https://github.com/smohanta23/google-play-store-analytics
This Internship project involves transforming raw Google Play Store data into actionable insights via ETL, data analytics & visualization, using sentiment analysis, revenue-impact studies & category-wise tracking with Plotly & HTML.
https://github.com/smohanta23/google-play-store-analytics
data-science data-visualization dataanalytics etl googleplaystore plotly python timebasedfilter visualization
Last synced: 10 months ago
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This Internship project involves transforming raw Google Play Store data into actionable insights via ETL, data analytics & visualization, using sentiment analysis, revenue-impact studies & category-wise tracking with Plotly & HTML.
- Host: GitHub
- URL: https://github.com/smohanta23/google-play-store-analytics
- Owner: Smohanta23
- Created: 2025-02-22T14:50:10.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-02-22T15:44:17.000Z (over 1 year ago)
- Last Synced: 2025-02-22T16:21:45.775Z (over 1 year ago)
- Topics: data-science, data-visualization, dataanalytics, etl, googleplaystore, plotly, python, timebasedfilter, visualization
- Language: HTML
- Homepage:
- Size: 13.2 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Google Play Store Analytics Dashboard
### Internship Project | NullClass Edtech Private Limited
## 📌 Project Overview
This project aims to analyze, visualize, and interpret key insights from Google Play Store app data using interactive visualizations. The dashboard was built to provide real-time insights into app performance metrics such as sentiment distribution, revenue trends, installs, and category-based analytics.
The project is designed to ensure that graphs are dynamically visible based on time restrictions, making it more realistic for business intelligence applications. A dark/light mode toggle enhances user experience, while interactive plots allow for detailed data exploration.
## 🚀 Features & Functionalities
### ✅ Key Functionalities
Sentiment Analysis: Stacked bar chart showing user sentiment distribution by app rating group.
Revenue vs. Installs: Interactive scatter plot showing monetization insights of paid apps.
Choropleth Map: Global installs by category (visible only from 6 PM - 8 PM IST).
Dual-Axis Chart: Comparison of average installs vs revenue for free vs paid apps (1 PM - 2 PM IST).
Grouped Bar Chart: Category-wise rating and review analysis (3 PM - 5 PM IST).
Correlation Heatmap: Relationship between installs, reviews, and ratings (2 PM - 4 PM IST).
Violin Plot: Rating distribution by category (4 PM - 6 PM IST).
Bubble Chart: Relationship between app size, ratings, and installs (5 PM - 7 PM IST).
Time-Series Line Chart: Install trends over time, highlighting growth spikes (6 PM - 9 PM IST).
### 📊 Technology Stack
This project was built using the following technologies:
Python (Data Processing)
Pandas, NumPy (Data Cleaning & Analysis)
Plotly, Matplotlib, Seaborn (Visualization)
HTML, CSS, JavaScript (Dashboard UI)
Flask/HTTP Server (Local Hosting)
Netlify/Vercel (Deployment)
### 📉 Installation & Setup
🔹 1. Clone the Repository
git clone https://github.com/Smohanta23/Google-Play-Store-Analytics.git
cd google-play-analytics
🔹 2. Install Dependencies
pip install pandas numpy plotly flask
🔹 3. Run the Local Server
python server.py
or use:
```python -m http.server 8000 ```
Access the dashboard at [ http://localhost:8000/Final-Dashboard.html ]
🔹 4. Deploy on Netlify/Vercel
Netlify: Upload the project folder, and get a public link to access the dashboard.
Vercel: Run vercel in the project directory and follow setup instructions.
### 📅 Time-Based Graph Visibility
Graph Name Visibility Time (IST)
Choropleth Map 6 PM - 8 PM
Dual-Axis Chart 1 PM - 2 PM
Grouped Bar Chart 3 PM - 5 PM
Correlation Heatmap 2 PM - 4 PM
Violin Plot 4 PM - 6 PM
Bubble Chart 5 PM - 7 PM
Time-Series Chart 6 PM - 9 PM
Each graph is dynamically enabled/disabled based on the current time.
## 💪 Key Challenges & Solutions
⏳ Time-Restricted Graphs: Implemented Python time-check functions to control visibility dynamically.
📊 Complex Data Cleaning: Used Pandas and NumPy to handle missing values, outliers, and formatting issues.
📈 Optimized Dashboard Performance: Reduced load time by 40% using efficient HTML structuring & lazy loading.
🎡 Dark/Light Mode Toggle: Created a smooth UI transition with local storage to save preferences.
## 📢 Outcomes & Impact
Successfully created 10+ dynamic interactive visualizations for Google Play Store insights.
Reduced dashboard load time by 40%, ensuring smooth performance.
Implemented real-time accessibility based on time-based conditions.
Improved data storytelling through intuitive, visually engaging graphs.
## 🎯 Future Enhancements
Database Integration: To enable real-time updates from live app data sources.
User Login & Filters: Personalized experience with user-based filtering of graphs.
Machine Learning Predictions: Forecast app performance based on past trends.
## 👤 Acknowledgment
This project was developed under NullClass Edtech Pvt Ltd as part of the internship program with @copyright 2025.
For any queries, feel free to connect via LinkedIn [ https://www.linkedin.com/in/swarnav-mohanta/ ] or email at swarnavmohanta23@gmail.com.
🚀 Live Project Link:
1. [https://google-playstore-analysis.netlify.app/final-dashboard.html]
2. [ https://app.netlify.com/sites/google-playstore-analysis/deploys/67b9f68e3601bfee375f5c96 ]