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Depression Detection Using Machine Learning - Best Final Year Engineering Project for Computer Science


In today's generation, depression has emerged as a significant concern, with an increasing number of individuals affected by it. While some individuals are aware of their depression, others may be unaware or hesitant to admit it. However, the widespread use of social media platforms has provided a unique opportunity to identify signs of depression through user posts. Machine learning algorithms have been employed in various studies to detect depression based on social media data. By analyzing readily available social media data, researchers can classify and differentiate depressive and non-depressive content using machine learning techniques. This project aims to detect depression in users using their social media data. The Twitter data will be fed into two separate machine learning algorithms: the Naive Bayes classifier and the hybrid model NBTree. The accuracy of both algorithms will be compared to determine the most effective approach for depression detection. The results indicate that both algorithms perform equally well, demonstrating the same level of accuracy.

This final year engineering project, based on an IEEE paper, offers an exceptional opportunity for computer science students. We are proud to provide the following components:

  1. Complete documentation support
  2. Implementation of working hardware and software in the student's environment
  3. Tailored classes to support project requirements

This project is available for purchase at the most valuable price, making it an ideal choice for students looking to undertake a highly impactful and relevant final year engineering project in computer science.

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