Smartphones Price Estimation System - B. Anil Kumar; S. Muni Kumar (International Journal of Scientific Research in Science, Engineering and Technology, 2025)
The goal of this system is to estimate the price of smartphones using various attributes such as brand, model, technical specifications, and other significant features. It employs machine learning techniques like Support Vector Machines (SVM), Random Forest, and XGBoost to deliver precise pricing predictions. The dataset, obtained from Kaggle, primarily includes information on Samsung smartphones. Key features analyzed include battery capacity, camera resolution, screen dimensions, and processor performance. The user interface is crafted using HTML, CSS, and JavaScript to ensure ease of use, while Python is utilized on the backend for data handling and integration of the machine learning models. This solution assists users in making informed purchasing decisions by offering real-time price predictions grounded in current market trends.
Keywords: Smartphone; Price Estimation; Machine Learning; SVM; Random Forest; XGBoost; Data Analysis; Web Development; Python