GitHub - Salma0-8/Youth-Suicide-Risk-Insights-ML-and-Visualization-: I leveraged Python libraries for data preprocessing and visualization. I applied machine learning models 'Random Forest and Logistic Regression' for risk prediction, with PCA and t-SNE for dimensionality reduction. SHAP were used for model interpretability, and metrics such as ROC AUC and F1 score ensured model performance evaluation.

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I leveraged Python libraries for data preprocessing and visualization. I applied machine learning models 'Random Forest and Logistic Regression' for risk prediction, with PCA and t-SNE for dimensionality reduction. SHAP were used for model interpretability, and metrics such as ROC AUC and F1 score ensured model performance evaluation.