Rating Prediction of Google Play Store apps with application of data mining techniques



KNN, Random Forest, Regression, Prediction


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 regres- sionsoftheapplicationsaccordingtosomecharacteristics: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.


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