description for github repoMachine learning project predicting depression risk from demographic, academic/work, and lifestyle data using Random Forest, XGBoost, and CatBoost (93-94% accuracy).
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Jun 30, 2026 - Python
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depression-prediction
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Built a machine learning model to predict depression using psychological data. Trained and evaluated Logistic Regression, Extra Trees, and XGBoost models, achieving over 90% accuracy and 80% precision. Demonstrates ML’s potential in mental health diagnostics with reliable, data-driven predictions.
python machine-learning xgboost logistic-regression binary-classification smote streamlit imbalanced-learn depression-prediction
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Sep 6, 2026 - Jupyter Notebook
🧠 ML web app predicting student depression risks. Showcases a complete pipeline: baseline modeling, advanced data preprocessing, and optimized Logistic Regression.
python data-science machine-learning scikit-learn logistic-regression feature-engineering imbalanced-data ml-pipeline streamlit mental-health-tech student-wellbeing depression-prediction
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Apr 21, 2026 - Jupyter Notebook
An end-to-end machine learning application for predicting depression risk based on user lifestyle and stress-related inputs, deployed with Streamlit and Hugging Face.
python data-science machine-learning numpy scikit-learn pandas kaggle xgboost predictive-modeling mental-health binary-classification model-deployment ml-pipeline huggingface streamlit depression-prediction
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Apr 18, 2026 - Python
End-to-end Machine Learning pipeline and EDA analyzing university students' academic stressors and depression levels using Scikit-Learn (Classification, PCA, K-Means).
python data-science machine-learning scikit-learn exploratory-data-analysis pca classification predictive-modeling bracucse422 cse422-bracu mental-health-analytics depression-prediction student-health
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Sep 27, 2026 - Jupyter Notebook
Demonstrate a comprehensive understanding of current advanced methods and techniques in data and text analytics. Design and implement data mining based applications to solve real-world problems. Critically analyse and evaluate the performance of different data mining techniques for text analysis and analyse and interpret the data mining results.
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May 13, 2026 - Jupyter Notebook
Projet de data science appliqué à la prédiction de la dépression avec optimisation des modèles et des variables
python data-science machine-learning scikit-learn feature-selection xgboost classification mental-health model-evaluation smote healthcare-ai depression-prediction
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Apr 5, 2026 - Jupyter Notebook
A Machine Learning and Streamlit-based application for analyzing teen mental health patterns and predicting depression risk using social media usage, sleep habits, stress levels, anxiety, academic performance, and lifestyle factors.
python data-science machine-learning random-forest scikit-learn plotly data-visualization classification mental-health predictive-analytics streamlit healthcare-ai mental-health-analysis depression-prediction teen-mental-health
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Jun 2, 2026
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