Welcome to the Google Play Store Apps and Reviews Analysis repository! This project delves into the vast world of Android applications, exploring the Google Play Store's treasure trove of apps and user reviews.
Mobile apps have transformed our lives, offering everything from productivity tools to entertainment. With app development becoming increasingly accessible, the Google Play Store has witnessed a surge in app offerings across diverse categories. This project provides an in-depth analysis of over ten thousand apps, aiming to uncover valuable insights and strategies for driving growth and user retention in this dynamic ecosystem.
This project is designed to put your skills in data manipulation, exploration, and analysis into action. We dived together headfirst into real-world dataset to draw meaningful conclusions.
- Data-Driven Insights: We utilize data manipulation and analysis skills to extract meaningful insights from real-world datasets.
- Comprehensive Exploration: Dive into app categories, ratings, sizes, pricing, user sentiment, and more to gain a holistic understanding of the Google Play Store.
- User Review Analysis: Use sentiment analysis techniques to gauge user satisfaction from user reviews.
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README.md: This README file provides an overview of the project, its objectives, and the tasks involved.
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Notebooks: Explore Jupyter notebooks containing code and analyses for each project task.
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Data: Access the dataset files: apps.csv and user_reviews.csv, which are essential for the project.
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Project Tasks: Dive into detailed analyses of different aspects of Google Play Store apps and reviews, organized into separate notebooks.
The dataset utilized in this project was meticulously scraped from the Google Play website. Unlike the Apple App Store, which boasts several popular datasets, Google Play Store datasets are relatively scarce. The scarcity can be attributed to the greater complexity of scraping data from Google Play compared to its Apple counterpart. The project incorporates two key data files:
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apps.csv: This file serves as a repository of information on Google Play applications. It encompasses 13 distinct features, each offering insights into the characteristics and attributes of an app.
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user_reviews.csv: Comprising a treasure trove of user reviews, this file contains 100 reviews for each app, meticulously sorted in descending order of helpfulness. Each review has undergone preprocessing and is enriched with three essential attributes: Sentiment (categorized as Positive, Negative, or Neutral), Sentiment Polarity, and Sentiment Subjectivity.
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Data Cleaning: Ensuring the dataset is free of inconsistencies and errors, making it ready for analysis.
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Exploring App Categories: Delving into the diverse app categories available in the Google Play Store and understanding their distribution.
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Distribution of App Ratings: Analyzing the distribution of app ratings to gain insights into user sentiment and preferences.
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Size and Price of an App: Investigating the relationship between the size and price of apps.
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Relation Between App Category and App Price: Examining how app categories correlate with their pricing.
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Filter Out "Junk" Apps: Identifying and filtering out low-quality or irrelevant apps.
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Popularity of Paid Apps vs. Free Apps: Comparing the popularity and user engagement of paid apps versus free apps.
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Sentiment Analysis of User Reviews: Utilizing sentiment analysis techniques to extract insights from user reviews and gauge user satisfaction.
This project is open-source and available in the DataCamp projects.
I hope this project inspires you to explore the world of data analysis and gain valuable insights into the Google Play Store's app market. Feel free to contribute, provide feedback, and enjoy your journey into the realm of Android apps and user reviews!