## 4 Processes and Methods
### Technique Selection  
Two of the most important and broad categories of ML techniques are supervised learning and unsupervised learning. These are applied in different ways and involve different processes. Supervised learning teaches the model using a labeled dataset, meaning that the data fed into the model go hand in hand with the desired output for each example fed. This type of learning aims for the model to learn a mapping between the input and the output to predict the class labels of new, unseen data accurately. This learning approach is normally implemented on projects like classification, where the goal is to categorize data into classes preset by the user, and regression, where the goal is to predict the relationship or estimate between variables. Methods like LR, LAR, RR, and ENR are among the supervised learning mathematical methods used. These methods use statistical methods to find the best-fitting line or best-fitting equation representing the relationship of input features to the target variable.  
Unsupervised learning involves learning without the use of labeled responses in the training data. The aim is instead to identify patterns, relationships, or structures within the data. Common tasks associated with unsupervised learning include clustering (where the goal is to find groups of similar data) and dimensionality reduction (where the aim is to reduce the number of variables considered). Techniques such as K-means clustering, hierarchical clustering, and Principal Component Analysis (PCA) are popular unsupervised learning methods. Moreover, unsupervised learning does not have a target to make the prediction, which makes it very useful for exploratory data analysis, anomaly detection, and the gaining of insights into the underlying structure of the data.  
These two learning paradigms differ fundamentally in the availability or absence of labeled data and the nature of the learning objectives. Supervised learning depends on the labels of the training data, through which the model learns a direct mapping from the inputs to the outputs for making predictions. On the contrary, unsupervised learning seekshidden patterns without guidance from any labeled output, making it ideal for unearthing the intrinsic structure of the data and for tasks where labeling is impractical or improbable. Both techniques play a critical role in the field of machine learning and are selected as the situation deems fit, according to the given requirements and goals.  
Therefore, we have opted to use supervised learning techniques in this paper since the latter enables the relationship between input features and output labels to be accurately modeled for precise prediction. Specifically, the choice landed on LR, LAR, RR, and ENR. Training these models means that the methods were trained on a labeled dataset in which an example was presented with its known output in order for the models to learn the underlying patterns and their relationships. This approach to be followed is well suited for our study, since the models will be effective in making predictions of SPVS performance based on historical and real-time data.  
In addition, the measures of Residual Mean Squared Error (RMSE) and the coefficient of determination (R Square) have been computed and chosen as performance criteria in order to compare the fitting between the proposed models.