Data Visualization

Inspiration

To find the profitable apps profile in App Store and Google Play markets .There are same categories of apps in both places and the inspiration was to find out the apps in the same categories so that developers can choose where they can launch the app and get better reach or response .

What it does

We only build apps that are free to download and install, and our main source of revenue consists of in-app ads. This means our revenue for any given app is mostly influenced by the number of users who use our app — the more users that see and engage with the ads, the better. Our goal for this project is to analyze data to help our developers understand what type of apps are likely to attract more users.

How we built it

There were various steps -

Data Cleaning In Data cleaning first we deleted the inaccurate data and the duplicate entries . We also removed the non-English terms .
Distinguish between free apps and non-free apps.
Analyzing the most prime genre in Apple Store and Google play store.
Finding the average number of installs.

Challenges we ran into

The data visualization is very descriptive and in tabular form , creating a better visualization like charts was challenging. In this project data is visualized mostly using frequency tables .

Accomplishments that we're proud of

Completed the project and got an interesting result about Apple store and Google play store.

What we learned

Learned about Data cleaning and major steps required before data visualization .Also the ratings difference of the app in Apple store in Google play store .