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  • Writer's pictureChockalingam Muthian

Analysing Google Play store

Today I have analysed the google play store data in terms of rating, category, reviews etc. I have taken apps from 2010 till 2018. Analysis is done using python specifically numpy, pandas, seaborn and finally keras.

Missing Values

We can see that only Rating column contains missing values. This could be due to app's recent release or uses doesn't review the app.

Below is the Exploratory data analysis of the store

As for now, I'll focus on Rating, Reviews and Installs; in context of app Categories, Type and Genres.

Rating Per application Type

Most of the apps which are free as well as paid have the rating from 4 - 4.5.

Rating per App as per category

Arts and Design, Events and Parenting has the highest rating category of apps.

Rating per Age Group

The App that has the Adult users have the highest rating compared to other age groups.

Top Reviewed Apps

['Messenger – Text and Video Chat for Free',

'WhatsApp Messenger',

'Clash of Clans',



'Clean Master- Space Cleaner & Antivirus']

Classification of top 3 apps using Keras




I have uploaded the code in github in the following location

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