App-Finder
Find out if your app idea already exist
Find the code to this project here
Find the app here
Data: This Dataset was obtained from Kaggle
Introduction:
People constantly have new app ideas. Whether it's an idea that will help everyday life or an idea that will benefit a specific niche, app ideas seem to pop up all over the place. The problem is that people will start to brainstorm or work through a concept that might already exist -- or worse, five or six successful versions of the idea already exist. This project's goal is to help figure out which app ideas are already taken in the Google Play store. Users can then determine whether or not their concept is worth pursuing.
How it works:
Select a category from the list. Users will see a list of all apps in the Google Play store that fall under that category. Additionally, users will see the average rating, average number of reviews, number of installs, and the average size of all apps in that category.
Next, type in a keyword and press submit. The web app will show users a list of apps within that category that contains those specified keywords. The web app also displays each app's total number of reviews, average rating, size, number of installs, type (free or paid), price, and content rating. In the charts section, users can view a series of bar plots and scatterplots. Compare elements such as category, reviews, installations, size, and rating to find patterns.
Finally, in the data section, users can view all of the apps in the Google Play store and search by keyword. After submitting a keyword, the results will display all apps with that specified keyword and the corresponding category, rating, number of reviews, size, number of installs, type (free or paid), price, and content rating.
The user can have a view of how the apps are distributed by category. This allows the customer to see where the productions of apps are concentrated. In this case, the largest number of apps can be found in the family category.
The customer can also see a specific plot that shows the distribution of average reviews vs ratings grouped by category.
This plot allows you to see that there also is no correlation between the number of reviews and the number of installs, which indicates that the number of installs does not guarantee a larger number of reviews.
It is possible to plot the number of reviews and compare it to others, grouped by category, and it’s possible to see that people are more open to reviewing apps in the parenting category.
On the other side, apps belonging to the communications category are the ones that most people install.
It is also possible for the customer to visualize, in this case, how most customers tend not to reviews the apps they download and how most apps have a small number of reviews.
It is also possible to see that most apps tend to be small in size, no matter their popularity or amount of reviews.
Finally, to understand how there is no correlation between variables like rating and installs, I decided to apply a linear model that shows this lack of relationship.
Only some categories have some relevance to the linear model, (the ones marked with three stars) which shows how it is common to see even successful apps not being highly reviewed.
Conclusion
This app is a great tool to find out what is already in the app market, which is a good new idea and which field are more successful.
In general, most apps have no clear correlation between variables, which makes it difficult to accurately predict success. However, this is still a great way to evaluate the possibilities and possible opportunities that the market can offer.
The skills the author demonstrated here can be learned through taking Data Science with Machine Learning bootcamp with NYC Data Science Academy.
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