Author
Listed:
- Gagana Lingshetti
- Sarthak Pawar
- Sakshi Kumari
- Vardhan Deokate
- Komal Munde
Abstract
A campus placement prediction system is to help students improve prediction system is developed to calculate their results, and academic performance and the possibility of a student getting jobs in a also develop another soft skill that will help company through campus placements. The them to maximize their chance of getting model takes many parameters that can be placed. Such a study will help the faculties of used to get an idea about the skill level of the the college to train the students accordingly student. While some data are taken from the and improve the placement department of college level like academic performance, their institutions. This will give an idea about obtained from tests conducted in the placement management system. Combining these data points, the model is to accurately predict if the student will or will not be placed in a company. Also, Data from past year students are used for training the model. We are using educational data mining by which we can get real past year's student data of that specific college. This will help the machine learning model to be more effective about the predictions of that particular college. CGPA, pointers, attendance etc. others are how students are doing and will ensure their institution can satisfy the needs of recruiters. For this supervised machine learning especially logistic regression is better, Logistic model designing plays a key role in getting correct predictions. This process includes the selection of tuples for training data and their pre-known outcome often known as real data.
Suggested Citation
Gagana Lingshetti & Sarthak Pawar & Sakshi Kumari & Vardhan Deokate & Komal Munde, 2025.
"Placement Prediction Website using Machine Learning,"
International Journal of Scientific Research in Computer Science, Engineering and Information Technology, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 11(6), pages 07-11, December.
Handle:
RePEc:jbh:ijsrcs:v11:y2025:i6:id:1759
DOI: 10.32628/CSEIT251117142
Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT251117142
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