Depression Analysis using Machine Learning Based on Musical Habits
Suyoga Srinivas1, Naveen N Bhat2, Yashwanth Venkat Chandolu3

1Suyoga Srinivas, Department of Information Science, PES University ECC, Bangalore (Karnataka), India.

2Naveen N Bhat, Department of Computer Science, PES University ECC, Bangalore (Karnataka), India.

3Yashwanth Venkat Chandolu, Department of Computer Science, RNSIT, Bangalore (Karnataka), India.

Manuscript received on 04 December 2019 | Revised Manuscript received on 12 December 2019 | Manuscript Published on 31 December 2019 | PP: 229-231 | Volume-9 Issue-2S December 2019 | Retrieval Number: B10161292S19/2019©BEIESP | DOI: 10.35940/ijitee.B1016.1292S19

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Abstract: Depression has been a main cause of mental illness. Depression results in vital impairment in lifestyle. A significant reason for suicidal cerebration is observed to be depression. Music varies the intensity of emotional experience by captivating the neurotransmitters and brain anatomy, including the brain’s dopaminergic projections. The popularity of using Regression Models in data analysis in both research and industry has driven the development of an array of prediction models. It relies on independent variables and can provide the prediction for the dependent variable. The paper outlines the development of a Regression model to get the depression score of a person based on the music the user listens to. A regression model is used to predict the depression score depending upon the data obtained from a varied span of individuals and the genre of music they have listened to. We generate a suitable report based on the depression score. The doctor can then use the report to give the necessary treatment to the depressed patient. With our research, we have obtained variance and r2 score of over 0.95.

Keywords: Multivariate Linear Regression, Music, Principal Component Analysis, Support Vector Regression.
Scope of the Article: Machine Learning