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Many apps struggle to gain downloads after launch, so anticipating performance early becomes strategically important. This study forecasts app success by predicting user ratings and installation counts before an app debuts. Machine learning models trained on historical app data and selected app features support data-driven decisions for improving launch 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and Applied Sciences,Universiti Malaysia Pahang,Pekan26600,Pahang,Malaysia,  \naleemmian380@gmail.com1,nhuzaimi@umpsa.edu.my2  \nHow to cite this article:Muhammad Aleem,Noorhuzaimi Mohd Noor (2024)Predicting Android App SuccessBefore Google Play Store Launch Using Machine Learning.Library Progress International,44(3),1907-1918  \n# ABSTRACT\n\nThe Google Play Store serves as a pivotal platform for Android app developers,drawing millions of users globally.With an overwhelming influx of new apps introduced daily,developers are faced with the critical challenge ofsustaining market share amidst fierce competition.Predicting an app's success before its official launch can providea strategic advantage,potentially transforming how developers approach app releases.In this study,we aim toforecast the success of new Android apps by predicting key indicators such as user ratings and installation numbersprior to their debut on the Google Play Store.Leveraging machine learning techniques,we analyze historical appdata and various app features to build predictive models that offer valuable insights into app performance,empowering developers to make data-driven decisions before launch.  \nKeywords:Mobile Application,Forecasting,Android,App Success,Google Play Store,Launch,PredictiveAnalytics  \n# INTRODUCTION\n\nGoogle Play Store is a prime destination for Android app developers,attracting a vast user base.With a largenumber of new apps being introduced daily,developers face the challenge of maintaining their market shareamidst intense competition.The ability to predict an app's performance before its release can provide a significantcompetitive advantage.Traditionally,an app's success is measured by user ratings and installation numbers.Thisstudy aims to forecast the popularity ofnew apps by predicting user ratings and installation numbers prior to theirlaunch on the Google Play Store.By leveraging advanced machine learning techniques and focusing on internalfeatures,we develop a comprehensive prediction model that offers valuable insights for developers,helping themto optimize their apps for better market performance.  \nMobile applications have become integral to daily life due to the widespread use of smartphones.This surge insmartphone usage has led to a significant increase in mobile apps,making the market both extensive and highlycompetitive.Nearly every digital need has resulted in the creation of multiple apps,offering users a wide rangeof choices for downloading and usage.In such a competitive environment,numerous factors determine an app'spopularity and success.Consequently,developers must carefully consider various aspects during the developmentand deployment phases.Despite the app store hosting millions of apps,many receive few downloads or go unused.Therefore,understanding how apps become essential to people's lives is crucial.The rapid expansion of the mobileapp market has spurred advanced innovation,and as the market continues to grow,so does the number ofdevelopers.This growth contributes significantly to the global mobile application sector's revenue.Mobile appshave become the primary utility for smartphones,offering diverse features and services such as social networking,entertainment,shopping,information access,and navigation (Venkata et al.,2014).An app store,such as GooglePlay,serves as a platform where users can download mobile applications for various services and software(Abdulet al.,2018).Users can browse and download apps from these stores,while developers can monitor their apps","cbCaivnpnz0Yb9yg","https://ap.wps.com/l/cbCaivnpnz0Yb9yg","pdf",155337,"English","# Abstract\n# Introduction","[{\"question\":\"What does the study predict before a Google Play Store launch?\",\"answer\":\"It predicts user ratings and installation numbers prior to the app’s official debut on the Google Play Store.\"},{\"question\":\"Why is early prediction important for Android developers?\",\"answer\":\"Early prediction provides a competitive advantage by helping developers understand likely performance and optimize apps before release.\"},{\"question\":\"How does the study build its prediction models?\",\"answer\":\"It uses machine learning techniques on historical app data and selected app features to create predictive models for app performance.\"}]","Predicting Android App Success Before Google Play Store Launch Using Machine Learning | PDF"]