The Google Play Store is one of the largest app marketplaces in the world, with millions of apps available for download. However, with so many options to choose from, it can be difficult for app developers to predict which apps will be successful and which will not. By understanding the factors that contribute to the success of an app, developers can better target their efforts and increase their chances of creating a successful app. The goal of this project is to use data mining techniques to predict the success of apps on the Google Play Store. Specifically, to:
- collect and clean a dataset of app information, including app details, user reviews, and download statistics.
- use machine learning algorithms to build a model that can predict the success of an app based on its characteristics and user feedback.
- evaluate the performance of the model and determine its accuracy in predicting app success.
- identify the most important factors that contribute to app success and recommend strategies for increasing it.