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Depression detection usiong machine learning

Abstract:
In the contemporary generation, depression has become a severe issue, and the number of those affected by it is rising daily. However, some of them are able to admit that they are depressed, while others are unaware of it. However, the enormous development of social media is turning into a "diary" to share their emotional state. Using machine learning algorithms, many types of study have been done to identify sadness from user posts on social media. The researcher can determine whether or not people are experiencing depression by the data that is readily available on social media. Data may be correctly categorised into groups and separated into depressive and non-depressive data using machine learning algorithms. The study project under consideration tries to identify user depression using social media user data. The Nave Bayes classifier and the hybrid model NBTree are then fed the Twitter data in two separate ways. For the purpose of choosing the most effective algorithm to identify depression, the results will be compared based on the highest accuracy value. The outcomes reveal that both algorithms function equally by demonstrating the same level of accuracy. final year project is based on IEEE Paper.this will be one of the best Final year engineering project for computer science. Components that we will provide are.
1.complete documentation support
2.complete working hardware/software implemented in students environment
3.classes will held accordingly.

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