A big issue that isn’t stressed enough for adolescents and emerging adults is depression. Depression can affect you in various ways, whether it be physically, mentally, or socially. Professional and academic performance, personal relationships, and other factors that are important to long-term success are negatively impacted. In trying to help those that need help, we come across a major issue; identifying depression. Social media has become increasingly popular over the years, with many younger generations spending vast amounts of time on it. Detecting depression from a platform that adolescents and emerging adults frequently visit would contribute significantly to helping those suffering from it. In this study, the datasets of tweets were utilized to create multiple machine-learning models that would accurately detect signs of depression. Out of 4 different machine learning models, this research used the Random Forest Classifier model to achieve an accuracy of 96.52%.

Depression is a mood disorder that negatively affects performance in work and school and also ruins your social life and leads to risk taking behaviors that endangers peoples’ lives. Timely intervention and diagnosis can help identify and mitigate the effects of depression. In this study, I used a dataset formed by a survey to create multiple machine learning models in order to create a model that would accurately predict if someone had depression. Out of 4 different machine learning models I used, I was able to create a machine learning model using the Random Forest Classifier to achieve an accuracy of 90.48%.