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A Review: Placement Prediction using Machine Learning


Shivaji Waghmode , S.B.Patil College of Engineering,Indapur, Pune-413106; Salve B. S, S.B.Patil College of Engineering,Indapur, Pune-413106; Amol Kharade, S.B.Patil College of Engineering,Indapur, Pune-413106; Manoj Kharade, S.B.Patil College of Engineering,Indapur, Pune-413106; Sajjan Sorate, S.B.Patil College of Engineering,Indapur, Pune-413106


Machine Learning, Placement, K-Nearest Neighbours [KNN], Naive Bayes


Engineering students are skeptical about what they want to pursue after graduation. Students studying in engineering colleges feel the exigency to know where they stand in comparison to others, and what kind of placement they would get. When a student enters final year, the T&P offices comes but they are of no use to a student planning for future studies. Prediction about the student’s performance is an integral part of an education system, as the overall growth of the student is directly proportional to the success rate of the students in their examinations and extra-curricular activities. Therefore, there are many situations where the performance of the student needs to be predicted, for example, in identifying weak performing students and taking actions for their betterment. The students having no platform to check current position and build their strengths. To achieve a better accuracy and a system that learns with every wrong prediction it has made, we intend to use Machine Learning and here we use two different machine learning classification algorithms, namely Naive Bayes Classifier and KNearest Neighbors [KNN] algorithm which will cause a continuous accuracy growth. We want to develop such system which student can use to know there current status using this web application. To ensure effective results, the model will be trained on a real data set and a vast number of qualitative as well as quantitative parameters will be considered.

Other Details

Paper ID: IJSRDV7I90206
Published in: Volume : 7, Issue : 9
Publication Date: 01/12/2019
Page(s): 253-255

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