Predicting App Ratings on Google Play Store: An Ensemble Learning Approach

As the Google Play Store continues to burgeon with millions of mobile applications catering to diverse needs and preferences, app developers face the challenge of understanding and predicting user satisfaction and ratings. Accurately predicting app ratings is crucial for developers to optimize their app's features, user experience, and overall performance. In this study, we present a comprehensive approach to predicting app ratings on the Google Play Store using machine learning techniques. Utilizing a rich dataset encompassing app metadata such as category, size, price, number of downloads, user reviews, and historical ratings, we employ various machine learning algorithms including regression models, decision trees, random forests, and gradient boosting. Feature engineering techniques are applied to extract useful insights and enhance the models to predict.