Google Play Store App Rating Prediction using Artificial Intelligence Technique

Abstract:-– With millions of mobile applications available on the Google Play Store, users face challenges in identifying the best apps suited to their needs. App ratings significantly influence the decision-making process, with higher ratings often leading to greater visibility and increased downloads. This research focuses on predicting app ratings on the Google Play Store using artificial intelligence (AI) techniques, specifically the K- Nearest Neighbors (KNN) and Random Forest algorithms. By leveraging app metadata, user reviews, and other relevant features, the study develops a model capable of forecasting app ratings with high accuracy. The KNN algorithm, known for its simplicity and effectiveness in classification problems, is utilized alongside Random Forest, a powerful ensemble learning method that excels in handling complex datasets and predicting continuous values. The proposed approach is evaluated on a dataset containing various features of Google Play Store apps, and the results demonstrate the potential of AI in predicting app ratings. This study provides valuable insights for both developers and users by improving app discoverability and rating prediction accuracy.

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