Student Placement Prediction and Skill Recommendation System using Machine Learning Algorithms

The Student Placement Prediction and Skill Recommendation System is an innovative application designed to assist students in enhancing their employability and securing suitable job placements. In today's competitive job market, students often face challenges in understanding their strengths, identifying required skills, and effectively positioning themselves for job opportunities. This research work aims to address these challenges by leveraging machine learning algorithms to predict a student's likelihood of placement and recommend skills to improve their employability. The dataset used in this proposed system comprises various attributes such as academic performance (CGPA, marks), internship experiences, projects undertaken, certifications earned, aptitude test scores, soft skills ratings, and extracurricular activities. Leveraging this dataset, machine learning models, particularly the Random Forest algorithm, were employed to predict the probability of a student getting placed and the potential offers from different companies based on their academic, skill, and experiential profiles. Moreover, the system also offers personalized skill recommendations to students based on their current profile. For students who are not yet placed, the system generates skill recommendations using cosine similarity vectorization techniques, analyzing various attributes, including academic achievements, internship experiences, projects, certifications, aptitude scores, and soft skills. Recommendations are tailored to bridge skill gaps and enhance the chances of securing suitable job placements. The research work demonstrates the effectiveness of machine learning techniques in predicting placement outcomes and recommending personalized skill improvements, thus providing valuable insights for students to strategize and improve their employability prospects. The system serves as a useful tool for students seeking career guidance and advancement in the competitive job market.