Mobile apps have become ubiquitous and highly lucrative. With the increasing number of apps being developed, it's crucial to understand the Android app market for driving growth and retention. This project provides a comprehensive analysis of over ten thousand apps in Google Play Store across various categories.
The analysis is based on two files:
apps.csv: Contains details of the applications on Google Play. It includes 13 features describing each app, such as name, category, rating, size, installs, price, content rating, genre, last updated date, current version, and Android version compatibility.user_reviews.csv: Includes 100 reviews for each app, sorted by helpfulness. The reviews have been pre-processed and tagged with sentiment analysis features, including sentiment (positive, negative, or neutral), sentiment polarity, and sentiment subjectivity.
The primary objectives of this analysis are:
- Identify trends and insights within the Android app market.
- Explore correlations between app features and user sentiment.
- Analyze app categories and their distribution.
- Formulate strategies to drive growth and improve user retention.
The analysis will involve various techniques, including data exploration, visualization, statistical analysis, and sentiment analysis. Python and Jupyter Notebook will be used as the main programming environment and tools.
The project folder contains the following files:
apps.csv: The dataset file containing app details.user_reviews.csv: The dataset file containing user reviews.analysis.ipynb: Jupyter Notebook file with the code, analysis, and visualizations.README.md: This file, providing an overview of the project.
This analysis aims to provide valuable insights into the Android app market on Google Play Store. The findings can help app developers, marketers, and stakeholders in formulating effective strategies for growth and improving user retention.
Feel free to explore the Jupyter Notebook for a detailed analysis of the data and insights.