Machine Learning Model For Forecasting The Rating Of Mobile Apps

Abstract

Customers' decisions are greatly influenced by what
they read or see online. Reviews by customers
demonstrate their knowledge about quality and
experience. In the Google Play store, applications'
success can be significantly influenced by phoney
numerical ratings. It's well knowledge that a positive
review may be strongly associated with a high star
rating. Nevertheless, the text format of reviews
typically differs from the information provided by
user star ratings. The effective machine learning
approach for forecasting app ratings on the Google
Play Store is displayed in this study. With the rapid
proliferation of mobile applications across various
digital platforms, understanding and predicting app
performance has become increasingly critical for
developers, marketers, and platform stakeholders.
Among various performance indicators, user ratings
serve as a key metric reflecting app quality, usability,
and user satisfaction. This study presents the design
and implementation of a machine learning-based
predictive model aimed at forecasting the ratings of
mobile applications. The model leverages a
comprehensive dataset comprising both quantitative
and qualitative features such as app category,
number of installs, user reviews, sentiment analysis,
update frequency, content rating, and in-app
purchases. Overall, this research contributes to the
field of mobile app analytics by showcasing how
machine learning and NLP techniques can be
effectively integrated to forecast app ratings,
thereby enabling proactive development and
marketing strategies in an increasingly competitive
app ecosystem.