Placement Prediction using Various Machine Learning Techniques

Based on the needs of the business, a placement predictor calculates the likelihood of placing a student there. The predictor considers a number of factors while determining the student’s proficiency. These factors are determined in part from assessments made inside the placement management system, and in part from surveys sent out to universities. Using all of this information, the predictor can accurately forecast whether or not a student will be hired by a business. The predictor is trained using historical data from previous students. By doing so, it helps in identifying the most suitable candidates for various company roles, thus improving the overall placement rate. In order to identify the most effective classification algorithm for our dataset, we faced the challenge of selecting one that would deliver maximum accuracy. We recognized that the performance of algorithms varies based on the specific problem and dataset they are tasked with. Consequently, we opted to evaluate four distinct algorithms: Multi-class methods such as KNN, SVM, Logistic Regression, and Random Forest. We evaluate the algorithms’ precision in light of our issue and data collection. we aimed to identify the most suitable algorithm for our predictor in the placement management system.