Predicting Laptop Prices in the Tunisian Market Using Data Mining and Machine Learning Methods

The laptop industry has undergone swift expansion and technological enhancements, resulting in a diverse array of laptop models and capabilities to cater to a broad spectrum of consumer demands. However, for businesses, the task of identifying the most suitable laptop that aligns with specific requirements can be quite formidable. To tackle this challenge, this study introduces a solution centered around predicting laptop prices through the application of supervised machine learning, with a particular focus on utilizing the Gradient Boost method. The system attains an impressive predictive accuracy of 94% when compared against actual market prices. Moreover, this investigation delves into the connections between laptop prices and various attributes including model, RAM, storage types (HDD/SSD), GPU, and CPU. By making use of this system and comprehending these correlations, enterprises can streamline the laptop selection process. This research significantly contributes to the domain of laptop price prognostication, providing enterprises with the tools to make well-informed decisions and optimize their laptop acquisition strategies.