Applying SMOTE with Decision Tree Classifier for Campus Placement Prediction

Vikas Rattan, Shikha Sharma, R. Mittal, Varun Malik
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引用次数: 2

Abstract

It is the dream of every student to attain an excellent career with decent remuneration. It will be an additional benefit if they get a high-profile job during their campus placement before they leave. The campus placement activities with the right resources at the right time and with minimal cost are of the greatest benefit to undergraduates regardless of any stream viz. engineering, business, medical, or sciences. The scope of the paper is to prepare an automated model that predicts or analyzes the probability of students getting positioned in a company by salient parameters like academic performance in terms of CGPA, test marks, or other professional degree evaluations and another non-academic parameter such as gender. For this intention, one of the classification algorithms named Decision Tree and up sampling technique “Synthetic Minority Oversampling Technique” had been used. The outcome of this analysis shall lend a hand to the organization to propose an approach that enhances the performance of students to get a better job in the pre-final years.
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基于决策树分类器的SMOTE校园布局预测
获得一份报酬体面的好工作是每个学生的梦想。如果他们在离开之前在校园实习期间得到一份引人注目的工作,这将是一个额外的好处。无论是工程、商业、医学还是科学专业,在合适的时间、合适的资源和最低成本的校园安置活动对本科生来说都是最大的好处。本文的范围是准备一个自动化模型,通过CGPA,考试分数或其他专业学位评估等重要参数和性别等非学术参数来预测或分析学生在公司中定位的概率。为此,采用了一种分类算法“决策树”和上采样技术“合成少数派过采样技术”。这一分析的结果将有助于组织提出一种方法,提高学生的表现,在最后几年获得更好的工作。
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