Comparative Review of Machine Learning Models for Mobile Price Prediction Based on Specifications: A Systematic Literature Analysis - Naaman, Diyar; Tahir Ahmed, Berivan; Sediq Kareem, Omar (Publisher: Fakultas Ilmu Komputer, Institution: Universitas Lancang Kuning, 2025)
This systematic literature review analyzes machine learning approaches for mobile phone price prediction based on device specifications through a comprehensive examination of 25 research studies from 2018 to 2024.The review reveals that ensemble methods, particularly Random Forest (achieving up to 97% accuracy) and Gradient Boosting (R² = 0.9829), consistently outperform individual algorithms across various datasets. Support Vector Machine models demonstrate superior classification performance with 96-97% accuracy, while neural networks show perfect best-performer ratios but remain underutilized (4.88% of implementations). The following keywords were used in this systematic review's extensive search strategy across IEEE Xplore, ACM Digital Library, ScienceDirect, and Google Scholar: (