Intelligent E-commerce Product Recommendation System using Machine Learning.

Abstract: Today, online shopping websites have millions of products. When a customer buys a main product, like a laptop or a smartphone, they often need accessories like a mouse, a bag, or a keyboard. However, finding accessories that are both affordable (within their budget) and technically compatible (matching the exact features) is very confusing and takes a lot of time. Traditional recommendation systems fail here because they only show items based on "what other people bought". They do not check if the accessory fits the customer's personal budget or if it actually connects with their specific device. To solve this real-world problem, this project introduces the "Intelligent E-commerce Product Recommendation System using Machine Learning". This system acts like a smart digital shopkeeper. Instead of showing random popular products, it uses Machine Learning (ML) algorithms to intelligently analyze two main things: the technical specifications of the selected primary product and the exact budget limit set by the user. By matching these details, the system filters the database and suggests only the most perfect, compatible, and pocket-friendly accessories. The project is built with a strong technical foundation, utilizing a relational database structure to safely store user and product details. To plan the software architecture perfectly, comprehensive UML models have been designed, including Use Case, Sequence, ER, Activity, Class, and Deployment diagrams, ensuring the web application is robust and logical. In conclusion, this intelligent system completely upgrades the online shopping experience. For the customers, it saves their valuable search time and provides highly personalized, cost-effective suggestions. For the e-commerce platform owners, it acts as a powerful tool to increase their daily sales and profit (cross-selling) by automatically showing the right product to the right user at the right time.

Keywords: Machine Learning, E-commerce, Product Recommendation System, Cross-selling, Collaborative Filtering, Content-Based Filtering, Personalization.