Quantitative analysis of historical data for prediction of job salary in India - A case study

Abstract

In this paper an attempt has been made to develop a quantitative approach for predicting the factors that affect the salary of an individual. The Aspiring Minds’ Employability Outcomes (AMEO-2015) dataset consisting of Aspiring Minds’ Computer Adaptive Test (AMCAT) score along with job seeker personal and employment details of Indian students has been considered for the study. It has been observed from the analysis that B.Tech is the most preferred course in India with Electronics and Communication engineering stream as the most preferred branch with highest package around 13 lakhs per annum and average package around 5 lakhs but with 50% of engineers are underemployed. It is observed that there is no linear relation between college score and salary, there are many other factors which play a role in deciding the different amount of salary for students who have same college scores. In order to analyze the effect of more than one independent variable on dependent variable multiple linear regression models has been applied. The model has been used on the training data to predict dependent variables and to extract features with highest impact on salary prediction. It has been observed that quant and logical scores are the best predictors for salary. The developed model has root relative squared error as 82.3056 %. The study concludes that efforts are required for developing the skills with amendment in the educational policies and course curriculum.