Laptop Price Prediction Using Real Time Data

Abstract

Online laptop sales are at an all-time high as a result of the pandemic. A laptop is a must-have for working from home, as well as e-learning and other activities. The buyer is aided in making a purchasing decision by a feature-based pricing prediction algorithm. Based on real-time data scraped from an e-commerce website, this study proposes a model for predicting laptop costs. The suggested method collects data from a real-time environment and predicts the model’s pricing with high accuracy. This study employs Support Vector Regression, Decision Tree Regression and Multi-Linear Regression to forecast laptop price.