Campus Placement Analysis: Predictive Insights with Machine Learning Models

The system is designed using machine learning to predict the placement of students which is being trained on the historical features such as academic achievements, demographics and extracurricular activities. The model is designed to help educational institutions and students gauge placement likelihood by utilizing attributes like academic performance, field of study, work experience, and specialization. As a result, the system implements multiple data preprocessing methods, such as missing data imputation, and label encoding for the purpose of converting categorical variables to numerical format. Utilizing performance assessment metrics such as precision and recall, a decision tree classifier is trained to predict whether or not a student will be placed in a company. In addition, the system contains visualization tools which help to illustrate the relationship among factors such as specialization and salary and gives a holistic view of how placement is affected by the different attributes. Streamlit is used to construct a nice interface and users are able to upload new data for prediction and analysis in a real time setting. Aiming to fuse both data-driven approach and an easy user interface, this paper is prepared to assist in placement forecasts thus aiding career guidance counsellors and students to use the available job opportunities more effectively. The system could, however, be developed further by including parameters relevant to the industry so that it can be applicable to a number of fields and professions.