Authors
Samy S. Abu-Naser
North Dakota State University (PhD)
Abstract
In this paper an Artificial Neural Network (ANN) model, was developed and tested for predicting the price range of a mobile phone. We used a dataset that contains mobile phones information, and there was a number of factors that influence the classification of mobile phone price. Factors as battery power, CPU clock speed, has dual sim support or not, Front Camera mega pixels, has 4G or not, has Wi-Fi or not, etc…. 20 attributes were used as input variables for the ANN model. A model based on the Multilayer Perceptron Topology was developed and trained, using data set, which its title is “Mobile Price Classification” and was obtained from Kaggle online community, and it is created by Abhishek Sharma. Test data evaluation shows that the ANN model is able to correctly predict the mobile price renge with 96.31 accuracy.
Keywords Artificial Neural Networks  mobile price prediction  Predictive Model  Data Mining
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Citations of this work BETA

ANN for Tic-Tac-Toe Learning.Dalffa Abu-Mohaned - 2020 - International Journal of Engineering and Information Systems (IJEAIS) 3 (2):9-17.
Apple Fruits Classification Using Deep Learning.Shawwa Mohammad - 2020 - International Journal of Academic Engineering Research (IJAER) 3 (12):1-6.
Machine Learning Application to Predict The Quality of Watermelon Using JustNN.Ibrahim M. Nasser - 2019 - International Journal of Engineering and Information Systems (IJEAIS) 3 (10):1-8.

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