In this research, a hybrid predictive model was proposed for the decision-making process. Predictive models can be built through the use of Machine Learning using different classifiers/algorithms to predict results as well as provide recommendations to the management for the student placement in appropriate programs of study and to the students for the adoption of appropriate study strategies and habits. A predictive model through Machine Learning was used in conjunction with probabilistic classification and clustering of specific segments within the data to increase the rate of success for an improved decision-making process. Variance in the actual and predicted results with respect to the difference in success rates can assist the decision makers in student placement. An aggregate of all the processes with the help of Cobb-Douglas utility function leads to a Hybrid Predictive Model, which combined two different phases for better placement, an increased rate of success, and an overall improved decision-making process. The introduction of Cobb-Douglas utility function can further streamline the process to check any external factors that may have influenced the predicted results. This model can also be applied in the corporate sector to maximize any program or individual efficiency by placing and training individuals with respect to individuals/employees aptitude, background, and personality type and learning styles. This type of model can also be applied in the same capacity in different organizations to maximize the program efficiency, placement, and employees’ capabilities.
