depression-prediction

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.

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.

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).

🧠 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

End-to-end Machine Learning pipeline and EDA analyzing university students' academic stressors and depression levels using Scikit-Learn (Classification, PCA, K-Means).

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.

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.