Measuring and Analyzing the Time Complexity of a Prediction Model in Different Scenarios

These days, almost every industry uses machine learning techniques. These techniques improve the accuracy of predicting the target output by using a wide range and velocity of data. The goal of each method is to quickly and accurately predict the target value. In this research, the execution time, which is the total time taken to predict the student’s grades, of the earlier proposed EMLRR model has been calculated. The model is based on an ensemble machine-learning technique: Stacking. Further, we have analyzed the time complexity of the model with other alternatives of stacking by choosing a variety of multiclassification models as meta-models. It has been observed that the proposed model has delivered an accuracy of up to 94% with an execution time of less than 3 seconds. This work uses various platforms, CPUs, and GPUs to analyze the execution time for two different datasets. Various student datasets have been tested to check the model’s efficiency in different scenarios. In addition, a comparative study has been done with other possible combinations of base models by increasing and decreasing the number of base models. The proposed prediction model uses the Stacking of four multiclass models to predict student performance with the best accuracy of up to 94% and 89% for two different student datasets.