![]() We removed size, last updated date, current version, and android version because they are not the factor that would affect the number of installs before publishing. ![]() Kuo Cleaning the Tableįirst, we have analyzed which information column is irrelevant to the number of installs of the app. Table for list of reviews according to apps | image by Jonathan C.T. Unfortunately, we could not directly use these two files as they are not joined. ![]() And the other is a list of reviews for each app with the sentiment if that particular content of the review was positive, neutral, or negative. It has information such as app name, category, rating, and more. We have two datasets from Kaggle for app reviews one is the list of apps with information. This project's result may show the importance of reviews to apps in the market as it could be one of the determining factors for the number of installs. Knowing the number of installs can help developers and business managers because they can predict the profit. We use this and some knowledge about the app to predict its success. Companies may run beta focus groups, or app developers may receive feedback from testers and get certain amounts of reviews. We hope that this project will help app developers predict their number of installs or investors who want to pick out the next big app. Our project goal is to predict the number of installs of apps by looking at app info and its reviews. ![]()
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