Predicting android app success before google play store launch using machine learning
The 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 of sustaining market share amidst fierce competition. Predicting an app’s succ...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English English |
| Published: |
Elsevier
2024
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/42620/1/Predicting%20android%20app%20success%20before%20google%20play%20store%20launch%20using%20machine%20learning_ABST.pdf http://umpir.ump.edu.my/id/eprint/42620/2/Predicting%20android%20app%20success%20before%20google%20play%20store%20launch%20using%20machine%20learning.pdf http://umpir.ump.edu.my/id/eprint/42620/ https://bpasjournals.com/library-science/index.php/journal/article/view/641 |
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| Summary: | The 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 of sustaining market share amidst fierce competition. Predicting an app’s success before its official launch can provide a strategic advantage, potentially transforming how developers approach app releases. In this study, we aim to forecast the success of new Android apps by predicting key indicators such as user ratings and installation numbers prior to their debut on the Google Play Store. Leveraging machine learning techniques, we analyze historical app data and various app features to build predictive models that offer valuable insights into app performance, empowering developers to make data-driven decisions before launch. |
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