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Our project will focus on analyzing the features behind successful apps

Our project will focus on analyzing the features behind successful apps nxWp7

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

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help
 developers and Google build apps that This phrase/word is too simple and can be replaced with:a plethora ofa number ofnumerous
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 This phrase/word (depending on the context) can be potentially replaced with :exploited
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 This phrase/word (depending on the context) can be potentially replaced with :reckonedassumedbelievedconsidered
thought
 would not significantly
 contribute to our You use this word too often, consider replacing it withsearchstudyscrutinyinvestigationinquirytest
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 You use this word too often, consider replacing it withsearchstudyscrutinyinvestigationinquirytest
analysis
. We wanted to understand how our data could be grouped
. We This phrase/word (depending on the context) can be potentially replaced with :exploited
used
 K-means clustering and our data was grouped
 into 3 This phrase/word is too simple and can be replaced with:various
different
 clusters reflecting differences in their installs, reviews, and ratings. Three successive sentences begin with the same word. Consider rewording the sentence or use a thesaurus to find a synonym.
We
 then identified the top categories and price distributions of the apps within those clusters to This phrase/word (depending on the context) can be potentially replaced with :interpret
explain
 the grouping. Three successive sentences begin with the same word. Consider rewording the sentence or use a thesaurus to find a synonym.
We
This phrase/word (depending on the context) can be potentially replaced with :exploited
used
 our findings to create forecasting You use this word too often, consider replacing it withsearchstudyscrutinyinvestigationinquirytest
analysis
 using K A determiner is probably missing here: "the nearest".the nearest
nearest
 neighbors and regression trees.

After compiling all the information we derived from our You use this word too often, consider replacing it withsearchstudyscrutinyinvestigationinquirytest
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