Study of Employment Salary Forecast using KNN Algorithm

DOI

10.2991/msbda-19.2019.26How to use a DOI?

Keywords

KNN, Prediction, Salary

Abstract

In the market analysis of the 2018 national recruitment data and the employment situation in a city, it was found that the current college students have serious imbalances in their actual ability and employment salary expectations. In order to predict the salary of employment, doing this research. For the Java back-end engineer position, there are the following seven influencing factors: computer network scores, Java programming basic results, database principle results, java web scores, framework programming scores, Linux scores, education. The above seven factors affect the salary of the java back-end engineers, using different levels of salary as a marker to build a sample set, using the KNN algorithm to build a salary level prediction classifier. The conclusion is as follows: When K=7, the classifier has better prediction effect with an accuracy of 88.10%. This model has better predictive effect when the degree is undergraduate.

Copyright

© 2019, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Junyu Zhang
AU  - Jinyong Cheng
PY  - 2019/08
DA  - 2019/08
TI  - Study of Employment Salary Forecast using KNN Algorithm
BT  - Proceedings of the 2019 International Conference on Modeling, Simulation and Big Data Analysis (MSBDA 2019)
PB  - Atlantis Press
SP  - 166
EP  - 170
SN  - 2352-538X
UR  - https://doi.org/10.2991/msbda-19.2019.26
DO  - 10.2991/msbda-19.2019.26
ID  - Zhang2019/08
ER  -