Project Summary
The Play Store apps data has enormous potential to drive app-making businesses to success. Actionable insights can be drawn for developers to work on and capture the Android market. Each app (row) has values for catergory, rating, size, and more. Another dataset contains customer reviews of the android apps.Explore and analyze the data to discover key factors responsible for app engagement and success.
Project Overview: Utilized Python to analyze Google Play Store app data to uncover insights into app performance, user ratings, and trends. The analysis aims to identify patterns and correlations in app attributes, such as ratings, installs, and categories, and to provide actionable insights for app developers and stakeholders.
- DELIVERABLES
- Developed Python scripts to preprocess and clean the Google Play Store app dataset, addressing missing values and standardizing data formats.
- Engineered features such as app age and rating distribution to enhance the depth and clarity of the analysis.
- Conducted exploratory data analysis (EDA) to uncover trends in app ratings, installs, and reviews, providing a comprehensive overview of app performance.
- Created descriptive statistics reports to summarize key metrics, including average ratings, number of installs, and review counts for various app categories.
- Generated visualizations such as histograms and box plots to illustrate the distribution of app ratings and installs across different categories.
- Demonstrated proficiency in Python and its libraries, such as Pandas, NumPy, Matplotlib, Seaborn, and TextBlob, for comprehensive data analysis and visualization.
- ANALYSIS IMPACT
- The analysis of Google Play Store app data offers valuable insights into app performance and trends. By leveraging Python for data cleaning, statistical analysis, and visualization, this project highlights key patterns, correlations, and trends in app metrics, providing actionable insights for app developers and stakeholders.