Mental Health Assessment System

About the Project
A machine learning–powered web application designed to estimate the likelihood of depression based on user-provided lifestyle and demographic factors.
The system analyzes inputs such as demographic information, academic workload, sleep habits, nutrition patterns, and more to generate an instant prediction along with general recommendations.

Tech Stack
Machine Learning & Data Science
• Python, Pandas, NumPy, Matplotlib, Scikit-learn: data cleaning, preprocessing, visualization, model training and comparison
• Pipeline: complete data-processing workflow (missing-value imputation, feature scaling)

Backend
• Flask (Python): REST API for communication with the ML model
• Integration: serves the production React build directly from Flask (Single-Server Deployment)

Frontend
• React + Vite + TypeScript: fast, modern, strongly-typed client interface
• Tailwind CSS: component styling and responsive design (mobile-first)
• React Select: customized UI components for improved usability on mobile devices


Project Highlights
Achieved strong F1-score/Accuracy through careful feature engineering and validation.
Developed a clean, user-friendly form with real-time validation and dynamic result visualization.
The system outputs not only a binary prediction but also the model’s confidence percentage.
Integrated a list of psychological support hotlines for user awareness and safety.
Full mobile compatibility, including fixes for native select issues on iOS/Android.

#machinelearining #ML #python #flask #React/TypeScript #React #TaillwindCSS