## 3 Methodology
### Analytics and Decision-Making Model
#### 3.3.3 Predictive Model Development  
After the feature extraction process, training and test features were obtained from the training and test datasets, respectively. Then, to train the model, two supervised regression machine learning algorithms were used: multi-layer perceptron neural network (MLP) and support vector machine (SVM). So, effective parameters needed to be tuned or set for the best regression result of the test dataset while training using hyperparameter tuning. The selection of two regression-based algorithms was based on the advantages; SVM works comparably well when there is an understandable margin of dissociation between classes, and it is also more productive in high-dimensional spaces, while MLP has the ability to solve complex nonlinear problems and also handles large amounts of input data well with quick predictions after training.  
Support vector machine (SVM) is a supervised classification and regression method. SVM's main idea is shifting nonlinear data to a new space in which the data may be linearly separated by employing a hyper-plane that accurately separates the data by following two crucial conditions; because distinct classes of vectors have various aspects, the distances between the hyperplane and the vectors must be used. In Equation (1), assumption function \(h\) has the following definition:  
\[h(x_{i})=\begin{cases}+1&\text{if }w.x+b\geq 0\\ -1&\text{if }w.x+b<0\end{cases} \tag{1}\]  
Class \(+1\) is assigned to points above or on the hyperplane, whereas class \(-1\) is assigned to points below the hyperplane.  
An artificial neural network (ANN) with one or more hidden layers is known as a multi-layer perceptron neural network. A perceptron is a neural network consisting of just one neural model. It simulates high nonlinear functions, which are the foundation of deep learning neural networks. Equation (2) shows the degree of inaccuracy in an output node j in the \(n\)th data point (training example), which can be represented by:  
\[e_{j}(n)=d_{j}(n)-y_{j}(n) \tag{2}\]  
where \"\(d\)\" is the goal value and \"\(y\)\" is the perceptron's output value. The node weights can then be modified depending on adjustments that reduce the overall output error, as shown in Equation (3):  
\[\in(n)=\frac{1}{2}\sum_{j}e_{j}^{2}(n) \tag{3}\]  
The difficulty in selecting a set of ideal hyperparameters for a model learning algorithm is known as hyperparameter tuning or optimization. A hyperparameter is the value of a parameter that is used to influence the learning process. The grid search technique was utilized, which is the most basic form of hyperparameter tuning. We generated a model for each conceivable combination of all the hyperparameter values supplied, evaluated each model, and chose the architecture that delivered the best results using this method. Below is a description of the technique for hyperparameter-guided adjusting of the model:  
Step 1: Define a machine learning model;  
Step 2: For the selected approach, define the range of possible values for all hyperparameters;  
Step 3: Define a sampling mechanism for hyperparameter values;  
Step 4: Create a criterion for judging the model;