لخّصلي

This study demonstrates how data-driven methods can be used to comprehend the dynamics of the cutthroat laptop market.We have created models that reveal the connections between important characteristics, like RAM, CPU type, brand, and screen resolution, and their effects on pricing by cleaning and preprocessing an unprocessed dataset of laptop prices and specifications.The findings of this study offer producers and consumers useful information.

This study demonstrates how data-driven methods can be used to comprehend the dynamics of the cutthroat laptop market. We have created models that reveal the connections between important characteristics, like RAM, CPU type, brand, and screen resolution, and their effects on pricing by cleaning and preprocessing an unprocessed dataset of laptop prices and specifications. Predictive modeling can categorize computers into different price groups and identify the most important aspects influencing value, as shown by the use of machine learning algorithms, particularly Decision Trees and k-Nearest Neighbors (k-NN).

The findings of this study offer producers and consumers useful information. These results can be used by manufacturers to improve their pricing policies and create laptops that better suit user preferences, such as giving mobility, processing speed, and battery life top priority. The study simplifies the difficult process of choosing a laptop that strikes a balance between features, performance, and price by providing users with a data-backed framework for making well-informed purchasing decisions.