Fashion-Intelligence-Systems
It is a machine learning project which aims to ease out the work of fashion designers by predicting the trendiness score of their designs (to be launched) based on their features such as material, colour, etc. The algorithm used for training of test data and prediction is Support Vector Machine. Training data is obtained by web-scraping from e-commerce websites such as amazon, flipkart, shein, etc. Top 10 trendy products from across all the fashion websites are also displayed on the leaderboard based on a rating which is normalized based on the website's relative ranking, no. of ratings/reviews done on the product.
The csv file to_be_predicted contains the features of the product input by a fashion designer to predict its trendiness. The csv file shein_data_webtool and flipkart_kids_data are example files of how data is scraped from the e-commerce websites.
Programming language used : Python. Platform Used - Jupyter Notebook.