Salary Prediction For Head Hunter Jobs

Student: Natalya Kozemaslova

Educational Programme: Machine Learning and Data-Intensive Systems (Master)

Year of Graduation: 2024

As part of the presented research work, a review and comparative analysis of existing methods was carried out for solving a practical regression problem - predicting wages based on a job description from the aggregator site HeadHunter.ru. Data describing vacancies was obtained through parsing. During pre-processing, categorical and binary features are encoded using Ordinal and One-hot-encoding methods, text features are processed using NLP tools. Experiments were conducted with such classic machine learning models as Random Forest, SGDRegressor, CatBoost, XGBoost, LightGBM; BERT transformer architecture was applied. The work used metrics to assess the quality of the MAE and MAPE model predictions; during the experiments, the best model for solving the problem was determined.

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