Salary Prediction via Sectoral Features in Turkey

Knowing the salary range of a position is beneficial for both job seekers and employers. This work examines the performance of different machine learning methods on salary estimation using industrial variables. The methods are applied to a dataset obtained from Turkey’s largest employment platform Kariyer.net. We perform various exploratory analyzes of the data, then use feature engineering techniques for improving the quality of the training data. The effect of the heavy-tailed distribution of salaries is mitigated with various response variable transformations. A timeliness standardization is performed using inflation rates as data from different time periods. Analyses and experiments show that standardization does not have a significant effect on the performance of the model. On the contrary, response variable transformation seems to have a significant effect. As for the models, we conclude that the XGBoost and the artificial neural networks achieve the highest success.