1. Rating Prediction of Google Play Store apps with application of data mining techniques
- Author
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Iago Richard Rodrigues Silva, Alexandre Magno Andrade Maciel, Emerson Lima, Roberta Andrade de Araújo Fagundes, Raniel Gomes da Silva, and Jailson de Oliveira Liberato Magalhaes
- Subjects
General Computer Science ,Relation (database) ,business.industry ,Computer science ,media_common.quotation_subject ,Machine learning ,computer.software_genre ,App store ,Regression ,Random forest ,ComputingMethodologies_PATTERNRECOGNITION ,Curiosity ,The Internet ,Artificial intelligence ,Electrical and Electronic Engineering ,Inference engine ,business ,computer ,media_common ,Statistical hypothesis testing - Abstract
The use of applications is part of people daily lives for various activities. In relation to development, the curiosity about the characteristics responsible for success arises. We use classifiers to meet the success requirements of the Google Play Store app store. Through the techniques of KNN and Random Forest, a statistical analysis was done performing the regressions of the applications according to some characteristics: as hypothesis test, correlation and regression metrics analysis. This work aims to create inference engines, allowing the prediction of application ratings, using the KNN and Random Forest regression techniques. The Random Forest showed better results than the KNN.
- Published
- 2021
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