I have some data from an ecommerce website with features like product_name, product_category product_link, product_id, free_delivery(1 or 0), price, discount, avg_rating, number of reviews, search_rank, date where search_rank is position of the product when a category webpage is opened.
I want to create a popularity_index based on above mentioned features.
My approach till now is to normalize the columns search_rank, ratings and avg_rating and assign weights a,b,c to these and assign popularity_index the value $ax+by+cz$ for each category.
Can I do it in a better way? Do I incorporate some common unsupervised algorithm that I am missing?