Machine Learning Approach for Predicting the Placement Status of Students

Ensuring the successful placement of students is a crucial objective for educational institutions as it directly impacts their reputation and annual admissions. To achieve this, institutions put in significant effort to strengthen their placement department. Any support in this area can have a positive impact on both the institution and the students. This study aims to forecast the placement chances of current students by analysing data from the previous year. The proposed model utilizes an algorithm to achieve this. However, the challenge was to find a suitable classification algorithm that could provide maximum accuracy for our dataset. To address this, we equated our model by using existing classification algorithms such as Decision Tree (DT), KNN, SVM, Logistic Regression (LR), and Random Forest based on accuracy, precision, and recall. We obtained the True Positive, False Positive, True Negative and False Negative values for each algorithm to determine accuracy of the model. Our results show that the proposed algorithm outpaces the other algorithms significantly.