Suicide prediction using machine learning techniques and interactive visualization of suicide information

Effective approaches to raising community awareness regarding suicide cases are critical to lowering the suicide rate over time. Many people do not commonly recognize suicide facts and rates since the method of dissemination is unappealing. Non- interactive and difficult methods of disseminating suicide information may lower suicide awareness, thus increasing the suicide rate. Therefore, an interactive web dashboard that contains suicide information and the prediction of the suicide rate has been  developed by using Tableau Desktop software. The web dashboard of interactive visualization can attract users to read suicide information and learn more about suicide history. Apart from that, the focus of this study is to test the ability of machine  learning to perform prediction by using the regression models of supervised machine learning. Three Machine Learning algorithms namely Random Forest Regressor, Decision Tree Regressor and Support Vector Regressor were used to predict the suicide  rate. These  Three algorithms were compared to find the best model for this study. Random Forest Regressor outperformed the two machine learning algorithms with the highest R2 and lowest prediction error.