Streamlit Analytics

Abstract

1. Introduction

This is a small extension for the fantastic streamlit framework. With just one line of code, it counts page views, tracks all widget interactions across users, and visualizes the results directly in your browser. Think Google Analytics but for streamlit.

That's it! All page views and user inputs are now tracked and counted. Of course, you can also use any other streamlit widget in the with block (both from st. and st.sidebar.).

2. Objective

👀 Track & visualize user interactions with your streamlit app

This project demonstrates how Python, Streamlit, Machine Learning can be applied to a real-world Digital Marketing & SEO problem.

4. Technology Stack

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Python 3.8 or later
• pip / virtualenv for dependencies
• VS Code, PyCharm or Jupyter Notebook
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/jrieke/streamlit-analytics.git
cd streamlit-analytics

pip install streamlit-analytics

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Export reports to Google Sheets or PDF
• Schedule weekly automated reports
• Add competitor comparison

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. Which marketing or SEO problem does this tool solve?
2. Which data sources or APIs does it use (Search Console, Analytics, social platforms)?
3. Which metrics or KPIs does it report and how are they calculated?
4. How could the output help a business make decisions?
5. How would you schedule it to run automatically?

9. Source Code & License

This project is developed by jrieke and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.