In today's rapidly evolving technological landscape, laptops have become indispensable tools for individuals and businesses alike. The laptop market offers a wide variety of products with varying specifications and price points, making it challenging for consumers to make informed purchasing decisions. This project aims to develop a machine learning model that predicts laptop prices based on their features. By analysing a dataset of laptop specifications and prices, the model will identify key factors influencing pricing, such as brand, processor type, RAM, storage capacity, screen size, and other relevant features. Various regression algorithms will be explored and evaluated to determine the most accurate model for price prediction. The results of this project will provide valuable insights for both consumers and retailers in the laptop market. Consumers can use the model to estimate the fair price of a laptop based on their desired specifications, while retailers can leverage it to optimize their pricing strategies and remain competitive. Ultimately, this project contributes to a more transparent and efficient laptop market, empowering consumers to make informed decisions and enabling retailers to price their products effectively.