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Project Summary

Project composition

  • Front End (FE): Web interface built with Vue.js.
  • Backend (BE): A Python backend built using the FastAPI framework and integrating machine learning (ML) models.
  • Machine Learning (ML): Use the PyCaret library for model training and deployment.
  • Utils: Stores Python modules for heart rate variability (HRV) feature extraction.

Project file structure

Main file

  • analyze.ipynb: Jupyter Notebook for data analysis.

  • preprocess.ipynb: Jupyter Notebook for data preprocessing.

  • deploy.py: Main deployment script, configures FastAPI and model deployment.

  • requirements.txt: Stores the list of dependent packages for the project.

  • logs.log: Log file that records events and errors when the application is running.

  • utils/: Directory that stores various tools and auxiliary functions.

Tool Module

  • hrv_feature_extraction.py: Python module for extracting features from heart rate data.

User operations

Machine Learning (ML)

  • Use analyze.ipynb for data analysis.
  • Use preprocess.ipynb to preprocess data.
  • Deploy machine learning models through deploy.py.

Front End (FE)

  • Use Vue.js to build web interfaces.

Backend (BE)

  • Build Python backend using FastAPI framework.

DevOps

  • Manage project dependency packages through requirements.txt.

Data process

  1. Data Analysis:

    • Use analyze.ipynb to analyze the data.

  2. Data preprocessing:

    • Use preprocess.ipynb for data preprocessing.

  3. Machine learning model training:

    • Use deploy.py to train and deploy machine learning models.

  4. Web interface construction:

    • Build web interfaces using Vue.js.

  5. Application Deployment and Running:

    • Deploy FastAPI backend application to run the entire system.

In conclusion

This project uses machine learning, front-end, and back-end integration to build a web application that includes data analysis and machine learning functions. Users can execute data processes, train models, and deploy applications by operating corresponding files and scripts.