PLACEMENT PREDICTION SYSTEM USING MACHINE LEARNING

Engineering students are not sure what they want to study after graduation. Students are confused by the many options offered by universities such as postgraduate admissions, and factors such as salaries and different jobs worsen the situation. There is no reliable platform that allows students to predict outcomes from the beginning of engineering and take action to bridge the gap and create a better future. Students studying in engineering faculties need to know where they stand compared to others and what kind of placement they will get. Training and workshops are available when students enter their final year, but these are not useful for students to plan their future studies. Student placement is one of the most important goals of the school. Schools work hard to accommodate students. The aim is to predict the current year’s student placement by analyzing the data students have collected from previous years. Prediction of student performance is an important part of the study because today all student development is directly related to the student’s success in tests and activities. Therefore, there are many situations where it is necessary to predict student performance, for example, identifying students who are not performing well and taking steps to improve them. There is no platform for girls to review their current work and highlight their strengths. . Currently, there are platforms that are not trained on real and complete data and cannot learn from error prediction, which reduces accuracy in the long run. Our goal is to create one. To ensure the results are acceptable, the model will be trained on real data and a large number of positive and negative results will be obtained. The model proposes an algorithm to estimate the model. Information is collected by the school where the prediction will be made, using the necessary information before the process or by examining the historical data of the previous year’s students, and the placement of the current students is predicted and helps to improve the placement percentage of the students. institute.