Analysis of Depression Level for User Using Social Media Post

Depression is a disabling disorder that takes in a number of forms. Although there are several treatments options for depression, predicting whether or not a person is suicidal is challenging. As a result, a plethora of models for predicting depression in individuals have been created, with this article focusing on three of the most frequently used: WEKA classifiers and methods for machine learning Suicidal individuals are not only mentally unfit, but also physically unfit. It degrades people's quality of life. Additionally, depression would not have to be severe to have an effect on a person's life. We assessed the reliability of three critical approaches used in those tests. Following a thorough review of machine learning classifiers, feature reduction mechanisms, cross validation techniques, and risk factors, it was determined that the Bayes net Classifier was the most accurate and efficient approach for Percentage Split testing. Numerous data sets can be used to assess the predictive model's accuracy. To increase accuracy, additional techniques for predicting depression should be studied in the future. The words SVM, machine learning, and tweeter upgrade are included in this article.