https://github.com/waynewbishop/bishop-app-runbuddy-python
A machine learning model that recommends athletic clothing based on weather conditions, elevation, and other relevant features.
https://github.com/waynewbishop/bishop-app-runbuddy-python
ai data-science machine-learning python random-forest-classifier rest-api
Last synced: over 1 year ago
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A machine learning model that recommends athletic clothing based on weather conditions, elevation, and other relevant features.
- Host: GitHub
- URL: https://github.com/waynewbishop/bishop-app-runbuddy-python
- Owner: waynewbishop
- Created: 2024-10-03T02:44:10.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-10-16T02:20:18.000Z (over 1 year ago)
- Last Synced: 2025-02-08T04:42:42.750Z (over 1 year ago)
- Topics: ai, data-science, machine-learning, python, random-forest-classifier, rest-api
- Language: Python
- Homepage: https://www.waynewbishop.com
- Size: 28.3 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Run Buddy: AI-Powered Running Outfit Recommender
The [Run Buddy](https://github.com/waynewbishop/bishop-app-runbuddy-swift) recommender leverages machine learning to provide personalized athletic clothing recommendations based on current weather conditions.
## Overview
The core of Run Buddy is a machine learning model (RandomForestClassifier) that predicts appropriate running attire categories based on temperature, humidity, wind speed and other factors. The system is designed to enhance runners' experiences by suggesting suitable clothing while integrating brand partnerships.
Key Features:
- Weather-based outfit prediction using machine learning
- JSON-based data management for model, brands, user preferences, and clothing interpretations
- Flask API for seamless integration with client applications
- Continuous model improvement through user feedback
The backend is built with Python, utilizing libraries such as **scikit-learn** for machine learning, **Flask** for API development, and **pandas** for model serialization. The system architecture includes a modular design with separate components for model management, data collection, API handling, and testing.
This project aims to demonstrate the practical application of machine learning in everyday scenarios, providing value to athletes while exploring opportunities for brand integration and personalized recommendations.
Future plans include publishing modeling data and research to Kaggle as well as Google Colab.
## Collaboration
Contributors are welcome to [help improve](https://github.com/waynewbishop/bishop-app-runbuddy-python/pulls) the model accuracy, expand the feature set, or enhance the API functionality.
## Other Projects
- [bishop-algorithms-swift-package](https://github.com/waynewbishop/bishop-algorithms-swift-package) - Examples of commonly used algorithms and data structures in Swift Package format.
- [bishop-app-runbuddy-swift](https://github.com/waynewbishop/bishop-app-runbuddy-swift) - Plan your next run using Generative AI. Implemented in Swift.
## Usage
Individuals are welcome to use the code with commercial and open-source projects. As a courtesy, please provide attribution to [waynewbishop.com](http://www.waynewbishop.com). For more information, review the complete license agreement.
## Questions
Have a question? Feel free to [contact me](https://www.linkedin.com/in/waynebishop/).