Published Paper Details:

The most frequently purchased item today is typically a mobile phone, which has become a common commodity. Every year, thousands of different mobile phone models with new features, specifications, and designs are released. Customers typically check whether they can buy the specified item at the projected price or not. So, before releasing the mobile device, estimating the pricing is crucial, as is learning about the market and competition. This study uses supervised machine learning to propose a mobile price prediction system. To anticipate "Whether the mobile with certain features will be Economical or Expensive" is the major motive of this project work. The actual dataset is gathered from online sources. Several independent variables are used in multiple linear regression, but there is only one dependent variable whose actual and predicted values are compared to determine the accuracy of results. The predictions are made using a variety of methodologies. The forecasts are then assessed and compared to identify those that deliver the best results. We want to make the predictions using more advanced algorithms in the future. The optimal feature selection technique and classifier for the given dataset are used to draw conclusions. Any form of marketing or business can use this study to identify the ideal product.