Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
Modern civilization is plagued by mental health problems, which calls for creative ways to support early identification and care. In this paper, a machine learning-based mental health treatment recommendation system is presented. Based on employment and demographic data, the system uses a Gradient Boosting Classifier to forecast a person's risk of needing therapy. Comprehensive data preprocessing, feature engineering, and pipeline-based model training are important procedures. The suggested system's 79.7% prediction accuracy shows that it has the ability to help both individuals and organizations effectively handle mental health issues.
Keywords:
ML, randomforest, classification, mental health detection, predictive model, flask, data preprocessing.
Cite Article:
"Mental Health Treatment Recommendation System Using Gradient Boost ", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 4, page no.a346-a349, April-2025, Available :http://www.ijrti.org/papers/IJRTI2504050.pdf
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000327
ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator