MakerYuichi / MakerYuichi/Depression_detection_teens
machine leaning model
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- Dominant language
- HTML
- Stars
- 1
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- 0
- PR merge metrics
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Description
Hi everyone,
I’ve developed a Depression Detection App using machine learning to predict depression levels in individuals aged 13-25. The app currently uses algorithms like SVM, KNN, XGBoost, and others to classify depression into four categories (Low, Mild, Moderate, High).
I’m looking for Increased model accuracy using deep learning techniques
Contributor guide
No contributing guide indexed for this repository
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
No files, tests, dataset, evaluation metric, or model entry point are named. Start by locating the current SVM, KNN, and XGBoost implementation and its accuracy evaluation; done would require a deep-learning approach that improves the agreed accuracy measure for the four depression categories.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- machine-learning
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100