Student's Placement Prediction using Machine Learning

Abstract— At academic institutions, student placement is critical. It is the decisive element in admission and the reputation of the school. To strengthen their placement department, all organizations attempt to improve their methods. In this essay, we hope to assess past student data, forecast job prospects for current students, and offer assistance in improving the institutions' placement rates. In this research, we present a method for predicting a student's placement status. We also anticipate the business to which the student will be sent based on data from previously placed students. Our method entails implementing two independent machine learning classification algorithms: The Random Forest Regressor and the K Nearest Neighbors [KNN] algorithm. We assess the performance of both algorithms by comparing their forecasting outcomes on the dataset. Using this model, a company's position cell could identify promising students and focus on developing both their technical and soft skills