AN MINH
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AI / Web App

Social Anxiety Level Prediction

A full-stack AI application designed to evaluate and predict a user's social anxiety levels based on psychological indicators and behavioral data.

Client
Personal Project
Role
AI & Full-stack Developer
Stack
Python, Scikit-learn, Pandas, FastAPI, React, TypeScript
Date
2024
Repository
Social Anxiety Level Prediction interface
Fig 1.0 — Social Anxiety Level Prediction interface

Technological & Algorithmic Highlights

Accurate Prediction Model

Utilizes Machine Learning algorithms to analyze complex psychological variables and behavioral data to output a risk classification.

Real-time Interactive UI

Provides a seamless and intuitive questionnaire experience built with React, delivering real-time prediction results upon submission.

Operation Flow

  1. 01User interacts with the React frontend to complete a comprehensive psychological survey.
  2. 02The collected data is securely transmitted to the FastAPI backend.
  3. 03A trained Machine Learning model processes the behavioral and psychological indicators.
  4. 04The system classifies the user's social anxiety risk level.
  5. 05Real-time feedback and results are displayed to the user via the interactive UI.

System Infrastructure

A decoupled, modern architecture separating the machine learning processing from the frontend presentation, ensuring performance, scalability, and data privacy without storing unnecessary PII.

Infrastructure parameters

  • • Backend API: FastAPI (Python)
  • • Frontend: React, TypeScript
  • • Machine Learning: Scikit-learn, Pandas, NumPy