Electronic Device Price Prediction using Machine Learning

The objective of this paper is to develop a software application that can forecast the price of a product using data collected from a database and present it in a web interface. This example demonstrates how the application accurately predicts the price of any electrical device based on its unique features and current pricing or price history. The application consists of four primary steps: database processing, machine learning training, exporting the model in a user interface, and making the prediction.