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A hybrid model for predicting Mobile Price Range using machine learning techniques
Ahsanul Hoque M Nazmus Sakib, Asif Khan Shakir, Khalid Been Biplop, Md Abu Saleh, Shanjoy Sutradhar, Washim Akram,

Machine learning based classification techniques helps to solve the problem related to decision making. In many areas of price prediction are used like housing price prediction, stock price prediction different classification algorithm used. Some of them are used artificial neural network. In this study, different classification techniques used for prediction the mobile price range. The first one is Decision Tree second one is Random forest machine learning algorithm. The accuracy got by first two techniques respectively 83% and 84%. So, for improving the accuracy of Decision Tree, the parameter has been pruned and later Random Forest has been used. It gives 90% accuracy for this dataset. Here, also a hybrid ensemble method is implemented using combination of five different heterogeneous weak learners because it has a record of manifesting performance in machine learning techniques and was not used before on this dataset. And also, performance evaluation is performed for Decision Tree, Random forest and also for hybrid model like Precision, Recall.

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