Performance Analysis of Decision Tree Models and M5P Models for Mobile Phone Price Prediction

The aim of this article is to develop a predictive model for mobile prices using mobile specs, and to identify the machine learning technique that produces the most accurate price forecasts. Predictive Analytics involves utilizing archival data to make precise predictions about future occurrences. In this example, the cost of the mobile device is used to determine the category into which it falls. The Mobile Price Class dataset, which is available on the Kaggle data science community website, is one of the datasets that was used to train the prediction model. The cost of mobile devices is broken down into numerous categories in the present dataset. In order to find the most effective classification algorithm for forecasting future mobile price tiers, the model is trained using a variety of different classification methods, and evaluation measures such as accuracy, precision, and recall are then applied.