\tabcellsep PRC area\tabcellsep Class\\
1.0\tabcellsep 1.0\tabcellsep 0.642\tabcellsep 1.0\tabcellsep 0.782\tabcellsep -\tabcellsep 0.500\tabcellsep 0.642\tabcellsep democrat\\
\multicolumn{5}{l}{Sensitivity \& Specificity Calculation for Model 8}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{5}{l}{Sensitivity = TP Rate = 1.0\& Specificity = 1.0}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{9}{l}{V. Revaluation of the Best, Second Best and Third Best Model}\end{longtable} \par
 
\caption{\label{tab_15}Table 11 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{18} \par 
\begin{longtable}{}
\end{longtable} \par
 
\caption{\label{tab_16}Table 18 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.11551724137931035\textwidth}P{0.1482758620689655\textwidth}P{0.12758620689655173\textwidth}P{0.05517241379310345\textwidth}P{0.13620689655172413\textwidth}P{0.05517241379310345\textwidth}P{0.04827586206896552\textwidth}P{0.16379310344827586\textwidth}}
Model Model 1 Model 2\tabcellsep Accuracy 96.7816\% 96.3303\%\tabcellsep \multicolumn{2}{l}{precision recall 0.981 0.966 0.985 0.957}\tabcellsep \multicolumn{2}{l}{sensitivity specificity 0.966 0.030 0.957 0.026}\tabcellsep Rank 2 nd best 3 rd best\tabcellsep Year 2 019\\
Model 3\tabcellsep 90.8046\%\tabcellsep 0.952\tabcellsep 0.895\tabcellsep 0.895\tabcellsep 0.071\tabcellsep \tabcellsep 41\\
Model 4 Model 5 Model 6 Model 7 Model 8\tabcellsep 90.8257\% 98.1609\% 97.2477\% 61.3793\% 64.2202\%\tabcellsep 0.969 0.989 0.986 0.614 0.642\tabcellsep 0.886 0.985 0.978 1.00 1.00\tabcellsep 0.886 0.981 0.971 1.00 1.00\tabcellsep 0.051 0.018 0.026 1.00 1.00\tabcellsep Best\tabcellsep Volume XIX Issue II Version I\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep ( ) C\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep Global Journal of Computer Science and Technology\end{longtable} \par
 
\caption{\label{tab_17}}\end{figure}
 			\footnote{© 2019 Global Journals} 			\footnote{Table 17: Model-8 Precision, Recall, F-measure rate according to Democrat class} 		 		\backmatter  			  				\begin{bibitemlist}{1}
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\end{document}
