Laptop Recommendation System (Web-Based Application)

Abstract

The Laptop Recommendation System is a web application that helps users choose the right laptop based on budget, brand preferences, and usage (gaming, business, programming, etc.). It uses a hybrid machine learning approach combining content-based filtering with Random Forest regression for price prediction and cosine similarity for matching. Features include user registration, personalized recommendations, cart management, price drop alerts, and an administrative interface for managing listings and orders.

Problem statement

Users face decision fatigue and difficulty selecting the right laptop due to the vast range of options, technical specifications, and fluctuating prices in the market.

Proposed solution

A web application utilizing machine learning algorithms (Random Forest and Cosine Similarity) to match technical specifications with user needs and budget, providing a streamlined path from recommendation to purchase.

Objectives

• Simplify and personalize the laptop selection and purchasing process
• Reduce decision fatigue for consumers through intelligent recommendations
• Provide a centralized platform for retailers to manage products and monitor sales trends
• Enhance user engagement via price-drop alerts and visual insights

Technologies used

Tools / frameworks

• Visual Studio Code
• AWS/Heroku
• OpenAI Platform
• Google Cloud APIs

Methodology

Incremental Software Process Model focusing on iterative development and object-oriented design.

Keywords