Fashion Recommender System

Project Overview:

A fashion recommendation system designed to suggest clothing items based on user preferences, trends, and historical data. The system utilizes machine learning and deep learning techniques to recommend outfits, ensuring an enhanced shopping experience.

Technologies Used:

• Backend: Python, Flask (for web app interaction)
• Machine Learning: TensorFlow, Keras, scikit-learn
• Frontend: HTML, CSS, JavaScript (for basic UI)
• Containerization: Docker
• Data Processing: Pandas, NumPy, OpenCV (for image processing)
• Visualization: Matplotlib, Seaborn

Project Structure:

• backend/: Contains the Flask-based backend logic that serves the recommendation API.
• public/: Stores static assets like images, CSS, and JavaScript files used for the front end of the web app.
• src/: Contains the core machine learning code for building and training the recommender system, including feature extraction, model building, and evaluation.
• test_images/: A folder that holds sample images used for testing and evaluating the recommender system.
• Dockerfile: Contains instructions to build the Docker container for the project, ensuring a consistent environment.
• entrypoint.sh: A shell script used for initializing the application in the Docker container.
• package-lock.json & package.json: These files manage the JavaScript dependencies and ensure consistency across environments.

Main Features:

• Personalized Fashion Recommendations: The system recommends clothing items based on user preferences, trending styles, and input criteria (e.g., occasion, size).
• Image-Based Recommendations: The recommender system can also suggest items based on clothing images uploaded by the user.
• Machine Learning Integration: Uses collaborative filtering, content-based filtering, or hybrid models to improve recommendation accuracy.
• User Interface: A simple and interactive web interface that allows users to input their preferences and view personalized clothing recommendations.

How It Works:

• The user interacts with the web app frontend to provide preferences or upload images.
• The Flask backend processes these inputs, sends them to the machine learning model for prediction, and returns the recommended items.
• The recommender system uses various algorithms, including content-based and collaborative filtering, to suggest items that best match the user's input.
• The results are displayed as a list of clothing items with images, descriptions, and recommendations.