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Exploratory Data Analysis On Google Playstore

Google Playstore, formerly Android Market, is a digital distribution service operated and developed by Google. It serves as the official app store for certified devices running on the Android operating system, allowing users to browse and download applications developed with the Android software development kit (SDK) and published through Google.

Android devices directly links up with the Google Playstore from where users are able to download the wide varieties of apps.

India is the highest consumer of Android Devices and all categories of people uses Android devices. Also app developments are also done for the Google Playstore the most as it provides the perfect platforms for the developers.

I got the dataset from https://www.kaggle.com/ which is a trusted open source dataset collection hub.

In this project I used famous library of python.

  • matplotlib, seaborn - For making graphs.
  • pandas - For working with dataframes.
  • numpy - For doing some arithematic operations.

I will give out the descriptions of what each of the attributes present in the dataset refers to for good understanding.

This datset contains the following features:

  1. App: Application name
  2. Category: Category the app belongs to
  3. Rating: Overall user rating of the app by users
  4. Reviews: Number of user reviews for the app after usage
  5. Size: Size of the app
  6. Installs: Total number of downloads of the particular application
  7. Type: If the app in the play store is paid or free
  8. Price: Price of the app
  9. Content Rating: Age group the app is targeted at - Children / Mature 21+ / Adult
  10. Genres: An app can belong to multiple genres (apart from its main category). For eg, a musical family game will belong to Music, Game, Family genres.
  11. Last Updated: Date when the app was last updated on Play Store
  12. Current Ver: Current version of the app available on Play Store
  13. Android Ver: Min required Android version

Jovian.ml platform taught us python from the very basic. And also taught us to use the python libraries to make your work easy, make our own functions or methods to some specific task, and in the last the most exciting thing that they made us to work with datasets and analyse the data.

Downloading the Dataset

There are several options for getting the dataset into Jupyter:

  • Using opendatasets helper library, we can download this data set from kaggle.
!pip install jovian opendatasets --upgrade --quiet

Let's begin by downloading the data, and listing the files within the dataset.

dataset_url = 'https://www.kaggle.com/lava18/google-play-store-apps'