Abstract:
Nowadays suicide has become a serious crime. People commit suicide for many reasons. Due to the advancement of social media, people post various types of posts before
committing suicide. Understanding the environmental risk factors that affect suicide
thoughts and behavior throughout time will be greatly aided by this study. We will collect
various types of data from various online platforms and identify them with the help of
machine learning models. We use some algorithms to find out the best accuracy. To find
out the best accurate results we implement classifier such as Naive Bayes, Support Vector
Machine (SVM), Logistic Regression, Decision Tree Classification, Random Forest and
Gradient Boosting Classifier. The models were evaluated based on their accuracy, precision, recall and F-1 score. Naive Bayes had the lowest accuracy at 73%, while
Support Vector Machine (SVM) secured the top positions with 92.32% accuracy. This
research highlights the importance of integrating advanced machine learning techniques
into mental health care to facilitate early intervention and support for at-risk adolescents. By leveraging technology, we can enhance the effectiveness of suicide prevention
strategies and ultimately save lives.
