## 3 Preference Model
### Prediction Improvement  
To improve the efficiency of the prediction, EPOC number, number of hidden nodes, number of hidden layers and value of sigmoid function are all increased. There are four hypotheses, i.e., Hypothesis I: to improve efficiency of the prediction models by increasing the EPOC number, EPOC numbers are varied from 500 to 20,000. Hypothesis II: increasing the number of hidden nodes. The number of nodes is increased, i.e., 4 nodes to 18 nodes, 4 nodes to 32 nodes, 4 nodes to 50 nodes and 4 nodes to 64 nodes at EPOC 500-20,000. Hypothesis III: increasing the number of hidden layers. The number of hidden layers is increased, i.e., from one hidden layer to two hidden layers and from one hidden layer to three hidden layers at EPOC 500-20,000. Hypothesis IV: increasing the value of the sigmoid function. The value of the sigmoid function is increased, i.e., the value of the sigmoid function changes from 0 to 0.2, 0 to 0.4, 0 to 0.6, 0 to 0.8 and 0 to 1 at EPOC 500-20,000. After proving the hypothesis for model prediction, the final structure for prediction Model 1 is shown in Table 3 below. From the above table, NNs using the Pareto technique and NNs using cutting 50% were accurate at 100% with no change in EPOC, hidden nodes and hidden layers. NN by considering only the basic features before the change had an accuracy of 62%. After improved prediction by four hypotheses, the best result was 84% with 1 input layer and output layer, 3 hidden layers, 32 hidden nodes and 4 output nodes. NN with full factors before the change had a significantly higher accuracy rate of 95%. After improved prediction by four hypotheses, the best result was 99% with 1 input layer, 1 output layer and 1 hidden layer, 18 hidden nodes and 4 output nodes. The model was tested on the website \"preferenceandapprovalmodels.com\". If the prediction result is a leasing company, the model will show no reason. However, if the prediction result is another financial institute which is not a leasing company, the model will present the reasons and recommendations. Table 4 illustrates an example of the output. In this case, a commercial bank is selected. The model then shows the reason why a customer selected the commercial bank service. \begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Prediction Results**} \\  & **Accuracy (\%)** & **MSE** & **EPOC** \\ \hline NN by considering & & & \\ only the basic & 62 & 0.01806 & 100 \\ features & 95 & 0.01802 & 100 \\ NN with cutting & & & \\ factors by Pareto & 100 & 0.01316 & 100 \\ (80:20) & & & \\ NN with cutting & 100 & 0.02485 & 100 \\ factors by 50\% & & & \\ \hline \hline \end{tabular} Note: EPOC is the number of training cycles.  
\end{table} 
Table 2: Summarized models predicting the structure. \begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Input** & **Input** & **Output** & **Output** & **Hidden** & **Accuracy** & **EPOC** & **MSE** \\  & **Layer** & **Nodes** & **Layer** & **Nodes** & **Layer** & **Nodes** & **(\%)** & **EPOC** & **MSE** \\ \hline NN by considering only & & 1 & 8 & 1 & 4 & 3 & 32 & 84 & 3000 & 0.00494 \\ NN with full factors & 1 & 68 & 1 & 4 & 1 & 18 & 99 & 200 & 0.01738 \\ NN with cutting & 1 & 42 & 1 & 4 & 1 & 8 & 100 & 100 & 0.01316 \\ factors by Pareto (80:20) & & & & & & & & & \\ NN with cutting & 1 & 36 & 1 & 4 & 1 & 8 & 100 & 100 & 0.02485 \\ factors by 50\% & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of model predicting the results of selecting the services of financial institutions by using NN. Market mix factors, indicated by the model, influence how customers choose their commercial bank and leasing business factors. SWOT's core principle is a survey analysis of a situation based on two aspects. It is composed of internal and external situations (situation analysis) as follows. The internal situation is to analyze resources and competence within the organization in all aspects.