Nintendo Games Dashboard

An interactive Streamlit dashboard providing insights into more than 22,000 Nintendo games.
The project was developed as part of a 3-day group challenge in the Data Science & AI Bootcamp (Batch 32) at Constructor Academy.

• Built with Python, Plotly, and Streamlit
• Data collected via web scraping from DekuDeals
• Designed for both Gamers and Developers

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Features

• Gamers' View (by Damla)

  • Search/filter by price, review scores, discounts
  • Quickly discover the best-value games

• Developers' View (by Debbie)

  • Analyze game success factors (genres, publishers, release trends)
  • Identify market trends and opportunities

• Data Foundation (by Karlo)

  • Scraped ~22,000 Nintendo games from DekuDeals
  • Attributes include: title, release date, price, publisher, review scores
  • Cleaned and transformed into structured dataset with Python/Pandas

• Fully interactive visualizations built with Plotly

• Deployed as a Streamlit web app

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Repository Structure

nintendo-games-dashboard/
├── data/                          # scraped datasets (CSV)
├── Welcome.py/                    # Streamlit dashboard entry point
├── pages/
│   ├── 01_Gamers.py               # gamers page in Streamlit dashboard
│   ├── 02_Developers.py           # developers page in Streamlit dashboard
├── requirements.txt               # dependencies
├── slides/                        # PowerPoint presentation
└── README.md

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Data Source

• Nintendo game data: DekuDeals

Thanks to DekuDeals for making comprehensive Nintendo game data accessible.

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Installation

Clone the repository and install requirements:

git clone https://github.com/nintendo-challenge/nintendo-games-dashboard.git
cd nintendo-games-dashboard
pip install -r requirements.txt

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Run the Streamlit App

streamlit run scripts/Welcome.py

The app will open in your browser.

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Contributors

This dashboard was developed collaboratively during a bootcamp group challenge:

• Damla – Gamers' Insights UI
• Debbie – Developers' Insights UI
• Karlo – Web Scraping & Data Collection

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Acknowledgements

This project was built as part of the Data Science & AI Bootcamp (Batch 32) at Constructor Academy.
Special thanks to the instructors and fellow participants for guidance and feedback throughout the project.