IJAIEM

Mohd Ibraheem,Padigela YashaSri , Surannagari Akhilesh, K.Surya Kanthi

Abstract

Mobile apps have become an essential part of our daily lives, and their ratings play a crucial role in determining their success. Accurate prediction of mobile app ratings can help developers and stakeholders make informed decisions. This study proposes a Random Forest-based approach for predicting mobile app ratings. The proposed model utilizes a combination of app metadata, user reviews, and performance metrics as input features. The Random Forest algorithm is used to handle the complexity and non-linearity of the data. The results show that the proposed model outperforms other machine learning algorithms and achieves a high accuracy in predicting mobile app ratings. The study also highlights the importance of feature selection and hyperparameter tuning in improving the performance of the model. The proposed approach can be used by developers and stakeholders to predict mobile app ratings and make informed decisions.