📊 Google Play Store Analytics Dashboard

Interactive data analysis dashboard exploring 10,000+ apps from the Google Play Store with live visualizations and machine learning insights.

🔗 Live Dashboard

📷 Screenshots of Graphs and Dashboard are in the Screenshots folder in this repo

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🎯 Features

• 16 Interactive Visualizations - Explore app trends, ratings, and revenue
• 6 Live Charts - Time-sensitive graphs that activate at specific hours (IST)
• Insights - Detialed Insights for each analysis
• Responsive Design - Works on desktop, tablet, and mobile

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🛠️ Tech Stack

Analysis: Python, Pandas, NumPy Visualization: Plotly (Express & GO)
ML/NLP: Scikit-learn, NLTK VADER
Frontend: HTML5, CSS3, JavaScript
Deployment: GitHub Pages

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🚀 Quick Start

# Clone repository
git clone https://github.com/AmanPhadke/Data_Analysis_Projects.git
cd Data_Analysis_Projects

# Install dependencies
pip install pandas numpy plotly scikit-learn nltk

# Download NLTK data
python -c "import nltk; nltk.download('vader_lexicon')"

# Run notebook
jupyter notebook Data_Analysis_Project.ipynb

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📈 Key Insights

• Family & Lifestyle apps generate the highest revenue
• Games dominate installations despite larger sizes
• More than 90% of the apps are free
• Strong correlation between sentiment scores and ratings

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⚡ Live Chart System

The dashboard features time-based interactive charts:

• Top 10 Categories: Rating vs Reviews - Live 3-5 PM IST
• Global Installs - Top 5 Categories - Live 6-8 PM IST
• Average Installs and Total Revenue Comparison - Free vs Paid Apps - Live 1-2 PM IST
• Total Installs Trend with more than 20% Growth Highlighted - Live 6-9 PM IST
• App Size vs Average Rating per number of Installs - Live 5-7 PM IST
• Total Installs over time by Category - Live 4-6 PM IST

Charts automatically activate/deactivate based on current IST time with real-time badge updates(live/not live)

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📁 Project Structure

├── Data_Analysis_Project.ipynb
├── index.html
├── README.md
└── *.html (visualization files)

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🎨 Visualizations Include

✅ Category distribution & rankings
✅ Revenue analysis by category
✅ Install patterns and trends
✅ App size vs. rating correlation
✅ Sentiment analysis results
✅ Time series with growth highlights
✅ Geographic distribution maps
✅ Paid vs. Free comparisons

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🔧 Customization

Change live chart times:

register_live_plot(
    'filename.html',
    'Title',
    15, 17  # Start and end hours (24hr format)
)

Modify styling: Edit Cell 108 in the notebook (dashboard_html template)

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📊 Data Source

Dataset: Google Play Store apps + user reviews
apps data file - https://www.kaggle.com/code/bansodesandeep/eda-google-play-store-apps/input

user_reviews file - https://www.kaggle.com/code/bansodesandeep/eda-google-play-store-apps/input?select=googleplaystore_user_reviews.csv

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🌐 Deployment

Hosted on GitHub with automatic updates on push to main branch.

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📧 Contact

Aman Phadke
GitHub: @AmanPhadke

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📄 License

MIT License - feel free to use and modify!

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⭐ Star this repo if you found it useful!

Made with ❤️ and Python by Aman