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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Updated
Jun 30, 2026 - Python
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).
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.
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.
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.
🧠 ML web app predicting student depression risks. Showcases a complete pipeline: baseline modeling, advanced data preprocessing, and optimized Logistic Regression.
Projet de data science appliqué à la prédiction de la dépression avec optimisation des modèles et des variables
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