Data Analysis and Rating Prediction on Google Play Store Using Data-Mining Techniques
Google Play Store was formerly known as Android Market. This biggest Android Application (App) provides a wide variety of details on requirements such as reviews, quality, number of installs, and explanations for device functionality. This study aims to predict the ratings of Google Play Store app...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
INTI International University
2022
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| Subjects: | |
| Online Access: | http://eprints.intimal.edu.my/1575/1/jods2022_01.pdf http://eprints.intimal.edu.my/1575/ https://ipublishing.intimal.edu.my/jods.html |
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| Institution: | INTI International University |
| Language: | English |
| Summary: | Google Play Store was formerly known as Android Market. This biggest Android Application (App) provides a wide variety of details on requirements such as reviews, quality, number of installs, and explanations for device functionality. This study aims to predict the ratings of Google Play Store apps using decision trees for classification in machine learning algorithms. The goal of using a Decision Tree is to create a training model that can use to predict the class or value of the target variable by learning simple decision rules inferred from prior data. This method classifies a population into branch-like segments that construct an inverted tree with a root node, internal nodes, and leaf nodes. The algorithm is non-parametric and can efficiently deal with large, complicated datasets without imposing a complicated parametric structure. This enables us to draw a comprehensive picture of the current situation on the process of analyzing Google Play Store by Number of Downloading Rate and Rating in current market trend. This will help the developers understand customers' great desires, attitudes, and trends in demand. To understand more in-depth, the similarity between the functionality of the device and to construct clusters of related applications. Then, analyze their characteristics following features of interest. The datasets that the author used are collected from Google Play Store (2019). In this research, the expected results have a more strong correlation between price and number of downloads and similarity between price and participation. |
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