Mobile Price Class prediction using Machine Learning Techniques

Abstract

To predict "If the mobile with given features will be Economical or Expensive" is the main motive of this research work. Real Dataset is collected from website www.GSMArena.com . Different feature selection algorithms are used to identify and remove less important and redundant features and have minimum computational complexity. Different classifiers are used to achieve as higher accuracy as possible. Results are compared in terms of highest accuracy achieved and minimum features selected. Conclusion is made on the base of best feature selection algorithm and best classifier for the given dataset. This work can be used in any type of marketing and business to find optimal product(with minimum cost and maximum features). Future work is suggested to extend this research and find more sophisticated solution to the given problem and more accurate tool for price estimation. General TermsMachine Learning

Prediction of Heart Disease using Artificial Neural Network

“…[8] The classification accuracies obtained with T2, 1R, IB1c and K* which were applied by WEKA, RA are 68.10%, 71.40%, 74.00% and 76.70%, respectively. [8] Robert Detrano used logistic regression algorithm and obtained 77.0% classification accuracy. The result of this fuzzy expert system in 79% as a well as the expert did.…”

“…[8] MLP+BP algorithm that was used by ToolDiag, RA reached to 65.60%. [8] The classification accuracies obtained with T2, 1R, IB1c and K* which were applied by WEKA, RA are 68.10%, 71.40%, 74.00% and 76.70%, respectively. [8] Robert Detrano used logistic regression algorithm and obtained 77.0% classification accuracy.…”

“…When the studies in the literature related with this classification application are examined, it can be seen that a great variety of methods were used which reached high classification accuracies using the dataset taken from the UCI machine learning repository. Among these, [8] ToolDiag, RA obtained 50.00% classification accuracy by using IB1-4 algorithms. [4] WEKA, RA obtained a classification accuracy of 58.50% using InductH algorithm while ToolDiag, RA reached to 60.00% with RBF algorithm.…”

“…[4] WEKA, RA obtained a classification accuracy of 58.50% using InductH algorithm while ToolDiag, RA reached to 60.00% with RBF algorithm. [8] Again, WEKA, RA applied FOIL algorithm to the problem and obtained a classification accuracy of 64.00%. [8] MLP+BP algorithm that was used by ToolDiag, RA reached to 65.60%.…”

“…[8] Again, WEKA, RA applied FOIL algorithm to the problem and obtained a classification accuracy of 64.00%. [8] MLP+BP algorithm that was used by ToolDiag, RA reached to 65.60%. [8] The classification accuracies obtained with T2, 1R, IB1c and K* which were applied by WEKA, RA are 68.10%, 71.40%, 74.00% and 76.70%, respectively.…”