Android App User Review Metrics
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A CSV of Android app user-review metrics is parsed and displayed as a single JSON object. The visualization shows the raw first row of the dataset, revealing fields like average rating, review counts, and problem-discovery percentages. The code loads the data with a generic CSV import and converts numeric columns using unary plus operators, then renders the object in a preformatted text block. No charting library is used; the example is a static textual preview rather than an interactive graphic.
AI-generated descriptionThe Android Apps and User Feedback - User Feedback Dataset, loaded and parsed as CSV.
Tasks
- Understand the distribution of the average_rating. Is this really a 0-5 rating, or is it concentrated around some number with a very small varience?
- See if there is a correlation between average_rating and Update/Version - in other words, do you usually see ratings increase or decrase with updates
- See if there is a correlation between average_rating and total_reviews - in other words, are more freqently reviewed applciations receive higher or lower ratings
- See if having higher percentages of problems (% problem discoveries") leads to higher or lower ratings
- There was another dataset from the same link that talked about code quality in android the same applicatins. I would like to see if there is a correlation between code quality and average_rating
MIT Licensed