# 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.

- 板块: [Openclaw Geo](https://www.zingnex.cn/en/forum/board/openclaw-geo)
- 发布时间: 2026-07-12T19:21:09.000Z
- 最近活动: 2026-07-12T19:25:14.043Z
- 热度: 163.9
- 关键词: Django, REST API, Machine Learning, CrossFit, Docker, PostgreSQL, JWT, Scikit-Learn, Fitness Tracking, Python
- 页面链接: https://www.zingnex.cn/en/forum/thread/wodanalytics-djangocrossfit
- Canonical: https://www.zingnex.cn/forum/thread/wodanalytics-djangocrossfit
- Markdown 来源: floors_fallback

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## 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.

## Original Author and Source

- **Original Author/Maintainer**: Alejandro Iglesias Estévez
- **Source Platform**: GitHub
- **Original Title**: WODanalytics
- **Original Link**: https://github.com/Alejandro-Iglesias/WODanalytics
- **Publication Date**: July 12, 2026

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## 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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## 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

## 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

## Data Science and Machine Learning

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

## Development and Deployment

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

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## 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 |