The problem statement is about the recommendation of products to the user without considering the ratings or the reviews provided, instead of that the product is recommended in the basis of considering the features like Quantity of the product purchased and the Unit Price of each product.
The product can be recommended to the user on the basis of the recommendation value of the product. The recommendation value can be calculated by multiplying the features which is provided in the dataset like Quantity and Unit Price. This multiplied value gives the recommendation value and it is trained GPU system using SVD algorithm. This SVD algorithm trains the model wisely and gives the highest accuracy to recommend a product to the user.
This system helps the customer to buy the product on the basis of Quantity purchased and Unit Price of the product. This helps them to buy a product from the Quantity which was frequently purchased by the user.
This system helps to buy the product on the basis of frequently purchased product and also considering the Unit Price of the product.This system overcomes the rating and review system which is currently prevailing in the present. So the proposed system will help the user to buy the product on the Quantity Basis and the Price of the product.
India's Sustainable Development
Recommending the products/services through advertisements is common and not secure. This project favors the people to buy the required products instantly/whenever needed. Avoidance of fake reviews in product purchasing is done by algorithms for accurate recommendations. No new and costly procedures involved instead using existing system with some additional techniques.
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