App-Finder

Avatar
Posted on Jun 19, 2019

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.

About Author

Leave a Comment

No comments found.

View Posts by Categories


Our Recent Popular Posts


View Posts by Tags

#python #trainwithnycdsa 2019 airbnb Alex Baransky alumni Alumni Interview Alumni Reviews Alumni Spotlight alumni story Alumnus API Application artist aws beautiful soup Best Bootcamp Best Data Science 2019 Best Data Science Bootcamp Best Data Science Bootcamp 2020 Best Ranked Big Data Book Launch Book-Signing bootcamp Bootcamp Alumni Bootcamp Prep Bundles California Cancer Research capstone Career Career Day citibike clustering Coding Course Demo Course Report D3.js data Data Analyst data science Data Science Academy Data Science Bootcamp Data science jobs Data Science Reviews Data Scientist Data Scientist Jobs data visualization Deep Learning Demo Day Discount dplyr employer networking feature engineering Finance Financial Data Science Flask gbm Get Hired ggplot2 googleVis Hadoop higgs boson Hiring hiring partner events Hiring Partners Industry Experts Instructor Blog Instructor Interview Job Job Placement Jobs Jon Krohn JP Morgan Chase Kaggle Kickstarter lasso regression Lead Data Scienctist Lead Data Scientist leaflet linear regression Logistic Regression machine learning Maps matplotlib Medical Research Meet the team meetup Networking neural network Neural networks New Courses nlp NYC NYC Data Science nyc data science academy NYC Open Data NYCDSA NYCDSA Alumni Online Online Bootcamp Online Training Open Data painter pandas Part-time Portfolio Development prediction Prework Programming PwC python python machine learning python scrapy python web scraping python webscraping Python Workshop R R language R Programming R Shiny r studio R Visualization R Workshop R-bloggers random forest Ranking recommendation recommendation system regression Remote remote data science bootcamp Scrapy scrapy visualization seaborn Selenium sentiment analysis Shiny Shiny Dashboard Spark Special Special Summer Sports statistics streaming Student Interview Student Showcase SVM Switchup Tableau team TensorFlow Testimonial tf-idf Top Data Science Bootcamp twitter visualization web scraping Weekend Course What to expect word cloud word2vec XGBoost yelp