📊 StreamSphere Analytics

Transforming Video Data into Actionable Business Insights

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🚀 Overview

StreamSphere Analytics is an interactive Product Analytics Dashboard developed using Python, Streamlit, PostgreSQL, SQL, Pandas, and Plotly.

The dashboard enables businesses to monitor product performance, analyze user engagement, evaluate video trends, explore geographic insights, and make data-driven decisions using interactive visualizations.

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🌐 Live Dashboard

🚀 Try StreamSphere Analytics Live

🔗 https://streamsphere-analytics-8sszywlgygewsnpo8zy74d.streamlit.app/

Interactive Product Analytics Dashboard built with Python, Streamlit, PostgreSQL, Pandas, and Plotly.

📸 Dashboard Preview

🏠 Main Dashboard

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🎥 Video Analytics

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👥 User Analytics

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🌍 Geography Analytics

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❤️ Engagement Analytics

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📅 Time Analytics

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✨ Features

• 📊 Interactive KPI Dashboard
• 🎥 Video Performance Analytics
• 👥 User Engagement Analysis
• 🌍 Country-wise Analytics
• 📅 Upload Trend Analysis
• 📂 Category-wise Insights
• 🔍 Dynamic Filters
• 📈 Interactive Plotly Charts
• 📥 Export Data to CSV
• 📱 Responsive Streamlit Dashboard

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💼 Business Value

The dashboard enables organizations to:

• 📈 Measure product and video performance
• 👥 Analyze user engagement patterns
• 🌍 Understand audience distribution
• 🎯 Identify trending content
• 📊 Monitor business KPIs
• 📉 Track platform growth over time
• 💡 Support data-driven decision making

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🛠 Technology Stack

Technology	Purpose
Python	Backend Development
Streamlit	Dashboard Development
PostgreSQL	Database
SQL	Data Retrieval
Pandas	Data Processing
Plotly	Interactive Visualizations
Git	Version Control
GitHub	Project Hosting

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📊 Dashboard Modules

📈 Product Dashboard

Displays overall platform KPIs and summary analytics.

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🎥 Video Analytics

• Most viewed videos
• Video popularity
• Category analysis
• View distribution

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👥 User Analytics

• Active users
• User engagement
• Watch history
• User behavior

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❤️ Engagement Analytics

• Likes
• Comments
• Shares
• Engagement Rate

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🌍 Geography Analytics

• Country-wise views
• Regional distribution
• Audience demographics

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📅 Time Analytics

• Upload trends
• Monthly performance
• Daily activity
• Growth over time

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📂 Project Structure

StreamSphere-Analytics/
│
├── assets/
│   ├── dashboard.png
│   ├── video.png
│   ├── user.png
│   ├── geography.png
│   ├── engagement.png
│   ├── time.png
│   └── logo.png
│
├── pages/
│   ├── About_Project.py
│   ├── Engagement_Analytics.py
│   ├── Geography_Analytics.py
│   ├── Time_Analytics.py
│   ├── User_Analytics.py
│   └── Video_Analytics.py
│
├── dashboard.py
├── app.py
├── db_connection.py
├── requirements.txt
├── README.md
├── LICENSE
└── .gitignore

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🗄 Database Schema

The project uses a PostgreSQL database containing the following tables:

• Users
• Videos
• Watch History

SQL queries retrieve the required information, which is processed using Pandas and visualized using Plotly.

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⚙ Installation

Clone Repository

git clone https://github.com/YukthaK215/StreamSphere-Analytics.git

Navigate to Project

cd StreamSphere-Analytics

Install Dependencies

pip install -r requirements.txt

Configure Database

Create a Streamlit secrets file.

.streamlit/secrets.toml

Example:

DATABASE_URL = "your_postgresql_connection_string"

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Run Application

streamlit run app.py

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📊 Project Highlights

✅ Interactive Dashboards

✅ SQL-Based Analytics

✅ PostgreSQL Integration

✅ Dynamic Filters

✅ Business KPI Cards

✅ CSV Export

✅ Responsive UI

✅ Interactive Plotly Charts

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📈 Skills Demonstrated

• Python
• SQL
• PostgreSQL
• Streamlit
• Pandas
• Plotly
• Data Visualization
• Business Analytics
• Dashboard Development
• Product Analytics
• KPI Reporting
• Data Cleaning
• Exploratory Data Analysis (EDA)

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🚀 Future Enhancements

• Product Analytics Dashboard
• Video Analytics
• User Analytics
• Geography Analytics
• Engagement Analytics
• Time Analytics
• User Authentication
• Machine Learning Recommendations
• Predictive Analytics
• Real-Time Streaming
• Cloud Deployment
• Admin Dashboard
• Role-Based Access Control

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🎯 Learning Outcomes

This project strengthened my understanding of:

• Dashboard Development
• SQL Query Optimization
• PostgreSQL Database Management
• Interactive Data Visualization
• Business Intelligence Concepts
• Streamlit Application Development
• Product Analytics
• Data Storytelling

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👩‍💻 Author

Yuktha K

Bachelor of Engineering (Electronics & Communication)

💻 Aspiring Data Analyst

📊 Interested in Data Analytics, Artificial Intelligence, Business Intelligence, and Software Development.

Connect with me

• GitHub: https://github.com/YukthaK215
• LinkedIn: https://www.linkedin.com/in/yuktha-k

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⭐ Support

If you found this project useful,

⭐ Please consider giving it a Star on GitHub!

It motivates me to continue building more open-source analytics projects.

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📄 License

This project is licensed under the MIT License.

See the LICENSE file for more information.