MOBILE PRICE RANGE CLASSIFICATION

In the rapidly growing digital era, smartphones have become an essential component of everyday life, with millions of users relying on them for communication, entertainment, education, and business activities. The mobile phone market is expanding rapidly with numerous models released each year, each equipped with diverse technical specifications and varying price ranges. This diversity often makes it difficult for customers, retailers, and manufacturers to accurately estimate the appropriate price category of a mobile device based solely on its specifications. Traditional price prediction methods rely heavily on manual evaluation and market comparisons, which can be time-consuming, inconsistent, and prone to errors when large datasets are involved. To address these challenges, this study proposes a Machine Learning-based Mobile Price Range Classification system that predicts the price category of a smartphone using its technical specifications. The system analyzes various attributes including battery power, RAM capacity, internal memory, screen resolution, processor speed, camera quality, and connectivity features. These features are processed using machine learning algorithms to classify smartphones into predefined price categories such as low, medium, high, and very high. The model is trained using a structured dataset and evaluated using classification performance metrics to ensure accurate predictions. By leveraging machine learning techniques, the proposed system enhances prediction accuracy, reduces human effort, and supports faster decision-making for manufacturers and customers. The results demonstrate that machine learning algorithms can effectively identify relationships between hardware specifications and market price categories, providing a reliable automated solution for smartphone price classification in the competitive mobile industry.