GooglePlay_Store_App_Analysis

The notebook intends to analyse over ten thousand apps from Google Play Store apps to devise pricing strategies.

The data consists of two files:

• apps.csv: contains all the details of the applications on Google Play. There are 13 features that describe a given app.
• user_reviews.csv: contains 100 reviews for each app. The ranking is based on the contribution to the sentiment analysis. The text in each review has been pre-processed and attributed with three new features:
  Sentiment (Positive, Negative or Neutral), Sentiment Polarity and Sentiment Subjectivity.

Structure

1. Import Packages
2. Dataset Information
3. Data Cleaning
4. Exploring app categories
5. Distribution of app ratings
6. Size and price of an app
7. Relation between app category and app price
8. Filter out "junk" apps
9. Popularity of paid apps vs free apps
10. Sentiment analysis of user reviews

Features to work wiht:
Installs, Size, Rating and Price

Key Findings:

1. Categories about personal growth and management tend to have higher rates.
   • Top 5 Ratings: Event (4.4), Education, Art_and_Design, Books_and_Reference, Personalization
2. Large Market Share and large installments does not help with app ratings
   • Tools holds the third largest market share and the third largest installment times, but is rated as the third from the bottom.
3. The downloading difference between paid/free apps are, unexpectedly, relatively low
4. From the sentiment analysis, it shows that free apps receive lots of negative comments, which is within expectations