Our project will focus on analyzing the features behind successful apps

Our project will focus on analyzing the features behind successful apps. Our goal is to help developers and Google build apps that many users will enjoy and increase revenue in the process. We will be analyzing apps on a general basis to obtain key variables responsible for high downloads. We will also group apps based on a combination of variables to obtain the ideal app types. The data we used is a Google Play Store dataset, containing over 10, 000 observations, and 12 variables describing app characteristics such as reviews, category, genres etc. We modified the dataset to remove variables we thought would not significantly contribute to our analysis such as “last updated” and “Android Version”. We also converted the Type column, referencing an app being Free or Paid, to a binary column to facilitate analysis. Our methods include a descriptive and clustering analysis. We wanted to understand how our data could be grouped. We used K-means clustering and our data was grouped into 3 different clusters reflecting differences in their installs, reviews, and ratings. We then identified the top categories and price distributions of the apps within those clusters to explain the grouping. We used our findings to create forecasting analysis using K nearest neighbors and regression trees. After compiling all the information we derived from our analysis, we were able to construct the characteristics of a successful application to provide developers with a framework and tips to build and present a successful application to the Play store. Introduction