Content-Based Fashion Recommendation System
Published:
Developed a content-based recommendation system for fashion apparel. The system takes an input image of a clothing item, uses a pre-trained ResNet-50 model to extract deep visual features, and then employs a K-Nearest Neighbors (KNN) algorithm to find and suggest similar-looking products from an inventory. The project was prototyped in Google Colab and deployed as an interactive web app using Streamlit.
Tech Stack: Python, TensorFlow, Numpy, Scikit-learn, Streamlit, Git, Google Colab.