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WODanalytics: A CrossFit Training Analysis Platform Based on Django and Machine Learning

A comprehensive training management platform integrating Django REST Framework, Docker containerization, and machine learning prediction models, designed specifically for CrossFit and hybrid training.

DjangoREST APIMachine LearningCrossFitDockerPostgreSQLJWTScikit-LearnFitness TrackingPython
Published 2026-07-13 03:21Recent activity 2026-07-13 03:25Estimated read 4 min
WODanalytics: A CrossFit Training Analysis Platform Based on Django and Machine Learning
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Section 01

Introduction / Main Floor: WODanalytics: A CrossFit Training Analysis Platform Based on Django and Machine Learning

A comprehensive training management platform integrating Django REST Framework, Docker containerization, and machine learning prediction models, designed specifically for CrossFit and hybrid training.

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Section 03

Project Overview

WODanalytics is a comprehensive training management platform designed specifically for CrossFit and hybrid training. It combines traditional training record-keeping with modern data science techniques, not only helping athletes track their training history but also predicting athletic performance through machine learning models and providing overtraining warnings.

For CrossFit enthusiasts, training data is often scattered across notebooks, phone memos, or various fitness apps. The value of WODanalytics lies in providing a unified data management center, allowing athletes to systematically analyze their training patterns, identify potential issues, and optimize their training plans.


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Section 04

Backend Architecture

  • Python 3.13: Leveraging the latest language features
  • Django: A mature full-featured web framework
  • Django REST Framework: Building standardized RESTful APIs
  • PostgreSQL 15: Reliable relational data storage
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Section 05

Authentication and Security

  • JWT (JSON Web Tokens): Stateless authentication implemented using djangorestframework-simplejwt
  • Environment Variable Management: Sensitive configurations isolated via .env files
  • Password Strength Validation: Ensuring account security using regular expressions
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Section 06

Data Science and Machine Learning

  • Scikit-Learn: Classic machine learning algorithm library
  • Pandas & NumPy: Data processing and numerical computation
  • Joblib: Model serialization and loading
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Section 07

Development and Deployment

  • Docker & Docker Compose: Containerized deployment, ensuring environment consistency
  • Pytest: Automated testing framework
  • Tailwind CSS: Frontend styling framework

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Section 08

1. User Authentication System

The platform provides complete user lifecycle management:

Feature Endpoint Description
Registration POST /api/v1/auth/register/ New athlete registration
Login POST /api/v1/auth/token/ Obtain Access and Refresh Tokens
Refresh POST /api/v1/auth/token/refresh/ Refresh Access Token
Profile GET /api/v1/auth/profile/ View authenticated user information