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Data Science Exercises based on real-world scenarios with explanatory comments and prettified output.

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Entertainment Media Analysis

Welcome to my cutting-edge entertainment analysis tool, where data meets entertainment! With our sleek and intuitive Streamlit web application, you can dive deep into the world of your favorite platforms and uncover insights that you never knew existed. Get ready to discover the hidden gems, trending hits, and everything in between with just a few clicks. Let's turn data into entertainment magic!

Screenshots

📕 Goodreads 🍿 Letterboxd 🎮 Steam
Dashboards In-Depth Analysis Awesome Stats
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Entertainment Data Analysis Web Application

This is a web application built with Streamlit for analyzing data from various entertainment platforms.

Supported Platforms

Currently, the following platforms are supported:

  • Goodreads
  • Steam
  • Letterboxd

Usage

To use this application, follow these steps:

  1. Clone this repository to your local machine
  2. cd into it and python -m venv env and env\Scripts\activate
  3. Install the required dependencies using pip install -r requirements.txt
  4. Run the application using streamlit run ⭐MEDIA_ANALYSIS.py
  5. Select a platform from the sidebar
  6. Upload your CSV file or download the example CSVs. To test, use the CSVs in pages/sample-csv
  7. Explore your data and gain insights!

Contributing

Contributions are always welcome! If you would like to contribute to this project, please follow these steps:

  1. Fork this repository
  2. Create a new branch with your feature or bug fix
  3. Make your changes and commit them with descriptive commit messages
  4. Push your changes to your fork
  5. Create a pull request to merge your changes into the main branch of this repository

Note: app.py is the main file for Streamlit application. The pages/ directory contains the code and the example CSVs for each subpage which is accessed from the sidebar.

Credits

This application was built by Atharva Shah using Streamlit.

License

This project is licensed under the MIT License.

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Data Science Exercises based on real-world scenarios with explanatory comments and prettified output.

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