Nowadays, every students expectation is primarily shaped by the offerings of educational institutions. This research paper mainly aims to reveal the impact of a policy on campus placement data over time using some machine learning algorithms like the Support Vector Machine, Decision Tree, Random Forest, and Ada boost algorithms. As an instance, the study tries to estimate the effectiveness of the implemented intervention measures, opened techniques, and algorithms that can be adapted to predict the future scope. This predictive analysis has a greater significance to higher learning institutions not only for making the right choice in the development of skills but also enables the students to pay attention to important matters before embarking on a certain path of development and career planning. Mainly, this work analyzes the campus placement data using different machine learning algorithms and deploys using IBM cloud. The results state that Random Forest gives a high accuracy of nearly 91% when compared to other algorithms to analyze the placement data.