Laptop Price Estimation Using Machine Learning

Abstract

Predicting the cost of a laptop is a crucial and significant undertaking, particularly when the device is being sent straight from the manufacturer to the electronic market or stores. Expert knowledge is necessary for accurate laptop price prediction because the price is typically influenced by a wide range of unique features and circumstances. In this project, we develop a Laptop Price Prediction Model aimed at aiding students and common individuals in making informed and budget-conscious decisions when purchasing laptops. Leveraging machine learning techniques, particularly the Random Forest algorithm, the model predicts laptop prices based on diverse features, ensuring accuracy in cost estimation. The web application, built using Streamlit, provides an intuitive interface for users to input laptop specifications, allowing them to receive real-time price predictions. This project addresses the practical need for budget-friendly laptop choices, enhancing accessibility and facilitating optimal decision-making in the realm of technology purchases. The streamlined web application ensures a user-friendly experience, making it a valuable tool for both students and the general public seeking reliable guidance in the complex landscape of laptop pricing.