#Introduction: In today’s digital age, mobile applications have become an integral part of our daily lives, serving various purposes from entertainment to productivity. Google Play Store, one of the largest repositories of mobile applications, offers a vast array of apps catering to different needs and preferences of users. This project delves into the realm of Play Store data analysis, aiming to uncover insights, trends, and correlations among various attributes of apps available on the platform. #Objective: The primary objective of this project is to conduct a comprehensive analysis of Google Play Store data to gain valuable insights into the characteristics, behavior, and preferences of app users. The key goals include:
- Data Cleaning: Preprocessing the raw data to handle missing values, outliers, and inconsis�tencies, ensuring data integrity and reliability.
- Exploratory Data Analysis (EDA): Exploring the dataset to identify patterns, distributions, and trends among different features such as app categories, genres, ratings, reviews, sizes, and installs.
- Correlation Analysis: Investigating the relationships and correlations between various at�tributes such as app ratings, reviews, sizes, and the number of installs to uncover underlying patterns and dependencies.
- Comparison between Free and Paid Apps: Analyzing the differences and similarities between free and paid apps in terms of their characteristics, user engagement, and popularity.
- Version Updates Analysis: Studying the impact of version updates on app performance, user satisfaction, and ratings.
- Genre and Category Trends: Identifying the most popular app genres and categories based on user preferences and demand.
#Methodology:
The project will involve the following steps:
- Data Acquisition: Gathering Play Store data using web scraping techniques or utilizing pub�licly available datasets.
- Data Cleaning: Performing data cleaning and preprocessing tasks including handling missing values, removing duplicates, and standardizing data formats.
- Exploratory Data Analysis: Conducting exploratory data analysis to visualize distributions, trends, and patterns among different attributes using statistical measures and visualization tools. 1
- Correlation Analysis: Calculating correlation coefficients and conducting statistical tests to identify relationships and dependencies among various features.
- Comparison Analysis: Comparing the characteristics and performance metrics of free and paid apps to discern any significant differences.
- Genre and Category Trends Analysis: Analyzing the popularity and trends of different app genres and categories based on user preferences and download statistics.
- Version Updates Analysis: Investigating the impact of version updates on app ratings, reviews, and user engagement metrics.
2 Dataset Descriptions
App: Name of the mobile application.
Category: Category or genre to which the app belongs.
Rating: User rating score of the app.
Reviews: Number of user reviews/ratings for the app.
Size: Size of the app installation package.
Installs: Number of times the app has been installed.
Type: Whether the app is free or paid.
Price: Price of the app if it’s paid; otherwise, ‘0’ or ‘Free’.
Content Rating: Content rating indicating suitability for different age groups.
Genres: Specific genres or themes associated with the app.
Last Updated: Date when the app was last updated.
Current Ver: Current version of the app.
Android Ver: Minimum required Android version to run the app.
#Observations and conclusions
#Based on the provided data, here is a summary Top Apps: The most popular app is ROBLOX with a rating of 9. Other notable apps include 8 Ball Pool, Bubble Shooter, Helix Jump, Zombie Catchers, Bowmasters, Candy Crush Saga, and Temple Run
App Categories: The majority of apps fall into the FAMILY category (1717), followed by GAME (1074) and TOOLS (733).
App Ratings: The most common ratings are 4.4, 4.3, and 4.5, indicating a generally positive user sentiment.
App Sizes: A significant number of apps have a size that varies with the device (1637), with other common sizes around 14M, 12M, 15M, and 11M.
Number of Installs: A large portion of apps has been installed over 1,000,000 times (1576), with other popular install ranges being 10,000,000+ and 100,000+.
App Types: 61 The majority of apps are Free (8275), with only a small portion being Paid (611).
App Genres: Top genres include Tools, Entertainment, Education, Action, and Productivity.
Content Ratings: The majority of apps are rated Everyone (7089), followed by Teen, Mature 17+, and Everyone 10+.
Market Diversity: There appears to be significant diversity in the app market, with a notable emphasis on family�oriented and gaming applications.
Positive Ratings: The distribution of user ratings suggests a general preference and liking, with a high proportion of apps receiving favorable ratings.
High Installations: A large number of apps have been installed over a million times, indicating widespread popularity among users.
Regular Updates: The presence of a substantial number of updates, especially around August 2018, indicates ongoing developer interest in enhancing and updating the applications.
Trend Towards Free Apps: It appears that the majority of apps are offered for free, reflecting the prevalent trend of providing
free services to users. Broad Android Version Support:
Developers seem to target a wide range of Android operating system versions to ensure app com�patibility with a diverse array of devices.
User Engagement:
Positive ratings and high installation numbers suggest strong user engagement with these applica�tions.