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Mobile apps are everywhere. They are easy to create and can be lucrative. Because of these two factors, more and more apps are being developed. In this project, you will do a comprehensive analysis of the Android app market by comparing over ten thousand apps in Google Play across different categories. You'll look for insights in the data to devise strategies to drive growth and retention. The [data](https://www.kaggle.com/lava18/google-play-store-apps) for this project was scraped from the [Google Play](https://play.google.com/store/apps?hl=en) website. While there are many popular datasets for Apple App Store, there aren't many for Google Play apps, which is partially due to the increased difficulty in scraping the latter as compared to the former. The data files are as follows: - `apps.csv `: contains all the details of the apps on Google Play. These are the features that describe an app. - `user_reviews.csv`: contains 100 reviews for each app, [most helpful first](https://www.androidpolice.com/2019/01/21/google-play-stores-redesigned-ratings-and-reviews-section-lets-you-easily-filter-by-star-rating/). The text in each review has been pre-processed, passed through a sentiment analyzer engine and tagged with its sentiment score.
- 1Google Play Store apps and reviews
- 2Data cleaning
- 3Correcting data types
- 4Exploring app categories
- 5Distribution of app ratings
- 6Size and price of an app
- 7Relation between app category and app price
- 8Filter out "junk" apps
- 9Popularity of paid apps vs free apps
- 10Sentiment analysis of user reviews
Machine Learning Engineer at PropTiger.com
Lavanya is a software engineer by profession with research interests in Data Science, Machine Learning and Deep Learning. She has a rich experience in leading data-driven production projects in the industry. She is a passionate programmer in Python, and loves to experiment with new datasets that she scrapes on her own!
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