Harnessing Ensemble Learning Approaches for Strong Mobile App Success Prediction Model

Abstract

With the creation of mobile applications across diverse domains, the ability to predict the success of these apps has become crucial for developers, investors, and marketers. This paper explores the efficacy of ensemble learning techniques in constructing robust prediction models for mobile app success. Ensemble learning combines multiple base learners to enhance prediction accuracy and generalization. We investigate various ensemble methods such as bagging, boosting, and stacking, employing diverse base learners including decision trees, neural networks, and support vector machines. The study utilizes a comprehensive dataset comprising various features including app characteristics, user reviews, download statistics, and market trends. Through rigorous experimentation and evaluation, we demonstrate the effectiveness of ensemble learning in improving prediction accuracy compared to traditional single-model approaches. Furthermore, we analyze the contribution of individual base learners within ensembles, highlighting their complementary strengths in capturing different aspects of app success. Google play store contains numerous apps, with new ones being added daily. It is challenging for a developer to determine if they are on the right path to creating a successful app due to the intense competition. Factors such as ratings, number of installs, and reviews can dictate the success of an app. In this study, we used Exploratory Data Analysis to identify connections between different aspects of an application in order to forecast its success. Information from the Google play store was utilized for training three distinct models - Random Forest, Support Vector Machine, and Linear Regression - to forecast the app's success. Our findings emphasize the importance of ensemble learning in predicting mobile app performance, offering insights that can help inform decisions on app development, marketing, and funding. In the growingly competitive mobile app sector, stakeholders can utilize powerful predictions from suggested ensemble models to recognize promising app opportunities, optimize resource distribution, and manage risks efficiently. Results of the study reveals that Successful Google Play Store apps often feature terms like "photo" and "share" in descriptions, and user ratings significantly influence app success, with sentiment analysis providing deeper insights. PCA revealed critical relationships among features, and models like Random Forest, SVM, and XGBoost showed high accuracy in predicting success. The research highlighted the need for aligning reviews with ratings, version-aware rating systems, and eliminating noisy data to enhance predictive accuracy.