zehan-alam / data-cleaning---viz--google-play-store

Analysis of Google Play Store Data

In this project, I conducted an in-depth analysis of Google Play Store data, comprising 10,841 entries. Leveraging Python's Pandas, Matplotlib, and Seaborn libraries, I performed data cleaning and visualization to extract meaningful insights from each column.

Key Findings

• Category: Corrected capitalization errors and visualized the distribution of app categories. The top three categories are Family, Game, and Tools.
• Rating: Most apps have a rating of 3.25 or higher.
• Reviews: Analyzed the distribution of reviews and categorized them into six groups ('Under 100', '100+', '10k+', '100k+', '1M+', '5M+'). The majority of apps have 100-10k reviews.
• Size: Extracted numeric values from the dataset and visualized the distribution using a KDE plot, revealing a right-skewed distribution with most apps ranging from 0-20MB.
• Installs: Created a bar plot to illustrate the number of apps in each installation category, sorted by installation groups.
• Type: Most apps are free.
• Price: Identified that most paid apps are priced around $5.
• Content Rating: Found that most apps are rated for everyone.
• Genres: Sorted genres by the number of apps and visualized them using a horizontal bar plot. Tools, Education, and Entertainment are the top genres.
• Last Updated: Most apps have been updated within the last year.
• Android Version: Apps are compatible with Android 4 and above.

This analysis provides valuable insights into the Google Play Store ecosystem, offering actionable information for developers and stakeholders.