The purpose of this model is to help individuals gain insights into how their music experiences affect their mental health.
Among the various algorithms used, Random Forest stood out with its exceptional parameter-learning capabilities, achieving a remarkable precision rate of 100%. While there is a possibility of overfitting, it successfully distinguished adverse outcomes from neutral or positive ones.
To further enhance this project, we can focus on feature engineering to identify the most crucial variables and fine-tuning, particularly adjusting parameters like 'max_depth.'
I consider this a promising start.
