The purpose of this study was to explore factors affecting job satisfaction and organizational commitment of employees using 7th wave of Human Capital Corporate Panel(HCCP) data. To do this, this study was to classify latent profiles based on job satisfaction and organizational commitment, and to apply four machine learing techniques(LASSO, Ridge, Randomforest and Support Vector Machine) to predict the identified latent profiles. As results, first, three different latent profiles were identified based on job satisfaction (middle job satisfaction, low job satisfaction and high job satisfaction) and three latent profiles were also identified based on organizational commitment(low organizational commitment, high organizational commitment, and middle organizational commitment). Second, Randomforest performed better than other techniques. Third. randomforest result revealed that main factors affecting job satisfaction were the annual earned income, year of birth, communication and trust relationship between the company and the worker, overtime work, stress and etc. For organizational commitment, communication and trust relationship, annual earned income, year of birth and overtime work were selected. Finally, based on the results, this study suggested implications to improve the employees’ job satisfaction and organizational commitment.