<![CDATA[Depression is a serious mental condition that affects both individuals and society. The Random Forest algorithm will be used in this project to create a depression categorization model based on work pressure and lifestyle characteristics. The Depression Professional Dataset was analyzed using the Knowledge Discovery in Databases (KDD) approach, which included 2,054 data points with 11 factors such as age, work pressure, working hours, sleep habits, and family mental health history. The results showed that the Random Forest algorithm classified depressive states with 91% accuracy. The investigation found that age was the most important predictor, followed by work pressure, working hours, and job happiness. In contrast, gender and family mental health history had a smaller impact. This study demonstrates that the risk factors for depression are multifaceted, including demography and work pressure. These findings can be used to develop mental health preventive and intervention methods in the workplace. Future model development can include new factors, such as socioeconomic status, to produce a more comprehensive study]]>
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