jhndacoder/rapid-crew repository overview

Rapid Crew is a state-of-the-art fashion recommendation system designed to help users discover fashion products tailored to their preferences. Utilizing algorithms like popularity-based, content-based, and collaborative filtering, it offers a variety of recommendation styles.

⁠Features

• Popularity-Based Recommendations: Suggests trendy products based on user ratings and popularity.
• Content-Based Recommendations: Recommends similar products using content analysis and cosine similarity.
• Personalized Recommendations: Tailored suggestions based on user interaction and preferences.
• Secure Authentication: Ensures authorized access using Firebase.
• Interactive UI: User-friendly interface built with ReactJS and Bootstrap.

⁠Dataset

The dataset includes 15K product listings from Myntra.com, covering the period from June 2019 to August 2019.

Dataset Link⁠

⁠Goals

The primary goal is to deliver a personalized fashion recommendation experience with an easy-to-navigate interface.

⁠Demo

YouTube Demo⁠ (Replace # with the actual link)

⁠Running the Application

• Frontend: Runs on localhost:3000
• Backend: Runs on localhost:5000

⁠Usage

To use this Docker image, follow the steps below:

• Pull the Docker image using:

  docker pull jhndacoder/rapid-crew:latest
  

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• Run the Docker container:
  • With GPU support:

    docker run --gpus all -p 3000:3000 -p 5000:5000 jhndacoder/rapid-crew:latest
    

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  • Without GPU support:

    docker run -p 3000:3000 -p 5000:5000 jhndacoder/rapid-crew:latest
    

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• Open localhost:3000. The application should be running there.

For more information, visit the project repository⁠.