Unveiling Optimal Mobile Price Classification.

In the context of mobile technology, where demand for cost-effective smartphones is high, this study focuses on mobile price classification. By employing Random Forest and Linear Regression algorithms, the research accurately estimates mobile prices. The dataset includes cellphone specifications like RAM, camera quality, battery capacity, and brand. The Random Forest model achieves 95% accuracy, while Linear Regression, though less precise, still performs competitively at 88%. This emphasizes the importance of a multi-algorithm approach for effective mobile price prediction.