• Kotha Amitabh
• Kopanathi Sai Kumar
• Singarapu Sahithi
• Deva Rajashekar

Campus placement performs an essential position in each academic group in supporting college students to achieve their goals. Managing placement and education information in any large corporation is tough because the scholar quantity is high; in such a situation differentiation and classification on different categories will become tedious. All college students dream to get employment to provide by their hands earlier than they depart their college. A placement probability predictor allows college students to possess a thought regarding wherever they stand and what to be performed to obtain a decent placement. The foremost objective of this paper is to investigate the preceding year’s student’s historic data and predict placement opportunities of present day college students and aids in the growth of the placement percent of the institutions. Here we use machine learning ensemble algorithms, namely bagging algorithms. These algorithms independently predict the results, and we then examine the efficiency of the algorithms, which is predicated on the dataset. The result acquired from this can assist the students to better understand their weak regions to work upon. Working in those regions will permit college students to obtain a better number of placements in a college institution.

Machine Learning, Prediction, Training, Ensemble learning, Bagging, Educational data mining.

"Campus Placement Prediction System Using Bagging Approach", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 8, page no.b306-b311, August-2021, Available :http://www.jetir.org/papers/JETIR2108168.pdf

"Campus Placement Prediction System Using Bagging Approach", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 8, page no. ppb306-b311, August-2021, Available at : http://www.jetir.org/papers/JETIR2108168.pdf