Treffer: Applications of data analytics in placements using machine learning.

The data of the various organizations how placements are done and the factors affecting the placements for students are analyzed. This kind of data is very useful for college management to train the students. Thus, college succeeds in developing and improving its organization in a better way and also develops in all aspects of placements and improves the various standards of college in terms of admissions too. In this paper, we are going to collect the placement information of students and their status whether they are placed or not. It is also compared with other college placements and thus helping the Organization to improve its existing processes in order to give a healthy and good competition. This consists of various tasks like gender who got placed the most, in which companies did the students got placed (core or IT), programming languages required for placement, minimum CGPA required, No. of backlogs for students that affects their placements. Demographic wise placements, which board students have placed in campus and many more. Based on the analysis the organization can take necessary steps and help students get their desired company. Data Analytics are performed using Python programming language. Data manipulation, analysis, and visualization is done. Anaconda software is used as a coding platform which provides a Jupiter Notebook and also Spyder which supports many libraries for data analytics and preferred by most of the data analysts. [ABSTRACT FROM AUTHOR]

Copyright of AIP Conference Proceedings is the property of American Institute of Physics and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)